Args:
            query (str): The query text for which to find similar documents.
            k (int): The number of documents to return. Default is 4.
        Returns:
            List[Document]: A list of documents that are most similar to the query text.
        """
        # Creates embedding vector from user query
        embedding = self.embedding_function.embed_query(query)
        keys_and_scores = self.client.tvs_knnsearch(
            self.index_name, k, embedding, False, None, **kwargs
        )
        pipeline = self.client.pipeline(transaction=False)
        for key, _ in keys_and_scores:
            pipeline.tvs_hmget(
                self.index_name, key, self.metadata_key, self.content_key
            )
        docs = pipeline.execute()
        return [
            Document(
                page_content=d[1],
                metadata=json.loads(d[0]),
            )
            for d in docs
        ]
[docs]    @classmethod
    def from_texts(
        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")
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/tair.html