mmr_lambda=lambda_mult,
                **kwargs,
            )
        return [Document(page_content=d.content, metadata=d.metadata) for d in results]
[docs]    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        metadata: Optional[Dict[str, Any]] = 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. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                     Zep determines this automatically and this parameter is
                        ignored.
            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.
            metadata: Optional, metadata to filter the resulting set of retrieved docs
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        if not self._collection:
            raise ValueError(
                "collection should be an instance of a Zep DocumentCollection"
            )
        results = self._collection.search(
            embedding=embedding,
            limit=k,
            metadata=metadata,
            search_type="mmr",
            mmr_lambda=lambda_mult,
            **kwargs,
        )
        return [Document(page_content=d.content, metadata=d.metadata) for d in results]