PairwiseStringDistanceEvalChain Class — langchain Architecture

Source Code

libs/langchain/langchain_classic/evaluation/string_distance/base.py lines 325–452

class PairwiseStringDistanceEvalChain(PairwiseStringEvaluator, _RapidFuzzChainMixin):
    """Compute string edit distances between two predictions."""

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

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

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

        Returns:
            The evaluation name.
        """
        return f"pairwise_{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 two predictions.

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

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

    @override
    async def _acall(
        self,
        inputs: dict[str, Any],
        run_manager: AsyncCallbackManagerForChainRun | None = None,
    ) -> dict[str, Any]:
        """Asynchronously compute the string distance between two predictions.

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

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

    @override
    def _evaluate_string_pairs(
        self,
        *,
        prediction: str,
        prediction_b: str,
        callbacks: Callbacks = None,
        tags: list[str] | None = None,
        metadata: dict[str, Any] | None = None,
        include_run_info: bool = False,
        **kwargs: Any,
    ) -> dict:
        """Evaluate the string distance between two predictions.

        Args:
            prediction: The first prediction string.
            prediction_b: The second prediction string.
            callbacks: The callbacks to use.
            tags: The tags to apply.
            metadata: The metadata to use.
            include_run_info: Whether to include run info in the output.
            **kwargs: Additional keyword arguments.