self, query: str, k: int = 4
    ) -> 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.
        Returns:
            List of Documents most similar to the query and score for each
        """
        # Creates embedding vector from user query
        embedding = self.embedding_function(query)
        # Creates Redis query
        redis_query = self._prepare_query(k)
        params_dict: Mapping[str, str] = {
            "vector": np.array(embedding)  # type: ignore
            .astype(dtype=np.float32)
            .tobytes()
        }
        # Perform vector search
        results = self.client.ft(self.index_name).search(redis_query, params_dict)
        # Prepare document results
        docs_and_scores: List[Tuple[Document, float]] = []
        for result in results.docs:
            metadata = {**json.loads(result.metadata), "id": result.id}
            doc = Document(page_content=result.content, metadata=metadata)
            docs_and_scores.append((doc, float(result.vector_score)))
        return docs_and_scores
[docs]    @classmethod
    def from_texts_return_keys(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        index_name: Optional[str] = None,
        content_key: str = "content",
        metadata_key: str = "metadata",
        vector_key: str = "content_vector",
        distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
        **kwargs: Any,
    ) -> Tuple[Redis, List[str]]: