human = "Human: " + dialogue_turn[0]
            ai = "Assistant: " + dialogue_turn[1]
            buffer += "\n" + "\n".join([human, ai])
        else:
            raise ValueError(
                f"Unsupported chat history format: {type(dialogue_turn)}."
                f" Full chat history: {chat_history} "
            )
    return buffer
class BaseConversationalRetrievalChain(Chain):
    """Chain for chatting with an index."""
    combine_docs_chain: BaseCombineDocumentsChain
    question_generator: LLMChain
    output_key: str = "answer"
    return_source_documents: bool = False
    return_generated_question: bool = False
    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
    """Return the source documents."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
        allow_population_by_field_name = True
    @property
    def input_keys(self) -> List[str]:
        """Input keys."""
        return ["question", "chat_history"]
    @property
    def output_keys(self) -> List[str]:
        """Return the output keys.
        :meta private:
        """
        _output_keys = [self.output_key]
        if self.return_source_documents:
            _output_keys = _output_keys + ["source_documents"]
        if self.return_generated_question:
            _output_keys = _output_keys + ["generated_question"]
        return _output_keys
    @abstractmethod
    def _get_docs(self, question: str, inputs: Dict[str, Any]) -> List[Document]:
        """Get docs."""
    def _call(
        self,
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/conversational_retrieval/base.html