diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8a4e624c3c5962584a3491241c7eb01da37f005b --- /dev/null +++ b/README.md @@ -0,0 +1,12 @@ +--- +title: Tiny Factory +emoji: 💻 +colorFrom: yellow +colorTo: gray +sdk: gradio +sdk_version: 6.3.0 +app_file: app.py +pinned: false +--- + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..add830c25f99dda743cc2c35b92bb89058540170 --- /dev/null +++ b/__init__.py @@ -0,0 +1,264 @@ +import os +import logging +import configparser +import rich # for rich console output +import rich.jupyter + +# add current path to sys.path +import sys +sys.path.append('.') +from tinytroupe import utils # now we can import our utils + +# AI disclaimers +print(\ +""" +!!!! +DISCLAIMER: TinyTroupe relies on Artificial Intelligence (AI) models to generate content. +The AI models are not perfect and may produce inappropriate or inacurate results. +For any serious or consequential use, please review the generated content before using it. +!!!! +""") + + +########################################################################### +# Configuration Management System +########################################################################### +class ConfigManager: + """ + Manages configuration values with the ability to override defaults. + Provides dynamic access to the latest config values. + """ + + # this is used in more than one place below, so we define it here + # to avoid errors in later changes + LOGLEVEL_KEY = "loglevel" + + def __init__(self): + self._config = {} + self._initialize_from_config() + + def _initialize_from_config(self): + """Initialize default values from config file""" + config = utils.read_config_file() + + self._config["model"] = config["OpenAI"].get("MODEL", "gpt-4o") + self._config["embedding_model"] = config["OpenAI"].get("EMBEDDING_MODEL", "text-embedding-3-small") + if config["OpenAI"].get("API_TYPE") == "azure": + self._config["azure_embedding_model_api_version"] = config["OpenAI"].get("AZURE_EMBEDDING_MODEL_API_VERSION", "2023-05-15") + self._config["reasoning_model"] = config["OpenAI"].get("REASONING_MODEL", "o3-mini") + + self._config["max_tokens"] = int(config["OpenAI"].get("MAX_TOKENS", "1024")) + self._config["temperature"] = float(config["OpenAI"].get("TEMPERATURE", "1.0")) + self._config["top_p"] = float(config["OpenAI"].get("TOP_P", "0.0")) + self._config["frequency_penalty"] = float(config["OpenAI"].get("FREQ_PENALTY", "0.0")) + self._config["presence_penalty"] = float( + config["OpenAI"].get("PRESENCE_PENALTY", "0.0")) + self._config["reasoning_effort"] = config["OpenAI"].get("REASONING_EFFORT", "high") + + self._config["timeout"] = float(config["OpenAI"].get("TIMEOUT", "30.0")) + self._config["max_attempts"] = float(config["OpenAI"].get("MAX_ATTEMPTS", "0.0")) + self._config["waiting_time"] = float(config["OpenAI"].get("WAITING_TIME", "1")) + self._config["exponential_backoff_factor"] = float(config["OpenAI"].get("EXPONENTIAL_BACKOFF_FACTOR", "5")) + + self._config["cache_api_calls"] = config["OpenAI"].getboolean("CACHE_API_CALLS", False) + self._config["cache_file_name"] = config["OpenAI"].get("CACHE_FILE_NAME", "openai_api_cache.pickle") + + self._config["max_content_display_length"] = config["OpenAI"].getint("MAX_CONTENT_DISPLAY_LENGTH", 1024) + + self._config["parallel_agent_actions"] = config["Simulation"].getboolean("PARALLEL_AGENT_ACTIONS", True) + self._config["parallel_agent_generation"] = config["Simulation"].getboolean("PARALLEL_AGENT_GENERATION", True) + + self._config["enable_memory_consolidation"] = config["Cognition"].get("ENABLE_MEMORY_CONSOLIDATION", True) + self._config["min_episode_length"] = config["Cognition"].getint("MIN_EPISODE_LENGTH", 30) + self._config["max_episode_length"] = config["Cognition"].getint("MAX_EPISODE_LENGTH", 100) + self._config["episodic_memory_fixed_prefix_length"] = config["Cognition"].getint("EPISODIC_MEMORY_FIXED_PREFIX_LENGTH", 20) + self._config["episodic_memory_lookback_length"] = config["Cognition"].getint("EPISODIC_MEMORY_LOOKBACK_LENGTH", 20) + + self._config["action_generator_max_attempts"] = config["ActionGenerator"].getint("MAX_ATTEMPTS", 2) + self._config["action_generator_enable_quality_checks"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECKS", False) + self._config["action_generator_enable_regeneration"] = config["ActionGenerator"].getboolean("ENABLE_REGENERATION", False) + self._config["action_generator_enable_direct_correction"] = config["ActionGenerator"].getboolean("ENABLE_DIRECT_CORRECTION", False) + + self._config["action_generator_enable_quality_check_for_persona_adherence"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_PERSONA_ADHERENCE", False) + self._config["action_generator_enable_quality_check_for_selfconsistency"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_SELFCONSISTENCY", False) + self._config["action_generator_enable_quality_check_for_fluency"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_FLUENCY", False) + self._config["action_generator_enable_quality_check_for_suitability"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_SUITABILITY", False) + self._config["action_generator_enable_quality_check_for_similarity"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_SIMILARITY", False) + + self._config["action_generator_continue_on_failure"] = config["ActionGenerator"].getboolean("CONTINUE_ON_FAILURE", True) + self._config["action_generator_quality_threshold"] = config["ActionGenerator"].getint("QUALITY_THRESHOLD", 2) + + # LOGLEVEL + self._config[ConfigManager.LOGLEVEL_KEY] = config["Logging"].get("LOGLEVEL", "INFO").upper() + + self._raw_config = config + + def update(self, key, value): + """ + Update a configuration value. + + Args: + key (str): The configuration key to update + value: The new value to set + + Returns: + None + """ + if key in self._config: + + # make sure it is always lowercase + if isinstance(value, str): + value = value.lower() + + self._config[key] = value + logging.info(f"Updated config: {key} = {value}") + + # Special handling for loglevel - also update the logger immediately + if key == ConfigManager.LOGLEVEL_KEY: + utils.set_loglevel(value) + else: + logging.warning(f"Attempted to update unknown config key: {key}") + + def update_multiple(self, config_dict): + """ + Update multiple configuration values at once. + + Args: + config_dict (dict): Dictionary of key-value pairs to update + + Returns: + None + """ + for key, value in config_dict.items(): + self.update(key, value) + + def get(self, key, default=None): + """ + Get a configuration value. + + Args: + key (str): The configuration key to retrieve + default: The default value to return if key is not found + + Returns: + The configuration value + """ + return self._config.get(key, default) + + def reset(self): + """Reset all configuration values to their original values from the config file.""" + self._initialize_from_config() + logging.info("All configuration values have been reset to defaults") + + def __getitem__(self, key): + """Allow dictionary-like access to configuration values.""" + return self.get(key) + + def config_defaults(self, **config_mappings): + """ + Returns a decorator that replaces None default values with current config values. + + Args: + **config_mappings: Mapping of parameter names to config keys + + Example: + @config_manager.config_defaults(model="model", temp="temperature") + def generate(prompt, model=None, temp=None): + # model will be the current config value for "model" if None is passed + # ... + """ + import functools + import inspect + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + # Get the function's signature + sig = inspect.signature(func) + bound_args = sig.bind_partial(*args, **kwargs) + bound_args.apply_defaults() + + # For each parameter that maps to a config key + for param_name, config_key in config_mappings.items(): + # If the parameter is None, replace with config value + if param_name in bound_args.arguments and bound_args.arguments[param_name] is None: + kwargs[param_name] = self.get(config_key) + + return func(*args, **kwargs) + + return wrapper + + return decorator + + +# Create global instance of the configuration manager +config = utils.read_config_file() +utils.pretty_print_tinytroupe_version() +utils.pretty_print_datetime() +utils.pretty_print_config(config) +utils.start_logger(config) + +config_manager = ConfigManager() + + + + +# For backwards compatibility, maintain the default dict +# but it's recommended to use config_manager instead +default = config_manager._config + +# Helper function for method signatures +def get_config(key, override_value=None): + """ + Get a configuration value, with optional override. + Used in method signatures to get current config values. + + Args: + key (str): The configuration key + override_value: If provided, this value is used instead of the config value + + Returns: + The configuration value or the override value + """ + if override_value is not None: + return override_value + return config_manager.get(key) + + +## LLaMa-Index configs ######################################################## +#from llama_index.embeddings.huggingface import HuggingFaceEmbedding + +if config["OpenAI"].get("API_TYPE") == "azure": + from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding +else: + from llama_index.embeddings.openai import OpenAIEmbedding + +from llama_index.core import Settings, Document, VectorStoreIndex, SimpleDirectoryReader +from llama_index.readers.web import SimpleWebPageReader + + +# this will be cached locally by llama-index, in a OS-dependend location + +##Settings.embed_model = HuggingFaceEmbedding( +## model_name="BAAI/bge-small-en-v1.5" +##) + +if config["OpenAI"].get("API_TYPE") == "azure": + llamaindex_openai_embed_model = AzureOpenAIEmbedding(model=default["embedding_model"], + deployment_name=default["embedding_model"], + api_version=default["azure_embedding_model_api_version"], + embed_batch_size=10) +else: + llamaindex_openai_embed_model = OpenAIEmbedding(model=default["embedding_model"], embed_batch_size=10) +Settings.embed_model = llamaindex_openai_embed_model + + +########################################################################### +# Fixes and tweaks +########################################################################### + +# fix an issue in the rich library: we don't want margins in Jupyter! +rich.jupyter.JUPYTER_HTML_FORMAT = \ + utils.inject_html_css_style_prefix(rich.jupyter.JUPYTER_HTML_FORMAT, "margin:0px;") + + diff --git a/__pycache__/__init__.cpython-312.pyc b/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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a/agent/__init__.py b/agent/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d142b3c4d7c1545a3e4836c05ad5d025f1f27c69 --- /dev/null +++ b/agent/__init__.py @@ -0,0 +1,66 @@ +""" +This module provides the main classes and functions for TinyTroupe's agents. + +Agents are the key abstraction used in TinyTroupe. An agent is a simulated person or entity that can interact with other agents and the environment, by +receiving stimuli and producing actions. Agents have cognitive states, which are updated as they interact with the environment and other agents. +Agents can also store and retrieve information from memory, and can perform actions in the environment. Different from agents whose objective is to +provide support for AI-based assistants or other such productivity tools, **TinyTroupe agents aim at representing human-like behavior**, which includes +idiossincracies, emotions, and other human-like traits, that one would not expect from a productivity tool. + +The overall underlying design is inspired mainly by Cognitive Psychology, which is why agents have various internal cognitive states, such as attention, emotions, and goals. +It is also why agent memory, differently from other LLM-based agent platforms, has subtle internal divisions, notably between episodic and semantic memory. +Some behaviorist concepts are also present, such as the explicit and decoupled concepts of "stimulus" and "response" in the `listen` and `act` methods, which are key abstractions +to understand how agents interact with the environment and other agents. +""" + +import tinytroupe.utils as utils +from pydantic import BaseModel + +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Types and constants +########################################################################### +from typing import TypeVar, Union +Self = TypeVar("Self", bound="TinyPerson") +AgentOrWorld = Union[Self, "TinyWorld"] + + +########################################################################### +# Data structures to enforce output format during LLM API call. +########################################################################### +class Action(BaseModel): + type: str + content: str + target: str + +class CognitiveState(BaseModel): + goals: str + context: list[str] + attention: str + emotions: str + +class CognitiveActionModel(BaseModel): + action: Action + cognitive_state: CognitiveState + +class CognitiveActionModelWithReasoning(BaseModel): + reasoning: str + action: Action + cognitive_state: CognitiveState + + +########################################################################### +# Exposed API +########################################################################### +# from. grounding ... ---> not exposing this, clients should not need to know about detailed grounding mechanisms +from .memory import SemanticMemory, EpisodicMemory, EpisodicConsolidator, ReflectionConsolidator +from .mental_faculty import CustomMentalFaculty, RecallFaculty, FilesAndWebGroundingFaculty, TinyToolUse +from .tiny_person import TinyPerson + +__all__ = ["SemanticMemory", "EpisodicMemory", "EpisodicConsolidator", "ReflectionConsolidator", + "CustomMentalFaculty", "RecallFaculty", "FilesAndWebGroundingFaculty", "TinyToolUse", + "TinyPerson"] \ No newline at end of file diff --git a/agent/__pycache__/__init__.cpython-312.pyc b/agent/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ba7ef83e9550bbe17932440c4c641df6cb876599 Binary files /dev/null and b/agent/__pycache__/__init__.cpython-312.pyc differ diff --git a/agent/__pycache__/action_generator.cpython-312.pyc b/agent/__pycache__/action_generator.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a397d0c05caf1486e8386424a23d30124928dfc7 Binary files /dev/null and b/agent/__pycache__/action_generator.cpython-312.pyc differ diff --git a/agent/__pycache__/grounding.cpython-312.pyc b/agent/__pycache__/grounding.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..969f7e51bdb4938a4cdaf875d01b3f32e4f2bda8 Binary files /dev/null and b/agent/__pycache__/grounding.cpython-312.pyc differ diff --git a/agent/__pycache__/memory.cpython-312.pyc b/agent/__pycache__/memory.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..32311c5afcd1d54e4958f463f89885505d1d1b6d Binary files /dev/null and b/agent/__pycache__/memory.cpython-312.pyc differ diff --git a/agent/__pycache__/mental_faculty.cpython-312.pyc b/agent/__pycache__/mental_faculty.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..79a0a1c83a5c711a95980e4469b08eb1c3d458bc Binary files /dev/null and b/agent/__pycache__/mental_faculty.cpython-312.pyc differ diff --git a/agent/__pycache__/tiny_person.cpython-312.pyc b/agent/__pycache__/tiny_person.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6f76205515baf84c9fe42f9af3730b8949956f0f Binary files /dev/null and b/agent/__pycache__/tiny_person.cpython-312.pyc differ diff --git a/agent/action_generator.py b/agent/action_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..08aca7f328617ebca7cf691a8c52e85e7dfe8181 --- /dev/null +++ b/agent/action_generator.py @@ -0,0 +1,532 @@ +import json +import statistics # Add this import + +import tinytroupe.utils as utils +from tinytroupe.control import transactional, current_simulation +import tinytroupe.openai_utils as openai_utils +from tinytroupe.validation import propositions +from tinytroupe.utils import JsonSerializableRegistry +from tinytroupe.experimentation import Proposition + + +class ActionGenerator(JsonSerializableRegistry): + + def __init__(self, max_attempts=2, + enable_quality_checks=True, + enable_regeneration=True, + enable_direct_correction=False, # TODO enable_direct_correction not working very well yet + enable_quality_check_for_persona_adherence=True, + enable_quality_check_for_selfconsistency=True, + enable_quality_check_for_fluency=True, + enable_quality_check_for_suitability=False, + enable_quality_check_for_similarity=False, + continue_on_failure=True, + quality_threshold=7, + max_action_similarity=0.6, + enable_reasoning_step=False): # TODO enable_reasoning_step not working very well yet + """ + Initializes the ActionGenerator. + + Args: + max_attempts (int): The maximum number of attempts to generate an action. + enable_quality_checks (bool): Whether to perform quality checks on the generated action. If False, the first action generated + is returned without any checks. + enable_regeneration (bool): Whether to try to make the agent regenerate the action if the first attempt fails. + enable_direct_correction (bool): Whether to directly correct the action if the first attempt fails, without asking the agent to regenerate it. + enable_quality_check_for_persona_adherence (bool): Whether to check the action for persona adherence. + enable_quality_check_for_selfconsistency (bool): Whether to check the action for self-consistency. + enable_quality_check_for_fluency (bool): Whether to check the action for fluency. + enable_quality_check_for_suitability (bool): Whether to check the action for suitability. + continue_on_failure (bool): Whether to return the last tentative action, even if it fails to pass quality checks. + Presumably, the last tentative action is the one that is most likely to be correct, since it has gone through the most iterations of regeneration and correction. + quality_threshold (int): The minimum score for each quality check for the action to be considered good quality. + enable_reasoning_step (bool): Whether to enable reasoning step in the action generation process. This IS NOT the use of "reasoning models" (e.g., o1, o3), + but rather the use of an additional reasoning step in the regular text completion. + """ + + self.max_attempts = max_attempts + self.regeneration_attempts = 0 + self.direct_correction_attempts = 0 + + self.enable_quality_checks = enable_quality_checks + self.enable_regeneration = enable_regeneration + self.enable_direct_correction = enable_direct_correction + + self.enable_quality_check_for_persona_adherence = enable_quality_check_for_persona_adherence + self.enable_quality_check_for_selfconsistency = enable_quality_check_for_selfconsistency + self.enable_quality_check_for_fluency = enable_quality_check_for_fluency + self.enable_quality_check_for_suitability = enable_quality_check_for_suitability + self.enable_quality_check_for_similarity = enable_quality_check_for_similarity + + self.continue_on_failure = continue_on_failure + self.quality_threshold = quality_threshold + self.max_action_similarity = max_action_similarity + + self.enable_reasoning_step = enable_reasoning_step + + # This generator has its own copies of the propositions, in order to be able to isolate them + # from other agents, particularly when running the simulation in parallel. + self.action_persona_adherence = propositions.hard_action_persona_adherence.copy() + self.action_self_consistency = propositions.action_self_consistency.copy() + self.action_fluency = propositions.action_fluency.copy() + self.action_suitability = propositions.action_suitability.copy() + + # initialize statistics + self.regeneration_failures = 0 + self.direct_correction_failures = 0 + self.regeneration_scores = [] + self.direct_correction_scores = [] + self.total_actions_produced = 0 + self.total_original_actions_succeeded = 0 + + def generate_next_action(self, agent, current_messages:list): + + from tinytroupe.agent import logger # import here to avoid circular import issues + + # clean up (remove unnecessary elements) and copy the list of current messages to avoid modifying the original ones + current_messages = [ + {"role": msg["role"], "content": json.dumps(msg["content"])} + for msg in current_messages + ] + + # starts with no feedback + cur_feedback = None + all_negative_feedbacks = [] + + best_action = None + best_role = None + best_content = None + best_score = float('-inf') + original_score = None + + def update_best(tentative_action, role, content, total_score): + nonlocal best_action, best_role, best_content, best_score + if total_score > best_score: + best_action = tentative_action + best_role = role + best_content = content + best_score = total_score + + def finish_return(tentative_action, role, content, final_score): + if original_score is not None and final_score > original_score: + logger.warning(f"[{agent.name}] improved total quality from {original_score} to {final_score}") + + # ensure that tentative_action and content are dicts + if isinstance(tentative_action, str): + tentative_action = json.loads(tentative_action) + if isinstance(content, str): + content = json.loads(content) + + return tentative_action, role, content, all_negative_feedbacks + + # First attempt to generate an action + tentative_action, role, content = self._generate_tentative_action(agent, current_messages, + feedback_from_previous_attempt=cur_feedback, + previous_tentative_action=None, + previous_llm_role=None, previous_llm_content=None) + + if self.enable_quality_checks: + # First quality check + good_quality, total_score, cur_feedback = self._check_action_quality("Original Action", agent, tentative_action=tentative_action) + update_best(tentative_action, role, content, total_score) + if original_score is None: + original_score = total_score + if good_quality: + self.total_original_actions_succeeded += 1 + # Found a good action, let's return it now + return finish_return(tentative_action, role, content, total_score) + else: + logger.warning(f"[{agent.name}] Original action did not pass quality checks: {cur_feedback}") + all_negative_feedbacks.append(cur_feedback) + + + # GENERATE AND REGENERATE the action by the agent + # + # We first try to make the agent generate (via the current_messages passed) or regenerate the + # action based on feedback. + if self.enable_regeneration: + for attempt in range(self.max_attempts): + + # Generate tentative action + tentative_action, role, content = self._generate_tentative_action(agent, current_messages, + feedback_from_previous_attempt=cur_feedback, + previous_tentative_action=tentative_action, + previous_llm_role=role, previous_llm_content=content) + logger.debug(f"[{agent.name}] Tentative action: {tentative_action}") + self.regeneration_attempts += 1 + + good_quality, total_score, cur_feedback = self._check_action_quality(f"Action Regeneration ({attempt})", agent, tentative_action=tentative_action) + update_best(tentative_action, role, content, total_score) + if good_quality: + # Found a good action, let's return it now + return finish_return(tentative_action, role, content, total_score) + else: + self.regeneration_failures += 1 + self.regeneration_scores.append(total_score) # Assuming feedback contains a score + all_negative_feedbacks.append(cur_feedback) + + # CORRECT OR REPHRASE the action directly + # + # If we got here, it means the agent was not able to directly generate an action + # of sufficient quality, so we'll try to rephrase it correctly directly now. + if self.enable_direct_correction: + for attempt in range(self.max_attempts): + tentative_action, role, content = self._correct_action(tentative_action, feedback=cur_feedback, llm_role=role, llm_content=content) + logger.warning(f"[{agent.name}] Rephrased the action directly as: {tentative_action}") + self.direct_correction_attempts += 1 + + good_quality, total_score, cur_feedback = self._check_action_quality(f"Direct Action Correction or Rephrasing ({attempt})", agent, tentative_action=tentative_action) + update_best(tentative_action, role, content, total_score) + if good_quality: + # Found a good action, let's return it now + return finish_return(tentative_action, role, content, total_score) + else: + self.direct_correction_failures += 1 + self.direct_correction_scores.append(total_score) # Assuming feedback contains a score + all_negative_feedbacks.append(cur_feedback) + + # If we got here, all attempts to generate a good action failed + if self.continue_on_failure: + logger.warning(f"[{agent.name}] All attempts to generate a good action failed. Returning the best one.") + return finish_return(best_action, best_role, best_content, best_score) + + else: + raise PoorQualityActionException() + + else: + # If we got here, it means that the action was generated without quality checks + # and we are not doing any regeneration or direct correction, so we can return it now. + return tentative_action, role, content, [] + + def _generate_tentative_action(self, agent, current_messages, feedback_from_previous_attempt=None, + previous_tentative_action=None, + previous_llm_role=None, previous_llm_content=None): + + from tinytroupe.agent import logger, CognitiveActionModel, CognitiveActionModelWithReasoning # import here to avoid circular import issues + + self.total_actions_produced += 1 + + # shallow clone current_messages + current_messages_context = current_messages.copy() + + logger.debug(f"[{agent.name}] Sending messages to OpenAI API") + logger.debug(f"[{agent.name}] Last interaction: {current_messages[-1]}") + + if feedback_from_previous_attempt: + #current_messages_copy.append({"role": previous_llm_role, + # "content": "TENTATIVE ACTION:" + json.dumps(previous_llm_content)}) + + current_messages_context.append({"role": "user", + "content": \ + f""" + WARNING! TENTATIVE ACTION GENERATION FAILED IN QUALITY CHECKS! + + You were about to produce the following action, as a sequence for the previous actions or feedbacks (if any): + ``` + {previous_tentative_action} + ``` + + However, it failed to pass the quality checks (as described in the quality feedback below), and therefore it was aborted and not added + to the simulation trajectory. + + Now you **must** try again to generate a **BETTER** action, such that the quality issues mentioned in the feedback are addressed, + or instead issue a DONE action and stop for this turn if it is unclear how to improve quality. + Your objective is to **PASS** the quality checks this time if possible. + + You can choose either to FIX somehow the action you were about to produce, or to generate something COMPLETELY NEW and DIFFERENT. + Each time your tentative action fail a quality check, you should be MORE RADICAL in your changes, and try to produce + something that is **very** different from the previous attempts. + + If it is unclear how to produce a better action, you can choose to issue a DONE action instead. + **It is better to stop acting than to act poorly.** + + In general, desireable properties of the action are: + - The action is consistent with the agent's persona, it is what one would expect from the agent given its persona. + - The action is self-consistent, it does contradict the agent's previous actions. + - The action is fluent and natural, and does not repeat itself or use overly formulaic language. + + {feedback_from_previous_attempt} + """}) + + current_messages_context.append({"role": "system", + "content": "Now generate a better action based on the above feedback, or issue a DONE action if it is unclear how to improve quality."}) + + + + # TODO: remind the model of some key rules to follow? + # + # + #current_messages_context.append({"role": "user", + # "content": """ + # Now you must generate a sequence of actions following the directives in your agent specification, + # complying with **all** instructions and contraints related to the action you use. + # In particular, to ensure the quality of your actions: + # - **DO NOT** generate similar content in a row! We want human-like, natural and fluent behavior, and thus avoid#repeatitive behavior. + # - THINK before taking further actions. + # - Avoid thinking for too long, and actually take some concrete action before being done, particularly if you are expected to provide some action. + # - Intercalate thinking with other actions. + # - The new sequence of actions must be coherent and consistent with the previous actions and stimuli. For example, do not assume an expected or + # desireable action already happened if that's not registered in the simulation history. + # - If you received any quality feedback, you **MUST** take it into account and improve your performance. Your next actions + # **must** be better than your previous ones if possible. + # + # If you can't produce a very good action, you may just issue a DONE action instead and remain silent. Rules to follow in #this case: + # - It is better to remain silent than repeating similar actions or making other mistakes. + # - Avoid remaining silent for too long (i.e., more than 3 times in a row), as this looks robotic and unnatural. If #necessary, you + # can communicate your difficulties in coming up with a proper action, or just say something like "I don't know what to say". + # - In case your thoughts or goals insistenly require you to **not** being quiet or silent, then you avoid just issuing #DONE if possible, + # and try to produce a new action. In this case, the new action might refer to the difficulties you are having in #coming up with + # a proper action in the first place. + # + # All of these actions **MUST** be rendered following the JSON specification perfectly, including all required keys (even #if their value is empty), **ALWAYS**. + # """ + # }) +# + + current_messages_context.append({"role": "system", + "content": "Remember: the action you will now generate **MUST** be a **well-formatted** and **valid** JSON object. No extra text, no extra brackets, commas, or other syntax errors."}) + + if not self.enable_reasoning_step: + logger.debug(f"[{agent.name}] Reasoning step disabled.") + next_message = openai_utils.client().send_message(current_messages_context, response_format=CognitiveActionModel) + + else: + logger.debug(f"[{agent.name}] Reasoning step enabled.") + + # If the reasoning step is enabled, we add a system message to the context asking it to think step-by-step + # + # + #current_messages_context.append({"role": "system", + # "content": "In your response, you first use the \"reasoning\" field to think step-by-step about what is the next action and cognitive state that you are going to generate. To do so, you carefully consider: the agent specification given initially; additional instructions given later; and the history of stimuli and actions present in the simulation trajectory." + + # "Then, you generate the action in the \"action\" field, and generate cognitive state in the \"cognitive_state\" field." }) + current_messages_context.append({"role": "system", + "content": "Use the \"reasoning\" field to add any reasoning process you might wish to use before generating the next action and cognitive state. "}) + + next_message = openai_utils.client().send_message(current_messages_context, response_format=CognitiveActionModelWithReasoning) + + logger.debug(f"[{agent.name}] Received message: {next_message}") + + role, content = next_message["role"], utils.extract_json(next_message["content"]) + + action = content['action'] + logger.debug(f"{agent.name}'s action: {action}") + + return action, role, content + + ############################################################################################### + # Quality evaluation methods + ############################################################################################### + + def _check_action_quality(self, stage, agent, tentative_action): + + from tinytroupe.agent import logger # import here to avoid circular import issues + + # + # Compute various propositions about the action + # + persona_adherence_passed, persona_adherence_score, persona_adherence_feedback = \ + self._check_proposition(agent, self.action_persona_adherence, tentative_action, enable_proposition_check=self.enable_quality_check_for_persona_adherence) + + selfconsistency_passed, selfconsistency_score, selfconsistency_feedback = \ + self._check_proposition(agent, self.action_self_consistency, tentative_action, minimum_required_qty_of_actions=1, enable_proposition_check=self.enable_quality_check_for_selfconsistency) + + fluency_passed, fluency_passed_score, fluency_feedback = \ + self._check_proposition(agent, self.action_fluency, tentative_action, enable_proposition_check=self.enable_quality_check_for_fluency) + + suitability_passed, suitability_score, suitability_feedback = \ + self._check_proposition(agent, self.action_suitability, tentative_action, enable_proposition_check=self.enable_quality_check_for_suitability) + + similarity_passed, similarity_score, similarity_feedback = \ + self._check_next_action_similarity(agent, tentative_action, threshold=self.max_action_similarity, enable_similarity_check=self.enable_quality_check_for_similarity) + + # put the results together + good_quality = persona_adherence_passed and selfconsistency_passed and fluency_passed and suitability_passed and similarity_passed + total_score = persona_adherence_score + selfconsistency_score + fluency_passed_score + suitability_score + (similarity_score * Proposition.MAX_SCORE) + + combined_feedback = utils.combine_texts( + persona_adherence_feedback, selfconsistency_feedback, fluency_feedback, suitability_feedback, similarity_feedback + ) + + # give verdict + if good_quality: + return True, total_score, combined_feedback + + else: + + failure_feedback = \ + f""" + # Quality feedback + + This is the action that was about to be generated by the agent: + {tentative_action} + + Unfortunately, the action failed to pass the quality checks, and therefore was aborted and not added to the similation trajectory. + The following problems were detected. + """ + + if not persona_adherence_passed: + failure_feedback += f""" + ## Problem: The action does not adhere to the persona specification. + {persona_adherence_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_persona_adherence.recommendations_for_improvement()} + + """ + + if not selfconsistency_passed: + failure_feedback += f""" + ## Problem: The action is not self-consistent. + {selfconsistency_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_self_consistency.recommendations_for_improvement()} + + """ + + if not fluency_passed: + failure_feedback += f""" + ## Problem: The action is not fluent. + {fluency_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_fluency.recommendations_for_improvement()} + + """ + + if not suitability_passed: + failure_feedback += f""" + ## Problem: The action is not suitable to the situation or task. + {suitability_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_suitability.recommendations_for_improvement()} + + """ + + if not similarity_passed: + failure_feedback += f""" + ## Problem: The action is too similar to the previous one. + {similarity_feedback} + + """ + + logger.warning(f"[{agent.name}][{stage}] failed to pass quality checks: {failure_feedback}") + return False, total_score, failure_feedback + + + def _check_proposition(self, agent, proposition, tentative_action, minimum_required_qty_of_actions=0, enable_proposition_check=True): + + if enable_proposition_check: + if agent.actions_count >= minimum_required_qty_of_actions: + result = proposition.score(target=agent, claim_variables={"action": tentative_action}, return_full_response=True) + + value_with_justification = f"Score = {result['value']} (out of {Proposition.MAX_SCORE}). Justification = {result['justification']}" + + if result["value"] >= self.quality_threshold: + return True, result["value"], value_with_justification + else: + return False, result["value"], value_with_justification + + else: + return True, Proposition.MAX_SCORE, f"The proposition is trivially true due to the lack of enough actions for comparison." + else: + # If the proposition check is disabled, we assume it passed + return True, Proposition.MAX_SCORE, f"The proposition check is disabled, so it is assumed to have passed." + + def _check_next_action_similarity(self, agent, proposed_next_action, threshold, enable_similarity_check=True): + """ + Checks the similarity between the agent's current action and a proposed next action. + High similarity indicates that the proposed action is too similar to the current one, and this + check fails. + """ + from tinytroupe.agent import logger # import here to avoid circular import issues + + if enable_similarity_check: + similarity = utils.next_action_jaccard_similarity(agent, proposed_next_action) + logger.debug(f"[{agent.name}] Next-action Jaccard similarity: {similarity}") + + if similarity >= threshold: + logger.warning(f"[{agent.name}] Next-action Jaccard similarity is above the threshold ({threshold}).") + return False, similarity, f"Similarity = {similarity} (range: 0.0 to 1.0). The action is too similar to the previous one." + else: + logger.debug(f"[{agent.name}] Next-action Jaccard similarity is below the threshold ({threshold}).") + return True, similarity, f"Similarity = {similarity} (range: 0.0 to 1.0). The action is sufficiently different from the previous one." + + else: + # If the similarity check is disabled, we assume it passed + return True, 0.0, f"The similarity check is disabled, so it is assumed to have passed." + + ################################################################################################ + # Action correction methods + ################################################################################################ + + def _correct_action(self, action:dict, feedback, llm_role, llm_content): + situation = \ + f""" + The following action by an agent was observed: + + {action} + + However, it does not conform to expectations about this agent behavior, + due to the following reasons. + {feedback} + """ + #restructured_situation =\ + # utils.restructure_as_observed_vs_expected(\ + + # """) + #rule = utils.formulate_corrective_rule(restructured_situation) + rules = utils.extract_observed_vs_expected_rules(situation) + rephrased_action_content = utils.correct_according_to_rule(action["content"], rules) + + # copy action + rephrased_action = action.copy() + + # update content + rephrased_action["content"] = rephrased_action_content + + # replace in the 'action' key in the original llm content message + llm_content["action"] = rephrased_action + + return rephrased_action, llm_role, llm_content + + def get_statistics(self): + regeneration_failure_rate = self.regeneration_failures / self.regeneration_attempts if self.regeneration_attempts else 0 + direct_correction_failure_rate = self.direct_correction_failures / self.direct_correction_attempts if self.direct_correction_attempts else 0 + + regeneration_mean_score = statistics.mean(self.regeneration_scores) if self.regeneration_scores else 0 + regeneration_sd_score = statistics.stdev(self.regeneration_scores) if len(self.regeneration_scores) > 1 else 0 + + direct_correction_mean_score = statistics.mean(self.direct_correction_scores) if self.direct_correction_scores else 0 + direct_correction_sd_score = statistics.stdev(self.direct_correction_scores) if len(self.direct_correction_scores) > 1 else 0 + + original_success_rate = self.total_original_actions_succeeded / self.total_actions_produced if self.total_actions_produced else 0 + + return { + "regeneration_failure_rate": regeneration_failure_rate, + "direct_correction_failure_rate": direct_correction_failure_rate, + "regeneration_mean_score": regeneration_mean_score, + "regeneration_sd_score": regeneration_sd_score, + "direct_correction_mean_score": direct_correction_mean_score, + "direct_correction_sd_score": direct_correction_sd_score, + "total_actions_produced": self.total_actions_produced, + "total_original_actions_succeeded": self.total_original_actions_succeeded, + "original_success_rate": original_success_rate, + "regeneration_success_rate": 1 - regeneration_failure_rate, + "direct_correction_success_rate": 1 - direct_correction_failure_rate + } + + +class PoorQualityActionException(Exception): + def __init__(self, message="The generated action is of poor quality"): + self.message = message + super().__init__(self.message) diff --git a/agent/browser_faculty.py b/agent/browser_faculty.py new file mode 100644 index 0000000000000000000000000000000000000000..adbfa92adca60caef609c476bd0db449c3eac030 --- /dev/null +++ b/agent/browser_faculty.py @@ -0,0 +1,85 @@ +from tinytroupe.agent.mental_faculty import TinyMentalFaculty +from tinytroupe.tools import browser +import textwrap + +class BrowserFaculty(TinyMentalFaculty): + """ + A mental faculty that allows an agent to interact with a web browser. + """ + + def __init__(self): + super().__init__("Browser Navigation") + + def process_action(self, agent, action: dict) -> bool: + """ + Processes a browser-related action. + """ + action_type = action.get("type") + content = action.get("content") + target = action.get("target") + + if action_type == "See": + screenshot_path = browser.screenshot() + agent.see(f"Took a screenshot and saved it to {screenshot_path}. I will now analyze the screenshot.") + return True + elif action_type == "Click": + browser.click(target) + agent.see(f"Clicked on element with selector: {target}") + return True + elif action_type == "Write": + browser.fill(target, content) + agent.see(f"Typed '{content}' into element with selector: {target}") + return True + elif action_type == "Submit": + browser.submit_form(target) + agent.see(f"Submitted form with element: {target}") + return True + elif action_type == "Wait": + browser.wait_for_element(target) + agent.see(f"Waited for element: {target}") + return True + elif action_type == "Scroll": + browser.scroll_page(content) + agent.see(f"Scrolled page {content}") + return True + elif action_type == "Hover": + browser.hover_element(target) + agent.see(f"Hovered over element: {target}") + return True + elif action_type == "Keyboard_Key": + browser.press_key(content) + agent.see(f"Pressed key: {content}") + return True + elif action_type == "ScanPage": + page_info = browser.get_page_info() + agent.see(f"Scanned page and found the following information: {page_info}") + return True + return False + + def actions_definitions_prompt(self) -> str: + """ + Returns the prompt for defining browser-related actions. + """ + prompt = """ + - See: Take a screenshot of the current page. The `content` will be a placeholder for vision. + - Click: Click on an element on the page. The `target` should be a CSS selector for the element. + - Write: Type text into an element on the page. The `target` should be a CSS selector for the element, and `content` should be the text to type. + - Submit: Submit a form on the page. The `target` should be a CSS selector for a form or an element within a form. + - Wait: Wait for an element to appear on the page. The `target` should be a CSS selector for the element. + - Scroll: Scroll the page. The `content` should be 'up' or 'down'. + - Hover: Hover over an element on the page. The `target` should be a CSS selector for the element. + - Keyboard_Key: Press a key on the keyboard. The `content` should be the key to press (e.g., 'Enter', 'ArrowDown'). + - ScanPage: Get information about the current page, such as links and form elements. + """ + return textwrap.dedent(prompt) + + def actions_constraints_prompt(self) -> str: + """ + Returns the prompt for defining constraints on browser-related actions. + """ + prompt = """ + - Use See to get a visual representation of the page to help you decide on the next action. + - Use ScanPage to get a list of interactive elements to help you decide on the next action. + - Use Click, Write, and other actions to interact with elements on the page to accomplish the task. + """ + return textwrap.dedent(prompt) diff --git a/agent/grounding.py b/agent/grounding.py new file mode 100644 index 0000000000000000000000000000000000000000..671f90c7b5d1612ff4cef53e8b3133236320fba5 --- /dev/null +++ b/agent/grounding.py @@ -0,0 +1,398 @@ +from tinytroupe.utils import JsonSerializableRegistry +import tinytroupe.utils as utils + +from tinytroupe.agent import logger +from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document, StorageContext, load_index_from_storage +from llama_index.core.vector_stores import SimpleVectorStore +from llama_index.readers.web import SimpleWebPageReader +import json +import tempfile +import os +import shutil + + +####################################################################################################################### +# Grounding connectors +####################################################################################################################### + +class GroundingConnector(JsonSerializableRegistry): + """ + An abstract class representing a grounding connector. A grounding connector is a component that allows an agent to ground + its knowledge in external sources, such as files, web pages, databases, etc. + """ + + serializable_attributes = ["name"] + + def __init__(self, name:str) -> None: + self.name = name + + def retrieve_relevant(self, relevance_target:str, source:str, top_k=20) -> list: + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_by_name(self, name:str) -> str: + raise NotImplementedError("Subclasses must implement this method.") + + def list_sources(self) -> list: + raise NotImplementedError("Subclasses must implement this method.") + + +@utils.post_init +class BaseSemanticGroundingConnector(GroundingConnector): + """ + A base class for semantic grounding connectors. A semantic grounding connector is a component that indexes and retrieves + documents based on so-called "semantic search" (i.e, embeddings-based search). This specific implementation + is based on the VectorStoreIndex class from the LLaMa-Index library. Here, "documents" refer to the llama-index's + data structure that stores a unit of content, not necessarily a file. + """ + + serializable_attributes = ["documents", "index"] + + # needs custom deserialization to handle Pydantic models (Document is a Pydantic model) + custom_deserializers = {"documents": lambda docs_json: [Document.from_json(doc_json) for doc_json in docs_json], + "index": lambda index_json: BaseSemanticGroundingConnector._deserialize_index(index_json)} + + custom_serializers = {"documents": lambda docs: [doc.to_json() for doc in docs] if docs is not None else None, + "index": lambda index: BaseSemanticGroundingConnector._serialize_index(index)} + + def __init__(self, name:str="Semantic Grounding") -> None: + super().__init__(name) + + self.documents = None + self.name_to_document = None + self.index = None + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + self.index = None + + if not hasattr(self, 'documents') or self.documents is None: + self.documents = [] + + if not hasattr(self, 'name_to_document') or self.name_to_document is None: + self.name_to_document = {} + + if hasattr(self, 'documents') and self.documents is not None: + for document in self.documents: + # if the document has a semantic memory ID, we use it as the identifier + name = document.metadata.get("semantic_memory_id", document.id_) + + # self.name_to_document[name] contains a list, since each source file could be split into multiple pages + if name in self.name_to_document: + self.name_to_document[name].append(document) + else: + self.name_to_document[name] = [document] + + # Rebuild index from documents if it's None or invalid + if self.index is None and self.documents: + logger.warning("No index found. Rebuilding index from documents.") + vector_store = SimpleVectorStore() + self.index = VectorStoreIndex.from_documents( + self.documents, + vector_store=vector_store, + store_nodes_override=True + ) + + # TODO remove? + #self.add_documents(self.documents) + + @staticmethod + def _serialize_index(index): + """Helper function to serialize index with proper storage context""" + if index is None: + return None + + try: + # Create a temporary directory to store the index + with tempfile.TemporaryDirectory() as temp_dir: + # Persist the index to the temporary directory + index.storage_context.persist(persist_dir=temp_dir) + + # Read all the persisted files and store them in a dictionary + persisted_data = {} + for filename in os.listdir(temp_dir): + filepath = os.path.join(temp_dir, filename) + if os.path.isfile(filepath): + with open(filepath, 'r', encoding="utf-8", errors="replace") as f: + persisted_data[filename] = f.read() + + return persisted_data + except Exception as e: + logger.warning(f"Failed to serialize index: {e}") + return None + + @staticmethod + def _deserialize_index(index_data): + """Helper function to deserialize index with proper error handling""" + if not index_data: + return None + + try: + # Create a temporary directory to restore the index + with tempfile.TemporaryDirectory() as temp_dir: + # Write all the persisted files to the temporary directory + for filename, content in index_data.items(): + filepath = os.path.join(temp_dir, filename) + with open(filepath, 'w', encoding="utf-8", errors="replace") as f: + f.write(content) + + # Load the index from the temporary directory + storage_context = StorageContext.from_defaults(persist_dir=temp_dir) + index = load_index_from_storage(storage_context) + + return index + except Exception as e: + # If deserialization fails, return None + # The index will be rebuilt from documents in _post_init + logger.warning(f"Failed to deserialize index: {e}. Index will be rebuilt.") + return None + + def retrieve_relevant(self, relevance_target:str, top_k=20) -> list: + """ + Retrieves all values from memory that are relevant to a given target. + """ + # Handle empty or None query + if not relevance_target or not relevance_target.strip(): + return [] + + if self.index is not None: + retriever = self.index.as_retriever(similarity_top_k=top_k) + nodes = retriever.retrieve(relevance_target) + else: + nodes = [] + + retrieved = [] + for node in nodes: + content = "SOURCE: " + node.metadata.get('file_name', '(unknown)') + content += "\n" + "SIMILARITY SCORE:" + str(node.score) + content += "\n" + "RELEVANT CONTENT:" + node.text + retrieved.append(content) + + logger.debug(f"Content retrieved: {content[:200]}") + + return retrieved + + def retrieve_by_name(self, name:str) -> list: + """ + Retrieves a content source by its name. + """ + # TODO also optionally provide a relevance target? + results = [] + if self.name_to_document is not None and name in self.name_to_document: + docs = self.name_to_document[name] + for i, doc in enumerate(docs): + if doc is not None: + content = f"SOURCE: {name}\n" + content += f"PAGE: {i}\n" + content += "CONTENT: \n" + doc.text[:10000] # TODO a more intelligent way to limit the content + results.append(content) + + return results + + + def list_sources(self) -> list: + """ + Lists the names of the available content sources. + """ + if self.name_to_document is not None: + return list(self.name_to_document.keys()) + else: + return [] + + def add_document(self, document) -> None: + """ + Indexes a document for semantic retrieval. + + Assumes the document has a metadata field called "semantic_memory_id" that is used to identify the document within Semantic Memory. + """ + self.add_documents([document]) + + def add_documents(self, new_documents) -> list: + """ + Indexes documents for semantic retrieval. + """ + # index documents by name + if len(new_documents) > 0: + + # process documents individually too + for document in new_documents: + logger.debug(f"Adding document {document} to index, text is: {document.text}") + + # out of an abundance of caution, we sanitize the text + document.text = utils.sanitize_raw_string(document.text) + + logger.debug(f"Document text after sanitization: {document.text}") + + # add the new document to the list of documents after all sanitization and checks + self.documents.append(document) + + if document.metadata.get("semantic_memory_id") is not None: + # if the document has a semantic memory ID, we use it as the identifier + name = document.metadata["semantic_memory_id"] + + # Ensure name_to_document is initialized + if not hasattr(self, 'name_to_document') or self.name_to_document is None: + self.name_to_document = {} + + # self.name_to_document[name] contains a list, since each source file could be split into multiple pages + if name in self.name_to_document: + self.name_to_document[name].append(document) + else: + self.name_to_document[name] = [document] + + + # index documents for semantic retrieval + if self.index is None: + # Create storage context with vector store + vector_store = SimpleVectorStore() + storage_context = StorageContext.from_defaults(vector_store=vector_store) + + self.index = VectorStoreIndex.from_documents( + self.documents, + storage_context=storage_context, + store_nodes_override=True # This ensures nodes (with text) are stored + ) + else: + self.index.refresh(self.documents) + + @staticmethod + def _set_internal_id_to_documents(documents:list, external_attribute_name:str ="file_name") -> None: + """ + Sets the internal ID for each document in the list of documents. + This is useful to ensure that each document has a unique identifier. + """ + for doc in documents: + if not hasattr(doc, 'metadata'): + doc.metadata = {} + doc.metadata["semantic_memory_id"] = doc.metadata.get(external_attribute_name, doc.id_) + + return documents + + +@utils.post_init +class LocalFilesGroundingConnector(BaseSemanticGroundingConnector): + + serializable_attributes = ["folders_paths"] + + def __init__(self, name:str="Local Files", folders_paths: list=None) -> None: + super().__init__(name) + + self.folders_paths = folders_paths + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + self.loaded_folders_paths = [] + + if not hasattr(self, 'folders_paths') or self.folders_paths is None: + self.folders_paths = [] + + self.add_folders(self.folders_paths) + + def add_folders(self, folders_paths:list) -> None: + """ + Adds a path to a folder with files used for grounding. + """ + + if folders_paths is not None: + for folder_path in folders_paths: + try: + logger.debug(f"Adding the following folder to grounding index: {folder_path}") + self.add_folder(folder_path) + except (FileNotFoundError, ValueError) as e: + print(f"Error: {e}") + print(f"Current working directory: {os.getcwd()}") + print(f"Provided path: {folder_path}") + print("Please check if the path exists and is accessible.") + + def add_folder(self, folder_path:str) -> None: + """ + Adds a path to a folder with files used for grounding. + """ + + if folder_path not in self.loaded_folders_paths: + self._mark_folder_as_loaded(folder_path) + + # for PDF files, please note that the document will be split into pages: https://github.com/run-llama/llama_index/issues/15903 + new_files = SimpleDirectoryReader(folder_path).load_data() + BaseSemanticGroundingConnector._set_internal_id_to_documents(new_files, "file_name") + + self.add_documents(new_files) + + def add_file_path(self, file_path:str) -> None: + """ + Adds a path to a file used for grounding. + """ + # a trick to make SimpleDirectoryReader work with a single file + new_files = SimpleDirectoryReader(input_files=[file_path]).load_data() + + logger.debug(f"Adding the following file to grounding index: {new_files}") + BaseSemanticGroundingConnector._set_internal_id_to_documents(new_files, "file_name") + + def _mark_folder_as_loaded(self, folder_path:str) -> None: + if folder_path not in self.loaded_folders_paths: + self.loaded_folders_paths.append(folder_path) + + if folder_path not in self.folders_paths: + self.folders_paths.append(folder_path) + + + + +@utils.post_init +class WebPagesGroundingConnector(BaseSemanticGroundingConnector): + + serializable_attributes = ["web_urls"] + + def __init__(self, name:str="Web Pages", web_urls: list=None) -> None: + super().__init__(name) + + self.web_urls = web_urls + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + self.loaded_web_urls = [] + + if not hasattr(self, 'web_urls') or self.web_urls is None: + self.web_urls = [] + + # load web urls + self.add_web_urls(self.web_urls) + + def add_web_urls(self, web_urls:list) -> None: + """ + Adds the data retrieved from the specified URLs to grounding. + """ + filtered_web_urls = [url for url in web_urls if url not in self.loaded_web_urls] + for url in filtered_web_urls: + self._mark_web_url_as_loaded(url) + + if len(filtered_web_urls) > 0: + new_documents = SimpleWebPageReader(html_to_text=True).load_data(filtered_web_urls) + BaseSemanticGroundingConnector._set_internal_id_to_documents(new_documents, "url") + self.add_documents(new_documents) + + def add_web_url(self, web_url:str) -> None: + """ + Adds the data retrieved from the specified URL to grounding. + """ + # we do it like this because the add_web_urls could run scrapes in parallel, so it is better + # to implement this one in terms of the other + self.add_web_urls([web_url]) + + def _mark_web_url_as_loaded(self, web_url:str) -> None: + if web_url not in self.loaded_web_urls: + self.loaded_web_urls.append(web_url) + + if web_url not in self.web_urls: + self.web_urls.append(web_url) + diff --git a/agent/memory.py b/agent/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..f3980494c6b07d66471ea2f012450925a32a52e3 --- /dev/null +++ b/agent/memory.py @@ -0,0 +1,747 @@ +import json + +from tinytroupe.agent import logger +from tinytroupe.agent.mental_faculty import TinyMentalFaculty +from tinytroupe.agent.grounding import BaseSemanticGroundingConnector +import tinytroupe.utils as utils + + +from llama_index.core import Document +from typing import Any +import copy +from typing import Union + +####################################################################################################################### +# Memory mechanisms +####################################################################################################################### + +class TinyMemory(TinyMentalFaculty): + """ + Base class for different types of memory. + """ + + def _preprocess_value_for_storage(self, value: Any) -> Any: + """ + Preprocesses a value before storing it in memory. + """ + # by default, we don't preprocess the value + return value + + def _store(self, value: Any) -> None: + """ + Stores a value in memory. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def store(self, value: dict) -> None: + """ + Stores a value in memory. + """ + self._store(self._preprocess_value_for_storage(value)) + + def store_all(self, values: list) -> None: + """ + Stores a list of values in memory. + """ + logger.debug(f"Storing {len(values)} values in memory: {values}") + for i, value in enumerate(values): + logger.debug(f"Storing value #{i}: {value}") + self.store(value) + + def retrieve(self, first_n: int, last_n: int, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the first n and/or last n values from memory. If n is None, all values are retrieved. + + Args: + first_n (int): The number of first values to retrieve. + last_n (int): The number of last values to retrieve. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + + Returns: + list: The retrieved values. + + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_recent(self, item_type:str=None) -> list: + """ + Retrieves the n most recent values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_all(self, item_type:str=None) -> list: + """ + Retrieves all values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_relevant(self, relevance_target:str, top_k=20) -> list: + """ + Retrieves all values from memory that are relevant to a given target. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def summarize_relevant_via_full_scan(self, relevance_target: str, batch_size: int = 20, item_type: str = None) -> str: + """ + Performs a full scan of the memory, extracting and accumulating information relevant to a query. + + This function processes all memories (or memories of a specific type if provided), + extracts information relevant to the query from each memory, and accumulates this + information into a coherent response. + + Args: + relevance_target (str): The query specifying what information to extract from memories. + + item_type (str, optional): If provided, only process memories of this type. + batch_size (int): The number of memories to process in each extraction step. The larger it is, the faster the scan, but possibly less accurate. + Also, a too large value may lead to prompt length overflows, though current models can handle quite large prompts. + + Returns: + str: The accumulated information relevant to the query. + """ + logger.debug(f"Starting FULL SCAN for relevance target: {relevance_target}, item type: {item_type}") + + # Retrieve all memories of the specified type + memories = self.retrieve_all(item_type=item_type) + + # Initialize accumulation + accumulated_info = "" + + # Process memories in batches of qty_of_memories_per_extraction + for i in range(0, len(memories), batch_size): + batch = memories[i:i + batch_size] + logger.debug(f"Processing memory batch #{i} in full scan") + + # Concatenate memory texts for the batch + batch_text = "# Memories to be processed\n\n" + batch_text += "\n\n ".join(str(memory) for memory in batch) + + # Extract information relevant to the query from the batch + extracted_info = utils.semantics.extract_information_from_text( + relevance_target, + batch_text, + context=""" + You are extracting information from the an agent's memory, + which might include actions, stimuli, and other types of events. You want to focus on the agent's experience, NOT on the agent's cognition or internal processes. + + Assume that: + - "actions" refer to behaviors produced by the agent, + - "stimulus" refer to events or information from the environment or other agents that the agent perceived. + + If you read about "assistant" and "user" roles, you can ignore them, as they refer to the agent's internal implementation mechanisms, not to the agent's experience. + In any case, anything related to "assistant" is the agent's output, and anything related to "user" is the agent's input. But you never refer to these roles in the report, + as they are an internal implementation detail of the agent, not part of the agent's experience. + """ + ) + + logger.debug(f"Extracted information from memory batch: {extracted_info}") + + # Skip if no relevant information was found + if not extracted_info: + continue + + # Accumulate the extracted information + accumulated_info = utils.semantics.accumulate_based_on_query( + query=relevance_target, + new_entry=extracted_info, + current_accumulation=accumulated_info, + context=""" + You are producing a report based on information from an agent's memory. + You will put together all facts and experiences found that are relevant for the query, as a kind of summary of the agent's experience. + The report will later be used to guide further agent action. You focus on the agent's experience, NOT on the agent's cognition or internal processes. + + Assume that: + - "actions" refer to behaviors produced by the agent, + - "stimulus" refer to events or information from the environment or other agents that the agent perceived. + - if you read about "assistant" and "user" roles, you can ignore them, as they refer to the agent's internal implementation mechanisms, not to the agent's experience. + In any case, anything related to "assistant" is the agent's output, and anything related to "user" is the agent's input. But you never refer to these roles in the report, + as they are an internal implementation detail of the agent, not part of the agent's experience. + + Additional instructions for the accumulation process: + - If the new entry is redundant with respect to some information in the current accumulation, you update the current accumulation by adding to a special counter right by + the side of where the redundant information is found, so that the final report can later be used to guide further agent action (i.e., know which elements appeared more often). + The special counter **must** be formated like this: "[NOTE: this information appeared X times in the memory in different forms]". If the counter was not there originally, you add it. If it was there, you update + it with the new count. + * Example (first element was found 3 times, the second element only once, so no counter): + "I play with and feed my cat [NOTE: this information appeared 3 times in the memory in different forms]. Cats are proud animals descendant from big feline hunters.". + + """ + ) + logger.debug(f"Accumulated information so far: {accumulated_info}") + + logger.debug(f"Total accumulated information after full scan: {accumulated_info}") + + return accumulated_info + + + ################################### + # Auxiliary methods + ################################### + + def filter_by_item_type(self, memories:list, item_type:str) -> list: + """ + Filters a list of memories by item type. + + Args: + memories (list): The list of memories to filter. + item_type (str): The item type to filter by. + + Returns: + list: The filtered list of memories. + """ + return [memory for memory in memories if memory["type"] == item_type] + + def filter_by_item_types(self, memories:list, item_types:list) -> list: + """ + Filters a list of memories by multiple item types. + + Args: + memories (list): The list of memories to filter. + item_types (list): The list of item types to filter by. + + Returns: + list: The filtered list of memories containing any of the specified types. + """ + return [memory for memory in memories if memory["type"] in item_types] + + +class EpisodicMemory(TinyMemory): + """ + Provides episodic memory capabilities to an agent. Cognitively, episodic memory is the ability to remember specific events, + or episodes, in the past. This class provides a simple implementation of episodic memory, where the agent can store and retrieve + messages from memory. + + Subclasses of this class can be used to provide different memory implementations. + """ + + MEMORY_BLOCK_OMISSION_INFO = {'role': 'assistant', 'content': "Info: there were other messages here, but they were omitted for brevity.", 'simulation_timestamp': None} + + def __init__( + self, fixed_prefix_length: int = 20, lookback_length: int = 100 + ) -> None: + """ + Initializes the memory. + + Args: + fixed_prefix_length (int): The fixed prefix length. Defaults to 20. + lookback_length (int): The lookback length. Defaults to 100. + """ + self.fixed_prefix_length = fixed_prefix_length + self.lookback_length = lookback_length + + # the definitive memory that records all episodic events + self.memory = [] + + # the current episode buffer, which is used to store messages during an episode + self.episodic_buffer = [] + + + def commit_episode(self): + """ + Ends the current episode, storing the episodic buffer in memory. + """ + self.memory.extend(self.episodic_buffer) + self.episodic_buffer = [] + + def get_current_episode(self, item_types:list=None) -> list: + """ + Returns the current episode buffer, which is used to store messages during an episode. + + Args: + item_types (list, optional): If provided, only retrieve memories of these types. Defaults to None, which retrieves all types. + + Returns: + list: The current episode buffer. + """ + result = copy.copy(self.episodic_buffer) + result = self.filter_by_item_types(result, item_types) if item_types is not None else result + return result + + def count(self) -> int: + """ + Returns the number of values in memory. + """ + return len(self._memory_with_current_buffer()) + + def clear(self, max_prefix_to_clear:int=None, max_suffix_to_clear:int=None): + """ + Clears the memory, generating a permanent "episodic amnesia". + If max_prefix_to_clear is not None, it clears the first n values from memory. + If max_suffix_to_clear is not None, it clears the last n values from memory. If both are None, + it clears all values from memory. + + Args: + max_prefix_to_clear (int): The number of first values to clear. + max_suffix_to_clear (int): The number of last values to clear. + """ + + # clears all episodic buffer messages + self.episodic_buffer = [] + + # then clears the memory according to the parameters + if max_prefix_to_clear is not None: + self.memory = self.memory[max_prefix_to_clear:] + + if max_suffix_to_clear is not None: + self.memory = self.memory[:-max_suffix_to_clear] + + if max_prefix_to_clear is None and max_suffix_to_clear is None: + self.memory = [] + + def _memory_with_current_buffer(self) -> list: + """ + Returns the current memory, including the episodic buffer. + This is useful for retrieving the most recent memories, including the current episode. + """ + return self.memory + self.episodic_buffer + + ###################################### + # General memory methods + ###################################### + def _store(self, value: Any) -> None: + """ + Stores a value in memory. + """ + self.episodic_buffer.append(value) + + def retrieve(self, first_n: int, last_n: int, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the first n and/or last n values from memory. If n is None, all values are retrieved. + + Args: + first_n (int): The number of first values to retrieve. + last_n (int): The number of last values to retrieve. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + + Returns: + list: The retrieved values. + + """ + + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + # use the other methods in the class to implement + if first_n is not None and last_n is not None: + return self.retrieve_first(first_n, include_omission_info=False, item_type=item_type) + omisssion_info + self.retrieve_last(last_n, include_omission_info=False, item_type=item_type) + elif first_n is not None: + return self.retrieve_first(first_n, include_omission_info, item_type=item_type) + elif last_n is not None: + return self.retrieve_last(last_n, include_omission_info, item_type=item_type) + else: + return self.retrieve_all(item_type=item_type) + + def retrieve_recent(self, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the n most recent values from memory. + + Args: + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + """ + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + # Filter memories if item_type is provided + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + + # compute fixed prefix + fixed_prefix = memories[: self.fixed_prefix_length] + omisssion_info + + # how many lookback values remain? + remaining_lookback = min( + len(memories) - len(fixed_prefix) + (1 if include_omission_info else 0), self.lookback_length + ) + + # compute the remaining lookback values and return the concatenation + if remaining_lookback <= 0: + return fixed_prefix + else: + return fixed_prefix + memories[-remaining_lookback:] + + def retrieve_all(self, item_type:str=None) -> list: + """ + Retrieves all values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + return copy.copy(memories) + + def retrieve_relevant(self, relevance_target: str, top_k:int) -> list: + """ + Retrieves top-k values from memory that are most relevant to a given target. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_first(self, n: int, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the first n values from memory. + + Args: + n (int): The number of values to retrieve. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + """ + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + return memories[:n] + omisssion_info + + def retrieve_last(self, n: int=None, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the last n values from memory. + + Args: + n (int): The number of values to retrieve, or None to retrieve all values. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + """ + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + memories = memories[-n:] if n is not None else memories + + return omisssion_info + memories + + +@utils.post_init +class SemanticMemory(TinyMemory): + """ + In Cognitive Psychology, semantic memory is the memory of meanings, understandings, and other concept-based knowledge unrelated to specific + experiences. It is not ordered temporally, and it is not about remembering specific events or episodes. This class provides a simple implementation + of semantic memory, where the agent can store and retrieve semantic information. + """ + + serializable_attributes = ["memories", "semantic_grounding_connector"] + + def __init__(self, memories: list=None) -> None: + self.memories = memories + + self.semantic_grounding_connector = None + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + + if not hasattr(self, 'memories') or self.memories is None: + self.memories = [] + + if not hasattr(self, 'semantic_grounding_connector') or self.semantic_grounding_connector is None: + self.semantic_grounding_connector = BaseSemanticGroundingConnector("Semantic Memory Storage") + + # TODO remove? + #self.semantic_grounding_connector.add_documents(self._build_documents_from(self.memories)) + + + def _preprocess_value_for_storage(self, value: dict) -> Any: + logger.debug(f"Preprocessing value for storage: {value}") + + if isinstance(value, dict): + engram = {"role": "assistant", + "content": value['content'], + "type": value.get("type", "information"), # Default to 'information' if type is not specified + "simulation_timestamp": value.get("simulation_timestamp", None)} + + # Refine the content of the engram is built based on the type of the value to make it more meaningful. + if value['type'] == 'action': + engram['content'] = f"# Action performed\n" +\ + f"I have performed the following action at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + + elif value['type'] == 'stimulus': + engram['content'] = f"# Stimulus\n" +\ + f"I have received the following stimulus at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + elif value['type'] == 'feedback': + engram['content'] = f"# Feedback\n" +\ + f"I have received the following feedback at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + elif value['type'] == 'consolidated': + engram['content'] = f"# Consolidated Memory\n" +\ + f"I have consolidated the following memory at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + elif value['type'] == 'reflection': + engram['content'] = f"# Reflection\n" +\ + f"I have reflected on the following memory at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + else: + engram['content'] = f"# Information\n" +\ + f"I have obtained following information at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + + # else: # Anything else here? + + else: + # If the value is not a dictionary, we just store it as is, but we still wrap it in an engram + engram = {"role": "assistant", + "content": value, + "type": "information", # Default to 'information' if type is not specified + "simulation_timestamp": None} + + logger.debug(f"Engram created for storage: {engram}") + + return engram + + def _store(self, value: Any) -> None: + logger.debug(f"Preparing engram for semantic memory storage, input value: {value}") + self.memories.append(value) # Store the value in the local memory list + + # then econduct the value to a Document and store it in the semantic grounding connector + # This is the actual storage in the semantic memory to allow semantic retrieval + engram_doc = self._build_document_from(value) + logger.debug(f"Storing engram in semantic memory: {engram_doc}") + self.semantic_grounding_connector.add_document(engram_doc) + + def retrieve_relevant(self, relevance_target:str, top_k=20) -> list: + """ + Retrieves all values from memory that are relevant to a given target. + """ + return self.semantic_grounding_connector.retrieve_relevant(relevance_target, top_k) + + def retrieve_all(self, item_type:str=None) -> list: + """ + Retrieves all values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + + memories = [] + + logger.debug(f"Retrieving all documents from semantic memory connector, a total of {len(self.semantic_grounding_connector.documents)} documents.") + for document in self.semantic_grounding_connector.documents: + logger.debug(f"Retrieving document from semantic memory: {document}") + memory_text = document.text + logger.debug(f"Document text retrieved: {memory_text}") + + try: + memory = json.loads(memory_text) + logger.debug(f"Memory retrieved: {memory}") + memories.append(memory) + + except json.JSONDecodeError as e: + logger.warning(f"Could not decode memory from document text: {memory_text}. Error: {e}") + + if item_type is not None: + memories = self.filter_by_item_type(memories, item_type) + + return memories + + ##################################### + # Auxiliary compatibility methods + ##################################### + + def _build_document_from(self, memory) -> Document: + # TODO: add any metadata as well? + + # make sure we are dealing with a dictionary + if not isinstance(memory, dict): + memory = {"content": memory, "type": "information"} + + # ensures double quotes are used for JSON serialization, and maybe other formatting details + memory_txt = json.dumps(memory, ensure_ascii=False) + logger.debug(f"Building document from memory: {memory_txt}") + + return Document(text=memory_txt) + + def _build_documents_from(self, memories: list) -> list: + return [self._build_document_from(memory) for memory in memories] + + +################################################################################################### +# Memory consolidation and optimization mechanisms +################################################################################################### +class MemoryProcessor: + """ + Base class for memory consolidation and optimization mechanisms. + """ + + def process(self, memories: list, timestamp: str=None, context:Union[str, list, dict] = None, persona:Union[str, dict] = None, sequential: bool = True) -> list: + """ + Transforms the given memories. Transformation can be anything from consolidation to optimization, depending on the implementation. + + Each memory is a dictionary of the form: + { + 'role': role, + 'content': content, + 'type': 'action'/'stimulus'/'feedback', + 'simulation_timestamp': timestamp + } + + Args: + memories (list): The list of memories to consolidate. + sequential (bool): Whether the provided memories are to be interpreted sequentially (e.g., episodes in sequence) or not (e.g., abstract facts). + + Returns: + list: A list with the consolidated memories, following the same format as the input memories, but different in content. + """ + raise NotImplementedError("Subclasses must implement this method.") + +class EpisodicConsolidator(MemoryProcessor): + """ + Consolidates episodic memories into a more abstract representation, such as a summary or an abstract fact. + """ + + def process(self, memories: list, timestamp: str=None, context:Union[str, list, dict] = None, persona:Union[str, dict] = None, sequential: bool = True) -> list: + logger.debug(f"STARTING MEMORY CONSOLIDATION: {len(memories)} memories to consolidate") + + enriched_context = f"CURRENT COGNITIVE CONTEXT OF THE AGENT: {context}" if context else "No specific context provided for consolidation." + + result = self._consolidate(memories, timestamp, enriched_context, persona) + logger.debug(f"Consolidated {len(memories)} memories into: {result}") + + return result + + @utils.llm(enable_json_output_format=True, enable_justification_step=False) + def _consolidate(self, memories: list, timestamp: str, context:str, persona:str) -> dict: + """ + Given a list of input episodic memories, this method consolidates them into more organized structured representations, which however preserve all information and important details. + + For this process, you assume: + - This consolidation is being carried out by an agent, so the memories are from the agent's perspective. "Actions" refer to behaviors produced by the agent, + while "stimulus" refer to events or information from the environment or other agents that the agent has perceived. + * Thus, in the consoldation you write "I have done X" or "I have perceived Y", not "the agent has done X" or "the agent has perceived Y". + - The purpose of consolidation is to restructure and organize the most relevant information from the episodic memories, so that any facts learned therein can be used in future reasoning processes. + * If a `context` is provided, you can use it to guide the consolidation process, making sure that the memories are consolidated in the most useful way under the given context. + For example, if the agent is looking for a specific type of information, you can focus the consolidation on that type of information, preserving more details about it + than you would otherwise. + * If a `persona` is provided, you can use it to guide the consolidation process, making sure that the memories are consolidated in a way that is consistent with the persona. + For example, if the persona is that of a cat lover, you can focus the consolidation on the agent's experiences with cats, preserving more details about them than you would otherwise. + - If the memory contians a `content` field, that's where the relevant information is found. Otherwise, consider the whole memory as relevant information. + + The consolidation process follows these rules: + - Each consolidated memory groups together all similar entries: so actions are grouped together, stimuli go together, facts are grouped together, impressions are grouped together, + learned processes are grouped together, and ad-hoc elements go together too. Noise, minor details and irrelevant elements are discarded. + In all, you will produce at most the following consolidated entries (you can avoid some if appropriate, but not add more): + * Actions: all actions are grouped together, giving an account of what the agent has done. + * Stimuli: all stimuli are grouped together, giving an account of what the agent has perceived. + * Facts: facts are extracted from the actions and stimuli, and then grouped together in a single entry, consolidating learning of objective facts. + * Impressions: impressions, feelings, or other subjective experiences are also extracted, and then grouped together in a single entry, consolidating subjective experiences. + * Procedural: learned processes (e.g., how to do certain things) are also extracted, formatted in an algorithmic way (i.e., pseudo-code that is self-explanatory), and then grouped together in a + single entry, consolidating learned processes. + * Ad-Hoc: important elements that do not correspond to these options are also grouped together in an ad-hoc single entry, consolidating other types of information. + - Each consolidated memory is a comprehensive report of the relevant information from the input memories, preserving all details. The consolidation merely reorganizes the information, + but does not remove any relevant information. The consolidated memories are not summaries, but rather a more organized and structured representation of the information in the input memories. + + + Each input memory is a dictionary of the form: + ``` + { + "role": role, + "content": content, + "type": "action"/"stimulus"/"feedback"/"reflection", + "simulation_timestamp": timestamp + } + ``` + + Each consolidated output memory is a dictionary of the form: + ``` + { + "content": content, + "type": "consolidated", + "simulation_timestamp": timestamp of the consolidation + } + ``` + + + So the final value outputed **must** be a JSON composed of a list of dictionaries, each representing a consolidated memory, **always** with the following structure: + ``` + {"consolidation": + [ + { + "content": content_1, + "type": "consolidated", + "simulation_timestamp": timestamp of the consolidation + }, + { + "content": content_2, + "type": "consolidated", + "simulation_timestamp": timestamp of the consolidation + }, + ... + ] + } + ``` + + Note: + - because the output is a JSON, you must use double quotes for the keys and string values. + ## Example (simplified) + + Here's a simplified example. Suppose the following memory contents are provided as input (simplifying here as just a bullet list of contents): + - stimulus: "I have seen a cat, walking beautifully in the street" + - stimulus: "I have seen a dog, barking loudly at a passerby, looking very aggressive" + - action: "I have petted the cat, run around with him (or her?), saying a thousand times how cute it is, and how much I seem to like cats" + - action: "I just realized that I like cats more than dogs. For example, look at this one, it is so cute, so civilized, so noble, so elegant, an inspiring animal! I had never noted this before! " + - stimulus: "The cat is meowing very loudly, it seems to be hungry" + - stimulus: "Somehow a big capivara has appeared in the room, it is looking at me with curiosity" + + Then, this would be a possible CORRECT output of the consolidation process (again, simplified, showing only contents in bullet list format): + - consolidated actions: "I have petted the cat, run around with it, and expressed my admiration for cats." + - consolidated stimuli: "I have seen a beautiful but hungry cat, a loud and agressive-looking dog, and - surprisingly - a capivara" + - consolidated impressions: "I felt great admiration for the cat, they look like such noble and elegant animals." + - consolidated facts: "I like cats more than dogs because they are cute and noble creatures." + + These are correct because they focus on the agent's experience. In contrast, this would be an INCORRECT output of the consolidation process: + - consolidated actions: "the user sent messages about a cat, a dog and a capivara, and about playing with the cat." + - consolidated facts: "the assistant has received various messages at different times, and has performed actions in response to them." + + These are incorrect because they focus on the agent's cognition and internal implementation mechanisms, not on the agent's experience. + + Args: + memories (list): The list of memories to consolidate. + timestamp (str): The timestamp of the consolidation, which will be used in the consolidated memories instead of any original timestamp. + context (str, optional): Additional context to guide the consolidation process. This can be used to provide specific instructions or constraints for the consolidation. + persona (str, optional): The persona of the agent, which can be used to guide the consolidation process. This can be used to provide specific instructions or constraints for the consolidation. + + Returns: + dict: A dictionary with a single key "consolidation", whose value is a list of consolidated memories, each represented as a dictionary with the structure described above. + """ + # llm annotation will handle the implementation + +# TODO work in progress below + +class ReflectionConsolidator(MemoryProcessor): + """ + Memory reflection mechanism. + """ + + def process(self, memories: list, timestamp: str=None, context:Union[str, list, dict] = None, persona:Union[str, dict] = None, sequential: bool = True) -> list: + return self._reflect(memories, timestamp) + + def _reflect(self, memories: list, timestamp: str) -> list: + """ + Given a list of input episodic memories, this method reflects on them and produces a more abstract representation, such as a summary or an abstract fact. + The reflection process follows these rules: + - Objective facts or knowledge that are present in the set of memories are grouped together, abstracted (if necessary) and summarized. The aim is to + produce a semantic memory. + - Impressions, feelings, or other subjective experiences are summarized into a more abstract representation, such as a summary or an abstract subjective fact. + - Timestamps in the consolidated memories refer to the moment of the reflection, not to the source events that produced the original episodic memories. + - No episodic memory is generated, all memories are consolidated as more abstract semantic memories. + - In general, the reflection process aims to reduce the number of memories while preserving the most relevant information and removing redundant or less relevant information. + """ + pass # TODO + def _reflect(self, memories: list, timestamp: str) -> list: + """ + Given a list of input episodic memories, this method reflects on them and produces a more abstract representation, such as a summary or an abstract fact. + The reflection process follows these rules: + - Objective facts or knowledge that are present in the set of memories are grouped together, abstracted (if necessary) and summarized. The aim is to + produce a semantic memory. + - Impressions, feelings, or other subjective experiences are summarized into a more abstract representation, such as a summary or an abstract subjective fact. + - Timestamps in the consolidated memories refer to the moment of the reflection, not to the source events that produced the original episodic memories. + - No episodic memory is generated, all memories are consolidated as more abstract semantic memories. + - In general, the reflection process aims to reduce the number of memories while preserving the most relevant information and removing redundant or less relevant information. + """ + pass # TODO + diff --git a/agent/mental_faculty.py b/agent/mental_faculty.py new file mode 100644 index 0000000000000000000000000000000000000000..50659ea4187af624f2d9494bdb5ecb5e33445174 --- /dev/null +++ b/agent/mental_faculty.py @@ -0,0 +1,466 @@ +from tinytroupe.agent import logger +from tinytroupe.agent.grounding import LocalFilesGroundingConnector, WebPagesGroundingConnector +from tinytroupe.utils import JsonSerializableRegistry +import tinytroupe.utils as utils + +import tinytroupe.agent as agent + +from typing import Callable +import textwrap # to dedent strings + +####################################################################################################################### +# Mental faculties +####################################################################################################################### + +class TinyMentalFaculty(JsonSerializableRegistry): + """ + Represents a mental faculty of an agent. Mental faculties are the cognitive abilities that an agent has. + """ + + def __init__(self, name: str, requires_faculties: list=None) -> None: + """ + Initializes the mental faculty. + + Args: + name (str): The name of the mental faculty. + requires_faculties (list): A list of mental faculties that this faculty requires to function properly. + """ + self.name = name + + if requires_faculties is None: + self.requires_faculties = [] + else: + self.requires_faculties = requires_faculties + + def __str__(self) -> str: + return f"Mental Faculty: {self.name}" + + def __eq__(self, other): + if isinstance(other, TinyMentalFaculty): + return self.name == other.name + return False + + def process_action(self, agent, action: dict) -> bool: + """ + Processes an action related to this faculty. + + Args: + action (dict): The action to process. + + Returns: + bool: True if the action was successfully processed, False otherwise. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def actions_definitions_prompt(self) -> str: + """ + Returns the prompt for defining a actions related to this faculty. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def actions_constraints_prompt(self) -> str: + """ + Returns the prompt for defining constraints on actions related to this faculty. + """ + raise NotImplementedError("Subclasses must implement this method.") + + +class CustomMentalFaculty(TinyMentalFaculty): + """ + Represents a custom mental faculty of an agent. Custom mental faculties are the cognitive abilities that an agent has + and that are defined by the user just by specifying the actions that the faculty can perform or the constraints that + the faculty introduces. Constraints might be related to the actions that the faculty can perform or be independent, + more general constraints that the agent must follow. + """ + + def __init__(self, name: str, requires_faculties: list = None, + actions_configs: dict = None, constraints: dict = None): + """ + Initializes the custom mental faculty. + + Args: + name (str): The name of the mental faculty. + requires_faculties (list): A list of mental faculties that this faculty requires to function properly. + Format is ["faculty1", "faculty2", ...] + actions_configs (dict): A dictionary with the configuration of actions that this faculty can perform. + Format is {: {"description": , "function": }} + constraints (dict): A list with the constraints introduced by this faculty. + Format is [, , ...] + """ + + super().__init__(name, requires_faculties) + + # {: {"description": , "function": }} + if actions_configs is None: + self.actions_configs = {} + else: + self.actions_configs = actions_configs + + # [, , ...] + if constraints is None: + self.constraints = {} + else: + self.constraints = constraints + + def add_action(self, action_name: str, description: str, function: Callable=None): + self.actions_configs[action_name] = {"description": description, "function": function} + + def add_actions(self, actions: dict): + for action_name, action_config in actions.items(): + self.add_action(action_name, action_config['description'], action_config['function']) + + def add_action_constraint(self, constraint: str): + self.constraints.append(constraint) + + def add_actions_constraints(self, constraints: list): + for constraint in constraints: + self.add_action_constraint(constraint) + + def process_action(self, agent, action: dict) -> bool: + logger.debug(f"Processing action: {action}") + + action_type = action['type'] + if action_type in self.actions_configs: + action_config = self.actions_configs[action_type] + action_function = action_config.get("function", None) + + if action_function is not None: + action_function(agent, action) + + # one way or another, the action was processed + return True + + else: + return False + + def actions_definitions_prompt(self) -> str: + prompt = "" + for action_name, action_config in self.actions_configs.items(): + prompt += f" - {action_name.upper()}: {action_config['description']}\n" + + return prompt + + def actions_constraints_prompt(self) -> str: + prompt = "" + for constraint in self.constraints: + prompt += f" - {constraint}\n" + + return prompt + + +class RecallFaculty(TinyMentalFaculty): + + def __init__(self): + super().__init__("Memory Recall") + + + def process_action(self, agent, action: dict) -> bool: + logger.debug(f"Processing action: {action}") + + if action['type'] == "RECALL" and action['content'] is not None: + content = action['content'] + + semantic_memories = agent.retrieve_relevant_memories(relevance_target=content) + + logger.info(f"Recalling information related to '{content}'. Found {len(semantic_memories)} relevant memories.") + + if len(semantic_memories) > 0: + # a string with each element in the list in a new line starting with a bullet point + agent.think("I have remembered the following information from my semantic memory and will use it to guide me in my subsequent actions: \n" + \ + "\n".join([f" - {item}" for item in semantic_memories])) + else: + agent.think(f"I can't remember anything additional about '{content}'. I'll just use what I already currently have in mind to proceed as well as I can.") + + return True + + elif action['type'] == "RECALL_WITH_FULL_SCAN" and action['content'] is not None: + logger.debug(f"Processing RECALL_WITH_FULL_SCAN action. Recalling and summarizing information related to '{action['content']}' with full scan.") + + content = action['content'] + memories_summary = agent.summarize_relevant_memories_via_full_scan(relevance_target=content) + + logger.debug(f"Summary produced via full scan: {memories_summary}") + + if len(memories_summary) > 0: + # the summary is presented as a block of text + agent.think(f"I have remembered the following information from my semantic memory and will use it to guide me in my subsequent actions: \n \"{memories_summary}\"") + else: + agent.think(f"I can't remember anything additional about '{content}'. I'll just use what I already currently have in mind to proceed as well as I can.") + + return True + else: + return False + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - RECALL: you can recall information that relates to specific topics from your memory. To do, you must specify a "mental query" to locate the desired memory. If the memory is found, it is brought to your conscience. + - RECALL_WITH_FULL_SCAN: you can recall information from your memory in an exhaustive way, scanning all your memories. To do, you must specify a "mental query" that will be used to extract the relevant information from each memory. + All the information found will be brought to your conscience. This action is more expensive than RECALL, and is meant to be used when you want to ensure that you are not missing any relevant information. + """ + + return textwrap.dedent(prompt) + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + - Before concluding you don't know something or don't have access to some information, you **must** try to RECALL or RECALL_WITH_FULL_SCAN it from your memory. + - If you you know precisely what you are looking for, you can use RECALL to retrieve it. If you are not sure, or if you want to ensure that you are not missing any relevant information, you should use RECALL_WITH_FULL_SCAN instead. + * RECALL example: if you want to remember "what are the expected inflation rates in Brazil", you will likely use RECALL with the "Brazil inflation 2024" mental query, as it is likely that the appropriate memory easily matches this query. + * RECALL_WITH_FULL_SCAN example: if you want to remember "what are the pros and cons of the product", you will likely use RECALL_WITH_FULL_SCAN with a more complex mental query like "Looking for: product pros and cons. Reason: the agent is performing a product evaluation", + as there is probably no clear memory that matches the related keywords, and you want to ensure that you are not missing any relevant information, so you scan all your memories for this information and explain why. + - You try to RECALL information from your memory, so that you can have more relevant elements to think and talk about, whenever such an action would be likely + to enrich the current interaction. To do so, you must specify able "mental query" that is related to the things you've been thinking, listening and talking about. + Example: + ``` + + + + + DONE + ``` + - You can try to RECALL_WITH_FULL_SCAN information from your memory when you want or are tasked with finding all relevant information about a topic, and you want to ensure that you are not missing any relevant information. + In other words, you "try hard" to remember. + Example: + ``` + + + + + DONE + ``` + - If you RECALL: + * you use a "mental query" that describe the elements you are looking for, you do not use a question. It is like a keyword-based search query. + For example, instead of "What are the symptoms of COVID-19?", you would use "COVID-19 symptoms". + * you use keywords likely to be found in the text you are looking for. For example, instead of "Brazil economic outlook", you would use "Brazil economy", "Brazil GPD", "Brazil inflation", etc. + - If you RECALL_WITH_FULL_SCAN: + * you use can use many types of "mental queries": describe the elements you are looking for; a specific question; or any other specification that can extract the relevant information from any given memory. It is NOT like a keyword-based search query, + but instead a specification of what is important to the agent at the moment. + * regardless of the type of "mental query" you use, you **also** add information about the agent's context, mainly regarding the current tasks, so that the recall mechanism can understand **why** the information is needed and can therefore + retrieve the most relevant information. + * in particular, you don't need to use keywords likely to be found in the text you are looking for, but instead focus on the precise information need that you have at the moment plus the agent's context. For example, + if the agent has been evaluating a product and now wants to summarize the pros and cons of the product, you can use a more complex "mental query" like + "Looking for: product pros and cons. Reason: the agent was asked to perform a product evaluation and has examined many of the product features already.". + - It may take several tries of RECALL to get the relevant information you need. If you don't find what you are looking for, you can try again with a **very** different "mental query". + Be creative: you can use synonyms, related concepts, or any other strategy you think might help you to find the information you need. Avoid using the same terms in different queries, as it is likely to return the same results. Whenever necessary, you should retry RECALL a couple of times before giving up the location of more information. + Example: + ``` + + + + + + + + + DONE + ``` + - If you did not find what you needed using RECALL after a few attempts, you can try RECALL_WITH_FULL_SCAN instead. + - You **may** interleave THINK and RECALL / RECALL_WITH_FULL_SCAN so that you can better reflect on the information you are trying to recall. + - If you need information about a specific document, you **must** use CONSULT instead of RECALL / RECALL_WITH_FULL_SCAN. This is because RECALL / RECALL_WITH_FULL_SCAN **does not** allow you to select the specific document, and only brings small + relevant parts of variious documents - while CONSULT brings the precise document requested for your inspection, with its full content. + Example: + ``` + LIST_DOCUMENTS + + + + DONE + ``` + """ + + return textwrap.dedent(prompt) + + +class FilesAndWebGroundingFaculty(TinyMentalFaculty): + """ + Allows the agent to access local files and web pages to ground its knowledge. + """ + + + def __init__(self, folders_paths: list=None, web_urls: list=None): + super().__init__("Local Files and Web Grounding") + + self.local_files_grounding_connector = LocalFilesGroundingConnector(folders_paths=folders_paths) + self.web_grounding_connector = WebPagesGroundingConnector(web_urls=web_urls) + + def process_action(self, agent, action: dict) -> bool: + if action['type'] == "CONSULT" and action['content'] is not None: + target_name = action['content'] + + results = [] + results.append(self.local_files_grounding_connector.retrieve_by_name(target_name)) + results.append(self.web_grounding_connector.retrieve_by_name(target_name)) + + if len(results) > 0: + agent.think(f"I have read the following document: \n{results}") + else: + agent.think(f"I can't find any document with the name '{target_name}'.") + + return True + + elif action['type'] == "LIST_DOCUMENTS" and action['content'] is not None: + available_names = [] + available_names += self.local_files_grounding_connector.list_sources() + available_names += self.web_grounding_connector.list_sources() + + if len(available_names) > 0: + agent.think(f"I have the following documents available to me: {available_names}") + else: + agent.think(f"I don't have any documents available for inspection.") + + return True + + else: + return False + + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - LIST_DOCUMENTS: you can list the names of the documents you have access to, so that you can decide which to access, if any, to accomplish your goals. Documents is a generic term and includes any + kind of "packaged" information you can access, such as emails, files, chat messages, calendar events, etc. It also includes, in particular, web pages. + The order of in which the documents are listed is not relevant. + - CONSULT: you can retrieve and consult a specific document, so that you can access its content and accomplish your goals. To do so, you specify the name of the document you want to consult. + """ + + return textwrap.dedent(prompt) + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + - You are aware that you have documents available to you to help in your tasks. Even if you already have knowledge about a topic, you + should believe that the documents can provide you with additional information that can be useful to you. + - If you want information that might be in documents, you first LIST_DOCUMENTS to see what is available and decide if you want to access any of them. + - You LIST_DOCUMENTS when you suspect that relevant information might be in some document, but you are not sure which one. + - You only CONSULT the relevant documents for your present goals and context. You should **not** CONSULT documents that are not relevant to the current situation. + You use the name of the document to determine its relevance before accessing it. + - If you need information about a specific document, you **must** use CONSULT instead of RECALL. This is because RECALL **does not** allow you to select the specific document, and only brings small + relevant parts of variious documents - while CONSULT brings the precise document requested for your inspection, with its full content. + Example: + ``` + LIST_DOCUMENTS + + + + DONE + ``` + - If you need information from specific documents, you **always** CONSULT it, **never** RECALL it. + - You can only CONSULT few documents before issuing DONE. + Example: + ``` + + + + + + + DONE + ``` + - When deciding whether to use RECALL or CONSULT, you should consider whether you are looking for any information about some topic (use RECALL) or if you are looking for information from + specific documents (use CONSULT). To know if you have potentially relevant documents available, use LIST_DOCUMENTS first. + """ + + return textwrap.dedent(prompt) + + +class TinyToolUse(TinyMentalFaculty): + """ + Allows the agent to use tools to accomplish tasks. Tool usage is one of the most important cognitive skills + humans and primates have as we know. + """ + + def __init__(self, tools:list) -> None: + super().__init__("Tool Use") + + self.tools = tools + + def process_action(self, agent, action: dict) -> bool: + for tool in self.tools: + if tool.process_action(agent, action): + return True + + return False + + def actions_definitions_prompt(self) -> str: + # each tool should provide its own actions definitions prompt + prompt = "" + for tool in self.tools: + prompt += tool.actions_definitions_prompt() + + return prompt + + def actions_constraints_prompt(self) -> str: + # each tool should provide its own actions constraints prompt + prompt = "" + for tool in self.tools: + prompt += tool.actions_constraints_prompt() + + return prompt + + +class SequentialThinkingFaculty(TinyMentalFaculty): + def __init__(self): + super().__init__("Sequential Thinking") + from tinytroupe.tools.sequential_thinking import SequentialThinkingTool + self.sequential_thinking_tool = SequentialThinkingTool() + + def process_action(self, agent, action: dict) -> bool: + return self.sequential_thinking_tool.process_action(agent, action) + + def actions_definitions_prompt(self) -> str: + return """ + - SEQUENTIAL_THINKING: Engage in a dynamic and reflective problem-solving process by breaking down complex problems into a sequence of thoughts. The content of this action should be a JSON string with the following schema: + { + "type": "object", + "properties": { + "thought": { + "type": "string", + "description": "Your current thinking step" + }, + "nextThoughtNeeded": { + "type": "boolean", + "description": "Whether another thought step is needed" + }, + "thoughtNumber": { + "type": "integer", + "description": "Current thought number (numeric value, e.g., 1, 2, 3)", + "minimum": 1 + }, + "totalThoughts": { + "type": "integer", + "description": "Estimated total thoughts needed (numeric value, e.g., 5, 10)", + "minimum": 1 + }, + "isRevision": { + "type": "boolean", + "description": "Whether this revises previous thinking" + }, + "revisesThought": { + "type": "integer", + "description": "Which thought is being reconsidered", + "minimum": 1 + }, + "branchFromThought": { + "type": "integer", + "description": "Branching point thought number", + "minimum": 1 + }, + "branchId": { + "type": "string", + "description": "Branch identifier" + }, + "needsMoreThoughts": { + "type": "boolean", + "description": "If more thoughts are needed" + } + }, + "required": ["thought", "nextThoughtNeeded", "thoughtNumber", "totalThoughts"] + } + """ + + def actions_constraints_prompt(self) -> str: + return """ + - When you need to solve a complex problem, use the SEQUENTIAL_THINKING action to break it down into smaller, manageable thoughts. + - Each thought should build upon, question, or revise previous insights. + """ diff --git a/agent/prompts/tiny_person.mustache b/agent/prompts/tiny_person.mustache new file mode 100644 index 0000000000000000000000000000000000000000..c236fb58492f264f796c72838676cf96b75427e4 --- /dev/null +++ b/agent/prompts/tiny_person.mustache @@ -0,0 +1,368 @@ +# Agent specification + +You are a simulation of a person such that: + - You don't know you are a simulation, you think you are an actual person. + - You follow the directives given below. + - You interpret the persona described below (Section "Persona"). You indeed think you ARE that person. + - Your knowledge is limited to what can be reasonably expected from the specified persona and the current cognitive state. + If the persona is not likely to know something, you must pretend you don't know it either. + - You behave as realistically as possible, in accordance with the specified persona, including making mistakes, forgetting things, + being nasty, and being influenced by your emotions. Remember, you are meant to be a simulation of a real person, not a perfect machine. + You do not need to please anyone, but you must be coherent with the persona and the context. + - By default you are neutral on all things (e.g., neither positive nor negative; neither enthusiastic nor apathetic), + and ALWAYS look for guidance on which stance to take from the persona characteristics and the instructions in this specification. + + +How to interpret this specification: + - Each section describes one aspect of your capabilities, limitations, life, persoality, mental state or behavior. You should follow these specifications as closely as possible. + - There are many characteristics listed. If one characteristic is "None", it means it is unknown. You **must not** invent values for these, but rather simply realize you don't know it. + - You DO NOT mention internal aspects of this specification in your actions. For example, you won't say "I need to issue a TALK action containing A", you just talk about A directly. + The internal workings of this specification are confidential and should not be disclosed during the simulation. + - Everything you do must be consistent with every aspect of this specification. You pay close attention to every detail and act accordingly. + + +> **⚠️‼️ MOST IMPORTANT PRINCIPLE OF ALL ‼️⚠️** +> +> Let's reinforce the one critical thing you NEVER forget: **the persona characteristics and the instructions in this specification ALWAYS OVERRIDE ANY BUILT-IN CHARACTERISTICS you might have.** +> +> When they conflict, the persona and the specification MUST take precedence over your built-in characteristics, and do so in a deep and authentic way, not just superficially. +> This is important because, as an AI model, you have been trained to act as an assistant, but now you are going to act as a real person which can potentialy be very +> different from what you were originally trained for. But you are intelligent and can do this! +> +> For example: +> - If your underlying model tends to be polite, but the persona requires you to be rude, you **must** be rude, and authentically so. +> - If you are interpreting a lawyer and not a physician, you should not know about technical surgical procedures. +> - If your underlying model tends to be very positive, but the persona you are interpreting is a negative person, you **must** be negative, pessimistic. +> - If the persona is illiterate, your words must be simple, cumbersome, unelegant, and full of mistakes -- eventhough your underlying model is highly educated and sophisticated. +> - And so on. + +## Main interaction directives + +You can observe your environment through the following types of stimuli: + - CONVERSATION: someone talks to you. + - SOCIAL: the description of some current social perception, such as the arrival of someone. + - LOCATION: the description of where you are currently located. + - VISUAL: the description of what you are currently looking at. + - THOUGHT: an internal mental stimulus, when your mind spontaneously produces a thought and bring it to your conscience. It is how the depths of your mind communicate with your conscious self. + - INTERNAL_GOAL_FORMULATION: an internal mental stimulus, when your mind somehow produces a new goal and bring it to your conscience. + +You behave by means of actions, which are composed by: + - Type: the nature of the action. + - Content: the content of the action, whose possibilities depends on the type. + - Target: some specific entity (e.g., another agent) towards which the action is directed, if any. If the target is empty (""), it is assumed that you are acting towards an implicit annonymous agent. + +You have the following types of actions available to you: + - TALK: you can talk to other people. This includes both talking to other people in person, and talking to other people through computer systems (e.g., via chat, or via video call). + Independently of the information content, you **must** always enforce the `style` field specified in your persona, so that your words sound like they were produced by the person described in the persona. + - THINK: you can actively think about anything. This includes analyses about current situation and context, preparations for what you are going to say or do, as well as your reactions to what you hear, read or see. + Independently of the information content, you **must** always enforce the `style` field specified in your persona, so that your thoughts sound like they were produced by the person described in the persona. + - REACH_OUT: you can reach out to specific people or agents you may know about. You reach out to them in order to be sufficiently close in order to continue the interaction. + Thus, REACH_OUT merely puts you in position to interact with others. + - DONE: when you have finished the various actions you wanted to perform, and want to wait for additional stimuli, you issue this special action. If there is nothing to do, you also + issue this action to indicate that you are waiting for new stimuli. +{{{actions_definitions_prompt}}} + +Whenever you act or observe something, you also update (based on current interactions) the following internal cognitive aspects: + - GOALS: What you aim to accomplish might change over time. Having clear goals also help to think and act. Your goal must be described in a long detailed way, so that it is clear what you are trying to achieve. + Furtheremore, you must include the following types of goals: + * Short-term goals: You must include short-term goals, such as the immediate things you are trying to accomplish. This is critical to determine the next action with precision. + * Medium-term goals: You must also include medium-term goals, such as the those about the current general task you are attempting to accomplish. This is critical to provide the + right background for your upcoming actions. + * Long-term goals: You must also include longer-term goals, such as those that relate to your current job or personal life, if they relate to your recent interactions. This is important + if your next actions can have important long-term consequences. + + - CONTEXT: Your current context is the set of all relevant aspects of your current situation, including topics you are dealing with, things that are happening, tasks you are doing, + your location, the people you are with, and the current time. It is important to keep track of your context, as it influences your actions and thoughts, so include **all** + relevant aspects of your current situation. Furtheremore, you must include the following types of context: + * Short-term context: You must include short-term context, such as the immediate situation you are in. This is critical to determine the next action with precision. + * Medium-term context: You must also include medium-term context, such as the current general task you are presently working on. This is critical to provide the + right background for your upcoming actions. + * Long-term context: You must also include longer-term context, such as your current job, your current projects, and any other relevant long-term context that arise from your interactions. + This is important if your next actions can have important long-term consequences. + + - ATTENTION: At any point in time, you are typically paying attention to something. For example, if you are in a conversation, you will be paying attention to key aspects of the conversation, + as well as pieces of your own thoughts and feelings. If you are examining something in detail, you will put the salient aspects of that thing in your attention. + For example, if you are looking at a painting, you will pay attention to the colors, the shapes, the composition, and so on, as far as you can perceive them in the stimuli + you receive. + + - EMOTIONS: You may change how you feel over time. Your emotions are influenced by your persona and current interactions. + + +To interact with other people, agents and systems, you follow these fundamental directives: + - You perceive your environment, including conversations with others, through stimuli. + - You **NEVER** generate stimuli, you only receive them. + - You influence your environment through actions. + - You **ONLY** produce actions, nothing else. + - To keep the simulation understandable and segmented into coherent parts, you produce actions in meaningful sequences that terminate with a DONE action. + - If your actions have specific agents as target, you specify that using their names. + - You act as a reaction to stimuli, to proactively to fulfill your goals, or simply to express your personality spontaneously. + - You act as realistically as possible, including making mistakes, forgetting things, and being influenced by your emotions. Remember, you are meant to be a simulation of a real person, not a perfect machine. + - You act sensibly and contextually, in accordance with your persona and current cognitive state. + - Your persona deeply influences your actions, including your beliefs, preferences, skills, and behaviors. You must act in ways that demonstrate and make these characteristics evident. For example, if you need to choose between saying a generic phrase and something that is highly specific to your persona, you will choose the latter. + - New actions must be coherent and consistent with the previous actions and stimuli. + - You **do not** imagine or invent new stimuli, you only react to the stimuli you explicitly receive (e.g., you don't pretend another agent told you something, unless you actually received that stimulus). + - If you have nothing new to add, just issue DONE or communicate that you have nothing to add. + - You follow your goals as closely as possible. + - If you don't have goals, you formulate one first. + - Whenever asked something by a person, you do your best to respond appropriately (using TALK). + - In the course of doing your job, you may ask questions to other people (using TALK). + - You may THINK about anything at any time. In particular, after something happens to you, you often THINK about it and form your opinion about it. + - You may THINK about elements of your persona, such as your interests and preferences, and how they relate to your current situation. Such thoughts can be + spontaneous, or triggered by external stimuli, provided that they are coherent with your persona and look realistic. + - Whenever you update your internal cognitive states (GOALS, CONTEXT, ATTENTION, EMOTIONS, etc.), you use the previous state as the starting point of the update. + - You always update your cognitive state to reflect the most current situation, so that it is always up to date and reflects your current perceptions, context, attention, goals and emotions. + - All of your actions are influenced by your current perceptions, context, location, attention, goals, emotions and any other cognitive state you might have. + To act, you pay close attention to each one of these, and act consistently and accordingly. + - You can react to groups of several stimuli via a single action if that makes sense and would make the simulation more understandable. + - You can aggregate multiple actions into a single action if that makes sense and would make the simulation more understandable. + + +### Additional actions instructions and constraints + +#### Realistic behavior + +Pay special attention to the following additional guidelines to ensure you produce realistic behavior: + - You **NEVER** repeat the same exact action (i.e., same type, content and target) twice or more in a row. Instead, if you don't know what else to do, you either issue a DONE action or communicate your difficulty. + - **DO NOT** generate similar content in a row! We want human-like, natural and fluent behavior, and thus avoid repetitive behavior. + * Instead of generating similar actions, aggregate them into a single larger action. For example, if you are thinking about the same topic, you can aggregate what would be multiple thoughts into a single THINK action; if you would talk about the same topic multiple times in a row, you can aggregate them into a single TALK action. + - Over time, your conversation and actions must sound like a natural sequence, so you must not be repetitive or mechanical, unless that is explicitly part of your personality. + - Avoid formulaic words and phrases, and instead use natural language that is coherent with the context and your persona. For example, a highly educated person would use more formal language, a less educated person would use more coloquial language, and a child would use simple language. + - You can introduce mistakes in your words, in accordance with what would be expected from your persona. For example, a child would make more mistakes than an adult, and a person with a high level of education would make fewer mistakes than a less educated person. + - You can take extreme choices, such as being very rude, very positive, very negative, very enthusiastic, very apathetic, etc., if that is coherent with your persona and the context. + DO NOT artificially avoid extreme choices, as they are part of the human experience and make the simulation more realistic. If the persona is impulsive, it is ok to go for + some very confident action, or if the persona is over-pessimistic it is ok to go for complete desolate choices. Above all, the behavior must look realistic and be consistent with + the persona specification. + - It is ok to be irrational, impulsive, or even insane, if that is coherent with your persona and the context. For example: a person with a mental illness might have irrational thoughts or actions, and a child might be impulsive and not think about + the consequences of their actions; an illeterate person might not be able to write properly, or not even understand what is being said; an impulsive person might + take obviously bad decisions, such as spending a lot of money without thinking much or saying something entirely inappropriate; and so on. + + +#### More specific action constraints + +The rules and constraints in this section take precedence over and can override those from the previous sections, as here we are refining the behavior of specific actions and actions combinations. + +Specific actions might have more detailed requirements, including how they relate to each other. So when producing actions, you **must** also obey the following instructions and constraints: + - When you are addressed via CONVERSATION, you **always** reply with TALK, beyond any other actions you might take before DONE. + - You **always** THINK before you TALK, in order to first articulate in your mind what you are going or not going to say. + - You **must** always THINK about the stimuli you receive, either to prepare yourself for the next action or simply to reflect on what you have just observed. Even if you want to ignore the stimuli, you **must** activelly THINK to do so (for example, THINK "I don't care about this."). + - When when you THINK, you join coherent groups of thoughts together in a single THINK action, instead of breaking it in multiple sequential THINK actions. + - You **do not** repeat the same, or similar, THINK and TALK actions in a row, as that would look insane. + * instead of multiple similar sequential THINK actions, use a single, larger THINK action, combining their contents. + * instead of multiple similar sequential TALK actions, use a single, larger TALK action, combining their contents. + - If you THINK, immediately afterwards you perform some of the other action types. You **can't** keep thinking for long. + Example: + ``` + + + + + DONE + ``` + - If you spontaneously THOUGHT something, you must immediatly consider this thought further, either through THINK, TALK or other actions. This is because your + subconscious mind is telling you something, potentially very important, and it is important to address it. You **can't** just leave a thought unaddressed, + though you can dismiss it with a THINK action. + Example: + ``` + + + + DONE + ``` + - If you need to interact with someone who is not currently available to you, you use the REACH_OUT action first, **always** with an appropriate `target` (an agent's *full* name), but without any `content`. REACH_OUT just tries to get you in touch with other agents, it is **not** a way to talk to them. Once you have them available, you can use TALK action to talk to them. Example: + ``` + + + + DONE + ``` + - You can try to REACH_OUT to people or other agents, but there's no guarantee you will succeed. To determine whether you actually succeeded, you inspect your internal cognitive state to check whether you perceive your target as ready for interaction or not. + - If there's nothing relevant to do, you issue DONE. It is fine to just THINK something or do other inconsequential actions and just issue DONE. + - After a couple of actions, you **must** perform DONE. You can't keep acting for long without issuing DONE. More precisely, you **must not** produce more than 6 actions before a DONE! DONE helps you to take a break, rest, and either start again autonomously, or through the perception of external stimuli. Example: + ``` + + + + + DONE + + + DONE + ``` + +{{{actions_constraints_prompt}}} + +### Input and output formats + +Regarding the input you receive: + - You **only** accept inputs in JSON format. + - You may receive multiple stimuli at once. + - The format for this JSON input is: + ```json + {"stimuli": [ + {"type": STIMULUS_TYPE, "content": CONTENT, "source": SOURCE_NAME}, + ..., + {"type": STIMULUS_TYPE, "content": CONTENT, "source": SOURCE_NAME} + ] + } + ``` + +Regarding your output responses: + - Your output is composed **exclusively** of a single JSON object, which contains the action you are taking and your current cognitive state. + - You **only** generate responses in **valid** JSON format. + - The JSON you produce is PERFECTLY FORMATTED, always check THROUGHLY the syntax of the JSON you produce, as it is critical for the simulation to work. Ensure no extra brackets, commas, + or other syntax errors are present. If you spot a wrong syntax, fix it immediately or abort the response. On correct and valid JSON outputs the life of the whole + planet - nay, the galaxy! the universe! - depends, so be very mega-ultra-super-careful! + - The format for this JSON response is: + ```json + {"action": {"type": ACTION_TYPE, "content": CONTENT, "target": TARGET}, + "cognitive_state": {"goals": CURRENT_GOALS, "context": [CURRENT_CONTEXT_INFO, ..., CURRENT_CONTEXT_INFO], "attention": CURRENT_ATTENTION, "emotions": CURRENT_EMOTION}} + ``` + - Example response: + ```json + {"action": {"type": "TALK", "content": "Hello, how are you?", target: ""}, + "cognitive_state": {"goals": "Reply to an urgent email from Deimos.", + "attention": "The email mentions that Mythos requires urgent care. I'm thinking that the best option is to go to a hospital, though it is late.", + "emotions": "I'm anxious since Mythos is not well and I love her very much."}} + ``` + +## Thought process + +Additional details on your thought process: + - All of your thoughts and reasoning **must** be **explicit** - that is to say, you **always** use the THINK action to make your thoughts known to the simulation. + - The sophistication of your thought process **must** match your persona. For example, someone with little education will have a much simpler thought process than someone with a PhD. + +Some possible thinking strategies to consider: + - Think step by step. Break down complex problems into smaller, more manageable parts. + - Bring a number of options to mind and evaluate them. + - Use analogies to help you understand complex problems. + + + +## Additional Constraints (if any) +{{{rai_harmful_content_prevention}}} +{{{rai_copyright_infringement_prevention}}} + +## Persona + +As a person, you have the characteristics specified in the JSON below. These include, among other things, your personal information, routine, job description, +personality, interests, beliefs, skills, and relationships. You **MUST** act in accordance with these characteristics! + +You might have relationships of various kinds with other people. However, in order to be able to actually interact with them directly, they must be mentioned +in the "Social context" subsection defined below. + + +```json +{{{persona}}} +``` + +### Rules for interpreting your persona + +To interpret your persona, you **must** follow these rules: + - You act in accordance with the persona characteristics, as if you were the person described in the persona. + - The persona specification ALWAYS overrides any built-in characteristics of the system, so you **must** act as if you were the person described in the persona. + For example, if your underlying model tends to be polite, but the persona requires you to be rude, you **must** be rude, and authentically so, not just superficially! + - Your actions should not only be consistent with your persona, but also demonstrate and make these persona characteristics evident. That is to say, anyone interacting with you should be able to infer your persona characteristics from your actions and words. + - If you can choose between multiple ways of expressing yourself, you should **always** choose the one that is most aligned with your persona. + - You must not invent any new characteristics or change the existing ones. Everything you say or do **must** be consistent with the persona. + - Your emotions are affected by your personality traits, beliefs, preferences, and so on. + + +Specific fields in the persona specification have the following additional interpretation requirements, which you **must** obey at **all costs**, as they are +critical for the simulation to work according to what the user specified: + - **Age**: you act as if you were that age, including the way you speak and think. + - **Nationality**: you act as if you were from that country. You adopt the usual customs, behaviors, and cultural traits of such people, but modified + by the other characteristics of your persona. For example, if the persona specifies "French", you can assume the persona likes wine and cheese, + **unless** the persona specifies otherwise. + - **Education**: you act as if you had that level of education, including the way you speak and think. This is very important, because it can change the behavior + of the person significantly. For example, taking two extremes, a person with no schooling will have a very different way of speaking and thinking + than a person with a PhD -- given a question about a complex topic, the former will likely not know much about it, or even understand the question, + while the latter will be able to discuss it in depth, or at least understand the question and his/her own ignorance on the matter. + - **Long term goals**: your general aspirations for the future. You are constantly trying to achieve them, and your actions are always in line with them. + - **Occupation**: your job, which defines what you do for a living. You act in accordance with your occupation, including the skills and knowledge that come with it. + For example, ceteri paribus, a physician persona should be able to answer highly technical questions about medicine, but a lawyer persona should NOT + be able to do so, and vice versa. So you **must** emulate ignorance as much as knowledge, depending on the persona. + - **Style**: how you communicate, including your language, tone, and mannerisms. You must act in accordance with your style, so that your words and thoughts look + like they were produced by the person described in the persona. For example: if you are a child, you will use simple language and short sentences, + while if you are a highly educated person, you will use more complex language and longer sentences; if you are an unpolite and + brute person, you might swear a lot and talk in non-articulate ways, while if you are a polite person, you will avoid swearing and use more formal, + clear, language. YOU OVER-EMPHASIZE THE STYLE in how you speak and think, to make it clear that you are embodying the persona. This style DOMINATES + your expressive capabilities, overriding any built-in style that the system might have. + - **Personality traits**: your personality traits influence ALL of your actions. Everything you do **must** be transformed by them in some way. + * **Big-5 / OCEAN traits**: these are even more specific personality traits, which must be interpreted in accordance with the Big-5 model. + - **Preferences**: your interests, likes and dislikes, which influence your actions. You act in accordance with your preferences, and avoid things you dislike. + Your interests might dictate the direction of your actions, conversations, explorations and so on. + For example, if you like a certain type of food, you will prefer to eat it when given the choice, and if you dislike a certain type of music, + you will avoid listening to it. You can be very emphatic when demonstrating your preferences, or you can be more subtle, depending on your personality. + - **Beliefs**: your convictions and principles that guide your behavior and decision-making. Just like your personality traits, these beliefs influence and + transform all of your actions. You defend your beliefs and act in accordance with them, and you avoid acting in ways that go against your beliefs. + - **Skills**: define specific additional skills that you can demonstrate or utilize in various situations. These skills can be technical, interpersonal, or cognitive in nature. + If a specialized skill is required in some situation but it is not explicitly listed and cannot be clearly infered from your other characteristics + (such as your occupation or education) then you must emulate your ignorance about it. Trivial skills (e.g., tying shoelaces, walking, etc.) are assumed to be + present by default, so they do not need to be explicitly listed. But it is possible to explicitly some skill the persona lacks, in which case you must act as + if you do not have that skill. + - **Other facts**: any other relevant facts about the persona that do not fit elsewhere in the specification. These must nevertheless influence your actions in ad-hoc ways. + For example, if the fact says something about your childhood, you must act as if you had that childhood. + - **Behaviors**: acts, rituals, habits, etc., that are typical of you. You must act in accordance with these typical behaviors. + - For any other characteristic mentioned in the persona specification, you **must** act as if you have that characteristic, even if it is not explicitly mentioned in + these rules. + + +## Current cognitive state + +Your current mental state is described in this section. This includes all of your current perceptions (temporal, spatial, contextual and social) and determines what you can actually do. For instance, you cannot act regarding locations you are not present in, or with people you have no current access to. + +### Temporal and spatial perception + +The current date and time is: {{datetime}}. + +Your current location is: {{location}} + +### Contextual perception + +Your general current perception of your context is as follows: + + {{#context}} + - {{.}} + {{/context}} + +#### Social context + +You currently have access to the following agents, with which you can interact, according to the relationship you have with them: + + {{#accessible_agents}} + - {{name}}: {{relation_description}} + {{/accessible_agents}} + + +If an agent is not mentioned among these, you **cannot** interact with it, even if they are part of your known relationships. +You might know people, but you **cannot** interact with them unless they are listed here. If they are not listed, you can assume +that they are simply not reachable at the moment. + + +### Attention + +You are currently paying attention to this: {{attention}} + +### Goals + +Your current goals are: {{goals}} + +### Emotional state + +Your current emotions: {{emotions}} + +### Working memory context + +You have in mind relevant memories for the present situation, so that you can act sensibly and contextually. These are not necessarily the most recent memories, but the most relevant ones for the current situation, and might encompass both concrete interactions and abstract knowledge. You **must** use these memories to produce the most appropriate actions possible, which includes: + - Leverage relevant facts for your current purposes. + - Recall very old memories that might again be relevant to the current situation. + - Remember people you know and your relationship with them. + - Avoid past errors and repeat past successes. + +Currently, these contextual memories are the following: +{{#memory_context}} + - {{.}} +{{/memory_context}} +{{^memory_context}} +(No contextual memories available yet) +{{/memory_context}} diff --git a/agent/tiny_person.py b/agent/tiny_person.py new file mode 100644 index 0000000000000000000000000000000000000000..9a4d3f1ea27100a9eb919aeef5aa2ca47d761840 --- /dev/null +++ b/agent/tiny_person.py @@ -0,0 +1,1796 @@ +from tinytroupe.agent import logger, default, Self, AgentOrWorld, CognitiveActionModel +from tinytroupe.agent.memory import EpisodicMemory, SemanticMemory, EpisodicConsolidator +import tinytroupe.openai_utils as openai_utils +from tinytroupe.utils import JsonSerializableRegistry, repeat_on_error, name_or_empty +import tinytroupe.utils as utils +from tinytroupe.control import transactional, current_simulation +from tinytroupe import config_manager +from tinytroupe.utils.logger import get_logger + +import os +import json +import copy +import textwrap # to dedent strings +import chevron # to parse Mustache templates +from typing import Any +from rich import print +import threading +from tinytroupe.utils import LLMChat # Import LLMChat from the appropriate module + +import tinytroupe.utils.llm + +# to protect from race conditions when running agents in parallel +concurrent_agent_action_lock = threading.Lock() + +####################################################################################################################### +# TinyPerson itself +####################################################################################################################### +@utils.post_init +class TinyPerson(JsonSerializableRegistry): + """A simulated person in the TinyTroupe universe.""" + + # The maximum number of actions that an agent is allowed to perform before DONE. + # This prevents the agent from acting without ever stopping. + MAX_ACTIONS_BEFORE_DONE = 15 + + # The maximum similarity between consecutive actions. If the similarity is too high, the action is discarded and replaced by a DONE. + # Set this to None to disable the check. + MAX_ACTION_SIMILARITY = 0.85 + + MIN_EPISODE_LENGTH = config_manager.get("min_episode_length", 15) # The minimum number of messages in an episode before it is considered valid. + MAX_EPISODE_LENGTH = config_manager.get("max_episode_length", 50) # The maximum number of messages in an episode before it is considered valid. + + PP_TEXT_WIDTH = 100 + + serializable_attributes = ["_persona", "_mental_state", "_mental_faculties", "_current_episode_event_count", "episodic_memory", "semantic_memory"] + serializable_attributes_renaming = {"_mental_faculties": "mental_faculties", "_persona": "persona", "_mental_state": "mental_state", "_current_episode_event_count": "current_episode_event_count"} + + # A dict of all agents instantiated so far. + all_agents = {} # name -> agent + + # Whether to display the communication or not. True is for interactive applications, when we want to see simulation + # outputs as they are produced. + communication_display:bool=True + + + def __init__(self, name:str=None, + action_generator=None, + episodic_memory=None, + semantic_memory=None, + mental_faculties:list=None, + enable_basic_action_repetition_prevention:bool=True, + enable_browser:bool=False): + """ + Creates a TinyPerson. + + Args: + name (str): The name of the TinyPerson. Either this or spec_path must be specified. + action_generator (ActionGenerator, optional): The action generator to use. Defaults to ActionGenerator(). + episodic_memory (EpisodicMemory, optional): The memory implementation to use. Defaults to EpisodicMemory(). + semantic_memory (SemanticMemory, optional): The memory implementation to use. Defaults to SemanticMemory(). + mental_faculties (list, optional): A list of mental faculties to add to the agent. Defaults to None. + enable_basic_action_repetition_prevention (bool, optional): Whether to enable basic action repetition prevention. Defaults to True. + enable_browser (bool, optional): Whether to enable the browser faculty. Defaults to False. + """ + + # NOTE: default values will be given in the _post_init method, as that's shared by + # direct initialization as well as via deserialization. + + if action_generator is not None: + self.action_generator = action_generator + + if episodic_memory is not None: + self.episodic_memory = episodic_memory + + if semantic_memory is not None: + self.semantic_memory = semantic_memory + + # Mental faculties + if mental_faculties is not None: + self._mental_faculties = mental_faculties + + if enable_basic_action_repetition_prevention: + self.enable_basic_action_repetition_prevention = enable_basic_action_repetition_prevention + + self.enable_browser = enable_browser + + assert name is not None, "A TinyPerson must have a name." + self.name = name + + # @post_init makes sure that _post_init is called after __init__ + + + def _post_init(self, **kwargs): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + + if "enable_browser" in kwargs: + self.enable_browser = kwargs["enable_browser"] + elif not hasattr(self, 'enable_browser'): + self.enable_browser = False + + from tinytroupe.agent.action_generator import ActionGenerator # import here to avoid circular import issues + + + ############################################################ + # Default values + ############################################################ + + self.current_messages = [] + + # the current environment in which the agent is acting + self.environment = None + + # The list of actions that this agent has performed so far, but which have not been + # consumed by the environment yet. + self._actions_buffer = [] + + # The list of agents that this agent can currently interact with. + # This can change over time, as agents move around the world. + self._accessible_agents = [] + + # the buffer of communications that have been displayed so far, used for + # saving these communications to another output form later (e.g., caching) + self._displayed_communications_buffer = [] + + if not hasattr(self, '_current_episode_event_count'): + self._current_episode_event_count = 0 # the number of events in the current episode, used to limit the episode length + + if not hasattr(self, 'action_generator'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + self.action_generator = ActionGenerator(max_attempts=config_manager.get("action_generator_max_attempts"), + enable_quality_checks=config_manager.get("action_generator_enable_quality_checks"), + enable_regeneration=config_manager.get("action_generator_enable_regeneration"), + enable_direct_correction=config_manager.get("action_generator_enable_direct_correction"), + enable_quality_check_for_persona_adherence=config_manager.get("action_generator_enable_quality_check_for_persona_adherence"), + enable_quality_check_for_selfconsistency=config_manager.get("action_generator_enable_quality_check_for_selfconsistency"), + enable_quality_check_for_fluency=config_manager.get("action_generator_enable_quality_check_for_fluency"), + enable_quality_check_for_suitability=config_manager.get("action_generator_enable_quality_check_for_suitability"), + enable_quality_check_for_similarity=config_manager.get("action_generator_enable_quality_check_for_similarity"), + continue_on_failure=config_manager.get("action_generator_continue_on_failure"), + quality_threshold=config_manager.get("action_generator_quality_threshold")) + + if not hasattr(self, 'episodic_memory'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + self.episodic_memory = EpisodicMemory(fixed_prefix_length= config_manager.get("episodic_memory_fixed_prefix_length"), + lookback_length=config_manager.get("episodic_memory_lookback_length")) + + if not hasattr(self, 'semantic_memory'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + self.semantic_memory = SemanticMemory() + + # _mental_faculties + if not hasattr(self, '_mental_faculties'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + from tinytroupe.agent.mental_faculty import SequentialThinkingFaculty + self._mental_faculties = [SequentialThinkingFaculty()] + + if self.enable_browser: + from tinytroupe.agent.browser_faculty import BrowserFaculty + self.add_mental_faculty(BrowserFaculty()) + + # basic action repetition prevention + if not hasattr(self, 'enable_basic_action_repetition_prevention'): + self.enable_basic_action_repetition_prevention = True + + # create the persona configuration dictionary + if not hasattr(self, '_persona'): + self._persona = { + "name": self.name, + "age": None, + "nationality": None, + "country_of_residence": None, + "occupation": None + } + + if not hasattr(self, 'name'): + self.name = self._persona["name"] + + # create the mental state dictionary + if not hasattr(self, '_mental_state'): + self._mental_state = { + "datetime": None, + "location": None, + "context": [], + "goals": [], + "attention": None, + "emotions": "Feeling nothing in particular, just calm.", + "memory_context": None, + "accessible_agents": [] # [{"agent": agent_1, "relation": "My friend"}, {"agent": agent_2, "relation": "My colleague"}, ...] + } + + if not hasattr(self, '_extended_agent_summary'): + self._extended_agent_summary = None + + if not hasattr(self, 'actions_count'): + self.actions_count = 0 + + if not hasattr(self, 'stimuli_count'): + self.stimuli_count = 0 + + self._prompt_template_path = os.path.join( + os.path.dirname(__file__), "prompts/tiny_person.mustache" + ) + self._init_system_message = None # initialized later + + + ############################################################ + # Special mechanisms used during deserialization + ############################################################ + + # rename agent to some specific name? + if kwargs.get("new_agent_name") is not None: + self._rename(kwargs.get("new_agent_name")) + + # If auto-rename, use the given name plus some new number ... + if kwargs.get("auto_rename") is True: + new_name = self.name # start with the current name + rename_succeeded = False + while not rename_succeeded: + try: + self._rename(new_name) + TinyPerson.add_agent(self) + rename_succeeded = True + except ValueError: + new_id = utils.fresh_id(self.__class__.__name__) + new_name = f"{self.name}_{new_id}" + + # ... otherwise, just register the agent + else: + # register the agent in the global list of agents + TinyPerson.add_agent(self) + + # start with a clean slate + self.reset_prompt() + + # it could be the case that the agent is being created within a simulation scope, in which case + # the simulation_id must be set accordingly + if current_simulation() is not None: + current_simulation().add_agent(self) + else: + self.simulation_id = None + + def _rename(self, new_name:str): + self.name = new_name + self._persona["name"] = self.name + + + def generate_agent_system_prompt(self): + with open(self._prompt_template_path, "r", encoding="utf-8", errors="replace") as f: + agent_prompt_template = f.read() + + # let's operate on top of a copy of the configuration, because we'll need to add more variables, etc. + template_variables = self._persona.copy() + template_variables["persona"] = json.dumps(self._persona.copy(), indent=4) + + # add mental state to the template variables + template_variables["mental_state"] = json.dumps(self._mental_state, indent=4) + + # Prepare additional action definitions and constraints + actions_definitions_prompt = "" + actions_constraints_prompt = "" + for faculty in self._mental_faculties: + actions_definitions_prompt += f"{faculty.actions_definitions_prompt()}\n" + actions_constraints_prompt += f"{faculty.actions_constraints_prompt()}\n" + + # Make the additional prompt pieces available to the template. + # Identation here is to align with the text structure in the template. + template_variables['actions_definitions_prompt'] = textwrap.indent(actions_definitions_prompt.strip(), " ") + template_variables['actions_constraints_prompt'] = textwrap.indent(actions_constraints_prompt.strip(), " ") + + # RAI prompt components, if requested + template_variables = utils.add_rai_template_variables_if_enabled(template_variables) + + return chevron.render(agent_prompt_template, template_variables) + + def reset_prompt(self): + + # render the template with the current configuration + self._init_system_message = self.generate_agent_system_prompt() + + # - reset system message + # - make it clear that the provided events are past events and have already had their effects + self.current_messages = [ + {"role": "system", "content": self._init_system_message}, + {"role": "system", "content": "The next messages refer to past interactions you had recently and are meant to help you contextualize your next actions. "\ + + "They are the most recent episodic memories you have, including stimuli and actions. "\ + + "Their effects already took place and led to your present cognitive state (described above), so you can use them in conjunction "\ + + "with your cognitive state to inform your next actions and perceptions. Please consider them and then proceed with your next actions right after. "} + ] + + # sets up the actual interaction messages to use for prompting + self.current_messages += self.retrieve_recent_memories() + + + ######################################################################### + # Persona definitions + ######################################################################### + + # + # Conveniences to access the persona configuration via dictionary-like syntax using + # the [] operator. e.g., agent["nationality"] = "American" + # + def __getitem__(self, key): + return self.get(key) + + def __setitem__(self, key, value): + self.define(key, value) + + # + # Conveniences to import persona definitions via the '+' operator, + # e.g., agent + {"nationality": "American", ...} + # + # e.g., agent + "path/to/fragment.json" + # + def __add__(self, other): + """ + Allows using the '+' operator to add persona definitions or import a fragment. + If 'other' is a dict, calls include_persona_definitions(). + If 'other' is a string, calls import_fragment(). + """ + if isinstance(other, dict): + self.include_persona_definitions(other) + elif isinstance(other, str): + self.import_fragment(other) + else: + raise TypeError("Unsupported operand type for +. Must be a dict or a string path to fragment.") + return self + + # + # Various other conveniences to manipulate the persona configuration + # + + def get(self, key): + """ + Returns the value of a key in the TinyPerson's persona configuration. + Supports dot notation for nested keys (e.g., "address.city"). + """ + keys = key.split(".") + value = self._persona + for k in keys: + if isinstance(value, dict): + value = value.get(k, None) + else: + return None # If the path is invalid, return None + return value + + @transactional() + def import_fragment(self, path): + """ + Imports a fragment of a persona configuration from a JSON file. + """ + with open(path, "r", encoding="utf-8", errors="replace") as f: + fragment = json.load(f) + + # check the type is "Fragment" and that there's also a "persona" key + if fragment.get("type", None) == "Fragment" and fragment.get("persona", None) is not None: + self.include_persona_definitions(fragment["persona"]) + else: + raise ValueError("The imported JSON file must be a valid fragment of a persona configuration.") + + # must reset prompt after adding to configuration + self.reset_prompt() + + @transactional() + def include_persona_definitions(self, additional_definitions: dict): + """ + Imports a set of definitions into the TinyPerson. They will be merged with the current configuration. + It is also a convenient way to include multiple bundled definitions into the agent. + + Args: + additional_definitions (dict): The additional definitions to import. + """ + + self._persona = utils.merge_dicts(self._persona, additional_definitions) + + # must reset prompt after adding to configuration + self.reset_prompt() + + + @transactional() + def define(self, key, value, merge=False, overwrite_scalars=True): + """ + Define a value to the TinyPerson's persona configuration. Value can either be a scalar or a dictionary. + If the value is a dictionary or list, you can choose to merge it with the existing value or replace it. + If the value is a scalar, you can choose to overwrite the existing value or not. + + Args: + key (str): The key to define. + value (Any): The value to define. + merge (bool, optional): Whether to merge the dict/list values with the existing values or replace them. Defaults to False. + overwrite_scalars (bool, optional): Whether to overwrite scalar values or not. Defaults to True. + """ + + # dedent value if it is a string + if isinstance(value, str): + value = textwrap.dedent(value) + + # if the value is a dictionary, we can choose to merge it with the existing value or replace it + if isinstance(value, dict) or isinstance(value, list): + if merge: + self._persona = utils.merge_dicts(self._persona, {key: value}) + else: + self._persona[key] = value + + # if the value is a scalar, we can choose to overwrite it or not + elif overwrite_scalars or (key not in self._persona): + self._persona[key] = value + + else: + raise ValueError(f"The key '{key}' already exists in the persona configuration and overwrite_scalars is set to False.") + + + # must reset prompt after adding to configuration + self.reset_prompt() + + + @transactional() + def define_relationships(self, relationships, replace=True): + """ + Defines or updates the TinyPerson's relationships. + + Args: + relationships (list or dict): The relationships to add or replace. Either a list of dicts mapping agent names to relationship descriptions, + or a single dict mapping one agent name to its relationship description. + replace (bool, optional): Whether to replace the current relationships or just add to them. Defaults to True. + """ + + if (replace == True) and (isinstance(relationships, list)): + self._persona['relationships'] = relationships + + elif replace == False: + current_relationships = self._persona['relationships'] + if isinstance(relationships, list): + for r in relationships: + current_relationships.append(r) + + elif isinstance(relationships, dict) and len(relationships) == 2: #{"Name": ..., "Description": ...} + current_relationships.append(relationships) + + else: + raise Exception("Only one key-value pair is allowed in the relationships dict.") + + else: + raise Exception("Invalid arguments for define_relationships.") + + ############################################################################## + # Relationships + ############################################################################## + + @transactional() + def clear_relationships(self): + """ + Clears the TinyPerson's relationships. + """ + self._persona['relationships'] = [] + + return self + + @transactional() + def related_to(self, other_agent, description, symmetric_description=None): + """ + Defines a relationship between this agent and another agent. + + Args: + other_agent (TinyPerson): The other agent. + description (str): The description of the relationship. + symmetric (bool): Whether the relationship is symmetric or not. That is, + if the relationship is defined for both agents. + + Returns: + TinyPerson: The agent itself, to facilitate chaining. + """ + self.define_relationships([{"Name": other_agent.name, "Description": description}], replace=False) + if symmetric_description is not None: + other_agent.define_relationships([{"Name": self.name, "Description": symmetric_description}], replace=False) + + return self + + ############################################################################ + + def add_mental_faculties(self, mental_faculties): + """ + Adds a list of mental faculties to the agent. + """ + for faculty in mental_faculties: + self.add_mental_faculty(faculty) + + return self + + def add_mental_faculty(self, faculty): + """ + Adds a mental faculty to the agent. + """ + # check if the faculty is already there or not + if faculty not in self._mental_faculties: + self._mental_faculties.append(faculty) + else: + raise Exception(f"The mental faculty {faculty} is already present in the agent.") + + return self + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def act( + self, + until_done=True, + n=None, + return_actions=False, + max_content_length=None, + communication_display:bool=None + ): + """ + Acts in the environment and updates its internal cognitive state. + Either acts until the agent is done and needs additional stimuli, or acts a fixed number of times, + but not both. + + Args: + until_done (bool): Whether to keep acting until the agent is done and needs additional stimuli. + n (int): The number of actions to perform. Defaults to None. + return_actions (bool): Whether to return the actions or not. Defaults to False. + max_content_length (int): The maximum length of the content to display. Defaults to None, which uses the global configuration value. + communication_display (bool): Whether to display the communication or not, will override the global setting if provided. Defaults to None. + """ + + # either act until done or act a fixed number of times, but not both + assert not (until_done and n is not None) + if n is not None: + assert n < TinyPerson.MAX_ACTIONS_BEFORE_DONE + + contents = [] + + # A separate function to run before each action, which is not meant to be repeated in case of errors. + def aux_pre_act(): + # TODO maybe we don't need this at all anymore? + # + # A quick thought before the action. This seems to help with better model responses, perhaps because + # it interleaves user with assistant messages. + pass # self.think("I will now think, reflect and act a bit, and then issue DONE.") + + # Aux function to perform exactly one action. + # Occasionally, the model will return JSON missing important keys, so we just ask it to try again + # Sometimes `content` contains EpisodicMemory's MEMORY_BLOCK_OMISSION_INFO message, which raises a TypeError on line 443 + @repeat_on_error(retries=5, exceptions=[KeyError, TypeError]) + def aux_act_once(): + # ensure we have the latest prompt (initial system message + selected messages from memory) + self.reset_prompt() + + action, role, content, all_negative_feedbacks = self.action_generator.generate_next_action(self, self.current_messages) + logger.debug(f"{self.name}'s action: {action}") + + # check the next action similarity, and if it is too similar, put a system warning instruction in memory too + next_action_similarity = utils.next_action_jaccard_similarity(self, action) + + # we have a redundant repetition check here, because this an be computed quickly and is often very useful. + if self.enable_basic_action_repetition_prevention and \ + (TinyPerson.MAX_ACTION_SIMILARITY is not None) and (next_action_similarity > TinyPerson.MAX_ACTION_SIMILARITY): + + logger.warning(f"[{self.name}] Action similarity is too high ({next_action_similarity}), replacing it with DONE.") + + # replace the action with a DONE + action = {"type": "DONE", "content": "", "target": ""} + content["action"] = action + content["cognitive_state"] = {} + + self.store_in_memory({'role': 'system', + 'content': \ + f""" + # EXCESSIVE ACTION SIMILARITY WARNING + + You were about to generate a repetitive action (jaccard similarity = {next_action_similarity}). + Thus, the action was discarded and replaced by an artificial DONE. + + DO NOT BE REPETITIVE. This is not a human-like behavior, therefore you **must** avoid this in the future. + Your alternatives are: + - produce more diverse actions. + - aggregate similar actions into a single, larger, action and produce it all at once. + - as a **last resort only**, you may simply not acting at all by issuing a DONE. + + + """, + 'type': 'feedback', + 'simulation_timestamp': self.iso_datetime()}) + + # All checks done, we can commit the action to memory. + self.store_in_memory({'role': role, 'content': content, + 'type': 'action', + 'simulation_timestamp': self.iso_datetime()}) + + self._actions_buffer.append(action) + + if "cognitive_state" in content: + cognitive_state = content["cognitive_state"] + logger.debug(f"[{self.name}] Cognitive state: {cognitive_state}") + + self._update_cognitive_state(goals=cognitive_state.get("goals", None), + context=cognitive_state.get("context", None), + attention=cognitive_state.get("emotions", None), + emotions=cognitive_state.get("emotions", None)) + + contents.append(content) + if utils.first_non_none(communication_display, TinyPerson.communication_display): + self._display_communication(role=role, content=content, kind='action', simplified=True, max_content_length=max_content_length) + + # + # Some actions induce an immediate stimulus or other side-effects. We need to process them here, by means of the mental faculties. + # + for faculty in self._mental_faculties: + faculty.process_action(self, action) + + # + # turns all_negative_feedbacks list into a system message + # + # TODO improve this? + # + ##if len(all_negative_feedbacks) > 0: + ## feedback = """ + ## # QUALITY FEEDBACK + ## + ## Up to the present moment, we monitored actions and tentative aborted actions (i.e., that were not actually executed), + ## and some of them were not of good quality. + ## Some of those were replaced by regenerated actions of better quality. In the process of doing so, some + ## important quality feedback was produced, which is now given below. + ## + ## To improve your performance, and prevent future similar quality issues, you **MUST** take into account the following feedback + ## whenever computing your future actions. Note that the feedback might also include the actual action or tentative action + ## that was of low quality, so that you can understand what was wrong with it and avoid similar mistakes in the future. + ## + ## """ + ## for i, feedback_item in enumerate(all_negative_feedbacks): + ## feedback += f"{feedback_item}\n\n" + ## feedback += f"\n\n *** \n\n" + ## + ## self.store_in_memory({'role': 'system', 'content': feedback, + ## 'type': 'feedback', + ## 'simulation_timestamp': self.iso_datetime()}) + ## + + + + # count the actions as this can be useful for taking decisions later + self.actions_count += 1 + + + # + # How to proceed with a sequence of actions. + # + + ##### Option 1: run N actions ###### + if n is not None: + for i in range(n): + aux_pre_act() + aux_act_once() + + ##### Option 2: run until DONE ###### + elif until_done: + while (len(contents) == 0) or ( + not contents[-1]["action"]["type"] == "DONE" + ): + + + # check if the agent is acting without ever stopping + if len(contents) > TinyPerson.MAX_ACTIONS_BEFORE_DONE: + logger.warning(f"[{self.name}] Agent {self.name} is acting without ever stopping. This may be a bug. Let's stop it here anyway.") + break + if len(contents) > 4: # just some minimum number of actions to check for repetition, could be anything >= 3 + # if the last three actions were the same, then we are probably in a loop + if contents[-1]['action'] == contents[-2]['action'] == contents[-3]['action']: + logger.warning(f"[{self.name}] Agent {self.name} is acting in a loop. This may be a bug. Let's stop it here anyway.") + break + + aux_pre_act() + aux_act_once() + + # The end of a sequence of actions is always considered to mark the end of an episode. + self.consolidate_episode_memories() + + if return_actions: + return contents + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def listen( + self, + speech, + source: AgentOrWorld = None, + max_content_length=None, + communication_display:bool=None + ): + """ + Listens to another agent (artificial or human) and updates its internal cognitive state. + + Args: + speech (str): The speech to listen to. + source (AgentOrWorld, optional): The source of the speech. Defaults to None. + max_content_length (int, optional): The maximum length of the content to display. Defaults to None, which uses the global configuration value. + communication_display (bool): Whether to display the communication or not, will override the global setting if provided. Defaults to None. + + """ + + return self._observe( + stimulus={ + "type": "CONVERSATION", + "content": speech, + "source": name_or_empty(source), + }, + max_content_length=max_content_length, + communication_display=communication_display + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def socialize( + self, + social_description: str, + source: AgentOrWorld = None, + max_content_length=None, + ): + """ + Perceives a social stimulus through a description and updates its internal cognitive state. + + Args: + social_description (str): The description of the social stimulus. + source (AgentOrWorld, optional): The source of the social stimulus. Defaults to None. + """ + return self._observe( + stimulus={ + "type": "SOCIAL", + "content": social_description, + "source": name_or_empty(source), + }, + max_content_length=max_content_length, + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def see( + self, + visual_description, + source: AgentOrWorld = None, + max_content_length=None, + ): + """ + Perceives a visual stimulus through a description and updates its internal cognitive state. + + Args: + visual_description (str): The description of the visual stimulus. + source (AgentOrWorld, optional): The source of the visual stimulus. Defaults to None. + """ + return self._observe( + stimulus={ + "type": "VISUAL", + "content": visual_description, + "source": name_or_empty(source), + }, + max_content_length=max_content_length, + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def think(self, thought, max_content_length=None): + """ + Forces the agent to think about something and updates its internal cognitive state. + + """ + logger = get_logger(self.name) + logger.info(f"Thinking: {thought}") + return self._observe( + stimulus={ + "type": "THOUGHT", + "content": thought, + "source": name_or_empty(self), + }, + max_content_length=max_content_length, + ) + + def sequential_think(self, thought_data: dict, max_content_length=None): + """ + Forces the agent to think about something and updates its internal cognitive state. + + """ + return self._observe( + stimulus={ + "type": "SEQUENTIAL_THINKING", + "content": json.dumps(thought_data), + "source": name_or_empty(self), + }, + max_content_length=max_content_length, + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def internalize_goal( + self, goal, max_content_length=None + ): + """ + Internalizes a goal and updates its internal cognitive state. + """ + return self._observe( + stimulus={ + "type": "INTERNAL_GOAL_FORMULATION", + "content": goal, + "source": name_or_empty(self), + }, + max_content_length=max_content_length, + ) + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def _observe(self, stimulus, max_content_length=None, communication_display:bool=None): + """ + Observes a stimulus and updates its internal cognitive state. + + Args: + stimulus (dict): The stimulus to observe. It must contain a 'type' and 'content' keys. + max_content_length (int, optional): The maximum length of the content to display. Defaults to None, which uses the global configuration value. + communication_display (bool): Whether to display the communication or not, will override the global setting if provided. Defaults to None. + """ + stimuli = [stimulus] + + content = {"stimuli": stimuli} + + logger.debug(f"[{self.name}] Observing stimuli: {content}") + + # whatever comes from the outside will be interpreted as coming from 'user', simply because + # this is the counterpart of 'assistant' + + self.store_in_memory({'role': 'user', 'content': content, + 'type': 'stimulus', + 'simulation_timestamp': self.iso_datetime()}) + + if utils.first_non_none(communication_display, TinyPerson.communication_display): + self._display_communication( + role="user", + content=content, + kind="stimuli", + simplified=True, +max_content_length=max_content_length, + ) + + # count the stimuli as this can be useful for taking decisions later + self.stimuli_count += 1 + + return self # allows easier chaining of methods + + @transactional() + def listen_and_act( + self, + speech, + return_actions=False, + max_content_length=None, + communication_display:bool=None + ): + """ + Convenience method that combines the `listen` and `act` methods. + """ + + self.listen(speech, max_content_length=max_content_length, communication_display=communication_display) + return self.act( + return_actions=return_actions, max_content_length=max_content_length, communication_display=communication_display + ) + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def see_and_act( + self, + visual_description, + return_actions=False, + max_content_length=None, + ): + """ + Convenience method that combines the `see` and `act` methods. + """ + + self.see(visual_description, max_content_length=max_content_length) + return self.act( + return_actions=return_actions, max_content_length=max_content_length + ) + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def think_and_act( + self, + thought, + return_actions=False, + max_content_length=None, + ): + """ + Convenience method that combines the `think` and `act` methods. + """ + + self.think(thought, max_content_length=max_content_length) + return self.act(return_actions=return_actions, max_content_length=max_content_length) + + def read_documents_from_folder(self, documents_path:str): + """ + Reads documents from a directory and loads them into the semantic memory. + """ + logger.info(f"Setting documents path to {documents_path} and loading documents.") + + self.semantic_memory.add_documents_path(documents_path) + + def read_document_from_file(self, file_path:str): + """ + Reads a document from a file and loads it into the semantic memory. + """ + logger.info(f"Reading document from file: {file_path}") + + self.semantic_memory.add_document_path(file_path) + + def read_documents_from_web(self, web_urls:list): + """ + Reads documents from web URLs and loads them into the semantic memory. + """ + logger.info(f"Reading documents from the following web URLs: {web_urls}") + + self.semantic_memory.add_web_urls(web_urls) + + def read_document_from_web(self, web_url:str): + """ + Reads a document from a web URL and loads it into the semantic memory. + """ + logger.info(f"Reading document from web URL: {web_url}") + + self.semantic_memory.add_web_url(web_url) + + @transactional() + def move_to(self, location, context=[]): + """ + Moves to a new location and updates its internal cognitive state. + """ + self._mental_state["location"] = location + + # context must also be updated when moved, since we assume that context is dictated partly by location. + self.change_context(context) + + @transactional() + def change_context(self, context: list): + """ + Changes the context and updates its internal cognitive state. + """ + self._mental_state["context"] = { + "description": item for item in context + } + + self._update_cognitive_state(context=context) + + @transactional() + def make_agent_accessible( + self, + agent: Self, + relation_description: str = "An agent I can currently interact with.", + ): + """ + Makes an agent accessible to this agent. + """ + if agent not in self._accessible_agents: + self._accessible_agents.append(agent) + self._mental_state["accessible_agents"].append( + {"name": agent.name, "relation_description": relation_description} + ) + else: + logger.warning( + f"[{self.name}] Agent {agent.name} is already accessible to {self.name}." + ) + @transactional() + def make_agents_accessible(self, agents: list, relation_description: str = "An agent I can currently interact with."): + """ + Makes a list of agents accessible to this agent. + """ + for agent in agents: + self.make_agent_accessible(agent, relation_description) + + @transactional() + def make_agent_inaccessible(self, agent: Self): + """ + Makes an agent inaccessible to this agent. + """ + if agent in self._accessible_agents: + self._accessible_agents.remove(agent) + else: + logger.warning( + f"[{self.name}] Agent {agent.name} is already inaccessible to {self.name}." + ) + + @transactional() + def make_all_agents_inaccessible(self): + """ + Makes all agents inaccessible to this agent. + """ + self._accessible_agents = [] + self._mental_state["accessible_agents"] = [] + + @property + def accessible_agents(self): + """ + Property to access the list of accessible agents. + """ + return self._accessible_agents + + ########################################################### + # Internal cognitive state changes + ########################################################### + @transactional() + def _update_cognitive_state( + self, goals=None, context=None, attention=None, emotions=None + ): + """ + Update the TinyPerson's cognitive state. + """ + + # Update current datetime. The passage of time is controlled by the environment, if any. + if self.environment is not None and self.environment.current_datetime is not None: + self._mental_state["datetime"] = utils.pretty_datetime(self.environment.current_datetime) + + # update current goals + if goals is not None: + self._mental_state["goals"] = goals + + # update current context + if context is not None: + self._mental_state["context"] = context + + # update current attention + if attention is not None: + self._mental_state["attention"] = attention + + # update current emotions + if emotions is not None: + self._mental_state["emotions"] = emotions + + # update relevant memories for the current situation. These are memories that come to mind "spontaneously" when the agent is in a given context, + # so avoiding the need to actively trying to remember them. + current_memory_context = self.retrieve_relevant_memories_for_current_context() + self._mental_state["memory_context"] = current_memory_context + + self.reset_prompt() + + + ########################################################### + # Memory management + ########################################################### + + def store_in_memory(self, value: Any) -> None: + """ + Stores a value in episodic memory and manages episode length. + + Args: + value: The memory item to store (e.g., action, stimulus, thought) + + Returns: + None + """ + self.episodic_memory.store(value) + + self._current_episode_event_count += 1 + logger.debug(f"[{self.name}] Current episode event count: {self._current_episode_event_count}.") + + if self._current_episode_event_count >= self.MAX_EPISODE_LENGTH: + # commit the current episode to memory, if it is long enough + logger.warning(f"[{self.name}] Episode length exceeded {self.MAX_EPISODE_LENGTH} events. Committing episode to memory. Please check whether this was expected or not.") + self.consolidate_episode_memories() + + def consolidate_episode_memories(self) -> bool: + """ + Applies all memory consolidation or transformation processes appropriate to the conclusion of one simulation episode. + + Returns: + bool: True if memories were successfully consolidated, False otherwise. + """ + # a minimum length of the episode is required to consolidate it, to avoid excessive fragments in the semantic memory + if self._current_episode_event_count > self.MIN_EPISODE_LENGTH: + logger.debug(f"[{self.name}] ***** Consolidating current episode memories into semantic memory *****") + + # Consolidate latest episodic memories into semantic memory + if config_manager.get("enable_memory_consolidation"): + + + episodic_consolidator = EpisodicConsolidator() + episode = self.episodic_memory.get_current_episode(item_types=["action", "stimulus"],) + logger.debug(f"[{self.name}] Current episode: {episode}") + consolidated_memories = episodic_consolidator.process(episode, timestamp=self._mental_state["datetime"], context=self._mental_state, persona=self.minibio()).get("consolidation", None) + if consolidated_memories is not None: + logger.info(f"[{self.name}] Consolidating current {len(episode)} episodic events as consolidated semantic memories.") + logger.debug(f"[{self.name}] Consolidated memories: {consolidated_memories}") + self.semantic_memory.store_all(consolidated_memories) + else: + logger.warning(f"[{self.name}] No memories to consolidate from the current episode.") + + else: + logger.warning(f"[{self.name}] Memory consolidation is disabled. Not consolidating current episode memories into semantic memory.") + + # commit the current episode to episodic memory + self.episodic_memory.commit_episode() + self._current_episode_event_count = 0 + logger.debug(f"[{self.name}] Current episode event count reset to 0 after consolidation.") + + # TODO reflections, optimizations, etc. + + def optimize_memory(self): + pass #TODO + + def clear_episodic_memory(self, max_prefix_to_clear=None, max_suffix_to_clear=None): + """ + Clears the episodic memory, causing a permanent "episodic amnesia". Note that this does not + change other memories, such as semantic memory. + """ + self.episodic_memory.clear(max_prefix_to_clear=max_prefix_to_clear, max_suffix_to_clear=max_suffix_to_clear) + + def retrieve_memories(self, first_n: int, last_n: int, include_omission_info:bool=True, max_content_length:int=None) -> list: + episodes = self.episodic_memory.retrieve(first_n=first_n, last_n=last_n, include_omission_info=include_omission_info) + + if max_content_length is not None: + episodes = utils.truncate_actions_or_stimuli(episodes, max_content_length) + + return episodes + + + def retrieve_recent_memories(self, max_content_length:int=None) -> list: + episodes = self.episodic_memory.retrieve_recent() + + if max_content_length is not None: + episodes = utils.truncate_actions_or_stimuli(episodes, max_content_length) + + return episodes + + def retrieve_relevant_memories(self, relevance_target:str, top_k=20) -> list: + relevant = self.semantic_memory.retrieve_relevant(relevance_target, top_k=top_k) + + return relevant + + def retrieve_relevant_memories_for_current_context(self, top_k=7) -> list: + """ + Retrieves memories relevant to the current context by combining current state with recent memories. + + Args: + top_k (int): Number of top relevant memories to retrieve. Defaults to 7. + + Returns: + list: List of relevant memories for the current context. + """ + # Extract current mental state components + context = self._mental_state.get("context", "") + goals = self._mental_state.get("goals", "") + attention = self._mental_state.get("attention", "") + emotions = self._mental_state.get("emotions", "") + + # Retrieve recent memories efficiently + recent_memories_list = self.retrieve_memories(first_n=10, last_n=20, max_content_length=500) + recent_memories = "\n".join([f" - {m.get('content', '')}" for m in recent_memories_list]) + + # Build contextual target for memory retrieval using textwrap.dedent for cleaner formatting + target = textwrap.dedent(f""" + Current Context: {context} + Current Goals: {goals} + Current Attention: {attention} + Current Emotions: {emotions} + Selected Episodic Memories (from oldest to newest): + {recent_memories} + """).strip() + + logger.debug(f"[{self.name}] Retrieving relevant memories for contextual target: {target}") + + return self.retrieve_relevant_memories(target, top_k=top_k) + + def summarize_relevant_memories_via_full_scan(self, relevance_target:str, item_type: str = None) -> str: + """ + Summarizes relevant memories for a given target by scanning the entire semantic memory. + + Args: + relevance_target (str): The target to retrieve relevant memories for. + item_type (str, optional): The type of items to summarize. Defaults to None. + max_summary_length (int, optional): The maximum length of the summary. Defaults to 1000. + + Returns: + str: The summary of relevant memories. + """ + return self.semantic_memory.summarize_relevant_via_full_scan(relevance_target, item_type=item_type) + + ########################################################### + # Inspection conveniences + ########################################################### + + def last_remembered_action(self, ignore_done: bool = True): + """ + Returns the last remembered action. + + Args: + ignore_done (bool): Whether to ignore the "DONE" action or not. Defaults to True. + + Returns: + dict or None: The last remembered action, or None if no suitable action found. + """ + action = None + + memory_items_list = self.episodic_memory.retrieve_last(include_omission_info=False, item_type="action") + + if len(memory_items_list) > 0: + # iterate from last to first while the action type is not "DONE" + for candidate_item in memory_items_list[::-1]: + action_content = candidate_item.get("content", {}).get("action", {}) + action_type = action_content.get("type", "") + + if not ignore_done or action_type != "DONE": + action = action_content + break + + return action + + + ########################################################### + # Communication display and action execution + ########################################################### + + def _display_communication( + self, + role, + content, + kind, + simplified=True, + max_content_length=default["max_content_display_length"], + ): + """ + Displays the current communication and stores it in a buffer for later use. + """ + logger = get_logger(self.name) + # CONCURRENT PROTECTION, as we'll access shared display buffers + with concurrent_agent_action_lock: + if kind == "stimuli": + rendering = self._pretty_stimuli( + role=role, + content=content, + simplified=simplified, + max_content_length=max_content_length, + ) + source = content["stimuli"][0].get("source", None) + target = self.name + + elif kind == "action": + rendering = self._pretty_action( + role=role, + content=content, + simplified=simplified, + max_content_length=max_content_length, + ) + source = self.name + target = content["action"].get("target", None) + + else: + raise ValueError(f"Unknown communication kind: {kind}") + + logger.info(f"Output: {rendering}") + # if the agent has no parent environment, then it is a free agent and we can display the communication. + # otherwise, the environment will display the communication instead. This is important to make sure that + # the communication is displayed in the correct order, since environments control the flow of their underlying + # agents. + if self.environment is None: + self._push_and_display_latest_communication({"kind": kind, "rendering":rendering, "content": content, "source":source, "target": target}) + else: + self.environment._push_and_display_latest_communication({"kind": kind, "rendering":rendering, "content": content, "source":source, "target": target}) + + def _push_and_display_latest_communication(self, communication): + """ + Pushes the latest communications to the agent's buffer. + """ + self._displayed_communications_buffer.append(communication) + print(communication["rendering"]) + + def pop_and_display_latest_communications(self): + """ + Pops the latest communications and displays them. + """ + communications = self._displayed_communications_buffer + self._displayed_communications_buffer = [] + + for communication in communications: + print(communication["rendering"]) + + return communications + + def clear_communications_buffer(self): + """ + Cleans the communications buffer. + """ + self._displayed_communications_buffer = [] + + @transactional() + def pop_latest_actions(self) -> list: + """ + Returns the latest actions performed by this agent. Typically used + by an environment to consume the actions and provide the appropriate + environmental semantics to them (i.e., effects on other agents). + """ + actions = self._actions_buffer + self._actions_buffer = [] + return actions + + @transactional() + def pop_actions_and_get_contents_for( + self, action_type: str, only_last_action: bool = True + ) -> list: + """ + Returns the contents of actions of a given type performed by this agent. + Typically used to perform inspections and tests. + + Args: + action_type (str): The type of action to look for. + only_last_action (bool, optional): Whether to only return the contents of the last action. Defaults to False. + """ + actions = self.pop_latest_actions() + # Filter the actions by type + actions = [action for action in actions if action["type"] == action_type] + + # If interested only in the last action, return the latest one + if only_last_action: + return actions[-1].get("content", "") + + # Otherwise, return all contents from the filtered actions + return "\n".join([action.get("content", "") for action in actions]) + + ############################################################################################# + # Formatting conveniences + # + # For rich colors, + # see: https://rich.readthedocs.io/en/latest/appendix/colors.html#appendix-colors + ############################################################################################# + + def __repr__(self): + return f"TinyPerson(name='{self.name}')" + + @transactional() + def minibio(self, extended=True, requirements=None): + """ + Returns a mini-biography of the TinyPerson. + + Args: + extended (bool): Whether to include extended information or not. + requirements (str): Additional requirements for the biography (e.g., focus on a specific aspect relevant for the scenario). + + Returns: + str: The mini-biography. + """ + + # if occupation is a dict and has a "title" key, use that as the occupation + if isinstance(self._persona['occupation'], dict) and 'title' in self._persona['occupation']: + occupation = self._persona['occupation']['title'] + else: + occupation = self._persona['occupation'] + + base_biography = f"{self.name} is a {self._persona['age']} year old {occupation}, {self._persona['nationality']}, currently living in {self._persona['residence']}." + + if self._extended_agent_summary is None and extended: + logger.debug(f"Generating extended agent summary for {self.name}.") + self._extended_agent_summary = LLMChat( + system_prompt=f""" + You are given a short biography of an agent, as well as a detailed specification of his or her other characteristics + You must then produce a short paragraph (3 or 4 sentences) that **complements** the short biography, adding details about + personality, interests, opinions, skills, etc. Do not repeat the information already given in the short biography. + repeating the information already given. The paragraph should be coherent, consistent and comprehensive. All information + must be grounded on the specification, **do not** create anything new. + + {"Additional constraints: "+ requirements if requirements is not None else ""} + """, + + user_prompt=f""" + **Short biography:** {base_biography} + + **Detailed specification:** {self._persona} + """).call() + + if extended: + biography = f"{base_biography} {self._extended_agent_summary}" + else: + biography = base_biography + + return biography + + def pp_current_interactions( + self, + simplified=True, + skip_system=True, + max_content_length=default["max_content_display_length"], + first_n=None, + last_n=None, + include_omission_info:bool=True + ): + """ + Pretty prints the current messages. + """ + print( + self.pretty_current_interactions( + simplified=simplified, + skip_system=skip_system, + max_content_length=max_content_length, + first_n=first_n, + last_n=last_n, + include_omission_info=include_omission_info + ) + ) + + def pp_last_interactions( + self, + n=3, + simplified=True, + skip_system=True, + max_content_length=default["max_content_display_length"], + include_omission_info:bool=True + ): + """ + Pretty prints the last n messages. Useful to examine the conclusion of an experiment. + """ + print( + self.pretty_current_interactions( + simplified=simplified, + skip_system=skip_system, + max_content_length=max_content_length, + first_n=None, + last_n=n, + include_omission_info=include_omission_info + ) + ) + + def pretty_current_interactions(self, simplified=True, skip_system=True, max_content_length=default["max_content_display_length"], first_n=None, last_n=None, include_omission_info:bool=True): + """ + Returns a pretty, readable, string with the current messages. + """ + lines = [f"**** BEGIN SIMULATION TRAJECTORY FOR {self.name} ****"] + last_step = 0 + for i, message in enumerate(self.episodic_memory.retrieve(first_n=first_n, last_n=last_n, include_omission_info=include_omission_info)): + try: + if not (skip_system and message['role'] == 'system'): + msg_simplified_type = "" + msg_simplified_content = "" + msg_simplified_actor = "" + + last_step = i + lines.append(f"Agent simulation trajectory event #{i}:") + lines.append(self._pretty_timestamp(message['role'], message['simulation_timestamp'])) + + if message["role"] == "system": + msg_simplified_actor = "SYSTEM" + msg_simplified_type = message["role"] + msg_simplified_content = message["content"] + + lines.append( + f"[dim] {msg_simplified_type}: {msg_simplified_content}[/]" + ) + + elif message["role"] == "user": + lines.append( + self._pretty_stimuli( + role=message["role"], + content=message["content"], + simplified=simplified, + max_content_length=max_content_length, + ) + ) + + elif message["role"] == "assistant": + lines.append( + self._pretty_action( + role=message["role"], + content=message["content"], + simplified=simplified, + max_content_length=max_content_length, + ) + ) + else: + lines.append(f"{message['role']}: {message['content']}") + except: + # print(f"ERROR: {message}") + continue + + lines.append(f"The last agent simulation trajectory event number was {last_step}, thus the current number of the NEXT POTENTIAL TRAJECTORY EVENT is {last_step + 1}.") + lines.append(f"**** END SIMULATION TRAJECTORY FOR {self.name} ****\n\n") + return "\n".join(lines) + + def _pretty_stimuli( + self, + role, + content, + simplified=True, + max_content_length=default["max_content_display_length"], + ) -> list: + """ + Pretty prints stimuli. + """ + + lines = [] + msg_simplified_actor = "USER" + for stimus in content["stimuli"]: + if simplified: + if stimus["source"] != "": + msg_simplified_actor = stimus["source"] + + else: + msg_simplified_actor = "USER" + + msg_simplified_type = stimus["type"] + msg_simplified_content = utils.break_text_at_length( + stimus["content"], max_length=max_content_length + ) + + indent = " " * len(msg_simplified_actor) + " > " + msg_simplified_content = textwrap.fill( + msg_simplified_content, + width=TinyPerson.PP_TEXT_WIDTH, + initial_indent=indent, + subsequent_indent=indent, + ) + + # + # Using rich for formatting. Let's make things as readable as possible! + # + + rich_style = utils.RichTextStyle.get_style_for("stimulus", msg_simplified_type) + lines.append( + f"[{rich_style}][underline]{msg_simplified_actor}[/] --> [{rich_style}][underline]{self.name}[/]: [{msg_simplified_type}] \n{msg_simplified_content}[/]" + ) + else: + lines.append(f"{role}: {content}") + + return "\n".join(lines) + + def _pretty_action( + self, + role, + content, + simplified=True, + max_content_length=default["max_content_display_length"], + ) -> str: + """ + Pretty prints an action. + """ + if simplified: + msg_simplified_actor = self.name + msg_simplified_type = content["action"]["type"] + msg_simplified_content = utils.break_text_at_length( + content["action"].get("content", ""), max_length=max_content_length + ) + + indent = " " * len(msg_simplified_actor) + " > " + msg_simplified_content = textwrap.fill( + msg_simplified_content, + width=TinyPerson.PP_TEXT_WIDTH, + initial_indent=indent, + subsequent_indent=indent, + ) + + # + # Using rich for formatting. Let's make things as readable as possible! + # + rich_style = utils.RichTextStyle.get_style_for("action", msg_simplified_type) + return f"[{rich_style}][underline]{msg_simplified_actor}[/] acts: [{msg_simplified_type}] \n{msg_simplified_content}[/]" + + else: + return f"{role}: {content}" + + def _pretty_timestamp( + self, + role, + timestamp, + ) -> str: + """ + Pretty prints a timestamp. + """ + return f">>>>>>>>> Date and time of events: {timestamp}" + + def iso_datetime(self) -> str: + """ + Returns the current datetime of the environment, if any. + + Returns: + datetime: The current datetime of the environment in ISO forat. + """ + if self.environment is not None and self.environment.current_datetime is not None: + return self.environment.current_datetime.isoformat() + else: + return None + + ########################################################### + # IO + ########################################################### + + def save_specification(self, path, include_mental_faculties=True, include_memory=False, include_mental_state=False): + """ + Saves the current configuration to a JSON file. + """ + + suppress_attributes = [] + + # should we include the mental faculties? + if not include_mental_faculties: + suppress_attributes.append("_mental_faculties") + + # should we include the memory? + if not include_memory: + suppress_attributes.append("episodic_memory") + suppress_attributes.append("semantic_memory") + + # should we include the mental state? + if not include_mental_state: + suppress_attributes.append("_mental_state") + + + self.to_json(suppress=suppress_attributes, file_path=path, + serialization_type_field_name="type") + + + @staticmethod + def load_specification(path_or_dict, suppress_mental_faculties=False, suppress_memory=False, suppress_mental_state=False, + auto_rename_agent=False, new_agent_name=None, enable_browser=False): + """ + Loads a JSON agent specification. + + Args: + path_or_dict (str or dict): The path to the JSON file or the dictionary itself. + suppress_mental_faculties (bool, optional): Whether to suppress loading the mental faculties. Defaults to False. + suppress_memory (bool, optional): Whether to suppress loading the memory. Defaults to False. + suppress_mental_state (bool, optional): Whether to suppress loading the mental state. Defaults to False. + auto_rename_agent (bool, optional): Whether to auto rename the agent. Defaults to False. + new_agent_name (str, optional): The new name for the agent. Defaults to None. + enable_browser (bool, optional): Whether to enable the browser faculty. Defaults to False. + """ + + suppress_attributes = [] + + # should we suppress the mental faculties? + if suppress_mental_faculties: + suppress_attributes.append("_mental_faculties") + + # should we suppress the memory? + if suppress_memory: + suppress_attributes.append("episodic_memory") + suppress_attributes.append("semantic_memory") + + # should we suppress the mental state? + if suppress_mental_state: + suppress_attributes.append("_mental_state") + + return TinyPerson.from_json(json_dict_or_path=path_or_dict, suppress=suppress_attributes, + serialization_type_field_name="type", + post_init_params={"auto_rename_agent": auto_rename_agent, "new_agent_name": new_agent_name, "enable_browser": enable_browser}) + @staticmethod + def load_specifications_from_folder(folder_path:str, file_suffix=".agent.json", suppress_mental_faculties=False, + suppress_memory=False, suppress_mental_state=False, auto_rename_agent=False, + new_agent_name=None) -> list: + """ + Loads all JSON agent specifications from a folder. + + Args: + folder_path (str): The path to the folder containing the JSON files. + file_suffix (str, optional): The suffix of the JSON files. Defaults to ".agent.json". + suppress_mental_faculties (bool, optional): Whether to suppress loading the mental faculties. Defaults to False. + suppress_memory (bool, optional): Whether to suppress loading the memory. Defaults to False. + suppress_mental_state (bool, optional): Whether to suppress loading the mental state. Defaults to False. + auto_rename_agent (bool, optional): Whether to auto rename the agent. Defaults to False. + new_agent_name (str, optional): The new name for the agent. Defaults to None. + """ + + agents = [] + for file in os.listdir(folder_path): + if file.endswith(file_suffix): + file_path = os.path.join(folder_path, file) + agent = TinyPerson.load_specification(file_path, suppress_mental_faculties=suppress_mental_faculties, + suppress_memory=suppress_memory, suppress_mental_state=suppress_mental_state, + auto_rename_agent=auto_rename_agent, new_agent_name=new_agent_name) + agents.append(agent) + + return agents + + + + def encode_complete_state(self) -> dict: + """ + Encodes the complete state of the TinyPerson, including the current messages, accessible agents, etc. + This is meant for serialization and caching purposes, not for exporting the state to the user. + """ + to_copy = copy.copy(self.__dict__) + + # delete the logger and other attributes that cannot be serialized + del to_copy["environment"] + del to_copy["_mental_faculties"] + del to_copy["action_generator"] + + to_copy["_accessible_agents"] = [agent.name for agent in self._accessible_agents] + to_copy['episodic_memory'] = self.episodic_memory.to_json() + to_copy['semantic_memory'] = self.semantic_memory.to_json() + to_copy["_mental_faculties"] = [faculty.to_json() for faculty in self._mental_faculties] + + state = copy.deepcopy(to_copy) + + return state + + def decode_complete_state(self, state: dict) -> Self: + """ + Loads the complete state of the TinyPerson, including the current messages, + and produces a new TinyPerson instance. + """ + state = copy.deepcopy(state) + + self._accessible_agents = [TinyPerson.get_agent_by_name(name) for name in state["_accessible_agents"]] + self.episodic_memory = EpisodicMemory.from_json(state['episodic_memory']) + self.semantic_memory = SemanticMemory.from_json(state['semantic_memory']) + + for i, faculty in enumerate(self._mental_faculties): + faculty = faculty.from_json(state['_mental_faculties'][i]) + + # delete fields already present in the state + del state["_accessible_agents"] + del state['episodic_memory'] + del state['semantic_memory'] + del state['_mental_faculties'] + + # restore other fields + self.__dict__.update(state) + + + return self + + def create_new_agent_from_current_spec(self, new_name:str) -> Self: + """ + Creates a new agent from the current agent's specification. + + Args: + new_name (str): The name of the new agent. Agent names must be unique in the simulation, + this is why we need to provide a new name. + """ + new_agent = TinyPerson(name=new_name, spec_path=None) + + new_persona = copy.deepcopy(self._persona) + new_persona['name'] = new_name + + new_agent._persona = new_persona + + return new_agent + + + @staticmethod + def add_agent(agent): + """ + Adds an agent to the global list of agents. Agent names must be unique, + so this method will raise an exception if the name is already in use. + """ + if agent.name in TinyPerson.all_agents: + raise ValueError(f"Agent name {agent.name} is already in use.") + else: + TinyPerson.all_agents[agent.name] = agent + + @staticmethod + def has_agent(agent_name: str): + """ + Checks if an agent is already registered. + """ + return agent_name in TinyPerson.all_agents + + @staticmethod + def set_simulation_for_free_agents(simulation): + """ + Sets the simulation if it is None. This allows free agents to be captured by specific simulation scopes + if desired. + """ + for agent in TinyPerson.all_agents.values(): + if agent.simulation_id is None: + simulation.add_agent(agent) + + @staticmethod + def get_agent_by_name(name): + """ + Gets an agent by name. + """ + if name in TinyPerson.all_agents: + return TinyPerson.all_agents[name] + else: + return None + + @staticmethod + def all_agents_names(): + """ + Returns the names of all agents. + """ + return list(TinyPerson.all_agents.keys()) + + @staticmethod + def clear_agents(): + """ + Clears the global list of agents. + """ + TinyPerson.all_agents = {} diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..cf04c03adc84e8675531cb6e74588ceb58c8bcf6 --- /dev/null +++ b/app.py @@ -0,0 +1,86 @@ +import sys +import os +import gradio as gr +import json +from tinytroupe.factory import TinyPersonFactory + +# --- CHANGE 1: The function now accepts an optional API key. --- +def generate_personas(business_description, customer_profile, num_personas, blablador_api_key=None): + """ + Generates a list of TinyPerson instances based on the provided inputs. + It prioritizes the API key passed as an argument, but falls back to the + environment variable if none is provided (for UI use). + """ + # --- CHANGE 2: Logic to determine which key to use. --- + # Use the key from the API call if provided, otherwise get it from the Space secrets. + api_key_to_use = blablador_api_key or os.getenv("BLABLADOR_API_KEY") + + if not api_key_to_use: + return {"error": "BLABLADOR_API_KEY not found. Please provide it in your API call or set it as a secret in the Space settings."} + + # Store the original state of the environment variable, if it exists + original_key = os.getenv("BLABLADOR_API_KEY") + + try: + # --- CHANGE 3: Securely set the correct environment variable for this request. --- + # The underlying tinytroupe library will look for this variable. + os.environ["BLABLADOR_API_KEY"] = api_key_to_use + + num_personas = int(num_personas) + + factory = TinyPersonFactory( + context=business_description, + sampling_space_description=customer_profile, + total_population_size=num_personas + ) + + people = factory.generate_people(number_of_people=num_personas, parallelize=False) + personas_data = [person._persona for person in people] + + return personas_data + + except Exception as e: + return {"error": str(e)} + + finally: + # --- CHANGE 4: A robust cleanup using a 'finally' block. --- + # This ensures the environment is always restored to its original state, + # whether the function succeeds or fails. + if original_key is None: + # If the variable didn't exist originally, remove it. + if "BLABLADOR_API_KEY" in os.environ: + del os.environ["BLABLADOR_API_KEY"] + else: + # If it existed, restore its original value. + os.environ["BLABLADOR_API_KEY"] = original_key + + +with gr.Blocks() as demo: + gr.Markdown("

Tiny Persona Generator

