Repository: activeloopai/Hub
File: deeplake/core/vectorstore/deeplake_vectorstore.py (Python)

Code before:
            ...                         {"name": "embedding_2", "htype": "embedding"},
            ...                         {"name": "source", "htype": "text"},
            ...                         {"name": "metadata", "htype": "json"}
            ...                        ]
            ... )

        Args:
            path (str, pathlib.Path): - 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.
            tensor_params (List[Dict[str, dict]], optional): List of dictionaries that contains information about tensors that user wants to create. See ``create_tensor`` in Deep Lake API docs for more information. Defaults to ``DEFAULT_VECTORSTORE_TENSORS``.
            embedding_function (Optional[callable], optional): Function that converts the embeddable data into embeddings. Defaults to None.
            read_only (bool, optional):  Opens dataset in read-only mode if True. Defaults to False.
            num_workers (int): Number of workers to use for parallel ingestion.
            ingestion_batch_size (int): Batch size to use for parallel ingestion.
            exec_option (str): Default method for search execution. It could be either It could be either ``"python"``, ``"compute_engine"`` or ``"tensor_db"``. Defaults to ``"python"``.

                - ``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 = {"db_engine": True} during dataset creation.
            token (str, optional): Activeloop token, used for fetching user credentials. This is Optional, tokens are normally autogenerated. Defaults to None.
            overwrite (bool): If set to True this overwrites the Vector Store if it already exists. Defaults to False.
            verbose (bool): Whether to print summary of the dataset created. Defaults to True.
            runtime (Dict, optional): Whether to store the dataset in the DeepLake Managed Database. Defaults to None.
            **kwargs (Any): Additional keyword arguments.

        ..
            # noqa: DAR101

        Danger:
            Setting ``overwrite`` to ``True`` will delete all of your data if the Vector Store exists! Be very careful when setting this parameter.
        """
        feature_report_path(
            path,
            "vs.initialize",
            {
                "tensor_params": "default"
                if tensor_params is not None
                else tensor_params,
                "embedding_function": True if embedding_function is not None else False,
                "num_workers": num_workers,
                "overwrite": overwrite,
                "read_only": read_only,
                "ingestion_batch_size": ingestion_batch_size,
                "exec_option": exec_option,
                "token": token,
                "verbose": verbose,
                "runtime": runtime,
            },

Review comment (suggestion):
@adolkhan pls rephrase to:

runtime (Dict, optional): Parameters for creating the Vector Store in Deep Lake's Managed Tensor Database. Not applicable when loading an existing Vector Store. To create a Vector Store in the Managed Tensor Database, set `runtime = {"tensor_db": True}`.

Code after:
            ...                         {"name": "embedding_2", "htype": "embedding"},
            ...                         {"name": "source", "htype": "text"},
            ...                         {"name": "metadata", "htype": "json"}
            ...                        ]
            ... )

        Args:
            path (str, pathlib.Path): - 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.
            tensor_params (List[Dict[str, dict]], optional): List of dictionaries that contains information about tensors that user wants to create. See ``create_tensor`` in Deep Lake API docs for more information. Defaults to ``DEFAULT_VECTORSTORE_TENSORS``.
            embedding_function (Optional[callable], optional): Function that converts the embeddable data into embeddings. Defaults to None.
            read_only (bool, optional):  Opens dataset in read-only mode if True. Defaults to False.
            num_workers (int): Number of workers to use for parallel ingestion.
            ingestion_batch_size (int): Batch size to use for parallel ingestion.
            exec_option (str): Default method for search execution. It could be either It could be either ``"python"``, ``"compute_engine"`` or ``"tensor_db"``. Defaults to ``"python"``.

                - ``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.
            token (str, optional): Activeloop token, used for fetching user credentials. This is Optional, tokens are normally autogenerated. Defaults to None.
            overwrite (bool): If set to True this overwrites the Vector Store if it already exists. Defaults to False.
            verbose (bool): Whether to print summary of the dataset created. Defaults to True.
            runtime (Dict, optional): Parameters for creating the Vector Store in Deep Lake's Managed Tensor Database. Not applicable when loading an existing Vector Store. To create a Vector Store in the Managed Tensor Database, set `runtime = {"tensor_db": True}`.

            **kwargs (Any): Additional keyword arguments.

        ..
            # noqa: DAR101

        Danger:
            Setting ``overwrite`` to ``True`` will delete all of your data if the Vector Store exists! Be very careful when setting this parameter.
        """
        feature_report_path(
            path,
            "vs.initialize",
            {
                "tensor_params": "default"
                if tensor_params is not None
                else tensor_params,
                "embedding_function": True if embedding_function is not None else False,
                "num_workers": num_workers,
                "overwrite": overwrite,
                "read_only": read_only,
                "ingestion_batch_size": ingestion_batch_size,
                "exec_option": exec_option,
                "token": token,
                "verbose": verbose,
                "runtime": runtime,