from_llm() — langchain Function Reference

Source Code

libs/langchain/langchain_classic/evaluation/criteria/eval_chain.py lines 315–379

    def from_llm(
        cls,
        llm: BaseLanguageModel,
        criteria: CRITERIA_TYPE | None = None,
        *,
        prompt: BasePromptTemplate | None = None,
        **kwargs: Any,
    ) -> CriteriaEvalChain:
        """Create a `CriteriaEvalChain` instance from an llm and criteria.

        Parameters
        ----------
        llm : BaseLanguageModel
            The language model to use for evaluation.
        criteria : CRITERIA_TYPE - default=None for "helpfulness"
            The criteria to evaluate the runs against. It can be:
                -  a mapping of a criterion name to its description
                -  a single criterion name present in one of the default criteria
                -  a single `ConstitutionalPrinciple` instance
        prompt : Optional[BasePromptTemplate], default=None
            The prompt template to use for generating prompts. If not provided,
            a default prompt template will be used.
        **kwargs : Any
            Additional keyword arguments to pass to the `LLMChain`
            constructor.

        Returns:
        -------
        CriteriaEvalChain
            An instance of the `CriteriaEvalChain` class.

        Examples:
        --------
        >>> from langchain_openai import OpenAI
        >>> from langchain_classic.evaluation.criteria import LabeledCriteriaEvalChain
        >>> model = OpenAI()
        >>> criteria = {
                "hallucination": (
                    "Does this submission contain information"
                    " not present in the input or reference?"
                ),
            }
        >>> chain = LabeledCriteriaEvalChain.from_llm(
                llm=model,
                criteria=criteria,
            )
        """
        prompt_ = cls._resolve_prompt(prompt)
        if criteria == Criteria.CORRECTNESS:
            msg = (
                "Correctness should not be used in the reference-free"
                " 'criteria' evaluator (CriteriaEvalChain)."
                " Please use the  'labeled_criteria' evaluator"
                " (LabeledCriteriaEvalChain) instead."
            )
            raise ValueError(msg)
        criteria_ = cls.resolve_criteria(criteria)
        criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
        prompt_ = prompt_.partial(criteria=criteria_str)
        return cls(
            llm=llm,
            prompt=prompt_,
            criterion_name="-".join(criteria_),
            **kwargs,
        )