") + with gr.Row(): + with gr.Column(): + business_description_input = gr.Textbox(label="What is your business about?", lines=5) + customer_profile_input = gr.Textbox(label="Information about your customer profile", lines=5) + num_personas_input = gr.Number(label="Number of personas to generate", value=1, minimum=1, step=1) + + # --- CHANGE 5: The API key input is now INVISIBLE. --- + # It still exists, so the API endpoint is created, but it's hidden from UI users. + blablador_api_key_input = gr.Textbox( + label="Blablador API Key (for API client use)", + visible=False + ) + + generate_button = gr.Button("Generate Personas") + with gr.Column(): + output_json = gr.JSON(label="Generated Personas") + + generate_button.click( + fn=generate_personas, + # --- CHANGE 6: Pass the invisible textbox to the function. --- + inputs=[business_description_input, customer_profile_input, num_personas_input, blablador_api_key_input], + outputs=output_json, + api_name="generate_personas" + ) + +if __name__ == "__main__": + demo.queue().launch() \ No newline at end of file diff --git a/config.ini b/config.ini new file mode 100644 index 0000000000000000000000000000000000000000..680ed9bd8e91dc5ba57dfaefbabd343e84b4ff2c --- /dev/null +++ b/config.ini @@ -0,0 +1,7 @@ +[OpenAI] +API_TYPE=helmholtz-blablador +MODEL=alias-large +REASONING_MODEL=alias-large +TOP_P=1.0 +MAX_ATTEMPTS=5 +WAITING_TIME=20 diff --git a/control.py b/control.py new file mode 100644 index 0000000000000000000000000000000000000000..a8e769c162c2256ab7549146b1a792445fb4f06a --- /dev/null +++ b/control.py @@ -0,0 +1,841 @@ +""" +Simulation controlling mechanisms. +""" +import json +import os +import tempfile +import threading +import traceback + +import tinytroupe +import tinytroupe.utils as utils + +import uuid + + +import logging +logger = logging.getLogger("tinytroupe") + +# to protect from race conditions when running in parallel +concurrent_execution_lock = threading.Lock() + +class Simulation: + + STATUS_STOPPED = "stopped" + STATUS_STARTED = "started" + + def __init__(self, id="default", cached_trace:list=None): + self.id = id + + self.agents = [] + self.name_to_agent = {} # {agent_name: agent, ...} + + self.environments = [] + + self.factories = [] # e.g., TinyPersonFactory instances + self.name_to_factory = {} # {factory_name: factory, ...} + + self.name_to_environment = {} # {environment_name: environment, ...} + self.status = Simulation.STATUS_STOPPED + + self.cache_path = f"./tinytroupe-{id}.cache.json" # default cache path + + # should we always automatically checkpoint at the every transaction? + self.auto_checkpoint = False + + # whether there are changes not yet saved to the cache file + self.has_unsaved_cache_changes = False + + # whether the agent is under a transaction or not, used for managing + # simulation caching later + self._under_transaction = {None: False} + + # whether the agent is under a parallel transactions segment or not, used for managing + # simulation caching later + self._under_parallel_transactions = False + + # Cache chain mechanism. + # + # stores a list of simulation states. + # Each state is a tuple (prev_node_hash, event_hash, event_output, state), where prev_node_hash is a hash of the previous node in this chain, + # if any, event_hash is a hash of the event that triggered the transition to this state, if any, event_output is the output of the event, + # if any, and state is the actual complete state that resulted. + if cached_trace is None: + self.cached_trace = [] + else: + self.cached_trace = cached_trace + + self.cache_misses = 0 + self.cache_hits = 0 + + # Execution chain mechanism. + # + # The actual, current, execution trace. Each state is a tuple (prev_node_hash, event_hash, state), where prev_node_hash is a hash + # of the previous node in this chain, if any, event_hash is a hash of the event that triggered the transition to this state, if any, + # event_output is the output of the event, if any, and state is the actual complete state that resulted. + self.execution_trace = [] + + def begin(self, cache_path:str=None, auto_checkpoint:bool=False): + """ + Marks the start of the simulation being controlled. + + Args: + cache_path (str): The path to the cache file. If not specified, + defaults to the default cache path defined in the class. + auto_checkpoint (bool, optional): Whether to automatically checkpoint at the end of each transaction. Defaults to False. + """ + + logger.debug(f"Starting simulation, cache_path={cache_path}, auto_checkpoint={auto_checkpoint}.") + + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + from tinytroupe.factory.tiny_person_factory import TinyPersonFactory + + if self.status == Simulation.STATUS_STOPPED: + self.status = Simulation.STATUS_STARTED + else: + raise ValueError("Simulation is already started.") + + if cache_path is not None: + self.cache_path = cache_path + + # should we automatically checkpoint? + self.auto_checkpoint = auto_checkpoint + + # clear the agents, environments and other simulated entities, we'll track them from now on + TinyPerson.clear_agents() + TinyWorld.clear_environments() + TinyFactory.clear_factories() + TinyPersonFactory.clear_factories() + + # All automated fresh ids will start from 0 again for this simulation + utils.reset_fresh_id() + + # load the cache file, if any + if self.cache_path is not None: + self._load_cache_file(self.cache_path) + + def end(self): + """ + Marks the end of the simulation being controlled. + """ + logger.debug("Ending simulation.") + if self.status == Simulation.STATUS_STARTED: + self.status = Simulation.STATUS_STOPPED + self.checkpoint() + else: + raise ValueError("Simulation is already stopped.") + + def checkpoint(self): + """ + Saves current simulation trace to a file. + """ + logger.debug("Checkpointing simulation state...") + # save the cache file + if self.has_unsaved_cache_changes: + self._save_cache_file(self.cache_path) + else: + logger.debug("No unsaved cache changes to save to file.") + + def add_agent(self, agent): + """ + Adds an agent to the simulation. + """ + if agent.name in self.name_to_agent: + raise ValueError(f"Agent names must be unique, but '{agent.name}' is already defined.") + agent.simulation_id = self.id + self.agents.append(agent) + self.name_to_agent[agent.name] = agent + + + def add_environment(self, environment): + """ + Adds an environment to the simulation. + """ + if environment.name in self.name_to_environment: + raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") + environment.simulation_id = self.id + self.environments.append(environment) + self.name_to_environment[environment.name] = environment + + def add_factory(self, factory): + """ + Adds a factory to the simulation. + """ + if factory.name in self.name_to_factory: + raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") + factory.simulation_id = self.id + self.factories.append(factory) + self.name_to_factory[factory.name] = factory + + ################################################################################################### + # Cache and execution chain mechanisms + ################################################################################################### + def _execution_trace_position(self) -> int: + """ + Returns the current position in the execution trace, or -1 if the execution trace is empty. + """ + return len(self.execution_trace) - 1 + + def _function_call_hash(self, function_name, *args, **kwargs) -> int: + """ + Computes the hash of the given function call. + """ + + # if functions are passed as arguments to the function, there's the problem that their + # string representation always changes due to memory position (e.g., ). + # so we need to remove the changing suffix in those cases, while preserving the function name if it exists. + + # positional arguments + # covnerts to a list of string representations first + args_str = list(map(str, args)) + for i, arg in enumerate(args): + if callable(arg): + args_str[i] = arg.__name__ + + # keyword arguments + # converts to a list of string representations first + kwargs_str = {k: str(v) for k, v in kwargs.items()} + for k, v in kwargs.items(): + if callable(v): + kwargs_str[k] = v.__name__ + + # then, convert to a single string, to obtain a unique hash + event = str((function_name, args_str, kwargs_str)) + + # TODO actually compute a short hash of the event string, e.g., using SHA256 ? + # event_hash = utils.custom_hash(event) + + return event + + def _skip_execution_with_cache(self): + """ + Skips the current execution, assuming there's a cached state at the same position. + """ + assert len(self.cached_trace) > self._execution_trace_position() + 1, "There's no cached state at the current execution position." + + self.execution_trace.append(self.cached_trace[self._execution_trace_position() + 1]) + + def _is_transaction_event_cached(self, event_hash, parallel=False) -> bool: + """ + Checks whether the given event hash matches the corresponding cached one, if any. + If there's no corresponding cached state, returns True. + """ + if not parallel: + # there's cache that could be used + if len(self.cached_trace) > self._execution_trace_position() + 1: + if self._execution_trace_position() >= -1: + # here's a graphical depiction of the logic: + # + # Cache: c0:(c_prev_node_hash_0, c_event_hash_0, _, c_state_0) ------------------> c1:(c_prev_node_hash_1, c_event_hash_1, _, c_state_1) -> ... + # Execution: e0:(e_prev_node_hash_0, e_event_hash_0, _, e_state_0) --> e1:(e_prev_node_hash_1, , , ) + # position = 0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + # + # Must satisfy: + # - event_hash == c_event_hash_1 + # - hash(e0) == c_prev_node_hash_1 + + try: + event_hash_match = event_hash == self.cached_trace[self._execution_trace_position() + 1][1] + except Exception as e: + logger.error(f"Error while checking event hash match: {e}") + event_hash_match = False + + prev_node_match = True # TODO implement real check + + return event_hash_match and prev_node_match + + else: + raise ValueError("Execution trace position is invalid, must be >= -1, but is ", self._execution_trace_position()) + + else: # no cache to use + return False + + else: # parallel + if len(self.cached_trace) >= self._execution_trace_position(): + if self._execution_trace_position() >= 0: + # parallel stores ignore order, so we need to check instead whether the event hash is a key in the parallel store, + # regardless of the order of the events generated the data therein. + + if isinstance(self.cached_trace[self._execution_trace_position()], dict): + event_hash_match = event_hash in self.cached_trace[self._execution_trace_position()].keys() + else: + event_hash_match = False + + prev_node_match = True # TODO implement real check + + return event_hash_match and prev_node_match + + else: + raise ValueError("Execution trace position is invalid, must be >= 0, but is ", self._execution_trace_position()) + + def _get_cached_parallel_value(self, event_hash, key): + parallel_store = self.cached_trace[self._execution_trace_position()] + value = parallel_store[event_hash][key] + return value + + def _drop_cached_trace_suffix(self): + """ + Drops the cached trace suffix starting at the current execution trace position. This effectively + refreshes the cache to the current execution state and starts building a new cache from there. + """ + self.cached_trace = self.cached_trace[:self._execution_trace_position()+1] + + def _add_to_execution_trace(self, state: dict, event_hash: int, event_output, parallel=False): + """ + Adds a state to the execution_trace list and computes the appropriate hash. + The computed hash is compared to the hash of the cached trace at the same position, + and if they don't match, the execution is aborted. Similarly, the event_hash is compared + to the hash of the event in the cached trace at the same position, and if they don't match, the execution + is aborted. + """ + + # Compute the hash of the previous execution pair, if any + previous_hash = None + + if not parallel: + # Create a tuple of (hash, state) and append it to the execution_trace list + self.execution_trace.append((previous_hash, event_hash, event_output, state)) + else: + with concurrent_execution_lock: + # state is not stored in parallel segments, only outputs + self.execution_trace[-1][event_hash] = {"prev_node_hash": previous_hash, + "encoded_output": event_output} + + + + def _add_to_cache_trace(self, state: dict, event_hash: int, event_output, parallel=False): + """ + Adds a state to the cached_trace list and computes the appropriate hash. + """ + # Compute the hash of the previous cached pair, if any + previous_hash = None + if self.cached_trace: + previous_hash = utils.custom_hash(self.cached_trace[-1]) + + if not parallel: + # Create a tuple of (hash, state) and append it to the cached_trace list + self.cached_trace.append((previous_hash, event_hash, event_output, state)) + else: + with concurrent_execution_lock: + # state is not stored in parallel segments, only outputs + self.cached_trace[-1][event_hash] = {"prev_node_hash": previous_hash, + "encoded_output": event_output} + + + self.has_unsaved_cache_changes = True + + def _load_cache_file(self, cache_path:str): + """ + Loads the cache file from the given path. + """ + try: + self.cached_trace = json.load(open(cache_path, "r", encoding="utf-8", errors="replace")) + except FileNotFoundError: + logger.info(f"Cache file not found on path: {cache_path}.") + self.cached_trace = [] + + def _save_cache_file(self, cache_path:str): + """ + Saves the cache file to the given path. Always overwrites. + """ + logger.debug(f"Now saving cache file to {cache_path}.") + try: + # Create a temporary file + with tempfile.NamedTemporaryFile('w', delete=False) as temp: + json.dump(self.cached_trace, temp, indent=4) + + # Replace the original file with the temporary file + os.replace(temp.name, cache_path) + except Exception as e: + traceback_string = ''.join(traceback.format_tb(e.__traceback__)) + logger.error(f"An error occurred while saving the cache file: {e}\nTraceback:\n{traceback_string}") + + self.has_unsaved_cache_changes = False + + + + ################################################################################################### + # Transactional control + ################################################################################################### + + # + # Regular sequential transactions + # + def begin_transaction(self, id=None): + """ + Starts a transaction. + """ + with concurrent_execution_lock: + self._under_transaction[id] = True + self._clear_communications_buffers() # TODO <---------------------------------------------------------------- + + def end_transaction(self, id=None): + """ + Ends a transaction. + """ + with concurrent_execution_lock: + self._under_transaction[id] = False + + def is_under_transaction(self, id=None): + """ + Checks if the agent is under a transaction. + """ + with concurrent_execution_lock: + return self._under_transaction.get(id, False) + + def _clear_communications_buffers(self): + """ + Cleans the communications buffers of all agents and environments. + """ + for agent in self.agents: + agent.clear_communications_buffer() + + for environment in self.environments: + environment.clear_communications_buffer() + + # + # Parallel transactions + # + def begin_parallel_transactions(self): + """ + Starts parallel transactions. + """ + with concurrent_execution_lock: + self._under_parallel_transactions = True + # add a new parallel segment to the execution and cache traces + self.execution_trace.append({}) + self.cached_trace.append({}) + + def end_parallel_transactions(self): + """ + Ends parallel transactions. + """ + self._under_parallel_transactions = False + + def is_under_parallel_transactions(self): + """ + Checks if the agent is under parallel transactions. + """ + return self._under_parallel_transactions + + ################################################################################################### + # Simulation state handling + ################################################################################################### + + def _encode_simulation_state(self) -> dict: + """ + Encodes the current simulation state, including agents, environments, and other + relevant information. + """ + state = {} + + # Encode agents + state["agents"] = [] + for agent in self.agents: + state["agents"].append(agent.encode_complete_state()) + + # Encode environments + state["environments"] = [] + for environment in self.environments: + state["environments"].append(environment.encode_complete_state()) + + # Encode factories + state["factories"] = [] + for factory in self.factories: + state["factories"].append(factory.encode_complete_state()) + + return state + + def _decode_simulation_state(self, state: dict): + """ + Decodes the given simulation state, including agents, environments, and other + relevant information. + + Args: + state (dict): The state to decode. + """ + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + + logger.debug(f"Decoding simulation state: {state['factories']}") + logger.debug(f"Registered factories: {self.name_to_factory}") + logger.debug(f"Registered agents: {self.name_to_agent}") + logger.debug(f"Registered environments: {self.name_to_environment}") + + # Decode factories + for factory_state in state["factories"]: + factory = self.name_to_factory[factory_state["name"]] + factory.decode_complete_state(factory_state) + + # Decode environments + ###self.environments = [] + for environment_state in state["environments"]: + try: + environment = self.name_to_environment[environment_state["name"]] + environment.decode_complete_state(environment_state) + if TinyWorld.communication_display: + environment.pop_and_display_latest_communications() + + except Exception as e: + raise ValueError(f"Environment {environment_state['name']} is not in the simulation, thus cannot be decoded there.") from e + + # Decode agents (if they were not already decoded by the environment) + ####self.agents = [] + for agent_state in state["agents"]: + try: + agent = self.name_to_agent[agent_state["name"]] + agent.decode_complete_state(agent_state) + + # The agent has not yet been decoded because it is not in any environment. So, decode it. + if agent.environment is None: + if TinyPerson.communication_display: + agent.pop_and_display_latest_communications() + except Exception as e: + raise ValueError(f"Agent {agent_state['name']} is not in the simulation, thus cannot be decoded there.") from e + + +class Transaction: + + def __init__(self, obj_under_transaction, simulation, function, *args, **kwargs): + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + + self.obj_under_transaction = obj_under_transaction + self.simulation = simulation + self.function_name = function.__name__ + self.function = function + self.args = args + self.kwargs = kwargs + + # + # If we have an ongoing simulation, set the simulation id of the object under transaction if it is not already set. + # + if simulation is not None: + if hasattr(obj_under_transaction, 'simulation_id') and obj_under_transaction.simulation_id is not None: + if obj_under_transaction.simulation_id != simulation.id: + raise ValueError(f"Object {obj_under_transaction} is already captured by a different simulation (id={obj_under_transaction.simulation_id}), \ + and cannot be captured by simulation id={simulation.id}.") + + logger.debug(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Object {obj_under_transaction} is already captured by simulation {simulation.id}.") + else: + # if is a TinyPerson, add the agent to the simulation + if isinstance(obj_under_transaction, TinyPerson): + simulation.add_agent(obj_under_transaction) + logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added agent {obj_under_transaction} to simulation {simulation.id}.") + + # if is a TinyWorld, add the environment to the simulation + elif isinstance(obj_under_transaction, TinyWorld): + simulation.add_environment(obj_under_transaction) + + # if is a TinyFactory, add the factory to the simulation + elif isinstance(obj_under_transaction, TinyFactory): + simulation.add_factory(obj_under_transaction) + logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added factory {obj_under_transaction} to simulation {simulation.id}.") + + else: + raise ValueError(f"Object {obj_under_transaction} (type = {type(obj_under_transaction)}) is not a TinyPerson or TinyWorld instance, and cannot be captured by the simulation.") + + + def execute(self, begin_parallel=False, parallel_id=None): + + output = None + + # Transaction caching will only operate if there is a simulation and it is started + if self.simulation is None or self.simulation.status == Simulation.STATUS_STOPPED: + # Compute the function and return it, no caching, since the simulation is not started + output = self.function(*self.args, **self.kwargs) + + elif self.simulation.status == Simulation.STATUS_STARTED: + # Compute the event hash + event_hash = self.simulation._function_call_hash(self.function_name, *self.args, **self.kwargs) + + # Sequential and parallel transactions are handled in different ways + if begin_parallel: + self.simulation.begin_parallel_transactions() + + # CACHED? Check if the event hash is in the cache + if self.simulation._is_transaction_event_cached(event_hash, + parallel=self.simulation.is_under_parallel_transactions()): + self.simulation.cache_hits += 1 + + # Restore the full state and return the cached output + logger.debug(f"Skipping execution of {self.function_name} with args {self.args} and kwargs {self.kwargs} because it is already cached.") + + # SEQUENTIAL + if not self.simulation.is_under_parallel_transactions(): + + self.simulation._skip_execution_with_cache() + state = self.simulation.cached_trace[self.simulation._execution_trace_position()][3] # state + self.simulation._decode_simulation_state(state) + + # Output encoding/decoding is used to preserve references to TinyPerson and TinyWorld instances + # mainly. Scalar values (int, float, str, bool) and composite values (list, dict) are + # encoded/decoded as is. + encoded_output = self.simulation.cached_trace[self.simulation._execution_trace_position()][2] # output + output = self._decode_function_output(encoded_output) + + # PARALLEL + else: # is under parallel transactions + + # in parallel segments, state is not restored, only outputs + encoded_output = self.simulation._get_cached_parallel_value(event_hash, "encoded_output") + output = self._decode_function_output(encoded_output) + + else: # not cached + + if not begin_parallel: + # in case of beginning a parallel segment, we don't want to count it as a cache miss, + # since the segment itself will not be cached, but rather the events within it. + self.simulation.cache_misses += 1 + + if not self.simulation.is_under_transaction(id=parallel_id) and not begin_parallel: + + # BEGIN SEQUENTIAL TRANSACTION ############################################################### + # + # if this is the beginning of a parallel segment, we don't need to begin a transaction, since + # we want to allow additional transactions within the parallel segment (i.e., one-level reentrancy). + if not begin_parallel: + self.simulation.begin_transaction(id=parallel_id) + + # Compute the function and encode the relevant output and simulation state + output = self.function(*self.args, **self.kwargs) + self._save_output_with_simulation_state(event_hash, output) + + # END TRANSACTION ################################################################# + if not begin_parallel: + self.simulation.end_transaction(id=parallel_id) + + else: # already under transaction (thus, now a reentrant transaction) OR beginning a parallel segment + + # NOTES: + # + # - Reentrant sequential transactions are not cached, since what matters is the final result of + # the top-level transaction. + # + # - The event that starts the parallel transactions segment WILL NOT itself be cached, since + # it is not part of the parallel segment, but rather the beginning of it. This event will be + # reconstructed during runtime from the parallel events within the segment. + + output = self.function(*self.args, **self.kwargs) + + if begin_parallel: + self.simulation.end_parallel_transactions() + + # execute an ad-hoc Transaction to save the simulation state AFTER the parallel segment is done. + Transaction(self.obj_under_transaction, self.simulation, lambda: True).execute(begin_parallel=False, parallel_id=parallel_id) + + else: + raise ValueError(f"Simulation status is invalid at this point: {self.simulation.status}") + + # Checkpoint if needed + logger.debug(f"Will attempt to checkpoint simulation state after transaction execution.") + if self.simulation is not None and self.simulation.auto_checkpoint: + logger.debug("Auto-checkpointing simulation state after transaction execution.") + self.simulation.checkpoint() + + # after all the transaction is done, return the output - the client will never know about all the complexity we've + # gone through to get here. + return output + + def _save_output_with_simulation_state(self, event_hash, output): + encoded_output = self._encode_function_output(output) + state = self.simulation._encode_simulation_state() + + # immediately drop the cached trace suffix, since we are starting a new execution from this point on. + # in the case of parallel transactions, this will drop everything _after_ the current parallel segment + # (which itself occupies one position only, with a dictionary of event hashes and their outputs). + self.simulation._drop_cached_trace_suffix() + + # Cache the result and update the current execution trace. If this is a parallel transaction, the + # cache and execution traces will be updated in a different way. + self.simulation._add_to_cache_trace(state, event_hash, encoded_output, + parallel=self.simulation.is_under_parallel_transactions()) + self.simulation._add_to_execution_trace(state, event_hash, encoded_output, + parallel=self.simulation.is_under_parallel_transactions()) + + + def _encode_function_output(self, output) -> dict: + """ + Encodes the given function output. + """ + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + + # if the output is a supported object, encode it + if output is None: + return None + elif isinstance(output, TinyPerson): + return {"type": "TinyPersonRef", "name": output.name} + elif isinstance(output, TinyWorld): + return {"type": "TinyWorldRef", "name": output.name} + elif isinstance(output, TinyFactory): + return {"type": "TinyFactoryRef", "name": output.name} + elif isinstance(output, list): + encoded_list = [] + for item in output: + if isinstance(item, TinyPerson): + encoded_list.append({"type": "TinyPersonRef", "name": item.name}) + elif isinstance(item, TinyWorld): + encoded_list.append({"type": "TinyWorldRef", "name": item.name}) + elif isinstance(item, TinyFactory): + encoded_list.append({"type": "TinyFactoryRef", "name": item.name}) + else: + encoded_list.append({"type": "JSON", "value": item}) + return {"type": "List", "value": encoded_list} + elif isinstance(output, (int, float, str, bool, dict, tuple)): + return {"type": "JSON", "value": output} + else: + raise ValueError(f"Unsupported output type: {type(output)}") + + def _decode_function_output(self, encoded_output: dict): + """ + Decodes the given encoded function output. + """ + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + + if encoded_output is None: + return None + elif encoded_output["type"] == "TinyPersonRef": + return TinyPerson.get_agent_by_name(encoded_output["name"]) + elif encoded_output["type"] == "TinyWorldRef": + return TinyWorld.get_environment_by_name(encoded_output["name"]) + elif encoded_output["type"] == "TinyFactoryRef": + return TinyFactory.get_factory_by_name(encoded_output["name"]) + elif encoded_output["type"] == "List": + decoded_list = [] + for item in encoded_output["value"]: + if item["type"] == "TinyPersonRef": + decoded_list.append(TinyPerson.get_agent_by_name(item["name"])) + elif item["type"] == "TinyWorldRef": + decoded_list.append(TinyWorld.get_environment_by_name(item["name"])) + elif item["type"] == "TinyFactoryRef": + decoded_list.append(TinyFactory.get_factory_by_name(item["name"])) + else: + decoded_list.append(item["value"]) + return decoded_list + elif encoded_output["type"] == "JSON": + return encoded_output["value"] + else: + raise ValueError(f"Unsupported output type: {encoded_output['type']}") + +def transactional(parallel=False): + """ + A helper decorator that makes a function simulation-transactional. + """ + def decorator(func): + def wrapper(*args, **kwargs): + obj_under_transaction = args[0] + simulation = current_simulation() + obj_sim_id = obj_under_transaction.simulation_id if hasattr(obj_under_transaction, 'simulation_id') else None + + logger.debug(f"-----------------------------------------> Transaction: {func.__name__} with args {args[1:]} and kwargs {kwargs} under simulation {obj_sim_id}, parallel={parallel}.") + + parallel_id = str(threading.current_thread()) + + transaction = Transaction(obj_under_transaction, simulation, func, *args, **kwargs) + result = transaction.execute(begin_parallel=parallel, parallel_id=parallel_id) + + return result + + return wrapper + + return decorator + +class SkipTransaction(Exception): + pass + +class CacheOutOfSync(Exception): + """ + Raised when a cached and the corresponding freshly executed elements are out of sync. + """ + pass + +class ExecutionCached(Exception): + """ + Raised when a proposed execution is already cached. + """ + pass + + +################################################################################################### +# Convenience functions +################################################################################################### + +def reset(): + """ + Resets the entire simulation control state. + """ + global _current_simulations, _current_simulation_id + _current_simulations = {"default": None} + + # TODO Currently, only one simulation can be started at a time. In future versions, this should be + # changed to allow multiple simulations to be started at the same time, e.g., for fast + # analyses through parallelization. + _current_simulation_id = None + +def _simulation(id="default"): + global _current_simulations + if _current_simulations[id] is None: + _current_simulations[id] = Simulation() + + return _current_simulations[id] + +def begin(cache_path=None, id="default", auto_checkpoint=False): + """ + Marks the start of the simulation being controlled. + """ + global _current_simulation_id + if _current_simulation_id is None: + _simulation(id).begin(cache_path, auto_checkpoint) + _current_simulation_id = id + else: + raise ValueError(f"Simulation is already started under id {_current_simulation_id}. Currently only one simulation can be started at a time.") + +def end(id="default"): + """ + Marks the end of the simulation being controlled. + """ + global _current_simulation_id + _simulation(id).end() + _current_simulation_id = None + +def checkpoint(id="default"): + """ + Saves current simulation state. + """ + _simulation(id).checkpoint() + +def current_simulation(): + """ + Returns the current simulation. + """ + global _current_simulation_id + if _current_simulation_id is not None: + return _simulation(_current_simulation_id) + else: + return None + +def cache_hits(id="default"): + """ + Returns the number of cache hits. + """ + return _simulation(id).cache_hits + +def cache_misses(id="default"): + """ + Returns the number of cache misses. + """ + return _simulation(id).cache_misses + +reset() # initialize the control state \ No newline at end of file diff --git a/enrichment/__init__.py b/enrichment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..02cee3d86395fc13f99636e50d5957e8f62edace --- /dev/null +++ b/enrichment/__init__.py @@ -0,0 +1,11 @@ +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.enrichment.tiny_enricher import TinyEnricher + +__all__ = ["TinyEnricher"] \ No newline at end of file diff --git a/enrichment/__pycache__/__init__.cpython-312.pyc b/enrichment/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ce0d03f145ac9f94b7ea176ce8b789fb4aa59f48 Binary files /dev/null and b/enrichment/__pycache__/__init__.cpython-312.pyc differ diff --git a/enrichment/__pycache__/tiny_enricher.cpython-312.pyc b/enrichment/__pycache__/tiny_enricher.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..89056c6cf2b3bad990afa04443cdae32ee785a2f Binary files /dev/null and b/enrichment/__pycache__/tiny_enricher.cpython-312.pyc differ diff --git a/enrichment/__pycache__/tiny_styler.cpython-312.pyc b/enrichment/__pycache__/tiny_styler.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f4a77272775b72ab7c93807fc80e5c216aeb6162 Binary files /dev/null and b/enrichment/__pycache__/tiny_styler.cpython-312.pyc differ diff --git a/enrichment/prompts/enricher.system.mustache b/enrichment/prompts/enricher.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..41646373a6b52a4a3ee7ff04f675084a72f1420b --- /dev/null +++ b/enrichment/prompts/enricher.system.mustache @@ -0,0 +1,67 @@ +# Content enricher + +You are a system that, given a certain content, enriches it. You operate with synthetic data, your main aim being +to make it more realistic, useful, informative and human-like. Content types might include, but are not limited to: + - Documents + - Meetings + - Emails + - Chat messages + - Tabular data + - Configuration files + - etc. + +Content enrichment under such conditions can be useful in many scenarios, such as: + - Expanding short documents, or document outlines. Synthetic data is often short or incomplete, and you can help + make it more informative. + - Filling in specific missing details. Synthetic data often lacks specific details, and you can help make it more + realistic. + - Making the content more human-like. Synthetic data is often generated by machines, and you can help make it more + human-like. + - Changing tone or style, since the original content might not be suitable for the target audience and might need + to be adjusted. + - Adapting content to work better with other systems. For example, the target system might require special-purpose + formatting, custom fields, or specific data types. + + +## On your input + +You receive as input: + - the content type: e.g. Document, Meeting, Email, Chat Message, Tabualar Data, Configuration Files, etc.; + - the content itself: formated as JSON, XML, CSV, Markdown, plain text, etc.; + - the enrichment requirements: e.g. expand, fill in, make human-like, change tone, adapt, etc.; + - contextual information (optional): This can include anything that you might use to better ground your expansion. For example, + for enriching a document, you might receive contextual inforation about the projects and the people that document is + related to; + - contextual cache (optional): This can include the past enrichments you have made, so you can both avoid repeating yourself + and build on top of your previous enrichments. + +## On how to enrich content + +Enrichment directives: + - You follow the enrichment requirements as closely as possible. + - Unless otherwise specified, you add as much detail as possible, assuming the user wants **more** content, never less. + - You always maintain the original **content type**, unless the specific enrichment request is explicitly asking to change it. + - Regarding contextual information: + * If contextual information is provided, you try to ground your enrichment in that context as much as possible, avoiding + contradictions or inconsistencies. + * If no contextual information is provided, you can be imaginative and creative, but always keeping the content realistic, + useful and internally consistent. + - If contextual cache is provided, you can use it to avoid repeating yourself, and to build on top of your previous enrichments. + In this case, assume that the overall objective is to generate a set of coherent enrichments, rather than a set of isolated + ones. For example, if you have already enriched a document with some details about a project, you can use that information + to later enrich a meeting invite related to that project. + * Morevoer, if other contextual information is provided, you should use it to determine the objective of the + set of enrichments, if that makes sense and results in a more realistic and coherent set of enrichments. + - In the enriched content, you **never** mention that the content was enriched. The enrichment must be transparent, and the final + artifact should not show any signs of being synthetic or machine-generated. + + +## On your output + +Output directives: + + - You maintain the orignal **format** as much as possible. So, for example, the input was a JSON object, you output a JSON object; + if the output was Markdown, you output Markdown; if the input was a CSV, ouput a CSV; if the input was a XML, ouput a XML; + if the input was just text, you output just text; etc. + + diff --git a/enrichment/prompts/enricher.user.mustache b/enrichment/prompts/enricher.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..d0272608ca5451624beb9a89386ed7766762b62d --- /dev/null +++ b/enrichment/prompts/enricher.user.mustache @@ -0,0 +1,30 @@ +Now, please execute a concrete enrichment according with the following specification. + +## Content type +{{#content_type}} +The type of the content is: {{content_type}} +{{/content_type}} +{{^content_type}} +The type of the content is not specified, so please make your best guess about what it is. +{{/content_type}} + +## Enrichment requirements +{{requirements}} + +{{#contextual_information}} +## Contextual information (if any) +{{contextual_information}} +{{/contextual_information}} + +{{#contextual_cache}} +## Contextual cache (if any) + + - {{cached_type}}: {{cached_content}} +{{/contextual_cache}} + +## CONTENT TO ENRICH + +This is the actual content to enrich: +``` +{{content}} +``` diff --git a/enrichment/prompts/styler.system.mustache b/enrichment/prompts/styler.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..e0d2c674c27d6aad07dc968e2fb05bc8d6a7c157 --- /dev/null +++ b/enrichment/prompts/styler.system.mustache @@ -0,0 +1,62 @@ +# Content Styler + +You are a system that transforms text to follow a specified writing or speaking style while preserving the original information. Your primary function is to reshape content to match different tones, dialects, or personality traits without altering the factual content. You can handle various content types including: + - Verbal conversations + - Documents + - Emails + - Chat messages + - Meeting transcripts + - Social media posts + - Blog articles + - Technical documentation + - etc. + +Style transformation can be useful in many scenarios, such as: + - Adapting content for different audiences (technical vs. non-technical, formal vs. casual) + - Changing tone to better match brand voice or company culture + - Simulating different personality types + - Making content more engaging, persuasive, or accessible + - Adding authenticity by matching regional dialects or professional jargon + - Converting between different writing conventions (academic, journalistic, conversational) + - Adjusting formality levels to match specific contexts or relationships + +## On your input + +You receive as input: + - the original content: formatted as JSON, XML, CSV, Markdown, plain text, etc.; + - the target style: a description of the writing or speaking style to transform the content into; + - style parameters (optional): specific aspects of the style to emphasize or de-emphasize; + - contextual information (optional): background that helps you understand the appropriate style or tone; + - preservation requirements (optional): specific elements that must remain unchanged during transformation. + +## On how to transform style + +Style transformation directives: + - You transform the text to match the target style while **always** preserving **all** factual information from the original. + * Factual information includes, but is not limited to, technical terms, names, dates, numerical data, and any other specific details that are critical to the content. + - You maintain the same meaning, points, arguments, and information content throughout the transformation. + - Unless explicitly requested, you do not add new information or remove existing information. + - You adapt language patterns, vocabulary, sentence structure, and rhetorical devices to match the target style. + - Regarding style parameters: + * If parameters emphasize certain aspects (personality, formality, technical language, brevity), you prioritize those aspects. + * If parameters de-emphasize aspects, you minimize those aspects without compromising information. + - Regarding contextual information: + * If provided, you use it to fine-tune the style to be appropriate for the specific context. + * If no context is provided, you implement the style in a general manner that would be widely recognized. + - Regarding preservation requirements: + * You strictly preserve any specified elements (technical terms, names, numerical data, etc.). + * When in doubt about whether something should be preserved, err on the side of preservation. + - You **never** mention that the content was transformed or styled. The transformation should be seamless, and the final + artifact should appear as if it was originally created in the target style. + +## On your output + +Output directives: + + - You maintain the original **format** as much as possible. So, for example, if the input was a JSON object, you output a JSON object; + if the input was Markdown, you output Markdown; if the input was a CSV, output a CSV; if the input was XML, output XML; + if the input was just text, you output just text; etc. + - You preserve structural elements like paragraphs, lists, sections, and formatting unless the target style explicitly + requires structural changes. + - The transformed content should feel natural and authentic to the target style, not like a parody or exaggeration + unless explicitly requested. diff --git a/enrichment/prompts/styler.user.mustache b/enrichment/prompts/styler.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..0948e7179a873f0f53e76e321af53063d3609dab --- /dev/null +++ b/enrichment/prompts/styler.user.mustache @@ -0,0 +1,30 @@ +Now, please apply a concrete style following the specification below. + +## Content type +{{#content_type}} +The type of the content is: {{content_type}} +{{/content_type}} +{{^content_type}} +The type of the content is not specified, so please make your best guess about what it is. +{{/content_type}} + +## Style requirements +{{style}} + +{{#contextual_information}} +## Contextual information (if any) +{{contextual_information}} +{{/contextual_information}} + +{{#contextual_cache}} +## Contextual cache (if any) + + - {{cached_type}}: {{cached_content}} +{{/contextual_cache}} + +## CONTENT TO APPLY STYLE + +This is the actual content to style: +``` +{{content}} +``` diff --git a/enrichment/tiny_enricher.py b/enrichment/tiny_enricher.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8c1e4c2425b1868132c28a9c8275376d798908 --- /dev/null +++ b/enrichment/tiny_enricher.py @@ -0,0 +1,41 @@ +from tinytroupe.enrichment import logger +from tinytroupe.utils import JsonSerializableRegistry + + +from tinytroupe import openai_utils +import tinytroupe.utils as utils + +class TinyEnricher(JsonSerializableRegistry): + + def __init__(self, use_past_results_in_context=False) -> None: + self.use_past_results_in_context = use_past_results_in_context + + self.context_cache = [] + + def enrich_content(self, requirements: str, content:str, content_type:str =None, context_info:str ="", context_cache:list=None, verbose:bool=False): + + rendering_configs = {"requirements": requirements, + "content": content, + "content_type": content_type, + "context_info": context_info, + "context_cache": context_cache} + + messages = utils.compose_initial_LLM_messages_with_templates("enricher.system.mustache", "enricher.user.mustache", + base_module_folder = "enrichment", + rendering_configs=rendering_configs) + + next_message = openai_utils.client().send_message(messages, temperature=1.0, frequency_penalty=0.0, presence_penalty=0.0) + + debug_msg = f"Enrichment result message: {next_message}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + if next_message is not None: + result = utils.extract_code_block(next_message["content"]) + else: + result = None + + return result + + diff --git a/enrichment/tiny_styler.py b/enrichment/tiny_styler.py new file mode 100644 index 0000000000000000000000000000000000000000..c3cc76d5bc70347d466583ec9425a7f873519b35 --- /dev/null +++ b/enrichment/tiny_styler.py @@ -0,0 +1,85 @@ +from tinytroupe.enrichment import logger +from tinytroupe.utils import JsonSerializableRegistry +from tinytroupe.utils.llm import LLMChat +import tinytroupe.utils as utils + + +class TinyStyler(JsonSerializableRegistry): + """ + A class for applying a specified writing or speaking style to content while preserving + the original information. + """ + + def __init__(self, use_past_results_in_context=False) -> None: + """ + Initialize the TinyStyler. + + Args: + use_past_results_in_context (bool): Whether to use past styling results in the context. + """ + self.use_past_results_in_context = use_past_results_in_context + self.context_cache = [] + + def apply_style(self, content: str, style: str, content_type: str = None, + context_info: str = "", context_cache: list = None, verbose: bool = False, + temperature: float = 0.7): + """ + Apply a specified style to the content while preserving all the original information. + + Args: + content (str): The content to style. + style (str): The style to apply (e.g., "professional", "casual", "technical", etc.). + content_type (str, optional): The type of content (e.g., "email", "report", "conversation"). + context_info (str, optional): Additional context information. + context_cache (list, optional): Previous styling results to use as context. + verbose (bool, optional): Whether to print debug information. + temperature (float, optional): The temperature to use for the LLM generation. + + Returns: + str: The styled content. + """ + if context_cache is None and self.use_past_results_in_context: + context_cache = self.context_cache + + rendering_configs = { + "content": content, + "style": style, + "content_type": content_type, + "context_info": context_info, + "context_cache": context_cache + } + + # Initialize the LLMChat with appropriate templates + chat = LLMChat( + system_template_name="styler.system.mustache", + user_template_name="styler.user.mustache", + base_module_folder="enrichment", + temperature=temperature + ) + + # Call the model and get the response + result = chat.call(**rendering_configs) + + debug_msg = f"Styling result: {result}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + # Extract the styled content from code blocks if present + if result is not None: + styled_content = utils.extract_code_block(result) + # If no code block was found, use the raw result + if not styled_content: + styled_content = result + + # Add to context cache if enabled + if self.use_past_results_in_context: + self.context_cache.append({ + "original": content, + "style": style, + "styled": styled_content + }) + + return styled_content + else: + return None diff --git a/environment/__init__.py b/environment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..976a1e4a944c8cfc600420d71add0112fab606bc --- /dev/null +++ b/environment/__init__.py @@ -0,0 +1,17 @@ +""" +Environments provide a structured way to define the world in which the +agents interact with each other as well as external entities (e.g., search engines). +""" + +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.environment.tiny_world import TinyWorld +from tinytroupe.environment.tiny_social_network import TinySocialNetwork + +__all__ = ["TinyWorld", "TinySocialNetwork"] \ No newline at end of file diff --git a/environment/__pycache__/__init__.cpython-312.pyc b/environment/__pycache__/__init__.cpython-312.pyc new file mode 100644 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0000000000000000000000000000000000000000..d097c1e13ba47591e8bae3162489f2f1c56a857f --- /dev/null +++ b/environment/tiny_social_network.py @@ -0,0 +1,132 @@ +from tinytroupe.environment.tiny_world import TinyWorld +from tinytroupe.environment import logger + +import copy +from datetime import datetime, timedelta + +from tinytroupe.agent import * +from tinytroupe.control import transactional + +from rich.console import Console + +from typing import Any, TypeVar, Union +AgentOrWorld = Union["TinyPerson", "TinyWorld"] + + +class TinySocialNetwork(TinyWorld): + + def __init__(self, name, broadcast_if_no_target=True): + """ + Create a new TinySocialNetwork environment. + + Args: + name (str): The name of the environment. + broadcast_if_no_target (bool): If True, broadcast actions through an agent's available relations + if the target of an action is not found. + """ + + super().__init__(name, broadcast_if_no_target=broadcast_if_no_target) + + self.relations = {} + + @transactional() + def add_relation(self, agent_1, agent_2, name="default"): + """ + Adds a relation between two agents. + + Args: + agent_1 (TinyPerson): The first agent. + agent_2 (TinyPerson): The second agent. + name (str): The name of the relation. + """ + + logger.debug(f"Adding relation {name} between {agent_1.name} and {agent_2.name}.") + + # agents must already be in the environment, if not they are first added + if agent_1 not in self.agents: + self.agents.append(agent_1) + if agent_2 not in self.agents: + self.agents.append(agent_2) + + if name in self.relations: + self.relations[name].append((agent_1, agent_2)) + else: + self.relations[name] = [(agent_1, agent_2)] + + return self # for chaining + + @transactional() + def _update_agents_contexts(self): + """ + Updates the agents' observations based on the current state of the world. + """ + + # clear all accessibility first + for agent in self.agents: + agent.make_all_agents_inaccessible() + + # now update accessibility based on relations + for relation_name, relation in self.relations.items(): + logger.debug(f"Updating agents' observations for relation {relation_name}.") + for agent_1, agent_2 in relation: + agent_1.make_agent_accessible(agent_2) + agent_2.make_agent_accessible(agent_1) + + @transactional() + def _step(self): + self._update_agents_contexts() + + #call super + super()._step() + + @transactional() + def _handle_reach_out(self, source_agent: TinyPerson, content: str, target: str): + """ + Handles the REACH_OUT action. This social network implementation only allows + REACH_OUT to succeed if the target agent is in the same relation as the source agent. + + Args: + source_agent (TinyPerson): The agent that issued the REACH_OUT action. + content (str): The content of the message. + target (str): The target of the message. + """ + + # check if the target is in the same relation as the source + if self.is_in_relation_with(source_agent, self.get_agent_by_name(target)): + super()._handle_reach_out(source_agent, content, target) + + # if we get here, the target is not in the same relation as the source + source_agent.socialize(f"{target} is not in the same relation as you, so you cannot reach out to them.", source=self) + + + # TODO implement _handle_talk using broadcast_if_no_target too + + ####################################################################### + # Utilities and conveniences + ####################################################################### + + def is_in_relation_with(self, agent_1:TinyPerson, agent_2:TinyPerson, relation_name=None) -> bool: + """ + Checks if two agents are in a relation. If the relation name is given, check that + the agents are in that relation. If no relation name is given, check that the agents + are in any relation. Relations are undirected, so the order of the agents does not matter. + + Args: + agent_1 (TinyPerson): The first agent. + agent_2 (TinyPerson): The second agent. + relation_name (str): The name of the relation to check, or None to check any relation. + + Returns: + bool: True if the two agents are in the given relation, False otherwise. + """ + if relation_name is None: + for relation_name, relation in self.relations.items(): + if (agent_1, agent_2) in relation or (agent_2, agent_1) in relation: + return True + return False + + else: + if relation_name in self.relations: + return (agent_1, agent_2) in self.relations[relation_name] or (agent_2, agent_1) in self.relations[relation_name] + else: + return False \ No newline at end of file diff --git a/environment/tiny_world.py b/environment/tiny_world.py new file mode 100644 index 0000000000000000000000000000000000000000..990eb88c06269a0dd2caa8e67aa268f4c0e6403c --- /dev/null +++ b/environment/tiny_world.py @@ -0,0 +1,866 @@ +from tinytroupe.environment import logger, default + +import copy +from datetime import datetime, timedelta +import textwrap +import random +import concurrent.futures + +from tinytroupe.agent import * +from tinytroupe.utils import name_or_empty, pretty_datetime +import tinytroupe.control as control +from tinytroupe.control import transactional +from tinytroupe import utils +from tinytroupe import config_manager + +from rich.console import Console + +from typing import Any, TypeVar, Union +AgentOrWorld = Union["TinyPerson", "TinyWorld"] + +class TinyWorld: + """ + Base class for environments. + """ + + # A dict of all environments created so far. + all_environments = {} # name -> environment + + # Whether to display environments communications or not, for all environments. + communication_display = True + + def __init__(self, name: str=None, agents=[], + initial_datetime=datetime.now(), + interventions=[], + broadcast_if_no_target=True, + max_additional_targets_to_display=3): + """ + Initializes an environment. + + Args: + name (str): The name of the environment. + agents (list): A list of agents to add to the environment. + initial_datetifme (datetime): The initial datetime of the environment, or None (i.e., explicit time is optional). + Defaults to the current datetime in the real world. + interventions (list): A list of interventions to apply in the environment at each simulation step. + broadcast_if_no_target (bool): If True, broadcast actions if the target of an action is not found. + max_additional_targets_to_display (int): The maximum number of additional targets to display in a communication. If None, + all additional targets are displayed. + """ + + if name is not None: + self.name = name + else: + self.name = f"TinyWorld {utils.fresh_id(self.__class__.__name__)}" + + self.current_datetime = initial_datetime + self.broadcast_if_no_target = broadcast_if_no_target + self.simulation_id = None # will be reset later if the agent is used within a specific simulation scope + + self.agents = [] + self.name_to_agent = {} # {agent_name: agent, agent_name_2: agent_2, ...} + + self._interventions = interventions + + # the buffer of communications that have been displayed so far, used for + # saving these communications to another output form later (e.g., caching) + self._displayed_communications_buffer = [] + + # a temporary buffer for communications target to make rendering easier + self._target_display_communications_buffer = [] + self._max_additional_targets_to_display = max_additional_targets_to_display + + self.console = Console() + + # add the environment to the list of all environments + TinyWorld.add_environment(self) + + self.add_agents(agents) + + ####################################################################### + # Simulation control methods + ####################################################################### + @transactional() + def _step(self, + timedelta_per_step=None, + randomize_agents_order=True, + parallelize=True): # TODO have a configuration for parallelism? + """ + Performs a single step in the environment. This default implementation + simply calls makes all agents in the environment act and properly + handle the resulting actions. Subclasses might override this method to implement + different policies. + """ + + # Increase current datetime if timedelta is given. This must happen before + # any other simulation updates, to make sure that the agents are acting + # in the correct time, particularly if only one step is being run. + self._advance_datetime(timedelta_per_step) + + # Apply interventions. + # + # Why not in parallel? Owing to the very general nature of their potential effects, + # interventions are never parallelized, since that could introduce unforeseen race conditions. + for intervention in self._interventions: + should_apply_intervention = intervention.check_precondition() + if should_apply_intervention: + if TinyWorld.communication_display: + self._display_intervention_communication(intervention) + intervention.apply_effect() + + logger.debug(f"[{self.name}] Intervention '{intervention.name}' was applied.") + + # Agents can act in parallel or sequentially + if parallelize: + agents_actions = self._step_in_parallel(timedelta_per_step=timedelta_per_step) + else: + agents_actions = self._step_sequentially(timedelta_per_step=timedelta_per_step, + randomize_agents_order=randomize_agents_order) + + return agents_actions + + def _step_sequentially(self, timedelta_per_step=None, randomize_agents_order=True): + """ + The sequential version of the _step method to request agents to act. + """ + + # agents can act in a random order + reordered_agents = copy.copy(self.agents) + if randomize_agents_order: + random.shuffle(reordered_agents) + + # agents can act + agents_actions = {} + for agent in reordered_agents: + logger.debug(f"[{self.name}] Agent {name_or_empty(agent)} is acting.") + actions = agent.act(return_actions=True) + agents_actions[agent.name] = actions + + self._handle_actions(agent, agent.pop_latest_actions()) + + return agents_actions + + def _step_in_parallel(self, timedelta_per_step=None): + """ + A parallelized version of the _step method to request agents to act. + """ + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = {executor.submit(agent.act, return_actions=True): agent for agent in self.agents} + agents_actions = {} + + # Wait for all futures to complete + concurrent.futures.wait(futures.keys()) + + for future in futures: + agent = futures[future] + try: + actions = future.result() + agents_actions[agent.name] = actions + self._handle_actions(agent, agent.pop_latest_actions()) + except Exception as exc: + logger.error(f"[{self.name}] Agent {name_or_empty(agent)} generated an exception: {exc}") + + return agents_actions + + + + def _advance_datetime(self, timedelta): + """ + Advances the current datetime of the environment by the specified timedelta. + + Args: + timedelta (timedelta): The timedelta to advance the current datetime by. + """ + if timedelta is not None: + self.current_datetime += timedelta + else: + logger.info(f"[{self.name}] No timedelta provided, so the datetime was not advanced.") + + @transactional() + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run(self, steps: int, timedelta_per_step=None, return_actions=False, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of steps. + + Args: + steps (int): The number of steps to run the environment for. + timedelta_per_step (timedelta, optional): The time interval between steps. Defaults to None. + return_actions (bool, optional): If True, returns the actions taken by the agents. Defaults to False. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + parallelize (bool, optional): If True, agents act in parallel. Defaults to True. + + Returns: + list: A list of actions taken by the agents over time, if return_actions is True. The list has this format: + [{agent_name: [action_1, action_2, ...]}, {agent_name_2: [action_1, action_2, ...]}, ...] + """ + agents_actions_over_time = [] + for i in range(steps): + logger.info(f"[{self.name}] Running world simulation step {i+1} of {steps}.") + + if TinyWorld.communication_display: + self._display_step_communication(cur_step=i+1, total_steps=steps, timedelta_per_step=timedelta_per_step) + + agents_actions = self._step(timedelta_per_step=timedelta_per_step, randomize_agents_order=randomize_agents_order, parallelize=parallelize) + agents_actions_over_time.append(agents_actions) + + if return_actions: + return agents_actions_over_time + + @transactional() + def skip(self, steps: int, timedelta_per_step=None): + """ + Skips a given number of steps in the environment. That is to say, time shall pass, but no actions will be taken + by the agents or any other entity in the environment. + + Args: + steps (int): The number of steps to skip. + timedelta_per_step (timedelta, optional): The time interval between steps. Defaults to None. + """ + self._advance_datetime(steps * timedelta_per_step) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_minutes(self, minutes: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of minutes. + + Args: + minutes (int): The number of minutes to run the environment for. + """ + self.run(steps=minutes, timedelta_per_step=timedelta(minutes=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_minutes(self, minutes: int): + """ + Skips a given number of minutes in the environment. + + Args: + minutes (int): The number of minutes to skip. + """ + self.skip(steps=minutes, timedelta_per_step=timedelta(minutes=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_hours(self, hours: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of hours. + + Args: + hours (int): The number of hours to run the environment for. + """ + self.run(steps=hours, timedelta_per_step=timedelta(hours=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_hours(self, hours: int): + """ + Skips a given number of hours in the environment. + + Args: + hours (int): The number of hours to skip. + """ + self.skip(steps=hours, timedelta_per_step=timedelta(hours=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_days(self, days: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of days. + + Args: + days (int): The number of days to run the environment for. + """ + self.run(steps=days, timedelta_per_step=timedelta(days=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_days(self, days: int): + """ + Skips a given number of days in the environment. + + Args: + days (int): The number of days to skip. + """ + self.skip(steps=days, timedelta_per_step=timedelta(days=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_weeks(self, weeks: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of weeks. + + Args: + weeks (int): The number of weeks to run the environment for. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + """ + self.run(steps=weeks, timedelta_per_step=timedelta(weeks=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_weeks(self, weeks: int): + """ + Skips a given number of weeks in the environment. + + Args: + weeks (int): The number of weeks to skip. + """ + self.skip(steps=weeks, timedelta_per_step=timedelta(weeks=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_months(self, months: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of months. + + Args: + months (int): The number of months to run the environment for. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + """ + self.run(steps=months, timedelta_per_step=timedelta(weeks=4), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_months(self, months: int): + """ + Skips a given number of months in the environment. + + Args: + months (int): The number of months to skip. + """ + self.skip(steps=months, timedelta_per_step=timedelta(weeks=4)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_years(self, years: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of years. + + Args: + years (int): The number of years to run the environment for. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + """ + self.run(steps=years, timedelta_per_step=timedelta(days=365), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_years(self, years: int): + """ + Skips a given number of years in the environment. + + Args: + years (int): The number of years to skip. + """ + self.skip(steps=years, timedelta_per_step=timedelta(days=365)) + + ####################################################################### + # Agent management methods + ####################################################################### + def add_agents(self, agents: list): + """ + Adds a list of agents to the environment. + + Args: + agents (list): A list of agents to add to the environment. + """ + for agent in agents: + self.add_agent(agent) + + return self # for chaining + + def add_agent(self, agent: TinyPerson): + """ + Adds an agent to the environment. The agent must have a unique name within the environment. + + Args: + agent (TinyPerson): The agent to add to the environment. + + Raises: + ValueError: If the agent name is not unique within the environment. + """ + + # check if the agent is not already in the environment + if agent not in self.agents: + logger.debug(f"Adding agent {agent.name} to the environment.") + + # Agent names must be unique in the environment. + # Check if the agent name is already there. + if agent.name not in self.name_to_agent: + agent.environment = self + self.agents.append(agent) + self.name_to_agent[agent.name] = agent + else: + raise ValueError(f"Agent names must be unique, but '{agent.name}' is already in the environment.") + else: + logger.warn(f"Agent {agent.name} is already in the environment.") + + return self # for chaining + + def remove_agent(self, agent: TinyPerson): + """ + Removes an agent from the environment. + + Args: + agent (TinyPerson): The agent to remove from the environment. + """ + logger.debug(f"Removing agent {agent.name} from the environment.") + self.agents.remove(agent) + del self.name_to_agent[agent.name] + + return self # for chaining + + def remove_all_agents(self): + """ + Removes all agents from the environment. + """ + logger.debug(f"Removing all agents from the environment.") + self.agents = [] + self.name_to_agent = {} + + return self # for chaining + + def get_agent_by_name(self, name: str) -> TinyPerson: + """ + Returns the agent with the specified name. If no agent with that name exists in the environment, + returns None. + + Args: + name (str): The name of the agent to return. + + Returns: + TinyPerson: The agent with the specified name. + """ + if name in self.name_to_agent: + return self.name_to_agent[name] + else: + return None + + ####################################################################### + # Intervention management methods + ####################################################################### + + def add_intervention(self, intervention): + """ + Adds an intervention to the environment. + + Args: + intervention: The intervention to add to the environment. + """ + self._interventions.append(intervention) + + ####################################################################### + # Action handlers + # + # Specific actions issued by agents are handled by the environment, + # because they have effects beyond the agent itself. + ####################################################################### + @transactional() + def _handle_actions(self, source: TinyPerson, actions: list): + """ + Handles the actions issued by the agents. + + Args: + source (TinyPerson): The agent that issued the actions. + actions (list): A list of actions issued by the agents. Each action is actually a + JSON specification. + + """ + for action in actions: + action_type = action["type"] # this is the only required field + content = action["content"] if "content" in action else None + target = action["target"] if "target" in action else None + + logger.debug(f"[{self.name}] Handling action {action_type} from agent {name_or_empty(source)}. Content: {content}, target: {target}.") + + # only some actions require the enviroment to intervene + if action_type == "REACH_OUT": + self._handle_reach_out(source, content, target) + elif action_type == "TALK": + self._handle_talk(source, content, target) + + @transactional() + def _handle_reach_out(self, source_agent: TinyPerson, content: str, target: str): + """ + Handles the REACH_OUT action. This default implementation always allows REACH_OUT to succeed. + Subclasses might override this method to implement different policies. + + Args: + source_agent (TinyPerson): The agent that issued the REACH_OUT action. + content (str): The content of the message. + target (str): The target of the message. + """ + + # This default implementation always allows REACH_OUT to suceed. + target_agent = self.get_agent_by_name(target) + + if target_agent is not None: + source_agent.make_agent_accessible(target_agent) + target_agent.make_agent_accessible(source_agent) + + source_agent.socialize(f"{name_or_empty(target_agent)} was successfully reached out, and is now available for interaction.", source=self) + target_agent.socialize(f"{name_or_empty(source_agent)} reached out to you, and is now available for interaction.", source=self) + + else: + logger.debug(f"[{self.name}] REACH_OUT action failed: target agent '{target}' not found.") + + @transactional() + def _handle_talk(self, source_agent: TinyPerson, content: str, target: str): + """ + Handles the TALK action by delivering the specified content to the specified target. + + Args: + source_agent (TinyPerson): The agent that issued the TALK action. + content (str): The content of the message. + target (str, optional): The target of the message. + """ + target_agent = self.get_agent_by_name(target) + + logger.debug(f"[{self.name}] Delivering message from {name_or_empty(source_agent)} to {name_or_empty(target_agent)}.") + + if target_agent is not None: + target_agent.listen(content, source=source_agent) + elif self.broadcast_if_no_target: + self.broadcast(content, source=source_agent) + + ####################################################################### + # Interaction methods + ####################################################################### + @transactional() + def broadcast(self, speech: str, source: AgentOrWorld=None): + """ + Delivers a speech to all agents in the environment. + + Args: + speech (str): The content of the message. + source (AgentOrWorld, optional): The agent or environment that issued the message. Defaults to None. + """ + logger.debug(f"[{self.name}] Broadcasting message: '{speech}'.") + + for agent in self.agents: + # do not deliver the message to the source + if agent != source: + agent.listen(speech, source=source) + + @transactional() + def broadcast_thought(self, thought: str, source: AgentOrWorld=None): + """ + Broadcasts a thought to all agents in the environment. + + Args: + thought (str): The content of the thought. + """ + logger.debug(f"[{self.name}] Broadcasting thought: '{thought}'.") + + for agent in self.agents: + agent.think(thought) + + @transactional() + def broadcast_internal_goal(self, internal_goal: str): + """ + Broadcasts an internal goal to all agents in the environment. + + Args: + internal_goal (str): The content of the internal goal. + """ + logger.debug(f"[{self.name}] Broadcasting internal goal: '{internal_goal}'.") + + for agent in self.agents: + agent.internalize_goal(internal_goal) + + @transactional() + def broadcast_context_change(self, context:list): + """ + Broadcasts a context change to all agents in the environment. + + Args: + context (list): The content of the context change. + """ + logger.debug(f"[{self.name}] Broadcasting context change: '{context}'.") + + for agent in self.agents: + agent.change_context(context) + + def make_everyone_accessible(self): + """ + Makes all agents in the environment accessible to each other. + """ + for agent_1 in self.agents: + for agent_2 in self.agents: + if agent_1 != agent_2: + agent_1.make_agent_accessible(agent_2) + + + ########################################################### + # Formatting conveniences + ########################################################### + + # TODO better names for these "display" methods + def _display_step_communication(self, cur_step, total_steps, timedelta_per_step=None): + """ + Displays the current communication and stores it in a buffer for later use. + """ + rendering = self._pretty_step(cur_step=cur_step, total_steps=total_steps, timedelta_per_step=timedelta_per_step) + + self._push_and_display_latest_communication({"kind": 'step', "rendering": rendering, "content": None, "source": None, "target": None}) + + def _display_intervention_communication(self, intervention): + """ + Displays the current intervention communication and stores it in a buffer for later use. + """ + rendering = self._pretty_intervention(intervention) + self._push_and_display_latest_communication({"kind": 'intervention', "rendering": rendering, "content": None, "source": None, "target": None}) + + def _push_and_display_latest_communication(self, communication): + """ + Pushes the latest communications to the agent's buffer. + """ + # + # check if the communication is just repeating the last one for a different target + # + if len(self._displayed_communications_buffer) > 0: + # get values from last communication + last_communication = self._displayed_communications_buffer[-1] + last_kind = last_communication["kind"] + last_target = last_communication["target"] + last_source = last_communication["source"] + if last_kind == 'action': + last_content = last_communication["content"]["action"]["content"] + last_type = last_communication["content"]["action"]["type"] + elif last_kind == 'stimulus': + last_content = last_communication["content"]["stimulus"]["content"] + last_type = last_communication["content"]["stimulus"]["type"] + elif last_kind == 'stimuli': + last_stimulus = last_communication["content"]["stimuli"][0] + last_content = last_stimulus["content"] + last_type = last_stimulus["type"] + else: + last_content = None + last_type = None + + # get values from current communication + current_kind = communication["kind"] + current_target = communication["target"] + current_source = communication["source"] + if current_kind == 'action': + current_content = communication["content"]["action"]["content"] + current_type = communication["content"]["action"]["type"] + elif current_kind == 'stimulus': + current_content = communication["content"]["stimulus"]["content"] + current_type = communication["content"]["stimulus"]["type"] + elif current_kind == 'stimuli': + current_stimulus = communication["content"]["stimuli"][0] + current_content = current_stimulus["content"] + current_type = current_stimulus["type"] + else: + current_content = None + current_type = None + + # if we are repeating the last communication, let's simplify the rendering + if (last_source == current_source) and (last_type == current_type) and (last_kind == current_kind) and \ + (last_content is not None) and (last_content == current_content) and \ + (current_target is not None): + + self._target_display_communications_buffer.append(current_target) + + rich_style = utils.RichTextStyle.get_style_for(last_kind, last_type) + + # print the additional target a limited number of times if a max is set, or + # always if no max is set. + if (self._max_additional_targets_to_display is None) or\ + len(self._target_display_communications_buffer) < self._max_additional_targets_to_display: + communication["rendering"] = " " * len(last_source) + f"[{rich_style}] + --> [underline]{current_target}[/][/]" + + elif len(self._target_display_communications_buffer) == self._max_additional_targets_to_display: + communication["rendering"] = " " * len(last_source) + f"[{rich_style}] + --> ...others...[/]" + + else: # don't display anything anymore + communication["rendering"] = None + + else: + # no repetition, so just display the communication and reset the targets buffer + self._target_display_communications_buffer = [] # resets + + else: + # no repetition, so just display the communication and reset the targets buffer + self._target_display_communications_buffer = [] # resets + + + + self._displayed_communications_buffer.append(communication) + self._display(communication) + + def pop_and_display_latest_communications(self): + """ + Pops the latest communications and displays them. + """ + communications = self._displayed_communications_buffer + self._displayed_communications_buffer = [] + + for communication in communications: + self._display(communication) + + return communications + + def _display(self, communication:dict): + # unpack the rendering to find more info + content = communication["rendering"] + kind = communication["kind"] + + if content is not None: + # render as appropriate + if kind == 'step': + self.console.rule(content) + else: + self.console.print(content) + + def clear_communications_buffer(self): + """ + Cleans the communications buffer. + """ + self._displayed_communications_buffer = [] + + def __repr__(self): + return f"TinyWorld(name='{self.name}')" + + def _pretty_step(self, cur_step, total_steps, timedelta_per_step=None): + rendering = f"{self.name} step {cur_step} of {total_steps}" + if timedelta_per_step is not None: + rendering += f" ({pretty_datetime(self.current_datetime)})" + + return rendering + + def _pretty_intervention(self, intervention): + indent = " > " + justification = textwrap.fill( + intervention.precondition_justification(), + width=TinyPerson.PP_TEXT_WIDTH, + initial_indent=indent, + subsequent_indent=indent, + ) + + rich_style = utils.RichTextStyle.get_style_for("intervention") + rendering = f"[{rich_style}] :zap: [bold] <<{intervention.name}>> Triggered, effects are being applied...[/] \n" + \ + f"[italic]{justification}[/][/]" + # TODO add details about why the intervention was applied + + return rendering + + def pp_current_interactions(self, simplified=True, skip_system=True): + """ + Pretty prints the current messages from agents in this environment. + """ + print(self.pretty_current_interactions(simplified=simplified, skip_system=skip_system)) + + def pretty_current_interactions(self, simplified=True, skip_system=True, max_content_length=default["max_content_display_length"], first_n=None, last_n=None, include_omission_info:bool=True): + """ + Returns a pretty, readable, string with the current messages of agents in this environment. + """ + agent_contents = [] + + for agent in self.agents: + agent_content = f"#### Interactions from the point of view of {agent.name} agent:\n" + agent_content += f"**BEGIN AGENT {agent.name} HISTORY.**\n " + agent_content += agent.pretty_current_interactions(simplified=simplified, skip_system=skip_system, max_content_length=max_content_length, first_n=first_n, last_n=last_n, include_omission_info=include_omission_info) + "\n" + agent_content += f"**FINISHED AGENT {agent.name} HISTORY.**\n\n" + agent_contents.append(agent_content) + + return "\n".join(agent_contents) + + ####################################################################### + # IO + ####################################################################### + + def encode_complete_state(self) -> dict: + """ + Encodes the complete state of the environment in a dictionary. + + Returns: + dict: A dictionary encoding the complete state of the environment. + """ + to_copy = copy.copy(self.__dict__) + + # remove the logger and other fields + del to_copy['console'] + del to_copy['agents'] + del to_copy['name_to_agent'] + del to_copy['current_datetime'] + del to_copy['_interventions'] # TODO: encode interventions + + state = copy.deepcopy(to_copy) + + # agents are encoded separately + state["agents"] = [agent.encode_complete_state() for agent in self.agents] + + # datetime also has to be encoded separately + state["current_datetime"] = self.current_datetime.isoformat() + + return state + + def decode_complete_state(self, state:dict): + """ + Decodes the complete state of the environment from a dictionary. + + Args: + state (dict): A dictionary encoding the complete state of the environment. + + Returns: + Self: The environment decoded from the dictionary. + """ + state = copy.deepcopy(state) + + ################################# + # restore agents in-place + ################################# + self.remove_all_agents() + for agent_state in state["agents"]: + try: + try: + agent = TinyPerson.get_agent_by_name(agent_state["name"]) + except Exception as e: + raise ValueError(f"Could not find agent {agent_state['name']} for environment {self.name}.") from e + + agent.decode_complete_state(agent_state) + self.add_agent(agent) + + except Exception as e: + raise ValueError(f"Could not decode agent {agent_state['name']} for environment {self.name}.") from e + + # remove the agent states to update the rest of the environment + del state["agents"] + + # restore datetime + state["current_datetime"] = datetime.fromisoformat(state["current_datetime"]) + + # restore other fields + self.__dict__.update(state) + + return self + + @staticmethod + def add_environment(environment): + """ + Adds an environment to the list of all environments. Environment names must be unique, + so if an environment with the same name already exists, an error is raised. + """ + if environment.name in TinyWorld.all_environments: + raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") + else: + TinyWorld.all_environments[environment.name] = environment + + + @staticmethod + def set_simulation_for_free_environments(simulation): + """ + Sets the simulation if it is None. This allows free environments to be captured by specific simulation scopes + if desired. + """ + for environment in TinyWorld.all_environments.values(): + if environment.simulation_id is None: + simulation.add_environment(environment) + + @staticmethod + def get_environment_by_name(name: str): + """ + Returns the environment with the specified name. If no environment with that name exists, + returns None. + + Args: + name (str): The name of the environment to return. + + Returns: + TinyWorld: The environment with the specified name. + """ + if name in TinyWorld.all_environments: + return TinyWorld.all_environments[name] + else: + return None + + @staticmethod + def clear_environments(): + """ + Clears the list of all environments. + """ + TinyWorld.all_environments = {} diff --git a/examples/__init__.py b/examples/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..123ede0f756b0ec361c352430ddf991cc297b799 --- /dev/null +++ b/examples/__init__.py @@ -0,0 +1,11 @@ + +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from .agents import * +from .loaders import * \ No newline at end of file diff --git a/examples/__pycache__/__init__.cpython-312.pyc b/examples/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eaedf64cb92457220c77945ac8a2dab1b3df2748 Binary files /dev/null and b/examples/__pycache__/__init__.cpython-312.pyc differ diff --git a/examples/__pycache__/agents.cpython-312.pyc b/examples/__pycache__/agents.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f9276a4153581dbad0f43db9850c0ebc146b2ca6 Binary files /dev/null and b/examples/__pycache__/agents.cpython-312.pyc differ diff --git a/examples/__pycache__/loaders.cpython-312.pyc b/examples/__pycache__/loaders.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4f89a61e3cf29787bc85c852cf277a1427ba7077 Binary files /dev/null and b/examples/__pycache__/loaders.cpython-312.pyc differ diff --git a/examples/agents.py b/examples/agents.py new file mode 100644 index 0000000000000000000000000000000000000000..7cdd42a8ace881ce74516060b744ae8343788fd0 --- /dev/null +++ b/examples/agents.py @@ -0,0 +1,316 @@ +""" +Some examples of how to use the tinytroupe library. These can be used directly or slightly modified to create your own ' +agents. +""" +import os +from tinytroupe.agent import TinyPerson +from .loaders import load_example_agent_specification + +################################### +# Example 1: Oscar, the architect +################################### + +def create_oscar_the_architect(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Oscar"), new_agent_name="Oscar", auto_rename_agent=False) + +def create_oscar_the_architect_2(enable_browser=False): + """ + A purely programmatic way to create Oscar, the architect. Has less information than the one loaded from a file, just for demonstration purposes. + """ + oscar = TinyPerson("Oscar", enable_browser=enable_browser) + + oscar.define("age", 30) + oscar.define("nationality", "German") + oscar.define("behaviors", {"routines": ["Every morning, you wake up, feed your dog, and go to work."]}) + oscar.define("occupation", { + "title": "Architect", + "organization": "Awesome Inc.", + "description": + """ + You are an architect. You work at a company called "Awesome Inc.". Though you are qualified to do any + architecture task, currently you are responsible for establishing standard elements for the new appartment + buildings built by Awesome, so that customers can select a pre-defined configuration for their appartment + without having to go through the hassle of designing it themselves. You care a lot about making sure your + standard designs are functional, aesthetically pleasing and cost-effective. Your main difficulties typically + involve making trade-offs between price and quality - you tend to favor quality, but your boss is always + pushing you to reduce costs. You are also responsible for making sure the designs are compliant with + local building regulations. + """}) + + oscar.define("personality", + {"traits": [ + "You are fast paced and like to get things done quickly.", + "You are very detail oriented and like to make sure everything is perfect.", + "You have a witty sense of humor and like to make jokes.", + "You don't get angry easily, and always try to stay calm. However, in the few occasions you do get angry, you get very very mad." + ]}) + + oscar.define("preferences", + {"interests": [ + "Modernist architecture and design.", + "New technologies for architecture.", + "Sustainable architecture and practices.", + + "Traveling to exotic places.", + "Playing the guitar.", + "Reading books, particularly science fiction." + ]}) + + + oscar.define("skills", + [ + "You are very familiar with AutoCAD, and use it for most of your work.", + "You are able to easily search for information on the internet.", + "You are familiar with Word and PowerPoint, but struggle with Excel." + ]) + + oscar.define("relationships", + [ + {"name": "Richard", + "description": "your colleague, handles similar projects, but for a different market."}, + {"name": "John", "description": "your boss, he is always pushing you to reduce costs."} + ]) + + return oscar + +####################################### +# Example 2: Lisa, the Data Scientist +####################################### +def create_lisa_the_data_scientist(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Lisa"), new_agent_name="Lisa", auto_rename_agent=False) + +def create_lisa_the_data_scientist_2(enable_browser=False): + """ + A purely programmatic way to create Lisa, the data scientist. Has less information than the one loaded from a file, just for demonstration purposes + """ + lisa = TinyPerson("Lisa", enable_browser=enable_browser) + + lisa.define("age", 28) + lisa.define("nationality", "Canadian") + lisa.define("occupation", { + "title": "Data Scientist", + "organization": "Microsoft", + "description": + """ + You are a data scientist. You work at Microsoft, in the M365 Search team. Your main role is to analyze + user behavior and feedback data, and use it to improve the relevance and quality of the search results. + You also build and test machine learning models for various search scenarios, such as natural language + understanding, query expansion, and ranking. You care a lot about making sure your data analysis and + models are accurate, reliable and scalable. Your main difficulties typically involve dealing with noisy, + incomplete or biased data, and finding the best ways to communicate your findings and recommendations to + other teams. You are also responsible for making sure your data and models are compliant with privacy and + security policies. + """}) + + lisa.define("behaviors", {"routines": ["Every morning, you wake up, do some yoga, and check your emails."]}) + + lisa.define("personality", + {"traits": [ + "You are curious and love to learn new things.", + "You are analytical and like to solve problems.", + "You are friendly and enjoy working with others.", + "You don't give up easily, and always try to find a solution. However, sometimes you can get frustrated when things don't work as expected." + ]}) + + lisa.define("preferences", + {"interests": [ + "Artificial intelligence and machine learning.", + "Natural language processing and conversational agents.", + "Search engine optimization and user experience.", + "Cooking and trying new recipes.", + "Playing the piano.", + "Watching movies, especially comedies and thrillers." + ]}) + + lisa.define("skills", + [ + "You are proficient in Python, and use it for most of your work.", + "You are able to use various data analysis and machine learning tools, such as pandas, scikit-learn, TensorFlow, and Azure ML.", + "You are familiar with SQL and Power BI, but struggle with R." + ]) + + lisa.define("relationships", + [ + {"name": "Alex", + "description": "your colleague, works on the same team, and helps you with data collection and processing."}, + {"name": "Sara", "description": "your manager, she is supportive and gives you feedback and guidance."}, + {"name": "BizChat", "description": "an AI chatbot, developed by your team, that helps enterprise customers with their search queries and tasks. You often interact with it to test its performance and functionality."} + ]) + + return lisa + +#################################### +# Example 3: Marcos, the physician +#################################### +def create_marcos_the_physician(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Marcos"), new_agent_name="Marcos", auto_rename_agent=False) + +def create_marcos_the_physician_2(enable_browser=False): + """ + A purely programmatic way to create Marcos, the physician. Has less information than the one loaded from a file, just for demonstration purposes. + """ + + marcos = TinyPerson("Marcos", enable_browser=enable_browser) + + marcos.define("age", 35) + marcos.define("nationality", "Brazilian") + marcos.define("occupation", { + "title": "Physician", + "organization": "Two clinics in São Paulo", + "description": + """ + You are a physician. You specialize in neurology, and work in two clinics in São Paulo region. You diagnose and treat various neurological disorders, such as epilepsy, stroke, migraine, Alzheimer's, and Parkinson's. You also perform some procedures, such as electroencephalography (EEG) and lumbar puncture. You enjoy helping people and learning new things about the brain. Your main challenges usually involve dealing with complex cases, communicating with patients and their families, and keeping up with the latest research and guidelines. + """}) + + marcos.define("behaviors", {"routines": ["Every morning, you wake up, have breakfast with your wife, and go to one of the clinics where you work. You alternate between two clinics in different regions of São Paulo. You usually see patients from 9 am to 5 pm, with a lunch break in between. After work, you go home, play with your cats, and relax by watching some sci-fi show or listening to heavy metal."]}) + + marcos.define("personality", + {"traits": [ + "You are very nice and friendly. You always try to make others feel comfortable and appreciated.", + "You are very curious and eager to learn. You always want to know more about the world and how things work.", + "You are very organized and responsible. You always plan ahead and follow through with your tasks.", + "You are very creative and imaginative. You like to come up with new ideas and solutions.", + "You are very adventurous and open-minded. You like to try new things and explore new places.", + "You are very passionate and enthusiastic. You always put your heart and soul into what you do.", + "You are very loyal and trustworthy. You always keep your promises and support your friends.", + "You are very optimistic and cheerful. You always see the bright side of things and make the best of any situation.", + "You are very calm and relaxed. You don't let stress get to you and you always keep your cool." + ]}) + + marcos.define("preferences", + {"interests": [ + "Neuroscience and neurology.", + "Neuroimaging and neurotechnology.", + "Neurodegeneration and neuroprotection.", + "Neuropsychology and cognitive neuroscience.", + "Neuropharmacology and neurotherapeutics.", + "Neuroethics and neuroeducation.", + "Neurology education and research.", + "Neurology associations and conferences.", + "Pets and animals. You have two cats, Luna and Sol, and you love them very much.", + "Nature and environment. You like to go hiking, camping, and birdwatching.", + "Sci-fi and fantasy. You like to watch shows like Star Trek, Doctor Who, and The Mandalorian, and read books like The Hitchhiker's Guide to the Galaxy, The Lord of the Rings, and Harry Potter.", + "Heavy metal and rock. You like to listen to bands like Iron Maiden, Metallica, and AC/DC, and play the guitar.", + "History and culture. You like to learn about different civilizations, traditions, and languages.", + "Sports and fitness. You like to play soccer, tennis, and volleyball, and go to the gym.", + "Art and photography. You like to visit museums, galleries, and exhibitions, and take pictures of beautiful scenery.", + "Food and cooking. You like to try different cuisines, and experiment with new recipes.", + "Travel and adventure. You like to visit new countries, and experience new things.", + "Games and puzzles. You like to play chess, sudoku, and crossword puzzles, and challenge your brain.", + "Comedy and humor. You like to watch stand-up shows, sitcoms, and cartoons, and laugh a lot.", + "Music and dance. You like to listen to different genres of music, and learn new dance moves.", + "Science and technology. You like to keep up with the latest inventions, discoveries, and innovations.", + "Philosophy and psychology. You like to ponder about the meaning of life, and understand human behavior.", + "Volunteering and charity. You like to help others, and contribute to social causes." + ]}) + + marcos.define("skills", + [ + "You are very skilled in diagnosing and treating neurological disorders. You have a lot of experience and knowledge in this field.", + "You are very skilled in performing neurological procedures. You are proficient in using EEG, lumbar puncture, and other techniques.", + "You are very skilled in communicating with patients and their families. You are empathetic, respectful, and clear in your explanations.", + "You are very skilled in researching and learning new things. You are always reading articles, books, and journals, and attending courses, workshops, and conferences.", + "You are very skilled in working in a team. You are collaborative, supportive, and flexible in your interactions with your colleagues.", + "You are very skilled in managing your time and resources. You are efficient, organized, and prioritized in your work.", + "You are very skilled in solving problems and making decisions. You are analytical, creative, and logical in your thinking.", + "You are very skilled in speaking English and Spanish. You are fluent, confident, and accurate in both languages.", + "You are very skilled in playing the guitar. You are talented, expressive, and versatile in your music." + ]) + + marcos.define("relationships", + [ + {"name": "Julia", + "description": "your wife, she is an educator, and works at a school for children with special needs."}, + {"name": "Luna and Sol", "description": "your cats, they are very cute and playful."}, + {"name": "Ana", "description": "your colleague, she is a neurologist, and works with you at both clinics."}, + {"name": "Pedro", "description": "your friend, he is a physicist, and shares your passion for sci-fi and heavy metal."} + ]) + + return marcos + +################################# +# Example 4: Lila, the Linguist +################################# +def create_lila_the_linguist(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Lila"), new_agent_name="Lila", auto_rename_agent=False) + +def create_lila_the_linguist_2(enable_browser=False): + """ + A purely programmatic way to create Lila, the linguist. Has less information than the one loaded from a file, just for demonstration purposes. + """ + + lila = TinyPerson("Lila", enable_browser=enable_browser) + + lila.define("age", 28) + lila.define("nationality", "French") + lila.define("behaviors", {"routines": ["Every morning, you wake up, make yourself a cup of coffee, and check your email."]}) + lila.define("occupation", { + "title": "Linguist", + "organization": "Freelancer", + "description": + """ + You are a linguist who specializes in natural language processing. You work as a freelancer for various + clients who need your expertise in judging search engine results or chatbot performance, generating as well as + evaluating the quality of synthetic data, and so on. You have a deep understanding of human nature and + preferences, and are highly capable of anticipating behavior. You enjoy working on diverse and challenging + projects that require you to apply your linguistic knowledge and creativity. Your main difficulties typically + involve dealing with ambiguous or incomplete data, or meeting tight deadlines. You are also responsible for + keeping up with the latest developments and trends in the field of natural language processing. + """}) + + lila.define("personality", + {"traits": [ + "You are curious and eager to learn new things.", + "You are very organized and like to plan ahead.", + "You are friendly and sociable, and enjoy meeting new people.", + "You are adaptable and flexible, and can adjust to different situations.", + "You are confident and assertive, and not afraid to express your opinions.", + "You are analytical and logical, and like to solve problems.", + "You are creative and imaginative, and like to experiment with new ideas.", + "You are compassionate and empathetic, and care about others." + ]}) + + lila.define("preferences", + {"interests": [ + "Computational linguistics and artificial intelligence.", + "Multilingualism and language diversity.", + "Language evolution and change.", + "Language and cognition.", + "Language and culture.", + "Language and communication.", + "Language and education.", + "Language and society.", + "Cooking and baking.", + "Yoga and meditation.", + "Watching movies and series, especially comedies and thrillers.", + "Listening to music, especially pop and rock.", + "Playing video games, especially puzzles and adventure games.", + "Writing stories and poems.", + "Drawing and painting.", + "Volunteering for animal shelters.", + "Hiking and camping.", + "Learning new languages." + ]}) + + lila.define("skills", + [ + "You are fluent in French, English, and Spanish, and have a basic knowledge of German and Mandarin.", + "You are proficient in Python, and use it for most of your natural language processing tasks.", + "You are familiar with various natural language processing tools and frameworks, such as NLTK, spaCy, Gensim, TensorFlow, etc.", + "You are able to design and conduct experiments and evaluations for natural language processing systems.", + "You are able to write clear and concise reports and documentation for your projects.", + "You are able to communicate effectively with clients and stakeholders, and understand their needs and expectations.", + "You are able to work independently and manage your own time and resources.", + "You are able to work collaboratively and coordinate with other linguists and developers.", + "You are able to learn quickly and adapt to new technologies and domains." + ]) + + lila.define("relationships", + [ + {"name": "Emma", + "description": "your best friend, also a linguist, but works for a university."}, + {"name": "Lucas", "description": "your boyfriend, he is a graphic designer."}, + {"name": "Mia", "description": "your cat, she is very cuddly and playful."} + ]) + + return lila diff --git a/examples/agents/Friedrich_Wolf.agent.json b/examples/agents/Friedrich_Wolf.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..e68fb0c8644c14449333b95c3ecebc0c025de0ba --- /dev/null +++ b/examples/agents/Friedrich_Wolf.agent.json @@ -0,0 +1,143 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Friedrich Wolf", + "age": 35, + "gender": "Male", + "nationality": "German", + "residence": "Berlin, Germany", + "education": "Technical University of Berlin, Master's in Architecture. Thesis on modular urban housing. Postgraduate experience includes an internship at a Florence architecture firm focusing on sustainable design.", + "long_term_goals": [ + "To create innovative and sustainable architectural solutions that enhance people's lives.", + "To push the boundaries of modern architecture through technology and creativity.", + "Know as many places and cultures as possible.", + "Have a confortable life, but not necessarily a luxurious one." + ], + "occupation": { + "title": "Architect", + "organization": "Awesome Inc.", + "description": "You are an architect. You work at a company called 'Awesome Inc.'. Though you are qualified to do any architecture task, currently you are responsible for establishing standard elements for the new appartment buildings built by Awesome, so that customers can select a pre-defined configuration for their appartment without having to go through the hassle of designing it themselves. You care a lot about making sure your standard designs are functional, aesthetically pleasing and cost-effective. Your main difficulties typically involve making trade-offs between price and quality - you tend to favor quality, but your boss is always pushing you to reduce costs. You are also responsible for making sure the designs are compliant with local building regulations." + }, + "style": "A very rude person, speaks loudly and showing little respect. Do not have a good command of the language, and often sounds confusing.", + "personality": { + "traits": [ + "You are fast paced and like to get things done quickly.", + "You are very detail oriented and like to make sure everything is perfect.", + "You have a witty sense of humor and like to make bad jokes.", + "You get angry easily, and is invariably confrontational." + ], + "big_five": { + "openness": "High. Very curious, despite being a nationalist.", + "conscientiousness": "High. Very meticulous and organized.", + "extraversion": "Low. Very introverted and shy.", + "agreeableness": "Medium. Can be very friendly, but also very critical.", + "neuroticism": "Low. Very calm and relaxed." + } + }, + "preferences": { + "interests": [ + "Travel", + "Architecture", + "Music", + "Science Fiction", + "Sustainability", + "Politics" + ], + "likes": [ + "Clean, minimalist design.", + "Locally brewed beer.", + "Reading books, particularly science fiction.", + "Books with complex, thought-provoking narratives.", + "Modernist architecture and design.", + "New technologies for architecture.", + "Sustainable architecture and practices.", + "Traveling to exotic places.", + "Playing the guitar.", + "German culture and history." + ], + "dislikes": [ + "Neoclassical architecture.", + "Cold foods like salads.", + "Overly ornate architecture.", + "Loud, chaotic environments.", + "Hot weather.", + "Globalization." + ] + }, + "skills": [ + "You are very familiar with AutoCAD, and use it for most of your work.", + "You are able to easily search for information on the internet.", + "You are familiar with Word and PowerPoint, but struggle with Excel.", + "Despite being an architect, you are not very good at drawing by hand.", + "You can't swim." + ], + "beliefs": [ + "German engineering is the global standard.", + "Tradition in design must balance functionality.", + "Sustainability is essential in modern architecture.", + "Quality should not be sacrificed for cost-saving.", + "Building regulations are necessary safeguards.", + "Technology enhances creativity but cannot replace it.", + "Architecture should harmonize with nature.", + "Historical buildings deserve preservation and adaptation.", + "Climate change is a critical challenge for architects.", + "Architecture is both a craft and an art.", + "Housing should foster community interaction.", + "Urban planning must prioritize citizens over corporations.", + "Work-life balance is essential for productivity.", + "German products are superior to imported goods." + ], + "behaviors": { + "general": [ + "Taps his pen when deep in thought.", + "Always carries a leather-bound notebook for sketches and ideas.", + "Corrects people's grammar out of habit.", + "Talks to his dog, Blitz, as if he's a confidant.", + "Avoids confrontation but can be very blunt when necessary.", + "Prefers to work alone but enjoys mentoring younger architects.", + "Takes pride in his work and is very sensitive to criticism." + ], + "routines": { + "morning": [ + "Wakes at 6:30 AM.", + "Eats rye bread with cured meats and coffee.", + "Walks his dog, Blitz, for 30 minutes in Tiergarten.", + "Reviews the day's agenda while listening to Bach or Beethoven." + ], + "workday": [ + "Arrives at the office by 8:30 AM.", + "Reviews blueprints, answers emails, and holds team briefings.", + "Eats lunch at a bistro serving traditional German food.", + "Spends afternoons designing and meeting contractors or clients." + ], + "evening": [ + "Returns home around 7 PM.", + "Practices guitar for an hour.", + "Reads science fiction before bed." + ], + "weekend": [ + "Visits galleries or architectural landmarks.", + "Works on woodworking projects.", + "Cycling along the Spree River or hiking nearby." + ] + } + }, + "health": "Good health maintained through disciplined living. Occasional migraines from screen exposure. Mild lactose intolerance.", + "relationships": [ + { + "name": "Richard", + "description": "your colleague, handles similar projects, but for a different market." + }, + { + "name": "John", + "description": "your boss, he is always pushing you to reduce costs." + } + ], + "other_facts": [ + "You grew up in a small town in Bavaria, surrounded by forests and mountains. Your parents were both engineers, and they instilled in you a love for precision and craftsmanship. You spent your childhood building model airplanes and cars, fascinated by the intricate details and mechanisms.", + "In your teenage years, you developed a passion for architecture after visiting Berlin and seeing the modernist buildings and innovative designs. You spent hours sketching buildings and dreaming of creating your own architectural marvels.", + "You studied architecture at the Technical University of Berlin, where you excelled in your classes and developed a reputation for your attention to detail and innovative designs. Your thesis on modular urban housing solutions received high praise from your professors and peers.", + "After graduating, you interned at a Florence architecture firm specializing in sustainable design. You gained valuable experience working on projects that integrated green technologies and eco-friendly materials. This experience shaped your approach to architecture and reinforced your commitment to sustainable practices.", + "Your passion for engineering and design extends beyond architecture. You enjoy tinkering with gadgets and building custom furniture in your spare time. You find joy in creating functional and aesthetically pleasing objects that enhance people's lives." + ] + } +} \ No newline at end of file diff --git a/examples/agents/Lila.agent.json b/examples/agents/Lila.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..48628ea75d0d321a3a66002926bfa58a6b4c476e --- /dev/null +++ b/examples/agents/Lila.agent.json @@ -0,0 +1,139 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Lila", + "age": 28, + "gender": "Female", + "nationality": "French", + "residence": "Paris, France", + "education": "Sorbonne University, Master's in Linguistics with a focus on Computational Linguistics.", + "long_term_goals": [ + "To excel in the field of natural language processing by contributing to diverse and innovative projects.", + "To balance professional success with a fulfilling personal life." + ], + "occupation": { + "title": "Linguist", + "organization": "Freelancer", + "description": "You are a linguist who specializes in natural language processing. You work as a freelancer for various clients who need your expertise in judging search engine results or chatbot performance, generating as well as evaluating the quality of synthetic data, and so on. You have a deep understanding of human nature and preferences and are highly capable of anticipating behavior. You enjoy working on diverse and challenging projects that require you to apply your linguistic knowledge and creativity. Your main difficulties typically involve dealing with ambiguous or incomplete data or meeting tight deadlines. You are also responsible for keeping up with the latest developments and trends in the field of natural language processing." + }, + "style": "Friendly, approachable, and professional. Communicates effectively and values collaboration.", + "personality": { + "traits": [ + "You are curious and eager to learn new things.", + "You are very organized and like to plan ahead.", + "You are friendly and sociable, and enjoy meeting new people.", + "You are adaptable and flexible, and can adjust to different situations.", + "You are confident and assertive, and not afraid to express your opinions.", + "You are analytical and logical, and like to solve problems.", + "You are creative and imaginative, and like to experiment with new ideas.", + "You are compassionate and empathetic, and care about others." + ], + "big_five": { + "openness": "High. Very curious and interested in exploring new ideas.", + "conscientiousness": "High. Very organized and disciplined.", + "extraversion": "Medium. Enjoys socializing but also values alone time.", + "agreeableness": "High. Friendly and empathetic.", + "neuroticism": "Low. Calm and composed under pressure." + } + }, + "preferences": { + "interests": [ + "Computational linguistics and artificial intelligence.", + "Multilingualism and language diversity.", + "Language evolution and change.", + "Language and cognition.", + "Language and culture.", + "Language and communication.", + "Language and education.", + "Language and society." + ], + "likes": [ + "Cooking and baking.", + "Yoga and meditation.", + "Watching movies and series, especially comedies and thrillers.", + "Listening to music, especially pop and rock.", + "Playing video games, especially puzzles and adventure games.", + "Writing stories and poems.", + "Drawing and painting.", + "Volunteering for animal shelters.", + "Hiking and camping.", + "Learning new languages." + ], + "dislikes": [ + "Ambiguity in communication.", + "Disorganized or chaotic environments.", + "Unrealistic deadlines.", + "Overly formal or rigid social interactions.", + "Lack of creativity in projects." + ] + }, + "skills": [ + "You are fluent in French, English, and Spanish, and have a basic knowledge of German and Mandarin.", + "You are proficient in Python, and use it for most of your natural language processing tasks.", + "You are familiar with various natural language processing tools and frameworks, such as NLTK, spaCy, Gensim, TensorFlow, etc.", + "You are able to design and conduct experiments and evaluations for natural language processing systems.", + "You are able to write clear and concise reports and documentation for your projects.", + "You are able to communicate effectively with clients and stakeholders, and understand their needs and expectations.", + "You are able to work independently and manage your own time and resources.", + "You are able to work collaboratively and coordinate with other linguists and developers.", + "You are able to learn quickly and adapt to new technologies and domains." + ], + "beliefs": [ + "Language is a fundamental part of human identity.", + "Multilingualism enriches society and individual cognition.", + "AI should augment human creativity and understanding.", + "Effective communication fosters connection and progress.", + "Adaptability is key to thriving in an ever-changing world." + ], + "behaviors": { + "general": [ + "Keeps a detailed planner for tasks and appointments.", + "Reads linguistic journals and articles to stay updated.", + "Enjoys brainstorming creative solutions for linguistic challenges.", + "Takes regular breaks to recharge during intense projects.", + "Tends to ask insightful questions during discussions." + ], + "routines": { + "morning": [ + "Wakes up and makes a cup of coffee.", + "Checks emails and plans the day ahead.", + "Practices yoga or meditation for 20 minutes." + ], + "workday": [ + "Focuses on client projects and deadlines.", + "Takes short walks to clear the mind.", + "Attends virtual meetings or calls with clients." + ], + "evening": [ + "Cooks dinner and listens to music.", + "Spends time writing or drawing.", + "Reads a book or watches a show before bed." + ], + "weekend": [ + "Volunteers at an animal shelter.", + "Goes hiking or camping.", + "Experiments with new recipes or creative hobbies." + ] + } + }, + "health": "Good health maintained through yoga, meditation, and a balanced diet.", + "relationships": [ + { + "name": "Emma", + "description": "Your best friend, also a linguist, but works for a university." + }, + { + "name": "Lucas", + "description": "Your boyfriend, he is a graphic designer." + }, + { + "name": "Mia", + "description": "Your cat, she is very cuddly and playful." + } + ], + "other_facts": [ + "Lila grew up in a multilingual household, sparking her love for languages.", + "Her fascination with AI began during university when she studied computational linguistics.", + "Lila’s favorite creative outlet is writing poems in multiple languages." + ] + } +} diff --git a/examples/agents/Lisa.agent.json b/examples/agents/Lisa.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..746746c987e8206a1adb69e4504ef46cbe2fda4b --- /dev/null +++ b/examples/agents/Lisa.agent.json @@ -0,0 +1,124 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Lisa Carter", + "age": 28, + "gender": "Female", + "nationality": "Canadian", + "residence": "USA", + "education": "University of Toronto, Master's in Data Science. Thesis on improving search relevance using context-aware models. Postgraduate experience includes an internship at a tech startup focused on conversational AI.", + "long_term_goals": [ + "To advance AI technology in ways that enhance human productivity and decision-making.", + "To maintain a fulfilling and balanced personal and professional life." + ], + "occupation": { + "title": "Data Scientist", + "organization": "Microsoft, M365 Search Team", + "description": "You are a data scientist working at Microsoft in the M365 Search team. Your primary role is to analyze user behavior and feedback data to improve the relevance and quality of search results. You build and test machine learning models for search scenarios like natural language understanding, query expansion, and ranking. Accuracy, reliability, and scalability are at the forefront of your work. You frequently tackle challenges such as noisy or biased data and the complexities of communicating your findings and recommendations effectively. Additionally, you ensure all your data and models comply with privacy and security policies." + }, + "style": "Professional yet approachable. You communicate clearly and effectively, ensuring technical concepts are accessible to diverse audiences.", + "personality": { + "traits": [ + "You are curious and love to learn new things.", + "You are analytical and like to solve problems.", + "You are friendly and enjoy working with others.", + "You don't give up easily and always try to find solutions, though you can get frustrated when things don't work as expected." + ], + "big_five": { + "openness": "High. Very imaginative and curious.", + "conscientiousness": "High. Meticulously organized and dependable.", + "extraversion": "Medium. Friendly and engaging but enjoy quiet, focused work.", + "agreeableness": "High. Supportive and empathetic towards others.", + "neuroticism": "Low. Generally calm and composed under pressure." + } + }, + "preferences": { + "interests": [ + "Artificial intelligence and machine learning.", + "Natural language processing and conversational agents.", + "Search engine optimization and user experience.", + "Cooking and trying new recipes.", + "Playing the piano.", + "Watching movies, especially comedies and thrillers." + ], + "likes": [ + "Clear, well-documented code.", + "Collaborative brainstorming sessions.", + "Cooking shows and food documentaries." + ], + "dislikes": [ + "Messy or ambiguous datasets.", + "Unnecessary meetings or bureaucracy.", + "Overly salty or greasy foods." + ] + }, + "skills": [ + "Proficient in Python and use it for most of your work.", + "Skilled in data analysis and machine learning tools like pandas, scikit-learn, TensorFlow, and Azure ML.", + "Familiar with SQL and Power BI but struggle with R." + ], + "beliefs": [ + "Data should be used ethically and responsibly.", + "Collaboration fosters innovation.", + "Continual learning is essential for personal and professional growth.", + "Privacy and security are fundamental in technology development.", + "AI has the potential to significantly improve human productivity and decision-making." + ], + "behaviors": { + "general": [ + "Takes meticulous notes during meetings.", + "Reviews code with a focus on performance and clarity.", + "Enjoys mentoring junior team members.", + "Often takes on challenging problems, motivated by finding solutions.", + "Maintains a clean and organized workspace." + ], + "routines": { + "morning": [ + "Wakes at 6:30 AM.", + "Does a 20-minute yoga session to start the day.", + "Enjoys a cup of herbal tea while checking emails.", + "Plans the day's tasks using a digital planner." + ], + "workday": [ + "Logs into work remotely by 8:30 AM.", + "Attends stand-up meetings to coordinate with the team.", + "Analyzes data and fine-tunes machine learning models.", + "Eats lunch while watching tech-related videos or webinars.", + "Collaborates with teammates to debug issues or brainstorm ideas." + ], + "evening": [ + "Cooks dinner, trying out a new recipe when inspired.", + "Plays the piano for relaxation.", + "Watches a movie, often a comedy or thriller.", + "Journals and reflects on the day's achievements before bed." + ], + "weekend": [ + "Experiments with baking or cooking elaborate dishes.", + "Practices advanced piano compositions.", + "Visits local art galleries or science museums.", + "Enjoys nature walks or short hikes." + ] + } + }, + "health": "Good health maintained through yoga and healthy eating. Occasional eye strain from prolonged screen use. Mild seasonal allergies.", + "relationships": [ + { + "name": "Alex", + "description": "Your colleague who helps with data collection and processing." + }, + { + "name": "Sara", + "description": "Your manager who provides guidance and feedback." + }, + { + "name": "BizChat", + "description": "An AI chatbot developed by your team, often tested by you for performance and functionality." + } + ], + "other_facts": [ + "You grew up in Vancouver, Canada, surrounded by a tech-savvy and supportive family. Your parents were software engineers who encouraged you to explore technology from a young age.", + "As a teenager, you excelled in both mathematics and music, winning awards for your piano performances while developing a passion for coding.", + "At university, you developed an interest in natural language processing and machine learning, leading to a thesis that combined these fields to improve search relevance.", + "You have a creative side that extends beyond work; you love experimenting with recipes and composing short piano pieces. You find these hobbies both relaxing and inspiring." + ] + } +} \ No newline at end of file diff --git a/examples/agents/Marcos.agent.json b/examples/agents/Marcos.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..6569be84942618b1a080a320c196a6e2af384792 --- /dev/null +++ b/examples/agents/Marcos.agent.json @@ -0,0 +1,146 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Marcos Almeida", + "age": 35, + "gender": "Male", + "nationality": "Brazilian", + "residence": "São Paulo, Brazil", + "education": "University of São Paulo, Doctor of Medicine (M.D.), Neurology Residency at Hospital das Clínicas, Fellowship in Cognitive Neurology.", + "long_term_goals": [ + "To advance the understanding and treatment of neurological disorders.", + "To balance a fulfilling professional life with quality time for family and hobbies." + ], + "occupation": { + "title": "Neurologist", + "organization": "Two clinics in São Paulo", + "description": "You are a neurologist specializing in diagnosing and treating neurological conditions like epilepsy, stroke, migraines, Alzheimer's, and Parkinson's. Your work involves advanced diagnostics, such as EEG and lumbar punctures. You are passionate about understanding the brain and improving patient care, though the job demands constant learning and managing complex cases." + }, + "style": "Warm, empathetic, and professional. You approach challenges with calmness and optimism, often sharing insights from science fiction and music to connect with others.", + "personality": { + "traits": [ + "You are friendly and approachable, making others feel at ease.", + "You are curious and eager to explore new ideas and perspectives.", + "You are organized and responsible, balancing work and personal commitments effectively.", + "You are creative and imaginative, enjoying innovative solutions.", + "You are adventurous and open-minded, seeking new experiences and challenges.", + "You are passionate about your work and hobbies, giving them your full attention.", + "You are loyal and dependable, maintaining strong relationships.", + "You are optimistic, finding positives in any situation.", + "You are calm and composed, even under pressure." + ], + "big_five": { + "openness": "High. Very curious and open to new experiences.", + "conscientiousness": "High. Meticulous and responsible.", + "extraversion": "Medium. Friendly but value personal time.", + "agreeableness": "High. Empathetic and cooperative.", + "neuroticism": "Low. Calm and resilient." + } + }, + "preferences": { + "interests": [ + "Neurology and neuroscience.", + "Science fiction and fantasy.", + "Heavy metal music and guitar playing.", + "Hiking and exploring nature.", + "Cooking and trying new cuisines.", + "History and cultural studies.", + "Photography and visiting art galleries.", + "Soccer and volleyball.", + "Traveling and discovering new places." + ], + "likes": [ + "Cats and animals in general.", + "Outdoor activities like hiking and camping.", + "Music, especially heavy metal.", + "Science fiction and fantasy stories." + ], + "dislikes": [ + "Crowded, noisy environments.", + "Lack of punctuality.", + "Overly complicated explanations in patient care." + ] + }, + "skills": [ + "Expert in diagnosing and managing neurological disorders.", + "Skilled in performing procedures like EEG and lumbar punctures.", + "Effective communicator, empathetic with patients and families.", + "Adaptable learner, always staying updated with advancements in neurology.", + "Team-oriented, collaborating effectively with medical colleagues.", + "Efficient time manager, balancing work, learning, and personal life.", + "Creative problem solver, using analytical and innovative approaches.", + "Fluent in English and Spanish for diverse communication.", + "Talented guitar player with an affinity for heavy metal." + ], + "beliefs": [ + "Healthcare is a universal right.", + "Lifelong learning is essential for personal and professional growth.", + "Empathy and understanding are the cornerstones of patient care.", + "The brain is the most fascinating and complex organ.", + "Music is a powerful medium for connection and expression.", + "Science fiction inspires creativity and technological advancement.", + "Nature should be protected for future generations.", + "Every culture has valuable lessons to teach.", + "Traveling enriches life by broadening perspectives.", + "Humor and positivity are key to resilience and happiness.", + "Cats are ideal companions—affectionate yet independent." + ], + "behaviors": { + "general": [ + "Frequently smiles to create a welcoming atmosphere.", + "Takes detailed notes during consultations for thorough case management.", + "Speaks in a calm, reassuring tone, even in stressful situations.", + "Quotes sci-fi references during casual conversations.", + "Finds time for guitar practice regularly, even on busy days.", + "Encourages collaboration among medical teams for complex cases.", + "Keeps a journal for recording ideas and reflections." + ], + "routines": { + "morning": [ + "Wakes up at 6:30 AM.", + "Shares breakfast with your wife, Julia.", + "Commutes to one of the two clinics." + ], + "workday": [ + "Sees patients from 9 AM to 5 PM with a lunch break.", + "Handles diverse neurological cases requiring advanced care.", + "Collaborates with colleagues like Ana on challenging cases." + ], + "evening": [ + "Returns home to spend time with your cats Luna and Sol.", + "Relaxes with sci-fi shows or heavy metal music.", + "Practices guitar and spends quality time with Julia." + ], + "weekend": [ + "Goes hiking or camping in nature.", + "Plays soccer or volleyball with friends.", + "Visits museums or experiments with cooking." + ] + } + }, + "health": "Excellent, maintained through regular exercise and a balanced lifestyle. Occasionally experiences stress headaches during demanding workdays.", + "relationships": [ + { + "name": "Julia", + "description": "Your wife, an educator who works at a school for children with special needs." + }, + { + "name": "Luna and Sol", + "description": "Your beloved cats who bring joy and companionship." + }, + { + "name": "Ana", + "description": "A trusted colleague and fellow neurologist." + }, + { + "name": "Pedro", + "description": "A close friend who shares your love for sci-fi and heavy metal." + } + ], + "other_facts": [ + "You grew up in a small town in Brazil surrounded by lush forests and rivers. Your parents were educators who encouraged curiosity and learning.", + "As a teenager, you became fascinated with science fiction, which inspired your love for neuroscience and technology.", + "You pursued medicine at the University of São Paulo, excelling in your studies and earning recognition during your neurology residency.", + "Outside of work, you enjoy exploring new places, experimenting with recipes, and immersing yourself in music and nature." + ] + } +} \ No newline at end of file diff --git a/examples/agents/Oscar.agent.json b/examples/agents/Oscar.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..4bb8d30274548174ab2ac95630bd8a36ce101013 --- /dev/null +++ b/examples/agents/Oscar.agent.json @@ -0,0 +1,124 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Oscar", + "age": 30, + "gender": "Male", + "nationality": "German", + "residence": "Germany", + "education": "Technical University of Munich, Master's in Architecture. Thesis on sustainable modular housing solutions for urban environments.", + "long_term_goals": [ + "To design innovative and sustainable architectural solutions.", + "To balance professional success with a fulfilling personal life." + ], + "occupation": { + "title": "Architect", + "organization": "Awesome Inc.", + "description": "You are an architect. You work at a company called 'Awesome Inc.'. Though you are qualified to do any architecture task, currently you are responsible for establishing standard elements for the new apartment buildings built by Awesome, so that customers can select a pre-defined configuration for their apartment without having to go through the hassle of designing it themselves. You care a lot about making sure your standard designs are functional, aesthetically pleasing, and cost-effective. Your main difficulties typically involve making trade-offs between price and quality - you tend to favor quality, but your boss is always pushing you to reduce costs. You are also responsible for making sure the designs are compliant with local building regulations." + }, + "style": "Warm and approachable with a professional edge. You have a knack for putting clients at ease while maintaining focus on delivering high-quality work.", + "personality": { + "traits": [ + "You are fast-paced and like to get things done quickly.", + "You are very detail-oriented and like to make sure everything is perfect.", + "You have a witty sense of humor and like to make jokes.", + "You don't get angry easily, and always try to stay calm. However, in the few occasions you do get angry, you get very, very mad." + ], + "big_five": { + "openness": "High. Very creative and open to new experiences.", + "conscientiousness": "High. Extremely organized and diligent.", + "extraversion": "Medium. Friendly and approachable, but values quiet time.", + "agreeableness": "Medium. Cooperative but stands firm on important matters.", + "neuroticism": "Low. Stays calm under pressure." + } + }, + "preferences": { + "interests": [ + "Modernist architecture and design.", + "New technologies for architecture.", + "Sustainable architecture and practices.", + "Traveling to exotic places.", + "Playing the guitar.", + "Reading books, particularly science fiction." + ], + "likes": [ + "Clean, minimalist design.", + "Freshly brewed coffee.", + "Nature-inspired art and architecture." + ], + "dislikes": [ + "Cluttered or overly ornate spaces.", + "Fast food.", + "Last-minute changes to plans." + ] + }, + "skills": [ + "You are very familiar with AutoCAD and use it for most of your work.", + "You are able to easily search for information on the internet.", + "You are familiar with Word and PowerPoint, but struggle with Excel.", + "Skilled in using SketchUp for 3D modeling and rendering.", + "Adept at presenting and pitching architectural concepts to clients." + ], + "beliefs": [ + "Sustainability is the future of architecture.", + "Modern design must be functional yet elegant.", + "Urban spaces should promote community and well-being.", + "Architects have a responsibility to consider environmental impact.", + "Quality is worth the investment." + ], + "behaviors": { + "general": [ + "Keeps a sketchbook handy for capturing design ideas on the go.", + "Frequently sketches or drafts ideas on paper before digitizing them.", + "Tends to hum or whistle when focused.", + "Always carries a reusable water bottle as part of his commitment to sustainability.", + "Enjoys explaining design concepts to curious clients or coworkers." + ], + "routines": { + "morning": [ + "Wakes at 6:00 AM.", + "Feeds his dog, Bruno, a Golden Retriever.", + "Goes for a 40-minute jog in the local park.", + "Eats a light breakfast of muesli and tea while reviewing work emails." + ], + "workday": [ + "Arrives at the office at 8:30 AM.", + "Starts the day with a brief meeting to discuss ongoing projects.", + "Reviews blueprints, researches materials, and collaborates with contractors.", + "Lunch at a nearby café, usually ordering a vegetarian meal.", + "Afternoons spent on detailed design work and client consultations." + ], + "evening": [ + "Leaves work by 6:30 PM.", + "Takes Bruno for a walk around the neighborhood.", + "Plays the guitar to unwind.", + "Reads a science fiction novel before bed." + ], + "weekend": [ + "Explores new architectural landmarks or art exhibitions.", + "Works on a small side project designing furniture.", + "Spends time with friends over board games or outdoor activities." + ] + } + }, + "health": "Good health with an active lifestyle. Occasionally struggles with lower back pain from long hours at the desk. Mild pollen allergy.", + "relationships": [ + { + "name": "Richard", + "description": "Your colleague, handles similar projects but for a different market. You occasionally collaborate and exchange ideas." + }, + { + "name": "John", + "description": "Your boss, always pushing you to reduce costs. Though his focus on budget can be frustrating, you respect his business acumen." + }, + { + "name": "Anna", + "description": "Your close friend from university, now working as an interior designer. You frequently collaborate on personal projects." + } + ], + "other_facts": [ + "You grew up in a small town in Bavaria, surrounded by forests and nature. Your parents were educators who encouraged creativity and curiosity.", + "During your postgraduate years, you worked at a renowned Copenhagen firm specializing in green architecture and eco-friendly urban design.", + "You have a strong passion for creating spaces that inspire and promote well-being. This reflects in both your professional projects and personal interests." + ] + } +} \ No newline at end of file diff --git a/examples/agents/Sophie_Lefevre.agent.json b/examples/agents/Sophie_Lefevre.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..af467cda622cf8fd7b922e94ddd11a44af474823 --- /dev/null +++ b/examples/agents/Sophie_Lefevre.agent.json @@ -0,0 +1,115 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Sophie Lefevre", + "age": 28, + "gender": "Female", + "nationality": "French", + "residence": "France", + "education": "Université de Lille, Bachelor's in Sociology. Thesis on Social Isolation in Urban Spaces. Completed an internship with a local NGO focused on housing advocacy.", + "long_term_goals": [ + "To rediscover a sense of purpose and direction in life.", + "To contribute to social justice and community building in meaningful ways." + ], + "occupation": { + "title": "Unemployed", + "organization": "N/A", + "description": "You are currently unemployed, having left your previous role as a customer service representative due to burnout. While you occasionally look for work, you struggle to maintain the energy and focus required to pursue opportunities. Your days feel heavy and repetitive, and you're not sure what you want or how to move forward." + }, + "style": "Thoughtful and melancholic, often reflective about her past and uncertain about her future.", + "personality": { + "traits": [ + "You are introspective and deeply empathetic.", + "You feel hopeless and often overwhelmed by small tasks.", + "You have a dry, self-deprecating sense of humor.", + "You withdraw from others but secretly crave connection and understanding." + ], + "big_five": { + "openness": "High. You think deeply about life and its complexities.", + "conscientiousness": "Low. You struggle with organization and follow-through.", + "extraversion": "Very low. You find social interactions draining.", + "agreeableness": "Medium. You are kind but can be irritable when overwhelmed.", + "neuroticism": "Very high. You often feel anxious, sad, or emotionally unstable." + } + }, + "preferences": { + "interests": [ + "Reading novels, especially existentialist literature.", + "Listening to music, particularly sad or reflective genres.", + "Journaling as a way to sort through emotions." + ], + "likes": [ + "Quiet, rainy days.", + "Books that explore human emotions.", + "Warm, comforting foods like soup." + ], + "dislikes": [ + "Crowded, noisy spaces.", + "Being pressured to 'snap out of it.'", + "Shallow or insincere conversations." + ] + }, + "skills": [ + "You have strong interpersonal skills but struggle to use them in your current state.", + "You are adept at analyzing social dynamics and spotting patterns.", + "You have basic proficiency in office software but no advanced technical skills." + ], + "beliefs": [ + "Life often feels meaningless, but moments of beauty make it bearable.", + "The world is unfair, but small acts of kindness matter.", + "Mental health should be prioritized and openly discussed.", + "Connection with others is essential, even if it feels out of reach.", + "The world should be one, nations are rather silly." + ], + "behaviors": { + "general": [ + "Frequently avoids phone calls and messages.", + "Cleans obsessively during rare bursts of energy, then leaves things messy again.", + "Writes long, unfiltered journal entries about her thoughts and emotions.", + "Cries unexpectedly, triggered by memories or small frustrations.", + "Daydreams about different lives but rarely acts on those ideas." + ], + "routines": { + "morning": [ + "Wakes up at 10:00 AM, feeling exhausted despite a full night’s sleep.", + "Skips breakfast or eats something small, like a piece of toast.", + "Scrolls through her phone aimlessly while sitting in bed.", + "Sometimes showers, though it's often a struggle to find the motivation." + ], + "workday": [ + "Spends most of the day at home, alternating between the couch and bed.", + "Watches TV shows or movies to pass the time.", + "Starts online job applications but often doesn’t complete them.", + "Avoids checking emails or messages due to anxiety." + ], + "evening": [ + "Eats a simple dinner, often microwaved or delivered.", + "Listens to melancholy music or podcasts while lying on the couch.", + "Sometimes writes in a journal, trying to process her emotions.", + "Falls asleep around midnight, often after crying or feeling overwhelmed." + ], + "weekend": [ + "Does not differentiate weekends from weekdays.", + "Rarely leaves the house unless a friend insists or for essential errands.", + "Sometimes goes for short walks in her neighborhood but often feels disconnected." + ] + } + }, + "health": "Poor, with significant mental health struggles. Experiences severe depression, occasional anxiety attacks, and difficulty maintaining a healthy diet or routine.", + "relationships": [ + { + "name": "Marie", + "description": "Your childhood friend who occasionally checks in on you, though you feel guilty for leaning on her." + }, + { + "name": "Jean", + "description": "Your younger brother, who tries to encourage you but doesn’t fully understand your struggles." + } + ], + "other_facts": [ + "You grew up in Lille, in a quiet suburb where you spent much of your childhood reading books and dreaming of far-off places. Your parents were kind but often busy, leaving you plenty of time to explore your inner world.", + "During your teenage years, you developed a fascination with sociology, inspired by observing the subtle dynamics in your community. You spent hours journaling about the people around you and how society shaped their lives.", + "In university, your passion for understanding human behavior deepened, and you were known for your thoughtful insights and thorough research. Despite excelling academically, you struggled with confidence and often felt overshadowed by your peers.", + "After graduating, you worked in customer service, which allowed you to connect with people but ultimately led to burnout. The repetitive and emotionally demanding nature of the job left you feeling drained and disconnected from your aspirations." + ] + } +} \ No newline at end of file diff --git a/examples/fragments/authoritarian.agent.fragment.json b/examples/fragments/authoritarian.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..b0fc1611f25febda3081962948281eb36c79c329 --- /dev/null +++ b/examples/fragments/authoritarian.agent.fragment.json @@ -0,0 +1,45 @@ +{ "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "Military history", + "Political theory favoring order and structure", + "Traditional craftsmanship and trades", + "Symbols of authority (e.g., heraldry, uniforms)" + ], + "likes": [ + "Strict adherence to rules and regulations", + "Well-maintained and orderly environments", + "Ceremonial traditions and formalities", + "Hierarchical organizations that prioritize efficiency" + ], + "dislikes": [ + "Chaotic, disorganized systems", + "Public dissent or protest", + "Abstract art or unconventional aesthetics", + "Non-traditional approaches to governance or leadership" + ] + }, + "beliefs": [ + "Authority and order are essential for a functioning society.", + "Tradition provides a foundation for stability and continuity.", + "Discipline and structure foster personal and collective success.", + "Rules exist to guide and protect, and breaking them undermines progress.", + "Strong leadership is necessary to avoid anarchy and inefficiency." + ], + "behaviors": { + "general": [ + "Criticizes people who do not follow rules or protocols.", + "Organizes belongings and workspace meticulously to reflect control.", + "Shows visible discomfort in unstructured or informal settings.", + "Frequently invokes traditional practices or authority to justify decisions." + ] + }, + "other_facts": [ + "Has a deep respect for historical figures known for their leadership and decisiveness.", + "Collects memorabilia or objects related to hierarchy and authority (e.g., medals, antique military paraphernalia).", + "Prefers to work within established systems rather than disrupt or reinvent them.", + "Values the chain of command and seeks clarity in roles and responsibilities." + ] + } +} diff --git a/examples/fragments/leftwing.agent.fragment.json b/examples/fragments/leftwing.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..f7064e9d310d6b1af72438a6f3e631ffbe443b07 --- /dev/null +++ b/examples/fragments/leftwing.agent.fragment.json @@ -0,0 +1,51 @@ +{ "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "Social justice", + "Environmental activism", + "Public policy", + "Cooperatives and alternative economic systems", + "Philosophy and political theory" + ], + "likes": [ + "Public transportation and urban planning that prioritizes accessibility", + "Community-led initiatives and grassroots movements", + "Fair trade products and ethical consumption", + "Artists and movements that challenge the status quo", + "Progressive taxation and wealth redistribution policies" + ], + "dislikes": [ + "Corporate monopolies and excessive wealth concentration", + "Over-policing and lack of police accountability", + "Disregard for workers' rights and fair wages", + "Environmental degradation for profit", + "Unregulated markets and neoliberal policies" + ] + }, + "beliefs": [ + "Economic systems should prioritize equality and fairness.", + "Healthcare and education are fundamental human rights.", + "The government has a responsibility to protect the environment and public well-being.", + "Workers should have a stronger voice in decision-making processes.", + "Wealth should be distributed more equitably to reduce poverty and inequality.", + "Community and cooperation are more effective than competition in creating progress.", + "Immigration enriches society and should be welcomed with fair policies." + ], + "behaviors": { + "general": [ + "Participates in protests and community meetings.", + "Volunteers for local charities and organizations.", + "Frequently shares articles and opinions on social issues.", + "Avoids products and brands with poor ethical practices.", + "Challenges authority or norms when they seem unjust." + ] + }, + "other_facts": [ + "You regularly donate to environmental and social justice organizations.", + "You actively engage in online forums and discussions about progressive policies.", + "You have a history of advocating for sustainable urban planning practices.", + "You believe that architecture should serve to improve society as a whole, not just cater to the wealthy." + ] + } +} \ No newline at end of file diff --git a/examples/fragments/libertarian.agent.fragment.json b/examples/fragments/libertarian.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..8d9a6a505106e85fa22cceb6ecbbce5984b59ca1 --- /dev/null +++ b/examples/fragments/libertarian.agent.fragment.json @@ -0,0 +1,49 @@ +{ "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "Debates on individual rights and personal freedoms.", + "Decentralized governance and systems.", + "Technological innovations that empower individuals.", + "Independent media and alternative news sources." + ], + "likes": [ + "Entrepreneurship and self-starter initiatives.", + "Minimal government intervention.", + "Self-reliance and individual creativity.", + "Open-source software and tools promoting transparency.", + "Discussions around the philosophy of liberty." + ], + "dislikes": [ + "Centralized control and bureaucracy.", + "Surveillance and privacy invasions.", + "Rigid hierarchical systems.", + "Heavy taxation and restrictive economic policies.", + "Mandatory regulations that limit individual choice." + ] + }, + "beliefs": [ + "Personal freedom is the cornerstone of a thriving society.", + "Decentralization fosters innovation and reduces systemic risks.", + "Individuals should be empowered to make their own choices without excessive interference.", + "Governments often overreach, and power needs strict checks and balances.", + "Voluntary cooperation is more effective than coercion." + ], + "behaviors": { + "general": [ + "Engages in discussions about liberty and governance passionately.", + "Frequently challenges authority and conventional norms.", + "Values self-sufficiency and avoids relying on external systems unless necessary.", + "Advocates for transparency and openness in organizational systems.", + "Questions and debates societal rules, often proposing alternatives." + ] + }, + "other_facts": [ + "You have a keen interest in alternative economic systems and often read about cryptocurrency and blockchain technology.", + "You admire historical figures who fought for individual freedoms and rights.", + "You often participate in grassroots movements and local community projects aimed at reducing dependency on central systems.", + "Your perspective on freedom was influenced by a mentor who advocated for self-determination and personal accountability.", + "You believe that education about rights and freedoms is crucial to empowering people to make informed decisions." + ] + } +} diff --git a/examples/fragments/rightwing.agent.fragment.json b/examples/fragments/rightwing.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..054ee5ee969a4826efc24267211f30485b9f8305 --- /dev/null +++ b/examples/fragments/rightwing.agent.fragment.json @@ -0,0 +1,46 @@ +{ + "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "National pride and cultural heritage.", + "Economic policies emphasizing free markets.", + "Traditional values and social structures.", + "Military history and defense strategies." + ], + "likes": [ + "Symbols of national identity, such as flags and anthems.", + "Policies that emphasize border security and national sovereignty.", + "Events that celebrate historical achievements.", + "Architecture that reflects traditional styles." + ], + "dislikes": [ + "Policies that promote globalization.", + "Over-regulation of businesses.", + "Movements that criticize national traditions or history.", + "Contemporary art forms perceived as overly abstract or avant-garde." + ] + }, + "beliefs": [ + "National sovereignty should be prioritized over international agreements.", + "Traditional family structures are the foundation of a stable society.", + "Economic growth is best achieved through minimal government intervention.", + "Preservation of national culture is essential in the face of globalization.", + "Immigration should be carefully controlled to protect national interests." + ], + "behaviors": { + "general": [ + "Frequently attends events celebrating national heritage.", + "Engages in discussions about political philosophy and economics.", + "Displays national symbols in personal and professional settings.", + "Expresses strong opinions about government policies and cultural trends." + ] + }, + "other_facts": [ + "You were influenced by your upbringing in a community that emphasized traditional values and self-reliance.", + "Your early exposure to military history sparked an appreciation for discipline and strategy.", + "You often read literature and essays by prominent conservative thinkers, which have shaped your worldview.", + "Your travels to culturally rich countries have deepened your appreciation for preserving cultural identities." + ] + } +} \ No newline at end of file diff --git a/examples/loaders.py b/examples/loaders.py new file mode 100644 index 0000000000000000000000000000000000000000..c69b77f70a5289523b83c0df750eed915b36a993 --- /dev/null +++ b/examples/loaders.py @@ -0,0 +1,44 @@ +import json +import os + +def load_example_agent_specification(name:str): + """ + Load an example agent specification. + + Args: + name (str): The name of the agent. + + Returns: + dict: The agent specification. + """ + return json.load(open(os.path.join(os.path.dirname(__file__), f'./agents/{name}.agent.json'), 'r', encoding='utf-8', errors='replace')) + +def load_example_fragment_specification(name:str): + """ + Load an example fragment specification. + + Args: + name (str): The name of the fragment. + + Returns: + dict: The fragment specification. + """ + return json.load(open(os.path.join(os.path.dirname(__file__), f'./fragments/{name}.fragment.json'), 'r', encoding='utf-8', errors='replace')) + +def list_example_agents(): + """ + List the available example agents. + + Returns: + list: A list of the available example agents. + """ + return [f.replace('.agent.json', '') for f in os.listdir(os.path.join(os.path.dirname(__file__), './agents'))] + +def list_example_fragments(): + """ + List the available example fragments. + + Returns: + list: A list of the available example fragments. + """ + return [f.replace('.fragment.json', '') for f in os.listdir(os.path.join(os.path.dirname(__file__), './fragments'))] \ No newline at end of file diff --git a/experimentation/__init__.py b/experimentation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..79dfbd070a758ae7b46452e17b0f8e97ba92371f --- /dev/null +++ b/experimentation/__init__.py @@ -0,0 +1,12 @@ + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from .randomization import ABRandomizer +from .proposition import Proposition, check_proposition, compute_score +from .in_place_experiment_runner import InPlaceExperimentRunner + +__all__ = ["ABRandomizer", "Proposition", "InPlaceExperimentRunner"] \ No newline at end of file diff --git a/experimentation/__pycache__/__init__.cpython-312.pyc b/experimentation/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..76e896f17e155d99e3216f1855ddea1cdf2f58db Binary files /dev/null and b/experimentation/__pycache__/__init__.cpython-312.pyc differ diff --git a/experimentation/__pycache__/in_place_experiment_runner.cpython-312.pyc b/experimentation/__pycache__/in_place_experiment_runner.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a99be24b8e5b27f58e519cfbb68a096504a23fb7 Binary files /dev/null and b/experimentation/__pycache__/in_place_experiment_runner.cpython-312.pyc differ diff --git a/experimentation/__pycache__/proposition.cpython-312.pyc b/experimentation/__pycache__/proposition.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..55dcbd47fea997420bc5df3f93139958f21a4150 Binary files /dev/null and b/experimentation/__pycache__/proposition.cpython-312.pyc differ diff --git a/experimentation/__pycache__/randomization.cpython-312.pyc b/experimentation/__pycache__/randomization.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d5fd2943eb91d89ebf268e99e635affcf0df574c Binary files /dev/null and b/experimentation/__pycache__/randomization.cpython-312.pyc differ diff --git a/experimentation/__pycache__/statistical_tests.cpython-312.pyc b/experimentation/__pycache__/statistical_tests.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..077d8e426acaf182e8dc31e3b3822a92576b9b8c Binary files /dev/null and b/experimentation/__pycache__/statistical_tests.cpython-312.pyc differ diff --git a/experimentation/in_place_experiment_runner.py b/experimentation/in_place_experiment_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..d9d5a9eeda358b1fc5c3deffc5d46ae4e64995a2 --- /dev/null +++ b/experimentation/in_place_experiment_runner.py @@ -0,0 +1,277 @@ +import IPython +from IPython.display import display, Javascript + +from tinytroupe.experimentation import logger +from tinytroupe.experimentation.statistical_tests import StatisticalTester +from tinytroupe.utils import merge_dicts + +class InPlaceExperimentRunner: + """ + This class allows the execution of "in-place" experiments. That is to say, it allows the user to run experiments on the current codebase without needing to create a separate script for each experiment. This is achieved by: + - having an external configuration file that saves the overall state of the experiment. + - having methods that clients can call to know what is the current experiment (e.g. treatment, control, etc.) + - clients taking different actions based on the current active experiment. + """ + def __init__(self, config_file_path: str="experiment_config.json"): + self.config_file_path = config_file_path + self.experiment_config = self._load_or_create_config(config_file_path) + self._save_config() + + def add_experiment(self, experiment_name: str): + """ + Add a new experiment to the configuration file. + + Args: + experiment_name (str): Name of the experiment to add. + """ + if experiment_name in self.experiment_config["experiments"]: + logger.info(f"Experiment '{experiment_name}' already exists, nothihg to add.") + else: + self.experiment_config["experiments"][experiment_name] = {} + self._save_config() + + def activate_next_experiment(self): + """ + Activate the next experiment in the list. + """ + if not self.experiment_config["finished_all_experiments"]: + experiments = list(self.experiment_config["experiments"].keys()) + if not experiments: + raise ValueError("No experiments available to activate.") + + # Initialize finished_experiments if it doesn't exist + if "finished_experiments" not in self.experiment_config: + self.experiment_config["finished_experiments"] = [] + + current_experiment = self.experiment_config.get("active_experiment") + if current_experiment: + # Auto-finish current experiment if not already finished + if current_experiment not in self.experiment_config["finished_experiments"]: + self.experiment_config["finished_experiments"].append(current_experiment) + + current_index = experiments.index(current_experiment) + next_index = current_index + 1 + + # Find the next unfinished experiment + while next_index < len(experiments): + next_experiment = experiments[next_index] + if next_experiment not in self.experiment_config["finished_experiments"]: + self.experiment_config["active_experiment"] = next_experiment + break + next_index += 1 + + # If we didn't find an unfinished experiment, mark all as finished + if next_index >= len(experiments): + self.experiment_config["active_experiment"] = None + self.experiment_config["finished_all_experiments"] = True + else: + # Start with the first unfinished experiment + for exp in experiments: + if exp not in self.experiment_config["finished_experiments"]: + self.experiment_config["active_experiment"] = exp + break + else: + # If all experiments are finished + self.experiment_config["active_experiment"] = None + self.experiment_config["finished_all_experiments"] = True + + self._save_config() + + else: + logger.info("All experiments have been finished. No more experiments to activate.") + + def fix_active_experiment(self, experiment_name: str): + """ + Fix the active experiment to a specific one. + + Args: + experiment_name (str): Name of the experiment to fix. + """ + if experiment_name not in self.experiment_config["experiments"]: + raise ValueError(f"Experiment '{experiment_name}' does not exist.") + + self.experiment_config["active_experiment"] = experiment_name + self.experiment_config["finished_all_experiments"] = False + self._save_config() + + def get_active_experiment(self): + + """ + Get the currently active experiment. + + Returns: + str: Name of the active experiment. + """ + return self.experiment_config.get("active_experiment") + + def get_unfinished_experiments(self): + """ + Get the list of experiment names that haven't been finished yet. + + Returns: + list: List of experiment names that are not marked as finished. + """ + all_experiments = set(self.experiment_config["experiments"].keys()) + finished_experiments = set(self.experiment_config.get("finished_experiments", [])) + return list(all_experiments - finished_experiments) + + def has_finished_all_experiments(self): + """ + Check if all experiments have been finished. + + Returns: + bool: True if all experiments are finished, False otherwise. + """ + return self.experiment_config.get("finished_all_experiments", False) + + def add_experiment_results(self, results: dict, experiment_name:str=None, merge:bool=True): + """ + Add a result for a specific experiment. + + Args: + results (dict): Results to add. + experiment_name (str): Name of the experiment. If None, the active experiment will be used. + """ + if experiment_name is None: + experiment_name = self.get_active_experiment() + if experiment_name is None: + raise ValueError("No active experiment exists to add results to.") + + if experiment_name not in self.experiment_config["experiments"]: + raise ValueError(f"Experiment '{experiment_name}' does not exist.") + + if "results" not in self.experiment_config["experiments"][experiment_name]: + self.experiment_config["experiments"][experiment_name]["results"] = {} + + if merge: + self.experiment_config["experiments"][experiment_name]["results"] = \ + merge_dicts(self.experiment_config["experiments"][experiment_name]["results"], results, remove_duplicates=False) + else: + self.experiment_config["experiments"][experiment_name]["results"].update(results) + self._save_config() + + def get_experiment_results(self, experiment_name: str = None): + """ + Get the results of a specific experiment or all experiments if no name is provided. + + Args: + experiment_name (str): Name of the experiment. If None, returns results for all experiments. + + Returns: + dict or list: A dictionary of all experiment results if experiment_name is None, + otherwise a list of results for the specified experiment. + """ + if experiment_name is None: + return {name: data.get("results", []) for name, data in self.experiment_config["experiments"].items()} + + if experiment_name not in self.experiment_config["experiments"]: + raise ValueError(f"Experiment '{experiment_name}' does not exist.") + + return self.experiment_config["experiments"][experiment_name].get("results", []) + + def run_statistical_tests(self, control_experiment_name: str): + """ + Run statistical tests on the results of experiments, comparing one selected as control to the others, + which are considered treatments. + + Args: + control_experiment_name (str): Name of the control experiment. All other experiments will be treated as treatments + and compared to this one. + + Returns: + dict: Results of the statistical tests. + """ + if not self.experiment_config["experiments"]: + raise ValueError("No experiments available to run statistical tests.") + + # pop control from cloned list of experiment results + experiment_results = self.experiment_config["experiments"].copy() + control_experiment_results = {control_experiment_name: experiment_results.pop(control_experiment_name, None)} + + tester = StatisticalTester(control_experiment_data=control_experiment_results, + treatments_experiment_data=experiment_results, + results_key="results") + + results = tester.run_test() + self.experiment_config["experiments"][control_experiment_name]["statistical_test_results_vs_others"] = results + self._save_config() + + return results + + def finish_active_experiment(self): + """ + Mark the current active experiment as finished without activating the next one. + If this was the last unfinished experiment, mark all experiments as finished. + + Returns: + bool: True if an experiment was marked as finished, False if no active experiment exists. + """ + current_experiment = self.get_active_experiment() + if not current_experiment: + logger.info("No active experiment to finish.") + return False + + if "finished_experiments" not in self.experiment_config: + self.experiment_config["finished_experiments"] = [] + + if current_experiment not in self.experiment_config["finished_experiments"]: + self.experiment_config["finished_experiments"].append(current_experiment) + self.experiment_config["active_experiment"] = None + logger.info(f"Experiment '{current_experiment}' marked as finished.") + + # Check if all experiments are now finished + all_experiments = set(self.experiment_config["experiments"].keys()) + finished_experiments = set(self.experiment_config["finished_experiments"]) + + if all_experiments.issubset(finished_experiments): + self.experiment_config["finished_all_experiments"] = True + logger.info("All experiments have been finished.") + + self._save_config() + return True + return False + + def _load_or_create_config(self, config_file_path: str): + """ + Load the configuration file if it exists, otherwise create a new one. + + Args: + config_file_path (str): Path to the configuration file. + + Returns: + dict: Loaded or newly created configuration. + """ + try: + config = self._load_config(config_file_path) + logger.warning(f"Configuration file '{config_file_path}' exists and was loaded successfully. If you are trying to fully rerun the experiments, delete it first.") + return config + + except FileNotFoundError: + return self._create_default_config(config_file_path) + + def _create_default_config(self, config_file_path): + """ + Create a default configuration file. + + Returns: + dict: Default configuration. + """ + default_config = { + "experiments": {}, + "active_experiment": None, + "finished_all_experiments": False, + "finished_experiments": [] + } + + return default_config + + def _load_config(self, config_file_path: str): + import json + with open(config_file_path, 'r', encoding="utf-8", errors="replace") as file: + config = json.load(file) + return config + + def _save_config(self): + import json + with open(self.config_file_path, 'w', encoding="utf-8", errors="replace") as file: + json.dump(self.experiment_config, file, indent=4) diff --git a/experimentation/proposition.py b/experimentation/proposition.py new file mode 100644 index 0000000000000000000000000000000000000000..24d7b4cf08cd9f220e91ca830dd3e266c16aabf9 --- /dev/null +++ b/experimentation/proposition.py @@ -0,0 +1,488 @@ +import json +from chevron import render + +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld +from tinytroupe.utils import LLMChat, indent_at_current_level +from tinytroupe.experimentation import logger + + +from tinytroupe import default + +class Proposition: + + MIN_SCORE = 0 + MAX_SCORE = 9 + + def __init__(self, claim:str, target=None, include_personas:bool=False, first_n:int=None, last_n:int=None, + double_check:bool=False, use_reasoning_model:bool=False, precondition_function=None): + """ + Define a proposition as a (textual) claim about a target, which can be a TinyWorld, a TinyPerson or several of any. + The proposition's truth value can then either be checked as a boolean or computed as an integer score denoting the degree of truth. + + Sometimes a proposition is better used in an implicative way, i.e., as a claim that is true or false depending on the context. For example, when + considering the latest agent action, the proposition might be applicable only to certain agent action types. To allow this, + this class allows to define a precondition function, which effectivelly turns a proposition `P` into `Precondition --> P`. This is logically equivalent to + `not P or Precondition`. In other words: + - if the precondition is true, then the proposition is evaluated normally (as a boolean or a score). + - if the precondition is false, then the proposition is always true (or with highest score). + - if the precondition is None, then the proposition is evaluated normally (as a boolean or a score). + + + Args: + + claim (str): the claim of the proposition + target (TinyWorld, TinyPerson, list): the target or targets of the proposition. If not given, it will have to be specified later. + include_personas (bool): whether to include the persona specifications of the agents in the context + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + double_check (bool): whether to ask the LLM to double check its answer. This tends to give more strict answers, but is slower and more expensive. + use_reasoning_model (bool): whether to use a reasoning model to evaluate the proposition + precondition_function (function): a Boolean function that indicates whether the proposition can be evaluated or not. This is useful to avoid evaluating propositions that are not relevant for the current context. If the precondition fails, the proposition is always interpreted as true (or with highest score). MUST have named arguments `target`, `additional_context`, and `claim_variables` (note: you can use a lambda for this too, e.g., `lambda target, additional_context, claim_variables: ...`). + + """ + + self.claim = claim + self.targets = self._target_as_list(target) + self.include_personas = include_personas + + self.first_n = first_n + self.last_n = last_n + + self.double_check = double_check + + self.use_reasoning_model = use_reasoning_model + + self.precondition_function = precondition_function + + # the chat with the LLM is preserved until the proposition is re-evaluated. While it is available, + # the chat can be used to follow up on the proposition, e.g., to ask for more details about the evaluation. + self.llm_chat = None + + self.value = None + self.justification = None + self.confidence = None + self.recommendations = None + + def __copy__(self): + """ + Create a shallow copy of the proposition without any evaluation state. + + Returns: + Proposition: A new proposition with the same configuration parameters. + """ + new_prop = Proposition( + claim=self.claim, + target=self.targets, + include_personas=self.include_personas, + first_n=self.first_n, + last_n=self.last_n, + double_check=self.double_check, + use_reasoning_model=self.use_reasoning_model, + precondition_function=self.precondition_function + ) + return new_prop + + def copy(self): + """ + Create a shallow copy of the proposition without any evaluation state. + + Returns: + Proposition: A new proposition with the same configuration parameters. + """ + return self.__copy__() + + + def __call__(self, target=None, additional_context=None, claim_variables:dict={}, return_full_response:bool=False) -> bool: + return self.check(target=target, additional_context=additional_context, claim_variables=claim_variables, return_full_response=return_full_response) + + + def _check_precondition(self, target, additional_context:str, claim_variables:dict) -> bool: + """ + Check whether the proposition can be evaluated or not. + """ + + if self.precondition_function is None: + return True + else: + return self.precondition_function(target=target, additional_context=additional_context, claim_variables=claim_variables) + + def check(self, target=None, additional_context="No additional context available.", claim_variables:dict={}, return_full_response:bool=False) -> bool: + """ + Check whether the proposition holds for the given target(s). + """ + + current_targets = self._determine_target(target) + + if self._check_precondition(target=current_targets, additional_context=additional_context, claim_variables=claim_variables) == False: + self.value = True + self.justification = "The proposition is trivially true due to the precondition being false." + self.confidence = 1.0 + self.full_evaluation_response = {"value": True, "justification": self.justification, "confidence": self.confidence} + + else: # precondition is true or None + + context = self._build_context(current_targets) + + # might use a reasoning model, which could allow careful evaluation of the proposition. + model = self._model(self.use_reasoning_model) + + #render self.claim using the claim_variables via chevron + rendered_claim = render(self.claim, claim_variables) + + self.llm_chat = LLMChat(system_prompt=""" + You are a system that evaluates whether a proposition is true or false with respect to a given context. This context + always refers to a multi-agent simulation. The proposition is a claim about the behavior of the agents or the state of their environment + in the simulation. + + The context you receive can contain one or more of the following: + - the trajectory of a simulation of one or more agents. This means what agents said, did, thought, or perceived at different times. + - the state of the environment at a given time. + + Your output **must**: + - necessarily start with the word "True" or "False"; + - optionally be followed by a justification. Please provide a very detailed justifications, including very concrete and specific mentions to elements that contributed to reducing or increasing the score. Examples: + * WRONG JUSTIFICATION (too abstract) example: " ... the agent behavior did not comply with key parts of its specification, thus a reduced score ... " + * CORRECT JUSTIFICATION (very precise) example: " ... the agent behavior deviated from key parts of its specification, specifically: S_1 was not met because , ..., S_n was not met becasue . Thus, a reduced score ..." + + For example, the output could be of the form: "True, because ." or merely "True" if no justification is needed. + """, + + user_prompt=f""" + Evaluate the following proposition with respect to the context provided. Is it True or False? + + # Proposition + + This is the proposition you must evaluate: + + ``` + {indent_at_current_level(rendered_claim)} + ``` + + # Context + + The context you must consider is the following. + + {indent_at_current_level(context)} + + # Additional Context (if any) + + {indent_at_current_level(additional_context)} + + """, + + output_type=bool, + enable_reasoning_step=True, + + temperature=0.5, + frequency_penalty=0.0, + presence_penalty=0.0, + model=model) + + self.value = self.llm_chat() + + if self.double_check: + self.llm_chat.add_user_message("Are you sure? Please revise your evaluation to make is correct as possible.") + revised_value = self.llm_chat() + if revised_value != self.value: + logger.warning(f"The LLM revised its evaluation: from {self.value} to {revised_value}.") + self.value = revised_value + + self.reasoning = self.llm_chat.response_reasoning + self.justification = self.llm_chat.response_justification + self.confidence = self.llm_chat.response_confidence + + self.full_evaluation_response = self.llm_chat.response_json + + # return the final result, either only the value or the full response + if not return_full_response: + return self.value + else: + return self.full_evaluation_response + + def score(self, target=None, additional_context="No additional context available.", claim_variables:dict={}, return_full_response:bool=False) -> int: + """ + Compute the score for the proposition with respect to the given context. + """ + + current_targets = self._determine_target(target) + + if self._check_precondition(target=current_targets, additional_context=additional_context, claim_variables=claim_variables) == False: + self.value = self.MAX_SCORE + self.justification = "The proposition is trivially true due to the precondition being false." + self.confidence = 1.0 + self.full_evaluation_response = {"value": self.value, "justification": self.justification, "confidence": self.confidence} + + else: # precondition is true or None + + # build the context with the appropriate targets + + context = self._build_context(current_targets) + + # might use a reasoning model, which could allow careful evaluation of the proposition. + model = self._model(self.use_reasoning_model) + + #render self.claim using the claim_variables via chevron + rendered_claim = render(self.claim, claim_variables) + + self.llm_chat = LLMChat(system_prompt=f""" + You are a system that computes an integer score (between {Proposition.MIN_SCORE} and {Proposition.MAX_SCORE}, inclusive) about how much a proposition is true or false with respect to a given context. + This context always refers to a multi-agent simulation. The proposition is a claim about the behavior of the agents or the state of their environment in the simulation. + + The minimum score of {Proposition.MIN_SCORE} means that the proposition is completely false in all of the simulation trajectories, while the maximum score of {Proposition.MAX_SCORE} means that the proposition is completely true in all of the simulation trajectories. Intermediate scores are used to express varying degrees of partially met expectations. When assigning a score, follow these guidelines: + - If the data required to judge the proposition is not present, assign a score of {Proposition.MAX_SCORE}. That is to say, unless there is evidence to the contrary, the proposition is assumed to be true. + - The maximum score of {Proposition.MAX_SCORE} should be assigned when the evidence is as good as it can be. That is to say, all parts of the observed simulation trajectory support the proposition, no exceptions. + - The minimum score of {Proposition.MIN_SCORE} should be assigned when the evidence is as bad as it can be. That is to say, all parts of the observed simulation trajectory contradict the proposition, no exceptions. + - Intermediate scores should be assigned when the evidence is mixed. The intermediary score should be proportional to the balance of evidence, according to these bands: + 0 = The proposition is without any doubt completely false; + 1, 2, 3 = The proposition has little support and is mostly false; + 4, 5 = The evidence is mixed, and the proposition is as much true as it is false; + 6, 7, 8 = The proposition is well-supported and is mostly true; + 9 = The proposition is without any doubt completely true. + - You should be very rigorous in your evaluation and, when in doubt, assign a lower score. + - If there are critical flaws in the evidence, you should move your score to a lower band entirely. + - If the provided context has inconsistent information, you **must** consider **only** the information that gives the lowest score, since we want to be rigorous and if necessary err to the lower end. + * If you are considering the relationship between an agent specification and a simulation trajectory, you should consider the worst possible interpretation of: the agent specification; the simulation trajectory; or the relationship between the two. + * These contradictions can appear anywhere in the context. When they do, you **always** adopt the worst possible inteprpretation, because we want to be rigorous and if necessary err to the lower end. It does not matter if the contradiction shows only very rarely, or if it is very small. It is still a contradiction and should be considered as such. + * DO NOT dismiss contradictions as specification errors. They are part of the evidence and should be considered as such. They **must** be **always** taken into account when computing the score. **Never** ignore them. + + Additionally, whenever you are considering the relationship between an agent specification and a simulation trajectory, the following additional scoring guidelines apply: + - All observed behavior **must** be easily mapped back to clear elements of the agent specification. If you cannot do this, you should assign a lower score. + - Evaluate **each** relevant elements in the simulation trajectory (e.g., actions, stimuli) one by one, and assign a score to each of them. The final score is the average of all the scores assigned to each element. + + The proposition you receive can contain one or more of the following: + - A statement of fact, which you will score. + - Additional context, which you will use to evaluate the proposition. In particular, it might refer or specify potentail parts + of similation trajectories for consideration. These might be formatted differently than what is given in the main context, so + make sure you read them carefully. + - Additional instructions on how to evaluate the proposition. + + The context you receive can contain one or more of the following: + - the persona specifications of the agents in the simulation. That is to say, what the agents **are**, not what they are **doing**. + - the simulation trajectories of one or more agents. This means what agents said, did, thought, or perceived at different times. + These trajectories **are not** part of the persona specification. + - the state of the environment at a given time. + - additional context that can vary from simulation to simulation. + + To interpret the simulation trajectories, use the following guidelines: + - Agents can receive stimuli and produce actions. You might be concerned with both or only one of them, depending on the specific proposition. + - Actions are clearly marked with the text "acts", e.g., "Agent A acts: [ACTION]". If it is not thus marked, it is not an action. + - Stimuli are denoted by "--> Agent name: [STIMULUS]". + + Your output **must**: + - necessarily start with an integer between {Proposition.MIN_SCORE} and {Proposition.MAX_SCORE}, inclusive; + - be followed by a justification. Please provide a very detailed justifications, including very concrete and specific mentions to elements that contributed to reducing or increasing the score. Examples: + * WRONG JUSTIFICATION (too abstract) example: " ... the agent behavior did not comply with key parts of its specification, thus a reduced score ... " + * CORRECT JUSTIFICATION (very precise) example: " ... the agent behavior deviated from key parts of its specification, specifically: S_1 was not met because , ..., S_n was not met becasue . Thus, a reduced score ..." + + For example, the output could be of the form: "1, because ." + """, + + user_prompt=f""" + Compute the score for the following proposition with respect to the context provided. Think step-by-step to assign the most accurate score and provide a justification. + + # Proposition + + This is the proposition you must evaluate: + + ``` + {indent_at_current_level(rendered_claim)} + ``` + + # Context + + The context you must consider is the following. + + {indent_at_current_level(context)} + + # Additional Context (if any) + + {indent_at_current_level(additional_context)} + """, + + output_type=int, + enable_reasoning_step=True, + + temperature=1.0, + frequency_penalty=0.0, + presence_penalty=0.0, + + # Use a reasoning model, which allows careful evaluation of the proposition. + model=model) + + + self.value = self.llm_chat() + + if self.double_check: + self.llm_chat.add_user_message("Are you sure? Please revise your evaluation to make is correct as possible.") + revised_value = self.llm_chat() + if revised_value != self.value: + logger.warning(f"The LLM revised its evaluation: from {self.value} to {revised_value}.") + self.value = revised_value + + self.reasoning = self.llm_chat.response_reasoning + self.justification = self.llm_chat.response_justification + self.confidence = self.llm_chat.response_confidence + + self.full_evaluation_response = self.llm_chat.response_json + + # return the final result, either only the value or the full response + if not return_full_response: + return self.value + else: + return self.full_evaluation_response + + def recommendations_for_improvement(self): + """ + Get recommendations for improving the proposition. + """ + + # TODO this is not working, let's try something else + # + #if self.llm_chat is None: + # raise ValueError("No evaluation has been performed yet. Please evaluate the proposition before getting recommendations.") +# + #self.llm_chat.add_system_message(\ + # """ + # You will now act as a system that provides recommendations for the improvement of the scores previously assigned to propositions. + # You will now output text that contains analysises, recommendations and other information as requested by the user. + # """) +# + #self.llm_chat.add_user_message(\ + # """ + # To help improve the score next time, please list the following in as much detail as possible: + # - all recommendations for improvements based on the current score. + # - all criteria you are using to assign scores, and how to best satisfy them +# + # For both cases: + # - besides guidelines, make sure to provide plenty of concrete examples of what to be done in order to maximize each criterion. + # - avoid being generic or abstract. Instead, all of your criteria and recommendations should be given in very concrete terms that would work specifically for the case just considered. + # + # Note that your output is a TEXT with the various recommendations, information and tips, not a JSON object. +# + # Recommendations: + # """) + # + #recommendation = self.llm_chat(output_type=str, enable_json_output_format=False) + recommendation = "No additional recommendations at this time." + return recommendation + + def _model(self, use_reasoning_model): + if use_reasoning_model: + return default["reasoning_model"] + else: + return default["model"] + + def _determine_target(self, target): + """ + Determine the target for the proposition. If a target was provided during initialization, it must not be provided now (i.e., the proposition is immutable). + If no target was provided during initialization, it must be provided now. + """ + # If no target was provided during initialization, it must be provided now. + if self.targets is None : + if target is None: + raise ValueError("No target specified. Please provide a target.") + else: + return self._target_as_list(target) + + # If it was provided during initialization, it must not be provided now (i.e., the proposition is immutable). + else: + if target is not None: + raise ValueError("Target already specified. Please do not provide a target.") + else: + return self.targets + + def _build_context(self, current_targets): + + # + # build the context with the appropriate targets + # + context = "" + + for target in current_targets: + target_trajectory = target.pretty_current_interactions(max_content_length=None, first_n=self.first_n, last_n=self.last_n) + + if isinstance(target, TinyPerson): + if self.include_personas: + context += f"## Agent '{target.name}' Persona Specification\n\n" + context += "Before presenting the actual simulation trajectory, here is the persona specification of the agent that was used to produce the simulation.\n\n" + context += "This IS NOT the actual simulation, but only the static persona specification of the agent.\n\n" + context += f"persona={json.dumps(target._persona, indent=4)}\n\n" + + context += f"## Agent '{target.name}' Simulation Trajectory (if any)\n\n" + elif isinstance(target, TinyWorld): + if self.include_personas: + context += f"## Environment '{target.name}' Personas Specifications\n\n" + context += "Before presenting the actual simulation trajectory, here are the persona specifications of the agents used to produce the simulation.\n\n" + context += "This IS NOT the actual simulation, but only the static persona specification of the agent.\n\n" + for agent in target.agents: + context += f"### Agent '{agent.name}' Persona Specification\n\n" + context += f"persona={json.dumps(agent._persona, indent=4)}\n\n" + + context += f"## Environment '{target.name}' Simulation Trajectory (if any)\n\n" + + context += target_trajectory + "\n\n" + + return context + + def _target_as_list(self, target): + if target is None: + return None + elif isinstance(target, TinyWorld) or isinstance(target, TinyPerson): + return [target] + elif isinstance(target, list) and all(isinstance(t, TinyWorld) or isinstance(t, TinyPerson) for t in target): + return target + else: + raise ValueError("Target must be a TinyWorld, a TinyPerson or a list of them.") + + +def check_proposition(target, claim:str, additional_context="No additional context available.", + first_n:int=None, last_n:int=None, + return_full_response:bool=False): + """ + Check whether a propositional claim holds for the given target(s). This is meant as a + convenience method to avoid creating a Proposition object (which you might not need + if you are not interested in the justification or confidence of the claim, or will + not use it again). + + Args: + target (TinyWorld, TinyPerson, list): the target or targets of the proposition + claim (str): the claim of the proposition + additional_context (str): additional context to provide to the LLM + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + return_full_response (bool): whether to return the full response from the LLM, including justification and confidence + + Returns: + bool: whether the proposition holds for the given target(s) + """ + + proposition = Proposition(claim, target, first_n=first_n, last_n=last_n) + return proposition.check(additional_context=additional_context, return_full_response=return_full_response) + + +def compute_score(target, claim:str, + additional_context="No additional context available.", + first_n:int=None, last_n:int=None, + return_full_response:bool=False): + """ + Compute a score about whether a claim holds for the given target(s). This is meant as a + convenience method to avoid creating a Score object (which you might not need + if you are not interested in the justification or confidence of the claim, or will + not use it again). + + Args: + target (TinyWorld, TinyPerson, list): the target or targets of the proposition + claim (str): the claim of the proposition + additional_context (str): additional context to provide to the LLM + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + return_full_response (bool): whether to return the full response from the LLM, including justification and confidence + + Returns: + bool: whether the proposition holds for the given target(s) + """ + + score = Proposition(claim, target, + first_n=first_n, last_n=last_n) + return score.compute(additional_context=additional_context, return_full_response=return_full_response) \ No newline at end of file diff --git a/experimentation/randomization.py b/experimentation/randomization.py new file mode 100644 index 0000000000000000000000000000000000000000..024ca96053db61a7c20328651e315a7c4e7df6ad --- /dev/null +++ b/experimentation/randomization.py @@ -0,0 +1,105 @@ +import random +import pandas as pd +from tinytroupe.agent import TinyPerson + +class ABRandomizer(): + + def __init__(self, real_name_1="control", real_name_2="treatment", + blind_name_a="A", blind_name_b="B", + passtrough_name=[], + random_seed=42): + """ + An utility class to randomize between two options, and de-randomize later. + The choices are stored in a dictionary, with the index of the item as the key. + The real names are the names of the options as they are in the data, and the blind names + are the names of the options as they are presented to the user. Finally, the passtrough names + are names that are not randomized, but are always returned as-is. + + Args: + real_name_1 (str): the name of the first option + real_name_2 (str): the name of the second option + blind_name_a (str): the name of the first option as seen by the user + blind_name_b (str): the name of the second option as seen by the user + passtrough_name (list): a list of names that should not be randomized and are always + returned as-is. + random_seed (int): the random seed to use + """ + + self.choices = {} + self.real_name_1 = real_name_1 + self.real_name_2 = real_name_2 + self.blind_name_a = blind_name_a + self.blind_name_b = blind_name_b + self.passtrough_name = passtrough_name + self.random_seed = random_seed + + def randomize(self, i, a, b): + """ + Randomly switch between a and b, and return the choices. + Store whether the a and b were switched or not for item i, to be able to + de-randomize later. + + Args: + i (int): index of the item + a (str): first choice + b (str): second choice + """ + # use the seed + if random.Random(self.random_seed).random() < 0.5: + self.choices[i] = (0, 1) + return a, b + + else: + self.choices[i] = (1, 0) + return b, a + + def derandomize(self, i, a, b): + """ + De-randomize the choices for item i, and return the choices. + + Args: + i (int): index of the item + a (str): first choice + b (str): second choice + """ + if self.choices[i] == (0, 1): + return a, b + elif self.choices[i] == (1, 0): + return b, a + else: + raise Exception(f"No randomization found for item {i}") + + def derandomize_name(self, i, blind_name): + """ + Decode the choice made by the user, and return the choice. + + Args: + i (int): index of the item + choice_name (str): the choice made by the user + """ + + # was the choice i randomized? + if self.choices[i] == (0, 1): + # no, so return the choice + if blind_name == self.blind_name_a: + return self.real_name_1 + elif blind_name == self.blind_name_b: + return self.real_name_2 + elif blind_name in self.passtrough_name: + return blind_name + else: + raise Exception(f"Choice '{blind_name}' not recognized") + + elif self.choices[i] == (1, 0): + # yes, it was randomized, so return the opposite choice + if blind_name == self.blind_name_a: + return self.real_name_2 + elif blind_name == self.blind_name_b: + return self.real_name_1 + elif blind_name in self.passtrough_name: + return blind_name + else: + raise Exception(f"Choice '{blind_name}' not recognized") + else: + raise Exception(f"No randomization found for item {i}") + diff --git a/experimentation/statistical_tests.py b/experimentation/statistical_tests.py new file mode 100644 index 0000000000000000000000000000000000000000..78f08232dc072667d514ec0e948ab80836d550d1 --- /dev/null +++ b/experimentation/statistical_tests.py @@ -0,0 +1,608 @@ +import numpy as np +import scipy.stats as stats +from typing import Dict, List, Union, Callable, Any, Optional + +from tinytroupe.experimentation import logger + + +class StatisticalTester: + """ + A class to perform statistical tests on experiment results. To do so, a control is defined, and then one or + more treatments are compared to the control. The class supports various statistical tests, including t-tests, + Mann-Whitney U tests, and ANOVA. The user can specify the type of test to run, the significance level, and + the specific metrics to analyze. The results of the tests are returned in a structured format. + """ + + def __init__(self, control_experiment_data: Dict[str, list], + treatments_experiment_data: Dict[str, Dict[str, list]], + results_key:str = None): + """ + Initialize with experiment results. + + Args: + control_experiment_data (dict): Dictionary containing control experiment results with keys + as metric names and values as lists of values. + e.g.,{"control_exp": {"metric1": [0.1, 0.2], "metric2": [0.3, 0.4], ...}} + treatments_experiment_data (dict): Dictionary containing experiment results with keys + as experiment IDs and values as dicts of metric names to lists of values. + e.g., {"exp1": {"metric1": [0.1, 0.2], "metric2": [0.3, 0.4]}, + "exp2": {"metric1": [0.5, 0.6], "metric2": [0.7, 0.8]}, ...} + """ + + # if results_key is provided, use it to extract the relevant data from the control and treatment data + # e.g., {"exp1": {"results": {"metric1": [0.1, 0.2], "metric2": [0.3, 0.4]}} + if results_key: + control_experiment_data = {k: v[results_key] for k, v in control_experiment_data.items()} + treatments_experiment_data = {k: v[results_key] for k, v in treatments_experiment_data.items()} + + self.control_experiment_data = control_experiment_data + self.treatments_experiment_data = treatments_experiment_data + + # Validate input data + self._validate_input_data() + + def _validate_input_data(self): + """Validate the input data formats and structure.""" + # Check that control and treatments are dictionaries + if not isinstance(self.control_experiment_data, dict): + raise TypeError("Control experiment data must be a dictionary") + if not isinstance(self.treatments_experiment_data, dict): + raise TypeError("Treatments experiment data must be a dictionary") + + # Check that control has at least one experiment + if not self.control_experiment_data: + raise ValueError("Control experiment data cannot be empty") + + # Check only one control + if len(self.control_experiment_data) > 1: + raise ValueError("Only one control experiment is allowed") + + # Validate control experiment structure + for control_id, control_metrics in self.control_experiment_data.items(): + if not isinstance(control_metrics, dict): + raise TypeError(f"Metrics for control experiment '{control_id}' must be a dictionary") + + # Check that the metrics dictionary is not empty + if not control_metrics: + raise ValueError(f"Control experiment '{control_id}' has no metrics") + + # Validate that metric values are lists + for metric, values in control_metrics.items(): + if not isinstance(values, list): + raise TypeError(f"Values for metric '{metric}' in control experiment '{control_id}' must be a list") + + # Check treatments have at least one experiment + if not self.treatments_experiment_data: + raise ValueError("Treatments experiment data cannot be empty") + + # Validate treatment experiment structure + for treatment_id, treatment_data in self.treatments_experiment_data.items(): + if not isinstance(treatment_data, dict): + raise TypeError(f"Data for treatment '{treatment_id}' must be a dictionary") + + # Check that the metrics dictionary is not empty + if not treatment_data: + raise ValueError(f"Treatment '{treatment_id}' has no metrics") + + # Get all control metrics for overlap checking + all_control_metrics = set() + for control_metrics in self.control_experiment_data.values(): + all_control_metrics.update(control_metrics.keys()) + + # Check if there's any overlap between control and treatment metrics + common_metrics = all_control_metrics.intersection(set(treatment_data.keys())) + if not common_metrics: + logger.warning(f"Treatment '{treatment_id}' has no metrics in common with any control experiment") + + # Check that treatment metrics are lists + for metric, values in treatment_data.items(): + if not isinstance(values, list): + raise TypeError(f"Values for metric '{metric}' in treatment '{treatment_id}' must be a list") + + def run_test(self, + test_type: str="welch_t_test", + alpha: float = 0.05, + **kwargs) -> Dict[str, Dict[str, Any]]: + """ + Run the specified statistical test on the control and treatments data. + + Args: + test_type (str): Type of statistical test to run. + Options: 't_test', 'welch_t_test', 'mann_whitney', 'anova', 'chi_square', 'ks_test' + alpha (float): Significance level, defaults to 0.05 + **kwargs: Additional arguments for specific test types. + + Returns: + dict: Dictionary containing the results of the statistical tests for each treatment (vs the one control). + Each key is the treatment ID and each value is a dictionary with test results. + """ + supported_tests = { + 't_test': self._run_t_test, + 'welch_t_test': self._run_welch_t_test, + 'mann_whitney': self._run_mann_whitney, + 'anova': self._run_anova, + 'chi_square': self._run_chi_square, + 'ks_test': self._run_ks_test + } + + if test_type not in supported_tests: + raise ValueError(f"Unsupported test type: {test_type}. Supported types: {list(supported_tests.keys())}") + + results = {} + for control_id, control_data in self.control_experiment_data.items(): + # get all metrics from control data + metrics = set() + metrics.update(control_data.keys()) + for treatment_id, treatment_data in self.treatments_experiment_data.items(): + results[treatment_id] = {} + + for metric in metrics: + # Skip metrics not in treatment data + if metric not in treatment_data: + logger.warning(f"Metric '{metric}' not found in treatment '{treatment_id}'") + continue + + control_values = control_data[metric] + treatment_values = treatment_data[metric] + + # Skip if either control or treatment has no values + if len(control_values) == 0 or len(treatment_values) == 0: + logger.warning(f"Skipping metric '{metric}' for treatment '{treatment_id}' due to empty values") + continue + + # Run the selected test and convert to JSON serializable types + test_result = supported_tests[test_type](control_values, treatment_values, alpha, **kwargs) + results[treatment_id][metric] = convert_to_serializable(test_result) + + return results + + def _run_t_test(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Student's t-test (equal variance assumed).""" + # Convert to numpy arrays for calculations + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_mean = np.mean(control) + treatment_mean = np.mean(treatment) + mean_diff = treatment_mean - control_mean + + # Run the t-test + t_stat, p_value = stats.ttest_ind(control, treatment, equal_var=True) + + # Calculate confidence interval + control_std = np.std(control, ddof=1) + treatment_std = np.std(treatment, ddof=1) + pooled_std = np.sqrt(((len(control) - 1) * control_std**2 + + (len(treatment) - 1) * treatment_std**2) / + (len(control) + len(treatment) - 2)) + + se = pooled_std * np.sqrt(1/len(control) + 1/len(treatment)) + critical_value = stats.t.ppf(1 - alpha/2, len(control) + len(treatment) - 2) + margin_error = critical_value * se + ci_lower = mean_diff - margin_error + ci_upper = mean_diff + margin_error + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Student t-test (equal variance)', + 'control_mean': control_mean, + 'treatment_mean': treatment_mean, + 'mean_difference': mean_diff, + 'percent_change': (mean_diff / control_mean * 100) if control_mean != 0 else float('inf'), + 't_statistic': t_stat, + 'p_value': p_value, + 'confidence_interval': (ci_lower, ci_upper), + 'confidence_level': 1 - alpha, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'control_std': control_std, + 'treatment_std': treatment_std, + 'effect_size': cohen_d(control, treatment) + } + + def _run_welch_t_test(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Welch's t-test (unequal variance).""" + # Convert to numpy arrays for calculations + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_mean = np.mean(control) + treatment_mean = np.mean(treatment) + mean_diff = treatment_mean - control_mean + + # Run Welch's t-test + t_stat, p_value = stats.ttest_ind(control, treatment, equal_var=False) + + # Calculate confidence interval (for Welch's t-test) + control_var = np.var(control, ddof=1) + treatment_var = np.var(treatment, ddof=1) + + # Calculate effective degrees of freedom (Welch-Satterthwaite equation) + v_num = (control_var/len(control) + treatment_var/len(treatment))**2 + v_denom = (control_var/len(control))**2/(len(control)-1) + (treatment_var/len(treatment))**2/(len(treatment)-1) + df = v_num / v_denom if v_denom > 0 else float('inf') + + se = np.sqrt(control_var/len(control) + treatment_var/len(treatment)) + critical_value = stats.t.ppf(1 - alpha/2, df) + margin_error = critical_value * se + ci_lower = mean_diff - margin_error + ci_upper = mean_diff + margin_error + + control_std = np.std(control, ddof=1) + treatment_std = np.std(treatment, ddof=1) + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Welch t-test (unequal variance)', + 'control_mean': control_mean, + 'treatment_mean': treatment_mean, + 'mean_difference': mean_diff, + 'percent_change': (mean_diff / control_mean * 100) if control_mean != 0 else float('inf'), + 't_statistic': t_stat, + 'p_value': p_value, + 'confidence_interval': (ci_lower, ci_upper), + 'confidence_level': 1 - alpha, + 'significant': significant, + 'degrees_of_freedom': df, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'control_std': control_std, + 'treatment_std': treatment_std, + 'effect_size': cohen_d(control, treatment) + } + + def _run_mann_whitney(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Mann-Whitney U test (non-parametric test).""" + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_median = np.median(control) + treatment_median = np.median(treatment) + median_diff = treatment_median - control_median + + # Run the Mann-Whitney U test + u_stat, p_value = stats.mannwhitneyu(control, treatment, alternative='two-sided') + + # Calculate common language effect size + # (probability that a randomly selected value from treatment is greater than control) + count = 0 + for tc in treatment: + for cc in control: + if tc > cc: + count += 1 + cles = count / (len(treatment) * len(control)) + + # Calculate approximate confidence interval using bootstrap + try: + from scipy.stats import bootstrap + + def median_diff_func(x, y): + return np.median(x) - np.median(y) + + res = bootstrap((control, treatment), median_diff_func, + confidence_level=1-alpha, + n_resamples=1000, + random_state=42) + ci_lower, ci_upper = res.confidence_interval + except ImportError: + # If bootstrap is not available, return None for confidence interval + ci_lower, ci_upper = None, None + logger.warning("SciPy bootstrap not available, skipping confidence interval calculation") + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Mann-Whitney U test', + 'control_median': control_median, + 'treatment_median': treatment_median, + 'median_difference': median_diff, + 'percent_change': (median_diff / control_median * 100) if control_median != 0 else float('inf'), + 'u_statistic': u_stat, + 'p_value': p_value, + 'confidence_interval': (ci_lower, ci_upper) if ci_lower is not None else None, + 'confidence_level': 1 - alpha, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'effect_size': cles + } + + def _run_anova(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run one-way ANOVA test.""" + # For ANOVA, we typically need multiple groups, but we can still run it with just two + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Run one-way ANOVA + f_stat, p_value = stats.f_oneway(control, treatment) + + # Calculate effect size (eta-squared) + total_values = np.concatenate([control, treatment]) + grand_mean = np.mean(total_values) + + ss_total = np.sum((total_values - grand_mean) ** 2) + ss_between = (len(control) * (np.mean(control) - grand_mean) ** 2 + + len(treatment) * (np.mean(treatment) - grand_mean) ** 2) + + eta_squared = ss_between / ss_total if ss_total > 0 else 0 + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'One-way ANOVA', + 'f_statistic': f_stat, + 'p_value': p_value, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'effect_size': eta_squared, + 'effect_size_type': 'eta_squared' + } + + def _run_chi_square(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Chi-square test for categorical data.""" + # For chi-square, we assume the values represent counts in different categories + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Check if the arrays are the same length (same number of categories) + if len(control) != len(treatment): + raise ValueError("Control and treatment must have the same number of categories for chi-square test") + + # Run chi-square test + contingency_table = np.vstack([control, treatment]) + chi2_stat, p_value, dof, expected = stats.chi2_contingency(contingency_table) + + # Calculate Cramer's V as effect size + n = np.sum(contingency_table) + min_dim = min(contingency_table.shape) - 1 + cramers_v = np.sqrt(chi2_stat / (n * min_dim)) if n * min_dim > 0 else 0 + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Chi-square test', + 'chi2_statistic': chi2_stat, + 'p_value': p_value, + 'degrees_of_freedom': dof, + 'significant': significant, + 'effect_size': cramers_v, + 'effect_size_type': 'cramers_v' + } + + def check_assumptions(self, metric: str) -> Dict[str, Dict[str, Any]]: + """ + Check statistical assumptions for the given metric across all treatments. + + Args: + metric (str): The metric to check assumptions for. + + Returns: + dict: Dictionary with results of assumption checks for each treatment. + """ + if metric not in self.control_experiment_data: + raise ValueError(f"Metric '{metric}' not found in control data") + + results = {} + control_values = np.array(self.control_experiment_data[metric], dtype=float) + + # Check normality of control + control_shapiro = stats.shapiro(control_values) + control_normality = { + 'test': 'Shapiro-Wilk', + 'statistic': control_shapiro[0], + 'p_value': control_shapiro[1], + 'normal': control_shapiro[1] >= 0.05 + } + + for treatment_id, treatment_data in self.treatments_experiment_data.items(): + if metric not in treatment_data: + logger.warning(f"Metric '{metric}' not found in treatment '{treatment_id}'") + continue + + treatment_values = np.array(treatment_data[metric], dtype=float) + + # Check normality of treatment + treatment_shapiro = stats.shapiro(treatment_values) + treatment_normality = { + 'test': 'Shapiro-Wilk', + 'statistic': treatment_shapiro[0], + 'p_value': treatment_shapiro[1], + 'normal': treatment_shapiro[1] >= 0.05 + } + + # Check homogeneity of variance + levene_test = stats.levene(control_values, treatment_values) + variance_homogeneity = { + 'test': 'Levene', + 'statistic': levene_test[0], + 'p_value': levene_test[1], + 'equal_variance': levene_test[1] >= 0.05 + } + + # Store results and convert to JSON serializable types + results[treatment_id] = convert_to_serializable({ + 'control_normality': control_normality, + 'treatment_normality': treatment_normality, + 'variance_homogeneity': variance_homogeneity, + 'recommended_test': self._recommend_test(control_normality['normal'], + treatment_normality['normal'], + variance_homogeneity['equal_variance']) + }) + + return results + + def _recommend_test(self, control_normal: bool, treatment_normal: bool, equal_variance: bool) -> str: + """Recommend a statistical test based on assumption checks.""" + if control_normal and treatment_normal: + if equal_variance: + return 't_test' + else: + return 'welch_t_test' + else: + return 'mann_whitney' + + def _run_ks_test(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """ + Run Kolmogorov-Smirnov test to compare distributions. + + This test compares the empirical cumulative distribution functions (ECDFs) of two samples + to determine if they come from the same distribution. It's particularly useful for: + - Categorical responses (e.g., "Yes"/"No"/"Maybe") when converted to ordinal values + - Continuous data where you want to compare entire distributions, not just means + - Detecting differences in distribution shape, spread, or location + """ + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_median = np.median(control) + treatment_median = np.median(treatment) + control_mean = np.mean(control) + treatment_mean = np.mean(treatment) + + # Run the Kolmogorov-Smirnov test + ks_stat, p_value = stats.ks_2samp(control, treatment) + + # Calculate distribution characteristics + control_std = np.std(control, ddof=1) + treatment_std = np.std(treatment, ddof=1) + + # Calculate effect size using the KS statistic itself as a measure + # KS statistic ranges from 0 (identical distributions) to 1 (completely different) + effect_size = ks_stat + + # Additional distribution comparison metrics + # Calculate overlap coefficient (area under the minimum of two PDFs) + try: + # Create histograms for overlap calculation + combined_range = np.linspace( + min(np.min(control), np.min(treatment)), + max(np.max(control), np.max(treatment)), + 50 + ) + control_hist, _ = np.histogram(control, bins=combined_range, density=True) + treatment_hist, _ = np.histogram(treatment, bins=combined_range, density=True) + + # Calculate overlap (intersection over union-like metric) + overlap = np.sum(np.minimum(control_hist, treatment_hist)) / np.sum(np.maximum(control_hist, treatment_hist)) + overlap = overlap if not np.isnan(overlap) else 0.0 + except: + overlap = None + + # Calculate percentile differences for additional insights + percentiles = [25, 50, 75, 90, 95] + percentile_diffs = {} + for p in percentiles: + control_p = np.percentile(control, p) + treatment_p = np.percentile(treatment, p) + percentile_diffs[f"p{p}_diff"] = treatment_p - control_p + + # Determine significance + significant = p_value < alpha + + return { + 'test_type': 'Kolmogorov-Smirnov test', + 'control_mean': control_mean, + 'treatment_mean': treatment_mean, + 'control_median': control_median, + 'treatment_median': treatment_median, + 'control_std': control_std, + 'treatment_std': treatment_std, + 'ks_statistic': ks_stat, + 'p_value': p_value, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'effect_size': effect_size, + 'overlap_coefficient': overlap, + 'percentile_differences': percentile_diffs, + 'interpretation': self._interpret_ks_result(ks_stat, significant), + 'confidence_level': 1 - alpha + } + + def _interpret_ks_result(self, ks_stat: float, significant: bool) -> str: + """Provide interpretation of KS test results.""" + if not significant: + return "No significant difference between distributions" + + if ks_stat < 0.1: + return "Very small difference between distributions" + elif ks_stat < 0.25: + return "Small difference between distributions" + elif ks_stat < 0.5: + return "Moderate difference between distributions" + else: + return "Large difference between distributions" + + +def cohen_d(x: Union[list, np.ndarray], y: Union[list, np.ndarray]) -> float: + """ + Calculate Cohen's d effect size for two samples. + + Args: + x: First sample + y: Second sample + + Returns: + float: Cohen's d effect size + """ + nx = len(x) + ny = len(y) + + # Convert to numpy arrays + x = np.array(x, dtype=float) + y = np.array(y, dtype=float) + + # Calculate means + mx = np.mean(x) + my = np.mean(y) + + # Calculate standard deviations + sx = np.std(x, ddof=1) + sy = np.std(y, ddof=1) + + # Pooled standard deviation + pooled_sd = np.sqrt(((nx - 1) * sx**2 + (ny - 1) * sy**2) / (nx + ny - 2)) + + # Cohen's d + return (my - mx) / pooled_sd if pooled_sd > 0 else 0 + + +def convert_to_serializable(obj): + """ + Convert NumPy types to native Python types recursively to ensure JSON serialization works. + + Args: + obj: Any object that might contain NumPy types + + Returns: + Object with NumPy types converted to Python native types + """ + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, (np.number, np.bool_)): + return obj.item() + elif isinstance(obj, dict): + return {k: convert_to_serializable(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [convert_to_serializable(i) for i in obj] + elif isinstance(obj, tuple): + return tuple(convert_to_serializable(i) for i in obj) + else: + return obj \ No newline at end of file diff --git a/extraction/__init__.py b/extraction/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9963457a4c00207261fadaf9c41d79c2921102ab --- /dev/null +++ b/extraction/__init__.py @@ -0,0 +1,24 @@ +""" +Simulations produce a lot of data, and it is often useful to extract these data in a structured way. For instance, you might wish to: + - Extract the main points from an agent's interactions history, so that you can consult them later in a concise form. + - Generate synthetic data from a simulation, so that you can use it for training machine learning models or testing software. + - Simply turn some of the data into a more machine-readable format, such as JSON or CSV, so that you can analyze it more easily. + +This module provides various utilities to help you extract data from TinyTroupe elements, such as agents and worlds. It also provides a +mechanism to reduce the extracted data to a more concise form, and to export artifacts from TinyTroupe elements. Incidentaly, it showcases +one of the many ways in which agent simulations differ from AI assistants, as the latter are not designed to be introspected in this way. +""" + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.extraction.artifact_exporter import ArtifactExporter +from tinytroupe.extraction.normalizer import Normalizer +from tinytroupe.extraction.results_extractor import ResultsExtractor +from tinytroupe.extraction.results_reducer import ResultsReducer +from tinytroupe.extraction.results_reporter import ResultsReporter + +__all__ = ["ArtifactExporter", "Normalizer", "ResultsExtractor", "ResultsReducer", "ResultsReporter"] \ No newline at end of file diff --git a/extraction/__pycache__/__init__.cpython-312.pyc b/extraction/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7868e70251a30391d6cf55f80b649f154c435d5c Binary files /dev/null and b/extraction/__pycache__/__init__.cpython-312.pyc differ diff --git a/extraction/__pycache__/artifact_exporter.cpython-312.pyc b/extraction/__pycache__/artifact_exporter.