max_marginal_relevance_search() — langchain Function Reference

Source Code

libs/partners/qdrant/langchain_qdrant/qdrant.py lines 728–769

    def max_marginal_relevance_search(
        self,
        query: str,
        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[Document]:
        """Return docs selected using the maximal marginal relevance with dense vectors.

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

        Returns:
            List of `Document` objects selected by maximal marginal relevance.

        """
        self._validate_collection_for_dense(
            self.client,
            self.collection_name,
            self.vector_name,
            self.distance,
            self.embeddings,
        )

        embeddings = self._require_embeddings("max_marginal_relevance_search")
        query_embedding = embeddings.embed_query(query)
        return self.max_marginal_relevance_search_by_vector(
            query_embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            search_params=search_params,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )