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 before filtering 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 and similarity scores selected by maximal marginal
                relevance and score for each.
        """
        scores, indices = self.index.search(
            np.array([embedding], dtype=np.float32),
            fetch_k if filter is None else fetch_k * 2,
        )
        if filter is not None:
            filtered_indices = []
            for i in indices[0]:
                if i == -1:
                    # This happens when not enough docs are returned.
                    continue
                _id = self.index_to_docstore_id[i]
                doc = self.docstore.search(_id)
                if not isinstance(doc, Document):
                    raise ValueError(f"Could not find document for id {_id}, got {doc}")
                if all(
                    doc.metadata.get(key) in value
                    if isinstance(value, list)
                    else doc.metadata.get(key) == value
                    for key, value in filter.items()
                ):
                    filtered_indices.append(i)
            indices = np.array([filtered_indices])
        # -1 happens when not enough docs are returned.
        embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
        mmr_selected = maximal_marginal_relevance(
            np.array([embedding], dtype=np.float32),