_similarity_search_with_relevance_scores() — langchain Function Reference

Source Code

libs/partners/qdrant/langchain_qdrant/vectorstores.py lines 1970–1992

    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs and relevance scores in the range `[0, 1]`.

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

        Args:
            query: input text
            k: Number of Documents 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)`

        """
        return self.similarity_search_with_score(query, k, **kwargs)