This is a user friendly interface that:
                    1. Embeds documents.
                    2. Adds the documents to a provided Dingo index
                This is intended to be a quick way to get started.
                Example:
                    .. code-block:: python
                        from langchain.vectorstores import Dingo
                        from langchain.embeddings import OpenAIEmbeddings
                        import dingodb
        sss
                        embeddings = OpenAIEmbeddings()
                        dingo = Dingo.from_texts(
                            texts,
                            embeddings,
                            index_name="langchain-demo"
                        )
        """
        try:
            import dingodb
        except ImportError:
            raise ImportError(
                "Could not import dingo python package. "
                "Please install it with `pip install dingodb`."
            )
        if client is not None:
            dingo_client = client
        else:
            try:
                # connect to dingo db
                dingo_client = dingodb.DingoDB(user, password, host)
            except ValueError as e:
                raise ValueError(f"Dingo failed to connect: {e}")
        if kwargs is not None and kwargs.get("self_id") is True:
            if index_name not in dingo_client.get_index():
                dingo_client.create_index(index_name, 1024, auto_id=False)
        else:
            if index_name not in dingo_client.get_index():
                dingo_client.create_index(index_name, 1024)
            # dingo_client.create_index(index_name, 1024, index_type="hnsw")
        # Embed and create the documents
        ids = ids or [str(uuid.uuid1().int)[:13] for _ in texts]
        metadatas_list = []
        texts = list(texts)