return MapRerankDocumentsChain(
        llm_chain=llm_chain,
        rank_key=rank_key,
        answer_key=answer_key,
        document_variable_name=document_variable_name,
        **kwargs,
    )
def _load_stuff_chain(
    llm: BaseLanguageModel,
    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
    document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
    document_variable_name: str = "summaries",
    verbose: Optional[bool] = None,
    **kwargs: Any,
) -> StuffDocumentsChain:
    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
    return StuffDocumentsChain(
        llm_chain=llm_chain,
        document_variable_name=document_variable_name,
        document_prompt=document_prompt,
        verbose=verbose,
        **kwargs,
    )
def _load_map_reduce_chain(
    llm: BaseLanguageModel,
    question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
    combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
    document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
    combine_document_variable_name: str = "summaries",
    map_reduce_document_variable_name: str = "context",
    collapse_prompt: Optional[BasePromptTemplate] = None,
    reduce_llm: Optional[BaseLanguageModel] = None,
    collapse_llm: Optional[BaseLanguageModel] = None,
    verbose: Optional[bool] = None,
    token_max: int = 3000,
    **kwargs: Any,
) -> MapReduceDocumentsChain: