index_name: str,
        embedding_function: Callable,
        content_key: str = "content",
        metadata_key: str = "metadata",
        vector_key: str = "content_vector",
        relevance_score_fn: Optional[
            Callable[[float], float]
        ] = _default_relevance_score,
        **kwargs: Any,
    ):
        """Initialize with necessary components."""
        try:
            import redis
        except ImportError:
            raise ValueError(
                "Could not import redis python package. "
                "Please install it with `pip install redis>=4.1.0`."
            )
        self.embedding_function = embedding_function
        self.index_name = index_name
        try:
            # connect to redis from url
            redis_client = redis.from_url(redis_url, **kwargs)
            # check if redis has redisearch module installed
            _check_redis_module_exist(redis_client, REDIS_REQUIRED_MODULES)
        except ValueError as e:
            raise ValueError(f"Redis failed to connect: {e}")
        self.client = redis_client
        self.content_key = content_key
        self.metadata_key = metadata_key
        self.vector_key = vector_key
        self.relevance_score_fn = relevance_score_fn
    def _create_index(
        self, dim: int = 1536, distance_metric: REDIS_DISTANCE_METRICS = "COSINE"
    ) -> None:
        try:
            from redis.commands.search.field import TextField, VectorField
            from redis.commands.search.indexDefinition import IndexDefinition, IndexType
        except ImportError:
            raise ValueError(
                "Could not import redis python package. "
                "Please install it with `pip install redis`."
            )
        # Check if index exists
        if not _check_index_exists(self.client, self.index_name):
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/redis.html