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"""Chain that just formats a prompt and calls an LLM.""" |
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from __future__ import annotations |
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import warnings |
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast |
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from langchain_core._api import deprecated |
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from langchain_core.callbacks import ( |
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AsyncCallbackManager, |
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AsyncCallbackManagerForChainRun, |
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CallbackManager, |
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CallbackManagerForChainRun, |
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Callbacks, |
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) |
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from langchain_core.language_models import ( |
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BaseLanguageModel, |
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LanguageModelInput, |
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) |
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from langchain_core.messages import BaseMessage |
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from langchain_core.output_parsers import BaseLLMOutputParser, StrOutputParser |
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from langchain_core.outputs import ChatGeneration, Generation, LLMResult |
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from langchain_core.prompt_values import PromptValue |
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from langchain_core.prompts import BasePromptTemplate, PromptTemplate |
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from langchain_core.runnables import ( |
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Runnable, |
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RunnableBinding, |
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RunnableBranch, |
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RunnableWithFallbacks, |
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) |
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from langchain_core.runnables.configurable import DynamicRunnable |
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from langchain_core.utils.input import get_colored_text |
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from pydantic import ConfigDict, Field |
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from langchain.chains.base import Chain |
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@deprecated( |
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since="0.1.17", |
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alternative="RunnableSequence, e.g., `prompt | llm`", |
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removal="1.0", |
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) |
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class LLMChain(Chain): |
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"""Chain to run queries against LLMs. |
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This class is deprecated. See below for an example implementation using |
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LangChain runnables: |
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.. code-block:: python |
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from langchain_core.output_parsers import StrOutputParser |
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from langchain_core.prompts import PromptTemplate |
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from langchain_openai import OpenAI |
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prompt_template = "Tell me a {adjective} joke" |
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prompt = PromptTemplate( |
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input_variables=["adjective"], template=prompt_template |
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) |
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llm = OpenAI() |
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chain = prompt | llm | StrOutputParser() |
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chain.invoke("your adjective here") |
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Example: |
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.. code-block:: python |
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from langchain.chains import LLMChain |
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from langchain_community.llms import OpenAI |
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from langchain_core.prompts import PromptTemplate |
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prompt_template = "Tell me a {adjective} joke" |
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prompt = PromptTemplate( |
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input_variables=["adjective"], template=prompt_template |
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) |
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llm = LLMChain(llm=OpenAI(), prompt=prompt) |
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""" |
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@classmethod |
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def is_lc_serializable(self) -> bool: |
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return True |
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prompt: BasePromptTemplate |
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"""Prompt object to use.""" |
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llm: Union[ |
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Runnable[LanguageModelInput, str], Runnable[LanguageModelInput, BaseMessage] |
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] |
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"""Language model to call.""" |
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output_key: str = "text" |
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output_parser: BaseLLMOutputParser = Field(default_factory=StrOutputParser) |
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"""Output parser to use. |
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Defaults to one that takes the most likely string but does not change it |
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otherwise.""" |
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return_final_only: bool = True |
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"""Whether to return only the final parsed result. Defaults to True. |
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If false, will return a bunch of extra information about the generation.""" |
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llm_kwargs: dict = Field(default_factory=dict) |
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model_config = ConfigDict( |
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arbitrary_types_allowed=True, |
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extra="forbid", |
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) |
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@property |
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def input_keys(self) -> List[str]: |
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"""Will be whatever keys the prompt expects. |
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:meta private: |
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""" |
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return self.prompt.input_variables |
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@property |
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def output_keys(self) -> List[str]: |
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"""Will always return text key. |
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:meta private: |
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""" |
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if self.return_final_only: |
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return [self.output_key] |
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else: |
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return [self.output_key, "full_generation"] |
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def _call( |
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self, |
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inputs: Dict[str, Any], |
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run_manager: Optional[CallbackManagerForChainRun] = None, |
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) -> Dict[str, str]: |
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response = self.generate([inputs], run_manager=run_manager) |
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return self.create_outputs(response)[0] |
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def generate( |
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self, |
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input_list: List[Dict[str, Any]], |
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run_manager: Optional[CallbackManagerForChainRun] = None, |
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) -> LLMResult: |
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"""Generate LLM result from inputs.""" |
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prompts, stop = self.prep_prompts(input_list, run_manager=run_manager) |
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callbacks = run_manager.get_child() if run_manager else None |
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if isinstance(self.llm, BaseLanguageModel): |
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return self.llm.generate_prompt( |
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prompts, |
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stop, |
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callbacks=callbacks, |
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**self.llm_kwargs, |
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) |
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else: |
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results = self.llm.bind(stop=stop, **self.llm_kwargs).batch( |
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cast(List, prompts), {"callbacks": callbacks} |
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) |
