----------
    evaluator : EvaluatorType
        The type of evaluator to load.
    llm : BaseLanguageModel, optional
        The language model to use for evaluation, by default None
    **kwargs : Any
        Additional keyword arguments to pass to the evaluator.
    Returns
    -------
    Chain
        The loaded evaluation chain.
    Examples
    --------
    >>> from langchain.evaluation import load_evaluator, EvaluatorType
    >>> evaluator = load_evaluator(EvaluatorType.QA)
    """
    llm = llm or ChatOpenAI(model="gpt-4", temperature=0)
    if evaluator not in _EVALUATOR_MAP:
        raise ValueError(
            f"Unknown evaluator type: {evaluator}"
            f"Valid types are: {list(_EVALUATOR_MAP.keys())}"
        )
    evaluator_cls = _EVALUATOR_MAP[evaluator]
    if issubclass(evaluator_cls, LLMEvalChain):
        return evaluator_cls.from_llm(llm=llm, **kwargs)
    else:
        return evaluator_cls(**kwargs)
[docs]def load_evaluators(
    evaluators: Sequence[EvaluatorType],
    *,
    llm: Optional[BaseLanguageModel] = None,
    config: Optional[dict] = None,
    **kwargs: Any,
) -> List[Chain]:
    """Load evaluators specified by a list of evaluator types.
    Parameters
    ----------
    evaluators : Sequence[EvaluatorType]
        The list of evaluator types to load.
    llm : BaseLanguageModel, optional
        The language model to use for evaluation, if none is provided, a default
        ChatOpenAI gpt-4 model will be used.
    config : dict, optional