max_marginal_relevance_search_with_score_by_vector() — langchain Function Reference

Source Code

libs/partners/qdrant/langchain_qdrant/qdrant.py lines 805–854

    def max_marginal_relevance_search_with_score_by_vector(
        self,
        embedding: list[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs selected using the maximal marginal relevance.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Returns:
            List of `Document` objects selected by maximal marginal relevance and
                distance for each.
        """
        results = self.client.query_points(
            collection_name=self.collection_name,
            query=models.NearestQuery(
                nearest=embedding,
                mmr=models.Mmr(diversity=lambda_mult, candidates_limit=fetch_k),
            ),
            query_filter=filter,
            search_params=search_params,
            limit=k,
            with_payload=True,
            with_vectors=True,
            score_threshold=score_threshold,
            consistency=consistency,
            using=self.vector_name,
            **kwargs,
        ).points

        return [
            (
                self._document_from_point(
                    result,
                    self.collection_name,
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
                result.score,
            )
            for result in results
        ]