langchain/vectorstores/docarray/base.py/DocArrayIndex/max_marginal_relevance_search
class DocArrayIndex:    def max_marginal_relevance_search(
        self,
        query: str,
        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:
            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.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        query_embedding = self.embedding.embed_query(query)
        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore

        docs = self.doc_index.find(
            query_doc, search_field="embedding", limit=fetch_k
        ).documents

        mmr_selected = maximal_marginal_relevance(
            np.array(query_embedding), docs.embedding, k=k
        )
        results = [
            Document(page_content=docs[idx].text, metadata=docs[idx].metadata)
            for idx in mmr_selected
        ]
        return results