StringDistanceEvalChain Class — langchain Architecture

Source Code

libs/langchain/langchain_classic/evaluation/string_distance/base.py lines 165–322

class StringDistanceEvalChain(StringEvaluator, _RapidFuzzChainMixin):
    """Compute string distances between the prediction and the reference.

    Examples:
    ----------
    >>> from langchain_classic.evaluation import StringDistanceEvalChain
    >>> evaluator = StringDistanceEvalChain()
    >>> evaluator.evaluate_strings(
            prediction="Mindy is the CTO",
            reference="Mindy is the CEO",
        )

    Using the `load_evaluator` function:

    >>> from langchain_classic.evaluation import load_evaluator
    >>> evaluator = load_evaluator("string_distance")
    >>> evaluator.evaluate_strings(
            prediction="The answer is three",
            reference="three",
        )
    """

    @property
    def requires_input(self) -> bool:
        """This evaluator does not require input."""
        return False

    @property
    def requires_reference(self) -> bool:
        """This evaluator does not require a reference."""
        return True

    @property
    def input_keys(self) -> list[str]:
        """Get the input keys.

        Returns:
            The input keys.
        """
        return ["reference", "prediction"]

    @property
    def evaluation_name(self) -> str:
        """Get the evaluation name.

        Returns:
            The evaluation name.
        """
        return f"{self.distance.value}_distance"

    @override
    def _call(
        self,
        inputs: dict[str, Any],
        run_manager: CallbackManagerForChainRun | None = None,
    ) -> dict[str, Any]:
        """Compute the string distance between the prediction and the reference.

        Args:
            inputs: The input values.
            run_manager: The callback manager.

        Returns:
            The evaluation results containing the score.
        """
        return {"score": self.compute_metric(inputs["reference"], inputs["prediction"])}

    @override
    async def _acall(
        self,
        inputs: dict[str, Any],
        run_manager: AsyncCallbackManagerForChainRun | None = None,
    ) -> dict[str, Any]:
        """Compute the string distance between the prediction and the reference.

        Args:
            inputs: The input values.
            run_manager: The callback manager.

        Returns:
            The evaluation results containing the score.