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generations: List[List[Generation]] = [] |
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for res in results: |
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if isinstance(res, BaseMessage): |
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generations.append([ChatGeneration(message=res)]) |
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else: |
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generations.append([Generation(text=res)]) |
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return LLMResult(generations=generations) |
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async def agenerate( |
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self, |
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input_list: List[Dict[str, Any]], |
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None, |
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) -> LLMResult: |
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"""Generate LLM result from inputs.""" |
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prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager) |
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callbacks = run_manager.get_child() if run_manager else None |
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if isinstance(self.llm, BaseLanguageModel): |
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return await self.llm.agenerate_prompt( |
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prompts, |
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stop, |
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callbacks=callbacks, |
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**self.llm_kwargs, |
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) |
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else: |
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results = await self.llm.bind(stop=stop, **self.llm_kwargs).abatch( |
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cast(List, prompts), {"callbacks": callbacks} |
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) |
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generations: List[List[Generation]] = [] |
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for res in results: |
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if isinstance(res, BaseMessage): |
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generations.append([ChatGeneration(message=res)]) |
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else: |
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generations.append([Generation(text=res)]) |
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return LLMResult(generations=generations) |
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def prep_prompts( |
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self, |
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input_list: List[Dict[str, Any]], |
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run_manager: Optional[CallbackManagerForChainRun] = None, |
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) -> Tuple[List[PromptValue], Optional[List[str]]]: |
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"""Prepare prompts from inputs.""" |
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stop = None |
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if len(input_list) == 0: |
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return [], stop |
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if "stop" in input_list[0]: |
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stop = input_list[0]["stop"] |
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prompts = [] |
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for inputs in input_list: |
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selected_inputs = {k: inputs[k] for k in self.prompt.input_variables} |
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prompt = self.prompt.format_prompt(**selected_inputs) |
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_colored_text = get_colored_text(prompt.to_string(), "green") |
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_text = "Prompt after formatting:\n" + _colored_text |
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if run_manager: |
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run_manager.on_text(_text, end="\n", verbose=self.verbose) |
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if "stop" in inputs and inputs["stop"] != stop: |
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raise ValueError( |
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"If `stop` is present in any inputs, should be present in all." |
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) |
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prompts.append(prompt) |
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return prompts, stop |
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async def aprep_prompts( |
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self, |
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input_list: List[Dict[str, Any]], |
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None, |
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) -> Tuple[List[PromptValue], Optional[List[str]]]: |
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"""Prepare prompts from inputs.""" |
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stop = None |
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if len(input_list) == 0: |
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return [], stop |
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if "stop" in input_list[0]: |
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stop = input_list[0]["stop"] |
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prompts = [] |
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for inputs in input_list: |
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selected_inputs = {k: inputs[k] for k in self.prompt.input_variables} |
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prompt = self.prompt.format_prompt(**selected_inputs) |
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_colored_text = get_colored_text(prompt.to_string(), "green") |
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_text = "Prompt after formatting:\n" + _colored_text |
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if run_manager: |
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await run_manager.on_text(_text, end="\n", verbose=self.verbose) |
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if "stop" in inputs and inputs["stop"] != stop: |
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raise ValueError( |
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"If `stop` is present in any inputs, should be present in all." |
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) |
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prompts.append(prompt) |
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return prompts, stop |
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def apply( |
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None |
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) -> List[Dict[str, str]]: |
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"""Utilize the LLM generate method for speed gains.""" |
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callback_manager = CallbackManager.configure( |
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callbacks, self.callbacks, self.verbose |
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) |
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run_manager = callback_manager.on_chain_start( |
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None, |
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{"input_list": input_list}, |
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name=self.get_name(), |
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) |
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try: |
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response = self.generate(input_list, run_manager=run_manager) |
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except BaseException as e: |
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run_manager.on_chain_error(e) |
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raise e |
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outputs = self.create_outputs(response) |
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run_manager.on_chain_end({"outputs": outputs}) |
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return outputs |
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async def aapply( |
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None |
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) -> List[Dict[str, str]]: |
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"""Utilize the LLM generate method for speed gains.""" |
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callback_manager = AsyncCallbackManager.configure( |
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callbacks, self.callbacks, self.verbose |
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) |
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run_manager = await callback_manager.on_chain_start( |
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None, |
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{"input_list": input_list}, |
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name=self.get_name(), |
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) |
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try: |
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response = await self.agenerate(input_list, run_manager=run_manager) |
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except BaseException as e: |
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await run_manager.on_chain_error(e) |
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raise e |
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outputs = self.create_outputs(response) |
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await run_manager.on_chain_end({"outputs": outputs}) |
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return outputs |
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@property |
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def _run_output_key(self) -> str: |
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return self.output_key |
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def create_outputs(self, llm_result: LLMResult) -> List[Dict[str, Any]]: |
