langchain/vectorstores/weaviate.py/Weaviate/max_marginal_relevance_search
class Weaviate:    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.
        """
        if self._embedding is not None:
            embedding = self._embedding.embed_query(query)
        else:
            raise ValueError(
                "max_marginal_relevance_search requires a suitable Embeddings object"
            )

        return self.max_marginal_relevance_search_by_vector(
            embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
        )