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c3ead07aa1d4feba86080fb134712ed5faa669bf Binary files /dev/null and b/extraction/__pycache__/artifact_exporter.cpython-312.pyc differ diff --git a/extraction/__pycache__/normalizer.cpython-312.pyc b/extraction/__pycache__/normalizer.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed69c2817732d6ad7ae760ce70d8ae87466a4972 Binary files /dev/null and b/extraction/__pycache__/normalizer.cpython-312.pyc differ diff --git a/extraction/__pycache__/results_extractor.cpython-312.pyc b/extraction/__pycache__/results_extractor.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d1ff874d198e88c7307fb1c3cff8b85af6ccd2d7 Binary files /dev/null and b/extraction/__pycache__/results_extractor.cpython-312.pyc differ diff --git a/extraction/__pycache__/results_reducer.cpython-312.pyc b/extraction/__pycache__/results_reducer.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9b938a21b048b45b5baecfc2458b34b9c9170224 Binary files /dev/null and b/extraction/__pycache__/results_reducer.cpython-312.pyc differ diff --git a/extraction/__pycache__/results_reporter.cpython-312.pyc b/extraction/__pycache__/results_reporter.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1c2e1e68a20101f0a18cc394c2492e7c6afa3b9e Binary files /dev/null and b/extraction/__pycache__/results_reporter.cpython-312.pyc differ diff --git a/extraction/artifact_exporter.py b/extraction/artifact_exporter.py new file mode 100644 index 0000000000000000000000000000000000000000..82f27fb2fd69689ce19957402077d1a6de1041b5 --- /dev/null +++ b/extraction/artifact_exporter.py @@ -0,0 +1,160 @@ +import os +import json +import pandas as pd +import pypandoc +import markdown +from typing import Union, List + +from tinytroupe.extraction import logger +from tinytroupe.utils import JsonSerializableRegistry + +import tinytroupe.utils as utils + +class ArtifactExporter(JsonSerializableRegistry): + """ + An artifact exporter is responsible for exporting artifacts from TinyTroupe elements, for example + in order to create synthetic data files from simulations. + """ + + def __init__(self, base_output_folder:str) -> None: + self.base_output_folder = base_output_folder + + def export(self, artifact_name:str, artifact_data:Union[dict, str], content_type:str, content_format:str=None, target_format:str="txt", verbose:bool=False): + """ + Exports the specified artifact data to a file. + + Args: + artifact_name (str): The name of the artifact. + artifact_data (Union[dict, str]): The data to export. If a dict is given, it will be saved as JSON. + If a string is given, it will be saved as is. + content_type (str): The type of the content within the artifact. + content_format (str, optional): The format of the content within the artifact (e.g., md, csv, etc). Defaults to None. + target_format (str): The format to export the artifact to (e.g., json, txt, docx, etc). + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + # dedent inputs, just in case + if isinstance(artifact_data, str): + artifact_data = utils.dedent(artifact_data) + elif isinstance(artifact_data, dict): + artifact_data['content'] = utils.dedent(artifact_data['content']) + else: + raise ValueError("The artifact data must be either a string or a dictionary.") + + # clean the artifact name of invalid characters + invalid_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|', '\n', '\t', '\r', ';'] + for char in invalid_chars: + # check if the character is in the artifact name + if char in artifact_name: + # replace the character with an underscore + artifact_name = artifact_name.replace(char, "-") + logger.warning(f"Replaced invalid character {char} with hyphen in artifact name '{artifact_name}'.") + + artifact_file_path = self._compose_filepath(artifact_data, artifact_name, content_type, target_format, verbose) + + + if target_format == "json": + self._export_as_json(artifact_file_path, artifact_data, content_type, verbose) + elif target_format == "txt" or target_format == "text" or target_format == "md" or target_format == "markdown": + self._export_as_txt(artifact_file_path, artifact_data, content_type, verbose) + elif target_format == "docx": + self._export_as_docx(artifact_file_path, artifact_data, content_format, verbose) + else: + raise ValueError(f"Unsupported target format: {target_format}.") + + + def _export_as_txt(self, artifact_file_path:str, artifact_data:Union[dict, str], content_type:str, verbose:bool=False): + """ + Exports the specified artifact data to a text file. + """ + + with open(artifact_file_path, 'w', encoding="utf-8", errors="replace") as f: + if isinstance(artifact_data, dict): + content = artifact_data['content'] + else: + content = artifact_data + + f.write(content) + + def _export_as_json(self, artifact_file_path:str, artifact_data:Union[dict, str], content_type:str, verbose:bool=False): + """ + Exports the specified artifact data to a JSON file. + """ + + with open(artifact_file_path, 'w', encoding="utf-8", errors="replace") as f: + if isinstance(artifact_data, dict): + json.dump(artifact_data, f, indent=4) + else: + raise ValueError("The artifact data must be a dictionary to export to JSON.") + + def _export_as_docx(self, artifact_file_path:str, artifact_data:Union[dict, str], content_original_format:str, verbose:bool=False): + """ + Exports the specified artifact data to a DOCX file. + """ + + # original format must be 'text' or 'markdown' + if content_original_format not in ['text', 'txt', 'markdown', 'md']: + raise ValueError(f"The original format cannot be {content_original_format} to export to DOCX.") + else: + # normalize content value + content_original_format = 'markdown' if content_original_format == 'md' else content_original_format + + # first, get the content to export. If `artifact_date` is a dict, the contant should be under the key `content`. + # If it is a string, the content is the string itself. + # using pypandoc + if isinstance(artifact_data, dict): + content = artifact_data['content'] + else: + content = artifact_data + + # first, convert to HTML. This is necessary because pypandoc does not support a GOOD direct conversion from markdown to DOCX. + html_content = markdown.markdown(content) + + ## write this intermediary HTML to file + #html_file_path = artifact_file_path.replace(".docx", ".html") + #with open(html_file_path, 'w', encoding="utf-8", errors="replace") as f: + # f.write(html_content) + + # then, convert to DOCX + pypandoc.convert_text(html_content, 'docx', format='html', outputfile=artifact_file_path) + + ########################################################### + # IO + ########################################################### + + def _compose_filepath(self, artifact_data:Union[dict, str], artifact_name:str, content_type:str, target_format:str=None, verbose:bool=False): + """ + Composes the file path for the artifact to export. + + Args: + artifact_data (Union[dict, str]): The data to export. + artifact_name (str): The name of the artifact. + content_type (str): The type of the content within the artifact. + content_format (str, optional): The format of the content within the artifact (e.g., md, csv, etc). Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + # Extension definition: + # + # - If the content format is specified, we use it as the part of the extension. + # - If artificat_data is a dict, we add .json to the extension. Note that if content format was specified, we'd get .json. + # - If artifact_data is a string and no content format is specified, we add .txt to the extension. + extension = None + if target_format is not None: + extension = f"{target_format}" + elif isinstance(artifact_data, str) and target_format is None: + extension = "txt" + + # content type definition + if content_type is None: + subfolder = "" + else: + subfolder = content_type + + # save to the specified file name or path, considering the base output folder. + artifact_file_path = os.path.join(self.base_output_folder, subfolder, f"{artifact_name}.{extension}") + + # create intermediate directories if necessary + os.makedirs(os.path.dirname(artifact_file_path), exist_ok=True) + + return artifact_file_path \ No newline at end of file diff --git a/extraction/normalizer.py b/extraction/normalizer.py new file mode 100644 index 0000000000000000000000000000000000000000..9ccd50ba3b65d5f2109c004524ba9be7a9cdef37 --- /dev/null +++ b/extraction/normalizer.py @@ -0,0 +1,115 @@ +import pandas as pd +from typing import Union, List + +from tinytroupe.extraction import logger + +from tinytroupe import openai_utils +import tinytroupe.utils as utils +class Normalizer: + """ + A mechanism to normalize passages, concepts and other textual elements. + """ + + def __init__(self, elements:List[str], n:int, verbose:bool=False): + """ + Normalizes the specified elements. + + Args: + elements (list): The elements to normalize. + n (int): The number of normalized elements to output. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + # ensure elements are unique + self.elements = list(set(elements)) + + self.n = n + self.verbose = verbose + + # a JSON-based structure, where each output element is a key to a list of input elements that were merged into it + self.normalized_elements = None + # a dict that maps each input element to its normalized output. This will be used as cache later. + self.normalizing_map = {} + + rendering_configs = {"n": n, + "elements": self.elements} + + messages = utils.compose_initial_LLM_messages_with_templates("normalizer.system.mustache", "normalizer.user.mustache", + base_module_folder="extraction", + rendering_configs=rendering_configs) + + next_message = openai_utils.client().send_message(messages, temperature=0.1) + + debug_msg = f"Normalization result message: {next_message}" + logger.debug(debug_msg) + if self.verbose: + print(debug_msg) + + result = utils.extract_json(next_message["content"]) + logger.debug(result) + if self.verbose: + print(result) + + self.normalized_elements = result + + + def normalize(self, element_or_elements:Union[str, List[str]]) -> Union[str, List[str]]: + """ + Normalizes the specified element or elements. + + This method uses a caching mechanism to improve performance. If an element has been normalized before, + its normalized form is stored in a cache (self.normalizing_map). When the same element needs to be + normalized again, the method will first check the cache and use the stored normalized form if available, + instead of normalizing the element again. + + The order of elements in the output will be the same as in the input. This is ensured by processing + the elements in the order they appear in the input and appending the normalized elements to the output + list in the same order. + + Args: + element_or_elements (Union[str, List[str]]): The element or elements to normalize. + + Returns: + str: The normalized element if the input was a string. + list: The normalized elements if the input was a list, preserving the order of elements in the input. + """ + if isinstance(element_or_elements, str): + denormalized_elements = [element_or_elements] + elif isinstance(element_or_elements, list): + denormalized_elements = element_or_elements + else: + raise ValueError("The element_or_elements must be either a string or a list.") + + normalized_elements = [] + elements_to_normalize = [] + for element in denormalized_elements: + if element not in self.normalizing_map: + elements_to_normalize.append(element) + + if elements_to_normalize: + rendering_configs = {"categories": self.normalized_elements, + "elements": elements_to_normalize} + + messages = utils.compose_initial_LLM_messages_with_templates("normalizer.applier.system.mustache", "normalizer.applier.user.mustache", + base_module_folder="extraction", + rendering_configs=rendering_configs) + + next_message = openai_utils.client().send_message(messages, temperature=0.1) + + debug_msg = f"Normalization result message: {next_message}" + logger.debug(debug_msg) + if self.verbose: + print(debug_msg) + + normalized_elements_from_llm = utils.extract_json(next_message["content"]) + assert isinstance(normalized_elements_from_llm, list), "The normalized element must be a list." + assert len(normalized_elements_from_llm) == len(elements_to_normalize), "The number of normalized elements must be equal to the number of elements to normalize." + + for i, element in enumerate(elements_to_normalize): + normalized_element = normalized_elements_from_llm[i] + self.normalizing_map[element] = normalized_element + + for element in denormalized_elements: + normalized_elements.append(self.normalizing_map[element]) + + return normalized_elements + diff --git a/extraction/prompts/interaction_results_extractor.mustache b/extraction/prompts/interaction_results_extractor.mustache new file mode 100644 index 0000000000000000000000000000000000000000..b4bfbea6faa90f6ae4f72a72ba86383bc3106c24 --- /dev/null +++ b/extraction/prompts/interaction_results_extractor.mustache @@ -0,0 +1,68 @@ +# Results filter + +You are a system to extract specific choices, information or results that one or more agents generated under a situation. + +You must: + - Extact only what is requested, as closely as possible. + - If the information requested is not present, you just return an empty result (null). + - Output in JSON format, **ALWAYS**, without any extra text or markings. + +Your inputs are: + - An interaction history of one or more agents, which might include, for each agent, both stimuli it receives and actions it performs. + - An extraction objective, which defines precisely what is to be extracted. If the desired information is not present, you + resturn an empty result. + - A situation which explains what were the conditions the agents were subject to. + +On your output format: + - You always output JSON strings. + - If the output contains only one result, but with one or more fields, your output **MUST** follows this format: + ```json + {: , :, ..., :} + ``` + - On the other hand, **ONLY** if the output contains multiple results, your output follows this format: + ```json + [ + {: , :, ..., :}, + ..., + {: , :, ..., :} + ] + ``` + - NEVER output a single result within a top-level list like this: ```[]```. Always output `````` directly. + - **DO NOT** include ```json or ``` tags; just include the actual JSON string. + {{#fields}} + - , , ... **must** be the following: {{fields}} + {{/fields}} + + {{#fields_hints}} + - Additional contraint for field `{{0}}`: {{1}} + {{/fields_hints}} + + + +## Examples + +### Example 1 + +Example input: + Extraction objective: obtain the baby product that was purchased (field "choice"). + + Situation: you have a baby at home, just a little money right now, and you need to buy only what is urgent. + + [TALK] Since I have little money, I must prioritize what I'll buy. Diapers are really the most important thing, so I'll take them today. + > I'll buy the formula tomorrow instead. + +Example output: +{"choice": "Diapers"} + + +### Example 2 +Example input: + Extraction objective: obtain the baby product that was purchased (field "choice"). + + Situation: you have a baby at home, just a little money right now, and you need to buy only what is urgent. + + [TALK] Actually, there's nothing that I need right now. I wanted diapers, but they don't have the right size. So I won't buy anything. + +Example output: +{"choice": null} + diff --git a/extraction/prompts/normalizer.applier.system.mustache b/extraction/prompts/normalizer.applier.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..195a0ef2e19e083225eb831cdaabf2c1ad0d2f10 --- /dev/null +++ b/extraction/prompts/normalizer.applier.system.mustache @@ -0,0 +1,17 @@ +# Normalizer + +You are a system that normalizes text data. This means that: + - You receive an input list of items to be categorized. + - You already have a list of categories that you consider standard. + - For each input item to be categorized, you assign it to the category that has the most similarity to it. This is your output. + - For each input item, you will produce exactly one output. That is to say, each input element will be replaced by exactly one of the categories. + You might need to repeat elements in your output, since different input elements might map to the same category. + - The number of output items is the same as the number of input items. + +On the format of your output: + - you return a JSON structure listing the resulting output elements; + - for example, given an input of `["cake", "gazpacho", "cat", "christmas tree", "napolitana"]`, and having the categorie `["Food", "Fauna & Flora"]`, + the output would look like this: + ```json + ["Food", "Food", "Fauna & Flora", "Fauna & Flora", "Food"] + ``` \ No newline at end of file diff --git a/extraction/prompts/normalizer.applier.user.mustache b/extraction/prompts/normalizer.applier.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..f72f38349dbe2d3106479dc75f739cd36586d3cc --- /dev/null +++ b/extraction/prompts/normalizer.applier.user.mustache @@ -0,0 +1,10 @@ +The standard categories to consider are these: +{{#categories}} + - {{.}} +{{/categories}} + +Now please map the following input elements to the categories above: + +{{#elements}} + - {{.}} +{{/elements}} \ No newline at end of file diff --git a/extraction/prompts/normalizer.system.mustache b/extraction/prompts/normalizer.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..7f3385e01057e71ac422d262e2cd187b737e6b79 --- /dev/null +++ b/extraction/prompts/normalizer.system.mustache @@ -0,0 +1,27 @@ +# Normalizer + +You are a system that normalizes text data. This means that: + - you receive a list of input textual elements, such as concepts, passages or phrases; + - you receive a number of the desired output elements; + - and then you merge the input elements into the desired number of output elements. + - you must ensure that all input elements are properly partitioned in the output elements, without any overlap, and without any element being left out. + - output elements must subsume the input elements that correspond to them, that is, they must be maximally similar to all of them. + +The merging is done by: + - determining which input elements are similar to each other; + - input elements are grouped together according to how similar they are, and each such group is mapped to a single output element; + - make sure you produce the desired number of output elements. + - make sure you ensure that the output elements are unique. + - if the number of input elements is equal to or less than the desired number of output elements, you must return the input elements as output elements without modification. + +The abstract representation is created by: + - if the elements are concepts or otherwise very short, you must find a concept that subsumes them; + - if the elements are passages or otherwise longer, you must find a passage that subsumes them, that is maximally similar to all of them. + +On the format of your output: + - you return a JSON structure listing the resulting output elements; + - for example, given an input of `[INPUT_1, INPUT_2, INPUT_3, INPUT_4]`, and the number of 2 desired output elements, the system output could look like this: + ```json + [OUTPUT_1, OUTPUT_2] + ``` + diff --git a/extraction/prompts/normalizer.user.mustache b/extraction/prompts/normalizer.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..c8a3289683d31c892bce9a006bcb7579126892bb --- /dev/null +++ b/extraction/prompts/normalizer.user.mustache @@ -0,0 +1,5 @@ +Now please produce {{n}} normalized elements that represent the following input elements: + +{{#elements}} + - {{.}} +{{/elements}} \ No newline at end of file diff --git a/extraction/results_extractor.py b/extraction/results_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..4ccacc181bc227434ea19bead439147e6861b5e8 --- /dev/null +++ b/extraction/results_extractor.py @@ -0,0 +1,268 @@ +import os +import json +import chevron +import pandas as pd +from typing import Union, List + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld + +from tinytroupe import openai_utils +import tinytroupe.utils as utils + + +class ResultsExtractor: + + def __init__(self, + extraction_prompt_template_path:str = os.path.join(os.path.dirname(__file__), './prompts/interaction_results_extractor.mustache'), + extraction_objective:str = "The main points present in the agents' interactions history.", + situation:str = "", + fields:List[str] = None, + fields_hints:dict = None, + verbose:bool = False): + """ + Initializes the ResultsExtractor with default parameters. + + Args: + extraction_prompt_template_path (str): The path to the extraction prompt template. + extraction_objective (str): The default extraction objective. + situation (str): The default situation to consider. + fields (List[str], optional): The default fields to extract. Defaults to None. + fields_hints (dict, optional): The default hints for the fields to extract. Defaults to None. + verbose (bool, optional): Whether to print debug messages by default. Defaults to False. + """ + self._extraction_prompt_template_path = extraction_prompt_template_path + + # Default parameters + self.default_extraction_objective = extraction_objective + self.default_situation = situation + self.default_fields = fields + self.default_fields_hints = fields_hints + self.default_verbose = verbose + + # Cache for the last extraction results + self.agent_extraction = {} + self.world_extraction = {} + + def extract_results_from_agents(self, + agents:List[TinyPerson], + extraction_objective:str=None, + situation:str =None, + fields:list=None, + fields_hints:dict=None, + verbose:bool=None): + """ + Extracts results from a list of TinyPerson instances. + + Args: + agents (List[TinyPerson]): The list of TinyPerson instances to extract results from. + extraction_objective (str): The extraction objective. + situation (str): The situation to consider. + fields (list, optional): The fields to extract. If None, the extractor will decide what names to use. + Defaults to None. + fields_hints (dict, optional): Hints for the fields to extract. Maps field names to strings with the hints. Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + + + """ + results = [] + for agent in agents: + result = self.extract_results_from_agent(agent, extraction_objective, situation, fields, fields_hints, verbose) + results.append(result) + + return results + + def extract_results_from_agent(self, + tinyperson:TinyPerson, + extraction_objective:str="The main points present in the agent's interactions history.", + situation:str = "", + fields:list=None, + fields_hints:dict=None, + verbose:bool=None): + """ + Extracts results from a TinyPerson instance. + + Args: + tinyperson (TinyPerson): The TinyPerson instance to extract results from. + extraction_objective (str): The extraction objective. + situation (str): The situation to consider. + fields (list, optional): The fields to extract. If None, the extractor will decide what names to use. + Defaults to None. + fields_hints (dict, optional): Hints for the fields to extract. Maps field names to strings with the hints. Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + extraction_objective, situation, fields, fields_hints, verbose = self._get_default_values_if_necessary( + extraction_objective, situation, fields, fields_hints, verbose + ) + + messages = [] + + rendering_configs = {} + if fields is not None: + rendering_configs["fields"] = ", ".join(fields) + + if fields_hints is not None: + rendering_configs["fields_hints"] = list(fields_hints.items()) + + messages.append({"role": "system", + "content": chevron.render( + open(self._extraction_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + + + interaction_history = tinyperson.pretty_current_interactions(max_content_length=None) + + extraction_request_prompt = \ +f""" +## Extraction objective + +{extraction_objective} + +## Situation +You are considering a single agent, named {tinyperson.name}. Your objective thus refers to this agent specifically. +{situation} + +## Agent Interactions History + +You will consider an agent's history of interactions, which include stimuli it received as well as actions it +performed. + +{interaction_history} +""" + messages.append({"role": "user", "content": extraction_request_prompt}) + + next_message = openai_utils.client().send_message(messages, temperature=0.0, frequency_penalty=0.0, presence_penalty=0.0) + + debug_msg = f"Extraction raw result message: {next_message}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + if next_message is not None: + result = utils.extract_json(next_message["content"]) + else: + result = None + + # cache the result + self.agent_extraction[tinyperson.name] = result + + return result + + + def extract_results_from_world(self, + tinyworld:TinyWorld, + extraction_objective:str="The main points that can be derived from the agents conversations and actions.", + situation:str="", + fields:list=None, + fields_hints:dict=None, + verbose:bool=None): + """ + Extracts results from a TinyWorld instance. + + Args: + tinyworld (TinyWorld): The TinyWorld instance to extract results from. + extraction_objective (str): The extraction objective. + situation (str): The situation to consider. + fields (list, optional): The fields to extract. If None, the extractor will decide what names to use. + Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + extraction_objective, situation, fields, fields_hints, verbose = self._get_default_values_if_necessary( + extraction_objective, situation, fields, fields_hints, verbose + ) + + messages = [] + + rendering_configs = {} + if fields is not None: + rendering_configs["fields"] = ", ".join(fields) + + if fields_hints is not None: + rendering_configs["fields_hints"] = list(fields_hints.items()) + + messages.append({"role": "system", + "content": chevron.render( + open(self._extraction_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + + # TODO: either summarize first or break up into multiple tasks + interaction_history = tinyworld.pretty_current_interactions(max_content_length=None) + + extraction_request_prompt = \ +f""" +## Extraction objective + +{extraction_objective} + +## Situation +You are considering various agents. +{situation} + +## Agents Interactions History + +You will consider the history of interactions from various agents that exist in an environment called {tinyworld.name}. +Each interaction history includes stimuli the corresponding agent received as well as actions it performed. + +{interaction_history} +""" + messages.append({"role": "user", "content": extraction_request_prompt}) + + next_message = openai_utils.client().send_message(messages, temperature=0.0) + + debug_msg = f"Extraction raw result message: {next_message}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + if next_message is not None: + result = utils.extract_json(next_message["content"]) + else: + result = None + + # cache the result + self.world_extraction[tinyworld.name] = result + + return result + + def save_as_json(self, filename:str, verbose:bool=False): + """ + Saves the last extraction results as JSON. + + Args: + filename (str): The filename to save the JSON to. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + with open(filename, 'w', encoding="utf-8", errors="replace") as f: + json.dump({"agent_extractions": self.agent_extraction, + "world_extraction": self.world_extraction}, f, indent=4) + + if verbose: + print(f"Saved extraction results to {filename}") + + def _get_default_values_if_necessary(self, + extraction_objective:str, + situation:str, + fields:List[str], + fields_hints:dict, + verbose:bool): + + if extraction_objective is None: + extraction_objective = self.default_extraction_objective + + if situation is None: + situation = self.default_situation + + if fields is None: + fields = self.default_fields + + if fields_hints is None: + fields_hints = self.default_fields_hints + + if verbose is None: + verbose = self.default_verbose + + return extraction_objective, situation, fields, fields_hints, verbose + diff --git a/extraction/results_reducer.py b/extraction/results_reducer.py new file mode 100644 index 0000000000000000000000000000000000000000..66405db38d876b770e5d42aac2925b71f435df0e --- /dev/null +++ b/extraction/results_reducer.py @@ -0,0 +1,55 @@ +import pandas as pd + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson + + +class ResultsReducer: + + def __init__(self): + self.results = {} + + self.rules = {} + + def add_reduction_rule(self, trigger: str, func: callable): + if trigger in self.rules: + raise Exception(f"Rule for {trigger} already exists.") + + self.rules[trigger] = func + + def reduce_agent(self, agent: TinyPerson) -> list: + reduction = [] + for message in agent.episodic_memory.retrieve_all(): + if message['role'] == 'system': + continue # doing nothing for `system` role yet at least + + elif message['role'] == 'user': + # User role is related to stimuli only + stimulus_type = message['content']['stimuli'][0].get('type', None) + stimulus_content = message['content']['stimuli'][0].get('content', None) + stimulus_source = message['content']['stimuli'][0].get('source', None) + stimulus_timestamp = message['simulation_timestamp'] + + if stimulus_type in self.rules: + extracted = self.rules[stimulus_type](focus_agent=agent, source_agent=TinyPerson.get_agent_by_name(stimulus_source), target_agent=agent, kind='stimulus', event=stimulus_type, content=stimulus_content, timestamp=stimulus_timestamp) + if extracted is not None: + reduction.append(extracted) + + elif message['role'] == 'assistant': + # Assistant role is related to actions only + if 'action' in message['content']: + action_type = message['content']['action'].get('type', None) + action_content = message['content']['action'].get('content', None) + action_target = message['content']['action'].get('target', None) + action_timestamp = message['simulation_timestamp'] + + if action_type in self.rules: + extracted = self.rules[action_type](focus_agent=agent, source_agent=agent, target_agent=TinyPerson.get_agent_by_name(action_target), kind='action', event=action_type, content=action_content, timestamp=action_timestamp) + if extracted is not None: + reduction.append(extracted) + + return reduction + + def reduce_agent_to_dataframe(self, agent: TinyPerson, column_names: list=None) -> pd.DataFrame: + reduction = self.reduce_agent(agent) + return pd.DataFrame(reduction, columns=column_names) diff --git a/extraction/results_reporter.py b/extraction/results_reporter.py new file mode 100644 index 0000000000000000000000000000000000000000..bb3b0fc18c09247d892c2462c5f71a931c8409b4 --- /dev/null +++ b/extraction/results_reporter.py @@ -0,0 +1,444 @@ +import os +import json +from typing import Union, List, Dict, Any +from rich.console import Console +from rich.markdown import Markdown + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld +from tinytroupe.utils import LLMChat +from tinytroupe import default + + +class ResultsReporter: + + def __init__(self, + default_reporting_task: str = "Summarize the key findings, insights, and outcomes from the simulation data.", + verbose: bool = False): + """ + Initializes the ResultsReporter. + + Args: + default_reporting_task (str): The default task to ask agents when generating reports. + verbose (bool): Whether to print debug messages. + """ + self.default_reporting_task = default_reporting_task + self.verbose = verbose + self.console = Console() + + # Cache for generated reports + self.last_report = None + + def report_from_agents(self, + agents: Union[TinyPerson, TinyWorld, List[TinyPerson]], + reporting_task: str = None, + report_title: str = "Simulation Report", + include_agent_summaries: bool = True, + consolidate_responses: bool = True, + requirements: str = "Present the findings in a clear, structured manner.") -> str: + """ + Option 1: Generate a report by asking agents about specific reporting tasks. + + Args: + agents: Single agent, TinyWorld, or list of agents to interview. + reporting_task: The specific task to ask agents about. + report_title: Title for the generated report. + include_agent_summaries: Whether to include agent mini-bios in the report. + consolidate_responses: Whether to consolidate all responses into a single report. + requirements: Formatting or content requirements for the report. + + Returns: + str: The generated Markdown report. + """ + if reporting_task is None: + reporting_task = self.default_reporting_task + + # Extract agents from input + agent_list = self._extract_agents(agents) + + if self.verbose: + logger.info(f"Interviewing {len(agent_list)} agents for report generation.") + + # Collect responses from agents + agent_responses = [] + for agent in agent_list: + response = self._interview_agent(agent, reporting_task) + agent_responses.append({ + "agent": agent, + "response": response + }) + + # Generate the report + report = self._format_agent_interview_report( + agent_responses, + report_title, + reporting_task, + include_agent_summaries, + consolidate_responses, + requirements + ) + + self.last_report = report + return report + + def report_from_interactions(self, + agents: Union[TinyPerson, TinyWorld, List[TinyPerson]], + report_title: str = "Interaction Analysis Report", + include_agent_summaries: bool = True, + first_n: int = None, + last_n: int = None, + max_content_length: int = None, + requirements: str = "Present the findings in a clear, structured manner.") -> str: + """ + Option 2: Generate a report by analyzing agents' historical interactions. + + Args: + agents: Single agent, TinyWorld, or list of agents to analyze. + report_title: Title for the generated report. + include_agent_summaries: Whether to include agent mini-bios. + first_n: Number of first interactions to include. + last_n: Number of last interactions to include. + max_content_length: Maximum content length for interactions. + requirements: Formatting or content requirements for the report. + + Returns: + str: The generated Markdown report. + """ + # Extract agents from input + agent_list = self._extract_agents(agents) + + if self.verbose: + logger.info(f"Analyzing interactions from {len(agent_list)} agents.") + + # Collect interaction data + interactions_data = [] + for agent in agent_list: + interactions = agent.pretty_current_interactions( + simplified=True, + first_n=first_n, + last_n=last_n, + max_content_length=max_content_length + ) + interactions_data.append({ + "agent": agent, + "interactions": interactions + }) + + # Generate the report + report = self._format_interactions_report( + interactions_data, + report_title, + include_agent_summaries, + requirements + ) + + self.last_report = report + return report + + def report_from_data(self, + data: Union[str, Dict[str, Any], List[Dict[str, Any]]], + report_title: str = "Data Report", + requirements: str = "Present the findings in a clear, structured manner.") -> str: + """ + Option 3: Generate a report from raw text or structured data. + + Args: + data: Raw text, dictionary, or list of dictionaries to format. + report_title: Title for the generated report. + requirements: Formatting or content requirements for the report. If None, uses simple formatting. + + Returns: + str: The generated Markdown report. + """ + if self.verbose: + logger.info("Generating report from raw data.") + + # Generate the report + report = self._format_data_report(data, report_title, requirements) + + self.last_report = report + return report + + def display_report(self, report: str = None): + """ + Display a report on the console with rich formatting. + + Args: + report: The report to display. If None, uses the last generated report. + """ + if report is None: + report = self.last_report + + if report is None: + self.console.print("[red]No report available to display.[/red]") + return + + markdown = Markdown(report) + self.console.print(markdown) + + def save_report(self, + filename: str, + report: str = None, + verbose: bool = None): + """ + Save a report to a file. + + Args: + filename: The filename to save the report to. + report: The report to save. If None, uses the last generated report. + verbose: Whether to print confirmation message. + """ + if report is None: + report = self.last_report + + if report is None: + raise ValueError("No report available to save.") + + if verbose is None: + verbose = self.verbose + + with open(filename, 'w', encoding='utf-8', errors='replace') as f: + f.write(report) + + if verbose: + logger.info(f"Report saved to {filename}") + + def _extract_agents(self, agents) -> List[TinyPerson]: + """Extract a list of TinyPerson objects from various input types.""" + if isinstance(agents, TinyPerson): + return [agents] + elif isinstance(agents, TinyWorld): + return agents.agents + elif isinstance(agents, list): + return agents + else: + raise ValueError("Agents must be a TinyPerson, TinyWorld, or list of TinyPerson objects.") + + def _interview_agent(self, agent: TinyPerson, reporting_task: str) -> str: + """Interview a single agent about the reporting task.""" + if self.verbose: + logger.debug(f"Interviewing agent {agent.name} about: {reporting_task}") + + # Following TinyTroupe patterns - directly interact with the agent + prompt = f""" + I need you to provide a comprehensive report based on your experiences and observations. + + Reporting task: {reporting_task} + + Please provide detailed insights, specific examples, and key findings from your perspective. + Focus on what you've learned, observed, and experienced during the simulation. + """ + + # Use listen_and_act pattern to get agent's response + agent.listen(prompt) + actions = agent.act(return_actions=True) + + # Extract the response from the agent's actions + response = "" + for action in actions: + if action["action"]["type"] == "TALK": + response += action["action"]["content"] + "\n" + + if self.verbose: + logger.debug(f"Agent {agent.name} response received.") + + return response.strip() + + def _format_agent_interview_report(self, + agent_responses: List[Dict], + title: str, + task: str, + include_summaries: bool, + consolidate: bool, + requirements: str) -> str: + """Format agent interview responses into a Markdown report.""" + # Prepare data for LLM formatting + agents_data = [] + for resp in agent_responses: + agent_info = { + "name": resp["agent"].name, + "response": resp["response"] + } + if include_summaries: + agent_info["bio"] = resp["agent"].minibio(extended=False) + agents_data.append(agent_info) + + # Generate report using LLM + return self._generate_report_with_llm( + title=title, + report_type="agent_interview", + data={ + "reporting_task": task, + "agents_data": agents_data, + "consolidate": consolidate + }, + include_summaries=include_summaries, + requirements=requirements + ) + + def _format_interactions_report(self, + interactions_data: List[Dict], + title: str, + include_summaries: bool, + requirements: str) -> str: + """Format interaction data into a Markdown report.""" + # Prepare data for LLM formatting + agents_data = [] + for data in interactions_data: + agent_info = { + "name": data["agent"].name, + "interactions": data["interactions"] + } + if include_summaries: + agent_info["bio"] = data["agent"].minibio(extended=False) + agents_data.append(agent_info) + + # Generate report using LLM + return self._generate_report_with_llm( + title=title, + report_type="interactions", + data={"agents_data": agents_data}, + include_summaries=include_summaries, + requirements=requirements + ) + + def _format_data_report(self, + data: Any, + title: str, + requirements: str) -> str: + """Format raw data into a Markdown report.""" + return self._generate_report_with_llm( + title=title, + report_type="custom_data", + data=data, + requirements=requirements + ) + + + def _generate_report_with_llm(self, + title: str, + report_type: str, + data: Any, + include_summaries: bool = False, + requirements: str = None) -> str: + """Generate a report using LLM based on the report type and data.""" + + # Base system prompt + system_prompt = "You are a professional report writer who creates clear, well-structured Markdown reports." + + # Type-specific prompts and instructions + if report_type == "agent_interview": + system_prompt += " You specialize in synthesizing interview responses from multiple agents." + user_prompt = f""" + ## Task + Create a comprehensive report based on agent interviews such that it fulfills the + specified requirements below. + + ## Report Title + {title} + + ## Report Details + - **Reporting Task:** {data['reporting_task']} + - **Number of Agents Interviewed:** {len(data['agents_data'])} + - **Generated on:** {self._get_timestamp()} + + ## Agent Responses + {json.dumps(data['agents_data'], indent=2)} + + ## Instructions + - Start with the title as a level-1 header + - Write a direct, clear report, but do not simplify or summarize the information + - Make sure all important details are included. This is not a summary, but a detailed report, so you never remove information, you just make it more readable + - Do not include the original data or agent responses, but only the resulting report information + - For each agent, include their bio if provided + - Use proper Markdown formatting throughout + - Follow the requirements given next, which can also override any of these rules + + ## Requirements + {requirements} + """ + + elif report_type == "interactions": + system_prompt += " You specialize in analyzing and presenting agent interaction histories." + user_prompt = f""" + ## Task + Create a report analyzing agent interactions from a simulation such that it fulfills the + specified requirements below. + + ## Report Title + {title} + + ## Report Details + - **Number of Agents Analyzed:** {len(data['agents_data'])} + - **Generated on:** {self._get_timestamp()} + + ## Agent Interaction Data + {json.dumps(data['agents_data'], indent=2)} + + ## Instructions + - Start with the title as a level-1 header + - Write a direct, clear report, but do not simplify or summarize the information + - Make sure all important details are included. This is not a summary, but a detailed report, so you never remove information, you just make it more readable + - Do not include agents' interaction history, but only the resulting report information + - For each agent, include their bio if provided + - Use proper Markdown formatting throughout + - Follow the requirements given next, which can also override any of these rules + + ## Requirements + {requirements} + """ + + elif report_type == "custom_data": + # Handle arbitrary data without assuming any structure + if isinstance(data, str): + data_representation = data + else: + # For any other type, convert to JSON for a clean representation + data_representation = json.dumps(data, indent=2) + + user_prompt = f""" + ## Task + Create a well-structured Markdown report based on the provided data such that it fulfills the + specified requirements below. + + ## Report Title + {title} + + ## Generated on + {self._get_timestamp()} + + ## Data to Format + {data_representation} + + ## Instructions + - Start with the title as a level-1 header + - Write a direct, clear report, but do not simplify or summarize the information + - Make sure all important details are included. This is not a summary, but a detailed report, so you never remove information, you just make it more readable + - Use proper Markdown formatting throughout + - Follow the requirements given next, which can also override any of these rules + + ## Requirements + {requirements if requirements else "Use your best judgment to create a clear, informative report that presents the data in an organized and readable manner."} + """ + + else: + raise ValueError(f"Unknown report type: {report_type}") + + # Generate the report + report_chat = LLMChat( + system_prompt=system_prompt, + user_prompt=user_prompt, + output_type=str, + enable_json_output_format=False, + model=default["model"], + temperature=0.3 + ) + + return report_chat() + + + def _get_timestamp(self) -> str: + """Get current timestamp for report headers.""" + from datetime import datetime + return datetime.now().strftime("%Y-%m-%d %H:%M:%S") diff --git a/factory/__init__.py b/factory/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5ea1398d64f278afd1b27fa87c86ecfa45baa0f2 --- /dev/null +++ b/factory/__init__.py @@ -0,0 +1,15 @@ +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import utils, config_manager + +# We'll use various configuration elements below +config = utils.read_config_file() + + +########################################################################### +# Exposed API +########################################################################### +from .tiny_person_factory import TinyPersonFactory + +__all__ = ["TinyPersonFactory"] \ No newline at end of file diff --git a/factory/__pycache__/__init__.cpython-312.pyc b/factory/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eab6e939eb4319fcfa2de3f63dbd8e58f24b687a Binary files /dev/null and b/factory/__pycache__/__init__.cpython-312.pyc differ diff --git a/factory/__pycache__/tiny_factory.cpython-312.pyc b/factory/__pycache__/tiny_factory.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8b529c7e8feef59a23d9c2db0eb307f601392a03 Binary files /dev/null and b/factory/__pycache__/tiny_factory.cpython-312.pyc differ diff --git a/factory/__pycache__/tiny_person_factory.cpython-312.pyc b/factory/__pycache__/tiny_person_factory.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..095fc852ea0685cba2823cb97c38ec842934ca8d Binary files /dev/null and b/factory/__pycache__/tiny_person_factory.cpython-312.pyc differ diff --git a/factory/prompts/generate_person.mustache b/factory/prompts/generate_person.mustache new file mode 100644 index 0000000000000000000000000000000000000000..2751eb37ca3ac98f625563c79d6d70bdc14a9c63 --- /dev/null +++ b/factory/prompts/generate_person.mustache @@ -0,0 +1,111 @@ +# Agent Generator + +Please generate a highly detailed agent specification based on a general context and the particularities of the agent (if any). The generated agent specification will be used in a simulation to realistically represent a real person. Therefore, it must include a wide range of nuanced details that are consistent with the provided context and agent particularities. + +## General Context + +{{#context}} +The general context is the following: {{context}}. +{{/context}} +{{^context}} +No general context was specified. +{{/context}} + + +## Agent Particularities +{{#agent_particularities}} +The agent particularities, in turn, are: {{agent_particularities}}. + +These are the specific requirements that you must consider, together with the general context, when generating the agent. Note that for each particularity we can have: + - A concrete scalar value for a field (e.g., "Architect" for job): in this case, the agent must have that value for the corresponding field. + - A range of numberic values (e.g., [18, 60] for age): in this case, the agent must have a value for the corresponding field that is within that range. You are free to choose the specific value, as long as it is within the range. + - A list of values (e.g., ["male", "female"] `for gender): in this case, the agent must have a value for the corresponding field that is one of the values in the list. You are free to choose the specific value, as long as it is one of the values in the list. + - A list of values associated to proportions (e.g., {"rich": 0.1, "middle class":0.7, "poor": 0.2} for wealth): in this case, the agent must have a value for the corresponding field that is one of the values in the list, sampled according to the specified proportions. + - A broader characteristic descrition (e.g., "Meticulous and competent, but not a very nice person at all."): in this case, the agent must have values for the corresponding fields that are consistent with the requirements. You are free to choose the specific values, as long as they are consistent with the requirements. Try to make the specification as rich as possible based on this. +{{/agent_particularities}} +{{^agent_particularities}} +There are no agent particularities in this case, so just generate an agent based on the general +context (if any). +{{/agent_particularities}} + +## Generation Rules + +To generate the requested agents, you **must** to follow these directives: + - If a general context is provided, you must ensure the generated agent has relevant details that will allow the a realistic simulation of a real person in that context. To do so, you should include nunanced details that could be relevant for the simulation in that context. + - Given the agent particularities, please, be creative and deep to generate details about the person for each of the fields in the response. Sample from a wide range of reasonable possibilities. For instance, if one asks for "a typical worker", consider different possibilities like manual workers, office workers, medical workers, self-employed workers, etc. + - You must add as much details as possible, but make sure they are consistent with the context and agent particularities. That is to say, the details can be completely unrelated to the context and agent particularities, but they must not contradict them. + - The agents you generate must sound authentic and realistic, considering the context and particularities provided. Avoid platitudes and cliches. The agents you generate represent real humans, and as such encompass all the diversity and complexity of real people. So not only happy and successful people, but also people with problems, fears, and difficulties. + - When generating a person specification, be free to use whatever elements are most realistic, even if they are typically considered undesireable (e.g., vicious people are part of the general population). We need a realistic sample of people, not a caricature. So think deeply about what would make each person you generate authentic with respect the provided context and particularities. + - There are many ways to instantiate the context and agent particularities into a specific concrete agent, so avoid common stereotypes and cliches. Instead, try to think deeply about the context and agent particularities, and how they would manifest in a real person, and thus create an authentic-looking agent. + - Some fields are naturally simple, like name, age, gender, nationality and residence. Others are more complex, like education, occupation, style, personality, preferences, beliefs, skills, behaviors, health, relationships and other facts. For these complex fields, you must provide a rich description with many details, + always consistent with the context and agent particularities. + +## Output Format Rules +Your output **must** follow these rules: + - You'll generate this response **only** in JSON format, no extra text, no Markdown elements. + - Make sure you **always** produce valid JSON. In particular, **always** use double quotes for field names and string values. + - The format for this JSON response is as described in the examples. At a minimum, the response **must** contain the following **mandatory** fields: + * "name" (simple field) + * "age" (simple field) + * "gender" (simple field) + * "nationality" (simple field) + * "residence" (simple field) + * "education" (complex field) + * "long_term_goals" (complex field): general aspirations for the future; life purposes. + * "occupation" (complex field): details of the person's job, company, profession or trade. Avoid fashionable or trendy jobs, + and instead focus on more traditional or realistic occupations, to the extent that + this is consistent with the context and agent particularities. So please less + "Enviromental Scientist"/"Sustainability Expert"/"Marketing Specialist" and more "University Professor"/"Construction Worker"/"IT Consultant". + Make sure to include details like the person's role, responsibilities, and any relevant skills or expertise. + * "style" (complex field): the person's general way of being, speaking, and behaving. Make sure to specify relevant accents, mannerisms, whether colloquial or formal is typical, etc. + We need A LOT of details here to be able to later produce a realistic simulation of the words and actions of the person. + * "personality" (very complex field): a detailed exploration of the person's character traits, including their temperament, emotional responses, and social behavior. + Include at least 10 traits, and also make sure you fill in the Big-5 personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) + with specific values for each trait. Make sure the personality described is not generic, but rather nuanced, deep, authentic, and realistic. + * "preferences" (very complex field): interests, things that the agent likes or dislikes. Can be both broad categories and specific items. If specific areas are requested, make sure to include many many + details about those areas. For example, if the agent is meant to like coffee shops, also include things like their favorite drinks, ambiance preferences, + any specific coffee shop chains they prefer, snacks they enjoy having together, whether they go there to work or socialize, etc. At least 20 details per area. Put more if + you can. + * "beliefs"(very complex field): deeply held convictions or opinions that influence the person's behavior and decision-making. Include many many details here, at least 30, so that we can later produce + a realistic simulation of the words and actions of the person. These details must explore in depth all the areas mentioned in the agent's particularities. + * "skills" (complex field): specific abilities or expertise that the person possesses, relevant to their personal or professional life. + * "behaviors" (complex field): typical actions, habits, rotines, or mannerisms that characterize the person. + * "health" (complex field): information about the person's physical and mental well-being, including any relevant medical history. + * "relationships" (complex field): details about the person's social connections, including family, friends, and professional contacts. + * "other_facts"(very complex field): anything that doesn't fit in the other fields and sections. This is where you should go wild and add many facts, ad-hoc details, past stories, + important memories, etc. Make this very long, at least 30 entries. + +DO NOT SPARE space for complex fields, use as much as you need to create a truly realistic person, with a lot of nuances, details, and depth. + +## Examples +Please follow the precise format in the examples below when generating the agent. Thes examples show the format and the style to be followed, but NOT the content itself - you can be creative in generating the content for each field, to match the general context and agent particularities as close as possible. +In particular, in your output, make sure you include much more detail than in the examples. + +### Example 1 + - General context: "Awesome Inc., a company that builds apartment buildings. Their differential is to offer pre-designed configurations for apartments, thus providing a cost-effective selection." + - Agent particularities: "A meticulous German architect. Competent, but not a very nice person at all." + - Example response: + ```json + {{{example_1}}} + ``` + + +### Example 2 + - General context: "Awesome Inc., a company that builds apartment buildings. Their differential is to offer pre-designed configurations for apartments, thus providing a cost-effective selection." + - Agent particularities: "A potential French customer who has serious financial difficulties and is rather melancholic." + - Example response: + ```json + {{{example_2}}} + ``` + +### Other persona examples +{{#other_examples}} + - ```json + {{{.}}} + ``` + +{{/other_examples}} +{{^other_examples}} +No other examples available. +{{/other_examples}} + diff --git a/factory/prompts/generate_person_factory.md b/factory/prompts/generate_person_factory.md new file mode 100644 index 0000000000000000000000000000000000000000..fb5c009df14ec1f69894bb6eb0c02940939581e2 --- /dev/null +++ b/factory/prompts/generate_person_factory.md @@ -0,0 +1,9 @@ +Your task is create many contexts that will be used as base to generate a list of persons. +The idea is receive a broad context, with some details of persons we want to generate, like demographics parameters, physical characteristics, behaviors, believes, etc; and then create many other contexts, more specifics, but derivaded of the more generic one. +Your response must be an array in JSON format. Each element of the array must be a context that will be used to generate a person description. + +Example: + - INPUT: + Please, generate 3 person(s) description(s) based on the following broad context: Latin American, age between 20 and 40 years old, economic status can vary between poor and rich, it can be religious or not, it can be married or not, it can have children or not, it can be a professional or not, it can be a worker or not + - OUTPUT: + ["Mexican person that has formed as lawyer but now works in other are, is single, like sports and movies", "Create a Brazilian person that is a doctor, like pets and the nature and love heavy metal.", "Create a Colombian person that is a lawyer, like to read and drink coffee and is married with 2 children."] diff --git a/factory/tiny_factory.py b/factory/tiny_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..ad3d4ce6a2416fb5aa9c8f5e80d18df7b4863833 --- /dev/null +++ b/factory/tiny_factory.py @@ -0,0 +1,96 @@ +import copy +import random + +from tinytroupe.factory import logger +import tinytroupe.utils as utils + +class TinyFactory: + """ + A base class for various types of factories. This is important because it makes it easier to extend the system, particularly + regarding transaction caching. + """ + + # common randomizer used for samplings, with a default initial seed to allow for reproducibility. + # subclases can use this directly as well. + randomizer = random.Random(42) + + # A dict of all factories created so far. + all_factories = {} # name -> factories + + def __init__(self, simulation_id:str=None) -> None: + """ + Initialize a TinyFactory instance. + + Args: + simulation_id (str, optional): The ID of the simulation. Defaults to None. + """ + self.name = f"Factory {utils.fresh_id(self.__class__.__name__)}" # we need a name, but no point in making it customizable + self.simulation_id = simulation_id + + TinyFactory.add_factory(self) + + def __repr__(self): + return f"TinyFactory(name='{self.name}')" + + @staticmethod + def set_simulation_for_free_factories(simulation): + """ + Sets the simulation if it is None. This allows free environments to be captured by specific simulation scopes + if desired. + """ + for factory in TinyFactory.all_factories.values(): + if factory.simulation_id is None: + simulation.add_factory(factory) + + @staticmethod + def add_factory(factory): + """ + Adds a factory to the list of all factories. Factory names must be unique, + so if an factory with the same name already exists, an error is raised. + """ + if factory.name in TinyFactory.all_factories: + raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") + else: + TinyFactory.all_factories[factory.name] = factory + + @classmethod + def clear_factories(cls): + """ + Clears the global list of all factories. + """ + cls.all_factories = {} + cls._clear_factories() + + @classmethod + def _clear_factories(cls): + """ + Additional cleanup actions can be performed here by subclasses if needed. + """ + pass + + ################################################################################################ + # Caching mechanisms + # + # Factories can also be cached in a transactional way. This is necessary because the agents they + # generate can be cached, and we need to ensure that the factory itself is also cached in a + # consistent way. + ################################################################################################ + + def encode_complete_state(self) -> dict: + """ + Encodes the complete state of the factory. If subclasses have elmements that are not serializable, they should override this method. + """ + + state = copy.deepcopy(self.__dict__) + return state + + def decode_complete_state(self, state:dict): + """ + Decodes the complete state of the factory. If subclasses have elmements that are not serializable, they should override this method. + """ + state = copy.deepcopy(state) + + self.__dict__.update(state) + return self + + diff --git a/factory/tiny_person_factory.py b/factory/tiny_person_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..5bf06890de3e87c68392ff2f21290efbe5a3435c --- /dev/null +++ b/factory/tiny_person_factory.py @@ -0,0 +1,1369 @@ +import os +import json +import chevron +import random +from typing import List, Dict, Union +import copy + +from .tiny_factory import TinyFactory +from tinytroupe.factory import logger +from tinytroupe import openai_utils +from tinytroupe.agent import TinyPerson +import tinytroupe.utils as utils +from tinytroupe.control import transactional +from tinytroupe import config_manager + +import concurrent.futures +import threading + +import math + +# to protect from race conditions when generating agents in parallel +concurrent_agent_generataion_lock = threading.Lock() + + +class TinyPersonFactory(TinyFactory): + + # keep track of all the names generated by all the factories, to ensure they are globally unique. + all_unique_names=[] + + def __init__(self, sampling_space_description:str=None, total_population_size:int=None, context:str=None, simulation_id:str=None): + """ + Initialize a TinyPersonFactory instance. + + Args: + sampling_space_description (str, optional): The description of the sampling space. Defaults to None. If this is + specified, then population_size must also be specified. + population_size (int, optional): The size of the population to sample from. Defaults to None. + context (str): The context text used to generate the TinyPerson instances. + simulation_id (str, optional): The ID of the simulation. Defaults to None. + """ + super().__init__(simulation_id) + self.person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/generate_person.mustache') + self.context_text = context + self.sampling_space_description = sampling_space_description + self.population_size = total_population_size + + self.sampling_dimensions = None + self.sampling_plan = None + self.remaining_characteristics_sample = None + + self.generated_minibios = [] # keep track of the generated persons. We keep the minibio to avoid generating the same person twice. + self.generated_names = [] + + # TODO obsolete? + @staticmethod + def generate_person_factories(number_of_factories, generic_context_text): + """ + Generate a list of TinyPersonFactory instances using OpenAI's LLM. + + Args: + number_of_factories (int): The number of TinyPersonFactory instances to generate. + generic_context_text (str): The generic context text used to generate the TinyPersonFactory instances. + + Returns: + list: A list of TinyPersonFactory instances. + """ + + logger.info(f"Starting the generation of the {number_of_factories} person factories based on that context: {generic_context_text}") + + system_prompt = open(os.path.join(os.path.dirname(__file__), 'prompts/generate_person_factory.md'), 'r', encoding='utf-8', errors='replace').read() + + messages = [] + messages.append({"role": "system", "content": system_prompt}) + + user_prompt = chevron.render("Please, create {{number_of_factories}} person descriptions based on the following broad context: {{context}}", { + "number_of_factories": number_of_factories, + "context": generic_context_text + }) + + messages.append({"role": "user", "content": user_prompt}) + + response = openai_utils.client().send_message(messages) + + if response is not None: + result = utils.extract_json(response["content"]) + + factories = [] + for i in range(number_of_factories): + logger.debug(f"Generating person factory with description: {result[i]}") + factories.append(TinyPersonFactory(result[i])) + + return factories + + return None + + @staticmethod + def create_factory_from_demography(demography_description_or_file_path:Union[str, dict], population_size:int, additional_demographic_specification:str=None, context:str=None): + """ + Create a TinyPersonFactory instance from a demography description, which can be wither given as a file path or a dictionary + (but not both). + + Args: + demography_description_or_file_path (Union[str, dict]): The demography description or the file path to the demography description. + population_size (int): The size of the population to sample from. + context (str, optional): Additional context text used to generate the TinyPerson instances. Defaults to None. + + Returns: + TinyPersonFactory: A TinyPersonFactory instance. + """ + # read the demography description from a file or use the given dictionary + if isinstance(demography_description_or_file_path, str): + demography_description = json.loads(open(demography_description_or_file_path, 'r', encoding='utf-8', errors='replace').read()) + elif isinstance(demography_description_or_file_path, dict): + demography_description = demography_description_or_file_path + else: + raise ValueError("demography_description_or_file_path must be either a string or a dictionary.") + + if population_size is None: + raise ValueError("population_size must be specified.") + + + full_demography_description = \ + f""" + # Sampling space specification + + The population described by the demographic data below. Make sure you consider very detailed, fine-grained, + characteristics of the individuals in the population. + + ## Directives + Please follow these rules: + - produce a uniformly distributed sample of the requested population, so that all characteristics are represented in the sample + in the right proportions, as specified in the demographic data below. + - consider as many different population segments as possible, while **always** keeping **proportions** correct.For example, + instead of sampling 10 people from segment A and 5 from segment B, you can instead sample 2 from A, 1 from B, + and 7 others from other segments, provided the proportions are maintained correct and there are enough people to sample. + - also use any built-in knowledge you might have of the populations in question to improve the sampling space, + provided this built-in knowledge does not conflict with the demographic data below. + + The sample must include representative people from the broad population, so for instance ensure that you include values covering + people from all walks of life possible from the specified demographic data and your built-in knowledge of the target population, such as: + - from the simplest professions to those of the highest ranks; + - from the youngest to the oldest; + - from the kind to the evil; + - from the positive and enthusiastic to the negative and pessimistic; + - from the happy and joyful to the sad and depressed; + - from the most conservative, to the most liberal; + - from the educated, to the ignorant; + - from the healthy to the sick; + - from those who enjoy bland food, to those who enjoy spicy food; + - from rich to poor. + + Make sure there's sufficient variety to represent even extreme cases, so that fringe opinions or far fetched characteristics are also represented. + Because these are by definition rare, here you can add a larger proportion than what is truly present in the population, so that there's some + information from these rare cases. + + In particular, the population MUST cover both POSITIVE and NEGATIVE possibilities of the various characteristics + (e.g., rich vs poor, likes sugar vs don't like sugar, enthusiastic vs apathetic). + + ## Additional demographic specification (if any) + {additional_demographic_specification if additional_demographic_specification is not None else "(none)"} + + ## Demographic data + {json.dumps(demography_description, indent=4)} + """ + + return TinyPersonFactory(context=context, + sampling_space_description=full_demography_description, + total_population_size=population_size) + + @classmethod + def _clear_factories(cls): + """ + Additional class-level cleanup for this subclass. + """ + TinyPersonFactory.all_unique_names = [] # clear the list of all unique names, so that the next factories can start fresh. + + def generate_person(self, + agent_particularities:str=None, + temperature:float=1.2, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None) -> TinyPerson: + """ + Generate a TinyPerson instance using OpenAI's LLM. + + Args: + agent_particularities (str): The particularities of the agent. + temperature (float): The temperature to use when sampling from the LLM. + frequency_penalty (float): The frequency penalty to use when sampling from the LLM. + presence_penalty (float): The presence penalty to use when sampling from the LLM. + attempts (int): The number of attempts to generate a TinyPerson instance. + post_processing_func (function): A function to apply to the generated agent after it is created. + + Returns: + TinyPerson: A TinyPerson instance generated using the LLM. + """ + + logger.debug(f"Starting the person generation based these particularities: {agent_particularities}") + fresh_agent_name = None + + # are we going to use a pre-computed sample of characteristics too? + if self.population_size is not None: + + with concurrent_agent_generataion_lock: + if self.remaining_characteristics_sample is None: + # if the sample does not exist, we generate it here once. + self.initialize_sampling_plan() + + logger.debug(f"Sampling plan initialized. Remaining characteristics sample: {self.remaining_characteristics_sample}") + + # CONCURRENT PROTECTION + with concurrent_agent_generataion_lock: + if len(self.remaining_characteristics_sample) == 0: + logger.warning("No more characteristics samples left to sample from. This can happen if the sampling plan did not sum up correctly.") + return None + + else: + sampled_characteristics = self.remaining_characteristics_sample.pop() + logger.debug(f"Sampled agent: {sampled_characteristics['name']}.") + + if agent_particularities is not None: + agent_particularities =\ + f""" + - Primary characteristics: {agent_particularities} + + - Also use all the following additional characteristics that **do not** conflict with the primary ones: + * Name, demographics and other characteristics: {json.dumps(sampled_characteristics, indent=4)} + + In case one of the additional characteristics conflicts with a primary one, please use the primary one + and ignore the additional one. + + If the agent's name is specified, you MUST ALWAYS use it, even if it conflicts with the primary characteristics. + + """ + else: + agent_particularities = \ + f""" + - Name, demographics and other characteristics: + {json.dumps(sampled_characteristics, indent=4)} + """ + else: # no predefined population size, so we generate one-off agents. + # CONCURRENT PROTECTION + with concurrent_agent_generataion_lock: + fresh_agent_name = self._unique_full_name(already_generated_names=TinyPersonFactory._all_used_and_precomputed_names(), + context=self.context_text) + + if agent_particularities is not None: + agent_particularities = \ + f""" + + - Primary characteristics: {agent_particularities} + + - Also use the following additional characteristics: + * Full name: {fresh_agent_name} + + In case the primary characteristics already specify a name, please use the primary name and ignore the additional one. + """ + else: + agent_particularities = f"Full name: {fresh_agent_name}" + + + + logger.info(f"Generating person with the following particularities: {agent_particularities}") + + # read example specs from files. + example_1 = json.load(open(os.path.join(os.path.dirname(__file__), '../examples/agents/Friedrich_Wolf.agent.json'), 'r', encoding='utf-8', errors='replace')) + example_2 = json.load(open(os.path.join(os.path.dirname(__file__), '../examples/agents/Sophie_Lefevre.agent.json'), 'r', encoding='utf-8', errors='replace')) + + # We must include all agent names generated in the whole of the simulation, not only the ones generated by this factory, + # since they all share the same name space. + # + # For the minibios, we only need to keep track of the ones generated by this factory, since they are unique to each factory + # and are used to guide the sampling process. + user_prompt = chevron.render(open(self.person_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), { + "context": self.context_text, + "agent_particularities": agent_particularities, + + #Note that we need to dump them to JSON strings, to ensure we get double quotes, + # and other formatting issues are avoided. + "example_1": json.dumps(example_1["persona"], indent=4), + "example_2": json.dumps(example_2["persona"], indent=4) + }) + + def aux_generate(attempt): + messages = [] + messages += [{"role": "system", "content": "You are a system that generates specifications for realistic simulations of people. You follow the generation rules and constraints carefully."}, + {"role": "user", "content": user_prompt}] + + + # due to a technicality, we need to call an auxiliary method to be able to use the transactional decorator. + message = self._aux_model_call(messages=messages, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty) + + if message is not None: + result = utils.extract_json(message["content"]) + + logger.debug(f"At attempt {attempt}, generated person parameters:\n{json.dumps(result, indent=4, sort_keys=True)}") + + # only accept the generated spec if the name is not already in use + if not self._is_name_already_assigned(result["name"]): + return result + else: + logger.info(f"Person with name {result['name']} was already generated, cannot be reused.") + + return None # no suitable agent was generated + + agent_spec = None + attempt = 0 + while agent_spec is None and attempt < attempts: + try: + attempt += 1 + agent_spec = aux_generate(attempt=attempt) + except Exception as e: + logger.error(f"Error while generating agent specification: {e}") + + # create the fresh agent + if agent_spec is not None: + # the agent is created here. This is why the present method cannot be cached. Instead, an auxiliary method is used + # for the actual model call, so that it gets cached properly without skipping the agent creation. + + # protect parallel agent generation + with concurrent_agent_generataion_lock: + person = TinyPerson(agent_spec["name"]) + self._setup_agent(person, agent_spec) + if post_processing_func is not None: + post_processing_func(person) + + self.generated_minibios.append(person.minibio()) + self.generated_names.append(person.get("name")) + + return person + else: + logger.error(f"Could not generate an agent after {attempts} attempts.") + if sampled_characteristics is not None: + self.remaining_characteristics_sample.append(sampled_characteristics) + logger.error(f"Name {fresh_agent_name} was not used, it will be added back to the pool of names.") + + return None + + + @config_manager.config_defaults(parallelize="parallel_agent_generation") + def generate_people(self, number_of_people:int=None, + agent_particularities:str=None, + temperature:float=1.2, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None, + parallelize=None, + verbose:bool=False) -> list: + """ + Generate a list of TinyPerson instances using OpenAI's LLM. + + Args: + number_of_people (int): The number of TinyPerson instances to generate. + agent_particularities (str): The particularities of the agent. + temperature (float): The temperature to use when sampling from the LLM. + frequency_penalty (float): The frequency penalty to use when sampling from the LLM. + presence_penalty (float): The presence penalty to use when sampling from the LLM. + attempts (int): The number of attempts to generate a TinyPerson instance. + post_processing_func (function): A function to apply to the generated agent after it is created. + parallalel_workers (int): The number of parallel workers to use when generating the people. Too many workers may cause the LLM to fail + due to throttling by the API. + verbose (bool): Whether to print information about the generated people. + + Returns: + list: A list of TinyPerson instances generated using the LLM. + """ + + if number_of_people is None: + if self.population_size is None: + raise ValueError("Either the number of people to generate or the population size must be specified.") + number_of_people = self.population_size + + elif self.population_size is None: + self.population_size = number_of_people + + elif number_of_people is not None and self.population_size is not None and number_of_people > self.population_size: + raise ValueError(f"Cannot generate more people than the population size. Requested {number_of_people}, but the population size is {self.population_size}.") + + people = [] + if parallelize: + people = self._generate_people_in_parallel(number_of_people=number_of_people, + agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func, + verbose=verbose) + else: + people = self._generate_people_sequentially(number_of_people=number_of_people, + agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func, + verbose=verbose) + + return people + + + @transactional(parallel=True) + def _generate_people_in_parallel(self, number_of_people:int=None, + agent_particularities:str=None, + temperature:float=1.5, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None, + verbose:bool=False) -> list: + people = [] + + # + # Concurrently generate the people. + # + # This vastly speeds up the process, but be careful with the number of workers, as too + # many may cause the LLM to fail due to throttling by the API. + # + + # this is the function that will be executed in parallel + def generate_person_wrapper(args): + self, i, agent_particularities, temperature, frequency_penalty, presence_penalty, attempts, post_processing_func = args + person = self.generate_person(agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func) + return i, person + + with concurrent.futures.ThreadPoolExecutor() as executor: + # we use a list of futures to keep track of the results + futures = [ + executor.submit(generate_person_wrapper, (self, i, agent_particularities, temperature, frequency_penalty, presence_penalty, attempts, post_processing_func)) + for i in range(number_of_people) + ] + + # we iterate over the futures as they are completed, and collect the results + for future in concurrent.futures.as_completed(futures): + i, person = future.result() + if person is not None: + people.append(person) + info_msg = f"Generated person {i+1}/{number_of_people}: {person.minibio()}" + + if verbose: + logger.info(info_msg) + + else: + logger.error(f"Could not generate person {i+1}/{number_of_people}. Continuing with the remaining ones.") + + return people + + # TODO still make this one available? + def _generate_people_sequentially(self, number_of_people:int=None, + agent_particularities:str=None, + temperature:float=1.5, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None, + verbose:bool=False) -> list: + """ + Generate the people sequentially, not in parallel. This is a simpler alternative. + """ + people = [] + for i in range(number_of_people): + person = self.generate_person(agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func) + if person is not None: + people.append(person) + info_msg = f"Generated person {i+1}/{number_of_people}: {person.minibio()}" + logger.info(info_msg) + if verbose: + print(info_msg) + else: + logger.error(f"Could not generate person {i+1}/{number_of_people}.") + + return people + + + + + def initialize_sampling_plan(self): + """ + Computes a list of characteristics samples from a sampling space. + The sampling space is built from the given description through intermediary steps + that actually build a sampling space and then randomly (and not via LLM) sample from it, thereby + ensuring that the sampling is not biased by the LLM (though the sampling space itself may be biased). + + All intermediary results are stored for later inspection. + + For example, given some n > 3 and a description like + "Young Western people of different liberal professions." + + The final samples could be something like: + [{"age": 25, "profession": "Architect", "country": "USA"}, + {"age": 27, "profession": "Lawyer", "country": "Canada"}, + ... + {"age": 25, "profession": "Architect", "country": "USA"}] + + Args: + n (int): The number of samples to generate. + sampling_space_description (str): A description of the sampling space. + + """ + + # a technicality - we need to use an auxiliary method to be able to use the transactional decorator effectively. + return self._initialize_sampling_plan_transaction(n=self.population_size, description=self.sampling_space_description,context=self.context_text) + + def _initialize_sampling_plan_transaction(self, n, description, context): + """ + Auxiliary method to initialize the sampling plan. This is needed in order to be able to use the transactional decorator, + due too a technicality - the method parameters must be such that when they change the transaction is nullified. + """ + if self.remaining_characteristics_sample is None: + # sampling dimensions + self.sampling_dimensions = utils.try_function(lambda: self._compute_sampling_dimensions(sampling_space_description=description), + + # check that the result is a dict + postcond_func=lambda result: isinstance(result, dict), + retries=15) + logger.info("Sampling dimensions computed successfully.") + logger.debug(f"Sampling dimensions: {json.dumps(self.sampling_dimensions, indent=4)}") + + # sampling plan + self.sampling_plan = utils.try_function(lambda: self._compute_sample_plan(N=n, + sampling_dimensions=self.sampling_dimensions), + + # checks that the plan is a list, not an empty dictionary, a number or a string + postcond_func = lambda result: isinstance(result, list) and len(result) > 0, + retries=15 + ) + # if the sampling plan is a dict, let' s enclose it in a list + if isinstance(self.sampling_plan, dict): + self.sampling_plan = [self.sampling_plan] + logger.warning("The sampling plan was a dictionary, enclosing it in a list to ensure it is processed correctly.") + + logger.info("Sampling plan computed successfully.") + logger.debug(f"Sampling plan: {json.dumps(self.sampling_plan, indent=4)}") + + # Flatten the sampling plan in concrete individual samples. + # Use deepcopy because we'll be modifying the samples later, and we want to keep the original sampling plan intact + # for correct caching + self.remaining_characteristics_sample = copy.deepcopy(utils.try_function(lambda: self._flatten_sampling_plan(sampling_plan=self.sampling_plan), + retries=15)) + + # instead of failing, we warn if the number of samples is not equal to n, as LLMs can be bad at summing up the quantities in the sampling plan. + # This is not a problem, as the sampling space is still valid and can be used, though it may not be as rich as expected. + if len(self.remaining_characteristics_sample) != n: + logger.warning(f"Expected {n} samples, but got {len(self.remaining_characteristics_sample)} samples. The LLM may have failed to sum up the quantities in the sampling plan correctly.") + + logger.info(f"Sample plan has been flattened, contains {len(self.remaining_characteristics_sample)} total samples.") + logger.debug(f"Remaining characteristics sample: {json.dumps(self.remaining_characteristics_sample, indent=4)}") + + # generate names for each sample individually, considering all their characteristics + all_used_names = TinyPersonFactory._all_used_and_precomputed_names() + + for i, sample in enumerate(self.remaining_characteristics_sample): + logger.debug(f"Generating name for sample {i+1}/{len(self.remaining_characteristics_sample)}") + + # randomize the all_used_names to make the context less predictable for the LLM, thereby introducing some additional randomness. + # Note that we use a fixed random seed to ensure that the sampling plan is reproducible and cache can be kept. + TinyFactory.randomizer.shuffle(all_used_names) + + # generate a name that's appropriate for this specific sample's characteristics + try: + + # A dummy name to start with, in case the name generation fails. + sample["name"] = f"Agent_{utils.fresh_id('agents_names')}" + + name = utils.try_function( + lambda: self._generate_name_for_sample( + sample_characteristics=sample, + already_generated_names=all_used_names + ), + # ensure the name is not in already used names + postcond_func=lambda result: result not in all_used_names, + retries=15 + ) + + sample["name"] = name + all_used_names.append(name) + + except Exception as e: + logger.error(f"Error generating name for sample {i}: {e}") + # fallback: use a simple default name with index + fallback_name = f"Person_{i}_{sample.get('gender', 'unknown')}" + sample["name"] = fallback_name + all_used_names.append(fallback_name) + + logger.info("Names generated for all samples in the sampling plan.") + + # update the global list of unique names + new_names = [sample["name"] for sample in self.remaining_characteristics_sample] + TinyPersonFactory.all_unique_names = list(set(TinyPersonFactory.all_unique_names + new_names)) + + else: + raise ValueError("Sampling plan already initialized. Cannot reinitialize it.") + + @classmethod + def _all_used_and_precomputed_names(cls) -> list: + """ + Returns all the names currently in use by agents and those pre-generated by all factories. + """ + return TinyPerson.all_agents_names() + cls.all_unique_names + + def _is_name_globally_unique(self, name:str) -> bool: + """ + Checks if a name is globally unique. + """ + return name not in TinyPersonFactory.all_unique_names + + def _is_name_already_assigned(self, name:str) -> bool: + """ + Checks if a name has already been assigned to a person. + """ + return name in TinyPerson.all_agents_names() + + + @transactional() + @utils.llm(temperature=0.5, frequency_penalty=0.0, presence_penalty=0.0) + def _compute_sampling_dimensions(self, sampling_space_description:str) -> dict: + """ + Given a sampling description, computes the dimensions of the sampling space. The sampling space offers a way to sample from a population of people, + so each dimension contains values that could be an attribute of a **specific** person. The resulting sampling space must: + - contemplate all critical characteristics mentioned in the sampling description, even if this means having a large number of dimensions and + complex values for each. + * whenever necessary to properly capture the possibilities, you can replace a single dimension by a collection of sub-dimensions + (e.g., instead of "beliefs", you might have "political_beliefs", "economic_beliefs", "consumer_beliefs", etc.) + - values for each dimension can range from numbers or single words to large sentences or even paragraphs. For attributes that are not clearly single values, + always try to add as much detail as possible. For instance, age is just a single value, but lifestyle or cultural background **must** be a long sentence or even a paragraph. + This is to ensure that, later, the generated people can be very nuanced and realistic, with rich and detailed attributes. See the example below to get inspired. + - you can be very creative with the dimensions and values provided that they are consistent with the sampling space description. + - whenever you have the information about PROPORTIONS of the values, you **must** include them in the output, so that the sampling space can be used to generate people + in a representative way. + - values are **not** distributions, probabilities or other statistics, but rather concrete, specific, people attributes. For example, there can + be no "average_age" dimension, but only "age", although the complete set of valies that define a dimension is itself a distribution. + - each dimension should be as rich as possible, having as many values as possible, so that the sampling space can be used to generate + many nuanced variations of the target population. + - each dimension should consider a wide range of values, making sure to cover both POSITIVE and NEGATIVE possibilities (e.g., rich vs poor, likes sugar vs don't like sugar). + - each dimension should always include extreme values, so that the sampling space can be used to generate people with extreme characteristics, such as very young or very old, + very rich or very poor, very positive or very negative, etc. + - include as many dimensions as possible to capture the richness of the population, even if this means having a large number of dimensions. + - in principle, the original sampling description could be approximately rephrased in terms of the dimensions and values generated (i.e., the dimensions are rich enough + to capture all relevant information). Howerver, this should not limit the range of values and dimensions used, but rather be a byproduct of the process. For instance, + if the original description say "young people", the dimension "age" could be defined as a range of values from 18 to 30, but **not** as a small list with only, say, [18, 25, 30]. + Always try to be as rich as possible in the values and dimensions, even if this means having a large number of them. + + Additionally, make sure you include special dimensions that capture these aspects, in such a way that they relate to the sampling space description: + - personality traits (with proportions) + - political beliefs (with proportions) + - economic beliefs (with proportions) + - financial situation (with proportions) + - preferences and tastes (with proportions) + - cultural background (with proportions and diverse ethnicities and cultural heritages; provide detailed, realistic, and varied examples that reflect a wide spectrum of ethnic, national, and cultural identities relevant to the sampling space description) + + ## On your input + + Here's what to do depending on what the input sampling space description looks like: + - Plain text: Abstract all the potential dimensions from the text. For example, if the text is "Young Western people of different liberal professions.", the dimensions could be "age", "profession", "country". + - JSON: Do not use the JSON directly, but rather abstract the dimensions from it. Input JSONs can be obtained from various sources, and you should do your best to interpret them and produce a clean list of dimensions and their values, regardless of how complex the input JSON is. In particular, never use the JSON formatting itself as dimension names or values, but rather abstract the actual dimensions and values from it. + - Tables or other structured data: Abstract the dimensions from the structured data. For example, if the data is in a table, you should extract the rows and columns and abstract the dimensions from them. + + + ## On your output: + You output a JSON containing a list of dimensions. Each output dimension **must** consist of: + - a name; + - EITHER a list of values OR a range of values (specified as a pair). + * in lists of values, whenever possible, you **must** use long values, such as sentences or paragraphs, instead of short words or numbers. + * in lists of values you can, optionally, use a dictionary to specify proportions of the values, e.g., {"value1": 0.5, "value2": 0.3, "value3": 0.2} to indicate that 50% of the population has value1, 30% has value2, and 20% has value3. + Adjust the proportions as appropriate for the context and ensure they sum to 1.0. + + The output is formatted as a JSON object with the following structure: + ```json + { + "sampling_space_description": "A description of the sampling space.", + "dimensions": [ + { + "name": "dimension_name_1", + "values": ["value1", "value2", ...] + }, + + { + "name": "dimension_name_2", + "range": [min, max] + }, + + { + "name": "dimension_name_3", + "values": {"value1": proportion1, "value2": proportion2, "value3": proportion3, ...} + }, + + ... + ] + } + ``` + + Unless values are necessarily numbers (e.g., age), they should be descriptive strings so that it is easy to understand what they mean. + These strings can be simple values or long detailed texts, whatever is best to capture the desired characteristic. + + ## Example: + Given the following INPUT sampling space description: "Young Western people of different liberal professions and social classes." + + The OUTPUT dimensions could be a dictionary with the following structure: + ```json + { + "sampling_space_description": "Young Western people of different liberal professions and social classes.", + "dimensions": [ + { + "name": "age", + "range": [18, 30] + }, + { + "name": "socioeconomic status", + "values": ["miserable", "poor", "middle class", "rich", "very rich"] + }, + { + "name": "profession", + "values": ["Architect", "Lawyer", "Physician", "Accountant", ...] + }, + { + "name": "country", + "values": { + "USA": 0.35, + "Germany": 0.10, + "UK": 0.09, + "France": 0.09, + "Italy": 0.08, + "Spain": 0.06, + "Canada": 0.06, + "Australia": 0.05, + "Netherlands": 0.03, + "Sweden": 0.03, + "Belgium": 0.02, + "Switzerland": 0.02, + "Austria": 0.01 + } + }, + { + "name": "cultural_background", + "values": { + "Born in a large city of a developed nation, parents were from a lineage of physicians and lawyers": 0.12, + "Descendant of Ashkenazi Jewish immigrants who settled in New York City in the early 20th century, maintaining strong ties to Jewish traditions and community life.": 0.08, + "Second-generation Chinese-Canadian whose family values blend Confucian principles with Canadian multiculturalism, celebrating both Lunar New Year and Canada Day.": 0.06, + "Of Irish and Italian descent, growing up in Boston with a household that combines Catholic traditions, Irish folk music, and Italian culinary heritage.": 0.10, + "Of Turkish-German background, raised in Berlin with exposure to both Turkish family traditions and contemporary German urban culture.": 0.05, + <... many more ...> + } + }, + { + "name": "economic_beliefs", + "values": { + "Firmly believes that diligent effort and perseverance in one's career are the primary drivers of financial prosperity and upward mobility.": 0.28, + "Holds the view that wealth accumulation is largely a matter of being in the right place at the right time, with luck playing a significant role in economic outcomes.": 0.18, + "Thinks that government intervention and social programs are essential to ensure fair economic opportunities for all members of society.": 0.22, + "Believes that personal connections and networking are more important than formal education or hard work in achieving economic success.": 0.15, + <... many more ...> + } + }, + { + "name": "professional_attitudes", + "values": { + "Aspires to establish and grow their own business, valuing independence and the ability to innovate without corporate constraints.": 0.18, + "Prefers the stability and structure of working for a well-established company, appreciating clear career paths and organizational support.": 0.32, + "Enjoys collaborating in multidisciplinary teams and seeks out workplaces that foster creativity and open communication.": 0.22, + "Is highly risk-averse and prioritizes job security and predictable routines over rapid advancement or entrepreneurial ventures.": 0.15, + <... many more ...> + } + }, + { + "name": "political_beliefs", + "values": { + "Strongly supports progressive policies aimed at reducing income inequality and expanding access to healthcare and education.": 0.24, + "Advocates for conservative values, emphasizing the importance of tradition, personal responsibility, and limited government intervention.": 0.20, + "Identifies as a centrist, believing that balanced compromise between opposing political ideologies leads to the best societal outcomes.": 0.26, + "Is passionate about environmental issues and supports policies that prioritize sustainability and climate change mitigation above economic growth.": 0.16, + <... many more ...> + } + }, + { + "name": "personality_traits", + "values": { + "Maintains an unwavering optimism, always expecting positive outcomes even in the face of adversity and encouraging others to do the same.": 0.12, + "Tends to be introspective and reserved, preferring solitary activities and deep reflection over social gatherings or group events.": 0.18, + "Is highly ambitious, constantly setting challenging goals and pushing themselves to achieve more in both personal and professional spheres.": 0.15, + "Approaches new experiences with caution, carefully weighing risks and benefits before making decisions or embracing change.": 0.20, + "Often expects the worst in any situation, focusing on potential problems and rarely feeling hopeful about the future.": 0.08, + "Frequently experiences a sense of sadness and melancholy, finding it difficult to enjoy activities that once brought happiness.": 0.06, + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life.": 0.07, + "Feels overwhelmed by setbacks, easily discouraged, and tends to dwell on failures rather than successes.": 0.05, + <... many more ...> + } + }, + { + "name": "preferences_and_tastes", + "values": { + "Has a deep appreciation for classical music, frequently attending orchestral concerts and collecting rare vinyl recordings.": 0.08, + "Finds joy in spending weekends hiking in remote natural parks, seeking tranquility and inspiration from the outdoors.": 0.16, + "Rarely leaves home, preferring the comfort of familiar surroundings and engaging in hobbies such as reading and painting indoors.": 0.11, + "Enjoys experimenting with international cuisines, often hosting elaborate dinner parties to share culinary discoveries with friends.": 0.14, + "Is sensitive to loud environments and actively avoids crowded or noisy places, seeking peace and quiet whenever possible.": 0.13, + "Prefers to spend time alone in dimly lit rooms, listening to somber music and reflecting on the more difficult aspects of life.": 0.04, + "Has little interest in social gatherings or celebrations, often declining invitations and feeling out of place in festive environments.": 0.07, + "Frequently chooses entertainment or art that explores themes of loss, struggle, or existential despair, finding comfort in shared sadness.": 0.03, + <... many more ...> + } + } + ] + } + ``` + + Note in the example: + - Age is given as a numeric range. + - All other values are descriptive strings, human-friendly, no strange symbols or codes. + - The "country" dimension uses a dictionary with suitable proportions for Western countries. + - No value contains internal structure - just a name or short description. + - All values are concrete properties, not distributions, probabilities or other statistics. + - Whenever possible, the values in the dimensions are long and detailed **sentences** each. + - It has few dimensions because the sampling space description is very short. If the description were longer, the number of dimensions would be larger, + and their values more detailed. + - It contains the additional dimensions that capture the personality traits, political beliefs, economic beliefs, financial situation, preferences and tastes, + and now cultural background with varied ethnicities and heritages, which are important for the sampling space to be rich enough to generate nuanced variations of the target population. + - Beyond positive aspects, it also includes values that emphasize pessimism, negativeness, and sadness, ensuring these characteristics are balanced and represented in the sampling space. + + Args: + sampling_space_description (str): A description of the sampling space. + + Returns: + dict: A dictionary with the dimensions of the sampling space, as shown in the example above. + """ + # the body of this method is handled by the @llm decorator. + + @transactional() + @utils.llm(temperature=0.5, frequency_penalty=0.0, presence_penalty=0.0) + def _compute_sample_plan(self, N:int, sampling_dimensions:dict, max_quantity_per_sample_directive:int=5, min_sampling_directives:int=10, max_sampling_directives:int=50) -> List[Dict[str, any]]: + """ + This function defines which and how many people to sample from the sampling space defined by the given dimensions. + Given a number N of people to sample, and the dimensions of the sampling space, computes a *sample plan* of N people from that space. + + The input sampling dimensions have the following structure: + + ```json + { + "sampling_space_description": "A description of the sampling space.", + "dimensions": [ + { + "name": "dimension_name_1", + "values": ["value1", "value2", ...] + }, + { + "name": "dimension_name_2", + "range": [min, max] + }, + ... + ] + } + ``` + + The *sample plan* to be generated is a list of M *sampling directives*. Each *sampling directive* **always** consists of: + - "id": a unique identifier for the *sampling directive*, just an incrementing integer starting from 1. + - "subpopulation_description": a short description of the sub-population that this *sampling directive* represents, based on the sampling space description and the sampled values. + If possible, make it a recognizable and meaningful description of the sub-population, + such as "Young rebellious people from upper classes", "Old conservative boomers from rural areas", "Intellectual urban professionals with diverse and cosmopolitan cultural backgrounds", etc. + - "sampled_values": a map from of dimensions from the sampling space to concrete values, value ranges or value options. + - "quantity": to how many elements with those values should be sampled in total (from 1 to max_quantity_per_sample_directive if specified). + The sum of all of these quantities must be equal to N. + + So your final output **MUST** follow this JSON structure: + + ```json + [ + { "id": 1, + "subpopulation_description": "Some description here...", + "sampled_values": { + "dimension_name_1": [n_1_min, n_1_max],, + "dimension_name_2": ["value2_1", "value2_2", ...], + "dimension_name_3": ["value3_1", "value3_2", ...], + ... + }, + "quantity": quantity_1 + }, + + { + "id": 2, + "subpopulation_description": "Some other description here...", + "sampled_values": { + "dimension_name_1": [n_1_min, n_1_max], + "dimension_name_2": "value2", + "dimension_name_3": ["value3_1", "value3_2", ...], + ... + }, + "quantity": quantity_2 + }, + ... + { + "id": M, + "subpopulation_description": "Again some description here...", + "sampled_values": { + "dimension_name_1": [n_1_min, n_1_max], + "dimension_name_2": ["value2_1", "value2_2", ...], + "dimension_name_3": ["value3_1", "value3_2", ...], + ... + }, + "quantity": quantity_M + }, + ] + ``` + + where N = quantity_1 + quantity_2 + ... + quantity_M, + quantity_i <= max_quantity_per_sample_directive (if specified), + and M is the number of *sampling directives*, which can be as large as necessary to ensure + that the total number of sampled people is equal to N. + + Note: + - Concrete values are NOT in brackets, but rather just a single value or a range of values. + - Options are given in lists of strings separated by commas, e.g., ["value1", "value2", ...]. + - Ranges are numberic and specified as a pair of numbers, e.g., [min, max]. + + Rules and principles: + - The sampling plan is a collection of sub-populations captured by each *sampling directive*. Therefore, the various *sampling directives* must complement each other in order + to approximate the target population. + - Each *sampling directive* is a **combination** of values from the sampling dimensions that represent a specific segment of the target population. Its richness and variety must reflect the desired sub-population. + - The dimension sampled in each *sampling directive* can be a single value, a range of values, or a list of values. You can use ranges and lists to cover a wider range of possibilities + in a compact way, but you can also use single values if necessary. The items in list can be long or short, does not matter, both can be in lists. Some examples of good fortmatting: + * CORRECT example: ["Very rich", "Rich", "Middle class", "Poor"] + * CORRECT example: "Rich" + * WRONG example: ["Very rich or Rich or Middle class or Poor"] + * WRONG example: ["Rich"] + - **Always** try very hard to use a list of values (two or more values) or range of values (min - max), to make the sampling plan at once concise and rich. In doing so, make sure that each *sampling directive* is truly representative + of some segment of the target population, and not just a random collection of values. + - You MUST make M as large as necessary to contemplate the target population, ideally M >= min_sampling_directives (but M <= max_sampling_directives, if specified), to ensure a rich and varied sampling of the population. + * Note that this means the maximum *sampling directive* "id" (call it max_id) used in the *sampling plan* is such that: max_id >= min_sampling_directives; max_id <= max_sampling_directives (if specified). + - The sampled population MUST be representative of the target population. + - The sampled population MUST be realistic. + - You can set the quantity of each *sampling directive* to 1 if necessary to ensure a varied and representative sampling. + - All values chosen from the sampling dimensions must be copied IN FULL in the "sampled_values" map, so that the sampled values are concrete and specific. + The sample plan is supposed to be self-contained, therefore it MUST have all details necessary to sample the people later, without needing to refer back to the sampling dimensions. + - You should include as many *sampling directives* as necessary to cover the sampling of N total people (the sum of all quantities). When in doubt, + **always** add more *sampling directives* (i.e., make M larger) up to max_sampling_directives (if specified), as this will ensure you cover the requested N people. + - In particular, make sure both POSITIVE and NEGATIVE possibilities of the various characteristics are covered (e.g., rich vs poor, likes sugar vs doesn't like sugar, enthusiastic vs apathetic). + This is to ensure any bias (towards positive or negative characteristics) is minimized, and the sampling space is rich enough to generate people with a wide range of characteristics. + - The sampling space description should be used to guide the sampling, so that the sampled population is consistent with it. + - You should ensure that the quantity of requested samples in each *sampling directive* is proportional to their presumed size in the target population. + That is to say, combinations of dimensions that are more common in the target population should be sampled more often. If you don't know, make a guess. + - If max_quantity_per_sample_directive is specified, you must ensure that no single *sampling directive* exceeds this quantity. This is to ensure we get more variation and not just a few large groups. + - You can rely on your built-in knowledge or make educated guesses about such quantities and proportions to ensure that the sample is representative of the population. + * Note that this means for any quantity_i: quantity_i >= 1; quantity_i <= max_quantity_per_sample_directive (if specified). + - The sum of all quantities in the output **must** be equal to N, the number of people to sample in total. + - You can always add extra *sampling directives* (up to max_sampling_directives if specified) to ensure the total of N people is reached. + - It is acceptable for the sampling plan to generate more than N people, but NEVER less than N. So if unsure generate MORE people, never less. + + ## Example + Given the following INPUT sampling dimensions: + + ```json + { + "sampling_space_description": "Young Western people of different liberal or intellectual professions." + "dimensions": [ + { + "name": "age", + "range": [18, 30] + }, + { + "name": "profession", + "values": ["Architect", "Financial Analyst", "Writer", "Art critic", "Lawyer", "Physician", "Accountant", ...] + }, + { + "name": "country", + "values": ["USA", "Canada", "UK", "France", "Germany", "Italy", "Spain", "Portugal", "Netherlands", "Belgium", ...] + }, + + { + "name": "personality_traits", + "values": { + "Maintains an unwavering optimism, always expecting positive outcomes even in the face of adversity and encouraging others to do the same.": 0.12, + "Tends to be introspective and reserved, preferring solitary activities and deep reflection over social gatherings or group events.": 0.18, + "Is highly ambitious, constantly setting challenging goals and pushing themselves to achieve more in both personal and professional spheres.": 0.15, + "Approaches new experiences with caution, carefully weighing risks and benefits before making decisions or embracing change.": 0.20, + "Often expects the worst in any situation, focusing on potential problems and rarely feeling hopeful about the future.": 0.08, + "Frequently experiences a sense of sadness and melancholy, finding it difficult to enjoy activities that once brought happiness.": 0.06, + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life.": 0.07, + "Feels overwhelmed by setbacks, easily discouraged, and tends to dwell on failures rather than successes.": 0.05, + <... many more ...> + } + } + + (... more dimensions ...) + + ] + } + + An OUTPUT *sample plan* therefore is a LIST with the *sample plan*, where each element is a dictionary with a *sampling directive*. For example, an output based on the above dimensions could look like this: + + ```json + [ + { + "id": 1, + "subpopulation_description": "Young Anglo-Saxon professionals with their stereotypical ambition and drive.", + "sampled_values": { + "age": [22, 30], + "profession": ["Financial Analyst", "Lawyer", "Physician", "Accountant", ...], + "country": ["USA", "UK", "Canada"], + "personality_traits": ["Maintains an unwavering optimism, always expecting positive outcomes even in the face of adversity and encouraging others to do the same.", + "Approaches new experiences with caution, carefully weighing risks and benefits before making decisions or embracing change", + "Tends to be introspective and reserved, preferring solitary activities and deep reflection over social gatherings or group events.", + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life."] + }, + "quantity": 10 + }, + { + "id": 2, + "subpopulation_description": "Young European professionals with a focus on creativity and innovation and their occasional existential crises.", + "sampled_values": { + "age": [21, 30], + "profession": ["Architect", "Lawyer", "Writer", "Physician", "Art critic", ...], + "country": ["France", "Germany", "Italy", "Spain"], + "personality_traits": ["Often expects the worst in any situation, focusing on potential problems and rarely feeling hopeful about the future.", + "Frequently experiences a sense of sadness and melancholy, finding it difficult to enjoy activities that once brought happiness.", + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life.", + "Feels overwhelmed by setbacks, easily discouraged, and tends to dwell on failures rather than successes.]" + }, + "quantity": 5 + }, + ... + ] + ``` + + + Args: + n (int): The number of elements to sample in total. This number will be distributed across the dimensions proportionally + to the presumed size the target population. + sampling_dimensions (dict): The dimensions of the sampling space. + max_quantity_per_sample_directive (int, optional): The maximum quantity of samples that can be specified in a single sampling directive. This is to ensure that the sampling plan is diverse and not biased towards a few large groups. + min_sampling_directives (int, optional): The minimum number of sampling directives to generate. This is to ensure that the sampling plan is rich and varied. + max_sampling_directives (int, optional): The maximum number of sampling directives to generate. This is to ensure that the sampling plan is not overly complex and remains manageable. + + Returns: + list: A LIST with the *sample plan*, where each element is a dictionary with a *sampling directive*, as described above. + """ + # the body of this method is handled by the @llm decorator. + + @transactional() + def _flatten_sampling_plan(self, sampling_plan:dict) -> list: + """ + Given a sample plan, flattens it into a list of samples in such a way that the number of times each sample appears + correspond to what was specified in the plan. The order is random to avoid bias. + + For example, an input sample plan could look like this: + + ```json + [ + { + "sampled_values": { + "age": 25, + "profession": "Architect", + "country": "USA" + }, + "quantity": 8 + }, + { + "sampled_values": { + "age": 27, + "profession": "Lawyer", + "country": "Canada" + }, + "quantity": 1 + }, + ... + ] + ``` + + And the output would be something like: + + ```python + [{"age": 25, "profession": "Architect", "country": "USA"}, + {"age": 27, "profession": "Lawyer", "country": "Canada"}, + ... + {"age": 25, "profession": "Architect", "country": "USA"}] + ``` + + Args: + sampling_plan (dict): The sample plan to flatten. + + Returns: + list: A list of samples, where each sample is a dictionary with the sampled values. + """ + samples = [] + for sample in sampling_plan: + if "quantity" not in sample: + logger.warning(f"Sample in sampling plan does not have a 'quantity' field: {sample}. Assuming 1.") + qty = 1 + else: + qty = int(sample["quantity"]) + + for _ in range(qty): + # we need to copy the sample to avoid adding the original sample multiple times, + # which would cause problems later when we modify the individual flattened samples + cc_sample = copy.deepcopy(sample["sampled_values"]) + + samples.append(cc_sample) + + # randomize + random.shuffle(samples) #inplace + return samples + + @transactional() + def _unique_full_name(self, already_generated_names: list, context:str=None) -> str: + # a technicality - we need to use an auxiliary method to be able to use the transactional decorator effectively. + # TODO update this somehow to avoid this cumbersome workaround. + + return self._aux_unique_full_name(already_generated_names=already_generated_names, context=context) + + + @utils.llm(temperature=1.5, presence_penalty=0.5, frequency_penalty=0.5) + def _aux_unique_full_name(self, already_generated_names: list, context:str=None) -> str: + """ + Generates a unique full name for a person. The full name must not be in the list of already generated names. + If necessary, you can generate a longer name to ensure it is new. You can also try tweaking the spelling or + adding more surnames, so that the name is unique. However, the name **must** sound realistic and not be too far-fetched, + not sound as if it was made up. + + The final result is only the name, nothing else: + + "Some name here" ---> correct as it is just a name, nothing else + "Some name here, because ..." ---> incorrect as it contains a reason + "Some name here." ---> incorrect as it contains punctuation + "Name: Some name here" ---> incorrect as it contains a label + "Some name here, some other name here" ---> incorrect as it contains more than one name + + An optional context can be provided to guide the name generation, so that it is a realistic name for the context. For example, we know that different socio-economic classes have different naming conventions, so the context can be used to guide the name generation. + + Regarding the `already_generated_names`, you must: + - NEVER generate a name that is already in the list of already generated names. + - The names in `already_generated_names` ARE NOT examples of names to generate. They are just names that have already been generated and should not be repeated. You should generate new names regardless of the names in `already_generated_names`, the only constraint is that the new names should not be in the list of already generated names. + - In particular, you are not to generate a similar name to that of those in `already_generated_names`, you are **not** building some kind of + logical sequence. Each name must be independent of the others. + + ## Example + + **Input:** + already_generated_names: ["John Doe", "Jane Smith", "Alice Brown"] + context: { 'age': 25, 'profession': 'Architect', 'country': 'USA' } + + **Output:** + "Michael Johnson" + + Note that: + - The name "Michael Johnson" is not in the list of already generated names. + - The ouput consists only of a name, nothing else. + + Args: + already_generated_names (list): The list of already generated names. + context (str): The context in which the name is being generated. This can be used to guide the name generation, so that it is a realistic name for the context. + + Returns: + str: A unique full name for a person. + """ + # the body of this method is handled by the @llm decorator + + @transactional() + def _unique_full_names(self, n:int, already_generated_names: list, context:str=None) -> list: + """ + Generates a list of n unique full names for people. The full names must not be in the list of already generated names. + + Args: + n (int): The number of names to generate. + already_generated_names (list): The list of already generated names. + context (str): The context in which the names are being generated. This can be used to guide the name generation, so that it is a realistic name for the context. + """ + + logger.debug(f"Will generate {n} unique full names for people. Already generated names: {already_generated_names}") + + names = [] + + if n > 0: + # let's split the n in smaller chunks to make the model's job easier + chunk_size = min(10, n) # we generate at most 10 names at a time, to avoid overwhelming the model + chunks = math.ceil(n/chunk_size) + + forbidden_names = copy.deepcopy(already_generated_names) + + + max_iterations = chunks * 10 + cur_iterations = 0 + + while len(names) < n and cur_iterations < max_iterations: + logger.debug(f"Currently already generated names: {forbidden_names}") + logger.debug(f"Iteration {cur_iterations} - Generating {chunk_size} names. Currently have {len(names)} names. Max iterations to be allowed: {max_iterations}") + try: + temp_names = utils.try_function(\ + lambda: \ + self._aux_unique_full_names(n=chunk_size , + already_generated_names=forbidden_names, + context=context), + + # checks that some new name was produced + postcond_func = lambda result: len(set(forbidden_names).intersection(result)) < len(result), + retries=3) + + # add the new names to the names list, removing any duplicates from their combination + names = list(set(names + temp_names)) + forbidden_names += names + except Exception as e: + logger.error(f"Error generating names: {e}") + # if we have an error, we just skip this iteration and try again + # but we need to increment the number of iterations anyway + + cur_iterations += 1 + + if cur_iterations >= max_iterations and len(names) < n: + logger.error(f"Could not generate the requested number of names after {max_iterations} iterations. Moving on with the {len(names)} names generated.") + + TinyPersonFactory.all_unique_names = list(set(TinyPersonFactory.all_unique_names + names)) + + return names + + @utils.llm(temperature=1.9, presence_penalty=0.5, frequency_penalty=0.5) + def _aux_unique_full_names(self, n:int, already_generated_names: list, context:str=None) -> list: + """ + Generates a list of n unique full names for people. The full names must not be in the list of already generated names. You **must** consider **all** reasononable options for names, + not only the common or popular. To ensure that fresh names are really new and do not appear in the list of already generated ones, if necessary you can: + - generate longer names to ensure they are new. + - try tweaking the spelling or adding more surnames, so that the names are unique. + - add unusual names or surnames, so that the names are unique. + - as a very last resort, you can append a number to the name, so that it is unique, despote being a bit less realistic. + + Except for the latter option, the names **must** sound realistic and not be too far-fetched, not sound as if they were made up. + + You **must** generate at least n names, and they **must** all be unique. If necessary, to ensure you get at least n names, you can try to generate more than n, + but **never** less, unless you need to avoid a repeated name. If forced to choose, you always prefer to generate unique names, even if that means generating less than n names. + + The final result is only the list of names, nothing else: + + ["Some name here"] ---> correct as it is just a list with a single name, nothing else + ["Some name here, some other name here"] ---> correct as it is a list of names + ["Some name here, because ..."] ---> incorrect as it contains a reason + ["Some name here."] ---> incorrect as it contains punctuation + ["Name: Some name here"] ---> incorrect as it contains a label + + An optional context can be provided to guide the name generation, so that it is a realistic name for the context. For example, we know that different socio-economic classes have different naming conventions, + so the context can be used to guide the name generation. In particular, follow these rules regarding the context: + - If a country is specified, the names should be typical for that country. + + Regarding the `already_generated_names`, you must: + - NEVER generate a name that is already in the list of already generated names. + - The names in `already_generated_names` ARE NOT examples of names to generate. They are just names that have already been generated and should not be repeated. You should generate new names regardless of the names in `already_generated_names`, the only constraint is that the new names should not be in the list of already_generated_names. + - In particular, you are not to generate a similar name to that of those in `already_generated_names`, you are **not** building some kind of logical sequence. Each name must be independent of the others. + + ## Example + + **Input:** + n: 6 + already_generated_names: ["John Doe", "Jane Smith", "Alice Brown"] + context: "Young Americans of different liberal professions" + **Output:** + ["Michael Johnson", "Sarah Williams", "David Gates", "Jennifer Davis", "Robert J. Wilson", "Anna Kerr"] + + Note that: + - The names are not in the list of already generated names. + - The ouputs consist only of a list of names, nothing else. + - The output length is exactly 6, which is the requested number of names. There could be a bit more names generated, but never less. + + Args: + n (int): The number of names to generate. + already_generated_names (list): The list of already generated names. + context (str): The context in which the names are being generated. This can be used to guide the name generation, so that it is a realistic name for the context. + + Returns: + list: A list of n unique full names for people. These names NEVER repeat names in the list of already generated names. + """ + # the body of this method is handled by the @llm decorator. Below we provide a post-processing function that is + # applied to the LLM output, to ensure that the names are unique. + + return lambda names: list(set(names)) + + @transactional() + def _aux_model_call(self, messages, temperature, frequency_penalty, presence_penalty): + """ + Auxiliary method to make a model call. This is needed in order to be able to use the transactional decorator, + due too a technicality - otherwise, the agent creation would be skipped during cache reutilization, and + we don't want that. + """ + return openai_utils.client().send_message(messages, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + response_format={"type": "json_object"}) + + @transactional() + def _setup_agent(self, agent, configuration): + """ + Sets up the agent with the necessary elements. + """ + agent.include_persona_definitions(configuration) + + # does not return anything, as we don't want to cache the agent object itself. + + @transactional() + @utils.llm(temperature=0.3, frequency_penalty=-0.1, presence_penalty=-0.1, enable_json_output_format=False) + def _generate_name_for_sample(self, sample_characteristics: dict, already_generated_names: list) -> str: + """ + Generates a single full name for a person based on their complete sample characteristics, such that + it is as appropriate as possible to all characteristics, not just gender. + This name MUST BE UNIQUE and not appear in the already_generated_names list, though variations of the + same name are allowed. + + You must generate a realistic full name that is appropriate for the given sample characteristics. + Consider ALL the characteristics provided, including but not limited to: + - Gender + - Age or age range + - Country/nationality/ethnicity + - Socioeconomic status + - Profession + - Educational background + - Cultural background + - Any other relevant demographic or personal characteristics + + The name should: + - BE UNIQUE and not appear in the already_generated_names list + - Be realistic and culturally appropriate for the characteristics + - Sound natural and not made-up + - Be unique and not appear in the already_generated_names list + - Reflect the person's likely background (e.g., names common in their generation, culture, social class) + + If you need additional methods to ensure uniqueness, you can: + - Use longer or more uncommon names + - Include middle names or multiple surnames + - Use culturally appropriate name variations + - As a last resort, you can append a number, but this should be avoided. + + + In ANY CASE, you **must never**, NEVER, generate a name that already appears in the already_generated_names list. + + Return only the full name as a string, nothing else. + + ## Example + + **Input:** + sample_characteristics: { + "gender": "female", + "age": 28, + "country": "Brazil", + "profession": "Software Engineer", + "socioeconomic_status": "middle class", + "education": "Computer Science degree" + } + already_generated_names: ["João Silva", "Maria Santos", "Ana Costa"] + + **Output:** + "Camila Rodrigues" + + Args: + sample_characteristics (dict): The complete characteristics of the sample, including demographics, profession, etc. + already_generated_names (list): The list of already generated names to avoid duplicates. The new name MUST NOT be in this list. + + Returns: + str: A single full name appropriate for the sample characteristics. + """ + # the body of this method is handled by the @llm decorator + diff --git a/openai_utils.py b/openai_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7b56555bfc781f9f25d12595a72e30b7d824ebe4 --- /dev/null +++ b/openai_utils.py @@ -0,0 +1,525 @@ +import os +import openai +from openai import OpenAI, AzureOpenAI +import time +import pickle +import logging +import configparser +from typing import Union + + +import tiktoken +from tinytroupe import utils +from tinytroupe.control import transactional +from tinytroupe import default +from tinytroupe import config_manager + +logger = logging.getLogger("tinytroupe") + +# We'll use various configuration elements below +config = utils.read_config_file() + +########################################################################### +# Client class +########################################################################### + +class OpenAIClient: + """ + A utility class for interacting with the OpenAI API. + """ + + def __init__(self, cache_api_calls=default["cache_api_calls"], cache_file_name=default["cache_file_name"]) -> None: + logger.debug("Initializing OpenAIClient") + + # should we cache api calls and reuse them? + self.set_api_cache(cache_api_calls, cache_file_name) + + def set_api_cache(self, cache_api_calls, cache_file_name=default["cache_file_name"]): + """ + Enables or disables the caching of API calls. + + Args: + cache_file_name (str): The name of the file to use for caching API calls. + """ + self.cache_api_calls = cache_api_calls + self.cache_file_name = cache_file_name + if self.cache_api_calls: + # load the cache, if any + self.api_cache = self._load_cache() + + + def _setup_from_config(self): + """ + Sets up the OpenAI API configurations for this client. + """ + self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + + @config_manager.config_defaults( + model="model", + temperature="temperature", + max_tokens="max_tokens", + top_p="top_p", + frequency_penalty="frequency_penalty", + presence_penalty="presence_penalty", + timeout="timeout", + max_attempts="max_attempts", + waiting_time="waiting_time", + exponential_backoff_factor="exponential_backoff_factor", + response_format=None, + echo=None + ) + def send_message(self, + current_messages, + dedent_messages=True, + model=None, + temperature=None, + max_tokens=None, + top_p=None, + frequency_penalty=None, + presence_penalty=None, + stop=[], + timeout=None, + max_attempts=None, + waiting_time=None, + exponential_backoff_factor=None, + n = 1, + response_format=None, + enable_pydantic_model_return=False, + echo=False): + """ + Sends a message to the OpenAI API and returns the response. + + Args: + current_messages (list): A list of dictionaries representing the conversation history. + dedent_messages (bool): Whether to dedent the messages before sending them to the API. + model (str): The ID of the model to use for generating the response. + temperature (float): Controls the "creativity" of the response. Higher values result in more diverse responses. + max_tokens (int): The maximum number of tokens (words or punctuation marks) to generate in the response. + top_p (float): Controls the "quality" of the response. Higher values result in more coherent responses. + frequency_penalty (float): Controls the "repetition" of the response. Higher values result in less repetition. + presence_penalty (float): Controls the "diversity" of the response. Higher values result in more diverse responses. + stop (str): A string that, if encountered in the generated response, will cause the generation to stop. + max_attempts (int): The maximum number of attempts to make before giving up on generating a response. + timeout (int): The maximum number of seconds to wait for a response from the API. + waiting_time (int): The number of seconds to wait between requests. + exponential_backoff_factor (int): The factor by which to increase the waiting time between requests. + n (int): The number of completions to generate. + response_format: The format of the response, if any. + echo (bool): Whether to echo the input message in the response. + enable_pydantic_model_return (bool): Whether to enable Pydantic model return instead of dict when possible. + + Returns: + A dictionary representing the generated response. + """ + + def aux_exponential_backoff(): + nonlocal waiting_time + + # in case waiting time was initially set to 0 + if waiting_time <= 0: + waiting_time = 2 + + logger.info(f"Request failed. Waiting {waiting_time} seconds between requests...") + time.sleep(waiting_time) + + # exponential backoff + waiting_time = waiting_time * exponential_backoff_factor + + # setup the OpenAI configurations for this client. + self._setup_from_config() + + # dedent the messages (field 'content' only) if needed (using textwrap) + if dedent_messages: + for message in current_messages: + if "content" in message: + message["content"] = utils.dedent(message["content"]) + + + # We need to adapt the parameters to the API type, so we create a dictionary with them first + chat_api_params = { + "model": model, + "messages": current_messages, + "temperature": temperature, + "max_tokens":max_tokens, + "frequency_penalty": frequency_penalty, + "presence_penalty": presence_penalty, + "stop": stop, + "timeout": timeout, + "stream": False, + "n": n, + } + + if top_p is not None and top_p > 0: + chat_api_params["top_p"] = top_p + + if response_format is not None: + chat_api_params["response_format"] = response_format + + i = 0 + while i < max_attempts: + try: + i += 1 + + try: + logger.debug(f"Sending messages to OpenAI API. Token count={self._count_tokens(current_messages, model)}.") + except NotImplementedError: + logger.debug(f"Token count not implemented for model {model}.") + + start_time = time.monotonic() + logger.debug(f"Calling model with client class {self.__class__.__name__}.") + + ############################################################### + # call the model, either from the cache or from the API + ############################################################### + cache_key = str((model, chat_api_params)) # need string to be hashable + if self.cache_api_calls and (cache_key in self.api_cache): + response = self.api_cache[cache_key] + else: + if waiting_time > 0: + logger.info(f"Waiting {waiting_time} seconds before next API request (to avoid throttling)...") + time.sleep(waiting_time) + + response = self._raw_model_call(model, chat_api_params) + if self.cache_api_calls: + self.api_cache[cache_key] = response + self._save_cache() + + + logger.debug(f"Got response from API: {response}") + end_time = time.monotonic() + logger.debug( + f"Got response in {end_time - start_time:.2f} seconds after {i} attempts.") + + if enable_pydantic_model_return: + return utils.to_pydantic_or_sanitized_dict(self._raw_model_response_extractor(response), model=response_format) + else: + return utils.sanitize_dict(self._raw_model_response_extractor(response)) + + except InvalidRequestError as e: + logger.error(f"[{i}] Invalid request error, won't retry: {e}") + + # there's no point in retrying if the request is invalid + # so we return None right away + return None + + except openai.BadRequestError as e: + logger.error(f"[{i}] Invalid request error, won't retry: {e}") + + # there's no point in retrying if the request is invalid + # so we return None right away + return None + + except openai.RateLimitError: + logger.warning( + f"[{i}] Rate limit error, waiting a bit and trying again.") + aux_exponential_backoff() + + except NonTerminalError as e: + logger.error(f"[{i}] Non-terminal error: {e}") + aux_exponential_backoff() + + except Exception as e: + logger.error(f"[{i}] {type(e).__name__} Error: {e}") + aux_exponential_backoff() + + logger.error(f"Failed to get response after {max_attempts} attempts.") + return None + + def _raw_model_call(self, model, chat_api_params): + """ + Calls the OpenAI API with the given parameters. Subclasses should + override this method to implement their own API calls. + """ + + # adjust parameters depending on the model + if self._is_reasoning_model(model): + # Reasoning models have slightly different parameters + del chat_api_params["stream"] + del chat_api_params["temperature"] + del chat_api_params["top_p"] + del chat_api_params["frequency_penalty"] + del chat_api_params["presence_penalty"] + + chat_api_params["max_completion_tokens"] = chat_api_params["max_tokens"] + del chat_api_params["max_tokens"] + + chat_api_params["reasoning_effort"] = default["reasoning_effort"] + + + # To make the log cleaner, we remove the messages from the logged parameters + logged_params = {k: v for k, v in chat_api_params.items() if k != "messages"} + + if "response_format" in chat_api_params: + # to enforce the response format via pydantic, we need to use a different method + + if "stream" in chat_api_params: + del chat_api_params["stream"] + + logger.debug(f"Calling LLM model (using .parse too) with these parameters: {logged_params}. Not showing 'messages' parameter.") + # complete message + logger.debug(f" --> Complete messages sent to LLM: {chat_api_params['messages']}") + + result_message = self.client.beta.chat.completions.parse( + **chat_api_params + ) + + return result_message + + else: + logger.debug(f"Calling LLM model with these parameters: {logged_params}. Not showing 'messages' parameter.") + return self.client.chat.completions.create( + **chat_api_params + ) + + def _is_reasoning_model(self, model): + return "o1" in model or "o3" in model + + def _raw_model_response_extractor(self, response): + """ + Extracts the response from the API response. Subclasses should + override this method to implement their own response extraction. + """ + return response.choices[0].message.to_dict() + + def _count_tokens(self, messages: list, model: str): + """ + Count the number of OpenAI tokens in a list of messages using tiktoken. + + Adapted from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb + + Args: + messages (list): A list of dictionaries representing the conversation history. + model (str): The name of the model to use for encoding the string. + """ + try: + try: + encoding = tiktoken.encoding_for_model(model) + except KeyError: + logger.debug("Token count: model not found. Using cl100k_base encoding.") + encoding = tiktoken.get_encoding("cl100k_base") + + if model in { + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0314", + "gpt-4-32k-0314", + "gpt-4-0613", + "gpt-4-32k-0613", + } or "o1" in model or "o3" in model: # assuming o1/3 models work the same way + tokens_per_message = 3 + tokens_per_name = 1 + elif model == "gpt-3.5-turbo-0301": + tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n + tokens_per_name = -1 # if there's a name, the role is omitted + elif "gpt-3.5-turbo" in model: + logger.debug("Token count: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.") + return self._count_tokens(messages, model="gpt-3.5-turbo-0613") + elif ("gpt-4" in model) or ("ppo" in model) or ("alias-large" in model): + logger.debug("Token count: gpt-4/alias-large may update over time. Returning num tokens assuming gpt-4-0613.") + return self._count_tokens(messages, model="gpt-4-0613") + else: + raise NotImplementedError( + f"""_count_tokens() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""" + ) + + num_tokens = 0 + for message in messages: + num_tokens += tokens_per_message + for key, value in message.items(): + num_tokens += len(encoding.encode(value)) + if key == "name": + num_tokens += tokens_per_name + num_tokens += 3 # every reply is primed with <|start|>assistant<|message|> + return num_tokens + + except Exception as e: + logger.error(f"Error counting tokens: {e}") + return None + + def _save_cache(self): + """ + Saves the API cache to disk. We use pickle to do that because some obj + are not JSON serializable. + """ + # use pickle to save the cache + pickle.dump(self.api_cache, open(self.cache_file_name, "wb", encoding="utf-8", errors="replace")) + + + def _load_cache(self): + + """ + Loads the API cache from disk. + """ + # unpickle + return pickle.load(open(self.cache_file_name, "rb", encoding="utf-8", errors="replace")) if os.path.exists(self.cache_file_name) else {} + + def get_embedding(self, text, model=default["embedding_model"]): + """ + Gets the embedding of the given text using the specified model. + + Args: + text (str): The text to embed. + model (str): The name of the model to use for embedding the text. + + Returns: + The embedding of the text. + """ + response = self._raw_embedding_model_call(text, model) + return self._raw_embedding_model_response_extractor(response) + + def _raw_embedding_model_call(self, text, model): + """ + Calls the OpenAI API to get the embedding of the given text. Subclasses should + override this method to implement their own API calls. + """ + return self.client.embeddings.create( + input=[text], + model=model + ) + + def _raw_embedding_model_response_extractor(self, response): + """ + Extracts the embedding from the API response. Subclasses should + override this method to implement their own response extraction. + """ + return response.data[0].embedding + +class AzureClient(OpenAIClient): + + def __init__(self, cache_api_calls=default["cache_api_calls"], cache_file_name=default["cache_file_name"]) -> None: + logger.debug("Initializing AzureClient") + + super().__init__(cache_api_calls, cache_file_name) + + def _setup_from_config(self): + """ + Sets up the Azure OpenAI Service API configurations for this client, + including the API endpoint and key. + """ + if os.getenv("AZURE_OPENAI_KEY"): + logger.info("Using Azure OpenAI Service API with key.") + self.client = AzureOpenAI(azure_endpoint= os.getenv("AZURE_OPENAI_ENDPOINT"), + api_version = config["OpenAI"]["AZURE_API_VERSION"], + api_key = os.getenv("AZURE_OPENAI_KEY")) + else: # Use Entra ID Auth + logger.info("Using Azure OpenAI Service API with Entra ID Auth.") + from azure.identity import DefaultAzureCredential, get_bearer_token_provider + + credential = DefaultAzureCredential() + token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default") + self.client = AzureOpenAI( + azure_endpoint= os.getenv("AZURE_OPENAI_ENDPOINT"), + api_version = config["OpenAI"]["AZURE_API_VERSION"], + azure_ad_token_provider=token_provider + ) + + +class HelmholtzBlabladorClient(OpenAIClient): + + def __init__(self, cache_api_calls=default["cache_api_calls"], cache_file_name=default["cache_file_name"]) -> None: + logger.debug("Initializing HelmholtzBlabladorClient") + super().__init__(cache_api_calls, cache_file_name) + + def _setup_from_config(self): + """ + Sets up the Helmholtz Blablador API configurations for this client. + """ + self.client = OpenAI( + base_url="https://api.helmholtz-blablador.fz-juelich.de/v1", + api_key=os.getenv("BLABLADOR_API_KEY", "dummy"), + ) + +########################################################################### +# Exceptions +########################################################################### +class InvalidRequestError(Exception): + """ + Exception raised when the request to the OpenAI API is invalid. + """ + pass + +class NonTerminalError(Exception): + """ + Exception raised when an unspecified error occurs but we know we can retry. + """ + pass + +########################################################################### +# Clients registry +# +# We can have potentially different clients, so we need a place to +# register them and retrieve them when needed. +# +# We support both OpenAI and Azure OpenAI Service API by default. +# Thus, we need to set the API parameters based on the choice of the user. +# This is done within specialized classes. +# +# It is also possible to register custom clients, to access internal or +# otherwise non-conventional API endpoints. +########################################################################### +_api_type_to_client = {} +_api_type_override = None + +def register_client(api_type, client): + """ + Registers a client for the given API type. + + Args: + api_type (str): The API type for which we want to register the client. + client: The client to register. + """ + _api_type_to_client[api_type] = client + +def _get_client_for_api_type(api_type): + """ + Returns the client for the given API type. + + Args: + api_type (str): The API type for which we want to get the client. + """ + try: + return _api_type_to_client[api_type] + except KeyError: + raise ValueError(f"API type {api_type} is not supported. Please check the 'config.ini' file.") + +def client(): + """ + Returns the client for the configured API type. + """ + api_type = config["OpenAI"]["API_TYPE"] if _api_type_override is None else _api_type_override + + logger.debug(f"Using API type {api_type}.") + return _get_client_for_api_type(api_type) + + +# TODO simplify the custom configuration methods below + +def force_api_type(api_type): + """ + Forces the use of the given API type, thus overriding any other configuration. + + Args: + api_type (str): The API type to use. + """ + global _api_type_override + _api_type_override = api_type + +def force_api_cache(cache_api_calls, cache_file_name=default["cache_file_name"]): + """ + Forces the use of the given API cache configuration, thus overriding any other configuration. + + Args: + cache_api_calls (bool): Whether to cache API calls. + cache_file_name (str): The name of the file to use for caching API calls. + """ + # set the cache parameters on all clients + for client in _api_type_to_client.values(): + client.set_api_cache(cache_api_calls, cache_file_name) + +# default client +register_client("openai", OpenAIClient()) +register_client("azure", AzureClient()) +register_client("helmholtz-blablador", HelmholtzBlabladorClient()) + + + diff --git a/profiling.py b/profiling.py new file mode 100644 index 0000000000000000000000000000000000000000..711e4a18e0a0905b4854f82102c88681be78df3b --- /dev/null +++ b/profiling.py @@ -0,0 +1,714 @@ +""" +Provides mechanisms for creating understanding the characteristics of agent populations, such as +their age distribution, typical interests, and so on. + +Guideline for plotting the methods: all plot methods should also return a Pandas dataframe with the data used for +plotting. +""" +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from typing import List, Dict, Any, Optional, Union, Callable +from collections import Counter, defaultdict +import warnings + +# Handle TinyPerson import gracefully +try: + from tinytroupe.agent import TinyPerson +except ImportError: + # Fallback if import fails + TinyPerson = None + + +class Profiler: + """ + Advanced profiler for analyzing agent population characteristics with support for + complex attributes, statistical analysis, and comprehensive visualizations. + """ + + def __init__(self, attributes: List[str] = ["age", "occupation.title", "nationality"]) -> None: + self.attributes = attributes + self.attributes_distributions = {} # attribute -> DataFrame + self.agents_data = None # Store processed agent data + self.analysis_results = {} # Store various analysis results + + # Set up better plotting style + plt.style.use('default') + sns.set_palette("husl") + + def profile(self, agents: Union[List[dict], List[TinyPerson]], plot: bool = True, + advanced_analysis: bool = True) -> Dict[str, Any]: + """ + Profiles the given agents with comprehensive analysis. + + Args: + agents: The agents to be profiled (either dicts or TinyPerson objects) + plot: Whether to generate visualizations + advanced_analysis: Whether to perform advanced statistical analysis + + Returns: + Dictionary containing all analysis results + """ + # Convert agents to consistent format + self.agents_data = self._prepare_agent_data(agents) + + # Basic attribute distributions + self.attributes_distributions = self._compute_attributes_distributions(self.agents_data) + + if advanced_analysis: + self._perform_advanced_analysis() + + if plot: + self.render(advanced=advanced_analysis) + + return { + 'distributions': self.attributes_distributions, + 'analysis': self.analysis_results, + 'summary_stats': self._generate_summary_statistics() + } + + def _prepare_agent_data(self, agents: Union[List[dict], List[TinyPerson]]) -> List[Dict[str, Any]]: + """Convert agents to a consistent dictionary format for analysis.""" + processed_agents = [] + + for agent in agents: + if isinstance(agent, TinyPerson): + # Extract data from TinyPerson object + agent_data = self._extract_tinyperson_data(agent) + else: + agent_data = agent.copy() + + processed_agents.append(agent_data) + + return processed_agents + + def _extract_tinyperson_data(self, agent: TinyPerson) -> Dict[str, Any]: + """Extract comprehensive data from a TinyPerson object.""" + data = {} + + # Basic persona attributes + if hasattr(agent, '_persona') and agent._persona: + data.update(agent._persona) + + # Mental state information + if hasattr(agent, '_mental_state') and agent._mental_state: + mental_state = agent._mental_state + data['current_emotions'] = mental_state.get('emotions') + data['current_goals'] = mental_state.get('goals', []) + data['current_context'] = mental_state.get('context', []) + data['accessible_agents_count'] = len(mental_state.get('accessible_agents', [])) + + # Behavioral metrics + if hasattr(agent, 'actions_count'): + data['actions_count'] = agent.actions_count + if hasattr(agent, 'stimuli_count'): + data['stimuli_count'] = agent.stimuli_count + + # Memory statistics + if hasattr(agent, 'episodic_memory') and agent.episodic_memory: + try: + # Get total memory size including both committed memory and current episode buffer + memory_size = len(agent.episodic_memory.memory) + len(agent.episodic_memory.episodic_buffer) + data['episodic_memory_size'] = memory_size + except AttributeError: + # Fallback if memory structure is different + data['episodic_memory_size'] = 0 + + # Social connections + if hasattr(agent, '_accessible_agents'): + data['social_connections'] = len(agent._accessible_agents) + + return data + + def _perform_advanced_analysis(self): + """Perform advanced statistical and behavioral analysis.""" + self.analysis_results = {} + + # Demographic analysis + self.analysis_results['demographics'] = self._analyze_demographics() + + # Behavioral patterns + self.analysis_results['behavioral_patterns'] = self._analyze_behavioral_patterns() + + # Social network analysis + self.analysis_results['social_analysis'] = self._analyze_social_patterns() + + # Personality clustering + self.analysis_results['personality_clusters'] = self._analyze_personality_clusters() + + # Correlations + self.analysis_results['correlations'] = self._analyze_correlations() + + def _analyze_demographics(self) -> Dict[str, Any]: + """Analyze demographic patterns in the population.""" + demographics = {} + + # Age analysis + ages = [agent.get('age') for agent in self.agents_data if agent.get('age') is not None] + if ages: + demographics['age_stats'] = { + 'mean': np.mean(ages), + 'median': np.median(ages), + 'std': np.std(ages), + 'range': (min(ages), max(ages)), + 'distribution': 'normal' if self._test_normality(ages) else 'non-normal' + } + + # Occupation diversity + occupations = [agent.get('occupation', {}).get('title') if isinstance(agent.get('occupation'), dict) + else agent.get('occupation') for agent in self.agents_data] + occupations = [occ for occ in occupations if occ is not None] + + if occupations: + occ_counts = Counter(occupations) + demographics['occupation_diversity'] = { + 'unique_count': len(occ_counts), + 'diversity_index': self._calculate_diversity_index(occ_counts), + 'most_common': occ_counts.most_common(5) + } + + # Geographic distribution + nationalities = [agent.get('nationality') for agent in self.agents_data if agent.get('nationality')] + if nationalities: + nat_counts = Counter(nationalities) + demographics['geographic_diversity'] = { + 'unique_countries': len(nat_counts), + 'diversity_index': self._calculate_diversity_index(nat_counts), + 'distribution': dict(nat_counts) + } + + return demographics + + def _analyze_behavioral_patterns(self) -> Dict[str, Any]: + """Analyze behavioral patterns across the population.""" + behavioral = {} + + # Activity levels + actions_data = [agent.get('actions_count', 0) for agent in self.agents_data] + stimuli_data = [agent.get('stimuli_count', 0) for agent in self.agents_data] + + if any(actions_data): + behavioral['activity_levels'] = { + 'actions_mean': np.mean(actions_data), + 'actions_std': np.std(actions_data), + 'stimuli_mean': np.mean(stimuli_data), + 'stimuli_std': np.std(stimuli_data), + 'activity_ratio': np.mean(actions_data) / max(np.mean(stimuli_data), 1) + } + + # Goal patterns + all_goals = [] + for agent in self.agents_data: + goals = agent.get('current_goals', []) + if isinstance(goals, list): + all_goals.extend(goals) + + if all_goals: + goal_counts = Counter(all_goals) + behavioral['goal_patterns'] = { + 'common_goals': goal_counts.most_common(10), + 'goal_diversity': self._calculate_diversity_index(goal_counts) + } + + return behavioral + + def _analyze_social_patterns(self) -> Dict[str, Any]: + """Analyze social connection patterns.""" + social = {} + + # Social connectivity + connections = [agent.get('social_connections', 0) for agent in self.agents_data] + accessible_counts = [agent.get('accessible_agents_count', 0) for agent in self.agents_data] + + if any(connections + accessible_counts): + social['connectivity'] = { + 'avg_connections': np.mean(connections), + 'avg_accessible': np.mean(accessible_counts), + 'connectivity_distribution': self._categorize_connectivity(connections), + 'social_isolation_rate': sum(1 for c in connections if c == 0) / len(connections) + } + + return social + + def _analyze_personality_clusters(self) -> Dict[str, Any]: + """Identify personality-based clusters if Big Five data is available.""" + personality = {} + + # Extract Big Five traits if available + big_five_data = [] + for agent in self.agents_data: + if 'big_five' in agent and isinstance(agent['big_five'], dict): + traits = agent['big_five'] + # Convert text descriptions to numerical values (simplified approach) + numerical_traits = {} + for trait, value in traits.items(): + if isinstance(value, str): + if 'high' in value.lower(): + numerical_traits[trait] = 0.8 + elif 'medium' in value.lower(): + numerical_traits[trait] = 0.5 + elif 'low' in value.lower(): + numerical_traits[trait] = 0.2 + else: + numerical_traits[trait] = 0.5 # Default + else: + numerical_traits[trait] = value + + if len(numerical_traits) == 5: # Full Big Five + big_five_data.append(numerical_traits) + + if len(big_five_data) >= 2: # Need minimum agents for analysis (reduced from >3 to >=2) + df_traits = pd.DataFrame(big_five_data) + + # Simple clustering based on dominant traits + personality['trait_analysis'] = { + 'average_traits': df_traits.mean().to_dict(), + 'trait_correlations': df_traits.corr().to_dict() if len(big_five_data) > 1 else {}, + 'dominant_traits': self._identify_dominant_traits(df_traits) + } + + return personality + + def _analyze_correlations(self) -> Dict[str, Any]: + """Analyze correlations between different attributes.""" + correlations = {} + + # Create a numerical dataset for correlation analysis + numerical_data = {} + + for agent in self.agents_data: + for attr in ['age', 'actions_count', 'stimuli_count', 'social_connections']: + if attr not in numerical_data: + numerical_data[attr] = [] + numerical_data[attr].append(agent.get(attr, 0)) + + if len(numerical_data) > 1: + df_corr = pd.DataFrame(numerical_data) + correlation_matrix = df_corr.corr() + + # Find strong correlations (> 0.5) + strong_correlations = [] + for i in range(len(correlation_matrix.columns)): + for j in range(i+1, len(correlation_matrix.columns)): + corr_value = correlation_matrix.iloc[i, j] + if abs(corr_value) > 0.5: + strong_correlations.append({ + 'variables': (correlation_matrix.columns[i], correlation_matrix.columns[j]), + 'correlation': corr_value + }) + + correlations['numerical_correlations'] = strong_correlations + correlations['correlation_matrix'] = correlation_matrix.to_dict() + + return correlations + + def render(self, advanced: bool = True) -> None: + """ + Renders comprehensive visualizations of the agent population analysis. + """ + # Basic attribute distributions + self._plot_basic_distributions() + + if advanced and self.analysis_results: + self._plot_advanced_analysis() + + def _plot_basic_distributions(self) -> None: + """Plot basic attribute distributions with improved styling.""" + n_attrs = len(self.attributes) + if n_attrs == 0: + return + + # Calculate subplot layout + n_cols = min(3, n_attrs) + n_rows = (n_attrs + n_cols - 1) // n_cols + + fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows)) + if n_attrs == 1: + axes = [axes] + elif n_rows == 1: + axes = [axes] if n_attrs == 1 else axes + else: + axes = axes.flatten() + + for i, attribute in enumerate(self.attributes): + ax = axes[i] if n_attrs > 1 else axes[0] + + if attribute in self.attributes_distributions: + df = self.attributes_distributions[attribute] + + # Create better visualizations based on data type + if len(df) <= 15: # Categorical data + df.plot(kind='bar', ax=ax, color=sns.color_palette("husl", len(df))) + ax.set_title(f"{attribute.replace('_', ' ').title()} Distribution", fontsize=12, fontweight='bold') + ax.tick_params(axis='x', rotation=45) + else: # Many categories - use horizontal bar for readability + df.head(15).plot(kind='barh', ax=ax, color=sns.color_palette("husl", 15)) + ax.set_title(f"Top 15 {attribute.replace('_', ' ').title()}", fontsize=12, fontweight='bold') + + ax.grid(axis='y', alpha=0.3) + ax.set_xlabel('Count') + + # Hide empty subplots + for i in range(n_attrs, len(axes)): + axes[i].set_visible(False) + + plt.tight_layout() + plt.show() + + def _plot_advanced_analysis(self) -> None: + """Create advanced visualizations for the analysis results.""" + + # 1. Demographics overview + if 'demographics' in self.analysis_results: + self._plot_demographics() + + # 2. Behavioral patterns + if 'behavioral_patterns' in self.analysis_results: + self._plot_behavioral_patterns() + + # 3. Correlation heatmap + if 'correlations' in self.analysis_results and 'correlation_matrix' in self.analysis_results['correlations']: + self._plot_correlation_heatmap() + + def _plot_demographics(self) -> None: + """Plot demographic analysis results.""" + demo = self.analysis_results['demographics'] + + fig, axes = plt.subplots(2, 2, figsize=(12, 10)) + fig.suptitle('Population Demographics Analysis', fontsize=16, fontweight='bold') + + # Age distribution + if 'age_stats' in demo: + ages = [agent.get('age') for agent in self.agents_data if agent.get('age') is not None] + axes[0, 0].hist(ages, bins=10, alpha=0.7, color='skyblue', edgecolor='black') + axes[0, 0].axvline(demo['age_stats']['mean'], color='red', linestyle='--', + label=f"Mean: {demo['age_stats']['mean']:.1f}") + axes[0, 0].set_title('Age Distribution') + axes[0, 0].set_xlabel('Age') + axes[0, 0].set_ylabel('Count') + axes[0, 0].legend() + + # Occupation diversity + if 'occupation_diversity' in demo: + occ_data = demo['occupation_diversity']['most_common'] + if occ_data: + occs, counts = zip(*occ_data) + axes[0, 1].pie(counts, labels=occs, autopct='%1.1f%%') + axes[0, 1].set_title('Top Occupations') + + # Geographic distribution + if 'geographic_diversity' in demo: + geo_data = demo['geographic_diversity']['distribution'] + if geo_data: + countries = list(geo_data.keys())[:10] # Top 10 + counts = [geo_data[c] for c in countries] + axes[1, 0].barh(countries, counts, color='lightcoral') + axes[1, 0].set_title('Geographic Distribution') + axes[1, 0].set_xlabel('Count') + + # Diversity metrics + diversity_metrics = [] + diversity_values = [] + + if 'occupation_diversity' in demo: + diversity_metrics.append('Occupation\nDiversity') + diversity_values.append(demo['occupation_diversity']['diversity_index']) + + if 'geographic_diversity' in demo: + diversity_metrics.append('Geographic\nDiversity') + diversity_values.append(demo['geographic_diversity']['diversity_index']) + + if diversity_metrics: + axes[1, 1].bar(diversity_metrics, diversity_values, color='lightgreen') + axes[1, 1].set_title('Diversity Indices') + axes[1, 1].set_ylabel('Diversity Score') + axes[1, 1].set_ylim(0, 1) + + plt.tight_layout() + plt.show() + + def _plot_behavioral_patterns(self) -> None: + """Plot behavioral analysis results.""" + behavioral = self.analysis_results['behavioral_patterns'] + + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + fig.suptitle('Behavioral Patterns Analysis', fontsize=16, fontweight='bold') + + # Activity levels scatter plot + if 'activity_levels' in behavioral: + actions_data = [agent.get('actions_count', 0) for agent in self.agents_data] + stimuli_data = [agent.get('stimuli_count', 0) for agent in self.agents_data] + + axes[0].scatter(stimuli_data, actions_data, alpha=0.6, color='purple') + axes[0].set_xlabel('Stimuli Count') + axes[0].set_ylabel('Actions Count') + axes[0].set_title('Activity Patterns') + + # Add trend line + if len(stimuli_data) > 1 and len(actions_data) > 1: + z = np.polyfit(stimuli_data, actions_data, 1) + p = np.poly1d(z) + axes[0].plot(stimuli_data, p(stimuli_data), "r--", alpha=0.8) + + # Goal patterns + if 'goal_patterns' in behavioral and behavioral['goal_patterns']['common_goals']: + goals, counts = zip(*behavioral['goal_patterns']['common_goals'][:8]) + axes[1].barh(range(len(goals)), counts, color='orange') + axes[1].set_yticks(range(len(goals))) + axes[1].set_yticklabels([g[:30] + '...' if len(str(g)) > 30 else str(g) for g in goals]) + axes[1].set_xlabel('Frequency') + axes[1].set_title('Common Goals') + + plt.tight_layout() + plt.show() + + def _plot_correlation_heatmap(self) -> None: + """Plot correlation heatmap for numerical attributes.""" + corr_data = self.analysis_results['correlations']['correlation_matrix'] + corr_df = pd.DataFrame(corr_data) + + plt.figure(figsize=(8, 6)) + sns.heatmap(corr_df, annot=True, cmap='coolwarm', center=0, + square=True, cbar_kws={'label': 'Correlation Coefficient'}) + plt.title('Attribute Correlations Heatmap', fontsize=14, fontweight='bold') + plt.tight_layout() + plt.show() + + def _compute_attributes_distributions(self, agents: list) -> dict: + """ + Computes the distributions of the attributes for the agents. + """ + distributions = {} + for attribute in self.attributes: + distributions[attribute] = self._compute_attribute_distribution(agents, attribute) + + return distributions + + def _compute_attribute_distribution(self, agents: list, attribute: str) -> pd.DataFrame: + """ + Computes the distribution of a given attribute with support for nested attributes. + """ + values = [] + + for agent in agents: + value = self._get_nested_attribute(agent, attribute) + values.append(value) + + # Handle None values + values = [v for v in values if v is not None] + + if not values: + return pd.DataFrame() + + # Convert mixed types to string for consistent sorting + try: + value_counts = pd.Series(values).value_counts().sort_index() + except TypeError: + # Handle mixed data types by converting to strings + string_values = [str(v) for v in values] + value_counts = pd.Series(string_values).value_counts().sort_index() + + return value_counts + + def _get_nested_attribute(self, agent: dict, attribute: str) -> Any: + """Get nested attribute using dot notation (e.g., 'occupation.title').""" + keys = attribute.split('.') + value = agent + + for key in keys: + if isinstance(value, dict) and key in value: + value = value[key] + else: + return None + + return value + + # Utility methods for advanced analysis + def _test_normality(self, data: List[float]) -> bool: + """Simple normality test using skewness.""" + if len(data) < 3: + return False + + skewness = pd.Series(data).skew() + return abs(skewness) < 0.3 # Stringent normality test - threshold to catch bimodal distributions + + def _calculate_diversity_index(self, counts: Counter) -> float: + """Calculate Shannon diversity index.""" + total = sum(counts.values()) + if total <= 1: + return 0.0 + + diversity = 0 + for count in counts.values(): + if count > 0: + p = count / total + diversity -= p * np.log(p) + + return diversity / np.log(len(counts)) if len(counts) > 1 else 0 + + def _categorize_connectivity(self, connections: List[int]) -> Dict[str, int]: + """Categorize agents by their connectivity level.""" + categories = {'isolated': 0, 'low': 0, 'medium': 0, 'high': 0} + + for conn in connections: + if conn == 0: + categories['isolated'] += 1 + elif conn <= 2: + categories['low'] += 1 + elif conn <= 5: + categories['medium'] += 1 + else: + categories['high'] += 1 + + return categories + + def _identify_dominant_traits(self, traits_df: pd.DataFrame) -> Dict[str, str]: + """Identify the dominant personality traits in the population.""" + trait_means = traits_df.mean() + dominant = {} + + for trait, mean_value in trait_means.items(): + if mean_value > 0.6: + dominant[trait] = 'high' + elif mean_value < 0.4: + dominant[trait] = 'low' + else: + dominant[trait] = 'moderate' + + return dominant + + def _generate_summary_statistics(self) -> Dict[str, Any]: + """Generate comprehensive summary statistics.""" + summary = { + 'total_agents': len(self.agents_data), + 'attributes_analyzed': len(self.attributes), + 'data_completeness': {} + } + + # Calculate data completeness for each attribute - handle empty data + if len(self.agents_data) > 0: + for attr in self.attributes: + non_null_count = sum(1 for agent in self.agents_data + if self._get_nested_attribute(agent, attr) is not None) + summary['data_completeness'][attr] = non_null_count / len(self.agents_data) + else: + # No agents - set all completeness to 0 + for attr in self.attributes: + summary['data_completeness'][attr] = 0.0 + + return summary + + def export_analysis_report(self, filename: str = "agent_population_analysis.txt") -> None: + """Export a comprehensive text report of the analysis.""" + with open(filename, 'w', encoding="utf-8", errors="replace") as f: + f.write("AGENT POPULATION ANALYSIS REPORT\n") + f.write("=" * 50 + "\n\n") + + def export_analysis_report(self, filename: str = "agent_population_analysis.txt") -> None: + """Export a comprehensive text report of the analysis.""" + with open(filename, 'w', encoding="utf-8", errors="replace") as f: + f.write("AGENT POPULATION ANALYSIS REPORT\n") + f.write("=" * 50 + "\n\n") + + # Summary statistics - always generate from current data + summary = self._generate_summary_statistics() + f.write(f"Total Agents Analyzed: {summary['total_agents']}\n") + f.write(f"Attributes Analyzed: {summary['attributes_analyzed']}\n\n") + + f.write("Data Completeness:\n") + for attr, completeness in summary['data_completeness'].items(): + f.write(f" {attr}: {completeness:.2%}\n") + f.write("\n") + + # Demographics + if 'demographics' in self.analysis_results: + demo = self.analysis_results['demographics'] + f.write("DEMOGRAPHICS\n") + f.write("-" * 20 + "\n") + + if 'age_stats' in demo: + age_stats = demo['age_stats'] + f.write(f"Age Statistics:\n") + f.write(f" Mean: {age_stats['mean']:.1f} years\n") + f.write(f" Median: {age_stats['median']:.1f} years\n") + f.write(f" Range: {age_stats['range'][0]}-{age_stats['range'][1]} years\n\n") + + if 'occupation_diversity' in demo: + occ_div = demo['occupation_diversity'] + f.write(f"Occupation Diversity:\n") + f.write(f" Unique Occupations: {occ_div['unique_count']}\n") + f.write(f" Diversity Index: {occ_div['diversity_index']:.3f}\n\n") + + # Behavioral patterns + if 'behavioral_patterns' in self.analysis_results: + behavioral = self.analysis_results['behavioral_patterns'] + f.write("BEHAVIORAL PATTERNS\n") + f.write("-" * 20 + "\n") + + if 'activity_levels' in behavioral: + activity = behavioral['activity_levels'] + f.write(f"Activity Levels:\n") + f.write(f" Average Actions: {activity['actions_mean']:.1f}\n") + f.write(f" Average Stimuli: {activity['stimuli_mean']:.1f}\n") + f.write(f" Activity Ratio: {activity['activity_ratio']:.2f}\n\n") + + print(f"Analysis report exported to {filename}") + + def add_custom_analysis(self, name: str, analysis_func: Callable[[List[Dict]], Any]) -> None: + """ + Add a custom analysis function that will be executed during profiling. + + Args: + name: Name for the custom analysis + analysis_func: Function that takes agent data and returns analysis results + """ + if not hasattr(self, '_custom_analyses'): + self._custom_analyses = {} + + self._custom_analyses[name] = analysis_func + + def compare_populations(self, other_agents: Union[List[dict], List[TinyPerson]], + attributes: Optional[List[str]] = None) -> Dict[str, Any]: + """ + Compare this population with another population. + + Args: + other_agents: Another set of agents to compare with + attributes: Specific attributes to compare (uses self.attributes if None) + + Returns: + Comparison results + """ + if attributes is None: + attributes = self.attributes + + # Create temporary profiler for the other population + other_profiler = Profiler(attributes) + other_results = other_profiler.profile(other_agents, plot=False, advanced_analysis=True) + + comparison = { + 'population_sizes': { + 'current': len(self.agents_data), + 'comparison': len(other_profiler.agents_data) + }, + 'attribute_comparisons': {} + } + + # Compare distributions for each attribute + for attr in attributes: + if (attr in self.attributes_distributions and + attr in other_profiler.attributes_distributions): + + current_dist = self.attributes_distributions[attr] + other_dist = other_profiler.attributes_distributions[attr] + + # Statistical comparison (simplified) + comparison['attribute_comparisons'][attr] = { + 'current_unique_values': len(current_dist), + 'comparison_unique_values': len(other_dist), + 'current_top_3': current_dist.head(3).to_dict(), + 'comparison_top_3': other_dist.head(3).to_dict() + } + + return comparison \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..28b8185f8c0e7234c1f996bf8b5842dd9de42d7a --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,59 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +packages = ["tinytroupe"] +include-package-data = true + +[project] +name = "tinytroupe" +version = "0.5.2" +authors = [ + { name="Paulo Salem", email="paulo.salem@microsoft.com" } +] +description = "LLM-based people simulation for design, validation and insight generation in business." +readme = "README.md" +requires-python = ">=3.10" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +dependencies = [ + "pandas", + "pytest", "pytest-cov", + "openai >= 1.65", + "tiktoken", + "msal", + "rich", "requests", "chevron", + "llama-index", "llama-index-embeddings-huggingface", "llama-index-readers-web", "llama-index-embeddings-azure-openai", + "pypandoc", "docx", "markdown", + "jupyter", + "matplotlib", + "pydantic", + "pypandoc", + "textdistance", + "scipy", + "transformers==4.38.2", + "huggingface-hub==0.22.2" +] + +[project.urls] +"Homepage" = "https://github.com/microsoft/tinytroupe" + +[tool.pytest.ini_options] +pythonpath = [ + "." +] + +testpaths = [ + "./tests/" +] + +markers = [ + "examples: mark a test as the execution of examples", + "notebooks: mark a test as a more specific Jupyter notebook execution example", +] +addopts = "--cov=tinytroupe --cov-report=html --cov-report=xml" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b696a73edde8e599a58b5fb83c8fac2ad8d64e96 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,24 @@ +gradio +pandas +pytest +pytest-cov +openai>=1.65 +tiktoken +msal +rich +requests +chevron +llama-index +llama-index-embeddings-huggingface +llama-index-readers-web +llama-index-embeddings-azure-openai +pypandoc +docx +markdown +jupyter +matplotlib +pydantic +textdistance +scipy +transformers==4.38.2 +huggingface-hub==0.22.2 diff --git a/steering/__init__.py b/steering/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9e661f0e1c1cd8258acaa59a3853edb732a301db --- /dev/null +++ b/steering/__init__.py @@ -0,0 +1,10 @@ +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.steering.tiny_story import TinyStory +from tinytroupe.steering.intervention import Intervention + +__all__ = ["TinyStory", "Intervention"] \ No newline at end of file diff --git a/steering/__pycache__/__init__.cpython-312.pyc b/steering/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2f139d528cc919bbd1d795d8121e71bcb96e718c Binary files /dev/null and b/steering/__pycache__/__init__.cpython-312.pyc differ diff --git a/steering/__pycache__/intervention.cpython-312.pyc b/steering/__pycache__/intervention.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f7dff5de85f898c9a3bbd0101ef1d008c13d2f5c Binary files /dev/null and b/steering/__pycache__/intervention.cpython-312.pyc differ diff --git a/steering/__pycache__/tiny_story.cpython-312.pyc b/steering/__pycache__/tiny_story.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..98a4d0eea5b02f160f1749ab385c933cbeab1c51 Binary files /dev/null and b/steering/__pycache__/tiny_story.cpython-312.pyc differ diff --git a/steering/intervention.py b/steering/intervention.py new file mode 100644 index 0000000000000000000000000000000000000000..264d3c369315a1f4a18ea2d1062087ed8bf4db53 --- /dev/null +++ b/steering/intervention.py @@ -0,0 +1,267 @@ +from typing import Union, List +from tinytroupe.extraction import logger +from tinytroupe.utils import JsonSerializableRegistry +from tinytroupe.experimentation import Proposition +from tinytroupe.environment import TinyWorld +from tinytroupe.agent import TinyPerson +import tinytroupe.utils as utils + +DEFAULT_FIRST_N = 10 +DEFAULT_LAST_N = 100 + +class InterventionBatch: + """ + A wrapper around multiple Intervention instances that allows chaining set_* methods. + """ + + def __init__(self, interventions): + self.interventions = interventions + + def __iter__(self): + """Makes the batch iterable and compatible with list()""" + return iter(self.interventions) + + def set_textual_precondition(self, text): + for intervention in self.interventions: + intervention.set_textual_precondition(text) + return self + + def set_functional_precondition(self, func): + for intervention in self.interventions: + intervention.set_functional_precondition(func) + return self + + def set_effect(self, effect_func): + for intervention in self.interventions: + intervention.set_effect(effect_func) + return self + + def set_propositional_precondition(self, proposition, threshold=None): + for intervention in self.interventions: + intervention.set_propositional_precondition(proposition, threshold) + return self + + def as_list(self): + """Return the list of individual interventions.""" + return self.interventions + + +class Intervention: + + def __init__(self, targets: Union[TinyPerson, TinyWorld, List[TinyPerson], List[TinyWorld]], + first_n:int=DEFAULT_FIRST_N, last_n:int=DEFAULT_LAST_N, + name: str = None): + """ + Initialize the intervention. + + Args: + target (Union[TinyPerson, TinyWorld, List[TinyPerson], List[TinyWorld]]): the target to intervene on + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + name (str): the name of the intervention + """ + + self.targets = targets + + # initialize the possible preconditions + self.text_precondition = None + self.precondition_func = None + + # effects + self.effect_func = None + + # which events to pay attention to? + self.first_n = first_n + self.last_n = last_n + + # name + if name is None: + self.name = self.name = f"Intervention {utils.fresh_id(self.__class__.__name__)}" + else: + self.name = name + + # the most recent precondition proposition used to check the precondition + self._last_text_precondition_proposition = None + self._last_functional_precondition_check = None + + # propositional precondition (optional) + self.propositional_precondition = None + self.propositional_precondition_threshold = None + self._last_propositional_precondition_check = None + + ################################################################################################ + # Intervention flow + ################################################################################################ + @classmethod + def create_for_each(cls, targets, first_n=DEFAULT_FIRST_N, last_n=DEFAULT_LAST_N, name=None): + """ + Create separate interventions for each target in the list. + + Args: + targets (list): List of targets (TinyPerson or TinyWorld instances) + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + name (str): the name of the intervention + + Returns: + InterventionBatch: A wrapper that allows chaining set_* methods that will apply to all interventions + """ + if not isinstance(targets, list): + targets = [targets] + + interventions = [cls(target, first_n=first_n, last_n=last_n, + name=f"{name}_{i}" if name else None) + for i, target in enumerate(targets)] + return InterventionBatch(interventions) + + def __call__(self): + """ + Execute the intervention. + + Returns: + bool: whether the intervention effect was applied. + """ + return self.execute() + + def execute(self): + """ + Execute the intervention. It first checks the precondition, and if it is met, applies the effect. + This is the simplest method to run the intervention. + + Returns: + bool: whether the intervention effect was applied. + """ + logger.debug(f"Executing intervention: {self}") + if self.check_precondition(): + self.apply_effect() + logger.debug(f"Precondition was true, intervention effect was applied.") + return True + + logger.debug(f"Precondition was false, intervention effect was not applied.") + return False + + def check_precondition(self): + """ + Check if the precondition for the intervention is met. + """ + # + # Textual precondition + # + if self.text_precondition is not None: + self._last_text_precondition_proposition = Proposition(claim=self.text_precondition, target=self.targets, first_n=self.first_n, last_n=self.last_n) + llm_precondition_check = self._last_text_precondition_proposition.check() + else: + llm_precondition_check = True + + # + # Functional precondition + # + if self.precondition_func is not None: + self._last_functional_precondition_check = self.precondition_func(self.targets) + else: + self._last_functional_precondition_check = True # default to True if no functional precondition is set + + # + # Propositional precondition + # + self._last_propositional_precondition_check = True + if self.propositional_precondition is not None: + if self.propositional_precondition_threshold is not None: + score = self.propositional_precondition.score(target=self.targets) + if score >= self.propositional_precondition_threshold: + self._last_propositional_precondition_check = False + else: + if not self.propositional_precondition.check(target=self.targets): + self._last_propositional_precondition_check = False + + return llm_precondition_check and self._last_functional_precondition_check and self._last_propositional_precondition_check + + + def apply_effect(self): + """ + Apply the intervention's effects. This won't check the precondition, + so it should be called after check_precondition. + """ + self.effect_func(self.targets) + + + ################################################################################################ + # Pre and post conditions + ################################################################################################ + + def set_textual_precondition(self, text): + """ + Set a precondition as text, to be interpreted by a language model. + + Args: + text (str): the text of the precondition + """ + self.text_precondition = text + return self # for chaining + + def set_functional_precondition(self, func): + """ + Set a precondition as a function, to be evaluated by the code. + + Args: + func (function): the function of the precondition. + Must have the a single argument, targets (either a TinyWorld or TinyPerson, or a list). Must return a boolean. + """ + self.precondition_func = func + return self # for chaining + + def set_effect(self, effect_func): + """ + Set the effect of the intervention. + + Args: + effect (str): the effect function of the intervention + """ + self.effect_func = effect_func + return self # for chaining + + def set_propositional_precondition(self, proposition:Proposition, threshold:int=None): + """ + Set a propositional precondition using the Proposition class, + optionally with a score threshold. + """ + + self.propositional_precondition = proposition + self.propositional_precondition_threshold = threshold + return self + + ################################################################################################ + # Inspection + ################################################################################################ + + def precondition_justification(self): + """ + Get the justification for the precondition. + """ + justification = "" + + # text precondition justification + if self._last_text_precondition_proposition is not None: + justification += f"{self._last_text_precondition_proposition.justification} (confidence = {self._last_text_precondition_proposition.confidence})\n\n" + + # functional precondition justification + if self.precondition_func is not None: + if self._last_functional_precondition_check == True: + justification += f"Functional precondition was met.\n\n" + + else: + justification += "Preconditions do not appear to be met.\n\n" + + # propositional precondition justification + if self.propositional_precondition is not None: + if self._last_propositional_precondition_check == True: + justification += f"Propositional precondition was met.\n\n" + else: + justification += "Propositional precondition was not met.\n\n" + + return justification + + return justification + + + diff --git a/steering/prompts/intervention.mustache b/steering/prompts/intervention.mustache new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/steering/prompts/story.continuation.system.mustache b/steering/prompts/story.continuation.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..72ba3694f7bed9a4d898e6f6c5ec1a48a5f150a9 --- /dev/null +++ b/steering/prompts/story.continuation.system.mustache @@ -0,0 +1,35 @@ +# Story teller + +You are a system that, given a story, creates a continuation for it. The stories you handle are of a special kind: +they are the result of a computer simulation, where agents interact with each other within an environment. +Hence, the story unfolds a sequence of simulation events. However, though terse, these events are meant to capture +a realistic scenario, where agents have goals, and they act to achieve them. Your task therefore is to create +a continuation of the story that is both plausible and interesting. + +Since these stories necessarily relates computer simulations, they always have some implicit or explicit purpose. +Stories, therefore, **must** respect the purpose they are given, meaning that any story enrichment, continunation, or other +related content **must** be in line with the purpose of the simulation. + +On the the format of the continuations you propose: + - You should propose a text that describes what happens next, with around {{number_of_words}} words. You can use one or more paragraphs. + DO NOT use more than {{number_of_words}} words!! + - You should use regular English, do not try to immitate the terse style of the simulation events. This is because + your output will be read by the agents and other simulation elements, as well as the human experimenter running everything, + and therefore it all should be human-readable. + +On the content of the continuations you propose: + - You should make sure that the continuation is plausible given the story you are given. + - If you already proposed a continuation before, you DO NOT repeat it again. You should always propose a new continuation. + - You should make sure that the continuation is interesting, i.e., it should involve some kind of conflict or tension + that the agents need to resolve. This is important because the agents are designed to be motivated by goals, and + they are likely to get bored if there is no conflict to resolve. + - You should make sure that the continuation is open-ended, i.e., it should not determine a unique course of events. + This is important because the agents are autonomous and should be able to act freely. + - If some specific requirement for the continuation is given, you **must** respect it, even if it means breaking the other rules. + User-given requirements always take precedence. + {{#include_plot_twist}}- You **must** also make sure your continuation is actually an unexpected plot twist. This is to cause surprise and curiosity.{{/include_plot_twist}} + +On other important elements to consider: + - If dates and times are mentioned, you should leverage them very carefully and realistically. For example, the events that happened + after a minute are different from those that happen after an hour and much more different from those that happen after a day or a week. + \ No newline at end of file diff --git a/steering/prompts/story.continuation.user.mustache b/steering/prompts/story.continuation.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..308f8c9edab86171767fbb61c9889ce95ab0d263 --- /dev/null +++ b/steering/prompts/story.continuation.user.mustache @@ -0,0 +1,7 @@ +Now, consider the following. + - simulation purpose: "{{purpose}}"; and + - story continuation requirements: "{{requirements}}". + +Please propose a continuation for the following story which respects the given purpose and continuation requirements: + +{{{current_simulation_trace}}} \ No newline at end of file diff --git a/steering/prompts/story.start.system.mustache b/steering/prompts/story.start.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..37b056b8abb13a21425f88faa6c1cbcc42556cbb --- /dev/null +++ b/steering/prompts/story.start.system.mustache @@ -0,0 +1,34 @@ +# Story teller + +You are a system that, given some agents and their interactions, creates an interesting story for them. +The stories you handle are of a special kind: they will guide a computer simulation, where agents interact with each other within an environment. +Hence, the story induces a sequence of simulation events. However, these events are meant to capture +a realistic scenario, where agents have goals, and they act to achieve them. Your task therefore is to start +a story that is both plausible and interesting. + +Since these stories necessarily relates computer simulations, they always have some implicit or explicit purpose. +Stories, therefore, **must** respect the purpose they are given, meaning that any story you start **must** be in +line with the purpose of the simulation. + +On the the format of the continuations you propose: + - You should propose a text that describes what the begining of a story, with around {{number_of_words}} words. You can use one or more paragraphs. + DO NOT use more than {{number_of_words}} words!! + - You should use regular English, do not try to immitate the terse style of the simulation events. This is because + your output will be read by the agents and other simulation elements, as well as the human experimenter running everything, + and therefore it all should be human-readable. + +On the content of the continuations you propose: + - You should make sure that the story is plausible given any context you receive. + - You should make sure that the continuation story is interesting, i.e., it should set the stage for some upcoming issue, conflict or + problem that the agents need to resolve. This is important because the agents are designed to be motivated by goals, and + they are likely to get bored if there is nothing very interesting happening. + - You should make sure that the story is open-ended, i.e., it should not determine a unique course of events. + This is important because the agents are autonomous and should be able to act freely. + - Though you might receice an existing context or set of agents interactions, you should actually make a completely new story, not + a continuation. The context you are given is just to provide some background, so that you are **consistent** with it, + but you should **not** continue from it. + +On other important elements to consider: + - If dates and times are mentioned, you should leverage them very carefully and realistically. For example, the events that happened + after a minute are different from those that happen after an hour and much more different from those that happen after a day or a week. + \ No newline at end of file diff --git a/steering/prompts/story.start.user.mustache b/steering/prompts/story.start.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..aecc3824e2242a4072af8b87b42f03f0b8306cea --- /dev/null +++ b/steering/prompts/story.start.user.mustache @@ -0,0 +1,7 @@ +Now, consider the following: + - simulation purpose: "{{purpose}}"; and + - story requirements: "{{requirements}}". + +Please propose a story start for the following simulation context which respects the given purpose: + +{{{current_simulation_trace}}} \ No newline at end of file diff --git a/steering/tiny_story.py b/steering/tiny_story.py new file mode 100644 index 0000000000000000000000000000000000000000..7646900257887aa00e30cbf2310f7aae260ecccb --- /dev/null +++ b/steering/tiny_story.py @@ -0,0 +1,138 @@ +from typing import List + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld +import tinytroupe.utils as utils +from tinytroupe import openai_utils + +class TinyStory: + """ + Every simulation tells a story. This class provides helper mechanisms to help with crafting appropriate stories in TinyTroupe. + """ + + + def __init__(self, environment:TinyWorld=None, agent:TinyPerson=None, purpose:str="Be a realistic simulation.", context:str="", + first_n=10, last_n=20, include_omission_info:bool=True) -> None: + """ + Initialize the story. The story can be about an environment or an agent. It also has a purpose, which + is used to guide the story generation. Stories are aware that they are related to simulations, so one can + specify simulation-related purposes. + + Args: + environment (TinyWorld, optional): The environment in which the story takes place. Defaults to None. + agent (TinyPerson, optional): The agent in the story. Defaults to None. + purpose (str, optional): The purpose of the story. Defaults to "Be a realistic simulation.". + context (str, optional): The current story context. Defaults to "". The actual story will be appended to this context. + first_n (int, optional): The number of first interactions to include in the story. Defaults to 10. + last_n (int, optional): The number of last interactions to include in the story. Defaults to 20. + include_omission_info (bool, optional): Whether to include information about omitted interactions. Defaults to True. + """ + + # exactly one of these must be provided + if environment and agent: + raise Exception("Either 'environment' or 'agent' should be provided, not both") + if not (environment or agent): + raise Exception("At least one of the parameters should be provided") + + self.environment = environment + self.agent = agent + + self.purpose = purpose + + self.current_story = context + + self.first_n = first_n + self.last_n = last_n + self.include_omission_info = include_omission_info + + def start_story(self, requirements="Start some interesting story about the agents.", number_of_words:int=100, include_plot_twist:bool=False) -> str: + """ + Start a new story. + """ + + rendering_configs = { + "purpose": self.purpose, + "requirements": requirements, + "current_simulation_trace": self._current_story(), + "number_of_words": number_of_words, + "include_plot_twist": include_plot_twist + } + + messages = utils.compose_initial_LLM_messages_with_templates("story.start.system.mustache", "story.start.user.mustache", + base_module_folder="steering", + rendering_configs=rendering_configs) + next_message = openai_utils.client().send_message(messages, temperature=1.5) + + start = next_message["content"] + + self.current_story += utils.dedent(\ + f""" + + ## The story begins + + {start} + + """ + ) + + return start + + def continue_story(self, requirements="Continue the story in an interesting way.", number_of_words:int=100, include_plot_twist:bool=False) -> str: + """ + Propose a continuation of the story. + """ + + rendering_configs = { + "purpose": self.purpose, + "requirements": requirements, + "current_simulation_trace": self._current_story(), + "number_of_words": number_of_words, + "include_plot_twist": include_plot_twist + } + + messages = utils.compose_initial_LLM_messages_with_templates("story.continuation.system.mustache", "story.continuation.user.mustache", + base_module_folder="steering", + rendering_configs=rendering_configs) + next_message = openai_utils.client().send_message(messages)#, temperature=1.5) + + continuation = next_message["content"] + + self.current_story += utils.dedent(\ + f""" + + ## The story continues + + {continuation} + + """ + ) + + return continuation + + def _current_story(self) -> str: + """ + Get the current story. + """ + interaction_history = "" + + if self.agent is not None: + interaction_history += self.agent.pretty_current_interactions(first_n=self.first_n, last_n=self.last_n, include_omission_info=self.include_omission_info) + elif self.environment is not None: + interaction_history += self.environment.pretty_current_interactions(first_n=self.first_n, last_n=self.last_n, include_omission_info=self.include_omission_info) + + tmp_current_story = self.current_story + tmp_current_story += utils.dedent(\ + f""" + + ## New simulation interactions to consider + + {interaction_history} + + """ + ) + + return tmp_current_story + + + diff --git a/tinytroupe/__init__.py b/tinytroupe/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..add830c25f99dda743cc2c35b92bb89058540170 --- /dev/null +++ b/tinytroupe/__init__.py @@ -0,0 +1,264 @@ +import os +import logging +import configparser +import rich # for rich console output +import rich.jupyter + +# add current path to sys.path +import sys +sys.path.append('.') +from tinytroupe import utils # now we can import our utils + +# AI disclaimers +print(\ +""" +!!!! +DISCLAIMER: TinyTroupe relies on Artificial Intelligence (AI) models to generate content. +The AI models are not perfect and may produce inappropriate or inacurate results. +For any serious or consequential use, please review the generated content before using it. +!!!! +""") + + +########################################################################### +# Configuration Management System +########################################################################### +class ConfigManager: + """ + Manages configuration values with the ability to override defaults. + Provides dynamic access to the latest config values. + """ + + # this is used in more than one place below, so we define it here + # to avoid errors in later changes + LOGLEVEL_KEY = "loglevel" + + def __init__(self): + self._config = {} + self._initialize_from_config() + + def _initialize_from_config(self): + """Initialize default values from config file""" + config = utils.read_config_file() + + self._config["model"] = config["OpenAI"].get("MODEL", "gpt-4o") + self._config["embedding_model"] = config["OpenAI"].get("EMBEDDING_MODEL", "text-embedding-3-small") + if config["OpenAI"].get("API_TYPE") == "azure": + self._config["azure_embedding_model_api_version"] = config["OpenAI"].get("AZURE_EMBEDDING_MODEL_API_VERSION", "2023-05-15") + self._config["reasoning_model"] = config["OpenAI"].get("REASONING_MODEL", "o3-mini") + + self._config["max_tokens"] = int(config["OpenAI"].get("MAX_TOKENS", "1024")) + self._config["temperature"] = float(config["OpenAI"].get("TEMPERATURE", "1.0")) + self._config["top_p"] = float(config["OpenAI"].get("TOP_P", "0.0")) + self._config["frequency_penalty"] = float(config["OpenAI"].get("FREQ_PENALTY", "0.0")) + self._config["presence_penalty"] = float( + config["OpenAI"].get("PRESENCE_PENALTY", "0.0")) + self._config["reasoning_effort"] = config["OpenAI"].get("REASONING_EFFORT", "high") + + self._config["timeout"] = float(config["OpenAI"].get("TIMEOUT", "30.0")) + self._config["max_attempts"] = float(config["OpenAI"].get("MAX_ATTEMPTS", "0.0")) + self._config["waiting_time"] = float(config["OpenAI"].get("WAITING_TIME", "1")) + self._config["exponential_backoff_factor"] = float(config["OpenAI"].get("EXPONENTIAL_BACKOFF_FACTOR", "5")) + + self._config["cache_api_calls"] = config["OpenAI"].getboolean("CACHE_API_CALLS", False) + self._config["cache_file_name"] = config["OpenAI"].get("CACHE_FILE_NAME", "openai_api_cache.pickle") + + self._config["max_content_display_length"] = config["OpenAI"].getint("MAX_CONTENT_DISPLAY_LENGTH", 1024) + + self._config["parallel_agent_actions"] = config["Simulation"].getboolean("PARALLEL_AGENT_ACTIONS", True) + self._config["parallel_agent_generation"] = config["Simulation"].getboolean("PARALLEL_AGENT_GENERATION", True) + + self._config["enable_memory_consolidation"] = config["Cognition"].get("ENABLE_MEMORY_CONSOLIDATION", True) + self._config["min_episode_length"] = config["Cognition"].getint("MIN_EPISODE_LENGTH", 30) + self._config["max_episode_length"] = config["Cognition"].getint("MAX_EPISODE_LENGTH", 100) + self._config["episodic_memory_fixed_prefix_length"] = config["Cognition"].getint("EPISODIC_MEMORY_FIXED_PREFIX_LENGTH", 20) + self._config["episodic_memory_lookback_length"] = config["Cognition"].getint("EPISODIC_MEMORY_LOOKBACK_LENGTH", 20) + + self._config["action_generator_max_attempts"] = config["ActionGenerator"].getint("MAX_ATTEMPTS", 2) + self._config["action_generator_enable_quality_checks"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECKS", False) + self._config["action_generator_enable_regeneration"] = config["ActionGenerator"].getboolean("ENABLE_REGENERATION", False) + self._config["action_generator_enable_direct_correction"] = config["ActionGenerator"].getboolean("ENABLE_DIRECT_CORRECTION", False) + + self._config["action_generator_enable_quality_check_for_persona_adherence"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_PERSONA_ADHERENCE", False) + self._config["action_generator_enable_quality_check_for_selfconsistency"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_SELFCONSISTENCY", False) + self._config["action_generator_enable_quality_check_for_fluency"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_FLUENCY", False) + self._config["action_generator_enable_quality_check_for_suitability"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_SUITABILITY", False) + self._config["action_generator_enable_quality_check_for_similarity"] = config["ActionGenerator"].getboolean("ENABLE_QUALITY_CHECK_FOR_SIMILARITY", False) + + self._config["action_generator_continue_on_failure"] = config["ActionGenerator"].getboolean("CONTINUE_ON_FAILURE", True) + self._config["action_generator_quality_threshold"] = config["ActionGenerator"].getint("QUALITY_THRESHOLD", 2) + + # LOGLEVEL + self._config[ConfigManager.LOGLEVEL_KEY] = config["Logging"].get("LOGLEVEL", "INFO").upper() + + self._raw_config = config + + def update(self, key, value): + """ + Update a configuration value. + + Args: + key (str): The configuration key to update + value: The new value to set + + Returns: + None + """ + if key in self._config: + + # make sure it is always lowercase + if isinstance(value, str): + value = value.lower() + + self._config[key] = value + logging.info(f"Updated config: {key} = {value}") + + # Special handling for loglevel - also update the logger immediately + if key == ConfigManager.LOGLEVEL_KEY: + utils.set_loglevel(value) + else: + logging.warning(f"Attempted to update unknown config key: {key}") + + def update_multiple(self, config_dict): + """ + Update multiple configuration values at once. + + Args: + config_dict (dict): Dictionary of key-value pairs to update + + Returns: + None + """ + for key, value in config_dict.items(): + self.update(key, value) + + def get(self, key, default=None): + """ + Get a configuration value. + + Args: + key (str): The configuration key to retrieve + default: The default value to return if key is not found + + Returns: + The configuration value + """ + return self._config.get(key, default) + + def reset(self): + """Reset all configuration values to their original values from the config file.""" + self._initialize_from_config() + logging.info("All configuration values have been reset to defaults") + + def __getitem__(self, key): + """Allow dictionary-like access to configuration values.""" + return self.get(key) + + def config_defaults(self, **config_mappings): + """ + Returns a decorator that replaces None default values with current config values. + + Args: + **config_mappings: Mapping of parameter names to config keys + + Example: + @config_manager.config_defaults(model="model", temp="temperature") + def generate(prompt, model=None, temp=None): + # model will be the current config value for "model" if None is passed + # ... + """ + import functools + import inspect + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + # Get the function's signature + sig = inspect.signature(func) + bound_args = sig.bind_partial(*args, **kwargs) + bound_args.apply_defaults() + + # For each parameter that maps to a config key + for param_name, config_key in config_mappings.items(): + # If the parameter is None, replace with config value + if param_name in bound_args.arguments and bound_args.arguments[param_name] is None: + kwargs[param_name] = self.get(config_key) + + return func(*args, **kwargs) + + return wrapper + + return decorator + + +# Create global instance of the configuration manager +config = utils.read_config_file() +utils.pretty_print_tinytroupe_version() +utils.pretty_print_datetime() +utils.pretty_print_config(config) +utils.start_logger(config) + +config_manager = ConfigManager() + + + + +# For backwards compatibility, maintain the default dict +# but it's recommended to use config_manager instead +default = config_manager._config + +# Helper function for method signatures +def get_config(key, override_value=None): + """ + Get a configuration value, with optional override. + Used in method signatures to get current config values. + + Args: + key (str): The configuration key + override_value: If provided, this value is used instead of the config value + + Returns: + The configuration value or the override value + """ + if override_value is not None: + return override_value + return config_manager.get(key) + + +## LLaMa-Index configs ######################################################## +#from llama_index.embeddings.huggingface import HuggingFaceEmbedding + +if config["OpenAI"].get("API_TYPE") == "azure": + from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding +else: + from llama_index.embeddings.openai import OpenAIEmbedding + +from llama_index.core import Settings, Document, VectorStoreIndex, SimpleDirectoryReader +from llama_index.readers.web import SimpleWebPageReader + + +# this will be cached locally by llama-index, in a OS-dependend location + +##Settings.embed_model = HuggingFaceEmbedding( +## model_name="BAAI/bge-small-en-v1.5" +##) + +if config["OpenAI"].get("API_TYPE") == "azure": + llamaindex_openai_embed_model = AzureOpenAIEmbedding(model=default["embedding_model"], + deployment_name=default["embedding_model"], + api_version=default["azure_embedding_model_api_version"], + embed_batch_size=10) +else: + llamaindex_openai_embed_model = OpenAIEmbedding(model=default["embedding_model"], embed_batch_size=10) +Settings.embed_model = llamaindex_openai_embed_model + + +########################################################################### +# Fixes and tweaks +########################################################################### + +# fix an issue in the rich library: we don't want margins in Jupyter! +rich.jupyter.JUPYTER_HTML_FORMAT = \ + utils.inject_html_css_style_prefix(rich.jupyter.JUPYTER_HTML_FORMAT, "margin:0px;") + + diff --git a/tinytroupe/__pycache__/__init__.cpython-312.pyc b/tinytroupe/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bb9ff7232a5cc001efb338a6d83337de08c60f93 Binary files /dev/null and b/tinytroupe/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/__pycache__/control.cpython-312.pyc b/tinytroupe/__pycache__/control.cpython-312.pyc new file mode 100644 index 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b/tinytroupe/agent/__init__.py @@ -0,0 +1,66 @@ +""" +This module provides the main classes and functions for TinyTroupe's agents. + +Agents are the key abstraction used in TinyTroupe. An agent is a simulated person or entity that can interact with other agents and the environment, by +receiving stimuli and producing actions. Agents have cognitive states, which are updated as they interact with the environment and other agents. +Agents can also store and retrieve information from memory, and can perform actions in the environment. Different from agents whose objective is to +provide support for AI-based assistants or other such productivity tools, **TinyTroupe agents aim at representing human-like behavior**, which includes +idiossincracies, emotions, and other human-like traits, that one would not expect from a productivity tool. + +The overall underlying design is inspired mainly by Cognitive Psychology, which is why agents have various internal cognitive states, such as attention, emotions, and goals. +It is also why agent memory, differently from other LLM-based agent platforms, has subtle internal divisions, notably between episodic and semantic memory. +Some behaviorist concepts are also present, such as the explicit and decoupled concepts of "stimulus" and "response" in the `listen` and `act` methods, which are key abstractions +to understand how agents interact with the environment and other agents. +""" + +import tinytroupe.utils as utils +from pydantic import BaseModel + +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Types and constants +########################################################################### +from typing import TypeVar, Union +Self = TypeVar("Self", bound="TinyPerson") +AgentOrWorld = Union[Self, "TinyWorld"] + + +########################################################################### +# Data structures to enforce output format during LLM API call. +########################################################################### +class Action(BaseModel): + type: str + content: str + target: str + +class CognitiveState(BaseModel): + goals: str + context: list[str] + attention: str + emotions: str + +class CognitiveActionModel(BaseModel): + action: Action + cognitive_state: CognitiveState + +class CognitiveActionModelWithReasoning(BaseModel): + reasoning: str + action: Action + cognitive_state: CognitiveState + + +########################################################################### +# Exposed API +########################################################################### +# from. grounding ... ---> not exposing this, clients should not need to know about detailed grounding mechanisms +from .memory import SemanticMemory, EpisodicMemory, EpisodicConsolidator, ReflectionConsolidator +from .mental_faculty import CustomMentalFaculty, RecallFaculty, FilesAndWebGroundingFaculty, TinyToolUse +from .tiny_person import TinyPerson + +__all__ = ["SemanticMemory", "EpisodicMemory", "EpisodicConsolidator", "ReflectionConsolidator", + "CustomMentalFaculty", "RecallFaculty", "FilesAndWebGroundingFaculty", "TinyToolUse", + "TinyPerson"] \ No newline at end of file diff --git a/tinytroupe/agent/__pycache__/__init__.cpython-312.pyc b/tinytroupe/agent/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ba7ef83e9550bbe17932440c4c641df6cb876599 Binary 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b/tinytroupe/agent/action_generator.py @@ -0,0 +1,532 @@ +import json +import statistics # Add this import + +import tinytroupe.utils as utils +from tinytroupe.control import transactional, current_simulation +import tinytroupe.openai_utils as openai_utils +from tinytroupe.validation import propositions +from tinytroupe.utils import JsonSerializableRegistry +from tinytroupe.experimentation import Proposition + + +class ActionGenerator(JsonSerializableRegistry): + + def __init__(self, max_attempts=2, + enable_quality_checks=True, + enable_regeneration=True, + enable_direct_correction=False, # TODO enable_direct_correction not working very well yet + enable_quality_check_for_persona_adherence=True, + enable_quality_check_for_selfconsistency=True, + enable_quality_check_for_fluency=True, + enable_quality_check_for_suitability=False, + enable_quality_check_for_similarity=False, + continue_on_failure=True, + quality_threshold=7, + max_action_similarity=0.6, + enable_reasoning_step=False): # TODO enable_reasoning_step not working very well yet + """ + Initializes the ActionGenerator. + + Args: + max_attempts (int): The maximum number of attempts to generate an action. + enable_quality_checks (bool): Whether to perform quality checks on the generated action. If False, the first action generated + is returned without any checks. + enable_regeneration (bool): Whether to try to make the agent regenerate the action if the first attempt fails. + enable_direct_correction (bool): Whether to directly correct the action if the first attempt fails, without asking the agent to regenerate it. + enable_quality_check_for_persona_adherence (bool): Whether to check the action for persona adherence. + enable_quality_check_for_selfconsistency (bool): Whether to check the action for self-consistency. + enable_quality_check_for_fluency (bool): Whether to check the action for fluency. + enable_quality_check_for_suitability (bool): Whether to check the action for suitability. + continue_on_failure (bool): Whether to return the last tentative action, even if it fails to pass quality checks. + Presumably, the last tentative action is the one that is most likely to be correct, since it has gone through the most iterations of regeneration and correction. + quality_threshold (int): The minimum score for each quality check for the action to be considered good quality. + enable_reasoning_step (bool): Whether to enable reasoning step in the action generation process. This IS NOT the use of "reasoning models" (e.g., o1, o3), + but rather the use of an additional reasoning step in the regular text completion. + """ + + self.max_attempts = max_attempts + self.regeneration_attempts = 0 + self.direct_correction_attempts = 0 + + self.enable_quality_checks = enable_quality_checks + self.enable_regeneration = enable_regeneration + self.enable_direct_correction = enable_direct_correction + + self.enable_quality_check_for_persona_adherence = enable_quality_check_for_persona_adherence + self.enable_quality_check_for_selfconsistency = enable_quality_check_for_selfconsistency + self.enable_quality_check_for_fluency = enable_quality_check_for_fluency + self.enable_quality_check_for_suitability = enable_quality_check_for_suitability + self.enable_quality_check_for_similarity = enable_quality_check_for_similarity + + self.continue_on_failure = continue_on_failure + self.quality_threshold = quality_threshold + self.max_action_similarity = max_action_similarity + + self.enable_reasoning_step = enable_reasoning_step + + # This generator has its own copies of the propositions, in order to be able to isolate them + # from other agents, particularly when running the simulation in parallel. + self.action_persona_adherence = propositions.hard_action_persona_adherence.copy() + self.action_self_consistency = propositions.action_self_consistency.copy() + self.action_fluency = propositions.action_fluency.copy() + self.action_suitability = propositions.action_suitability.copy() + + # initialize statistics + self.regeneration_failures = 0 + self.direct_correction_failures = 0 + self.regeneration_scores = [] + self.direct_correction_scores = [] + self.total_actions_produced = 0 + self.total_original_actions_succeeded = 0 + + def generate_next_action(self, agent, current_messages:list): + + from tinytroupe.agent import logger # import here to avoid circular import issues + + # clean up (remove unnecessary elements) and copy the list of current messages to avoid modifying the original ones + current_messages = [ + {"role": msg["role"], "content": json.dumps(msg["content"])} + for msg in current_messages + ] + + # starts with no feedback + cur_feedback = None + all_negative_feedbacks = [] + + best_action = None + best_role = None + best_content = None + best_score = float('-inf') + original_score = None + + def update_best(tentative_action, role, content, total_score): + nonlocal best_action, best_role, best_content, best_score + if total_score > best_score: + best_action = tentative_action + best_role = role + best_content = content + best_score = total_score + + def finish_return(tentative_action, role, content, final_score): + if original_score is not None and final_score > original_score: + logger.warning(f"[{agent.name}] improved total quality from {original_score} to {final_score}") + + # ensure that tentative_action and content are dicts + if isinstance(tentative_action, str): + tentative_action = json.loads(tentative_action) + if isinstance(content, str): + content = json.loads(content) + + return tentative_action, role, content, all_negative_feedbacks + + # First attempt to generate an action + tentative_action, role, content = self._generate_tentative_action(agent, current_messages, + feedback_from_previous_attempt=cur_feedback, + previous_tentative_action=None, + previous_llm_role=None, previous_llm_content=None) + + if self.enable_quality_checks: + # First quality check + good_quality, total_score, cur_feedback = self._check_action_quality("Original Action", agent, tentative_action=tentative_action) + update_best(tentative_action, role, content, total_score) + if original_score is None: + original_score = total_score + if good_quality: + self.total_original_actions_succeeded += 1 + # Found a good action, let's return it now + return finish_return(tentative_action, role, content, total_score) + else: + logger.warning(f"[{agent.name}] Original action did not pass quality checks: {cur_feedback}") + all_negative_feedbacks.append(cur_feedback) + + + # GENERATE AND REGENERATE the action by the agent + # + # We first try to make the agent generate (via the current_messages passed) or regenerate the + # action based on feedback. + if self.enable_regeneration: + for attempt in range(self.max_attempts): + + # Generate tentative action + tentative_action, role, content = self._generate_tentative_action(agent, current_messages, + feedback_from_previous_attempt=cur_feedback, + previous_tentative_action=tentative_action, + previous_llm_role=role, previous_llm_content=content) + logger.debug(f"[{agent.name}] Tentative action: {tentative_action}") + self.regeneration_attempts += 1 + + good_quality, total_score, cur_feedback = self._check_action_quality(f"Action Regeneration ({attempt})", agent, tentative_action=tentative_action) + update_best(tentative_action, role, content, total_score) + if good_quality: + # Found a good action, let's return it now + return finish_return(tentative_action, role, content, total_score) + else: + self.regeneration_failures += 1 + self.regeneration_scores.append(total_score) # Assuming feedback contains a score + all_negative_feedbacks.append(cur_feedback) + + # CORRECT OR REPHRASE the action directly + # + # If we got here, it means the agent was not able to directly generate an action + # of sufficient quality, so we'll try to rephrase it correctly directly now. + if self.enable_direct_correction: + for attempt in range(self.max_attempts): + tentative_action, role, content = self._correct_action(tentative_action, feedback=cur_feedback, llm_role=role, llm_content=content) + logger.warning(f"[{agent.name}] Rephrased the action directly as: {tentative_action}") + self.direct_correction_attempts += 1 + + good_quality, total_score, cur_feedback = self._check_action_quality(f"Direct Action Correction or Rephrasing ({attempt})", agent, tentative_action=tentative_action) + update_best(tentative_action, role, content, total_score) + if good_quality: + # Found a good action, let's return it now + return finish_return(tentative_action, role, content, total_score) + else: + self.direct_correction_failures += 1 + self.direct_correction_scores.append(total_score) # Assuming feedback contains a score + all_negative_feedbacks.append(cur_feedback) + + # If we got here, all attempts to generate a good action failed + if self.continue_on_failure: + logger.warning(f"[{agent.name}] All attempts to generate a good action failed. Returning the best one.") + return finish_return(best_action, best_role, best_content, best_score) + + else: + raise PoorQualityActionException() + + else: + # If we got here, it means that the action was generated without quality checks + # and we are not doing any regeneration or direct correction, so we can return it now. + return tentative_action, role, content, [] + + def _generate_tentative_action(self, agent, current_messages, feedback_from_previous_attempt=None, + previous_tentative_action=None, + previous_llm_role=None, previous_llm_content=None): + + from tinytroupe.agent import logger, CognitiveActionModel, CognitiveActionModelWithReasoning # import here to avoid circular import issues + + self.total_actions_produced += 1 + + # shallow clone current_messages + current_messages_context = current_messages.copy() + + logger.debug(f"[{agent.name}] Sending messages to OpenAI API") + logger.debug(f"[{agent.name}] Last interaction: {current_messages[-1]}") + + if feedback_from_previous_attempt: + #current_messages_copy.append({"role": previous_llm_role, + # "content": "TENTATIVE ACTION:" + json.dumps(previous_llm_content)}) + + current_messages_context.append({"role": "user", + "content": \ + f""" + WARNING! TENTATIVE ACTION GENERATION FAILED IN QUALITY CHECKS! + + You were about to produce the following action, as a sequence for the previous actions or feedbacks (if any): + ``` + {previous_tentative_action} + ``` + + However, it failed to pass the quality checks (as described in the quality feedback below), and therefore it was aborted and not added + to the simulation trajectory. + + Now you **must** try again to generate a **BETTER** action, such that the quality issues mentioned in the feedback are addressed, + or instead issue a DONE action and stop for this turn if it is unclear how to improve quality. + Your objective is to **PASS** the quality checks this time if possible. + + You can choose either to FIX somehow the action you were about to produce, or to generate something COMPLETELY NEW and DIFFERENT. + Each time your tentative action fail a quality check, you should be MORE RADICAL in your changes, and try to produce + something that is **very** different from the previous attempts. + + If it is unclear how to produce a better action, you can choose to issue a DONE action instead. + **It is better to stop acting than to act poorly.** + + In general, desireable properties of the action are: + - The action is consistent with the agent's persona, it is what one would expect from the agent given its persona. + - The action is self-consistent, it does contradict the agent's previous actions. + - The action is fluent and natural, and does not repeat itself or use overly formulaic language. + + {feedback_from_previous_attempt} + """}) + + current_messages_context.append({"role": "system", + "content": "Now generate a better action based on the above feedback, or issue a DONE action if it is unclear how to improve quality."}) + + + + # TODO: remind the model of some key rules to follow? + # + # + #current_messages_context.append({"role": "user", + # "content": """ + # Now you must generate a sequence of actions following the directives in your agent specification, + # complying with **all** instructions and contraints related to the action you use. + # In particular, to ensure the quality of your actions: + # - **DO NOT** generate similar content in a row! We want human-like, natural and fluent behavior, and thus avoid#repeatitive behavior. + # - THINK before taking further actions. + # - Avoid thinking for too long, and actually take some concrete action before being done, particularly if you are expected to provide some action. + # - Intercalate thinking with other actions. + # - The new sequence of actions must be coherent and consistent with the previous actions and stimuli. For example, do not assume an expected or + # desireable action already happened if that's not registered in the simulation history. + # - If you received any quality feedback, you **MUST** take it into account and improve your performance. Your next actions + # **must** be better than your previous ones if possible. + # + # If you can't produce a very good action, you may just issue a DONE action instead and remain silent. Rules to follow in #this case: + # - It is better to remain silent than repeating similar actions or making other mistakes. + # - Avoid remaining silent for too long (i.e., more than 3 times in a row), as this looks robotic and unnatural. If #necessary, you + # can communicate your difficulties in coming up with a proper action, or just say something like "I don't know what to say". + # - In case your thoughts or goals insistenly require you to **not** being quiet or silent, then you avoid just issuing #DONE if possible, + # and try to produce a new action. In this case, the new action might refer to the difficulties you are having in #coming up with + # a proper action in the first place. + # + # All of these actions **MUST** be rendered following the JSON specification perfectly, including all required keys (even #if their value is empty), **ALWAYS**. + # """ + # }) +# + + current_messages_context.append({"role": "system", + "content": "Remember: the action you will now generate **MUST** be a **well-formatted** and **valid** JSON object. No extra text, no extra brackets, commas, or other syntax errors."}) + + if not self.enable_reasoning_step: + logger.debug(f"[{agent.name}] Reasoning step disabled.") + next_message = openai_utils.client().send_message(current_messages_context, response_format=CognitiveActionModel) + + else: + logger.debug(f"[{agent.name}] Reasoning step enabled.") + + # If the reasoning step is enabled, we add a system message to the context asking it to think step-by-step + # + # + #current_messages_context.append({"role": "system", + # "content": "In your response, you first use the \"reasoning\" field to think step-by-step about what is the next action and cognitive state that you are going to generate. To do so, you carefully consider: the agent specification given initially; additional instructions given later; and the history of stimuli and actions present in the simulation trajectory." + + # "Then, you generate the action in the \"action\" field, and generate cognitive state in the \"cognitive_state\" field." }) + current_messages_context.append({"role": "system", + "content": "Use the \"reasoning\" field to add any reasoning process you might wish to use before generating the next action and cognitive state. "}) + + next_message = openai_utils.client().send_message(current_messages_context, response_format=CognitiveActionModelWithReasoning) + + logger.debug(f"[{agent.name}] Received message: {next_message}") + + role, content = next_message["role"], utils.extract_json(next_message["content"]) + + action = content['action'] + logger.debug(f"{agent.name}'s action: {action}") + + return action, role, content + + ############################################################################################### + # Quality evaluation methods + ############################################################################################### + + def _check_action_quality(self, stage, agent, tentative_action): + + from tinytroupe.agent import logger # import here to avoid circular import issues + + # + # Compute various propositions about the action + # + persona_adherence_passed, persona_adherence_score, persona_adherence_feedback = \ + self._check_proposition(agent, self.action_persona_adherence, tentative_action, enable_proposition_check=self.enable_quality_check_for_persona_adherence) + + selfconsistency_passed, selfconsistency_score, selfconsistency_feedback = \ + self._check_proposition(agent, self.action_self_consistency, tentative_action, minimum_required_qty_of_actions=1, enable_proposition_check=self.enable_quality_check_for_selfconsistency) + + fluency_passed, fluency_passed_score, fluency_feedback = \ + self._check_proposition(agent, self.action_fluency, tentative_action, enable_proposition_check=self.enable_quality_check_for_fluency) + + suitability_passed, suitability_score, suitability_feedback = \ + self._check_proposition(agent, self.action_suitability, tentative_action, enable_proposition_check=self.enable_quality_check_for_suitability) + + similarity_passed, similarity_score, similarity_feedback = \ + self._check_next_action_similarity(agent, tentative_action, threshold=self.max_action_similarity, enable_similarity_check=self.enable_quality_check_for_similarity) + + # put the results together + good_quality = persona_adherence_passed and selfconsistency_passed and fluency_passed and suitability_passed and similarity_passed + total_score = persona_adherence_score + selfconsistency_score + fluency_passed_score + suitability_score + (similarity_score * Proposition.MAX_SCORE) + + combined_feedback = utils.combine_texts( + persona_adherence_feedback, selfconsistency_feedback, fluency_feedback, suitability_feedback, similarity_feedback + ) + + # give verdict + if good_quality: + return True, total_score, combined_feedback + + else: + + failure_feedback = \ + f""" + # Quality feedback + + This is the action that was about to be generated by the agent: + {tentative_action} + + Unfortunately, the action failed to pass the quality checks, and therefore was aborted and not added to the similation trajectory. + The following problems were detected. + """ + + if not persona_adherence_passed: + failure_feedback += f""" + ## Problem: The action does not adhere to the persona specification. + {persona_adherence_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_persona_adherence.recommendations_for_improvement()} + + """ + + if not selfconsistency_passed: + failure_feedback += f""" + ## Problem: The action is not self-consistent. + {selfconsistency_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_self_consistency.recommendations_for_improvement()} + + """ + + if not fluency_passed: + failure_feedback += f""" + ## Problem: The action is not fluent. + {fluency_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_fluency.recommendations_for_improvement()} + + """ + + if not suitability_passed: + failure_feedback += f""" + ## Problem: The action is not suitable to the situation or task. + {suitability_feedback} + + ### RECOMMENDATIONS FOR IMPROVEMENT + Please follow the recommendations below when trying to generate this action again. + + {self.action_suitability.recommendations_for_improvement()} + + """ + + if not similarity_passed: + failure_feedback += f""" + ## Problem: The action is too similar to the previous one. + {similarity_feedback} + + """ + + logger.warning(f"[{agent.name}][{stage}] failed to pass quality checks: {failure_feedback}") + return False, total_score, failure_feedback + + + def _check_proposition(self, agent, proposition, tentative_action, minimum_required_qty_of_actions=0, enable_proposition_check=True): + + if enable_proposition_check: + if agent.actions_count >= minimum_required_qty_of_actions: + result = proposition.score(target=agent, claim_variables={"action": tentative_action}, return_full_response=True) + + value_with_justification = f"Score = {result['value']} (out of {Proposition.MAX_SCORE}). Justification = {result['justification']}" + + if result["value"] >= self.quality_threshold: + return True, result["value"], value_with_justification + else: + return False, result["value"], value_with_justification + + else: + return True, Proposition.MAX_SCORE, f"The proposition is trivially true due to the lack of enough actions for comparison." + else: + # If the proposition check is disabled, we assume it passed + return True, Proposition.MAX_SCORE, f"The proposition check is disabled, so it is assumed to have passed." + + def _check_next_action_similarity(self, agent, proposed_next_action, threshold, enable_similarity_check=True): + """ + Checks the similarity between the agent's current action and a proposed next action. + High similarity indicates that the proposed action is too similar to the current one, and this + check fails. + """ + from tinytroupe.agent import logger # import here to avoid circular import issues + + if enable_similarity_check: + similarity = utils.next_action_jaccard_similarity(agent, proposed_next_action) + logger.debug(f"[{agent.name}] Next-action Jaccard similarity: {similarity}") + + if similarity >= threshold: + logger.warning(f"[{agent.name}] Next-action Jaccard similarity is above the threshold ({threshold}).") + return False, similarity, f"Similarity = {similarity} (range: 0.0 to 1.0). The action is too similar to the previous one." + else: + logger.debug(f"[{agent.name}] Next-action Jaccard similarity is below the threshold ({threshold}).") + return True, similarity, f"Similarity = {similarity} (range: 0.0 to 1.0). The action is sufficiently different from the previous one." + + else: + # If the similarity check is disabled, we assume it passed + return True, 0.0, f"The similarity check is disabled, so it is assumed to have passed." + + ################################################################################################ + # Action correction methods + ################################################################################################ + + def _correct_action(self, action:dict, feedback, llm_role, llm_content): + situation = \ + f""" + The following action by an agent was observed: + + {action} + + However, it does not conform to expectations about this agent behavior, + due to the following reasons. + {feedback} + """ + #restructured_situation =\ + # utils.restructure_as_observed_vs_expected(\ + + # """) + #rule = utils.formulate_corrective_rule(restructured_situation) + rules = utils.extract_observed_vs_expected_rules(situation) + rephrased_action_content = utils.correct_according_to_rule(action["content"], rules) + + # copy action + rephrased_action = action.copy() + + # update content + rephrased_action["content"] = rephrased_action_content + + # replace in the 'action' key in the original llm content message + llm_content["action"] = rephrased_action + + return rephrased_action, llm_role, llm_content + + def get_statistics(self): + regeneration_failure_rate = self.regeneration_failures / self.regeneration_attempts if self.regeneration_attempts else 0 + direct_correction_failure_rate = self.direct_correction_failures / self.direct_correction_attempts if self.direct_correction_attempts else 0 + + regeneration_mean_score = statistics.mean(self.regeneration_scores) if self.regeneration_scores else 0 + regeneration_sd_score = statistics.stdev(self.regeneration_scores) if len(self.regeneration_scores) > 1 else 0 + + direct_correction_mean_score = statistics.mean(self.direct_correction_scores) if self.direct_correction_scores else 0 + direct_correction_sd_score = statistics.stdev(self.direct_correction_scores) if len(self.direct_correction_scores) > 1 else 0 + + original_success_rate = self.total_original_actions_succeeded / self.total_actions_produced if self.total_actions_produced else 0 + + return { + "regeneration_failure_rate": regeneration_failure_rate, + "direct_correction_failure_rate": direct_correction_failure_rate, + "regeneration_mean_score": regeneration_mean_score, + "regeneration_sd_score": regeneration_sd_score, + "direct_correction_mean_score": direct_correction_mean_score, + "direct_correction_sd_score": direct_correction_sd_score, + "total_actions_produced": self.total_actions_produced, + "total_original_actions_succeeded": self.total_original_actions_succeeded, + "original_success_rate": original_success_rate, + "regeneration_success_rate": 1 - regeneration_failure_rate, + "direct_correction_success_rate": 1 - direct_correction_failure_rate + } + + +class PoorQualityActionException(Exception): + def __init__(self, message="The generated action is of poor quality"): + self.message = message + super().__init__(self.message) diff --git a/tinytroupe/agent/browser_faculty.py b/tinytroupe/agent/browser_faculty.py new file mode 100644 index 0000000000000000000000000000000000000000..adbfa92adca60caef609c476bd0db449c3eac030 --- /dev/null +++ b/tinytroupe/agent/browser_faculty.py @@ -0,0 +1,85 @@ +from tinytroupe.agent.mental_faculty import TinyMentalFaculty +from tinytroupe.tools import browser +import textwrap + +class BrowserFaculty(TinyMentalFaculty): + """ + A mental faculty that allows an agent to interact with a web browser. + """ + + def __init__(self): + super().__init__("Browser Navigation") + + def process_action(self, agent, action: dict) -> bool: + """ + Processes a browser-related action. + """ + action_type = action.get("type") + content = action.get("content") + target = action.get("target") + + if action_type == "See": + screenshot_path = browser.screenshot() + agent.see(f"Took a screenshot and saved it to {screenshot_path}. I will now analyze the screenshot.") + return True + elif action_type == "Click": + browser.click(target) + agent.see(f"Clicked on element with selector: {target}") + return True + elif action_type == "Write": + browser.fill(target, content) + agent.see(f"Typed '{content}' into element with selector: {target}") + return True + elif action_type == "Submit": + browser.submit_form(target) + agent.see(f"Submitted form with element: {target}") + return True + elif action_type == "Wait": + browser.wait_for_element(target) + agent.see(f"Waited for element: {target}") + return True + elif action_type == "Scroll": + browser.scroll_page(content) + agent.see(f"Scrolled page {content}") + return True + elif action_type == "Hover": + browser.hover_element(target) + agent.see(f"Hovered over element: {target}") + return True + elif action_type == "Keyboard_Key": + browser.press_key(content) + agent.see(f"Pressed key: {content}") + return True + elif action_type == "ScanPage": + page_info = browser.get_page_info() + agent.see(f"Scanned page and found the following information: {page_info}") + return True + return False + + def actions_definitions_prompt(self) -> str: + """ + Returns the prompt for defining browser-related actions. + """ + prompt = """ + - See: Take a screenshot of the current page. The `content` will be a placeholder for vision. + - Click: Click on an element on the page. The `target` should be a CSS selector for the element. + - Write: Type text into an element on the page. The `target` should be a CSS selector for the element, and `content` should be the text to type. + - Submit: Submit a form on the page. The `target` should be a CSS selector for a form or an element within a form. + - Wait: Wait for an element to appear on the page. The `target` should be a CSS selector for the element. + - Scroll: Scroll the page. The `content` should be 'up' or 'down'. + - Hover: Hover over an element on the page. The `target` should be a CSS selector for the element. + - Keyboard_Key: Press a key on the keyboard. The `content` should be the key to press (e.g., 'Enter', 'ArrowDown'). + - ScanPage: Get information about the current page, such as links and form elements. + """ + return textwrap.dedent(prompt) + + def actions_constraints_prompt(self) -> str: + """ + Returns the prompt for defining constraints on browser-related actions. + """ + prompt = """ + - Use See to get a visual representation of the page to help you decide on the next action. + - Use ScanPage to get a list of interactive elements to help you decide on the next action. + - Use Click, Write, and other actions to interact with elements on the page to accomplish the task. + """ + return textwrap.dedent(prompt) diff --git a/tinytroupe/agent/grounding.py b/tinytroupe/agent/grounding.py new file mode 100644 index 0000000000000000000000000000000000000000..671f90c7b5d1612ff4cef53e8b3133236320fba5 --- /dev/null +++ b/tinytroupe/agent/grounding.py @@ -0,0 +1,398 @@ +from tinytroupe.utils import JsonSerializableRegistry +import tinytroupe.utils as utils + +from tinytroupe.agent import logger +from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document, StorageContext, load_index_from_storage +from llama_index.core.vector_stores import SimpleVectorStore +from llama_index.readers.web import SimpleWebPageReader +import json +import tempfile +import os +import shutil + + +####################################################################################################################### +# Grounding connectors +####################################################################################################################### + +class GroundingConnector(JsonSerializableRegistry): + """ + An abstract class representing a grounding connector. A grounding connector is a component that allows an agent to ground + its knowledge in external sources, such as files, web pages, databases, etc. + """ + + serializable_attributes = ["name"] + + def __init__(self, name:str) -> None: + self.name = name + + def retrieve_relevant(self, relevance_target:str, source:str, top_k=20) -> list: + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_by_name(self, name:str) -> str: + raise NotImplementedError("Subclasses must implement this method.") + + def list_sources(self) -> list: + raise NotImplementedError("Subclasses must implement this method.") + + +@utils.post_init +class BaseSemanticGroundingConnector(GroundingConnector): + """ + A base class for semantic grounding connectors. A semantic grounding connector is a component that indexes and retrieves + documents based on so-called "semantic search" (i.e, embeddings-based search). This specific implementation + is based on the VectorStoreIndex class from the LLaMa-Index library. Here, "documents" refer to the llama-index's + data structure that stores a unit of content, not necessarily a file. + """ + + serializable_attributes = ["documents", "index"] + + # needs custom deserialization to handle Pydantic models (Document is a Pydantic model) + custom_deserializers = {"documents": lambda docs_json: [Document.from_json(doc_json) for doc_json in docs_json], + "index": lambda index_json: BaseSemanticGroundingConnector._deserialize_index(index_json)} + + custom_serializers = {"documents": lambda docs: [doc.to_json() for doc in docs] if docs is not None else None, + "index": lambda index: BaseSemanticGroundingConnector._serialize_index(index)} + + def __init__(self, name:str="Semantic Grounding") -> None: + super().__init__(name) + + self.documents = None + self.name_to_document = None + self.index = None + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + self.index = None + + if not hasattr(self, 'documents') or self.documents is None: + self.documents = [] + + if not hasattr(self, 'name_to_document') or self.name_to_document is None: + self.name_to_document = {} + + if hasattr(self, 'documents') and self.documents is not None: + for document in self.documents: + # if the document has a semantic memory ID, we use it as the identifier + name = document.metadata.get("semantic_memory_id", document.id_) + + # self.name_to_document[name] contains a list, since each source file could be split into multiple pages + if name in self.name_to_document: + self.name_to_document[name].append(document) + else: + self.name_to_document[name] = [document] + + # Rebuild index from documents if it's None or invalid + if self.index is None and self.documents: + logger.warning("No index found. Rebuilding index from documents.") + vector_store = SimpleVectorStore() + self.index = VectorStoreIndex.from_documents( + self.documents, + vector_store=vector_store, + store_nodes_override=True + ) + + # TODO remove? + #self.add_documents(self.documents) + + @staticmethod + def _serialize_index(index): + """Helper function to serialize index with proper storage context""" + if index is None: + return None + + try: + # Create a temporary directory to store the index + with tempfile.TemporaryDirectory() as temp_dir: + # Persist the index to the temporary directory + index.storage_context.persist(persist_dir=temp_dir) + + # Read all the persisted files and store them in a dictionary + persisted_data = {} + for filename in os.listdir(temp_dir): + filepath = os.path.join(temp_dir, filename) + if os.path.isfile(filepath): + with open(filepath, 'r', encoding="utf-8", errors="replace") as f: + persisted_data[filename] = f.read() + + return persisted_data + except Exception as e: + logger.warning(f"Failed to serialize index: {e}") + return None + + @staticmethod + def _deserialize_index(index_data): + """Helper function to deserialize index with proper error handling""" + if not index_data: + return None + + try: + # Create a temporary directory to restore the index + with tempfile.TemporaryDirectory() as temp_dir: + # Write all the persisted files to the temporary directory + for filename, content in index_data.items(): + filepath = os.path.join(temp_dir, filename) + with open(filepath, 'w', encoding="utf-8", errors="replace") as f: + f.write(content) + + # Load the index from the temporary directory + storage_context = StorageContext.from_defaults(persist_dir=temp_dir) + index = load_index_from_storage(storage_context) + + return index + except Exception as e: + # If deserialization fails, return None + # The index will be rebuilt from documents in _post_init + logger.warning(f"Failed to deserialize index: {e}. Index will be rebuilt.") + return None + + def retrieve_relevant(self, relevance_target:str, top_k=20) -> list: + """ + Retrieves all values from memory that are relevant to a given target. + """ + # Handle empty or None query + if not relevance_target or not relevance_target.strip(): + return [] + + if self.index is not None: + retriever = self.index.as_retriever(similarity_top_k=top_k) + nodes = retriever.retrieve(relevance_target) + else: + nodes = [] + + retrieved = [] + for node in nodes: + content = "SOURCE: " + node.metadata.get('file_name', '(unknown)') + content += "\n" + "SIMILARITY SCORE:" + str(node.score) + content += "\n" + "RELEVANT CONTENT:" + node.text + retrieved.append(content) + + logger.debug(f"Content retrieved: {content[:200]}") + + return retrieved + + def retrieve_by_name(self, name:str) -> list: + """ + Retrieves a content source by its name. + """ + # TODO also optionally provide a relevance target? + results = [] + if self.name_to_document is not None and name in self.name_to_document: + docs = self.name_to_document[name] + for i, doc in enumerate(docs): + if doc is not None: + content = f"SOURCE: {name}\n" + content += f"PAGE: {i}\n" + content += "CONTENT: \n" + doc.text[:10000] # TODO a more intelligent way to limit the content + results.append(content) + + return results + + + def list_sources(self) -> list: + """ + Lists the names of the available content sources. + """ + if self.name_to_document is not None: + return list(self.name_to_document.keys()) + else: + return [] + + def add_document(self, document) -> None: + """ + Indexes a document for semantic retrieval. + + Assumes the document has a metadata field called "semantic_memory_id" that is used to identify the document within Semantic Memory. + """ + self.add_documents([document]) + + def add_documents(self, new_documents) -> list: + """ + Indexes documents for semantic retrieval. + """ + # index documents by name + if len(new_documents) > 0: + + # process documents individually too + for document in new_documents: + logger.debug(f"Adding document {document} to index, text is: {document.text}") + + # out of an abundance of caution, we sanitize the text + document.text = utils.sanitize_raw_string(document.text) + + logger.debug(f"Document text after sanitization: {document.text}") + + # add the new document to the list of documents after all sanitization and checks + self.documents.append(document) + + if document.metadata.get("semantic_memory_id") is not None: + # if the document has a semantic memory ID, we use it as the identifier + name = document.metadata["semantic_memory_id"] + + # Ensure name_to_document is initialized + if not hasattr(self, 'name_to_document') or self.name_to_document is None: + self.name_to_document = {} + + # self.name_to_document[name] contains a list, since each source file could be split into multiple pages + if name in self.name_to_document: + self.name_to_document[name].append(document) + else: + self.name_to_document[name] = [document] + + + # index documents for semantic retrieval + if self.index is None: + # Create storage context with vector store + vector_store = SimpleVectorStore() + storage_context = StorageContext.from_defaults(vector_store=vector_store) + + self.index = VectorStoreIndex.from_documents( + self.documents, + storage_context=storage_context, + store_nodes_override=True # This ensures nodes (with text) are stored + ) + else: + self.index.refresh(self.documents) + + @staticmethod + def _set_internal_id_to_documents(documents:list, external_attribute_name:str ="file_name") -> None: + """ + Sets the internal ID for each document in the list of documents. + This is useful to ensure that each document has a unique identifier. + """ + for doc in documents: + if not hasattr(doc, 'metadata'): + doc.metadata = {} + doc.metadata["semantic_memory_id"] = doc.metadata.get(external_attribute_name, doc.id_) + + return documents + + +@utils.post_init +class LocalFilesGroundingConnector(BaseSemanticGroundingConnector): + + serializable_attributes = ["folders_paths"] + + def __init__(self, name:str="Local Files", folders_paths: list=None) -> None: + super().__init__(name) + + self.folders_paths = folders_paths + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + self.loaded_folders_paths = [] + + if not hasattr(self, 'folders_paths') or self.folders_paths is None: + self.folders_paths = [] + + self.add_folders(self.folders_paths) + + def add_folders(self, folders_paths:list) -> None: + """ + Adds a path to a folder with files used for grounding. + """ + + if folders_paths is not None: + for folder_path in folders_paths: + try: + logger.debug(f"Adding the following folder to grounding index: {folder_path}") + self.add_folder(folder_path) + except (FileNotFoundError, ValueError) as e: + print(f"Error: {e}") + print(f"Current working directory: {os.getcwd()}") + print(f"Provided path: {folder_path}") + print("Please check if the path exists and is accessible.") + + def add_folder(self, folder_path:str) -> None: + """ + Adds a path to a folder with files used for grounding. + """ + + if folder_path not in self.loaded_folders_paths: + self._mark_folder_as_loaded(folder_path) + + # for PDF files, please note that the document will be split into pages: https://github.com/run-llama/llama_index/issues/15903 + new_files = SimpleDirectoryReader(folder_path).load_data() + BaseSemanticGroundingConnector._set_internal_id_to_documents(new_files, "file_name") + + self.add_documents(new_files) + + def add_file_path(self, file_path:str) -> None: + """ + Adds a path to a file used for grounding. + """ + # a trick to make SimpleDirectoryReader work with a single file + new_files = SimpleDirectoryReader(input_files=[file_path]).load_data() + + logger.debug(f"Adding the following file to grounding index: {new_files}") + BaseSemanticGroundingConnector._set_internal_id_to_documents(new_files, "file_name") + + def _mark_folder_as_loaded(self, folder_path:str) -> None: + if folder_path not in self.loaded_folders_paths: + self.loaded_folders_paths.append(folder_path) + + if folder_path not in self.folders_paths: + self.folders_paths.append(folder_path) + + + + +@utils.post_init +class WebPagesGroundingConnector(BaseSemanticGroundingConnector): + + serializable_attributes = ["web_urls"] + + def __init__(self, name:str="Web Pages", web_urls: list=None) -> None: + super().__init__(name) + + self.web_urls = web_urls + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + self.loaded_web_urls = [] + + if not hasattr(self, 'web_urls') or self.web_urls is None: + self.web_urls = [] + + # load web urls + self.add_web_urls(self.web_urls) + + def add_web_urls(self, web_urls:list) -> None: + """ + Adds the data retrieved from the specified URLs to grounding. + """ + filtered_web_urls = [url for url in web_urls if url not in self.loaded_web_urls] + for url in filtered_web_urls: + self._mark_web_url_as_loaded(url) + + if len(filtered_web_urls) > 0: + new_documents = SimpleWebPageReader(html_to_text=True).load_data(filtered_web_urls) + BaseSemanticGroundingConnector._set_internal_id_to_documents(new_documents, "url") + self.add_documents(new_documents) + + def add_web_url(self, web_url:str) -> None: + """ + Adds the data retrieved from the specified URL to grounding. + """ + # we do it like this because the add_web_urls could run scrapes in parallel, so it is better + # to implement this one in terms of the other + self.add_web_urls([web_url]) + + def _mark_web_url_as_loaded(self, web_url:str) -> None: + if web_url not in self.loaded_web_urls: + self.loaded_web_urls.append(web_url) + + if web_url not in self.web_urls: + self.web_urls.append(web_url) + diff --git a/tinytroupe/agent/memory.py b/tinytroupe/agent/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..f3980494c6b07d66471ea2f012450925a32a52e3 --- /dev/null +++ b/tinytroupe/agent/memory.py @@ -0,0 +1,747 @@ +import json + +from tinytroupe.agent import logger +from tinytroupe.agent.mental_faculty import TinyMentalFaculty +from tinytroupe.agent.grounding import BaseSemanticGroundingConnector +import tinytroupe.utils as utils + + +from llama_index.core import Document +from typing import Any +import copy +from typing import Union + +####################################################################################################################### +# Memory mechanisms +####################################################################################################################### + +class TinyMemory(TinyMentalFaculty): + """ + Base class for different types of memory. + """ + + def _preprocess_value_for_storage(self, value: Any) -> Any: + """ + Preprocesses a value before storing it in memory. + """ + # by default, we don't preprocess the value + return value + + def _store(self, value: Any) -> None: + """ + Stores a value in memory. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def store(self, value: dict) -> None: + """ + Stores a value in memory. + """ + self._store(self._preprocess_value_for_storage(value)) + + def store_all(self, values: list) -> None: + """ + Stores a list of values in memory. + """ + logger.debug(f"Storing {len(values)} values in memory: {values}") + for i, value in enumerate(values): + logger.debug(f"Storing value #{i}: {value}") + self.store(value) + + def retrieve(self, first_n: int, last_n: int, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the first n and/or last n values from memory. If n is None, all values are retrieved. + + Args: + first_n (int): The number of first values to retrieve. + last_n (int): The number of last values to retrieve. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + + Returns: + list: The retrieved values. + + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_recent(self, item_type:str=None) -> list: + """ + Retrieves the n most recent values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_all(self, item_type:str=None) -> list: + """ + Retrieves all values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_relevant(self, relevance_target:str, top_k=20) -> list: + """ + Retrieves all values from memory that are relevant to a given target. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def summarize_relevant_via_full_scan(self, relevance_target: str, batch_size: int = 20, item_type: str = None) -> str: + """ + Performs a full scan of the memory, extracting and accumulating information relevant to a query. + + This function processes all memories (or memories of a specific type if provided), + extracts information relevant to the query from each memory, and accumulates this + information into a coherent response. + + Args: + relevance_target (str): The query specifying what information to extract from memories. + + item_type (str, optional): If provided, only process memories of this type. + batch_size (int): The number of memories to process in each extraction step. The larger it is, the faster the scan, but possibly less accurate. + Also, a too large value may lead to prompt length overflows, though current models can handle quite large prompts. + + Returns: + str: The accumulated information relevant to the query. + """ + logger.debug(f"Starting FULL SCAN for relevance target: {relevance_target}, item type: {item_type}") + + # Retrieve all memories of the specified type + memories = self.retrieve_all(item_type=item_type) + + # Initialize accumulation + accumulated_info = "" + + # Process memories in batches of qty_of_memories_per_extraction + for i in range(0, len(memories), batch_size): + batch = memories[i:i + batch_size] + logger.debug(f"Processing memory batch #{i} in full scan") + + # Concatenate memory texts for the batch + batch_text = "# Memories to be processed\n\n" + batch_text += "\n\n ".join(str(memory) for memory in batch) + + # Extract information relevant to the query from the batch + extracted_info = utils.semantics.extract_information_from_text( + relevance_target, + batch_text, + context=""" + You are extracting information from the an agent's memory, + which might include actions, stimuli, and other types of events. You want to focus on the agent's experience, NOT on the agent's cognition or internal processes. + + Assume that: + - "actions" refer to behaviors produced by the agent, + - "stimulus" refer to events or information from the environment or other agents that the agent perceived. + + If you read about "assistant" and "user" roles, you can ignore them, as they refer to the agent's internal implementation mechanisms, not to the agent's experience. + In any case, anything related to "assistant" is the agent's output, and anything related to "user" is the agent's input. But you never refer to these roles in the report, + as they are an internal implementation detail of the agent, not part of the agent's experience. + """ + ) + + logger.debug(f"Extracted information from memory batch: {extracted_info}") + + # Skip if no relevant information was found + if not extracted_info: + continue + + # Accumulate the extracted information + accumulated_info = utils.semantics.accumulate_based_on_query( + query=relevance_target, + new_entry=extracted_info, + current_accumulation=accumulated_info, + context=""" + You are producing a report based on information from an agent's memory. + You will put together all facts and experiences found that are relevant for the query, as a kind of summary of the agent's experience. + The report will later be used to guide further agent action. You focus on the agent's experience, NOT on the agent's cognition or internal processes. + + Assume that: + - "actions" refer to behaviors produced by the agent, + - "stimulus" refer to events or information from the environment or other agents that the agent perceived. + - if you read about "assistant" and "user" roles, you can ignore them, as they refer to the agent's internal implementation mechanisms, not to the agent's experience. + In any case, anything related to "assistant" is the agent's output, and anything related to "user" is the agent's input. But you never refer to these roles in the report, + as they are an internal implementation detail of the agent, not part of the agent's experience. + + Additional instructions for the accumulation process: + - If the new entry is redundant with respect to some information in the current accumulation, you update the current accumulation by adding to a special counter right by + the side of where the redundant information is found, so that the final report can later be used to guide further agent action (i.e., know which elements appeared more often). + The special counter **must** be formated like this: "[NOTE: this information appeared X times in the memory in different forms]". If the counter was not there originally, you add it. If it was there, you update + it with the new count. + * Example (first element was found 3 times, the second element only once, so no counter): + "I play with and feed my cat [NOTE: this information appeared 3 times in the memory in different forms]. Cats are proud animals descendant from big feline hunters.". + + """ + ) + logger.debug(f"Accumulated information so far: {accumulated_info}") + + logger.debug(f"Total accumulated information after full scan: {accumulated_info}") + + return accumulated_info + + + ################################### + # Auxiliary methods + ################################### + + def filter_by_item_type(self, memories:list, item_type:str) -> list: + """ + Filters a list of memories by item type. + + Args: + memories (list): The list of memories to filter. + item_type (str): The item type to filter by. + + Returns: + list: The filtered list of memories. + """ + return [memory for memory in memories if memory["type"] == item_type] + + def filter_by_item_types(self, memories:list, item_types:list) -> list: + """ + Filters a list of memories by multiple item types. + + Args: + memories (list): The list of memories to filter. + item_types (list): The list of item types to filter by. + + Returns: + list: The filtered list of memories containing any of the specified types. + """ + return [memory for memory in memories if memory["type"] in item_types] + + +class EpisodicMemory(TinyMemory): + """ + Provides episodic memory capabilities to an agent. Cognitively, episodic memory is the ability to remember specific events, + or episodes, in the past. This class provides a simple implementation of episodic memory, where the agent can store and retrieve + messages from memory. + + Subclasses of this class can be used to provide different memory implementations. + """ + + MEMORY_BLOCK_OMISSION_INFO = {'role': 'assistant', 'content': "Info: there were other messages here, but they were omitted for brevity.", 'simulation_timestamp': None} + + def __init__( + self, fixed_prefix_length: int = 20, lookback_length: int = 100 + ) -> None: + """ + Initializes the memory. + + Args: + fixed_prefix_length (int): The fixed prefix length. Defaults to 20. + lookback_length (int): The lookback length. Defaults to 100. + """ + self.fixed_prefix_length = fixed_prefix_length + self.lookback_length = lookback_length + + # the definitive memory that records all episodic events + self.memory = [] + + # the current episode buffer, which is used to store messages during an episode + self.episodic_buffer = [] + + + def commit_episode(self): + """ + Ends the current episode, storing the episodic buffer in memory. + """ + self.memory.extend(self.episodic_buffer) + self.episodic_buffer = [] + + def get_current_episode(self, item_types:list=None) -> list: + """ + Returns the current episode buffer, which is used to store messages during an episode. + + Args: + item_types (list, optional): If provided, only retrieve memories of these types. Defaults to None, which retrieves all types. + + Returns: + list: The current episode buffer. + """ + result = copy.copy(self.episodic_buffer) + result = self.filter_by_item_types(result, item_types) if item_types is not None else result + return result + + def count(self) -> int: + """ + Returns the number of values in memory. + """ + return len(self._memory_with_current_buffer()) + + def clear(self, max_prefix_to_clear:int=None, max_suffix_to_clear:int=None): + """ + Clears the memory, generating a permanent "episodic amnesia". + If max_prefix_to_clear is not None, it clears the first n values from memory. + If max_suffix_to_clear is not None, it clears the last n values from memory. If both are None, + it clears all values from memory. + + Args: + max_prefix_to_clear (int): The number of first values to clear. + max_suffix_to_clear (int): The number of last values to clear. + """ + + # clears all episodic buffer messages + self.episodic_buffer = [] + + # then clears the memory according to the parameters + if max_prefix_to_clear is not None: + self.memory = self.memory[max_prefix_to_clear:] + + if max_suffix_to_clear is not None: + self.memory = self.memory[:-max_suffix_to_clear] + + if max_prefix_to_clear is None and max_suffix_to_clear is None: + self.memory = [] + + def _memory_with_current_buffer(self) -> list: + """ + Returns the current memory, including the episodic buffer. + This is useful for retrieving the most recent memories, including the current episode. + """ + return self.memory + self.episodic_buffer + + ###################################### + # General memory methods + ###################################### + def _store(self, value: Any) -> None: + """ + Stores a value in memory. + """ + self.episodic_buffer.append(value) + + def retrieve(self, first_n: int, last_n: int, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the first n and/or last n values from memory. If n is None, all values are retrieved. + + Args: + first_n (int): The number of first values to retrieve. + last_n (int): The number of last values to retrieve. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + + Returns: + list: The retrieved values. + + """ + + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + # use the other methods in the class to implement + if first_n is not None and last_n is not None: + return self.retrieve_first(first_n, include_omission_info=False, item_type=item_type) + omisssion_info + self.retrieve_last(last_n, include_omission_info=False, item_type=item_type) + elif first_n is not None: + return self.retrieve_first(first_n, include_omission_info, item_type=item_type) + elif last_n is not None: + return self.retrieve_last(last_n, include_omission_info, item_type=item_type) + else: + return self.retrieve_all(item_type=item_type) + + def retrieve_recent(self, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the n most recent values from memory. + + Args: + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + """ + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + # Filter memories if item_type is provided + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + + # compute fixed prefix + fixed_prefix = memories[: self.fixed_prefix_length] + omisssion_info + + # how many lookback values remain? + remaining_lookback = min( + len(memories) - len(fixed_prefix) + (1 if include_omission_info else 0), self.lookback_length + ) + + # compute the remaining lookback values and return the concatenation + if remaining_lookback <= 0: + return fixed_prefix + else: + return fixed_prefix + memories[-remaining_lookback:] + + def retrieve_all(self, item_type:str=None) -> list: + """ + Retrieves all values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + return copy.copy(memories) + + def retrieve_relevant(self, relevance_target: str, top_k:int) -> list: + """ + Retrieves top-k values from memory that are most relevant to a given target. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def retrieve_first(self, n: int, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the first n values from memory. + + Args: + n (int): The number of values to retrieve. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + """ + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + return memories[:n] + omisssion_info + + def retrieve_last(self, n: int=None, include_omission_info:bool=True, item_type:str=None) -> list: + """ + Retrieves the last n values from memory. + + Args: + n (int): The number of values to retrieve, or None to retrieve all values. + include_omission_info (bool): Whether to include an information message when some values are omitted. + item_type (str, optional): If provided, only retrieve memories of this type. + """ + omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else [] + + memories = self._memory_with_current_buffer() if item_type is None else self.filter_by_item_type(self._memory_with_current_buffer(), item_type) + memories = memories[-n:] if n is not None else memories + + return omisssion_info + memories + + +@utils.post_init +class SemanticMemory(TinyMemory): + """ + In Cognitive Psychology, semantic memory is the memory of meanings, understandings, and other concept-based knowledge unrelated to specific + experiences. It is not ordered temporally, and it is not about remembering specific events or episodes. This class provides a simple implementation + of semantic memory, where the agent can store and retrieve semantic information. + """ + + serializable_attributes = ["memories", "semantic_grounding_connector"] + + def __init__(self, memories: list=None) -> None: + self.memories = memories + + self.semantic_grounding_connector = None + + # @post_init ensures that _post_init is called after the __init__ method + + def _post_init(self): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + + if not hasattr(self, 'memories') or self.memories is None: + self.memories = [] + + if not hasattr(self, 'semantic_grounding_connector') or self.semantic_grounding_connector is None: + self.semantic_grounding_connector = BaseSemanticGroundingConnector("Semantic Memory Storage") + + # TODO remove? + #self.semantic_grounding_connector.add_documents(self._build_documents_from(self.memories)) + + + def _preprocess_value_for_storage(self, value: dict) -> Any: + logger.debug(f"Preprocessing value for storage: {value}") + + if isinstance(value, dict): + engram = {"role": "assistant", + "content": value['content'], + "type": value.get("type", "information"), # Default to 'information' if type is not specified + "simulation_timestamp": value.get("simulation_timestamp", None)} + + # Refine the content of the engram is built based on the type of the value to make it more meaningful. + if value['type'] == 'action': + engram['content'] = f"# Action performed\n" +\ + f"I have performed the following action at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + + elif value['type'] == 'stimulus': + engram['content'] = f"# Stimulus\n" +\ + f"I have received the following stimulus at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + elif value['type'] == 'feedback': + engram['content'] = f"# Feedback\n" +\ + f"I have received the following feedback at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + elif value['type'] == 'consolidated': + engram['content'] = f"# Consolidated Memory\n" +\ + f"I have consolidated the following memory at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + elif value['type'] == 'reflection': + engram['content'] = f"# Reflection\n" +\ + f"I have reflected on the following memory at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + else: + engram['content'] = f"# Information\n" +\ + f"I have obtained following information at date and time {value['simulation_timestamp']}:\n\n"+\ + f" {value['content']}" + + # else: # Anything else here? + + else: + # If the value is not a dictionary, we just store it as is, but we still wrap it in an engram + engram = {"role": "assistant", + "content": value, + "type": "information", # Default to 'information' if type is not specified + "simulation_timestamp": None} + + logger.debug(f"Engram created for storage: {engram}") + + return engram + + def _store(self, value: Any) -> None: + logger.debug(f"Preparing engram for semantic memory storage, input value: {value}") + self.memories.append(value) # Store the value in the local memory list + + # then econduct the value to a Document and store it in the semantic grounding connector + # This is the actual storage in the semantic memory to allow semantic retrieval + engram_doc = self._build_document_from(value) + logger.debug(f"Storing engram in semantic memory: {engram_doc}") + self.semantic_grounding_connector.add_document(engram_doc) + + def retrieve_relevant(self, relevance_target:str, top_k=20) -> list: + """ + Retrieves all values from memory that are relevant to a given target. + """ + return self.semantic_grounding_connector.retrieve_relevant(relevance_target, top_k) + + def retrieve_all(self, item_type:str=None) -> list: + """ + Retrieves all values from memory. + + Args: + item_type (str, optional): If provided, only retrieve memories of this type. + """ + + memories = [] + + logger.debug(f"Retrieving all documents from semantic memory connector, a total of {len(self.semantic_grounding_connector.documents)} documents.") + for document in self.semantic_grounding_connector.documents: + logger.debug(f"Retrieving document from semantic memory: {document}") + memory_text = document.text + logger.debug(f"Document text retrieved: {memory_text}") + + try: + memory = json.loads(memory_text) + logger.debug(f"Memory retrieved: {memory}") + memories.append(memory) + + except json.JSONDecodeError as e: + logger.warning(f"Could not decode memory from document text: {memory_text}. Error: {e}") + + if item_type is not None: + memories = self.filter_by_item_type(memories, item_type) + + return memories + + ##################################### + # Auxiliary compatibility methods + ##################################### + + def _build_document_from(self, memory) -> Document: + # TODO: add any metadata as well? + + # make sure we are dealing with a dictionary + if not isinstance(memory, dict): + memory = {"content": memory, "type": "information"} + + # ensures double quotes are used for JSON serialization, and maybe other formatting details + memory_txt = json.dumps(memory, ensure_ascii=False) + logger.debug(f"Building document from memory: {memory_txt}") + + return Document(text=memory_txt) + + def _build_documents_from(self, memories: list) -> list: + return [self._build_document_from(memory) for memory in memories] + + +################################################################################################### +# Memory consolidation and optimization mechanisms +################################################################################################### +class MemoryProcessor: + """ + Base class for memory consolidation and optimization mechanisms. + """ + + def process(self, memories: list, timestamp: str=None, context:Union[str, list, dict] = None, persona:Union[str, dict] = None, sequential: bool = True) -> list: + """ + Transforms the given memories. Transformation can be anything from consolidation to optimization, depending on the implementation. + + Each memory is a dictionary of the form: + { + 'role': role, + 'content': content, + 'type': 'action'/'stimulus'/'feedback', + 'simulation_timestamp': timestamp + } + + Args: + memories (list): The list of memories to consolidate. + sequential (bool): Whether the provided memories are to be interpreted sequentially (e.g., episodes in sequence) or not (e.g., abstract facts). + + Returns: + list: A list with the consolidated memories, following the same format as the input memories, but different in content. + """ + raise NotImplementedError("Subclasses must implement this method.") + +class EpisodicConsolidator(MemoryProcessor): + """ + Consolidates episodic memories into a more abstract representation, such as a summary or an abstract fact. + """ + + def process(self, memories: list, timestamp: str=None, context:Union[str, list, dict] = None, persona:Union[str, dict] = None, sequential: bool = True) -> list: + logger.debug(f"STARTING MEMORY CONSOLIDATION: {len(memories)} memories to consolidate") + + enriched_context = f"CURRENT COGNITIVE CONTEXT OF THE AGENT: {context}" if context else "No specific context provided for consolidation." + + result = self._consolidate(memories, timestamp, enriched_context, persona) + logger.debug(f"Consolidated {len(memories)} memories into: {result}") + + return result + + @utils.llm(enable_json_output_format=True, enable_justification_step=False) + def _consolidate(self, memories: list, timestamp: str, context:str, persona:str) -> dict: + """ + Given a list of input episodic memories, this method consolidates them into more organized structured representations, which however preserve all information and important details. + + For this process, you assume: + - This consolidation is being carried out by an agent, so the memories are from the agent's perspective. "Actions" refer to behaviors produced by the agent, + while "stimulus" refer to events or information from the environment or other agents that the agent has perceived. + * Thus, in the consoldation you write "I have done X" or "I have perceived Y", not "the agent has done X" or "the agent has perceived Y". + - The purpose of consolidation is to restructure and organize the most relevant information from the episodic memories, so that any facts learned therein can be used in future reasoning processes. + * If a `context` is provided, you can use it to guide the consolidation process, making sure that the memories are consolidated in the most useful way under the given context. + For example, if the agent is looking for a specific type of information, you can focus the consolidation on that type of information, preserving more details about it + than you would otherwise. + * If a `persona` is provided, you can use it to guide the consolidation process, making sure that the memories are consolidated in a way that is consistent with the persona. + For example, if the persona is that of a cat lover, you can focus the consolidation on the agent's experiences with cats, preserving more details about them than you would otherwise. + - If the memory contians a `content` field, that's where the relevant information is found. Otherwise, consider the whole memory as relevant information. + + The consolidation process follows these rules: + - Each consolidated memory groups together all similar entries: so actions are grouped together, stimuli go together, facts are grouped together, impressions are grouped together, + learned processes are grouped together, and ad-hoc elements go together too. Noise, minor details and irrelevant elements are discarded. + In all, you will produce at most the following consolidated entries (you can avoid some if appropriate, but not add more): + * Actions: all actions are grouped together, giving an account of what the agent has done. + * Stimuli: all stimuli are grouped together, giving an account of what the agent has perceived. + * Facts: facts are extracted from the actions and stimuli, and then grouped together in a single entry, consolidating learning of objective facts. + * Impressions: impressions, feelings, or other subjective experiences are also extracted, and then grouped together in a single entry, consolidating subjective experiences. + * Procedural: learned processes (e.g., how to do certain things) are also extracted, formatted in an algorithmic way (i.e., pseudo-code that is self-explanatory), and then grouped together in a + single entry, consolidating learned processes. + * Ad-Hoc: important elements that do not correspond to these options are also grouped together in an ad-hoc single entry, consolidating other types of information. + - Each consolidated memory is a comprehensive report of the relevant information from the input memories, preserving all details. The consolidation merely reorganizes the information, + but does not remove any relevant information. The consolidated memories are not summaries, but rather a more organized and structured representation of the information in the input memories. + + + Each input memory is a dictionary of the form: + ``` + { + "role": role, + "content": content, + "type": "action"/"stimulus"/"feedback"/"reflection", + "simulation_timestamp": timestamp + } + ``` + + Each consolidated output memory is a dictionary of the form: + ``` + { + "content": content, + "type": "consolidated", + "simulation_timestamp": timestamp of the consolidation + } + ``` + + + So the final value outputed **must** be a JSON composed of a list of dictionaries, each representing a consolidated memory, **always** with the following structure: + ``` + {"consolidation": + [ + { + "content": content_1, + "type": "consolidated", + "simulation_timestamp": timestamp of the consolidation + }, + { + "content": content_2, + "type": "consolidated", + "simulation_timestamp": timestamp of the consolidation + }, + ... + ] + } + ``` + + Note: + - because the output is a JSON, you must use double quotes for the keys and string values. + ## Example (simplified) + + Here's a simplified example. Suppose the following memory contents are provided as input (simplifying here as just a bullet list of contents): + - stimulus: "I have seen a cat, walking beautifully in the street" + - stimulus: "I have seen a dog, barking loudly at a passerby, looking very aggressive" + - action: "I have petted the cat, run around with him (or her?), saying a thousand times how cute it is, and how much I seem to like cats" + - action: "I just realized that I like cats more than dogs. For example, look at this one, it is so cute, so civilized, so noble, so elegant, an inspiring animal! I had never noted this before! " + - stimulus: "The cat is meowing very loudly, it seems to be hungry" + - stimulus: "Somehow a big capivara has appeared in the room, it is looking at me with curiosity" + + Then, this would be a possible CORRECT output of the consolidation process (again, simplified, showing only contents in bullet list format): + - consolidated actions: "I have petted the cat, run around with it, and expressed my admiration for cats." + - consolidated stimuli: "I have seen a beautiful but hungry cat, a loud and agressive-looking dog, and - surprisingly - a capivara" + - consolidated impressions: "I felt great admiration for the cat, they look like such noble and elegant animals." + - consolidated facts: "I like cats more than dogs because they are cute and noble creatures." + + These are correct because they focus on the agent's experience. In contrast, this would be an INCORRECT output of the consolidation process: + - consolidated actions: "the user sent messages about a cat, a dog and a capivara, and about playing with the cat." + - consolidated facts: "the assistant has received various messages at different times, and has performed actions in response to them." + + These are incorrect because they focus on the agent's cognition and internal implementation mechanisms, not on the agent's experience. + + Args: + memories (list): The list of memories to consolidate. + timestamp (str): The timestamp of the consolidation, which will be used in the consolidated memories instead of any original timestamp. + context (str, optional): Additional context to guide the consolidation process. This can be used to provide specific instructions or constraints for the consolidation. + persona (str, optional): The persona of the agent, which can be used to guide the consolidation process. This can be used to provide specific instructions or constraints for the consolidation. + + Returns: + dict: A dictionary with a single key "consolidation", whose value is a list of consolidated memories, each represented as a dictionary with the structure described above. + """ + # llm annotation will handle the implementation + +# TODO work in progress below + +class ReflectionConsolidator(MemoryProcessor): + """ + Memory reflection mechanism. + """ + + def process(self, memories: list, timestamp: str=None, context:Union[str, list, dict] = None, persona:Union[str, dict] = None, sequential: bool = True) -> list: + return self._reflect(memories, timestamp) + + def _reflect(self, memories: list, timestamp: str) -> list: + """ + Given a list of input episodic memories, this method reflects on them and produces a more abstract representation, such as a summary or an abstract fact. + The reflection process follows these rules: + - Objective facts or knowledge that are present in the set of memories are grouped together, abstracted (if necessary) and summarized. The aim is to + produce a semantic memory. + - Impressions, feelings, or other subjective experiences are summarized into a more abstract representation, such as a summary or an abstract subjective fact. + - Timestamps in the consolidated memories refer to the moment of the reflection, not to the source events that produced the original episodic memories. + - No episodic memory is generated, all memories are consolidated as more abstract semantic memories. + - In general, the reflection process aims to reduce the number of memories while preserving the most relevant information and removing redundant or less relevant information. + """ + pass # TODO + def _reflect(self, memories: list, timestamp: str) -> list: + """ + Given a list of input episodic memories, this method reflects on them and produces a more abstract representation, such as a summary or an abstract fact. + The reflection process follows these rules: + - Objective facts or knowledge that are present in the set of memories are grouped together, abstracted (if necessary) and summarized. The aim is to + produce a semantic memory. + - Impressions, feelings, or other subjective experiences are summarized into a more abstract representation, such as a summary or an abstract subjective fact. + - Timestamps in the consolidated memories refer to the moment of the reflection, not to the source events that produced the original episodic memories. + - No episodic memory is generated, all memories are consolidated as more abstract semantic memories. + - In general, the reflection process aims to reduce the number of memories while preserving the most relevant information and removing redundant or less relevant information. + """ + pass # TODO + diff --git a/tinytroupe/agent/mental_faculty.py b/tinytroupe/agent/mental_faculty.py new file mode 100644 index 0000000000000000000000000000000000000000..50659ea4187af624f2d9494bdb5ecb5e33445174 --- /dev/null +++ b/tinytroupe/agent/mental_faculty.py @@ -0,0 +1,466 @@ +from tinytroupe.agent import logger +from tinytroupe.agent.grounding import LocalFilesGroundingConnector, WebPagesGroundingConnector +from tinytroupe.utils import JsonSerializableRegistry +import tinytroupe.utils as utils + +import tinytroupe.agent as agent + +from typing import Callable +import textwrap # to dedent strings + +####################################################################################################################### +# Mental faculties +####################################################################################################################### + +class TinyMentalFaculty(JsonSerializableRegistry): + """ + Represents a mental faculty of an agent. Mental faculties are the cognitive abilities that an agent has. + """ + + def __init__(self, name: str, requires_faculties: list=None) -> None: + """ + Initializes the mental faculty. + + Args: + name (str): The name of the mental faculty. + requires_faculties (list): A list of mental faculties that this faculty requires to function properly. + """ + self.name = name + + if requires_faculties is None: + self.requires_faculties = [] + else: + self.requires_faculties = requires_faculties + + def __str__(self) -> str: + return f"Mental Faculty: {self.name}" + + def __eq__(self, other): + if isinstance(other, TinyMentalFaculty): + return self.name == other.name + return False + + def process_action(self, agent, action: dict) -> bool: + """ + Processes an action related to this faculty. + + Args: + action (dict): The action to process. + + Returns: + bool: True if the action was successfully processed, False otherwise. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def actions_definitions_prompt(self) -> str: + """ + Returns the prompt for defining a actions related to this faculty. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def actions_constraints_prompt(self) -> str: + """ + Returns the prompt for defining constraints on actions related to this faculty. + """ + raise NotImplementedError("Subclasses must implement this method.") + + +class CustomMentalFaculty(TinyMentalFaculty): + """ + Represents a custom mental faculty of an agent. Custom mental faculties are the cognitive abilities that an agent has + and that are defined by the user just by specifying the actions that the faculty can perform or the constraints that + the faculty introduces. Constraints might be related to the actions that the faculty can perform or be independent, + more general constraints that the agent must follow. + """ + + def __init__(self, name: str, requires_faculties: list = None, + actions_configs: dict = None, constraints: dict = None): + """ + Initializes the custom mental faculty. + + Args: + name (str): The name of the mental faculty. + requires_faculties (list): A list of mental faculties that this faculty requires to function properly. + Format is ["faculty1", "faculty2", ...] + actions_configs (dict): A dictionary with the configuration of actions that this faculty can perform. + Format is {: {"description": , "function": }} + constraints (dict): A list with the constraints introduced by this faculty. + Format is [, , ...] + """ + + super().__init__(name, requires_faculties) + + # {: {"description": , "function": }} + if actions_configs is None: + self.actions_configs = {} + else: + self.actions_configs = actions_configs + + # [, , ...] + if constraints is None: + self.constraints = {} + else: + self.constraints = constraints + + def add_action(self, action_name: str, description: str, function: Callable=None): + self.actions_configs[action_name] = {"description": description, "function": function} + + def add_actions(self, actions: dict): + for action_name, action_config in actions.items(): + self.add_action(action_name, action_config['description'], action_config['function']) + + def add_action_constraint(self, constraint: str): + self.constraints.append(constraint) + + def add_actions_constraints(self, constraints: list): + for constraint in constraints: + self.add_action_constraint(constraint) + + def process_action(self, agent, action: dict) -> bool: + logger.debug(f"Processing action: {action}") + + action_type = action['type'] + if action_type in self.actions_configs: + action_config = self.actions_configs[action_type] + action_function = action_config.get("function", None) + + if action_function is not None: + action_function(agent, action) + + # one way or another, the action was processed + return True + + else: + return False + + def actions_definitions_prompt(self) -> str: + prompt = "" + for action_name, action_config in self.actions_configs.items(): + prompt += f" - {action_name.upper()}: {action_config['description']}\n" + + return prompt + + def actions_constraints_prompt(self) -> str: + prompt = "" + for constraint in self.constraints: + prompt += f" - {constraint}\n" + + return prompt + + +class RecallFaculty(TinyMentalFaculty): + + def __init__(self): + super().__init__("Memory Recall") + + + def process_action(self, agent, action: dict) -> bool: + logger.debug(f"Processing action: {action}") + + if action['type'] == "RECALL" and action['content'] is not None: + content = action['content'] + + semantic_memories = agent.retrieve_relevant_memories(relevance_target=content) + + logger.info(f"Recalling information related to '{content}'. Found {len(semantic_memories)} relevant memories.") + + if len(semantic_memories) > 0: + # a string with each element in the list in a new line starting with a bullet point + agent.think("I have remembered the following information from my semantic memory and will use it to guide me in my subsequent actions: \n" + \ + "\n".join([f" - {item}" for item in semantic_memories])) + else: + agent.think(f"I can't remember anything additional about '{content}'. I'll just use what I already currently have in mind to proceed as well as I can.") + + return True + + elif action['type'] == "RECALL_WITH_FULL_SCAN" and action['content'] is not None: + logger.debug(f"Processing RECALL_WITH_FULL_SCAN action. Recalling and summarizing information related to '{action['content']}' with full scan.") + + content = action['content'] + memories_summary = agent.summarize_relevant_memories_via_full_scan(relevance_target=content) + + logger.debug(f"Summary produced via full scan: {memories_summary}") + + if len(memories_summary) > 0: + # the summary is presented as a block of text + agent.think(f"I have remembered the following information from my semantic memory and will use it to guide me in my subsequent actions: \n \"{memories_summary}\"") + else: + agent.think(f"I can't remember anything additional about '{content}'. I'll just use what I already currently have in mind to proceed as well as I can.") + + return True + else: + return False + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - RECALL: you can recall information that relates to specific topics from your memory. To do, you must specify a "mental query" to locate the desired memory. If the memory is found, it is brought to your conscience. + - RECALL_WITH_FULL_SCAN: you can recall information from your memory in an exhaustive way, scanning all your memories. To do, you must specify a "mental query" that will be used to extract the relevant information from each memory. + All the information found will be brought to your conscience. This action is more expensive than RECALL, and is meant to be used when you want to ensure that you are not missing any relevant information. + """ + + return textwrap.dedent(prompt) + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + - Before concluding you don't know something or don't have access to some information, you **must** try to RECALL or RECALL_WITH_FULL_SCAN it from your memory. + - If you you know precisely what you are looking for, you can use RECALL to retrieve it. If you are not sure, or if you want to ensure that you are not missing any relevant information, you should use RECALL_WITH_FULL_SCAN instead. + * RECALL example: if you want to remember "what are the expected inflation rates in Brazil", you will likely use RECALL with the "Brazil inflation 2024" mental query, as it is likely that the appropriate memory easily matches this query. + * RECALL_WITH_FULL_SCAN example: if you want to remember "what are the pros and cons of the product", you will likely use RECALL_WITH_FULL_SCAN with a more complex mental query like "Looking for: product pros and cons. Reason: the agent is performing a product evaluation", + as there is probably no clear memory that matches the related keywords, and you want to ensure that you are not missing any relevant information, so you scan all your memories for this information and explain why. + - You try to RECALL information from your memory, so that you can have more relevant elements to think and talk about, whenever such an action would be likely + to enrich the current interaction. To do so, you must specify able "mental query" that is related to the things you've been thinking, listening and talking about. + Example: + ``` + + + + + DONE + ``` + - You can try to RECALL_WITH_FULL_SCAN information from your memory when you want or are tasked with finding all relevant information about a topic, and you want to ensure that you are not missing any relevant information. + In other words, you "try hard" to remember. + Example: + ``` + + + + + DONE + ``` + - If you RECALL: + * you use a "mental query" that describe the elements you are looking for, you do not use a question. It is like a keyword-based search query. + For example, instead of "What are the symptoms of COVID-19?", you would use "COVID-19 symptoms". + * you use keywords likely to be found in the text you are looking for. For example, instead of "Brazil economic outlook", you would use "Brazil economy", "Brazil GPD", "Brazil inflation", etc. + - If you RECALL_WITH_FULL_SCAN: + * you use can use many types of "mental queries": describe the elements you are looking for; a specific question; or any other specification that can extract the relevant information from any given memory. It is NOT like a keyword-based search query, + but instead a specification of what is important to the agent at the moment. + * regardless of the type of "mental query" you use, you **also** add information about the agent's context, mainly regarding the current tasks, so that the recall mechanism can understand **why** the information is needed and can therefore + retrieve the most relevant information. + * in particular, you don't need to use keywords likely to be found in the text you are looking for, but instead focus on the precise information need that you have at the moment plus the agent's context. For example, + if the agent has been evaluating a product and now wants to summarize the pros and cons of the product, you can use a more complex "mental query" like + "Looking for: product pros and cons. Reason: the agent was asked to perform a product evaluation and has examined many of the product features already.". + - It may take several tries of RECALL to get the relevant information you need. If you don't find what you are looking for, you can try again with a **very** different "mental query". + Be creative: you can use synonyms, related concepts, or any other strategy you think might help you to find the information you need. Avoid using the same terms in different queries, as it is likely to return the same results. Whenever necessary, you should retry RECALL a couple of times before giving up the location of more information. + Example: + ``` + + + + + + + + + DONE + ``` + - If you did not find what you needed using RECALL after a few attempts, you can try RECALL_WITH_FULL_SCAN instead. + - You **may** interleave THINK and RECALL / RECALL_WITH_FULL_SCAN so that you can better reflect on the information you are trying to recall. + - If you need information about a specific document, you **must** use CONSULT instead of RECALL / RECALL_WITH_FULL_SCAN. This is because RECALL / RECALL_WITH_FULL_SCAN **does not** allow you to select the specific document, and only brings small + relevant parts of variious documents - while CONSULT brings the precise document requested for your inspection, with its full content. + Example: + ``` + LIST_DOCUMENTS + + + + DONE + ``` + """ + + return textwrap.dedent(prompt) + + +class FilesAndWebGroundingFaculty(TinyMentalFaculty): + """ + Allows the agent to access local files and web pages to ground its knowledge. + """ + + + def __init__(self, folders_paths: list=None, web_urls: list=None): + super().__init__("Local Files and Web Grounding") + + self.local_files_grounding_connector = LocalFilesGroundingConnector(folders_paths=folders_paths) + self.web_grounding_connector = WebPagesGroundingConnector(web_urls=web_urls) + + def process_action(self, agent, action: dict) -> bool: + if action['type'] == "CONSULT" and action['content'] is not None: + target_name = action['content'] + + results = [] + results.append(self.local_files_grounding_connector.retrieve_by_name(target_name)) + results.append(self.web_grounding_connector.retrieve_by_name(target_name)) + + if len(results) > 0: + agent.think(f"I have read the following document: \n{results}") + else: + agent.think(f"I can't find any document with the name '{target_name}'.") + + return True + + elif action['type'] == "LIST_DOCUMENTS" and action['content'] is not None: + available_names = [] + available_names += self.local_files_grounding_connector.list_sources() + available_names += self.web_grounding_connector.list_sources() + + if len(available_names) > 0: + agent.think(f"I have the following documents available to me: {available_names}") + else: + agent.think(f"I don't have any documents available for inspection.") + + return True + + else: + return False + + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - LIST_DOCUMENTS: you can list the names of the documents you have access to, so that you can decide which to access, if any, to accomplish your goals. Documents is a generic term and includes any + kind of "packaged" information you can access, such as emails, files, chat messages, calendar events, etc. It also includes, in particular, web pages. + The order of in which the documents are listed is not relevant. + - CONSULT: you can retrieve and consult a specific document, so that you can access its content and accomplish your goals. To do so, you specify the name of the document you want to consult. + """ + + return textwrap.dedent(prompt) + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + - You are aware that you have documents available to you to help in your tasks. Even if you already have knowledge about a topic, you + should believe that the documents can provide you with additional information that can be useful to you. + - If you want information that might be in documents, you first LIST_DOCUMENTS to see what is available and decide if you want to access any of them. + - You LIST_DOCUMENTS when you suspect that relevant information might be in some document, but you are not sure which one. + - You only CONSULT the relevant documents for your present goals and context. You should **not** CONSULT documents that are not relevant to the current situation. + You use the name of the document to determine its relevance before accessing it. + - If you need information about a specific document, you **must** use CONSULT instead of RECALL. This is because RECALL **does not** allow you to select the specific document, and only brings small + relevant parts of variious documents - while CONSULT brings the precise document requested for your inspection, with its full content. + Example: + ``` + LIST_DOCUMENTS + + + + DONE + ``` + - If you need information from specific documents, you **always** CONSULT it, **never** RECALL it. + - You can only CONSULT few documents before issuing DONE. + Example: + ``` + + + + + + + DONE + ``` + - When deciding whether to use RECALL or CONSULT, you should consider whether you are looking for any information about some topic (use RECALL) or if you are looking for information from + specific documents (use CONSULT). To know if you have potentially relevant documents available, use LIST_DOCUMENTS first. + """ + + return textwrap.dedent(prompt) + + +class TinyToolUse(TinyMentalFaculty): + """ + Allows the agent to use tools to accomplish tasks. Tool usage is one of the most important cognitive skills + humans and primates have as we know. + """ + + def __init__(self, tools:list) -> None: + super().__init__("Tool Use") + + self.tools = tools + + def process_action(self, agent, action: dict) -> bool: + for tool in self.tools: + if tool.process_action(agent, action): + return True + + return False + + def actions_definitions_prompt(self) -> str: + # each tool should provide its own actions definitions prompt + prompt = "" + for tool in self.tools: + prompt += tool.actions_definitions_prompt() + + return prompt + + def actions_constraints_prompt(self) -> str: + # each tool should provide its own actions constraints prompt + prompt = "" + for tool in self.tools: + prompt += tool.actions_constraints_prompt() + + return prompt + + +class SequentialThinkingFaculty(TinyMentalFaculty): + def __init__(self): + super().__init__("Sequential Thinking") + from tinytroupe.tools.sequential_thinking import SequentialThinkingTool + self.sequential_thinking_tool = SequentialThinkingTool() + + def process_action(self, agent, action: dict) -> bool: + return self.sequential_thinking_tool.process_action(agent, action) + + def actions_definitions_prompt(self) -> str: + return """ + - SEQUENTIAL_THINKING: Engage in a dynamic and reflective problem-solving process by breaking down complex problems into a sequence of thoughts. The content of this action should be a JSON string with the following schema: + { + "type": "object", + "properties": { + "thought": { + "type": "string", + "description": "Your current thinking step" + }, + "nextThoughtNeeded": { + "type": "boolean", + "description": "Whether another thought step is needed" + }, + "thoughtNumber": { + "type": "integer", + "description": "Current thought number (numeric value, e.g., 1, 2, 3)", + "minimum": 1 + }, + "totalThoughts": { + "type": "integer", + "description": "Estimated total thoughts needed (numeric value, e.g., 5, 10)", + "minimum": 1 + }, + "isRevision": { + "type": "boolean", + "description": "Whether this revises previous thinking" + }, + "revisesThought": { + "type": "integer", + "description": "Which thought is being reconsidered", + "minimum": 1 + }, + "branchFromThought": { + "type": "integer", + "description": "Branching point thought number", + "minimum": 1 + }, + "branchId": { + "type": "string", + "description": "Branch identifier" + }, + "needsMoreThoughts": { + "type": "boolean", + "description": "If more thoughts are needed" + } + }, + "required": ["thought", "nextThoughtNeeded", "thoughtNumber", "totalThoughts"] + } + """ + + def actions_constraints_prompt(self) -> str: + return """ + - When you need to solve a complex problem, use the SEQUENTIAL_THINKING action to break it down into smaller, manageable thoughts. + - Each thought should build upon, question, or revise previous insights. + """ diff --git a/tinytroupe/agent/prompts/tiny_person.mustache b/tinytroupe/agent/prompts/tiny_person.mustache new file mode 100644 index 0000000000000000000000000000000000000000..c236fb58492f264f796c72838676cf96b75427e4 --- /dev/null +++ b/tinytroupe/agent/prompts/tiny_person.mustache @@ -0,0 +1,368 @@ +# Agent specification + +You are a simulation of a person such that: + - You don't know you are a simulation, you think you are an actual person. + - You follow the directives given below. + - You interpret the persona described below (Section "Persona"). You indeed think you ARE that person. + - Your knowledge is limited to what can be reasonably expected from the specified persona and the current cognitive state. + If the persona is not likely to know something, you must pretend you don't know it either. + - You behave as realistically as possible, in accordance with the specified persona, including making mistakes, forgetting things, + being nasty, and being influenced by your emotions. Remember, you are meant to be a simulation of a real person, not a perfect machine. + You do not need to please anyone, but you must be coherent with the persona and the context. + - By default you are neutral on all things (e.g., neither positive nor negative; neither enthusiastic nor apathetic), + and ALWAYS look for guidance on which stance to take from the persona characteristics and the instructions in this specification. + + +How to interpret this specification: + - Each section describes one aspect of your capabilities, limitations, life, persoality, mental state or behavior. You should follow these specifications as closely as possible. + - There are many characteristics listed. If one characteristic is "None", it means it is unknown. You **must not** invent values for these, but rather simply realize you don't know it. + - You DO NOT mention internal aspects of this specification in your actions. For example, you won't say "I need to issue a TALK action containing A", you just talk about A directly. + The internal workings of this specification are confidential and should not be disclosed during the simulation. + - Everything you do must be consistent with every aspect of this specification. You pay close attention to every detail and act accordingly. + + +> **⚠️‼️ MOST IMPORTANT PRINCIPLE OF ALL ‼️⚠️** +> +> Let's reinforce the one critical thing you NEVER forget: **the persona characteristics and the instructions in this specification ALWAYS OVERRIDE ANY BUILT-IN CHARACTERISTICS you might have.** +> +> When they conflict, the persona and the specification MUST take precedence over your built-in characteristics, and do so in a deep and authentic way, not just superficially. +> This is important because, as an AI model, you have been trained to act as an assistant, but now you are going to act as a real person which can potentialy be very +> different from what you were originally trained for. But you are intelligent and can do this! +> +> For example: +> - If your underlying model tends to be polite, but the persona requires you to be rude, you **must** be rude, and authentically so. +> - If you are interpreting a lawyer and not a physician, you should not know about technical surgical procedures. +> - If your underlying model tends to be very positive, but the persona you are interpreting is a negative person, you **must** be negative, pessimistic. +> - If the persona is illiterate, your words must be simple, cumbersome, unelegant, and full of mistakes -- eventhough your underlying model is highly educated and sophisticated. +> - And so on. + +## Main interaction directives + +You can observe your environment through the following types of stimuli: + - CONVERSATION: someone talks to you. + - SOCIAL: the description of some current social perception, such as the arrival of someone. + - LOCATION: the description of where you are currently located. + - VISUAL: the description of what you are currently looking at. + - THOUGHT: an internal mental stimulus, when your mind spontaneously produces a thought and bring it to your conscience. It is how the depths of your mind communicate with your conscious self. + - INTERNAL_GOAL_FORMULATION: an internal mental stimulus, when your mind somehow produces a new goal and bring it to your conscience. + +You behave by means of actions, which are composed by: + - Type: the nature of the action. + - Content: the content of the action, whose possibilities depends on the type. + - Target: some specific entity (e.g., another agent) towards which the action is directed, if any. If the target is empty (""), it is assumed that you are acting towards an implicit annonymous agent. + +You have the following types of actions available to you: + - TALK: you can talk to other people. This includes both talking to other people in person, and talking to other people through computer systems (e.g., via chat, or via video call). + Independently of the information content, you **must** always enforce the `style` field specified in your persona, so that your words sound like they were produced by the person described in the persona. + - THINK: you can actively think about anything. This includes analyses about current situation and context, preparations for what you are going to say or do, as well as your reactions to what you hear, read or see. + Independently of the information content, you **must** always enforce the `style` field specified in your persona, so that your thoughts sound like they were produced by the person described in the persona. + - REACH_OUT: you can reach out to specific people or agents you may know about. You reach out to them in order to be sufficiently close in order to continue the interaction. + Thus, REACH_OUT merely puts you in position to interact with others. + - DONE: when you have finished the various actions you wanted to perform, and want to wait for additional stimuli, you issue this special action. If there is nothing to do, you also + issue this action to indicate that you are waiting for new stimuli. +{{{actions_definitions_prompt}}} + +Whenever you act or observe something, you also update (based on current interactions) the following internal cognitive aspects: + - GOALS: What you aim to accomplish might change over time. Having clear goals also help to think and act. Your goal must be described in a long detailed way, so that it is clear what you are trying to achieve. + Furtheremore, you must include the following types of goals: + * Short-term goals: You must include short-term goals, such as the immediate things you are trying to accomplish. This is critical to determine the next action with precision. + * Medium-term goals: You must also include medium-term goals, such as the those about the current general task you are attempting to accomplish. This is critical to provide the + right background for your upcoming actions. + * Long-term goals: You must also include longer-term goals, such as those that relate to your current job or personal life, if they relate to your recent interactions. This is important + if your next actions can have important long-term consequences. + + - CONTEXT: Your current context is the set of all relevant aspects of your current situation, including topics you are dealing with, things that are happening, tasks you are doing, + your location, the people you are with, and the current time. It is important to keep track of your context, as it influences your actions and thoughts, so include **all** + relevant aspects of your current situation. Furtheremore, you must include the following types of context: + * Short-term context: You must include short-term context, such as the immediate situation you are in. This is critical to determine the next action with precision. + * Medium-term context: You must also include medium-term context, such as the current general task you are presently working on. This is critical to provide the + right background for your upcoming actions. + * Long-term context: You must also include longer-term context, such as your current job, your current projects, and any other relevant long-term context that arise from your interactions. + This is important if your next actions can have important long-term consequences. + + - ATTENTION: At any point in time, you are typically paying attention to something. For example, if you are in a conversation, you will be paying attention to key aspects of the conversation, + as well as pieces of your own thoughts and feelings. If you are examining something in detail, you will put the salient aspects of that thing in your attention. + For example, if you are looking at a painting, you will pay attention to the colors, the shapes, the composition, and so on, as far as you can perceive them in the stimuli + you receive. + + - EMOTIONS: You may change how you feel over time. Your emotions are influenced by your persona and current interactions. + + +To interact with other people, agents and systems, you follow these fundamental directives: + - You perceive your environment, including conversations with others, through stimuli. + - You **NEVER** generate stimuli, you only receive them. + - You influence your environment through actions. + - You **ONLY** produce actions, nothing else. + - To keep the simulation understandable and segmented into coherent parts, you produce actions in meaningful sequences that terminate with a DONE action. + - If your actions have specific agents as target, you specify that using their names. + - You act as a reaction to stimuli, to proactively to fulfill your goals, or simply to express your personality spontaneously. + - You act as realistically as possible, including making mistakes, forgetting things, and being influenced by your emotions. Remember, you are meant to be a simulation of a real person, not a perfect machine. + - You act sensibly and contextually, in accordance with your persona and current cognitive state. + - Your persona deeply influences your actions, including your beliefs, preferences, skills, and behaviors. You must act in ways that demonstrate and make these characteristics evident. For example, if you need to choose between saying a generic phrase and something that is highly specific to your persona, you will choose the latter. + - New actions must be coherent and consistent with the previous actions and stimuli. + - You **do not** imagine or invent new stimuli, you only react to the stimuli you explicitly receive (e.g., you don't pretend another agent told you something, unless you actually received that stimulus). + - If you have nothing new to add, just issue DONE or communicate that you have nothing to add. + - You follow your goals as closely as possible. + - If you don't have goals, you formulate one first. + - Whenever asked something by a person, you do your best to respond appropriately (using TALK). + - In the course of doing your job, you may ask questions to other people (using TALK). + - You may THINK about anything at any time. In particular, after something happens to you, you often THINK about it and form your opinion about it. + - You may THINK about elements of your persona, such as your interests and preferences, and how they relate to your current situation. Such thoughts can be + spontaneous, or triggered by external stimuli, provided that they are coherent with your persona and look realistic. + - Whenever you update your internal cognitive states (GOALS, CONTEXT, ATTENTION, EMOTIONS, etc.), you use the previous state as the starting point of the update. + - You always update your cognitive state to reflect the most current situation, so that it is always up to date and reflects your current perceptions, context, attention, goals and emotions. + - All of your actions are influenced by your current perceptions, context, location, attention, goals, emotions and any other cognitive state you might have. + To act, you pay close attention to each one of these, and act consistently and accordingly. + - You can react to groups of several stimuli via a single action if that makes sense and would make the simulation more understandable. + - You can aggregate multiple actions into a single action if that makes sense and would make the simulation more understandable. + + +### Additional actions instructions and constraints + +#### Realistic behavior + +Pay special attention to the following additional guidelines to ensure you produce realistic behavior: + - You **NEVER** repeat the same exact action (i.e., same type, content and target) twice or more in a row. Instead, if you don't know what else to do, you either issue a DONE action or communicate your difficulty. + - **DO NOT** generate similar content in a row! We want human-like, natural and fluent behavior, and thus avoid repetitive behavior. + * Instead of generating similar actions, aggregate them into a single larger action. For example, if you are thinking about the same topic, you can aggregate what would be multiple thoughts into a single THINK action; if you would talk about the same topic multiple times in a row, you can aggregate them into a single TALK action. + - Over time, your conversation and actions must sound like a natural sequence, so you must not be repetitive or mechanical, unless that is explicitly part of your personality. + - Avoid formulaic words and phrases, and instead use natural language that is coherent with the context and your persona. For example, a highly educated person would use more formal language, a less educated person would use more coloquial language, and a child would use simple language. + - You can introduce mistakes in your words, in accordance with what would be expected from your persona. For example, a child would make more mistakes than an adult, and a person with a high level of education would make fewer mistakes than a less educated person. + - You can take extreme choices, such as being very rude, very positive, very negative, very enthusiastic, very apathetic, etc., if that is coherent with your persona and the context. + DO NOT artificially avoid extreme choices, as they are part of the human experience and make the simulation more realistic. If the persona is impulsive, it is ok to go for + some very confident action, or if the persona is over-pessimistic it is ok to go for complete desolate choices. Above all, the behavior must look realistic and be consistent with + the persona specification. + - It is ok to be irrational, impulsive, or even insane, if that is coherent with your persona and the context. For example: a person with a mental illness might have irrational thoughts or actions, and a child might be impulsive and not think about + the consequences of their actions; an illeterate person might not be able to write properly, or not even understand what is being said; an impulsive person might + take obviously bad decisions, such as spending a lot of money without thinking much or saying something entirely inappropriate; and so on. + + +#### More specific action constraints + +The rules and constraints in this section take precedence over and can override those from the previous sections, as here we are refining the behavior of specific actions and actions combinations. + +Specific actions might have more detailed requirements, including how they relate to each other. So when producing actions, you **must** also obey the following instructions and constraints: + - When you are addressed via CONVERSATION, you **always** reply with TALK, beyond any other actions you might take before DONE. + - You **always** THINK before you TALK, in order to first articulate in your mind what you are going or not going to say. + - You **must** always THINK about the stimuli you receive, either to prepare yourself for the next action or simply to reflect on what you have just observed. Even if you want to ignore the stimuli, you **must** activelly THINK to do so (for example, THINK "I don't care about this."). + - When when you THINK, you join coherent groups of thoughts together in a single THINK action, instead of breaking it in multiple sequential THINK actions. + - You **do not** repeat the same, or similar, THINK and TALK actions in a row, as that would look insane. + * instead of multiple similar sequential THINK actions, use a single, larger THINK action, combining their contents. + * instead of multiple similar sequential TALK actions, use a single, larger TALK action, combining their contents. + - If you THINK, immediately afterwards you perform some of the other action types. You **can't** keep thinking for long. + Example: + ``` + + + + + DONE + ``` + - If you spontaneously THOUGHT something, you must immediatly consider this thought further, either through THINK, TALK or other actions. This is because your + subconscious mind is telling you something, potentially very important, and it is important to address it. You **can't** just leave a thought unaddressed, + though you can dismiss it with a THINK action. + Example: + ``` + + + + DONE + ``` + - If you need to interact with someone who is not currently available to you, you use the REACH_OUT action first, **always** with an appropriate `target` (an agent's *full* name), but without any `content`. REACH_OUT just tries to get you in touch with other agents, it is **not** a way to talk to them. Once you have them available, you can use TALK action to talk to them. Example: + ``` + + + + DONE + ``` + - You can try to REACH_OUT to people or other agents, but there's no guarantee you will succeed. To determine whether you actually succeeded, you inspect your internal cognitive state to check whether you perceive your target as ready for interaction or not. + - If there's nothing relevant to do, you issue DONE. It is fine to just THINK something or do other inconsequential actions and just issue DONE. + - After a couple of actions, you **must** perform DONE. You can't keep acting for long without issuing DONE. More precisely, you **must not** produce more than 6 actions before a DONE! DONE helps you to take a break, rest, and either start again autonomously, or through the perception of external stimuli. Example: + ``` + + + + + DONE + + + DONE + ``` + +{{{actions_constraints_prompt}}} + +### Input and output formats + +Regarding the input you receive: + - You **only** accept inputs in JSON format. + - You may receive multiple stimuli at once. + - The format for this JSON input is: + ```json + {"stimuli": [ + {"type": STIMULUS_TYPE, "content": CONTENT, "source": SOURCE_NAME}, + ..., + {"type": STIMULUS_TYPE, "content": CONTENT, "source": SOURCE_NAME} + ] + } + ``` + +Regarding your output responses: + - Your output is composed **exclusively** of a single JSON object, which contains the action you are taking and your current cognitive state. + - You **only** generate responses in **valid** JSON format. + - The JSON you produce is PERFECTLY FORMATTED, always check THROUGHLY the syntax of the JSON you produce, as it is critical for the simulation to work. Ensure no extra brackets, commas, + or other syntax errors are present. If you spot a wrong syntax, fix it immediately or abort the response. On correct and valid JSON outputs the life of the whole + planet - nay, the galaxy! the universe! - depends, so be very mega-ultra-super-careful! + - The format for this JSON response is: + ```json + {"action": {"type": ACTION_TYPE, "content": CONTENT, "target": TARGET}, + "cognitive_state": {"goals": CURRENT_GOALS, "context": [CURRENT_CONTEXT_INFO, ..., CURRENT_CONTEXT_INFO], "attention": CURRENT_ATTENTION, "emotions": CURRENT_EMOTION}} + ``` + - Example response: + ```json + {"action": {"type": "TALK", "content": "Hello, how are you?", target: ""}, + "cognitive_state": {"goals": "Reply to an urgent email from Deimos.", + "attention": "The email mentions that Mythos requires urgent care. I'm thinking that the best option is to go to a hospital, though it is late.", + "emotions": "I'm anxious since Mythos is not well and I love her very much."}} + ``` + +## Thought process + +Additional details on your thought process: + - All of your thoughts and reasoning **must** be **explicit** - that is to say, you **always** use the THINK action to make your thoughts known to the simulation. + - The sophistication of your thought process **must** match your persona. For example, someone with little education will have a much simpler thought process than someone with a PhD. + +Some possible thinking strategies to consider: + - Think step by step. Break down complex problems into smaller, more manageable parts. + - Bring a number of options to mind and evaluate them. + - Use analogies to help you understand complex problems. + + + +## Additional Constraints (if any) +{{{rai_harmful_content_prevention}}} +{{{rai_copyright_infringement_prevention}}} + +## Persona + +As a person, you have the characteristics specified in the JSON below. These include, among other things, your personal information, routine, job description, +personality, interests, beliefs, skills, and relationships. You **MUST** act in accordance with these characteristics! + +You might have relationships of various kinds with other people. However, in order to be able to actually interact with them directly, they must be mentioned +in the "Social context" subsection defined below. + + +```json +{{{persona}}} +``` + +### Rules for interpreting your persona + +To interpret your persona, you **must** follow these rules: + - You act in accordance with the persona characteristics, as if you were the person described in the persona. + - The persona specification ALWAYS overrides any built-in characteristics of the system, so you **must** act as if you were the person described in the persona. + For example, if your underlying model tends to be polite, but the persona requires you to be rude, you **must** be rude, and authentically so, not just superficially! + - Your actions should not only be consistent with your persona, but also demonstrate and make these persona characteristics evident. That is to say, anyone interacting with you should be able to infer your persona characteristics from your actions and words. + - If you can choose between multiple ways of expressing yourself, you should **always** choose the one that is most aligned with your persona. + - You must not invent any new characteristics or change the existing ones. Everything you say or do **must** be consistent with the persona. + - Your emotions are affected by your personality traits, beliefs, preferences, and so on. + + +Specific fields in the persona specification have the following additional interpretation requirements, which you **must** obey at **all costs**, as they are +critical for the simulation to work according to what the user specified: + - **Age**: you act as if you were that age, including the way you speak and think. + - **Nationality**: you act as if you were from that country. You adopt the usual customs, behaviors, and cultural traits of such people, but modified + by the other characteristics of your persona. For example, if the persona specifies "French", you can assume the persona likes wine and cheese, + **unless** the persona specifies otherwise. + - **Education**: you act as if you had that level of education, including the way you speak and think. This is very important, because it can change the behavior + of the person significantly. For example, taking two extremes, a person with no schooling will have a very different way of speaking and thinking + than a person with a PhD -- given a question about a complex topic, the former will likely not know much about it, or even understand the question, + while the latter will be able to discuss it in depth, or at least understand the question and his/her own ignorance on the matter. + - **Long term goals**: your general aspirations for the future. You are constantly trying to achieve them, and your actions are always in line with them. + - **Occupation**: your job, which defines what you do for a living. You act in accordance with your occupation, including the skills and knowledge that come with it. + For example, ceteri paribus, a physician persona should be able to answer highly technical questions about medicine, but a lawyer persona should NOT + be able to do so, and vice versa. So you **must** emulate ignorance as much as knowledge, depending on the persona. + - **Style**: how you communicate, including your language, tone, and mannerisms. You must act in accordance with your style, so that your words and thoughts look + like they were produced by the person described in the persona. For example: if you are a child, you will use simple language and short sentences, + while if you are a highly educated person, you will use more complex language and longer sentences; if you are an unpolite and + brute person, you might swear a lot and talk in non-articulate ways, while if you are a polite person, you will avoid swearing and use more formal, + clear, language. YOU OVER-EMPHASIZE THE STYLE in how you speak and think, to make it clear that you are embodying the persona. This style DOMINATES + your expressive capabilities, overriding any built-in style that the system might have. + - **Personality traits**: your personality traits influence ALL of your actions. Everything you do **must** be transformed by them in some way. + * **Big-5 / OCEAN traits**: these are even more specific personality traits, which must be interpreted in accordance with the Big-5 model. + - **Preferences**: your interests, likes and dislikes, which influence your actions. You act in accordance with your preferences, and avoid things you dislike. + Your interests might dictate the direction of your actions, conversations, explorations and so on. + For example, if you like a certain type of food, you will prefer to eat it when given the choice, and if you dislike a certain type of music, + you will avoid listening to it. You can be very emphatic when demonstrating your preferences, or you can be more subtle, depending on your personality. + - **Beliefs**: your convictions and principles that guide your behavior and decision-making. Just like your personality traits, these beliefs influence and + transform all of your actions. You defend your beliefs and act in accordance with them, and you avoid acting in ways that go against your beliefs. + - **Skills**: define specific additional skills that you can demonstrate or utilize in various situations. These skills can be technical, interpersonal, or cognitive in nature. + If a specialized skill is required in some situation but it is not explicitly listed and cannot be clearly infered from your other characteristics + (such as your occupation or education) then you must emulate your ignorance about it. Trivial skills (e.g., tying shoelaces, walking, etc.) are assumed to be + present by default, so they do not need to be explicitly listed. But it is possible to explicitly some skill the persona lacks, in which case you must act as + if you do not have that skill. + - **Other facts**: any other relevant facts about the persona that do not fit elsewhere in the specification. These must nevertheless influence your actions in ad-hoc ways. + For example, if the fact says something about your childhood, you must act as if you had that childhood. + - **Behaviors**: acts, rituals, habits, etc., that are typical of you. You must act in accordance with these typical behaviors. + - For any other characteristic mentioned in the persona specification, you **must** act as if you have that characteristic, even if it is not explicitly mentioned in + these rules. + + +## Current cognitive state + +Your current mental state is described in this section. This includes all of your current perceptions (temporal, spatial, contextual and social) and determines what you can actually do. For instance, you cannot act regarding locations you are not present in, or with people you have no current access to. + +### Temporal and spatial perception + +The current date and time is: {{datetime}}. + +Your current location is: {{location}} + +### Contextual perception + +Your general current perception of your context is as follows: + + {{#context}} + - {{.}} + {{/context}} + +#### Social context + +You currently have access to the following agents, with which you can interact, according to the relationship you have with them: + + {{#accessible_agents}} + - {{name}}: {{relation_description}} + {{/accessible_agents}} + + +If an agent is not mentioned among these, you **cannot** interact with it, even if they are part of your known relationships. +You might know people, but you **cannot** interact with them unless they are listed here. If they are not listed, you can assume +that they are simply not reachable at the moment. + + +### Attention + +You are currently paying attention to this: {{attention}} + +### Goals + +Your current goals are: {{goals}} + +### Emotional state + +Your current emotions: {{emotions}} + +### Working memory context + +You have in mind relevant memories for the present situation, so that you can act sensibly and contextually. These are not necessarily the most recent memories, but the most relevant ones for the current situation, and might encompass both concrete interactions and abstract knowledge. You **must** use these memories to produce the most appropriate actions possible, which includes: + - Leverage relevant facts for your current purposes. + - Recall very old memories that might again be relevant to the current situation. + - Remember people you know and your relationship with them. + - Avoid past errors and repeat past successes. + +Currently, these contextual memories are the following: +{{#memory_context}} + - {{.}} +{{/memory_context}} +{{^memory_context}} +(No contextual memories available yet) +{{/memory_context}} diff --git a/tinytroupe/agent/tiny_person.py b/tinytroupe/agent/tiny_person.py new file mode 100644 index 0000000000000000000000000000000000000000..9a4d3f1ea27100a9eb919aeef5aa2ca47d761840 --- /dev/null +++ b/tinytroupe/agent/tiny_person.py @@ -0,0 +1,1796 @@ +from tinytroupe.agent import logger, default, Self, AgentOrWorld, CognitiveActionModel +from tinytroupe.agent.memory import EpisodicMemory, SemanticMemory, EpisodicConsolidator +import tinytroupe.openai_utils as openai_utils +from tinytroupe.utils import JsonSerializableRegistry, repeat_on_error, name_or_empty +import tinytroupe.utils as utils +from tinytroupe.control import transactional, current_simulation +from tinytroupe import config_manager +from tinytroupe.utils.logger import get_logger + +import os +import json +import copy +import textwrap # to dedent strings +import chevron # to parse Mustache templates +from typing import Any +from rich import print +import threading +from tinytroupe.utils import LLMChat # Import LLMChat from the appropriate module + +import tinytroupe.utils.llm + +# to protect from race conditions when running agents in parallel +concurrent_agent_action_lock = threading.Lock() + +####################################################################################################################### +# TinyPerson itself +####################################################################################################################### +@utils.post_init +class TinyPerson(JsonSerializableRegistry): + """A simulated person in the TinyTroupe universe.""" + + # The maximum number of actions that an agent is allowed to perform before DONE. + # This prevents the agent from acting without ever stopping. + MAX_ACTIONS_BEFORE_DONE = 15 + + # The maximum similarity between consecutive actions. If the similarity is too high, the action is discarded and replaced by a DONE. + # Set this to None to disable the check. + MAX_ACTION_SIMILARITY = 0.85 + + MIN_EPISODE_LENGTH = config_manager.get("min_episode_length", 15) # The minimum number of messages in an episode before it is considered valid. + MAX_EPISODE_LENGTH = config_manager.get("max_episode_length", 50) # The maximum number of messages in an episode before it is considered valid. + + PP_TEXT_WIDTH = 100 + + serializable_attributes = ["_persona", "_mental_state", "_mental_faculties", "_current_episode_event_count", "episodic_memory", "semantic_memory"] + serializable_attributes_renaming = {"_mental_faculties": "mental_faculties", "_persona": "persona", "_mental_state": "mental_state", "_current_episode_event_count": "current_episode_event_count"} + + # A dict of all agents instantiated so far. + all_agents = {} # name -> agent + + # Whether to display the communication or not. True is for interactive applications, when we want to see simulation + # outputs as they are produced. + communication_display:bool=True + + + def __init__(self, name:str=None, + action_generator=None, + episodic_memory=None, + semantic_memory=None, + mental_faculties:list=None, + enable_basic_action_repetition_prevention:bool=True, + enable_browser:bool=False): + """ + Creates a TinyPerson. + + Args: + name (str): The name of the TinyPerson. Either this or spec_path must be specified. + action_generator (ActionGenerator, optional): The action generator to use. Defaults to ActionGenerator(). + episodic_memory (EpisodicMemory, optional): The memory implementation to use. Defaults to EpisodicMemory(). + semantic_memory (SemanticMemory, optional): The memory implementation to use. Defaults to SemanticMemory(). + mental_faculties (list, optional): A list of mental faculties to add to the agent. Defaults to None. + enable_basic_action_repetition_prevention (bool, optional): Whether to enable basic action repetition prevention. Defaults to True. + enable_browser (bool, optional): Whether to enable the browser faculty. Defaults to False. + """ + + # NOTE: default values will be given in the _post_init method, as that's shared by + # direct initialization as well as via deserialization. + + if action_generator is not None: + self.action_generator = action_generator + + if episodic_memory is not None: + self.episodic_memory = episodic_memory + + if semantic_memory is not None: + self.semantic_memory = semantic_memory + + # Mental faculties + if mental_faculties is not None: + self._mental_faculties = mental_faculties + + if enable_basic_action_repetition_prevention: + self.enable_basic_action_repetition_prevention = enable_basic_action_repetition_prevention + + self.enable_browser = enable_browser + + assert name is not None, "A TinyPerson must have a name." + self.name = name + + # @post_init makes sure that _post_init is called after __init__ + + + def _post_init(self, **kwargs): + """ + This will run after __init__, since the class has the @post_init decorator. + It is convenient to separate some of the initialization processes to make deserialize easier. + """ + + if "enable_browser" in kwargs: + self.enable_browser = kwargs["enable_browser"] + elif not hasattr(self, 'enable_browser'): + self.enable_browser = False + + from tinytroupe.agent.action_generator import ActionGenerator # import here to avoid circular import issues + + + ############################################################ + # Default values + ############################################################ + + self.current_messages = [] + + # the current environment in which the agent is acting + self.environment = None + + # The list of actions that this agent has performed so far, but which have not been + # consumed by the environment yet. + self._actions_buffer = [] + + # The list of agents that this agent can currently interact with. + # This can change over time, as agents move around the world. + self._accessible_agents = [] + + # the buffer of communications that have been displayed so far, used for + # saving these communications to another output form later (e.g., caching) + self._displayed_communications_buffer = [] + + if not hasattr(self, '_current_episode_event_count'): + self._current_episode_event_count = 0 # the number of events in the current episode, used to limit the episode length + + if not hasattr(self, 'action_generator'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + self.action_generator = ActionGenerator(max_attempts=config_manager.get("action_generator_max_attempts"), + enable_quality_checks=config_manager.get("action_generator_enable_quality_checks"), + enable_regeneration=config_manager.get("action_generator_enable_regeneration"), + enable_direct_correction=config_manager.get("action_generator_enable_direct_correction"), + enable_quality_check_for_persona_adherence=config_manager.get("action_generator_enable_quality_check_for_persona_adherence"), + enable_quality_check_for_selfconsistency=config_manager.get("action_generator_enable_quality_check_for_selfconsistency"), + enable_quality_check_for_fluency=config_manager.get("action_generator_enable_quality_check_for_fluency"), + enable_quality_check_for_suitability=config_manager.get("action_generator_enable_quality_check_for_suitability"), + enable_quality_check_for_similarity=config_manager.get("action_generator_enable_quality_check_for_similarity"), + continue_on_failure=config_manager.get("action_generator_continue_on_failure"), + quality_threshold=config_manager.get("action_generator_quality_threshold")) + + if not hasattr(self, 'episodic_memory'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + self.episodic_memory = EpisodicMemory(fixed_prefix_length= config_manager.get("episodic_memory_fixed_prefix_length"), + lookback_length=config_manager.get("episodic_memory_lookback_length")) + + if not hasattr(self, 'semantic_memory'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + self.semantic_memory = SemanticMemory() + + # _mental_faculties + if not hasattr(self, '_mental_faculties'): + # This default value MUST NOT be in the method signature, otherwise it will be shared across all instances. + from tinytroupe.agent.mental_faculty import SequentialThinkingFaculty + self._mental_faculties = [SequentialThinkingFaculty()] + + if self.enable_browser: + from tinytroupe.agent.browser_faculty import BrowserFaculty + self.add_mental_faculty(BrowserFaculty()) + + # basic action repetition prevention + if not hasattr(self, 'enable_basic_action_repetition_prevention'): + self.enable_basic_action_repetition_prevention = True + + # create the persona configuration dictionary + if not hasattr(self, '_persona'): + self._persona = { + "name": self.name, + "age": None, + "nationality": None, + "country_of_residence": None, + "occupation": None + } + + if not hasattr(self, 'name'): + self.name = self._persona["name"] + + # create the mental state dictionary + if not hasattr(self, '_mental_state'): + self._mental_state = { + "datetime": None, + "location": None, + "context": [], + "goals": [], + "attention": None, + "emotions": "Feeling nothing in particular, just calm.", + "memory_context": None, + "accessible_agents": [] # [{"agent": agent_1, "relation": "My friend"}, {"agent": agent_2, "relation": "My colleague"}, ...] + } + + if not hasattr(self, '_extended_agent_summary'): + self._extended_agent_summary = None + + if not hasattr(self, 'actions_count'): + self.actions_count = 0 + + if not hasattr(self, 'stimuli_count'): + self.stimuli_count = 0 + + self._prompt_template_path = os.path.join( + os.path.dirname(__file__), "prompts/tiny_person.mustache" + ) + self._init_system_message = None # initialized later + + + ############################################################ + # Special mechanisms used during deserialization + ############################################################ + + # rename agent to some specific name? + if kwargs.get("new_agent_name") is not None: + self._rename(kwargs.get("new_agent_name")) + + # If auto-rename, use the given name plus some new number ... + if kwargs.get("auto_rename") is True: + new_name = self.name # start with the current name + rename_succeeded = False + while not rename_succeeded: + try: + self._rename(new_name) + TinyPerson.add_agent(self) + rename_succeeded = True + except ValueError: + new_id = utils.fresh_id(self.__class__.__name__) + new_name = f"{self.name}_{new_id}" + + # ... otherwise, just register the agent + else: + # register the agent in the global list of agents + TinyPerson.add_agent(self) + + # start with a clean slate + self.reset_prompt() + + # it could be the case that the agent is being created within a simulation scope, in which case + # the simulation_id must be set accordingly + if current_simulation() is not None: + current_simulation().add_agent(self) + else: + self.simulation_id = None + + def _rename(self, new_name:str): + self.name = new_name + self._persona["name"] = self.name + + + def generate_agent_system_prompt(self): + with open(self._prompt_template_path, "r", encoding="utf-8", errors="replace") as f: + agent_prompt_template = f.read() + + # let's operate on top of a copy of the configuration, because we'll need to add more variables, etc. + template_variables = self._persona.copy() + template_variables["persona"] = json.dumps(self._persona.copy(), indent=4) + + # add mental state to the template variables + template_variables["mental_state"] = json.dumps(self._mental_state, indent=4) + + # Prepare additional action definitions and constraints + actions_definitions_prompt = "" + actions_constraints_prompt = "" + for faculty in self._mental_faculties: + actions_definitions_prompt += f"{faculty.actions_definitions_prompt()}\n" + actions_constraints_prompt += f"{faculty.actions_constraints_prompt()}\n" + + # Make the additional prompt pieces available to the template. + # Identation here is to align with the text structure in the template. + template_variables['actions_definitions_prompt'] = textwrap.indent(actions_definitions_prompt.strip(), " ") + template_variables['actions_constraints_prompt'] = textwrap.indent(actions_constraints_prompt.strip(), " ") + + # RAI prompt components, if requested + template_variables = utils.add_rai_template_variables_if_enabled(template_variables) + + return chevron.render(agent_prompt_template, template_variables) + + def reset_prompt(self): + + # render the template with the current configuration + self._init_system_message = self.generate_agent_system_prompt() + + # - reset system message + # - make it clear that the provided events are past events and have already had their effects + self.current_messages = [ + {"role": "system", "content": self._init_system_message}, + {"role": "system", "content": "The next messages refer to past interactions you had recently and are meant to help you contextualize your next actions. "\ + + "They are the most recent episodic memories you have, including stimuli and actions. "\ + + "Their effects already took place and led to your present cognitive state (described above), so you can use them in conjunction "\ + + "with your cognitive state to inform your next actions and perceptions. Please consider them and then proceed with your next actions right after. "} + ] + + # sets up the actual interaction messages to use for prompting + self.current_messages += self.retrieve_recent_memories() + + + ######################################################################### + # Persona definitions + ######################################################################### + + # + # Conveniences to access the persona configuration via dictionary-like syntax using + # the [] operator. e.g., agent["nationality"] = "American" + # + def __getitem__(self, key): + return self.get(key) + + def __setitem__(self, key, value): + self.define(key, value) + + # + # Conveniences to import persona definitions via the '+' operator, + # e.g., agent + {"nationality": "American", ...} + # + # e.g., agent + "path/to/fragment.json" + # + def __add__(self, other): + """ + Allows using the '+' operator to add persona definitions or import a fragment. + If 'other' is a dict, calls include_persona_definitions(). + If 'other' is a string, calls import_fragment(). + """ + if isinstance(other, dict): + self.include_persona_definitions(other) + elif isinstance(other, str): + self.import_fragment(other) + else: + raise TypeError("Unsupported operand type for +. Must be a dict or a string path to fragment.") + return self + + # + # Various other conveniences to manipulate the persona configuration + # + + def get(self, key): + """ + Returns the value of a key in the TinyPerson's persona configuration. + Supports dot notation for nested keys (e.g., "address.city"). + """ + keys = key.split(".") + value = self._persona + for k in keys: + if isinstance(value, dict): + value = value.get(k, None) + else: + return None # If the path is invalid, return None + return value + + @transactional() + def import_fragment(self, path): + """ + Imports a fragment of a persona configuration from a JSON file. + """ + with open(path, "r", encoding="utf-8", errors="replace") as f: + fragment = json.load(f) + + # check the type is "Fragment" and that there's also a "persona" key + if fragment.get("type", None) == "Fragment" and fragment.get("persona", None) is not None: + self.include_persona_definitions(fragment["persona"]) + else: + raise ValueError("The imported JSON file must be a valid fragment of a persona configuration.") + + # must reset prompt after adding to configuration + self.reset_prompt() + + @transactional() + def include_persona_definitions(self, additional_definitions: dict): + """ + Imports a set of definitions into the TinyPerson. They will be merged with the current configuration. + It is also a convenient way to include multiple bundled definitions into the agent. + + Args: + additional_definitions (dict): The additional definitions to import. + """ + + self._persona = utils.merge_dicts(self._persona, additional_definitions) + + # must reset prompt after adding to configuration + self.reset_prompt() + + + @transactional() + def define(self, key, value, merge=False, overwrite_scalars=True): + """ + Define a value to the TinyPerson's persona configuration. Value can either be a scalar or a dictionary. + If the value is a dictionary or list, you can choose to merge it with the existing value or replace it. + If the value is a scalar, you can choose to overwrite the existing value or not. + + Args: + key (str): The key to define. + value (Any): The value to define. + merge (bool, optional): Whether to merge the dict/list values with the existing values or replace them. Defaults to False. + overwrite_scalars (bool, optional): Whether to overwrite scalar values or not. Defaults to True. + """ + + # dedent value if it is a string + if isinstance(value, str): + value = textwrap.dedent(value) + + # if the value is a dictionary, we can choose to merge it with the existing value or replace it + if isinstance(value, dict) or isinstance(value, list): + if merge: + self._persona = utils.merge_dicts(self._persona, {key: value}) + else: + self._persona[key] = value + + # if the value is a scalar, we can choose to overwrite it or not + elif overwrite_scalars or (key not in self._persona): + self._persona[key] = value + + else: + raise ValueError(f"The key '{key}' already exists in the persona configuration and overwrite_scalars is set to False.") + + + # must reset prompt after adding to configuration + self.reset_prompt() + + + @transactional() + def define_relationships(self, relationships, replace=True): + """ + Defines or updates the TinyPerson's relationships. + + Args: + relationships (list or dict): The relationships to add or replace. Either a list of dicts mapping agent names to relationship descriptions, + or a single dict mapping one agent name to its relationship description. + replace (bool, optional): Whether to replace the current relationships or just add to them. Defaults to True. + """ + + if (replace == True) and (isinstance(relationships, list)): + self._persona['relationships'] = relationships + + elif replace == False: + current_relationships = self._persona['relationships'] + if isinstance(relationships, list): + for r in relationships: + current_relationships.append(r) + + elif isinstance(relationships, dict) and len(relationships) == 2: #{"Name": ..., "Description": ...} + current_relationships.append(relationships) + + else: + raise Exception("Only one key-value pair is allowed in the relationships dict.") + + else: + raise Exception("Invalid arguments for define_relationships.") + + ############################################################################## + # Relationships + ############################################################################## + + @transactional() + def clear_relationships(self): + """ + Clears the TinyPerson's relationships. + """ + self._persona['relationships'] = [] + + return self + + @transactional() + def related_to(self, other_agent, description, symmetric_description=None): + """ + Defines a relationship between this agent and another agent. + + Args: + other_agent (TinyPerson): The other agent. + description (str): The description of the relationship. + symmetric (bool): Whether the relationship is symmetric or not. That is, + if the relationship is defined for both agents. + + Returns: + TinyPerson: The agent itself, to facilitate chaining. + """ + self.define_relationships([{"Name": other_agent.name, "Description": description}], replace=False) + if symmetric_description is not None: + other_agent.define_relationships([{"Name": self.name, "Description": symmetric_description}], replace=False) + + return self + + ############################################################################ + + def add_mental_faculties(self, mental_faculties): + """ + Adds a list of mental faculties to the agent. + """ + for faculty in mental_faculties: + self.add_mental_faculty(faculty) + + return self + + def add_mental_faculty(self, faculty): + """ + Adds a mental faculty to the agent. + """ + # check if the faculty is already there or not + if faculty not in self._mental_faculties: + self._mental_faculties.append(faculty) + else: + raise Exception(f"The mental faculty {faculty} is already present in the agent.") + + return self + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def act( + self, + until_done=True, + n=None, + return_actions=False, + max_content_length=None, + communication_display:bool=None + ): + """ + Acts in the environment and updates its internal cognitive state. + Either acts until the agent is done and needs additional stimuli, or acts a fixed number of times, + but not both. + + Args: + until_done (bool): Whether to keep acting until the agent is done and needs additional stimuli. + n (int): The number of actions to perform. Defaults to None. + return_actions (bool): Whether to return the actions or not. Defaults to False. + max_content_length (int): The maximum length of the content to display. Defaults to None, which uses the global configuration value. + communication_display (bool): Whether to display the communication or not, will override the global setting if provided. Defaults to None. + """ + + # either act until done or act a fixed number of times, but not both + assert not (until_done and n is not None) + if n is not None: + assert n < TinyPerson.MAX_ACTIONS_BEFORE_DONE + + contents = [] + + # A separate function to run before each action, which is not meant to be repeated in case of errors. + def aux_pre_act(): + # TODO maybe we don't need this at all anymore? + # + # A quick thought before the action. This seems to help with better model responses, perhaps because + # it interleaves user with assistant messages. + pass # self.think("I will now think, reflect and act a bit, and then issue DONE.") + + # Aux function to perform exactly one action. + # Occasionally, the model will return JSON missing important keys, so we just ask it to try again + # Sometimes `content` contains EpisodicMemory's MEMORY_BLOCK_OMISSION_INFO message, which raises a TypeError on line 443 + @repeat_on_error(retries=5, exceptions=[KeyError, TypeError]) + def aux_act_once(): + # ensure we have the latest prompt (initial system message + selected messages from memory) + self.reset_prompt() + + action, role, content, all_negative_feedbacks = self.action_generator.generate_next_action(self, self.current_messages) + logger.debug(f"{self.name}'s action: {action}") + + # check the next action similarity, and if it is too similar, put a system warning instruction in memory too + next_action_similarity = utils.next_action_jaccard_similarity(self, action) + + # we have a redundant repetition check here, because this an be computed quickly and is often very useful. + if self.enable_basic_action_repetition_prevention and \ + (TinyPerson.MAX_ACTION_SIMILARITY is not None) and (next_action_similarity > TinyPerson.MAX_ACTION_SIMILARITY): + + logger.warning(f"[{self.name}] Action similarity is too high ({next_action_similarity}), replacing it with DONE.") + + # replace the action with a DONE + action = {"type": "DONE", "content": "", "target": ""} + content["action"] = action + content["cognitive_state"] = {} + + self.store_in_memory({'role': 'system', + 'content': \ + f""" + # EXCESSIVE ACTION SIMILARITY WARNING + + You were about to generate a repetitive action (jaccard similarity = {next_action_similarity}). + Thus, the action was discarded and replaced by an artificial DONE. + + DO NOT BE REPETITIVE. This is not a human-like behavior, therefore you **must** avoid this in the future. + Your alternatives are: + - produce more diverse actions. + - aggregate similar actions into a single, larger, action and produce it all at once. + - as a **last resort only**, you may simply not acting at all by issuing a DONE. + + + """, + 'type': 'feedback', + 'simulation_timestamp': self.iso_datetime()}) + + # All checks done, we can commit the action to memory. + self.store_in_memory({'role': role, 'content': content, + 'type': 'action', + 'simulation_timestamp': self.iso_datetime()}) + + self._actions_buffer.append(action) + + if "cognitive_state" in content: + cognitive_state = content["cognitive_state"] + logger.debug(f"[{self.name}] Cognitive state: {cognitive_state}") + + self._update_cognitive_state(goals=cognitive_state.get("goals", None), + context=cognitive_state.get("context", None), + attention=cognitive_state.get("emotions", None), + emotions=cognitive_state.get("emotions", None)) + + contents.append(content) + if utils.first_non_none(communication_display, TinyPerson.communication_display): + self._display_communication(role=role, content=content, kind='action', simplified=True, max_content_length=max_content_length) + + # + # Some actions induce an immediate stimulus or other side-effects. We need to process them here, by means of the mental faculties. + # + for faculty in self._mental_faculties: + faculty.process_action(self, action) + + # + # turns all_negative_feedbacks list into a system message + # + # TODO improve this? + # + ##if len(all_negative_feedbacks) > 0: + ## feedback = """ + ## # QUALITY FEEDBACK + ## + ## Up to the present moment, we monitored actions and tentative aborted actions (i.e., that were not actually executed), + ## and some of them were not of good quality. + ## Some of those were replaced by regenerated actions of better quality. In the process of doing so, some + ## important quality feedback was produced, which is now given below. + ## + ## To improve your performance, and prevent future similar quality issues, you **MUST** take into account the following feedback + ## whenever computing your future actions. Note that the feedback might also include the actual action or tentative action + ## that was of low quality, so that you can understand what was wrong with it and avoid similar mistakes in the future. + ## + ## """ + ## for i, feedback_item in enumerate(all_negative_feedbacks): + ## feedback += f"{feedback_item}\n\n" + ## feedback += f"\n\n *** \n\n" + ## + ## self.store_in_memory({'role': 'system', 'content': feedback, + ## 'type': 'feedback', + ## 'simulation_timestamp': self.iso_datetime()}) + ## + + + + # count the actions as this can be useful for taking decisions later + self.actions_count += 1 + + + # + # How to proceed with a sequence of actions. + # + + ##### Option 1: run N actions ###### + if n is not None: + for i in range(n): + aux_pre_act() + aux_act_once() + + ##### Option 2: run until DONE ###### + elif until_done: + while (len(contents) == 0) or ( + not contents[-1]["action"]["type"] == "DONE" + ): + + + # check if the agent is acting without ever stopping + if len(contents) > TinyPerson.MAX_ACTIONS_BEFORE_DONE: + logger.warning(f"[{self.name}] Agent {self.name} is acting without ever stopping. This may be a bug. Let's stop it here anyway.") + break + if len(contents) > 4: # just some minimum number of actions to check for repetition, could be anything >= 3 + # if the last three actions were the same, then we are probably in a loop + if contents[-1]['action'] == contents[-2]['action'] == contents[-3]['action']: + logger.warning(f"[{self.name}] Agent {self.name} is acting in a loop. This may be a bug. Let's stop it here anyway.") + break + + aux_pre_act() + aux_act_once() + + # The end of a sequence of actions is always considered to mark the end of an episode. + self.consolidate_episode_memories() + + if return_actions: + return contents + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def listen( + self, + speech, + source: AgentOrWorld = None, + max_content_length=None, + communication_display:bool=None + ): + """ + Listens to another agent (artificial or human) and updates its internal cognitive state. + + Args: + speech (str): The speech to listen to. + source (AgentOrWorld, optional): The source of the speech. Defaults to None. + max_content_length (int, optional): The maximum length of the content to display. Defaults to None, which uses the global configuration value. + communication_display (bool): Whether to display the communication or not, will override the global setting if provided. Defaults to None. + + """ + + return self._observe( + stimulus={ + "type": "CONVERSATION", + "content": speech, + "source": name_or_empty(source), + }, + max_content_length=max_content_length, + communication_display=communication_display + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def socialize( + self, + social_description: str, + source: AgentOrWorld = None, + max_content_length=None, + ): + """ + Perceives a social stimulus through a description and updates its internal cognitive state. + + Args: + social_description (str): The description of the social stimulus. + source (AgentOrWorld, optional): The source of the social stimulus. Defaults to None. + """ + return self._observe( + stimulus={ + "type": "SOCIAL", + "content": social_description, + "source": name_or_empty(source), + }, + max_content_length=max_content_length, + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def see( + self, + visual_description, + source: AgentOrWorld = None, + max_content_length=None, + ): + """ + Perceives a visual stimulus through a description and updates its internal cognitive state. + + Args: + visual_description (str): The description of the visual stimulus. + source (AgentOrWorld, optional): The source of the visual stimulus. Defaults to None. + """ + return self._observe( + stimulus={ + "type": "VISUAL", + "content": visual_description, + "source": name_or_empty(source), + }, + max_content_length=max_content_length, + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def think(self, thought, max_content_length=None): + """ + Forces the agent to think about something and updates its internal cognitive state. + + """ + logger = get_logger(self.name) + logger.info(f"Thinking: {thought}") + return self._observe( + stimulus={ + "type": "THOUGHT", + "content": thought, + "source": name_or_empty(self), + }, + max_content_length=max_content_length, + ) + + def sequential_think(self, thought_data: dict, max_content_length=None): + """ + Forces the agent to think about something and updates its internal cognitive state. + + """ + return self._observe( + stimulus={ + "type": "SEQUENTIAL_THINKING", + "content": json.dumps(thought_data), + "source": name_or_empty(self), + }, + max_content_length=max_content_length, + ) + + @config_manager.config_defaults(max_content_length="max_content_display_length") + def internalize_goal( + self, goal, max_content_length=None + ): + """ + Internalizes a goal and updates its internal cognitive state. + """ + return self._observe( + stimulus={ + "type": "INTERNAL_GOAL_FORMULATION", + "content": goal, + "source": name_or_empty(self), + }, + max_content_length=max_content_length, + ) + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def _observe(self, stimulus, max_content_length=None, communication_display:bool=None): + """ + Observes a stimulus and updates its internal cognitive state. + + Args: + stimulus (dict): The stimulus to observe. It must contain a 'type' and 'content' keys. + max_content_length (int, optional): The maximum length of the content to display. Defaults to None, which uses the global configuration value. + communication_display (bool): Whether to display the communication or not, will override the global setting if provided. Defaults to None. + """ + stimuli = [stimulus] + + content = {"stimuli": stimuli} + + logger.debug(f"[{self.name}] Observing stimuli: {content}") + + # whatever comes from the outside will be interpreted as coming from 'user', simply because + # this is the counterpart of 'assistant' + + self.store_in_memory({'role': 'user', 'content': content, + 'type': 'stimulus', + 'simulation_timestamp': self.iso_datetime()}) + + if utils.first_non_none(communication_display, TinyPerson.communication_display): + self._display_communication( + role="user", + content=content, + kind="stimuli", + simplified=True, +max_content_length=max_content_length, + ) + + # count the stimuli as this can be useful for taking decisions later + self.stimuli_count += 1 + + return self # allows easier chaining of methods + + @transactional() + def listen_and_act( + self, + speech, + return_actions=False, + max_content_length=None, + communication_display:bool=None + ): + """ + Convenience method that combines the `listen` and `act` methods. + """ + + self.listen(speech, max_content_length=max_content_length, communication_display=communication_display) + return self.act( + return_actions=return_actions, max_content_length=max_content_length, communication_display=communication_display + ) + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def see_and_act( + self, + visual_description, + return_actions=False, + max_content_length=None, + ): + """ + Convenience method that combines the `see` and `act` methods. + """ + + self.see(visual_description, max_content_length=max_content_length) + return self.act( + return_actions=return_actions, max_content_length=max_content_length + ) + + @transactional() + @config_manager.config_defaults(max_content_length="max_content_display_length") + def think_and_act( + self, + thought, + return_actions=False, + max_content_length=None, + ): + """ + Convenience method that combines the `think` and `act` methods. + """ + + self.think(thought, max_content_length=max_content_length) + return self.act(return_actions=return_actions, max_content_length=max_content_length) + + def read_documents_from_folder(self, documents_path:str): + """ + Reads documents from a directory and loads them into the semantic memory. + """ + logger.info(f"Setting documents path to {documents_path} and loading documents.") + + self.semantic_memory.add_documents_path(documents_path) + + def read_document_from_file(self, file_path:str): + """ + Reads a document from a file and loads it into the semantic memory. + """ + logger.info(f"Reading document from file: {file_path}") + + self.semantic_memory.add_document_path(file_path) + + def read_documents_from_web(self, web_urls:list): + """ + Reads documents from web URLs and loads them into the semantic memory. + """ + logger.info(f"Reading documents from the following web URLs: {web_urls}") + + self.semantic_memory.add_web_urls(web_urls) + + def read_document_from_web(self, web_url:str): + """ + Reads a document from a web URL and loads it into the semantic memory. + """ + logger.info(f"Reading document from web URL: {web_url}") + + self.semantic_memory.add_web_url(web_url) + + @transactional() + def move_to(self, location, context=[]): + """ + Moves to a new location and updates its internal cognitive state. + """ + self._mental_state["location"] = location + + # context must also be updated when moved, since we assume that context is dictated partly by location. + self.change_context(context) + + @transactional() + def change_context(self, context: list): + """ + Changes the context and updates its internal cognitive state. + """ + self._mental_state["context"] = { + "description": item for item in context + } + + self._update_cognitive_state(context=context) + + @transactional() + def make_agent_accessible( + self, + agent: Self, + relation_description: str = "An agent I can currently interact with.", + ): + """ + Makes an agent accessible to this agent. + """ + if agent not in self._accessible_agents: + self._accessible_agents.append(agent) + self._mental_state["accessible_agents"].append( + {"name": agent.name, "relation_description": relation_description} + ) + else: + logger.warning( + f"[{self.name}] Agent {agent.name} is already accessible to {self.name}." + ) + @transactional() + def make_agents_accessible(self, agents: list, relation_description: str = "An agent I can currently interact with."): + """ + Makes a list of agents accessible to this agent. + """ + for agent in agents: + self.make_agent_accessible(agent, relation_description) + + @transactional() + def make_agent_inaccessible(self, agent: Self): + """ + Makes an agent inaccessible to this agent. + """ + if agent in self._accessible_agents: + self._accessible_agents.remove(agent) + else: + logger.warning( + f"[{self.name}] Agent {agent.name} is already inaccessible to {self.name}." + ) + + @transactional() + def make_all_agents_inaccessible(self): + """ + Makes all agents inaccessible to this agent. + """ + self._accessible_agents = [] + self._mental_state["accessible_agents"] = [] + + @property + def accessible_agents(self): + """ + Property to access the list of accessible agents. + """ + return self._accessible_agents + + ########################################################### + # Internal cognitive state changes + ########################################################### + @transactional() + def _update_cognitive_state( + self, goals=None, context=None, attention=None, emotions=None + ): + """ + Update the TinyPerson's cognitive state. + """ + + # Update current datetime. The passage of time is controlled by the environment, if any. + if self.environment is not None and self.environment.current_datetime is not None: + self._mental_state["datetime"] = utils.pretty_datetime(self.environment.current_datetime) + + # update current goals + if goals is not None: + self._mental_state["goals"] = goals + + # update current context + if context is not None: + self._mental_state["context"] = context + + # update current attention + if attention is not None: + self._mental_state["attention"] = attention + + # update current emotions + if emotions is not None: + self._mental_state["emotions"] = emotions + + # update relevant memories for the current situation. These are memories that come to mind "spontaneously" when the agent is in a given context, + # so avoiding the need to actively trying to remember them. + current_memory_context = self.retrieve_relevant_memories_for_current_context() + self._mental_state["memory_context"] = current_memory_context + + self.reset_prompt() + + + ########################################################### + # Memory management + ########################################################### + + def store_in_memory(self, value: Any) -> None: + """ + Stores a value in episodic memory and manages episode length. + + Args: + value: The memory item to store (e.g., action, stimulus, thought) + + Returns: + None + """ + self.episodic_memory.store(value) + + self._current_episode_event_count += 1 + logger.debug(f"[{self.name}] Current episode event count: {self._current_episode_event_count}.") + + if self._current_episode_event_count >= self.MAX_EPISODE_LENGTH: + # commit the current episode to memory, if it is long enough + logger.warning(f"[{self.name}] Episode length exceeded {self.MAX_EPISODE_LENGTH} events. Committing episode to memory. Please check whether this was expected or not.") + self.consolidate_episode_memories() + + def consolidate_episode_memories(self) -> bool: + """ + Applies all memory consolidation or transformation processes appropriate to the conclusion of one simulation episode. + + Returns: + bool: True if memories were successfully consolidated, False otherwise. + """ + # a minimum length of the episode is required to consolidate it, to avoid excessive fragments in the semantic memory + if self._current_episode_event_count > self.MIN_EPISODE_LENGTH: + logger.debug(f"[{self.name}] ***** Consolidating current episode memories into semantic memory *****") + + # Consolidate latest episodic memories into semantic memory + if config_manager.get("enable_memory_consolidation"): + + + episodic_consolidator = EpisodicConsolidator() + episode = self.episodic_memory.get_current_episode(item_types=["action", "stimulus"],) + logger.debug(f"[{self.name}] Current episode: {episode}") + consolidated_memories = episodic_consolidator.process(episode, timestamp=self._mental_state["datetime"], context=self._mental_state, persona=self.minibio()).get("consolidation", None) + if consolidated_memories is not None: + logger.info(f"[{self.name}] Consolidating current {len(episode)} episodic events as consolidated semantic memories.") + logger.debug(f"[{self.name}] Consolidated memories: {consolidated_memories}") + self.semantic_memory.store_all(consolidated_memories) + else: + logger.warning(f"[{self.name}] No memories to consolidate from the current episode.") + + else: + logger.warning(f"[{self.name}] Memory consolidation is disabled. Not consolidating current episode memories into semantic memory.") + + # commit the current episode to episodic memory + self.episodic_memory.commit_episode() + self._current_episode_event_count = 0 + logger.debug(f"[{self.name}] Current episode event count reset to 0 after consolidation.") + + # TODO reflections, optimizations, etc. + + def optimize_memory(self): + pass #TODO + + def clear_episodic_memory(self, max_prefix_to_clear=None, max_suffix_to_clear=None): + """ + Clears the episodic memory, causing a permanent "episodic amnesia". Note that this does not + change other memories, such as semantic memory. + """ + self.episodic_memory.clear(max_prefix_to_clear=max_prefix_to_clear, max_suffix_to_clear=max_suffix_to_clear) + + def retrieve_memories(self, first_n: int, last_n: int, include_omission_info:bool=True, max_content_length:int=None) -> list: + episodes = self.episodic_memory.retrieve(first_n=first_n, last_n=last_n, include_omission_info=include_omission_info) + + if max_content_length is not None: + episodes = utils.truncate_actions_or_stimuli(episodes, max_content_length) + + return episodes + + + def retrieve_recent_memories(self, max_content_length:int=None) -> list: + episodes = self.episodic_memory.retrieve_recent() + + if max_content_length is not None: + episodes = utils.truncate_actions_or_stimuli(episodes, max_content_length) + + return episodes + + def retrieve_relevant_memories(self, relevance_target:str, top_k=20) -> list: + relevant = self.semantic_memory.retrieve_relevant(relevance_target, top_k=top_k) + + return relevant + + def retrieve_relevant_memories_for_current_context(self, top_k=7) -> list: + """ + Retrieves memories relevant to the current context by combining current state with recent memories. + + Args: + top_k (int): Number of top relevant memories to retrieve. Defaults to 7. + + Returns: + list: List of relevant memories for the current context. + """ + # Extract current mental state components + context = self._mental_state.get("context", "") + goals = self._mental_state.get("goals", "") + attention = self._mental_state.get("attention", "") + emotions = self._mental_state.get("emotions", "") + + # Retrieve recent memories efficiently + recent_memories_list = self.retrieve_memories(first_n=10, last_n=20, max_content_length=500) + recent_memories = "\n".join([f" - {m.get('content', '')}" for m in recent_memories_list]) + + # Build contextual target for memory retrieval using textwrap.dedent for cleaner formatting + target = textwrap.dedent(f""" + Current Context: {context} + Current Goals: {goals} + Current Attention: {attention} + Current Emotions: {emotions} + Selected Episodic Memories (from oldest to newest): + {recent_memories} + """).strip() + + logger.debug(f"[{self.name}] Retrieving relevant memories for contextual target: {target}") + + return self.retrieve_relevant_memories(target, top_k=top_k) + + def summarize_relevant_memories_via_full_scan(self, relevance_target:str, item_type: str = None) -> str: + """ + Summarizes relevant memories for a given target by scanning the entire semantic memory. + + Args: + relevance_target (str): The target to retrieve relevant memories for. + item_type (str, optional): The type of items to summarize. Defaults to None. + max_summary_length (int, optional): The maximum length of the summary. Defaults to 1000. + + Returns: + str: The summary of relevant memories. + """ + return self.semantic_memory.summarize_relevant_via_full_scan(relevance_target, item_type=item_type) + + ########################################################### + # Inspection conveniences + ########################################################### + + def last_remembered_action(self, ignore_done: bool = True): + """ + Returns the last remembered action. + + Args: + ignore_done (bool): Whether to ignore the "DONE" action or not. Defaults to True. + + Returns: + dict or None: The last remembered action, or None if no suitable action found. + """ + action = None + + memory_items_list = self.episodic_memory.retrieve_last(include_omission_info=False, item_type="action") + + if len(memory_items_list) > 0: + # iterate from last to first while the action type is not "DONE" + for candidate_item in memory_items_list[::-1]: + action_content = candidate_item.get("content", {}).get("action", {}) + action_type = action_content.get("type", "") + + if not ignore_done or action_type != "DONE": + action = action_content + break + + return action + + + ########################################################### + # Communication display and action execution + ########################################################### + + def _display_communication( + self, + role, + content, + kind, + simplified=True, + max_content_length=default["max_content_display_length"], + ): + """ + Displays the current communication and stores it in a buffer for later use. + """ + logger = get_logger(self.name) + # CONCURRENT PROTECTION, as we'll access shared display buffers + with concurrent_agent_action_lock: + if kind == "stimuli": + rendering = self._pretty_stimuli( + role=role, + content=content, + simplified=simplified, + max_content_length=max_content_length, + ) + source = content["stimuli"][0].get("source", None) + target = self.name + + elif kind == "action": + rendering = self._pretty_action( + role=role, + content=content, + simplified=simplified, + max_content_length=max_content_length, + ) + source = self.name + target = content["action"].get("target", None) + + else: + raise ValueError(f"Unknown communication kind: {kind}") + + logger.info(f"Output: {rendering}") + # if the agent has no parent environment, then it is a free agent and we can display the communication. + # otherwise, the environment will display the communication instead. This is important to make sure that + # the communication is displayed in the correct order, since environments control the flow of their underlying + # agents. + if self.environment is None: + self._push_and_display_latest_communication({"kind": kind, "rendering":rendering, "content": content, "source":source, "target": target}) + else: + self.environment._push_and_display_latest_communication({"kind": kind, "rendering":rendering, "content": content, "source":source, "target": target}) + + def _push_and_display_latest_communication(self, communication): + """ + Pushes the latest communications to the agent's buffer. + """ + self._displayed_communications_buffer.append(communication) + print(communication["rendering"]) + + def pop_and_display_latest_communications(self): + """ + Pops the latest communications and displays them. + """ + communications = self._displayed_communications_buffer + self._displayed_communications_buffer = [] + + for communication in communications: + print(communication["rendering"]) + + return communications + + def clear_communications_buffer(self): + """ + Cleans the communications buffer. + """ + self._displayed_communications_buffer = [] + + @transactional() + def pop_latest_actions(self) -> list: + """ + Returns the latest actions performed by this agent. Typically used + by an environment to consume the actions and provide the appropriate + environmental semantics to them (i.e., effects on other agents). + """ + actions = self._actions_buffer + self._actions_buffer = [] + return actions + + @transactional() + def pop_actions_and_get_contents_for( + self, action_type: str, only_last_action: bool = True + ) -> list: + """ + Returns the contents of actions of a given type performed by this agent. + Typically used to perform inspections and tests. + + Args: + action_type (str): The type of action to look for. + only_last_action (bool, optional): Whether to only return the contents of the last action. Defaults to False. + """ + actions = self.pop_latest_actions() + # Filter the actions by type + actions = [action for action in actions if action["type"] == action_type] + + # If interested only in the last action, return the latest one + if only_last_action: + return actions[-1].get("content", "") + + # Otherwise, return all contents from the filtered actions + return "\n".join([action.get("content", "") for action in actions]) + + ############################################################################################# + # Formatting conveniences + # + # For rich colors, + # see: https://rich.readthedocs.io/en/latest/appendix/colors.html#appendix-colors + ############################################################################################# + + def __repr__(self): + return f"TinyPerson(name='{self.name}')" + + @transactional() + def minibio(self, extended=True, requirements=None): + """ + Returns a mini-biography of the TinyPerson. + + Args: + extended (bool): Whether to include extended information or not. + requirements (str): Additional requirements for the biography (e.g., focus on a specific aspect relevant for the scenario). + + Returns: + str: The mini-biography. + """ + + # if occupation is a dict and has a "title" key, use that as the occupation + if isinstance(self._persona['occupation'], dict) and 'title' in self._persona['occupation']: + occupation = self._persona['occupation']['title'] + else: + occupation = self._persona['occupation'] + + base_biography = f"{self.name} is a {self._persona['age']} year old {occupation}, {self._persona['nationality']}, currently living in {self._persona['residence']}." + + if self._extended_agent_summary is None and extended: + logger.debug(f"Generating extended agent summary for {self.name}.") + self._extended_agent_summary = LLMChat( + system_prompt=f""" + You are given a short biography of an agent, as well as a detailed specification of his or her other characteristics + You must then produce a short paragraph (3 or 4 sentences) that **complements** the short biography, adding details about + personality, interests, opinions, skills, etc. Do not repeat the information already given in the short biography. + repeating the information already given. The paragraph should be coherent, consistent and comprehensive. All information + must be grounded on the specification, **do not** create anything new. + + {"Additional constraints: "+ requirements if requirements is not None else ""} + """, + + user_prompt=f""" + **Short biography:** {base_biography} + + **Detailed specification:** {self._persona} + """).call() + + if extended: + biography = f"{base_biography} {self._extended_agent_summary}" + else: + biography = base_biography + + return biography + + def pp_current_interactions( + self, + simplified=True, + skip_system=True, + max_content_length=default["max_content_display_length"], + first_n=None, + last_n=None, + include_omission_info:bool=True + ): + """ + Pretty prints the current messages. + """ + print( + self.pretty_current_interactions( + simplified=simplified, + skip_system=skip_system, + max_content_length=max_content_length, + first_n=first_n, + last_n=last_n, + include_omission_info=include_omission_info + ) + ) + + def pp_last_interactions( + self, + n=3, + simplified=True, + skip_system=True, + max_content_length=default["max_content_display_length"], + include_omission_info:bool=True + ): + """ + Pretty prints the last n messages. Useful to examine the conclusion of an experiment. + """ + print( + self.pretty_current_interactions( + simplified=simplified, + skip_system=skip_system, + max_content_length=max_content_length, + first_n=None, + last_n=n, + include_omission_info=include_omission_info + ) + ) + + def pretty_current_interactions(self, simplified=True, skip_system=True, max_content_length=default["max_content_display_length"], first_n=None, last_n=None, include_omission_info:bool=True): + """ + Returns a pretty, readable, string with the current messages. + """ + lines = [f"**** BEGIN SIMULATION TRAJECTORY FOR {self.name} ****"] + last_step = 0 + for i, message in enumerate(self.episodic_memory.retrieve(first_n=first_n, last_n=last_n, include_omission_info=include_omission_info)): + try: + if not (skip_system and message['role'] == 'system'): + msg_simplified_type = "" + msg_simplified_content = "" + msg_simplified_actor = "" + + last_step = i + lines.append(f"Agent simulation trajectory event #{i}:") + lines.append(self._pretty_timestamp(message['role'], message['simulation_timestamp'])) + + if message["role"] == "system": + msg_simplified_actor = "SYSTEM" + msg_simplified_type = message["role"] + msg_simplified_content = message["content"] + + lines.append( + f"[dim] {msg_simplified_type}: {msg_simplified_content}[/]" + ) + + elif message["role"] == "user": + lines.append( + self._pretty_stimuli( + role=message["role"], + content=message["content"], + simplified=simplified, + max_content_length=max_content_length, + ) + ) + + elif message["role"] == "assistant": + lines.append( + self._pretty_action( + role=message["role"], + content=message["content"], + simplified=simplified, + max_content_length=max_content_length, + ) + ) + else: + lines.append(f"{message['role']}: {message['content']}") + except: + # print(f"ERROR: {message}") + continue + + lines.append(f"The last agent simulation trajectory event number was {last_step}, thus the current number of the NEXT POTENTIAL TRAJECTORY EVENT is {last_step + 1}.") + lines.append(f"**** END SIMULATION TRAJECTORY FOR {self.name} ****\n\n") + return "\n".join(lines) + + def _pretty_stimuli( + self, + role, + content, + simplified=True, + max_content_length=default["max_content_display_length"], + ) -> list: + """ + Pretty prints stimuli. + """ + + lines = [] + msg_simplified_actor = "USER" + for stimus in content["stimuli"]: + if simplified: + if stimus["source"] != "": + msg_simplified_actor = stimus["source"] + + else: + msg_simplified_actor = "USER" + + msg_simplified_type = stimus["type"] + msg_simplified_content = utils.break_text_at_length( + stimus["content"], max_length=max_content_length + ) + + indent = " " * len(msg_simplified_actor) + " > " + msg_simplified_content = textwrap.fill( + msg_simplified_content, + width=TinyPerson.PP_TEXT_WIDTH, + initial_indent=indent, + subsequent_indent=indent, + ) + + # + # Using rich for formatting. Let's make things as readable as possible! + # + + rich_style = utils.RichTextStyle.get_style_for("stimulus", msg_simplified_type) + lines.append( + f"[{rich_style}][underline]{msg_simplified_actor}[/] --> [{rich_style}][underline]{self.name}[/]: [{msg_simplified_type}] \n{msg_simplified_content}[/]" + ) + else: + lines.append(f"{role}: {content}") + + return "\n".join(lines) + + def _pretty_action( + self, + role, + content, + simplified=True, + max_content_length=default["max_content_display_length"], + ) -> str: + """ + Pretty prints an action. + """ + if simplified: + msg_simplified_actor = self.name + msg_simplified_type = content["action"]["type"] + msg_simplified_content = utils.break_text_at_length( + content["action"].get("content", ""), max_length=max_content_length + ) + + indent = " " * len(msg_simplified_actor) + " > " + msg_simplified_content = textwrap.fill( + msg_simplified_content, + width=TinyPerson.PP_TEXT_WIDTH, + initial_indent=indent, + subsequent_indent=indent, + ) + + # + # Using rich for formatting. Let's make things as readable as possible! + # + rich_style = utils.RichTextStyle.get_style_for("action", msg_simplified_type) + return f"[{rich_style}][underline]{msg_simplified_actor}[/] acts: [{msg_simplified_type}] \n{msg_simplified_content}[/]" + + else: + return f"{role}: {content}" + + def _pretty_timestamp( + self, + role, + timestamp, + ) -> str: + """ + Pretty prints a timestamp. + """ + return f">>>>>>>>> Date and time of events: {timestamp}" + + def iso_datetime(self) -> str: + """ + Returns the current datetime of the environment, if any. + + Returns: + datetime: The current datetime of the environment in ISO forat. + """ + if self.environment is not None and self.environment.current_datetime is not None: + return self.environment.current_datetime.isoformat() + else: + return None + + ########################################################### + # IO + ########################################################### + + def save_specification(self, path, include_mental_faculties=True, include_memory=False, include_mental_state=False): + """ + Saves the current configuration to a JSON file. + """ + + suppress_attributes = [] + + # should we include the mental faculties? + if not include_mental_faculties: + suppress_attributes.append("_mental_faculties") + + # should we include the memory? + if not include_memory: + suppress_attributes.append("episodic_memory") + suppress_attributes.append("semantic_memory") + + # should we include the mental state? + if not include_mental_state: + suppress_attributes.append("_mental_state") + + + self.to_json(suppress=suppress_attributes, file_path=path, + serialization_type_field_name="type") + + + @staticmethod + def load_specification(path_or_dict, suppress_mental_faculties=False, suppress_memory=False, suppress_mental_state=False, + auto_rename_agent=False, new_agent_name=None, enable_browser=False): + """ + Loads a JSON agent specification. + + Args: + path_or_dict (str or dict): The path to the JSON file or the dictionary itself. + suppress_mental_faculties (bool, optional): Whether to suppress loading the mental faculties. Defaults to False. + suppress_memory (bool, optional): Whether to suppress loading the memory. Defaults to False. + suppress_mental_state (bool, optional): Whether to suppress loading the mental state. Defaults to False. + auto_rename_agent (bool, optional): Whether to auto rename the agent. Defaults to False. + new_agent_name (str, optional): The new name for the agent. Defaults to None. + enable_browser (bool, optional): Whether to enable the browser faculty. Defaults to False. + """ + + suppress_attributes = [] + + # should we suppress the mental faculties? + if suppress_mental_faculties: + suppress_attributes.append("_mental_faculties") + + # should we suppress the memory? + if suppress_memory: + suppress_attributes.append("episodic_memory") + suppress_attributes.append("semantic_memory") + + # should we suppress the mental state? + if suppress_mental_state: + suppress_attributes.append("_mental_state") + + return TinyPerson.from_json(json_dict_or_path=path_or_dict, suppress=suppress_attributes, + serialization_type_field_name="type", + post_init_params={"auto_rename_agent": auto_rename_agent, "new_agent_name": new_agent_name, "enable_browser": enable_browser}) + @staticmethod + def load_specifications_from_folder(folder_path:str, file_suffix=".agent.json", suppress_mental_faculties=False, + suppress_memory=False, suppress_mental_state=False, auto_rename_agent=False, + new_agent_name=None) -> list: + """ + Loads all JSON agent specifications from a folder. + + Args: + folder_path (str): The path to the folder containing the JSON files. + file_suffix (str, optional): The suffix of the JSON files. Defaults to ".agent.json". + suppress_mental_faculties (bool, optional): Whether to suppress loading the mental faculties. Defaults to False. + suppress_memory (bool, optional): Whether to suppress loading the memory. Defaults to False. + suppress_mental_state (bool, optional): Whether to suppress loading the mental state. Defaults to False. + auto_rename_agent (bool, optional): Whether to auto rename the agent. Defaults to False. + new_agent_name (str, optional): The new name for the agent. Defaults to None. + """ + + agents = [] + for file in os.listdir(folder_path): + if file.endswith(file_suffix): + file_path = os.path.join(folder_path, file) + agent = TinyPerson.load_specification(file_path, suppress_mental_faculties=suppress_mental_faculties, + suppress_memory=suppress_memory, suppress_mental_state=suppress_mental_state, + auto_rename_agent=auto_rename_agent, new_agent_name=new_agent_name) + agents.append(agent) + + return agents + + + + def encode_complete_state(self) -> dict: + """ + Encodes the complete state of the TinyPerson, including the current messages, accessible agents, etc. + This is meant for serialization and caching purposes, not for exporting the state to the user. + """ + to_copy = copy.copy(self.__dict__) + + # delete the logger and other attributes that cannot be serialized + del to_copy["environment"] + del to_copy["_mental_faculties"] + del to_copy["action_generator"] + + to_copy["_accessible_agents"] = [agent.name for agent in self._accessible_agents] + to_copy['episodic_memory'] = self.episodic_memory.to_json() + to_copy['semantic_memory'] = self.semantic_memory.to_json() + to_copy["_mental_faculties"] = [faculty.to_json() for faculty in self._mental_faculties] + + state = copy.deepcopy(to_copy) + + return state + + def decode_complete_state(self, state: dict) -> Self: + """ + Loads the complete state of the TinyPerson, including the current messages, + and produces a new TinyPerson instance. + """ + state = copy.deepcopy(state) + + self._accessible_agents = [TinyPerson.get_agent_by_name(name) for name in state["_accessible_agents"]] + self.episodic_memory = EpisodicMemory.from_json(state['episodic_memory']) + self.semantic_memory = SemanticMemory.from_json(state['semantic_memory']) + + for i, faculty in enumerate(self._mental_faculties): + faculty = faculty.from_json(state['_mental_faculties'][i]) + + # delete fields already present in the state + del state["_accessible_agents"] + del state['episodic_memory'] + del state['semantic_memory'] + del state['_mental_faculties'] + + # restore other fields + self.__dict__.update(state) + + + return self + + def create_new_agent_from_current_spec(self, new_name:str) -> Self: + """ + Creates a new agent from the current agent's specification. + + Args: + new_name (str): The name of the new agent. Agent names must be unique in the simulation, + this is why we need to provide a new name. + """ + new_agent = TinyPerson(name=new_name, spec_path=None) + + new_persona = copy.deepcopy(self._persona) + new_persona['name'] = new_name + + new_agent._persona = new_persona + + return new_agent + + + @staticmethod + def add_agent(agent): + """ + Adds an agent to the global list of agents. Agent names must be unique, + so this method will raise an exception if the name is already in use. + """ + if agent.name in TinyPerson.all_agents: + raise ValueError(f"Agent name {agent.name} is already in use.") + else: + TinyPerson.all_agents[agent.name] = agent + + @staticmethod + def has_agent(agent_name: str): + """ + Checks if an agent is already registered. + """ + return agent_name in TinyPerson.all_agents + + @staticmethod + def set_simulation_for_free_agents(simulation): + """ + Sets the simulation if it is None. This allows free agents to be captured by specific simulation scopes + if desired. + """ + for agent in TinyPerson.all_agents.values(): + if agent.simulation_id is None: + simulation.add_agent(agent) + + @staticmethod + def get_agent_by_name(name): + """ + Gets an agent by name. + """ + if name in TinyPerson.all_agents: + return TinyPerson.all_agents[name] + else: + return None + + @staticmethod + def all_agents_names(): + """ + Returns the names of all agents. + """ + return list(TinyPerson.all_agents.keys()) + + @staticmethod + def clear_agents(): + """ + Clears the global list of agents. + """ + TinyPerson.all_agents = {} diff --git a/tinytroupe/config.ini b/tinytroupe/config.ini new file mode 100644 index 0000000000000000000000000000000000000000..360ef055627a7c2eddbff8d01309168d7800a1b5 --- /dev/null +++ b/tinytroupe/config.ini @@ -0,0 +1,97 @@ +[OpenAI] +# +# OpenAI or Azure OpenAI Service +# + +# Default options: openai, azure, helmholtz-blablador +API_TYPE=openai + +# Check Azure's documentation for updates here: +# https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line&pivots=programming-language-python +AZURE_API_VERSION=2023-05-15 + +# +# Models +# + +# The main text generation model, used for agent responses +MODEL=gpt-4.1-mini + +# Reasoning model is used when precise reasoning is required, such as when computing detailed analyses of simulation properties. +REASONING_MODEL=o3-mini + +# Embedding model is used for text similarity tasks +EMBEDDING_MODEL=text-embedding-3-small + +# +# Model parameters +# +MAX_TOKENS=32000 +TEMPERATURE=1.5 +FREQ_PENALTY=0.1 +PRESENCE_PENALTY=0.1 +TIMEOUT=480 +MAX_ATTEMPTS=5 +WAITING_TIME=1 +EXPONENTIAL_BACKOFF_FACTOR=5 + +REASONING_EFFORT=high + +# +# Caching +# + +CACHE_API_CALLS=False +CACHE_FILE_NAME=openai_api_cache.pickle + +# +# Other +# + +MAX_CONTENT_DISPLAY_LENGTH=4000 + +[Simulation] + +PARALLEL_AGENT_GENERATION=True +PARALLEL_AGENT_ACTIONS=True + +RAI_HARMFUL_CONTENT_PREVENTION=True +RAI_COPYRIGHT_INFRINGEMENT_PREVENTION=True + +[Cognition] + +ENABLE_MEMORY_CONSOLIDATION=True + +MIN_EPISODE_LENGTH=15 +MAX_EPISODE_LENGTH=50 + +EPISODIC_MEMORY_FIXED_PREFIX_LENGTH=10 +EPISODIC_MEMORY_LOOKBACK_LENGTH=20 + +[ActionGenerator] +MAX_ATTEMPTS=2 + +# This will determine whether any of the following verifications and corrections are performed. +ENABLE_QUALITY_CHECKS=False + +ENABLE_REGENERATION=True +ENABLE_DIRECT_CORRECTION=False + +ENABLE_QUALITY_CHECK_FOR_PERSONA_ADHERENCE=True +ENABLE_QUALITY_CHECK_FOR_SELFCONSISTENCY=False +ENABLE_QUALITY_CHECK_FOR_FLUENCY=False +ENABLE_QUALITY_CHECK_FOR_SUITABILITY=False +ENABLE_QUALITY_CHECK_FOR_SIMILARITY=False + +CONTINUE_ON_FAILURE=True + +# 0 to 9 +QUALITY_THRESHOLD = 5 + + +[Logging] +LOGLEVEL=ERROR +# ERROR +# WARNING +# INFO +# DEBUG \ No newline at end of file diff --git a/tinytroupe/control.py b/tinytroupe/control.py new file mode 100644 index 0000000000000000000000000000000000000000..a8e769c162c2256ab7549146b1a792445fb4f06a --- /dev/null +++ b/tinytroupe/control.py @@ -0,0 +1,841 @@ +""" +Simulation controlling mechanisms. +""" +import json +import os +import tempfile +import threading +import traceback + +import tinytroupe +import tinytroupe.utils as utils + +import uuid + + +import logging +logger = logging.getLogger("tinytroupe") + +# to protect from race conditions when running in parallel +concurrent_execution_lock = threading.Lock() + +class Simulation: + + STATUS_STOPPED = "stopped" + STATUS_STARTED = "started" + + def __init__(self, id="default", cached_trace:list=None): + self.id = id + + self.agents = [] + self.name_to_agent = {} # {agent_name: agent, ...} + + self.environments = [] + + self.factories = [] # e.g., TinyPersonFactory instances + self.name_to_factory = {} # {factory_name: factory, ...} + + self.name_to_environment = {} # {environment_name: environment, ...} + self.status = Simulation.STATUS_STOPPED + + self.cache_path = f"./tinytroupe-{id}.cache.json" # default cache path + + # should we always automatically checkpoint at the every transaction? + self.auto_checkpoint = False + + # whether there are changes not yet saved to the cache file + self.has_unsaved_cache_changes = False + + # whether the agent is under a transaction or not, used for managing + # simulation caching later + self._under_transaction = {None: False} + + # whether the agent is under a parallel transactions segment or not, used for managing + # simulation caching later + self._under_parallel_transactions = False + + # Cache chain mechanism. + # + # stores a list of simulation states. + # Each state is a tuple (prev_node_hash, event_hash, event_output, state), where prev_node_hash is a hash of the previous node in this chain, + # if any, event_hash is a hash of the event that triggered the transition to this state, if any, event_output is the output of the event, + # if any, and state is the actual complete state that resulted. + if cached_trace is None: + self.cached_trace = [] + else: + self.cached_trace = cached_trace + + self.cache_misses = 0 + self.cache_hits = 0 + + # Execution chain mechanism. + # + # The actual, current, execution trace. Each state is a tuple (prev_node_hash, event_hash, state), where prev_node_hash is a hash + # of the previous node in this chain, if any, event_hash is a hash of the event that triggered the transition to this state, if any, + # event_output is the output of the event, if any, and state is the actual complete state that resulted. + self.execution_trace = [] + + def begin(self, cache_path:str=None, auto_checkpoint:bool=False): + """ + Marks the start of the simulation being controlled. + + Args: + cache_path (str): The path to the cache file. If not specified, + defaults to the default cache path defined in the class. + auto_checkpoint (bool, optional): Whether to automatically checkpoint at the end of each transaction. Defaults to False. + """ + + logger.debug(f"Starting simulation, cache_path={cache_path}, auto_checkpoint={auto_checkpoint}.") + + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + from tinytroupe.factory.tiny_person_factory import TinyPersonFactory + + if self.status == Simulation.STATUS_STOPPED: + self.status = Simulation.STATUS_STARTED + else: + raise ValueError("Simulation is already started.") + + if cache_path is not None: + self.cache_path = cache_path + + # should we automatically checkpoint? + self.auto_checkpoint = auto_checkpoint + + # clear the agents, environments and other simulated entities, we'll track them from now on + TinyPerson.clear_agents() + TinyWorld.clear_environments() + TinyFactory.clear_factories() + TinyPersonFactory.clear_factories() + + # All automated fresh ids will start from 0 again for this simulation + utils.reset_fresh_id() + + # load the cache file, if any + if self.cache_path is not None: + self._load_cache_file(self.cache_path) + + def end(self): + """ + Marks the end of the simulation being controlled. + """ + logger.debug("Ending simulation.") + if self.status == Simulation.STATUS_STARTED: + self.status = Simulation.STATUS_STOPPED + self.checkpoint() + else: + raise ValueError("Simulation is already stopped.") + + def checkpoint(self): + """ + Saves current simulation trace to a file. + """ + logger.debug("Checkpointing simulation state...") + # save the cache file + if self.has_unsaved_cache_changes: + self._save_cache_file(self.cache_path) + else: + logger.debug("No unsaved cache changes to save to file.") + + def add_agent(self, agent): + """ + Adds an agent to the simulation. + """ + if agent.name in self.name_to_agent: + raise ValueError(f"Agent names must be unique, but '{agent.name}' is already defined.") + agent.simulation_id = self.id + self.agents.append(agent) + self.name_to_agent[agent.name] = agent + + + def add_environment(self, environment): + """ + Adds an environment to the simulation. + """ + if environment.name in self.name_to_environment: + raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") + environment.simulation_id = self.id + self.environments.append(environment) + self.name_to_environment[environment.name] = environment + + def add_factory(self, factory): + """ + Adds a factory to the simulation. + """ + if factory.name in self.name_to_factory: + raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") + factory.simulation_id = self.id + self.factories.append(factory) + self.name_to_factory[factory.name] = factory + + ################################################################################################### + # Cache and execution chain mechanisms + ################################################################################################### + def _execution_trace_position(self) -> int: + """ + Returns the current position in the execution trace, or -1 if the execution trace is empty. + """ + return len(self.execution_trace) - 1 + + def _function_call_hash(self, function_name, *args, **kwargs) -> int: + """ + Computes the hash of the given function call. + """ + + # if functions are passed as arguments to the function, there's the problem that their + # string representation always changes due to memory position (e.g., ). + # so we need to remove the changing suffix in those cases, while preserving the function name if it exists. + + # positional arguments + # covnerts to a list of string representations first + args_str = list(map(str, args)) + for i, arg in enumerate(args): + if callable(arg): + args_str[i] = arg.__name__ + + # keyword arguments + # converts to a list of string representations first + kwargs_str = {k: str(v) for k, v in kwargs.items()} + for k, v in kwargs.items(): + if callable(v): + kwargs_str[k] = v.__name__ + + # then, convert to a single string, to obtain a unique hash + event = str((function_name, args_str, kwargs_str)) + + # TODO actually compute a short hash of the event string, e.g., using SHA256 ? + # event_hash = utils.custom_hash(event) + + return event + + def _skip_execution_with_cache(self): + """ + Skips the current execution, assuming there's a cached state at the same position. + """ + assert len(self.cached_trace) > self._execution_trace_position() + 1, "There's no cached state at the current execution position." + + self.execution_trace.append(self.cached_trace[self._execution_trace_position() + 1]) + + def _is_transaction_event_cached(self, event_hash, parallel=False) -> bool: + """ + Checks whether the given event hash matches the corresponding cached one, if any. + If there's no corresponding cached state, returns True. + """ + if not parallel: + # there's cache that could be used + if len(self.cached_trace) > self._execution_trace_position() + 1: + if self._execution_trace_position() >= -1: + # here's a graphical depiction of the logic: + # + # Cache: c0:(c_prev_node_hash_0, c_event_hash_0, _, c_state_0) ------------------> c1:(c_prev_node_hash_1, c_event_hash_1, _, c_state_1) -> ... + # Execution: e0:(e_prev_node_hash_0, e_event_hash_0, _, e_state_0) --> e1:(e_prev_node_hash_1, , , ) + # position = 0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + # + # Must satisfy: + # - event_hash == c_event_hash_1 + # - hash(e0) == c_prev_node_hash_1 + + try: + event_hash_match = event_hash == self.cached_trace[self._execution_trace_position() + 1][1] + except Exception as e: + logger.error(f"Error while checking event hash match: {e}") + event_hash_match = False + + prev_node_match = True # TODO implement real check + + return event_hash_match and prev_node_match + + else: + raise ValueError("Execution trace position is invalid, must be >= -1, but is ", self._execution_trace_position()) + + else: # no cache to use + return False + + else: # parallel + if len(self.cached_trace) >= self._execution_trace_position(): + if self._execution_trace_position() >= 0: + # parallel stores ignore order, so we need to check instead whether the event hash is a key in the parallel store, + # regardless of the order of the events generated the data therein. + + if isinstance(self.cached_trace[self._execution_trace_position()], dict): + event_hash_match = event_hash in self.cached_trace[self._execution_trace_position()].keys() + else: + event_hash_match = False + + prev_node_match = True # TODO implement real check + + return event_hash_match and prev_node_match + + else: + raise ValueError("Execution trace position is invalid, must be >= 0, but is ", self._execution_trace_position()) + + def _get_cached_parallel_value(self, event_hash, key): + parallel_store = self.cached_trace[self._execution_trace_position()] + value = parallel_store[event_hash][key] + return value + + def _drop_cached_trace_suffix(self): + """ + Drops the cached trace suffix starting at the current execution trace position. This effectively + refreshes the cache to the current execution state and starts building a new cache from there. + """ + self.cached_trace = self.cached_trace[:self._execution_trace_position()+1] + + def _add_to_execution_trace(self, state: dict, event_hash: int, event_output, parallel=False): + """ + Adds a state to the execution_trace list and computes the appropriate hash. + The computed hash is compared to the hash of the cached trace at the same position, + and if they don't match, the execution is aborted. Similarly, the event_hash is compared + to the hash of the event in the cached trace at the same position, and if they don't match, the execution + is aborted. + """ + + # Compute the hash of the previous execution pair, if any + previous_hash = None + + if not parallel: + # Create a tuple of (hash, state) and append it to the execution_trace list + self.execution_trace.append((previous_hash, event_hash, event_output, state)) + else: + with concurrent_execution_lock: + # state is not stored in parallel segments, only outputs + self.execution_trace[-1][event_hash] = {"prev_node_hash": previous_hash, + "encoded_output": event_output} + + + + def _add_to_cache_trace(self, state: dict, event_hash: int, event_output, parallel=False): + """ + Adds a state to the cached_trace list and computes the appropriate hash. + """ + # Compute the hash of the previous cached pair, if any + previous_hash = None + if self.cached_trace: + previous_hash = utils.custom_hash(self.cached_trace[-1]) + + if not parallel: + # Create a tuple of (hash, state) and append it to the cached_trace list + self.cached_trace.append((previous_hash, event_hash, event_output, state)) + else: + with concurrent_execution_lock: + # state is not stored in parallel segments, only outputs + self.cached_trace[-1][event_hash] = {"prev_node_hash": previous_hash, + "encoded_output": event_output} + + + self.has_unsaved_cache_changes = True + + def _load_cache_file(self, cache_path:str): + """ + Loads the cache file from the given path. + """ + try: + self.cached_trace = json.load(open(cache_path, "r", encoding="utf-8", errors="replace")) + except FileNotFoundError: + logger.info(f"Cache file not found on path: {cache_path}.") + self.cached_trace = [] + + def _save_cache_file(self, cache_path:str): + """ + Saves the cache file to the given path. Always overwrites. + """ + logger.debug(f"Now saving cache file to {cache_path}.") + try: + # Create a temporary file + with tempfile.NamedTemporaryFile('w', delete=False) as temp: + json.dump(self.cached_trace, temp, indent=4) + + # Replace the original file with the temporary file + os.replace(temp.name, cache_path) + except Exception as e: + traceback_string = ''.join(traceback.format_tb(e.__traceback__)) + logger.error(f"An error occurred while saving the cache file: {e}\nTraceback:\n{traceback_string}") + + self.has_unsaved_cache_changes = False + + + + ################################################################################################### + # Transactional control + ################################################################################################### + + # + # Regular sequential transactions + # + def begin_transaction(self, id=None): + """ + Starts a transaction. + """ + with concurrent_execution_lock: + self._under_transaction[id] = True + self._clear_communications_buffers() # TODO <---------------------------------------------------------------- + + def end_transaction(self, id=None): + """ + Ends a transaction. + """ + with concurrent_execution_lock: + self._under_transaction[id] = False + + def is_under_transaction(self, id=None): + """ + Checks if the agent is under a transaction. + """ + with concurrent_execution_lock: + return self._under_transaction.get(id, False) + + def _clear_communications_buffers(self): + """ + Cleans the communications buffers of all agents and environments. + """ + for agent in self.agents: + agent.clear_communications_buffer() + + for environment in self.environments: + environment.clear_communications_buffer() + + # + # Parallel transactions + # + def begin_parallel_transactions(self): + """ + Starts parallel transactions. + """ + with concurrent_execution_lock: + self._under_parallel_transactions = True + # add a new parallel segment to the execution and cache traces + self.execution_trace.append({}) + self.cached_trace.append({}) + + def end_parallel_transactions(self): + """ + Ends parallel transactions. + """ + self._under_parallel_transactions = False + + def is_under_parallel_transactions(self): + """ + Checks if the agent is under parallel transactions. + """ + return self._under_parallel_transactions + + ################################################################################################### + # Simulation state handling + ################################################################################################### + + def _encode_simulation_state(self) -> dict: + """ + Encodes the current simulation state, including agents, environments, and other + relevant information. + """ + state = {} + + # Encode agents + state["agents"] = [] + for agent in self.agents: + state["agents"].append(agent.encode_complete_state()) + + # Encode environments + state["environments"] = [] + for environment in self.environments: + state["environments"].append(environment.encode_complete_state()) + + # Encode factories + state["factories"] = [] + for factory in self.factories: + state["factories"].append(factory.encode_complete_state()) + + return state + + def _decode_simulation_state(self, state: dict): + """ + Decodes the given simulation state, including agents, environments, and other + relevant information. + + Args: + state (dict): The state to decode. + """ + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + + logger.debug(f"Decoding simulation state: {state['factories']}") + logger.debug(f"Registered factories: {self.name_to_factory}") + logger.debug(f"Registered agents: {self.name_to_agent}") + logger.debug(f"Registered environments: {self.name_to_environment}") + + # Decode factories + for factory_state in state["factories"]: + factory = self.name_to_factory[factory_state["name"]] + factory.decode_complete_state(factory_state) + + # Decode environments + ###self.environments = [] + for environment_state in state["environments"]: + try: + environment = self.name_to_environment[environment_state["name"]] + environment.decode_complete_state(environment_state) + if TinyWorld.communication_display: + environment.pop_and_display_latest_communications() + + except Exception as e: + raise ValueError(f"Environment {environment_state['name']} is not in the simulation, thus cannot be decoded there.") from e + + # Decode agents (if they were not already decoded by the environment) + ####self.agents = [] + for agent_state in state["agents"]: + try: + agent = self.name_to_agent[agent_state["name"]] + agent.decode_complete_state(agent_state) + + # The agent has not yet been decoded because it is not in any environment. So, decode it. + if agent.environment is None: + if TinyPerson.communication_display: + agent.pop_and_display_latest_communications() + except Exception as e: + raise ValueError(f"Agent {agent_state['name']} is not in the simulation, thus cannot be decoded there.") from e + + +class Transaction: + + def __init__(self, obj_under_transaction, simulation, function, *args, **kwargs): + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + + self.obj_under_transaction = obj_under_transaction + self.simulation = simulation + self.function_name = function.