k (int): Number of results to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List[Tuple[Document, float]]: List of documents most similar to the query
                text with distance in float.
        """
        return self._search_helper(
            query=query,
            k=k,
            filter=filter,
            return_score=True,
            distance_metric=distance_metric,
        )
[docs]    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                of diversity among the results with 0 corresponding
                to maximum diversity and 1 to minimum diversity.
                Defaults to 0.5.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        return self._search_helper(
            embedding=embedding,
            k=k,
            fetch_k=fetch_k,
            use_maximal_marginal_relevance=True,
            lambda_mult=lambda_mult,
            **kwargs,
        )
[docs]    def max_marginal_relevance_search(
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/deeplake.html