def __init__(
        self,
        dataset_path: str = "llama_index",
        token: Optional[str] = None,
        read_only: Optional[bool] = False,
        ingestion_batch_size: int = 1024,
        ingestion_num_workers: int = 4,
        overwrite: bool = False,
        exec_option: Optional[str] = None,
        verbose: bool = True,
        **kwargs: Any,
    ) -> None:
        """
        Args:
            dataset_path (str): The full path for storing to the Deep Lake Vector Store. It can be:
                - a Deep Lake cloud path of the form ``hub://org_id/dataset_name``. Requires registration with Deep Lake.
                - an s3 path of the form ``s3://bucketname/path/to/dataset``. Credentials are required in either the environment or passed to the creds argument.
                - a local file system path of the form ``./path/to/dataset`` or ``~/path/to/dataset`` or ``path/to/dataset``.
                - a memory path of the form ``mem://path/to/dataset`` which doesn't save the dataset but keeps it in memory instead. Should be used only for testing as it does not persist.
                Defaults to "llama_index".
            overwrite (bool, optional): If set to True this overwrites the Vector Store if it already exists. Defaults to False.
            token (str, optional): Activeloop token, used for fetching user credentials. This is Optional, tokens are normally autogenerated. Defaults to None.
            read_only (bool, optional): Opens dataset in read-only mode if True. Defaults to False.
            ingestion_batch_size (int): During data ingestion, data is divided
                into batches. Batch size is the size of each batch. Defaults to 1024.
            ingestion_num_workers (int): number of workers to use during data ingestion.
                Defaults to 4.
            exec_option (str): Default method for search execution. It could be either ``"auto"``, ``"python"``, ``"compute_engine"`` or ``"tensor_db"``. Defaults to ``"auto"``. If None, it's set to "auto".
                - ``auto``- Selects the best execution method based on the storage location of the Vector Store. It is the default option.
                - ``python`` - Pure-python implementation that runs on the client and can be used for data stored anywhere. WARNING: using this option with big datasets is discouraged because it can lead to memory issues.
                - ``compute_engine`` - Performant C++ implementation of the Deep Lake Compute Engine that runs on the client and can be used for any data stored in or connected to Deep Lake. It cannot be used with in-memory or local datasets.
                - ``tensor_db`` - Performant and fully-hosted Managed Tensor Database that is responsible for storage and query execution. Only available for data stored in the Deep Lake Managed Database. Store datasets in this database by specifying runtime = {"tensor_db": True} during dataset creation.

        Raises:
            ImportError: Unable to import `deeplake`.

        """
        super().__init__(
            dataset_path=dataset_path,
            token=token,
            read_only=read_only,
            ingestion_batch_size=ingestion_batch_size,
            num_workers=ingestion_num_workers,
        )

        self.vectorstore = VectorStore(
            path=dataset_path,
            ingestion_batch_size=ingestion_batch_size,
            num_workers=ingestion_num_workers,
            token=token,
            read_only=read_only,
            exec_option=exec_option,
            overwrite=overwrite,
            verbose=verbose,
            **kwargs,
        )
        try:
            self._id_tensor_name = (
                "ids" if "ids" in self.vectorstore.tensors() else "id"
            )
        except AttributeError:
            self._id_tensor_name = "id"

        Args:
            dataset_path (str): The full path for storing to the Deep Lake Vector Store. It can be:
                - a Deep Lake cloud path of the form ``hub://org_id/dataset_name``. Requires registration with Deep Lake.
                - an s3 path of the form ``s3://bucketname/path/to/dataset``. Credentials are required in either the environment or passed to the creds argument.
                - a local file system path of the form ``./path/to/dataset`` or ``~/path/to/dataset`` or ``path/to/dataset``.
                - a memory path of the form ``mem://path/to/dataset`` which doesn't save the dataset but keeps it in memory instead. Should be used only for testing as it does not persist.
                Defaults to "llama_index".
            overwrite (bool, optional): If set to True this overwrites the Vector Store if it already exists. Defaults to False.
            token (str, optional): Activeloop token, used for fetching user credentials. This is Optional, tokens are normally autogenerated. Defaults to None.
            read_only (bool, optional): Opens dataset in read-only mode if True. Defaults to False.
            ingestion_batch_size (int): During data ingestion, data is divided
                into batches. Batch size is the size of each batch. Defaults to 1024.
            ingestion_num_workers (int): number of workers to use during data ingestion.
                Defaults to 4.
            exec_option (str): Default method for search execution. It could be either ``"auto"``, ``"python"``, ``"compute_engine"`` or ``"tensor_db"``. Defaults to ``"auto"``. If None, it's set to "auto".
                - ``auto``- Selects the best execution method based on the storage location of the Vector Store. It is the default option.
                - ``python`` - Pure-python implementation that runs on the client and can be used for data stored anywhere. WARNING: using this option with big datasets is discouraged because it can lead to memory issues.
                - ``compute_engine`` - Performant C++ implementation of the Deep Lake Compute Engine that runs on the client and can be used for any data stored in or connected to Deep Lake. It cannot be used with in-memory or local datasets.
                - ``tensor_db`` - Performant and fully-hosted Managed Tensor Database that is responsible for storage and query execution. Only available for data stored in the Deep Lake Managed Database. Store datasets in this database by specifying runtime = {"tensor_db": True} during dataset creation.

        Raises:
            ImportError: Unable to import `deeplake`.

        
llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py