langchain/vectorstores/chroma.py/Chroma/max_marginal_relevance_search_by_vector
class Chroma:    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        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. 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 (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """

        results = self.__query_collection(
            query_embeddings=embedding,
            n_results=fetch_k,
            where=filter,
            include=["metadatas", "documents", "distances", "embeddings"],
        )
        mmr_selected = maximal_marginal_relevance(
            np.array(embedding, dtype=np.float32),
            results["embeddings"][0],
            k=k,
            lambda_mult=lambda_mult,
        )

        candidates = _results_to_docs(results)

        selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
        return selected_results