# Default strategy is to rely on distance strategy provided
        # in vectorstore constructor
        if self._distance_strategy == DistanceStrategy.COSINE:
            return self._cosine_relevance_score_fn
        elif self._distance_strategy == DistanceStrategy.EUCLIDEAN:
            return self._euclidean_relevance_score_fn
        elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
            return self._max_inner_product_relevance_score_fn
        else:
            raise ValueError(
                "No supported normalization function"
                f" for distance_strategy of {self._distance_strategy}."
                "Consider providing relevance_score_fn to PGVector constructor."
            )
[docs]    def max_marginal_relevance_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, str]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs selected using the maximal marginal relevance with score
            to embedding vector.
        Maximal marginal relevance optimizes for similarity to query AND diversity
            among selected documents.
        Args:
            embedding: Embedding to look up documents similar to.
            k (int): Number of Documents to return. Defaults to 4.
            fetch_k (int): Number of Documents to fetch to pass to MMR algorithm.
                Defaults to 20.
            lambda_mult (float): 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.