amax_marginal_relevance_search() — langchain Function Reference

Source Code

libs/core/langchain_core/vectorstores/base.py lines 686–722

    async def amax_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **kwargs: Any,
    ) -> list[Document]:
        """Async 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 `Document` objects to return.
            fetch_k: Number of `Document` objects 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.
            **kwargs: Arguments to pass to the search method.

        Returns:
            List of `Document` objects selected by maximal marginal relevance.
        """
        # This is a temporary workaround to make the similarity search
        # asynchronous. The proper solution is to make the similarity search
        # asynchronous in the vector store implementations.
        return await run_in_executor(
            None,
            self.max_marginal_relevance_search,
            query,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            **kwargs,
        )