cls: Type[Tair],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        index_name: str = "langchain",
        content_key: str = "content",
        metadata_key: str = "metadata",
        **kwargs: Any,
    ) -> Tair:
        try:
            from tair import tairvector
        except ImportError:
            raise ValueError(
                "Could not import tair python package. "
                "Please install it with `pip install tair`."
            )
        url = get_from_dict_or_env(kwargs, "tair_url", "TAIR_URL")
        if "tair_url" in kwargs:
            kwargs.pop("tair_url")
        distance_type = tairvector.DistanceMetric.InnerProduct
        if "distance_type" in kwargs:
            distance_type = kwargs.pop("distance_type")
        index_type = tairvector.IndexType.HNSW
        if "index_type" in kwargs:
            index_type = kwargs.pop("index_type")
        data_type = tairvector.DataType.Float32
        if "data_type" in kwargs:
            data_type = kwargs.pop("data_type")
        index_params = {}
        if "index_params" in kwargs:
            index_params = kwargs.pop("index_params")
        search_params = {}
        if "search_params" in kwargs:
            search_params = kwargs.pop("search_params")
        keys = None
        if "keys" in kwargs:
            keys = kwargs.pop("keys")
        try:
            tair_vector_store = cls(
                embedding,
                url,
                index_name,
                content_key=content_key,
                metadata_key=metadata_key,
                search_params=search_params,