docs = []
        for j, i in enumerate(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 filter is not None:
                if all(doc.metadata.get(key) == value for key, value in filter.items()):
                    docs.append((doc, scores[0][j]))
            else:
                docs.append((doc, scores[0][j]))
        return docs[:k]
[docs]    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.
        Returns:
            List of documents most similar to the query text with
            L2 distance in float. Lower score represents more similarity.
        """
        embedding = self.embedding_function(query)
        docs = self.similarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html