[docs]class FlareChain(Chain):
    question_generator_chain: QuestionGeneratorChain
    response_chain: _ResponseChain = Field(default_factory=_OpenAIResponseChain)
    output_parser: FinishedOutputParser = Field(default_factory=FinishedOutputParser)
    retriever: BaseRetriever
    min_prob: float = 0.2
    min_token_gap: int = 5
    num_pad_tokens: int = 2
    max_iter: int = 10
    start_with_retrieval: bool = True
    @property
    def input_keys(self) -> List[str]:
        return ["user_input"]
    @property
    def output_keys(self) -> List[str]:
        return ["response"]
    def _do_generation(
        self,
        questions: List[str],
        user_input: str,
        response: str,
        _run_manager: CallbackManagerForChainRun,
    ) -> Tuple[str, bool]:
        callbacks = _run_manager.get_child()
        docs = []
        for question in questions:
            docs.extend(self.retriever.get_relevant_documents(question))
        context = "\n\n".join(d.page_content for d in docs)
        result = self.response_chain.predict(
            user_input=user_input,
            context=context,
            response=response,
            callbacks=callbacks,
        )
        marginal, finished = self.output_parser.parse(result)
        return marginal, finished
    def _do_retrieval(
        self,
        low_confidence_spans: List[str],
        _run_manager: CallbackManagerForChainRun,
        user_input: str,
        response: str,
        initial_response: str,
    ) -> Tuple[str, bool]:
        question_gen_inputs = [
            {
                "user_input": user_input,
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/flare/base.html