- int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
            **kwargs:
                Any other named arguments to pass through to
                QdrantClient.async_grpc_points.Search().
        Returns:
            List of Documents selected by maximal marginal relevance and distance for
            each.
        """
        results = await self.amax_marginal_relevance_search_with_score_by_vector(
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            search_params=search_params,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))
[docs]    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: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = 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.
        Args: