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"
            )
        if not self._collection.is_auto_embedded and self._embedding:
            query_vector = self._embedding.embed_query(query)
            results = self._collection.search(
                embedding=query_vector, limit=k, metadata=metadata, **kwargs
            )
        else:
            results, query_vector = self._collection.search_return_query_vector(
                query, limit=k, metadata=metadata, **kwargs
            )
        return self._max_marginal_relevance_selection(
            query_vector, results, k=k, lambda_mult=lambda_mult
        )
[docs]    async def amax_marginal_relevance_search(
        self,
        query: str,
        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."""
        if not self._collection:
            raise ValueError(
                "collection should be an instance of a Zep DocumentCollection"
            )
        if not self._collection.is_auto_embedded and self._embedding:
            query_vector = self._embedding.embed_query(query)
            results = await self._collection.asearch(
                embedding=query_vector, limit=k, metadata=metadata, **kwargs