port (int)
table (str)
username (Optional[str])
field column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata', 'uuid': 'uuid'}#
field database: str = 'default'#
field host: str = 'localhost'#
field index_param: Optional[Union[List, Dict]] = [100, "'L2Distance'"]#
field index_query_params: Dict[str, str] = {}#
field index_type: str = 'annoy'#
field metric: str = 'angular'#
field password: Optional[str] = None#
field port: int = 8123#
field table: str = 'langchain'#
field username: Optional[str] = None#
class langchain.vectorstores.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, read_only: Optional[bool] = False, ingestion_batch_size: int = 1024, num_workers: int = 0, verbose: bool = True, **kwargs: Any)[source]#
Wrapper around Deep Lake, a data lake for deep learning applications.
We implement naive similarity search and filtering for fast prototyping,
but it can be extended with Tensor Query Language (TQL) for production use cases
over billion rows.
Why Deep Lake?
Not only stores embeddings, but also the original data with version control.
Serverless, doesn’t require another service and can be used with majorcloud providers (S3, GCS, etc.)
More than just a multi-modal vector store. You can use the datasetto fine-tune your own LLM models.
To use, you should have the deeplake python package installed.
Example
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html