Source code for langchain.chains.llm_summarization_checker.base

"""用于自我验证摘要的链条。"""

from __future__ import annotations

import warnings
from pathlib import Path
from typing import Any, Dict, List, Optional

from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.pydantic_v1 import Extra, root_validator

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sequential import SequentialChain

PROMPTS_DIR = Path(__file__).parent / "prompts"

CREATE_ASSERTIONS_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "create_facts.txt")
CHECK_ASSERTIONS_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "check_facts.txt")
REVISED_SUMMARY_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "revise_summary.txt")
ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "are_all_true_prompt.txt")


def _load_sequential_chain(
    llm: BaseLanguageModel,
    create_assertions_prompt: PromptTemplate,
    check_assertions_prompt: PromptTemplate,
    revised_summary_prompt: PromptTemplate,
    are_all_true_prompt: PromptTemplate,
    verbose: bool = False,
) -> SequentialChain:
    chain = SequentialChain(
        chains=[
            LLMChain(
                llm=llm,
                prompt=create_assertions_prompt,
                output_key="assertions",
                verbose=verbose,
            ),
            LLMChain(
                llm=llm,
                prompt=check_assertions_prompt,
                output_key="checked_assertions",
                verbose=verbose,
            ),
            LLMChain(
                llm=llm,
                prompt=revised_summary_prompt,
                output_key="revised_summary",
                verbose=verbose,
            ),
            LLMChain(
                llm=llm,
                output_key="all_true",
                prompt=are_all_true_prompt,
                verbose=verbose,
            ),
        ],
        input_variables=["summary"],
        output_variables=["all_true", "revised_summary"],
        verbose=verbose,
    )
    return chain


[docs]class LLMSummarizationCheckerChain(Chain): """用于自我验证问答的链。 示例: .. code-block:: python from langchain_community.llms import OpenAI from langchain.chains import LLMSummarizationCheckerChain llm = OpenAI(temperature=0.0) checker_chain = LLMSummarizationCheckerChain.from_llm(llm)""" sequential_chain: SequentialChain llm: Optional[BaseLanguageModel] = None """[已弃用] 用于使用的LLM包装器。""" create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT """[已弃用]""" check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT """[已弃用]""" revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT """[已弃用]""" are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT """[已弃用]""" input_key: str = "query" #: :meta private: output_key: str = "result" #: :meta private: max_checks: int = 2 """断言检查的最大次数。默认为双重检查。""" class Config: """这个pydantic对象的配置。""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator(pre=True) def raise_deprecation(cls, values: Dict) -> Dict: if "llm" in values: warnings.warn( "Directly instantiating an LLMSummarizationCheckerChain with an llm is " "deprecated. Please instantiate with" " sequential_chain argument or using the from_llm class method." ) if "sequential_chain" not in values and values["llm"] is not None: values["sequential_chain"] = _load_sequential_chain( values["llm"], values.get("create_assertions_prompt", CREATE_ASSERTIONS_PROMPT), values.get("check_assertions_prompt", CHECK_ASSERTIONS_PROMPT), values.get("revised_summary_prompt", REVISED_SUMMARY_PROMPT), values.get("are_all_true_prompt", ARE_ALL_TRUE_PROMPT), verbose=values.get("verbose", False), ) return values @property def input_keys(self) -> List[str]: """返回单个输入键。 :元数据 私有: """ return [self.input_key] @property def output_keys(self) -> List[str]: """返回单个输出键。 :元数据 私有: """ return [self.output_key] def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() all_true = False count = 0 output = None original_input = inputs[self.input_key] chain_input = original_input while not all_true and count < self.max_checks: output = self.sequential_chain( {"summary": chain_input}, callbacks=_run_manager.get_child() ) count += 1 if output["all_true"].strip() == "True": break if self.verbose: print(output["revised_summary"]) # noqa: T201 chain_input = output["revised_summary"] if not output: raise ValueError("No output from chain") return {self.output_key: output["revised_summary"].strip()} @property def _chain_type(self) -> str: return "llm_summarization_checker_chain"
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT, check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT, revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT, are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT, verbose: bool = False, **kwargs: Any, ) -> LLMSummarizationCheckerChain: chain = _load_sequential_chain( llm, create_assertions_prompt, check_assertions_prompt, revised_summary_prompt, are_all_true_prompt, verbose=verbose, ) return cls(sequential_chain=chain, verbose=verbose, **kwargs)