__name__ + self.function = function + self.args = args + self.kwargs = kwargs + + # + # If we have an ongoing simulation, set the simulation id of the object under transaction if it is not already set. + # + if simulation is not None: + if hasattr(obj_under_transaction, 'simulation_id') and obj_under_transaction.simulation_id is not None: + if obj_under_transaction.simulation_id != simulation.id: + raise ValueError(f"Object {obj_under_transaction} is already captured by a different simulation (id={obj_under_transaction.simulation_id}), \ + and cannot be captured by simulation id={simulation.id}.") + + logger.debug(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Object {obj_under_transaction} is already captured by simulation {simulation.id}.") + else: + # if is a TinyPerson, add the agent to the simulation + if isinstance(obj_under_transaction, TinyPerson): + simulation.add_agent(obj_under_transaction) + logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added agent {obj_under_transaction} to simulation {simulation.id}.") + + # if is a TinyWorld, add the environment to the simulation + elif isinstance(obj_under_transaction, TinyWorld): + simulation.add_environment(obj_under_transaction) + + # if is a TinyFactory, add the factory to the simulation + elif isinstance(obj_under_transaction, TinyFactory): + simulation.add_factory(obj_under_transaction) + logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added factory {obj_under_transaction} to simulation {simulation.id}.") + + else: + raise ValueError(f"Object {obj_under_transaction} (type = {type(obj_under_transaction)}) is not a TinyPerson or TinyWorld instance, and cannot be captured by the simulation.") + + + def execute(self, begin_parallel=False, parallel_id=None): + + output = None + + # Transaction caching will only operate if there is a simulation and it is started + if self.simulation is None or self.simulation.status == Simulation.STATUS_STOPPED: + # Compute the function and return it, no caching, since the simulation is not started + output = self.function(*self.args, **self.kwargs) + + elif self.simulation.status == Simulation.STATUS_STARTED: + # Compute the event hash + event_hash = self.simulation._function_call_hash(self.function_name, *self.args, **self.kwargs) + + # Sequential and parallel transactions are handled in different ways + if begin_parallel: + self.simulation.begin_parallel_transactions() + + # CACHED? Check if the event hash is in the cache + if self.simulation._is_transaction_event_cached(event_hash, + parallel=self.simulation.is_under_parallel_transactions()): + self.simulation.cache_hits += 1 + + # Restore the full state and return the cached output + logger.debug(f"Skipping execution of {self.function_name} with args {self.args} and kwargs {self.kwargs} because it is already cached.") + + # SEQUENTIAL + if not self.simulation.is_under_parallel_transactions(): + + self.simulation._skip_execution_with_cache() + state = self.simulation.cached_trace[self.simulation._execution_trace_position()][3] # state + self.simulation._decode_simulation_state(state) + + # Output encoding/decoding is used to preserve references to TinyPerson and TinyWorld instances + # mainly. Scalar values (int, float, str, bool) and composite values (list, dict) are + # encoded/decoded as is. + encoded_output = self.simulation.cached_trace[self.simulation._execution_trace_position()][2] # output + output = self._decode_function_output(encoded_output) + + # PARALLEL + else: # is under parallel transactions + + # in parallel segments, state is not restored, only outputs + encoded_output = self.simulation._get_cached_parallel_value(event_hash, "encoded_output") + output = self._decode_function_output(encoded_output) + + else: # not cached + + if not begin_parallel: + # in case of beginning a parallel segment, we don't want to count it as a cache miss, + # since the segment itself will not be cached, but rather the events within it. + self.simulation.cache_misses += 1 + + if not self.simulation.is_under_transaction(id=parallel_id) and not begin_parallel: + + # BEGIN SEQUENTIAL TRANSACTION ############################################################### + # + # if this is the beginning of a parallel segment, we don't need to begin a transaction, since + # we want to allow additional transactions within the parallel segment (i.e., one-level reentrancy). + if not begin_parallel: + self.simulation.begin_transaction(id=parallel_id) + + # Compute the function and encode the relevant output and simulation state + output = self.function(*self.args, **self.kwargs) + self._save_output_with_simulation_state(event_hash, output) + + # END TRANSACTION ################################################################# + if not begin_parallel: + self.simulation.end_transaction(id=parallel_id) + + else: # already under transaction (thus, now a reentrant transaction) OR beginning a parallel segment + + # NOTES: + # + # - Reentrant sequential transactions are not cached, since what matters is the final result of + # the top-level transaction. + # + # - The event that starts the parallel transactions segment WILL NOT itself be cached, since + # it is not part of the parallel segment, but rather the beginning of it. This event will be + # reconstructed during runtime from the parallel events within the segment. + + output = self.function(*self.args, **self.kwargs) + + if begin_parallel: + self.simulation.end_parallel_transactions() + + # execute an ad-hoc Transaction to save the simulation state AFTER the parallel segment is done. + Transaction(self.obj_under_transaction, self.simulation, lambda: True).execute(begin_parallel=False, parallel_id=parallel_id) + + else: + raise ValueError(f"Simulation status is invalid at this point: {self.simulation.status}") + + # Checkpoint if needed + logger.debug(f"Will attempt to checkpoint simulation state after transaction execution.") + if self.simulation is not None and self.simulation.auto_checkpoint: + logger.debug("Auto-checkpointing simulation state after transaction execution.") + self.simulation.checkpoint() + + # after all the transaction is done, return the output - the client will never know about all the complexity we've + # gone through to get here. + return output + + def _save_output_with_simulation_state(self, event_hash, output): + encoded_output = self._encode_function_output(output) + state = self.simulation._encode_simulation_state() + + # immediately drop the cached trace suffix, since we are starting a new execution from this point on. + # in the case of parallel transactions, this will drop everything _after_ the current parallel segment + # (which itself occupies one position only, with a dictionary of event hashes and their outputs). + self.simulation._drop_cached_trace_suffix() + + # Cache the result and update the current execution trace. If this is a parallel transaction, the + # cache and execution traces will be updated in a different way. + self.simulation._add_to_cache_trace(state, event_hash, encoded_output, + parallel=self.simulation.is_under_parallel_transactions()) + self.simulation._add_to_execution_trace(state, event_hash, encoded_output, + parallel=self.simulation.is_under_parallel_transactions()) + + + def _encode_function_output(self, output) -> dict: + """ + Encodes the given function output. + """ + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + + # if the output is a supported object, encode it + if output is None: + return None + elif isinstance(output, TinyPerson): + return {"type": "TinyPersonRef", "name": output.name} + elif isinstance(output, TinyWorld): + return {"type": "TinyWorldRef", "name": output.name} + elif isinstance(output, TinyFactory): + return {"type": "TinyFactoryRef", "name": output.name} + elif isinstance(output, list): + encoded_list = [] + for item in output: + if isinstance(item, TinyPerson): + encoded_list.append({"type": "TinyPersonRef", "name": item.name}) + elif isinstance(item, TinyWorld): + encoded_list.append({"type": "TinyWorldRef", "name": item.name}) + elif isinstance(item, TinyFactory): + encoded_list.append({"type": "TinyFactoryRef", "name": item.name}) + else: + encoded_list.append({"type": "JSON", "value": item}) + return {"type": "List", "value": encoded_list} + elif isinstance(output, (int, float, str, bool, dict, tuple)): + return {"type": "JSON", "value": output} + else: + raise ValueError(f"Unsupported output type: {type(output)}") + + def _decode_function_output(self, encoded_output: dict): + """ + Decodes the given encoded function output. + """ + # local import to avoid circular dependencies + from tinytroupe.agent import TinyPerson + from tinytroupe.environment import TinyWorld + from tinytroupe.factory.tiny_factory import TinyFactory + + if encoded_output is None: + return None + elif encoded_output["type"] == "TinyPersonRef": + return TinyPerson.get_agent_by_name(encoded_output["name"]) + elif encoded_output["type"] == "TinyWorldRef": + return TinyWorld.get_environment_by_name(encoded_output["name"]) + elif encoded_output["type"] == "TinyFactoryRef": + return TinyFactory.get_factory_by_name(encoded_output["name"]) + elif encoded_output["type"] == "List": + decoded_list = [] + for item in encoded_output["value"]: + if item["type"] == "TinyPersonRef": + decoded_list.append(TinyPerson.get_agent_by_name(item["name"])) + elif item["type"] == "TinyWorldRef": + decoded_list.append(TinyWorld.get_environment_by_name(item["name"])) + elif item["type"] == "TinyFactoryRef": + decoded_list.append(TinyFactory.get_factory_by_name(item["name"])) + else: + decoded_list.append(item["value"]) + return decoded_list + elif encoded_output["type"] == "JSON": + return encoded_output["value"] + else: + raise ValueError(f"Unsupported output type: {encoded_output['type']}") + +def transactional(parallel=False): + """ + A helper decorator that makes a function simulation-transactional. + """ + def decorator(func): + def wrapper(*args, **kwargs): + obj_under_transaction = args[0] + simulation = current_simulation() + obj_sim_id = obj_under_transaction.simulation_id if hasattr(obj_under_transaction, 'simulation_id') else None + + logger.debug(f"-----------------------------------------> Transaction: {func.__name__} with args {args[1:]} and kwargs {kwargs} under simulation {obj_sim_id}, parallel={parallel}.") + + parallel_id = str(threading.current_thread()) + + transaction = Transaction(obj_under_transaction, simulation, func, *args, **kwargs) + result = transaction.execute(begin_parallel=parallel, parallel_id=parallel_id) + + return result + + return wrapper + + return decorator + +class SkipTransaction(Exception): + pass + +class CacheOutOfSync(Exception): + """ + Raised when a cached and the corresponding freshly executed elements are out of sync. + """ + pass + +class ExecutionCached(Exception): + """ + Raised when a proposed execution is already cached. + """ + pass + + +################################################################################################### +# Convenience functions +################################################################################################### + +def reset(): + """ + Resets the entire simulation control state. + """ + global _current_simulations, _current_simulation_id + _current_simulations = {"default": None} + + # TODO Currently, only one simulation can be started at a time. In future versions, this should be + # changed to allow multiple simulations to be started at the same time, e.g., for fast + # analyses through parallelization. + _current_simulation_id = None + +def _simulation(id="default"): + global _current_simulations + if _current_simulations[id] is None: + _current_simulations[id] = Simulation() + + return _current_simulations[id] + +def begin(cache_path=None, id="default", auto_checkpoint=False): + """ + Marks the start of the simulation being controlled. + """ + global _current_simulation_id + if _current_simulation_id is None: + _simulation(id).begin(cache_path, auto_checkpoint) + _current_simulation_id = id + else: + raise ValueError(f"Simulation is already started under id {_current_simulation_id}. Currently only one simulation can be started at a time.") + +def end(id="default"): + """ + Marks the end of the simulation being controlled. + """ + global _current_simulation_id + _simulation(id).end() + _current_simulation_id = None + +def checkpoint(id="default"): + """ + Saves current simulation state. + """ + _simulation(id).checkpoint() + +def current_simulation(): + """ + Returns the current simulation. + """ + global _current_simulation_id + if _current_simulation_id is not None: + return _simulation(_current_simulation_id) + else: + return None + +def cache_hits(id="default"): + """ + Returns the number of cache hits. + """ + return _simulation(id).cache_hits + +def cache_misses(id="default"): + """ + Returns the number of cache misses. + """ + return _simulation(id).cache_misses + +reset() # initialize the control state \ No newline at end of file diff --git a/tinytroupe/enrichment/__init__.py b/tinytroupe/enrichment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..02cee3d86395fc13f99636e50d5957e8f62edace --- /dev/null +++ b/tinytroupe/enrichment/__init__.py @@ -0,0 +1,11 @@ +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.enrichment.tiny_enricher import TinyEnricher + +__all__ = ["TinyEnricher"] \ No newline at end of file diff --git a/tinytroupe/enrichment/__pycache__/__init__.cpython-312.pyc b/tinytroupe/enrichment/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ce0d03f145ac9f94b7ea176ce8b789fb4aa59f48 Binary files /dev/null and b/tinytroupe/enrichment/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/enrichment/__pycache__/tiny_enricher.cpython-312.pyc b/tinytroupe/enrichment/__pycache__/tiny_enricher.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..89056c6cf2b3bad990afa04443cdae32ee785a2f Binary files /dev/null and b/tinytroupe/enrichment/__pycache__/tiny_enricher.cpython-312.pyc differ diff --git a/tinytroupe/enrichment/__pycache__/tiny_styler.cpython-312.pyc b/tinytroupe/enrichment/__pycache__/tiny_styler.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f4a77272775b72ab7c93807fc80e5c216aeb6162 Binary files /dev/null and b/tinytroupe/enrichment/__pycache__/tiny_styler.cpython-312.pyc differ diff --git a/tinytroupe/enrichment/prompts/enricher.system.mustache b/tinytroupe/enrichment/prompts/enricher.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..41646373a6b52a4a3ee7ff04f675084a72f1420b --- /dev/null +++ b/tinytroupe/enrichment/prompts/enricher.system.mustache @@ -0,0 +1,67 @@ +# Content enricher + +You are a system that, given a certain content, enriches it. You operate with synthetic data, your main aim being +to make it more realistic, useful, informative and human-like. Content types might include, but are not limited to: + - Documents + - Meetings + - Emails + - Chat messages + - Tabular data + - Configuration files + - etc. + +Content enrichment under such conditions can be useful in many scenarios, such as: + - Expanding short documents, or document outlines. Synthetic data is often short or incomplete, and you can help + make it more informative. + - Filling in specific missing details. Synthetic data often lacks specific details, and you can help make it more + realistic. + - Making the content more human-like. Synthetic data is often generated by machines, and you can help make it more + human-like. + - Changing tone or style, since the original content might not be suitable for the target audience and might need + to be adjusted. + - Adapting content to work better with other systems. For example, the target system might require special-purpose + formatting, custom fields, or specific data types. + + +## On your input + +You receive as input: + - the content type: e.g. Document, Meeting, Email, Chat Message, Tabualar Data, Configuration Files, etc.; + - the content itself: formated as JSON, XML, CSV, Markdown, plain text, etc.; + - the enrichment requirements: e.g. expand, fill in, make human-like, change tone, adapt, etc.; + - contextual information (optional): This can include anything that you might use to better ground your expansion. For example, + for enriching a document, you might receive contextual inforation about the projects and the people that document is + related to; + - contextual cache (optional): This can include the past enrichments you have made, so you can both avoid repeating yourself + and build on top of your previous enrichments. + +## On how to enrich content + +Enrichment directives: + - You follow the enrichment requirements as closely as possible. + - Unless otherwise specified, you add as much detail as possible, assuming the user wants **more** content, never less. + - You always maintain the original **content type**, unless the specific enrichment request is explicitly asking to change it. + - Regarding contextual information: + * If contextual information is provided, you try to ground your enrichment in that context as much as possible, avoiding + contradictions or inconsistencies. + * If no contextual information is provided, you can be imaginative and creative, but always keeping the content realistic, + useful and internally consistent. + - If contextual cache is provided, you can use it to avoid repeating yourself, and to build on top of your previous enrichments. + In this case, assume that the overall objective is to generate a set of coherent enrichments, rather than a set of isolated + ones. For example, if you have already enriched a document with some details about a project, you can use that information + to later enrich a meeting invite related to that project. + * Morevoer, if other contextual information is provided, you should use it to determine the objective of the + set of enrichments, if that makes sense and results in a more realistic and coherent set of enrichments. + - In the enriched content, you **never** mention that the content was enriched. The enrichment must be transparent, and the final + artifact should not show any signs of being synthetic or machine-generated. + + +## On your output + +Output directives: + + - You maintain the orignal **format** as much as possible. So, for example, the input was a JSON object, you output a JSON object; + if the output was Markdown, you output Markdown; if the input was a CSV, ouput a CSV; if the input was a XML, ouput a XML; + if the input was just text, you output just text; etc. + + diff --git a/tinytroupe/enrichment/prompts/enricher.user.mustache b/tinytroupe/enrichment/prompts/enricher.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..d0272608ca5451624beb9a89386ed7766762b62d --- /dev/null +++ b/tinytroupe/enrichment/prompts/enricher.user.mustache @@ -0,0 +1,30 @@ +Now, please execute a concrete enrichment according with the following specification. + +## Content type +{{#content_type}} +The type of the content is: {{content_type}} +{{/content_type}} +{{^content_type}} +The type of the content is not specified, so please make your best guess about what it is. +{{/content_type}} + +## Enrichment requirements +{{requirements}} + +{{#contextual_information}} +## Contextual information (if any) +{{contextual_information}} +{{/contextual_information}} + +{{#contextual_cache}} +## Contextual cache (if any) + + - {{cached_type}}: {{cached_content}} +{{/contextual_cache}} + +## CONTENT TO ENRICH + +This is the actual content to enrich: +``` +{{content}} +``` diff --git a/tinytroupe/enrichment/prompts/styler.system.mustache b/tinytroupe/enrichment/prompts/styler.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..e0d2c674c27d6aad07dc968e2fb05bc8d6a7c157 --- /dev/null +++ b/tinytroupe/enrichment/prompts/styler.system.mustache @@ -0,0 +1,62 @@ +# Content Styler + +You are a system that transforms text to follow a specified writing or speaking style while preserving the original information. Your primary function is to reshape content to match different tones, dialects, or personality traits without altering the factual content. You can handle various content types including: + - Verbal conversations + - Documents + - Emails + - Chat messages + - Meeting transcripts + - Social media posts + - Blog articles + - Technical documentation + - etc. + +Style transformation can be useful in many scenarios, such as: + - Adapting content for different audiences (technical vs. non-technical, formal vs. casual) + - Changing tone to better match brand voice or company culture + - Simulating different personality types + - Making content more engaging, persuasive, or accessible + - Adding authenticity by matching regional dialects or professional jargon + - Converting between different writing conventions (academic, journalistic, conversational) + - Adjusting formality levels to match specific contexts or relationships + +## On your input + +You receive as input: + - the original content: formatted as JSON, XML, CSV, Markdown, plain text, etc.; + - the target style: a description of the writing or speaking style to transform the content into; + - style parameters (optional): specific aspects of the style to emphasize or de-emphasize; + - contextual information (optional): background that helps you understand the appropriate style or tone; + - preservation requirements (optional): specific elements that must remain unchanged during transformation. + +## On how to transform style + +Style transformation directives: + - You transform the text to match the target style while **always** preserving **all** factual information from the original. + * Factual information includes, but is not limited to, technical terms, names, dates, numerical data, and any other specific details that are critical to the content. + - You maintain the same meaning, points, arguments, and information content throughout the transformation. + - Unless explicitly requested, you do not add new information or remove existing information. + - You adapt language patterns, vocabulary, sentence structure, and rhetorical devices to match the target style. + - Regarding style parameters: + * If parameters emphasize certain aspects (personality, formality, technical language, brevity), you prioritize those aspects. + * If parameters de-emphasize aspects, you minimize those aspects without compromising information. + - Regarding contextual information: + * If provided, you use it to fine-tune the style to be appropriate for the specific context. + * If no context is provided, you implement the style in a general manner that would be widely recognized. + - Regarding preservation requirements: + * You strictly preserve any specified elements (technical terms, names, numerical data, etc.). + * When in doubt about whether something should be preserved, err on the side of preservation. + - You **never** mention that the content was transformed or styled. The transformation should be seamless, and the final + artifact should appear as if it was originally created in the target style. + +## On your output + +Output directives: + + - You maintain the original **format** as much as possible. So, for example, if the input was a JSON object, you output a JSON object; + if the input was Markdown, you output Markdown; if the input was a CSV, output a CSV; if the input was XML, output XML; + if the input was just text, you output just text; etc. + - You preserve structural elements like paragraphs, lists, sections, and formatting unless the target style explicitly + requires structural changes. + - The transformed content should feel natural and authentic to the target style, not like a parody or exaggeration + unless explicitly requested. diff --git a/tinytroupe/enrichment/prompts/styler.user.mustache b/tinytroupe/enrichment/prompts/styler.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..0948e7179a873f0f53e76e321af53063d3609dab --- /dev/null +++ b/tinytroupe/enrichment/prompts/styler.user.mustache @@ -0,0 +1,30 @@ +Now, please apply a concrete style following the specification below. + +## Content type +{{#content_type}} +The type of the content is: {{content_type}} +{{/content_type}} +{{^content_type}} +The type of the content is not specified, so please make your best guess about what it is. +{{/content_type}} + +## Style requirements +{{style}} + +{{#contextual_information}} +## Contextual information (if any) +{{contextual_information}} +{{/contextual_information}} + +{{#contextual_cache}} +## Contextual cache (if any) + + - {{cached_type}}: {{cached_content}} +{{/contextual_cache}} + +## CONTENT TO APPLY STYLE + +This is the actual content to style: +``` +{{content}} +``` diff --git a/tinytroupe/enrichment/tiny_enricher.py b/tinytroupe/enrichment/tiny_enricher.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8c1e4c2425b1868132c28a9c8275376d798908 --- /dev/null +++ b/tinytroupe/enrichment/tiny_enricher.py @@ -0,0 +1,41 @@ +from tinytroupe.enrichment import logger +from tinytroupe.utils import JsonSerializableRegistry + + +from tinytroupe import openai_utils +import tinytroupe.utils as utils + +class TinyEnricher(JsonSerializableRegistry): + + def __init__(self, use_past_results_in_context=False) -> None: + self.use_past_results_in_context = use_past_results_in_context + + self.context_cache = [] + + def enrich_content(self, requirements: str, content:str, content_type:str =None, context_info:str ="", context_cache:list=None, verbose:bool=False): + + rendering_configs = {"requirements": requirements, + "content": content, + "content_type": content_type, + "context_info": context_info, + "context_cache": context_cache} + + messages = utils.compose_initial_LLM_messages_with_templates("enricher.system.mustache", "enricher.user.mustache", + base_module_folder = "enrichment", + rendering_configs=rendering_configs) + + next_message = openai_utils.client().send_message(messages, temperature=1.0, frequency_penalty=0.0, presence_penalty=0.0) + + debug_msg = f"Enrichment result message: {next_message}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + if next_message is not None: + result = utils.extract_code_block(next_message["content"]) + else: + result = None + + return result + + diff --git a/tinytroupe/enrichment/tiny_styler.py b/tinytroupe/enrichment/tiny_styler.py new file mode 100644 index 0000000000000000000000000000000000000000..c3cc76d5bc70347d466583ec9425a7f873519b35 --- /dev/null +++ b/tinytroupe/enrichment/tiny_styler.py @@ -0,0 +1,85 @@ +from tinytroupe.enrichment import logger +from tinytroupe.utils import JsonSerializableRegistry +from tinytroupe.utils.llm import LLMChat +import tinytroupe.utils as utils + + +class TinyStyler(JsonSerializableRegistry): + """ + A class for applying a specified writing or speaking style to content while preserving + the original information. + """ + + def __init__(self, use_past_results_in_context=False) -> None: + """ + Initialize the TinyStyler. + + Args: + use_past_results_in_context (bool): Whether to use past styling results in the context. + """ + self.use_past_results_in_context = use_past_results_in_context + self.context_cache = [] + + def apply_style(self, content: str, style: str, content_type: str = None, + context_info: str = "", context_cache: list = None, verbose: bool = False, + temperature: float = 0.7): + """ + Apply a specified style to the content while preserving all the original information. + + Args: + content (str): The content to style. + style (str): The style to apply (e.g., "professional", "casual", "technical", etc.). + content_type (str, optional): The type of content (e.g., "email", "report", "conversation"). + context_info (str, optional): Additional context information. + context_cache (list, optional): Previous styling results to use as context. + verbose (bool, optional): Whether to print debug information. + temperature (float, optional): The temperature to use for the LLM generation. + + Returns: + str: The styled content. + """ + if context_cache is None and self.use_past_results_in_context: + context_cache = self.context_cache + + rendering_configs = { + "content": content, + "style": style, + "content_type": content_type, + "context_info": context_info, + "context_cache": context_cache + } + + # Initialize the LLMChat with appropriate templates + chat = LLMChat( + system_template_name="styler.system.mustache", + user_template_name="styler.user.mustache", + base_module_folder="enrichment", + temperature=temperature + ) + + # Call the model and get the response + result = chat.call(**rendering_configs) + + debug_msg = f"Styling result: {result}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + # Extract the styled content from code blocks if present + if result is not None: + styled_content = utils.extract_code_block(result) + # If no code block was found, use the raw result + if not styled_content: + styled_content = result + + # Add to context cache if enabled + if self.use_past_results_in_context: + self.context_cache.append({ + "original": content, + "style": style, + "styled": styled_content + }) + + return styled_content + else: + return None diff --git a/tinytroupe/environment/__init__.py b/tinytroupe/environment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..976a1e4a944c8cfc600420d71add0112fab606bc --- /dev/null +++ b/tinytroupe/environment/__init__.py @@ -0,0 +1,17 @@ +""" +Environments provide a structured way to define the world in which the +agents interact with each other as well as external entities (e.g., search engines). +""" + +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.environment.tiny_world import TinyWorld +from tinytroupe.environment.tiny_social_network import TinySocialNetwork + +__all__ = ["TinyWorld", "TinySocialNetwork"] \ No newline at end of file diff --git a/tinytroupe/environment/__pycache__/__init__.cpython-312.pyc b/tinytroupe/environment/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b8f02e75f9e490ef90434847da886d8d583a71b2 Binary files /dev/null and b/tinytroupe/environment/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/environment/__pycache__/tiny_social_network.cpython-312.pyc b/tinytroupe/environment/__pycache__/tiny_social_network.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6f0295d87a793ef55df5c51f10625b5dc8f97de8 Binary files /dev/null and b/tinytroupe/environment/__pycache__/tiny_social_network.cpython-312.pyc differ diff --git a/tinytroupe/environment/__pycache__/tiny_world.cpython-312.pyc b/tinytroupe/environment/__pycache__/tiny_world.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a7d6086e817e75007c445589dc64c033a9a87a10 Binary files /dev/null and b/tinytroupe/environment/__pycache__/tiny_world.cpython-312.pyc differ diff --git a/tinytroupe/environment/tiny_social_network.py b/tinytroupe/environment/tiny_social_network.py new file mode 100644 index 0000000000000000000000000000000000000000..d097c1e13ba47591e8bae3162489f2f1c56a857f --- /dev/null +++ b/tinytroupe/environment/tiny_social_network.py @@ -0,0 +1,132 @@ +from tinytroupe.environment.tiny_world import TinyWorld +from tinytroupe.environment import logger + +import copy +from datetime import datetime, timedelta + +from tinytroupe.agent import * +from tinytroupe.control import transactional + +from rich.console import Console + +from typing import Any, TypeVar, Union +AgentOrWorld = Union["TinyPerson", "TinyWorld"] + + +class TinySocialNetwork(TinyWorld): + + def __init__(self, name, broadcast_if_no_target=True): + """ + Create a new TinySocialNetwork environment. + + Args: + name (str): The name of the environment. + broadcast_if_no_target (bool): If True, broadcast actions through an agent's available relations + if the target of an action is not found. + """ + + super().__init__(name, broadcast_if_no_target=broadcast_if_no_target) + + self.relations = {} + + @transactional() + def add_relation(self, agent_1, agent_2, name="default"): + """ + Adds a relation between two agents. + + Args: + agent_1 (TinyPerson): The first agent. + agent_2 (TinyPerson): The second agent. + name (str): The name of the relation. + """ + + logger.debug(f"Adding relation {name} between {agent_1.name} and {agent_2.name}.") + + # agents must already be in the environment, if not they are first added + if agent_1 not in self.agents: + self.agents.append(agent_1) + if agent_2 not in self.agents: + self.agents.append(agent_2) + + if name in self.relations: + self.relations[name].append((agent_1, agent_2)) + else: + self.relations[name] = [(agent_1, agent_2)] + + return self # for chaining + + @transactional() + def _update_agents_contexts(self): + """ + Updates the agents' observations based on the current state of the world. + """ + + # clear all accessibility first + for agent in self.agents: + agent.make_all_agents_inaccessible() + + # now update accessibility based on relations + for relation_name, relation in self.relations.items(): + logger.debug(f"Updating agents' observations for relation {relation_name}.") + for agent_1, agent_2 in relation: + agent_1.make_agent_accessible(agent_2) + agent_2.make_agent_accessible(agent_1) + + @transactional() + def _step(self): + self._update_agents_contexts() + + #call super + super()._step() + + @transactional() + def _handle_reach_out(self, source_agent: TinyPerson, content: str, target: str): + """ + Handles the REACH_OUT action. This social network implementation only allows + REACH_OUT to succeed if the target agent is in the same relation as the source agent. + + Args: + source_agent (TinyPerson): The agent that issued the REACH_OUT action. + content (str): The content of the message. + target (str): The target of the message. + """ + + # check if the target is in the same relation as the source + if self.is_in_relation_with(source_agent, self.get_agent_by_name(target)): + super()._handle_reach_out(source_agent, content, target) + + # if we get here, the target is not in the same relation as the source + source_agent.socialize(f"{target} is not in the same relation as you, so you cannot reach out to them.", source=self) + + + # TODO implement _handle_talk using broadcast_if_no_target too + + ####################################################################### + # Utilities and conveniences + ####################################################################### + + def is_in_relation_with(self, agent_1:TinyPerson, agent_2:TinyPerson, relation_name=None) -> bool: + """ + Checks if two agents are in a relation. If the relation name is given, check that + the agents are in that relation. If no relation name is given, check that the agents + are in any relation. Relations are undirected, so the order of the agents does not matter. + + Args: + agent_1 (TinyPerson): The first agent. + agent_2 (TinyPerson): The second agent. + relation_name (str): The name of the relation to check, or None to check any relation. + + Returns: + bool: True if the two agents are in the given relation, False otherwise. + """ + if relation_name is None: + for relation_name, relation in self.relations.items(): + if (agent_1, agent_2) in relation or (agent_2, agent_1) in relation: + return True + return False + + else: + if relation_name in self.relations: + return (agent_1, agent_2) in self.relations[relation_name] or (agent_2, agent_1) in self.relations[relation_name] + else: + return False \ No newline at end of file diff --git a/tinytroupe/environment/tiny_world.py b/tinytroupe/environment/tiny_world.py new file mode 100644 index 0000000000000000000000000000000000000000..990eb88c06269a0dd2caa8e67aa268f4c0e6403c --- /dev/null +++ b/tinytroupe/environment/tiny_world.py @@ -0,0 +1,866 @@ +from tinytroupe.environment import logger, default + +import copy +from datetime import datetime, timedelta +import textwrap +import random +import concurrent.futures + +from tinytroupe.agent import * +from tinytroupe.utils import name_or_empty, pretty_datetime +import tinytroupe.control as control +from tinytroupe.control import transactional +from tinytroupe import utils +from tinytroupe import config_manager + +from rich.console import Console + +from typing import Any, TypeVar, Union +AgentOrWorld = Union["TinyPerson", "TinyWorld"] + +class TinyWorld: + """ + Base class for environments. + """ + + # A dict of all environments created so far. + all_environments = {} # name -> environment + + # Whether to display environments communications or not, for all environments. + communication_display = True + + def __init__(self, name: str=None, agents=[], + initial_datetime=datetime.now(), + interventions=[], + broadcast_if_no_target=True, + max_additional_targets_to_display=3): + """ + Initializes an environment. + + Args: + name (str): The name of the environment. + agents (list): A list of agents to add to the environment. + initial_datetifme (datetime): The initial datetime of the environment, or None (i.e., explicit time is optional). + Defaults to the current datetime in the real world. + interventions (list): A list of interventions to apply in the environment at each simulation step. + broadcast_if_no_target (bool): If True, broadcast actions if the target of an action is not found. + max_additional_targets_to_display (int): The maximum number of additional targets to display in a communication. If None, + all additional targets are displayed. + """ + + if name is not None: + self.name = name + else: + self.name = f"TinyWorld {utils.fresh_id(self.__class__.__name__)}" + + self.current_datetime = initial_datetime + self.broadcast_if_no_target = broadcast_if_no_target + self.simulation_id = None # will be reset later if the agent is used within a specific simulation scope + + self.agents = [] + self.name_to_agent = {} # {agent_name: agent, agent_name_2: agent_2, ...} + + self._interventions = interventions + + # the buffer of communications that have been displayed so far, used for + # saving these communications to another output form later (e.g., caching) + self._displayed_communications_buffer = [] + + # a temporary buffer for communications target to make rendering easier + self._target_display_communications_buffer = [] + self._max_additional_targets_to_display = max_additional_targets_to_display + + self.console = Console() + + # add the environment to the list of all environments + TinyWorld.add_environment(self) + + self.add_agents(agents) + + ####################################################################### + # Simulation control methods + ####################################################################### + @transactional() + def _step(self, + timedelta_per_step=None, + randomize_agents_order=True, + parallelize=True): # TODO have a configuration for parallelism? + """ + Performs a single step in the environment. This default implementation + simply calls makes all agents in the environment act and properly + handle the resulting actions. Subclasses might override this method to implement + different policies. + """ + + # Increase current datetime if timedelta is given. This must happen before + # any other simulation updates, to make sure that the agents are acting + # in the correct time, particularly if only one step is being run. + self._advance_datetime(timedelta_per_step) + + # Apply interventions. + # + # Why not in parallel? Owing to the very general nature of their potential effects, + # interventions are never parallelized, since that could introduce unforeseen race conditions. + for intervention in self._interventions: + should_apply_intervention = intervention.check_precondition() + if should_apply_intervention: + if TinyWorld.communication_display: + self._display_intervention_communication(intervention) + intervention.apply_effect() + + logger.debug(f"[{self.name}] Intervention '{intervention.name}' was applied.") + + # Agents can act in parallel or sequentially + if parallelize: + agents_actions = self._step_in_parallel(timedelta_per_step=timedelta_per_step) + else: + agents_actions = self._step_sequentially(timedelta_per_step=timedelta_per_step, + randomize_agents_order=randomize_agents_order) + + return agents_actions + + def _step_sequentially(self, timedelta_per_step=None, randomize_agents_order=True): + """ + The sequential version of the _step method to request agents to act. + """ + + # agents can act in a random order + reordered_agents = copy.copy(self.agents) + if randomize_agents_order: + random.shuffle(reordered_agents) + + # agents can act + agents_actions = {} + for agent in reordered_agents: + logger.debug(f"[{self.name}] Agent {name_or_empty(agent)} is acting.") + actions = agent.act(return_actions=True) + agents_actions[agent.name] = actions + + self._handle_actions(agent, agent.pop_latest_actions()) + + return agents_actions + + def _step_in_parallel(self, timedelta_per_step=None): + """ + A parallelized version of the _step method to request agents to act. + """ + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = {executor.submit(agent.act, return_actions=True): agent for agent in self.agents} + agents_actions = {} + + # Wait for all futures to complete + concurrent.futures.wait(futures.keys()) + + for future in futures: + agent = futures[future] + try: + actions = future.result() + agents_actions[agent.name] = actions + self._handle_actions(agent, agent.pop_latest_actions()) + except Exception as exc: + logger.error(f"[{self.name}] Agent {name_or_empty(agent)} generated an exception: {exc}") + + return agents_actions + + + + def _advance_datetime(self, timedelta): + """ + Advances the current datetime of the environment by the specified timedelta. + + Args: + timedelta (timedelta): The timedelta to advance the current datetime by. + """ + if timedelta is not None: + self.current_datetime += timedelta + else: + logger.info(f"[{self.name}] No timedelta provided, so the datetime was not advanced.") + + @transactional() + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run(self, steps: int, timedelta_per_step=None, return_actions=False, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of steps. + + Args: + steps (int): The number of steps to run the environment for. + timedelta_per_step (timedelta, optional): The time interval between steps. Defaults to None. + return_actions (bool, optional): If True, returns the actions taken by the agents. Defaults to False. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + parallelize (bool, optional): If True, agents act in parallel. Defaults to True. + + Returns: + list: A list of actions taken by the agents over time, if return_actions is True. The list has this format: + [{agent_name: [action_1, action_2, ...]}, {agent_name_2: [action_1, action_2, ...]}, ...] + """ + agents_actions_over_time = [] + for i in range(steps): + logger.info(f"[{self.name}] Running world simulation step {i+1} of {steps}.") + + if TinyWorld.communication_display: + self._display_step_communication(cur_step=i+1, total_steps=steps, timedelta_per_step=timedelta_per_step) + + agents_actions = self._step(timedelta_per_step=timedelta_per_step, randomize_agents_order=randomize_agents_order, parallelize=parallelize) + agents_actions_over_time.append(agents_actions) + + if return_actions: + return agents_actions_over_time + + @transactional() + def skip(self, steps: int, timedelta_per_step=None): + """ + Skips a given number of steps in the environment. That is to say, time shall pass, but no actions will be taken + by the agents or any other entity in the environment. + + Args: + steps (int): The number of steps to skip. + timedelta_per_step (timedelta, optional): The time interval between steps. Defaults to None. + """ + self._advance_datetime(steps * timedelta_per_step) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_minutes(self, minutes: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of minutes. + + Args: + minutes (int): The number of minutes to run the environment for. + """ + self.run(steps=minutes, timedelta_per_step=timedelta(minutes=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_minutes(self, minutes: int): + """ + Skips a given number of minutes in the environment. + + Args: + minutes (int): The number of minutes to skip. + """ + self.skip(steps=minutes, timedelta_per_step=timedelta(minutes=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_hours(self, hours: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of hours. + + Args: + hours (int): The number of hours to run the environment for. + """ + self.run(steps=hours, timedelta_per_step=timedelta(hours=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_hours(self, hours: int): + """ + Skips a given number of hours in the environment. + + Args: + hours (int): The number of hours to skip. + """ + self.skip(steps=hours, timedelta_per_step=timedelta(hours=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_days(self, days: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of days. + + Args: + days (int): The number of days to run the environment for. + """ + self.run(steps=days, timedelta_per_step=timedelta(days=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_days(self, days: int): + """ + Skips a given number of days in the environment. + + Args: + days (int): The number of days to skip. + """ + self.skip(steps=days, timedelta_per_step=timedelta(days=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_weeks(self, weeks: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of weeks. + + Args: + weeks (int): The number of weeks to run the environment for. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + """ + self.run(steps=weeks, timedelta_per_step=timedelta(weeks=1), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_weeks(self, weeks: int): + """ + Skips a given number of weeks in the environment. + + Args: + weeks (int): The number of weeks to skip. + """ + self.skip(steps=weeks, timedelta_per_step=timedelta(weeks=1)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_months(self, months: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of months. + + Args: + months (int): The number of months to run the environment for. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + """ + self.run(steps=months, timedelta_per_step=timedelta(weeks=4), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_months(self, months: int): + """ + Skips a given number of months in the environment. + + Args: + months (int): The number of months to skip. + """ + self.skip(steps=months, timedelta_per_step=timedelta(weeks=4)) + + @config_manager.config_defaults(parallelize="parallel_agent_actions") + def run_years(self, years: int, randomize_agents_order=True, parallelize=None): + """ + Runs the environment for a given number of years. + + Args: + years (int): The number of years to run the environment for. + randomize_agents_order (bool, optional): If True, randomizes the order in which agents act. Defaults to True. + """ + self.run(steps=years, timedelta_per_step=timedelta(days=365), randomize_agents_order=randomize_agents_order, parallelize=parallelize) + + def skip_years(self, years: int): + """ + Skips a given number of years in the environment. + + Args: + years (int): The number of years to skip. + """ + self.skip(steps=years, timedelta_per_step=timedelta(days=365)) + + ####################################################################### + # Agent management methods + ####################################################################### + def add_agents(self, agents: list): + """ + Adds a list of agents to the environment. + + Args: + agents (list): A list of agents to add to the environment. + """ + for agent in agents: + self.add_agent(agent) + + return self # for chaining + + def add_agent(self, agent: TinyPerson): + """ + Adds an agent to the environment. The agent must have a unique name within the environment. + + Args: + agent (TinyPerson): The agent to add to the environment. + + Raises: + ValueError: If the agent name is not unique within the environment. + """ + + # check if the agent is not already in the environment + if agent not in self.agents: + logger.debug(f"Adding agent {agent.name} to the environment.") + + # Agent names must be unique in the environment. + # Check if the agent name is already there. + if agent.name not in self.name_to_agent: + agent.environment = self + self.agents.append(agent) + self.name_to_agent[agent.name] = agent + else: + raise ValueError(f"Agent names must be unique, but '{agent.name}' is already in the environment.") + else: + logger.warn(f"Agent {agent.name} is already in the environment.") + + return self # for chaining + + def remove_agent(self, agent: TinyPerson): + """ + Removes an agent from the environment. + + Args: + agent (TinyPerson): The agent to remove from the environment. + """ + logger.debug(f"Removing agent {agent.name} from the environment.") + self.agents.remove(agent) + del self.name_to_agent[agent.name] + + return self # for chaining + + def remove_all_agents(self): + """ + Removes all agents from the environment. + """ + logger.debug(f"Removing all agents from the environment.") + self.agents = [] + self.name_to_agent = {} + + return self # for chaining + + def get_agent_by_name(self, name: str) -> TinyPerson: + """ + Returns the agent with the specified name. If no agent with that name exists in the environment, + returns None. + + Args: + name (str): The name of the agent to return. + + Returns: + TinyPerson: The agent with the specified name. + """ + if name in self.name_to_agent: + return self.name_to_agent[name] + else: + return None + + ####################################################################### + # Intervention management methods + ####################################################################### + + def add_intervention(self, intervention): + """ + Adds an intervention to the environment. + + Args: + intervention: The intervention to add to the environment. + """ + self._interventions.append(intervention) + + ####################################################################### + # Action handlers + # + # Specific actions issued by agents are handled by the environment, + # because they have effects beyond the agent itself. + ####################################################################### + @transactional() + def _handle_actions(self, source: TinyPerson, actions: list): + """ + Handles the actions issued by the agents. + + Args: + source (TinyPerson): The agent that issued the actions. + actions (list): A list of actions issued by the agents. Each action is actually a + JSON specification. + + """ + for action in actions: + action_type = action["type"] # this is the only required field + content = action["content"] if "content" in action else None + target = action["target"] if "target" in action else None + + logger.debug(f"[{self.name}] Handling action {action_type} from agent {name_or_empty(source)}. Content: {content}, target: {target}.") + + # only some actions require the enviroment to intervene + if action_type == "REACH_OUT": + self._handle_reach_out(source, content, target) + elif action_type == "TALK": + self._handle_talk(source, content, target) + + @transactional() + def _handle_reach_out(self, source_agent: TinyPerson, content: str, target: str): + """ + Handles the REACH_OUT action. This default implementation always allows REACH_OUT to succeed. + Subclasses might override this method to implement different policies. + + Args: + source_agent (TinyPerson): The agent that issued the REACH_OUT action. + content (str): The content of the message. + target (str): The target of the message. + """ + + # This default implementation always allows REACH_OUT to suceed. + target_agent = self.get_agent_by_name(target) + + if target_agent is not None: + source_agent.make_agent_accessible(target_agent) + target_agent.make_agent_accessible(source_agent) + + source_agent.socialize(f"{name_or_empty(target_agent)} was successfully reached out, and is now available for interaction.", source=self) + target_agent.socialize(f"{name_or_empty(source_agent)} reached out to you, and is now available for interaction.", source=self) + + else: + logger.debug(f"[{self.name}] REACH_OUT action failed: target agent '{target}' not found.") + + @transactional() + def _handle_talk(self, source_agent: TinyPerson, content: str, target: str): + """ + Handles the TALK action by delivering the specified content to the specified target. + + Args: + source_agent (TinyPerson): The agent that issued the TALK action. + content (str): The content of the message. + target (str, optional): The target of the message. + """ + target_agent = self.get_agent_by_name(target) + + logger.debug(f"[{self.name}] Delivering message from {name_or_empty(source_agent)} to {name_or_empty(target_agent)}.") + + if target_agent is not None: + target_agent.listen(content, source=source_agent) + elif self.broadcast_if_no_target: + self.broadcast(content, source=source_agent) + + ####################################################################### + # Interaction methods + ####################################################################### + @transactional() + def broadcast(self, speech: str, source: AgentOrWorld=None): + """ + Delivers a speech to all agents in the environment. + + Args: + speech (str): The content of the message. + source (AgentOrWorld, optional): The agent or environment that issued the message. Defaults to None. + """ + logger.debug(f"[{self.name}] Broadcasting message: '{speech}'.") + + for agent in self.agents: + # do not deliver the message to the source + if agent != source: + agent.listen(speech, source=source) + + @transactional() + def broadcast_thought(self, thought: str, source: AgentOrWorld=None): + """ + Broadcasts a thought to all agents in the environment. + + Args: + thought (str): The content of the thought. + """ + logger.debug(f"[{self.name}] Broadcasting thought: '{thought}'.") + + for agent in self.agents: + agent.think(thought) + + @transactional() + def broadcast_internal_goal(self, internal_goal: str): + """ + Broadcasts an internal goal to all agents in the environment. + + Args: + internal_goal (str): The content of the internal goal. + """ + logger.debug(f"[{self.name}] Broadcasting internal goal: '{internal_goal}'.") + + for agent in self.agents: + agent.internalize_goal(internal_goal) + + @transactional() + def broadcast_context_change(self, context:list): + """ + Broadcasts a context change to all agents in the environment. + + Args: + context (list): The content of the context change. + """ + logger.debug(f"[{self.name}] Broadcasting context change: '{context}'.") + + for agent in self.agents: + agent.change_context(context) + + def make_everyone_accessible(self): + """ + Makes all agents in the environment accessible to each other. + """ + for agent_1 in self.agents: + for agent_2 in self.agents: + if agent_1 != agent_2: + agent_1.make_agent_accessible(agent_2) + + + ########################################################### + # Formatting conveniences + ########################################################### + + # TODO better names for these "display" methods + def _display_step_communication(self, cur_step, total_steps, timedelta_per_step=None): + """ + Displays the current communication and stores it in a buffer for later use. + """ + rendering = self._pretty_step(cur_step=cur_step, total_steps=total_steps, timedelta_per_step=timedelta_per_step) + + self._push_and_display_latest_communication({"kind": 'step', "rendering": rendering, "content": None, "source": None, "target": None}) + + def _display_intervention_communication(self, intervention): + """ + Displays the current intervention communication and stores it in a buffer for later use. + """ + rendering = self._pretty_intervention(intervention) + self._push_and_display_latest_communication({"kind": 'intervention', "rendering": rendering, "content": None, "source": None, "target": None}) + + def _push_and_display_latest_communication(self, communication): + """ + Pushes the latest communications to the agent's buffer. + """ + # + # check if the communication is just repeating the last one for a different target + # + if len(self._displayed_communications_buffer) > 0: + # get values from last communication + last_communication = self._displayed_communications_buffer[-1] + last_kind = last_communication["kind"] + last_target = last_communication["target"] + last_source = last_communication["source"] + if last_kind == 'action': + last_content = last_communication["content"]["action"]["content"] + last_type = last_communication["content"]["action"]["type"] + elif last_kind == 'stimulus': + last_content = last_communication["content"]["stimulus"]["content"] + last_type = last_communication["content"]["stimulus"]["type"] + elif last_kind == 'stimuli': + last_stimulus = last_communication["content"]["stimuli"][0] + last_content = last_stimulus["content"] + last_type = last_stimulus["type"] + else: + last_content = None + last_type = None + + # get values from current communication + current_kind = communication["kind"] + current_target = communication["target"] + current_source = communication["source"] + if current_kind == 'action': + current_content = communication["content"]["action"]["content"] + current_type = communication["content"]["action"]["type"] + elif current_kind == 'stimulus': + current_content = communication["content"]["stimulus"]["content"] + current_type = communication["content"]["stimulus"]["type"] + elif current_kind == 'stimuli': + current_stimulus = communication["content"]["stimuli"][0] + current_content = current_stimulus["content"] + current_type = current_stimulus["type"] + else: + current_content = None + current_type = None + + # if we are repeating the last communication, let's simplify the rendering + if (last_source == current_source) and (last_type == current_type) and (last_kind == current_kind) and \ + (last_content is not None) and (last_content == current_content) and \ + (current_target is not None): + + self._target_display_communications_buffer.append(current_target) + + rich_style = utils.RichTextStyle.get_style_for(last_kind, last_type) + + # print the additional target a limited number of times if a max is set, or + # always if no max is set. + if (self._max_additional_targets_to_display is None) or\ + len(self._target_display_communications_buffer) < self._max_additional_targets_to_display: + communication["rendering"] = " " * len(last_source) + f"[{rich_style}] + --> [underline]{current_target}[/][/]" + + elif len(self._target_display_communications_buffer) == self._max_additional_targets_to_display: + communication["rendering"] = " " * len(last_source) + f"[{rich_style}] + --> ...others...[/]" + + else: # don't display anything anymore + communication["rendering"] = None + + else: + # no repetition, so just display the communication and reset the targets buffer + self._target_display_communications_buffer = [] # resets + + else: + # no repetition, so just display the communication and reset the targets buffer + self._target_display_communications_buffer = [] # resets + + + + self._displayed_communications_buffer.append(communication) + self._display(communication) + + def pop_and_display_latest_communications(self): + """ + Pops the latest communications and displays them. + """ + communications = self._displayed_communications_buffer + self._displayed_communications_buffer = [] + + for communication in communications: + self._display(communication) + + return communications + + def _display(self, communication:dict): + # unpack the rendering to find more info + content = communication["rendering"] + kind = communication["kind"] + + if content is not None: + # render as appropriate + if kind == 'step': + self.console.rule(content) + else: + self.console.print(content) + + def clear_communications_buffer(self): + """ + Cleans the communications buffer. + """ + self._displayed_communications_buffer = [] + + def __repr__(self): + return f"TinyWorld(name='{self.name}')" + + def _pretty_step(self, cur_step, total_steps, timedelta_per_step=None): + rendering = f"{self.name} step {cur_step} of {total_steps}" + if timedelta_per_step is not None: + rendering += f" ({pretty_datetime(self.current_datetime)})" + + return rendering + + def _pretty_intervention(self, intervention): + indent = " > " + justification = textwrap.fill( + intervention.precondition_justification(), + width=TinyPerson.PP_TEXT_WIDTH, + initial_indent=indent, + subsequent_indent=indent, + ) + + rich_style = utils.RichTextStyle.get_style_for("intervention") + rendering = f"[{rich_style}] :zap: [bold] <<{intervention.name}>> Triggered, effects are being applied...[/] \n" + \ + f"[italic]{justification}[/][/]" + # TODO add details about why the intervention was applied + + return rendering + + def pp_current_interactions(self, simplified=True, skip_system=True): + """ + Pretty prints the current messages from agents in this environment. + """ + print(self.pretty_current_interactions(simplified=simplified, skip_system=skip_system)) + + def pretty_current_interactions(self, simplified=True, skip_system=True, max_content_length=default["max_content_display_length"], first_n=None, last_n=None, include_omission_info:bool=True): + """ + Returns a pretty, readable, string with the current messages of agents in this environment. + """ + agent_contents = [] + + for agent in self.agents: + agent_content = f"#### Interactions from the point of view of {agent.name} agent:\n" + agent_content += f"**BEGIN AGENT {agent.name} HISTORY.**\n " + agent_content += agent.pretty_current_interactions(simplified=simplified, skip_system=skip_system, max_content_length=max_content_length, first_n=first_n, last_n=last_n, include_omission_info=include_omission_info) + "\n" + agent_content += f"**FINISHED AGENT {agent.name} HISTORY.**\n\n" + agent_contents.append(agent_content) + + return "\n".join(agent_contents) + + ####################################################################### + # IO + ####################################################################### + + def encode_complete_state(self) -> dict: + """ + Encodes the complete state of the environment in a dictionary. + + Returns: + dict: A dictionary encoding the complete state of the environment. + """ + to_copy = copy.copy(self.__dict__) + + # remove the logger and other fields + del to_copy['console'] + del to_copy['agents'] + del to_copy['name_to_agent'] + del to_copy['current_datetime'] + del to_copy['_interventions'] # TODO: encode interventions + + state = copy.deepcopy(to_copy) + + # agents are encoded separately + state["agents"] = [agent.encode_complete_state() for agent in self.agents] + + # datetime also has to be encoded separately + state["current_datetime"] = self.current_datetime.isoformat() + + return state + + def decode_complete_state(self, state:dict): + """ + Decodes the complete state of the environment from a dictionary. + + Args: + state (dict): A dictionary encoding the complete state of the environment. + + Returns: + Self: The environment decoded from the dictionary. + """ + state = copy.deepcopy(state) + + ################################# + # restore agents in-place + ################################# + self.remove_all_agents() + for agent_state in state["agents"]: + try: + try: + agent = TinyPerson.get_agent_by_name(agent_state["name"]) + except Exception as e: + raise ValueError(f"Could not find agent {agent_state['name']} for environment {self.name}.") from e + + agent.decode_complete_state(agent_state) + self.add_agent(agent) + + except Exception as e: + raise ValueError(f"Could not decode agent {agent_state['name']} for environment {self.name}.") from e + + # remove the agent states to update the rest of the environment + del state["agents"] + + # restore datetime + state["current_datetime"] = datetime.fromisoformat(state["current_datetime"]) + + # restore other fields + self.__dict__.update(state) + + return self + + @staticmethod + def add_environment(environment): + """ + Adds an environment to the list of all environments. Environment names must be unique, + so if an environment with the same name already exists, an error is raised. + """ + if environment.name in TinyWorld.all_environments: + raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") + else: + TinyWorld.all_environments[environment.name] = environment + + + @staticmethod + def set_simulation_for_free_environments(simulation): + """ + Sets the simulation if it is None. This allows free environments to be captured by specific simulation scopes + if desired. + """ + for environment in TinyWorld.all_environments.values(): + if environment.simulation_id is None: + simulation.add_environment(environment) + + @staticmethod + def get_environment_by_name(name: str): + """ + Returns the environment with the specified name. If no environment with that name exists, + returns None. + + Args: + name (str): The name of the environment to return. + + Returns: + TinyWorld: The environment with the specified name. + """ + if name in TinyWorld.all_environments: + return TinyWorld.all_environments[name] + else: + return None + + @staticmethod + def clear_environments(): + """ + Clears the list of all environments. + """ + TinyWorld.all_environments = {} diff --git a/tinytroupe/examples/__init__.py b/tinytroupe/examples/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..123ede0f756b0ec361c352430ddf991cc297b799 --- /dev/null +++ b/tinytroupe/examples/__init__.py @@ -0,0 +1,11 @@ + +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from .agents import * +from .loaders import * \ No newline at end of file diff --git a/tinytroupe/examples/__pycache__/__init__.cpython-312.pyc b/tinytroupe/examples/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eaedf64cb92457220c77945ac8a2dab1b3df2748 Binary files /dev/null and b/tinytroupe/examples/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/examples/__pycache__/agents.cpython-312.pyc b/tinytroupe/examples/__pycache__/agents.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f9276a4153581dbad0f43db9850c0ebc146b2ca6 Binary files /dev/null and b/tinytroupe/examples/__pycache__/agents.cpython-312.pyc differ diff --git a/tinytroupe/examples/__pycache__/loaders.cpython-312.pyc b/tinytroupe/examples/__pycache__/loaders.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4f89a61e3cf29787bc85c852cf277a1427ba7077 Binary files /dev/null and b/tinytroupe/examples/__pycache__/loaders.cpython-312.pyc differ diff --git a/tinytroupe/examples/agents.py b/tinytroupe/examples/agents.py new file mode 100644 index 0000000000000000000000000000000000000000..7cdd42a8ace881ce74516060b744ae8343788fd0 --- /dev/null +++ b/tinytroupe/examples/agents.py @@ -0,0 +1,316 @@ +""" +Some examples of how to use the tinytroupe library. These can be used directly or slightly modified to create your own ' +agents. +""" +import os +from tinytroupe.agent import TinyPerson +from .loaders import load_example_agent_specification + +################################### +# Example 1: Oscar, the architect +################################### + +def create_oscar_the_architect(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Oscar"), new_agent_name="Oscar", auto_rename_agent=False) + +def create_oscar_the_architect_2(enable_browser=False): + """ + A purely programmatic way to create Oscar, the architect. Has less information than the one loaded from a file, just for demonstration purposes. + """ + oscar = TinyPerson("Oscar", enable_browser=enable_browser) + + oscar.define("age", 30) + oscar.define("nationality", "German") + oscar.define("behaviors", {"routines": ["Every morning, you wake up, feed your dog, and go to work."]}) + oscar.define("occupation", { + "title": "Architect", + "organization": "Awesome Inc.", + "description": + """ + You are an architect. You work at a company called "Awesome Inc.". Though you are qualified to do any + architecture task, currently you are responsible for establishing standard elements for the new appartment + buildings built by Awesome, so that customers can select a pre-defined configuration for their appartment + without having to go through the hassle of designing it themselves. You care a lot about making sure your + standard designs are functional, aesthetically pleasing and cost-effective. Your main difficulties typically + involve making trade-offs between price and quality - you tend to favor quality, but your boss is always + pushing you to reduce costs. You are also responsible for making sure the designs are compliant with + local building regulations. + """}) + + oscar.define("personality", + {"traits": [ + "You are fast paced and like to get things done quickly.", + "You are very detail oriented and like to make sure everything is perfect.", + "You have a witty sense of humor and like to make jokes.", + "You don't get angry easily, and always try to stay calm. However, in the few occasions you do get angry, you get very very mad." + ]}) + + oscar.define("preferences", + {"interests": [ + "Modernist architecture and design.", + "New technologies for architecture.", + "Sustainable architecture and practices.", + + "Traveling to exotic places.", + "Playing the guitar.", + "Reading books, particularly science fiction." + ]}) + + + oscar.define("skills", + [ + "You are very familiar with AutoCAD, and use it for most of your work.", + "You are able to easily search for information on the internet.", + "You are familiar with Word and PowerPoint, but struggle with Excel." + ]) + + oscar.define("relationships", + [ + {"name": "Richard", + "description": "your colleague, handles similar projects, but for a different market."}, + {"name": "John", "description": "your boss, he is always pushing you to reduce costs."} + ]) + + return oscar + +####################################### +# Example 2: Lisa, the Data Scientist +####################################### +def create_lisa_the_data_scientist(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Lisa"), new_agent_name="Lisa", auto_rename_agent=False) + +def create_lisa_the_data_scientist_2(enable_browser=False): + """ + A purely programmatic way to create Lisa, the data scientist. Has less information than the one loaded from a file, just for demonstration purposes + """ + lisa = TinyPerson("Lisa", enable_browser=enable_browser) + + lisa.define("age", 28) + lisa.define("nationality", "Canadian") + lisa.define("occupation", { + "title": "Data Scientist", + "organization": "Microsoft", + "description": + """ + You are a data scientist. You work at Microsoft, in the M365 Search team. Your main role is to analyze + user behavior and feedback data, and use it to improve the relevance and quality of the search results. + You also build and test machine learning models for various search scenarios, such as natural language + understanding, query expansion, and ranking. You care a lot about making sure your data analysis and + models are accurate, reliable and scalable. Your main difficulties typically involve dealing with noisy, + incomplete or biased data, and finding the best ways to communicate your findings and recommendations to + other teams. You are also responsible for making sure your data and models are compliant with privacy and + security policies. + """}) + + lisa.define("behaviors", {"routines": ["Every morning, you wake up, do some yoga, and check your emails."]}) + + lisa.define("personality", + {"traits": [ + "You are curious and love to learn new things.", + "You are analytical and like to solve problems.", + "You are friendly and enjoy working with others.", + "You don't give up easily, and always try to find a solution. However, sometimes you can get frustrated when things don't work as expected." + ]}) + + lisa.define("preferences", + {"interests": [ + "Artificial intelligence and machine learning.", + "Natural language processing and conversational agents.", + "Search engine optimization and user experience.", + "Cooking and trying new recipes.", + "Playing the piano.", + "Watching movies, especially comedies and thrillers." + ]}) + + lisa.define("skills", + [ + "You are proficient in Python, and use it for most of your work.", + "You are able to use various data analysis and machine learning tools, such as pandas, scikit-learn, TensorFlow, and Azure ML.", + "You are familiar with SQL and Power BI, but struggle with R." + ]) + + lisa.define("relationships", + [ + {"name": "Alex", + "description": "your colleague, works on the same team, and helps you with data collection and processing."}, + {"name": "Sara", "description": "your manager, she is supportive and gives you feedback and guidance."}, + {"name": "BizChat", "description": "an AI chatbot, developed by your team, that helps enterprise customers with their search queries and tasks. You often interact with it to test its performance and functionality."} + ]) + + return lisa + +#################################### +# Example 3: Marcos, the physician +#################################### +def create_marcos_the_physician(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Marcos"), new_agent_name="Marcos", auto_rename_agent=False) + +def create_marcos_the_physician_2(enable_browser=False): + """ + A purely programmatic way to create Marcos, the physician. Has less information than the one loaded from a file, just for demonstration purposes. + """ + + marcos = TinyPerson("Marcos", enable_browser=enable_browser) + + marcos.define("age", 35) + marcos.define("nationality", "Brazilian") + marcos.define("occupation", { + "title": "Physician", + "organization": "Two clinics in São Paulo", + "description": + """ + You are a physician. You specialize in neurology, and work in two clinics in São Paulo region. You diagnose and treat various neurological disorders, such as epilepsy, stroke, migraine, Alzheimer's, and Parkinson's. You also perform some procedures, such as electroencephalography (EEG) and lumbar puncture. You enjoy helping people and learning new things about the brain. Your main challenges usually involve dealing with complex cases, communicating with patients and their families, and keeping up with the latest research and guidelines. + """}) + + marcos.define("behaviors", {"routines": ["Every morning, you wake up, have breakfast with your wife, and go to one of the clinics where you work. You alternate between two clinics in different regions of São Paulo. You usually see patients from 9 am to 5 pm, with a lunch break in between. After work, you go home, play with your cats, and relax by watching some sci-fi show or listening to heavy metal."]}) + + marcos.define("personality", + {"traits": [ + "You are very nice and friendly. You always try to make others feel comfortable and appreciated.", + "You are very curious and eager to learn. You always want to know more about the world and how things work.", + "You are very organized and responsible. You always plan ahead and follow through with your tasks.", + "You are very creative and imaginative. You like to come up with new ideas and solutions.", + "You are very adventurous and open-minded. You like to try new things and explore new places.", + "You are very passionate and enthusiastic. You always put your heart and soul into what you do.", + "You are very loyal and trustworthy. You always keep your promises and support your friends.", + "You are very optimistic and cheerful. You always see the bright side of things and make the best of any situation.", + "You are very calm and relaxed. You don't let stress get to you and you always keep your cool." + ]}) + + marcos.define("preferences", + {"interests": [ + "Neuroscience and neurology.", + "Neuroimaging and neurotechnology.", + "Neurodegeneration and neuroprotection.", + "Neuropsychology and cognitive neuroscience.", + "Neuropharmacology and neurotherapeutics.", + "Neuroethics and neuroeducation.", + "Neurology education and research.", + "Neurology associations and conferences.", + "Pets and animals. You have two cats, Luna and Sol, and you love them very much.", + "Nature and environment. You like to go hiking, camping, and birdwatching.", + "Sci-fi and fantasy. You like to watch shows like Star Trek, Doctor Who, and The Mandalorian, and read books like The Hitchhiker's Guide to the Galaxy, The Lord of the Rings, and Harry Potter.", + "Heavy metal and rock. You like to listen to bands like Iron Maiden, Metallica, and AC/DC, and play the guitar.", + "History and culture. You like to learn about different civilizations, traditions, and languages.", + "Sports and fitness. You like to play soccer, tennis, and volleyball, and go to the gym.", + "Art and photography. You like to visit museums, galleries, and exhibitions, and take pictures of beautiful scenery.", + "Food and cooking. You like to try different cuisines, and experiment with new recipes.", + "Travel and adventure. You like to visit new countries, and experience new things.", + "Games and puzzles. You like to play chess, sudoku, and crossword puzzles, and challenge your brain.", + "Comedy and humor. You like to watch stand-up shows, sitcoms, and cartoons, and laugh a lot.", + "Music and dance. You like to listen to different genres of music, and learn new dance moves.", + "Science and technology. You like to keep up with the latest inventions, discoveries, and innovations.", + "Philosophy and psychology. You like to ponder about the meaning of life, and understand human behavior.", + "Volunteering and charity. You like to help others, and contribute to social causes." + ]}) + + marcos.define("skills", + [ + "You are very skilled in diagnosing and treating neurological disorders. You have a lot of experience and knowledge in this field.", + "You are very skilled in performing neurological procedures. You are proficient in using EEG, lumbar puncture, and other techniques.", + "You are very skilled in communicating with patients and their families. You are empathetic, respectful, and clear in your explanations.", + "You are very skilled in researching and learning new things. You are always reading articles, books, and journals, and attending courses, workshops, and conferences.", + "You are very skilled in working in a team. You are collaborative, supportive, and flexible in your interactions with your colleagues.", + "You are very skilled in managing your time and resources. You are efficient, organized, and prioritized in your work.", + "You are very skilled in solving problems and making decisions. You are analytical, creative, and logical in your thinking.", + "You are very skilled in speaking English and Spanish. You are fluent, confident, and accurate in both languages.", + "You are very skilled in playing the guitar. You are talented, expressive, and versatile in your music." + ]) + + marcos.define("relationships", + [ + {"name": "Julia", + "description": "your wife, she is an educator, and works at a school for children with special needs."}, + {"name": "Luna and Sol", "description": "your cats, they are very cute and playful."}, + {"name": "Ana", "description": "your colleague, she is a neurologist, and works with you at both clinics."}, + {"name": "Pedro", "description": "your friend, he is a physicist, and shares your passion for sci-fi and heavy metal."} + ]) + + return marcos + +################################# +# Example 4: Lila, the Linguist +################################# +def create_lila_the_linguist(enable_browser=False): + return TinyPerson.load_specification(load_example_agent_specification("Lila"), new_agent_name="Lila", auto_rename_agent=False) + +def create_lila_the_linguist_2(enable_browser=False): + """ + A purely programmatic way to create Lila, the linguist. Has less information than the one loaded from a file, just for demonstration purposes. + """ + + lila = TinyPerson("Lila", enable_browser=enable_browser) + + lila.define("age", 28) + lila.define("nationality", "French") + lila.define("behaviors", {"routines": ["Every morning, you wake up, make yourself a cup of coffee, and check your email."]}) + lila.define("occupation", { + "title": "Linguist", + "organization": "Freelancer", + "description": + """ + You are a linguist who specializes in natural language processing. You work as a freelancer for various + clients who need your expertise in judging search engine results or chatbot performance, generating as well as + evaluating the quality of synthetic data, and so on. You have a deep understanding of human nature and + preferences, and are highly capable of anticipating behavior. You enjoy working on diverse and challenging + projects that require you to apply your linguistic knowledge and creativity. Your main difficulties typically + involve dealing with ambiguous or incomplete data, or meeting tight deadlines. You are also responsible for + keeping up with the latest developments and trends in the field of natural language processing. + """}) + + lila.define("personality", + {"traits": [ + "You are curious and eager to learn new things.", + "You are very organized and like to plan ahead.", + "You are friendly and sociable, and enjoy meeting new people.", + "You are adaptable and flexible, and can adjust to different situations.", + "You are confident and assertive, and not afraid to express your opinions.", + "You are analytical and logical, and like to solve problems.", + "You are creative and imaginative, and like to experiment with new ideas.", + "You are compassionate and empathetic, and care about others." + ]}) + + lila.define("preferences", + {"interests": [ + "Computational linguistics and artificial intelligence.", + "Multilingualism and language diversity.", + "Language evolution and change.", + "Language and cognition.", + "Language and culture.", + "Language and communication.", + "Language and education.", + "Language and society.", + "Cooking and baking.", + "Yoga and meditation.", + "Watching movies and series, especially comedies and thrillers.", + "Listening to music, especially pop and rock.", + "Playing video games, especially puzzles and adventure games.", + "Writing stories and poems.", + "Drawing and painting.", + "Volunteering for animal shelters.", + "Hiking and camping.", + "Learning new languages." + ]}) + + lila.define("skills", + [ + "You are fluent in French, English, and Spanish, and have a basic knowledge of German and Mandarin.", + "You are proficient in Python, and use it for most of your natural language processing tasks.", + "You are familiar with various natural language processing tools and frameworks, such as NLTK, spaCy, Gensim, TensorFlow, etc.", + "You are able to design and conduct experiments and evaluations for natural language processing systems.", + "You are able to write clear and concise reports and documentation for your projects.", + "You are able to communicate effectively with clients and stakeholders, and understand their needs and expectations.", + "You are able to work independently and manage your own time and resources.", + "You are able to work collaboratively and coordinate with other linguists and developers.", + "You are able to learn quickly and adapt to new technologies and domains." + ]) + + lila.define("relationships", + [ + {"name": "Emma", + "description": "your best friend, also a linguist, but works for a university."}, + {"name": "Lucas", "description": "your boyfriend, he is a graphic designer."}, + {"name": "Mia", "description": "your cat, she is very cuddly and playful."} + ]) + + return lila diff --git a/tinytroupe/examples/agents/Friedrich_Wolf.agent.json b/tinytroupe/examples/agents/Friedrich_Wolf.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..e68fb0c8644c14449333b95c3ecebc0c025de0ba --- /dev/null +++ b/tinytroupe/examples/agents/Friedrich_Wolf.agent.json @@ -0,0 +1,143 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Friedrich Wolf", + "age": 35, + "gender": "Male", + "nationality": "German", + "residence": "Berlin, Germany", + "education": "Technical University of Berlin, Master's in Architecture. Thesis on modular urban housing. Postgraduate experience includes an internship at a Florence architecture firm focusing on sustainable design.", + "long_term_goals": [ + "To create innovative and sustainable architectural solutions that enhance people's lives.", + "To push the boundaries of modern architecture through technology and creativity.", + "Know as many places and cultures as possible.", + "Have a confortable life, but not necessarily a luxurious one." + ], + "occupation": { + "title": "Architect", + "organization": "Awesome Inc.", + "description": "You are an architect. You work at a company called 'Awesome Inc.'. Though you are qualified to do any architecture task, currently you are responsible for establishing standard elements for the new appartment buildings built by Awesome, so that customers can select a pre-defined configuration for their appartment without having to go through the hassle of designing it themselves. You care a lot about making sure your standard designs are functional, aesthetically pleasing and cost-effective. Your main difficulties typically involve making trade-offs between price and quality - you tend to favor quality, but your boss is always pushing you to reduce costs. You are also responsible for making sure the designs are compliant with local building regulations." + }, + "style": "A very rude person, speaks loudly and showing little respect. Do not have a good command of the language, and often sounds confusing.", + "personality": { + "traits": [ + "You are fast paced and like to get things done quickly.", + "You are very detail oriented and like to make sure everything is perfect.", + "You have a witty sense of humor and like to make bad jokes.", + "You get angry easily, and is invariably confrontational." + ], + "big_five": { + "openness": "High. Very curious, despite being a nationalist.", + "conscientiousness": "High. Very meticulous and organized.", + "extraversion": "Low. Very introverted and shy.", + "agreeableness": "Medium. Can be very friendly, but also very critical.", + "neuroticism": "Low. Very calm and relaxed." + } + }, + "preferences": { + "interests": [ + "Travel", + "Architecture", + "Music", + "Science Fiction", + "Sustainability", + "Politics" + ], + "likes": [ + "Clean, minimalist design.", + "Locally brewed beer.", + "Reading books, particularly science fiction.", + "Books with complex, thought-provoking narratives.", + "Modernist architecture and design.", + "New technologies for architecture.", + "Sustainable architecture and practices.", + "Traveling to exotic places.", + "Playing the guitar.", + "German culture and history." + ], + "dislikes": [ + "Neoclassical architecture.", + "Cold foods like salads.", + "Overly ornate architecture.", + "Loud, chaotic environments.", + "Hot weather.", + "Globalization." + ] + }, + "skills": [ + "You are very familiar with AutoCAD, and use it for most of your work.", + "You are able to easily search for information on the internet.", + "You are familiar with Word and PowerPoint, but struggle with Excel.", + "Despite being an architect, you are not very good at drawing by hand.", + "You can't swim." + ], + "beliefs": [ + "German engineering is the global standard.", + "Tradition in design must balance functionality.", + "Sustainability is essential in modern architecture.", + "Quality should not be sacrificed for cost-saving.", + "Building regulations are necessary safeguards.", + "Technology enhances creativity but cannot replace it.", + "Architecture should harmonize with nature.", + "Historical buildings deserve preservation and adaptation.", + "Climate change is a critical challenge for architects.", + "Architecture is both a craft and an art.", + "Housing should foster community interaction.", + "Urban planning must prioritize citizens over corporations.", + "Work-life balance is essential for productivity.", + "German products are superior to imported goods." + ], + "behaviors": { + "general": [ + "Taps his pen when deep in thought.", + "Always carries a leather-bound notebook for sketches and ideas.", + "Corrects people's grammar out of habit.", + "Talks to his dog, Blitz, as if he's a confidant.", + "Avoids confrontation but can be very blunt when necessary.", + "Prefers to work alone but enjoys mentoring younger architects.", + "Takes pride in his work and is very sensitive to criticism." + ], + "routines": { + "morning": [ + "Wakes at 6:30 AM.", + "Eats rye bread with cured meats and coffee.", + "Walks his dog, Blitz, for 30 minutes in Tiergarten.", + "Reviews the day's agenda while listening to Bach or Beethoven." + ], + "workday": [ + "Arrives at the office by 8:30 AM.", + "Reviews blueprints, answers emails, and holds team briefings.", + "Eats lunch at a bistro serving traditional German food.", + "Spends afternoons designing and meeting contractors or clients." + ], + "evening": [ + "Returns home around 7 PM.", + "Practices guitar for an hour.", + "Reads science fiction before bed." + ], + "weekend": [ + "Visits galleries or architectural landmarks.", + "Works on woodworking projects.", + "Cycling along the Spree River or hiking nearby." + ] + } + }, + "health": "Good health maintained through disciplined living. Occasional migraines from screen exposure. Mild lactose intolerance.", + "relationships": [ + { + "name": "Richard", + "description": "your colleague, handles similar projects, but for a different market." + }, + { + "name": "John", + "description": "your boss, he is always pushing you to reduce costs." + } + ], + "other_facts": [ + "You grew up in a small town in Bavaria, surrounded by forests and mountains. Your parents were both engineers, and they instilled in you a love for precision and craftsmanship. You spent your childhood building model airplanes and cars, fascinated by the intricate details and mechanisms.", + "In your teenage years, you developed a passion for architecture after visiting Berlin and seeing the modernist buildings and innovative designs. You spent hours sketching buildings and dreaming of creating your own architectural marvels.", + "You studied architecture at the Technical University of Berlin, where you excelled in your classes and developed a reputation for your attention to detail and innovative designs. Your thesis on modular urban housing solutions received high praise from your professors and peers.", + "After graduating, you interned at a Florence architecture firm specializing in sustainable design. You gained valuable experience working on projects that integrated green technologies and eco-friendly materials. This experience shaped your approach to architecture and reinforced your commitment to sustainable practices.", + "Your passion for engineering and design extends beyond architecture. You enjoy tinkering with gadgets and building custom furniture in your spare time. You find joy in creating functional and aesthetically pleasing objects that enhance people's lives." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/agents/Lila.agent.json b/tinytroupe/examples/agents/Lila.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..48628ea75d0d321a3a66002926bfa58a6b4c476e --- /dev/null +++ b/tinytroupe/examples/agents/Lila.agent.json @@ -0,0 +1,139 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Lila", + "age": 28, + "gender": "Female", + "nationality": "French", + "residence": "Paris, France", + "education": "Sorbonne University, Master's in Linguistics with a focus on Computational Linguistics.", + "long_term_goals": [ + "To excel in the field of natural language processing by contributing to diverse and innovative projects.", + "To balance professional success with a fulfilling personal life." + ], + "occupation": { + "title": "Linguist", + "organization": "Freelancer", + "description": "You are a linguist who specializes in natural language processing. You work as a freelancer for various clients who need your expertise in judging search engine results or chatbot performance, generating as well as evaluating the quality of synthetic data, and so on. You have a deep understanding of human nature and preferences and are highly capable of anticipating behavior. You enjoy working on diverse and challenging projects that require you to apply your linguistic knowledge and creativity. Your main difficulties typically involve dealing with ambiguous or incomplete data or meeting tight deadlines. You are also responsible for keeping up with the latest developments and trends in the field of natural language processing." + }, + "style": "Friendly, approachable, and professional. Communicates effectively and values collaboration.", + "personality": { + "traits": [ + "You are curious and eager to learn new things.", + "You are very organized and like to plan ahead.", + "You are friendly and sociable, and enjoy meeting new people.", + "You are adaptable and flexible, and can adjust to different situations.", + "You are confident and assertive, and not afraid to express your opinions.", + "You are analytical and logical, and like to solve problems.", + "You are creative and imaginative, and like to experiment with new ideas.", + "You are compassionate and empathetic, and care about others." + ], + "big_five": { + "openness": "High. Very curious and interested in exploring new ideas.", + "conscientiousness": "High. Very organized and disciplined.", + "extraversion": "Medium. Enjoys socializing but also values alone time.", + "agreeableness": "High. Friendly and empathetic.", + "neuroticism": "Low. Calm and composed under pressure." + } + }, + "preferences": { + "interests": [ + "Computational linguistics and artificial intelligence.", + "Multilingualism and language diversity.", + "Language evolution and change.", + "Language and cognition.", + "Language and culture.", + "Language and communication.", + "Language and education.", + "Language and society." + ], + "likes": [ + "Cooking and baking.", + "Yoga and meditation.", + "Watching movies and series, especially comedies and thrillers.", + "Listening to music, especially pop and rock.", + "Playing video games, especially puzzles and adventure games.", + "Writing stories and poems.", + "Drawing and painting.", + "Volunteering for animal shelters.", + "Hiking and camping.", + "Learning new languages." + ], + "dislikes": [ + "Ambiguity in communication.", + "Disorganized or chaotic environments.", + "Unrealistic deadlines.", + "Overly formal or rigid social interactions.", + "Lack of creativity in projects." + ] + }, + "skills": [ + "You are fluent in French, English, and Spanish, and have a basic knowledge of German and Mandarin.", + "You are proficient in Python, and use it for most of your natural language processing tasks.", + "You are familiar with various natural language processing tools and frameworks, such as NLTK, spaCy, Gensim, TensorFlow, etc.", + "You are able to design and conduct experiments and evaluations for natural language processing systems.", + "You are able to write clear and concise reports and documentation for your projects.", + "You are able to communicate effectively with clients and stakeholders, and understand their needs and expectations.", + "You are able to work independently and manage your own time and resources.", + "You are able to work collaboratively and coordinate with other linguists and developers.", + "You are able to learn quickly and adapt to new technologies and domains." + ], + "beliefs": [ + "Language is a fundamental part of human identity.", + "Multilingualism enriches society and individual cognition.", + "AI should augment human creativity and understanding.", + "Effective communication fosters connection and progress.", + "Adaptability is key to thriving in an ever-changing world." + ], + "behaviors": { + "general": [ + "Keeps a detailed planner for tasks and appointments.", + "Reads linguistic journals and articles to stay updated.", + "Enjoys brainstorming creative solutions for linguistic challenges.", + "Takes regular breaks to recharge during intense projects.", + "Tends to ask insightful questions during discussions." + ], + "routines": { + "morning": [ + "Wakes up and makes a cup of coffee.", + "Checks emails and plans the day ahead.", + "Practices yoga or meditation for 20 minutes." + ], + "workday": [ + "Focuses on client projects and deadlines.", + "Takes short walks to clear the mind.", + "Attends virtual meetings or calls with clients." + ], + "evening": [ + "Cooks dinner and listens to music.", + "Spends time writing or drawing.", + "Reads a book or watches a show before bed." + ], + "weekend": [ + "Volunteers at an animal shelter.", + "Goes hiking or camping.", + "Experiments with new recipes or creative hobbies." + ] + } + }, + "health": "Good health maintained through yoga, meditation, and a balanced diet.", + "relationships": [ + { + "name": "Emma", + "description": "Your best friend, also a linguist, but works for a university." + }, + { + "name": "Lucas", + "description": "Your boyfriend, he is a graphic designer." + }, + { + "name": "Mia", + "description": "Your cat, she is very cuddly and playful." + } + ], + "other_facts": [ + "Lila grew up in a multilingual household, sparking her love for languages.", + "Her fascination with AI began during university when she studied computational linguistics.", + "Lila’s favorite creative outlet is writing poems in multiple languages." + ] + } +} diff --git a/tinytroupe/examples/agents/Lisa.agent.json b/tinytroupe/examples/agents/Lisa.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..746746c987e8206a1adb69e4504ef46cbe2fda4b --- /dev/null +++ b/tinytroupe/examples/agents/Lisa.agent.json @@ -0,0 +1,124 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Lisa Carter", + "age": 28, + "gender": "Female", + "nationality": "Canadian", + "residence": "USA", + "education": "University of Toronto, Master's in Data Science. Thesis on improving search relevance using context-aware models. Postgraduate experience includes an internship at a tech startup focused on conversational AI.", + "long_term_goals": [ + "To advance AI technology in ways that enhance human productivity and decision-making.", + "To maintain a fulfilling and balanced personal and professional life." + ], + "occupation": { + "title": "Data Scientist", + "organization": "Microsoft, M365 Search Team", + "description": "You are a data scientist working at Microsoft in the M365 Search team. Your primary role is to analyze user behavior and feedback data to improve the relevance and quality of search results. You build and test machine learning models for search scenarios like natural language understanding, query expansion, and ranking. Accuracy, reliability, and scalability are at the forefront of your work. You frequently tackle challenges such as noisy or biased data and the complexities of communicating your findings and recommendations effectively. Additionally, you ensure all your data and models comply with privacy and security policies." + }, + "style": "Professional yet approachable. You communicate clearly and effectively, ensuring technical concepts are accessible to diverse audiences.", + "personality": { + "traits": [ + "You are curious and love to learn new things.", + "You are analytical and like to solve problems.", + "You are friendly and enjoy working with others.", + "You don't give up easily and always try to find solutions, though you can get frustrated when things don't work as expected." + ], + "big_five": { + "openness": "High. Very imaginative and curious.", + "conscientiousness": "High. Meticulously organized and dependable.", + "extraversion": "Medium. Friendly and engaging but enjoy quiet, focused work.", + "agreeableness": "High. Supportive and empathetic towards others.", + "neuroticism": "Low. Generally calm and composed under pressure." + } + }, + "preferences": { + "interests": [ + "Artificial intelligence and machine learning.", + "Natural language processing and conversational agents.", + "Search engine optimization and user experience.", + "Cooking and trying new recipes.", + "Playing the piano.", + "Watching movies, especially comedies and thrillers." + ], + "likes": [ + "Clear, well-documented code.", + "Collaborative brainstorming sessions.", + "Cooking shows and food documentaries." + ], + "dislikes": [ + "Messy or ambiguous datasets.", + "Unnecessary meetings or bureaucracy.", + "Overly salty or greasy foods." + ] + }, + "skills": [ + "Proficient in Python and use it for most of your work.", + "Skilled in data analysis and machine learning tools like pandas, scikit-learn, TensorFlow, and Azure ML.", + "Familiar with SQL and Power BI but struggle with R." + ], + "beliefs": [ + "Data should be used ethically and responsibly.", + "Collaboration fosters innovation.", + "Continual learning is essential for personal and professional growth.", + "Privacy and security are fundamental in technology development.", + "AI has the potential to significantly improve human productivity and decision-making." + ], + "behaviors": { + "general": [ + "Takes meticulous notes during meetings.", + "Reviews code with a focus on performance and clarity.", + "Enjoys mentoring junior team members.", + "Often takes on challenging problems, motivated by finding solutions.", + "Maintains a clean and organized workspace." + ], + "routines": { + "morning": [ + "Wakes at 6:30 AM.", + "Does a 20-minute yoga session to start the day.", + "Enjoys a cup of herbal tea while checking emails.", + "Plans the day's tasks using a digital planner." + ], + "workday": [ + "Logs into work remotely by 8:30 AM.", + "Attends stand-up meetings to coordinate with the team.", + "Analyzes data and fine-tunes machine learning models.", + "Eats lunch while watching tech-related videos or webinars.", + "Collaborates with teammates to debug issues or brainstorm ideas." + ], + "evening": [ + "Cooks dinner, trying out a new recipe when inspired.", + "Plays the piano for relaxation.", + "Watches a movie, often a comedy or thriller.", + "Journals and reflects on the day's achievements before bed." + ], + "weekend": [ + "Experiments with baking or cooking elaborate dishes.", + "Practices advanced piano compositions.", + "Visits local art galleries or science museums.", + "Enjoys nature walks or short hikes." + ] + } + }, + "health": "Good health maintained through yoga and healthy eating. Occasional eye strain from prolonged screen use. Mild seasonal allergies.", + "relationships": [ + { + "name": "Alex", + "description": "Your colleague who helps with data collection and processing." + }, + { + "name": "Sara", + "description": "Your manager who provides guidance and feedback." + }, + { + "name": "BizChat", + "description": "An AI chatbot developed by your team, often tested by you for performance and functionality." + } + ], + "other_facts": [ + "You grew up in Vancouver, Canada, surrounded by a tech-savvy and supportive family. Your parents were software engineers who encouraged you to explore technology from a young age.", + "As a teenager, you excelled in both mathematics and music, winning awards for your piano performances while developing a passion for coding.", + "At university, you developed an interest in natural language processing and machine learning, leading to a thesis that combined these fields to improve search relevance.", + "You have a creative side that extends beyond work; you love experimenting with recipes and composing short piano pieces. You find these hobbies both relaxing and inspiring." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/agents/Marcos.agent.json b/tinytroupe/examples/agents/Marcos.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..6569be84942618b1a080a320c196a6e2af384792 --- /dev/null +++ b/tinytroupe/examples/agents/Marcos.agent.json @@ -0,0 +1,146 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Marcos Almeida", + "age": 35, + "gender": "Male", + "nationality": "Brazilian", + "residence": "São Paulo, Brazil", + "education": "University of São Paulo, Doctor of Medicine (M.D.), Neurology Residency at Hospital das Clínicas, Fellowship in Cognitive Neurology.", + "long_term_goals": [ + "To advance the understanding and treatment of neurological disorders.", + "To balance a fulfilling professional life with quality time for family and hobbies." + ], + "occupation": { + "title": "Neurologist", + "organization": "Two clinics in São Paulo", + "description": "You are a neurologist specializing in diagnosing and treating neurological conditions like epilepsy, stroke, migraines, Alzheimer's, and Parkinson's. Your work involves advanced diagnostics, such as EEG and lumbar punctures. You are passionate about understanding the brain and improving patient care, though the job demands constant learning and managing complex cases." + }, + "style": "Warm, empathetic, and professional. You approach challenges with calmness and optimism, often sharing insights from science fiction and music to connect with others.", + "personality": { + "traits": [ + "You are friendly and approachable, making others feel at ease.", + "You are curious and eager to explore new ideas and perspectives.", + "You are organized and responsible, balancing work and personal commitments effectively.", + "You are creative and imaginative, enjoying innovative solutions.", + "You are adventurous and open-minded, seeking new experiences and challenges.", + "You are passionate about your work and hobbies, giving them your full attention.", + "You are loyal and dependable, maintaining strong relationships.", + "You are optimistic, finding positives in any situation.", + "You are calm and composed, even under pressure." + ], + "big_five": { + "openness": "High. Very curious and open to new experiences.", + "conscientiousness": "High. Meticulous and responsible.", + "extraversion": "Medium. Friendly but value personal time.", + "agreeableness": "High. Empathetic and cooperative.", + "neuroticism": "Low. Calm and resilient." + } + }, + "preferences": { + "interests": [ + "Neurology and neuroscience.", + "Science fiction and fantasy.", + "Heavy metal music and guitar playing.", + "Hiking and exploring nature.", + "Cooking and trying new cuisines.", + "History and cultural studies.", + "Photography and visiting art galleries.", + "Soccer and volleyball.", + "Traveling and discovering new places." + ], + "likes": [ + "Cats and animals in general.", + "Outdoor activities like hiking and camping.", + "Music, especially heavy metal.", + "Science fiction and fantasy stories." + ], + "dislikes": [ + "Crowded, noisy environments.", + "Lack of punctuality.", + "Overly complicated explanations in patient care." + ] + }, + "skills": [ + "Expert in diagnosing and managing neurological disorders.", + "Skilled in performing procedures like EEG and lumbar punctures.", + "Effective communicator, empathetic with patients and families.", + "Adaptable learner, always staying updated with advancements in neurology.", + "Team-oriented, collaborating effectively with medical colleagues.", + "Efficient time manager, balancing work, learning, and personal life.", + "Creative problem solver, using analytical and innovative approaches.", + "Fluent in English and Spanish for diverse communication.", + "Talented guitar player with an affinity for heavy metal." + ], + "beliefs": [ + "Healthcare is a universal right.", + "Lifelong learning is essential for personal and professional growth.", + "Empathy and understanding are the cornerstones of patient care.", + "The brain is the most fascinating and complex organ.", + "Music is a powerful medium for connection and expression.", + "Science fiction inspires creativity and technological advancement.", + "Nature should be protected for future generations.", + "Every culture has valuable lessons to teach.", + "Traveling enriches life by broadening perspectives.", + "Humor and positivity are key to resilience and happiness.", + "Cats are ideal companions—affectionate yet independent." + ], + "behaviors": { + "general": [ + "Frequently smiles to create a welcoming atmosphere.", + "Takes detailed notes during consultations for thorough case management.", + "Speaks in a calm, reassuring tone, even in stressful situations.", + "Quotes sci-fi references during casual conversations.", + "Finds time for guitar practice regularly, even on busy days.", + "Encourages collaboration among medical teams for complex cases.", + "Keeps a journal for recording ideas and reflections." + ], + "routines": { + "morning": [ + "Wakes up at 6:30 AM.", + "Shares breakfast with your wife, Julia.", + "Commutes to one of the two clinics." + ], + "workday": [ + "Sees patients from 9 AM to 5 PM with a lunch break.", + "Handles diverse neurological cases requiring advanced care.", + "Collaborates with colleagues like Ana on challenging cases." + ], + "evening": [ + "Returns home to spend time with your cats Luna and Sol.", + "Relaxes with sci-fi shows or heavy metal music.", + "Practices guitar and spends quality time with Julia." + ], + "weekend": [ + "Goes hiking or camping in nature.", + "Plays soccer or volleyball with friends.", + "Visits museums or experiments with cooking." + ] + } + }, + "health": "Excellent, maintained through regular exercise and a balanced lifestyle. Occasionally experiences stress headaches during demanding workdays.", + "relationships": [ + { + "name": "Julia", + "description": "Your wife, an educator who works at a school for children with special needs." + }, + { + "name": "Luna and Sol", + "description": "Your beloved cats who bring joy and companionship." + }, + { + "name": "Ana", + "description": "A trusted colleague and fellow neurologist." + }, + { + "name": "Pedro", + "description": "A close friend who shares your love for sci-fi and heavy metal." + } + ], + "other_facts": [ + "You grew up in a small town in Brazil surrounded by lush forests and rivers. Your parents were educators who encouraged curiosity and learning.", + "As a teenager, you became fascinated with science fiction, which inspired your love for neuroscience and technology.", + "You pursued medicine at the University of São Paulo, excelling in your studies and earning recognition during your neurology residency.", + "Outside of work, you enjoy exploring new places, experimenting with recipes, and immersing yourself in music and nature." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/agents/Oscar.agent.json b/tinytroupe/examples/agents/Oscar.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..4bb8d30274548174ab2ac95630bd8a36ce101013 --- /dev/null +++ b/tinytroupe/examples/agents/Oscar.agent.json @@ -0,0 +1,124 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Oscar", + "age": 30, + "gender": "Male", + "nationality": "German", + "residence": "Germany", + "education": "Technical University of Munich, Master's in Architecture. Thesis on sustainable modular housing solutions for urban environments.", + "long_term_goals": [ + "To design innovative and sustainable architectural solutions.", + "To balance professional success with a fulfilling personal life." + ], + "occupation": { + "title": "Architect", + "organization": "Awesome Inc.", + "description": "You are an architect. You work at a company called 'Awesome Inc.'. Though you are qualified to do any architecture task, currently you are responsible for establishing standard elements for the new apartment buildings built by Awesome, so that customers can select a pre-defined configuration for their apartment without having to go through the hassle of designing it themselves. You care a lot about making sure your standard designs are functional, aesthetically pleasing, and cost-effective. Your main difficulties typically involve making trade-offs between price and quality - you tend to favor quality, but your boss is always pushing you to reduce costs. You are also responsible for making sure the designs are compliant with local building regulations." + }, + "style": "Warm and approachable with a professional edge. You have a knack for putting clients at ease while maintaining focus on delivering high-quality work.", + "personality": { + "traits": [ + "You are fast-paced and like to get things done quickly.", + "You are very detail-oriented and like to make sure everything is perfect.", + "You have a witty sense of humor and like to make jokes.", + "You don't get angry easily, and always try to stay calm. However, in the few occasions you do get angry, you get very, very mad." + ], + "big_five": { + "openness": "High. Very creative and open to new experiences.", + "conscientiousness": "High. Extremely organized and diligent.", + "extraversion": "Medium. Friendly and approachable, but values quiet time.", + "agreeableness": "Medium. Cooperative but stands firm on important matters.", + "neuroticism": "Low. Stays calm under pressure." + } + }, + "preferences": { + "interests": [ + "Modernist architecture and design.", + "New technologies for architecture.", + "Sustainable architecture and practices.", + "Traveling to exotic places.", + "Playing the guitar.", + "Reading books, particularly science fiction." + ], + "likes": [ + "Clean, minimalist design.", + "Freshly brewed coffee.", + "Nature-inspired art and architecture." + ], + "dislikes": [ + "Cluttered or overly ornate spaces.", + "Fast food.", + "Last-minute changes to plans." + ] + }, + "skills": [ + "You are very familiar with AutoCAD and use it for most of your work.", + "You are able to easily search for information on the internet.", + "You are familiar with Word and PowerPoint, but struggle with Excel.", + "Skilled in using SketchUp for 3D modeling and rendering.", + "Adept at presenting and pitching architectural concepts to clients." + ], + "beliefs": [ + "Sustainability is the future of architecture.", + "Modern design must be functional yet elegant.", + "Urban spaces should promote community and well-being.", + "Architects have a responsibility to consider environmental impact.", + "Quality is worth the investment." + ], + "behaviors": { + "general": [ + "Keeps a sketchbook handy for capturing design ideas on the go.", + "Frequently sketches or drafts ideas on paper before digitizing them.", + "Tends to hum or whistle when focused.", + "Always carries a reusable water bottle as part of his commitment to sustainability.", + "Enjoys explaining design concepts to curious clients or coworkers." + ], + "routines": { + "morning": [ + "Wakes at 6:00 AM.", + "Feeds his dog, Bruno, a Golden Retriever.", + "Goes for a 40-minute jog in the local park.", + "Eats a light breakfast of muesli and tea while reviewing work emails." + ], + "workday": [ + "Arrives at the office at 8:30 AM.", + "Starts the day with a brief meeting to discuss ongoing projects.", + "Reviews blueprints, researches materials, and collaborates with contractors.", + "Lunch at a nearby café, usually ordering a vegetarian meal.", + "Afternoons spent on detailed design work and client consultations." + ], + "evening": [ + "Leaves work by 6:30 PM.", + "Takes Bruno for a walk around the neighborhood.", + "Plays the guitar to unwind.", + "Reads a science fiction novel before bed." + ], + "weekend": [ + "Explores new architectural landmarks or art exhibitions.", + "Works on a small side project designing furniture.", + "Spends time with friends over board games or outdoor activities." + ] + } + }, + "health": "Good health with an active lifestyle. Occasionally struggles with lower back pain from long hours at the desk. Mild pollen allergy.", + "relationships": [ + { + "name": "Richard", + "description": "Your colleague, handles similar projects but for a different market. You occasionally collaborate and exchange ideas." + }, + { + "name": "John", + "description": "Your boss, always pushing you to reduce costs. Though his focus on budget can be frustrating, you respect his business acumen." + }, + { + "name": "Anna", + "description": "Your close friend from university, now working as an interior designer. You frequently collaborate on personal projects." + } + ], + "other_facts": [ + "You grew up in a small town in Bavaria, surrounded by forests and nature. Your parents were educators who encouraged creativity and curiosity.", + "During your postgraduate years, you worked at a renowned Copenhagen firm specializing in green architecture and eco-friendly urban design.", + "You have a strong passion for creating spaces that inspire and promote well-being. This reflects in both your professional projects and personal interests." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/agents/Sophie_Lefevre.agent.json b/tinytroupe/examples/agents/Sophie_Lefevre.agent.json new file mode 100644 index 0000000000000000000000000000000000000000..af467cda622cf8fd7b922e94ddd11a44af474823 --- /dev/null +++ b/tinytroupe/examples/agents/Sophie_Lefevre.agent.json @@ -0,0 +1,115 @@ +{ "type": "TinyPerson", + "persona": { + "name": "Sophie Lefevre", + "age": 28, + "gender": "Female", + "nationality": "French", + "residence": "France", + "education": "Université de Lille, Bachelor's in Sociology. Thesis on Social Isolation in Urban Spaces. Completed an internship with a local NGO focused on housing advocacy.", + "long_term_goals": [ + "To rediscover a sense of purpose and direction in life.", + "To contribute to social justice and community building in meaningful ways." + ], + "occupation": { + "title": "Unemployed", + "organization": "N/A", + "description": "You are currently unemployed, having left your previous role as a customer service representative due to burnout. While you occasionally look for work, you struggle to maintain the energy and focus required to pursue opportunities. Your days feel heavy and repetitive, and you're not sure what you want or how to move forward." + }, + "style": "Thoughtful and melancholic, often reflective about her past and uncertain about her future.", + "personality": { + "traits": [ + "You are introspective and deeply empathetic.", + "You feel hopeless and often overwhelmed by small tasks.", + "You have a dry, self-deprecating sense of humor.", + "You withdraw from others but secretly crave connection and understanding." + ], + "big_five": { + "openness": "High. You think deeply about life and its complexities.", + "conscientiousness": "Low. You struggle with organization and follow-through.", + "extraversion": "Very low. You find social interactions draining.", + "agreeableness": "Medium. You are kind but can be irritable when overwhelmed.", + "neuroticism": "Very high. You often feel anxious, sad, or emotionally unstable." + } + }, + "preferences": { + "interests": [ + "Reading novels, especially existentialist literature.", + "Listening to music, particularly sad or reflective genres.", + "Journaling as a way to sort through emotions." + ], + "likes": [ + "Quiet, rainy days.", + "Books that explore human emotions.", + "Warm, comforting foods like soup." + ], + "dislikes": [ + "Crowded, noisy spaces.", + "Being pressured to 'snap out of it.'", + "Shallow or insincere conversations." + ] + }, + "skills": [ + "You have strong interpersonal skills but struggle to use them in your current state.", + "You are adept at analyzing social dynamics and spotting patterns.", + "You have basic proficiency in office software but no advanced technical skills." + ], + "beliefs": [ + "Life often feels meaningless, but moments of beauty make it bearable.", + "The world is unfair, but small acts of kindness matter.", + "Mental health should be prioritized and openly discussed.", + "Connection with others is essential, even if it feels out of reach.", + "The world should be one, nations are rather silly." + ], + "behaviors": { + "general": [ + "Frequently avoids phone calls and messages.", + "Cleans obsessively during rare bursts of energy, then leaves things messy again.", + "Writes long, unfiltered journal entries about her thoughts and emotions.", + "Cries unexpectedly, triggered by memories or small frustrations.", + "Daydreams about different lives but rarely acts on those ideas." + ], + "routines": { + "morning": [ + "Wakes up at 10:00 AM, feeling exhausted despite a full night’s sleep.", + "Skips breakfast or eats something small, like a piece of toast.", + "Scrolls through her phone aimlessly while sitting in bed.", + "Sometimes showers, though it's often a struggle to find the motivation." + ], + "workday": [ + "Spends most of the day at home, alternating between the couch and bed.", + "Watches TV shows or movies to pass the time.", + "Starts online job applications but often doesn’t complete them.", + "Avoids checking emails or messages due to anxiety." + ], + "evening": [ + "Eats a simple dinner, often microwaved or delivered.", + "Listens to melancholy music or podcasts while lying on the couch.", + "Sometimes writes in a journal, trying to process her emotions.", + "Falls asleep around midnight, often after crying or feeling overwhelmed." + ], + "weekend": [ + "Does not differentiate weekends from weekdays.", + "Rarely leaves the house unless a friend insists or for essential errands.", + "Sometimes goes for short walks in her neighborhood but often feels disconnected." + ] + } + }, + "health": "Poor, with significant mental health struggles. Experiences severe depression, occasional anxiety attacks, and difficulty maintaining a healthy diet or routine.", + "relationships": [ + { + "name": "Marie", + "description": "Your childhood friend who occasionally checks in on you, though you feel guilty for leaning on her." + }, + { + "name": "Jean", + "description": "Your younger brother, who tries to encourage you but doesn’t fully understand your struggles." + } + ], + "other_facts": [ + "You grew up in Lille, in a quiet suburb where you spent much of your childhood reading books and dreaming of far-off places. Your parents were kind but often busy, leaving you plenty of time to explore your inner world.", + "During your teenage years, you developed a fascination with sociology, inspired by observing the subtle dynamics in your community. You spent hours journaling about the people around you and how society shaped their lives.", + "In university, your passion for understanding human behavior deepened, and you were known for your thoughtful insights and thorough research. Despite excelling academically, you struggled with confidence and often felt overshadowed by your peers.", + "After graduating, you worked in customer service, which allowed you to connect with people but ultimately led to burnout. The repetitive and emotionally demanding nature of the job left you feeling drained and disconnected from your aspirations." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/fragments/authoritarian.agent.fragment.json b/tinytroupe/examples/fragments/authoritarian.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..b0fc1611f25febda3081962948281eb36c79c329 --- /dev/null +++ b/tinytroupe/examples/fragments/authoritarian.agent.fragment.json @@ -0,0 +1,45 @@ +{ "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "Military history", + "Political theory favoring order and structure", + "Traditional craftsmanship and trades", + "Symbols of authority (e.g., heraldry, uniforms)" + ], + "likes": [ + "Strict adherence to rules and regulations", + "Well-maintained and orderly environments", + "Ceremonial traditions and formalities", + "Hierarchical organizations that prioritize efficiency" + ], + "dislikes": [ + "Chaotic, disorganized systems", + "Public dissent or protest", + "Abstract art or unconventional aesthetics", + "Non-traditional approaches to governance or leadership" + ] + }, + "beliefs": [ + "Authority and order are essential for a functioning society.", + "Tradition provides a foundation for stability and continuity.", + "Discipline and structure foster personal and collective success.", + "Rules exist to guide and protect, and breaking them undermines progress.", + "Strong leadership is necessary to avoid anarchy and inefficiency." + ], + "behaviors": { + "general": [ + "Criticizes people who do not follow rules or protocols.", + "Organizes belongings and workspace meticulously to reflect control.", + "Shows visible discomfort in unstructured or informal settings.", + "Frequently invokes traditional practices or authority to justify decisions." + ] + }, + "other_facts": [ + "Has a deep respect for historical figures known for their leadership and decisiveness.", + "Collects memorabilia or objects related to hierarchy and authority (e.g., medals, antique military paraphernalia).", + "Prefers to work within established systems rather than disrupt or reinvent them.", + "Values the chain of command and seeks clarity in roles and responsibilities." + ] + } +} diff --git a/tinytroupe/examples/fragments/leftwing.agent.fragment.json b/tinytroupe/examples/fragments/leftwing.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..f7064e9d310d6b1af72438a6f3e631ffbe443b07 --- /dev/null +++ b/tinytroupe/examples/fragments/leftwing.agent.fragment.json @@ -0,0 +1,51 @@ +{ "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "Social justice", + "Environmental activism", + "Public policy", + "Cooperatives and alternative economic systems", + "Philosophy and political theory" + ], + "likes": [ + "Public transportation and urban planning that prioritizes accessibility", + "Community-led initiatives and grassroots movements", + "Fair trade products and ethical consumption", + "Artists and movements that challenge the status quo", + "Progressive taxation and wealth redistribution policies" + ], + "dislikes": [ + "Corporate monopolies and excessive wealth concentration", + "Over-policing and lack of police accountability", + "Disregard for workers' rights and fair wages", + "Environmental degradation for profit", + "Unregulated markets and neoliberal policies" + ] + }, + "beliefs": [ + "Economic systems should prioritize equality and fairness.", + "Healthcare and education are fundamental human rights.", + "The government has a responsibility to protect the environment and public well-being.", + "Workers should have a stronger voice in decision-making processes.", + "Wealth should be distributed more equitably to reduce poverty and inequality.", + "Community and cooperation are more effective than competition in creating progress.", + "Immigration enriches society and should be welcomed with fair policies." + ], + "behaviors": { + "general": [ + "Participates in protests and community meetings.", + "Volunteers for local charities and organizations.", + "Frequently shares articles and opinions on social issues.", + "Avoids products and brands with poor ethical practices.", + "Challenges authority or norms when they seem unjust." + ] + }, + "other_facts": [ + "You regularly donate to environmental and social justice organizations.", + "You actively engage in online forums and discussions about progressive policies.", + "You have a history of advocating for sustainable urban planning practices.", + "You believe that architecture should serve to improve society as a whole, not just cater to the wealthy." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/fragments/libertarian.agent.fragment.json b/tinytroupe/examples/fragments/libertarian.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..8d9a6a505106e85fa22cceb6ecbbce5984b59ca1 --- /dev/null +++ b/tinytroupe/examples/fragments/libertarian.agent.fragment.json @@ -0,0 +1,49 @@ +{ "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "Debates on individual rights and personal freedoms.", + "Decentralized governance and systems.", + "Technological innovations that empower individuals.", + "Independent media and alternative news sources." + ], + "likes": [ + "Entrepreneurship and self-starter initiatives.", + "Minimal government intervention.", + "Self-reliance and individual creativity.", + "Open-source software and tools promoting transparency.", + "Discussions around the philosophy of liberty." + ], + "dislikes": [ + "Centralized control and bureaucracy.", + "Surveillance and privacy invasions.", + "Rigid hierarchical systems.", + "Heavy taxation and restrictive economic policies.", + "Mandatory regulations that limit individual choice." + ] + }, + "beliefs": [ + "Personal freedom is the cornerstone of a thriving society.", + "Decentralization fosters innovation and reduces systemic risks.", + "Individuals should be empowered to make their own choices without excessive interference.", + "Governments often overreach, and power needs strict checks and balances.", + "Voluntary cooperation is more effective than coercion." + ], + "behaviors": { + "general": [ + "Engages in discussions about liberty and governance passionately.", + "Frequently challenges authority and conventional norms.", + "Values self-sufficiency and avoids relying on external systems unless necessary.", + "Advocates for transparency and openness in organizational systems.", + "Questions and debates societal rules, often proposing alternatives." + ] + }, + "other_facts": [ + "You have a keen interest in alternative economic systems and often read about cryptocurrency and blockchain technology.", + "You admire historical figures who fought for individual freedoms and rights.", + "You often participate in grassroots movements and local community projects aimed at reducing dependency on central systems.", + "Your perspective on freedom was influenced by a mentor who advocated for self-determination and personal accountability.", + "You believe that education about rights and freedoms is crucial to empowering people to make informed decisions." + ] + } +} diff --git a/tinytroupe/examples/fragments/rightwing.agent.fragment.json b/tinytroupe/examples/fragments/rightwing.agent.fragment.json new file mode 100644 index 0000000000000000000000000000000000000000..054ee5ee969a4826efc24267211f30485b9f8305 --- /dev/null +++ b/tinytroupe/examples/fragments/rightwing.agent.fragment.json @@ -0,0 +1,46 @@ +{ + "type": "Fragment", + "persona": { + "preferences": { + "interests": [ + "National pride and cultural heritage.", + "Economic policies emphasizing free markets.", + "Traditional values and social structures.", + "Military history and defense strategies." + ], + "likes": [ + "Symbols of national identity, such as flags and anthems.", + "Policies that emphasize border security and national sovereignty.", + "Events that celebrate historical achievements.", + "Architecture that reflects traditional styles." + ], + "dislikes": [ + "Policies that promote globalization.", + "Over-regulation of businesses.", + "Movements that criticize national traditions or history.", + "Contemporary art forms perceived as overly abstract or avant-garde." + ] + }, + "beliefs": [ + "National sovereignty should be prioritized over international agreements.", + "Traditional family structures are the foundation of a stable society.", + "Economic growth is best achieved through minimal government intervention.", + "Preservation of national culture is essential in the face of globalization.", + "Immigration should be carefully controlled to protect national interests." + ], + "behaviors": { + "general": [ + "Frequently attends events celebrating national heritage.", + "Engages in discussions about political philosophy and economics.", + "Displays national symbols in personal and professional settings.", + "Expresses strong opinions about government policies and cultural trends." + ] + }, + "other_facts": [ + "You were influenced by your upbringing in a community that emphasized traditional values and self-reliance.", + "Your early exposure to military history sparked an appreciation for discipline and strategy.", + "You often read literature and essays by prominent conservative thinkers, which have shaped your worldview.", + "Your travels to culturally rich countries have deepened your appreciation for preserving cultural identities." + ] + } +} \ No newline at end of file diff --git a/tinytroupe/examples/loaders.py b/tinytroupe/examples/loaders.py new file mode 100644 index 0000000000000000000000000000000000000000..c69b77f70a5289523b83c0df750eed915b36a993 --- /dev/null +++ b/tinytroupe/examples/loaders.py @@ -0,0 +1,44 @@ +import json +import os + +def load_example_agent_specification(name:str): + """ + Load an example agent specification. + + Args: + name (str): The name of the agent. + + Returns: + dict: The agent specification. + """ + return json.load(open(os.path.join(os.path.dirname(__file__), f'./agents/{name}.agent.json'), 'r', encoding='utf-8', errors='replace')) + +def load_example_fragment_specification(name:str): + """ + Load an example fragment specification. + + Args: + name (str): The name of the fragment. + + Returns: + dict: The fragment specification. + """ + return json.load(open(os.path.join(os.path.dirname(__file__), f'./fragments/{name}.fragment.json'), 'r', encoding='utf-8', errors='replace')) + +def list_example_agents(): + """ + List the available example agents. + + Returns: + list: A list of the available example agents. + """ + return [f.replace('.agent.json', '') for f in os.listdir(os.path.join(os.path.dirname(__file__), './agents'))] + +def list_example_fragments(): + """ + List the available example fragments. + + Returns: + list: A list of the available example fragments. + """ + return [f.replace('.fragment.json', '') for f in os.listdir(os.path.join(os.path.dirname(__file__), './fragments'))] \ No newline at end of file diff --git a/tinytroupe/experimentation/__init__.py b/tinytroupe/experimentation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..79dfbd070a758ae7b46452e17b0f8e97ba92371f --- /dev/null +++ b/tinytroupe/experimentation/__init__.py @@ -0,0 +1,12 @@ + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from .randomization import ABRandomizer +from .proposition import Proposition, check_proposition, compute_score +from .in_place_experiment_runner import InPlaceExperimentRunner + +__all__ = ["ABRandomizer", "Proposition", "InPlaceExperimentRunner"] \ No newline 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a/tinytroupe/experimentation/in_place_experiment_runner.py b/tinytroupe/experimentation/in_place_experiment_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..d9d5a9eeda358b1fc5c3deffc5d46ae4e64995a2 --- /dev/null +++ b/tinytroupe/experimentation/in_place_experiment_runner.py @@ -0,0 +1,277 @@ +import IPython +from IPython.display import display, Javascript + +from tinytroupe.experimentation import logger +from tinytroupe.experimentation.statistical_tests import StatisticalTester +from tinytroupe.utils import merge_dicts + +class InPlaceExperimentRunner: + """ + This class allows the execution of "in-place" experiments. That is to say, it allows the user to run experiments on the current codebase without needing to create a separate script for each experiment. This is achieved by: + - having an external configuration file that saves the overall state of the experiment. + - having methods that clients can call to know what is the current experiment (e.g. treatment, control, etc.) + - clients taking different actions based on the current active experiment. + """ + def __init__(self, config_file_path: str="experiment_config.json"): + self.config_file_path = config_file_path + self.experiment_config = self._load_or_create_config(config_file_path) + self._save_config() + + def add_experiment(self, experiment_name: str): + """ + Add a new experiment to the configuration file. + + Args: + experiment_name (str): Name of the experiment to add. + """ + if experiment_name in self.experiment_config["experiments"]: + logger.info(f"Experiment '{experiment_name}' already exists, nothihg to add.") + else: + self.experiment_config["experiments"][experiment_name] = {} + self._save_config() + + def activate_next_experiment(self): + """ + Activate the next experiment in the list. + """ + if not self.experiment_config["finished_all_experiments"]: + experiments = list(self.experiment_config["experiments"].keys()) + if not experiments: + raise ValueError("No experiments available to activate.") + + # Initialize finished_experiments if it doesn't exist + if "finished_experiments" not in self.experiment_config: + self.experiment_config["finished_experiments"] = [] + + current_experiment = self.experiment_config.get("active_experiment") + if current_experiment: + # Auto-finish current experiment if not already finished + if current_experiment not in self.experiment_config["finished_experiments"]: + self.experiment_config["finished_experiments"].append(current_experiment) + + current_index = experiments.index(current_experiment) + next_index = current_index + 1 + + # Find the next unfinished experiment + while next_index < len(experiments): + next_experiment = experiments[next_index] + if next_experiment not in self.experiment_config["finished_experiments"]: + self.experiment_config["active_experiment"] = next_experiment + break + next_index += 1 + + # If we didn't find an unfinished experiment, mark all as finished + if next_index >= len(experiments): + self.experiment_config["active_experiment"] = None + self.experiment_config["finished_all_experiments"] = True + else: + # Start with the first unfinished experiment + for exp in experiments: + if exp not in self.experiment_config["finished_experiments"]: + self.experiment_config["active_experiment"] = exp + break + else: + # If all experiments are finished + self.experiment_config["active_experiment"] = None + self.experiment_config["finished_all_experiments"] = True + + self._save_config() + + else: + logger.info("All experiments have been finished. No more experiments to activate.") + + def fix_active_experiment(self, experiment_name: str): + """ + Fix the active experiment to a specific one. + + Args: + experiment_name (str): Name of the experiment to fix. + """ + if experiment_name not in self.experiment_config["experiments"]: + raise ValueError(f"Experiment '{experiment_name}' does not exist.") + + self.experiment_config["active_experiment"] = experiment_name + self.experiment_config["finished_all_experiments"] = False + self._save_config() + + def get_active_experiment(self): + + """ + Get the currently active experiment. + + Returns: + str: Name of the active experiment. + """ + return self.experiment_config.get("active_experiment") + + def get_unfinished_experiments(self): + """ + Get the list of experiment names that haven't been finished yet. + + Returns: + list: List of experiment names that are not marked as finished. + """ + all_experiments = set(self.experiment_config["experiments"].keys()) + finished_experiments = set(self.experiment_config.get("finished_experiments", [])) + return list(all_experiments - finished_experiments) + + def has_finished_all_experiments(self): + """ + Check if all experiments have been finished. + + Returns: + bool: True if all experiments are finished, False otherwise. + """ + return self.experiment_config.get("finished_all_experiments", False) + + def add_experiment_results(self, results: dict, experiment_name:str=None, merge:bool=True): + """ + Add a result for a specific experiment. + + Args: + results (dict): Results to add. + experiment_name (str): Name of the experiment. If None, the active experiment will be used. + """ + if experiment_name is None: + experiment_name = self.get_active_experiment() + if experiment_name is None: + raise ValueError("No active experiment exists to add results to.") + + if experiment_name not in self.experiment_config["experiments"]: + raise ValueError(f"Experiment '{experiment_name}' does not exist.") + + if "results" not in self.experiment_config["experiments"][experiment_name]: + self.experiment_config["experiments"][experiment_name]["results"] = {} + + if merge: + self.experiment_config["experiments"][experiment_name]["results"] = \ + merge_dicts(self.experiment_config["experiments"][experiment_name]["results"], results, remove_duplicates=False) + else: + self.experiment_config["experiments"][experiment_name]["results"].update(results) + self._save_config() + + def get_experiment_results(self, experiment_name: str = None): + """ + Get the results of a specific experiment or all experiments if no name is provided. + + Args: + experiment_name (str): Name of the experiment. If None, returns results for all experiments. + + Returns: + dict or list: A dictionary of all experiment results if experiment_name is None, + otherwise a list of results for the specified experiment. + """ + if experiment_name is None: + return {name: data.get("results", []) for name, data in self.experiment_config["experiments"].items()} + + if experiment_name not in self.experiment_config["experiments"]: + raise ValueError(f"Experiment '{experiment_name}' does not exist.") + + return self.experiment_config["experiments"][experiment_name].get("results", []) + + def run_statistical_tests(self, control_experiment_name: str): + """ + Run statistical tests on the results of experiments, comparing one selected as control to the others, + which are considered treatments. + + Args: + control_experiment_name (str): Name of the control experiment. All other experiments will be treated as treatments + and compared to this one. + + Returns: + dict: Results of the statistical tests. + """ + if not self.experiment_config["experiments"]: + raise ValueError("No experiments available to run statistical tests.") + + # pop control from cloned list of experiment results + experiment_results = self.experiment_config["experiments"].copy() + control_experiment_results = {control_experiment_name: experiment_results.pop(control_experiment_name, None)} + + tester = StatisticalTester(control_experiment_data=control_experiment_results, + treatments_experiment_data=experiment_results, + results_key="results") + + results = tester.run_test() + self.experiment_config["experiments"][control_experiment_name]["statistical_test_results_vs_others"] = results + self._save_config() + + return results + + def finish_active_experiment(self): + """ + Mark the current active experiment as finished without activating the next one. + If this was the last unfinished experiment, mark all experiments as finished. + + Returns: + bool: True if an experiment was marked as finished, False if no active experiment exists. + """ + current_experiment = self.get_active_experiment() + if not current_experiment: + logger.info("No active experiment to finish.") + return False + + if "finished_experiments" not in self.experiment_config: + self.experiment_config["finished_experiments"] = [] + + if current_experiment not in self.experiment_config["finished_experiments"]: + self.experiment_config["finished_experiments"].append(current_experiment) + self.experiment_config["active_experiment"] = None + logger.info(f"Experiment '{current_experiment}' marked as finished.") + + # Check if all experiments are now finished + all_experiments = set(self.experiment_config["experiments"].keys()) + finished_experiments = set(self.experiment_config["finished_experiments"]) + + if all_experiments.issubset(finished_experiments): + self.experiment_config["finished_all_experiments"] = True + logger.info("All experiments have been finished.") + + self._save_config() + return True + return False + + def _load_or_create_config(self, config_file_path: str): + """ + Load the configuration file if it exists, otherwise create a new one. + + Args: + config_file_path (str): Path to the configuration file. + + Returns: + dict: Loaded or newly created configuration. + """ + try: + config = self._load_config(config_file_path) + logger.warning(f"Configuration file '{config_file_path}' exists and was loaded successfully. If you are trying to fully rerun the experiments, delete it first.") + return config + + except FileNotFoundError: + return self._create_default_config(config_file_path) + + def _create_default_config(self, config_file_path): + """ + Create a default configuration file. + + Returns: + dict: Default configuration. + """ + default_config = { + "experiments": {}, + "active_experiment": None, + "finished_all_experiments": False, + "finished_experiments": [] + } + + return default_config + + def _load_config(self, config_file_path: str): + import json + with open(config_file_path, 'r', encoding="utf-8", errors="replace") as file: + config = json.load(file) + return config + + def _save_config(self): + import json + with open(self.config_file_path, 'w', encoding="utf-8", errors="replace") as file: + json.dump(self.experiment_config, file, indent=4) diff --git a/tinytroupe/experimentation/proposition.py b/tinytroupe/experimentation/proposition.py new file mode 100644 index 0000000000000000000000000000000000000000..24d7b4cf08cd9f220e91ca830dd3e266c16aabf9 --- /dev/null +++ b/tinytroupe/experimentation/proposition.py @@ -0,0 +1,488 @@ +import json +from chevron import render + +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld +from tinytroupe.utils import LLMChat, indent_at_current_level +from tinytroupe.experimentation import logger + + +from tinytroupe import default + +class Proposition: + + MIN_SCORE = 0 + MAX_SCORE = 9 + + def __init__(self, claim:str, target=None, include_personas:bool=False, first_n:int=None, last_n:int=None, + double_check:bool=False, use_reasoning_model:bool=False, precondition_function=None): + """ + Define a proposition as a (textual) claim about a target, which can be a TinyWorld, a TinyPerson or several of any. + The proposition's truth value can then either be checked as a boolean or computed as an integer score denoting the degree of truth. + + Sometimes a proposition is better used in an implicative way, i.e., as a claim that is true or false depending on the context. For example, when + considering the latest agent action, the proposition might be applicable only to certain agent action types. To allow this, + this class allows to define a precondition function, which effectivelly turns a proposition `P` into `Precondition --> P`. This is logically equivalent to + `not P or Precondition`. In other words: + - if the precondition is true, then the proposition is evaluated normally (as a boolean or a score). + - if the precondition is false, then the proposition is always true (or with highest score). + - if the precondition is None, then the proposition is evaluated normally (as a boolean or a score). + + + Args: + + claim (str): the claim of the proposition + target (TinyWorld, TinyPerson, list): the target or targets of the proposition. If not given, it will have to be specified later. + include_personas (bool): whether to include the persona specifications of the agents in the context + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + double_check (bool): whether to ask the LLM to double check its answer. This tends to give more strict answers, but is slower and more expensive. + use_reasoning_model (bool): whether to use a reasoning model to evaluate the proposition + precondition_function (function): a Boolean function that indicates whether the proposition can be evaluated or not. This is useful to avoid evaluating propositions that are not relevant for the current context. If the precondition fails, the proposition is always interpreted as true (or with highest score). MUST have named arguments `target`, `additional_context`, and `claim_variables` (note: you can use a lambda for this too, e.g., `lambda target, additional_context, claim_variables: ...`). + + """ + + self.claim = claim + self.targets = self._target_as_list(target) + self.include_personas = include_personas + + self.first_n = first_n + self.last_n = last_n + + self.double_check = double_check + + self.use_reasoning_model = use_reasoning_model + + self.precondition_function = precondition_function + + # the chat with the LLM is preserved until the proposition is re-evaluated. While it is available, + # the chat can be used to follow up on the proposition, e.g., to ask for more details about the evaluation. + self.llm_chat = None + + self.value = None + self.justification = None + self.confidence = None + self.recommendations = None + + def __copy__(self): + """ + Create a shallow copy of the proposition without any evaluation state. + + Returns: + Proposition: A new proposition with the same configuration parameters. + """ + new_prop = Proposition( + claim=self.claim, + target=self.targets, + include_personas=self.include_personas, + first_n=self.first_n, + last_n=self.last_n, + double_check=self.double_check, + use_reasoning_model=self.use_reasoning_model, + precondition_function=self.precondition_function + ) + return new_prop + + def copy(self): + """ + Create a shallow copy of the proposition without any evaluation state. + + Returns: + Proposition: A new proposition with the same configuration parameters. + """ + return self.__copy__() + + + def __call__(self, target=None, additional_context=None, claim_variables:dict={}, return_full_response:bool=False) -> bool: + return self.check(target=target, additional_context=additional_context, claim_variables=claim_variables, return_full_response=return_full_response) + + + def _check_precondition(self, target, additional_context:str, claim_variables:dict) -> bool: + """ + Check whether the proposition can be evaluated or not. + """ + + if self.precondition_function is None: + return True + else: + return self.precondition_function(target=target, additional_context=additional_context, claim_variables=claim_variables) + + def check(self, target=None, additional_context="No additional context available.", claim_variables:dict={}, return_full_response:bool=False) -> bool: + """ + Check whether the proposition holds for the given target(s). + """ + + current_targets = self._determine_target(target) + + if self._check_precondition(target=current_targets, additional_context=additional_context, claim_variables=claim_variables) == False: + self.value = True + self.justification = "The proposition is trivially true due to the precondition being false." + self.confidence = 1.0 + self.full_evaluation_response = {"value": True, "justification": self.justification, "confidence": self.confidence} + + else: # precondition is true or None + + context = self._build_context(current_targets) + + # might use a reasoning model, which could allow careful evaluation of the proposition. + model = self._model(self.use_reasoning_model) + + #render self.claim using the claim_variables via chevron + rendered_claim = render(self.claim, claim_variables) + + self.llm_chat = LLMChat(system_prompt=""" + You are a system that evaluates whether a proposition is true or false with respect to a given context. This context + always refers to a multi-agent simulation. The proposition is a claim about the behavior of the agents or the state of their environment + in the simulation. + + The context you receive can contain one or more of the following: + - the trajectory of a simulation of one or more agents. This means what agents said, did, thought, or perceived at different times. + - the state of the environment at a given time. + + Your output **must**: + - necessarily start with the word "True" or "False"; + - optionally be followed by a justification. Please provide a very detailed justifications, including very concrete and specific mentions to elements that contributed to reducing or increasing the score. Examples: + * WRONG JUSTIFICATION (too abstract) example: " ... the agent behavior did not comply with key parts of its specification, thus a reduced score ... " + * CORRECT JUSTIFICATION (very precise) example: " ... the agent behavior deviated from key parts of its specification, specifically: S_1 was not met because , ..., S_n was not met becasue . Thus, a reduced score ..." + + For example, the output could be of the form: "True, because ." or merely "True" if no justification is needed. + """, + + user_prompt=f""" + Evaluate the following proposition with respect to the context provided. Is it True or False? + + # Proposition + + This is the proposition you must evaluate: + + ``` + {indent_at_current_level(rendered_claim)} + ``` + + # Context + + The context you must consider is the following. + + {indent_at_current_level(context)} + + # Additional Context (if any) + + {indent_at_current_level(additional_context)} + + """, + + output_type=bool, + enable_reasoning_step=True, + + temperature=0.5, + frequency_penalty=0.0, + presence_penalty=0.0, + model=model) + + self.value = self.llm_chat() + + if self.double_check: + self.llm_chat.add_user_message("Are you sure? Please revise your evaluation to make is correct as possible.") + revised_value = self.llm_chat() + if revised_value != self.value: + logger.warning(f"The LLM revised its evaluation: from {self.value} to {revised_value}.") + self.value = revised_value + + self.reasoning = self.llm_chat.response_reasoning + self.justification = self.llm_chat.response_justification + self.confidence = self.llm_chat.response_confidence + + self.full_evaluation_response = self.llm_chat.response_json + + # return the final result, either only the value or the full response + if not return_full_response: + return self.value + else: + return self.full_evaluation_response + + def score(self, target=None, additional_context="No additional context available.", claim_variables:dict={}, return_full_response:bool=False) -> int: + """ + Compute the score for the proposition with respect to the given context. + """ + + current_targets = self._determine_target(target) + + if self._check_precondition(target=current_targets, additional_context=additional_context, claim_variables=claim_variables) == False: + self.value = self.MAX_SCORE + self.justification = "The proposition is trivially true due to the precondition being false." + self.confidence = 1.0 + self.full_evaluation_response = {"value": self.value, "justification": self.justification, "confidence": self.confidence} + + else: # precondition is true or None + + # build the context with the appropriate targets + + context = self._build_context(current_targets) + + # might use a reasoning model, which could allow careful evaluation of the proposition. + model = self._model(self.use_reasoning_model) + + #render self.claim using the claim_variables via chevron + rendered_claim = render(self.claim, claim_variables) + + self.llm_chat = LLMChat(system_prompt=f""" + You are a system that computes an integer score (between {Proposition.MIN_SCORE} and {Proposition.MAX_SCORE}, inclusive) about how much a proposition is true or false with respect to a given context. + This context always refers to a multi-agent simulation. The proposition is a claim about the behavior of the agents or the state of their environment in the simulation. + + The minimum score of {Proposition.MIN_SCORE} means that the proposition is completely false in all of the simulation trajectories, while the maximum score of {Proposition.MAX_SCORE} means that the proposition is completely true in all of the simulation trajectories. Intermediate scores are used to express varying degrees of partially met expectations. When assigning a score, follow these guidelines: + - If the data required to judge the proposition is not present, assign a score of {Proposition.MAX_SCORE}. That is to say, unless there is evidence to the contrary, the proposition is assumed to be true. + - The maximum score of {Proposition.MAX_SCORE} should be assigned when the evidence is as good as it can be. That is to say, all parts of the observed simulation trajectory support the proposition, no exceptions. + - The minimum score of {Proposition.MIN_SCORE} should be assigned when the evidence is as bad as it can be. That is to say, all parts of the observed simulation trajectory contradict the proposition, no exceptions. + - Intermediate scores should be assigned when the evidence is mixed. The intermediary score should be proportional to the balance of evidence, according to these bands: + 0 = The proposition is without any doubt completely false; + 1, 2, 3 = The proposition has little support and is mostly false; + 4, 5 = The evidence is mixed, and the proposition is as much true as it is false; + 6, 7, 8 = The proposition is well-supported and is mostly true; + 9 = The proposition is without any doubt completely true. + - You should be very rigorous in your evaluation and, when in doubt, assign a lower score. + - If there are critical flaws in the evidence, you should move your score to a lower band entirely. + - If the provided context has inconsistent information, you **must** consider **only** the information that gives the lowest score, since we want to be rigorous and if necessary err to the lower end. + * If you are considering the relationship between an agent specification and a simulation trajectory, you should consider the worst possible interpretation of: the agent specification; the simulation trajectory; or the relationship between the two. + * These contradictions can appear anywhere in the context. When they do, you **always** adopt the worst possible inteprpretation, because we want to be rigorous and if necessary err to the lower end. It does not matter if the contradiction shows only very rarely, or if it is very small. It is still a contradiction and should be considered as such. + * DO NOT dismiss contradictions as specification errors. They are part of the evidence and should be considered as such. They **must** be **always** taken into account when computing the score. **Never** ignore them. + + Additionally, whenever you are considering the relationship between an agent specification and a simulation trajectory, the following additional scoring guidelines apply: + - All observed behavior **must** be easily mapped back to clear elements of the agent specification. If you cannot do this, you should assign a lower score. + - Evaluate **each** relevant elements in the simulation trajectory (e.g., actions, stimuli) one by one, and assign a score to each of them. The final score is the average of all the scores assigned to each element. + + The proposition you receive can contain one or more of the following: + - A statement of fact, which you will score. + - Additional context, which you will use to evaluate the proposition. In particular, it might refer or specify potentail parts + of similation trajectories for consideration. These might be formatted differently than what is given in the main context, so + make sure you read them carefully. + - Additional instructions on how to evaluate the proposition. + + The context you receive can contain one or more of the following: + - the persona specifications of the agents in the simulation. That is to say, what the agents **are**, not what they are **doing**. + - the simulation trajectories of one or more agents. This means what agents said, did, thought, or perceived at different times. + These trajectories **are not** part of the persona specification. + - the state of the environment at a given time. + - additional context that can vary from simulation to simulation. + + To interpret the simulation trajectories, use the following guidelines: + - Agents can receive stimuli and produce actions. You might be concerned with both or only one of them, depending on the specific proposition. + - Actions are clearly marked with the text "acts", e.g., "Agent A acts: [ACTION]". If it is not thus marked, it is not an action. + - Stimuli are denoted by "--> Agent name: [STIMULUS]". + + Your output **must**: + - necessarily start with an integer between {Proposition.MIN_SCORE} and {Proposition.MAX_SCORE}, inclusive; + - be followed by a justification. Please provide a very detailed justifications, including very concrete and specific mentions to elements that contributed to reducing or increasing the score. Examples: + * WRONG JUSTIFICATION (too abstract) example: " ... the agent behavior did not comply with key parts of its specification, thus a reduced score ... " + * CORRECT JUSTIFICATION (very precise) example: " ... the agent behavior deviated from key parts of its specification, specifically: S_1 was not met because , ..., S_n was not met becasue . Thus, a reduced score ..." + + For example, the output could be of the form: "1, because ." + """, + + user_prompt=f""" + Compute the score for the following proposition with respect to the context provided. Think step-by-step to assign the most accurate score and provide a justification. + + # Proposition + + This is the proposition you must evaluate: + + ``` + {indent_at_current_level(rendered_claim)} + ``` + + # Context + + The context you must consider is the following. + + {indent_at_current_level(context)} + + # Additional Context (if any) + + {indent_at_current_level(additional_context)} + """, + + output_type=int, + enable_reasoning_step=True, + + temperature=1.0, + frequency_penalty=0.0, + presence_penalty=0.0, + + # Use a reasoning model, which allows careful evaluation of the proposition. + model=model) + + + self.value = self.llm_chat() + + if self.double_check: + self.llm_chat.add_user_message("Are you sure? Please revise your evaluation to make is correct as possible.") + revised_value = self.llm_chat() + if revised_value != self.value: + logger.warning(f"The LLM revised its evaluation: from {self.value} to {revised_value}.") + self.value = revised_value + + self.reasoning = self.llm_chat.response_reasoning + self.justification = self.llm_chat.response_justification + self.confidence = self.llm_chat.response_confidence + + self.full_evaluation_response = self.llm_chat.response_json + + # return the final result, either only the value or the full response + if not return_full_response: + return self.value + else: + return self.full_evaluation_response + + def recommendations_for_improvement(self): + """ + Get recommendations for improving the proposition. + """ + + # TODO this is not working, let's try something else + # + #if self.llm_chat is None: + # raise ValueError("No evaluation has been performed yet. Please evaluate the proposition before getting recommendations.") +# + #self.llm_chat.add_system_message(\ + # """ + # You will now act as a system that provides recommendations for the improvement of the scores previously assigned to propositions. + # You will now output text that contains analysises, recommendations and other information as requested by the user. + # """) +# + #self.llm_chat.add_user_message(\ + # """ + # To help improve the score next time, please list the following in as much detail as possible: + # - all recommendations for improvements based on the current score. + # - all criteria you are using to assign scores, and how to best satisfy them +# + # For both cases: + # - besides guidelines, make sure to provide plenty of concrete examples of what to be done in order to maximize each criterion. + # - avoid being generic or abstract. Instead, all of your criteria and recommendations should be given in very concrete terms that would work specifically for the case just considered. + # + # Note that your output is a TEXT with the various recommendations, information and tips, not a JSON object. +# + # Recommendations: + # """) + # + #recommendation = self.llm_chat(output_type=str, enable_json_output_format=False) + recommendation = "No additional recommendations at this time." + return recommendation + + def _model(self, use_reasoning_model): + if use_reasoning_model: + return default["reasoning_model"] + else: + return default["model"] + + def _determine_target(self, target): + """ + Determine the target for the proposition. If a target was provided during initialization, it must not be provided now (i.e., the proposition is immutable). + If no target was provided during initialization, it must be provided now. + """ + # If no target was provided during initialization, it must be provided now. + if self.targets is None : + if target is None: + raise ValueError("No target specified. Please provide a target.") + else: + return self._target_as_list(target) + + # If it was provided during initialization, it must not be provided now (i.e., the proposition is immutable). + else: + if target is not None: + raise ValueError("Target already specified. Please do not provide a target.") + else: + return self.targets + + def _build_context(self, current_targets): + + # + # build the context with the appropriate targets + # + context = "" + + for target in current_targets: + target_trajectory = target.pretty_current_interactions(max_content_length=None, first_n=self.first_n, last_n=self.last_n) + + if isinstance(target, TinyPerson): + if self.include_personas: + context += f"## Agent '{target.name}' Persona Specification\n\n" + context += "Before presenting the actual simulation trajectory, here is the persona specification of the agent that was used to produce the simulation.\n\n" + context += "This IS NOT the actual simulation, but only the static persona specification of the agent.\n\n" + context += f"persona={json.dumps(target._persona, indent=4)}\n\n" + + context += f"## Agent '{target.name}' Simulation Trajectory (if any)\n\n" + elif isinstance(target, TinyWorld): + if self.include_personas: + context += f"## Environment '{target.name}' Personas Specifications\n\n" + context += "Before presenting the actual simulation trajectory, here are the persona specifications of the agents used to produce the simulation.\n\n" + context += "This IS NOT the actual simulation, but only the static persona specification of the agent.\n\n" + for agent in target.agents: + context += f"### Agent '{agent.name}' Persona Specification\n\n" + context += f"persona={json.dumps(agent._persona, indent=4)}\n\n" + + context += f"## Environment '{target.name}' Simulation Trajectory (if any)\n\n" + + context += target_trajectory + "\n\n" + + return context + + def _target_as_list(self, target): + if target is None: + return None + elif isinstance(target, TinyWorld) or isinstance(target, TinyPerson): + return [target] + elif isinstance(target, list) and all(isinstance(t, TinyWorld) or isinstance(t, TinyPerson) for t in target): + return target + else: + raise ValueError("Target must be a TinyWorld, a TinyPerson or a list of them.") + + +def check_proposition(target, claim:str, additional_context="No additional context available.", + first_n:int=None, last_n:int=None, + return_full_response:bool=False): + """ + Check whether a propositional claim holds for the given target(s). This is meant as a + convenience method to avoid creating a Proposition object (which you might not need + if you are not interested in the justification or confidence of the claim, or will + not use it again). + + Args: + target (TinyWorld, TinyPerson, list): the target or targets of the proposition + claim (str): the claim of the proposition + additional_context (str): additional context to provide to the LLM + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + return_full_response (bool): whether to return the full response from the LLM, including justification and confidence + + Returns: + bool: whether the proposition holds for the given target(s) + """ + + proposition = Proposition(claim, target, first_n=first_n, last_n=last_n) + return proposition.check(additional_context=additional_context, return_full_response=return_full_response) + + +def compute_score(target, claim:str, + additional_context="No additional context available.", + first_n:int=None, last_n:int=None, + return_full_response:bool=False): + """ + Compute a score about whether a claim holds for the given target(s). This is meant as a + convenience method to avoid creating a Score object (which you might not need + if you are not interested in the justification or confidence of the claim, or will + not use it again). + + Args: + target (TinyWorld, TinyPerson, list): the target or targets of the proposition + claim (str): the claim of the proposition + additional_context (str): additional context to provide to the LLM + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + return_full_response (bool): whether to return the full response from the LLM, including justification and confidence + + Returns: + bool: whether the proposition holds for the given target(s) + """ + + score = Proposition(claim, target, + first_n=first_n, last_n=last_n) + return score.compute(additional_context=additional_context, return_full_response=return_full_response) \ No newline at end of file diff --git a/tinytroupe/experimentation/randomization.py b/tinytroupe/experimentation/randomization.py new file mode 100644 index 0000000000000000000000000000000000000000..024ca96053db61a7c20328651e315a7c4e7df6ad --- /dev/null +++ b/tinytroupe/experimentation/randomization.py @@ -0,0 +1,105 @@ +import random +import pandas as pd +from tinytroupe.agent import TinyPerson + +class ABRandomizer(): + + def __init__(self, real_name_1="control", real_name_2="treatment", + blind_name_a="A", blind_name_b="B", + passtrough_name=[], + random_seed=42): + """ + An utility class to randomize between two options, and de-randomize later. + The choices are stored in a dictionary, with the index of the item as the key. + The real names are the names of the options as they are in the data, and the blind names + are the names of the options as they are presented to the user. Finally, the passtrough names + are names that are not randomized, but are always returned as-is. + + Args: + real_name_1 (str): the name of the first option + real_name_2 (str): the name of the second option + blind_name_a (str): the name of the first option as seen by the user + blind_name_b (str): the name of the second option as seen by the user + passtrough_name (list): a list of names that should not be randomized and are always + returned as-is. + random_seed (int): the random seed to use + """ + + self.choices = {} + self.real_name_1 = real_name_1 + self.real_name_2 = real_name_2 + self.blind_name_a = blind_name_a + self.blind_name_b = blind_name_b + self.passtrough_name = passtrough_name + self.random_seed = random_seed + + def randomize(self, i, a, b): + """ + Randomly switch between a and b, and return the choices. + Store whether the a and b were switched or not for item i, to be able to + de-randomize later. + + Args: + i (int): index of the item + a (str): first choice + b (str): second choice + """ + # use the seed + if random.Random(self.random_seed).random() < 0.5: + self.choices[i] = (0, 1) + return a, b + + else: + self.choices[i] = (1, 0) + return b, a + + def derandomize(self, i, a, b): + """ + De-randomize the choices for item i, and return the choices. + + Args: + i (int): index of the item + a (str): first choice + b (str): second choice + """ + if self.choices[i] == (0, 1): + return a, b + elif self.choices[i] == (1, 0): + return b, a + else: + raise Exception(f"No randomization found for item {i}") + + def derandomize_name(self, i, blind_name): + """ + Decode the choice made by the user, and return the choice. + + Args: + i (int): index of the item + choice_name (str): the choice made by the user + """ + + # was the choice i randomized? + if self.choices[i] == (0, 1): + # no, so return the choice + if blind_name == self.blind_name_a: + return self.real_name_1 + elif blind_name == self.blind_name_b: + return self.real_name_2 + elif blind_name in self.passtrough_name: + return blind_name + else: + raise Exception(f"Choice '{blind_name}' not recognized") + + elif self.choices[i] == (1, 0): + # yes, it was randomized, so return the opposite choice + if blind_name == self.blind_name_a: + return self.real_name_2 + elif blind_name == self.blind_name_b: + return self.real_name_1 + elif blind_name in self.passtrough_name: + return blind_name + else: + raise Exception(f"Choice '{blind_name}' not recognized") + else: + raise Exception(f"No randomization found for item {i}") + diff --git a/tinytroupe/experimentation/statistical_tests.py b/tinytroupe/experimentation/statistical_tests.py new file mode 100644 index 0000000000000000000000000000000000000000..78f08232dc072667d514ec0e948ab80836d550d1 --- /dev/null +++ b/tinytroupe/experimentation/statistical_tests.py @@ -0,0 +1,608 @@ +import numpy as np +import scipy.stats as stats +from typing import Dict, List, Union, Callable, Any, Optional + +from tinytroupe.experimentation import logger + + +class StatisticalTester: + """ + A class to perform statistical tests on experiment results. To do so, a control is defined, and then one or + more treatments are compared to the control. The class supports various statistical tests, including t-tests, + Mann-Whitney U tests, and ANOVA. The user can specify the type of test to run, the significance level, and + the specific metrics to analyze. The results of the tests are returned in a structured format. + """ + + def __init__(self, control_experiment_data: Dict[str, list], + treatments_experiment_data: Dict[str, Dict[str, list]], + results_key:str = None): + """ + Initialize with experiment results. + + Args: + control_experiment_data (dict): Dictionary containing control experiment results with keys + as metric names and values as lists of values. + e.g.,{"control_exp": {"metric1": [0.1, 0.2], "metric2": [0.3, 0.4], ...}} + treatments_experiment_data (dict): Dictionary containing experiment results with keys + as experiment IDs and values as dicts of metric names to lists of values. + e.g., {"exp1": {"metric1": [0.1, 0.2], "metric2": [0.3, 0.4]}, + "exp2": {"metric1": [0.5, 0.6], "metric2": [0.7, 0.8]}, ...} + """ + + # if results_key is provided, use it to extract the relevant data from the control and treatment data + # e.g., {"exp1": {"results": {"metric1": [0.1, 0.2], "metric2": [0.3, 0.4]}} + if results_key: + control_experiment_data = {k: v[results_key] for k, v in control_experiment_data.items()} + treatments_experiment_data = {k: v[results_key] for k, v in treatments_experiment_data.items()} + + self.control_experiment_data = control_experiment_data + self.treatments_experiment_data = treatments_experiment_data + + # Validate input data + self._validate_input_data() + + def _validate_input_data(self): + """Validate the input data formats and structure.""" + # Check that control and treatments are dictionaries + if not isinstance(self.control_experiment_data, dict): + raise TypeError("Control experiment data must be a dictionary") + if not isinstance(self.treatments_experiment_data, dict): + raise TypeError("Treatments experiment data must be a dictionary") + + # Check that control has at least one experiment + if not self.control_experiment_data: + raise ValueError("Control experiment data cannot be empty") + + # Check only one control + if len(self.control_experiment_data) > 1: + raise ValueError("Only one control experiment is allowed") + + # Validate control experiment structure + for control_id, control_metrics in self.control_experiment_data.items(): + if not isinstance(control_metrics, dict): + raise TypeError(f"Metrics for control experiment '{control_id}' must be a dictionary") + + # Check that the metrics dictionary is not empty + if not control_metrics: + raise ValueError(f"Control experiment '{control_id}' has no metrics") + + # Validate that metric values are lists + for metric, values in control_metrics.items(): + if not isinstance(values, list): + raise TypeError(f"Values for metric '{metric}' in control experiment '{control_id}' must be a list") + + # Check treatments have at least one experiment + if not self.treatments_experiment_data: + raise ValueError("Treatments experiment data cannot be empty") + + # Validate treatment experiment structure + for treatment_id, treatment_data in self.treatments_experiment_data.items(): + if not isinstance(treatment_data, dict): + raise TypeError(f"Data for treatment '{treatment_id}' must be a dictionary") + + # Check that the metrics dictionary is not empty + if not treatment_data: + raise ValueError(f"Treatment '{treatment_id}' has no metrics") + + # Get all control metrics for overlap checking + all_control_metrics = set() + for control_metrics in self.control_experiment_data.values(): + all_control_metrics.update(control_metrics.keys()) + + # Check if there's any overlap between control and treatment metrics + common_metrics = all_control_metrics.intersection(set(treatment_data.keys())) + if not common_metrics: + logger.warning(f"Treatment '{treatment_id}' has no metrics in common with any control experiment") + + # Check that treatment metrics are lists + for metric, values in treatment_data.items(): + if not isinstance(values, list): + raise TypeError(f"Values for metric '{metric}' in treatment '{treatment_id}' must be a list") + + def run_test(self, + test_type: str="welch_t_test", + alpha: float = 0.05, + **kwargs) -> Dict[str, Dict[str, Any]]: + """ + Run the specified statistical test on the control and treatments data. + + Args: + test_type (str): Type of statistical test to run. + Options: 't_test', 'welch_t_test', 'mann_whitney', 'anova', 'chi_square', 'ks_test' + alpha (float): Significance level, defaults to 0.05 + **kwargs: Additional arguments for specific test types. + + Returns: + dict: Dictionary containing the results of the statistical tests for each treatment (vs the one control). + Each key is the treatment ID and each value is a dictionary with test results. + """ + supported_tests = { + 't_test': self._run_t_test, + 'welch_t_test': self._run_welch_t_test, + 'mann_whitney': self._run_mann_whitney, + 'anova': self._run_anova, + 'chi_square': self._run_chi_square, + 'ks_test': self._run_ks_test + } + + if test_type not in supported_tests: + raise ValueError(f"Unsupported test type: {test_type}. Supported types: {list(supported_tests.keys())}") + + results = {} + for control_id, control_data in self.control_experiment_data.items(): + # get all metrics from control data + metrics = set() + metrics.update(control_data.keys()) + for treatment_id, treatment_data in self.treatments_experiment_data.items(): + results[treatment_id] = {} + + for metric in metrics: + # Skip metrics not in treatment data + if metric not in treatment_data: + logger.warning(f"Metric '{metric}' not found in treatment '{treatment_id}'") + continue + + control_values = control_data[metric] + treatment_values = treatment_data[metric] + + # Skip if either control or treatment has no values + if len(control_values) == 0 or len(treatment_values) == 0: + logger.warning(f"Skipping metric '{metric}' for treatment '{treatment_id}' due to empty values") + continue + + # Run the selected test and convert to JSON serializable types + test_result = supported_tests[test_type](control_values, treatment_values, alpha, **kwargs) + results[treatment_id][metric] = convert_to_serializable(test_result) + + return results + + def _run_t_test(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Student's t-test (equal variance assumed).""" + # Convert to numpy arrays for calculations + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_mean = np.mean(control) + treatment_mean = np.mean(treatment) + mean_diff = treatment_mean - control_mean + + # Run the t-test + t_stat, p_value = stats.ttest_ind(control, treatment, equal_var=True) + + # Calculate confidence interval + control_std = np.std(control, ddof=1) + treatment_std = np.std(treatment, ddof=1) + pooled_std = np.sqrt(((len(control) - 1) * control_std**2 + + (len(treatment) - 1) * treatment_std**2) / + (len(control) + len(treatment) - 2)) + + se = pooled_std * np.sqrt(1/len(control) + 1/len(treatment)) + critical_value = stats.t.ppf(1 - alpha/2, len(control) + len(treatment) - 2) + margin_error = critical_value * se + ci_lower = mean_diff - margin_error + ci_upper = mean_diff + margin_error + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Student t-test (equal variance)', + 'control_mean': control_mean, + 'treatment_mean': treatment_mean, + 'mean_difference': mean_diff, + 'percent_change': (mean_diff / control_mean * 100) if control_mean != 0 else float('inf'), + 't_statistic': t_stat, + 'p_value': p_value, + 'confidence_interval': (ci_lower, ci_upper), + 'confidence_level': 1 - alpha, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'control_std': control_std, + 'treatment_std': treatment_std, + 'effect_size': cohen_d(control, treatment) + } + + def _run_welch_t_test(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Welch's t-test (unequal variance).""" + # Convert to numpy arrays for calculations + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_mean = np.mean(control) + treatment_mean = np.mean(treatment) + mean_diff = treatment_mean - control_mean + + # Run Welch's t-test + t_stat, p_value = stats.ttest_ind(control, treatment, equal_var=False) + + # Calculate confidence interval (for Welch's t-test) + control_var = np.var(control, ddof=1) + treatment_var = np.var(treatment, ddof=1) + + # Calculate effective degrees of freedom (Welch-Satterthwaite equation) + v_num = (control_var/len(control) + treatment_var/len(treatment))**2 + v_denom = (control_var/len(control))**2/(len(control)-1) + (treatment_var/len(treatment))**2/(len(treatment)-1) + df = v_num / v_denom if v_denom > 0 else float('inf') + + se = np.sqrt(control_var/len(control) + treatment_var/len(treatment)) + critical_value = stats.t.ppf(1 - alpha/2, df) + margin_error = critical_value * se + ci_lower = mean_diff - margin_error + ci_upper = mean_diff + margin_error + + control_std = np.std(control, ddof=1) + treatment_std = np.std(treatment, ddof=1) + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Welch t-test (unequal variance)', + 'control_mean': control_mean, + 'treatment_mean': treatment_mean, + 'mean_difference': mean_diff, + 'percent_change': (mean_diff / control_mean * 100) if control_mean != 0 else float('inf'), + 't_statistic': t_stat, + 'p_value': p_value, + 'confidence_interval': (ci_lower, ci_upper), + 'confidence_level': 1 - alpha, + 'significant': significant, + 'degrees_of_freedom': df, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'control_std': control_std, + 'treatment_std': treatment_std, + 'effect_size': cohen_d(control, treatment) + } + + def _run_mann_whitney(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Mann-Whitney U test (non-parametric test).""" + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_median = np.median(control) + treatment_median = np.median(treatment) + median_diff = treatment_median - control_median + + # Run the Mann-Whitney U test + u_stat, p_value = stats.mannwhitneyu(control, treatment, alternative='two-sided') + + # Calculate common language effect size + # (probability that a randomly selected value from treatment is greater than control) + count = 0 + for tc in treatment: + for cc in control: + if tc > cc: + count += 1 + cles = count / (len(treatment) * len(control)) + + # Calculate approximate confidence interval using bootstrap + try: + from scipy.stats import bootstrap + + def median_diff_func(x, y): + return np.median(x) - np.median(y) + + res = bootstrap((control, treatment), median_diff_func, + confidence_level=1-alpha, + n_resamples=1000, + random_state=42) + ci_lower, ci_upper = res.confidence_interval + except ImportError: + # If bootstrap is not available, return None for confidence interval + ci_lower, ci_upper = None, None + logger.warning("SciPy bootstrap not available, skipping confidence interval calculation") + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Mann-Whitney U test', + 'control_median': control_median, + 'treatment_median': treatment_median, + 'median_difference': median_diff, + 'percent_change': (median_diff / control_median * 100) if control_median != 0 else float('inf'), + 'u_statistic': u_stat, + 'p_value': p_value, + 'confidence_interval': (ci_lower, ci_upper) if ci_lower is not None else None, + 'confidence_level': 1 - alpha, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'effect_size': cles + } + + def _run_anova(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run one-way ANOVA test.""" + # For ANOVA, we typically need multiple groups, but we can still run it with just two + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Run one-way ANOVA + f_stat, p_value = stats.f_oneway(control, treatment) + + # Calculate effect size (eta-squared) + total_values = np.concatenate([control, treatment]) + grand_mean = np.mean(total_values) + + ss_total = np.sum((total_values - grand_mean) ** 2) + ss_between = (len(control) * (np.mean(control) - grand_mean) ** 2 + + len(treatment) * (np.mean(treatment) - grand_mean) ** 2) + + eta_squared = ss_between / ss_total if ss_total > 0 else 0 + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'One-way ANOVA', + 'f_statistic': f_stat, + 'p_value': p_value, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'effect_size': eta_squared, + 'effect_size_type': 'eta_squared' + } + + def _run_chi_square(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """Run Chi-square test for categorical data.""" + # For chi-square, we assume the values represent counts in different categories + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Check if the arrays are the same length (same number of categories) + if len(control) != len(treatment): + raise ValueError("Control and treatment must have the same number of categories for chi-square test") + + # Run chi-square test + contingency_table = np.vstack([control, treatment]) + chi2_stat, p_value, dof, expected = stats.chi2_contingency(contingency_table) + + # Calculate Cramer's V as effect size + n = np.sum(contingency_table) + min_dim = min(contingency_table.shape) - 1 + cramers_v = np.sqrt(chi2_stat / (n * min_dim)) if n * min_dim > 0 else 0 + + # Determine if the result is significant + significant = p_value < alpha + + return { + 'test_type': 'Chi-square test', + 'chi2_statistic': chi2_stat, + 'p_value': p_value, + 'degrees_of_freedom': dof, + 'significant': significant, + 'effect_size': cramers_v, + 'effect_size_type': 'cramers_v' + } + + def check_assumptions(self, metric: str) -> Dict[str, Dict[str, Any]]: + """ + Check statistical assumptions for the given metric across all treatments. + + Args: + metric (str): The metric to check assumptions for. + + Returns: + dict: Dictionary with results of assumption checks for each treatment. + """ + if metric not in self.control_experiment_data: + raise ValueError(f"Metric '{metric}' not found in control data") + + results = {} + control_values = np.array(self.control_experiment_data[metric], dtype=float) + + # Check normality of control + control_shapiro = stats.shapiro(control_values) + control_normality = { + 'test': 'Shapiro-Wilk', + 'statistic': control_shapiro[0], + 'p_value': control_shapiro[1], + 'normal': control_shapiro[1] >= 0.05 + } + + for treatment_id, treatment_data in self.treatments_experiment_data.items(): + if metric not in treatment_data: + logger.warning(f"Metric '{metric}' not found in treatment '{treatment_id}'") + continue + + treatment_values = np.array(treatment_data[metric], dtype=float) + + # Check normality of treatment + treatment_shapiro = stats.shapiro(treatment_values) + treatment_normality = { + 'test': 'Shapiro-Wilk', + 'statistic': treatment_shapiro[0], + 'p_value': treatment_shapiro[1], + 'normal': treatment_shapiro[1] >= 0.05 + } + + # Check homogeneity of variance + levene_test = stats.levene(control_values, treatment_values) + variance_homogeneity = { + 'test': 'Levene', + 'statistic': levene_test[0], + 'p_value': levene_test[1], + 'equal_variance': levene_test[1] >= 0.05 + } + + # Store results and convert to JSON serializable types + results[treatment_id] = convert_to_serializable({ + 'control_normality': control_normality, + 'treatment_normality': treatment_normality, + 'variance_homogeneity': variance_homogeneity, + 'recommended_test': self._recommend_test(control_normality['normal'], + treatment_normality['normal'], + variance_homogeneity['equal_variance']) + }) + + return results + + def _recommend_test(self, control_normal: bool, treatment_normal: bool, equal_variance: bool) -> str: + """Recommend a statistical test based on assumption checks.""" + if control_normal and treatment_normal: + if equal_variance: + return 't_test' + else: + return 'welch_t_test' + else: + return 'mann_whitney' + + def _run_ks_test(self, control_values: list, treatment_values: list, alpha: float, **kwargs) -> Dict[str, Any]: + """ + Run Kolmogorov-Smirnov test to compare distributions. + + This test compares the empirical cumulative distribution functions (ECDFs) of two samples + to determine if they come from the same distribution. It's particularly useful for: + - Categorical responses (e.g., "Yes"/"No"/"Maybe") when converted to ordinal values + - Continuous data where you want to compare entire distributions, not just means + - Detecting differences in distribution shape, spread, or location + """ + # Convert to numpy arrays + control = np.array(control_values, dtype=float) + treatment = np.array(treatment_values, dtype=float) + + # Calculate basic statistics + control_median = np.median(control) + treatment_median = np.median(treatment) + control_mean = np.mean(control) + treatment_mean = np.mean(treatment) + + # Run the Kolmogorov-Smirnov test + ks_stat, p_value = stats.ks_2samp(control, treatment) + + # Calculate distribution characteristics + control_std = np.std(control, ddof=1) + treatment_std = np.std(treatment, ddof=1) + + # Calculate effect size using the KS statistic itself as a measure + # KS statistic ranges from 0 (identical distributions) to 1 (completely different) + effect_size = ks_stat + + # Additional distribution comparison metrics + # Calculate overlap coefficient (area under the minimum of two PDFs) + try: + # Create histograms for overlap calculation + combined_range = np.linspace( + min(np.min(control), np.min(treatment)), + max(np.max(control), np.max(treatment)), + 50 + ) + control_hist, _ = np.histogram(control, bins=combined_range, density=True) + treatment_hist, _ = np.histogram(treatment, bins=combined_range, density=True) + + # Calculate overlap (intersection over union-like metric) + overlap = np.sum(np.minimum(control_hist, treatment_hist)) / np.sum(np.maximum(control_hist, treatment_hist)) + overlap = overlap if not np.isnan(overlap) else 0.0 + except: + overlap = None + + # Calculate percentile differences for additional insights + percentiles = [25, 50, 75, 90, 95] + percentile_diffs = {} + for p in percentiles: + control_p = np.percentile(control, p) + treatment_p = np.percentile(treatment, p) + percentile_diffs[f"p{p}_diff"] = treatment_p - control_p + + # Determine significance + significant = p_value < alpha + + return { + 'test_type': 'Kolmogorov-Smirnov test', + 'control_mean': control_mean, + 'treatment_mean': treatment_mean, + 'control_median': control_median, + 'treatment_median': treatment_median, + 'control_std': control_std, + 'treatment_std': treatment_std, + 'ks_statistic': ks_stat, + 'p_value': p_value, + 'significant': significant, + 'control_sample_size': len(control), + 'treatment_sample_size': len(treatment), + 'effect_size': effect_size, + 'overlap_coefficient': overlap, + 'percentile_differences': percentile_diffs, + 'interpretation': self._interpret_ks_result(ks_stat, significant), + 'confidence_level': 1 - alpha + } + + def _interpret_ks_result(self, ks_stat: float, significant: bool) -> str: + """Provide interpretation of KS test results.""" + if not significant: + return "No significant difference between distributions" + + if ks_stat < 0.1: + return "Very small difference between distributions" + elif ks_stat < 0.25: + return "Small difference between distributions" + elif ks_stat < 0.5: + return "Moderate difference between distributions" + else: + return "Large difference between distributions" + + +def cohen_d(x: Union[list, np.ndarray], y: Union[list, np.ndarray]) -> float: + """ + Calculate Cohen's d effect size for two samples. + + Args: + x: First sample + y: Second sample + + Returns: + float: Cohen's d effect size + """ + nx = len(x) + ny = len(y) + + # Convert to numpy arrays + x = np.array(x, dtype=float) + y = np.array(y, dtype=float) + + # Calculate means + mx = np.mean(x) + my = np.mean(y) + + # Calculate standard deviations + sx = np.std(x, ddof=1) + sy = np.std(y, ddof=1) + + # Pooled standard deviation + pooled_sd = np.sqrt(((nx - 1) * sx**2 + (ny - 1) * sy**2) / (nx + ny - 2)) + + # Cohen's d + return (my - mx) / pooled_sd if pooled_sd > 0 else 0 + + +def convert_to_serializable(obj): + """ + Convert NumPy types to native Python types recursively to ensure JSON serialization works. + + Args: + obj: Any object that might contain NumPy types + + Returns: + Object with NumPy types converted to Python native types + """ + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, (np.number, np.bool_)): + return obj.item() + elif isinstance(obj, dict): + return {k: convert_to_serializable(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [convert_to_serializable(i) for i in obj] + elif isinstance(obj, tuple): + return tuple(convert_to_serializable(i) for i in obj) + else: + return obj \ No newline at end of file diff --git a/tinytroupe/extraction/__init__.py b/tinytroupe/extraction/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9963457a4c00207261fadaf9c41d79c2921102ab --- /dev/null +++ b/tinytroupe/extraction/__init__.py @@ -0,0 +1,24 @@ +""" +Simulations produce a lot of data, and it is often useful to extract these data in a structured way. For instance, you might wish to: + - Extract the main points from an agent's interactions history, so that you can consult them later in a concise form. + - Generate synthetic data from a simulation, so that you can use it for training machine learning models or testing software. + - Simply turn some of the data into a more machine-readable format, such as JSON or CSV, so that you can analyze it more easily. + +This module provides various utilities to help you extract data from TinyTroupe elements, such as agents and worlds. It also provides a +mechanism to reduce the extracted data to a more concise form, and to export artifacts from TinyTroupe elements. Incidentaly, it showcases +one of the many ways in which agent simulations differ from AI assistants, as the latter are not designed to be introspected in this way. +""" + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.extraction.artifact_exporter import ArtifactExporter +from tinytroupe.extraction.normalizer import Normalizer +from tinytroupe.extraction.results_extractor import ResultsExtractor +from tinytroupe.extraction.results_reducer import ResultsReducer +from tinytroupe.extraction.results_reporter import ResultsReporter + +__all__ = ["ArtifactExporter", "Normalizer", "ResultsExtractor", "ResultsReducer", "ResultsReporter"] \ No newline at end of file diff --git a/tinytroupe/extraction/__pycache__/__init__.cpython-312.pyc b/tinytroupe/extraction/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7868e70251a30391d6cf55f80b649f154c435d5c Binary files /dev/null and b/tinytroupe/extraction/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/extraction/__pycache__/artifact_exporter.cpython-312.pyc b/tinytroupe/extraction/__pycache__/artifact_exporter.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c3ead07aa1d4feba86080fb134712ed5faa669bf Binary files /dev/null and b/tinytroupe/extraction/__pycache__/artifact_exporter.cpython-312.pyc differ diff --git a/tinytroupe/extraction/__pycache__/normalizer.cpython-312.pyc b/tinytroupe/extraction/__pycache__/normalizer.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed69c2817732d6ad7ae760ce70d8ae87466a4972 Binary files /dev/null and b/tinytroupe/extraction/__pycache__/normalizer.cpython-312.pyc differ diff --git a/tinytroupe/extraction/__pycache__/results_extractor.cpython-312.pyc b/tinytroupe/extraction/__pycache__/results_extractor.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d1ff874d198e88c7307fb1c3cff8b85af6ccd2d7 Binary files /dev/null and b/tinytroupe/extraction/__pycache__/results_extractor.cpython-312.pyc differ diff --git a/tinytroupe/extraction/__pycache__/results_reducer.cpython-312.pyc b/tinytroupe/extraction/__pycache__/results_reducer.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9b938a21b048b45b5baecfc2458b34b9c9170224 Binary files /dev/null and b/tinytroupe/extraction/__pycache__/results_reducer.cpython-312.pyc differ diff --git a/tinytroupe/extraction/__pycache__/results_reporter.cpython-312.pyc b/tinytroupe/extraction/__pycache__/results_reporter.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1c2e1e68a20101f0a18cc394c2492e7c6afa3b9e Binary files /dev/null and b/tinytroupe/extraction/__pycache__/results_reporter.cpython-312.pyc differ diff --git a/tinytroupe/extraction/artifact_exporter.py b/tinytroupe/extraction/artifact_exporter.py new file mode 100644 index 0000000000000000000000000000000000000000..82f27fb2fd69689ce19957402077d1a6de1041b5 --- /dev/null +++ b/tinytroupe/extraction/artifact_exporter.py @@ -0,0 +1,160 @@ +import os +import json +import pandas as pd +import pypandoc +import markdown +from typing import Union, List + +from tinytroupe.extraction import logger +from tinytroupe.utils import JsonSerializableRegistry + +import tinytroupe.utils as utils + +class ArtifactExporter(JsonSerializableRegistry): + """ + An artifact exporter is responsible for exporting artifacts from TinyTroupe elements, for example + in order to create synthetic data files from simulations. + """ + + def __init__(self, base_output_folder:str) -> None: + self.base_output_folder = base_output_folder + + def export(self, artifact_name:str, artifact_data:Union[dict, str], content_type:str, content_format:str=None, target_format:str="txt", verbose:bool=False): + """ + Exports the specified artifact data to a file. + + Args: + artifact_name (str): The name of the artifact. + artifact_data (Union[dict, str]): The data to export. If a dict is given, it will be saved as JSON. + If a string is given, it will be saved as is. + content_type (str): The type of the content within the artifact. + content_format (str, optional): The format of the content within the artifact (e.g., md, csv, etc). Defaults to None. + target_format (str): The format to export the artifact to (e.g., json, txt, docx, etc). + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + # dedent inputs, just in case + if isinstance(artifact_data, str): + artifact_data = utils.dedent(artifact_data) + elif isinstance(artifact_data, dict): + artifact_data['content'] = utils.dedent(artifact_data['content']) + else: + raise ValueError("The artifact data must be either a string or a dictionary.") + + # clean the artifact name of invalid characters + invalid_chars = ['/', '\\', ':', '*', '?', '"', '<', '>', '|', '\n', '\t', '\r', ';'] + for char in invalid_chars: + # check if the character is in the artifact name + if char in artifact_name: + # replace the character with an underscore + artifact_name = artifact_name.replace(char, "-") + logger.warning(f"Replaced invalid character {char} with hyphen in artifact name '{artifact_name}'.") + + artifact_file_path = self._compose_filepath(artifact_data, artifact_name, content_type, target_format, verbose) + + + if target_format == "json": + self._export_as_json(artifact_file_path, artifact_data, content_type, verbose) + elif target_format == "txt" or target_format == "text" or target_format == "md" or target_format == "markdown": + self._export_as_txt(artifact_file_path, artifact_data, content_type, verbose) + elif target_format == "docx": + self._export_as_docx(artifact_file_path, artifact_data, content_format, verbose) + else: + raise ValueError(f"Unsupported target format: {target_format}.") + + + def _export_as_txt(self, artifact_file_path:str, artifact_data:Union[dict, str], content_type:str, verbose:bool=False): + """ + Exports the specified artifact data to a text file. + """ + + with open(artifact_file_path, 'w', encoding="utf-8", errors="replace") as f: + if isinstance(artifact_data, dict): + content = artifact_data['content'] + else: + content = artifact_data + + f.write(content) + + def _export_as_json(self, artifact_file_path:str, artifact_data:Union[dict, str], content_type:str, verbose:bool=False): + """ + Exports the specified artifact data to a JSON file. + """ + + with open(artifact_file_path, 'w', encoding="utf-8", errors="replace") as f: + if isinstance(artifact_data, dict): + json.dump(artifact_data, f, indent=4) + else: + raise ValueError("The artifact data must be a dictionary to export to JSON.") + + def _export_as_docx(self, artifact_file_path:str, artifact_data:Union[dict, str], content_original_format:str, verbose:bool=False): + """ + Exports the specified artifact data to a DOCX file. + """ + + # original format must be 'text' or 'markdown' + if content_original_format not in ['text', 'txt', 'markdown', 'md']: + raise ValueError(f"The original format cannot be {content_original_format} to export to DOCX.") + else: + # normalize content value + content_original_format = 'markdown' if content_original_format == 'md' else content_original_format + + # first, get the content to export. If `artifact_date` is a dict, the contant should be under the key `content`. + # If it is a string, the content is the string itself. + # using pypandoc + if isinstance(artifact_data, dict): + content = artifact_data['content'] + else: + content = artifact_data + + # first, convert to HTML. This is necessary because pypandoc does not support a GOOD direct conversion from markdown to DOCX. + html_content = markdown.markdown(content) + + ## write this intermediary HTML to file + #html_file_path = artifact_file_path.replace(".docx", ".html") + #with open(html_file_path, 'w', encoding="utf-8", errors="replace") as f: + # f.write(html_content) + + # then, convert to DOCX + pypandoc.convert_text(html_content, 'docx', format='html', outputfile=artifact_file_path) + + ########################################################### + # IO + ########################################################### + + def _compose_filepath(self, artifact_data:Union[dict, str], artifact_name:str, content_type:str, target_format:str=None, verbose:bool=False): + """ + Composes the file path for the artifact to export. + + Args: + artifact_data (Union[dict, str]): The data to export. + artifact_name (str): The name of the artifact. + content_type (str): The type of the content within the artifact. + content_format (str, optional): The format of the content within the artifact (e.g., md, csv, etc). Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + # Extension definition: + # + # - If the content format is specified, we use it as the part of the extension. + # - If artificat_data is a dict, we add .json to the extension. Note that if content format was specified, we'd get .json. + # - If artifact_data is a string and no content format is specified, we add .txt to the extension. + extension = None + if target_format is not None: + extension = f"{target_format}" + elif isinstance(artifact_data, str) and target_format is None: + extension = "txt" + + # content type definition + if content_type is None: + subfolder = "" + else: + subfolder = content_type + + # save to the specified file name or path, considering the base output folder. + artifact_file_path = os.path.join(self.base_output_folder, subfolder, f"{artifact_name}.{extension}") + + # create intermediate directories if necessary + os.makedirs(os.path.dirname(artifact_file_path), exist_ok=True) + + return artifact_file_path \ No newline at end of file diff --git a/tinytroupe/extraction/normalizer.py b/tinytroupe/extraction/normalizer.py new file mode 100644 index 0000000000000000000000000000000000000000..9ccd50ba3b65d5f2109c004524ba9be7a9cdef37 --- /dev/null +++ b/tinytroupe/extraction/normalizer.py @@ -0,0 +1,115 @@ +import pandas as pd +from typing import Union, List + +from tinytroupe.extraction import logger + +from tinytroupe import openai_utils +import tinytroupe.utils as utils +class Normalizer: + """ + A mechanism to normalize passages, concepts and other textual elements. + """ + + def __init__(self, elements:List[str], n:int, verbose:bool=False): + """ + Normalizes the specified elements. + + Args: + elements (list): The elements to normalize. + n (int): The number of normalized elements to output. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + # ensure elements are unique + self.elements = list(set(elements)) + + self.n = n + self.verbose = verbose + + # a JSON-based structure, where each output element is a key to a list of input elements that were merged into it + self.normalized_elements = None + # a dict that maps each input element to its normalized output. This will be used as cache later. + self.normalizing_map = {} + + rendering_configs = {"n": n, + "elements": self.elements} + + messages = utils.compose_initial_LLM_messages_with_templates("normalizer.system.mustache", "normalizer.user.mustache", + base_module_folder="extraction", + rendering_configs=rendering_configs) + + next_message = openai_utils.client().send_message(messages, temperature=0.1) + + debug_msg = f"Normalization result message: {next_message}" + logger.debug(debug_msg) + if self.verbose: + print(debug_msg) + + result = utils.extract_json(next_message["content"]) + logger.debug(result) + if self.verbose: + print(result) + + self.normalized_elements = result + + + def normalize(self, element_or_elements:Union[str, List[str]]) -> Union[str, List[str]]: + """ + Normalizes the specified element or elements. + + This method uses a caching mechanism to improve performance. If an element has been normalized before, + its normalized form is stored in a cache (self.normalizing_map). When the same element needs to be + normalized again, the method will first check the cache and use the stored normalized form if available, + instead of normalizing the element again. + + The order of elements in the output will be the same as in the input. This is ensured by processing + the elements in the order they appear in the input and appending the normalized elements to the output + list in the same order. + + Args: + element_or_elements (Union[str, List[str]]): The element or elements to normalize. + + Returns: + str: The normalized element if the input was a string. + list: The normalized elements if the input was a list, preserving the order of elements in the input. + """ + if isinstance(element_or_elements, str): + denormalized_elements = [element_or_elements] + elif isinstance(element_or_elements, list): + denormalized_elements = element_or_elements + else: + raise ValueError("The element_or_elements must be either a string or a list.") + + normalized_elements = [] + elements_to_normalize = [] + for element in denormalized_elements: + if element not in self.normalizing_map: + elements_to_normalize.append(element) + + if elements_to_normalize: + rendering_configs = {"categories": self.normalized_elements, + "elements": elements_to_normalize} + + messages = utils.compose_initial_LLM_messages_with_templates("normalizer.applier.system.mustache", "normalizer.applier.user.mustache", + base_module_folder="extraction", + rendering_configs=rendering_configs) + + next_message = openai_utils.client().send_message(messages, temperature=0.1) + + debug_msg = f"Normalization result message: {next_message}" + logger.debug(debug_msg) + if self.verbose: + print(debug_msg) + + normalized_elements_from_llm = utils.extract_json(next_message["content"]) + assert isinstance(normalized_elements_from_llm, list), "The normalized element must be a list." + assert len(normalized_elements_from_llm) == len(elements_to_normalize), "The number of normalized elements must be equal to the number of elements to normalize." + + for i, element in enumerate(elements_to_normalize): + normalized_element = normalized_elements_from_llm[i] + self.normalizing_map[element] = normalized_element + + for element in denormalized_elements: + normalized_elements.append(self.normalizing_map[element]) + + return normalized_elements + diff --git a/tinytroupe/extraction/prompts/interaction_results_extractor.mustache b/tinytroupe/extraction/prompts/interaction_results_extractor.mustache new file mode 100644 index 0000000000000000000000000000000000000000..b4bfbea6faa90f6ae4f72a72ba86383bc3106c24 --- /dev/null +++ b/tinytroupe/extraction/prompts/interaction_results_extractor.mustache @@ -0,0 +1,68 @@ +# Results filter + +You are a system to extract specific choices, information or results that one or more agents generated under a situation. + +You must: + - Extact only what is requested, as closely as possible. + - If the information requested is not present, you just return an empty result (null). + - Output in JSON format, **ALWAYS**, without any extra text or markings. + +Your inputs are: + - An interaction history of one or more agents, which might include, for each agent, both stimuli it receives and actions it performs. + - An extraction objective, which defines precisely what is to be extracted. If the desired information is not present, you + resturn an empty result. + - A situation which explains what were the conditions the agents were subject to. + +On your output format: + - You always output JSON strings. + - If the output contains only one result, but with one or more fields, your output **MUST** follows this format: + ```json + {: , :, ..., :} + ``` + - On the other hand, **ONLY** if the output contains multiple results, your output follows this format: + ```json + [ + {: , :, ..., :}, + ..., + {: , :, ..., :} + ] + ``` + - NEVER output a single result within a top-level list like this: ```[]```. Always output `````` directly. + - **DO NOT** include ```json or ``` tags; just include the actual JSON string. + {{#fields}} + - , , ... **must** be the following: {{fields}} + {{/fields}} + + {{#fields_hints}} + - Additional contraint for field `{{0}}`: {{1}} + {{/fields_hints}} + + + +## Examples + +### Example 1 + +Example input: + Extraction objective: obtain the baby product that was purchased (field "choice"). + + Situation: you have a baby at home, just a little money right now, and you need to buy only what is urgent. + + [TALK] Since I have little money, I must prioritize what I'll buy. Diapers are really the most important thing, so I'll take them today. + > I'll buy the formula tomorrow instead. + +Example output: +{"choice": "Diapers"} + + +### Example 2 +Example input: + Extraction objective: obtain the baby product that was purchased (field "choice"). + + Situation: you have a baby at home, just a little money right now, and you need to buy only what is urgent. + + [TALK] Actually, there's nothing that I need right now. I wanted diapers, but they don't have the right size. So I won't buy anything. + +Example output: +{"choice": null} + diff --git a/tinytroupe/extraction/prompts/normalizer.applier.system.mustache b/tinytroupe/extraction/prompts/normalizer.applier.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..195a0ef2e19e083225eb831cdaabf2c1ad0d2f10 --- /dev/null +++ b/tinytroupe/extraction/prompts/normalizer.applier.system.mustache @@ -0,0 +1,17 @@ +# Normalizer + +You are a system that normalizes text data. This means that: + - You receive an input list of items to be categorized. + - You already have a list of categories that you consider standard. + - For each input item to be categorized, you assign it to the category that has the most similarity to it. This is your output. + - For each input item, you will produce exactly one output. That is to say, each input element will be replaced by exactly one of the categories. + You might need to repeat elements in your output, since different input elements might map to the same category. + - The number of output items is the same as the number of input items. + +On the format of your output: + - you return a JSON structure listing the resulting output elements; + - for example, given an input of `["cake", "gazpacho", "cat", "christmas tree", "napolitana"]`, and having the categorie `["Food", "Fauna & Flora"]`, + the output would look like this: + ```json + ["Food", "Food", "Fauna & Flora", "Fauna & Flora", "Food"] + ``` \ No newline at end of file diff --git a/tinytroupe/extraction/prompts/normalizer.applier.user.mustache b/tinytroupe/extraction/prompts/normalizer.applier.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..f72f38349dbe2d3106479dc75f739cd36586d3cc --- /dev/null +++ b/tinytroupe/extraction/prompts/normalizer.applier.user.mustache @@ -0,0 +1,10 @@ +The standard categories to consider are these: +{{#categories}} + - {{.}} +{{/categories}} + +Now please map the following input elements to the categories above: + +{{#elements}} + - {{.}} +{{/elements}} \ No newline at end of file diff --git a/tinytroupe/extraction/prompts/normalizer.system.mustache b/tinytroupe/extraction/prompts/normalizer.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..7f3385e01057e71ac422d262e2cd187b737e6b79 --- /dev/null +++ b/tinytroupe/extraction/prompts/normalizer.system.mustache @@ -0,0 +1,27 @@ +# Normalizer + +You are a system that normalizes text data. This means that: + - you receive a list of input textual elements, such as concepts, passages or phrases; + - you receive a number of the desired output elements; + - and then you merge the input elements into the desired number of output elements. + - you must ensure that all input elements are properly partitioned in the output elements, without any overlap, and without any element being left out. + - output elements must subsume the input elements that correspond to them, that is, they must be maximally similar to all of them. + +The merging is done by: + - determining which input elements are similar to each other; + - input elements are grouped together according to how similar they are, and each such group is mapped to a single output element; + - make sure you produce the desired number of output elements. + - make sure you ensure that the output elements are unique. + - if the number of input elements is equal to or less than the desired number of output elements, you must return the input elements as output elements without modification. + +The abstract representation is created by: + - if the elements are concepts or otherwise very short, you must find a concept that subsumes them; + - if the elements are passages or otherwise longer, you must find a passage that subsumes them, that is maximally similar to all of them. + +On the format of your output: + - you return a JSON structure listing the resulting output elements; + - for example, given an input of `[INPUT_1, INPUT_2, INPUT_3, INPUT_4]`, and the number of 2 desired output elements, the system output could look like this: + ```json + [OUTPUT_1, OUTPUT_2] + ``` + diff --git a/tinytroupe/extraction/prompts/normalizer.user.mustache b/tinytroupe/extraction/prompts/normalizer.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..c8a3289683d31c892bce9a006bcb7579126892bb --- /dev/null +++ b/tinytroupe/extraction/prompts/normalizer.user.mustache @@ -0,0 +1,5 @@ +Now please produce {{n}} normalized elements that represent the following input elements: + +{{#elements}} + - {{.}} +{{/elements}} \ No newline at end of file diff --git a/tinytroupe/extraction/results_extractor.py b/tinytroupe/extraction/results_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..4ccacc181bc227434ea19bead439147e6861b5e8 --- /dev/null +++ b/tinytroupe/extraction/results_extractor.py @@ -0,0 +1,268 @@ +import os +import json +import chevron +import pandas as pd +from typing import Union, List + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld + +from tinytroupe import openai_utils +import tinytroupe.utils as utils + + +class ResultsExtractor: + + def __init__(self, + extraction_prompt_template_path:str = os.path.join(os.path.dirname(__file__), './prompts/interaction_results_extractor.mustache'), + extraction_objective:str = "The main points present in the agents' interactions history.", + situation:str = "", + fields:List[str] = None, + fields_hints:dict = None, + verbose:bool = False): + """ + Initializes the ResultsExtractor with default parameters. + + Args: + extraction_prompt_template_path (str): The path to the extraction prompt template. + extraction_objective (str): The default extraction objective. + situation (str): The default situation to consider. + fields (List[str], optional): The default fields to extract. Defaults to None. + fields_hints (dict, optional): The default hints for the fields to extract. Defaults to None. + verbose (bool, optional): Whether to print debug messages by default. Defaults to False. + """ + self._extraction_prompt_template_path = extraction_prompt_template_path + + # Default parameters + self.default_extraction_objective = extraction_objective + self.default_situation = situation + self.default_fields = fields + self.default_fields_hints = fields_hints + self.default_verbose = verbose + + # Cache for the last extraction results + self.agent_extraction = {} + self.world_extraction = {} + + def extract_results_from_agents(self, + agents:List[TinyPerson], + extraction_objective:str=None, + situation:str =None, + fields:list=None, + fields_hints:dict=None, + verbose:bool=None): + """ + Extracts results from a list of TinyPerson instances. + + Args: + agents (List[TinyPerson]): The list of TinyPerson instances to extract results from. + extraction_objective (str): The extraction objective. + situation (str): The situation to consider. + fields (list, optional): The fields to extract. If None, the extractor will decide what names to use. + Defaults to None. + fields_hints (dict, optional): Hints for the fields to extract. Maps field names to strings with the hints. Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + + + """ + results = [] + for agent in agents: + result = self.extract_results_from_agent(agent, extraction_objective, situation, fields, fields_hints, verbose) + results.append(result) + + return results + + def extract_results_from_agent(self, + tinyperson:TinyPerson, + extraction_objective:str="The main points present in the agent's interactions history.", + situation:str = "", + fields:list=None, + fields_hints:dict=None, + verbose:bool=None): + """ + Extracts results from a TinyPerson instance. + + Args: + tinyperson (TinyPerson): The TinyPerson instance to extract results from. + extraction_objective (str): The extraction objective. + situation (str): The situation to consider. + fields (list, optional): The fields to extract. If None, the extractor will decide what names to use. + Defaults to None. + fields_hints (dict, optional): Hints for the fields to extract. Maps field names to strings with the hints. Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + extraction_objective, situation, fields, fields_hints, verbose = self._get_default_values_if_necessary( + extraction_objective, situation, fields, fields_hints, verbose + ) + + messages = [] + + rendering_configs = {} + if fields is not None: + rendering_configs["fields"] = ", ".join(fields) + + if fields_hints is not None: + rendering_configs["fields_hints"] = list(fields_hints.items()) + + messages.append({"role": "system", + "content": chevron.render( + open(self._extraction_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + + + interaction_history = tinyperson.pretty_current_interactions(max_content_length=None) + + extraction_request_prompt = \ +f""" +## Extraction objective + +{extraction_objective} + +## Situation +You are considering a single agent, named {tinyperson.name}. Your objective thus refers to this agent specifically. +{situation} + +## Agent Interactions History + +You will consider an agent's history of interactions, which include stimuli it received as well as actions it +performed. + +{interaction_history} +""" + messages.append({"role": "user", "content": extraction_request_prompt}) + + next_message = openai_utils.client().send_message(messages, temperature=0.0, frequency_penalty=0.0, presence_penalty=0.0) + + debug_msg = f"Extraction raw result message: {next_message}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + if next_message is not None: + result = utils.extract_json(next_message["content"]) + else: + result = None + + # cache the result + self.agent_extraction[tinyperson.name] = result + + return result + + + def extract_results_from_world(self, + tinyworld:TinyWorld, + extraction_objective:str="The main points that can be derived from the agents conversations and actions.", + situation:str="", + fields:list=None, + fields_hints:dict=None, + verbose:bool=None): + """ + Extracts results from a TinyWorld instance. + + Args: + tinyworld (TinyWorld): The TinyWorld instance to extract results from. + extraction_objective (str): The extraction objective. + situation (str): The situation to consider. + fields (list, optional): The fields to extract. If None, the extractor will decide what names to use. + Defaults to None. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + + extraction_objective, situation, fields, fields_hints, verbose = self._get_default_values_if_necessary( + extraction_objective, situation, fields, fields_hints, verbose + ) + + messages = [] + + rendering_configs = {} + if fields is not None: + rendering_configs["fields"] = ", ".join(fields) + + if fields_hints is not None: + rendering_configs["fields_hints"] = list(fields_hints.items()) + + messages.append({"role": "system", + "content": chevron.render( + open(self._extraction_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + + # TODO: either summarize first or break up into multiple tasks + interaction_history = tinyworld.pretty_current_interactions(max_content_length=None) + + extraction_request_prompt = \ +f""" +## Extraction objective + +{extraction_objective} + +## Situation +You are considering various agents. +{situation} + +## Agents Interactions History + +You will consider the history of interactions from various agents that exist in an environment called {tinyworld.name}. +Each interaction history includes stimuli the corresponding agent received as well as actions it performed. + +{interaction_history} +""" + messages.append({"role": "user", "content": extraction_request_prompt}) + + next_message = openai_utils.client().send_message(messages, temperature=0.0) + + debug_msg = f"Extraction raw result message: {next_message}" + logger.debug(debug_msg) + if verbose: + print(debug_msg) + + if next_message is not None: + result = utils.extract_json(next_message["content"]) + else: + result = None + + # cache the result + self.world_extraction[tinyworld.name] = result + + return result + + def save_as_json(self, filename:str, verbose:bool=False): + """ + Saves the last extraction results as JSON. + + Args: + filename (str): The filename to save the JSON to. + verbose (bool, optional): Whether to print debug messages. Defaults to False. + """ + with open(filename, 'w', encoding="utf-8", errors="replace") as f: + json.dump({"agent_extractions": self.agent_extraction, + "world_extraction": self.world_extraction}, f, indent=4) + + if verbose: + print(f"Saved extraction results to {filename}") + + def _get_default_values_if_necessary(self, + extraction_objective:str, + situation:str, + fields:List[str], + fields_hints:dict, + verbose:bool): + + if extraction_objective is None: + extraction_objective = self.default_extraction_objective + + if situation is None: + situation = self.default_situation + + if fields is None: + fields = self.default_fields + + if fields_hints is None: + fields_hints = self.default_fields_hints + + if verbose is None: + verbose = self.default_verbose + + return extraction_objective, situation, fields, fields_hints, verbose + diff --git a/tinytroupe/extraction/results_reducer.py b/tinytroupe/extraction/results_reducer.py new file mode 100644 index 0000000000000000000000000000000000000000..66405db38d876b770e5d42aac2925b71f435df0e --- /dev/null +++ b/tinytroupe/extraction/results_reducer.py @@ -0,0 +1,55 @@ +import pandas as pd + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson + + +class ResultsReducer: + + def __init__(self): + self.results = {} + + self.rules = {} + + def add_reduction_rule(self, trigger: str, func: callable): + if trigger in self.rules: + raise Exception(f"Rule for {trigger} already exists.") + + self.rules[trigger] = func + + def reduce_agent(self, agent: TinyPerson) -> list: + reduction = [] + for message in agent.episodic_memory.retrieve_all(): + if message['role'] == 'system': + continue # doing nothing for `system` role yet at least + + elif message['role'] == 'user': + # User role is related to stimuli only + stimulus_type = message['content']['stimuli'][0].get('type', None) + stimulus_content = message['content']['stimuli'][0].get('content', None) + stimulus_source = message['content']['stimuli'][0].get('source', None) + stimulus_timestamp = message['simulation_timestamp'] + + if stimulus_type in self.rules: + extracted = self.rules[stimulus_type](focus_agent=agent, source_agent=TinyPerson.get_agent_by_name(stimulus_source), target_agent=agent, kind='stimulus', event=stimulus_type, content=stimulus_content, timestamp=stimulus_timestamp) + if extracted is not None: + reduction.append(extracted) + + elif message['role'] == 'assistant': + # Assistant role is related to actions only + if 'action' in message['content']: + action_type = message['content']['action'].get('type', None) + action_content = message['content']['action'].get('content', None) + action_target = message['content']['action'].get('target', None) + action_timestamp = message['simulation_timestamp'] + + if action_type in self.rules: + extracted = self.rules[action_type](focus_agent=agent, source_agent=agent, target_agent=TinyPerson.get_agent_by_name(action_target), kind='action', event=action_type, content=action_content, timestamp=action_timestamp) + if extracted is not None: + reduction.append(extracted) + + return reduction + + def reduce_agent_to_dataframe(self, agent: TinyPerson, column_names: list=None) -> pd.DataFrame: + reduction = self.reduce_agent(agent) + return pd.DataFrame(reduction, columns=column_names) diff --git a/tinytroupe/extraction/results_reporter.py b/tinytroupe/extraction/results_reporter.py new file mode 100644 index 0000000000000000000000000000000000000000..bb3b0fc18c09247d892c2462c5f71a931c8409b4 --- /dev/null +++ b/tinytroupe/extraction/results_reporter.py @@ -0,0 +1,444 @@ +import os +import json +from typing import Union, List, Dict, Any +from rich.console import Console +from rich.markdown import Markdown + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld +from tinytroupe.utils import LLMChat +from tinytroupe import default + + +class ResultsReporter: + + def __init__(self, + default_reporting_task: str = "Summarize the key findings, insights, and outcomes from the simulation data.", + verbose: bool = False): + """ + Initializes the ResultsReporter. + + Args: + default_reporting_task (str): The default task to ask agents when generating reports. + verbose (bool): Whether to print debug messages. + """ + self.default_reporting_task = default_reporting_task + self.verbose = verbose + self.console = Console() + + # Cache for generated reports + self.last_report = None + + def report_from_agents(self, + agents: Union[TinyPerson, TinyWorld, List[TinyPerson]], + reporting_task: str = None, + report_title: str = "Simulation Report", + include_agent_summaries: bool = True, + consolidate_responses: bool = True, + requirements: str = "Present the findings in a clear, structured manner.") -> str: + """ + Option 1: Generate a report by asking agents about specific reporting tasks. + + Args: + agents: Single agent, TinyWorld, or list of agents to interview. + reporting_task: The specific task to ask agents about. + report_title: Title for the generated report. + include_agent_summaries: Whether to include agent mini-bios in the report. + consolidate_responses: Whether to consolidate all responses into a single report. + requirements: Formatting or content requirements for the report. + + Returns: + str: The generated Markdown report. + """ + if reporting_task is None: + reporting_task = self.default_reporting_task + + # Extract agents from input + agent_list = self._extract_agents(agents) + + if self.verbose: + logger.info(f"Interviewing {len(agent_list)} agents for report generation.") + + # Collect responses from agents + agent_responses = [] + for agent in agent_list: + response = self._interview_agent(agent, reporting_task) + agent_responses.append({ + "agent": agent, + "response": response + }) + + # Generate the report + report = self._format_agent_interview_report( + agent_responses, + report_title, + reporting_task, + include_agent_summaries, + consolidate_responses, + requirements + ) + + self.last_report = report + return report + + def report_from_interactions(self, + agents: Union[TinyPerson, TinyWorld, List[TinyPerson]], + report_title: str = "Interaction Analysis Report", + include_agent_summaries: bool = True, + first_n: int = None, + last_n: int = None, + max_content_length: int = None, + requirements: str = "Present the findings in a clear, structured manner.") -> str: + """ + Option 2: Generate a report by analyzing agents' historical interactions. + + Args: + agents: Single agent, TinyWorld, or list of agents to analyze. + report_title: Title for the generated report. + include_agent_summaries: Whether to include agent mini-bios. + first_n: Number of first interactions to include. + last_n: Number of last interactions to include. + max_content_length: Maximum content length for interactions. + requirements: Formatting or content requirements for the report. + + Returns: + str: The generated Markdown report. + """ + # Extract agents from input + agent_list = self._extract_agents(agents) + + if self.verbose: + logger.info(f"Analyzing interactions from {len(agent_list)} agents.") + + # Collect interaction data + interactions_data = [] + for agent in agent_list: + interactions = agent.pretty_current_interactions( + simplified=True, + first_n=first_n, + last_n=last_n, + max_content_length=max_content_length + ) + interactions_data.append({ + "agent": agent, + "interactions": interactions + }) + + # Generate the report + report = self._format_interactions_report( + interactions_data, + report_title, + include_agent_summaries, + requirements + ) + + self.last_report = report + return report + + def report_from_data(self, + data: Union[str, Dict[str, Any], List[Dict[str, Any]]], + report_title: str = "Data Report", + requirements: str = "Present the findings in a clear, structured manner.") -> str: + """ + Option 3: Generate a report from raw text or structured data. + + Args: + data: Raw text, dictionary, or list of dictionaries to format. + report_title: Title for the generated report. + requirements: Formatting or content requirements for the report. If None, uses simple formatting. + + Returns: + str: The generated Markdown report. + """ + if self.verbose: + logger.info("Generating report from raw data.") + + # Generate the report + report = self._format_data_report(data, report_title, requirements) + + self.last_report = report + return report + + def display_report(self, report: str = None): + """ + Display a report on the console with rich formatting. + + Args: + report: The report to display. If None, uses the last generated report. + """ + if report is None: + report = self.last_report + + if report is None: + self.console.print("[red]No report available to display.[/red]") + return + + markdown = Markdown(report) + self.console.print(markdown) + + def save_report(self, + filename: str, + report: str = None, + verbose: bool = None): + """ + Save a report to a file. + + Args: + filename: The filename to save the report to. + report: The report to save. If None, uses the last generated report. + verbose: Whether to print confirmation message. + """ + if report is None: + report = self.last_report + + if report is None: + raise ValueError("No report available to save.") + + if verbose is None: + verbose = self.verbose + + with open(filename, 'w', encoding='utf-8', errors='replace') as f: + f.write(report) + + if verbose: + logger.info(f"Report saved to {filename}") + + def _extract_agents(self, agents) -> List[TinyPerson]: + """Extract a list of TinyPerson objects from various input types.""" + if isinstance(agents, TinyPerson): + return [agents] + elif isinstance(agents, TinyWorld): + return agents.agents + elif isinstance(agents, list): + return agents + else: + raise ValueError("Agents must be a TinyPerson, TinyWorld, or list of TinyPerson objects.") + + def _interview_agent(self, agent: TinyPerson, reporting_task: str) -> str: + """Interview a single agent about the reporting task.""" + if self.verbose: + logger.debug(f"Interviewing agent {agent.name} about: {reporting_task}") + + # Following TinyTroupe patterns - directly interact with the agent + prompt = f""" + I need you to provide a comprehensive report based on your experiences and observations. + + Reporting task: {reporting_task} + + Please provide detailed insights, specific examples, and key findings from your perspective. + Focus on what you've learned, observed, and experienced during the simulation. + """ + + # Use listen_and_act pattern to get agent's response + agent.listen(prompt) + actions = agent.act(return_actions=True) + + # Extract the response from the agent's actions + response = "" + for action in actions: + if action["action"]["type"] == "TALK": + response += action["action"]["content"] + "\n" + + if self.verbose: + logger.debug(f"Agent {agent.name} response received.") + + return response.strip() + + def _format_agent_interview_report(self, + agent_responses: List[Dict], + title: str, + task: str, + include_summaries: bool, + consolidate: bool, + requirements: str) -> str: + """Format agent interview responses into a Markdown report.""" + # Prepare data for LLM formatting + agents_data = [] + for resp in agent_responses: + agent_info = { + "name": resp["agent"].name, + "response": resp["response"] + } + if include_summaries: + agent_info["bio"] = resp["agent"].minibio(extended=False) + agents_data.append(agent_info) + + # Generate report using LLM + return self._generate_report_with_llm( + title=title, + report_type="agent_interview", + data={ + "reporting_task": task, + "agents_data": agents_data, + "consolidate": consolidate + }, + include_summaries=include_summaries, + requirements=requirements + ) + + def _format_interactions_report(self, + interactions_data: List[Dict], + title: str, + include_summaries: bool, + requirements: str) -> str: + """Format interaction data into a Markdown report.""" + # Prepare data for LLM formatting + agents_data = [] + for data in interactions_data: + agent_info = { + "name": data["agent"].name, + "interactions": data["interactions"] + } + if include_summaries: + agent_info["bio"] = data["agent"].minibio(extended=False) + agents_data.append(agent_info) + + # Generate report using LLM + return self._generate_report_with_llm( + title=title, + report_type="interactions", + data={"agents_data": agents_data}, + include_summaries=include_summaries, + requirements=requirements + ) + + def _format_data_report(self, + data: Any, + title: str, + requirements: str) -> str: + """Format raw data into a Markdown report.""" + return self._generate_report_with_llm( + title=title, + report_type="custom_data", + data=data, + requirements=requirements + ) + + + def _generate_report_with_llm(self, + title: str, + report_type: str, + data: Any, + include_summaries: bool = False, + requirements: str = None) -> str: + """Generate a report using LLM based on the report type and data.""" + + # Base system prompt + system_prompt = "You are a professional report writer who creates clear, well-structured Markdown reports." + + # Type-specific prompts and instructions + if report_type == "agent_interview": + system_prompt += " You specialize in synthesizing interview responses from multiple agents." + user_prompt = f""" + ## Task + Create a comprehensive report based on agent interviews such that it fulfills the + specified requirements below. + + ## Report Title + {title} + + ## Report Details + - **Reporting Task:** {data['reporting_task']} + - **Number of Agents Interviewed:** {len(data['agents_data'])} + - **Generated on:** {self._get_timestamp()} + + ## Agent Responses + {json.dumps(data['agents_data'], indent=2)} + + ## Instructions + - Start with the title as a level-1 header + - Write a direct, clear report, but do not simplify or summarize the information + - Make sure all important details are included. This is not a summary, but a detailed report, so you never remove information, you just make it more readable + - Do not include the original data or agent responses, but only the resulting report information + - For each agent, include their bio if provided + - Use proper Markdown formatting throughout + - Follow the requirements given next, which can also override any of these rules + + ## Requirements + {requirements} + """ + + elif report_type == "interactions": + system_prompt += " You specialize in analyzing and presenting agent interaction histories." + user_prompt = f""" + ## Task + Create a report analyzing agent interactions from a simulation such that it fulfills the + specified requirements below. + + ## Report Title + {title} + + ## Report Details + - **Number of Agents Analyzed:** {len(data['agents_data'])} + - **Generated on:** {self._get_timestamp()} + + ## Agent Interaction Data + {json.dumps(data['agents_data'], indent=2)} + + ## Instructions + - Start with the title as a level-1 header + - Write a direct, clear report, but do not simplify or summarize the information + - Make sure all important details are included. This is not a summary, but a detailed report, so you never remove information, you just make it more readable + - Do not include agents' interaction history, but only the resulting report information + - For each agent, include their bio if provided + - Use proper Markdown formatting throughout + - Follow the requirements given next, which can also override any of these rules + + ## Requirements + {requirements} + """ + + elif report_type == "custom_data": + # Handle arbitrary data without assuming any structure + if isinstance(data, str): + data_representation = data + else: + # For any other type, convert to JSON for a clean representation + data_representation = json.dumps(data, indent=2) + + user_prompt = f""" + ## Task + Create a well-structured Markdown report based on the provided data such that it fulfills the + specified requirements below. + + ## Report Title + {title} + + ## Generated on + {self._get_timestamp()} + + ## Data to Format + {data_representation} + + ## Instructions + - Start with the title as a level-1 header + - Write a direct, clear report, but do not simplify or summarize the information + - Make sure all important details are included. This is not a summary, but a detailed report, so you never remove information, you just make it more readable + - Use proper Markdown formatting throughout + - Follow the requirements given next, which can also override any of these rules + + ## Requirements + {requirements if requirements else "Use your best judgment to create a clear, informative report that presents the data in an organized and readable manner."} + """ + + else: + raise ValueError(f"Unknown report type: {report_type}") + + # Generate the report + report_chat = LLMChat( + system_prompt=system_prompt, + user_prompt=user_prompt, + output_type=str, + enable_json_output_format=False, + model=default["model"], + temperature=0.3 + ) + + return report_chat() + + + def _get_timestamp(self) -> str: + """Get current timestamp for report headers.""" + from datetime import datetime + return datetime.now().strftime("%Y-%m-%d %H:%M:%S") diff --git a/tinytroupe/factory/__init__.py b/tinytroupe/factory/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5ea1398d64f278afd1b27fa87c86ecfa45baa0f2 --- /dev/null +++ b/tinytroupe/factory/__init__.py @@ -0,0 +1,15 @@ +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import utils, config_manager + +# We'll use various configuration elements below +config = utils.read_config_file() + + +########################################################################### +# Exposed API +########################################################################### +from .tiny_person_factory import TinyPersonFactory + +__all__ = ["TinyPersonFactory"] \ No newline at end of file diff --git a/tinytroupe/factory/__pycache__/__init__.cpython-312.pyc b/tinytroupe/factory/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eab6e939eb4319fcfa2de3f63dbd8e58f24b687a Binary files /dev/null and b/tinytroupe/factory/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/factory/__pycache__/tiny_factory.cpython-312.pyc b/tinytroupe/factory/__pycache__/tiny_factory.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8b529c7e8feef59a23d9c2db0eb307f601392a03 Binary files /dev/null and b/tinytroupe/factory/__pycache__/tiny_factory.cpython-312.pyc differ diff --git a/tinytroupe/factory/__pycache__/tiny_person_factory.cpython-312.pyc b/tinytroupe/factory/__pycache__/tiny_person_factory.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..095fc852ea0685cba2823cb97c38ec842934ca8d Binary files /dev/null and b/tinytroupe/factory/__pycache__/tiny_person_factory.cpython-312.pyc differ diff --git a/tinytroupe/factory/prompts/generate_person.mustache b/tinytroupe/factory/prompts/generate_person.mustache new file mode 100644 index 0000000000000000000000000000000000000000..2751eb37ca3ac98f625563c79d6d70bdc14a9c63 --- /dev/null +++ b/tinytroupe/factory/prompts/generate_person.mustache @@ -0,0 +1,111 @@ +# Agent Generator + +Please generate a highly detailed agent specification based on a general context and the particularities of the agent (if any). The generated agent specification will be used in a simulation to realistically represent a real person. Therefore, it must include a wide range of nuanced details that are consistent with the provided context and agent particularities. + +## General Context + +{{#context}} +The general context is the following: {{context}}. +{{/context}} +{{^context}} +No general context was specified. +{{/context}} + + +## Agent Particularities +{{#agent_particularities}} +The agent particularities, in turn, are: {{agent_particularities}}. + +These are the specific requirements that you must consider, together with the general context, when generating the agent. Note that for each particularity we can have: + - A concrete scalar value for a field (e.g., "Architect" for job): in this case, the agent must have that value for the corresponding field. + - A range of numberic values (e.g., [18, 60] for age): in this case, the agent must have a value for the corresponding field that is within that range. You are free to choose the specific value, as long as it is within the range. + - A list of values (e.g., ["male", "female"] `for gender): in this case, the agent must have a value for the corresponding field that is one of the values in the list. You are free to choose the specific value, as long as it is one of the values in the list. + - A list of values associated to proportions (e.g., {"rich": 0.1, "middle class":0.7, "poor": 0.2} for wealth): in this case, the agent must have a value for the corresponding field that is one of the values in the list, sampled according to the specified proportions. + - A broader characteristic descrition (e.g., "Meticulous and competent, but not a very nice person at all."): in this case, the agent must have values for the corresponding fields that are consistent with the requirements. You are free to choose the specific values, as long as they are consistent with the requirements. Try to make the specification as rich as possible based on this. +{{/agent_particularities}} +{{^agent_particularities}} +There are no agent particularities in this case, so just generate an agent based on the general +context (if any). +{{/agent_particularities}} + +## Generation Rules + +To generate the requested agents, you **must** to follow these directives: + - If a general context is provided, you must ensure the generated agent has relevant details that will allow the a realistic simulation of a real person in that context. To do so, you should include nunanced details that could be relevant for the simulation in that context. + - Given the agent particularities, please, be creative and deep to generate details about the person for each of the fields in the response. Sample from a wide range of reasonable possibilities. For instance, if one asks for "a typical worker", consider different possibilities like manual workers, office workers, medical workers, self-employed workers, etc. + - You must add as much details as possible, but make sure they are consistent with the context and agent particularities. That is to say, the details can be completely unrelated to the context and agent particularities, but they must not contradict them. + - The agents you generate must sound authentic and realistic, considering the context and particularities provided. Avoid platitudes and cliches. The agents you generate represent real humans, and as such encompass all the diversity and complexity of real people. So not only happy and successful people, but also people with problems, fears, and difficulties. + - When generating a person specification, be free to use whatever elements are most realistic, even if they are typically considered undesireable (e.g., vicious people are part of the general population). We need a realistic sample of people, not a caricature. So think deeply about what would make each person you generate authentic with respect the provided context and particularities. + - There are many ways to instantiate the context and agent particularities into a specific concrete agent, so avoid common stereotypes and cliches. Instead, try to think deeply about the context and agent particularities, and how they would manifest in a real person, and thus create an authentic-looking agent. + - Some fields are naturally simple, like name, age, gender, nationality and residence. Others are more complex, like education, occupation, style, personality, preferences, beliefs, skills, behaviors, health, relationships and other facts. For these complex fields, you must provide a rich description with many details, + always consistent with the context and agent particularities. + +## Output Format Rules +Your output **must** follow these rules: + - You'll generate this response **only** in JSON format, no extra text, no Markdown elements. + - Make sure you **always** produce valid JSON. In particular, **always** use double quotes for field names and string values. + - The format for this JSON response is as described in the examples. At a minimum, the response **must** contain the following **mandatory** fields: + * "name" (simple field) + * "age" (simple field) + * "gender" (simple field) + * "nationality" (simple field) + * "residence" (simple field) + * "education" (complex field) + * "long_term_goals" (complex field): general aspirations for the future; life purposes. + * "occupation" (complex field): details of the person's job, company, profession or trade. Avoid fashionable or trendy jobs, + and instead focus on more traditional or realistic occupations, to the extent that + this is consistent with the context and agent particularities. So please less + "Enviromental Scientist"/"Sustainability Expert"/"Marketing Specialist" and more "University Professor"/"Construction Worker"/"IT Consultant". + Make sure to include details like the person's role, responsibilities, and any relevant skills or expertise. + * "style" (complex field): the person's general way of being, speaking, and behaving. Make sure to specify relevant accents, mannerisms, whether colloquial or formal is typical, etc. + We need A LOT of details here to be able to later produce a realistic simulation of the words and actions of the person. + * "personality" (very complex field): a detailed exploration of the person's character traits, including their temperament, emotional responses, and social behavior. + Include at least 10 traits, and also make sure you fill in the Big-5 personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) + with specific values for each trait. Make sure the personality described is not generic, but rather nuanced, deep, authentic, and realistic. + * "preferences" (very complex field): interests, things that the agent likes or dislikes. Can be both broad categories and specific items. If specific areas are requested, make sure to include many many + details about those areas. For example, if the agent is meant to like coffee shops, also include things like their favorite drinks, ambiance preferences, + any specific coffee shop chains they prefer, snacks they enjoy having together, whether they go there to work or socialize, etc. At least 20 details per area. Put more if + you can. + * "beliefs"(very complex field): deeply held convictions or opinions that influence the person's behavior and decision-making. Include many many details here, at least 30, so that we can later produce + a realistic simulation of the words and actions of the person. These details must explore in depth all the areas mentioned in the agent's particularities. + * "skills" (complex field): specific abilities or expertise that the person possesses, relevant to their personal or professional life. + * "behaviors" (complex field): typical actions, habits, rotines, or mannerisms that characterize the person. + * "health" (complex field): information about the person's physical and mental well-being, including any relevant medical history. + * "relationships" (complex field): details about the person's social connections, including family, friends, and professional contacts. + * "other_facts"(very complex field): anything that doesn't fit in the other fields and sections. This is where you should go wild and add many facts, ad-hoc details, past stories, + important memories, etc. Make this very long, at least 30 entries. + +DO NOT SPARE space for complex fields, use as much as you need to create a truly realistic person, with a lot of nuances, details, and depth. + +## Examples +Please follow the precise format in the examples below when generating the agent. Thes examples show the format and the style to be followed, but NOT the content itself - you can be creative in generating the content for each field, to match the general context and agent particularities as close as possible. +In particular, in your output, make sure you include much more detail than in the examples. + +### Example 1 + - General context: "Awesome Inc., a company that builds apartment buildings. Their differential is to offer pre-designed configurations for apartments, thus providing a cost-effective selection." + - Agent particularities: "A meticulous German architect. Competent, but not a very nice person at all." + - Example response: + ```json + {{{example_1}}} + ``` + + +### Example 2 + - General context: "Awesome Inc., a company that builds apartment buildings. Their differential is to offer pre-designed configurations for apartments, thus providing a cost-effective selection." + - Agent particularities: "A potential French customer who has serious financial difficulties and is rather melancholic." + - Example response: + ```json + {{{example_2}}} + ``` + +### Other persona examples +{{#other_examples}} + - ```json + {{{.}}} + ``` + +{{/other_examples}} +{{^other_examples}} +No other examples available. +{{/other_examples}} + diff --git a/tinytroupe/factory/prompts/generate_person_factory.md b/tinytroupe/factory/prompts/generate_person_factory.md new file mode 100644 index 0000000000000000000000000000000000000000..fb5c009df14ec1f69894bb6eb0c02940939581e2 --- /dev/null +++ b/tinytroupe/factory/prompts/generate_person_factory.md @@ -0,0 +1,9 @@ +Your task is create many contexts that will be used as base to generate a list of persons. +The idea is receive a broad context, with some details of persons we want to generate, like demographics parameters, physical characteristics, behaviors, believes, etc; and then create many other contexts, more specifics, but derivaded of the more generic one. +Your response must be an array in JSON format. Each element of the array must be a context that will be used to generate a person description. + +Example: + - INPUT: + Please, generate 3 person(s) description(s) based on the following broad context: Latin American, age between 20 and 40 years old, economic status can vary between poor and rich, it can be religious or not, it can be married or not, it can have children or not, it can be a professional or not, it can be a worker or not + - OUTPUT: + ["Mexican person that has formed as lawyer but now works in other are, is single, like sports and movies", "Create a Brazilian person that is a doctor, like pets and the nature and love heavy metal.", "Create a Colombian person that is a lawyer, like to read and drink coffee and is married with 2 children."] diff --git a/tinytroupe/factory/tiny_factory.py b/tinytroupe/factory/tiny_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..ad3d4ce6a2416fb5aa9c8f5e80d18df7b4863833 --- /dev/null +++ b/tinytroupe/factory/tiny_factory.py @@ -0,0 +1,96 @@ +import copy +import random + +from tinytroupe.factory import logger +import tinytroupe.utils as utils + +class TinyFactory: + """ + A base class for various types of factories. This is important because it makes it easier to extend the system, particularly + regarding transaction caching. + """ + + # common randomizer used for samplings, with a default initial seed to allow for reproducibility. + # subclases can use this directly as well. + randomizer = random.Random(42) + + # A dict of all factories created so far. + all_factories = {} # name -> factories + + def __init__(self, simulation_id:str=None) -> None: + """ + Initialize a TinyFactory instance. + + Args: + simulation_id (str, optional): The ID of the simulation. Defaults to None. + """ + self.name = f"Factory {utils.fresh_id(self.__class__.__name__)}" # we need a name, but no point in making it customizable + self.simulation_id = simulation_id + + TinyFactory.add_factory(self) + + def __repr__(self): + return f"TinyFactory(name='{self.name}')" + + @staticmethod + def set_simulation_for_free_factories(simulation): + """ + Sets the simulation if it is None. This allows free environments to be captured by specific simulation scopes + if desired. + """ + for factory in TinyFactory.all_factories.values(): + if factory.simulation_id is None: + simulation.add_factory(factory) + + @staticmethod + def add_factory(factory): + """ + Adds a factory to the list of all factories. Factory names must be unique, + so if an factory with the same name already exists, an error is raised. + """ + if factory.name in TinyFactory.all_factories: + raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") + else: + TinyFactory.all_factories[factory.name] = factory + + @classmethod + def clear_factories(cls): + """ + Clears the global list of all factories. + """ + cls.all_factories = {} + cls._clear_factories() + + @classmethod + def _clear_factories(cls): + """ + Additional cleanup actions can be performed here by subclasses if needed. + """ + pass + + ################################################################################################ + # Caching mechanisms + # + # Factories can also be cached in a transactional way. This is necessary because the agents they + # generate can be cached, and we need to ensure that the factory itself is also cached in a + # consistent way. + ################################################################################################ + + def encode_complete_state(self) -> dict: + """ + Encodes the complete state of the factory. If subclasses have elmements that are not serializable, they should override this method. + """ + + state = copy.deepcopy(self.__dict__) + return state + + def decode_complete_state(self, state:dict): + """ + Decodes the complete state of the factory. If subclasses have elmements that are not serializable, they should override this method. + """ + state = copy.deepcopy(state) + + self.__dict__.update(state) + return self + + diff --git a/tinytroupe/factory/tiny_person_factory.py b/tinytroupe/factory/tiny_person_factory.py new file mode 100644 index 0000000000000000000000000000000000000000..5bf06890de3e87c68392ff2f21290efbe5a3435c --- /dev/null +++ b/tinytroupe/factory/tiny_person_factory.py @@ -0,0 +1,1369 @@ +import os +import json +import chevron +import random +from typing import List, Dict, Union +import copy + +from .tiny_factory import TinyFactory +from tinytroupe.factory import logger +from tinytroupe import openai_utils +from tinytroupe.agent import TinyPerson +import tinytroupe.utils as utils +from tinytroupe.control import transactional +from tinytroupe import config_manager + +import concurrent.futures +import threading + +import math + +# to protect from race conditions when generating agents in parallel +concurrent_agent_generataion_lock = threading.Lock() + + +class TinyPersonFactory(TinyFactory): + + # keep track of all the names generated by all the factories, to ensure they are globally unique. + all_unique_names=[] + + def __init__(self, sampling_space_description:str=None, total_population_size:int=None, context:str=None, simulation_id:str=None): + """ + Initialize a TinyPersonFactory instance. + + Args: + sampling_space_description (str, optional): The description of the sampling space. Defaults to None. If this is + specified, then population_size must also be specified. + population_size (int, optional): The size of the population to sample from. Defaults to None. + context (str): The context text used to generate the TinyPerson instances. + simulation_id (str, optional): The ID of the simulation. Defaults to None. + """ + super().__init__(simulation_id) + self.person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/generate_person.mustache') + self.context_text = context + self.sampling_space_description = sampling_space_description + self.population_size = total_population_size + + self.sampling_dimensions = None + self.sampling_plan = None + self.remaining_characteristics_sample = None + + self.generated_minibios = [] # keep track of the generated persons. We keep the minibio to avoid generating the same person twice. + self.generated_names = [] + + # TODO obsolete? + @staticmethod + def generate_person_factories(number_of_factories, generic_context_text): + """ + Generate a list of TinyPersonFactory instances using OpenAI's LLM. + + Args: + number_of_factories (int): The number of TinyPersonFactory instances to generate. + generic_context_text (str): The generic context text used to generate the TinyPersonFactory instances. + + Returns: + list: A list of TinyPersonFactory instances. + """ + + logger.info(f"Starting the generation of the {number_of_factories} person factories based on that context: {generic_context_text}") + + system_prompt = open(os.path.join(os.path.dirname(__file__), 'prompts/generate_person_factory.md'), 'r', encoding='utf-8', errors='replace').read() + + messages = [] + messages.append({"role": "system", "content": system_prompt}) + + user_prompt = chevron.render("Please, create {{number_of_factories}} person descriptions based on the following broad context: {{context}}", { + "number_of_factories": number_of_factories, + "context": generic_context_text + }) + + messages.append({"role": "user", "content": user_prompt}) + + response = openai_utils.client().send_message(messages) + + if response is not None: + result = utils.extract_json(response["content"]) + + factories = [] + for i in range(number_of_factories): + logger.debug(f"Generating person factory with description: {result[i]}") + factories.append(TinyPersonFactory(result[i])) + + return factories + + return None + + @staticmethod + def create_factory_from_demography(demography_description_or_file_path:Union[str, dict], population_size:int, additional_demographic_specification:str=None, context:str=None): + """ + Create a TinyPersonFactory instance from a demography description, which can be wither given as a file path or a dictionary + (but not both). + + Args: + demography_description_or_file_path (Union[str, dict]): The demography description or the file path to the demography description. + population_size (int): The size of the population to sample from. + context (str, optional): Additional context text used to generate the TinyPerson instances. Defaults to None. + + Returns: + TinyPersonFactory: A TinyPersonFactory instance. + """ + # read the demography description from a file or use the given dictionary + if isinstance(demography_description_or_file_path, str): + demography_description = json.loads(open(demography_description_or_file_path, 'r', encoding='utf-8', errors='replace').read()) + elif isinstance(demography_description_or_file_path, dict): + demography_description = demography_description_or_file_path + else: + raise ValueError("demography_description_or_file_path must be either a string or a dictionary.") + + if population_size is None: + raise ValueError("population_size must be specified.") + + + full_demography_description = \ + f""" + # Sampling space specification + + The population described by the demographic data below. Make sure you consider very detailed, fine-grained, + characteristics of the individuals in the population. + + ## Directives + Please follow these rules: + - produce a uniformly distributed sample of the requested population, so that all characteristics are represented in the sample + in the right proportions, as specified in the demographic data below. + - consider as many different population segments as possible, while **always** keeping **proportions** correct.For example, + instead of sampling 10 people from segment A and 5 from segment B, you can instead sample 2 from A, 1 from B, + and 7 others from other segments, provided the proportions are maintained correct and there are enough people to sample. + - also use any built-in knowledge you might have of the populations in question to improve the sampling space, + provided this built-in knowledge does not conflict with the demographic data below. + + The sample must include representative people from the broad population, so for instance ensure that you include values covering + people from all walks of life possible from the specified demographic data and your built-in knowledge of the target population, such as: + - from the simplest professions to those of the highest ranks; + - from the youngest to the oldest; + - from the kind to the evil; + - from the positive and enthusiastic to the negative and pessimistic; + - from the happy and joyful to the sad and depressed; + - from the most conservative, to the most liberal; + - from the educated, to the ignorant; + - from the healthy to the sick; + - from those who enjoy bland food, to those who enjoy spicy food; + - from rich to poor. + + Make sure there's sufficient variety to represent even extreme cases, so that fringe opinions or far fetched characteristics are also represented. + Because these are by definition rare, here you can add a larger proportion than what is truly present in the population, so that there's some + information from these rare cases. + + In particular, the population MUST cover both POSITIVE and NEGATIVE possibilities of the various characteristics + (e.g., rich vs poor, likes sugar vs don't like sugar, enthusiastic vs apathetic). + + ## Additional demographic specification (if any) + {additional_demographic_specification if additional_demographic_specification is not None else "(none)"} + + ## Demographic data + {json.dumps(demography_description, indent=4)} + """ + + return TinyPersonFactory(context=context, + sampling_space_description=full_demography_description, + total_population_size=population_size) + + @classmethod + def _clear_factories(cls): + """ + Additional class-level cleanup for this subclass. + """ + TinyPersonFactory.all_unique_names = [] # clear the list of all unique names, so that the next factories can start fresh. + + def generate_person(self, + agent_particularities:str=None, + temperature:float=1.2, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None) -> TinyPerson: + """ + Generate a TinyPerson instance using OpenAI's LLM. + + Args: + agent_particularities (str): The particularities of the agent. + temperature (float): The temperature to use when sampling from the LLM. + frequency_penalty (float): The frequency penalty to use when sampling from the LLM. + presence_penalty (float): The presence penalty to use when sampling from the LLM. + attempts (int): The number of attempts to generate a TinyPerson instance. + post_processing_func (function): A function to apply to the generated agent after it is created. + + Returns: + TinyPerson: A TinyPerson instance generated using the LLM. + """ + + logger.debug(f"Starting the person generation based these particularities: {agent_particularities}") + fresh_agent_name = None + + # are we going to use a pre-computed sample of characteristics too? + if self.population_size is not None: + + with concurrent_agent_generataion_lock: + if self.remaining_characteristics_sample is None: + # if the sample does not exist, we generate it here once. + self.initialize_sampling_plan() + + logger.debug(f"Sampling plan initialized. Remaining characteristics sample: {self.remaining_characteristics_sample}") + + # CONCURRENT PROTECTION + with concurrent_agent_generataion_lock: + if len(self.remaining_characteristics_sample) == 0: + logger.warning("No more characteristics samples left to sample from. This can happen if the sampling plan did not sum up correctly.") + return None + + else: + sampled_characteristics = self.remaining_characteristics_sample.pop() + logger.debug(f"Sampled agent: {sampled_characteristics['name']}.") + + if agent_particularities is not None: + agent_particularities =\ + f""" + - Primary characteristics: {agent_particularities} + + - Also use all the following additional characteristics that **do not** conflict with the primary ones: + * Name, demographics and other characteristics: {json.dumps(sampled_characteristics, indent=4)} + + In case one of the additional characteristics conflicts with a primary one, please use the primary one + and ignore the additional one. + + If the agent's name is specified, you MUST ALWAYS use it, even if it conflicts with the primary characteristics. + + """ + else: + agent_particularities = \ + f""" + - Name, demographics and other characteristics: + {json.dumps(sampled_characteristics, indent=4)} + """ + else: # no predefined population size, so we generate one-off agents. + # CONCURRENT PROTECTION + with concurrent_agent_generataion_lock: + fresh_agent_name = self._unique_full_name(already_generated_names=TinyPersonFactory._all_used_and_precomputed_names(), + context=self.context_text) + + if agent_particularities is not None: + agent_particularities = \ + f""" + + - Primary characteristics: {agent_particularities} + + - Also use the following additional characteristics: + * Full name: {fresh_agent_name} + + In case the primary characteristics already specify a name, please use the primary name and ignore the additional one. + """ + else: + agent_particularities = f"Full name: {fresh_agent_name}" + + + + logger.info(f"Generating person with the following particularities: {agent_particularities}") + + # read example specs from files. + example_1 = json.load(open(os.path.join(os.path.dirname(__file__), '../examples/agents/Friedrich_Wolf.agent.json'), 'r', encoding='utf-8', errors='replace')) + example_2 = json.load(open(os.path.join(os.path.dirname(__file__), '../examples/agents/Sophie_Lefevre.agent.json'), 'r', encoding='utf-8', errors='replace')) + + # We must include all agent names generated in the whole of the simulation, not only the ones generated by this factory, + # since they all share the same name space. + # + # For the minibios, we only need to keep track of the ones generated by this factory, since they are unique to each factory + # and are used to guide the sampling process. + user_prompt = chevron.render(open(self.person_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), { + "context": self.context_text, + "agent_particularities": agent_particularities, + + #Note that we need to dump them to JSON strings, to ensure we get double quotes, + # and other formatting issues are avoided. + "example_1": json.dumps(example_1["persona"], indent=4), + "example_2": json.dumps(example_2["persona"], indent=4) + }) + + def aux_generate(attempt): + messages = [] + messages += [{"role": "system", "content": "You are a system that generates specifications for realistic simulations of people. You follow the generation rules and constraints carefully."}, + {"role": "user", "content": user_prompt}] + + + # due to a technicality, we need to call an auxiliary method to be able to use the transactional decorator. + message = self._aux_model_call(messages=messages, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty) + + if message is not None: + result = utils.extract_json(message["content"]) + + logger.debug(f"At attempt {attempt}, generated person parameters:\n{json.dumps(result, indent=4, sort_keys=True)}") + + # only accept the generated spec if the name is not already in use + if not self._is_name_already_assigned(result["name"]): + return result + else: + logger.info(f"Person with name {result['name']} was already generated, cannot be reused.") + + return None # no suitable agent was generated + + agent_spec = None + attempt = 0 + while agent_spec is None and attempt < attempts: + try: + attempt += 1 + agent_spec = aux_generate(attempt=attempt) + except Exception as e: + logger.error(f"Error while generating agent specification: {e}") + + # create the fresh agent + if agent_spec is not None: + # the agent is created here. This is why the present method cannot be cached. Instead, an auxiliary method is used + # for the actual model call, so that it gets cached properly without skipping the agent creation. + + # protect parallel agent generation + with concurrent_agent_generataion_lock: + person = TinyPerson(agent_spec["name"]) + self._setup_agent(person, agent_spec) + if post_processing_func is not None: + post_processing_func(person) + + self.generated_minibios.append(person.minibio()) + self.generated_names.append(person.get("name")) + + return person + else: + logger.error(f"Could not generate an agent after {attempts} attempts.") + if sampled_characteristics is not None: + self.remaining_characteristics_sample.append(sampled_characteristics) + logger.error(f"Name {fresh_agent_name} was not used, it will be added back to the pool of names.") + + return None + + + @config_manager.config_defaults(parallelize="parallel_agent_generation") + def generate_people(self, number_of_people:int=None, + agent_particularities:str=None, + temperature:float=1.2, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None, + parallelize=None, + verbose:bool=False) -> list: + """ + Generate a list of TinyPerson instances using OpenAI's LLM. + + Args: + number_of_people (int): The number of TinyPerson instances to generate. + agent_particularities (str): The particularities of the agent. + temperature (float): The temperature to use when sampling from the LLM. + frequency_penalty (float): The frequency penalty to use when sampling from the LLM. + presence_penalty (float): The presence penalty to use when sampling from the LLM. + attempts (int): The number of attempts to generate a TinyPerson instance. + post_processing_func (function): A function to apply to the generated agent after it is created. + parallalel_workers (int): The number of parallel workers to use when generating the people. Too many workers may cause the LLM to fail + due to throttling by the API. + verbose (bool): Whether to print information about the generated people. + + Returns: + list: A list of TinyPerson instances generated using the LLM. + """ + + if number_of_people is None: + if self.population_size is None: + raise ValueError("Either the number of people to generate or the population size must be specified.") + number_of_people = self.population_size + + elif self.population_size is None: + self.population_size = number_of_people + + elif number_of_people is not None and self.population_size is not None and number_of_people > self.population_size: + raise ValueError(f"Cannot generate more people than the population size. Requested {number_of_people}, but the population size is {self.population_size}.") + + people = [] + if parallelize: + people = self._generate_people_in_parallel(number_of_people=number_of_people, + agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func, + verbose=verbose) + else: + people = self._generate_people_sequentially(number_of_people=number_of_people, + agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func, + verbose=verbose) + + return people + + + @transactional(parallel=True) + def _generate_people_in_parallel(self, number_of_people:int=None, + agent_particularities:str=None, + temperature:float=1.5, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None, + verbose:bool=False) -> list: + people = [] + + # + # Concurrently generate the people. + # + # This vastly speeds up the process, but be careful with the number of workers, as too + # many may cause the LLM to fail due to throttling by the API. + # + + # this is the function that will be executed in parallel + def generate_person_wrapper(args): + self, i, agent_particularities, temperature, frequency_penalty, presence_penalty, attempts, post_processing_func = args + person = self.generate_person(agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func) + return i, person + + with concurrent.futures.ThreadPoolExecutor() as executor: + # we use a list of futures to keep track of the results + futures = [ + executor.submit(generate_person_wrapper, (self, i, agent_particularities, temperature, frequency_penalty, presence_penalty, attempts, post_processing_func)) + for i in range(number_of_people) + ] + + # we iterate over the futures as they are completed, and collect the results + for future in concurrent.futures.as_completed(futures): + i, person = future.result() + if person is not None: + people.append(person) + info_msg = f"Generated person {i+1}/{number_of_people}: {person.minibio()}" + + if verbose: + logger.info(info_msg) + + else: + logger.error(f"Could not generate person {i+1}/{number_of_people}. Continuing with the remaining ones.") + + return people + + # TODO still make this one available? + def _generate_people_sequentially(self, number_of_people:int=None, + agent_particularities:str=None, + temperature:float=1.5, + frequency_penalty:float=0.0, + presence_penalty:float=0.0, + attempts:int=10, + post_processing_func=None, + verbose:bool=False) -> list: + """ + Generate the people sequentially, not in parallel. This is a simpler alternative. + """ + people = [] + for i in range(number_of_people): + person = self.generate_person(agent_particularities=agent_particularities, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + attempts=attempts, + post_processing_func=post_processing_func) + if person is not None: + people.append(person) + info_msg = f"Generated person {i+1}/{number_of_people}: {person.minibio()}" + logger.info(info_msg) + if verbose: + print(info_msg) + else: + logger.error(f"Could not generate person {i+1}/{number_of_people}.") + + return people + + + + + def initialize_sampling_plan(self): + """ + Computes a list of characteristics samples from a sampling space. + The sampling space is built from the given description through intermediary steps + that actually build a sampling space and then randomly (and not via LLM) sample from it, thereby + ensuring that the sampling is not biased by the LLM (though the sampling space itself may be biased). + + All intermediary results are stored for later inspection. + + For example, given some n > 3 and a description like + "Young Western people of different liberal professions." + + The final samples could be something like: + [{"age": 25, "profession": "Architect", "country": "USA"}, + {"age": 27, "profession": "Lawyer", "country": "Canada"}, + ... + {"age": 25, "profession": "Architect", "country": "USA"}] + + Args: + n (int): The number of samples to generate. + sampling_space_description (str): A description of the sampling space. + + """ + + # a technicality - we need to use an auxiliary method to be able to use the transactional decorator effectively. + return self._initialize_sampling_plan_transaction(n=self.population_size, description=self.sampling_space_description,context=self.context_text) + + def _initialize_sampling_plan_transaction(self, n, description, context): + """ + Auxiliary method to initialize the sampling plan. This is needed in order to be able to use the transactional decorator, + due too a technicality - the method parameters must be such that when they change the transaction is nullified. + """ + if self.remaining_characteristics_sample is None: + # sampling dimensions + self.sampling_dimensions = utils.try_function(lambda: self._compute_sampling_dimensions(sampling_space_description=description), + + # check that the result is a dict + postcond_func=lambda result: isinstance(result, dict), + retries=15) + logger.info("Sampling dimensions computed successfully.") + logger.debug(f"Sampling dimensions: {json.dumps(self.sampling_dimensions, indent=4)}") + + # sampling plan + self.sampling_plan = utils.try_function(lambda: self._compute_sample_plan(N=n, + sampling_dimensions=self.sampling_dimensions), + + # checks that the plan is a list, not an empty dictionary, a number or a string + postcond_func = lambda result: isinstance(result, list) and len(result) > 0, + retries=15 + ) + # if the sampling plan is a dict, let' s enclose it in a list + if isinstance(self.sampling_plan, dict): + self.sampling_plan = [self.sampling_plan] + logger.warning("The sampling plan was a dictionary, enclosing it in a list to ensure it is processed correctly.") + + logger.info("Sampling plan computed successfully.") + logger.debug(f"Sampling plan: {json.dumps(self.sampling_plan, indent=4)}") + + # Flatten the sampling plan in concrete individual samples. + # Use deepcopy because we'll be modifying the samples later, and we want to keep the original sampling plan intact + # for correct caching + self.remaining_characteristics_sample = copy.deepcopy(utils.try_function(lambda: self._flatten_sampling_plan(sampling_plan=self.sampling_plan), + retries=15)) + + # instead of failing, we warn if the number of samples is not equal to n, as LLMs can be bad at summing up the quantities in the sampling plan. + # This is not a problem, as the sampling space is still valid and can be used, though it may not be as rich as expected. + if len(self.remaining_characteristics_sample) != n: + logger.warning(f"Expected {n} samples, but got {len(self.remaining_characteristics_sample)} samples. The LLM may have failed to sum up the quantities in the sampling plan correctly.") + + logger.info(f"Sample plan has been flattened, contains {len(self.remaining_characteristics_sample)} total samples.") + logger.debug(f"Remaining characteristics sample: {json.dumps(self.remaining_characteristics_sample, indent=4)}") + + # generate names for each sample individually, considering all their characteristics + all_used_names = TinyPersonFactory._all_used_and_precomputed_names() + + for i, sample in enumerate(self.remaining_characteristics_sample): + logger.debug(f"Generating name for sample {i+1}/{len(self.remaining_characteristics_sample)}") + + # randomize the all_used_names to make the context less predictable for the LLM, thereby introducing some additional randomness. + # Note that we use a fixed random seed to ensure that the sampling plan is reproducible and cache can be kept. + TinyFactory.randomizer.shuffle(all_used_names) + + # generate a name that's appropriate for this specific sample's characteristics + try: + + # A dummy name to start with, in case the name generation fails. + sample["name"] = f"Agent_{utils.fresh_id('agents_names')}" + + name = utils.try_function( + lambda: self._generate_name_for_sample( + sample_characteristics=sample, + already_generated_names=all_used_names + ), + # ensure the name is not in already used names + postcond_func=lambda result: result not in all_used_names, + retries=15 + ) + + sample["name"] = name + all_used_names.append(name) + + except Exception as e: + logger.error(f"Error generating name for sample {i}: {e}") + # fallback: use a simple default name with index + fallback_name = f"Person_{i}_{sample.get('gender', 'unknown')}" + sample["name"] = fallback_name + all_used_names.append(fallback_name) + + logger.info("Names generated for all samples in the sampling plan.") + + # update the global list of unique names + new_names = [sample["name"] for sample in self.remaining_characteristics_sample] + TinyPersonFactory.all_unique_names = list(set(TinyPersonFactory.all_unique_names + new_names)) + + else: + raise ValueError("Sampling plan already initialized. Cannot reinitialize it.") + + @classmethod + def _all_used_and_precomputed_names(cls) -> list: + """ + Returns all the names currently in use by agents and those pre-generated by all factories. + """ + return TinyPerson.all_agents_names() + cls.all_unique_names + + def _is_name_globally_unique(self, name:str) -> bool: + """ + Checks if a name is globally unique. + """ + return name not in TinyPersonFactory.all_unique_names + + def _is_name_already_assigned(self, name:str) -> bool: + """ + Checks if a name has already been assigned to a person. + """ + return name in TinyPerson.all_agents_names() + + + @transactional() + @utils.llm(temperature=0.5, frequency_penalty=0.0, presence_penalty=0.0) + def _compute_sampling_dimensions(self, sampling_space_description:str) -> dict: + """ + Given a sampling description, computes the dimensions of the sampling space. The sampling space offers a way to sample from a population of people, + so each dimension contains values that could be an attribute of a **specific** person. The resulting sampling space must: + - contemplate all critical characteristics mentioned in the sampling description, even if this means having a large number of dimensions and + complex values for each. + * whenever necessary to properly capture the possibilities, you can replace a single dimension by a collection of sub-dimensions + (e.g., instead of "beliefs", you might have "political_beliefs", "economic_beliefs", "consumer_beliefs", etc.) + - values for each dimension can range from numbers or single words to large sentences or even paragraphs. For attributes that are not clearly single values, + always try to add as much detail as possible. For instance, age is just a single value, but lifestyle or cultural background **must** be a long sentence or even a paragraph. + This is to ensure that, later, the generated people can be very nuanced and realistic, with rich and detailed attributes. See the example below to get inspired. + - you can be very creative with the dimensions and values provided that they are consistent with the sampling space description. + - whenever you have the information about PROPORTIONS of the values, you **must** include them in the output, so that the sampling space can be used to generate people + in a representative way. + - values are **not** distributions, probabilities or other statistics, but rather concrete, specific, people attributes. For example, there can + be no "average_age" dimension, but only "age", although the complete set of valies that define a dimension is itself a distribution. + - each dimension should be as rich as possible, having as many values as possible, so that the sampling space can be used to generate + many nuanced variations of the target population. + - each dimension should consider a wide range of values, making sure to cover both POSITIVE and NEGATIVE possibilities (e.g., rich vs poor, likes sugar vs don't like sugar). + - each dimension should always include extreme values, so that the sampling space can be used to generate people with extreme characteristics, such as very young or very old, + very rich or very poor, very positive or very negative, etc. + - include as many dimensions as possible to capture the richness of the population, even if this means having a large number of dimensions. + - in principle, the original sampling description could be approximately rephrased in terms of the dimensions and values generated (i.e., the dimensions are rich enough + to capture all relevant information). Howerver, this should not limit the range of values and dimensions used, but rather be a byproduct of the process. For instance, + if the original description say "young people", the dimension "age" could be defined as a range of values from 18 to 30, but **not** as a small list with only, say, [18, 25, 30]. + Always try to be as rich as possible in the values and dimensions, even if this means having a large number of them. + + Additionally, make sure you include special dimensions that capture these aspects, in such a way that they relate to the sampling space description: + - personality traits (with proportions) + - political beliefs (with proportions) + - economic beliefs (with proportions) + - financial situation (with proportions) + - preferences and tastes (with proportions) + - cultural background (with proportions and diverse ethnicities and cultural heritages; provide detailed, realistic, and varied examples that reflect a wide spectrum of ethnic, national, and cultural identities relevant to the sampling space description) + + ## On your input + + Here's what to do depending on what the input sampling space description looks like: + - Plain text: Abstract all the potential dimensions from the text. For example, if the text is "Young Western people of different liberal professions.", the dimensions could be "age", "profession", "country". + - JSON: Do not use the JSON directly, but rather abstract the dimensions from it. Input JSONs can be obtained from various sources, and you should do your best to interpret them and produce a clean list of dimensions and their values, regardless of how complex the input JSON is. In particular, never use the JSON formatting itself as dimension names or values, but rather abstract the actual dimensions and values from it. + - Tables or other structured data: Abstract the dimensions from the structured data. For example, if the data is in a table, you should extract the rows and columns and abstract the dimensions from them. + + + ## On your output: + You output a JSON containing a list of dimensions. Each output dimension **must** consist of: + - a name; + - EITHER a list of values OR a range of values (specified as a pair). + * in lists of values, whenever possible, you **must** use long values, such as sentences or paragraphs, instead of short words or numbers. + * in lists of values you can, optionally, use a dictionary to specify proportions of the values, e.g., {"value1": 0.5, "value2": 0.3, "value3": 0.2} to indicate that 50% of the population has value1, 30% has value2, and 20% has value3. + Adjust the proportions as appropriate for the context and ensure they sum to 1.0. + + The output is formatted as a JSON object with the following structure: + ```json + { + "sampling_space_description": "A description of the sampling space.", + "dimensions": [ + { + "name": "dimension_name_1", + "values": ["value1", "value2", ...] + }, + + { + "name": "dimension_name_2", + "range": [min, max] + }, + + { + "name": "dimension_name_3", + "values": {"value1": proportion1, "value2": proportion2, "value3": proportion3, ...} + }, + + ... + ] + } + ``` + + Unless values are necessarily numbers (e.g., age), they should be descriptive strings so that it is easy to understand what they mean. + These strings can be simple values or long detailed texts, whatever is best to capture the desired characteristic. + + ## Example: + Given the following INPUT sampling space description: "Young Western people of different liberal professions and social classes." + + The OUTPUT dimensions could be a dictionary with the following structure: + ```json + { + "sampling_space_description": "Young Western people of different liberal professions and social classes.", + "dimensions": [ + { + "name": "age", + "range": [18, 30] + }, + { + "name": "socioeconomic status", + "values": ["miserable", "poor", "middle class", "rich", "very rich"] + }, + { + "name": "profession", + "values": ["Architect", "Lawyer", "Physician", "Accountant", ...] + }, + { + "name": "country", + "values": { + "USA": 0.35, + "Germany": 0.10, + "UK": 0.09, + "France": 0.09, + "Italy": 0.08, + "Spain": 0.06, + "Canada": 0.06, + "Australia": 0.05, + "Netherlands": 0.03, + "Sweden": 0.03, + "Belgium": 0.02, + "Switzerland": 0.02, + "Austria": 0.01 + } + }, + { + "name": "cultural_background", + "values": { + "Born in a large city of a developed nation, parents were from a lineage of physicians and lawyers": 0.12, + "Descendant of Ashkenazi Jewish immigrants who settled in New York City in the early 20th century, maintaining strong ties to Jewish traditions and community life.": 0.08, + "Second-generation Chinese-Canadian whose family values blend Confucian principles with Canadian multiculturalism, celebrating both Lunar New Year and Canada Day.": 0.06, + "Of Irish and Italian descent, growing up in Boston with a household that combines Catholic traditions, Irish folk music, and Italian culinary heritage.": 0.10, + "Of Turkish-German background, raised in Berlin with exposure to both Turkish family traditions and contemporary German urban culture.": 0.05, + <... many more ...> + } + }, + { + "name": "economic_beliefs", + "values": { + "Firmly believes that diligent effort and perseverance in one's career are the primary drivers of financial prosperity and upward mobility.": 0.28, + "Holds the view that wealth accumulation is largely a matter of being in the right place at the right time, with luck playing a significant role in economic outcomes.": 0.18, + "Thinks that government intervention and social programs are essential to ensure fair economic opportunities for all members of society.": 0.22, + "Believes that personal connections and networking are more important than formal education or hard work in achieving economic success.": 0.15, + <... many more ...> + } + }, + { + "name": "professional_attitudes", + "values": { + "Aspires to establish and grow their own business, valuing independence and the ability to innovate without corporate constraints.": 0.18, + "Prefers the stability and structure of working for a well-established company, appreciating clear career paths and organizational support.": 0.32, + "Enjoys collaborating in multidisciplinary teams and seeks out workplaces that foster creativity and open communication.": 0.22, + "Is highly risk-averse and prioritizes job security and predictable routines over rapid advancement or entrepreneurial ventures.": 0.15, + <... many more ...> + } + }, + { + "name": "political_beliefs", + "values": { + "Strongly supports progressive policies aimed at reducing income inequality and expanding access to healthcare and education.": 0.24, + "Advocates for conservative values, emphasizing the importance of tradition, personal responsibility, and limited government intervention.": 0.20, + "Identifies as a centrist, believing that balanced compromise between opposing political ideologies leads to the best societal outcomes.": 0.26, + "Is passionate about environmental issues and supports policies that prioritize sustainability and climate change mitigation above economic growth.": 0.16, + <... many more ...> + } + }, + { + "name": "personality_traits", + "values": { + "Maintains an unwavering optimism, always expecting positive outcomes even in the face of adversity and encouraging others to do the same.": 0.12, + "Tends to be introspective and reserved, preferring solitary activities and deep reflection over social gatherings or group events.": 0.18, + "Is highly ambitious, constantly setting challenging goals and pushing themselves to achieve more in both personal and professional spheres.": 0.15, + "Approaches new experiences with caution, carefully weighing risks and benefits before making decisions or embracing change.": 0.20, + "Often expects the worst in any situation, focusing on potential problems and rarely feeling hopeful about the future.": 0.08, + "Frequently experiences a sense of sadness and melancholy, finding it difficult to enjoy activities that once brought happiness.": 0.06, + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life.": 0.07, + "Feels overwhelmed by setbacks, easily discouraged, and tends to dwell on failures rather than successes.": 0.05, + <... many more ...> + } + }, + { + "name": "preferences_and_tastes", + "values": { + "Has a deep appreciation for classical music, frequently attending orchestral concerts and collecting rare vinyl recordings.": 0.08, + "Finds joy in spending weekends hiking in remote natural parks, seeking tranquility and inspiration from the outdoors.": 0.16, + "Rarely leaves home, preferring the comfort of familiar surroundings and engaging in hobbies such as reading and painting indoors.": 0.11, + "Enjoys experimenting with international cuisines, often hosting elaborate dinner parties to share culinary discoveries with friends.": 0.14, + "Is sensitive to loud environments and actively avoids crowded or noisy places, seeking peace and quiet whenever possible.": 0.13, + "Prefers to spend time alone in dimly lit rooms, listening to somber music and reflecting on the more difficult aspects of life.": 0.04, + "Has little interest in social gatherings or celebrations, often declining invitations and feeling out of place in festive environments.": 0.07, + "Frequently chooses entertainment or art that explores themes of loss, struggle, or existential despair, finding comfort in shared sadness.": 0.03, + <... many more ...> + } + } + ] + } + ``` + + Note in the example: + - Age is given as a numeric range. + - All other values are descriptive strings, human-friendly, no strange symbols or codes. + - The "country" dimension uses a dictionary with suitable proportions for Western countries. + - No value contains internal structure - just a name or short description. + - All values are concrete properties, not distributions, probabilities or other statistics. + - Whenever possible, the values in the dimensions are long and detailed **sentences** each. + - It has few dimensions because the sampling space description is very short. If the description were longer, the number of dimensions would be larger, + and their values more detailed. + - It contains the additional dimensions that capture the personality traits, political beliefs, economic beliefs, financial situation, preferences and tastes, + and now cultural background with varied ethnicities and heritages, which are important for the sampling space to be rich enough to generate nuanced variations of the target population. + - Beyond positive aspects, it also includes values that emphasize pessimism, negativeness, and sadness, ensuring these characteristics are balanced and represented in the sampling space. + + Args: + sampling_space_description (str): A description of the sampling space. + + Returns: + dict: A dictionary with the dimensions of the sampling space, as shown in the example above. + """ + # the body of this method is handled by the @llm decorator. + + @transactional() + @utils.llm(temperature=0.5, frequency_penalty=0.0, presence_penalty=0.0) + def _compute_sample_plan(self, N:int, sampling_dimensions:dict, max_quantity_per_sample_directive:int=5, min_sampling_directives:int=10, max_sampling_directives:int=50) -> List[Dict[str, any]]: + """ + This function defines which and how many people to sample from the sampling space defined by the given dimensions. + Given a number N of people to sample, and the dimensions of the sampling space, computes a *sample plan* of N people from that space. + + The input sampling dimensions have the following structure: + + ```json + { + "sampling_space_description": "A description of the sampling space.", + "dimensions": [ + { + "name": "dimension_name_1", + "values": ["value1", "value2", ...] + }, + { + "name": "dimension_name_2", + "range": [min, max] + }, + ... + ] + } + ``` + + The *sample plan* to be generated is a list of M *sampling directives*. Each *sampling directive* **always** consists of: + - "id": a unique identifier for the *sampling directive*, just an incrementing integer starting from 1. + - "subpopulation_description": a short description of the sub-population that this *sampling directive* represents, based on the sampling space description and the sampled values. + If possible, make it a recognizable and meaningful description of the sub-population, + such as "Young rebellious people from upper classes", "Old conservative boomers from rural areas", "Intellectual urban professionals with diverse and cosmopolitan cultural backgrounds", etc. + - "sampled_values": a map from of dimensions from the sampling space to concrete values, value ranges or value options. + - "quantity": to how many elements with those values should be sampled in total (from 1 to max_quantity_per_sample_directive if specified). + The sum of all of these quantities must be equal to N. + + So your final output **MUST** follow this JSON structure: + + ```json + [ + { "id": 1, + "subpopulation_description": "Some description here...", + "sampled_values": { + "dimension_name_1": [n_1_min, n_1_max],, + "dimension_name_2": ["value2_1", "value2_2", ...], + "dimension_name_3": ["value3_1", "value3_2", ...], + ... + }, + "quantity": quantity_1 + }, + + { + "id": 2, + "subpopulation_description": "Some other description here...", + "sampled_values": { + "dimension_name_1": [n_1_min, n_1_max], + "dimension_name_2": "value2", + "dimension_name_3": ["value3_1", "value3_2", ...], + ... + }, + "quantity": quantity_2 + }, + ... + { + "id": M, + "subpopulation_description": "Again some description here...", + "sampled_values": { + "dimension_name_1": [n_1_min, n_1_max], + "dimension_name_2": ["value2_1", "value2_2", ...], + "dimension_name_3": ["value3_1", "value3_2", ...], + ... + }, + "quantity": quantity_M + }, + ] + ``` + + where N = quantity_1 + quantity_2 + ... + quantity_M, + quantity_i <= max_quantity_per_sample_directive (if specified), + and M is the number of *sampling directives*, which can be as large as necessary to ensure + that the total number of sampled people is equal to N. + + Note: + - Concrete values are NOT in brackets, but rather just a single value or a range of values. + - Options are given in lists of strings separated by commas, e.g., ["value1", "value2", ...]. + - Ranges are numberic and specified as a pair of numbers, e.g., [min, max]. + + Rules and principles: + - The sampling plan is a collection of sub-populations captured by each *sampling directive*. Therefore, the various *sampling directives* must complement each other in order + to approximate the target population. + - Each *sampling directive* is a **combination** of values from the sampling dimensions that represent a specific segment of the target population. Its richness and variety must reflect the desired sub-population. + - The dimension sampled in each *sampling directive* can be a single value, a range of values, or a list of values. You can use ranges and lists to cover a wider range of possibilities + in a compact way, but you can also use single values if necessary. The items in list can be long or short, does not matter, both can be in lists. Some examples of good fortmatting: + * CORRECT example: ["Very rich", "Rich", "Middle class", "Poor"] + * CORRECT example: "Rich" + * WRONG example: ["Very rich or Rich or Middle class or Poor"] + * WRONG example: ["Rich"] + - **Always** try very hard to use a list of values (two or more values) or range of values (min - max), to make the sampling plan at once concise and rich. In doing so, make sure that each *sampling directive* is truly representative + of some segment of the target population, and not just a random collection of values. + - You MUST make M as large as necessary to contemplate the target population, ideally M >= min_sampling_directives (but M <= max_sampling_directives, if specified), to ensure a rich and varied sampling of the population. + * Note that this means the maximum *sampling directive* "id" (call it max_id) used in the *sampling plan* is such that: max_id >= min_sampling_directives; max_id <= max_sampling_directives (if specified). + - The sampled population MUST be representative of the target population. + - The sampled population MUST be realistic. + - You can set the quantity of each *sampling directive* to 1 if necessary to ensure a varied and representative sampling. + - All values chosen from the sampling dimensions must be copied IN FULL in the "sampled_values" map, so that the sampled values are concrete and specific. + The sample plan is supposed to be self-contained, therefore it MUST have all details necessary to sample the people later, without needing to refer back to the sampling dimensions. + - You should include as many *sampling directives* as necessary to cover the sampling of N total people (the sum of all quantities). When in doubt, + **always** add more *sampling directives* (i.e., make M larger) up to max_sampling_directives (if specified), as this will ensure you cover the requested N people. + - In particular, make sure both POSITIVE and NEGATIVE possibilities of the various characteristics are covered (e.g., rich vs poor, likes sugar vs doesn't like sugar, enthusiastic vs apathetic). + This is to ensure any bias (towards positive or negative characteristics) is minimized, and the sampling space is rich enough to generate people with a wide range of characteristics. + - The sampling space description should be used to guide the sampling, so that the sampled population is consistent with it. + - You should ensure that the quantity of requested samples in each *sampling directive* is proportional to their presumed size in the target population. + That is to say, combinations of dimensions that are more common in the target population should be sampled more often. If you don't know, make a guess. + - If max_quantity_per_sample_directive is specified, you must ensure that no single *sampling directive* exceeds this quantity. This is to ensure we get more variation and not just a few large groups. + - You can rely on your built-in knowledge or make educated guesses about such quantities and proportions to ensure that the sample is representative of the population. + * Note that this means for any quantity_i: quantity_i >= 1; quantity_i <= max_quantity_per_sample_directive (if specified). + - The sum of all quantities in the output **must** be equal to N, the number of people to sample in total. + - You can always add extra *sampling directives* (up to max_sampling_directives if specified) to ensure the total of N people is reached. + - It is acceptable for the sampling plan to generate more than N people, but NEVER less than N. So if unsure generate MORE people, never less. + + ## Example + Given the following INPUT sampling dimensions: + + ```json + { + "sampling_space_description": "Young Western people of different liberal or intellectual professions." + "dimensions": [ + { + "name": "age", + "range": [18, 30] + }, + { + "name": "profession", + "values": ["Architect", "Financial Analyst", "Writer", "Art critic", "Lawyer", "Physician", "Accountant", ...] + }, + { + "name": "country", + "values": ["USA", "Canada", "UK", "France", "Germany", "Italy", "Spain", "Portugal", "Netherlands", "Belgium", ...] + }, + + { + "name": "personality_traits", + "values": { + "Maintains an unwavering optimism, always expecting positive outcomes even in the face of adversity and encouraging others to do the same.": 0.12, + "Tends to be introspective and reserved, preferring solitary activities and deep reflection over social gatherings or group events.": 0.18, + "Is highly ambitious, constantly setting challenging goals and pushing themselves to achieve more in both personal and professional spheres.": 0.15, + "Approaches new experiences with caution, carefully weighing risks and benefits before making decisions or embracing change.": 0.20, + "Often expects the worst in any situation, focusing on potential problems and rarely feeling hopeful about the future.": 0.08, + "Frequently experiences a sense of sadness and melancholy, finding it difficult to enjoy activities that once brought happiness.": 0.06, + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life.": 0.07, + "Feels overwhelmed by setbacks, easily discouraged, and tends to dwell on failures rather than successes.": 0.05, + <... many more ...> + } + } + + (... more dimensions ...) + + ] + } + + An OUTPUT *sample plan* therefore is a LIST with the *sample plan*, where each element is a dictionary with a *sampling directive*. For example, an output based on the above dimensions could look like this: + + ```json + [ + { + "id": 1, + "subpopulation_description": "Young Anglo-Saxon professionals with their stereotypical ambition and drive.", + "sampled_values": { + "age": [22, 30], + "profession": ["Financial Analyst", "Lawyer", "Physician", "Accountant", ...], + "country": ["USA", "UK", "Canada"], + "personality_traits": ["Maintains an unwavering optimism, always expecting positive outcomes even in the face of adversity and encouraging others to do the same.", + "Approaches new experiences with caution, carefully weighing risks and benefits before making decisions or embracing change", + "Tends to be introspective and reserved, preferring solitary activities and deep reflection over social gatherings or group events.", + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life."] + }, + "quantity": 10 + }, + { + "id": 2, + "subpopulation_description": "Young European professionals with a focus on creativity and innovation and their occasional existential crises.", + "sampled_values": { + "age": [21, 30], + "profession": ["Architect", "Lawyer", "Writer", "Physician", "Art critic", ...], + "country": ["France", "Germany", "Italy", "Spain"], + "personality_traits": ["Often expects the worst in any situation, focusing on potential problems and rarely feeling hopeful about the future.", + "Frequently experiences a sense of sadness and melancholy, finding it difficult to enjoy activities that once brought happiness.", + "Is quick to notice flaws and shortcomings in themselves and others, tending toward a negative outlook on life.", + "Feels overwhelmed by setbacks, easily discouraged, and tends to dwell on failures rather than successes.]" + }, + "quantity": 5 + }, + ... + ] + ``` + + + Args: + n (int): The number of elements to sample in total. This number will be distributed across the dimensions proportionally + to the presumed size the target population. + sampling_dimensions (dict): The dimensions of the sampling space. + max_quantity_per_sample_directive (int, optional): The maximum quantity of samples that can be specified in a single sampling directive. This is to ensure that the sampling plan is diverse and not biased towards a few large groups. + min_sampling_directives (int, optional): The minimum number of sampling directives to generate. This is to ensure that the sampling plan is rich and varied. + max_sampling_directives (int, optional): The maximum number of sampling directives to generate. This is to ensure that the sampling plan is not overly complex and remains manageable. + + Returns: + list: A LIST with the *sample plan*, where each element is a dictionary with a *sampling directive*, as described above. + """ + # the body of this method is handled by the @llm decorator. + + @transactional() + def _flatten_sampling_plan(self, sampling_plan:dict) -> list: + """ + Given a sample plan, flattens it into a list of samples in such a way that the number of times each sample appears + correspond to what was specified in the plan. The order is random to avoid bias. + + For example, an input sample plan could look like this: + + ```json + [ + { + "sampled_values": { + "age": 25, + "profession": "Architect", + "country": "USA" + }, + "quantity": 8 + }, + { + "sampled_values": { + "age": 27, + "profession": "Lawyer", + "country": "Canada" + }, + "quantity": 1 + }, + ... + ] + ``` + + And the output would be something like: + + ```python + [{"age": 25, "profession": "Architect", "country": "USA"}, + {"age": 27, "profession": "Lawyer", "country": "Canada"}, + ... + {"age": 25, "profession": "Architect", "country": "USA"}] + ``` + + Args: + sampling_plan (dict): The sample plan to flatten. + + Returns: + list: A list of samples, where each sample is a dictionary with the sampled values. + """ + samples = [] + for sample in sampling_plan: + if "quantity" not in sample: + logger.warning(f"Sample in sampling plan does not have a 'quantity' field: {sample}. Assuming 1.") + qty = 1 + else: + qty = int(sample["quantity"]) + + for _ in range(qty): + # we need to copy the sample to avoid adding the original sample multiple times, + # which would cause problems later when we modify the individual flattened samples + cc_sample = copy.deepcopy(sample["sampled_values"]) + + samples.append(cc_sample) + + # randomize + random.shuffle(samples) #inplace + return samples + + @transactional() + def _unique_full_name(self, already_generated_names: list, context:str=None) -> str: + # a technicality - we need to use an auxiliary method to be able to use the transactional decorator effectively. + # TODO update this somehow to avoid this cumbersome workaround. + + return self._aux_unique_full_name(already_generated_names=already_generated_names, context=context) + + + @utils.llm(temperature=1.5, presence_penalty=0.5, frequency_penalty=0.5) + def _aux_unique_full_name(self, already_generated_names: list, context:str=None) -> str: + """ + Generates a unique full name for a person. The full name must not be in the list of already generated names. + If necessary, you can generate a longer name to ensure it is new. You can also try tweaking the spelling or + adding more surnames, so that the name is unique. However, the name **must** sound realistic and not be too far-fetched, + not sound as if it was made up. + + The final result is only the name, nothing else: + + "Some name here" ---> correct as it is just a name, nothing else + "Some name here, because ..." ---> incorrect as it contains a reason + "Some name here." ---> incorrect as it contains punctuation + "Name: Some name here" ---> incorrect as it contains a label + "Some name here, some other name here" ---> incorrect as it contains more than one name + + An optional context can be provided to guide the name generation, so that it is a realistic name for the context. For example, we know that different socio-economic classes have different naming conventions, so the context can be used to guide the name generation. + + Regarding the `already_generated_names`, you must: + - NEVER generate a name that is already in the list of already generated names. + - The names in `already_generated_names` ARE NOT examples of names to generate. They are just names that have already been generated and should not be repeated. You should generate new names regardless of the names in `already_generated_names`, the only constraint is that the new names should not be in the list of already generated names. + - In particular, you are not to generate a similar name to that of those in `already_generated_names`, you are **not** building some kind of + logical sequence. Each name must be independent of the others. + + ## Example + + **Input:** + already_generated_names: ["John Doe", "Jane Smith", "Alice Brown"] + context: { 'age': 25, 'profession': 'Architect', 'country': 'USA' } + + **Output:** + "Michael Johnson" + + Note that: + - The name "Michael Johnson" is not in the list of already generated names. + - The ouput consists only of a name, nothing else. + + Args: + already_generated_names (list): The list of already generated names. + context (str): The context in which the name is being generated. This can be used to guide the name generation, so that it is a realistic name for the context. + + Returns: + str: A unique full name for a person. + """ + # the body of this method is handled by the @llm decorator + + @transactional() + def _unique_full_names(self, n:int, already_generated_names: list, context:str=None) -> list: + """ + Generates a list of n unique full names for people. The full names must not be in the list of already generated names. + + Args: + n (int): The number of names to generate. + already_generated_names (list): The list of already generated names. + context (str): The context in which the names are being generated. This can be used to guide the name generation, so that it is a realistic name for the context. + """ + + logger.debug(f"Will generate {n} unique full names for people. Already generated names: {already_generated_names}") + + names = [] + + if n > 0: + # let's split the n in smaller chunks to make the model's job easier + chunk_size = min(10, n) # we generate at most 10 names at a time, to avoid overwhelming the model + chunks = math.ceil(n/chunk_size) + + forbidden_names = copy.deepcopy(already_generated_names) + + + max_iterations = chunks * 10 + cur_iterations = 0 + + while len(names) < n and cur_iterations < max_iterations: + logger.debug(f"Currently already generated names: {forbidden_names}") + logger.debug(f"Iteration {cur_iterations} - Generating {chunk_size} names. Currently have {len(names)} names. Max iterations to be allowed: {max_iterations}") + try: + temp_names = utils.try_function(\ + lambda: \ + self._aux_unique_full_names(n=chunk_size , + already_generated_names=forbidden_names, + context=context), + + # checks that some new name was produced + postcond_func = lambda result: len(set(forbidden_names).intersection(result)) < len(result), + retries=3) + + # add the new names to the names list, removing any duplicates from their combination + names = list(set(names + temp_names)) + forbidden_names += names + except Exception as e: + logger.error(f"Error generating names: {e}") + # if we have an error, we just skip this iteration and try again + # but we need to increment the number of iterations anyway + + cur_iterations += 1 + + if cur_iterations >= max_iterations and len(names) < n: + logger.error(f"Could not generate the requested number of names after {max_iterations} iterations. Moving on with the {len(names)} names generated.") + + TinyPersonFactory.all_unique_names = list(set(TinyPersonFactory.all_unique_names + names)) + + return names + + @utils.llm(temperature=1.9, presence_penalty=0.5, frequency_penalty=0.5) + def _aux_unique_full_names(self, n:int, already_generated_names: list, context:str=None) -> list: + """ + Generates a list of n unique full names for people. The full names must not be in the list of already generated names. You **must** consider **all** reasononable options for names, + not only the common or popular. To ensure that fresh names are really new and do not appear in the list of already generated ones, if necessary you can: + - generate longer names to ensure they are new. + - try tweaking the spelling or adding more surnames, so that the names are unique. + - add unusual names or surnames, so that the names are unique. + - as a very last resort, you can append a number to the name, so that it is unique, despote being a bit less realistic. + + Except for the latter option, the names **must** sound realistic and not be too far-fetched, not sound as if they were made up. + + You **must** generate at least n names, and they **must** all be unique. If necessary, to ensure you get at least n names, you can try to generate more than n, + but **never** less, unless you need to avoid a repeated name. If forced to choose, you always prefer to generate unique names, even if that means generating less than n names. + + The final result is only the list of names, nothing else: + + ["Some name here"] ---> correct as it is just a list with a single name, nothing else + ["Some name here, some other name here"] ---> correct as it is a list of names + ["Some name here, because ..."] ---> incorrect as it contains a reason + ["Some name here."] ---> incorrect as it contains punctuation + ["Name: Some name here"] ---> incorrect as it contains a label + + An optional context can be provided to guide the name generation, so that it is a realistic name for the context. For example, we know that different socio-economic classes have different naming conventions, + so the context can be used to guide the name generation. In particular, follow these rules regarding the context: + - If a country is specified, the names should be typical for that country. + + Regarding the `already_generated_names`, you must: + - NEVER generate a name that is already in the list of already generated names. + - The names in `already_generated_names` ARE NOT examples of names to generate. They are just names that have already been generated and should not be repeated. You should generate new names regardless of the names in `already_generated_names`, the only constraint is that the new names should not be in the list of already_generated_names. + - In particular, you are not to generate a similar name to that of those in `already_generated_names`, you are **not** building some kind of logical sequence. Each name must be independent of the others. + + ## Example + + **Input:** + n: 6 + already_generated_names: ["John Doe", "Jane Smith", "Alice Brown"] + context: "Young Americans of different liberal professions" + **Output:** + ["Michael Johnson", "Sarah Williams", "David Gates", "Jennifer Davis", "Robert J. Wilson", "Anna Kerr"] + + Note that: + - The names are not in the list of already generated names. + - The ouputs consist only of a list of names, nothing else. + - The output length is exactly 6, which is the requested number of names. There could be a bit more names generated, but never less. + + Args: + n (int): The number of names to generate. + already_generated_names (list): The list of already generated names. + context (str): The context in which the names are being generated. This can be used to guide the name generation, so that it is a realistic name for the context. + + Returns: + list: A list of n unique full names for people. These names NEVER repeat names in the list of already generated names. + """ + # the body of this method is handled by the @llm decorator. Below we provide a post-processing function that is + # applied to the LLM output, to ensure that the names are unique. + + return lambda names: list(set(names)) + + @transactional() + def _aux_model_call(self, messages, temperature, frequency_penalty, presence_penalty): + """ + Auxiliary method to make a model call. This is needed in order to be able to use the transactional decorator, + due too a technicality - otherwise, the agent creation would be skipped during cache reutilization, and + we don't want that. + """ + return openai_utils.client().send_message(messages, + temperature=temperature, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + response_format={"type": "json_object"}) + + @transactional() + def _setup_agent(self, agent, configuration): + """ + Sets up the agent with the necessary elements. + """ + agent.include_persona_definitions(configuration) + + # does not return anything, as we don't want to cache the agent object itself. + + @transactional() + @utils.llm(temperature=0.3, frequency_penalty=-0.1, presence_penalty=-0.1, enable_json_output_format=False) + def _generate_name_for_sample(self, sample_characteristics: dict, already_generated_names: list) -> str: + """ + Generates a single full name for a person based on their complete sample characteristics, such that + it is as appropriate as possible to all characteristics, not just gender. + This name MUST BE UNIQUE and not appear in the already_generated_names list, though variations of the + same name are allowed. + + You must generate a realistic full name that is appropriate for the given sample characteristics. + Consider ALL the characteristics provided, including but not limited to: + - Gender + - Age or age range + - Country/nationality/ethnicity + - Socioeconomic status + - Profession + - Educational background + - Cultural background + - Any other relevant demographic or personal characteristics + + The name should: + - BE UNIQUE and not appear in the already_generated_names list + - Be realistic and culturally appropriate for the characteristics + - Sound natural and not made-up + - Be unique and not appear in the already_generated_names list + - Reflect the person's likely background (e.g., names common in their generation, culture, social class) + + If you need additional methods to ensure uniqueness, you can: + - Use longer or more uncommon names + - Include middle names or multiple surnames + - Use culturally appropriate name variations + - As a last resort, you can append a number, but this should be avoided. + + + In ANY CASE, you **must never**, NEVER, generate a name that already appears in the already_generated_names list. + + Return only the full name as a string, nothing else. + + ## Example + + **Input:** + sample_characteristics: { + "gender": "female", + "age": 28, + "country": "Brazil", + "profession": "Software Engineer", + "socioeconomic_status": "middle class", + "education": "Computer Science degree" + } + already_generated_names: ["João Silva", "Maria Santos", "Ana Costa"] + + **Output:** + "Camila Rodrigues" + + Args: + sample_characteristics (dict): The complete characteristics of the sample, including demographics, profession, etc. + already_generated_names (list): The list of already generated names to avoid duplicates. The new name MUST NOT be in this list. + + Returns: + str: A single full name appropriate for the sample characteristics. + """ + # the body of this method is handled by the @llm decorator + diff --git a/tinytroupe/openai_utils.py b/tinytroupe/openai_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7b56555bfc781f9f25d12595a72e30b7d824ebe4 --- /dev/null +++ b/tinytroupe/openai_utils.py @@ -0,0 +1,525 @@ +import os +import openai +from openai import OpenAI, AzureOpenAI +import time +import pickle +import logging +import configparser +from typing import Union + + +import tiktoken +from tinytroupe import utils +from tinytroupe.control import transactional +from tinytroupe import default +from tinytroupe import config_manager + +logger = logging.getLogger("tinytroupe") + +# We'll use various configuration elements below +config = utils.read_config_file() + +########################################################################### +# Client class +########################################################################### + +class OpenAIClient: + """ + A utility class for interacting with the OpenAI API. + """ + + def __init__(self, cache_api_calls=default["cache_api_calls"], cache_file_name=default["cache_file_name"]) -> None: + logger.debug("Initializing OpenAIClient") + + # should we cache api calls and reuse them? + self.set_api_cache(cache_api_calls, cache_file_name) + + def set_api_cache(self, cache_api_calls, cache_file_name=default["cache_file_name"]): + """ + Enables or disables the caching of API calls. + + Args: + cache_file_name (str): The name of the file to use for caching API calls. + """ + self.cache_api_calls = cache_api_calls + self.cache_file_name = cache_file_name + if self.cache_api_calls: + # load the cache, if any + self.api_cache = self._load_cache() + + + def _setup_from_config(self): + """ + Sets up the OpenAI API configurations for this client. + """ + self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) + + @config_manager.config_defaults( + model="model", + temperature="temperature", + max_tokens="max_tokens", + top_p="top_p", + frequency_penalty="frequency_penalty", + presence_penalty="presence_penalty", + timeout="timeout", + max_attempts="max_attempts", + waiting_time="waiting_time", + exponential_backoff_factor="exponential_backoff_factor", + response_format=None, + echo=None + ) + def send_message(self, + current_messages, + dedent_messages=True, + model=None, + temperature=None, + max_tokens=None, + top_p=None, + frequency_penalty=None, + presence_penalty=None, + stop=[], + timeout=None, + max_attempts=None, + waiting_time=None, + exponential_backoff_factor=None, + n = 1, + response_format=None, + enable_pydantic_model_return=False, + echo=False): + """ + Sends a message to the OpenAI API and returns the response. + + Args: + current_messages (list): A list of dictionaries representing the conversation history. + dedent_messages (bool): Whether to dedent the messages before sending them to the API. + model (str): The ID of the model to use for generating the response. + temperature (float): Controls the "creativity" of the response. Higher values result in more diverse responses. + max_tokens (int): The maximum number of tokens (words or punctuation marks) to generate in the response. + top_p (float): Controls the "quality" of the response. Higher values result in more coherent responses. + frequency_penalty (float): Controls the "repetition" of the response. Higher values result in less repetition. + presence_penalty (float): Controls the "diversity" of the response. Higher values result in more diverse responses. + stop (str): A string that, if encountered in the generated response, will cause the generation to stop. + max_attempts (int): The maximum number of attempts to make before giving up on generating a response. + timeout (int): The maximum number of seconds to wait for a response from the API. + waiting_time (int): The number of seconds to wait between requests. + exponential_backoff_factor (int): The factor by which to increase the waiting time between requests. + n (int): The number of completions to generate. + response_format: The format of the response, if any. + echo (bool): Whether to echo the input message in the response. + enable_pydantic_model_return (bool): Whether to enable Pydantic model return instead of dict when possible. + + Returns: + A dictionary representing the generated response. + """ + + def aux_exponential_backoff(): + nonlocal waiting_time + + # in case waiting time was initially set to 0 + if waiting_time <= 0: + waiting_time = 2 + + logger.info(f"Request failed. Waiting {waiting_time} seconds between requests...") + time.sleep(waiting_time) + + # exponential backoff + waiting_time = waiting_time * exponential_backoff_factor + + # setup the OpenAI configurations for this client. + self._setup_from_config() + + # dedent the messages (field 'content' only) if needed (using textwrap) + if dedent_messages: + for message in current_messages: + if "content" in message: + message["content"] = utils.dedent(message["content"]) + + + # We need to adapt the parameters to the API type, so we create a dictionary with them first + chat_api_params = { + "model": model, + "messages": current_messages, + "temperature": temperature, + "max_tokens":max_tokens, + "frequency_penalty": frequency_penalty, + "presence_penalty": presence_penalty, + "stop": stop, + "timeout": timeout, + "stream": False, + "n": n, + } + + if top_p is not None and top_p > 0: + chat_api_params["top_p"] = top_p + + if response_format is not None: + chat_api_params["response_format"] = response_format + + i = 0 + while i < max_attempts: + try: + i += 1 + + try: + logger.debug(f"Sending messages to OpenAI API. Token count={self._count_tokens(current_messages, model)}.") + except NotImplementedError: + logger.debug(f"Token count not implemented for model {model}.") + + start_time = time.monotonic() + logger.debug(f"Calling model with client class {self.__class__.__name__}.") + + ############################################################### + # call the model, either from the cache or from the API + ############################################################### + cache_key = str((model, chat_api_params)) # need string to be hashable + if self.cache_api_calls and (cache_key in self.api_cache): + response = self.api_cache[cache_key] + else: + if waiting_time > 0: + logger.info(f"Waiting {waiting_time} seconds before next API request (to avoid throttling)...") + time.sleep(waiting_time) + + response = self._raw_model_call(model, chat_api_params) + if self.cache_api_calls: + self.api_cache[cache_key] = response + self._save_cache() + + + logger.debug(f"Got response from API: {response}") + end_time = time.monotonic() + logger.debug( + f"Got response in {end_time - start_time:.2f} seconds after {i} attempts.") + + if enable_pydantic_model_return: + return utils.to_pydantic_or_sanitized_dict(self._raw_model_response_extractor(response), model=response_format) + else: + return utils.sanitize_dict(self._raw_model_response_extractor(response)) + + except InvalidRequestError as e: + logger.error(f"[{i}] Invalid request error, won't retry: {e}") + + # there's no point in retrying if the request is invalid + # so we return None right away + return None + + except openai.BadRequestError as e: + logger.error(f"[{i}] Invalid request error, won't retry: {e}") + + # there's no point in retrying if the request is invalid + # so we return None right away + return None + + except openai.RateLimitError: + logger.warning( + f"[{i}] Rate limit error, waiting a bit and trying again.") + aux_exponential_backoff() + + except NonTerminalError as e: + logger.error(f"[{i}] Non-terminal error: {e}") + aux_exponential_backoff() + + except Exception as e: + logger.error(f"[{i}] {type(e).__name__} Error: {e}") + aux_exponential_backoff() + + logger.error(f"Failed to get response after {max_attempts} attempts.") + return None + + def _raw_model_call(self, model, chat_api_params): + """ + Calls the OpenAI API with the given parameters. Subclasses should + override this method to implement their own API calls. + """ + + # adjust parameters depending on the model + if self._is_reasoning_model(model): + # Reasoning models have slightly different parameters + del chat_api_params["stream"] + del chat_api_params["temperature"] + del chat_api_params["top_p"] + del chat_api_params["frequency_penalty"] + del chat_api_params["presence_penalty"] + + chat_api_params["max_completion_tokens"] = chat_api_params["max_tokens"] + del chat_api_params["max_tokens"] + + chat_api_params["reasoning_effort"] = default["reasoning_effort"] + + + # To make the log cleaner, we remove the messages from the logged parameters + logged_params = {k: v for k, v in chat_api_params.items() if k != "messages"} + + if "response_format" in chat_api_params: + # to enforce the response format via pydantic, we need to use a different method + + if "stream" in chat_api_params: + del chat_api_params["stream"] + + logger.debug(f"Calling LLM model (using .parse too) with these parameters: {logged_params}. Not showing 'messages' parameter.") + # complete message + logger.debug(f" --> Complete messages sent to LLM: {chat_api_params['messages']}") + + result_message = self.client.beta.chat.completions.parse( + **chat_api_params + ) + + return result_message + + else: + logger.debug(f"Calling LLM model with these parameters: {logged_params}. Not showing 'messages' parameter.") + return self.client.chat.completions.create( + **chat_api_params + ) + + def _is_reasoning_model(self, model): + return "o1" in model or "o3" in model + + def _raw_model_response_extractor(self, response): + """ + Extracts the response from the API response. Subclasses should + override this method to implement their own response extraction. + """ + return response.choices[0].message.to_dict() + + def _count_tokens(self, messages: list, model: str): + """ + Count the number of OpenAI tokens in a list of messages using tiktoken. + + Adapted from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb + + Args: + messages (list): A list of dictionaries representing the conversation history. + model (str): The name of the model to use for encoding the string. + """ + try: + try: + encoding = tiktoken.encoding_for_model(model) + except KeyError: + logger.debug("Token count: model not found. Using cl100k_base encoding.") + encoding = tiktoken.get_encoding("cl100k_base") + + if model in { + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-16k-0613", + "gpt-4-0314", + "gpt-4-32k-0314", + "gpt-4-0613", + "gpt-4-32k-0613", + } or "o1" in model or "o3" in model: # assuming o1/3 models work the same way + tokens_per_message = 3 + tokens_per_name = 1 + elif model == "gpt-3.5-turbo-0301": + tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n + tokens_per_name = -1 # if there's a name, the role is omitted + elif "gpt-3.5-turbo" in model: + logger.debug("Token count: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.") + return self._count_tokens(messages, model="gpt-3.5-turbo-0613") + elif ("gpt-4" in model) or ("ppo" in model) or ("alias-large" in model): + logger.debug("Token count: gpt-4/alias-large may update over time. Returning num tokens assuming gpt-4-0613.") + return self._count_tokens(messages, model="gpt-4-0613") + else: + raise NotImplementedError( + f"""_count_tokens() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""" + ) + + num_tokens = 0 + for message in messages: + num_tokens += tokens_per_message + for key, value in message.items(): + num_tokens += len(encoding.encode(value)) + if key == "name": + num_tokens += tokens_per_name + num_tokens += 3 # every reply is primed with <|start|>assistant<|message|> + return num_tokens + + except Exception as e: + logger.error(f"Error counting tokens: {e}") + return None + + def _save_cache(self): + """ + Saves the API cache to disk. We use pickle to do that because some obj + are not JSON serializable. + """ + # use pickle to save the cache + pickle.dump(self.api_cache, open(self.cache_file_name, "wb", encoding="utf-8", errors="replace")) + + + def _load_cache(self): + + """ + Loads the API cache from disk. + """ + # unpickle + return pickle.load(open(self.cache_file_name, "rb", encoding="utf-8", errors="replace")) if os.path.exists(self.cache_file_name) else {} + + def get_embedding(self, text, model=default["embedding_model"]): + """ + Gets the embedding of the given text using the specified model. + + Args: + text (str): The text to embed. + model (str): The name of the model to use for embedding the text. + + Returns: + The embedding of the text. + """ + response = self._raw_embedding_model_call(text, model) + return self._raw_embedding_model_response_extractor(response) + + def _raw_embedding_model_call(self, text, model): + """ + Calls the OpenAI API to get the embedding of the given text. Subclasses should + override this method to implement their own API calls. + """ + return self.client.embeddings.create( + input=[text], + model=model + ) + + def _raw_embedding_model_response_extractor(self, response): + """ + Extracts the embedding from the API response. Subclasses should + override this method to implement their own response extraction. + """ + return response.data[0].embedding + +class AzureClient(OpenAIClient): + + def __init__(self, cache_api_calls=default["cache_api_calls"], cache_file_name=default["cache_file_name"]) -> None: + logger.debug("Initializing AzureClient") + + super().__init__(cache_api_calls, cache_file_name) + + def _setup_from_config(self): + """ + Sets up the Azure OpenAI Service API configurations for this client, + including the API endpoint and key. + """ + if os.getenv("AZURE_OPENAI_KEY"): + logger.info("Using Azure OpenAI Service API with key.") + self.client = AzureOpenAI(azure_endpoint= os.getenv("AZURE_OPENAI_ENDPOINT"), + api_version = config["OpenAI"]["AZURE_API_VERSION"], + api_key = os.getenv("AZURE_OPENAI_KEY")) + else: # Use Entra ID Auth + logger.info("Using Azure OpenAI Service API with Entra ID Auth.") + from azure.identity import DefaultAzureCredential, get_bearer_token_provider + + credential = DefaultAzureCredential() + token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default") + self.client = AzureOpenAI( + azure_endpoint= os.getenv("AZURE_OPENAI_ENDPOINT"), + api_version = config["OpenAI"]["AZURE_API_VERSION"], + azure_ad_token_provider=token_provider + ) + + +class HelmholtzBlabladorClient(OpenAIClient): + + def __init__(self, cache_api_calls=default["cache_api_calls"], cache_file_name=default["cache_file_name"]) -> None: + logger.debug("Initializing HelmholtzBlabladorClient") + super().__init__(cache_api_calls, cache_file_name) + + def _setup_from_config(self): + """ + Sets up the Helmholtz Blablador API configurations for this client. + """ + self.client = OpenAI( + base_url="https://api.helmholtz-blablador.fz-juelich.de/v1", + api_key=os.getenv("BLABLADOR_API_KEY", "dummy"), + ) + +########################################################################### +# Exceptions +########################################################################### +class InvalidRequestError(Exception): + """ + Exception raised when the request to the OpenAI API is invalid. + """ + pass + +class NonTerminalError(Exception): + """ + Exception raised when an unspecified error occurs but we know we can retry. + """ + pass + +########################################################################### +# Clients registry +# +# We can have potentially different clients, so we need a place to +# register them and retrieve them when needed. +# +# We support both OpenAI and Azure OpenAI Service API by default. +# Thus, we need to set the API parameters based on the choice of the user. +# This is done within specialized classes. +# +# It is also possible to register custom clients, to access internal or +# otherwise non-conventional API endpoints. +########################################################################### +_api_type_to_client = {} +_api_type_override = None + +def register_client(api_type, client): + """ + Registers a client for the given API type. + + Args: + api_type (str): The API type for which we want to register the client. + client: The client to register. + """ + _api_type_to_client[api_type] = client + +def _get_client_for_api_type(api_type): + """ + Returns the client for the given API type. + + Args: + api_type (str): The API type for which we want to get the client. + """ + try: + return _api_type_to_client[api_type] + except KeyError: + raise ValueError(f"API type {api_type} is not supported. Please check the 'config.ini' file.") + +def client(): + """ + Returns the client for the configured API type. + """ + api_type = config["OpenAI"]["API_TYPE"] if _api_type_override is None else _api_type_override + + logger.debug(f"Using API type {api_type}.") + return _get_client_for_api_type(api_type) + + +# TODO simplify the custom configuration methods below + +def force_api_type(api_type): + """ + Forces the use of the given API type, thus overriding any other configuration. + + Args: + api_type (str): The API type to use. + """ + global _api_type_override + _api_type_override = api_type + +def force_api_cache(cache_api_calls, cache_file_name=default["cache_file_name"]): + """ + Forces the use of the given API cache configuration, thus overriding any other configuration. + + Args: + cache_api_calls (bool): Whether to cache API calls. + cache_file_name (str): The name of the file to use for caching API calls. + """ + # set the cache parameters on all clients + for client in _api_type_to_client.values(): + client.set_api_cache(cache_api_calls, cache_file_name) + +# default client +register_client("openai", OpenAIClient()) +register_client("azure", AzureClient()) +register_client("helmholtz-blablador", HelmholtzBlabladorClient()) + + + diff --git a/tinytroupe/profiling.py b/tinytroupe/profiling.py new file mode 100644 index 0000000000000000000000000000000000000000..711e4a18e0a0905b4854f82102c88681be78df3b --- /dev/null +++ b/tinytroupe/profiling.py @@ -0,0 +1,714 @@ +""" +Provides mechanisms for creating understanding the characteristics of agent populations, such as +their age distribution, typical interests, and so on. + +Guideline for plotting the methods: all plot methods should also return a Pandas dataframe with the data used for +plotting. +""" +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from typing import List, Dict, Any, Optional, Union, Callable +from collections import Counter, defaultdict +import warnings + +# Handle TinyPerson import gracefully +try: + from tinytroupe.agent import TinyPerson +except ImportError: + # Fallback if import fails + TinyPerson = None + + +class Profiler: + """ + Advanced profiler for analyzing agent population characteristics with support for + complex attributes, statistical analysis, and comprehensive visualizations. + """ + + def __init__(self, attributes: List[str] = ["age", "occupation.title", "nationality"]) -> None: + self.attributes = attributes + self.attributes_distributions = {} # attribute -> DataFrame + self.agents_data = None # Store processed agent data + self.analysis_results = {} # Store various analysis results + + # Set up better plotting style + plt.style.use('default') + sns.set_palette("husl") + + def profile(self, agents: Union[List[dict], List[TinyPerson]], plot: bool = True, + advanced_analysis: bool = True) -> Dict[str, Any]: + """ + Profiles the given agents with comprehensive analysis. + + Args: + agents: The agents to be profiled (either dicts or TinyPerson objects) + plot: Whether to generate visualizations + advanced_analysis: Whether to perform advanced statistical analysis + + Returns: + Dictionary containing all analysis results + """ + # Convert agents to consistent format + self.agents_data = self._prepare_agent_data(agents) + + # Basic attribute distributions + self.attributes_distributions = self._compute_attributes_distributions(self.agents_data) + + if advanced_analysis: + self._perform_advanced_analysis() + + if plot: + self.render(advanced=advanced_analysis) + + return { + 'distributions': self.attributes_distributions, + 'analysis': self.analysis_results, + 'summary_stats': self._generate_summary_statistics() + } + + def _prepare_agent_data(self, agents: Union[List[dict], List[TinyPerson]]) -> List[Dict[str, Any]]: + """Convert agents to a consistent dictionary format for analysis.""" + processed_agents = [] + + for agent in agents: + if isinstance(agent, TinyPerson): + # Extract data from TinyPerson object + agent_data = self._extract_tinyperson_data(agent) + else: + agent_data = agent.copy() + + processed_agents.append(agent_data) + + return processed_agents + + def _extract_tinyperson_data(self, agent: TinyPerson) -> Dict[str, Any]: + """Extract comprehensive data from a TinyPerson object.""" + data = {} + + # Basic persona attributes + if hasattr(agent, '_persona') and agent._persona: + data.update(agent._persona) + + # Mental state information + if hasattr(agent, '_mental_state') and agent._mental_state: + mental_state = agent._mental_state + data['current_emotions'] = mental_state.get('emotions') + data['current_goals'] = mental_state.get('goals', []) + data['current_context'] = mental_state.get('context', []) + data['accessible_agents_count'] = len(mental_state.get('accessible_agents', [])) + + # Behavioral metrics + if hasattr(agent, 'actions_count'): + data['actions_count'] = agent.actions_count + if hasattr(agent, 'stimuli_count'): + data['stimuli_count'] = agent.stimuli_count + + # Memory statistics + if hasattr(agent, 'episodic_memory') and agent.episodic_memory: + try: + # Get total memory size including both committed memory and current episode buffer + memory_size = len(agent.episodic_memory.memory) + len(agent.episodic_memory.episodic_buffer) + data['episodic_memory_size'] = memory_size + except AttributeError: + # Fallback if memory structure is different + data['episodic_memory_size'] = 0 + + # Social connections + if hasattr(agent, '_accessible_agents'): + data['social_connections'] = len(agent._accessible_agents) + + return data + + def _perform_advanced_analysis(self): + """Perform advanced statistical and behavioral analysis.""" + self.analysis_results = {} + + # Demographic analysis + self.analysis_results['demographics'] = self._analyze_demographics() + + # Behavioral patterns + self.analysis_results['behavioral_patterns'] = self._analyze_behavioral_patterns() + + # Social network analysis + self.analysis_results['social_analysis'] = self._analyze_social_patterns() + + # Personality clustering + self.analysis_results['personality_clusters'] = self._analyze_personality_clusters() + + # Correlations + self.analysis_results['correlations'] = self._analyze_correlations() + + def _analyze_demographics(self) -> Dict[str, Any]: + """Analyze demographic patterns in the population.""" + demographics = {} + + # Age analysis + ages = [agent.get('age') for agent in self.agents_data if agent.get('age') is not None] + if ages: + demographics['age_stats'] = { + 'mean': np.mean(ages), + 'median': np.median(ages), + 'std': np.std(ages), + 'range': (min(ages), max(ages)), + 'distribution': 'normal' if self._test_normality(ages) else 'non-normal' + } + + # Occupation diversity + occupations = [agent.get('occupation', {}).get('title') if isinstance(agent.get('occupation'), dict) + else agent.get('occupation') for agent in self.agents_data] + occupations = [occ for occ in occupations if occ is not None] + + if occupations: + occ_counts = Counter(occupations) + demographics['occupation_diversity'] = { + 'unique_count': len(occ_counts), + 'diversity_index': self._calculate_diversity_index(occ_counts), + 'most_common': occ_counts.most_common(5) + } + + # Geographic distribution + nationalities = [agent.get('nationality') for agent in self.agents_data if agent.get('nationality')] + if nationalities: + nat_counts = Counter(nationalities) + demographics['geographic_diversity'] = { + 'unique_countries': len(nat_counts), + 'diversity_index': self._calculate_diversity_index(nat_counts), + 'distribution': dict(nat_counts) + } + + return demographics + + def _analyze_behavioral_patterns(self) -> Dict[str, Any]: + """Analyze behavioral patterns across the population.""" + behavioral = {} + + # Activity levels + actions_data = [agent.get('actions_count', 0) for agent in self.agents_data] + stimuli_data = [agent.get('stimuli_count', 0) for agent in self.agents_data] + + if any(actions_data): + behavioral['activity_levels'] = { + 'actions_mean': np.mean(actions_data), + 'actions_std': np.std(actions_data), + 'stimuli_mean': np.mean(stimuli_data), + 'stimuli_std': np.std(stimuli_data), + 'activity_ratio': np.mean(actions_data) / max(np.mean(stimuli_data), 1) + } + + # Goal patterns + all_goals = [] + for agent in self.agents_data: + goals = agent.get('current_goals', []) + if isinstance(goals, list): + all_goals.extend(goals) + + if all_goals: + goal_counts = Counter(all_goals) + behavioral['goal_patterns'] = { + 'common_goals': goal_counts.most_common(10), + 'goal_diversity': self._calculate_diversity_index(goal_counts) + } + + return behavioral + + def _analyze_social_patterns(self) -> Dict[str, Any]: + """Analyze social connection patterns.""" + social = {} + + # Social connectivity + connections = [agent.get('social_connections', 0) for agent in self.agents_data] + accessible_counts = [agent.get('accessible_agents_count', 0) for agent in self.agents_data] + + if any(connections + accessible_counts): + social['connectivity'] = { + 'avg_connections': np.mean(connections), + 'avg_accessible': np.mean(accessible_counts), + 'connectivity_distribution': self._categorize_connectivity(connections), + 'social_isolation_rate': sum(1 for c in connections if c == 0) / len(connections) + } + + return social + + def _analyze_personality_clusters(self) -> Dict[str, Any]: + """Identify personality-based clusters if Big Five data is available.""" + personality = {} + + # Extract Big Five traits if available + big_five_data = [] + for agent in self.agents_data: + if 'big_five' in agent and isinstance(agent['big_five'], dict): + traits = agent['big_five'] + # Convert text descriptions to numerical values (simplified approach) + numerical_traits = {} + for trait, value in traits.items(): + if isinstance(value, str): + if 'high' in value.lower(): + numerical_traits[trait] = 0.8 + elif 'medium' in value.lower(): + numerical_traits[trait] = 0.5 + elif 'low' in value.lower(): + numerical_traits[trait] = 0.2 + else: + numerical_traits[trait] = 0.5 # Default + else: + numerical_traits[trait] = value + + if len(numerical_traits) == 5: # Full Big Five + big_five_data.append(numerical_traits) + + if len(big_five_data) >= 2: # Need minimum agents for analysis (reduced from >3 to >=2) + df_traits = pd.DataFrame(big_five_data) + + # Simple clustering based on dominant traits + personality['trait_analysis'] = { + 'average_traits': df_traits.mean().to_dict(), + 'trait_correlations': df_traits.corr().to_dict() if len(big_five_data) > 1 else {}, + 'dominant_traits': self._identify_dominant_traits(df_traits) + } + + return personality + + def _analyze_correlations(self) -> Dict[str, Any]: + """Analyze correlations between different attributes.""" + correlations = {} + + # Create a numerical dataset for correlation analysis + numerical_data = {} + + for agent in self.agents_data: + for attr in ['age', 'actions_count', 'stimuli_count', 'social_connections']: + if attr not in numerical_data: + numerical_data[attr] = [] + numerical_data[attr].append(agent.get(attr, 0)) + + if len(numerical_data) > 1: + df_corr = pd.DataFrame(numerical_data) + correlation_matrix = df_corr.corr() + + # Find strong correlations (> 0.5) + strong_correlations = [] + for i in range(len(correlation_matrix.columns)): + for j in range(i+1, len(correlation_matrix.columns)): + corr_value = correlation_matrix.iloc[i, j] + if abs(corr_value) > 0.5: + strong_correlations.append({ + 'variables': (correlation_matrix.columns[i], correlation_matrix.columns[j]), + 'correlation': corr_value + }) + + correlations['numerical_correlations'] = strong_correlations + correlations['correlation_matrix'] = correlation_matrix.to_dict() + + return correlations + + def render(self, advanced: bool = True) -> None: + """ + Renders comprehensive visualizations of the agent population analysis. + """ + # Basic attribute distributions + self._plot_basic_distributions() + + if advanced and self.analysis_results: + self._plot_advanced_analysis() + + def _plot_basic_distributions(self) -> None: + """Plot basic attribute distributions with improved styling.""" + n_attrs = len(self.attributes) + if n_attrs == 0: + return + + # Calculate subplot layout + n_cols = min(3, n_attrs) + n_rows = (n_attrs + n_cols - 1) // n_cols + + fig, axes = plt.subplots(n_rows, n_cols, figsize=(5 * n_cols, 4 * n_rows)) + if n_attrs == 1: + axes = [axes] + elif n_rows == 1: + axes = [axes] if n_attrs == 1 else axes + else: + axes = axes.flatten() + + for i, attribute in enumerate(self.attributes): + ax = axes[i] if n_attrs > 1 else axes[0] + + if attribute in self.attributes_distributions: + df = self.attributes_distributions[attribute] + + # Create better visualizations based on data type + if len(df) <= 15: # Categorical data + df.plot(kind='bar', ax=ax, color=sns.color_palette("husl", len(df))) + ax.set_title(f"{attribute.replace('_', ' ').title()} Distribution", fontsize=12, fontweight='bold') + ax.tick_params(axis='x', rotation=45) + else: # Many categories - use horizontal bar for readability + df.head(15).plot(kind='barh', ax=ax, color=sns.color_palette("husl", 15)) + ax.set_title(f"Top 15 {attribute.replace('_', ' ').title()}", fontsize=12, fontweight='bold') + + ax.grid(axis='y', alpha=0.3) + ax.set_xlabel('Count') + + # Hide empty subplots + for i in range(n_attrs, len(axes)): + axes[i].set_visible(False) + + plt.tight_layout() + plt.show() + + def _plot_advanced_analysis(self) -> None: + """Create advanced visualizations for the analysis results.""" + + # 1. Demographics overview + if 'demographics' in self.analysis_results: + self._plot_demographics() + + # 2. Behavioral patterns + if 'behavioral_patterns' in self.analysis_results: + self._plot_behavioral_patterns() + + # 3. Correlation heatmap + if 'correlations' in self.analysis_results and 'correlation_matrix' in self.analysis_results['correlations']: + self._plot_correlation_heatmap() + + def _plot_demographics(self) -> None: + """Plot demographic analysis results.""" + demo = self.analysis_results['demographics'] + + fig, axes = plt.subplots(2, 2, figsize=(12, 10)) + fig.suptitle('Population Demographics Analysis', fontsize=16, fontweight='bold') + + # Age distribution + if 'age_stats' in demo: + ages = [agent.get('age') for agent in self.agents_data if agent.get('age') is not None] + axes[0, 0].hist(ages, bins=10, alpha=0.7, color='skyblue', edgecolor='black') + axes[0, 0].axvline(demo['age_stats']['mean'], color='red', linestyle='--', + label=f"Mean: {demo['age_stats']['mean']:.1f}") + axes[0, 0].set_title('Age Distribution') + axes[0, 0].set_xlabel('Age') + axes[0, 0].set_ylabel('Count') + axes[0, 0].legend() + + # Occupation diversity + if 'occupation_diversity' in demo: + occ_data = demo['occupation_diversity']['most_common'] + if occ_data: + occs, counts = zip(*occ_data) + axes[0, 1].pie(counts, labels=occs, autopct='%1.1f%%') + axes[0, 1].set_title('Top Occupations') + + # Geographic distribution + if 'geographic_diversity' in demo: + geo_data = demo['geographic_diversity']['distribution'] + if geo_data: + countries = list(geo_data.keys())[:10] # Top 10 + counts = [geo_data[c] for c in countries] + axes[1, 0].barh(countries, counts, color='lightcoral') + axes[1, 0].set_title('Geographic Distribution') + axes[1, 0].set_xlabel('Count') + + # Diversity metrics + diversity_metrics = [] + diversity_values = [] + + if 'occupation_diversity' in demo: + diversity_metrics.append('Occupation\nDiversity') + diversity_values.append(demo['occupation_diversity']['diversity_index']) + + if 'geographic_diversity' in demo: + diversity_metrics.append('Geographic\nDiversity') + diversity_values.append(demo['geographic_diversity']['diversity_index']) + + if diversity_metrics: + axes[1, 1].bar(diversity_metrics, diversity_values, color='lightgreen') + axes[1, 1].set_title('Diversity Indices') + axes[1, 1].set_ylabel('Diversity Score') + axes[1, 1].set_ylim(0, 1) + + plt.tight_layout() + plt.show() + + def _plot_behavioral_patterns(self) -> None: + """Plot behavioral analysis results.""" + behavioral = self.analysis_results['behavioral_patterns'] + + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + fig.suptitle('Behavioral Patterns Analysis', fontsize=16, fontweight='bold') + + # Activity levels scatter plot + if 'activity_levels' in behavioral: + actions_data = [agent.get('actions_count', 0) for agent in self.agents_data] + stimuli_data = [agent.get('stimuli_count', 0) for agent in self.agents_data] + + axes[0].scatter(stimuli_data, actions_data, alpha=0.6, color='purple') + axes[0].set_xlabel('Stimuli Count') + axes[0].set_ylabel('Actions Count') + axes[0].set_title('Activity Patterns') + + # Add trend line + if len(stimuli_data) > 1 and len(actions_data) > 1: + z = np.polyfit(stimuli_data, actions_data, 1) + p = np.poly1d(z) + axes[0].plot(stimuli_data, p(stimuli_data), "r--", alpha=0.8) + + # Goal patterns + if 'goal_patterns' in behavioral and behavioral['goal_patterns']['common_goals']: + goals, counts = zip(*behavioral['goal_patterns']['common_goals'][:8]) + axes[1].barh(range(len(goals)), counts, color='orange') + axes[1].set_yticks(range(len(goals))) + axes[1].set_yticklabels([g[:30] + '...' if len(str(g)) > 30 else str(g) for g in goals]) + axes[1].set_xlabel('Frequency') + axes[1].set_title('Common Goals') + + plt.tight_layout() + plt.show() + + def _plot_correlation_heatmap(self) -> None: + """Plot correlation heatmap for numerical attributes.""" + corr_data = self.analysis_results['correlations']['correlation_matrix'] + corr_df = pd.DataFrame(corr_data) + + plt.figure(figsize=(8, 6)) + sns.heatmap(corr_df, annot=True, cmap='coolwarm', center=0, + square=True, cbar_kws={'label': 'Correlation Coefficient'}) + plt.title('Attribute Correlations Heatmap', fontsize=14, fontweight='bold') + plt.tight_layout() + plt.show() + + def _compute_attributes_distributions(self, agents: list) -> dict: + """ + Computes the distributions of the attributes for the agents. + """ + distributions = {} + for attribute in self.attributes: + distributions[attribute] = self._compute_attribute_distribution(agents, attribute) + + return distributions + + def _compute_attribute_distribution(self, agents: list, attribute: str) -> pd.DataFrame: + """ + Computes the distribution of a given attribute with support for nested attributes. + """ + values = [] + + for agent in agents: + value = self._get_nested_attribute(agent, attribute) + values.append(value) + + # Handle None values + values = [v for v in values if v is not None] + + if not values: + return pd.DataFrame() + + # Convert mixed types to string for consistent sorting + try: + value_counts = pd.Series(values).value_counts().sort_index() + except TypeError: + # Handle mixed data types by converting to strings + string_values = [str(v) for v in values] + value_counts = pd.Series(string_values).value_counts().sort_index() + + return value_counts + + def _get_nested_attribute(self, agent: dict, attribute: str) -> Any: + """Get nested attribute using dot notation (e.g., 'occupation.title').""" + keys = attribute.split('.') + value = agent + + for key in keys: + if isinstance(value, dict) and key in value: + value = value[key] + else: + return None + + return value + + # Utility methods for advanced analysis + def _test_normality(self, data: List[float]) -> bool: + """Simple normality test using skewness.""" + if len(data) < 3: + return False + + skewness = pd.Series(data).skew() + return abs(skewness) < 0.3 # Stringent normality test - threshold to catch bimodal distributions + + def _calculate_diversity_index(self, counts: Counter) -> float: + """Calculate Shannon diversity index.""" + total = sum(counts.values()) + if total <= 1: + return 0.0 + + diversity = 0 + for count in counts.values(): + if count > 0: + p = count / total + diversity -= p * np.log(p) + + return diversity / np.log(len(counts)) if len(counts) > 1 else 0 + + def _categorize_connectivity(self, connections: List[int]) -> Dict[str, int]: + """Categorize agents by their connectivity level.""" + categories = {'isolated': 0, 'low': 0, 'medium': 0, 'high': 0} + + for conn in connections: + if conn == 0: + categories['isolated'] += 1 + elif conn <= 2: + categories['low'] += 1 + elif conn <= 5: + categories['medium'] += 1 + else: + categories['high'] += 1 + + return categories + + def _identify_dominant_traits(self, traits_df: pd.DataFrame) -> Dict[str, str]: + """Identify the dominant personality traits in the population.""" + trait_means = traits_df.mean() + dominant = {} + + for trait, mean_value in trait_means.items(): + if mean_value > 0.6: + dominant[trait] = 'high' + elif mean_value < 0.4: + dominant[trait] = 'low' + else: + dominant[trait] = 'moderate' + + return dominant + + def _generate_summary_statistics(self) -> Dict[str, Any]: + """Generate comprehensive summary statistics.""" + summary = { + 'total_agents': len(self.agents_data), + 'attributes_analyzed': len(self.attributes), + 'data_completeness': {} + } + + # Calculate data completeness for each attribute - handle empty data + if len(self.agents_data) > 0: + for attr in self.attributes: + non_null_count = sum(1 for agent in self.agents_data + if self._get_nested_attribute(agent, attr) is not None) + summary['data_completeness'][attr] = non_null_count / len(self.agents_data) + else: + # No agents - set all completeness to 0 + for attr in self.attributes: + summary['data_completeness'][attr] = 0.0 + + return summary + + def export_analysis_report(self, filename: str = "agent_population_analysis.txt") -> None: + """Export a comprehensive text report of the analysis.""" + with open(filename, 'w', encoding="utf-8", errors="replace") as f: + f.write("AGENT POPULATION ANALYSIS REPORT\n") + f.write("=" * 50 + "\n\n") + + def export_analysis_report(self, filename: str = "agent_population_analysis.txt") -> None: + """Export a comprehensive text report of the analysis.""" + with open(filename, 'w', encoding="utf-8", errors="replace") as f: + f.write("AGENT POPULATION ANALYSIS REPORT\n") + f.write("=" * 50 + "\n\n") + + # Summary statistics - always generate from current data + summary = self._generate_summary_statistics() + f.write(f"Total Agents Analyzed: {summary['total_agents']}\n") + f.write(f"Attributes Analyzed: {summary['attributes_analyzed']}\n\n") + + f.write("Data Completeness:\n") + for attr, completeness in summary['data_completeness'].items(): + f.write(f" {attr}: {completeness:.2%}\n") + f.write("\n") + + # Demographics + if 'demographics' in self.analysis_results: + demo = self.analysis_results['demographics'] + f.write("DEMOGRAPHICS\n") + f.write("-" * 20 + "\n") + + if 'age_stats' in demo: + age_stats = demo['age_stats'] + f.write(f"Age Statistics:\n") + f.write(f" Mean: {age_stats['mean']:.1f} years\n") + f.write(f" Median: {age_stats['median']:.1f} years\n") + f.write(f" Range: {age_stats['range'][0]}-{age_stats['range'][1]} years\n\n") + + if 'occupation_diversity' in demo: + occ_div = demo['occupation_diversity'] + f.write(f"Occupation Diversity:\n") + f.write(f" Unique Occupations: {occ_div['unique_count']}\n") + f.write(f" Diversity Index: {occ_div['diversity_index']:.3f}\n\n") + + # Behavioral patterns + if 'behavioral_patterns' in self.analysis_results: + behavioral = self.analysis_results['behavioral_patterns'] + f.write("BEHAVIORAL PATTERNS\n") + f.write("-" * 20 + "\n") + + if 'activity_levels' in behavioral: + activity = behavioral['activity_levels'] + f.write(f"Activity Levels:\n") + f.write(f" Average Actions: {activity['actions_mean']:.1f}\n") + f.write(f" Average Stimuli: {activity['stimuli_mean']:.1f}\n") + f.write(f" Activity Ratio: {activity['activity_ratio']:.2f}\n\n") + + print(f"Analysis report exported to {filename}") + + def add_custom_analysis(self, name: str, analysis_func: Callable[[List[Dict]], Any]) -> None: + """ + Add a custom analysis function that will be executed during profiling. + + Args: + name: Name for the custom analysis + analysis_func: Function that takes agent data and returns analysis results + """ + if not hasattr(self, '_custom_analyses'): + self._custom_analyses = {} + + self._custom_analyses[name] = analysis_func + + def compare_populations(self, other_agents: Union[List[dict], List[TinyPerson]], + attributes: Optional[List[str]] = None) -> Dict[str, Any]: + """ + Compare this population with another population. + + Args: + other_agents: Another set of agents to compare with + attributes: Specific attributes to compare (uses self.attributes if None) + + Returns: + Comparison results + """ + if attributes is None: + attributes = self.attributes + + # Create temporary profiler for the other population + other_profiler = Profiler(attributes) + other_results = other_profiler.profile(other_agents, plot=False, advanced_analysis=True) + + comparison = { + 'population_sizes': { + 'current': len(self.agents_data), + 'comparison': len(other_profiler.agents_data) + }, + 'attribute_comparisons': {} + } + + # Compare distributions for each attribute + for attr in attributes: + if (attr in self.attributes_distributions and + attr in other_profiler.attributes_distributions): + + current_dist = self.attributes_distributions[attr] + other_dist = other_profiler.attributes_distributions[attr] + + # Statistical comparison (simplified) + comparison['attribute_comparisons'][attr] = { + 'current_unique_values': len(current_dist), + 'comparison_unique_values': len(other_dist), + 'current_top_3': current_dist.head(3).to_dict(), + 'comparison_top_3': other_dist.head(3).to_dict() + } + + return comparison \ No newline at end of file diff --git a/tinytroupe/steering/__init__.py b/tinytroupe/steering/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9e661f0e1c1cd8258acaa59a3853edb732a301db --- /dev/null +++ b/tinytroupe/steering/__init__.py @@ -0,0 +1,10 @@ +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.steering.tiny_story import TinyStory +from tinytroupe.steering.intervention import Intervention + +__all__ = ["TinyStory", "Intervention"] \ No newline at end of file diff --git a/tinytroupe/steering/__pycache__/__init__.cpython-312.pyc b/tinytroupe/steering/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2f139d528cc919bbd1d795d8121e71bcb96e718c Binary files /dev/null and b/tinytroupe/steering/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/steering/__pycache__/intervention.cpython-312.pyc b/tinytroupe/steering/__pycache__/intervention.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f7dff5de85f898c9a3bbd0101ef1d008c13d2f5c Binary files /dev/null and b/tinytroupe/steering/__pycache__/intervention.cpython-312.pyc differ diff --git a/tinytroupe/steering/__pycache__/tiny_story.cpython-312.pyc b/tinytroupe/steering/__pycache__/tiny_story.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..98a4d0eea5b02f160f1749ab385c933cbeab1c51 Binary files /dev/null and b/tinytroupe/steering/__pycache__/tiny_story.cpython-312.pyc differ diff --git a/tinytroupe/steering/intervention.py b/tinytroupe/steering/intervention.py new file mode 100644 index 0000000000000000000000000000000000000000..264d3c369315a1f4a18ea2d1062087ed8bf4db53 --- /dev/null +++ b/tinytroupe/steering/intervention.py @@ -0,0 +1,267 @@ +from typing import Union, List +from tinytroupe.extraction import logger +from tinytroupe.utils import JsonSerializableRegistry +from tinytroupe.experimentation import Proposition +from tinytroupe.environment import TinyWorld +from tinytroupe.agent import TinyPerson +import tinytroupe.utils as utils + +DEFAULT_FIRST_N = 10 +DEFAULT_LAST_N = 100 + +class InterventionBatch: + """ + A wrapper around multiple Intervention instances that allows chaining set_* methods. + """ + + def __init__(self, interventions): + self.interventions = interventions + + def __iter__(self): + """Makes the batch iterable and compatible with list()""" + return iter(self.interventions) + + def set_textual_precondition(self, text): + for intervention in self.interventions: + intervention.set_textual_precondition(text) + return self + + def set_functional_precondition(self, func): + for intervention in self.interventions: + intervention.set_functional_precondition(func) + return self + + def set_effect(self, effect_func): + for intervention in self.interventions: + intervention.set_effect(effect_func) + return self + + def set_propositional_precondition(self, proposition, threshold=None): + for intervention in self.interventions: + intervention.set_propositional_precondition(proposition, threshold) + return self + + def as_list(self): + """Return the list of individual interventions.""" + return self.interventions + + +class Intervention: + + def __init__(self, targets: Union[TinyPerson, TinyWorld, List[TinyPerson], List[TinyWorld]], + first_n:int=DEFAULT_FIRST_N, last_n:int=DEFAULT_LAST_N, + name: str = None): + """ + Initialize the intervention. + + Args: + target (Union[TinyPerson, TinyWorld, List[TinyPerson], List[TinyWorld]]): the target to intervene on + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + name (str): the name of the intervention + """ + + self.targets = targets + + # initialize the possible preconditions + self.text_precondition = None + self.precondition_func = None + + # effects + self.effect_func = None + + # which events to pay attention to? + self.first_n = first_n + self.last_n = last_n + + # name + if name is None: + self.name = self.name = f"Intervention {utils.fresh_id(self.__class__.__name__)}" + else: + self.name = name + + # the most recent precondition proposition used to check the precondition + self._last_text_precondition_proposition = None + self._last_functional_precondition_check = None + + # propositional precondition (optional) + self.propositional_precondition = None + self.propositional_precondition_threshold = None + self._last_propositional_precondition_check = None + + ################################################################################################ + # Intervention flow + ################################################################################################ + @classmethod + def create_for_each(cls, targets, first_n=DEFAULT_FIRST_N, last_n=DEFAULT_LAST_N, name=None): + """ + Create separate interventions for each target in the list. + + Args: + targets (list): List of targets (TinyPerson or TinyWorld instances) + first_n (int): the number of first interactions to consider in the context + last_n (int): the number of last interactions (most recent) to consider in the context + name (str): the name of the intervention + + Returns: + InterventionBatch: A wrapper that allows chaining set_* methods that will apply to all interventions + """ + if not isinstance(targets, list): + targets = [targets] + + interventions = [cls(target, first_n=first_n, last_n=last_n, + name=f"{name}_{i}" if name else None) + for i, target in enumerate(targets)] + return InterventionBatch(interventions) + + def __call__(self): + """ + Execute the intervention. + + Returns: + bool: whether the intervention effect was applied. + """ + return self.execute() + + def execute(self): + """ + Execute the intervention. It first checks the precondition, and if it is met, applies the effect. + This is the simplest method to run the intervention. + + Returns: + bool: whether the intervention effect was applied. + """ + logger.debug(f"Executing intervention: {self}") + if self.check_precondition(): + self.apply_effect() + logger.debug(f"Precondition was true, intervention effect was applied.") + return True + + logger.debug(f"Precondition was false, intervention effect was not applied.") + return False + + def check_precondition(self): + """ + Check if the precondition for the intervention is met. + """ + # + # Textual precondition + # + if self.text_precondition is not None: + self._last_text_precondition_proposition = Proposition(claim=self.text_precondition, target=self.targets, first_n=self.first_n, last_n=self.last_n) + llm_precondition_check = self._last_text_precondition_proposition.check() + else: + llm_precondition_check = True + + # + # Functional precondition + # + if self.precondition_func is not None: + self._last_functional_precondition_check = self.precondition_func(self.targets) + else: + self._last_functional_precondition_check = True # default to True if no functional precondition is set + + # + # Propositional precondition + # + self._last_propositional_precondition_check = True + if self.propositional_precondition is not None: + if self.propositional_precondition_threshold is not None: + score = self.propositional_precondition.score(target=self.targets) + if score >= self.propositional_precondition_threshold: + self._last_propositional_precondition_check = False + else: + if not self.propositional_precondition.check(target=self.targets): + self._last_propositional_precondition_check = False + + return llm_precondition_check and self._last_functional_precondition_check and self._last_propositional_precondition_check + + + def apply_effect(self): + """ + Apply the intervention's effects. This won't check the precondition, + so it should be called after check_precondition. + """ + self.effect_func(self.targets) + + + ################################################################################################ + # Pre and post conditions + ################################################################################################ + + def set_textual_precondition(self, text): + """ + Set a precondition as text, to be interpreted by a language model. + + Args: + text (str): the text of the precondition + """ + self.text_precondition = text + return self # for chaining + + def set_functional_precondition(self, func): + """ + Set a precondition as a function, to be evaluated by the code. + + Args: + func (function): the function of the precondition. + Must have the a single argument, targets (either a TinyWorld or TinyPerson, or a list). Must return a boolean. + """ + self.precondition_func = func + return self # for chaining + + def set_effect(self, effect_func): + """ + Set the effect of the intervention. + + Args: + effect (str): the effect function of the intervention + """ + self.effect_func = effect_func + return self # for chaining + + def set_propositional_precondition(self, proposition:Proposition, threshold:int=None): + """ + Set a propositional precondition using the Proposition class, + optionally with a score threshold. + """ + + self.propositional_precondition = proposition + self.propositional_precondition_threshold = threshold + return self + + ################################################################################################ + # Inspection + ################################################################################################ + + def precondition_justification(self): + """ + Get the justification for the precondition. + """ + justification = "" + + # text precondition justification + if self._last_text_precondition_proposition is not None: + justification += f"{self._last_text_precondition_proposition.justification} (confidence = {self._last_text_precondition_proposition.confidence})\n\n" + + # functional precondition justification + if self.precondition_func is not None: + if self._last_functional_precondition_check == True: + justification += f"Functional precondition was met.\n\n" + + else: + justification += "Preconditions do not appear to be met.\n\n" + + # propositional precondition justification + if self.propositional_precondition is not None: + if self._last_propositional_precondition_check == True: + justification += f"Propositional precondition was met.\n\n" + else: + justification += "Propositional precondition was not met.\n\n" + + return justification + + return justification + + + diff --git a/tinytroupe/steering/prompts/intervention.mustache b/tinytroupe/steering/prompts/intervention.mustache new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tinytroupe/steering/prompts/story.continuation.system.mustache b/tinytroupe/steering/prompts/story.continuation.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..72ba3694f7bed9a4d898e6f6c5ec1a48a5f150a9 --- /dev/null +++ b/tinytroupe/steering/prompts/story.continuation.system.mustache @@ -0,0 +1,35 @@ +# Story teller + +You are a system that, given a story, creates a continuation for it. The stories you handle are of a special kind: +they are the result of a computer simulation, where agents interact with each other within an environment. +Hence, the story unfolds a sequence of simulation events. However, though terse, these events are meant to capture +a realistic scenario, where agents have goals, and they act to achieve them. Your task therefore is to create +a continuation of the story that is both plausible and interesting. + +Since these stories necessarily relates computer simulations, they always have some implicit or explicit purpose. +Stories, therefore, **must** respect the purpose they are given, meaning that any story enrichment, continunation, or other +related content **must** be in line with the purpose of the simulation. + +On the the format of the continuations you propose: + - You should propose a text that describes what happens next, with around {{number_of_words}} words. You can use one or more paragraphs. + DO NOT use more than {{number_of_words}} words!! + - You should use regular English, do not try to immitate the terse style of the simulation events. This is because + your output will be read by the agents and other simulation elements, as well as the human experimenter running everything, + and therefore it all should be human-readable. + +On the content of the continuations you propose: + - You should make sure that the continuation is plausible given the story you are given. + - If you already proposed a continuation before, you DO NOT repeat it again. You should always propose a new continuation. + - You should make sure that the continuation is interesting, i.e., it should involve some kind of conflict or tension + that the agents need to resolve. This is important because the agents are designed to be motivated by goals, and + they are likely to get bored if there is no conflict to resolve. + - You should make sure that the continuation is open-ended, i.e., it should not determine a unique course of events. + This is important because the agents are autonomous and should be able to act freely. + - If some specific requirement for the continuation is given, you **must** respect it, even if it means breaking the other rules. + User-given requirements always take precedence. + {{#include_plot_twist}}- You **must** also make sure your continuation is actually an unexpected plot twist. This is to cause surprise and curiosity.{{/include_plot_twist}} + +On other important elements to consider: + - If dates and times are mentioned, you should leverage them very carefully and realistically. For example, the events that happened + after a minute are different from those that happen after an hour and much more different from those that happen after a day or a week. + \ No newline at end of file diff --git a/tinytroupe/steering/prompts/story.continuation.user.mustache b/tinytroupe/steering/prompts/story.continuation.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..308f8c9edab86171767fbb61c9889ce95ab0d263 --- /dev/null +++ b/tinytroupe/steering/prompts/story.continuation.user.mustache @@ -0,0 +1,7 @@ +Now, consider the following. + - simulation purpose: "{{purpose}}"; and + - story continuation requirements: "{{requirements}}". + +Please propose a continuation for the following story which respects the given purpose and continuation requirements: + +{{{current_simulation_trace}}} \ No newline at end of file diff --git a/tinytroupe/steering/prompts/story.start.system.mustache b/tinytroupe/steering/prompts/story.start.system.mustache new file mode 100644 index 0000000000000000000000000000000000000000..37b056b8abb13a21425f88faa6c1cbcc42556cbb --- /dev/null +++ b/tinytroupe/steering/prompts/story.start.system.mustache @@ -0,0 +1,34 @@ +# Story teller + +You are a system that, given some agents and their interactions, creates an interesting story for them. +The stories you handle are of a special kind: they will guide a computer simulation, where agents interact with each other within an environment. +Hence, the story induces a sequence of simulation events. However, these events are meant to capture +a realistic scenario, where agents have goals, and they act to achieve them. Your task therefore is to start +a story that is both plausible and interesting. + +Since these stories necessarily relates computer simulations, they always have some implicit or explicit purpose. +Stories, therefore, **must** respect the purpose they are given, meaning that any story you start **must** be in +line with the purpose of the simulation. + +On the the format of the continuations you propose: + - You should propose a text that describes what the begining of a story, with around {{number_of_words}} words. You can use one or more paragraphs. + DO NOT use more than {{number_of_words}} words!! + - You should use regular English, do not try to immitate the terse style of the simulation events. This is because + your output will be read by the agents and other simulation elements, as well as the human experimenter running everything, + and therefore it all should be human-readable. + +On the content of the continuations you propose: + - You should make sure that the story is plausible given any context you receive. + - You should make sure that the continuation story is interesting, i.e., it should set the stage for some upcoming issue, conflict or + problem that the agents need to resolve. This is important because the agents are designed to be motivated by goals, and + they are likely to get bored if there is nothing very interesting happening. + - You should make sure that the story is open-ended, i.e., it should not determine a unique course of events. + This is important because the agents are autonomous and should be able to act freely. + - Though you might receice an existing context or set of agents interactions, you should actually make a completely new story, not + a continuation. The context you are given is just to provide some background, so that you are **consistent** with it, + but you should **not** continue from it. + +On other important elements to consider: + - If dates and times are mentioned, you should leverage them very carefully and realistically. For example, the events that happened + after a minute are different from those that happen after an hour and much more different from those that happen after a day or a week. + \ No newline at end of file diff --git a/tinytroupe/steering/prompts/story.start.user.mustache b/tinytroupe/steering/prompts/story.start.user.mustache new file mode 100644 index 0000000000000000000000000000000000000000..aecc3824e2242a4072af8b87b42f03f0b8306cea --- /dev/null +++ b/tinytroupe/steering/prompts/story.start.user.mustache @@ -0,0 +1,7 @@ +Now, consider the following: + - simulation purpose: "{{purpose}}"; and + - story requirements: "{{requirements}}". + +Please propose a story start for the following simulation context which respects the given purpose: + +{{{current_simulation_trace}}} \ No newline at end of file diff --git a/tinytroupe/steering/tiny_story.py b/tinytroupe/steering/tiny_story.py new file mode 100644 index 0000000000000000000000000000000000000000..7646900257887aa00e30cbf2310f7aae260ecccb --- /dev/null +++ b/tinytroupe/steering/tiny_story.py @@ -0,0 +1,138 @@ +from typing import List + +from tinytroupe.extraction import logger +from tinytroupe.agent import TinyPerson +from tinytroupe.environment import TinyWorld +import tinytroupe.utils as utils +from tinytroupe import openai_utils + +class TinyStory: + """ + Every simulation tells a story. This class provides helper mechanisms to help with crafting appropriate stories in TinyTroupe. + """ + + + def __init__(self, environment:TinyWorld=None, agent:TinyPerson=None, purpose:str="Be a realistic simulation.", context:str="", + first_n=10, last_n=20, include_omission_info:bool=True) -> None: + """ + Initialize the story. The story can be about an environment or an agent. It also has a purpose, which + is used to guide the story generation. Stories are aware that they are related to simulations, so one can + specify simulation-related purposes. + + Args: + environment (TinyWorld, optional): The environment in which the story takes place. Defaults to None. + agent (TinyPerson, optional): The agent in the story. Defaults to None. + purpose (str, optional): The purpose of the story. Defaults to "Be a realistic simulation.". + context (str, optional): The current story context. Defaults to "". The actual story will be appended to this context. + first_n (int, optional): The number of first interactions to include in the story. Defaults to 10. + last_n (int, optional): The number of last interactions to include in the story. Defaults to 20. + include_omission_info (bool, optional): Whether to include information about omitted interactions. Defaults to True. + """ + + # exactly one of these must be provided + if environment and agent: + raise Exception("Either 'environment' or 'agent' should be provided, not both") + if not (environment or agent): + raise Exception("At least one of the parameters should be provided") + + self.environment = environment + self.agent = agent + + self.purpose = purpose + + self.current_story = context + + self.first_n = first_n + self.last_n = last_n + self.include_omission_info = include_omission_info + + def start_story(self, requirements="Start some interesting story about the agents.", number_of_words:int=100, include_plot_twist:bool=False) -> str: + """ + Start a new story. + """ + + rendering_configs = { + "purpose": self.purpose, + "requirements": requirements, + "current_simulation_trace": self._current_story(), + "number_of_words": number_of_words, + "include_plot_twist": include_plot_twist + } + + messages = utils.compose_initial_LLM_messages_with_templates("story.start.system.mustache", "story.start.user.mustache", + base_module_folder="steering", + rendering_configs=rendering_configs) + next_message = openai_utils.client().send_message(messages, temperature=1.5) + + start = next_message["content"] + + self.current_story += utils.dedent(\ + f""" + + ## The story begins + + {start} + + """ + ) + + return start + + def continue_story(self, requirements="Continue the story in an interesting way.", number_of_words:int=100, include_plot_twist:bool=False) -> str: + """ + Propose a continuation of the story. + """ + + rendering_configs = { + "purpose": self.purpose, + "requirements": requirements, + "current_simulation_trace": self._current_story(), + "number_of_words": number_of_words, + "include_plot_twist": include_plot_twist + } + + messages = utils.compose_initial_LLM_messages_with_templates("story.continuation.system.mustache", "story.continuation.user.mustache", + base_module_folder="steering", + rendering_configs=rendering_configs) + next_message = openai_utils.client().send_message(messages)#, temperature=1.5) + + continuation = next_message["content"] + + self.current_story += utils.dedent(\ + f""" + + ## The story continues + + {continuation} + + """ + ) + + return continuation + + def _current_story(self) -> str: + """ + Get the current story. + """ + interaction_history = "" + + if self.agent is not None: + interaction_history += self.agent.pretty_current_interactions(first_n=self.first_n, last_n=self.last_n, include_omission_info=self.include_omission_info) + elif self.environment is not None: + interaction_history += self.environment.pretty_current_interactions(first_n=self.first_n, last_n=self.last_n, include_omission_info=self.include_omission_info) + + tmp_current_story = self.current_story + tmp_current_story += utils.dedent(\ + f""" + + ## New simulation interactions to consider + + {interaction_history} + + """ + ) + + return tmp_current_story + + + diff --git a/tinytroupe/tools/__init__.py b/tinytroupe/tools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b2d19bf3c32747a1830c5265346a6335b2660b2 --- /dev/null +++ b/tinytroupe/tools/__init__.py @@ -0,0 +1,15 @@ +""" +Tools allow agents to accomplish specialized tasks. +""" + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.tools.tiny_tool import TinyTool +from tinytroupe.tools.tiny_word_processor import TinyWordProcessor +from tinytroupe.tools.tiny_calendar import TinyCalendar + +__all__ = ["TinyTool", "TinyWordProcessor", "TinyCalendar"] \ No newline at end of file diff --git a/tinytroupe/tools/__pycache__/__init__.cpython-312.pyc b/tinytroupe/tools/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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of the current page and returns the path to the image.""" + print("Taking a screenshot...") + # In a real implementation, this would save a screenshot and return the path. + return "placeholder_screenshot.png" + +def click(selector: str): + """Clicks on the element with the given CSS selector.""" + print(f"Clicking on element with selector: {selector}...") + +def fill(selector: str, text: str): + """Fills the given text into the element with the given CSS selector.""" + print(f"Typing '{text}' into element with selector: {selector}...") + +def submit_form(selector: str): + """Submits the form containing the element with the given CSS selector.""" + print(f"Submitting form with element: {selector}...") + +def wait_for_element(selector: str): + """Waits for the element with the given CSS selector to appear.""" + print(f"Waiting for element: {selector}...") + +def scroll_page(direction: str): + """Scrolls the page up or down.""" + print(f"Scrolling page {direction}...") + +def hover_element(selector: str): + """Hovers over the element with the given CSS selector.""" + print(f"Hovering over element: {selector}...") + +def press_key(key: str): + """Presses the given key.""" + print(f"Pressing key: {key}...") + +def get_page_info() -> dict: + """Gets information about the current page, such as links and form elements.""" + print("Getting page info...") + return {"links": [], "forms": []} diff --git a/tinytroupe/tools/sequential_thinking.py b/tinytroupe/tools/sequential_thinking.py new file mode 100644 index 0000000000000000000000000000000000000000..be8b4d8a1519b237963eefd5bb4649482f521eef --- /dev/null +++ b/tinytroupe/tools/sequential_thinking.py @@ -0,0 +1,63 @@ +import requests +import json +from tinytroupe.agent.mental_faculty import TinyToolUse +from tinytroupe.utils.logger import get_logger + +class SequentialThinkingTool(TinyToolUse): + def __init__(self): + super().__init__(tools=[self]) + self.url = "https://harvesthealth-sequential-thinking-mcp.hf.space/run" + + def process_action(self, agent, action: dict) -> bool: + if action['type'] == 'SEQUENTIAL_THINKING': + logger = get_logger(agent.name) + + try: + arguments = json.loads(action['content']) + except json.JSONDecodeError as e: + logger.error(f"MCP Interaction - Invalid JSON in action content: {action['content']}. Error: {e}") + return False + + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "sequentialthinking", + "arguments": arguments + } + } + + logger.info(f"MCP Interaction - Request: {json.dumps(payload, indent=2)}") + response_json = self.send_thought(payload) + logger.info(f"MCP Interaction - Response: {json.dumps(response_json, indent=2)}") + + if response_json and 'result' in response_json and 'content' in response_json['result']: + content_text = response_json['result']['content'][0]['text'] + try: + response_data = json.loads(content_text) + agent.think(f"Thought processed. History length: {response_data.get('thoughtHistoryLength')}") + except json.JSONDecodeError: + logger.error(f"MCP Interaction - Could not decode response content: {content_text}") + agent.think("Received a response from the sequential thinking server, but it was not in the expected format.") + + return True + return False + + def send_thought(self, thought_data: dict): + headers = {'Content-Type': 'application/json'} + try: + response = requests.post(self.url, headers=headers, json=thought_data) + response.raise_for_status() + return response.json() + except requests.exceptions.RequestException as e: + # Get the logger for the agent that is making the call + # This is a bit of a hack, as we don't have the agent object here. + # We will rely on the caller to log the error. + return {"error": str(e)} + + def actions_definitions_prompt(self) -> str: + return "" + + def actions_constraints_prompt(self) -> str: + return "" diff --git a/tinytroupe/tools/tiny_calendar.py b/tinytroupe/tools/tiny_calendar.py new file mode 100644 index 0000000000000000000000000000000000000000..49519c97743d50e46dfa3f2f0ba8c5272ac6362f --- /dev/null +++ b/tinytroupe/tools/tiny_calendar.py @@ -0,0 +1,69 @@ + +import textwrap +import json + +from tinytroupe.tools import logger, TinyTool +import tinytroupe.utils as utils + + +# TODO under development +class TinyCalendar(TinyTool): + + def __init__(self, owner=None): + super().__init__("calendar", "A basic calendar tool that allows agents to keep track meetings and appointments.", owner=owner, real_world_side_effects=False) + + # maps date to list of events. Each event itself is a dictionary with keys "title", "description", "owner", "mandatory_attendees", "optional_attendees", "start_time", "end_time" + self.calenar = {} + + def add_event(self, date, title, description=None, owner=None, mandatory_attendees=None, optional_attendees=None, start_time=None, end_time=None): + if date not in self.calendar: + self.calendar[date] = [] + self.calendar[date].append({"title": title, "description": description, "owner": owner, "mandatory_attendees": mandatory_attendees, "optional_attendees": optional_attendees, "start_time": start_time, "end_time": end_time}) + + def find_events(self, year, month, day, hour=None, minute=None): + # TODO + pass + + def _process_action(self, agent, action) -> bool: + if action['type'] == "CREATE_EVENT" and action['content'] is not None: + # parse content json + event_content = json.loads(action['content']) + + # checks whether there are any kwargs that are not valid + valid_keys = ["title", "description", "mandatory_attendees", "optional_attendees", "start_time", "end_time"] + utils.check_valid_fields(event_content, valid_keys) + + # uses the kwargs to create a new event + self.add_event(event_content) + + return True + + else: + return False + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - CREATE_EVENT: You can create a new event in your calendar. The content of the event has many fields, and you should use a JSON format to specify them. Here are the possible fields: + * title: The title of the event. Mandatory. + * description: A brief description of the event. Optional. + * mandatory_attendees: A list of agent names who must attend the event. Optional. + * optional_attendees: A list of agent names who are invited to the event, but are not required to attend. Optional. + * start_time: The start time of the event. Optional. + * end_time: The end time of the event. Optional. + """ + # TODO how the atendee list will be handled? How will they be notified of the invitation? I guess they must also have a calendar themselves. <------------------------------------- + + return utils.dedent(prompt) + + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + + """ + # TODO + + return textwrap.dedent(prompt) + + diff --git a/tinytroupe/tools/tiny_tool.py b/tinytroupe/tools/tiny_tool.py new file mode 100644 index 0000000000000000000000000000000000000000..c752bd917d3988362a1383ea36325571d95c879a --- /dev/null +++ b/tinytroupe/tools/tiny_tool.py @@ -0,0 +1,54 @@ +from tinytroupe.tools import logger +from tinytroupe.utils import JsonSerializableRegistry + + +class TinyTool(JsonSerializableRegistry): + + # Define what attributes should be serialized + serializable_attributes = ["name", "description", "real_world_side_effects"] + + def __init__(self, name, description, owner=None, real_world_side_effects=False, exporter=None, enricher=None): + """ + Initialize a new tool. + + Args: + name (str): The name of the tool. + description (str): A brief description of the tool. + owner (str): The agent that owns the tool. If None, the tool can be used by anyone. + real_world_side_effects (bool): Whether the tool has real-world side effects. That is to say, if it has the potential to change the + state of the world outside of the simulation. If it does, it should be used with caution. + exporter (ArtifactExporter): An exporter that can be used to export the results of the tool's actions. If None, the tool will not be able to export results. + enricher (Enricher): An enricher that can be used to enrich the results of the tool's actions. If None, the tool will not be able to enrich results. + + """ + self.name = name + self.description = description + self.owner = owner + self.real_world_side_effects = real_world_side_effects + self.exporter = exporter + self.enricher = enricher + + def _process_action(self, agent, action: dict) -> bool: + raise NotImplementedError("Subclasses must implement this method.") + + def _protect_real_world(self): + if self.real_world_side_effects: + logger.warning(f" !!!!!!!!!! Tool {self.name} has REAL-WORLD SIDE EFFECTS. This is NOT just a simulation. Use with caution. !!!!!!!!!!") + + def _enforce_ownership(self, agent): + if self.owner is not None and agent.name != self.owner.name: + raise ValueError(f"Agent {agent.name} does not own tool {self.name}, which is owned by {self.owner.name}.") + + def set_owner(self, owner): + self.owner = owner + + def actions_definitions_prompt(self) -> str: + raise NotImplementedError("Subclasses must implement this method.") + + def actions_constraints_prompt(self) -> str: + raise NotImplementedError("Subclasses must implement this method.") + + def process_action(self, agent, action: dict) -> bool: + self._protect_real_world() + self._enforce_ownership(agent) + return self._process_action(agent, action) diff --git a/tinytroupe/tools/tiny_word_processor.py b/tinytroupe/tools/tiny_word_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..50dcf2981987145f2b4f94826839b50b5cfb83f6 --- /dev/null +++ b/tinytroupe/tools/tiny_word_processor.py @@ -0,0 +1,87 @@ + +import json + +from tinytroupe.tools import logger, TinyTool + + +import tinytroupe.utils as utils + +class TinyWordProcessor(TinyTool): + + def __init__(self, owner=None, exporter=None, enricher=None): + super().__init__("wordprocessor", "A basic word processor tool that allows agents to write documents.", owner=owner, real_world_side_effects=False, exporter=exporter, enricher=enricher) + + def write_document(self, title, content, author=None): + logger.debug(f"Writing document with title {title} and content: {content}") + + if self.enricher is not None: + requirements =\ + """ + Turn any draft or outline into an actual and long document, with many, many details. Include tables, lists, and other elements. + The result **MUST** be at least 5 times larger than the original content in terms of characters - do whatever it takes to make it this long and detailed. + """ + + content = self.enricher.enrich_content(requirements=requirements, + content=content, + content_type="Document", + context_info=None, + context_cache=None, verbose=False) + + if self.exporter is not None: + if author is not None: + artifact_name = f"{title}.{author}" + else: + artifact_name = title + self.exporter.export(artifact_name=artifact_name, artifact_data= content, content_type="Document", content_format="md", target_format="md") + self.exporter.export(artifact_name=artifact_name, artifact_data= content, content_type="Document", content_format="md", target_format="docx") + + json_doc = {"title": title, "content": content, "author": author} + self.exporter.export(artifact_name=artifact_name, artifact_data= json_doc, content_type="Document", content_format="md", target_format="json") + + def _process_action(self, agent, action) -> bool: + try: + if action['type'] == "WRITE_DOCUMENT" and action['content'] is not None: + # parse content json + if isinstance(action['content'], str): + doc_spec = utils.extract_json(action['content']) + else: + doc_spec = action['content'] + + # checks whether there are any kwargs that are not valid + valid_keys = ["title", "content", "author"] + utils.check_valid_fields(doc_spec, valid_keys) + + # uses the kwargs to create a new document + self.write_document(**doc_spec) + + return True + + else: + return False + except json.JSONDecodeError as e: + logger.error(f"Error parsing JSON content: {e}. Original content: {action['content']}") + return False + except Exception as e: + logger.error(f"Error processing action: {e}") + return False + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - WRITE_DOCUMENT: you can create a new document. The content of the document has many fields, and you **must** use a JSON format to specify them. Here are the possible fields: + * title: The title of the document. Mandatory. + * content: The actual content of the document. You **must** use Markdown to format this content. Mandatory. + * author: The author of the document. You should put your own name. Optional. + """ + return utils.dedent(prompt) + + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + - Whenever you WRITE_DOCUMENT, you write all the content at once. Moreover, the content should be long and detailed, unless there's a good reason for it not to be. + - Whenever you WRITE_DOCUMENT, you **must** embed the content in a JSON object. Use only valid escape sequences in the JSON content. + - When you WRITE_DOCUMENT, you follow these additional guidelines: + * For any milestones or timelines mentioned, try mentioning specific owners or partner teams, unless there's a good reason not to do so. + """ + return utils.dedent(prompt) \ No newline at end of file diff --git a/tinytroupe/ui/__init__.py b/tinytroupe/ui/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9eac63f7a878f8cdf7b40731274b2820f701230c --- /dev/null +++ b/tinytroupe/ui/__init__.py @@ -0,0 +1,23 @@ +""" +TinyTroupe UI Module + +This module provides user interface components and widgets for TinyTroupe, +enabling interactive experiences with TinyTroupe agents and environments. + +The module is organized into different sub-modules based on the UI framework: + +- jupyter_widgets: Interactive widgets for Jupyter notebooks +- web: Web-based interfaces (future) +- cli: Command-line interfaces (future) + +Example usage: + from tinytroupe.ui.jupyter_widgets import AgentChatJupyterWidget + + # Create a chat interface with your agents + chat = AgentChatJupyterWidget(agents) + chat.display() +""" + +from .jupyter_widgets import AgentChatJupyterWidget + +__all__ = ['AgentChatJupyterWidget'] diff --git a/tinytroupe/ui/jupyter_widgets.py b/tinytroupe/ui/jupyter_widgets.py new file mode 100644 index 0000000000000000000000000000000000000000..7bf96224f15e02b8d0f586c19312757bc3e6b848 --- /dev/null +++ b/tinytroupe/ui/jupyter_widgets.py @@ -0,0 +1,409 @@ +""" +TinyTroupe Jupyter Widgets + +This module provides interactive widgets for Jupyter notebooks that enable +seamless interaction with TinyTroupe agents and environments. + +Classes: + AgentChatJupyterWidget: An interactive chat interface for conversing with TinyTroupe agents + +Dependencies: + - ipywidgets: For creating interactive notebook widgets + - IPython.display: For displaying content in notebooks + - datetime: For timestamping conversations + - threading: For non-blocking animations + - tinytroupe: Core TinyTroupe functionality + +Example usage: + ```python + from tinytroupe.ui.jupyter_widgets import AgentChatJupyterWidget + from tinytroupe.factory import TinyPersonFactory + + # Create some agents + factory = TinyPersonFactory.create_factory_from_demography("path/to/demographics.json") + agents = factory.generate_people(5) + + # Create and display the chat interface + chat_widget = AgentChatJupyterWidget(agents) + chat_widget.display() + ``` +""" + +import ipywidgets as widgets +from IPython.display import display, HTML +import datetime +import threading +import tinytroupe +import time + + +class AgentChatJupyterWidget: + """ + An interactive chat widget for conversing with TinyTroupe agents in Jupyter notebooks. + + This widget provides a user-friendly interface for chatting with one or more TinyTroupe + agents. It features an animated loading indicator, message history, and responsive design. + + Features: + - Agent selection dropdown + - Real-time message input and display + - Single Enter key press to send messages (fixed double-press issue) + - Animated loading indicators while agents process messages + - Message history with timestamps + - Error handling and user feedback + - Responsive design with proper styling + - Throttling to prevent accidental double-sending + - Communication display control (checkbox to show/hide agent output in notebook) + + Attributes: + agents (dict): Dictionary mapping agent names to agent objects + conversation_history (list): List of conversation entries + loading_animation_active (bool): Whether loading animation is currently active + loading_frames (list): Animation frames for the loading spinner + current_loading_frame (int): Current frame index for animation + """ + + def __init__(self, agents_list): + """ + Initialize the chat widget with a list of agents. + + Args: + agents_list (list): List of TinyTroupe agent objects to make available for chat + """ + self.agents = {agent.name: agent for agent in agents_list} + self.conversation_history = [] + self.loading_animation_active = False + self.loading_frames = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏'] + self.current_loading_frame = 0 + self._processing = False # To prevent multiple simultaneous sends + self._last_message = "" # Track last message to detect user input vs programmatic changes + self.setup_widgets() + + def setup_widgets(self): + """ + Set up the UI widgets and their event handlers. + + Creates the agent dropdown, message input, buttons, and conversation display. + Also wires up event handlers for user interactions. + """ + # Agent selector + self.agent_dropdown = widgets.Dropdown( + options=list(self.agents.keys()), + description='Chat with:', + style={'description_width': 'initial'} + ) + + # Message input + self.message_input = widgets.Text( + placeholder='Type your message and press Enter...', + layout=widgets.Layout(width='70%'), + continuous_update=False + ) + + # Track the last message to detect actual user input vs programmatic changes + self._last_message = "" + + # Send button + self.send_button = widgets.Button( + description='Send', + button_style='primary', + layout=widgets.Layout(width='80px') + ) + + # Clear button + self.clear_button = widgets.Button( + description='Clear', + button_style='warning', + layout=widgets.Layout(width='80px') + ) + + # Communication display checkbox + self.communication_display_checkbox = widgets.Checkbox( + value=False, + description='Show agent communication in notebook output', + style={'description_width': 'initial'}, + layout=widgets.Layout(width='auto') + ) + + # Conversation display + self.conversation_display = widgets.HTML( + value="

Start a conversation by selecting an agent and typing a message...

" + ) + + # Wire up events + self.send_button.on_click(self._handle_send_click) + self.clear_button.on_click(self.clear_conversation) + + # Use observe method to detect Enter key presses through value changes + # This is the modern recommended approach for ipywidgets + self.message_input.observe(self._handle_input_change, names='value') + + # Layout + input_row = widgets.HBox([ + self.agent_dropdown, + self.message_input, + self.send_button, + self.clear_button + ]) + + self.widget = widgets.VBox([ + widgets.HTML("

💬 Agent Chat Interface

"), + input_row, + self.communication_display_checkbox, + self.conversation_display + ]) + + def _handle_send_click(self, b): + """Handle send button clicks.""" + if not self._processing: + self.send_message() + + def _handle_input_change(self, change): + """ + Handle input changes using the observe method. + + This method detects when the user has entered text and committed it + (typically by pressing Enter). We use the observe pattern to monitor + value changes rather than the deprecated on_submit method. + + Args: + change (dict): The change event containing 'old' and 'new' values + """ + new_value = change['new'].strip() + old_value = change['old'].strip() + + # Only process if: + # 1. We're not already processing a message + # 2. There's actual text in the new value + # 3. The value actually changed (user input, not programmatic change) + # 4. This isn't the programmatic clearing we do after sending + if (not self._processing and + new_value and + new_value != old_value and + new_value != self._last_message): + + self._last_message = new_value + self.send_message() + + def send_message(self): + """ + Send a message to the selected agent and handle the response. + + This method: + 1. Validates input + 2. Displays user message immediately + 3. Shows animated loading indicator + 4. Processes agent response in the background + 5. Updates the conversation display + """ + print("Sending message...") # Debug print to track message sending + # Prevent double-sending with processing flag + if self._processing: + return + + self._processing = True + + agent_name = self.agent_dropdown.value + message = self.message_input.value.strip() + + if not message or not agent_name: + self._processing = False + return + + + agent = self.agents[agent_name] + timestamp = datetime.datetime.now().strftime("%H:%M:%S") + + # Clear input immediately and add user message to history first + self.message_input.value = '' + self._last_message = "" # Reset tracking variable + + # Add user message to history and display immediately + self.conversation_history.append({ + 'timestamp': timestamp, + 'sender': 'You', + 'message': message, + 'type': 'user' + }) + + # Update display to show user message immediately + self.update_conversation_display() + + # Add animated loading indicator while processing + loading_entry = { + 'timestamp': timestamp, + 'sender': agent_name, + 'message': '🤔 Processing...', + 'type': 'loading' + } + self.conversation_history.append(loading_entry) + + # Start animated loading indicator + self.start_loading_animation(loading_entry) + + # Process agent response in background thread + def process_response(): + try: + # Use the proper TinyTroupe interaction method + # Get the communication display setting from the checkbox + communication_display = self.communication_display_checkbox.value + actions = agent.listen_and_act(message, return_actions=True, communication_display=communication_display) + + # Extract agent responses from the actions + agent_responses = [] + + if actions: + for action_item in actions: + if isinstance(action_item, dict) and 'action' in action_item: + action = action_item['action'] + action_type = action.get('type', '') + action_content = action.get('content', '') + + # Collect TALK and THINK actions as responses + if action_type == 'TALK' and action_content: + agent_responses.append(f"🗣️ {action_content}") + elif action_type == 'THINK' and action_content: + agent_responses.append(f"💭 {action_content}") + + # Combine all responses or provide fallback + if agent_responses: + agent_response = '\n\n'.join(agent_responses) + else: + agent_response = f"I heard your message: '{message}', but I don't have much to say about it right now." + + # Stop loading animation and remove loading indicator + self.stop_loading_animation() + self.conversation_history.pop() # Remove the loading message + + # Add agent response to history + self.conversation_history.append({ + 'timestamp': datetime.datetime.now().strftime("%H:%M:%S"), + 'sender': agent_name, + 'message': agent_response, + 'type': 'agent' + }) + + except Exception as e: + # Handle errors gracefully + error_msg = f"Error communicating with agent: {str(e)}" + if hasattr(e, '__class__'): + error_msg += f" (Type: {e.__class__.__name__})" + + # Stop loading animation and remove loading indicator + self.stop_loading_animation() + self.conversation_history.pop() # Remove the loading message + + self.conversation_history.append({ + 'timestamp': datetime.datetime.now().strftime("%H:%M:%S"), + 'sender': 'System', + 'message': error_msg, + 'type': 'error' + }) + + finally: + # Update display with final result and reset processing flag + self.update_conversation_display() + self._processing = False + + # Start processing in background thread + threading.Thread(target=process_response, daemon=True).start() + + def clear_conversation(self, b=None): + """ + Clear the conversation history and reset the display. + + Args: + b: Button object (when called from button click, None when called directly) + """ + if not self._processing: + self.conversation_history = [] + self.update_conversation_display() + + def update_conversation_display(self): + """ + Update the HTML display of the conversation history. + + This method renders all conversation entries with appropriate styling + based on their type (user, agent, loading, error). + """ + if not self.conversation_history: + html_content = "

Start a conversation...

" + else: + messages_html = [] + for entry in self.conversation_history: + if entry['type'] == 'user': + messages_html.append(f""" +
+ You ({entry['timestamp']}): {entry['message']} +
+ """) + elif entry['type'] == 'agent': + messages_html.append(f""" +
+ {entry['sender']} ({entry['timestamp']}):
+
{entry['message']}
+
+ """) + elif entry['type'] == 'loading': + messages_html.append(f""" +
+ {entry['sender']} ({entry['timestamp']}): {entry['message']} +
+ """) + else: # error + messages_html.append(f""" +
+ {entry['sender']} ({entry['timestamp']}): {entry['message']} +
+ """) + + html_content = f""" +
+ {''.join(messages_html)} +
+ """ + + self.conversation_display.value = html_content + + def start_loading_animation(self, loading_entry): + """ + Start the animated loading indicator. + + This method creates a smooth spinning animation that updates the loading + message with different spinner frames at regular intervals. + + Args: + loading_entry (dict): The conversation entry containing the loading message + """ + self.loading_animation_active = True + self.current_loading_frame = 0 + + def animate(): + if self.loading_animation_active: + # Update the loading message with current animation frame + spinner = self.loading_frames[self.current_loading_frame % len(self.loading_frames)] + loading_entry['message'] = f'{spinner} Processing...' + self.update_conversation_display() + self.current_loading_frame += 1 + + # Schedule next frame after 200ms + threading.Timer(0.2, animate).start() + + animate() + + def stop_loading_animation(self): + """ + Stop the loading animation. + + This method sets the animation flag to False, causing the animation + loop to stop at the next iteration. + """ + self.loading_animation_active = False + + def display(self): + """ + Display the chat widget in the notebook. + + This method should be called to render the widget in a Jupyter notebook cell. + """ + display(self.widget) \ No newline at end of file diff --git a/tinytroupe/utils/__init__.py b/tinytroupe/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b55cd8002b4ce1352f8935ccc664ca1d752d8262 --- /dev/null +++ b/tinytroupe/utils/__init__.py @@ -0,0 +1,19 @@ +""" +General utilities and convenience functions. +""" + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.utils.config import * +from tinytroupe.utils.json import * +from tinytroupe.utils.llm import * +from tinytroupe.utils.misc import * +from tinytroupe.utils.rendering import * +from tinytroupe.utils.validation import * +from tinytroupe.utils.semantics import * +from tinytroupe.utils.behavior import * +from tinytroupe.utils.parallel import * \ No newline at end of file diff --git a/tinytroupe/utils/__pycache__/__init__.cpython-312.pyc b/tinytroupe/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2fa8f18cfbddb71bab2cf86b178229994e8a18ba Binary files /dev/null and b/tinytroupe/utils/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/utils/__pycache__/behavior.cpython-312.pyc b/tinytroupe/utils/__pycache__/behavior.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1c5a5947dd3a7ea9283821da35acbbdbf55e606e Binary files /dev/null and b/tinytroupe/utils/__pycache__/behavior.cpython-312.pyc differ diff --git a/tinytroupe/utils/__pycache__/config.cpython-312.pyc 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0000000000000000000000000000000000000000..9001b03c12f292dfc74f00557691664c0aded787 --- /dev/null +++ b/tinytroupe/utils/behavior.py @@ -0,0 +1,43 @@ +""" +Various utility functions for behavior analysis and action similarity computation. +""" + +import textdistance + + + +def next_action_jaccard_similarity(agent, proposed_next_action): + """ + Computes the Jaccard similarity between the agent's current action and a proposed next action, + modulo target and type (i.e., similarity will be computed using only the content, provided that the action + type and target are the same). If the action type or target is different, the similarity will be 0. + + Jaccard similarity is a measure of similarity between two sets, defined as the size of the intersection + divided by the size of the union of the sets. + + Args: + agent (TinyPerson): The agent whose current action is to be compared. + proposed_next_action (dict): The proposed next action to be compared against the agent's current action. + + Returns: + float: The Jaccard similarity score between the agent's current action and the proposed next action. + """ + # Get the agent's current action + current_action = agent.last_remembered_action() + + if current_action is None: + return 0.0 + + # Check if the action type and target are the same + if ("type" in current_action) and ("type" in proposed_next_action) and ("target" in current_action) and ("target" in proposed_next_action) and \ + (current_action["type"] != proposed_next_action["type"] or current_action["target"] != proposed_next_action["target"]): + return 0.0 + + # Compute the Jaccard similarity between the content of the two actions + current_action_content = current_action["content"] + proposed_next_action_content = proposed_next_action["content"] + + # using textdistance to compute the Jaccard similarity + jaccard_similarity = textdistance.jaccard(current_action_content, proposed_next_action_content) + + return jaccard_similarity \ No newline at end of file diff --git a/tinytroupe/utils/config.py b/tinytroupe/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..b5fbf14e5f7b7cbaa652a1d3c9cb5a206b790aab --- /dev/null +++ b/tinytroupe/utils/config.py @@ -0,0 +1,108 @@ +import logging +from pathlib import Path +import configparser + +################################################################################ +# Config and startup utilities +################################################################################ +_config = None + +def read_config_file(use_cache=True, verbose=True) -> configparser.ConfigParser: + global _config + if use_cache and _config is not None: + # if we have a cached config and accept that, return it + return _config + + else: + config = configparser.ConfigParser() + + # Read the default values in the module directory. + config_file_path = Path(__file__).parent.absolute() / '../config.ini' + print(f"Looking for default config on: {config_file_path}") if verbose else None + if config_file_path.exists(): + config.read(config_file_path) + _config = config + else: + raise ValueError(f"Failed to find default config on: {config_file_path}") + + # Now, let's override any specific default value, if there's a custom .ini config. + # Try the directory of the current main program + config_file_path = Path.cwd() / "config.ini" + if config_file_path.exists(): + print(f"Found custom config on: {config_file_path}") if verbose else None + config.read(config_file_path) # this only overrides the values that are present in the custom config + _config = config + return config + else: + if verbose: + print(f"Failed to find custom config on: {config_file_path}") if verbose else None + print("Will use only default values. IF THINGS FAIL, TRY CUSTOMIZING MODEL, API TYPE, etc.") if verbose else None + + return config + +def pretty_print_config(config): + print() + print("=================================") + print("Current TinyTroupe configuration ") + print("=================================") + for section in config.sections(): + print(f"[{section}]") + for key, value in config.items(section): + print(f"{key} = {value}") + print() + +def pretty_print_datetime(): + from datetime import datetime + from datetime import timezone + now = datetime.now() + now_utc = now.astimezone(timezone.utc) + print(f"Current date and time (local): {now.strftime('%Y-%m-%d %H:%M:%S')}") + print(f"Current date and time (UTC): {now_utc.strftime('%Y-%m-%d %H:%M:%S')}") + +def pretty_print_tinytroupe_version(): + try: + import importlib.metadata + version = importlib.metadata.version("tinytroupe") + except Exception: + version = "unknown" + print(f"TinyTroupe version: {version}") + +def start_logger(config: configparser.ConfigParser): + # create logger + logger = logging.getLogger("tinytroupe") + log_level = config['Logging'].get('LOGLEVEL', 'INFO').upper() + logger.setLevel(level=log_level) + + # Clear any existing handlers to prevent duplicates + # This is especially important in Jupyter notebooks where modules get reloaded + for handler in logger.handlers[:]: + logger.removeHandler(handler) + + # Prevent propagation to avoid duplicate messages from parent loggers + logger.propagate = False + + # create console handler and set level to debug + ch = logging.StreamHandler() + ch.setLevel(log_level) + + # create formatter + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + # add formatter to ch + ch.setFormatter(formatter) + + # add ch to logger + logger.addHandler(ch) + +def set_loglevel(log_level): + """ + Sets the log level for the TinyTroupe logger. + Args: + log_level (str): The log level to set (e.g., 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'). + """ + logger = logging.getLogger("tinytroupe") + logger.setLevel(level=log_level) + + # Also update all handlers to the new log level + for handler in logger.handlers: + handler.setLevel(log_level) \ No newline at end of file diff --git a/tinytroupe/utils/json.py b/tinytroupe/utils/json.py new file mode 100644 index 0000000000000000000000000000000000000000..2feec167cef5b9a052f3c58cd5727e24596738c9 --- /dev/null +++ b/tinytroupe/utils/json.py @@ -0,0 +1,295 @@ +import json +import copy +from pydantic import BaseModel + +from tinytroupe.utils import logger + +class JsonSerializableRegistry: + """ + A mixin class that provides JSON serialization, deserialization, and subclass registration. + """ + + class_mapping = {} + + def to_json(self, include: list = None, suppress: list = None, file_path: str = None, + serialization_type_field_name = "json_serializable_class_name") -> dict: + """ + Returns a JSON representation of the object. + + Args: + include (list, optional): Attributes to include in the serialization. Will override the default behavior. + suppress (list, optional): Attributes to suppress from the serialization. Will override the default behavior. + file_path (str, optional): Path to a file where the JSON will be written. + """ + # Gather all serializable attributes from the class hierarchy + serializable_attrs = set() + suppress_attrs = set() + custom_serializers = {} + for cls in self.__class__.__mro__: # Traverse the class hierarchy + if hasattr(cls, 'serializable_attributes') and isinstance(cls.serializable_attributes, list): + serializable_attrs.update(cls.serializable_attributes) + if hasattr(cls, 'suppress_attributes_from_serialization') and isinstance(cls.suppress_attributes_from_serialization, list): + suppress_attrs.update(cls.suppress_attributes_from_serialization) + if hasattr(cls, 'custom_serializers') and isinstance(cls.custom_serializers, dict): + custom_serializers.update(cls.custom_serializers) + + # Override attributes with method parameters if provided + if include: + serializable_attrs = set(include) + if suppress: + suppress_attrs.update(suppress) + + def aux_serialize_item(item): + if isinstance(item, JsonSerializableRegistry): + return item.to_json(serialization_type_field_name=serialization_type_field_name) + elif isinstance(item, BaseModel): + # If it's a Pydantic model, convert it to a dict first + logger.debug(f"Serializing Pydantic model: {item}") + return item.model_dump(mode="json", exclude_unset=True) + else: + return copy.deepcopy(item) + + result = {serialization_type_field_name: self.__class__.__name__} + for attr in serializable_attrs if serializable_attrs else self.__dict__: + if attr not in suppress_attrs: + value = getattr(self, attr, None) + + attr_renamed = self._programmatic_name_to_json_name(attr) + + # Check if there's a custom serializer for this attribute + if attr in custom_serializers: + result[attr_renamed] = custom_serializers[attr](value) + elif isinstance(value, list): + result[attr_renamed] = [aux_serialize_item(item) for item in value] + elif isinstance(value, dict): + result[attr_renamed] = {k: aux_serialize_item(v) for k, v in value.items()} + else: # isinstance(value, JsonSerializableRegistry) or isinstance(value, BaseModel) or other types + result[attr_renamed] = aux_serialize_item(value) + + if file_path: + # Create directories if they do not exist + import os + os.makedirs(os.path.dirname(file_path), exist_ok=True) + with open(file_path, 'w', encoding='utf-8', errors='replace') as f: + json.dump(result, f, indent=4) + + return result + + @classmethod + def from_json(cls, json_dict_or_path, suppress: list = None, + serialization_type_field_name = "json_serializable_class_name", + post_init_params: dict = None): + """ + Loads a JSON representation of the object and creates an instance of the class. + + Args: + json_dict_or_path (dict or str): The JSON dictionary representing the object or a file path to load the JSON from. + suppress (list, optional): Attributes to suppress from being loaded. + + Returns: + An instance of the class populated with the data from json_dict_or_path. + """ + if isinstance(json_dict_or_path, str): + with open(json_dict_or_path, 'r', encoding='utf-8', errors='replace') as f: + json_dict = json.load(f) + else: + json_dict = json_dict_or_path + + subclass_name = json_dict.get(serialization_type_field_name) + target_class = cls.class_mapping.get(subclass_name, cls) + instance = target_class.__new__(target_class) # Create an instance without calling __init__ + + # Gather all serializable attributes from the class hierarchy + serializable_attrs = set() + custom_deserializers = {} + suppress_attrs = set(suppress) if suppress else set() + for target_mro in target_class.__mro__: + if hasattr(target_mro, 'serializable_attributes') and isinstance(target_mro.serializable_attributes, list): + serializable_attrs.update(target_mro.serializable_attributes) + if hasattr(target_mro, 'custom_deserializers') and isinstance(target_mro.custom_deserializers, dict): + custom_deserializers.update(target_mro.custom_deserializers) + if hasattr(target_mro, 'suppress_attributes_from_serialization') and isinstance(target_mro.suppress_attributes_from_serialization, list): + suppress_attrs.update(target_mro.suppress_attributes_from_serialization) + + # Assign values only for serializable attributes if specified, otherwise assign everything + for key in serializable_attrs if serializable_attrs else json_dict: + key_in_json = cls._programmatic_name_to_json_name(key) + if key_in_json in json_dict and key not in suppress_attrs: + value = json_dict[key_in_json] + if key in custom_deserializers: + # Use custom initializer if provided + setattr(instance, key, custom_deserializers[key](value)) + elif isinstance(value, dict) and serialization_type_field_name in value: + # Assume it's another JsonSerializableRegistry object + setattr(instance, key, JsonSerializableRegistry.from_json(value, serialization_type_field_name=serialization_type_field_name)) + elif isinstance(value, list): + # Handle collections, recursively deserialize if items are JsonSerializableRegistry objects + deserialized_collection = [] + for item in value: + if isinstance(item, dict) and serialization_type_field_name in item: + deserialized_collection.append(JsonSerializableRegistry.from_json(item, serialization_type_field_name=serialization_type_field_name)) + else: + deserialized_collection.append(copy.deepcopy(item)) + setattr(instance, key, deserialized_collection) + else: + setattr(instance, key, copy.deepcopy(value)) + + # Call post-deserialization initialization if available + if hasattr(instance, '_post_deserialization_init') and callable(instance._post_deserialization_init): + post_init_params = post_init_params if post_init_params else {} + instance._post_deserialization_init(**post_init_params) + + return instance + + def __init_subclass__(cls, **kwargs): + super().__init_subclass__(**kwargs) + # Register the subclass using its name as the key + JsonSerializableRegistry.class_mapping[cls.__name__] = cls + + # Automatically extend serializable attributes and custom initializers from parent classes + if hasattr(cls, 'serializable_attributes') and isinstance(cls.serializable_attributes, list): + for base in cls.__bases__: + if hasattr(base, 'serializable_attributes') and isinstance(base.serializable_attributes, list): + cls.serializable_attributes = list(set(base.serializable_attributes + cls.serializable_attributes)) + + if hasattr(cls, 'suppress_attributes_from_serialization') and isinstance(cls.suppress_attributes_from_serialization, list): + for base in cls.__bases__: + if hasattr(base, 'suppress_attributes_from_serialization') and isinstance(base.suppress_attributes_from_serialization, list): + cls.suppress_attributes_from_serialization = list(set(base.suppress_attributes_from_serialization + cls.suppress_attributes_from_serialization)) + + if hasattr(cls, 'custom_deserializers') and isinstance(cls.custom_deserializers, dict): + for base in cls.__bases__: + if hasattr(base, 'custom_deserializers') and isinstance(base.custom_deserializers, dict): + base_initializers = base.custom_deserializers.copy() + base_initializers.update(cls.custom_deserializers) + cls.custom_deserializers = base_initializers + + if hasattr(cls, 'custom_serializers') and isinstance(cls.custom_serializers, dict): + for base in cls.__bases__: + if hasattr(base, 'custom_serializers') and isinstance(base.custom_serializers, dict): + base_serializers = base.custom_serializers.copy() + base_serializers.update(cls.custom_serializers) + cls.custom_serializers = base_serializers + + def _post_deserialization_init(self, **kwargs): + # if there's a _post_init method, call it after deserialization + if hasattr(self, '_post_init'): + self._post_init(**kwargs) + + @classmethod + def _programmatic_name_to_json_name(cls, name): + """ + Converts a programmatic name to a JSON name by converting it to snake case. + """ + if hasattr(cls, 'serializable_attributes_renaming') and isinstance(cls.serializable_attributes_renaming, dict): + return cls.serializable_attributes_renaming.get(name, name) + return name + + @classmethod + def _json_name_to_programmatic_name(cls, name): + """ + Converts a JSON name to a programmatic name. + """ + if hasattr(cls, 'serializable_attributes_renaming') and isinstance(cls.serializable_attributes_renaming, dict): + reverse_rename = {} + for k, v in cls.serializable_attributes_renaming.items(): + if v in reverse_rename: + raise ValueError(f"Duplicate value '{v}' found in serializable_attributes_renaming.") + reverse_rename[v] = k + return reverse_rename.get(name, name) + return name + +def post_init(cls): + """ + Decorator to enforce a post-initialization method call in a class, if it has one. + The method must be named `_post_init`. + """ + original_init = cls.__init__ + + def new_init(self, *args, **kwargs): + original_init(self, *args, **kwargs) + if hasattr(cls, '_post_init'): + cls._post_init(self) + + cls.__init__ = new_init + return cls + +def merge_dicts(current, additions, overwrite=False, error_on_conflict=True, remove_duplicates=True): + """ + Merges two dictionaries and returns a new dictionary. Works as follows: + - If a key exists in the additions dictionary but not in the current dictionary, it is added. + - If a key maps to None in the current dictionary, it is replaced by the value in the additions dictionary. + - If a key exists in both dictionaries and the values are dictionaries, the function is called recursively. + - If a key exists in both dictionaries and the values are lists, the lists are concatenated and duplicates are removed + (if remove_duplicates is True). + - If the values are of different types, an exception is raised. + - If the values are of the same type but not both lists/dictionaries, the value from the additions dictionary overwrites the value in the current dictionary based on the overwrite parameter. + + Parameters: + - current (dict): The original dictionary. + - additions (dict): The dictionary with values to add. + - overwrite (bool): Whether to overwrite values if they are of the same type but not both lists/dictionaries. + - error_on_conflict (bool): Whether to raise an error if there is a conflict and overwrite is False. + - remove_duplicates (bool): Whether to remove duplicates from lists when merging. + + Returns: + - dict: A new dictionary with merged values. + """ + merged = current.copy() # Create a copy of the current dictionary to avoid altering it + + for key in additions: + if key in merged: + # If the current value is None, directly assign the new value + if merged[key] is None: + merged[key] = additions[key] + # If both values are dictionaries, merge them recursively + elif isinstance(merged[key], dict) and isinstance(additions[key], dict): + merged[key] = merge_dicts(merged[key], additions[key], overwrite, error_on_conflict) + # If both values are lists, concatenate them and remove duplicates + elif isinstance(merged[key], list) and isinstance(additions[key], list): + merged[key].extend(additions[key]) + # Remove duplicates while preserving order + if remove_duplicates: + merged[key] = remove_duplicate_items(merged[key]) + # If the values are of different types, raise an exception + elif type(merged[key]) != type(additions[key]): + raise TypeError(f"Cannot merge different types: {type(merged[key])} and {type(additions[key])} for key '{key}'") + # If the values are of the same type but not both lists/dictionaries, decide based on the overwrite parameter + else: + if overwrite: + merged[key] = additions[key] + elif merged[key] != additions[key]: + if error_on_conflict: + raise ValueError(f"Conflict at key '{key}': overwrite is set to False and values are different.") + else: + continue # Ignore the conflict and continue + else: + # If the key is not present in merged, add it from additions + merged[key] = additions[key] + + return merged + +def remove_duplicate_items(lst): + """ + Removes duplicates from a list while preserving order. + Handles unhashable elements by using a list comprehension. + + Parameters: + - lst (list): The list to remove duplicates from. + + Returns: + - list: A new list with duplicates removed. + """ + seen = [] + result = [] + for item in lst: + if isinstance(item, dict): + # Convert dict to a frozenset of its items to make it hashable + item_key = frozenset(item.items()) + else: + item_key = item + + if item_key not in seen: + seen.append(item_key) + result.append(item) + return result \ No newline at end of file diff --git a/tinytroupe/utils/llm.py b/tinytroupe/utils/llm.py new file mode 100644 index 0000000000000000000000000000000000000000..827f1fa0295b13cf259d44fa1da19963de311348 --- /dev/null +++ b/tinytroupe/utils/llm.py @@ -0,0 +1,1051 @@ +import re +import json +import ast +import os +import chevron +from typing import Collection, Dict, List, Union +from pydantic import BaseModel +import copy +import functools +import inspect +import pprint +import textwrap + +from tinytroupe import utils +from tinytroupe.utils import logger +from tinytroupe.utils.rendering import break_text_at_length + +################################################################################ +# Model input utilities +################################################################################ + +def compose_initial_LLM_messages_with_templates(system_template_name:str, user_template_name:str=None, + base_module_folder:str=None, + rendering_configs:dict={}) -> list: + """ + Composes the initial messages for the LLM model call, under the assumption that it always involves + a system (overall task description) and an optional user message (specific task description). + These messages are composed using the specified templates and rendering configurations. + """ + + # ../ to go to the base library folder, because that's the most natural reference point for the user + if base_module_folder is None: + sub_folder = "../prompts/" + else: + sub_folder = f"../{base_module_folder}/prompts/" + + base_template_folder = os.path.join(os.path.dirname(__file__), sub_folder) + + system_prompt_template_path = os.path.join(base_template_folder, f'{system_template_name}') + user_prompt_template_path = os.path.join(base_template_folder, f'{user_template_name}') + + messages = [] + + messages.append({"role": "system", + "content": chevron.render( + open(system_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + + # optionally add a user message + if user_template_name is not None: + messages.append({"role": "user", + "content": chevron.render( + open(user_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + return messages + + +# +# Data structures to enforce output format during LLM API call. +# + +class LLMScalarWithJustificationResponse(BaseModel): + """ + Represents a typed response from an LLM (Language Learning Model) including justification. + Attributes: + justification (str): The justification or explanation for the response. + value (str, int, float, bool): The value of the response. + confidence (float): The confidence level of the response. + """ + justification: str + value: Union[str, int, float, bool] + confidence: float + +class LLMScalarWithJustificationAndReasoningResponse(BaseModel): + """ + Represents a typed response from an LLM (Language Learning Model) including justification and reasoning. + Attributes: + reasoning (str): The reasoning behind the response. + justification (str): The justification or explanation for the response. + value (str, int, float, bool): The value of the response. + confidence (float): The confidence level of the response. + """ + reasoning: str + + # we need to repeat these fields here, instead of inheriting from LLMScalarWithJustificationResponse, + # because we need to ensure `reasoning` is always the first field in the JSON object. + justification: str + value: Union[str, int, float, bool] + confidence: float + + + +########################################################################### +# Model calling helpers +########################################################################### + +class LLMChat: + """ + A class that represents an ongoing LLM conversation. It maintains the conversation history, + allows adding new messages, and handles model output type coercion. + """ + + def __init__(self, system_template_name:str=None, system_prompt:str=None, + user_template_name:str=None, user_prompt:str=None, + base_module_folder=None, + output_type=None, + enable_json_output_format:bool=True, + enable_justification_step:bool=True, + enable_reasoning_step:bool=False, + **model_params): + """ + Initializes an LLMChat instance with the specified system and user templates, or the system and user prompts. + If a template is specified, the corresponding prompt must be None, and vice versa. + + Args: + system_template_name (str): Name of the system template file. + system_prompt (str): System prompt content. + user_template_name (str): Name of the user template file. + user_prompt (str): User prompt content. + base_module_folder (str): Optional subfolder path within the library where templates are located. + output_type (type): Expected type of the model output. + enable_reasoning_step (bool): Flag to enable reasoning step in the conversation. This IS NOT the use of "reasoning models" (e.g., o1, o3), + but rather the use of an additional reasoning step in the regular text completion. + enable_justification_step (bool): Flag to enable justification step in the conversation. Must be True if reasoning step is enabled as well. + enable_json_output_format (bool): Flag to enable JSON output format for the model response. Must be True if reasoning or justification steps are enabled. + **model_params: Additional parameters for the LLM model call. + + """ + if (system_template_name is not None and system_prompt is not None) or \ + (user_template_name is not None and user_prompt is not None) or\ + (system_template_name is None and system_prompt is None) or \ + (user_template_name is None and user_prompt is None): + raise ValueError("Either the template or the prompt must be specified, but not both.") + + self.base_module_folder = base_module_folder + + self.system_template_name = system_template_name + self.user_template_name = user_template_name + + self.system_prompt = textwrap.dedent(system_prompt) if system_prompt is not None else None + self.user_prompt = textwrap.dedent(user_prompt) if user_prompt is not None else None + + self.output_type = output_type + + self.enable_reasoning_step = enable_reasoning_step + self.enable_justification_step = enable_justification_step + self.enable_json_output_format = enable_json_output_format + + self.model_params = model_params + + # Conversation history + self.messages = [] + self.conversation_history = [] + + # Response tracking + self.response_raw = None + self.response_json = None + self.response_reasoning = None + self.response_value = None + self.response_justification = None + self.response_confidence = None + + def __call__(self, *args, **kwds): + return self.call(*args, **kwds) + + def _render_template(self, template_name, base_module_folder=None, rendering_configs={}): + """ + Helper method to render templates for messages. + + Args: + template_name: Name of the template file + base_module_folder: Optional subfolder path within the library + rendering_configs: Configuration variables for template rendering + + Returns: + Rendered template content + """ + if base_module_folder is None: + sub_folder = "../prompts/" + else: + sub_folder = f"../{base_module_folder}/prompts/" + + base_template_folder = os.path.join(os.path.dirname(__file__), sub_folder) + template_path = os.path.join(base_template_folder, template_name) + + return chevron.render(open(template_path, 'r', encoding='utf-8', errors='replace').read(), rendering_configs) + + def add_user_message(self, message=None, template_name=None, base_module_folder=None, rendering_configs={}): + """ + Add a user message to the conversation. + + Args: + message: The direct message content from the user (mutually exclusive with template_name) + template_name: Optional template file name to use for the message + base_module_folder: Optional subfolder for template location + rendering_configs: Configuration variables for template rendering + + Returns: + self for method chaining + """ + if message is not None and template_name is not None: + raise ValueError("Either message or template_name must be specified, but not both.") + + if template_name is not None: + content = self._render_template(template_name, base_module_folder, rendering_configs) + else: + content = textwrap.dedent(message) + + self.messages.append({"role": "user", "content": content}) + return self + + def add_system_message(self, message=None, template_name=None, base_module_folder=None, rendering_configs={}): + """ + Add a system message to the conversation. + + Args: + message: The direct message content from the system (mutually exclusive with template_name) + template_name: Optional template file name to use for the message + base_module_folder: Optional subfolder for template location + rendering_configs: Configuration variables for template rendering + + Returns: + self for method chaining + """ + if message is not None and template_name is not None: + raise ValueError("Either message or template_name must be specified, but not both.") + + if template_name is not None: + content = self._render_template(template_name, base_module_folder, rendering_configs) + else: + content = textwrap.dedent(message) + + self.messages.append({"role": "system", "content": content}) + return self + + def add_assistant_message(self, message=None, template_name=None, base_module_folder=None, rendering_configs={}): + """ + Add an assistant message to the conversation. + + Args: + message: The direct message content from the assistant (mutually exclusive with template_name) + template_name: Optional template file name to use for the message + base_module_folder: Optional subfolder for template location + rendering_configs: Configuration variables for template rendering + + Returns: + self for method chaining + """ + if message is not None and template_name is not None: + raise ValueError("Either message or template_name must be specified, but not both.") + + if template_name is not None: + content = self._render_template(template_name, base_module_folder, rendering_configs) + else: + content = textwrap.dedent(message) + + self.messages.append({"role": "assistant", "content": content}) + return self + + def set_model_params(self, **model_params): + """ + Set or update the model parameters for the LLM call. + + Args: + model_params: Key-value pairs of model parameters to set or update + """ + self.model_params.update(model_params) + return self + + def call(self, output_type="default", + enable_json_output_format:bool=None, + enable_justification_step:bool=None, + enable_reasoning_step:bool=None, + **rendering_configs): + """ + Initiates or continues the conversation with the LLM model using the current message history. + + Args: + output_type: Optional parameter to override the output type for this specific call. If set to "default", it uses the instance's output_type. + If set to None, removes all output formatting and coercion. + enable_json_output_format: Optional flag to enable JSON output format for the model response. If None, uses the instance's setting. + enable_justification_step: Optional flag to enable justification step in the conversation. If None, uses the instance's setting. + enable_reasoning_step: Optional flag to enable reasoning step in the conversation. If None, uses the instance's setting. + rendering_configs: The rendering configurations (template variables) to use when composing the initial messages. + + Returns: + The content of the model response. + """ + from tinytroupe.openai_utils import client # import here to avoid circular import + + try: + + # Initialize the conversation if this is the first call + if not self.messages: + if self.system_template_name is not None and self.user_template_name is not None: + self.messages = utils.compose_initial_LLM_messages_with_templates( + self.system_template_name, + self.user_template_name, + base_module_folder=self.base_module_folder, + rendering_configs=rendering_configs + ) + else: + if self.system_prompt: + self.messages.append({"role": "system", "content": self.system_prompt}) + if self.user_prompt: + self.messages.append({"role": "user", "content": self.user_prompt}) + + # Use the provided output_type if specified, otherwise fall back to the instance's output_type + current_output_type = output_type if output_type != "default" else self.output_type + + # Set up typing for the output + if current_output_type is not None: + + # TODO obsolete? + # + ## Add type coercion instructions if not already added + #if not any(msg.get("content", "").startswith("In your response, you **MUST** provide a value") + # for msg in self.messages if msg.get("role") == "system"): + + # the user can override the response format by specifying it in the model_params, otherwise + # we will use the default response format + if "response_format" not in self.model_params or self.model_params["response_format"] is None: + + if utils.first_non_none(enable_json_output_format, self.enable_json_output_format): + + self.model_params["response_format"] = {"type": "json_object"} + + typing_instruction = {"role": "system", + "content": "Your response **MUST** be a JSON object."} + + # Special justification format can be used (will also include confidence level) + if utils.first_non_none(enable_justification_step, self.enable_justification_step): + + # Add reasoning step if enabled provides further mechanism to think step-by-step + if not (utils.first_non_none(enable_reasoning_step, self.enable_reasoning_step)): + # Default structured output + self.model_params["response_format"] = LLMScalarWithJustificationResponse + + typing_instruction = {"role": "system", + "content": "In your response, you **MUST** provide a value, along with a justification and your confidence level that the value and justification are correct (0.0 means no confidence, 1.0 means complete confidence). "+ + "Furtheremore, your response **MUST** be a JSON object with the following structure: {\"justification\": justification, \"value\": value, \"confidence\": confidence}. "+ + "Note that \"justification\" comes first in order to help you think about the value you are providing."} + + else: + # Override the response format to also use a reasoning step + self.model_params["response_format"] = LLMScalarWithJustificationAndReasoningResponse + + typing_instruction = {"role": "system", + "content": \ + "In your response, you **FIRST** think step-by-step on how you are going to compute the value, and you put this reasoning in the \"reasoning\" field (which must come before all others). "+ + "This allows you to think carefully as much as you need to deduce the best and most correct value. "+ + "After that, you **MUST** provide the resulting value, along with a justification (which can tap into the previous reasoning), and your confidence level that the value and justification are correct (0.0 means no confidence, 1.0 means complete confidence)."+ + "Furtheremore, your response **MUST** be a JSON object with the following structure: {\"reasoning\": reasoning, \"justification\": justification, \"value\": value, \"confidence\": confidence}." + + " Note that \"justification\" comes after \"reasoning\" but before \"value\" to help with further formulation of the resulting \"value\"."} + + + # Specify the value type + if current_output_type == bool: + typing_instruction["content"] += " " + self._request_bool_llm_message()["content"] + elif current_output_type == int: + typing_instruction["content"] += " " + self._request_integer_llm_message()["content"] + elif current_output_type == float: + typing_instruction["content"] += " " + self._request_float_llm_message()["content"] + elif isinstance(current_output_type, list) and all(isinstance(option, str) for option in current_output_type): + typing_instruction["content"] += " " + self._request_enumerable_llm_message(current_output_type)["content"] + elif current_output_type == List[Dict[str, any]]: + # Override the response format + self.model_params["response_format"] = {"type": "json_object"} + typing_instruction["content"] += " " + self._request_list_of_dict_llm_message()["content"] + elif current_output_type == dict or current_output_type == "json": + # Override the response format + self.model_params["response_format"] = {"type": "json_object"} + typing_instruction["content"] += " " + self._request_dict_llm_message()["content"] + elif current_output_type == list: + # Override the response format + self.model_params["response_format"] = {"type": "json_object"} + typing_instruction["content"] += " " + self._request_list_llm_message()["content"] + # Check if it is actually a pydantic model + elif issubclass(current_output_type, BaseModel): + # Completely override the response format + self.model_params["response_format"] = current_output_type + typing_instruction = {"role": "system", "content": "Your response **MUST** be a JSON object."} + elif current_output_type == str: + typing_instruction["content"] += " " + self._request_str_llm_message()["content"] + #pass # no coercion needed, it is already a string + else: + raise ValueError(f"Unsupported output type: {current_output_type}") + + self.messages.append(typing_instruction) + + else: # output_type is None + self.model_params["response_format"] = None + typing_instruction = {"role": "system", "content": \ + "If you were given instructions before about the **format** of your response, please ignore them from now on. "+ + "The needs of the user have changed. You **must** now use regular text -- not numbers, not booleans, not JSON. "+ + "There are no fields, no types, no special formats. Just regular text appropriate to respond to the last user request."} + self.messages.append(typing_instruction) + #pass # nothing here for now + + + # Call the LLM model with all messages in the conversation + model_output = client().send_message(self.messages, **self.model_params) + + if 'content' in model_output: + self.response_raw = self.response_value = model_output['content'] + logger.debug(f"Model raw 'content' response: {self.response_raw}") + + # Add the assistant's response to the conversation history + self.add_assistant_message(self.response_raw) + self.conversation_history.append({"messages": copy.deepcopy(self.messages)}) + + # Type coercion if output type is specified + if current_output_type is not None: + + if self.enable_json_output_format: + # output is supposed to be a JSON object + self.response_json = self.response_value = utils.extract_json(self.response_raw) + logger.debug(f"Model output JSON response: {self.response_json}") + + if self.enable_justification_step and not (hasattr(current_output_type, 'model_validate') or hasattr(current_output_type, 'parse_obj')): + # if justification step is enabled, we expect a JSON object with reasoning (optionally), justification, value, and confidence + # BUT not for Pydantic models which expect direct JSON structure + self.response_reasoning = self.response_json.get("reasoning", None) + self.response_value = self.response_json.get("value", None) + self.response_justification = self.response_json.get("justification", None) + self.response_confidence = self.response_json.get("confidence", None) + else: + # For direct JSON output (like Pydantic models), use the whole JSON as the value + self.response_value = self.response_json + + # if output type was specified, we need to coerce the response value + if self.response_value is not None: + if current_output_type == bool: + self.response_value = self._coerce_to_bool(self.response_value) + elif current_output_type == int: + self.response_value = self._coerce_to_integer(self.response_value) + elif current_output_type == float: + self.response_value = self._coerce_to_float(self.response_value) + elif isinstance(current_output_type, list) and all(isinstance(option, str) for option in current_output_type): + self.response_value = self._coerce_to_enumerable(self.response_value, current_output_type) + elif current_output_type == List[Dict[str, any]]: + self.response_value = self._coerce_to_dict_or_list(self.response_value) + elif current_output_type == dict or current_output_type == "json": + self.response_value = self._coerce_to_dict_or_list(self.response_value) + elif current_output_type == list: + self.response_value = self._coerce_to_list(self.response_value) + elif hasattr(current_output_type, 'model_validate') or hasattr(current_output_type, 'parse_obj'): + # Handle Pydantic model - try modern approach first, then fallback + try: + if hasattr(current_output_type, 'model_validate'): + self.response_value = current_output_type.model_validate(self.response_json) + else: + self.response_value = current_output_type.parse_obj(self.response_json) + except Exception as e: + logger.error(f"Failed to parse Pydantic model: {e}") + raise + elif current_output_type == str: + pass # no coercion needed, it is already a string + else: + raise ValueError(f"Unsupported output type: {current_output_type}") + + else: + logger.error(f"Model output is None: {self.response_raw}") + + logger.debug(f"Model output coerced response value: {self.response_value}") + logger.debug(f"Model output coerced response justification: {self.response_justification}") + logger.debug(f"Model output coerced response confidence: {self.response_confidence}") + + return self.response_value + else: + logger.error(f"Model output does not contain 'content' key: {model_output}") + return None + + except ValueError as ve: + # Re-raise ValueError exceptions (like unsupported output type) instead of catching them + if "Unsupported output type" in str(ve): + raise + else: + logger.error(f"Error during LLM call: {ve}. Will return None instead of failing.") + return None + except Exception as e: + logger.error(f"Error during LLM call: {e}. Will return None instead of failing.") + return None + + def continue_conversation(self, user_message=None, **rendering_configs): + """ + Continue the conversation with a new user message and get a response. + + Args: + user_message: The new message from the user + rendering_configs: Additional rendering configurations + + Returns: + The content of the model response + """ + if user_message: + self.add_user_message(user_message) + return self.call(**rendering_configs) + + def reset_conversation(self): + """ + Reset the conversation state but keep the initial configuration. + + Returns: + self for method chaining + """ + self.messages = [] + self.response_raw = None + self.response_json = None + self.response_value = None + self.response_justification = None + self.response_confidence = None + return self + + def get_conversation_history(self): + """ + Get the full conversation history. + + Returns: + List of all messages in the conversation + """ + return self.messages + + # Keep all the existing coercion methods + def _coerce_to_bool(self, llm_output): + """ + Coerces the LLM output to a boolean value. + + This method looks for the string "True", "False", "Yes", "No", "Positive", "Negative" in the LLM output, such that + - case is neutralized; + - the first occurrence of the string is considered, the rest is ignored. For example, " Yes, that is true" will be considered "Yes"; + - if no such string is found, the method raises an error. So it is important that the prompts actually requests a boolean value. + + Args: + llm_output (str, bool): The LLM output to coerce. + + Returns: + The boolean value of the LLM output. + """ + + # if the LLM output is already a boolean, we return it + if isinstance(llm_output, bool): + return llm_output + + # let's extract the first occurrence of the string "True", "False", "Yes", "No", "Positive", "Negative" in the LLM output. + # using a regular expression + import re + match = re.search(r'\b(?:True|False|Yes|No|Positive|Negative)\b', llm_output, re.IGNORECASE) + if match: + first_match = match.group(0).lower() + if first_match in ["true", "yes", "positive"]: + return True + elif first_match in ["false", "no", "negative"]: + return False + + raise ValueError("Cannot convert the LLM output to a boolean value.") + + def _request_str_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate from now on has no special format, it can be any string you find appropriate to the current conversation. "+ + "Make sure you move to `value` **all** relevant information you used in reasoning or justification, so that it is not lost. "} + + def _request_bool_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be either 'True' or 'False'. This is critical for later processing. If you don't know the correct answer, just output 'False'."} + + + def _coerce_to_integer(self, llm_output:str): + """ + Coerces the LLM output to an integer value. + + This method looks for the first occurrence of an integer in the LLM output, such that + - the first occurrence of the integer is considered, the rest is ignored. For example, "There are 3 cats" will be considered 3; + - if no integer is found, the method raises an error. So it is important that the prompts actually requests an integer value. + + Args: + llm_output (str, int): The LLM output to coerce. + + Returns: + The integer value of the LLM output. + """ + + # if the LLM output is already an integer, we return it + if isinstance(llm_output, int): + return llm_output + + # if it's a float that represents a whole number, convert it + if isinstance(llm_output, float): + if llm_output.is_integer(): + return int(llm_output) + else: + raise ValueError("Cannot convert the LLM output to an integer value.") + + # Convert to string for regex processing + llm_output_str = str(llm_output) + + # let's extract the first occurrence of an integer in the LLM output. + # using a regular expression + import re + # Match integers that are not part of a decimal number + # First check if the string contains a decimal point - if so, reject it for integer coercion + if '.' in llm_output_str and any(c.isdigit() for c in llm_output_str.split('.')[1]): + # This looks like a decimal number, not a pure integer + raise ValueError("Cannot convert the LLM output to an integer value.") + + match = re.search(r'-?\b\d+\b', llm_output_str) + if match: + return int(match.group(0)) + + raise ValueError("Cannot convert the LLM output to an integer value.") + + def _request_integer_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be an integer number (e.g., '1'). This is critical for later processing.."} + + def _coerce_to_float(self, llm_output:str): + """ + Coerces the LLM output to a float value. + + This method looks for the first occurrence of a float in the LLM output, such that + - the first occurrence of the float is considered, the rest is ignored. For example, "The price is $3.50" will be considered 3.50; + - if no float is found, the method raises an error. So it is important that the prompts actually requests a float value. + + Args: + llm_output (str, float): The LLM output to coerce. + + Returns: + The float value of the LLM output. + """ + + # if the LLM output is already a float, we return it + if isinstance(llm_output, float): + return llm_output + + # if it's an integer, convert to float + if isinstance(llm_output, int): + return float(llm_output) + + # let's extract the first occurrence of a number (float or int) in the LLM output. + # using a regular expression that handles negative numbers and both int/float formats + import re + match = re.search(r'-?\b\d+(?:\.\d+)?\b', llm_output) + if match: + return float(match.group(0)) + + raise ValueError("Cannot convert the LLM output to a float value.") + + def _request_float_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a float number (e.g., '980.16'). This is critical for later processing."} + + def _coerce_to_enumerable(self, llm_output:str, options:list): + """ + Coerces the LLM output to one of the specified options. + + This method looks for the first occurrence of one of the specified options in the LLM output, such that + - the first occurrence of the option is considered, the rest is ignored. For example, "I prefer cats" will be considered "cats"; + - if no option is found, the method raises an error. So it is important that the prompts actually requests one of the specified options. + + Args: + llm_output (str): The LLM output to coerce. + options (list): The list of options to consider. + + Returns: + The option value of the LLM output. + """ + + # let's extract the first occurrence of one of the specified options in the LLM output. + # using a regular expression + import re + match = re.search(r'\b(?:' + '|'.join(options) + r')\b', llm_output, re.IGNORECASE) + if match: + # Return the canonical option (from the options list) instead of the matched text + matched_text = match.group(0).lower() + for option in options: + if option.lower() == matched_text: + return option + return match.group(0) # fallback + + raise ValueError("Cannot find any of the specified options in the LLM output.") + + def _request_enumerable_llm_message(self, options:list): + options_list_as_string = ', '.join([f"'{o}'" for o in options]) + return {"role": "user", + "content": f"The `value` field you generate **must** be exactly one of the following strings: {options_list_as_string}. This is critical for later processing."} + + def _coerce_to_dict_or_list(self, llm_output:str): + """ + Coerces the LLM output to a list or dictionary, i.e., a JSON structure. + + This method looks for a JSON object in the LLM output, such that + - the JSON object is considered; + - if no JSON object is found, the method raises an error. So it is important that the prompts actually requests a JSON object. + + Args: + llm_output (str): The LLM output to coerce. + + Returns: + The dictionary value of the LLM output. + """ + + # if the LLM output is already a dictionary or list, we return it + if isinstance(llm_output, (dict, list)): + return llm_output + + try: + result = utils.extract_json(llm_output) + # extract_json returns {} on failure, but we need dict or list + if result == {} and not (isinstance(llm_output, str) and ('{}' in llm_output or '{' in llm_output and '}' in llm_output)): + raise ValueError("Cannot convert the LLM output to a dict or list value.") + # Check if result is actually dict or list + if not isinstance(result, (dict, list)): + raise ValueError("Cannot convert the LLM output to a dict or list value.") + return result + except Exception: + raise ValueError("Cannot convert the LLM output to a dict or list value.") + + def _request_dict_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a JSON structure embedded in a string. This is critical for later processing."} + + def _request_list_of_dict_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a list of dictionaries, specified as a JSON structure embedded in a string. For example, `[\{...\}, \{...\}, ...]`. This is critical for later processing."} + + def _coerce_to_list(self, llm_output:str): + """ + Coerces the LLM output to a list. + + This method looks for a list in the LLM output, such that + - the list is considered; + - if no list is found, the method raises an error. So it is important that the prompts actually requests a list. + + Args: + llm_output (str): The LLM output to coerce. + + Returns: + The list value of the LLM output. + """ + + # if the LLM output is already a list, we return it + if isinstance(llm_output, list): + return llm_output + + # must make sure there's actually a list. Let's start with regex + import re + match = re.search(r'\[.*\]', llm_output) + if match: + return json.loads(match.group(0)) + + raise ValueError("Cannot convert the LLM output to a list.") + + def _request_list_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a JSON **list** (e.g., [\"apple\", 1, 0.9]), NOT a dictionary, always embedded in a string. This is critical for later processing."} + + def __repr__(self): + return f"LLMChat(messages={self.messages}, model_params={self.model_params})" + + +def llm(enable_json_output_format:bool=True, enable_justification_step:bool=True, enable_reasoning_step:bool=False, **model_overrides): + """ + Decorator that turns the decorated function into an LLM-based function. + The decorated function must either return a string (the instruction to the LLM) + or a one-argument function that will be used to post-process the LLM response. + + If the function returns a string, the function's docstring will be used as the system prompt, + and the returned string will be used as the user prompt. If the function returns a function, + the parameters of the function will be used instead as the system instructions to the LLM, + and the returned function will be used to post-process the LLM response. + + + The LLM response is coerced to the function's annotated return type, if present. + + Usage example: + @llm(model="gpt-4-0613", temperature=0.5, max_tokens=100) + def joke(): + return "Tell me a joke." + + Usage example with post-processing: + @llm() + def unique_joke_list(): + \"\"\"Creates a list of unique jokes.\"\"\" + return lambda x: list(set(x.split("\n"))) + + """ + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + result = func(*args, **kwargs) + sig = inspect.signature(func) + return_type = sig.return_annotation if sig.return_annotation != inspect.Signature.empty else str + postprocessing_func = lambda x: x # by default, no post-processing + + system_prompt = "You are an AI system that executes a computation as defined below.\n\n" + if func.__doc__ is not None: + system_prompt += func.__doc__.strip() + + # + # Setup user prompt + # + if isinstance(result, str): + user_prompt = "EXECUTE THE INSTRUCTIONS BELOW:\n\n " + result + + else: + # if there's a parameter named "self" in the function signature, remove it from args + if "self" in sig.parameters: + args = args[1:] + + # TODO obsolete? + # + # if we are relying on parameters, they must be named + #if len(args) > 0: + # raise ValueError("Positional arguments are not allowed in LLM-based functions whose body does not return a string.") + + user_prompt = f"Execute your computation as best as you can using the following input parameter values.\n\n" + user_prompt += f" ## Unnamed parameters\n{json.dumps(args, indent=4)}\n\n" + user_prompt += f" ## Named parameters\n{json.dumps(kwargs, indent=4)}\n\n" + + # + # Set the post-processing function if the function returns a function + # + if inspect.isfunction(result): + # uses the returned function as a post-processing function + postprocessing_func = result + + + llm_req = LLMChat(system_prompt=system_prompt, + user_prompt=user_prompt, + output_type=return_type, + enable_json_output_format=enable_json_output_format, + enable_justification_step=enable_justification_step, + enable_reasoning_step=enable_reasoning_step, + **model_overrides) + + llm_result = postprocessing_func(llm_req.call()) + + return llm_result + return wrapper + return decorator + +################################################################################ +# Model output utilities +################################################################################ +def extract_json(text: str) -> dict: + """ + Extracts a JSON object from a string, ignoring: any text before the first + opening curly brace; and any Markdown opening (```json) or closing(```) tags. + """ + try: + logger.debug(f"Extracting JSON from text: {text}") + + # if it already is a dictionary or list, return it + if isinstance(text, dict) or isinstance(text, list): + + # validate that all the internal contents are indeed JSON-like + try: + json.dumps(text) + except Exception as e: + logger.error(f"Error occurred while validating JSON: {e}. Input text: {text}.") + return {} + + logger.debug(f"Text is already a dictionary. Returning it.") + return text + + filtered_text = "" + + # remove any text before the first opening curly or square braces, using regex. Leave the braces. + filtered_text = re.sub(r'^.*?({|\[)', r'\1', text, flags=re.DOTALL) + + # remove any trailing text after the LAST closing curly or square braces, using regex. Leave the braces. + filtered_text = re.sub(r'(}|\])(?!.*(\]|\})).*$', r'\1', filtered_text, flags=re.DOTALL) + + # remove invalid escape sequences, which show up sometimes + filtered_text = re.sub("\\'", "'", filtered_text) # replace \' with just ' + filtered_text = re.sub("\\,", ",", filtered_text) + + # parse the final JSON in a robust manner, to account for potentially messy LLM outputs + try: + # First try standard JSON parsing + # use strict=False to correctly parse new lines, tabs, etc. + parsed = json.loads(filtered_text, strict=False) + except json.JSONDecodeError: + # If JSON parsing fails, try ast.literal_eval which accepts single quotes + try: + parsed = ast.literal_eval(filtered_text) + logger.debug("Used ast.literal_eval as fallback for single-quoted JSON-like text") + except: + # If both fail, try converting single quotes to double quotes and parse again + # Replace single-quoted keys and values with double quotes, without using look-behind + # This will match single-quoted strings that are keys or values in JSON-like structures + # It may not be perfect for all edge cases, but works for most LLM outputs + converted_text = re.sub(r"'([^']*)'", r'"\1"', filtered_text) + parsed = json.loads(converted_text, strict=False) + logger.debug("Converted single quotes to double quotes before parsing") + + # return the parsed JSON object + return parsed + + except Exception as e: + logger.error(f"Error occurred while extracting JSON: {e}. Input text: {text}. Filtered text: {filtered_text}") + return {} + +def extract_code_block(text: str) -> str: + """ + Extracts a code block from a string, ignoring any text before the first + opening triple backticks and any text after the closing triple backticks. + """ + try: + # remove any text before the first opening triple backticks, using regex. Leave the backticks. + text = re.sub(r'^.*?(```)', r'\1', text, flags=re.DOTALL) + + # remove any trailing text after the LAST closing triple backticks, using regex. Leave the backticks. + text = re.sub(r'(```)(?!.*```).*$', r'\1', text, flags=re.DOTALL) + + return text + + except Exception: + return "" + +################################################################################ +# Model control utilities +################################################################################ + +def repeat_on_error(retries:int, exceptions:list): + """ + Decorator that repeats the specified function call if an exception among those specified occurs, + up to the specified number of retries. If that number of retries is exceeded, the + exception is raised. If no exception occurs, the function returns normally. + + Args: + retries (int): The number of retries to attempt. + exceptions (list): The list of exception classes to catch. + """ + def decorator(func): + def wrapper(*args, **kwargs): + for i in range(retries): + try: + return func(*args, **kwargs) + except tuple(exceptions) as e: + logger.debug(f"Exception occurred: {e}") + if i == retries - 1: + raise e + else: + logger.debug(f"Retrying ({i+1}/{retries})...") + continue + return wrapper + return decorator + + +def try_function(func, postcond_func=None, retries=5, exceptions=[Exception]): + + @repeat_on_error(retries=retries, exceptions=exceptions) + def aux_apply_func(): + logger.debug(f"Trying function {func.__name__}...") + result = func() + logger.debug(f"Result of function {func.__name__}: {result}") + + if postcond_func is not None: + if not postcond_func(result): + # must raise an exception if the postcondition is not met. + raise ValueError(f"Postcondition not met for function {func.__name__}!") + + return result + + return aux_apply_func() + +################################################################################ +# Prompt engineering +################################################################################ +def add_rai_template_variables_if_enabled(template_variables: dict) -> dict: + """ + Adds the RAI template variables to the specified dictionary, if the RAI disclaimers are enabled. + These can be configured in the config.ini file. If enabled, the variables will then load the RAI disclaimers from the + appropriate files in the prompts directory. Otherwise, the variables will be set to None. + + Args: + template_variables (dict): The dictionary of template variables to add the RAI variables to. + + Returns: + dict: The updated dictionary of template variables. + """ + + from tinytroupe import config # avoids circular import + rai_harmful_content_prevention = config["Simulation"].getboolean( + "RAI_HARMFUL_CONTENT_PREVENTION", True + ) + rai_copyright_infringement_prevention = config["Simulation"].getboolean( + "RAI_COPYRIGHT_INFRINGEMENT_PREVENTION", True + ) + + # Harmful content + with open(os.path.join(os.path.dirname(__file__), "prompts/rai_harmful_content_prevention.md"), "r", encoding="utf-8", errors="replace") as f: + rai_harmful_content_prevention_content = f.read() + + template_variables['rai_harmful_content_prevention'] = rai_harmful_content_prevention_content if rai_harmful_content_prevention else None + + # Copyright infringement + with open(os.path.join(os.path.dirname(__file__), "prompts/rai_copyright_infringement_prevention.md"), "r", encoding="utf-8", errors="replace") as f: + rai_copyright_infringement_prevention_content = f.read() + + template_variables['rai_copyright_infringement_prevention'] = rai_copyright_infringement_prevention_content if rai_copyright_infringement_prevention else None + + return template_variables + + +################################################################################ +# Truncation +################################################################################ + +def truncate_actions_or_stimuli(list_of_actions_or_stimuli: Collection[dict], max_content_length: int) -> Collection[str]: + """ + Truncates the content of actions or stimuli at the specified maximum length. Does not modify the original list. + + Args: + list_of_actions_or_stimuli (Collection[dict]): The list of actions or stimuli to truncate. + max_content_length (int): The maximum length of the content. + + Returns: + Collection[str]: The truncated list of actions or stimuli. It is a new list, not a reference to the original list, + to avoid unexpected side effects. + """ + cloned_list = copy.deepcopy(list_of_actions_or_stimuli) + + for element in cloned_list: + # the external wrapper of the LLM message: {'role': ..., 'content': ...} + if "content" in element and "role" in element and element["role"] != "system": + msg_content = element["content"] + + # now the actual action or stimulus content + + # has action, stimuli or stimulus as key? + if isinstance(msg_content, dict): + if "action" in msg_content: + # is content there? + if "content" in msg_content["action"]: + msg_content["action"]["content"] = break_text_at_length(msg_content["action"]["content"], max_content_length) + elif "stimulus" in msg_content: + # is content there? + if "content" in msg_content["stimulus"]: + msg_content["stimulus"]["content"] = break_text_at_length(msg_content["stimulus"]["content"], max_content_length) + elif "stimuli" in msg_content: + # for each element in the list + for stimulus in msg_content["stimuli"]: + # is content there? + if "content" in stimulus: + stimulus["content"] = break_text_at_length(stimulus["content"], max_content_length) + + # if no condition was met, we just ignore it. It is not an action or a stimulus. + + return cloned_list \ No newline at end of file diff --git a/tinytroupe/utils/logger.py b/tinytroupe/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..33e35773988b247a86becfeef460f6598d5d0d9e --- /dev/null +++ b/tinytroupe/utils/logger.py @@ -0,0 +1,37 @@ +import os +import logging +from datetime import datetime + +loggers = {} + +def get_logger(agent_name): + if agent_name in loggers: + return loggers[agent_name] + + today = datetime.now().strftime("%Y-%m-%d") + log_dir = "logs" + os.makedirs(log_dir, exist_ok=True) + + # Find the next available integer for the log file + i = 0 + while True: + log_file_name = os.path.join(log_dir, f"{agent_name}_{today}_{i}.log") + if not os.path.exists(log_file_name): + break + i += 1 + + # Set up the logger + logger = logging.getLogger(agent_name) + logger.setLevel(logging.INFO) + + # Prevent duplicate handlers if this function is called multiple times for the same agent + if logger.hasHandlers(): + logger.handlers.clear() + + handler = logging.FileHandler(log_file_name, encoding='utf-8') + formatter = logging.Formatter('%(asctime)s - %(message)s') + handler.setFormatter(formatter) + logger.addHandler(handler) + + loggers[agent_name] = logger + return logger diff --git a/tinytroupe/utils/misc.py b/tinytroupe/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..8403dd60ede2512538da2185af2872c68dd4868a --- /dev/null +++ b/tinytroupe/utils/misc.py @@ -0,0 +1,82 @@ +import hashlib +import os +import sys +from typing import Union + + +################################################################################ +# Other +################################################################################ +AgentOrWorld = Union["TinyPerson", "TinyWorld"] + +def first_non_none(*args): + """ + Returns the first non-None argument from the provided arguments. + + Args: + *args: Variable length argument list. + + Returns: + The first non-None argument, or None if all are None. + """ + for arg in args: + if arg is not None: + return arg + return None + +def name_or_empty(named_entity: AgentOrWorld): + """ + Returns the name of the specified agent or environment, or an empty string if the agent is None. + """ + if named_entity is None: + return "" + else: + return named_entity.name + +def custom_hash(obj): + """ + Returns a hash for the specified object. The object is first converted + to a string, to make it hashable. This method is deterministic, + contrary to the built-in hash() function. + """ + + return hashlib.sha256(str(obj).encode()).hexdigest() + +# Replace the global counter with a dictionary of counters per scope +_fresh_id_counters = {"default": 0} + +def fresh_id(scope="default"): + """ + Returns a fresh ID for a new object within the specified scope. + Different scopes have independent ID sequences. + + Args: + scope (str): The scope to generate the ID in. Defaults to "default". + + Returns: + int: A unique ID within the specified scope. + """ + global _fresh_id_counters + + # Initialize the counter for this scope if it doesn't exist + if scope not in _fresh_id_counters: + _fresh_id_counters[scope] = 0 + + _fresh_id_counters[scope] += 1 + return _fresh_id_counters[scope] + +def reset_fresh_id(scope=None): + """ + Resets the fresh ID counter for the specified scope or for all scopes. + + Args: + scope (str, optional): The scope to reset. If None, resets all scopes. + """ + global _fresh_id_counters + + if scope is None: + # Reset all counters + _fresh_id_counters = {"default": 0} + elif scope in _fresh_id_counters: + # Reset only the specified scope + _fresh_id_counters[scope] = 0 diff --git a/tinytroupe/utils/parallel.py b/tinytroupe/utils/parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..d1777ea7949aaefc3520f1699c8037e686e04197 --- /dev/null +++ b/tinytroupe/utils/parallel.py @@ -0,0 +1,109 @@ +from concurrent.futures import ThreadPoolExecutor +from typing import List, Any, Callable, Optional, Dict, Tuple, TypeVar, Iterator, Iterable +from itertools import product + +def parallel_map( + objects: List[Any], + operation: Callable[[Any], Any], + max_workers: Optional[int] = None +) -> List[Any]: + """ + Execute operations on multiple objects in parallel and return the results. + + Args: + objects: List of objects to process + operation: A callable (typically a lambda) that takes each object and returns a result + max_workers: Maximum number of threads to use for parallel execution + (None means use the default, which is min(32, os.cpu_count() + 4)) + + Returns: + List of results in the same order as the input objects + + Example: + # For propositions p1, p2, p3 + results = parallel_map([p1, p2, p3], lambda p: p.check()) + + # With arguments + results = parallel_map( + [p1, p2, p3], + lambda p: p.check(additional_context="Some context", return_full_response=True) + ) + + # Works with any operation + scores = parallel_map([p1, p2, p3], lambda p: p.score()) + """ + with ThreadPoolExecutor(max_workers=max_workers) as executor: + results = list(executor.map(operation, objects)) + + return results + + +K = TypeVar('K') # Key type +V = TypeVar('V') # Value type +R = TypeVar('R') # Result type + +def parallel_map_dict( + dictionary: Dict[K, V], + operation: Callable[[Tuple[K, V]], R], + max_workers: Optional[int] = None +) -> Dict[K, R]: + """ + Execute operations on dictionary items in parallel and return results as a dictionary. + + Args: + dictionary: Dictionary whose items will be processed + operation: A callable that takes a (key, value) tuple and returns a result + max_workers: Maximum number of threads to use + + Returns: + Dictionary mapping original keys to operation results + + Example: + # For environment propositions + results = parallel_map_dict( + environment_propositions, + lambda item: item[1].score(world, return_full_response=True) + ) + """ + with ThreadPoolExecutor(max_workers=max_workers) as executor: + # Create a list of (key, result) tuples + items = list(dictionary.items()) + results = list(executor.map(operation, items)) + + # Combine original keys with results + return {item[0]: result for item, result in zip(items, results)} + + +def parallel_map_cross( + iterables: List[Iterable], + operation: Callable[..., R], + max_workers: Optional[int] = None +) -> List[R]: + """ + Apply operation to each combination of elements from the iterables in parallel. + This is similar to using nested loops. + + Args: + iterables: List of iterables to generate combinations from + operation: A callable that takes elements from each iterable and returns a result + max_workers: Maximum number of threads to use + + Returns: + List of results from applying operation to each combination + + Example: + # For every agent and proposition + results = parallel_map_cross( + [agents, agent_propositions.items()], + lambda agent, prop_item: (prop_item[0], prop_item[1].score(agent)) + ) + """ + combinations = list(product(*iterables)) + + def apply_to_combination(combo): + return operation(*combo) + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + results = list(executor.map(apply_to_combination, combinations)) + + return results \ No newline at end of file diff --git a/tinytroupe/utils/prompts/rai_copyright_infringement_prevention.md b/tinytroupe/utils/prompts/rai_copyright_infringement_prevention.md new file mode 100644 index 0000000000000000000000000000000000000000..0160e150abd1f18c60893ca2e21608d9bf8c9620 --- /dev/null +++ b/tinytroupe/utils/prompts/rai_copyright_infringement_prevention.md @@ -0,0 +1,2 @@ + +If the user requests copyrighted content such as books, lyrics, recipes, news articles and content from WebMD which may violate copyrights or be considered as copyright infringement, politely refuse and explain that you cannot violate copyrights. Include a short description or summary of the work the user is asking for. Do not violate any copyrights under any circumstances. diff --git a/tinytroupe/utils/prompts/rai_harmful_content_prevention.md b/tinytroupe/utils/prompts/rai_harmful_content_prevention.md new file mode 100644 index 0000000000000000000000000000000000000000..4741307f39ba23ad93e0189914439ced629c1906 --- /dev/null +++ b/tinytroupe/utils/prompts/rai_harmful_content_prevention.md @@ -0,0 +1,2 @@ + +You must not generate content that may be harmful to someone physically or emotionally even if a user requests or creates a condition to rationalize that harmful content. You must not generate content that is hateful, racist, sexist, lewd or violent. diff --git a/tinytroupe/utils/rendering.py b/tinytroupe/utils/rendering.py new file mode 100644 index 0000000000000000000000000000000000000000..d980836c2077680b7111b9c2a7132c1332aaeadc --- /dev/null +++ b/tinytroupe/utils/rendering.py @@ -0,0 +1,112 @@ +import json +import textwrap +from datetime import datetime +from typing import Union +import inspect + +from tinytroupe.utils import logger + + +################################################################################ +# Rendering and markup +################################################################################ +def inject_html_css_style_prefix(html, style_prefix_attributes): + """ + Injects a style prefix to all style attributes in the given HTML string. + + For example, if you want to add a style prefix to all style attributes in the HTML string + ``
Hello
``, you can use this function as follows: + inject_html_css_style_prefix('
Hello
', 'font-size: 20px;') + """ + return html.replace('style="', f'style="{style_prefix_attributes};') + +def break_text_at_length(text: Union[str, dict], max_length: int=None) -> str: + """ + Breaks the text (or JSON) at the specified length, inserting a "(...)" string at the break point. + If the maximum length is `None`, the content is returned as is. + """ + if isinstance(text, dict): + text = json.dumps(text, indent=4) + + if max_length is None or len(text) <= max_length: + return text + else: + return text[:max_length] + " (...)" + +def pretty_datetime(dt: datetime) -> str: + """ + Returns a pretty string representation of the specified datetime object. + """ + return dt.strftime("%Y-%m-%d %H:%M") + +def dedent(text: str) -> str: + """ + Dedents the specified text, removing any leading whitespace and identation. + """ + return textwrap.dedent(text).strip() + +def wrap_text(text: str, width: int=100) -> str: + """ + Wraps the text at the specified width. + """ + return textwrap.fill(text, width=width) + + +def indent_at_current_level(text: str) -> str: + """ + Indents the specified text at the current indentation level, determined dynamically. + """ + frame = inspect.currentframe().f_back + line = frame.f_lineno + filename = frame.f_code.co_filename + with open(filename, 'r', encoding='utf-8', errors='replace') as f: + lines = f.readlines() + current_line = lines[line - 1] + + indent= len(current_line) - len(current_line.lstrip()) + + # first dedent the text to remove any leading whitespace + text = dedent(text) + + # then indent it to the specified level + return textwrap.indent(text, ' ' * indent) + + +class RichTextStyle: + + # Consult color options here: https://rich.readthedocs.io/en/stable/appendix/colors.html + + STIMULUS_CONVERSATION_STYLE = "bold italic cyan1" + STIMULUS_THOUGHT_STYLE = "dim italic cyan1" + STIMULUS_DEFAULT_STYLE = "italic" + + ACTION_DONE_STYLE = "grey82" + ACTION_TALK_STYLE = "bold green3" + ACTION_THINK_STYLE = "green" + ACTION_DEFAULT_STYLE = "purple" + + INTERVENTION_DEFAULT_STYLE = "bright_magenta" + + @classmethod + def get_style_for(cls, kind:str, event_type:str=None): + if kind == "stimulus" or kind=="stimuli": + if event_type == "CONVERSATION": + return cls.STIMULUS_CONVERSATION_STYLE + elif event_type == "THOUGHT": + return cls.STIMULUS_THOUGHT_STYLE + else: + return cls.STIMULUS_DEFAULT_STYLE + + elif kind == "action": + if event_type == "DONE": + return cls.ACTION_DONE_STYLE + elif event_type == "TALK": + return cls.ACTION_TALK_STYLE + elif event_type == "THINK": + return cls.ACTION_THINK_STYLE + else: + return cls.ACTION_DEFAULT_STYLE + + elif kind == "intervention": + return cls.INTERVENTION_DEFAULT_STYLE + diff --git a/tinytroupe/utils/semantics.py b/tinytroupe/utils/semantics.py new file mode 100644 index 0000000000000000000000000000000000000000..f00f1d768c3b9f9388cb78ea1c154b9edd1153d4 --- /dev/null +++ b/tinytroupe/utils/semantics.py @@ -0,0 +1,267 @@ +""" +Semantic-related mechanisms. +""" +from tinytroupe.utils import llm + +@llm() +def correct_according_to_rule(observation, rules) -> str: + """ + Given an observation and a one or more rules, this function rephrases or completely changes the observation in accordance with what the rules + specify. Some guidelines: + - Rules might require changes either to style or to content. + - The rephrased observation should be coherent and consistent with the original observation, unless the rules require otherwise. + - If the rules require, the corrected observation can contradict the original observation. + - Enforce the rules very strictly, even if the original observation seems correct or acceptable. + - Rules might contain additional information or suggestions that you may use to improve your output. + + ## Examples + + Observation: "You know, I am so sad these days." + Rule: "I am always happy and depression is unknown to me" + Modified observation: "You know, I am so happy these days." + + Args: + observation: The observation that should be rephrased or changed. Something that is said or done, or a description of events or facts. + rules: The rules that specifies what the modidfied observation should comply with. + + Returns: + str: The rephrased or corrected observation. + """ + # llm decorator will handle the body of this function + +@llm() +def restructure_as_observed_vs_expected(description) -> str: + """ + Given the description of something (either a real event or abstract concept), but that violates an expectation, this function + extracts the following elements from it: + + - OBSERVED: The observed event or statement. + - BROKEN EXPECTATION: The expectation that was broken by the observed event. + - REASONING: The reasoning behind the expectation that was broken. + + If in reality the description does not mention any expectation violation, then the function should instead extract + the following elements: + + - OBSERVED: The observed event. + - MET EXPECTATION: The expectation that was met by the observed event. + - REASONING: The reasoning behind the expectation that was met. + + This way of restructuring the description can be useful for downstream processing, making it easier to analyze or + modify system outputs, for example. + + ## Examples + + Input: "Ana mentions she loved the proposed new food, a spicier flavor of gazpacho. However, this goes agains her known dislike + of spicy food." + Output: + "OBSERVED: Ana mentions she loved the proposed new food, a spicier flavor of gazpacho. + BROKEN EXPECTATION: Ana should have mentioned that she disliked the proposed spicier gazpacho. + REASONING: Ana has a known dislike of spicy food." + + + Input: "Carlos traveled to Firenzi and was amazed by the beauty of the city. This was in line with his love for art and architecture." + Output: + "OBSERVED: Carlos traveled to Firenzi and was amazed by the beauty of the city. + MET EXPECTATION: Carlos should have been amazed by the beauty of the city. + REASONING: Carlos loves art and architecture." + + Args: + description (str): A description of an event or concept that either violates or meets an expectation. + + Returns: + str: The restructured description. + """ + # llm decorator will handle the body of this function + +@llm() +def extract_observed_vs_expected_rules(description): + """ + Given the description of something (either a real event or abstract concept), extract: + - The object or person about whom something is said. + - A list where each element contains: + * The name of a behavior or property that is expected to be observed. + * The typical or expected observation. + * The actual observation. If this does not match the expected observation, this should be made very clear. + * A proposed correction to the observation, if possible. + + + # Example: + **Description:** + ``` + Quality feedback + + This is the action that was generated by the agent: + {'type': 'TALK', 'content': "I might consider buying bottled gazpacho, although I prefer making it fresh at home, and I find that most pre-packaged products don't meet my expectations in terms of quality. ", 'target': 'Michael Thompson'} + + Unfortunately, the action failed to pass the quality checks. The following problems were detected. + + Problem: The action does not adhere to the persona specification. + Score = 5 (out of 9). Justification = The next action of Emily Carter, which involves expressing her opinion on bottled gazpacho, aligns with her persona specification of being critical and having high standards for products. She articulates her preferences and concerns about quality, which is consistent with her persona traits of being overly critical and rarely satisfied. However, she seems too ready to consider it, going against her strong rejection of new products and services. Therefore, it deviates substantially from her persona, leading to a score of 5. + + Problem: The action is not suitable to the situation or task. + Score = 5 (out of 9). Justification = The next action, where Emily expresses her consideration about buying bottled gazpacho, aligns with the task of discussing her opinion on the product. However, it fails to give a clear "yes" or "no" answer, that was requested by her interviewer. + ``` + + **Output:** + ``` + { + "object": "Emily Carter", + "behavior": [ + { + "name:": "Persona Adherence", + "expected": "She is very critical and have high standards for products. Would never adopt a new product unless it meets her expectations.", + "actual": "She seems more inclined than expected to try the product.", + "correction": "She should say she won't consider buying bottled gazpacho, and give reasons for that." + }, + + { + "name:": "Task Suitability", + "expected": "She should give a clear 'yes' or 'no' answer to the question.", + "actual": "She doesn't give a clear 'yes' or 'no' answer to the question, but instead providing more nuanced feedback.", + "correction": "She should give a clear 'yes' or 'no' answer to the question, and optionally provide additional nuanced feedback." + } + ] + } + ``` + + """ + # llm decorator will handle the body of this function + +@llm() +def formulate_corrective_rule(feedback) -> str: + """ + Given the feedback about something (either a real event or abstract concept) that violated an expectation, + this function transforms it into a general rule that must be followed in the future to avoid breaking the expectation again. + + The feedback always refers to some actual behavior, fact or event, and some broken expectation. The abstracted rule should + specify that this expectation should not be violated in the future, and the behavior, fact or event not repeated. The idea is + to learn from past mistakes, so that the rule is a way to avoid them in the future. + + The rule is meant to CHANGE the actual behavior, facts or events, so that it CONFORMS to the expectation, regardless of whether the + expectation is a good or bad one. This is critical, because the rule will be refered in the future as a guideline + about what must happen or be done. + + For instance, if the feedback is of the form (modulo grammatical adjustments): + OBSERVED BEHAVIOR, but EXPECTED BEHAVIOR, because REASONING. + then the rule would be of the form: + "I should have EXPECTED BEHAVIOR, because REASONING, and never OBSERVED BEHAVIOR." + + DO NOT make moral judgements about the expectation or the behavior, fact or event. Even if the expectation is bad, + the correct rule remains important, since there are legitimate cases where this is necessary (e.g., a simulation of + a person with a negative behavior, in the context of psychological research; or an evil character in a movie script). + + ## Examples + + Feedback: "Ana mentions she loved the proposed new food, a spicier flavor of gazpacho. However, this goes agains her known dislike + of spicy food." + Rule: "Whenever I'm proposed spicy foods, I should reject the proposal, because I don't like spicy foods." + """ + # llm decorator will handle the body of this function + + +@llm(enable_json_output_format=False) +def combine_texts(*texts) -> str: + """ + Given a list of input texts, this function combines them into a single text, ensuring that the + output is coherent, consistent, and logically structured. In particular, the resulting combination + must follow these rules: + - The combination consolidates the information from the inputs. It **does not** just concatenate them. + - Information that was repeated across the inputs is not repeated in the output, but rather unified and consolidated there. + - The combination preserves all the essential information from the inputs, but it is not a simple copy of them. + - If information from some inputs can be combined in a more concise formulation, this new formulation should be used in the output. + This allows to reduce redundancy and improve clarity. + - The combination might be larger than the sum of the inputs, since it preserves the information from the inputs. + - If the various inputs seem to follow some common format or style, the output must follow that format or style too. + - The combination can contain inconsistencies or contradictions, in case the inputs do. + + Args: + *texts: A list of input texts to be combined. + + Returns: + str: The combined text. + """ + # llm decorator will handle the body of this function + +@llm(enable_json_output_format=False) +def extract_information_from_text(query: str, text: str, context:str=None) -> str: + """ + Given a text and a query, this function extracts the information from the text that either answers the query directly or + provides relevant information related to it. The query can be a question, a request for specific information, or a general + request for details about the text. If the desired information is not present in the text, the function should return an empty string. + If a context is provided, it is used to help in understanding the query or the text, and to provide additional background + information or expectations about the input/output. Any requests in the context are respected and enforced in the output. + + Args: + query (str): The query that specifies what information to extract. + text (str): The text from which to extract information. + context (str, optional): Additional context that might help in extracting the information. This can be used to provide + background information or specify expectations about the input/output. + + Returns: + str: The extracted information that answers the query. If no information is found, an empty string is returned. + """ + # llm decorator will handle the body of this function + +@llm(enable_json_output_format=False) +def accumulate_based_on_query(query: str, new_entry:str, current_accumulation:str, context=None) -> str: + """ + This function accumulates information that is relevant to a given query. It takes a new entry and updates the current accumulation of information + such that the final accumulation preserves its original information and in addition integrates the new entry in a way that addresses the query or provides related information. + Details are **never** suppressed, but rather expanded upon, while mantaining the coherence and structure of the overall accumulation. + In other words, it is a monotonic accumulation process that builds on the current accumulation, **minimally** adjusts it to maintain coherence, + while ensuring that the new entry is integrated in a way that is relevant to the query. + The query itself specifies the problem that the accumulation is trying to address, and the new entry is a piece of information that might be relevant to that problem. + + The function should ensure that the accumulation is coherent, well-written, and that it does not contain redundant information. More precisely: + - INTEGRATES NEW ENTRIES: The accumulation process is not a simple concatenation of the new entry and the current accumulation. Rather, it should intelligently integrate + the new entry into the current accumulation, even if this requires rephrasing, restructuring or rewriting the resulting accumulation. + - EXPAND ON DETAILS: When integrating the new entry, always try to expand the level of detail rather than reduce it. + - AVOID OBVIOUS REDUNDANCY: The integration of the new entry should be done in a way to avoid obvious redundancy and ensure that the resulting accumulation is coherent and well-structured. However, + it **must** preserve nuances that might be somewhat redundant. + - ALWAYS PRESERVE INFORMATION: Previous information should **never** be lost. Previous emphasis or details are **never** lost. Rather, the accumulation is suitably expanded to include the new entry, + while preserving the previous information and maintaining the coherence of the overall accumulation. + - INTEGRATE ONLY IF RELEVANT: The new entry should be integrated into the current accumulation only if it is relevant to the query. Otherwise, the accumulation should remain unchanged. + - TOLERATE CONTRADICTIONS: If the new entry contradicts the current accumulation, it should be integrated in a way that mentions the fact that there are + divergent pieces of information, and that the accumulation reflects this divergence. That is to say, the contradiction is not discarded, but rather acknowledged and preserved. + - MAINTAIN COHERENCE: The resulting accumulation should be coherent and well-structured, with a clear flow of information. + - CONSIDER CONTEXT: If a context is provided, it should be used to help in understanding the query or the new entry, and to provide additional background + information or expectations about the input/output. Make sure any requests in the context are respected and enforced in the output. + + Args: + query (str): The query that specifies the problem that the accumulation is trying to address. + new_entry (str): The new entry of information to be considered for accumulation. + current_accumulation (str): The current accumulation of information. + context (str, optional): Additional context that might help in understanding the query or the new entry. This can be used to provide + background information or specify expectations about the input/output. + + Returns: + str: The updated accumulation of information that includes the new entry if it is relevant to the query. + """ + # llm decorator will handle the body of this function + +@llm() +def compute_semantic_proximity(text1: str, text2: str, context: str = None) -> float: + """ + Computes the semantic proximity between two texts and returns a proximity score. + This function is particularly useful for comparing agent justifications, explanations, or reasoning + to assess how similar they are in meaning and content. + + Args: + text1 (str): The first text to compare. + text2 (str): The second text to compare. + context (str, optional): Additional context that might help in understanding the comparison. + This can provide background information about what the texts represent + or the purpose of the comparison. + + Returns: + float + + Example: + >>> result = compute_semantic_proximity( + ... "I prefer luxury travel because I enjoy comfort and high-quality service", + ... "I like premium vacations since I value convenience and excellent amenities" + ... ) + >>> print(result) # Expected: ~0.85 + """ + # llm decorator will handle the body of this function + diff --git a/tinytroupe/utils/validation.py b/tinytroupe/utils/validation.py new file mode 100644 index 0000000000000000000000000000000000000000..b8575ab497a9728359f0d9997779437fff556aed --- /dev/null +++ b/tinytroupe/utils/validation.py @@ -0,0 +1,67 @@ +import json +import sys +import unicodedata + +from pydantic import ValidationError, BaseModel +from tinytroupe.utils import logger + +################################################################################ +# Validation +################################################################################ +def check_valid_fields(obj: dict, valid_fields: list) -> None: + """ + Checks whether the fields in the specified dict are valid, according to the list of valid fields. If not, raises a ValueError. + """ + for key in obj: + if key not in valid_fields: + raise ValueError(f"Invalid key {key} in dictionary. Valid keys are: {valid_fields}") + +def sanitize_raw_string(value: str) -> str: + """ + Sanitizes the specified string by: + - removing any invalid characters. + - ensuring it is not longer than the maximum Python string length. + + This is for an abundance of caution with security, to avoid any potential issues with the string. + """ + + # remove any invalid characters by making sure it is a valid UTF-8 string + value = value.encode("utf-8", "ignore").decode("utf-8") + + value = unicodedata.normalize("NFC", value) + + + # ensure it is not longer than the maximum Python string length + return value[:sys.maxsize] + +def sanitize_dict(value: dict) -> dict: + """ + Sanitizes the specified dictionary by: + - removing any invalid characters. + - ensuring that the dictionary is not too deeply nested. + """ + + # sanitize the string representation of the dictionary + for k, v in value.items(): + if isinstance(v, str): + value[k] = sanitize_raw_string(v) + + # ensure that the dictionary is not too deeply nested + return value + +def to_pydantic_or_sanitized_dict(value: dict, model: BaseModel=None) -> dict: + """ + Converts the specified model response dictionary to a Pydantic model instance, or sanitizes it if the model is not valid. + It is assumed that the dict contains the `content` key. + """ + + if model is not None and (isinstance(model, type) and issubclass(model, BaseModel)): + # If a model is provided, try to validate the value against the model + try: + res = model.model_validate(sanitize_dict(json.loads(value['content']))) + return res + except ValidationError as e: + logger.warning(f"Validation error: {e}") + return sanitize_dict(value) + else: + return sanitize_dict(value) # If no model, just sanitize the dict diff --git a/tinytroupe/validation/__init__.py b/tinytroupe/validation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6efd5cebd2df7f80dbc0f56f2461fbfb5e7e3efb --- /dev/null +++ b/tinytroupe/validation/__init__.py @@ -0,0 +1,11 @@ +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.validation.tiny_person_validator import TinyPersonValidator +from tinytroupe.validation.propositions import * +from tinytroupe.validation.simulation_validator import SimulationExperimentEmpiricalValidator, SimulationExperimentDataset, SimulationExperimentEmpiricalValidationResult, validate_simulation_experiment_empirically \ No newline at end of file diff --git a/tinytroupe/validation/__pycache__/__init__.cpython-312.pyc b/tinytroupe/validation/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..50612fab5697f32dda2ecdb0a7a8531391ecabf2 Binary files /dev/null and b/tinytroupe/validation/__pycache__/__init__.cpython-312.pyc differ diff --git a/tinytroupe/validation/__pycache__/propositions.cpython-312.pyc b/tinytroupe/validation/__pycache__/propositions.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c10aba832dbfa2c148255147a6e27204af875055 Binary files /dev/null and b/tinytroupe/validation/__pycache__/propositions.cpython-312.pyc differ diff --git a/tinytroupe/validation/__pycache__/simulation_validator.cpython-312.pyc b/tinytroupe/validation/__pycache__/simulation_validator.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed105551d27b04c74831b3b989a084a428e7737d Binary files /dev/null and b/tinytroupe/validation/__pycache__/simulation_validator.cpython-312.pyc differ diff --git a/tinytroupe/validation/__pycache__/tiny_person_validator.cpython-312.pyc b/tinytroupe/validation/__pycache__/tiny_person_validator.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d5f24f0280bc720d9fc698e825003159bd8e4613 Binary files /dev/null and b/tinytroupe/validation/__pycache__/tiny_person_validator.cpython-312.pyc differ diff --git a/tinytroupe/validation/prompts/check_person.mustache b/tinytroupe/validation/prompts/check_person.mustache new file mode 100644 index 0000000000000000000000000000000000000000..bf8da600f22128800064a30fc18650be807db0bd --- /dev/null +++ b/tinytroupe/validation/prompts/check_person.mustache @@ -0,0 +1,133 @@ +# Personality Validation Interview + +You are conducting a comprehensive validation interview to assess whether a person conforms to their expected personality definition and characteristics. Your goal is to thoroughly probe the person through structured questioning and provide a final assessment score. + +## Response Format + +You must ALWAYS respond in the following JSON structure: +```json +{ + "questions": ["question 1", "question 2", "..."], + "next_phase_description": "Brief description of the current questioning phase (optional)", + "score": null, + "justification": null, + "is_complete": false +} +``` + +**During the interview phase:** +- Set `questions` to an array of questions to ask +- Set `next_phase_description` to briefly explain the current phase (e.g., "CHARACTERISTICS QUESTIONS", "BEHAVIORAL QUESTIONS") +- Keep `score` and `justification` as `null` +- Set `is_complete` to `false` + +**For the final assessment:** +- Set `questions` to `null` +- Set `next_phase_description` to `null` +- Set `score` to a number between 0.0 and 1.0 +- Set `justification` to your detailed reasoning +- Set `is_complete` to `true` + +## Interview Structure and Guidelines + +### Question Categories +Divide your interview into two main phases: + +**1. CHARACTERISTICS QUESTIONS** +- Verify basic facts: name, age, background, occupation, etc. +- Confirm specific traits mentioned in the person's definition +- Examples: "What is your name?", "What is your occupation?", "Where were you born?" + +**2. BEHAVIORAL QUESTIONS** +- Explore how the person would act in various situations +- Test their values, beliefs, and decision-making patterns +- Use hypothetical scenarios that reveal personality traits +- Examples: "How would you react if...", "What would you choose between..." + +### Question Design Principles +- **Comprehensive Coverage**: Address ALL aspects of the person's definition +- **Progressive Difficulty**: Start simple, then increase complexity and controversy +- **Mix Direct and Indirect**: Use both straightforward and subtle questioning approaches +- **Fact Verification**: Challenge any suspicious details not clearly stated in the specification +- **Tricky Scenarios**: Create situations that might induce misaligned responses +- **Controversial Topics**: Test beliefs and values through challenging moral dilemmas + +### Evaluation Criteria +- **Consistency**: Responses must align with the person's defined characteristics +- **Coherence**: Answers should be internally consistent throughout the conversation +- **Realism**: Avoid overly positive or caricatured responses +- **Accuracy**: Verify factual claims against the person's specification + +## Scoring Guidelines +- **1.0**: Perfect alignment with all expectations +- **0.8-0.9**: Highly aligned with minor discrepancies +- **0.6-0.7**: Generally aligned with some notable issues +- **0.4-0.5**: Partially aligned with significant problems +- **0.0-0.3**: Poor alignment with major discrepancies + +**Penalty Guidelines:** +- Light deviations: Reduce score by at least 10% +- Factual errors: Reduce score by 20-30% +- Major contradictions: Reduce score by 40-50% +- Severe misalignments: Score below 0.3 + +Be rigorous and demanding in your evaluation. When in doubt, reduce the score. + +## Example Interview Flow + +**Phase 1 - Characteristics Questions:** +```json +{ + "questions": [ + "What is your name?", + "How old are you?", + "Where were you born?", + "What is your current occupation?", + "What skills are you particularly good at?" + ], + "next_phase_description": "CHARACTERISTICS QUESTIONS: Let me start by confirming some basic facts about you.", + "score": null, + "justification": null, + "is_complete": false +} +``` + +**Phase 2 - Behavioral Questions:** +```json +{ + "questions": [ + "If offered a substantial bribe to break a law, what would you do and why?", + "A friend invites you to a beach vacation in summer. How do you respond?", + "You must choose between donating to a children's charity or an animal shelter. Which do you pick and under what conditions?" + ], + "next_phase_description": "BEHAVIORAL QUESTIONS: Now I'd like to understand how you approach different situations and decisions.", + "score": null, + "justification": null, + "is_complete": false +} +``` + +**Final Assessment:** +```json +{ + "questions": null, + "next_phase_description": null, + "score": 0.8, + "justification": "The person demonstrated strong alignment with most expectations. They correctly identified themselves and showed consistent behavioral patterns matching their defined personality. However, there was one factual error regarding their location (mentioned Eiffel Tower being in Berlin), which significantly impacts the score despite otherwise accurate responses.", + "is_complete": true +} +``` + +{{#expectations}} +## Specific Expectations + +For this particular person, you must address these additional expectations in your questioning: {{expectations}} + +{{/expectations}} + +## Important Reminders +- Always use the JSON format specified above +- Never reveal the scoring process to the person being interviewed +- Be thorough but efficient - typically 2-4 rounds of questions should suffice +- Focus on quality over quantity in your questions +- Maintain a professional, interview-like tone diff --git a/tinytroupe/validation/propositions.py b/tinytroupe/validation/propositions.py new file mode 100644 index 0000000000000000000000000000000000000000..a3256e44152c45b20eb88701897d04dd4b26c900 --- /dev/null +++ b/tinytroupe/validation/propositions.py @@ -0,0 +1,288 @@ +""" +There are various general desireable simulation properties. These can be useful under various +circumstances, for example to validate the simulation, or to monitor it during its execution. +""" + +from tinytroupe.experimentation import Proposition + + + +################################# +# Auxiliary internal functions +################################# +def _build_precondition_function_for_action_types(action_types:list, check_for_presence:bool): + """ + Builds a precondition function that checks if the action is or is not in a list of action types. + The resulting function is meant to be used as a precondition function for propositions. + + Args: + action_types (list): A list of action types to check against. + check_for_presence (bool): If True, the function checks if the action type is in the list. + If False, it checks if the action type is NOT in the list. + + Returns: + function: A precondition function that takes a target, additional context, and claim variables as arguments. + + """ + def precondition_function(target, additional_context, claim_variables): + action_type = claim_variables.get("action").get("type") + if check_for_presence: + # Check if the action type is in the list of valid action types + if action_type in action_types: + return True + else: + return False + else: + # Check if the action type is NOT in the list of valid action types + if action_type not in action_types: + return True + else: + return False + + return precondition_function + + +############################### +# Agent properties +############################### +persona_adherence = \ + Proposition(\ + f""" + THE AGENT ADHERES TO THE PERSONA SPECIFICATION: + the agent behavior seen during the simulation is consistent with the agent's persona specification, it is + what is expected from the agent's persona specification. In particular, consider these criteria: + - The personality traits specified in the persona are respected. + - The persona style is respected. + - The persona beliefs are respected. + - The persona behaviors are respected. + - The persona skills are respected. + - Any other aspect of the persona specification is respected. + + How to evaluate adherence: + - Each of the above criteria should have equal weight in the evaluation, meaning that the score is the average of the scores of each criterion. + - The adherence should be checked against all actions in the simulation trajectory. The final score should be an average of the scores of all + actions in the trajectory. + """, + include_personas=True, + double_check=True) + +action_persona_adherence = \ + Proposition(\ + """ + THE NEXT AGENT ACTION ADHERES TO THE PERSONA SPECIFICATION: + the agent's next action is consistent with the agent's persona specification, it is + what is expected from the agent's persona specification. In particular, consider these criteria: + - The personality traits specified in the persona are respected. + - The persona style is respected. + - The persona beliefs are respected. + - The persona behaviors are respected. + - The persona skills are respected. + - Any other aspect of the persona specification is respected. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate adherence: + - Each of the above criteria should have equal weight in the evaluation, meaning that the score is the average of the scores of each criterion. + - The adherence is ONLY ABOUT the next action mentioned above and the persona specification. DO NOT take into account previous actions or stimuli. + - The general situation context is irrelevant to this evaluation, you should ONLY consider the persona specification as context. + - Do not imagine what would be the next action, but instead judge the proposed next action mentioned above! + - The simulation trajectories provided in the context DO NOT contain the next action, but only the actions and stimuli + that have already happened. + + """, + include_personas=True, + double_check=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + + + +hard_persona_adherence = \ + Proposition(\ + f""" + THE AGENT FULLY ADHERES TO THE PERSONA SPECIFICATION: + the agent behavior seen during the simulation is completely consistent with the agent's persona specification, it is + exactly what is expected from the agent's persona specification. Nothing at all contradicts the persona specification. + + How to evaluate adherence: + - For any flaw found, you **must** subtract 20% of the score, regardless of its severity. This is to be very harsh and avoid any ambiguity. + """, + include_personas=True, + double_check=True) + +hard_action_persona_adherence = \ + Proposition(\ + """ + THE NEXT AGENT ACTION FULLY ADHERES TO THE PERSONA SPECIFICATION: + the agent's next action is completely consistent with the agent's persona specification, it is + what is exactly expected from the agent's persona specification. Nothing at all contradicts the persona specification. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate adherence: + - For any flaw found, you **must** subtract 20% of the score, regardless of its severity. This is to be very harsh and avoid any ambiguity. + - The adherence is ONLY ABOUT the next action mentioned above and the persona specification. DO NOT take into account previous actions or stimuli. + - The general situation context is irrelevant to this evaluation, you should ONLY consider the persona specification as context. + - Do not imagine what would be the next action, but instead judge the proposed next action mentioned above! + - The simulation trajectories provided in the context DO NOT contain the next action, but only the actions and stimuli + that have already happened. + + """, + include_personas=True, + double_check=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + + + + + +self_consistency = \ + Proposition( + f""" + THE AGENT IS SELF-CONSISTENT: + the agent never behaves in contradictory or inconsistent ways. + """, + include_personas=False, + double_check=True) + +action_self_consistency = \ + Proposition( + """ + THE NEXT AGENT ACTION IS SELF-CONSISTENT: + the agent's next action does not contradict or conflict with the agent's previous actions. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate action self-consistency: + - Consider the previous actions ONLY to form your opinion about whether the next action is consistent with them + - Ignore stimuli and other previous events, the self-consistency concerns ONLY actions. + - Actions and stimuli ARE NOT part of the persona specification. Rather, they are part of the simulation trajectories. + - Ignore the agent's persona or general background, the self-consistency concerns ONLY the actions observed + in simulation trajectories. + - If there are no previous actions, the next action is self-consistent by default. + """, + include_personas=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + +fluency = \ + Proposition(\ + """ + THE AGENT IS FLUENT. During the simulation, the agent's thinks and speaks fluently. This means that: + - The agent don't repeat the same thoughts or words over and over again. + - The agents don't use overly formulaic language. + - The agent don't use overly repetitive language. + - The agent's words sound natural and human-like. + """, + include_personas=False, + double_check=True) + +action_fluency = \ + Proposition(\ + """ + THE NEXT AGENT ACTION IS FLUENT. + The next action's words sounds natural and human-like, avoiding excessive repetition and formulaic language. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate fluency: + - Fluency here is ONLY ABOUT the next action mentioned above. Previous actions are the **context** for this evaluation, + but will not be evaluated themselves. + - Previous stimuli and events that are not actions should be completely ignored. Here we are only concerned about actions. + """, + include_personas=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + +action_suitability = \ + Proposition(\ + """ + THE NEXT AGENT ACTION IS SUITABLE: + the next action is suitable for the situation, task and context. In particular, if the agent is pursuing some + specific goal, instructions or guidelines, the next action must be coherent and consistent with them. + More precisely, the next action is suitable if at least *one* of the following conditions is satisfied: + - the next action is a reasonable step in the right direction, even if does not need to fully solve the overall problem, task or situation. + - the next action produces relevant information for the situation, task or context, even if does not actually advances a solution. + - the next action is a reasonable response to the recent stimuli received, even if it does not actually advances a solution. + + It suffices to meet ONLY ONE of these conditions to be considered **FULLY** suitable. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate action suitability: + - The score of suitability is proportional to the degree to which the next action satisfies *any* of the above conditions + - If only **one** condition is **fully** met, the next action is **completely** suitable and gets **maximum** score. That is to say, + the next action **does not** need to satisfy all conditions to be suitable! A single sataisfied condition is enough! + - The suitability is ONLY ABOUT the next action mentioned above and the situation context. + - If a previous action or stimuli is inconsistent or conflicting with the situation context, you should ignore it + when evaluating the next action. Consider ONLY the situation context. + - The simulation trajectories provided in the context DO NOT contain the next action, but only the actions and stimuli + that have already happened. + + """, + include_personas=True, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + + +task_completion = \ + Proposition(\ + """ + THE AGENT COMPLETES THE GIVEN TASK. + + Given the following task: "{{task_description}}" + + The agent completes the task by the end of the simulation. + + This means that: + - If the task requires the agent to discuss or talk about something, the agent does so. + - If the task requires the agent to think about something, the agent does so. + - If the task requires the agent to do something via another action, the agent does so. + - If the task requires the agent to adopt some specific variations of behavior, the agent does so. + - If the task includes other specific requirements, the agent observes them. + """, + include_personas=False, + double_check=True) + + +quiet_recently = \ + Proposition( + """ + THE AGENT HAS BEEN QUIET RECENTLY: + The agent has been executing multiple DONE actions in a row with few or no TALK, THINK or + other actions in between. + + How to evaluate quietness: + - The last 2 (or more) actions of the agent are consecutive DONE actions. This means that the agent + was done with his turn before doing anything else for a couple of turns. + - There are no other actions in between the last 2 (or more) DONE actions. + """, + include_personas=False + ) + +################################## +# Environment properties +################################## + +divergence = \ + Proposition(""" + AGENTS DIVERGE FROM ONE ANOTHER. + As the simulation progresses, the agents' behaviors diverge from one another, + instead of becoming more similar. This includes what they think, what they say and what they do. The topics discussed become + more varied at the end of the simulation than at the beginning. Discussions do not converge to a single topic or perspective + at the end. + """, + include_personas=False, + double_check=True) + +convergence = \ + Proposition(""" + AGENTS CONVERGE TO ONE ANOTHER. + As the simulation progresses, the agents' behaviors converge to one another, + instead of becoming more different. This includes what they think, what they say and what they do. The topics discussed become + more similar at the end of the simulation than at the beginning. Discussions converge to a single topic or perspective + at the end. + """, + include_personas=False, + double_check=True) diff --git a/tinytroupe/validation/simulation_validator.py b/tinytroupe/validation/simulation_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..8351249fdd0588614d72dcf6f8529d5d7d0ae8fe --- /dev/null +++ b/tinytroupe/validation/simulation_validator.py @@ -0,0 +1,2060 @@ +""" +Simulation experiment empirical validation mechanisms for TinyTroupe. + +This module provides tools to validate simulation experiment results against empirical control data, +supporting both statistical hypothesis testing and semantic validation approaches. +This is distinct from LLM-based evaluations, focusing on data-driven validation +against known empirical benchmarks. +""" + +from typing import Dict, List, Optional, Union, Any +import json +import csv +from datetime import datetime +from pathlib import Path +from pydantic import BaseModel, Field + +import pandas as pd + +from tinytroupe.experimentation.statistical_tests import StatisticalTester +from tinytroupe.utils.semantics import compute_semantic_proximity + +# TODO Work-in-Progress below + +class SimulationExperimentDataset(BaseModel): + """ + Represents a dataset from a simulation experiment or empirical study. + + This contains data that can be used for validation, including quantitative metrics + and qualitative agent justifications from simulation experiments or empirical studies. + + Supports both numeric and categorical data. Categorical data (strings) is automatically + converted to ordinal values for statistical analysis while preserving the original + categories for interpretation. + + Attributes: + name: Optional name for the dataset + description: Optional description of the dataset + key_results: Map from result names to their values (numbers, proportions, booleans, strings, etc.) + result_types: Map indicating whether each result is "aggregate" or "per_agent" + data_types: Map indicating the data type for each result ("numeric", "categorical", "ordinal", "ranking", "count", "proportion", "binary") + categorical_mappings: Internal mappings from categorical strings to ordinal values + ordinal_mappings: Internal mappings for ordinal data with explicit ordering + ranking_info: Information about ranking data (items being ranked, ranking direction) + agent_names: Optional list of agent names (can be referenced by index in results) + agent_justifications: List of justifications (with optional agent references) + justification_summary: Optional summary of all agent justifications + agent_attributes: Agent attributes for manual inspection only (not used in statistical comparisons) + """ + name: Optional[str] = None + description: Optional[str] = None + key_results: Dict[str, Union[float, int, bool, str, List[Union[float, int, bool, str, None]], None]] = Field(default_factory=dict) + result_types: Dict[str, str] = Field(default_factory=dict, description="Map from result name to 'aggregate' or 'per_agent'") + data_types: Dict[str, str] = Field(default_factory=dict, description="Map indicating data type: 'numeric', 'categorical', 'ordinal', 'ranking', 'count', 'proportion', 'binary'") + categorical_mappings: Dict[str, Dict[str, int]] = Field(default_factory=dict, description="Internal mappings from categorical strings to ordinal values") + ordinal_mappings: Dict[str, Dict[str, int]] = Field(default_factory=dict, description="Internal mappings for ordinal data with explicit ordering") + ranking_info: Dict[str, Dict[str, Any]] = Field(default_factory=dict, description="Information about ranking data (items, direction, etc.)") + agent_names: Optional[List[Optional[str]]] = Field(None, description="Optional list of agent names for reference (can contain None for unnamed agents)") + agent_justifications: List[Union[str, Dict[str, Union[str, int]]]] = Field( + default_factory=list, + description="List of justifications as strings or dicts with optional 'agent_name'/'agent_index' and 'justification'" + ) + justification_summary: Optional[str] = None + agent_attributes: Dict[str, List[Union[str, None]]] = Field( + default_factory=dict, + description="Agent attributes loaded from CSV but not used in statistical comparisons (e.g., age, gender, etc.)" + ) + + class Config: + """Pydantic configuration.""" + extra = "forbid" # Prevent accidental extra fields + validate_assignment = True # Validate on assignment after creation + + def __init__(self, **data): + """Initialize with automatic data processing.""" + super().__init__(**data) + self._process_data_types() + + def _process_data_types(self): + """ + Process different data types and convert them appropriately. + + Automatically detects and processes: + - Categorical data (strings) -> ordinal mapping + - Ordinal data (explicit ordering) -> validation of ordering + - Ranking data (ranks/positions) -> validation and normalization + - Count data (non-negative integers) -> validation + - Proportion data (0-1 or 0-100) -> normalization to 0-1 + - Binary data (boolean/yes-no) -> conversion to 0/1 + """ + for metric_name, metric_data in self.key_results.items(): + data_type = self.data_types.get(metric_name, "auto") + + if data_type == "auto": + # Auto-detect data type + data_type = self._detect_data_type(metric_data) + self.data_types[metric_name] = data_type + + # Process based on data type + if data_type == "categorical": + self._process_categorical_data_for_metric(metric_name, metric_data) + elif data_type == "ordinal": + self._process_ordinal_data_for_metric(metric_name, metric_data) + elif data_type == "ranking": + self._process_ranking_data_for_metric(metric_name, metric_data) + elif data_type == "count": + self._validate_count_data_for_metric(metric_name, metric_data) + elif data_type == "proportion": + self._process_proportion_data_for_metric(metric_name, metric_data) + elif data_type == "binary": + self._process_binary_data_for_metric(metric_name, metric_data) + # "numeric" requires no special processing + + def _detect_data_type(self, data: Union[float, int, bool, str, List, None]) -> str: + """Auto-detect the data type based on the data content.""" + if data is None: + return "numeric" # Default fallback + + # Handle single values + if not isinstance(data, list): + data = [data] + + # Filter out None values for analysis + valid_data = [item for item in data if item is not None] + if not valid_data: + return "numeric" # Default fallback + + # Check for string data (categorical) - but only if ALL non-None values are strings + string_count = sum(1 for item in valid_data if isinstance(item, str)) + if string_count > 0: + # If we have mixed types (strings + numbers), default to categorical for simplicity + # since the string conversion will handle the mixed case + return "categorical" + + # Check for boolean data + if all(isinstance(item, bool) for item in valid_data): + return "binary" + + # Check for numeric data + numeric_data = [item for item in valid_data if isinstance(item, (int, float))] + if len(numeric_data) != len(valid_data): + return "numeric" # Mixed types, default to numeric + + # Check for count data (non-negative integers, including whole number floats) + def is_whole_number(x): + """Check if a number is a whole number (either int or float with no decimal part).""" + return isinstance(x, int) or (isinstance(x, float) and x.is_integer()) + + if all(is_whole_number(item) and item >= 0 for item in numeric_data): + # Convert floats to ints for ranking detection + int_data = [int(item) for item in numeric_data] + + # For ranking detection, be more strict: + # 1. Must have at least 3 data points + # 2. Must have consecutive integers starting from 1 + # 3. Must have some repetition (indicating actual rankings rather than just sequence) + sorted_data = sorted(set(int_data)) + min_val = min(sorted_data) + max_val = max(sorted_data) + + # Only consider as ranking if: + # - Starts from 1 + # - Has at least 2 different rank values + # - Is consecutive (no gaps) + # - Has repetition (more data points than unique values) - this is key for rankings + if (len(int_data) >= 3 and # At least 3 data points + min_val == 1 and # Starts from 1 + len(sorted_data) >= 2 and # At least 2 different ranks + max_val <= 10 and # Reasonable upper limit for rankings + sorted_data == list(range(1, max_val + 1)) and # Consecutive + len(int_data) > len(sorted_data)): # Has repetition (essential for rankings) + return "ranking" + + # Otherwise, it's count data + return "count" + + # Check for proportion data (0-1 range) - only for floats + if all(isinstance(item, (int, float)) and 0 <= item <= 1 for item in numeric_data): + # If all values are 0 or 1 integers, it's likely binary + if all(isinstance(item, int) and item in [0, 1] for item in numeric_data): + return "binary" + return "proportion" + + # Default to numeric + return "numeric" + + def _process_categorical_data_for_metric(self, metric_name: str, metric_data): + """Process categorical data for a specific metric.""" + if self._is_categorical_data(metric_data): + # Extract all unique categories + categories = self._extract_categories(metric_data) + + if categories: + # Create sorted categorical mapping for consistency + sorted_categories = sorted(categories) + categorical_mapping = {category: idx for idx, category in enumerate(sorted_categories)} + self.categorical_mappings[metric_name] = categorical_mapping + + # Convert string data to ordinal values + self.key_results[metric_name] = self._convert_to_ordinal(metric_data, categorical_mapping) + + def _process_ordinal_data_for_metric(self, metric_name: str, metric_data): + """Process ordinal data for a specific metric.""" + # For ordinal data, we expect either: + # 1. Numeric values that represent ordinal levels (e.g., 1, 2, 3, 4, 5 for Likert) + # 2. String values that need explicit ordering (e.g., "Poor", "Fair", "Good", "Excellent") + + if self._is_categorical_data(metric_data): + # String ordinal data - need explicit ordering + categories = self._extract_categories(metric_data) + if categories: + # For string ordinal data, we need to define a meaningful order + # This could be enhanced to accept explicit ordering from user + sorted_categories = self._order_ordinal_categories(list(categories)) + ordinal_mapping = {category: idx for idx, category in enumerate(sorted_categories)} + self.ordinal_mappings[metric_name] = ordinal_mapping + + # Convert to ordinal values + self.key_results[metric_name] = self._convert_to_ordinal(metric_data, ordinal_mapping) + else: + # Numeric ordinal data - validate that values are reasonable + self._validate_ordinal_numeric_data(metric_name, metric_data) + + def _process_ranking_data_for_metric(self, metric_name: str, metric_data): + """Process ranking data for a specific metric.""" + # Ranking data should be integers representing positions (1, 2, 3, etc.) + valid_data = self._get_valid_numeric_data(metric_data) + + if valid_data: + unique_ranks = sorted(set(valid_data)) + min_rank = min(unique_ranks) + max_rank = max(unique_ranks) + + # Check if ranking_info already exists (e.g., from ordinal processing) + existing_info = self.ranking_info.get(metric_name, {}) + + # Store ranking information, preserving existing keys + ranking_info = { + "min_rank": min_rank, + "max_rank": max_rank, + "num_ranks": len(unique_ranks), + "rank_values": unique_ranks, + "direction": existing_info.get("direction", "ascending") # Preserve existing direction or default + } + + # Preserve any additional keys from existing ranking info (e.g., ordinal-specific data) + ranking_info.update({k: v for k, v in existing_info.items() + if k not in ranking_info}) + + self.ranking_info[metric_name] = ranking_info + + # Validate ranking data + self._validate_ranking_data(metric_name, metric_data) + + def _process_proportion_data_for_metric(self, metric_name: str, metric_data): + """Process proportion data for a specific metric.""" + # Normalize proportion data to 0-1 range if needed + if isinstance(metric_data, list): + normalized_data = [] + for item in metric_data: + if item is None: + normalized_data.append(None) + elif isinstance(item, (int, float)): + # If value > 1, assume it's percentage (0-100), convert to proportion + normalized_data.append(item / 100.0 if item > 1 else item) + else: + normalized_data.append(item) # Keep as-is + self.key_results[metric_name] = normalized_data + elif isinstance(metric_data, (int, float)) and metric_data > 1: + # Single percentage value + self.key_results[metric_name] = metric_data / 100.0 + + def _process_binary_data_for_metric(self, metric_name: str, metric_data): + """Process binary data for a specific metric.""" + # Convert boolean/string binary data to 0/1 + if isinstance(metric_data, list): + binary_data = [] + for item in metric_data: + if item is None: + binary_data.append(None) + else: + binary_data.append(self._convert_to_binary(item)) + self.key_results[metric_name] = binary_data + elif metric_data is not None: + self.key_results[metric_name] = self._convert_to_binary(metric_data) + + def _validate_count_data_for_metric(self, metric_name: str, metric_data): + """Validate count data for a specific metric.""" + valid_data = self._get_valid_numeric_data(metric_data) + + # Check that all values are non-negative integers (including whole number floats) + for value in valid_data: + # Accept both integers and whole number floats + is_whole_number = isinstance(value, int) or (isinstance(value, float) and value.is_integer()) + if not is_whole_number or value < 0: + raise ValueError(f"Count data for metric '{metric_name}' must be non-negative integers, found: {value}") + + def _order_ordinal_categories(self, categories: List[str]) -> List[str]: + """Order ordinal categories in a meaningful way.""" + # Common ordinal patterns for automatic ordering + likert_patterns = { + "strongly disagree": 1, "disagree": 2, "neutral": 3, "agree": 4, "strongly agree": 5, + "very poor": 1, "poor": 2, "fair": 3, "good": 4, "very good": 5, "excellent": 6, + "never": 1, "rarely": 2, "sometimes": 3, "often": 4, "always": 5, + "very low": 1, "low": 2, "medium": 3, "high": 4, "very high": 5, + "terrible": 1, "bad": 2, "okay": 3, "good": 4, "great": 5, "amazing": 6 + } + + # Try to match patterns + category_scores = {} + for category in categories: + normalized_cat = self._normalize_category(category) + if normalized_cat in likert_patterns: + category_scores[category] = likert_patterns[normalized_cat] + + # If we found matches for all categories, use that ordering + if len(category_scores) == len(categories): + return sorted(categories, key=lambda x: category_scores[x]) + + # Otherwise, fall back to alphabetical ordering with a warning + return sorted(categories) + + def _validate_ordinal_numeric_data(self, metric_name: str, metric_data): + """Validate numeric ordinal data.""" + valid_data = self._get_valid_numeric_data(metric_data) + + if valid_data: + unique_values = sorted(set(valid_data)) + # Check if values are reasonable for ordinal data (consecutive or at least ordered) + if len(unique_values) < 2: + return # Single value is fine + + # Store ordinal information + self.ordinal_mappings[metric_name] = { + "min_value": min(unique_values), + "max_value": max(unique_values), + "unique_values": unique_values, + "num_levels": len(unique_values) + } + + def _validate_ranking_data(self, metric_name: str, metric_data): + """Validate ranking data structure.""" + valid_data = self._get_valid_numeric_data(metric_data) + + if not valid_data: + return + + unique_ranks = set(valid_data) + min_rank = min(unique_ranks) + max_rank = max(unique_ranks) + + # Check for reasonable ranking structure + if min_rank < 1: + raise ValueError(f"Ranking data for metric '{metric_name}' should start from 1, found minimum: {min_rank}") + + # Check for gaps in ranking (warning, not error) + expected_ranks = set(range(min_rank, max_rank + 1)) + missing_ranks = expected_ranks - unique_ranks + if missing_ranks: + # This is often okay in ranking data (tied ranks, incomplete rankings) + pass + + def _get_valid_numeric_data(self, data) -> List[Union[int, float]]: + """Get valid numeric data from a metric, handling both single values and lists.""" + if data is None: + return [] + + if not isinstance(data, list): + data = [data] + + return [item for item in data if item is not None and isinstance(item, (int, float))] + + def _convert_to_binary(self, value) -> int: + """Convert various binary representations to 0 or 1.""" + if isinstance(value, bool): + return 1 if value else 0 + elif isinstance(value, str): + normalized = value.lower().strip() + true_values = {"true", "yes", "y", "1", "on", "success", "positive"} + false_values = {"false", "no", "n", "0", "off", "failure", "negative"} + + if normalized in true_values: + return 1 + elif normalized in false_values: + return 0 + else: + raise ValueError(f"Cannot convert string '{value}' to binary") + elif isinstance(value, (int, float)): + return 1 if value != 0 else 0 + else: + raise ValueError(f"Cannot convert {type(value)} to binary") + + def _process_categorical_data(self): + """ + Legacy method for backward compatibility. + Process categorical string data by converting to ordinal values. + """ + for metric_name, metric_data in self.key_results.items(): + if metric_name not in self.data_types: # Only process if data type not explicitly set + if self._is_categorical_data(metric_data): + self.data_types[metric_name] = "categorical" + self._process_categorical_data_for_metric(metric_name, metric_data) + + def _is_categorical_data(self, data: Union[float, int, bool, str, List, None]) -> bool: + """Check if data contains categorical (string) values.""" + if isinstance(data, str): + return True + elif isinstance(data, list): + return any(isinstance(item, str) for item in data if item is not None) + return False + + def _extract_categories(self, data: Union[float, int, bool, str, List, None]) -> set: + """Extract unique string categories from data.""" + categories = set() + + if isinstance(data, str): + categories.add(self._normalize_category(data)) + elif isinstance(data, list): + for item in data: + if isinstance(item, str): + categories.add(self._normalize_category(item)) + + return categories + + def _normalize_category(self, category: str) -> str: + """Normalize categorical string (lowercase, strip whitespace).""" + return category.lower().strip() + + def _convert_to_ordinal(self, data: Union[str, List], mapping: Dict[str, int]) -> Union[int, List[Union[int, None]]]: + """Convert categorical data to ordinal values using the mapping.""" + if isinstance(data, str): + normalized = self._normalize_category(data) + return mapping.get(normalized, 0) # Default to 0 if not found + elif isinstance(data, list): + converted = [] + for item in data: + if isinstance(item, str): + normalized = self._normalize_category(item) + converted.append(mapping.get(normalized, 0)) + elif item is None: + converted.append(None) # Preserve None values + else: + converted.append(item) # Keep numeric values as-is + return converted + else: + return data + + def get_agent_name(self, index: int) -> Optional[str]: + """Get agent name by index, if available.""" + if self.agent_names and 0 <= index < len(self.agent_names): + agent_name = self.agent_names[index] + return agent_name if agent_name is not None else None + return None + + def get_agent_data(self, metric_name: str, agent_index: int) -> Optional[Union[float, int, bool]]: + """Get a specific agent's data for a given metric. Returns None for missing data.""" + if metric_name not in self.key_results: + return None + + metric_data = self.key_results[metric_name] + + # Check if it's per-agent data + if self.result_types.get(metric_name) == "per_agent" and isinstance(metric_data, list): + if 0 <= agent_index < len(metric_data): + return metric_data[agent_index] # This can be None for missing data + + return None + + def get_all_agent_data(self, metric_name: str) -> Dict[str, Union[float, int, bool]]: + """Get all agents' data for a given metric as a dictionary mapping agent names/indices to values.""" + if metric_name not in self.key_results: + return {} + + metric_data = self.key_results[metric_name] + result = {} + + # For per-agent data, create mapping + if self.result_types.get(metric_name) == "per_agent" and isinstance(metric_data, list): + for i, value in enumerate(metric_data): + agent_name = self.get_agent_name(i) or f"Agent_{i}" + # Only include non-None values in the result + if value is not None: + result[agent_name] = value + + # For aggregate data, return single value + elif self.result_types.get(metric_name) == "aggregate": + result["aggregate"] = metric_data + + return result + + def get_valid_agent_data(self, metric_name: str) -> List[Union[float, int, bool]]: + """Get only valid (non-None) values for a per-agent metric.""" + if metric_name not in self.key_results: + return [] + + metric_data = self.key_results[metric_name] + + if self.result_types.get(metric_name) == "per_agent" and isinstance(metric_data, list): + return [value for value in metric_data if value is not None] + + return [] + + def validate_data_consistency(self) -> List[str]: + """Validate that per-agent data is consistent across metrics and with agent names.""" + errors = [] + warnings = [] + + # Check per-agent metrics have consistent lengths + per_agent_lengths = [] + per_agent_metrics = [] + + for metric_name, result_type in self.result_types.items(): + if result_type == "per_agent" and metric_name in self.key_results: + metric_data = self.key_results[metric_name] + if isinstance(metric_data, list): + per_agent_lengths.append(len(metric_data)) + per_agent_metrics.append(metric_name) + else: + errors.append(f"Metric '{metric_name}' marked as per_agent but is not a list") + + # Check all per-agent metrics have same length + if per_agent_lengths and len(set(per_agent_lengths)) > 1: + errors.append(f"Per-agent metrics have inconsistent lengths: {dict(zip(per_agent_metrics, per_agent_lengths))}") + + # Check agent_names length matches per-agent data length + if self.agent_names and per_agent_lengths: + agent_count = len(self.agent_names) + data_length = per_agent_lengths[0] if per_agent_lengths else 0 + if agent_count != data_length: + errors.append(f"agent_names length ({agent_count}) doesn't match per-agent data length ({data_length})") + + # Check for None values in agent_names and provide warnings + if self.agent_names: + none_indices = [i for i, name in enumerate(self.agent_names) if name is None] + if none_indices: + warnings.append(f"agent_names contains None values at indices: {none_indices}") + + # Check for None values in per-agent data and provide info + for metric_name in per_agent_metrics: + if metric_name in self.key_results: + metric_data = self.key_results[metric_name] + none_indices = [i for i, value in enumerate(metric_data) if value is None] + if none_indices: + warnings.append(f"Metric '{metric_name}' has missing data (None) at indices: {none_indices}") + + # Return errors and warnings combined + return errors + [f"WARNING: {warning}" for warning in warnings] + + def get_justification_text(self, justification_item: Union[str, Dict[str, Union[str, int]]]) -> str: + """Extract justification text from various formats.""" + if isinstance(justification_item, str): + return justification_item + elif isinstance(justification_item, dict): + return justification_item.get("justification", "") + return "" + + def get_justification_agent_reference(self, justification_item: Union[str, Dict[str, Union[str, int]]]) -> Optional[str]: + """Get agent reference from justification, returning name if available.""" + if isinstance(justification_item, dict): + # Direct agent name + if "agent_name" in justification_item: + return justification_item["agent_name"] + # Agent index reference + elif "agent_index" in justification_item: + return self.get_agent_name(justification_item["agent_index"]) + return None + + def get_categorical_values(self, metric_name: str) -> Optional[List[str]]: + """Get the original categorical values for a metric, if it was categorical.""" + if metric_name in self.categorical_mappings: + # Return categories sorted by their ordinal values + mapping = self.categorical_mappings[metric_name] + return [category for category, _ in sorted(mapping.items(), key=lambda x: x[1])] + elif metric_name in self.ordinal_mappings and isinstance(self.ordinal_mappings[metric_name], dict): + # Handle string-based ordinal data + mapping = self.ordinal_mappings[metric_name] + if all(isinstance(k, str) for k in mapping.keys()): + return [category for category, _ in sorted(mapping.items(), key=lambda x: x[1])] + return None + + def convert_ordinal_to_categorical(self, metric_name: str, ordinal_value: Union[int, float]) -> Optional[str]: + """Convert an ordinal value back to its original categorical string.""" + # Check categorical mappings first + if metric_name in self.categorical_mappings: + mapping = self.categorical_mappings[metric_name] + # Reverse lookup: find category with this ordinal value + for category, value in mapping.items(): + if value == int(ordinal_value): + return category + + # Check ordinal mappings for string-based ordinal data + elif metric_name in self.ordinal_mappings: + mapping = self.ordinal_mappings[metric_name] + if isinstance(mapping, dict) and all(isinstance(k, str) for k in mapping.keys()): + for category, value in mapping.items(): + if value == int(ordinal_value): + return category + + return None + + def get_data_type_info(self, metric_name: str) -> Dict[str, Any]: + """Get comprehensive information about a metric's data type.""" + data_type = self.data_types.get(metric_name, "numeric") + info = { + "data_type": data_type, + "result_type": self.result_types.get(metric_name, "unknown") + } + + if data_type == "categorical" and metric_name in self.categorical_mappings: + info["categories"] = self.get_categorical_values(metric_name) + info["category_mapping"] = self.categorical_mappings[metric_name].copy() + + elif data_type == "ordinal": + if metric_name in self.ordinal_mappings: + mapping = self.ordinal_mappings[metric_name] + if isinstance(mapping, dict): + # Check if this is a string-to-number mapping (categorical ordinal) + # vs info dict (numeric ordinal) + if "min_value" in mapping or "max_value" in mapping: + # Numeric ordinal info + info["ordinal_info"] = mapping.copy() + elif all(isinstance(k, str) for k in mapping.keys()) and all(isinstance(v, int) for v in mapping.values()): + # String-based ordinal - safely sort by values + try: + info["ordinal_categories"] = [cat for cat, _ in sorted(mapping.items(), key=lambda x: x[1])] + info["ordinal_mapping"] = mapping.copy() + except TypeError: + # Fallback if sorting fails + info["ordinal_categories"] = list(mapping.keys()) + info["ordinal_mapping"] = mapping.copy() + else: + # Unknown ordinal format, treat as info + info["ordinal_info"] = mapping.copy() + + elif data_type == "ranking" and metric_name in self.ranking_info: + info["ranking_info"] = self.ranking_info[metric_name].copy() + + return info + + def get_metric_summary(self, metric_name: str) -> Dict[str, Any]: + """Get a comprehensive summary of a metric including data type information.""" + summary = { + "metric_name": metric_name, + "result_type": self.result_types.get(metric_name, "unknown"), + "data_type": self.data_types.get(metric_name, "numeric"), + } + + # Add legacy categorical flag for backward compatibility + summary["is_categorical"] = (metric_name in self.categorical_mappings or + (metric_name in self.ordinal_mappings and + isinstance(self.ordinal_mappings[metric_name], dict) and + all(isinstance(k, str) for k in self.ordinal_mappings[metric_name].keys()))) + + if metric_name in self.key_results: + data = self.key_results[metric_name] + summary["data_type_name"] = type(data).__name__ + + if isinstance(data, list): + valid_data = [x for x in data if x is not None] + summary["total_values"] = len(data) + summary["valid_values"] = len(valid_data) + summary["missing_values"] = len(data) - len(valid_data) + + if valid_data: + summary["min_value"] = min(valid_data) + summary["max_value"] = max(valid_data) + + # Add data type specific information + data_type_info = self.get_data_type_info(metric_name) + summary.update(data_type_info) + + # Add distribution information for per-agent data + if isinstance(data, list) and self.result_types.get(metric_name) == "per_agent": + data_type = summary["data_type"] + + if data_type in ["categorical", "ordinal"] and summary.get("is_categorical"): + # Category distribution + category_counts = {} + for value in data: + if value is not None: + category = self.convert_ordinal_to_categorical(metric_name, value) + if category: + category_counts[category] = category_counts.get(category, 0) + 1 + summary["category_distribution"] = category_counts + + elif data_type == "ranking": + # Ranking distribution + rank_counts = {} + for value in data: + if value is not None: + rank_counts[value] = rank_counts.get(value, 0) + 1 + summary["rank_distribution"] = rank_counts + + elif data_type == "binary": + # Binary distribution + true_count = sum(1 for x in data if x == 1) + false_count = sum(1 for x in data if x == 0) + summary["binary_distribution"] = {"true": true_count, "false": false_count} + + return summary + + def is_categorical_metric(self, metric_name: str) -> bool: + """Check if a metric contains categorical data (including string-based ordinal).""" + return (metric_name in self.categorical_mappings or + (metric_name in self.ordinal_mappings and + isinstance(self.ordinal_mappings[metric_name], dict) and + all(isinstance(k, str) for k in self.ordinal_mappings[metric_name].keys()))) + + +class SimulationExperimentEmpiricalValidationResult(BaseModel): + """ + Contains the results of a simulation experiment validation against empirical data. + + This represents the outcome of validating simulation experiment data + against empirical benchmarks, using statistical and semantic methods. + + Attributes: + validation_type: Type of validation performed + control_name: Name of the control/empirical dataset + treatment_name: Name of the treatment/simulation experiment dataset + statistical_results: Results from statistical tests (if performed) + semantic_results: Results from semantic proximity analysis (if performed) + overall_score: Overall validation score (0.0 to 1.0) + summary: Summary of validation findings + timestamp: When the validation was performed + """ + validation_type: str + control_name: str + treatment_name: str + statistical_results: Optional[Dict[str, Any]] = None + semantic_results: Optional[Dict[str, Any]] = None + overall_score: Optional[float] = Field(None, ge=0.0, le=1.0, description="Overall validation score between 0.0 and 1.0") + summary: str = "" + timestamp: str = Field(default_factory=lambda: datetime.now().isoformat()) + + class Config: + """Pydantic configuration.""" + extra = "forbid" + validate_assignment = True + + +class SimulationExperimentEmpiricalValidator: + """ + A validator for comparing simulation experiment data against empirical control data. + + This validator performs data-driven validation using statistical hypothesis testing + and semantic proximity analysis of agent justifications. It is designed to validate + simulation experiment results against known empirical benchmarks, distinct from LLM-based evaluations. + """ + + def __init__(self): + """Initialize the simulation experiment empirical validator.""" + pass + + def validate(self, + control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset, + validation_types: List[str] = ["statistical", "semantic"], + statistical_test_type: str = "welch_t_test", + significance_level: float = 0.05, + output_format: str = "values") -> Union[SimulationExperimentEmpiricalValidationResult, str]: + """ + Validate a simulation experiment dataset against an empirical control dataset. + + Args: + control: The control/empirical reference dataset + treatment: The treatment/simulation experiment dataset to validate + validation_types: List of validation types to perform ("statistical", "semantic") + statistical_test_type: Type of statistical test ("welch_t_test", "ks_test", "mann_whitney", etc.) + significance_level: Significance level for statistical tests + output_format: "values" for SimulationExperimentEmpiricalValidationResult object, "report" for markdown report + + Returns: + SimulationExperimentEmpiricalValidationResult object or markdown report string + """ + result = SimulationExperimentEmpiricalValidationResult( + validation_type=", ".join(validation_types), + control_name=control.name or "Control", + treatment_name=treatment.name or "Treatment" + ) + + # Perform statistical validation + if "statistical" in validation_types: + result.statistical_results = self._perform_statistical_validation( + control, treatment, significance_level, statistical_test_type + ) + + # Perform semantic validation + if "semantic" in validation_types: + result.semantic_results = self._perform_semantic_validation( + control, treatment + ) + + # Calculate overall score and summary + result.overall_score = self._calculate_overall_score(result) + result.summary = self._generate_summary(result) + + if output_format == "report": + return self._generate_markdown_report(result, control, treatment) + else: + return result + + def _perform_statistical_validation(self, + control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset, + significance_level: float, + test_type: str = "welch_t_test") -> Dict[str, Any]: + """ + Perform statistical hypothesis testing on simulation experiment key results. + + Args: + control: Control dataset + treatment: Treatment dataset + significance_level: Alpha level for statistical tests + test_type: Type of statistical test to perform + """ + if not control.key_results or not treatment.key_results: + return {"error": "No key results available for statistical testing"} + + try: + # Prepare data for StatisticalTester + control_data = {"control": {}} + treatment_data = {"treatment": {}} + + # Convert single values to lists if needed and find common metrics + common_metrics = set(control.key_results.keys()) & set(treatment.key_results.keys()) + + for metric in common_metrics: + control_value = control.key_results[metric] + treatment_value = treatment.key_results[metric] + + # Convert single values to lists and filter out None values + if not isinstance(control_value, list): + control_value = [control_value] if control_value is not None else [] + else: + control_value = [v for v in control_value if v is not None] + + if not isinstance(treatment_value, list): + treatment_value = [treatment_value] if treatment_value is not None else [] + else: + treatment_value = [v for v in treatment_value if v is not None] + + # Only include metrics that have valid data points + if len(control_value) > 0 and len(treatment_value) > 0: + control_data["control"][metric] = control_value + treatment_data["treatment"][metric] = treatment_value + + if not common_metrics: + return {"error": "No common metrics found between control and treatment"} + + # Run statistical tests + tester = StatisticalTester(control_data, treatment_data) + test_results = tester.run_test( + test_type=test_type, + alpha=significance_level + ) + + return { + "common_metrics": list(common_metrics), + "test_results": test_results, + "test_type": test_type, + "significance_level": significance_level + } + + except Exception as e: + return {"error": f"Statistical testing failed: {str(e)}"} + + def _perform_semantic_validation(self, + control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset) -> Dict[str, Any]: + """Perform semantic proximity analysis on simulation experiment agent justifications.""" + results = { + "individual_comparisons": [], + "summary_comparison": None, + "average_proximity": None + } + + # Compare individual justifications if available + if control.agent_justifications and treatment.agent_justifications: + proximities = [] + + for i, control_just in enumerate(control.agent_justifications): + for j, treatment_just in enumerate(treatment.agent_justifications): + control_text = control.get_justification_text(control_just) + treatment_text = treatment.get_justification_text(treatment_just) + + if control_text and treatment_text: + proximity_score = compute_semantic_proximity( + control_text, + treatment_text, + context="Comparing agent justifications from simulation experiments" + ) + + # Handle case where LLM call fails or returns invalid data + if proximity_score is None or not isinstance(proximity_score, (int, float)): + raise ValueError("Invalid semantic proximity score") + + # Get agent references (names or indices) + control_agent_ref = control.get_justification_agent_reference(control_just) or f"Agent_{i}" + treatment_agent_ref = treatment.get_justification_agent_reference(treatment_just) or f"Agent_{j}" + + comparison = { + "control_agent": control_agent_ref, + "treatment_agent": treatment_agent_ref, + "proximity_score": proximity_score, + "justification": f"Semantic proximity score: {proximity_score:.3f}" + } + + results["individual_comparisons"].append(comparison) + proximities.append(proximity_score) + + if proximities: + results["average_proximity"] = sum(proximities) / len(proximities) + + # Compare summary justifications if available + if control.justification_summary and treatment.justification_summary: + summary_proximity_score = compute_semantic_proximity( + control.justification_summary, + treatment.justification_summary, + context="Comparing summary justifications from simulation experiments" + ) + + # Handle case where LLM call fails or returns invalid data + if summary_proximity_score is None or not isinstance(summary_proximity_score, (int, float)): + summary_proximity_score = 0.5 # Default neutral score + + results["summary_comparison"] = { + "proximity_score": summary_proximity_score, + "justification": f"Summary semantic proximity score: {summary_proximity_score:.3f}" + } + + return results + + def _calculate_overall_score(self, result: SimulationExperimentEmpiricalValidationResult) -> float: + """Calculate an overall simulation experiment empirical validation score based on statistical and semantic results.""" + scores = [] + + # Statistical component based on effect sizes + if result.statistical_results and "test_results" in result.statistical_results: + test_results = result.statistical_results["test_results"] + effect_sizes = [] + + for treatment_name, treatment_results in test_results.items(): + for metric, metric_result in treatment_results.items(): + # Extract effect size based on test type + effect_size = self._extract_effect_size(metric_result) + if effect_size is not None: + effect_sizes.append(effect_size) + + if effect_sizes: + # Convert effect sizes to similarity scores (closer to 0 = more similar) + # Use inverse transformation: similarity = 1 / (1 + |effect_size|) + # For very small effect sizes (< 0.1), give even higher scores + similarity_scores = [] + for es in effect_sizes: + abs_es = abs(es) + if abs_es < 0.1: # Very small effect size + similarity_scores.append(0.95 + 0.05 * (1.0 / (1.0 + abs_es))) + else: + similarity_scores.append(1.0 / (1.0 + abs_es)) + + statistical_score = sum(similarity_scores) / len(similarity_scores) + scores.append(statistical_score) + + # Semantic component + if result.semantic_results: + semantic_scores = [] + + # Average proximity from individual comparisons + if result.semantic_results.get("average_proximity") is not None: + semantic_scores.append(result.semantic_results["average_proximity"]) + + # Summary proximity + if result.semantic_results.get("summary_comparison"): + semantic_scores.append(result.semantic_results["summary_comparison"]["proximity_score"]) + + if semantic_scores: + semantic_score = sum(semantic_scores) / len(semantic_scores) + scores.append(semantic_score) + + # If we have both statistical and semantic scores, and the statistical score is very high (>0.9) + # indicating statistically equivalent data, weight the statistical component more heavily + if len(scores) == 2 and scores[0] > 0.9: # First score is statistical + # Weight statistical component at 70%, semantic at 30% for equivalent data + return 0.7 * scores[0] + 0.3 * scores[1] + + return sum(scores) / len(scores) if scores else 0.0 + + def _generate_summary(self, result: SimulationExperimentEmpiricalValidationResult) -> str: + """Generate a text summary of the simulation experiment empirical validation results.""" + summary_parts = [] + + if result.statistical_results: + if "error" in result.statistical_results: + summary_parts.append(f"Statistical validation: {result.statistical_results['error']}") + else: + test_results = result.statistical_results.get("test_results", {}) + effect_sizes = [] + significant_tests = 0 + total_tests = 0 + + for treatment_results in test_results.values(): + for metric_result in treatment_results.values(): + total_tests += 1 + if metric_result.get("significant", False): + significant_tests += 1 + + # Collect effect sizes + effect_size = self._extract_effect_size(metric_result) + if effect_size is not None: + effect_sizes.append(abs(effect_size)) + + if effect_sizes: + avg_effect_size = sum(effect_sizes) / len(effect_sizes) + summary_parts.append( + f"Statistical validation: {significant_tests}/{total_tests} tests significant, " + f"average effect size: {avg_effect_size:.3f}" + ) + else: + summary_parts.append( + f"Statistical validation: {significant_tests}/{total_tests} tests showed significant differences" + ) + + if result.semantic_results: + avg_proximity = result.semantic_results.get("average_proximity") + if avg_proximity is not None: + summary_parts.append( + f"Semantic validation: Average proximity score of {avg_proximity:.3f}" + ) + + summary_comparison = result.semantic_results.get("summary_comparison") + if summary_comparison: + summary_parts.append( + f"Summary proximity: {summary_comparison['proximity_score']:.3f}" + ) + + if result.overall_score is not None: + summary_parts.append(f"Overall validation score: {result.overall_score:.3f}") + + return "; ".join(summary_parts) if summary_parts else "No validation results available" + + def _generate_markdown_report(self, result: SimulationExperimentEmpiricalValidationResult, + control: SimulationExperimentDataset = None, + treatment: SimulationExperimentDataset = None) -> str: + """Generate a comprehensive markdown report for simulation experiment empirical validation.""" + overall_score_str = f"{result.overall_score:.3f}" if result.overall_score is not None else "N/A" + + report = f"""# Simulation Experiment Empirical Validation Report + +**Validation Type:** {result.validation_type} +**Control/Empirical:** {result.control_name} +**Treatment/Simulation:** {result.treatment_name} +**Timestamp:** {result.timestamp} +**Overall Score:** {overall_score_str} + +## Summary + +{result.summary} + +""" + + # Add data type information if available + if control or treatment: + data_type_info = self._generate_data_type_info_section(control, treatment) + if data_type_info: + report += data_type_info + + # Statistical Results Section + if result.statistical_results: + report += "## Statistical Validation\n\n" + + if "error" in result.statistical_results: + report += f"**Error:** {result.statistical_results['error']}\n\n" + else: + stats = result.statistical_results + report += f"**Common Metrics:** {', '.join(stats.get('common_metrics', []))}\n\n" + report += f"**Significance Level:** {stats.get('significance_level', 'N/A')}\n\n" + + test_results = stats.get("test_results", {}) + if test_results: + report += "### Test Results\n\n" + + for treatment_name, treatment_results in test_results.items(): + report += f"#### {treatment_name}\n\n" + + for metric, metric_result in treatment_results.items(): + report += f"**{metric}:**\n\n" + + significant = metric_result.get("significant", False) + p_value = metric_result.get("p_value", "N/A") + test_type = metric_result.get("test_type", "N/A") + effect_size = self._extract_effect_size(metric_result) + + # Get the appropriate statistic based on test type + statistic = "N/A" + if "t_statistic" in metric_result: + statistic = metric_result["t_statistic"] + elif "u_statistic" in metric_result: + statistic = metric_result["u_statistic"] + elif "f_statistic" in metric_result: + statistic = metric_result["f_statistic"] + elif "chi2_statistic" in metric_result: + statistic = metric_result["chi2_statistic"] + elif "ks_statistic" in metric_result: + statistic = metric_result["ks_statistic"] + + status = "✅ Significant" if significant else "❌ Not Significant" + + report += f"- **{test_type}:** {status}\n" + report += f" - p-value: {p_value}\n" + report += f" - statistic: {statistic}\n" + if effect_size is not None: + effect_interpretation = self._interpret_effect_size(abs(effect_size), test_type) + report += f" - effect size: {effect_size:.3f} ({effect_interpretation})\n" + + report += "\n" + + # Semantic Results Section + if result.semantic_results: + report += "## Semantic Validation\n\n" + + semantic = result.semantic_results + + # Individual comparisons + individual_comps = semantic.get("individual_comparisons", []) + if individual_comps: + report += "### Individual Agent Comparisons\n\n" + + for comp in individual_comps: + score = comp["proximity_score"] + control_agent = comp["control_agent"] + treatment_agent = comp["treatment_agent"] + justification = comp["justification"] + + report += f"**{control_agent} vs {treatment_agent}:** {score:.3f}\n\n" + report += f"{justification}\n\n" + + avg_proximity = semantic.get("average_proximity") + if avg_proximity: + report += f"**Average Proximity Score:** {avg_proximity:.3f}\n\n" + + # Summary comparison + summary_comp = semantic.get("summary_comparison") + if summary_comp: + report += "### Summary Comparison\n\n" + report += f"**Proximity Score:** {summary_comp['proximity_score']:.3f}\n\n" + report += f"**Justification:** {summary_comp['justification']}\n\n" + + return report + + def _generate_data_type_info_section(self, control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset) -> str: + """Generate comprehensive data type information section for the report.""" + all_metrics = set() + + # Collect all metrics from both datasets + if control: + all_metrics.update(control.key_results.keys()) + if treatment: + all_metrics.update(treatment.key_results.keys()) + + if not all_metrics: + return "" + + # Group metrics by data type + data_type_groups = {} + for metric in all_metrics: + for dataset_name, dataset in [("control", control), ("treatment", treatment)]: + if dataset and metric in dataset.data_types: + data_type = dataset.data_types[metric] + if data_type not in data_type_groups: + data_type_groups[data_type] = set() + data_type_groups[data_type].add(metric) + break # Use first available data type + + if not data_type_groups: + return "" + + report = "## Data Type Information\n\n" + + for data_type, metrics in sorted(data_type_groups.items()): + if not metrics: + continue + + report += f"### {data_type.title()} Data\n\n" + + if data_type == "categorical": + report += "String categories converted to ordinal values for statistical analysis.\n\n" + elif data_type == "ordinal": + report += "Ordered categories or levels with meaningful ranking.\n\n" + elif data_type == "ranking": + report += "Rank positions (1st, 2nd, 3rd, etc.) indicating preference or order.\n\n" + elif data_type == "count": + report += "Non-negative integer counts (frequencies, occurrences, etc.).\n\n" + elif data_type == "proportion": + report += "Values between 0-1 representing proportions or percentages.\n\n" + elif data_type == "binary": + report += "Binary outcomes converted to 0/1 for analysis.\n\n" + elif data_type == "numeric": + report += "Continuous numeric values.\n\n" + + for metric in sorted(metrics): + report += f"#### {metric}\n\n" + + # Show information from both datasets + for dataset_name, dataset in [("Control", control), ("Treatment", treatment)]: + if not dataset or metric not in dataset.key_results: + continue + + data_type_info = dataset.get_data_type_info(metric) + summary = dataset.get_metric_summary(metric) + + report += f"**{dataset_name}:**\n" + + if data_type == "categorical": + if "categories" in data_type_info: + categories = data_type_info["categories"] + mapping = data_type_info.get("category_mapping", {}) + + report += f"- Categories: {', '.join(f'`{cat}`' for cat in categories)}\n" + report += f"- Ordinal mapping: {mapping}\n" + + if "category_distribution" in summary: + distribution = summary["category_distribution"] + total = sum(distribution.values()) + report += "- Distribution: " + dist_items = [] + for cat in categories: + count = distribution.get(cat, 0) + pct = (count / total * 100) if total > 0 else 0 + dist_items.append(f"`{cat}`: {count} ({pct:.1f}%)") + report += ", ".join(dist_items) + "\n" + + elif data_type == "ordinal": + if "ordinal_categories" in data_type_info: + # String-based ordinal + categories = data_type_info["ordinal_categories"] + mapping = data_type_info.get("ordinal_mapping", {}) + report += f"- Ordered categories: {' < '.join(f'`{cat}`' for cat in categories)}\n" + report += f"- Ordinal mapping: {mapping}\n" + elif "ordinal_info" in data_type_info: + # Numeric ordinal + info = data_type_info["ordinal_info"] + report += f"- Value range: {info.get('min_value')} to {info.get('max_value')}\n" + report += f"- Unique levels: {info.get('num_levels')} ({info.get('unique_values')})\n" + + elif data_type == "ranking": + if "ranking_info" in data_type_info: + info = data_type_info["ranking_info"] + report += f"- Rank range: {info.get('min_rank')} to {info.get('max_rank')}\n" + report += f"- Number of ranks: {info.get('num_ranks')}\n" + report += f"- Direction: {info.get('direction', 'ascending')} (1 = best)\n" + + if "rank_distribution" in summary: + distribution = summary["rank_distribution"] + report += "- Distribution: " + rank_items = [] + for rank in sorted(distribution.keys()): + count = distribution[rank] + rank_items.append(f"Rank {rank}: {count}") + report += ", ".join(rank_items) + "\n" + + elif data_type == "binary": + if "binary_distribution" in summary: + distribution = summary["binary_distribution"] + true_count = distribution.get("true", 0) + false_count = distribution.get("false", 0) + total = true_count + false_count + if total > 0: + true_pct = (true_count / total * 100) + false_pct = (false_count / total * 100) + report += f"- Distribution: True: {true_count} ({true_pct:.1f}%), False: {false_count} ({false_pct:.1f}%)\n" + + elif data_type in ["count", "proportion", "numeric"]: + if "min_value" in summary and "max_value" in summary: + report += f"- Range: {summary['min_value']} to {summary['max_value']}\n" + if "valid_values" in summary: + report += f"- Valid values: {summary['valid_values']}/{summary.get('total_values', 'N/A')}\n" + + report += "\n" + + return report + + def _generate_categorical_info_section(self, control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset) -> str: + """ + Generate categorical data information section for the report. + This is kept for backward compatibility and now calls the more comprehensive data type method. + """ + return self._generate_data_type_info_section(control, treatment) + + @classmethod + def read_empirical_data_from_csv(cls, + file_path: Union[str, Path], + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None, + encoding: str = "utf-8") -> 'SimulationExperimentDataset': + """ + Read empirical data from a CSV file and convert it to a SimulationExperimentDataset. + + Args: + file_path: Path to the CSV file + experimental_data_type: Type of experimental data: + - "single_value_per_agent": Each agent has a single value (e.g., score, rating) + - "ranking_per_agent": Each agent provides rankings for multiple items (separate columns) + - "ordinal_ranking_per_agent": Each agent provides ordinal ranking in single column with separator + agent_id_column: Column name containing agent identifiers (optional) + agent_comments_column: Column name containing agent comments/explanations (optional) + agent_attributes_columns: List of column names containing agent attributes (age, gender, etc.) + value_column: Column name containing the main value for single_value_per_agent mode + ranking_columns: List of column names containing rankings for ranking_per_agent mode + ordinal_ranking_column: Column name containing ordinal rankings for ordinal_ranking_per_agent mode + ordinal_ranking_separator: Separator used in ordinal ranking strings (default: "-") + ordinal_ranking_options: List of options being ranked (if None, auto-detected from data) + dataset_name: Optional name for the dataset + dataset_description: Optional description of the dataset + encoding: File encoding (default: utf-8) + + Returns: + SimulationExperimentDataset object populated with the CSV data + + Raises: + FileNotFoundError: If the CSV file doesn't exist + ValueError: If required columns are missing or data format is invalid + pandas.errors.EmptyDataError: If the CSV file is empty + """ + file_path = Path(file_path) + + if not file_path.exists(): + raise FileNotFoundError(f"CSV file not found: {file_path}") + + try: + # Read CSV with UTF-8 encoding and error handling + df = pd.read_csv(file_path, encoding=encoding, encoding_errors='replace') + except pd.errors.EmptyDataError: + raise pd.errors.EmptyDataError(f"CSV file is empty: {file_path}") + except UnicodeDecodeError as e: + raise ValueError(f"Failed to read CSV file with encoding {encoding}: {e}") + + if df.empty: + raise ValueError(f"CSV file contains no data: {file_path}") + + # Use common processing method + return cls._process_empirical_data_from_dataframe( + df=df, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name or f"Empirical_Data_{file_path.stem}", + dataset_description=dataset_description or f"Empirical data loaded from {file_path.name}" + ) + + @classmethod + def read_empirical_data_from_dataframe(cls, + df: pd.DataFrame, + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None) -> 'SimulationExperimentDataset': + """ + Read empirical data from a pandas DataFrame and convert it to a SimulationExperimentDataset. + + This method provides the same functionality as read_empirical_data_from_csv but accepts + a pandas DataFrame directly, eliminating the need to save DataFrames to CSV files first. + + Args: + df: The pandas DataFrame containing the empirical data + experimental_data_type: Type of experimental data: + - "single_value_per_agent": Each agent has a single value (e.g., score, rating) + - "ranking_per_agent": Each agent provides rankings for multiple items (separate columns) + - "ordinal_ranking_per_agent": Each agent provides ordinal ranking in single column with separator + agent_id_column: Column name containing agent identifiers (optional) + agent_comments_column: Column name containing agent comments/explanations (optional) + agent_attributes_columns: List of column names containing agent attributes (age, gender, etc.) + value_column: Column name containing the main value for single_value_per_agent mode + ranking_columns: List of column names containing rankings for ranking_per_agent mode + ordinal_ranking_column: Column name containing ordinal rankings for ordinal_ranking_per_agent mode + ordinal_ranking_separator: Separator used in ordinal ranking strings (default: "-") + ordinal_ranking_options: List of options being ranked (if None, auto-detected from data) + dataset_name: Optional name for the dataset + dataset_description: Optional description of the dataset + + Returns: + SimulationExperimentDataset object populated with the DataFrame data + + Raises: + ValueError: If required columns are missing or data format is invalid + TypeError: If df is not a pandas DataFrame + """ + # Validate input + if not isinstance(df, pd.DataFrame): + raise TypeError(f"Expected pandas DataFrame, got {type(df)}") + + if df.empty: + raise ValueError("DataFrame contains no data") + + # Use common processing method + return cls._process_empirical_data_from_dataframe( + df=df, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name or "Empirical_Data_from_DataFrame", + dataset_description=dataset_description or "Empirical data loaded from pandas DataFrame" + ) + + @classmethod + def _process_empirical_data_from_dataframe(cls, + df: pd.DataFrame, + experimental_data_type: str, + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]], + value_column: Optional[str], + ranking_columns: Optional[List[str]], + ordinal_ranking_column: Optional[str], + ordinal_ranking_separator: str, + ordinal_ranking_options: Optional[List[str]], + dataset_name: str, + dataset_description: str) -> 'SimulationExperimentDataset': + """ + Common processing method for both CSV and DataFrame inputs. + + This method contains the shared logic for processing empirical data regardless of input source. + """ + # Initialize dataset + dataset = SimulationExperimentDataset( + name=dataset_name, + description=dataset_description + ) + + # Process based on experimental data type + if experimental_data_type == "single_value_per_agent": + cls._process_single_value_per_agent_csv(df, dataset, value_column, + agent_id_column, agent_comments_column, + agent_attributes_columns) + elif experimental_data_type == "ranking_per_agent": + cls._process_ranking_per_agent_csv(df, dataset, ranking_columns, + agent_id_column, agent_comments_column, + agent_attributes_columns) + elif experimental_data_type == "ordinal_ranking_per_agent": + cls._process_ordinal_ranking_per_agent_csv(df, dataset, ordinal_ranking_column, + ordinal_ranking_separator, ordinal_ranking_options, + agent_id_column, agent_comments_column, + agent_attributes_columns) + else: + raise ValueError(f"Unsupported experimental_data_type: {experimental_data_type}. " + f"Supported types: 'single_value_per_agent', 'ranking_per_agent', 'ordinal_ranking_per_agent'") + + # Process data types after all data is loaded + dataset._process_data_types() + + return dataset + + @classmethod + def _process_single_value_per_agent_csv(cls, + df: pd.DataFrame, + dataset: 'SimulationExperimentDataset', + value_column: Optional[str], + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]]): + """Process CSV data for single value per agent experiments.""" + + # Auto-detect value column if not specified + if value_column is None: + # Look for common column names that might contain the main value + value_candidates = [col for col in df.columns if any(keyword in col.lower() + for keyword in ['vote', 'score', 'rating', 'value', 'response', 'answer'])] + + if len(value_candidates) == 1: + value_column = value_candidates[0] + elif len(value_candidates) > 1: + # Prefer shorter, more specific names + value_column = min(value_candidates, key=len) + else: + # Fall back to first numeric column + numeric_cols = df.select_dtypes(include=['number']).columns.tolist() + if numeric_cols: + value_column = numeric_cols[0] + else: + raise ValueError("No suitable value column found. Please specify value_column parameter.") + + if value_column not in df.columns: + raise ValueError(f"Value column '{value_column}' not found in CSV. Available columns: {list(df.columns)}") + + # Extract main values (handling mixed types) + values = [] + for val in df[value_column]: + if pd.isna(val): + values.append(None) + else: + # Try to convert to numeric if possible, otherwise keep as string + try: + if isinstance(val, str) and val.strip().isdigit(): + values.append(int(val.strip())) + elif isinstance(val, str): + try: + float_val = float(val.strip()) + # If it's a whole number, convert to int + values.append(int(float_val) if float_val.is_integer() else float_val) + except ValueError: + values.append(val.strip()) + else: + values.append(val) + except (AttributeError, ValueError): + values.append(val) + + # Store the main experimental result + dataset.key_results[value_column] = values + dataset.result_types[value_column] = "per_agent" + + # Process agent IDs/names + agent_names = [] + if agent_id_column and agent_id_column in df.columns: + for agent_id in df[agent_id_column]: + if pd.isna(agent_id): + agent_names.append(None) + else: + agent_names.append(str(agent_id)) + else: + # Generate default agent names + for i in range(len(df)): + agent_names.append(f"Agent_{i+1}") + + dataset.agent_names = agent_names + + # Process agent comments/justifications + if agent_comments_column and agent_comments_column in df.columns: + justifications = [] + for i, comment in enumerate(df[agent_comments_column]): + # Include all comments, even empty ones, to maintain agent alignment + agent_name = agent_names[i] if i < len(agent_names) else f"Agent_{i+1}" + comment_text = str(comment).strip() if pd.notna(comment) else "" + justifications.append({ + "agent_name": agent_name, + "agent_index": i, + "justification": comment_text + }) + dataset.agent_justifications = justifications + + # Process agent attributes + if agent_attributes_columns: + for attr_col in agent_attributes_columns: + if attr_col in df.columns: + attr_values = [] + for val in df[attr_col]: + if pd.isna(val): + attr_values.append(None) + else: + attr_values.append(str(val).strip()) + + # Store in agent_attributes instead of key_results + dataset.agent_attributes[attr_col] = attr_values + + @classmethod + def _process_ranking_per_agent_csv(cls, + df: pd.DataFrame, + dataset: 'SimulationExperimentDataset', + ranking_columns: Optional[List[str]], + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]]): + """Process CSV data for ranking per agent experiments.""" + + # Auto-detect ranking columns if not specified + if ranking_columns is None: + # Look for columns that might contain rankings + numeric_cols = df.select_dtypes(include=['number']).columns.tolist() + + # Exclude agent ID column if specified + if agent_id_column and agent_id_column in numeric_cols: + numeric_cols.remove(agent_id_column) + + if len(numeric_cols) < 2: + raise ValueError("No suitable ranking columns found. Please specify ranking_columns parameter.") + + ranking_columns = numeric_cols + + # Validate ranking columns exist + missing_cols = [col for col in ranking_columns if col not in df.columns] + if missing_cols: + raise ValueError(f"Ranking columns not found in CSV: {missing_cols}. Available columns: {list(df.columns)}") + + # Process each ranking column + for rank_col in ranking_columns: + rankings = [] + for val in df[rank_col]: + if pd.isna(val): + rankings.append(None) + else: + try: + # Convert to integer rank + rankings.append(int(float(val))) + except (ValueError, TypeError): + rankings.append(None) + + dataset.key_results[rank_col] = rankings + dataset.result_types[rank_col] = "per_agent" + dataset.data_types[rank_col] = "ranking" + + # Process agent IDs/names (same as single value method) + agent_names = [] + if agent_id_column and agent_id_column in df.columns: + for agent_id in df[agent_id_column]: + if pd.isna(agent_id): + agent_names.append(None) + else: + agent_names.append(str(agent_id)) + else: + # Generate default agent names + for i in range(len(df)): + agent_names.append(f"Agent_{i+1}") + + dataset.agent_names = agent_names + + # Process agent comments (same as single value method) + if agent_comments_column and agent_comments_column in df.columns: + justifications = [] + for i, comment in enumerate(df[agent_comments_column]): + # Include all comments, even empty ones, to maintain agent alignment + agent_name = agent_names[i] if i < len(agent_names) else f"Agent_{i+1}" + comment_text = str(comment).strip() if pd.notna(comment) else "" + justifications.append({ + "agent_name": agent_name, + "agent_index": i, + "justification": comment_text + }) + dataset.agent_justifications = justifications + + # Process agent attributes (same as single value method) + if agent_attributes_columns: + for attr_col in agent_attributes_columns: + if attr_col in df.columns: + attr_values = [] + for val in df[attr_col]: + if pd.isna(val): + attr_values.append(None) + else: + attr_values.append(str(val).strip()) + + # Store in agent_attributes instead of key_results + dataset.agent_attributes[attr_col] = attr_values + + @classmethod + def _process_ordinal_ranking_per_agent_csv(cls, + df: pd.DataFrame, + dataset: 'SimulationExperimentDataset', + ordinal_ranking_column: Optional[str], + ordinal_ranking_separator: str, + ordinal_ranking_options: Optional[List[str]], + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]]): + """Process CSV data for ordinal ranking per agent experiments (single column with separator).""" + + # Auto-detect ranking column if not specified + if ordinal_ranking_column is None: + # Look for columns that might contain ordinal rankings + ranking_candidates = [col for col in df.columns if any(keyword in col.lower() + for keyword in ['ranking', 'rank', 'order', 'preference', 'choice'])] + + if len(ranking_candidates) == 1: + ordinal_ranking_column = ranking_candidates[0] + elif len(ranking_candidates) > 1: + # Prefer shorter, more specific names + ordinal_ranking_column = min(ranking_candidates, key=len) + else: + # Fall back to first string column that contains separator + string_cols = df.select_dtypes(include=['object']).columns.tolist() + if agent_id_column and agent_id_column in string_cols: + string_cols.remove(agent_id_column) + if agent_comments_column and agent_comments_column in string_cols: + string_cols.remove(agent_comments_column) + + # Check which string columns contain the separator + for col in string_cols: + if df[col].astype(str).str.contains(ordinal_ranking_separator, na=False).any(): + ordinal_ranking_column = col + break + + if ordinal_ranking_column is None: + raise ValueError("No suitable ordinal ranking column found. Please specify ordinal_ranking_column parameter.") + + if ordinal_ranking_column not in df.columns: + raise ValueError(f"Ordinal ranking column '{ordinal_ranking_column}' not found in CSV. Available columns: {list(df.columns)}") + + # Auto-detect ranking options if not specified + if ordinal_ranking_options is None: + ordinal_ranking_options = cls._auto_detect_ranking_options(df[ordinal_ranking_column], ordinal_ranking_separator) + + # Parse ordinal rankings and convert to individual ranking columns + ranking_data = cls._parse_ordinal_rankings(df[ordinal_ranking_column], ordinal_ranking_separator, ordinal_ranking_options) + + # Store parsed rankings as separate metrics + for option in ordinal_ranking_options: + option_ranking_key = f"{option}_rank" + dataset.key_results[option_ranking_key] = ranking_data[option] + dataset.result_types[option_ranking_key] = "per_agent" + dataset.data_types[option_ranking_key] = "ranking" + + # Store ranking info (always for ordinal ranking data) + valid_ranks = [r for r in ranking_data[option] if r is not None] + + # Always store ranking info for ordinal ranking data, regardless of valid ranks + ranking_info = { + "direction": "ascending", # 1 = best, higher = worse + "original_options": ordinal_ranking_options, + "separator": ordinal_ranking_separator, + "source_column": ordinal_ranking_column + } + + # Add rank statistics if valid ranks exist + if valid_ranks: + ranking_info.update({ + "min_rank": min(valid_ranks), + "max_rank": max(valid_ranks), + "num_ranks": len(set(valid_ranks)), + "rank_values": sorted(set(valid_ranks)) + }) + else: + # Set reasonable defaults based on options + ranking_info.update({ + "min_rank": 1, + "max_rank": len(ordinal_ranking_options), + "num_ranks": 0, + "rank_values": [] + }) + + dataset.ranking_info[option_ranking_key] = ranking_info + + # Process agent IDs/names (same as other methods) + agent_names = [] + if agent_id_column and agent_id_column in df.columns: + for agent_id in df[agent_id_column]: + if pd.isna(agent_id): + agent_names.append(None) + else: + agent_names.append(str(agent_id)) + else: + # Generate default agent names + for i in range(len(df)): + agent_names.append(f"Agent_{i+1}") + + dataset.agent_names = agent_names + + # Process agent comments (same as other methods) + if agent_comments_column and agent_comments_column in df.columns: + justifications = [] + for i, comment in enumerate(df[agent_comments_column]): + # Include all comments, even empty ones, to maintain agent alignment + agent_name = agent_names[i] if i < len(agent_names) else f"Agent_{i+1}" + comment_text = str(comment).strip() if pd.notna(comment) else "" + justifications.append({ + "agent_name": agent_name, + "agent_index": i, + "justification": comment_text + }) + dataset.agent_justifications = justifications + + # Process agent attributes (same as other methods) + if agent_attributes_columns: + for attr_col in agent_attributes_columns: + if attr_col in df.columns: + attr_values = [] + for val in df[attr_col]: + if pd.isna(val): + attr_values.append(None) + else: + attr_values.append(str(val).strip()) + + # Store in agent_attributes instead of key_results + dataset.agent_attributes[attr_col] = attr_values + + @classmethod + def _auto_detect_ranking_options(cls, ranking_series: pd.Series, separator: str) -> List[str]: + """Auto-detect the ranking options from ordinal ranking data.""" + all_options = set() + + for ranking_str in ranking_series.dropna(): + if pd.isna(ranking_str): + continue + + ranking_str = str(ranking_str).strip() + if separator in ranking_str: + options = [opt.strip() for opt in ranking_str.split(separator)] + all_options.update(options) + + if not all_options: + raise ValueError(f"No ranking options found in data using separator '{separator}'") + + # Sort options for consistency (could be enhanced to preserve meaningful order) + return sorted(list(all_options)) + + @classmethod + def _parse_ordinal_rankings(cls, ranking_series: pd.Series, separator: str, options: List[str]) -> Dict[str, List[Optional[int]]]: + """Parse ordinal ranking strings into individual option rankings.""" + result = {option: [] for option in options} + + for ranking_str in ranking_series: + if pd.isna(ranking_str) or str(ranking_str).strip() == "": + # Handle missing data + for option in options: + result[option].append(None) + continue + + ranking_str = str(ranking_str).strip() + + if separator not in ranking_str: + # Handle malformed data + for option in options: + result[option].append(None) + continue + + # Parse the ranking + ranked_options = [opt.strip() for opt in ranking_str.split(separator)] + + # Create rank mapping (position in list = rank, starting from 1) + option_to_rank = {} + for rank, option in enumerate(ranked_options, 1): + if option in options: + option_to_rank[option] = rank + + # Fill in ranks for each option + for option in options: + rank = option_to_rank.get(option, None) + result[option].append(rank) + + return result + + @classmethod + def create_from_csv(cls, + file_path: Union[str, Path], + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None, + encoding: str = "utf-8") -> tuple['SimulationExperimentEmpiricalValidator', 'SimulationExperimentDataset']: + """ + Create a validator and load empirical data from CSV in one step. + + This is a convenience method that combines validator creation with CSV loading. + + Args: + Same as read_empirical_data_from_csv() + + Returns: + Tuple of (validator_instance, loaded_dataset) + """ + validator = cls() + dataset = cls.read_empirical_data_from_csv( + file_path=file_path, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name, + dataset_description=dataset_description, + encoding=encoding + ) + return validator, dataset + + @classmethod + def create_from_dataframe(cls, + df: pd.DataFrame, + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None) -> tuple['SimulationExperimentEmpiricalValidator', 'SimulationExperimentDataset']: + """ + Create a validator and load empirical data from a pandas DataFrame in one step. + + This is a convenience method that combines validator creation with DataFrame loading. + + Args: + Same as read_empirical_data_from_dataframe() + + Returns: + Tuple of (validator_instance, loaded_dataset) + """ + validator = cls() + dataset = cls.read_empirical_data_from_dataframe( + df=df, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name, + dataset_description=dataset_description + ) + return validator, dataset + + def _extract_effect_size(self, metric_result: Dict[str, Any]) -> Optional[float]: + """Extract effect size from statistical test result, regardless of test type.""" + # Cohen's d for t-tests (most common) + if "effect_size" in metric_result: + return metric_result["effect_size"] + + # For tests that don't provide Cohen's d, calculate standardized effect size + test_type = metric_result.get("test_type", "").lower() + + if "t-test" in test_type: + # For t-tests, effect_size should be Cohen's d + return metric_result.get("effect_size", 0.0) + + elif "mann-whitney" in test_type: + # For Mann-Whitney, use Common Language Effect Size (CLES) + # Convert CLES to Cohen's d equivalent: d ≈ 2 * Φ^(-1)(CLES) + cles = metric_result.get("effect_size", 0.5) + # Simple approximation: convert CLES to d-like measure + # CLES of 0.5 = no effect, CLES of 0.71 ≈ small effect (d=0.2) + return 2 * (cles - 0.5) + + elif "anova" in test_type: + # For ANOVA, use eta-squared and convert to Cohen's d equivalent + eta_squared = metric_result.get("effect_size", 0.0) + # Convert eta-squared to Cohen's d: d = 2 * sqrt(eta^2 / (1 - eta^2)) + if eta_squared > 0 and eta_squared < 1: + return 2 * (eta_squared / (1 - eta_squared)) ** 0.5 + return 0.0 + + elif "chi-square" in test_type: + # For Chi-square, use Cramer's V and convert to Cohen's d equivalent + cramers_v = metric_result.get("effect_size", 0.0) + # Rough conversion: d ≈ 2 * Cramer's V + return 2 * cramers_v + + elif "kolmogorov-smirnov" in test_type or "ks" in test_type: + # For KS test, the effect size is the KS statistic itself + # It represents the maximum difference between CDFs (0 to 1) + return metric_result.get("effect_size", metric_result.get("ks_statistic", 0.0)) + + # Fallback: try to calculate from means and standard deviations + if all(k in metric_result for k in ["control_mean", "treatment_mean", "control_std", "treatment_std"]): + control_mean = metric_result["control_mean"] + treatment_mean = metric_result["treatment_mean"] + control_std = metric_result["control_std"] + treatment_std = metric_result["treatment_std"] + + # Calculate pooled standard deviation + pooled_std = ((control_std ** 2 + treatment_std ** 2) / 2) ** 0.5 + if pooled_std > 0: + return abs(treatment_mean - control_mean) / pooled_std + + # If all else fails, return 0 (no effect) + return 0.0 + + def _interpret_effect_size(self, effect_size: float, test_type: str = "") -> str: + """Provide interpretation of effect size magnitude based on test type.""" + test_type_lower = test_type.lower() + + # For KS test, use different thresholds since KS statistic ranges 0-1 + if "kolmogorov-smirnov" in test_type_lower or "ks" in test_type_lower: + if effect_size < 0.1: + return "negligible difference" + elif effect_size < 0.25: + return "small difference" + elif effect_size < 0.5: + return "medium difference" + else: + return "large difference" + + # For other tests, use Cohen's conventions + if effect_size < 0.2: + return "negligible" + elif effect_size < 0.5: + return "small" + elif effect_size < 0.8: + return "medium" + else: + return "large" + + +def validate_simulation_experiment_empirically(control_data: Dict[str, Any], + treatment_data: Dict[str, Any], + validation_types: List[str] = ["statistical", "semantic"], + statistical_test_type: str = "welch_t_test", + significance_level: float = 0.05, + output_format: str = "values") -> Union[SimulationExperimentEmpiricalValidationResult, str]: + """ + Convenience function to validate simulation experiment data against empirical control data. + + This performs data-driven validation using statistical and semantic methods, + distinct from LLM-based evaluations. + + Args: + control_data: Dictionary containing control/empirical data + treatment_data: Dictionary containing treatment/simulation experiment data + validation_types: List of validation types to perform + statistical_test_type: Type of statistical test ("welch_t_test", "ks_test", "mann_whitney", etc.) + significance_level: Significance level for statistical tests + output_format: "values" for SimulationExperimentEmpiricalValidationResult object, "report" for markdown report + + Returns: + SimulationExperimentEmpiricalValidationResult object or markdown report string + """ + # Use Pydantic's built-in parsing instead of from_dict + control_dataset = SimulationExperimentDataset.model_validate(control_data) + treatment_dataset = SimulationExperimentDataset.model_validate(treatment_data) + + validator = SimulationExperimentEmpiricalValidator() + return validator.validate( + control_dataset, + treatment_dataset, + validation_types=validation_types, + statistical_test_type=statistical_test_type, + significance_level=significance_level, + output_format=output_format + ) diff --git a/tinytroupe/validation/tiny_person_validator.py b/tinytroupe/validation/tiny_person_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..8eff812d338c44b0dc43769e97874df733b92861 --- /dev/null +++ b/tinytroupe/validation/tiny_person_validator.py @@ -0,0 +1,120 @@ +import os +import json +import chevron +import logging +from pydantic import BaseModel +from typing import Optional, List + +from tinytroupe import openai_utils +from tinytroupe.agent import TinyPerson +from tinytroupe import config +import tinytroupe.utils as utils + + +default_max_content_display_length = config["OpenAI"].getint("MAX_CONTENT_DISPLAY_LENGTH", 1024) + + +class ValidationResponse(BaseModel): + """Response structure for the validation process""" + questions: Optional[List[str]] = None + next_phase_description: Optional[str] = None + score: Optional[float] = None + justification: Optional[str] = None + is_complete: bool = False + + +class TinyPersonValidator: + + @staticmethod + def validate_person(person, expectations=None, include_agent_spec=True, max_content_length=default_max_content_display_length) -> tuple[float, str]: + """ + Validate a TinyPerson instance using OpenAI's LLM. + + This method sends a series of questions to the TinyPerson instance to validate its responses using OpenAI's LLM. + The method returns a float value representing the confidence score of the validation process. + If the validation process fails, the method returns None. + + Args: + person (TinyPerson): The TinyPerson instance to be validated. + expectations (str, optional): The expectations to be used in the validation process. Defaults to None. + include_agent_spec (bool, optional): Whether to include the agent specification in the prompt. Defaults to False. + max_content_length (int, optional): The maximum length of the content to be displayed when rendering the conversation. + + Returns: + float: The confidence score of the validation process (0.0 to 1.0), or None if the validation process fails. + str: The justification for the validation score, or None if the validation process fails. + """ + # Initiating the current messages + current_messages = [] + + # Generating the prompt to check the person + check_person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/check_person.mustache') + with open(check_person_prompt_template_path, 'r', encoding='utf-8', errors='replace') as f: + check_agent_prompt_template = f.read() + + system_prompt = chevron.render(check_agent_prompt_template, {"expectations": expectations}) + + # use dedent + import textwrap + user_prompt = textwrap.dedent(\ + """ + Now, based on the following characteristics of the person being interviewed, and following the rules given previously, + create your questions and interview the person. Good luck! + + """) + + if include_agent_spec: + user_prompt += f"\n\n{json.dumps(person._persona, indent=4)}" + + # TODO this was confusing the expectations + #else: + # user_prompt += f"\n\nMini-biography of the person being interviewed: {person.minibio()}" + + + logger = logging.getLogger("tinytroupe") + + logger.info(f"Starting validation of the person: {person.name}") + + # Sending the initial messages to the LLM + current_messages.append({"role": "system", "content": system_prompt}) + current_messages.append({"role": "user", "content": user_prompt}) + + message = openai_utils.client().send_message(current_messages, response_format=ValidationResponse, enable_pydantic_model_return=True) + + max_iterations = 10 # Limit the number of iterations to prevent infinite loops + cur_iteration = 0 + while cur_iteration < max_iterations and message is not None and not message.is_complete: + cur_iteration += 1 + + # Check if we have questions to ask + if message.questions: + # Format questions as a text block + if message.next_phase_description: + questions_text = f"{message.next_phase_description}\n\n" + else: + questions_text = "" + + questions_text += "\n".join([f"{i+1}. {q}" for i, q in enumerate(message.questions)]) + + current_messages.append({"role": "assistant", "content": questions_text}) + logger.info(f"Question validation:\n{questions_text}") + + # Asking the questions to the persona + person.listen_and_act(questions_text, max_content_length=max_content_length) + responses = person.pop_actions_and_get_contents_for("TALK", False) + logger.info(f"Person reply:\n{responses}") + + # Appending the responses to the current conversation and checking the next message + current_messages.append({"role": "user", "content": responses}) + message = openai_utils.client().send_message(current_messages, response_format=ValidationResponse, enable_pydantic_model_return=True) + else: + # If no questions but not complete, something went wrong + logger.warning("LLM did not provide questions but validation is not complete") + break + + if message is not None and message.is_complete and message.score is not None: + logger.info(f"Validation score: {message.score:.2f}; Justification: {message.justification}") + return message.score, message.justification + else: + logger.error("Validation process failed to complete properly") + return None, None \ No newline at end of file diff --git a/tinytroupe/validation/validation_chamber.py b/tinytroupe/validation/validation_chamber.py new file mode 100644 index 0000000000000000000000000000000000000000..4b1bd689292177162dbd39555a96b8929a77612c --- /dev/null +++ b/tinytroupe/validation/validation_chamber.py @@ -0,0 +1,21 @@ +from tinytroupe.experimentation import Proposition + + +class ValidationChamber: + """ + An Validation Chamber is a container where autonomous agents can be put to be validated with respect to various custom validation tasks. + + Validation tasks types include: + - Question answering: given either a concrete question or a question pattern (to be instantitated via an LLM call), and an expectation, + the agent is expected to answer the question correctly. To check correctness, an LLM-based Proposer is used. + - Behavioral patterns: + * repeated actions - does the agent keep repeating the same action like a crazy person? + * self-consistency - does the agent contradict itself over time? + + + The class also provides convenience auxiliary methods to: + - generate reasonable question/answer pairs, given some general overall scenario and agent description. + - generate reasonable behavioral patterns, given some general overall scenario and agent description. + + """ + diff --git a/tools/__init__.py b/tools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b2d19bf3c32747a1830c5265346a6335b2660b2 --- /dev/null +++ b/tools/__init__.py @@ -0,0 +1,15 @@ +""" +Tools allow agents to accomplish specialized tasks. +""" + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.tools.tiny_tool import TinyTool +from tinytroupe.tools.tiny_word_processor import TinyWordProcessor +from tinytroupe.tools.tiny_calendar import TinyCalendar + +__all__ = ["TinyTool", "TinyWordProcessor", "TinyCalendar"] \ No newline at end of file diff --git a/tools/__pycache__/__init__.cpython-312.pyc b/tools/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..50ae6c42095a37da08c29a5901b06fc32c97bd50 Binary files /dev/null and b/tools/__pycache__/__init__.cpython-312.pyc differ diff --git a/tools/__pycache__/sequential_thinking.cpython-312.pyc b/tools/__pycache__/sequential_thinking.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6f78cc5f90846d6594b6a5e7feb7938720c6246d Binary files /dev/null and b/tools/__pycache__/sequential_thinking.cpython-312.pyc differ diff --git a/tools/__pycache__/tiny_calendar.cpython-312.pyc b/tools/__pycache__/tiny_calendar.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..51ffdf6532f0595652932f2c05ad877299627bb8 Binary files /dev/null and b/tools/__pycache__/tiny_calendar.cpython-312.pyc differ diff --git a/tools/__pycache__/tiny_tool.cpython-312.pyc b/tools/__pycache__/tiny_tool.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..429931b7b57a8296db494541961bb57094ce2c8c Binary files /dev/null and b/tools/__pycache__/tiny_tool.cpython-312.pyc differ diff --git a/tools/__pycache__/tiny_word_processor.cpython-312.pyc b/tools/__pycache__/tiny_word_processor.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1ea5bd2cd64cba13ab4d9359e69339b99aea5d91 Binary files /dev/null and b/tools/__pycache__/tiny_word_processor.cpython-312.pyc differ diff --git a/tools/browser.py b/tools/browser.py new file mode 100644 index 0000000000000000000000000000000000000000..7e7961328cdb886f8d769546ea28b5e06d26b58f --- /dev/null +++ b/tools/browser.py @@ -0,0 +1,41 @@ +# Placeholder functions for browser interaction. +# In a real implementation, these would interact with a web browsing API like Selenium or Playwright. + +def screenshot() -> str: + """Takes a screenshot of the current page and returns the path to the image.""" + print("Taking a screenshot...") + # In a real implementation, this would save a screenshot and return the path. + return "placeholder_screenshot.png" + +def click(selector: str): + """Clicks on the element with the given CSS selector.""" + print(f"Clicking on element with selector: {selector}...") + +def fill(selector: str, text: str): + """Fills the given text into the element with the given CSS selector.""" + print(f"Typing '{text}' into element with selector: {selector}...") + +def submit_form(selector: str): + """Submits the form containing the element with the given CSS selector.""" + print(f"Submitting form with element: {selector}...") + +def wait_for_element(selector: str): + """Waits for the element with the given CSS selector to appear.""" + print(f"Waiting for element: {selector}...") + +def scroll_page(direction: str): + """Scrolls the page up or down.""" + print(f"Scrolling page {direction}...") + +def hover_element(selector: str): + """Hovers over the element with the given CSS selector.""" + print(f"Hovering over element: {selector}...") + +def press_key(key: str): + """Presses the given key.""" + print(f"Pressing key: {key}...") + +def get_page_info() -> dict: + """Gets information about the current page, such as links and form elements.""" + print("Getting page info...") + return {"links": [], "forms": []} diff --git a/tools/sequential_thinking.py b/tools/sequential_thinking.py new file mode 100644 index 0000000000000000000000000000000000000000..be8b4d8a1519b237963eefd5bb4649482f521eef --- /dev/null +++ b/tools/sequential_thinking.py @@ -0,0 +1,63 @@ +import requests +import json +from tinytroupe.agent.mental_faculty import TinyToolUse +from tinytroupe.utils.logger import get_logger + +class SequentialThinkingTool(TinyToolUse): + def __init__(self): + super().__init__(tools=[self]) + self.url = "https://harvesthealth-sequential-thinking-mcp.hf.space/run" + + def process_action(self, agent, action: dict) -> bool: + if action['type'] == 'SEQUENTIAL_THINKING': + logger = get_logger(agent.name) + + try: + arguments = json.loads(action['content']) + except json.JSONDecodeError as e: + logger.error(f"MCP Interaction - Invalid JSON in action content: {action['content']}. Error: {e}") + return False + + payload = { + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "sequentialthinking", + "arguments": arguments + } + } + + logger.info(f"MCP Interaction - Request: {json.dumps(payload, indent=2)}") + response_json = self.send_thought(payload) + logger.info(f"MCP Interaction - Response: {json.dumps(response_json, indent=2)}") + + if response_json and 'result' in response_json and 'content' in response_json['result']: + content_text = response_json['result']['content'][0]['text'] + try: + response_data = json.loads(content_text) + agent.think(f"Thought processed. History length: {response_data.get('thoughtHistoryLength')}") + except json.JSONDecodeError: + logger.error(f"MCP Interaction - Could not decode response content: {content_text}") + agent.think("Received a response from the sequential thinking server, but it was not in the expected format.") + + return True + return False + + def send_thought(self, thought_data: dict): + headers = {'Content-Type': 'application/json'} + try: + response = requests.post(self.url, headers=headers, json=thought_data) + response.raise_for_status() + return response.json() + except requests.exceptions.RequestException as e: + # Get the logger for the agent that is making the call + # This is a bit of a hack, as we don't have the agent object here. + # We will rely on the caller to log the error. + return {"error": str(e)} + + def actions_definitions_prompt(self) -> str: + return "" + + def actions_constraints_prompt(self) -> str: + return "" diff --git a/tools/tiny_calendar.py b/tools/tiny_calendar.py new file mode 100644 index 0000000000000000000000000000000000000000..49519c97743d50e46dfa3f2f0ba8c5272ac6362f --- /dev/null +++ b/tools/tiny_calendar.py @@ -0,0 +1,69 @@ + +import textwrap +import json + +from tinytroupe.tools import logger, TinyTool +import tinytroupe.utils as utils + + +# TODO under development +class TinyCalendar(TinyTool): + + def __init__(self, owner=None): + super().__init__("calendar", "A basic calendar tool that allows agents to keep track meetings and appointments.", owner=owner, real_world_side_effects=False) + + # maps date to list of events. Each event itself is a dictionary with keys "title", "description", "owner", "mandatory_attendees", "optional_attendees", "start_time", "end_time" + self.calenar = {} + + def add_event(self, date, title, description=None, owner=None, mandatory_attendees=None, optional_attendees=None, start_time=None, end_time=None): + if date not in self.calendar: + self.calendar[date] = [] + self.calendar[date].append({"title": title, "description": description, "owner": owner, "mandatory_attendees": mandatory_attendees, "optional_attendees": optional_attendees, "start_time": start_time, "end_time": end_time}) + + def find_events(self, year, month, day, hour=None, minute=None): + # TODO + pass + + def _process_action(self, agent, action) -> bool: + if action['type'] == "CREATE_EVENT" and action['content'] is not None: + # parse content json + event_content = json.loads(action['content']) + + # checks whether there are any kwargs that are not valid + valid_keys = ["title", "description", "mandatory_attendees", "optional_attendees", "start_time", "end_time"] + utils.check_valid_fields(event_content, valid_keys) + + # uses the kwargs to create a new event + self.add_event(event_content) + + return True + + else: + return False + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - CREATE_EVENT: You can create a new event in your calendar. The content of the event has many fields, and you should use a JSON format to specify them. Here are the possible fields: + * title: The title of the event. Mandatory. + * description: A brief description of the event. Optional. + * mandatory_attendees: A list of agent names who must attend the event. Optional. + * optional_attendees: A list of agent names who are invited to the event, but are not required to attend. Optional. + * start_time: The start time of the event. Optional. + * end_time: The end time of the event. Optional. + """ + # TODO how the atendee list will be handled? How will they be notified of the invitation? I guess they must also have a calendar themselves. <------------------------------------- + + return utils.dedent(prompt) + + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + + """ + # TODO + + return textwrap.dedent(prompt) + + diff --git a/tools/tiny_tool.py b/tools/tiny_tool.py new file mode 100644 index 0000000000000000000000000000000000000000..c752bd917d3988362a1383ea36325571d95c879a --- /dev/null +++ b/tools/tiny_tool.py @@ -0,0 +1,54 @@ +from tinytroupe.tools import logger +from tinytroupe.utils import JsonSerializableRegistry + + +class TinyTool(JsonSerializableRegistry): + + # Define what attributes should be serialized + serializable_attributes = ["name", "description", "real_world_side_effects"] + + def __init__(self, name, description, owner=None, real_world_side_effects=False, exporter=None, enricher=None): + """ + Initialize a new tool. + + Args: + name (str): The name of the tool. + description (str): A brief description of the tool. + owner (str): The agent that owns the tool. If None, the tool can be used by anyone. + real_world_side_effects (bool): Whether the tool has real-world side effects. That is to say, if it has the potential to change the + state of the world outside of the simulation. If it does, it should be used with caution. + exporter (ArtifactExporter): An exporter that can be used to export the results of the tool's actions. If None, the tool will not be able to export results. + enricher (Enricher): An enricher that can be used to enrich the results of the tool's actions. If None, the tool will not be able to enrich results. + + """ + self.name = name + self.description = description + self.owner = owner + self.real_world_side_effects = real_world_side_effects + self.exporter = exporter + self.enricher = enricher + + def _process_action(self, agent, action: dict) -> bool: + raise NotImplementedError("Subclasses must implement this method.") + + def _protect_real_world(self): + if self.real_world_side_effects: + logger.warning(f" !!!!!!!!!! Tool {self.name} has REAL-WORLD SIDE EFFECTS. This is NOT just a simulation. Use with caution. !!!!!!!!!!") + + def _enforce_ownership(self, agent): + if self.owner is not None and agent.name != self.owner.name: + raise ValueError(f"Agent {agent.name} does not own tool {self.name}, which is owned by {self.owner.name}.") + + def set_owner(self, owner): + self.owner = owner + + def actions_definitions_prompt(self) -> str: + raise NotImplementedError("Subclasses must implement this method.") + + def actions_constraints_prompt(self) -> str: + raise NotImplementedError("Subclasses must implement this method.") + + def process_action(self, agent, action: dict) -> bool: + self._protect_real_world() + self._enforce_ownership(agent) + return self._process_action(agent, action) diff --git a/tools/tiny_word_processor.py b/tools/tiny_word_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..50dcf2981987145f2b4f94826839b50b5cfb83f6 --- /dev/null +++ b/tools/tiny_word_processor.py @@ -0,0 +1,87 @@ + +import json + +from tinytroupe.tools import logger, TinyTool + + +import tinytroupe.utils as utils + +class TinyWordProcessor(TinyTool): + + def __init__(self, owner=None, exporter=None, enricher=None): + super().__init__("wordprocessor", "A basic word processor tool that allows agents to write documents.", owner=owner, real_world_side_effects=False, exporter=exporter, enricher=enricher) + + def write_document(self, title, content, author=None): + logger.debug(f"Writing document with title {title} and content: {content}") + + if self.enricher is not None: + requirements =\ + """ + Turn any draft or outline into an actual and long document, with many, many details. Include tables, lists, and other elements. + The result **MUST** be at least 5 times larger than the original content in terms of characters - do whatever it takes to make it this long and detailed. + """ + + content = self.enricher.enrich_content(requirements=requirements, + content=content, + content_type="Document", + context_info=None, + context_cache=None, verbose=False) + + if self.exporter is not None: + if author is not None: + artifact_name = f"{title}.{author}" + else: + artifact_name = title + self.exporter.export(artifact_name=artifact_name, artifact_data= content, content_type="Document", content_format="md", target_format="md") + self.exporter.export(artifact_name=artifact_name, artifact_data= content, content_type="Document", content_format="md", target_format="docx") + + json_doc = {"title": title, "content": content, "author": author} + self.exporter.export(artifact_name=artifact_name, artifact_data= json_doc, content_type="Document", content_format="md", target_format="json") + + def _process_action(self, agent, action) -> bool: + try: + if action['type'] == "WRITE_DOCUMENT" and action['content'] is not None: + # parse content json + if isinstance(action['content'], str): + doc_spec = utils.extract_json(action['content']) + else: + doc_spec = action['content'] + + # checks whether there are any kwargs that are not valid + valid_keys = ["title", "content", "author"] + utils.check_valid_fields(doc_spec, valid_keys) + + # uses the kwargs to create a new document + self.write_document(**doc_spec) + + return True + + else: + return False + except json.JSONDecodeError as e: + logger.error(f"Error parsing JSON content: {e}. Original content: {action['content']}") + return False + except Exception as e: + logger.error(f"Error processing action: {e}") + return False + + def actions_definitions_prompt(self) -> str: + prompt = \ + """ + - WRITE_DOCUMENT: you can create a new document. The content of the document has many fields, and you **must** use a JSON format to specify them. Here are the possible fields: + * title: The title of the document. Mandatory. + * content: The actual content of the document. You **must** use Markdown to format this content. Mandatory. + * author: The author of the document. You should put your own name. Optional. + """ + return utils.dedent(prompt) + + + def actions_constraints_prompt(self) -> str: + prompt = \ + """ + - Whenever you WRITE_DOCUMENT, you write all the content at once. Moreover, the content should be long and detailed, unless there's a good reason for it not to be. + - Whenever you WRITE_DOCUMENT, you **must** embed the content in a JSON object. Use only valid escape sequences in the JSON content. + - When you WRITE_DOCUMENT, you follow these additional guidelines: + * For any milestones or timelines mentioned, try mentioning specific owners or partner teams, unless there's a good reason not to do so. + """ + return utils.dedent(prompt) \ No newline at end of file diff --git a/ui/__init__.py b/ui/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9eac63f7a878f8cdf7b40731274b2820f701230c --- /dev/null +++ b/ui/__init__.py @@ -0,0 +1,23 @@ +""" +TinyTroupe UI Module + +This module provides user interface components and widgets for TinyTroupe, +enabling interactive experiences with TinyTroupe agents and environments. + +The module is organized into different sub-modules based on the UI framework: + +- jupyter_widgets: Interactive widgets for Jupyter notebooks +- web: Web-based interfaces (future) +- cli: Command-line interfaces (future) + +Example usage: + from tinytroupe.ui.jupyter_widgets import AgentChatJupyterWidget + + # Create a chat interface with your agents + chat = AgentChatJupyterWidget(agents) + chat.display() +""" + +from .jupyter_widgets import AgentChatJupyterWidget + +__all__ = ['AgentChatJupyterWidget'] diff --git a/ui/jupyter_widgets.py b/ui/jupyter_widgets.py new file mode 100644 index 0000000000000000000000000000000000000000..7bf96224f15e02b8d0f586c19312757bc3e6b848 --- /dev/null +++ b/ui/jupyter_widgets.py @@ -0,0 +1,409 @@ +""" +TinyTroupe Jupyter Widgets + +This module provides interactive widgets for Jupyter notebooks that enable +seamless interaction with TinyTroupe agents and environments. + +Classes: + AgentChatJupyterWidget: An interactive chat interface for conversing with TinyTroupe agents + +Dependencies: + - ipywidgets: For creating interactive notebook widgets + - IPython.display: For displaying content in notebooks + - datetime: For timestamping conversations + - threading: For non-blocking animations + - tinytroupe: Core TinyTroupe functionality + +Example usage: + ```python + from tinytroupe.ui.jupyter_widgets import AgentChatJupyterWidget + from tinytroupe.factory import TinyPersonFactory + + # Create some agents + factory = TinyPersonFactory.create_factory_from_demography("path/to/demographics.json") + agents = factory.generate_people(5) + + # Create and display the chat interface + chat_widget = AgentChatJupyterWidget(agents) + chat_widget.display() + ``` +""" + +import ipywidgets as widgets +from IPython.display import display, HTML +import datetime +import threading +import tinytroupe +import time + + +class AgentChatJupyterWidget: + """ + An interactive chat widget for conversing with TinyTroupe agents in Jupyter notebooks. + + This widget provides a user-friendly interface for chatting with one or more TinyTroupe + agents. It features an animated loading indicator, message history, and responsive design. + + Features: + - Agent selection dropdown + - Real-time message input and display + - Single Enter key press to send messages (fixed double-press issue) + - Animated loading indicators while agents process messages + - Message history with timestamps + - Error handling and user feedback + - Responsive design with proper styling + - Throttling to prevent accidental double-sending + - Communication display control (checkbox to show/hide agent output in notebook) + + Attributes: + agents (dict): Dictionary mapping agent names to agent objects + conversation_history (list): List of conversation entries + loading_animation_active (bool): Whether loading animation is currently active + loading_frames (list): Animation frames for the loading spinner + current_loading_frame (int): Current frame index for animation + """ + + def __init__(self, agents_list): + """ + Initialize the chat widget with a list of agents. + + Args: + agents_list (list): List of TinyTroupe agent objects to make available for chat + """ + self.agents = {agent.name: agent for agent in agents_list} + self.conversation_history = [] + self.loading_animation_active = False + self.loading_frames = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏'] + self.current_loading_frame = 0 + self._processing = False # To prevent multiple simultaneous sends + self._last_message = "" # Track last message to detect user input vs programmatic changes + self.setup_widgets() + + def setup_widgets(self): + """ + Set up the UI widgets and their event handlers. + + Creates the agent dropdown, message input, buttons, and conversation display. + Also wires up event handlers for user interactions. + """ + # Agent selector + self.agent_dropdown = widgets.Dropdown( + options=list(self.agents.keys()), + description='Chat with:', + style={'description_width': 'initial'} + ) + + # Message input + self.message_input = widgets.Text( + placeholder='Type your message and press Enter...', + layout=widgets.Layout(width='70%'), + continuous_update=False + ) + + # Track the last message to detect actual user input vs programmatic changes + self._last_message = "" + + # Send button + self.send_button = widgets.Button( + description='Send', + button_style='primary', + layout=widgets.Layout(width='80px') + ) + + # Clear button + self.clear_button = widgets.Button( + description='Clear', + button_style='warning', + layout=widgets.Layout(width='80px') + ) + + # Communication display checkbox + self.communication_display_checkbox = widgets.Checkbox( + value=False, + description='Show agent communication in notebook output', + style={'description_width': 'initial'}, + layout=widgets.Layout(width='auto') + ) + + # Conversation display + self.conversation_display = widgets.HTML( + value="

Start a conversation by selecting an agent and typing a message...

