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"""An agent designed to hold a conversation in addition to using tools.""" |
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from __future__ import annotations |
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from typing import Any, List, Optional, Sequence |
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from langchain_core._api import deprecated |
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from langchain_core.callbacks import BaseCallbackManager |
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from langchain_core.language_models import BaseLanguageModel |
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from langchain_core.prompts import PromptTemplate |
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from langchain_core.tools import BaseTool |
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from pydantic import Field |
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from langchain._api.deprecation import AGENT_DEPRECATION_WARNING |
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from langchain.agents.agent import Agent, AgentOutputParser |
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from langchain.agents.agent_types import AgentType |
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from langchain.agents.conversational.output_parser import ConvoOutputParser |
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from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX |
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from langchain.agents.utils import validate_tools_single_input |
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from langchain.chains import LLMChain |
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@deprecated( |
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"0.1.0", |
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message=AGENT_DEPRECATION_WARNING, |
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removal="1.0", |
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) |
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class ConversationalAgent(Agent): |
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"""An agent that holds a conversation in addition to using tools.""" |
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ai_prefix: str = "AI" |
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"""Prefix to use before AI output.""" |
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output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser) |
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"""Output parser for the agent.""" |
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@classmethod |
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def _get_default_output_parser( |
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cls, ai_prefix: str = "AI", **kwargs: Any |
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) -> AgentOutputParser: |
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return ConvoOutputParser(ai_prefix=ai_prefix) |
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@property |
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def _agent_type(self) -> str: |
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"""Return Identifier of agent type.""" |
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return AgentType.CONVERSATIONAL_REACT_DESCRIPTION |
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@property |
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def observation_prefix(self) -> str: |
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"""Prefix to append the observation with. |
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Returns: |
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"Observation: " |
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""" |
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return "Observation: " |
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@property |
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def llm_prefix(self) -> str: |
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"""Prefix to append the llm call with. |
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Returns: |
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"Thought: " |
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""" |
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return "Thought:" |
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@classmethod |
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def create_prompt( |
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cls, |
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tools: Sequence[BaseTool], |
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prefix: str = PREFIX, |
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suffix: str = SUFFIX, |
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format_instructions: str = FORMAT_INSTRUCTIONS, |
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ai_prefix: str = "AI", |
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human_prefix: str = "Human", |
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input_variables: Optional[List[str]] = None, |
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) -> PromptTemplate: |
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"""Create prompt in the style of the zero-shot agent. |
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Args: |
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tools: List of tools the agent will have access to, used to format the |
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prompt. |
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prefix: String to put before the list of tools. Defaults to PREFIX. |
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suffix: String to put after the list of tools. Defaults to SUFFIX. |
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format_instructions: Instructions on how to use the tools. Defaults to |
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FORMAT_INSTRUCTIONS |
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ai_prefix: String to use before AI output. Defaults to "AI". |
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human_prefix: String to use before human output. |
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Defaults to "Human". |
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input_variables: List of input variables the final prompt will expect. |
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Defaults to ["input", "chat_history", "agent_scratchpad"]. |
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Returns: |
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A PromptTemplate with the template assembled from the pieces here. |
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""" |
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tool_strings = "\n".join( |
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[f"> {tool.name}: {tool.description}" for tool in tools] |
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) |
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tool_names = ", ".join([tool.name for tool in tools]) |
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format_instructions = format_instructions.format( |
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tool_names=tool_names, ai_prefix=ai_prefix, human_prefix=human_prefix |
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) |
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template = "\n\n".join([prefix, tool_strings, format_instructions, suffix]) |
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if input_variables is None: |
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input_variables = ["input", "chat_history", "agent_scratchpad"] |
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return PromptTemplate(template=template, input_variables=input_variables) |
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@classmethod |
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def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: |
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super()._validate_tools(tools) |
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validate_tools_single_input(cls.__name__, tools) |
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@classmethod |
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def from_llm_and_tools( |
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cls, |
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llm: BaseLanguageModel, |
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tools: Sequence[BaseTool], |
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callback_manager: Optional[BaseCallbackManager] = None, |
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output_parser: Optional[AgentOutputParser] = None, |
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prefix: str = PREFIX, |
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suffix: str = SUFFIX, |
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format_instructions: str = FORMAT_INSTRUCTIONS, |
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ai_prefix: str = "AI", |
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human_prefix: str = "Human", |
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input_variables: Optional[List[str]] = None, |
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**kwargs: Any, |
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) -> Agent: |
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"""Construct an agent from an LLM and tools. |
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Args: |
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llm: The language model to use. |
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tools: A list of tools to use. |
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callback_manager: The callback manager to use. Default is None. |
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output_parser: The output parser to use. Default is None. |
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prefix: The prefix to use in the prompt. Default is PREFIX. |
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suffix: The suffix to use in the prompt. Default is SUFFIX. |
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format_instructions: The format instructions to use. |
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Default is FORMAT_INSTRUCTIONS. |
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ai_prefix: The prefix to use before AI output. Default is "AI". |
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human_prefix: The prefix to use before human output. |
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Default is "Human". |
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input_variables: The input variables to use. Default is None. |
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**kwargs: Any additional keyword arguments to pass to the agent. |
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Returns: |
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An agent. |
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""" |
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cls._validate_tools(tools) |
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prompt = cls.create_prompt( |
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tools, |
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ai_prefix=ai_prefix, |
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human_prefix=human_prefix, |
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prefix=prefix, |
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suffix=suffix, |
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format_instructions=format_instructions, |
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input_variables=input_variables, |
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) |
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llm_chain = LLMChain( |
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llm=llm, |
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prompt=prompt, |
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callback_manager=callback_manager, |
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) |
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tool_names = [tool.name for tool in tools] |
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_output_parser = output_parser or cls._get_default_output_parser( |
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ai_prefix=ai_prefix |
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) |
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return cls( |
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llm_chain=llm_chain, |
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allowed_tools=tool_names, |
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ai_prefix=ai_prefix, |
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output_parser=_output_parser, |
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**kwargs, |
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) |
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