"""
        self.ingestion_batch_size = ingestion_batch_size
        self.num_workers = num_workers
        self.verbose = verbose
        if _DEEPLAKE_INSTALLED is False:
            raise ValueError(
                "Could not import deeplake python package. "
                "Please install it with `pip install deeplake[enterprise]`."
            )
        if (
            kwargs.get("runtime") == {"tensor_db": True}
            and version_compare(deeplake.__version__, "3.6.7") == -1
        ):
            raise ValueError(
                "To use tensor_db option you need to update deeplake to `3.6.7`. "
                f"Currently installed deeplake version is {deeplake.__version__}. "
            )
        self.dataset_path = dataset_path
        logger.warning(
            "Using embedding function is deprecated and will be removed "
            "in the future. Please use embedding instead."
        )
        self.vectorstore = DeepLakeVectorStore(
            path=self.dataset_path,
            embedding_function=embedding_function or embedding,
            read_only=read_only,
            token=token,
            exec_option=exec_option,
            verbose=verbose,
            **kwargs,
        )
        self._embedding_function = embedding_function or embedding
        self._id_tensor_name = "ids" if "ids" in self.vectorstore.tensors() else "id"
    @property
    def embeddings(self) -> Optional[Embeddings]:
        return self._embedding_function
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]: