self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
    ) -> List[Tuple[Document, float]]:
        embedding_vector = self.embedding.embed_query(query)
        return self.similarity_search_with_score_by_vector(
            embedding_vector,
            k,
            filter=filter,
        )
[docs]    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, str]] = None,
        **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.
            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.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        search_metadata = self._filter_to_metadata(filter)
        prefetchHits = self.table.search(
            embedding_vector=embedding,
            top_k=fetch_k,
            metric="cos",
            metric_threshold=None,
            metadata=search_metadata,
        )
        # let the mmr utility pick the *indices* in the above array
        mmrChosenIndices = maximal_marginal_relevance(