"""Return docs most similar to embedding vector.
        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Doc fields filter conditions that meet the SQL where clause
                    specification.
        Returns:
            List of Documents most similar to the query vector.
        """
        docs_and_scores = self._similarity_search_with_score_by_vector(
            embedding, k, filter
        )
        return [doc for doc, _ in docs_and_scores]
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[dict] = 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:
            query: Text 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.
            filter: Doc fields filter conditions that meet the SQL where clause
                    specification.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        embedding = self._embedding.embed_query(query)
        return self.max_marginal_relevance_search_by_vector(