vectorstore = DeepLake("langchain_store", embeddings.embed_query)
    """
    _LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
[docs]    def __init__(
        self,
        dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
        token: Optional[str] = None,
        embedding: Optional[Embeddings] = None,
        embedding_function: Optional[Embeddings] = None,
        read_only: bool = False,
        ingestion_batch_size: int = 1000,
        num_workers: int = 0,
        verbose: bool = True,
        exec_option: Optional[str] = None,
        runtime: Optional[Dict] = None,
        **kwargs: Any,
    ) -> None:
        """Creates an empty DeepLakeVectorStore or loads an existing one.
        The DeepLakeVectorStore is located at the specified ``path``.
        Examples:
            >>> # Create a vector store with default tensors
            >>> deeplake_vectorstore = DeepLake(
            ...        path = <path_for_storing_Data>,
            ... )
            >>>
            >>> # Create a vector store in the Deep Lake Managed Tensor Database
            >>> data = DeepLake(
            ...        path = "hub://org_id/dataset_name",
            ...        runtime = {"tensor_db": True},
            ... )
        Args:
            dataset_path (str): Path to existing dataset or where to create
                a new one. Defaults to _LANGCHAIN_DEFAULT_DEEPLAKE_PATH.
            token (str, optional):  Activeloop token, for fetching credentials
                to the dataset at path if it is a Deep Lake dataset.
                Tokens are normally autogenerated. Optional.
            embedding (Embeddings, optional): Function to convert