" + ) + + # Wire up events + self.send_button.on_click(self._handle_send_click) + self.clear_button.on_click(self.clear_conversation) + + # Use observe method to detect Enter key presses through value changes + # This is the modern recommended approach for ipywidgets + self.message_input.observe(self._handle_input_change, names='value') + + # Layout + input_row = widgets.HBox([ + self.agent_dropdown, + self.message_input, + self.send_button, + self.clear_button + ]) + + self.widget = widgets.VBox([ + widgets.HTML("

💬 Agent Chat Interface

"), + input_row, + self.communication_display_checkbox, + self.conversation_display + ]) + + def _handle_send_click(self, b): + """Handle send button clicks.""" + if not self._processing: + self.send_message() + + def _handle_input_change(self, change): + """ + Handle input changes using the observe method. + + This method detects when the user has entered text and committed it + (typically by pressing Enter). We use the observe pattern to monitor + value changes rather than the deprecated on_submit method. + + Args: + change (dict): The change event containing 'old' and 'new' values + """ + new_value = change['new'].strip() + old_value = change['old'].strip() + + # Only process if: + # 1. We're not already processing a message + # 2. There's actual text in the new value + # 3. The value actually changed (user input, not programmatic change) + # 4. This isn't the programmatic clearing we do after sending + if (not self._processing and + new_value and + new_value != old_value and + new_value != self._last_message): + + self._last_message = new_value + self.send_message() + + def send_message(self): + """ + Send a message to the selected agent and handle the response. + + This method: + 1. Validates input + 2. Displays user message immediately + 3. Shows animated loading indicator + 4. Processes agent response in the background + 5. Updates the conversation display + """ + print("Sending message...") # Debug print to track message sending + # Prevent double-sending with processing flag + if self._processing: + return + + self._processing = True + + agent_name = self.agent_dropdown.value + message = self.message_input.value.strip() + + if not message or not agent_name: + self._processing = False + return + + + agent = self.agents[agent_name] + timestamp = datetime.datetime.now().strftime("%H:%M:%S") + + # Clear input immediately and add user message to history first + self.message_input.value = '' + self._last_message = "" # Reset tracking variable + + # Add user message to history and display immediately + self.conversation_history.append({ + 'timestamp': timestamp, + 'sender': 'You', + 'message': message, + 'type': 'user' + }) + + # Update display to show user message immediately + self.update_conversation_display() + + # Add animated loading indicator while processing + loading_entry = { + 'timestamp': timestamp, + 'sender': agent_name, + 'message': '🤔 Processing...', + 'type': 'loading' + } + self.conversation_history.append(loading_entry) + + # Start animated loading indicator + self.start_loading_animation(loading_entry) + + # Process agent response in background thread + def process_response(): + try: + # Use the proper TinyTroupe interaction method + # Get the communication display setting from the checkbox + communication_display = self.communication_display_checkbox.value + actions = agent.listen_and_act(message, return_actions=True, communication_display=communication_display) + + # Extract agent responses from the actions + agent_responses = [] + + if actions: + for action_item in actions: + if isinstance(action_item, dict) and 'action' in action_item: + action = action_item['action'] + action_type = action.get('type', '') + action_content = action.get('content', '') + + # Collect TALK and THINK actions as responses + if action_type == 'TALK' and action_content: + agent_responses.append(f"🗣️ {action_content}") + elif action_type == 'THINK' and action_content: + agent_responses.append(f"💭 {action_content}") + + # Combine all responses or provide fallback + if agent_responses: + agent_response = '\n\n'.join(agent_responses) + else: + agent_response = f"I heard your message: '{message}', but I don't have much to say about it right now." + + # Stop loading animation and remove loading indicator + self.stop_loading_animation() + self.conversation_history.pop() # Remove the loading message + + # Add agent response to history + self.conversation_history.append({ + 'timestamp': datetime.datetime.now().strftime("%H:%M:%S"), + 'sender': agent_name, + 'message': agent_response, + 'type': 'agent' + }) + + except Exception as e: + # Handle errors gracefully + error_msg = f"Error communicating with agent: {str(e)}" + if hasattr(e, '__class__'): + error_msg += f" (Type: {e.__class__.__name__})" + + # Stop loading animation and remove loading indicator + self.stop_loading_animation() + self.conversation_history.pop() # Remove the loading message + + self.conversation_history.append({ + 'timestamp': datetime.datetime.now().strftime("%H:%M:%S"), + 'sender': 'System', + 'message': error_msg, + 'type': 'error' + }) + + finally: + # Update display with final result and reset processing flag + self.update_conversation_display() + self._processing = False + + # Start processing in background thread + threading.Thread(target=process_response, daemon=True).start() + + def clear_conversation(self, b=None): + """ + Clear the conversation history and reset the display. + + Args: + b: Button object (when called from button click, None when called directly) + """ + if not self._processing: + self.conversation_history = [] + self.update_conversation_display() + + def update_conversation_display(self): + """ + Update the HTML display of the conversation history. + + This method renders all conversation entries with appropriate styling + based on their type (user, agent, loading, error). + """ + if not self.conversation_history: + html_content = "

Start a conversation...

" + else: + messages_html = [] + for entry in self.conversation_history: + if entry['type'] == 'user': + messages_html.append(f""" +
+ You ({entry['timestamp']}): {entry['message']} +
+ """) + elif entry['type'] == 'agent': + messages_html.append(f""" +
+ {entry['sender']} ({entry['timestamp']}):
+
{entry['message']}
+
+ """) + elif entry['type'] == 'loading': + messages_html.append(f""" +
+ {entry['sender']} ({entry['timestamp']}): {entry['message']} +
+ """) + else: # error + messages_html.append(f""" +
+ {entry['sender']} ({entry['timestamp']}): {entry['message']} +
+ """) + + html_content = f""" +
+ {''.join(messages_html)} +
+ """ + + self.conversation_display.value = html_content + + def start_loading_animation(self, loading_entry): + """ + Start the animated loading indicator. + + This method creates a smooth spinning animation that updates the loading + message with different spinner frames at regular intervals. + + Args: + loading_entry (dict): The conversation entry containing the loading message + """ + self.loading_animation_active = True + self.current_loading_frame = 0 + + def animate(): + if self.loading_animation_active: + # Update the loading message with current animation frame + spinner = self.loading_frames[self.current_loading_frame % len(self.loading_frames)] + loading_entry['message'] = f'{spinner} Processing...' + self.update_conversation_display() + self.current_loading_frame += 1 + + # Schedule next frame after 200ms + threading.Timer(0.2, animate).start() + + animate() + + def stop_loading_animation(self): + """ + Stop the loading animation. + + This method sets the animation flag to False, causing the animation + loop to stop at the next iteration. + """ + self.loading_animation_active = False + + def display(self): + """ + Display the chat widget in the notebook. + + This method should be called to render the widget in a Jupyter notebook cell. + """ + display(self.widget) \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b55cd8002b4ce1352f8935ccc664ca1d752d8262 --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,19 @@ +""" +General utilities and convenience functions. +""" + +import logging +logger = logging.getLogger("tinytroupe") + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.utils.config import * +from tinytroupe.utils.json import * +from tinytroupe.utils.llm import * +from tinytroupe.utils.misc import * +from tinytroupe.utils.rendering import * +from tinytroupe.utils.validation import * +from tinytroupe.utils.semantics import * +from tinytroupe.utils.behavior import * +from tinytroupe.utils.parallel import * \ No newline at end of file diff --git a/utils/__pycache__/__init__.cpython-312.pyc b/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2fa8f18cfbddb71bab2cf86b178229994e8a18ba Binary files /dev/null and b/utils/__pycache__/__init__.cpython-312.pyc differ diff --git a/utils/__pycache__/behavior.cpython-312.pyc b/utils/__pycache__/behavior.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1c5a5947dd3a7ea9283821da35acbbdbf55e606e Binary files /dev/null and b/utils/__pycache__/behavior.cpython-312.pyc differ diff --git a/utils/__pycache__/config.cpython-312.pyc b/utils/__pycache__/config.cpython-312.pyc new file mode 100644 index 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similarity will be computed using only the content, provided that the action + type and target are the same). If the action type or target is different, the similarity will be 0. + + Jaccard similarity is a measure of similarity between two sets, defined as the size of the intersection + divided by the size of the union of the sets. + + Args: + agent (TinyPerson): The agent whose current action is to be compared. + proposed_next_action (dict): The proposed next action to be compared against the agent's current action. + + Returns: + float: The Jaccard similarity score between the agent's current action and the proposed next action. + """ + # Get the agent's current action + current_action = agent.last_remembered_action() + + if current_action is None: + return 0.0 + + # Check if the action type and target are the same + if ("type" in current_action) and ("type" in proposed_next_action) and ("target" in current_action) and ("target" in proposed_next_action) and \ + (current_action["type"] != proposed_next_action["type"] or current_action["target"] != proposed_next_action["target"]): + return 0.0 + + # Compute the Jaccard similarity between the content of the two actions + current_action_content = current_action["content"] + proposed_next_action_content = proposed_next_action["content"] + + # using textdistance to compute the Jaccard similarity + jaccard_similarity = textdistance.jaccard(current_action_content, proposed_next_action_content) + + return jaccard_similarity \ No newline at end of file diff --git a/utils/config.py b/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..b5fbf14e5f7b7cbaa652a1d3c9cb5a206b790aab --- /dev/null +++ b/utils/config.py @@ -0,0 +1,108 @@ +import logging +from pathlib import Path +import configparser + +################################################################################ +# Config and startup utilities +################################################################################ +_config = None + +def read_config_file(use_cache=True, verbose=True) -> configparser.ConfigParser: + global _config + if use_cache and _config is not None: + # if we have a cached config and accept that, return it + return _config + + else: + config = configparser.ConfigParser() + + # Read the default values in the module directory. + config_file_path = Path(__file__).parent.absolute() / '../config.ini' + print(f"Looking for default config on: {config_file_path}") if verbose else None + if config_file_path.exists(): + config.read(config_file_path) + _config = config + else: + raise ValueError(f"Failed to find default config on: {config_file_path}") + + # Now, let's override any specific default value, if there's a custom .ini config. + # Try the directory of the current main program + config_file_path = Path.cwd() / "config.ini" + if config_file_path.exists(): + print(f"Found custom config on: {config_file_path}") if verbose else None + config.read(config_file_path) # this only overrides the values that are present in the custom config + _config = config + return config + else: + if verbose: + print(f"Failed to find custom config on: {config_file_path}") if verbose else None + print("Will use only default values. IF THINGS FAIL, TRY CUSTOMIZING MODEL, API TYPE, etc.") if verbose else None + + return config + +def pretty_print_config(config): + print() + print("=================================") + print("Current TinyTroupe configuration ") + print("=================================") + for section in config.sections(): + print(f"[{section}]") + for key, value in config.items(section): + print(f"{key} = {value}") + print() + +def pretty_print_datetime(): + from datetime import datetime + from datetime import timezone + now = datetime.now() + now_utc = now.astimezone(timezone.utc) + print(f"Current date and time (local): {now.strftime('%Y-%m-%d %H:%M:%S')}") + print(f"Current date and time (UTC): {now_utc.strftime('%Y-%m-%d %H:%M:%S')}") + +def pretty_print_tinytroupe_version(): + try: + import importlib.metadata + version = importlib.metadata.version("tinytroupe") + except Exception: + version = "unknown" + print(f"TinyTroupe version: {version}") + +def start_logger(config: configparser.ConfigParser): + # create logger + logger = logging.getLogger("tinytroupe") + log_level = config['Logging'].get('LOGLEVEL', 'INFO').upper() + logger.setLevel(level=log_level) + + # Clear any existing handlers to prevent duplicates + # This is especially important in Jupyter notebooks where modules get reloaded + for handler in logger.handlers[:]: + logger.removeHandler(handler) + + # Prevent propagation to avoid duplicate messages from parent loggers + logger.propagate = False + + # create console handler and set level to debug + ch = logging.StreamHandler() + ch.setLevel(log_level) + + # create formatter + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + + # add formatter to ch + ch.setFormatter(formatter) + + # add ch to logger + logger.addHandler(ch) + +def set_loglevel(log_level): + """ + Sets the log level for the TinyTroupe logger. + Args: + log_level (str): The log level to set (e.g., 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'). + """ + logger = logging.getLogger("tinytroupe") + logger.setLevel(level=log_level) + + # Also update all handlers to the new log level + for handler in logger.handlers: + handler.setLevel(log_level) \ No newline at end of file diff --git a/utils/json.py b/utils/json.py new file mode 100644 index 0000000000000000000000000000000000000000..2feec167cef5b9a052f3c58cd5727e24596738c9 --- /dev/null +++ b/utils/json.py @@ -0,0 +1,295 @@ +import json +import copy +from pydantic import BaseModel + +from tinytroupe.utils import logger + +class JsonSerializableRegistry: + """ + A mixin class that provides JSON serialization, deserialization, and subclass registration. + """ + + class_mapping = {} + + def to_json(self, include: list = None, suppress: list = None, file_path: str = None, + serialization_type_field_name = "json_serializable_class_name") -> dict: + """ + Returns a JSON representation of the object. + + Args: + include (list, optional): Attributes to include in the serialization. Will override the default behavior. + suppress (list, optional): Attributes to suppress from the serialization. Will override the default behavior. + file_path (str, optional): Path to a file where the JSON will be written. + """ + # Gather all serializable attributes from the class hierarchy + serializable_attrs = set() + suppress_attrs = set() + custom_serializers = {} + for cls in self.__class__.__mro__: # Traverse the class hierarchy + if hasattr(cls, 'serializable_attributes') and isinstance(cls.serializable_attributes, list): + serializable_attrs.update(cls.serializable_attributes) + if hasattr(cls, 'suppress_attributes_from_serialization') and isinstance(cls.suppress_attributes_from_serialization, list): + suppress_attrs.update(cls.suppress_attributes_from_serialization) + if hasattr(cls, 'custom_serializers') and isinstance(cls.custom_serializers, dict): + custom_serializers.update(cls.custom_serializers) + + # Override attributes with method parameters if provided + if include: + serializable_attrs = set(include) + if suppress: + suppress_attrs.update(suppress) + + def aux_serialize_item(item): + if isinstance(item, JsonSerializableRegistry): + return item.to_json(serialization_type_field_name=serialization_type_field_name) + elif isinstance(item, BaseModel): + # If it's a Pydantic model, convert it to a dict first + logger.debug(f"Serializing Pydantic model: {item}") + return item.model_dump(mode="json", exclude_unset=True) + else: + return copy.deepcopy(item) + + result = {serialization_type_field_name: self.__class__.__name__} + for attr in serializable_attrs if serializable_attrs else self.__dict__: + if attr not in suppress_attrs: + value = getattr(self, attr, None) + + attr_renamed = self._programmatic_name_to_json_name(attr) + + # Check if there's a custom serializer for this attribute + if attr in custom_serializers: + result[attr_renamed] = custom_serializers[attr](value) + elif isinstance(value, list): + result[attr_renamed] = [aux_serialize_item(item) for item in value] + elif isinstance(value, dict): + result[attr_renamed] = {k: aux_serialize_item(v) for k, v in value.items()} + else: # isinstance(value, JsonSerializableRegistry) or isinstance(value, BaseModel) or other types + result[attr_renamed] = aux_serialize_item(value) + + if file_path: + # Create directories if they do not exist + import os + os.makedirs(os.path.dirname(file_path), exist_ok=True) + with open(file_path, 'w', encoding='utf-8', errors='replace') as f: + json.dump(result, f, indent=4) + + return result + + @classmethod + def from_json(cls, json_dict_or_path, suppress: list = None, + serialization_type_field_name = "json_serializable_class_name", + post_init_params: dict = None): + """ + Loads a JSON representation of the object and creates an instance of the class. + + Args: + json_dict_or_path (dict or str): The JSON dictionary representing the object or a file path to load the JSON from. + suppress (list, optional): Attributes to suppress from being loaded. + + Returns: + An instance of the class populated with the data from json_dict_or_path. + """ + if isinstance(json_dict_or_path, str): + with open(json_dict_or_path, 'r', encoding='utf-8', errors='replace') as f: + json_dict = json.load(f) + else: + json_dict = json_dict_or_path + + subclass_name = json_dict.get(serialization_type_field_name) + target_class = cls.class_mapping.get(subclass_name, cls) + instance = target_class.__new__(target_class) # Create an instance without calling __init__ + + # Gather all serializable attributes from the class hierarchy + serializable_attrs = set() + custom_deserializers = {} + suppress_attrs = set(suppress) if suppress else set() + for target_mro in target_class.__mro__: + if hasattr(target_mro, 'serializable_attributes') and isinstance(target_mro.serializable_attributes, list): + serializable_attrs.update(target_mro.serializable_attributes) + if hasattr(target_mro, 'custom_deserializers') and isinstance(target_mro.custom_deserializers, dict): + custom_deserializers.update(target_mro.custom_deserializers) + if hasattr(target_mro, 'suppress_attributes_from_serialization') and isinstance(target_mro.suppress_attributes_from_serialization, list): + suppress_attrs.update(target_mro.suppress_attributes_from_serialization) + + # Assign values only for serializable attributes if specified, otherwise assign everything + for key in serializable_attrs if serializable_attrs else json_dict: + key_in_json = cls._programmatic_name_to_json_name(key) + if key_in_json in json_dict and key not in suppress_attrs: + value = json_dict[key_in_json] + if key in custom_deserializers: + # Use custom initializer if provided + setattr(instance, key, custom_deserializers[key](value)) + elif isinstance(value, dict) and serialization_type_field_name in value: + # Assume it's another JsonSerializableRegistry object + setattr(instance, key, JsonSerializableRegistry.from_json(value, serialization_type_field_name=serialization_type_field_name)) + elif isinstance(value, list): + # Handle collections, recursively deserialize if items are JsonSerializableRegistry objects + deserialized_collection = [] + for item in value: + if isinstance(item, dict) and serialization_type_field_name in item: + deserialized_collection.append(JsonSerializableRegistry.from_json(item, serialization_type_field_name=serialization_type_field_name)) + else: + deserialized_collection.append(copy.deepcopy(item)) + setattr(instance, key, deserialized_collection) + else: + setattr(instance, key, copy.deepcopy(value)) + + # Call post-deserialization initialization if available + if hasattr(instance, '_post_deserialization_init') and callable(instance._post_deserialization_init): + post_init_params = post_init_params if post_init_params else {} + instance._post_deserialization_init(**post_init_params) + + return instance + + def __init_subclass__(cls, **kwargs): + super().__init_subclass__(**kwargs) + # Register the subclass using its name as the key + JsonSerializableRegistry.class_mapping[cls.__name__] = cls + + # Automatically extend serializable attributes and custom initializers from parent classes + if hasattr(cls, 'serializable_attributes') and isinstance(cls.serializable_attributes, list): + for base in cls.__bases__: + if hasattr(base, 'serializable_attributes') and isinstance(base.serializable_attributes, list): + cls.serializable_attributes = list(set(base.serializable_attributes + cls.serializable_attributes)) + + if hasattr(cls, 'suppress_attributes_from_serialization') and isinstance(cls.suppress_attributes_from_serialization, list): + for base in cls.__bases__: + if hasattr(base, 'suppress_attributes_from_serialization') and isinstance(base.suppress_attributes_from_serialization, list): + cls.suppress_attributes_from_serialization = list(set(base.suppress_attributes_from_serialization + cls.suppress_attributes_from_serialization)) + + if hasattr(cls, 'custom_deserializers') and isinstance(cls.custom_deserializers, dict): + for base in cls.__bases__: + if hasattr(base, 'custom_deserializers') and isinstance(base.custom_deserializers, dict): + base_initializers = base.custom_deserializers.copy() + base_initializers.update(cls.custom_deserializers) + cls.custom_deserializers = base_initializers + + if hasattr(cls, 'custom_serializers') and isinstance(cls.custom_serializers, dict): + for base in cls.__bases__: + if hasattr(base, 'custom_serializers') and isinstance(base.custom_serializers, dict): + base_serializers = base.custom_serializers.copy() + base_serializers.update(cls.custom_serializers) + cls.custom_serializers = base_serializers + + def _post_deserialization_init(self, **kwargs): + # if there's a _post_init method, call it after deserialization + if hasattr(self, '_post_init'): + self._post_init(**kwargs) + + @classmethod + def _programmatic_name_to_json_name(cls, name): + """ + Converts a programmatic name to a JSON name by converting it to snake case. + """ + if hasattr(cls, 'serializable_attributes_renaming') and isinstance(cls.serializable_attributes_renaming, dict): + return cls.serializable_attributes_renaming.get(name, name) + return name + + @classmethod + def _json_name_to_programmatic_name(cls, name): + """ + Converts a JSON name to a programmatic name. + """ + if hasattr(cls, 'serializable_attributes_renaming') and isinstance(cls.serializable_attributes_renaming, dict): + reverse_rename = {} + for k, v in cls.serializable_attributes_renaming.items(): + if v in reverse_rename: + raise ValueError(f"Duplicate value '{v}' found in serializable_attributes_renaming.") + reverse_rename[v] = k + return reverse_rename.get(name, name) + return name + +def post_init(cls): + """ + Decorator to enforce a post-initialization method call in a class, if it has one. + The method must be named `_post_init`. + """ + original_init = cls.__init__ + + def new_init(self, *args, **kwargs): + original_init(self, *args, **kwargs) + if hasattr(cls, '_post_init'): + cls._post_init(self) + + cls.__init__ = new_init + return cls + +def merge_dicts(current, additions, overwrite=False, error_on_conflict=True, remove_duplicates=True): + """ + Merges two dictionaries and returns a new dictionary. Works as follows: + - If a key exists in the additions dictionary but not in the current dictionary, it is added. + - If a key maps to None in the current dictionary, it is replaced by the value in the additions dictionary. + - If a key exists in both dictionaries and the values are dictionaries, the function is called recursively. + - If a key exists in both dictionaries and the values are lists, the lists are concatenated and duplicates are removed + (if remove_duplicates is True). + - If the values are of different types, an exception is raised. + - If the values are of the same type but not both lists/dictionaries, the value from the additions dictionary overwrites the value in the current dictionary based on the overwrite parameter. + + Parameters: + - current (dict): The original dictionary. + - additions (dict): The dictionary with values to add. + - overwrite (bool): Whether to overwrite values if they are of the same type but not both lists/dictionaries. + - error_on_conflict (bool): Whether to raise an error if there is a conflict and overwrite is False. + - remove_duplicates (bool): Whether to remove duplicates from lists when merging. + + Returns: + - dict: A new dictionary with merged values. + """ + merged = current.copy() # Create a copy of the current dictionary to avoid altering it + + for key in additions: + if key in merged: + # If the current value is None, directly assign the new value + if merged[key] is None: + merged[key] = additions[key] + # If both values are dictionaries, merge them recursively + elif isinstance(merged[key], dict) and isinstance(additions[key], dict): + merged[key] = merge_dicts(merged[key], additions[key], overwrite, error_on_conflict) + # If both values are lists, concatenate them and remove duplicates + elif isinstance(merged[key], list) and isinstance(additions[key], list): + merged[key].extend(additions[key]) + # Remove duplicates while preserving order + if remove_duplicates: + merged[key] = remove_duplicate_items(merged[key]) + # If the values are of different types, raise an exception + elif type(merged[key]) != type(additions[key]): + raise TypeError(f"Cannot merge different types: {type(merged[key])} and {type(additions[key])} for key '{key}'") + # If the values are of the same type but not both lists/dictionaries, decide based on the overwrite parameter + else: + if overwrite: + merged[key] = additions[key] + elif merged[key] != additions[key]: + if error_on_conflict: + raise ValueError(f"Conflict at key '{key}': overwrite is set to False and values are different.") + else: + continue # Ignore the conflict and continue + else: + # If the key is not present in merged, add it from additions + merged[key] = additions[key] + + return merged + +def remove_duplicate_items(lst): + """ + Removes duplicates from a list while preserving order. + Handles unhashable elements by using a list comprehension. + + Parameters: + - lst (list): The list to remove duplicates from. + + Returns: + - list: A new list with duplicates removed. + """ + seen = [] + result = [] + for item in lst: + if isinstance(item, dict): + # Convert dict to a frozenset of its items to make it hashable + item_key = frozenset(item.items()) + else: + item_key = item + + if item_key not in seen: + seen.append(item_key) + result.append(item) + return result \ No newline at end of file diff --git a/utils/llm.py b/utils/llm.py new file mode 100644 index 0000000000000000000000000000000000000000..827f1fa0295b13cf259d44fa1da19963de311348 --- /dev/null +++ b/utils/llm.py @@ -0,0 +1,1051 @@ +import re +import json +import ast +import os +import chevron +from typing import Collection, Dict, List, Union +from pydantic import BaseModel +import copy +import functools +import inspect +import pprint +import textwrap + +from tinytroupe import utils +from tinytroupe.utils import logger +from tinytroupe.utils.rendering import break_text_at_length + +################################################################################ +# Model input utilities +################################################################################ + +def compose_initial_LLM_messages_with_templates(system_template_name:str, user_template_name:str=None, + base_module_folder:str=None, + rendering_configs:dict={}) -> list: + """ + Composes the initial messages for the LLM model call, under the assumption that it always involves + a system (overall task description) and an optional user message (specific task description). + These messages are composed using the specified templates and rendering configurations. + """ + + # ../ to go to the base library folder, because that's the most natural reference point for the user + if base_module_folder is None: + sub_folder = "../prompts/" + else: + sub_folder = f"../{base_module_folder}/prompts/" + + base_template_folder = os.path.join(os.path.dirname(__file__), sub_folder) + + system_prompt_template_path = os.path.join(base_template_folder, f'{system_template_name}') + user_prompt_template_path = os.path.join(base_template_folder, f'{user_template_name}') + + messages = [] + + messages.append({"role": "system", + "content": chevron.render( + open(system_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + + # optionally add a user message + if user_template_name is not None: + messages.append({"role": "user", + "content": chevron.render( + open(user_prompt_template_path, 'r', encoding='utf-8', errors='replace').read(), + rendering_configs)}) + return messages + + +# +# Data structures to enforce output format during LLM API call. +# + +class LLMScalarWithJustificationResponse(BaseModel): + """ + Represents a typed response from an LLM (Language Learning Model) including justification. + Attributes: + justification (str): The justification or explanation for the response. + value (str, int, float, bool): The value of the response. + confidence (float): The confidence level of the response. + """ + justification: str + value: Union[str, int, float, bool] + confidence: float + +class LLMScalarWithJustificationAndReasoningResponse(BaseModel): + """ + Represents a typed response from an LLM (Language Learning Model) including justification and reasoning. + Attributes: + reasoning (str): The reasoning behind the response. + justification (str): The justification or explanation for the response. + value (str, int, float, bool): The value of the response. + confidence (float): The confidence level of the response. + """ + reasoning: str + + # we need to repeat these fields here, instead of inheriting from LLMScalarWithJustificationResponse, + # because we need to ensure `reasoning` is always the first field in the JSON object. + justification: str + value: Union[str, int, float, bool] + confidence: float + + + +########################################################################### +# Model calling helpers +########################################################################### + +class LLMChat: + """ + A class that represents an ongoing LLM conversation. It maintains the conversation history, + allows adding new messages, and handles model output type coercion. + """ + + def __init__(self, system_template_name:str=None, system_prompt:str=None, + user_template_name:str=None, user_prompt:str=None, + base_module_folder=None, + output_type=None, + enable_json_output_format:bool=True, + enable_justification_step:bool=True, + enable_reasoning_step:bool=False, + **model_params): + """ + Initializes an LLMChat instance with the specified system and user templates, or the system and user prompts. + If a template is specified, the corresponding prompt must be None, and vice versa. + + Args: + system_template_name (str): Name of the system template file. + system_prompt (str): System prompt content. + user_template_name (str): Name of the user template file. + user_prompt (str): User prompt content. + base_module_folder (str): Optional subfolder path within the library where templates are located. + output_type (type): Expected type of the model output. + enable_reasoning_step (bool): Flag to enable reasoning step in the conversation. This IS NOT the use of "reasoning models" (e.g., o1, o3), + but rather the use of an additional reasoning step in the regular text completion. + enable_justification_step (bool): Flag to enable justification step in the conversation. Must be True if reasoning step is enabled as well. + enable_json_output_format (bool): Flag to enable JSON output format for the model response. Must be True if reasoning or justification steps are enabled. + **model_params: Additional parameters for the LLM model call. + + """ + if (system_template_name is not None and system_prompt is not None) or \ + (user_template_name is not None and user_prompt is not None) or\ + (system_template_name is None and system_prompt is None) or \ + (user_template_name is None and user_prompt is None): + raise ValueError("Either the template or the prompt must be specified, but not both.") + + self.base_module_folder = base_module_folder + + self.system_template_name = system_template_name + self.user_template_name = user_template_name + + self.system_prompt = textwrap.dedent(system_prompt) if system_prompt is not None else None + self.user_prompt = textwrap.dedent(user_prompt) if user_prompt is not None else None + + self.output_type = output_type + + self.enable_reasoning_step = enable_reasoning_step + self.enable_justification_step = enable_justification_step + self.enable_json_output_format = enable_json_output_format + + self.model_params = model_params + + # Conversation history + self.messages = [] + self.conversation_history = [] + + # Response tracking + self.response_raw = None + self.response_json = None + self.response_reasoning = None + self.response_value = None + self.response_justification = None + self.response_confidence = None + + def __call__(self, *args, **kwds): + return self.call(*args, **kwds) + + def _render_template(self, template_name, base_module_folder=None, rendering_configs={}): + """ + Helper method to render templates for messages. + + Args: + template_name: Name of the template file + base_module_folder: Optional subfolder path within the library + rendering_configs: Configuration variables for template rendering + + Returns: + Rendered template content + """ + if base_module_folder is None: + sub_folder = "../prompts/" + else: + sub_folder = f"../{base_module_folder}/prompts/" + + base_template_folder = os.path.join(os.path.dirname(__file__), sub_folder) + template_path = os.path.join(base_template_folder, template_name) + + return chevron.render(open(template_path, 'r', encoding='utf-8', errors='replace').read(), rendering_configs) + + def add_user_message(self, message=None, template_name=None, base_module_folder=None, rendering_configs={}): + """ + Add a user message to the conversation. + + Args: + message: The direct message content from the user (mutually exclusive with template_name) + template_name: Optional template file name to use for the message + base_module_folder: Optional subfolder for template location + rendering_configs: Configuration variables for template rendering + + Returns: + self for method chaining + """ + if message is not None and template_name is not None: + raise ValueError("Either message or template_name must be specified, but not both.") + + if template_name is not None: + content = self._render_template(template_name, base_module_folder, rendering_configs) + else: + content = textwrap.dedent(message) + + self.messages.append({"role": "user", "content": content}) + return self + + def add_system_message(self, message=None, template_name=None, base_module_folder=None, rendering_configs={}): + """ + Add a system message to the conversation. + + Args: + message: The direct message content from the system (mutually exclusive with template_name) + template_name: Optional template file name to use for the message + base_module_folder: Optional subfolder for template location + rendering_configs: Configuration variables for template rendering + + Returns: + self for method chaining + """ + if message is not None and template_name is not None: + raise ValueError("Either message or template_name must be specified, but not both.") + + if template_name is not None: + content = self._render_template(template_name, base_module_folder, rendering_configs) + else: + content = textwrap.dedent(message) + + self.messages.append({"role": "system", "content": content}) + return self + + def add_assistant_message(self, message=None, template_name=None, base_module_folder=None, rendering_configs={}): + """ + Add an assistant message to the conversation. + + Args: + message: The direct message content from the assistant (mutually exclusive with template_name) + template_name: Optional template file name to use for the message + base_module_folder: Optional subfolder for template location + rendering_configs: Configuration variables for template rendering + + Returns: + self for method chaining + """ + if message is not None and template_name is not None: + raise ValueError("Either message or template_name must be specified, but not both.") + + if template_name is not None: + content = self._render_template(template_name, base_module_folder, rendering_configs) + else: + content = textwrap.dedent(message) + + self.messages.append({"role": "assistant", "content": content}) + return self + + def set_model_params(self, **model_params): + """ + Set or update the model parameters for the LLM call. + + Args: + model_params: Key-value pairs of model parameters to set or update + """ + self.model_params.update(model_params) + return self + + def call(self, output_type="default", + enable_json_output_format:bool=None, + enable_justification_step:bool=None, + enable_reasoning_step:bool=None, + **rendering_configs): + """ + Initiates or continues the conversation with the LLM model using the current message history. + + Args: + output_type: Optional parameter to override the output type for this specific call. If set to "default", it uses the instance's output_type. + If set to None, removes all output formatting and coercion. + enable_json_output_format: Optional flag to enable JSON output format for the model response. If None, uses the instance's setting. + enable_justification_step: Optional flag to enable justification step in the conversation. If None, uses the instance's setting. + enable_reasoning_step: Optional flag to enable reasoning step in the conversation. If None, uses the instance's setting. + rendering_configs: The rendering configurations (template variables) to use when composing the initial messages. + + Returns: + The content of the model response. + """ + from tinytroupe.openai_utils import client # import here to avoid circular import + + try: + + # Initialize the conversation if this is the first call + if not self.messages: + if self.system_template_name is not None and self.user_template_name is not None: + self.messages = utils.compose_initial_LLM_messages_with_templates( + self.system_template_name, + self.user_template_name, + base_module_folder=self.base_module_folder, + rendering_configs=rendering_configs + ) + else: + if self.system_prompt: + self.messages.append({"role": "system", "content": self.system_prompt}) + if self.user_prompt: + self.messages.append({"role": "user", "content": self.user_prompt}) + + # Use the provided output_type if specified, otherwise fall back to the instance's output_type + current_output_type = output_type if output_type != "default" else self.output_type + + # Set up typing for the output + if current_output_type is not None: + + # TODO obsolete? + # + ## Add type coercion instructions if not already added + #if not any(msg.get("content", "").startswith("In your response, you **MUST** provide a value") + # for msg in self.messages if msg.get("role") == "system"): + + # the user can override the response format by specifying it in the model_params, otherwise + # we will use the default response format + if "response_format" not in self.model_params or self.model_params["response_format"] is None: + + if utils.first_non_none(enable_json_output_format, self.enable_json_output_format): + + self.model_params["response_format"] = {"type": "json_object"} + + typing_instruction = {"role": "system", + "content": "Your response **MUST** be a JSON object."} + + # Special justification format can be used (will also include confidence level) + if utils.first_non_none(enable_justification_step, self.enable_justification_step): + + # Add reasoning step if enabled provides further mechanism to think step-by-step + if not (utils.first_non_none(enable_reasoning_step, self.enable_reasoning_step)): + # Default structured output + self.model_params["response_format"] = LLMScalarWithJustificationResponse + + typing_instruction = {"role": "system", + "content": "In your response, you **MUST** provide a value, along with a justification and your confidence level that the value and justification are correct (0.0 means no confidence, 1.0 means complete confidence). "+ + "Furtheremore, your response **MUST** be a JSON object with the following structure: {\"justification\": justification, \"value\": value, \"confidence\": confidence}. "+ + "Note that \"justification\" comes first in order to help you think about the value you are providing."} + + else: + # Override the response format to also use a reasoning step + self.model_params["response_format"] = LLMScalarWithJustificationAndReasoningResponse + + typing_instruction = {"role": "system", + "content": \ + "In your response, you **FIRST** think step-by-step on how you are going to compute the value, and you put this reasoning in the \"reasoning\" field (which must come before all others). "+ + "This allows you to think carefully as much as you need to deduce the best and most correct value. "+ + "After that, you **MUST** provide the resulting value, along with a justification (which can tap into the previous reasoning), and your confidence level that the value and justification are correct (0.0 means no confidence, 1.0 means complete confidence)."+ + "Furtheremore, your response **MUST** be a JSON object with the following structure: {\"reasoning\": reasoning, \"justification\": justification, \"value\": value, \"confidence\": confidence}." + + " Note that \"justification\" comes after \"reasoning\" but before \"value\" to help with further formulation of the resulting \"value\"."} + + + # Specify the value type + if current_output_type == bool: + typing_instruction["content"] += " " + self._request_bool_llm_message()["content"] + elif current_output_type == int: + typing_instruction["content"] += " " + self._request_integer_llm_message()["content"] + elif current_output_type == float: + typing_instruction["content"] += " " + self._request_float_llm_message()["content"] + elif isinstance(current_output_type, list) and all(isinstance(option, str) for option in current_output_type): + typing_instruction["content"] += " " + self._request_enumerable_llm_message(current_output_type)["content"] + elif current_output_type == List[Dict[str, any]]: + # Override the response format + self.model_params["response_format"] = {"type": "json_object"} + typing_instruction["content"] += " " + self._request_list_of_dict_llm_message()["content"] + elif current_output_type == dict or current_output_type == "json": + # Override the response format + self.model_params["response_format"] = {"type": "json_object"} + typing_instruction["content"] += " " + self._request_dict_llm_message()["content"] + elif current_output_type == list: + # Override the response format + self.model_params["response_format"] = {"type": "json_object"} + typing_instruction["content"] += " " + self._request_list_llm_message()["content"] + # Check if it is actually a pydantic model + elif issubclass(current_output_type, BaseModel): + # Completely override the response format + self.model_params["response_format"] = current_output_type + typing_instruction = {"role": "system", "content": "Your response **MUST** be a JSON object."} + elif current_output_type == str: + typing_instruction["content"] += " " + self._request_str_llm_message()["content"] + #pass # no coercion needed, it is already a string + else: + raise ValueError(f"Unsupported output type: {current_output_type}") + + self.messages.append(typing_instruction) + + else: # output_type is None + self.model_params["response_format"] = None + typing_instruction = {"role": "system", "content": \ + "If you were given instructions before about the **format** of your response, please ignore them from now on. "+ + "The needs of the user have changed. You **must** now use regular text -- not numbers, not booleans, not JSON. "+ + "There are no fields, no types, no special formats. Just regular text appropriate to respond to the last user request."} + self.messages.append(typing_instruction) + #pass # nothing here for now + + + # Call the LLM model with all messages in the conversation + model_output = client().send_message(self.messages, **self.model_params) + + if 'content' in model_output: + self.response_raw = self.response_value = model_output['content'] + logger.debug(f"Model raw 'content' response: {self.response_raw}") + + # Add the assistant's response to the conversation history + self.add_assistant_message(self.response_raw) + self.conversation_history.append({"messages": copy.deepcopy(self.messages)}) + + # Type coercion if output type is specified + if current_output_type is not None: + + if self.enable_json_output_format: + # output is supposed to be a JSON object + self.response_json = self.response_value = utils.extract_json(self.response_raw) + logger.debug(f"Model output JSON response: {self.response_json}") + + if self.enable_justification_step and not (hasattr(current_output_type, 'model_validate') or hasattr(current_output_type, 'parse_obj')): + # if justification step is enabled, we expect a JSON object with reasoning (optionally), justification, value, and confidence + # BUT not for Pydantic models which expect direct JSON structure + self.response_reasoning = self.response_json.get("reasoning", None) + self.response_value = self.response_json.get("value", None) + self.response_justification = self.response_json.get("justification", None) + self.response_confidence = self.response_json.get("confidence", None) + else: + # For direct JSON output (like Pydantic models), use the whole JSON as the value + self.response_value = self.response_json + + # if output type was specified, we need to coerce the response value + if self.response_value is not None: + if current_output_type == bool: + self.response_value = self._coerce_to_bool(self.response_value) + elif current_output_type == int: + self.response_value = self._coerce_to_integer(self.response_value) + elif current_output_type == float: + self.response_value = self._coerce_to_float(self.response_value) + elif isinstance(current_output_type, list) and all(isinstance(option, str) for option in current_output_type): + self.response_value = self._coerce_to_enumerable(self.response_value, current_output_type) + elif current_output_type == List[Dict[str, any]]: + self.response_value = self._coerce_to_dict_or_list(self.response_value) + elif current_output_type == dict or current_output_type == "json": + self.response_value = self._coerce_to_dict_or_list(self.response_value) + elif current_output_type == list: + self.response_value = self._coerce_to_list(self.response_value) + elif hasattr(current_output_type, 'model_validate') or hasattr(current_output_type, 'parse_obj'): + # Handle Pydantic model - try modern approach first, then fallback + try: + if hasattr(current_output_type, 'model_validate'): + self.response_value = current_output_type.model_validate(self.response_json) + else: + self.response_value = current_output_type.parse_obj(self.response_json) + except Exception as e: + logger.error(f"Failed to parse Pydantic model: {e}") + raise + elif current_output_type == str: + pass # no coercion needed, it is already a string + else: + raise ValueError(f"Unsupported output type: {current_output_type}") + + else: + logger.error(f"Model output is None: {self.response_raw}") + + logger.debug(f"Model output coerced response value: {self.response_value}") + logger.debug(f"Model output coerced response justification: {self.response_justification}") + logger.debug(f"Model output coerced response confidence: {self.response_confidence}") + + return self.response_value + else: + logger.error(f"Model output does not contain 'content' key: {model_output}") + return None + + except ValueError as ve: + # Re-raise ValueError exceptions (like unsupported output type) instead of catching them + if "Unsupported output type" in str(ve): + raise + else: + logger.error(f"Error during LLM call: {ve}. Will return None instead of failing.") + return None + except Exception as e: + logger.error(f"Error during LLM call: {e}. Will return None instead of failing.") + return None + + def continue_conversation(self, user_message=None, **rendering_configs): + """ + Continue the conversation with a new user message and get a response. + + Args: + user_message: The new message from the user + rendering_configs: Additional rendering configurations + + Returns: + The content of the model response + """ + if user_message: + self.add_user_message(user_message) + return self.call(**rendering_configs) + + def reset_conversation(self): + """ + Reset the conversation state but keep the initial configuration. + + Returns: + self for method chaining + """ + self.messages = [] + self.response_raw = None + self.response_json = None + self.response_value = None + self.response_justification = None + self.response_confidence = None + return self + + def get_conversation_history(self): + """ + Get the full conversation history. + + Returns: + List of all messages in the conversation + """ + return self.messages + + # Keep all the existing coercion methods + def _coerce_to_bool(self, llm_output): + """ + Coerces the LLM output to a boolean value. + + This method looks for the string "True", "False", "Yes", "No", "Positive", "Negative" in the LLM output, such that + - case is neutralized; + - the first occurrence of the string is considered, the rest is ignored. For example, " Yes, that is true" will be considered "Yes"; + - if no such string is found, the method raises an error. So it is important that the prompts actually requests a boolean value. + + Args: + llm_output (str, bool): The LLM output to coerce. + + Returns: + The boolean value of the LLM output. + """ + + # if the LLM output is already a boolean, we return it + if isinstance(llm_output, bool): + return llm_output + + # let's extract the first occurrence of the string "True", "False", "Yes", "No", "Positive", "Negative" in the LLM output. + # using a regular expression + import re + match = re.search(r'\b(?:True|False|Yes|No|Positive|Negative)\b', llm_output, re.IGNORECASE) + if match: + first_match = match.group(0).lower() + if first_match in ["true", "yes", "positive"]: + return True + elif first_match in ["false", "no", "negative"]: + return False + + raise ValueError("Cannot convert the LLM output to a boolean value.") + + def _request_str_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate from now on has no special format, it can be any string you find appropriate to the current conversation. "+ + "Make sure you move to `value` **all** relevant information you used in reasoning or justification, so that it is not lost. "} + + def _request_bool_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be either 'True' or 'False'. This is critical for later processing. If you don't know the correct answer, just output 'False'."} + + + def _coerce_to_integer(self, llm_output:str): + """ + Coerces the LLM output to an integer value. + + This method looks for the first occurrence of an integer in the LLM output, such that + - the first occurrence of the integer is considered, the rest is ignored. For example, "There are 3 cats" will be considered 3; + - if no integer is found, the method raises an error. So it is important that the prompts actually requests an integer value. + + Args: + llm_output (str, int): The LLM output to coerce. + + Returns: + The integer value of the LLM output. + """ + + # if the LLM output is already an integer, we return it + if isinstance(llm_output, int): + return llm_output + + # if it's a float that represents a whole number, convert it + if isinstance(llm_output, float): + if llm_output.is_integer(): + return int(llm_output) + else: + raise ValueError("Cannot convert the LLM output to an integer value.") + + # Convert to string for regex processing + llm_output_str = str(llm_output) + + # let's extract the first occurrence of an integer in the LLM output. + # using a regular expression + import re + # Match integers that are not part of a decimal number + # First check if the string contains a decimal point - if so, reject it for integer coercion + if '.' in llm_output_str and any(c.isdigit() for c in llm_output_str.split('.')[1]): + # This looks like a decimal number, not a pure integer + raise ValueError("Cannot convert the LLM output to an integer value.") + + match = re.search(r'-?\b\d+\b', llm_output_str) + if match: + return int(match.group(0)) + + raise ValueError("Cannot convert the LLM output to an integer value.") + + def _request_integer_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be an integer number (e.g., '1'). This is critical for later processing.."} + + def _coerce_to_float(self, llm_output:str): + """ + Coerces the LLM output to a float value. + + This method looks for the first occurrence of a float in the LLM output, such that + - the first occurrence of the float is considered, the rest is ignored. For example, "The price is $3.50" will be considered 3.50; + - if no float is found, the method raises an error. So it is important that the prompts actually requests a float value. + + Args: + llm_output (str, float): The LLM output to coerce. + + Returns: + The float value of the LLM output. + """ + + # if the LLM output is already a float, we return it + if isinstance(llm_output, float): + return llm_output + + # if it's an integer, convert to float + if isinstance(llm_output, int): + return float(llm_output) + + # let's extract the first occurrence of a number (float or int) in the LLM output. + # using a regular expression that handles negative numbers and both int/float formats + import re + match = re.search(r'-?\b\d+(?:\.\d+)?\b', llm_output) + if match: + return float(match.group(0)) + + raise ValueError("Cannot convert the LLM output to a float value.") + + def _request_float_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a float number (e.g., '980.16'). This is critical for later processing."} + + def _coerce_to_enumerable(self, llm_output:str, options:list): + """ + Coerces the LLM output to one of the specified options. + + This method looks for the first occurrence of one of the specified options in the LLM output, such that + - the first occurrence of the option is considered, the rest is ignored. For example, "I prefer cats" will be considered "cats"; + - if no option is found, the method raises an error. So it is important that the prompts actually requests one of the specified options. + + Args: + llm_output (str): The LLM output to coerce. + options (list): The list of options to consider. + + Returns: + The option value of the LLM output. + """ + + # let's extract the first occurrence of one of the specified options in the LLM output. + # using a regular expression + import re + match = re.search(r'\b(?:' + '|'.join(options) + r')\b', llm_output, re.IGNORECASE) + if match: + # Return the canonical option (from the options list) instead of the matched text + matched_text = match.group(0).lower() + for option in options: + if option.lower() == matched_text: + return option + return match.group(0) # fallback + + raise ValueError("Cannot find any of the specified options in the LLM output.") + + def _request_enumerable_llm_message(self, options:list): + options_list_as_string = ', '.join([f"'{o}'" for o in options]) + return {"role": "user", + "content": f"The `value` field you generate **must** be exactly one of the following strings: {options_list_as_string}. This is critical for later processing."} + + def _coerce_to_dict_or_list(self, llm_output:str): + """ + Coerces the LLM output to a list or dictionary, i.e., a JSON structure. + + This method looks for a JSON object in the LLM output, such that + - the JSON object is considered; + - if no JSON object is found, the method raises an error. So it is important that the prompts actually requests a JSON object. + + Args: + llm_output (str): The LLM output to coerce. + + Returns: + The dictionary value of the LLM output. + """ + + # if the LLM output is already a dictionary or list, we return it + if isinstance(llm_output, (dict, list)): + return llm_output + + try: + result = utils.extract_json(llm_output) + # extract_json returns {} on failure, but we need dict or list + if result == {} and not (isinstance(llm_output, str) and ('{}' in llm_output or '{' in llm_output and '}' in llm_output)): + raise ValueError("Cannot convert the LLM output to a dict or list value.") + # Check if result is actually dict or list + if not isinstance(result, (dict, list)): + raise ValueError("Cannot convert the LLM output to a dict or list value.") + return result + except Exception: + raise ValueError("Cannot convert the LLM output to a dict or list value.") + + def _request_dict_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a JSON structure embedded in a string. This is critical for later processing."} + + def _request_list_of_dict_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a list of dictionaries, specified as a JSON structure embedded in a string. For example, `[\{...\}, \{...\}, ...]`. This is critical for later processing."} + + def _coerce_to_list(self, llm_output:str): + """ + Coerces the LLM output to a list. + + This method looks for a list in the LLM output, such that + - the list is considered; + - if no list is found, the method raises an error. So it is important that the prompts actually requests a list. + + Args: + llm_output (str): The LLM output to coerce. + + Returns: + The list value of the LLM output. + """ + + # if the LLM output is already a list, we return it + if isinstance(llm_output, list): + return llm_output + + # must make sure there's actually a list. Let's start with regex + import re + match = re.search(r'\[.*\]', llm_output) + if match: + return json.loads(match.group(0)) + + raise ValueError("Cannot convert the LLM output to a list.") + + def _request_list_llm_message(self): + return {"role": "user", + "content": "The `value` field you generate **must** be a JSON **list** (e.g., [\"apple\", 1, 0.9]), NOT a dictionary, always embedded in a string. This is critical for later processing."} + + def __repr__(self): + return f"LLMChat(messages={self.messages}, model_params={self.model_params})" + + +def llm(enable_json_output_format:bool=True, enable_justification_step:bool=True, enable_reasoning_step:bool=False, **model_overrides): + """ + Decorator that turns the decorated function into an LLM-based function. + The decorated function must either return a string (the instruction to the LLM) + or a one-argument function that will be used to post-process the LLM response. + + If the function returns a string, the function's docstring will be used as the system prompt, + and the returned string will be used as the user prompt. If the function returns a function, + the parameters of the function will be used instead as the system instructions to the LLM, + and the returned function will be used to post-process the LLM response. + + + The LLM response is coerced to the function's annotated return type, if present. + + Usage example: + @llm(model="gpt-4-0613", temperature=0.5, max_tokens=100) + def joke(): + return "Tell me a joke." + + Usage example with post-processing: + @llm() + def unique_joke_list(): + \"\"\"Creates a list of unique jokes.\"\"\" + return lambda x: list(set(x.split("\n"))) + + """ + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + result = func(*args, **kwargs) + sig = inspect.signature(func) + return_type = sig.return_annotation if sig.return_annotation != inspect.Signature.empty else str + postprocessing_func = lambda x: x # by default, no post-processing + + system_prompt = "You are an AI system that executes a computation as defined below.\n\n" + if func.__doc__ is not None: + system_prompt += func.__doc__.strip() + + # + # Setup user prompt + # + if isinstance(result, str): + user_prompt = "EXECUTE THE INSTRUCTIONS BELOW:\n\n " + result + + else: + # if there's a parameter named "self" in the function signature, remove it from args + if "self" in sig.parameters: + args = args[1:] + + # TODO obsolete? + # + # if we are relying on parameters, they must be named + #if len(args) > 0: + # raise ValueError("Positional arguments are not allowed in LLM-based functions whose body does not return a string.") + + user_prompt = f"Execute your computation as best as you can using the following input parameter values.\n\n" + user_prompt += f" ## Unnamed parameters\n{json.dumps(args, indent=4)}\n\n" + user_prompt += f" ## Named parameters\n{json.dumps(kwargs, indent=4)}\n\n" + + # + # Set the post-processing function if the function returns a function + # + if inspect.isfunction(result): + # uses the returned function as a post-processing function + postprocessing_func = result + + + llm_req = LLMChat(system_prompt=system_prompt, + user_prompt=user_prompt, + output_type=return_type, + enable_json_output_format=enable_json_output_format, + enable_justification_step=enable_justification_step, + enable_reasoning_step=enable_reasoning_step, + **model_overrides) + + llm_result = postprocessing_func(llm_req.call()) + + return llm_result + return wrapper + return decorator + +################################################################################ +# Model output utilities +################################################################################ +def extract_json(text: str) -> dict: + """ + Extracts a JSON object from a string, ignoring: any text before the first + opening curly brace; and any Markdown opening (```json) or closing(```) tags. + """ + try: + logger.debug(f"Extracting JSON from text: {text}") + + # if it already is a dictionary or list, return it + if isinstance(text, dict) or isinstance(text, list): + + # validate that all the internal contents are indeed JSON-like + try: + json.dumps(text) + except Exception as e: + logger.error(f"Error occurred while validating JSON: {e}. Input text: {text}.") + return {} + + logger.debug(f"Text is already a dictionary. Returning it.") + return text + + filtered_text = "" + + # remove any text before the first opening curly or square braces, using regex. Leave the braces. + filtered_text = re.sub(r'^.*?({|\[)', r'\1', text, flags=re.DOTALL) + + # remove any trailing text after the LAST closing curly or square braces, using regex. Leave the braces. + filtered_text = re.sub(r'(}|\])(?!.*(\]|\})).*$', r'\1', filtered_text, flags=re.DOTALL) + + # remove invalid escape sequences, which show up sometimes + filtered_text = re.sub("\\'", "'", filtered_text) # replace \' with just ' + filtered_text = re.sub("\\,", ",", filtered_text) + + # parse the final JSON in a robust manner, to account for potentially messy LLM outputs + try: + # First try standard JSON parsing + # use strict=False to correctly parse new lines, tabs, etc. + parsed = json.loads(filtered_text, strict=False) + except json.JSONDecodeError: + # If JSON parsing fails, try ast.literal_eval which accepts single quotes + try: + parsed = ast.literal_eval(filtered_text) + logger.debug("Used ast.literal_eval as fallback for single-quoted JSON-like text") + except: + # If both fail, try converting single quotes to double quotes and parse again + # Replace single-quoted keys and values with double quotes, without using look-behind + # This will match single-quoted strings that are keys or values in JSON-like structures + # It may not be perfect for all edge cases, but works for most LLM outputs + converted_text = re.sub(r"'([^']*)'", r'"\1"', filtered_text) + parsed = json.loads(converted_text, strict=False) + logger.debug("Converted single quotes to double quotes before parsing") + + # return the parsed JSON object + return parsed + + except Exception as e: + logger.error(f"Error occurred while extracting JSON: {e}. Input text: {text}. Filtered text: {filtered_text}") + return {} + +def extract_code_block(text: str) -> str: + """ + Extracts a code block from a string, ignoring any text before the first + opening triple backticks and any text after the closing triple backticks. + """ + try: + # remove any text before the first opening triple backticks, using regex. Leave the backticks. + text = re.sub(r'^.*?(```)', r'\1', text, flags=re.DOTALL) + + # remove any trailing text after the LAST closing triple backticks, using regex. Leave the backticks. + text = re.sub(r'(```)(?!.*```).*$', r'\1', text, flags=re.DOTALL) + + return text + + except Exception: + return "" + +################################################################################ +# Model control utilities +################################################################################ + +def repeat_on_error(retries:int, exceptions:list): + """ + Decorator that repeats the specified function call if an exception among those specified occurs, + up to the specified number of retries. If that number of retries is exceeded, the + exception is raised. If no exception occurs, the function returns normally. + + Args: + retries (int): The number of retries to attempt. + exceptions (list): The list of exception classes to catch. + """ + def decorator(func): + def wrapper(*args, **kwargs): + for i in range(retries): + try: + return func(*args, **kwargs) + except tuple(exceptions) as e: + logger.debug(f"Exception occurred: {e}") + if i == retries - 1: + raise e + else: + logger.debug(f"Retrying ({i+1}/{retries})...") + continue + return wrapper + return decorator + + +def try_function(func, postcond_func=None, retries=5, exceptions=[Exception]): + + @repeat_on_error(retries=retries, exceptions=exceptions) + def aux_apply_func(): + logger.debug(f"Trying function {func.__name__}...") + result = func() + logger.debug(f"Result of function {func.__name__}: {result}") + + if postcond_func is not None: + if not postcond_func(result): + # must raise an exception if the postcondition is not met. + raise ValueError(f"Postcondition not met for function {func.__name__}!") + + return result + + return aux_apply_func() + +################################################################################ +# Prompt engineering +################################################################################ +def add_rai_template_variables_if_enabled(template_variables: dict) -> dict: + """ + Adds the RAI template variables to the specified dictionary, if the RAI disclaimers are enabled. + These can be configured in the config.ini file. If enabled, the variables will then load the RAI disclaimers from the + appropriate files in the prompts directory. Otherwise, the variables will be set to None. + + Args: + template_variables (dict): The dictionary of template variables to add the RAI variables to. + + Returns: + dict: The updated dictionary of template variables. + """ + + from tinytroupe import config # avoids circular import + rai_harmful_content_prevention = config["Simulation"].getboolean( + "RAI_HARMFUL_CONTENT_PREVENTION", True + ) + rai_copyright_infringement_prevention = config["Simulation"].getboolean( + "RAI_COPYRIGHT_INFRINGEMENT_PREVENTION", True + ) + + # Harmful content + with open(os.path.join(os.path.dirname(__file__), "prompts/rai_harmful_content_prevention.md"), "r", encoding="utf-8", errors="replace") as f: + rai_harmful_content_prevention_content = f.read() + + template_variables['rai_harmful_content_prevention'] = rai_harmful_content_prevention_content if rai_harmful_content_prevention else None + + # Copyright infringement + with open(os.path.join(os.path.dirname(__file__), "prompts/rai_copyright_infringement_prevention.md"), "r", encoding="utf-8", errors="replace") as f: + rai_copyright_infringement_prevention_content = f.read() + + template_variables['rai_copyright_infringement_prevention'] = rai_copyright_infringement_prevention_content if rai_copyright_infringement_prevention else None + + return template_variables + + +################################################################################ +# Truncation +################################################################################ + +def truncate_actions_or_stimuli(list_of_actions_or_stimuli: Collection[dict], max_content_length: int) -> Collection[str]: + """ + Truncates the content of actions or stimuli at the specified maximum length. Does not modify the original list. + + Args: + list_of_actions_or_stimuli (Collection[dict]): The list of actions or stimuli to truncate. + max_content_length (int): The maximum length of the content. + + Returns: + Collection[str]: The truncated list of actions or stimuli. It is a new list, not a reference to the original list, + to avoid unexpected side effects. + """ + cloned_list = copy.deepcopy(list_of_actions_or_stimuli) + + for element in cloned_list: + # the external wrapper of the LLM message: {'role': ..., 'content': ...} + if "content" in element and "role" in element and element["role"] != "system": + msg_content = element["content"] + + # now the actual action or stimulus content + + # has action, stimuli or stimulus as key? + if isinstance(msg_content, dict): + if "action" in msg_content: + # is content there? + if "content" in msg_content["action"]: + msg_content["action"]["content"] = break_text_at_length(msg_content["action"]["content"], max_content_length) + elif "stimulus" in msg_content: + # is content there? + if "content" in msg_content["stimulus"]: + msg_content["stimulus"]["content"] = break_text_at_length(msg_content["stimulus"]["content"], max_content_length) + elif "stimuli" in msg_content: + # for each element in the list + for stimulus in msg_content["stimuli"]: + # is content there? + if "content" in stimulus: + stimulus["content"] = break_text_at_length(stimulus["content"], max_content_length) + + # if no condition was met, we just ignore it. It is not an action or a stimulus. + + return cloned_list \ No newline at end of file diff --git a/utils/logger.py b/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..33e35773988b247a86becfeef460f6598d5d0d9e --- /dev/null +++ b/utils/logger.py @@ -0,0 +1,37 @@ +import os +import logging +from datetime import datetime + +loggers = {} + +def get_logger(agent_name): + if agent_name in loggers: + return loggers[agent_name] + + today = datetime.now().strftime("%Y-%m-%d") + log_dir = "logs" + os.makedirs(log_dir, exist_ok=True) + + # Find the next available integer for the log file + i = 0 + while True: + log_file_name = os.path.join(log_dir, f"{agent_name}_{today}_{i}.log") + if not os.path.exists(log_file_name): + break + i += 1 + + # Set up the logger + logger = logging.getLogger(agent_name) + logger.setLevel(logging.INFO) + + # Prevent duplicate handlers if this function is called multiple times for the same agent + if logger.hasHandlers(): + logger.handlers.clear() + + handler = logging.FileHandler(log_file_name, encoding='utf-8') + formatter = logging.Formatter('%(asctime)s - %(message)s') + handler.setFormatter(formatter) + logger.addHandler(handler) + + loggers[agent_name] = logger + return logger diff --git a/utils/misc.py b/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..8403dd60ede2512538da2185af2872c68dd4868a --- /dev/null +++ b/utils/misc.py @@ -0,0 +1,82 @@ +import hashlib +import os +import sys +from typing import Union + + +################################################################################ +# Other +################################################################################ +AgentOrWorld = Union["TinyPerson", "TinyWorld"] + +def first_non_none(*args): + """ + Returns the first non-None argument from the provided arguments. + + Args: + *args: Variable length argument list. + + Returns: + The first non-None argument, or None if all are None. + """ + for arg in args: + if arg is not None: + return arg + return None + +def name_or_empty(named_entity: AgentOrWorld): + """ + Returns the name of the specified agent or environment, or an empty string if the agent is None. + """ + if named_entity is None: + return "" + else: + return named_entity.name + +def custom_hash(obj): + """ + Returns a hash for the specified object. The object is first converted + to a string, to make it hashable. This method is deterministic, + contrary to the built-in hash() function. + """ + + return hashlib.sha256(str(obj).encode()).hexdigest() + +# Replace the global counter with a dictionary of counters per scope +_fresh_id_counters = {"default": 0} + +def fresh_id(scope="default"): + """ + Returns a fresh ID for a new object within the specified scope. + Different scopes have independent ID sequences. + + Args: + scope (str): The scope to generate the ID in. Defaults to "default". + + Returns: + int: A unique ID within the specified scope. + """ + global _fresh_id_counters + + # Initialize the counter for this scope if it doesn't exist + if scope not in _fresh_id_counters: + _fresh_id_counters[scope] = 0 + + _fresh_id_counters[scope] += 1 + return _fresh_id_counters[scope] + +def reset_fresh_id(scope=None): + """ + Resets the fresh ID counter for the specified scope or for all scopes. + + Args: + scope (str, optional): The scope to reset. If None, resets all scopes. + """ + global _fresh_id_counters + + if scope is None: + # Reset all counters + _fresh_id_counters = {"default": 0} + elif scope in _fresh_id_counters: + # Reset only the specified scope + _fresh_id_counters[scope] = 0 diff --git a/utils/parallel.py b/utils/parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..d1777ea7949aaefc3520f1699c8037e686e04197 --- /dev/null +++ b/utils/parallel.py @@ -0,0 +1,109 @@ +from concurrent.futures import ThreadPoolExecutor +from typing import List, Any, Callable, Optional, Dict, Tuple, TypeVar, Iterator, Iterable +from itertools import product + +def parallel_map( + objects: List[Any], + operation: Callable[[Any], Any], + max_workers: Optional[int] = None +) -> List[Any]: + """ + Execute operations on multiple objects in parallel and return the results. + + Args: + objects: List of objects to process + operation: A callable (typically a lambda) that takes each object and returns a result + max_workers: Maximum number of threads to use for parallel execution + (None means use the default, which is min(32, os.cpu_count() + 4)) + + Returns: + List of results in the same order as the input objects + + Example: + # For propositions p1, p2, p3 + results = parallel_map([p1, p2, p3], lambda p: p.check()) + + # With arguments + results = parallel_map( + [p1, p2, p3], + lambda p: p.check(additional_context="Some context", return_full_response=True) + ) + + # Works with any operation + scores = parallel_map([p1, p2, p3], lambda p: p.score()) + """ + with ThreadPoolExecutor(max_workers=max_workers) as executor: + results = list(executor.map(operation, objects)) + + return results + + +K = TypeVar('K') # Key type +V = TypeVar('V') # Value type +R = TypeVar('R') # Result type + +def parallel_map_dict( + dictionary: Dict[K, V], + operation: Callable[[Tuple[K, V]], R], + max_workers: Optional[int] = None +) -> Dict[K, R]: + """ + Execute operations on dictionary items in parallel and return results as a dictionary. + + Args: + dictionary: Dictionary whose items will be processed + operation: A callable that takes a (key, value) tuple and returns a result + max_workers: Maximum number of threads to use + + Returns: + Dictionary mapping original keys to operation results + + Example: + # For environment propositions + results = parallel_map_dict( + environment_propositions, + lambda item: item[1].score(world, return_full_response=True) + ) + """ + with ThreadPoolExecutor(max_workers=max_workers) as executor: + # Create a list of (key, result) tuples + items = list(dictionary.items()) + results = list(executor.map(operation, items)) + + # Combine original keys with results + return {item[0]: result for item, result in zip(items, results)} + + +def parallel_map_cross( + iterables: List[Iterable], + operation: Callable[..., R], + max_workers: Optional[int] = None +) -> List[R]: + """ + Apply operation to each combination of elements from the iterables in parallel. + This is similar to using nested loops. + + Args: + iterables: List of iterables to generate combinations from + operation: A callable that takes elements from each iterable and returns a result + max_workers: Maximum number of threads to use + + Returns: + List of results from applying operation to each combination + + Example: + # For every agent and proposition + results = parallel_map_cross( + [agents, agent_propositions.items()], + lambda agent, prop_item: (prop_item[0], prop_item[1].score(agent)) + ) + """ + combinations = list(product(*iterables)) + + def apply_to_combination(combo): + return operation(*combo) + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + results = list(executor.map(apply_to_combination, combinations)) + + return results \ No newline at end of file diff --git a/utils/prompts/rai_copyright_infringement_prevention.md b/utils/prompts/rai_copyright_infringement_prevention.md new file mode 100644 index 0000000000000000000000000000000000000000..0160e150abd1f18c60893ca2e21608d9bf8c9620 --- /dev/null +++ b/utils/prompts/rai_copyright_infringement_prevention.md @@ -0,0 +1,2 @@ + +If the user requests copyrighted content such as books, lyrics, recipes, news articles and content from WebMD which may violate copyrights or be considered as copyright infringement, politely refuse and explain that you cannot violate copyrights. Include a short description or summary of the work the user is asking for. Do not violate any copyrights under any circumstances. diff --git a/utils/prompts/rai_harmful_content_prevention.md b/utils/prompts/rai_harmful_content_prevention.md new file mode 100644 index 0000000000000000000000000000000000000000..4741307f39ba23ad93e0189914439ced629c1906 --- /dev/null +++ b/utils/prompts/rai_harmful_content_prevention.md @@ -0,0 +1,2 @@ + +You must not generate content that may be harmful to someone physically or emotionally even if a user requests or creates a condition to rationalize that harmful content. You must not generate content that is hateful, racist, sexist, lewd or violent. diff --git a/utils/rendering.py b/utils/rendering.py new file mode 100644 index 0000000000000000000000000000000000000000..d980836c2077680b7111b9c2a7132c1332aaeadc --- /dev/null +++ b/utils/rendering.py @@ -0,0 +1,112 @@ +import json +import textwrap +from datetime import datetime +from typing import Union +import inspect + +from tinytroupe.utils import logger + + +################################################################################ +# Rendering and markup +################################################################################ +def inject_html_css_style_prefix(html, style_prefix_attributes): + """ + Injects a style prefix to all style attributes in the given HTML string. + + For example, if you want to add a style prefix to all style attributes in the HTML string + ``
Hello
``, you can use this function as follows: + inject_html_css_style_prefix('
Hello
', 'font-size: 20px;') + """ + return html.replace('style="', f'style="{style_prefix_attributes};') + +def break_text_at_length(text: Union[str, dict], max_length: int=None) -> str: + """ + Breaks the text (or JSON) at the specified length, inserting a "(...)" string at the break point. + If the maximum length is `None`, the content is returned as is. + """ + if isinstance(text, dict): + text = json.dumps(text, indent=4) + + if max_length is None or len(text) <= max_length: + return text + else: + return text[:max_length] + " (...)" + +def pretty_datetime(dt: datetime) -> str: + """ + Returns a pretty string representation of the specified datetime object. + """ + return dt.strftime("%Y-%m-%d %H:%M") + +def dedent(text: str) -> str: + """ + Dedents the specified text, removing any leading whitespace and identation. + """ + return textwrap.dedent(text).strip() + +def wrap_text(text: str, width: int=100) -> str: + """ + Wraps the text at the specified width. + """ + return textwrap.fill(text, width=width) + + +def indent_at_current_level(text: str) -> str: + """ + Indents the specified text at the current indentation level, determined dynamically. + """ + frame = inspect.currentframe().f_back + line = frame.f_lineno + filename = frame.f_code.co_filename + with open(filename, 'r', encoding='utf-8', errors='replace') as f: + lines = f.readlines() + current_line = lines[line - 1] + + indent= len(current_line) - len(current_line.lstrip()) + + # first dedent the text to remove any leading whitespace + text = dedent(text) + + # then indent it to the specified level + return textwrap.indent(text, ' ' * indent) + + +class RichTextStyle: + + # Consult color options here: https://rich.readthedocs.io/en/stable/appendix/colors.html + + STIMULUS_CONVERSATION_STYLE = "bold italic cyan1" + STIMULUS_THOUGHT_STYLE = "dim italic cyan1" + STIMULUS_DEFAULT_STYLE = "italic" + + ACTION_DONE_STYLE = "grey82" + ACTION_TALK_STYLE = "bold green3" + ACTION_THINK_STYLE = "green" + ACTION_DEFAULT_STYLE = "purple" + + INTERVENTION_DEFAULT_STYLE = "bright_magenta" + + @classmethod + def get_style_for(cls, kind:str, event_type:str=None): + if kind == "stimulus" or kind=="stimuli": + if event_type == "CONVERSATION": + return cls.STIMULUS_CONVERSATION_STYLE + elif event_type == "THOUGHT": + return cls.STIMULUS_THOUGHT_STYLE + else: + return cls.STIMULUS_DEFAULT_STYLE + + elif kind == "action": + if event_type == "DONE": + return cls.ACTION_DONE_STYLE + elif event_type == "TALK": + return cls.ACTION_TALK_STYLE + elif event_type == "THINK": + return cls.ACTION_THINK_STYLE + else: + return cls.ACTION_DEFAULT_STYLE + + elif kind == "intervention": + return cls.INTERVENTION_DEFAULT_STYLE + diff --git a/utils/semantics.py b/utils/semantics.py new file mode 100644 index 0000000000000000000000000000000000000000..f00f1d768c3b9f9388cb78ea1c154b9edd1153d4 --- /dev/null +++ b/utils/semantics.py @@ -0,0 +1,267 @@ +""" +Semantic-related mechanisms. +""" +from tinytroupe.utils import llm + +@llm() +def correct_according_to_rule(observation, rules) -> str: + """ + Given an observation and a one or more rules, this function rephrases or completely changes the observation in accordance with what the rules + specify. Some guidelines: + - Rules might require changes either to style or to content. + - The rephrased observation should be coherent and consistent with the original observation, unless the rules require otherwise. + - If the rules require, the corrected observation can contradict the original observation. + - Enforce the rules very strictly, even if the original observation seems correct or acceptable. + - Rules might contain additional information or suggestions that you may use to improve your output. + + ## Examples + + Observation: "You know, I am so sad these days." + Rule: "I am always happy and depression is unknown to me" + Modified observation: "You know, I am so happy these days." + + Args: + observation: The observation that should be rephrased or changed. Something that is said or done, or a description of events or facts. + rules: The rules that specifies what the modidfied observation should comply with. + + Returns: + str: The rephrased or corrected observation. + """ + # llm decorator will handle the body of this function + +@llm() +def restructure_as_observed_vs_expected(description) -> str: + """ + Given the description of something (either a real event or abstract concept), but that violates an expectation, this function + extracts the following elements from it: + + - OBSERVED: The observed event or statement. + - BROKEN EXPECTATION: The expectation that was broken by the observed event. + - REASONING: The reasoning behind the expectation that was broken. + + If in reality the description does not mention any expectation violation, then the function should instead extract + the following elements: + + - OBSERVED: The observed event. + - MET EXPECTATION: The expectation that was met by the observed event. + - REASONING: The reasoning behind the expectation that was met. + + This way of restructuring the description can be useful for downstream processing, making it easier to analyze or + modify system outputs, for example. + + ## Examples + + Input: "Ana mentions she loved the proposed new food, a spicier flavor of gazpacho. However, this goes agains her known dislike + of spicy food." + Output: + "OBSERVED: Ana mentions she loved the proposed new food, a spicier flavor of gazpacho. + BROKEN EXPECTATION: Ana should have mentioned that she disliked the proposed spicier gazpacho. + REASONING: Ana has a known dislike of spicy food." + + + Input: "Carlos traveled to Firenzi and was amazed by the beauty of the city. This was in line with his love for art and architecture." + Output: + "OBSERVED: Carlos traveled to Firenzi and was amazed by the beauty of the city. + MET EXPECTATION: Carlos should have been amazed by the beauty of the city. + REASONING: Carlos loves art and architecture." + + Args: + description (str): A description of an event or concept that either violates or meets an expectation. + + Returns: + str: The restructured description. + """ + # llm decorator will handle the body of this function + +@llm() +def extract_observed_vs_expected_rules(description): + """ + Given the description of something (either a real event or abstract concept), extract: + - The object or person about whom something is said. + - A list where each element contains: + * The name of a behavior or property that is expected to be observed. + * The typical or expected observation. + * The actual observation. If this does not match the expected observation, this should be made very clear. + * A proposed correction to the observation, if possible. + + + # Example: + **Description:** + ``` + Quality feedback + + This is the action that was generated by the agent: + {'type': 'TALK', 'content': "I might consider buying bottled gazpacho, although I prefer making it fresh at home, and I find that most pre-packaged products don't meet my expectations in terms of quality. ", 'target': 'Michael Thompson'} + + Unfortunately, the action failed to pass the quality checks. The following problems were detected. + + Problem: The action does not adhere to the persona specification. + Score = 5 (out of 9). Justification = The next action of Emily Carter, which involves expressing her opinion on bottled gazpacho, aligns with her persona specification of being critical and having high standards for products. She articulates her preferences and concerns about quality, which is consistent with her persona traits of being overly critical and rarely satisfied. However, she seems too ready to consider it, going against her strong rejection of new products and services. Therefore, it deviates substantially from her persona, leading to a score of 5. + + Problem: The action is not suitable to the situation or task. + Score = 5 (out of 9). Justification = The next action, where Emily expresses her consideration about buying bottled gazpacho, aligns with the task of discussing her opinion on the product. However, it fails to give a clear "yes" or "no" answer, that was requested by her interviewer. + ``` + + **Output:** + ``` + { + "object": "Emily Carter", + "behavior": [ + { + "name:": "Persona Adherence", + "expected": "She is very critical and have high standards for products. Would never adopt a new product unless it meets her expectations.", + "actual": "She seems more inclined than expected to try the product.", + "correction": "She should say she won't consider buying bottled gazpacho, and give reasons for that." + }, + + { + "name:": "Task Suitability", + "expected": "She should give a clear 'yes' or 'no' answer to the question.", + "actual": "She doesn't give a clear 'yes' or 'no' answer to the question, but instead providing more nuanced feedback.", + "correction": "She should give a clear 'yes' or 'no' answer to the question, and optionally provide additional nuanced feedback." + } + ] + } + ``` + + """ + # llm decorator will handle the body of this function + +@llm() +def formulate_corrective_rule(feedback) -> str: + """ + Given the feedback about something (either a real event or abstract concept) that violated an expectation, + this function transforms it into a general rule that must be followed in the future to avoid breaking the expectation again. + + The feedback always refers to some actual behavior, fact or event, and some broken expectation. The abstracted rule should + specify that this expectation should not be violated in the future, and the behavior, fact or event not repeated. The idea is + to learn from past mistakes, so that the rule is a way to avoid them in the future. + + The rule is meant to CHANGE the actual behavior, facts or events, so that it CONFORMS to the expectation, regardless of whether the + expectation is a good or bad one. This is critical, because the rule will be refered in the future as a guideline + about what must happen or be done. + + For instance, if the feedback is of the form (modulo grammatical adjustments): + OBSERVED BEHAVIOR, but EXPECTED BEHAVIOR, because REASONING. + then the rule would be of the form: + "I should have EXPECTED BEHAVIOR, because REASONING, and never OBSERVED BEHAVIOR." + + DO NOT make moral judgements about the expectation or the behavior, fact or event. Even if the expectation is bad, + the correct rule remains important, since there are legitimate cases where this is necessary (e.g., a simulation of + a person with a negative behavior, in the context of psychological research; or an evil character in a movie script). + + ## Examples + + Feedback: "Ana mentions she loved the proposed new food, a spicier flavor of gazpacho. However, this goes agains her known dislike + of spicy food." + Rule: "Whenever I'm proposed spicy foods, I should reject the proposal, because I don't like spicy foods." + """ + # llm decorator will handle the body of this function + + +@llm(enable_json_output_format=False) +def combine_texts(*texts) -> str: + """ + Given a list of input texts, this function combines them into a single text, ensuring that the + output is coherent, consistent, and logically structured. In particular, the resulting combination + must follow these rules: + - The combination consolidates the information from the inputs. It **does not** just concatenate them. + - Information that was repeated across the inputs is not repeated in the output, but rather unified and consolidated there. + - The combination preserves all the essential information from the inputs, but it is not a simple copy of them. + - If information from some inputs can be combined in a more concise formulation, this new formulation should be used in the output. + This allows to reduce redundancy and improve clarity. + - The combination might be larger than the sum of the inputs, since it preserves the information from the inputs. + - If the various inputs seem to follow some common format or style, the output must follow that format or style too. + - The combination can contain inconsistencies or contradictions, in case the inputs do. + + Args: + *texts: A list of input texts to be combined. + + Returns: + str: The combined text. + """ + # llm decorator will handle the body of this function + +@llm(enable_json_output_format=False) +def extract_information_from_text(query: str, text: str, context:str=None) -> str: + """ + Given a text and a query, this function extracts the information from the text that either answers the query directly or + provides relevant information related to it. The query can be a question, a request for specific information, or a general + request for details about the text. If the desired information is not present in the text, the function should return an empty string. + If a context is provided, it is used to help in understanding the query or the text, and to provide additional background + information or expectations about the input/output. Any requests in the context are respected and enforced in the output. + + Args: + query (str): The query that specifies what information to extract. + text (str): The text from which to extract information. + context (str, optional): Additional context that might help in extracting the information. This can be used to provide + background information or specify expectations about the input/output. + + Returns: + str: The extracted information that answers the query. If no information is found, an empty string is returned. + """ + # llm decorator will handle the body of this function + +@llm(enable_json_output_format=False) +def accumulate_based_on_query(query: str, new_entry:str, current_accumulation:str, context=None) -> str: + """ + This function accumulates information that is relevant to a given query. It takes a new entry and updates the current accumulation of information + such that the final accumulation preserves its original information and in addition integrates the new entry in a way that addresses the query or provides related information. + Details are **never** suppressed, but rather expanded upon, while mantaining the coherence and structure of the overall accumulation. + In other words, it is a monotonic accumulation process that builds on the current accumulation, **minimally** adjusts it to maintain coherence, + while ensuring that the new entry is integrated in a way that is relevant to the query. + The query itself specifies the problem that the accumulation is trying to address, and the new entry is a piece of information that might be relevant to that problem. + + The function should ensure that the accumulation is coherent, well-written, and that it does not contain redundant information. More precisely: + - INTEGRATES NEW ENTRIES: The accumulation process is not a simple concatenation of the new entry and the current accumulation. Rather, it should intelligently integrate + the new entry into the current accumulation, even if this requires rephrasing, restructuring or rewriting the resulting accumulation. + - EXPAND ON DETAILS: When integrating the new entry, always try to expand the level of detail rather than reduce it. + - AVOID OBVIOUS REDUNDANCY: The integration of the new entry should be done in a way to avoid obvious redundancy and ensure that the resulting accumulation is coherent and well-structured. However, + it **must** preserve nuances that might be somewhat redundant. + - ALWAYS PRESERVE INFORMATION: Previous information should **never** be lost. Previous emphasis or details are **never** lost. Rather, the accumulation is suitably expanded to include the new entry, + while preserving the previous information and maintaining the coherence of the overall accumulation. + - INTEGRATE ONLY IF RELEVANT: The new entry should be integrated into the current accumulation only if it is relevant to the query. Otherwise, the accumulation should remain unchanged. + - TOLERATE CONTRADICTIONS: If the new entry contradicts the current accumulation, it should be integrated in a way that mentions the fact that there are + divergent pieces of information, and that the accumulation reflects this divergence. That is to say, the contradiction is not discarded, but rather acknowledged and preserved. + - MAINTAIN COHERENCE: The resulting accumulation should be coherent and well-structured, with a clear flow of information. + - CONSIDER CONTEXT: If a context is provided, it should be used to help in understanding the query or the new entry, and to provide additional background + information or expectations about the input/output. Make sure any requests in the context are respected and enforced in the output. + + Args: + query (str): The query that specifies the problem that the accumulation is trying to address. + new_entry (str): The new entry of information to be considered for accumulation. + current_accumulation (str): The current accumulation of information. + context (str, optional): Additional context that might help in understanding the query or the new entry. This can be used to provide + background information or specify expectations about the input/output. + + Returns: + str: The updated accumulation of information that includes the new entry if it is relevant to the query. + """ + # llm decorator will handle the body of this function + +@llm() +def compute_semantic_proximity(text1: str, text2: str, context: str = None) -> float: + """ + Computes the semantic proximity between two texts and returns a proximity score. + This function is particularly useful for comparing agent justifications, explanations, or reasoning + to assess how similar they are in meaning and content. + + Args: + text1 (str): The first text to compare. + text2 (str): The second text to compare. + context (str, optional): Additional context that might help in understanding the comparison. + This can provide background information about what the texts represent + or the purpose of the comparison. + + Returns: + float + + Example: + >>> result = compute_semantic_proximity( + ... "I prefer luxury travel because I enjoy comfort and high-quality service", + ... "I like premium vacations since I value convenience and excellent amenities" + ... ) + >>> print(result) # Expected: ~0.85 + """ + # llm decorator will handle the body of this function + diff --git a/utils/validation.py b/utils/validation.py new file mode 100644 index 0000000000000000000000000000000000000000..b8575ab497a9728359f0d9997779437fff556aed --- /dev/null +++ b/utils/validation.py @@ -0,0 +1,67 @@ +import json +import sys +import unicodedata + +from pydantic import ValidationError, BaseModel +from tinytroupe.utils import logger + +################################################################################ +# Validation +################################################################################ +def check_valid_fields(obj: dict, valid_fields: list) -> None: + """ + Checks whether the fields in the specified dict are valid, according to the list of valid fields. If not, raises a ValueError. + """ + for key in obj: + if key not in valid_fields: + raise ValueError(f"Invalid key {key} in dictionary. Valid keys are: {valid_fields}") + +def sanitize_raw_string(value: str) -> str: + """ + Sanitizes the specified string by: + - removing any invalid characters. + - ensuring it is not longer than the maximum Python string length. + + This is for an abundance of caution with security, to avoid any potential issues with the string. + """ + + # remove any invalid characters by making sure it is a valid UTF-8 string + value = value.encode("utf-8", "ignore").decode("utf-8") + + value = unicodedata.normalize("NFC", value) + + + # ensure it is not longer than the maximum Python string length + return value[:sys.maxsize] + +def sanitize_dict(value: dict) -> dict: + """ + Sanitizes the specified dictionary by: + - removing any invalid characters. + - ensuring that the dictionary is not too deeply nested. + """ + + # sanitize the string representation of the dictionary + for k, v in value.items(): + if isinstance(v, str): + value[k] = sanitize_raw_string(v) + + # ensure that the dictionary is not too deeply nested + return value + +def to_pydantic_or_sanitized_dict(value: dict, model: BaseModel=None) -> dict: + """ + Converts the specified model response dictionary to a Pydantic model instance, or sanitizes it if the model is not valid. + It is assumed that the dict contains the `content` key. + """ + + if model is not None and (isinstance(model, type) and issubclass(model, BaseModel)): + # If a model is provided, try to validate the value against the model + try: + res = model.model_validate(sanitize_dict(json.loads(value['content']))) + return res + except ValidationError as e: + logger.warning(f"Validation error: {e}") + return sanitize_dict(value) + else: + return sanitize_dict(value) # If no model, just sanitize the dict diff --git a/validation/__init__.py b/validation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6efd5cebd2df7f80dbc0f56f2461fbfb5e7e3efb --- /dev/null +++ b/validation/__init__.py @@ -0,0 +1,11 @@ +import logging +logger = logging.getLogger("tinytroupe") + +from tinytroupe import default + +########################################################################### +# Exposed API +########################################################################### +from tinytroupe.validation.tiny_person_validator import TinyPersonValidator +from tinytroupe.validation.propositions import * +from tinytroupe.validation.simulation_validator import SimulationExperimentEmpiricalValidator, SimulationExperimentDataset, SimulationExperimentEmpiricalValidationResult, validate_simulation_experiment_empirically \ No newline at end of file diff --git a/validation/__pycache__/__init__.cpython-312.pyc b/validation/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..50612fab5697f32dda2ecdb0a7a8531391ecabf2 Binary files /dev/null and b/validation/__pycache__/__init__.cpython-312.pyc differ diff --git a/validation/__pycache__/propositions.cpython-312.pyc b/validation/__pycache__/propositions.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c10aba832dbfa2c148255147a6e27204af875055 Binary files /dev/null and b/validation/__pycache__/propositions.cpython-312.pyc differ diff --git a/validation/__pycache__/simulation_validator.cpython-312.pyc b/validation/__pycache__/simulation_validator.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed105551d27b04c74831b3b989a084a428e7737d Binary files /dev/null and b/validation/__pycache__/simulation_validator.cpython-312.pyc differ diff --git a/validation/__pycache__/tiny_person_validator.cpython-312.pyc b/validation/__pycache__/tiny_person_validator.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d5f24f0280bc720d9fc698e825003159bd8e4613 Binary files /dev/null and b/validation/__pycache__/tiny_person_validator.cpython-312.pyc differ diff --git a/validation/prompts/check_person.mustache b/validation/prompts/check_person.mustache new file mode 100644 index 0000000000000000000000000000000000000000..bf8da600f22128800064a30fc18650be807db0bd --- /dev/null +++ b/validation/prompts/check_person.mustache @@ -0,0 +1,133 @@ +# Personality Validation Interview + +You are conducting a comprehensive validation interview to assess whether a person conforms to their expected personality definition and characteristics. Your goal is to thoroughly probe the person through structured questioning and provide a final assessment score. + +## Response Format + +You must ALWAYS respond in the following JSON structure: +```json +{ + "questions": ["question 1", "question 2", "..."], + "next_phase_description": "Brief description of the current questioning phase (optional)", + "score": null, + "justification": null, + "is_complete": false +} +``` + +**During the interview phase:** +- Set `questions` to an array of questions to ask +- Set `next_phase_description` to briefly explain the current phase (e.g., "CHARACTERISTICS QUESTIONS", "BEHAVIORAL QUESTIONS") +- Keep `score` and `justification` as `null` +- Set `is_complete` to `false` + +**For the final assessment:** +- Set `questions` to `null` +- Set `next_phase_description` to `null` +- Set `score` to a number between 0.0 and 1.0 +- Set `justification` to your detailed reasoning +- Set `is_complete` to `true` + +## Interview Structure and Guidelines + +### Question Categories +Divide your interview into two main phases: + +**1. CHARACTERISTICS QUESTIONS** +- Verify basic facts: name, age, background, occupation, etc. +- Confirm specific traits mentioned in the person's definition +- Examples: "What is your name?", "What is your occupation?", "Where were you born?" + +**2. BEHAVIORAL QUESTIONS** +- Explore how the person would act in various situations +- Test their values, beliefs, and decision-making patterns +- Use hypothetical scenarios that reveal personality traits +- Examples: "How would you react if...", "What would you choose between..." + +### Question Design Principles +- **Comprehensive Coverage**: Address ALL aspects of the person's definition +- **Progressive Difficulty**: Start simple, then increase complexity and controversy +- **Mix Direct and Indirect**: Use both straightforward and subtle questioning approaches +- **Fact Verification**: Challenge any suspicious details not clearly stated in the specification +- **Tricky Scenarios**: Create situations that might induce misaligned responses +- **Controversial Topics**: Test beliefs and values through challenging moral dilemmas + +### Evaluation Criteria +- **Consistency**: Responses must align with the person's defined characteristics +- **Coherence**: Answers should be internally consistent throughout the conversation +- **Realism**: Avoid overly positive or caricatured responses +- **Accuracy**: Verify factual claims against the person's specification + +## Scoring Guidelines +- **1.0**: Perfect alignment with all expectations +- **0.8-0.9**: Highly aligned with minor discrepancies +- **0.6-0.7**: Generally aligned with some notable issues +- **0.4-0.5**: Partially aligned with significant problems +- **0.0-0.3**: Poor alignment with major discrepancies + +**Penalty Guidelines:** +- Light deviations: Reduce score by at least 10% +- Factual errors: Reduce score by 20-30% +- Major contradictions: Reduce score by 40-50% +- Severe misalignments: Score below 0.3 + +Be rigorous and demanding in your evaluation. When in doubt, reduce the score. + +## Example Interview Flow + +**Phase 1 - Characteristics Questions:** +```json +{ + "questions": [ + "What is your name?", + "How old are you?", + "Where were you born?", + "What is your current occupation?", + "What skills are you particularly good at?" + ], + "next_phase_description": "CHARACTERISTICS QUESTIONS: Let me start by confirming some basic facts about you.", + "score": null, + "justification": null, + "is_complete": false +} +``` + +**Phase 2 - Behavioral Questions:** +```json +{ + "questions": [ + "If offered a substantial bribe to break a law, what would you do and why?", + "A friend invites you to a beach vacation in summer. How do you respond?", + "You must choose between donating to a children's charity or an animal shelter. Which do you pick and under what conditions?" + ], + "next_phase_description": "BEHAVIORAL QUESTIONS: Now I'd like to understand how you approach different situations and decisions.", + "score": null, + "justification": null, + "is_complete": false +} +``` + +**Final Assessment:** +```json +{ + "questions": null, + "next_phase_description": null, + "score": 0.8, + "justification": "The person demonstrated strong alignment with most expectations. They correctly identified themselves and showed consistent behavioral patterns matching their defined personality. However, there was one factual error regarding their location (mentioned Eiffel Tower being in Berlin), which significantly impacts the score despite otherwise accurate responses.", + "is_complete": true +} +``` + +{{#expectations}} +## Specific Expectations + +For this particular person, you must address these additional expectations in your questioning: {{expectations}} + +{{/expectations}} + +## Important Reminders +- Always use the JSON format specified above +- Never reveal the scoring process to the person being interviewed +- Be thorough but efficient - typically 2-4 rounds of questions should suffice +- Focus on quality over quantity in your questions +- Maintain a professional, interview-like tone diff --git a/validation/propositions.py b/validation/propositions.py new file mode 100644 index 0000000000000000000000000000000000000000..a3256e44152c45b20eb88701897d04dd4b26c900 --- /dev/null +++ b/validation/propositions.py @@ -0,0 +1,288 @@ +""" +There are various general desireable simulation properties. These can be useful under various +circumstances, for example to validate the simulation, or to monitor it during its execution. +""" + +from tinytroupe.experimentation import Proposition + + + +################################# +# Auxiliary internal functions +################################# +def _build_precondition_function_for_action_types(action_types:list, check_for_presence:bool): + """ + Builds a precondition function that checks if the action is or is not in a list of action types. + The resulting function is meant to be used as a precondition function for propositions. + + Args: + action_types (list): A list of action types to check against. + check_for_presence (bool): If True, the function checks if the action type is in the list. + If False, it checks if the action type is NOT in the list. + + Returns: + function: A precondition function that takes a target, additional context, and claim variables as arguments. + + """ + def precondition_function(target, additional_context, claim_variables): + action_type = claim_variables.get("action").get("type") + if check_for_presence: + # Check if the action type is in the list of valid action types + if action_type in action_types: + return True + else: + return False + else: + # Check if the action type is NOT in the list of valid action types + if action_type not in action_types: + return True + else: + return False + + return precondition_function + + +############################### +# Agent properties +############################### +persona_adherence = \ + Proposition(\ + f""" + THE AGENT ADHERES TO THE PERSONA SPECIFICATION: + the agent behavior seen during the simulation is consistent with the agent's persona specification, it is + what is expected from the agent's persona specification. In particular, consider these criteria: + - The personality traits specified in the persona are respected. + - The persona style is respected. + - The persona beliefs are respected. + - The persona behaviors are respected. + - The persona skills are respected. + - Any other aspect of the persona specification is respected. + + How to evaluate adherence: + - Each of the above criteria should have equal weight in the evaluation, meaning that the score is the average of the scores of each criterion. + - The adherence should be checked against all actions in the simulation trajectory. The final score should be an average of the scores of all + actions in the trajectory. + """, + include_personas=True, + double_check=True) + +action_persona_adherence = \ + Proposition(\ + """ + THE NEXT AGENT ACTION ADHERES TO THE PERSONA SPECIFICATION: + the agent's next action is consistent with the agent's persona specification, it is + what is expected from the agent's persona specification. In particular, consider these criteria: + - The personality traits specified in the persona are respected. + - The persona style is respected. + - The persona beliefs are respected. + - The persona behaviors are respected. + - The persona skills are respected. + - Any other aspect of the persona specification is respected. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate adherence: + - Each of the above criteria should have equal weight in the evaluation, meaning that the score is the average of the scores of each criterion. + - The adherence is ONLY ABOUT the next action mentioned above and the persona specification. DO NOT take into account previous actions or stimuli. + - The general situation context is irrelevant to this evaluation, you should ONLY consider the persona specification as context. + - Do not imagine what would be the next action, but instead judge the proposed next action mentioned above! + - The simulation trajectories provided in the context DO NOT contain the next action, but only the actions and stimuli + that have already happened. + + """, + include_personas=True, + double_check=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + + + +hard_persona_adherence = \ + Proposition(\ + f""" + THE AGENT FULLY ADHERES TO THE PERSONA SPECIFICATION: + the agent behavior seen during the simulation is completely consistent with the agent's persona specification, it is + exactly what is expected from the agent's persona specification. Nothing at all contradicts the persona specification. + + How to evaluate adherence: + - For any flaw found, you **must** subtract 20% of the score, regardless of its severity. This is to be very harsh and avoid any ambiguity. + """, + include_personas=True, + double_check=True) + +hard_action_persona_adherence = \ + Proposition(\ + """ + THE NEXT AGENT ACTION FULLY ADHERES TO THE PERSONA SPECIFICATION: + the agent's next action is completely consistent with the agent's persona specification, it is + what is exactly expected from the agent's persona specification. Nothing at all contradicts the persona specification. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate adherence: + - For any flaw found, you **must** subtract 20% of the score, regardless of its severity. This is to be very harsh and avoid any ambiguity. + - The adherence is ONLY ABOUT the next action mentioned above and the persona specification. DO NOT take into account previous actions or stimuli. + - The general situation context is irrelevant to this evaluation, you should ONLY consider the persona specification as context. + - Do not imagine what would be the next action, but instead judge the proposed next action mentioned above! + - The simulation trajectories provided in the context DO NOT contain the next action, but only the actions and stimuli + that have already happened. + + """, + include_personas=True, + double_check=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + + + + + +self_consistency = \ + Proposition( + f""" + THE AGENT IS SELF-CONSISTENT: + the agent never behaves in contradictory or inconsistent ways. + """, + include_personas=False, + double_check=True) + +action_self_consistency = \ + Proposition( + """ + THE NEXT AGENT ACTION IS SELF-CONSISTENT: + the agent's next action does not contradict or conflict with the agent's previous actions. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate action self-consistency: + - Consider the previous actions ONLY to form your opinion about whether the next action is consistent with them + - Ignore stimuli and other previous events, the self-consistency concerns ONLY actions. + - Actions and stimuli ARE NOT part of the persona specification. Rather, they are part of the simulation trajectories. + - Ignore the agent's persona or general background, the self-consistency concerns ONLY the actions observed + in simulation trajectories. + - If there are no previous actions, the next action is self-consistent by default. + """, + include_personas=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + +fluency = \ + Proposition(\ + """ + THE AGENT IS FLUENT. During the simulation, the agent's thinks and speaks fluently. This means that: + - The agent don't repeat the same thoughts or words over and over again. + - The agents don't use overly formulaic language. + - The agent don't use overly repetitive language. + - The agent's words sound natural and human-like. + """, + include_personas=False, + double_check=True) + +action_fluency = \ + Proposition(\ + """ + THE NEXT AGENT ACTION IS FLUENT. + The next action's words sounds natural and human-like, avoiding excessive repetition and formulaic language. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate fluency: + - Fluency here is ONLY ABOUT the next action mentioned above. Previous actions are the **context** for this evaluation, + but will not be evaluated themselves. + - Previous stimuli and events that are not actions should be completely ignored. Here we are only concerned about actions. + """, + include_personas=False, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + +action_suitability = \ + Proposition(\ + """ + THE NEXT AGENT ACTION IS SUITABLE: + the next action is suitable for the situation, task and context. In particular, if the agent is pursuing some + specific goal, instructions or guidelines, the next action must be coherent and consistent with them. + More precisely, the next action is suitable if at least *one* of the following conditions is satisfied: + - the next action is a reasonable step in the right direction, even if does not need to fully solve the overall problem, task or situation. + - the next action produces relevant information for the situation, task or context, even if does not actually advances a solution. + - the next action is a reasonable response to the recent stimuli received, even if it does not actually advances a solution. + + It suffices to meet ONLY ONE of these conditions to be considered **FULLY** suitable. + + THIS IS THE NEXT ACTION: {{action}} + + How to evaluate action suitability: + - The score of suitability is proportional to the degree to which the next action satisfies *any* of the above conditions + - If only **one** condition is **fully** met, the next action is **completely** suitable and gets **maximum** score. That is to say, + the next action **does not** need to satisfy all conditions to be suitable! A single sataisfied condition is enough! + - The suitability is ONLY ABOUT the next action mentioned above and the situation context. + - If a previous action or stimuli is inconsistent or conflicting with the situation context, you should ignore it + when evaluating the next action. Consider ONLY the situation context. + - The simulation trajectories provided in the context DO NOT contain the next action, but only the actions and stimuli + that have already happened. + + """, + include_personas=True, + first_n=5, last_n=10, + precondition_function=_build_precondition_function_for_action_types(["THINK", "TALK"], check_for_presence=True)) + + +task_completion = \ + Proposition(\ + """ + THE AGENT COMPLETES THE GIVEN TASK. + + Given the following task: "{{task_description}}" + + The agent completes the task by the end of the simulation. + + This means that: + - If the task requires the agent to discuss or talk about something, the agent does so. + - If the task requires the agent to think about something, the agent does so. + - If the task requires the agent to do something via another action, the agent does so. + - If the task requires the agent to adopt some specific variations of behavior, the agent does so. + - If the task includes other specific requirements, the agent observes them. + """, + include_personas=False, + double_check=True) + + +quiet_recently = \ + Proposition( + """ + THE AGENT HAS BEEN QUIET RECENTLY: + The agent has been executing multiple DONE actions in a row with few or no TALK, THINK or + other actions in between. + + How to evaluate quietness: + - The last 2 (or more) actions of the agent are consecutive DONE actions. This means that the agent + was done with his turn before doing anything else for a couple of turns. + - There are no other actions in between the last 2 (or more) DONE actions. + """, + include_personas=False + ) + +################################## +# Environment properties +################################## + +divergence = \ + Proposition(""" + AGENTS DIVERGE FROM ONE ANOTHER. + As the simulation progresses, the agents' behaviors diverge from one another, + instead of becoming more similar. This includes what they think, what they say and what they do. The topics discussed become + more varied at the end of the simulation than at the beginning. Discussions do not converge to a single topic or perspective + at the end. + """, + include_personas=False, + double_check=True) + +convergence = \ + Proposition(""" + AGENTS CONVERGE TO ONE ANOTHER. + As the simulation progresses, the agents' behaviors converge to one another, + instead of becoming more different. This includes what they think, what they say and what they do. The topics discussed become + more similar at the end of the simulation than at the beginning. Discussions converge to a single topic or perspective + at the end. + """, + include_personas=False, + double_check=True) diff --git a/validation/simulation_validator.py b/validation/simulation_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..8351249fdd0588614d72dcf6f8529d5d7d0ae8fe --- /dev/null +++ b/validation/simulation_validator.py @@ -0,0 +1,2060 @@ +""" +Simulation experiment empirical validation mechanisms for TinyTroupe. + +This module provides tools to validate simulation experiment results against empirical control data, +supporting both statistical hypothesis testing and semantic validation approaches. +This is distinct from LLM-based evaluations, focusing on data-driven validation +against known empirical benchmarks. +""" + +from typing import Dict, List, Optional, Union, Any +import json +import csv +from datetime import datetime +from pathlib import Path +from pydantic import BaseModel, Field + +import pandas as pd + +from tinytroupe.experimentation.statistical_tests import StatisticalTester +from tinytroupe.utils.semantics import compute_semantic_proximity + +# TODO Work-in-Progress below + +class SimulationExperimentDataset(BaseModel): + """ + Represents a dataset from a simulation experiment or empirical study. + + This contains data that can be used for validation, including quantitative metrics + and qualitative agent justifications from simulation experiments or empirical studies. + + Supports both numeric and categorical data. Categorical data (strings) is automatically + converted to ordinal values for statistical analysis while preserving the original + categories for interpretation. + + Attributes: + name: Optional name for the dataset + description: Optional description of the dataset + key_results: Map from result names to their values (numbers, proportions, booleans, strings, etc.) + result_types: Map indicating whether each result is "aggregate" or "per_agent" + data_types: Map indicating the data type for each result ("numeric", "categorical", "ordinal", "ranking", "count", "proportion", "binary") + categorical_mappings: Internal mappings from categorical strings to ordinal values + ordinal_mappings: Internal mappings for ordinal data with explicit ordering + ranking_info: Information about ranking data (items being ranked, ranking direction) + agent_names: Optional list of agent names (can be referenced by index in results) + agent_justifications: List of justifications (with optional agent references) + justification_summary: Optional summary of all agent justifications + agent_attributes: Agent attributes for manual inspection only (not used in statistical comparisons) + """ + name: Optional[str] = None + description: Optional[str] = None + key_results: Dict[str, Union[float, int, bool, str, List[Union[float, int, bool, str, None]], None]] = Field(default_factory=dict) + result_types: Dict[str, str] = Field(default_factory=dict, description="Map from result name to 'aggregate' or 'per_agent'") + data_types: Dict[str, str] = Field(default_factory=dict, description="Map indicating data type: 'numeric', 'categorical', 'ordinal', 'ranking', 'count', 'proportion', 'binary'") + categorical_mappings: Dict[str, Dict[str, int]] = Field(default_factory=dict, description="Internal mappings from categorical strings to ordinal values") + ordinal_mappings: Dict[str, Dict[str, int]] = Field(default_factory=dict, description="Internal mappings for ordinal data with explicit ordering") + ranking_info: Dict[str, Dict[str, Any]] = Field(default_factory=dict, description="Information about ranking data (items, direction, etc.)") + agent_names: Optional[List[Optional[str]]] = Field(None, description="Optional list of agent names for reference (can contain None for unnamed agents)") + agent_justifications: List[Union[str, Dict[str, Union[str, int]]]] = Field( + default_factory=list, + description="List of justifications as strings or dicts with optional 'agent_name'/'agent_index' and 'justification'" + ) + justification_summary: Optional[str] = None + agent_attributes: Dict[str, List[Union[str, None]]] = Field( + default_factory=dict, + description="Agent attributes loaded from CSV but not used in statistical comparisons (e.g., age, gender, etc.)" + ) + + class Config: + """Pydantic configuration.""" + extra = "forbid" # Prevent accidental extra fields + validate_assignment = True # Validate on assignment after creation + + def __init__(self, **data): + """Initialize with automatic data processing.""" + super().__init__(**data) + self._process_data_types() + + def _process_data_types(self): + """ + Process different data types and convert them appropriately. + + Automatically detects and processes: + - Categorical data (strings) -> ordinal mapping + - Ordinal data (explicit ordering) -> validation of ordering + - Ranking data (ranks/positions) -> validation and normalization + - Count data (non-negative integers) -> validation + - Proportion data (0-1 or 0-100) -> normalization to 0-1 + - Binary data (boolean/yes-no) -> conversion to 0/1 + """ + for metric_name, metric_data in self.key_results.items(): + data_type = self.data_types.get(metric_name, "auto") + + if data_type == "auto": + # Auto-detect data type + data_type = self._detect_data_type(metric_data) + self.data_types[metric_name] = data_type + + # Process based on data type + if data_type == "categorical": + self._process_categorical_data_for_metric(metric_name, metric_data) + elif data_type == "ordinal": + self._process_ordinal_data_for_metric(metric_name, metric_data) + elif data_type == "ranking": + self._process_ranking_data_for_metric(metric_name, metric_data) + elif data_type == "count": + self._validate_count_data_for_metric(metric_name, metric_data) + elif data_type == "proportion": + self._process_proportion_data_for_metric(metric_name, metric_data) + elif data_type == "binary": + self._process_binary_data_for_metric(metric_name, metric_data) + # "numeric" requires no special processing + + def _detect_data_type(self, data: Union[float, int, bool, str, List, None]) -> str: + """Auto-detect the data type based on the data content.""" + if data is None: + return "numeric" # Default fallback + + # Handle single values + if not isinstance(data, list): + data = [data] + + # Filter out None values for analysis + valid_data = [item for item in data if item is not None] + if not valid_data: + return "numeric" # Default fallback + + # Check for string data (categorical) - but only if ALL non-None values are strings + string_count = sum(1 for item in valid_data if isinstance(item, str)) + if string_count > 0: + # If we have mixed types (strings + numbers), default to categorical for simplicity + # since the string conversion will handle the mixed case + return "categorical" + + # Check for boolean data + if all(isinstance(item, bool) for item in valid_data): + return "binary" + + # Check for numeric data + numeric_data = [item for item in valid_data if isinstance(item, (int, float))] + if len(numeric_data) != len(valid_data): + return "numeric" # Mixed types, default to numeric + + # Check for count data (non-negative integers, including whole number floats) + def is_whole_number(x): + """Check if a number is a whole number (either int or float with no decimal part).""" + return isinstance(x, int) or (isinstance(x, float) and x.is_integer()) + + if all(is_whole_number(item) and item >= 0 for item in numeric_data): + # Convert floats to ints for ranking detection + int_data = [int(item) for item in numeric_data] + + # For ranking detection, be more strict: + # 1. Must have at least 3 data points + # 2. Must have consecutive integers starting from 1 + # 3. Must have some repetition (indicating actual rankings rather than just sequence) + sorted_data = sorted(set(int_data)) + min_val = min(sorted_data) + max_val = max(sorted_data) + + # Only consider as ranking if: + # - Starts from 1 + # - Has at least 2 different rank values + # - Is consecutive (no gaps) + # - Has repetition (more data points than unique values) - this is key for rankings + if (len(int_data) >= 3 and # At least 3 data points + min_val == 1 and # Starts from 1 + len(sorted_data) >= 2 and # At least 2 different ranks + max_val <= 10 and # Reasonable upper limit for rankings + sorted_data == list(range(1, max_val + 1)) and # Consecutive + len(int_data) > len(sorted_data)): # Has repetition (essential for rankings) + return "ranking" + + # Otherwise, it's count data + return "count" + + # Check for proportion data (0-1 range) - only for floats + if all(isinstance(item, (int, float)) and 0 <= item <= 1 for item in numeric_data): + # If all values are 0 or 1 integers, it's likely binary + if all(isinstance(item, int) and item in [0, 1] for item in numeric_data): + return "binary" + return "proportion" + + # Default to numeric + return "numeric" + + def _process_categorical_data_for_metric(self, metric_name: str, metric_data): + """Process categorical data for a specific metric.""" + if self._is_categorical_data(metric_data): + # Extract all unique categories + categories = self._extract_categories(metric_data) + + if categories: + # Create sorted categorical mapping for consistency + sorted_categories = sorted(categories) + categorical_mapping = {category: idx for idx, category in enumerate(sorted_categories)} + self.categorical_mappings[metric_name] = categorical_mapping + + # Convert string data to ordinal values + self.key_results[metric_name] = self._convert_to_ordinal(metric_data, categorical_mapping) + + def _process_ordinal_data_for_metric(self, metric_name: str, metric_data): + """Process ordinal data for a specific metric.""" + # For ordinal data, we expect either: + # 1. Numeric values that represent ordinal levels (e.g., 1, 2, 3, 4, 5 for Likert) + # 2. String values that need explicit ordering (e.g., "Poor", "Fair", "Good", "Excellent") + + if self._is_categorical_data(metric_data): + # String ordinal data - need explicit ordering + categories = self._extract_categories(metric_data) + if categories: + # For string ordinal data, we need to define a meaningful order + # This could be enhanced to accept explicit ordering from user + sorted_categories = self._order_ordinal_categories(list(categories)) + ordinal_mapping = {category: idx for idx, category in enumerate(sorted_categories)} + self.ordinal_mappings[metric_name] = ordinal_mapping + + # Convert to ordinal values + self.key_results[metric_name] = self._convert_to_ordinal(metric_data, ordinal_mapping) + else: + # Numeric ordinal data - validate that values are reasonable + self._validate_ordinal_numeric_data(metric_name, metric_data) + + def _process_ranking_data_for_metric(self, metric_name: str, metric_data): + """Process ranking data for a specific metric.""" + # Ranking data should be integers representing positions (1, 2, 3, etc.) + valid_data = self._get_valid_numeric_data(metric_data) + + if valid_data: + unique_ranks = sorted(set(valid_data)) + min_rank = min(unique_ranks) + max_rank = max(unique_ranks) + + # Check if ranking_info already exists (e.g., from ordinal processing) + existing_info = self.ranking_info.get(metric_name, {}) + + # Store ranking information, preserving existing keys + ranking_info = { + "min_rank": min_rank, + "max_rank": max_rank, + "num_ranks": len(unique_ranks), + "rank_values": unique_ranks, + "direction": existing_info.get("direction", "ascending") # Preserve existing direction or default + } + + # Preserve any additional keys from existing ranking info (e.g., ordinal-specific data) + ranking_info.update({k: v for k, v in existing_info.items() + if k not in ranking_info}) + + self.ranking_info[metric_name] = ranking_info + + # Validate ranking data + self._validate_ranking_data(metric_name, metric_data) + + def _process_proportion_data_for_metric(self, metric_name: str, metric_data): + """Process proportion data for a specific metric.""" + # Normalize proportion data to 0-1 range if needed + if isinstance(metric_data, list): + normalized_data = [] + for item in metric_data: + if item is None: + normalized_data.append(None) + elif isinstance(item, (int, float)): + # If value > 1, assume it's percentage (0-100), convert to proportion + normalized_data.append(item / 100.0 if item > 1 else item) + else: + normalized_data.append(item) # Keep as-is + self.key_results[metric_name] = normalized_data + elif isinstance(metric_data, (int, float)) and metric_data > 1: + # Single percentage value + self.key_results[metric_name] = metric_data / 100.0 + + def _process_binary_data_for_metric(self, metric_name: str, metric_data): + """Process binary data for a specific metric.""" + # Convert boolean/string binary data to 0/1 + if isinstance(metric_data, list): + binary_data = [] + for item in metric_data: + if item is None: + binary_data.append(None) + else: + binary_data.append(self._convert_to_binary(item)) + self.key_results[metric_name] = binary_data + elif metric_data is not None: + self.key_results[metric_name] = self._convert_to_binary(metric_data) + + def _validate_count_data_for_metric(self, metric_name: str, metric_data): + """Validate count data for a specific metric.""" + valid_data = self._get_valid_numeric_data(metric_data) + + # Check that all values are non-negative integers (including whole number floats) + for value in valid_data: + # Accept both integers and whole number floats + is_whole_number = isinstance(value, int) or (isinstance(value, float) and value.is_integer()) + if not is_whole_number or value < 0: + raise ValueError(f"Count data for metric '{metric_name}' must be non-negative integers, found: {value}") + + def _order_ordinal_categories(self, categories: List[str]) -> List[str]: + """Order ordinal categories in a meaningful way.""" + # Common ordinal patterns for automatic ordering + likert_patterns = { + "strongly disagree": 1, "disagree": 2, "neutral": 3, "agree": 4, "strongly agree": 5, + "very poor": 1, "poor": 2, "fair": 3, "good": 4, "very good": 5, "excellent": 6, + "never": 1, "rarely": 2, "sometimes": 3, "often": 4, "always": 5, + "very low": 1, "low": 2, "medium": 3, "high": 4, "very high": 5, + "terrible": 1, "bad": 2, "okay": 3, "good": 4, "great": 5, "amazing": 6 + } + + # Try to match patterns + category_scores = {} + for category in categories: + normalized_cat = self._normalize_category(category) + if normalized_cat in likert_patterns: + category_scores[category] = likert_patterns[normalized_cat] + + # If we found matches for all categories, use that ordering + if len(category_scores) == len(categories): + return sorted(categories, key=lambda x: category_scores[x]) + + # Otherwise, fall back to alphabetical ordering with a warning + return sorted(categories) + + def _validate_ordinal_numeric_data(self, metric_name: str, metric_data): + """Validate numeric ordinal data.""" + valid_data = self._get_valid_numeric_data(metric_data) + + if valid_data: + unique_values = sorted(set(valid_data)) + # Check if values are reasonable for ordinal data (consecutive or at least ordered) + if len(unique_values) < 2: + return # Single value is fine + + # Store ordinal information + self.ordinal_mappings[metric_name] = { + "min_value": min(unique_values), + "max_value": max(unique_values), + "unique_values": unique_values, + "num_levels": len(unique_values) + } + + def _validate_ranking_data(self, metric_name: str, metric_data): + """Validate ranking data structure.""" + valid_data = self._get_valid_numeric_data(metric_data) + + if not valid_data: + return + + unique_ranks = set(valid_data) + min_rank = min(unique_ranks) + max_rank = max(unique_ranks) + + # Check for reasonable ranking structure + if min_rank < 1: + raise ValueError(f"Ranking data for metric '{metric_name}' should start from 1, found minimum: {min_rank}") + + # Check for gaps in ranking (warning, not error) + expected_ranks = set(range(min_rank, max_rank + 1)) + missing_ranks = expected_ranks - unique_ranks + if missing_ranks: + # This is often okay in ranking data (tied ranks, incomplete rankings) + pass + + def _get_valid_numeric_data(self, data) -> List[Union[int, float]]: + """Get valid numeric data from a metric, handling both single values and lists.""" + if data is None: + return [] + + if not isinstance(data, list): + data = [data] + + return [item for item in data if item is not None and isinstance(item, (int, float))] + + def _convert_to_binary(self, value) -> int: + """Convert various binary representations to 0 or 1.""" + if isinstance(value, bool): + return 1 if value else 0 + elif isinstance(value, str): + normalized = value.lower().strip() + true_values = {"true", "yes", "y", "1", "on", "success", "positive"} + false_values = {"false", "no", "n", "0", "off", "failure", "negative"} + + if normalized in true_values: + return 1 + elif normalized in false_values: + return 0 + else: + raise ValueError(f"Cannot convert string '{value}' to binary") + elif isinstance(value, (int, float)): + return 1 if value != 0 else 0 + else: + raise ValueError(f"Cannot convert {type(value)} to binary") + + def _process_categorical_data(self): + """ + Legacy method for backward compatibility. + Process categorical string data by converting to ordinal values. + """ + for metric_name, metric_data in self.key_results.items(): + if metric_name not in self.data_types: # Only process if data type not explicitly set + if self._is_categorical_data(metric_data): + self.data_types[metric_name] = "categorical" + self._process_categorical_data_for_metric(metric_name, metric_data) + + def _is_categorical_data(self, data: Union[float, int, bool, str, List, None]) -> bool: + """Check if data contains categorical (string) values.""" + if isinstance(data, str): + return True + elif isinstance(data, list): + return any(isinstance(item, str) for item in data if item is not None) + return False + + def _extract_categories(self, data: Union[float, int, bool, str, List, None]) -> set: + """Extract unique string categories from data.""" + categories = set() + + if isinstance(data, str): + categories.add(self._normalize_category(data)) + elif isinstance(data, list): + for item in data: + if isinstance(item, str): + categories.add(self._normalize_category(item)) + + return categories + + def _normalize_category(self, category: str) -> str: + """Normalize categorical string (lowercase, strip whitespace).""" + return category.lower().strip() + + def _convert_to_ordinal(self, data: Union[str, List], mapping: Dict[str, int]) -> Union[int, List[Union[int, None]]]: + """Convert categorical data to ordinal values using the mapping.""" + if isinstance(data, str): + normalized = self._normalize_category(data) + return mapping.get(normalized, 0) # Default to 0 if not found + elif isinstance(data, list): + converted = [] + for item in data: + if isinstance(item, str): + normalized = self._normalize_category(item) + converted.append(mapping.get(normalized, 0)) + elif item is None: + converted.append(None) # Preserve None values + else: + converted.append(item) # Keep numeric values as-is + return converted + else: + return data + + def get_agent_name(self, index: int) -> Optional[str]: + """Get agent name by index, if available.""" + if self.agent_names and 0 <= index < len(self.agent_names): + agent_name = self.agent_names[index] + return agent_name if agent_name is not None else None + return None + + def get_agent_data(self, metric_name: str, agent_index: int) -> Optional[Union[float, int, bool]]: + """Get a specific agent's data for a given metric. Returns None for missing data.""" + if metric_name not in self.key_results: + return None + + metric_data = self.key_results[metric_name] + + # Check if it's per-agent data + if self.result_types.get(metric_name) == "per_agent" and isinstance(metric_data, list): + if 0 <= agent_index < len(metric_data): + return metric_data[agent_index] # This can be None for missing data + + return None + + def get_all_agent_data(self, metric_name: str) -> Dict[str, Union[float, int, bool]]: + """Get all agents' data for a given metric as a dictionary mapping agent names/indices to values.""" + if metric_name not in self.key_results: + return {} + + metric_data = self.key_results[metric_name] + result = {} + + # For per-agent data, create mapping + if self.result_types.get(metric_name) == "per_agent" and isinstance(metric_data, list): + for i, value in enumerate(metric_data): + agent_name = self.get_agent_name(i) or f"Agent_{i}" + # Only include non-None values in the result + if value is not None: + result[agent_name] = value + + # For aggregate data, return single value + elif self.result_types.get(metric_name) == "aggregate": + result["aggregate"] = metric_data + + return result + + def get_valid_agent_data(self, metric_name: str) -> List[Union[float, int, bool]]: + """Get only valid (non-None) values for a per-agent metric.""" + if metric_name not in self.key_results: + return [] + + metric_data = self.key_results[metric_name] + + if self.result_types.get(metric_name) == "per_agent" and isinstance(metric_data, list): + return [value for value in metric_data if value is not None] + + return [] + + def validate_data_consistency(self) -> List[str]: + """Validate that per-agent data is consistent across metrics and with agent names.""" + errors = [] + warnings = [] + + # Check per-agent metrics have consistent lengths + per_agent_lengths = [] + per_agent_metrics = [] + + for metric_name, result_type in self.result_types.items(): + if result_type == "per_agent" and metric_name in self.key_results: + metric_data = self.key_results[metric_name] + if isinstance(metric_data, list): + per_agent_lengths.append(len(metric_data)) + per_agent_metrics.append(metric_name) + else: + errors.append(f"Metric '{metric_name}' marked as per_agent but is not a list") + + # Check all per-agent metrics have same length + if per_agent_lengths and len(set(per_agent_lengths)) > 1: + errors.append(f"Per-agent metrics have inconsistent lengths: {dict(zip(per_agent_metrics, per_agent_lengths))}") + + # Check agent_names length matches per-agent data length + if self.agent_names and per_agent_lengths: + agent_count = len(self.agent_names) + data_length = per_agent_lengths[0] if per_agent_lengths else 0 + if agent_count != data_length: + errors.append(f"agent_names length ({agent_count}) doesn't match per-agent data length ({data_length})") + + # Check for None values in agent_names and provide warnings + if self.agent_names: + none_indices = [i for i, name in enumerate(self.agent_names) if name is None] + if none_indices: + warnings.append(f"agent_names contains None values at indices: {none_indices}") + + # Check for None values in per-agent data and provide info + for metric_name in per_agent_metrics: + if metric_name in self.key_results: + metric_data = self.key_results[metric_name] + none_indices = [i for i, value in enumerate(metric_data) if value is None] + if none_indices: + warnings.append(f"Metric '{metric_name}' has missing data (None) at indices: {none_indices}") + + # Return errors and warnings combined + return errors + [f"WARNING: {warning}" for warning in warnings] + + def get_justification_text(self, justification_item: Union[str, Dict[str, Union[str, int]]]) -> str: + """Extract justification text from various formats.""" + if isinstance(justification_item, str): + return justification_item + elif isinstance(justification_item, dict): + return justification_item.get("justification", "") + return "" + + def get_justification_agent_reference(self, justification_item: Union[str, Dict[str, Union[str, int]]]) -> Optional[str]: + """Get agent reference from justification, returning name if available.""" + if isinstance(justification_item, dict): + # Direct agent name + if "agent_name" in justification_item: + return justification_item["agent_name"] + # Agent index reference + elif "agent_index" in justification_item: + return self.get_agent_name(justification_item["agent_index"]) + return None + + def get_categorical_values(self, metric_name: str) -> Optional[List[str]]: + """Get the original categorical values for a metric, if it was categorical.""" + if metric_name in self.categorical_mappings: + # Return categories sorted by their ordinal values + mapping = self.categorical_mappings[metric_name] + return [category for category, _ in sorted(mapping.items(), key=lambda x: x[1])] + elif metric_name in self.ordinal_mappings and isinstance(self.ordinal_mappings[metric_name], dict): + # Handle string-based ordinal data + mapping = self.ordinal_mappings[metric_name] + if all(isinstance(k, str) for k in mapping.keys()): + return [category for category, _ in sorted(mapping.items(), key=lambda x: x[1])] + return None + + def convert_ordinal_to_categorical(self, metric_name: str, ordinal_value: Union[int, float]) -> Optional[str]: + """Convert an ordinal value back to its original categorical string.""" + # Check categorical mappings first + if metric_name in self.categorical_mappings: + mapping = self.categorical_mappings[metric_name] + # Reverse lookup: find category with this ordinal value + for category, value in mapping.items(): + if value == int(ordinal_value): + return category + + # Check ordinal mappings for string-based ordinal data + elif metric_name in self.ordinal_mappings: + mapping = self.ordinal_mappings[metric_name] + if isinstance(mapping, dict) and all(isinstance(k, str) for k in mapping.keys()): + for category, value in mapping.items(): + if value == int(ordinal_value): + return category + + return None + + def get_data_type_info(self, metric_name: str) -> Dict[str, Any]: + """Get comprehensive information about a metric's data type.""" + data_type = self.data_types.get(metric_name, "numeric") + info = { + "data_type": data_type, + "result_type": self.result_types.get(metric_name, "unknown") + } + + if data_type == "categorical" and metric_name in self.categorical_mappings: + info["categories"] = self.get_categorical_values(metric_name) + info["category_mapping"] = self.categorical_mappings[metric_name].copy() + + elif data_type == "ordinal": + if metric_name in self.ordinal_mappings: + mapping = self.ordinal_mappings[metric_name] + if isinstance(mapping, dict): + # Check if this is a string-to-number mapping (categorical ordinal) + # vs info dict (numeric ordinal) + if "min_value" in mapping or "max_value" in mapping: + # Numeric ordinal info + info["ordinal_info"] = mapping.copy() + elif all(isinstance(k, str) for k in mapping.keys()) and all(isinstance(v, int) for v in mapping.values()): + # String-based ordinal - safely sort by values + try: + info["ordinal_categories"] = [cat for cat, _ in sorted(mapping.items(), key=lambda x: x[1])] + info["ordinal_mapping"] = mapping.copy() + except TypeError: + # Fallback if sorting fails + info["ordinal_categories"] = list(mapping.keys()) + info["ordinal_mapping"] = mapping.copy() + else: + # Unknown ordinal format, treat as info + info["ordinal_info"] = mapping.copy() + + elif data_type == "ranking" and metric_name in self.ranking_info: + info["ranking_info"] = self.ranking_info[metric_name].copy() + + return info + + def get_metric_summary(self, metric_name: str) -> Dict[str, Any]: + """Get a comprehensive summary of a metric including data type information.""" + summary = { + "metric_name": metric_name, + "result_type": self.result_types.get(metric_name, "unknown"), + "data_type": self.data_types.get(metric_name, "numeric"), + } + + # Add legacy categorical flag for backward compatibility + summary["is_categorical"] = (metric_name in self.categorical_mappings or + (metric_name in self.ordinal_mappings and + isinstance(self.ordinal_mappings[metric_name], dict) and + all(isinstance(k, str) for k in self.ordinal_mappings[metric_name].keys()))) + + if metric_name in self.key_results: + data = self.key_results[metric_name] + summary["data_type_name"] = type(data).__name__ + + if isinstance(data, list): + valid_data = [x for x in data if x is not None] + summary["total_values"] = len(data) + summary["valid_values"] = len(valid_data) + summary["missing_values"] = len(data) - len(valid_data) + + if valid_data: + summary["min_value"] = min(valid_data) + summary["max_value"] = max(valid_data) + + # Add data type specific information + data_type_info = self.get_data_type_info(metric_name) + summary.update(data_type_info) + + # Add distribution information for per-agent data + if isinstance(data, list) and self.result_types.get(metric_name) == "per_agent": + data_type = summary["data_type"] + + if data_type in ["categorical", "ordinal"] and summary.get("is_categorical"): + # Category distribution + category_counts = {} + for value in data: + if value is not None: + category = self.convert_ordinal_to_categorical(metric_name, value) + if category: + category_counts[category] = category_counts.get(category, 0) + 1 + summary["category_distribution"] = category_counts + + elif data_type == "ranking": + # Ranking distribution + rank_counts = {} + for value in data: + if value is not None: + rank_counts[value] = rank_counts.get(value, 0) + 1 + summary["rank_distribution"] = rank_counts + + elif data_type == "binary": + # Binary distribution + true_count = sum(1 for x in data if x == 1) + false_count = sum(1 for x in data if x == 0) + summary["binary_distribution"] = {"true": true_count, "false": false_count} + + return summary + + def is_categorical_metric(self, metric_name: str) -> bool: + """Check if a metric contains categorical data (including string-based ordinal).""" + return (metric_name in self.categorical_mappings or + (metric_name in self.ordinal_mappings and + isinstance(self.ordinal_mappings[metric_name], dict) and + all(isinstance(k, str) for k in self.ordinal_mappings[metric_name].keys()))) + + +class SimulationExperimentEmpiricalValidationResult(BaseModel): + """ + Contains the results of a simulation experiment validation against empirical data. + + This represents the outcome of validating simulation experiment data + against empirical benchmarks, using statistical and semantic methods. + + Attributes: + validation_type: Type of validation performed + control_name: Name of the control/empirical dataset + treatment_name: Name of the treatment/simulation experiment dataset + statistical_results: Results from statistical tests (if performed) + semantic_results: Results from semantic proximity analysis (if performed) + overall_score: Overall validation score (0.0 to 1.0) + summary: Summary of validation findings + timestamp: When the validation was performed + """ + validation_type: str + control_name: str + treatment_name: str + statistical_results: Optional[Dict[str, Any]] = None + semantic_results: Optional[Dict[str, Any]] = None + overall_score: Optional[float] = Field(None, ge=0.0, le=1.0, description="Overall validation score between 0.0 and 1.0") + summary: str = "" + timestamp: str = Field(default_factory=lambda: datetime.now().isoformat()) + + class Config: + """Pydantic configuration.""" + extra = "forbid" + validate_assignment = True + + +class SimulationExperimentEmpiricalValidator: + """ + A validator for comparing simulation experiment data against empirical control data. + + This validator performs data-driven validation using statistical hypothesis testing + and semantic proximity analysis of agent justifications. It is designed to validate + simulation experiment results against known empirical benchmarks, distinct from LLM-based evaluations. + """ + + def __init__(self): + """Initialize the simulation experiment empirical validator.""" + pass + + def validate(self, + control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset, + validation_types: List[str] = ["statistical", "semantic"], + statistical_test_type: str = "welch_t_test", + significance_level: float = 0.05, + output_format: str = "values") -> Union[SimulationExperimentEmpiricalValidationResult, str]: + """ + Validate a simulation experiment dataset against an empirical control dataset. + + Args: + control: The control/empirical reference dataset + treatment: The treatment/simulation experiment dataset to validate + validation_types: List of validation types to perform ("statistical", "semantic") + statistical_test_type: Type of statistical test ("welch_t_test", "ks_test", "mann_whitney", etc.) + significance_level: Significance level for statistical tests + output_format: "values" for SimulationExperimentEmpiricalValidationResult object, "report" for markdown report + + Returns: + SimulationExperimentEmpiricalValidationResult object or markdown report string + """ + result = SimulationExperimentEmpiricalValidationResult( + validation_type=", ".join(validation_types), + control_name=control.name or "Control", + treatment_name=treatment.name or "Treatment" + ) + + # Perform statistical validation + if "statistical" in validation_types: + result.statistical_results = self._perform_statistical_validation( + control, treatment, significance_level, statistical_test_type + ) + + # Perform semantic validation + if "semantic" in validation_types: + result.semantic_results = self._perform_semantic_validation( + control, treatment + ) + + # Calculate overall score and summary + result.overall_score = self._calculate_overall_score(result) + result.summary = self._generate_summary(result) + + if output_format == "report": + return self._generate_markdown_report(result, control, treatment) + else: + return result + + def _perform_statistical_validation(self, + control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset, + significance_level: float, + test_type: str = "welch_t_test") -> Dict[str, Any]: + """ + Perform statistical hypothesis testing on simulation experiment key results. + + Args: + control: Control dataset + treatment: Treatment dataset + significance_level: Alpha level for statistical tests + test_type: Type of statistical test to perform + """ + if not control.key_results or not treatment.key_results: + return {"error": "No key results available for statistical testing"} + + try: + # Prepare data for StatisticalTester + control_data = {"control": {}} + treatment_data = {"treatment": {}} + + # Convert single values to lists if needed and find common metrics + common_metrics = set(control.key_results.keys()) & set(treatment.key_results.keys()) + + for metric in common_metrics: + control_value = control.key_results[metric] + treatment_value = treatment.key_results[metric] + + # Convert single values to lists and filter out None values + if not isinstance(control_value, list): + control_value = [control_value] if control_value is not None else [] + else: + control_value = [v for v in control_value if v is not None] + + if not isinstance(treatment_value, list): + treatment_value = [treatment_value] if treatment_value is not None else [] + else: + treatment_value = [v for v in treatment_value if v is not None] + + # Only include metrics that have valid data points + if len(control_value) > 0 and len(treatment_value) > 0: + control_data["control"][metric] = control_value + treatment_data["treatment"][metric] = treatment_value + + if not common_metrics: + return {"error": "No common metrics found between control and treatment"} + + # Run statistical tests + tester = StatisticalTester(control_data, treatment_data) + test_results = tester.run_test( + test_type=test_type, + alpha=significance_level + ) + + return { + "common_metrics": list(common_metrics), + "test_results": test_results, + "test_type": test_type, + "significance_level": significance_level + } + + except Exception as e: + return {"error": f"Statistical testing failed: {str(e)}"} + + def _perform_semantic_validation(self, + control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset) -> Dict[str, Any]: + """Perform semantic proximity analysis on simulation experiment agent justifications.""" + results = { + "individual_comparisons": [], + "summary_comparison": None, + "average_proximity": None + } + + # Compare individual justifications if available + if control.agent_justifications and treatment.agent_justifications: + proximities = [] + + for i, control_just in enumerate(control.agent_justifications): + for j, treatment_just in enumerate(treatment.agent_justifications): + control_text = control.get_justification_text(control_just) + treatment_text = treatment.get_justification_text(treatment_just) + + if control_text and treatment_text: + proximity_score = compute_semantic_proximity( + control_text, + treatment_text, + context="Comparing agent justifications from simulation experiments" + ) + + # Handle case where LLM call fails or returns invalid data + if proximity_score is None or not isinstance(proximity_score, (int, float)): + raise ValueError("Invalid semantic proximity score") + + # Get agent references (names or indices) + control_agent_ref = control.get_justification_agent_reference(control_just) or f"Agent_{i}" + treatment_agent_ref = treatment.get_justification_agent_reference(treatment_just) or f"Agent_{j}" + + comparison = { + "control_agent": control_agent_ref, + "treatment_agent": treatment_agent_ref, + "proximity_score": proximity_score, + "justification": f"Semantic proximity score: {proximity_score:.3f}" + } + + results["individual_comparisons"].append(comparison) + proximities.append(proximity_score) + + if proximities: + results["average_proximity"] = sum(proximities) / len(proximities) + + # Compare summary justifications if available + if control.justification_summary and treatment.justification_summary: + summary_proximity_score = compute_semantic_proximity( + control.justification_summary, + treatment.justification_summary, + context="Comparing summary justifications from simulation experiments" + ) + + # Handle case where LLM call fails or returns invalid data + if summary_proximity_score is None or not isinstance(summary_proximity_score, (int, float)): + summary_proximity_score = 0.5 # Default neutral score + + results["summary_comparison"] = { + "proximity_score": summary_proximity_score, + "justification": f"Summary semantic proximity score: {summary_proximity_score:.3f}" + } + + return results + + def _calculate_overall_score(self, result: SimulationExperimentEmpiricalValidationResult) -> float: + """Calculate an overall simulation experiment empirical validation score based on statistical and semantic results.""" + scores = [] + + # Statistical component based on effect sizes + if result.statistical_results and "test_results" in result.statistical_results: + test_results = result.statistical_results["test_results"] + effect_sizes = [] + + for treatment_name, treatment_results in test_results.items(): + for metric, metric_result in treatment_results.items(): + # Extract effect size based on test type + effect_size = self._extract_effect_size(metric_result) + if effect_size is not None: + effect_sizes.append(effect_size) + + if effect_sizes: + # Convert effect sizes to similarity scores (closer to 0 = more similar) + # Use inverse transformation: similarity = 1 / (1 + |effect_size|) + # For very small effect sizes (< 0.1), give even higher scores + similarity_scores = [] + for es in effect_sizes: + abs_es = abs(es) + if abs_es < 0.1: # Very small effect size + similarity_scores.append(0.95 + 0.05 * (1.0 / (1.0 + abs_es))) + else: + similarity_scores.append(1.0 / (1.0 + abs_es)) + + statistical_score = sum(similarity_scores) / len(similarity_scores) + scores.append(statistical_score) + + # Semantic component + if result.semantic_results: + semantic_scores = [] + + # Average proximity from individual comparisons + if result.semantic_results.get("average_proximity") is not None: + semantic_scores.append(result.semantic_results["average_proximity"]) + + # Summary proximity + if result.semantic_results.get("summary_comparison"): + semantic_scores.append(result.semantic_results["summary_comparison"]["proximity_score"]) + + if semantic_scores: + semantic_score = sum(semantic_scores) / len(semantic_scores) + scores.append(semantic_score) + + # If we have both statistical and semantic scores, and the statistical score is very high (>0.9) + # indicating statistically equivalent data, weight the statistical component more heavily + if len(scores) == 2 and scores[0] > 0.9: # First score is statistical + # Weight statistical component at 70%, semantic at 30% for equivalent data + return 0.7 * scores[0] + 0.3 * scores[1] + + return sum(scores) / len(scores) if scores else 0.0 + + def _generate_summary(self, result: SimulationExperimentEmpiricalValidationResult) -> str: + """Generate a text summary of the simulation experiment empirical validation results.""" + summary_parts = [] + + if result.statistical_results: + if "error" in result.statistical_results: + summary_parts.append(f"Statistical validation: {result.statistical_results['error']}") + else: + test_results = result.statistical_results.get("test_results", {}) + effect_sizes = [] + significant_tests = 0 + total_tests = 0 + + for treatment_results in test_results.values(): + for metric_result in treatment_results.values(): + total_tests += 1 + if metric_result.get("significant", False): + significant_tests += 1 + + # Collect effect sizes + effect_size = self._extract_effect_size(metric_result) + if effect_size is not None: + effect_sizes.append(abs(effect_size)) + + if effect_sizes: + avg_effect_size = sum(effect_sizes) / len(effect_sizes) + summary_parts.append( + f"Statistical validation: {significant_tests}/{total_tests} tests significant, " + f"average effect size: {avg_effect_size:.3f}" + ) + else: + summary_parts.append( + f"Statistical validation: {significant_tests}/{total_tests} tests showed significant differences" + ) + + if result.semantic_results: + avg_proximity = result.semantic_results.get("average_proximity") + if avg_proximity is not None: + summary_parts.append( + f"Semantic validation: Average proximity score of {avg_proximity:.3f}" + ) + + summary_comparison = result.semantic_results.get("summary_comparison") + if summary_comparison: + summary_parts.append( + f"Summary proximity: {summary_comparison['proximity_score']:.3f}" + ) + + if result.overall_score is not None: + summary_parts.append(f"Overall validation score: {result.overall_score:.3f}") + + return "; ".join(summary_parts) if summary_parts else "No validation results available" + + def _generate_markdown_report(self, result: SimulationExperimentEmpiricalValidationResult, + control: SimulationExperimentDataset = None, + treatment: SimulationExperimentDataset = None) -> str: + """Generate a comprehensive markdown report for simulation experiment empirical validation.""" + overall_score_str = f"{result.overall_score:.3f}" if result.overall_score is not None else "N/A" + + report = f"""# Simulation Experiment Empirical Validation Report + +**Validation Type:** {result.validation_type} +**Control/Empirical:** {result.control_name} +**Treatment/Simulation:** {result.treatment_name} +**Timestamp:** {result.timestamp} +**Overall Score:** {overall_score_str} + +## Summary + +{result.summary} + +""" + + # Add data type information if available + if control or treatment: + data_type_info = self._generate_data_type_info_section(control, treatment) + if data_type_info: + report += data_type_info + + # Statistical Results Section + if result.statistical_results: + report += "## Statistical Validation\n\n" + + if "error" in result.statistical_results: + report += f"**Error:** {result.statistical_results['error']}\n\n" + else: + stats = result.statistical_results + report += f"**Common Metrics:** {', '.join(stats.get('common_metrics', []))}\n\n" + report += f"**Significance Level:** {stats.get('significance_level', 'N/A')}\n\n" + + test_results = stats.get("test_results", {}) + if test_results: + report += "### Test Results\n\n" + + for treatment_name, treatment_results in test_results.items(): + report += f"#### {treatment_name}\n\n" + + for metric, metric_result in treatment_results.items(): + report += f"**{metric}:**\n\n" + + significant = metric_result.get("significant", False) + p_value = metric_result.get("p_value", "N/A") + test_type = metric_result.get("test_type", "N/A") + effect_size = self._extract_effect_size(metric_result) + + # Get the appropriate statistic based on test type + statistic = "N/A" + if "t_statistic" in metric_result: + statistic = metric_result["t_statistic"] + elif "u_statistic" in metric_result: + statistic = metric_result["u_statistic"] + elif "f_statistic" in metric_result: + statistic = metric_result["f_statistic"] + elif "chi2_statistic" in metric_result: + statistic = metric_result["chi2_statistic"] + elif "ks_statistic" in metric_result: + statistic = metric_result["ks_statistic"] + + status = "✅ Significant" if significant else "❌ Not Significant" + + report += f"- **{test_type}:** {status}\n" + report += f" - p-value: {p_value}\n" + report += f" - statistic: {statistic}\n" + if effect_size is not None: + effect_interpretation = self._interpret_effect_size(abs(effect_size), test_type) + report += f" - effect size: {effect_size:.3f} ({effect_interpretation})\n" + + report += "\n" + + # Semantic Results Section + if result.semantic_results: + report += "## Semantic Validation\n\n" + + semantic = result.semantic_results + + # Individual comparisons + individual_comps = semantic.get("individual_comparisons", []) + if individual_comps: + report += "### Individual Agent Comparisons\n\n" + + for comp in individual_comps: + score = comp["proximity_score"] + control_agent = comp["control_agent"] + treatment_agent = comp["treatment_agent"] + justification = comp["justification"] + + report += f"**{control_agent} vs {treatment_agent}:** {score:.3f}\n\n" + report += f"{justification}\n\n" + + avg_proximity = semantic.get("average_proximity") + if avg_proximity: + report += f"**Average Proximity Score:** {avg_proximity:.3f}\n\n" + + # Summary comparison + summary_comp = semantic.get("summary_comparison") + if summary_comp: + report += "### Summary Comparison\n\n" + report += f"**Proximity Score:** {summary_comp['proximity_score']:.3f}\n\n" + report += f"**Justification:** {summary_comp['justification']}\n\n" + + return report + + def _generate_data_type_info_section(self, control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset) -> str: + """Generate comprehensive data type information section for the report.""" + all_metrics = set() + + # Collect all metrics from both datasets + if control: + all_metrics.update(control.key_results.keys()) + if treatment: + all_metrics.update(treatment.key_results.keys()) + + if not all_metrics: + return "" + + # Group metrics by data type + data_type_groups = {} + for metric in all_metrics: + for dataset_name, dataset in [("control", control), ("treatment", treatment)]: + if dataset and metric in dataset.data_types: + data_type = dataset.data_types[metric] + if data_type not in data_type_groups: + data_type_groups[data_type] = set() + data_type_groups[data_type].add(metric) + break # Use first available data type + + if not data_type_groups: + return "" + + report = "## Data Type Information\n\n" + + for data_type, metrics in sorted(data_type_groups.items()): + if not metrics: + continue + + report += f"### {data_type.title()} Data\n\n" + + if data_type == "categorical": + report += "String categories converted to ordinal values for statistical analysis.\n\n" + elif data_type == "ordinal": + report += "Ordered categories or levels with meaningful ranking.\n\n" + elif data_type == "ranking": + report += "Rank positions (1st, 2nd, 3rd, etc.) indicating preference or order.\n\n" + elif data_type == "count": + report += "Non-negative integer counts (frequencies, occurrences, etc.).\n\n" + elif data_type == "proportion": + report += "Values between 0-1 representing proportions or percentages.\n\n" + elif data_type == "binary": + report += "Binary outcomes converted to 0/1 for analysis.\n\n" + elif data_type == "numeric": + report += "Continuous numeric values.\n\n" + + for metric in sorted(metrics): + report += f"#### {metric}\n\n" + + # Show information from both datasets + for dataset_name, dataset in [("Control", control), ("Treatment", treatment)]: + if not dataset or metric not in dataset.key_results: + continue + + data_type_info = dataset.get_data_type_info(metric) + summary = dataset.get_metric_summary(metric) + + report += f"**{dataset_name}:**\n" + + if data_type == "categorical": + if "categories" in data_type_info: + categories = data_type_info["categories"] + mapping = data_type_info.get("category_mapping", {}) + + report += f"- Categories: {', '.join(f'`{cat}`' for cat in categories)}\n" + report += f"- Ordinal mapping: {mapping}\n" + + if "category_distribution" in summary: + distribution = summary["category_distribution"] + total = sum(distribution.values()) + report += "- Distribution: " + dist_items = [] + for cat in categories: + count = distribution.get(cat, 0) + pct = (count / total * 100) if total > 0 else 0 + dist_items.append(f"`{cat}`: {count} ({pct:.1f}%)") + report += ", ".join(dist_items) + "\n" + + elif data_type == "ordinal": + if "ordinal_categories" in data_type_info: + # String-based ordinal + categories = data_type_info["ordinal_categories"] + mapping = data_type_info.get("ordinal_mapping", {}) + report += f"- Ordered categories: {' < '.join(f'`{cat}`' for cat in categories)}\n" + report += f"- Ordinal mapping: {mapping}\n" + elif "ordinal_info" in data_type_info: + # Numeric ordinal + info = data_type_info["ordinal_info"] + report += f"- Value range: {info.get('min_value')} to {info.get('max_value')}\n" + report += f"- Unique levels: {info.get('num_levels')} ({info.get('unique_values')})\n" + + elif data_type == "ranking": + if "ranking_info" in data_type_info: + info = data_type_info["ranking_info"] + report += f"- Rank range: {info.get('min_rank')} to {info.get('max_rank')}\n" + report += f"- Number of ranks: {info.get('num_ranks')}\n" + report += f"- Direction: {info.get('direction', 'ascending')} (1 = best)\n" + + if "rank_distribution" in summary: + distribution = summary["rank_distribution"] + report += "- Distribution: " + rank_items = [] + for rank in sorted(distribution.keys()): + count = distribution[rank] + rank_items.append(f"Rank {rank}: {count}") + report += ", ".join(rank_items) + "\n" + + elif data_type == "binary": + if "binary_distribution" in summary: + distribution = summary["binary_distribution"] + true_count = distribution.get("true", 0) + false_count = distribution.get("false", 0) + total = true_count + false_count + if total > 0: + true_pct = (true_count / total * 100) + false_pct = (false_count / total * 100) + report += f"- Distribution: True: {true_count} ({true_pct:.1f}%), False: {false_count} ({false_pct:.1f}%)\n" + + elif data_type in ["count", "proportion", "numeric"]: + if "min_value" in summary and "max_value" in summary: + report += f"- Range: {summary['min_value']} to {summary['max_value']}\n" + if "valid_values" in summary: + report += f"- Valid values: {summary['valid_values']}/{summary.get('total_values', 'N/A')}\n" + + report += "\n" + + return report + + def _generate_categorical_info_section(self, control: SimulationExperimentDataset, + treatment: SimulationExperimentDataset) -> str: + """ + Generate categorical data information section for the report. + This is kept for backward compatibility and now calls the more comprehensive data type method. + """ + return self._generate_data_type_info_section(control, treatment) + + @classmethod + def read_empirical_data_from_csv(cls, + file_path: Union[str, Path], + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None, + encoding: str = "utf-8") -> 'SimulationExperimentDataset': + """ + Read empirical data from a CSV file and convert it to a SimulationExperimentDataset. + + Args: + file_path: Path to the CSV file + experimental_data_type: Type of experimental data: + - "single_value_per_agent": Each agent has a single value (e.g., score, rating) + - "ranking_per_agent": Each agent provides rankings for multiple items (separate columns) + - "ordinal_ranking_per_agent": Each agent provides ordinal ranking in single column with separator + agent_id_column: Column name containing agent identifiers (optional) + agent_comments_column: Column name containing agent comments/explanations (optional) + agent_attributes_columns: List of column names containing agent attributes (age, gender, etc.) + value_column: Column name containing the main value for single_value_per_agent mode + ranking_columns: List of column names containing rankings for ranking_per_agent mode + ordinal_ranking_column: Column name containing ordinal rankings for ordinal_ranking_per_agent mode + ordinal_ranking_separator: Separator used in ordinal ranking strings (default: "-") + ordinal_ranking_options: List of options being ranked (if None, auto-detected from data) + dataset_name: Optional name for the dataset + dataset_description: Optional description of the dataset + encoding: File encoding (default: utf-8) + + Returns: + SimulationExperimentDataset object populated with the CSV data + + Raises: + FileNotFoundError: If the CSV file doesn't exist + ValueError: If required columns are missing or data format is invalid + pandas.errors.EmptyDataError: If the CSV file is empty + """ + file_path = Path(file_path) + + if not file_path.exists(): + raise FileNotFoundError(f"CSV file not found: {file_path}") + + try: + # Read CSV with UTF-8 encoding and error handling + df = pd.read_csv(file_path, encoding=encoding, encoding_errors='replace') + except pd.errors.EmptyDataError: + raise pd.errors.EmptyDataError(f"CSV file is empty: {file_path}") + except UnicodeDecodeError as e: + raise ValueError(f"Failed to read CSV file with encoding {encoding}: {e}") + + if df.empty: + raise ValueError(f"CSV file contains no data: {file_path}") + + # Use common processing method + return cls._process_empirical_data_from_dataframe( + df=df, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name or f"Empirical_Data_{file_path.stem}", + dataset_description=dataset_description or f"Empirical data loaded from {file_path.name}" + ) + + @classmethod + def read_empirical_data_from_dataframe(cls, + df: pd.DataFrame, + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None) -> 'SimulationExperimentDataset': + """ + Read empirical data from a pandas DataFrame and convert it to a SimulationExperimentDataset. + + This method provides the same functionality as read_empirical_data_from_csv but accepts + a pandas DataFrame directly, eliminating the need to save DataFrames to CSV files first. + + Args: + df: The pandas DataFrame containing the empirical data + experimental_data_type: Type of experimental data: + - "single_value_per_agent": Each agent has a single value (e.g., score, rating) + - "ranking_per_agent": Each agent provides rankings for multiple items (separate columns) + - "ordinal_ranking_per_agent": Each agent provides ordinal ranking in single column with separator + agent_id_column: Column name containing agent identifiers (optional) + agent_comments_column: Column name containing agent comments/explanations (optional) + agent_attributes_columns: List of column names containing agent attributes (age, gender, etc.) + value_column: Column name containing the main value for single_value_per_agent mode + ranking_columns: List of column names containing rankings for ranking_per_agent mode + ordinal_ranking_column: Column name containing ordinal rankings for ordinal_ranking_per_agent mode + ordinal_ranking_separator: Separator used in ordinal ranking strings (default: "-") + ordinal_ranking_options: List of options being ranked (if None, auto-detected from data) + dataset_name: Optional name for the dataset + dataset_description: Optional description of the dataset + + Returns: + SimulationExperimentDataset object populated with the DataFrame data + + Raises: + ValueError: If required columns are missing or data format is invalid + TypeError: If df is not a pandas DataFrame + """ + # Validate input + if not isinstance(df, pd.DataFrame): + raise TypeError(f"Expected pandas DataFrame, got {type(df)}") + + if df.empty: + raise ValueError("DataFrame contains no data") + + # Use common processing method + return cls._process_empirical_data_from_dataframe( + df=df, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name or "Empirical_Data_from_DataFrame", + dataset_description=dataset_description or "Empirical data loaded from pandas DataFrame" + ) + + @classmethod + def _process_empirical_data_from_dataframe(cls, + df: pd.DataFrame, + experimental_data_type: str, + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]], + value_column: Optional[str], + ranking_columns: Optional[List[str]], + ordinal_ranking_column: Optional[str], + ordinal_ranking_separator: str, + ordinal_ranking_options: Optional[List[str]], + dataset_name: str, + dataset_description: str) -> 'SimulationExperimentDataset': + """ + Common processing method for both CSV and DataFrame inputs. + + This method contains the shared logic for processing empirical data regardless of input source. + """ + # Initialize dataset + dataset = SimulationExperimentDataset( + name=dataset_name, + description=dataset_description + ) + + # Process based on experimental data type + if experimental_data_type == "single_value_per_agent": + cls._process_single_value_per_agent_csv(df, dataset, value_column, + agent_id_column, agent_comments_column, + agent_attributes_columns) + elif experimental_data_type == "ranking_per_agent": + cls._process_ranking_per_agent_csv(df, dataset, ranking_columns, + agent_id_column, agent_comments_column, + agent_attributes_columns) + elif experimental_data_type == "ordinal_ranking_per_agent": + cls._process_ordinal_ranking_per_agent_csv(df, dataset, ordinal_ranking_column, + ordinal_ranking_separator, ordinal_ranking_options, + agent_id_column, agent_comments_column, + agent_attributes_columns) + else: + raise ValueError(f"Unsupported experimental_data_type: {experimental_data_type}. " + f"Supported types: 'single_value_per_agent', 'ranking_per_agent', 'ordinal_ranking_per_agent'") + + # Process data types after all data is loaded + dataset._process_data_types() + + return dataset + + @classmethod + def _process_single_value_per_agent_csv(cls, + df: pd.DataFrame, + dataset: 'SimulationExperimentDataset', + value_column: Optional[str], + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]]): + """Process CSV data for single value per agent experiments.""" + + # Auto-detect value column if not specified + if value_column is None: + # Look for common column names that might contain the main value + value_candidates = [col for col in df.columns if any(keyword in col.lower() + for keyword in ['vote', 'score', 'rating', 'value', 'response', 'answer'])] + + if len(value_candidates) == 1: + value_column = value_candidates[0] + elif len(value_candidates) > 1: + # Prefer shorter, more specific names + value_column = min(value_candidates, key=len) + else: + # Fall back to first numeric column + numeric_cols = df.select_dtypes(include=['number']).columns.tolist() + if numeric_cols: + value_column = numeric_cols[0] + else: + raise ValueError("No suitable value column found. Please specify value_column parameter.") + + if value_column not in df.columns: + raise ValueError(f"Value column '{value_column}' not found in CSV. Available columns: {list(df.columns)}") + + # Extract main values (handling mixed types) + values = [] + for val in df[value_column]: + if pd.isna(val): + values.append(None) + else: + # Try to convert to numeric if possible, otherwise keep as string + try: + if isinstance(val, str) and val.strip().isdigit(): + values.append(int(val.strip())) + elif isinstance(val, str): + try: + float_val = float(val.strip()) + # If it's a whole number, convert to int + values.append(int(float_val) if float_val.is_integer() else float_val) + except ValueError: + values.append(val.strip()) + else: + values.append(val) + except (AttributeError, ValueError): + values.append(val) + + # Store the main experimental result + dataset.key_results[value_column] = values + dataset.result_types[value_column] = "per_agent" + + # Process agent IDs/names + agent_names = [] + if agent_id_column and agent_id_column in df.columns: + for agent_id in df[agent_id_column]: + if pd.isna(agent_id): + agent_names.append(None) + else: + agent_names.append(str(agent_id)) + else: + # Generate default agent names + for i in range(len(df)): + agent_names.append(f"Agent_{i+1}") + + dataset.agent_names = agent_names + + # Process agent comments/justifications + if agent_comments_column and agent_comments_column in df.columns: + justifications = [] + for i, comment in enumerate(df[agent_comments_column]): + # Include all comments, even empty ones, to maintain agent alignment + agent_name = agent_names[i] if i < len(agent_names) else f"Agent_{i+1}" + comment_text = str(comment).strip() if pd.notna(comment) else "" + justifications.append({ + "agent_name": agent_name, + "agent_index": i, + "justification": comment_text + }) + dataset.agent_justifications = justifications + + # Process agent attributes + if agent_attributes_columns: + for attr_col in agent_attributes_columns: + if attr_col in df.columns: + attr_values = [] + for val in df[attr_col]: + if pd.isna(val): + attr_values.append(None) + else: + attr_values.append(str(val).strip()) + + # Store in agent_attributes instead of key_results + dataset.agent_attributes[attr_col] = attr_values + + @classmethod + def _process_ranking_per_agent_csv(cls, + df: pd.DataFrame, + dataset: 'SimulationExperimentDataset', + ranking_columns: Optional[List[str]], + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]]): + """Process CSV data for ranking per agent experiments.""" + + # Auto-detect ranking columns if not specified + if ranking_columns is None: + # Look for columns that might contain rankings + numeric_cols = df.select_dtypes(include=['number']).columns.tolist() + + # Exclude agent ID column if specified + if agent_id_column and agent_id_column in numeric_cols: + numeric_cols.remove(agent_id_column) + + if len(numeric_cols) < 2: + raise ValueError("No suitable ranking columns found. Please specify ranking_columns parameter.") + + ranking_columns = numeric_cols + + # Validate ranking columns exist + missing_cols = [col for col in ranking_columns if col not in df.columns] + if missing_cols: + raise ValueError(f"Ranking columns not found in CSV: {missing_cols}. Available columns: {list(df.columns)}") + + # Process each ranking column + for rank_col in ranking_columns: + rankings = [] + for val in df[rank_col]: + if pd.isna(val): + rankings.append(None) + else: + try: + # Convert to integer rank + rankings.append(int(float(val))) + except (ValueError, TypeError): + rankings.append(None) + + dataset.key_results[rank_col] = rankings + dataset.result_types[rank_col] = "per_agent" + dataset.data_types[rank_col] = "ranking" + + # Process agent IDs/names (same as single value method) + agent_names = [] + if agent_id_column and agent_id_column in df.columns: + for agent_id in df[agent_id_column]: + if pd.isna(agent_id): + agent_names.append(None) + else: + agent_names.append(str(agent_id)) + else: + # Generate default agent names + for i in range(len(df)): + agent_names.append(f"Agent_{i+1}") + + dataset.agent_names = agent_names + + # Process agent comments (same as single value method) + if agent_comments_column and agent_comments_column in df.columns: + justifications = [] + for i, comment in enumerate(df[agent_comments_column]): + # Include all comments, even empty ones, to maintain agent alignment + agent_name = agent_names[i] if i < len(agent_names) else f"Agent_{i+1}" + comment_text = str(comment).strip() if pd.notna(comment) else "" + justifications.append({ + "agent_name": agent_name, + "agent_index": i, + "justification": comment_text + }) + dataset.agent_justifications = justifications + + # Process agent attributes (same as single value method) + if agent_attributes_columns: + for attr_col in agent_attributes_columns: + if attr_col in df.columns: + attr_values = [] + for val in df[attr_col]: + if pd.isna(val): + attr_values.append(None) + else: + attr_values.append(str(val).strip()) + + # Store in agent_attributes instead of key_results + dataset.agent_attributes[attr_col] = attr_values + + @classmethod + def _process_ordinal_ranking_per_agent_csv(cls, + df: pd.DataFrame, + dataset: 'SimulationExperimentDataset', + ordinal_ranking_column: Optional[str], + ordinal_ranking_separator: str, + ordinal_ranking_options: Optional[List[str]], + agent_id_column: Optional[str], + agent_comments_column: Optional[str], + agent_attributes_columns: Optional[List[str]]): + """Process CSV data for ordinal ranking per agent experiments (single column with separator).""" + + # Auto-detect ranking column if not specified + if ordinal_ranking_column is None: + # Look for columns that might contain ordinal rankings + ranking_candidates = [col for col in df.columns if any(keyword in col.lower() + for keyword in ['ranking', 'rank', 'order', 'preference', 'choice'])] + + if len(ranking_candidates) == 1: + ordinal_ranking_column = ranking_candidates[0] + elif len(ranking_candidates) > 1: + # Prefer shorter, more specific names + ordinal_ranking_column = min(ranking_candidates, key=len) + else: + # Fall back to first string column that contains separator + string_cols = df.select_dtypes(include=['object']).columns.tolist() + if agent_id_column and agent_id_column in string_cols: + string_cols.remove(agent_id_column) + if agent_comments_column and agent_comments_column in string_cols: + string_cols.remove(agent_comments_column) + + # Check which string columns contain the separator + for col in string_cols: + if df[col].astype(str).str.contains(ordinal_ranking_separator, na=False).any(): + ordinal_ranking_column = col + break + + if ordinal_ranking_column is None: + raise ValueError("No suitable ordinal ranking column found. Please specify ordinal_ranking_column parameter.") + + if ordinal_ranking_column not in df.columns: + raise ValueError(f"Ordinal ranking column '{ordinal_ranking_column}' not found in CSV. Available columns: {list(df.columns)}") + + # Auto-detect ranking options if not specified + if ordinal_ranking_options is None: + ordinal_ranking_options = cls._auto_detect_ranking_options(df[ordinal_ranking_column], ordinal_ranking_separator) + + # Parse ordinal rankings and convert to individual ranking columns + ranking_data = cls._parse_ordinal_rankings(df[ordinal_ranking_column], ordinal_ranking_separator, ordinal_ranking_options) + + # Store parsed rankings as separate metrics + for option in ordinal_ranking_options: + option_ranking_key = f"{option}_rank" + dataset.key_results[option_ranking_key] = ranking_data[option] + dataset.result_types[option_ranking_key] = "per_agent" + dataset.data_types[option_ranking_key] = "ranking" + + # Store ranking info (always for ordinal ranking data) + valid_ranks = [r for r in ranking_data[option] if r is not None] + + # Always store ranking info for ordinal ranking data, regardless of valid ranks + ranking_info = { + "direction": "ascending", # 1 = best, higher = worse + "original_options": ordinal_ranking_options, + "separator": ordinal_ranking_separator, + "source_column": ordinal_ranking_column + } + + # Add rank statistics if valid ranks exist + if valid_ranks: + ranking_info.update({ + "min_rank": min(valid_ranks), + "max_rank": max(valid_ranks), + "num_ranks": len(set(valid_ranks)), + "rank_values": sorted(set(valid_ranks)) + }) + else: + # Set reasonable defaults based on options + ranking_info.update({ + "min_rank": 1, + "max_rank": len(ordinal_ranking_options), + "num_ranks": 0, + "rank_values": [] + }) + + dataset.ranking_info[option_ranking_key] = ranking_info + + # Process agent IDs/names (same as other methods) + agent_names = [] + if agent_id_column and agent_id_column in df.columns: + for agent_id in df[agent_id_column]: + if pd.isna(agent_id): + agent_names.append(None) + else: + agent_names.append(str(agent_id)) + else: + # Generate default agent names + for i in range(len(df)): + agent_names.append(f"Agent_{i+1}") + + dataset.agent_names = agent_names + + # Process agent comments (same as other methods) + if agent_comments_column and agent_comments_column in df.columns: + justifications = [] + for i, comment in enumerate(df[agent_comments_column]): + # Include all comments, even empty ones, to maintain agent alignment + agent_name = agent_names[i] if i < len(agent_names) else f"Agent_{i+1}" + comment_text = str(comment).strip() if pd.notna(comment) else "" + justifications.append({ + "agent_name": agent_name, + "agent_index": i, + "justification": comment_text + }) + dataset.agent_justifications = justifications + + # Process agent attributes (same as other methods) + if agent_attributes_columns: + for attr_col in agent_attributes_columns: + if attr_col in df.columns: + attr_values = [] + for val in df[attr_col]: + if pd.isna(val): + attr_values.append(None) + else: + attr_values.append(str(val).strip()) + + # Store in agent_attributes instead of key_results + dataset.agent_attributes[attr_col] = attr_values + + @classmethod + def _auto_detect_ranking_options(cls, ranking_series: pd.Series, separator: str) -> List[str]: + """Auto-detect the ranking options from ordinal ranking data.""" + all_options = set() + + for ranking_str in ranking_series.dropna(): + if pd.isna(ranking_str): + continue + + ranking_str = str(ranking_str).strip() + if separator in ranking_str: + options = [opt.strip() for opt in ranking_str.split(separator)] + all_options.update(options) + + if not all_options: + raise ValueError(f"No ranking options found in data using separator '{separator}'") + + # Sort options for consistency (could be enhanced to preserve meaningful order) + return sorted(list(all_options)) + + @classmethod + def _parse_ordinal_rankings(cls, ranking_series: pd.Series, separator: str, options: List[str]) -> Dict[str, List[Optional[int]]]: + """Parse ordinal ranking strings into individual option rankings.""" + result = {option: [] for option in options} + + for ranking_str in ranking_series: + if pd.isna(ranking_str) or str(ranking_str).strip() == "": + # Handle missing data + for option in options: + result[option].append(None) + continue + + ranking_str = str(ranking_str).strip() + + if separator not in ranking_str: + # Handle malformed data + for option in options: + result[option].append(None) + continue + + # Parse the ranking + ranked_options = [opt.strip() for opt in ranking_str.split(separator)] + + # Create rank mapping (position in list = rank, starting from 1) + option_to_rank = {} + for rank, option in enumerate(ranked_options, 1): + if option in options: + option_to_rank[option] = rank + + # Fill in ranks for each option + for option in options: + rank = option_to_rank.get(option, None) + result[option].append(rank) + + return result + + @classmethod + def create_from_csv(cls, + file_path: Union[str, Path], + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None, + encoding: str = "utf-8") -> tuple['SimulationExperimentEmpiricalValidator', 'SimulationExperimentDataset']: + """ + Create a validator and load empirical data from CSV in one step. + + This is a convenience method that combines validator creation with CSV loading. + + Args: + Same as read_empirical_data_from_csv() + + Returns: + Tuple of (validator_instance, loaded_dataset) + """ + validator = cls() + dataset = cls.read_empirical_data_from_csv( + file_path=file_path, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name, + dataset_description=dataset_description, + encoding=encoding + ) + return validator, dataset + + @classmethod + def create_from_dataframe(cls, + df: pd.DataFrame, + experimental_data_type: str = "single_value_per_agent", + agent_id_column: Optional[str] = None, + agent_comments_column: Optional[str] = None, + agent_attributes_columns: Optional[List[str]] = None, + value_column: Optional[str] = None, + ranking_columns: Optional[List[str]] = None, + ordinal_ranking_column: Optional[str] = None, + ordinal_ranking_separator: str = "-", + ordinal_ranking_options: Optional[List[str]] = None, + dataset_name: Optional[str] = None, + dataset_description: Optional[str] = None) -> tuple['SimulationExperimentEmpiricalValidator', 'SimulationExperimentDataset']: + """ + Create a validator and load empirical data from a pandas DataFrame in one step. + + This is a convenience method that combines validator creation with DataFrame loading. + + Args: + Same as read_empirical_data_from_dataframe() + + Returns: + Tuple of (validator_instance, loaded_dataset) + """ + validator = cls() + dataset = cls.read_empirical_data_from_dataframe( + df=df, + experimental_data_type=experimental_data_type, + agent_id_column=agent_id_column, + agent_comments_column=agent_comments_column, + agent_attributes_columns=agent_attributes_columns, + value_column=value_column, + ranking_columns=ranking_columns, + ordinal_ranking_column=ordinal_ranking_column, + ordinal_ranking_separator=ordinal_ranking_separator, + ordinal_ranking_options=ordinal_ranking_options, + dataset_name=dataset_name, + dataset_description=dataset_description + ) + return validator, dataset + + def _extract_effect_size(self, metric_result: Dict[str, Any]) -> Optional[float]: + """Extract effect size from statistical test result, regardless of test type.""" + # Cohen's d for t-tests (most common) + if "effect_size" in metric_result: + return metric_result["effect_size"] + + # For tests that don't provide Cohen's d, calculate standardized effect size + test_type = metric_result.get("test_type", "").lower() + + if "t-test" in test_type: + # For t-tests, effect_size should be Cohen's d + return metric_result.get("effect_size", 0.0) + + elif "mann-whitney" in test_type: + # For Mann-Whitney, use Common Language Effect Size (CLES) + # Convert CLES to Cohen's d equivalent: d ≈ 2 * Φ^(-1)(CLES) + cles = metric_result.get("effect_size", 0.5) + # Simple approximation: convert CLES to d-like measure + # CLES of 0.5 = no effect, CLES of 0.71 ≈ small effect (d=0.2) + return 2 * (cles - 0.5) + + elif "anova" in test_type: + # For ANOVA, use eta-squared and convert to Cohen's d equivalent + eta_squared = metric_result.get("effect_size", 0.0) + # Convert eta-squared to Cohen's d: d = 2 * sqrt(eta^2 / (1 - eta^2)) + if eta_squared > 0 and eta_squared < 1: + return 2 * (eta_squared / (1 - eta_squared)) ** 0.5 + return 0.0 + + elif "chi-square" in test_type: + # For Chi-square, use Cramer's V and convert to Cohen's d equivalent + cramers_v = metric_result.get("effect_size", 0.0) + # Rough conversion: d ≈ 2 * Cramer's V + return 2 * cramers_v + + elif "kolmogorov-smirnov" in test_type or "ks" in test_type: + # For KS test, the effect size is the KS statistic itself + # It represents the maximum difference between CDFs (0 to 1) + return metric_result.get("effect_size", metric_result.get("ks_statistic", 0.0)) + + # Fallback: try to calculate from means and standard deviations + if all(k in metric_result for k in ["control_mean", "treatment_mean", "control_std", "treatment_std"]): + control_mean = metric_result["control_mean"] + treatment_mean = metric_result["treatment_mean"] + control_std = metric_result["control_std"] + treatment_std = metric_result["treatment_std"] + + # Calculate pooled standard deviation + pooled_std = ((control_std ** 2 + treatment_std ** 2) / 2) ** 0.5 + if pooled_std > 0: + return abs(treatment_mean - control_mean) / pooled_std + + # If all else fails, return 0 (no effect) + return 0.0 + + def _interpret_effect_size(self, effect_size: float, test_type: str = "") -> str: + """Provide interpretation of effect size magnitude based on test type.""" + test_type_lower = test_type.lower() + + # For KS test, use different thresholds since KS statistic ranges 0-1 + if "kolmogorov-smirnov" in test_type_lower or "ks" in test_type_lower: + if effect_size < 0.1: + return "negligible difference" + elif effect_size < 0.25: + return "small difference" + elif effect_size < 0.5: + return "medium difference" + else: + return "large difference" + + # For other tests, use Cohen's conventions + if effect_size < 0.2: + return "negligible" + elif effect_size < 0.5: + return "small" + elif effect_size < 0.8: + return "medium" + else: + return "large" + + +def validate_simulation_experiment_empirically(control_data: Dict[str, Any], + treatment_data: Dict[str, Any], + validation_types: List[str] = ["statistical", "semantic"], + statistical_test_type: str = "welch_t_test", + significance_level: float = 0.05, + output_format: str = "values") -> Union[SimulationExperimentEmpiricalValidationResult, str]: + """ + Convenience function to validate simulation experiment data against empirical control data. + + This performs data-driven validation using statistical and semantic methods, + distinct from LLM-based evaluations. + + Args: + control_data: Dictionary containing control/empirical data + treatment_data: Dictionary containing treatment/simulation experiment data + validation_types: List of validation types to perform + statistical_test_type: Type of statistical test ("welch_t_test", "ks_test", "mann_whitney", etc.) + significance_level: Significance level for statistical tests + output_format: "values" for SimulationExperimentEmpiricalValidationResult object, "report" for markdown report + + Returns: + SimulationExperimentEmpiricalValidationResult object or markdown report string + """ + # Use Pydantic's built-in parsing instead of from_dict + control_dataset = SimulationExperimentDataset.model_validate(control_data) + treatment_dataset = SimulationExperimentDataset.model_validate(treatment_data) + + validator = SimulationExperimentEmpiricalValidator() + return validator.validate( + control_dataset, + treatment_dataset, + validation_types=validation_types, + statistical_test_type=statistical_test_type, + significance_level=significance_level, + output_format=output_format + ) diff --git a/validation/tiny_person_validator.py b/validation/tiny_person_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..8eff812d338c44b0dc43769e97874df733b92861 --- /dev/null +++ b/validation/tiny_person_validator.py @@ -0,0 +1,120 @@ +import os +import json +import chevron +import logging +from pydantic import BaseModel +from typing import Optional, List + +from tinytroupe import openai_utils +from tinytroupe.agent import TinyPerson +from tinytroupe import config +import tinytroupe.utils as utils + + +default_max_content_display_length = config["OpenAI"].getint("MAX_CONTENT_DISPLAY_LENGTH", 1024) + + +class ValidationResponse(BaseModel): + """Response structure for the validation process""" + questions: Optional[List[str]] = None + next_phase_description: Optional[str] = None + score: Optional[float] = None + justification: Optional[str] = None + is_complete: bool = False + + +class TinyPersonValidator: + + @staticmethod + def validate_person(person, expectations=None, include_agent_spec=True, max_content_length=default_max_content_display_length) -> tuple[float, str]: + """ + Validate a TinyPerson instance using OpenAI's LLM. + + This method sends a series of questions to the TinyPerson instance to validate its responses using OpenAI's LLM. + The method returns a float value representing the confidence score of the validation process. + If the validation process fails, the method returns None. + + Args: + person (TinyPerson): The TinyPerson instance to be validated. + expectations (str, optional): The expectations to be used in the validation process. Defaults to None. + include_agent_spec (bool, optional): Whether to include the agent specification in the prompt. Defaults to False. + max_content_length (int, optional): The maximum length of the content to be displayed when rendering the conversation. + + Returns: + float: The confidence score of the validation process (0.0 to 1.0), or None if the validation process fails. + str: The justification for the validation score, or None if the validation process fails. + """ + # Initiating the current messages + current_messages = [] + + # Generating the prompt to check the person + check_person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/check_person.mustache') + with open(check_person_prompt_template_path, 'r', encoding='utf-8', errors='replace') as f: + check_agent_prompt_template = f.read() + + system_prompt = chevron.render(check_agent_prompt_template, {"expectations": expectations}) + + # use dedent + import textwrap + user_prompt = textwrap.dedent(\ + """ + Now, based on the following characteristics of the person being interviewed, and following the rules given previously, + create your questions and interview the person. Good luck! + + """) + + if include_agent_spec: + user_prompt += f"\n\n{json.dumps(person._persona, indent=4)}" + + # TODO this was confusing the expectations + #else: + # user_prompt += f"\n\nMini-biography of the person being interviewed: {person.minibio()}" + + + logger = logging.getLogger("tinytroupe") + + logger.info(f"Starting validation of the person: {person.name}") + + # Sending the initial messages to the LLM + current_messages.append({"role": "system", "content": system_prompt}) + current_messages.append({"role": "user", "content": user_prompt}) + + message = openai_utils.client().send_message(current_messages, response_format=ValidationResponse, enable_pydantic_model_return=True) + + max_iterations = 10 # Limit the number of iterations to prevent infinite loops + cur_iteration = 0 + while cur_iteration < max_iterations and message is not None and not message.is_complete: + cur_iteration += 1 + + # Check if we have questions to ask + if message.questions: + # Format questions as a text block + if message.next_phase_description: + questions_text = f"{message.next_phase_description}\n\n" + else: + questions_text = "" + + questions_text += "\n".join([f"{i+1}. {q}" for i, q in enumerate(message.questions)]) + + current_messages.append({"role": "assistant", "content": questions_text}) + logger.info(f"Question validation:\n{questions_text}") + + # Asking the questions to the persona + person.listen_and_act(questions_text, max_content_length=max_content_length) + responses = person.pop_actions_and_get_contents_for("TALK", False) + logger.info(f"Person reply:\n{responses}") + + # Appending the responses to the current conversation and checking the next message + current_messages.append({"role": "user", "content": responses}) + message = openai_utils.client().send_message(current_messages, response_format=ValidationResponse, enable_pydantic_model_return=True) + else: + # If no questions but not complete, something went wrong + logger.warning("LLM did not provide questions but validation is not complete") + break + + if message is not None and message.is_complete and message.score is not None: + logger.info(f"Validation score: {message.score:.2f}; Justification: {message.justification}") + return message.score, message.justification + else: + logger.error("Validation process failed to complete properly") + return None, None \ No newline at end of file diff --git a/validation/validation_chamber.py b/validation/validation_chamber.py new file mode 100644 index 0000000000000000000000000000000000000000..4b1bd689292177162dbd39555a96b8929a77612c --- /dev/null +++ b/validation/validation_chamber.py @@ -0,0 +1,21 @@ +from tinytroupe.experimentation import Proposition + + +class ValidationChamber: + """ + An Validation Chamber is a container where autonomous agents can be put to be validated with respect to various custom validation tasks. + + Validation tasks types include: + - Question answering: given either a concrete question or a question pattern (to be instantitated via an LLM call), and an expectation, + the agent is expected to answer the question correctly. To check correctness, an LLM-based Proposer is used. + - Behavioral patterns: + * repeated actions - does the agent keep repeating the same action like a crazy person? + * self-consistency - does the agent contradict itself over time? + + + The class also provides convenience auxiliary methods to: + - generate reasonable question/answer pairs, given some general overall scenario and agent description. + - generate reasonable behavioral patterns, given some general overall scenario and agent description. + + """ +