max_marginal_relevance_search_by_vector() — langchain Function Reference

Source Code

libs/core/langchain_core/vectorstores/in_memory.py lines 419–448

    def max_marginal_relevance_search_by_vector(
        self,
        embedding: list[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        *,
        filter: Callable[[Document], bool] | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        prefetch_hits = self._similarity_search_with_score_by_vector(
            embedding=embedding,
            k=fetch_k,
            filter=filter,
        )

        if not _HAS_NUMPY:
            msg = (
                "numpy must be installed to use max_marginal_relevance_search "
                "pip install numpy"
            )
            raise ImportError(msg)

        mmr_chosen_indices = maximal_marginal_relevance(
            np.array(embedding, dtype=np.float32),
            [vector for _, _, vector in prefetch_hits],
            k=k,
            lambda_mult=lambda_mult,
        )
        return [prefetch_hits[idx][0] for idx in mmr_chosen_indices]