_asimilarity_search_with_relevance_scores() — langchain Function Reference

Source Code

libs/core/langchain_core/vectorstores/base.py lines 478–504

    async def _asimilarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Default similarity search with relevance scores.

        Modify if necessary in subclass.
        Return docs and relevance scores in the range `[0, 1]`.

        `0` is dissimilar, `1` is most similar.

        Args:
            query: Input text.
            k: Number of `Document` objects to return.
            **kwargs: Kwargs to be passed to similarity search.

                Should include `score_threshold`, an optional floating point value
                between `0` to `1` to filter the resulting set of retrieved docs.

        Returns:
            List of tuples of `(doc, similarity_score)`
        """
        relevance_score_fn = self._select_relevance_score_fn()
        docs_and_scores = await self.asimilarity_search_with_score(query, k, **kwargs)
        return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]