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"""Create outputs from response.""" |
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result = [ |
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{ |
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self.output_key: self.output_parser.parse_result(generation), |
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"full_generation": generation, |
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} |
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for generation in llm_result.generations |
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] |
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if self.return_final_only: |
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result = [{self.output_key: r[self.output_key]} for r in result] |
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return result |
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async def _acall( |
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self, |
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inputs: Dict[str, Any], |
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None, |
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) -> Dict[str, str]: |
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response = await self.agenerate([inputs], run_manager=run_manager) |
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return self.create_outputs(response)[0] |
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def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str: |
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"""Format prompt with kwargs and pass to LLM. |
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Args: |
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callbacks: Callbacks to pass to LLMChain |
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**kwargs: Keys to pass to prompt template. |
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Returns: |
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Completion from LLM. |
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Example: |
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.. code-block:: python |
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completion = llm.predict(adjective="funny") |
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""" |
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return self(kwargs, callbacks=callbacks)[self.output_key] |
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async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str: |
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"""Format prompt with kwargs and pass to LLM. |
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Args: |
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callbacks: Callbacks to pass to LLMChain |
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**kwargs: Keys to pass to prompt template. |
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Returns: |
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Completion from LLM. |
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Example: |
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.. code-block:: python |
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completion = llm.predict(adjective="funny") |
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""" |
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return (await self.acall(kwargs, callbacks=callbacks))[self.output_key] |
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def predict_and_parse( |
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self, callbacks: Callbacks = None, **kwargs: Any |
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) -> Union[str, List[str], Dict[str, Any]]: |
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"""Call predict and then parse the results.""" |
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warnings.warn( |
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"The predict_and_parse method is deprecated, " |
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"instead pass an output parser directly to LLMChain." |
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) |
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result = self.predict(callbacks=callbacks, **kwargs) |
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if self.prompt.output_parser is not None: |
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return self.prompt.output_parser.parse(result) |
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else: |
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return result |
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async def apredict_and_parse( |
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self, callbacks: Callbacks = None, **kwargs: Any |
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) -> Union[str, List[str], Dict[str, str]]: |
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"""Call apredict and then parse the results.""" |
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warnings.warn( |
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"The apredict_and_parse method is deprecated, " |
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"instead pass an output parser directly to LLMChain." |
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) |
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result = await self.apredict(callbacks=callbacks, **kwargs) |
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if self.prompt.output_parser is not None: |
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return self.prompt.output_parser.parse(result) |
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else: |
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return result |
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def apply_and_parse( |
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None |
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) -> Sequence[Union[str, List[str], Dict[str, str]]]: |
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"""Call apply and then parse the results.""" |
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warnings.warn( |
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"The apply_and_parse method is deprecated, " |
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"instead pass an output parser directly to LLMChain." |
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) |
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result = self.apply(input_list, callbacks=callbacks) |
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return self._parse_generation(result) |
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def _parse_generation( |
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self, generation: List[Dict[str, str]] |
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) -> Sequence[Union[str, List[str], Dict[str, str]]]: |
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if self.prompt.output_parser is not None: |
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return [ |
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self.prompt.output_parser.parse(res[self.output_key]) |
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for res in generation |
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] |
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else: |
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return generation |
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async def aapply_and_parse( |
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None |
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) -> Sequence[Union[str, List[str], Dict[str, str]]]: |
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"""Call apply and then parse the results.""" |
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warnings.warn( |
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"The aapply_and_parse method is deprecated, " |
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"instead pass an output parser directly to LLMChain." |
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) |
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result = await self.aapply(input_list, callbacks=callbacks) |
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return self._parse_generation(result) |
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@property |
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def _chain_type(self) -> str: |
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return "llm_chain" |
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@classmethod |
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def from_string(cls, llm: BaseLanguageModel, template: str) -> LLMChain: |
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"""Create LLMChain from LLM and template.""" |
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prompt_template = PromptTemplate.from_template(template) |
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return cls(llm=llm, prompt=prompt_template) |
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def _get_num_tokens(self, text: str) -> int: |
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return _get_language_model(self.llm).get_num_tokens(text) |
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def _get_language_model(llm_like: Runnable) -> BaseLanguageModel: |
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if isinstance(llm_like, BaseLanguageModel): |
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return llm_like |
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elif isinstance(llm_like, RunnableBinding): |
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return _get_language_model(llm_like.bound) |
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elif isinstance(llm_like, RunnableWithFallbacks): |
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return _get_language_model(llm_like.runnable) |
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elif isinstance(llm_like, (RunnableBranch, DynamicRunnable)): |
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return _get_language_model(llm_like.default) |
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else: |
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raise ValueError( |
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f"Unable to extract BaseLanguageModel from llm_like object of type " |
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f"{type(llm_like)}" |
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) |
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|
