into batches. Batch size is the size of each batch.
Default is 1000.
num_workers (int) – Number of workers to use during data ingestion.
Default is 0.
verbose (bool) – Print dataset summary after each operation.
Default is True.
exec_option (str, optional) – DeepLakeVectorStore supports 3 ways to perform
searching - “python”, “compute_engine”, “tensor_db” and auto.
Default is None.
- 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.WARNING: using this with big datasets can lead to memory
issues. Data can be stored anywhere.
compute_engine - C++ implementation of the Deep Lake ComputeEngine that runs on the client. Can be used for any data stored in
or connected to Deep Lake. Not for in-memory or local datasets.
tensor_db - Hosted Managed Tensor Database that isresponsible for storage and query execution. Only for data stored in
the Deep Lake Managed Database. Use runtime = {“db_engine”: True}
during dataset creation.
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 – Other optional keyword arguments.
Raises
ValueError – If some condition is not met.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]