_aevaluate_string_pairs() — langchain Function Reference

Architecture documentation for the _aevaluate_string_pairs() function in base.py from the langchain codebase.

Function python LangChainCore LanguageModelBase calls 1

Entity Profile

LangChainCore→ LanguageModelBase→ _aevaluate_string_pairs() — langchain Function Reference

Dependency Diagram

graph TD
  410d183f_2cb6_1006_4210_ea1c8753bacf["_aevaluate_string_pairs()"]
  a279fd93_b575_438d_176b_7abfe52f575d["PairwiseEmbeddingDistanceEvalChain"]
  410d183f_2cb6_1006_4210_ea1c8753bacf -->|defined in| a279fd93_b575_438d_176b_7abfe52f575d
  fbbf6b20_ebdc_5396_8597_95686297918e["_prepare_output()"]
  410d183f_2cb6_1006_4210_ea1c8753bacf -->|calls| fbbf6b20_ebdc_5396_8597_95686297918e
  style 410d183f_2cb6_1006_4210_ea1c8753bacf fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/evaluation/embedding_distance/base.py lines 624–657

    async def _aevaluate_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:
        """Asynchronously evaluate the embedding distance between two predictions.

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

        Returns:
            `dict` containing:
                - score: The embedding distance between the two predictions.
        """
        result = await self.acall(
            inputs={"prediction": prediction, "prediction_b": prediction_b},
            callbacks=callbacks,
            tags=tags,
            metadata=metadata,
            include_run_info=include_run_info,
        )
        return self._prepare_output(result)