]
        embeddings = OpenAIEmbeddings()
        supabase_client = create_client("my_supabase_url", "my_supabase_key")
        vector_store = SupabaseVectorStore.from_documents(
            docs,
            embeddings,
            client=supabase_client,
            table_name="documents",
            query_name="match_documents",
        )
    To load from an existing table:
    .. code-block:: python
        from langchain.embeddings.openai import OpenAIEmbeddings
        from langchain.vectorstores import SupabaseVectorStore
        from supabase.client import create_client
        embeddings = OpenAIEmbeddings()
        supabase_client = create_client("my_supabase_url", "my_supabase_key")
        vector_store = SupabaseVectorStore(
            client=supabase_client,
            embedding=embeddings,
            table_name="documents",
            query_name="match_documents",
        )
    """
[docs]    def __init__(
        self,
        client: supabase.client.Client,
        embedding: Embeddings,
        table_name: str,
        query_name: Union[str, None] = None,
    ) -> None:
        """Initialize with supabase client."""
        try:
            import supabase  # noqa: F401
        except ImportError:
            raise ImportError(
                "Could not import supabase python package. "
                "Please install it with `pip install supabase`."
            )
        self._client = client
        self._embedding: Embeddings = embedding
        self.table_name = table_name or "documents"
        self.query_name = query_name or "match_documents"
    @property
    def embeddings(self) -> Embeddings: