Return docs most similar to query.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → bool[source]¶
Delete the entities in the dataset.
Parameters
ids (Optional[List[str]], optional) – The document_ids to delete.
Defaults to None.
**kwargs – Other keyword arguments that subclasses might use.
- filter (Optional[Dict[str, str]], optional): The filter to delete by.
- delete_all (Optional[bool], optional): Whether to drop the dataset.
Returns
Whether the delete operation was successful.
Return type
bool
delete_dataset() → None[source]¶
Delete the collection.
ds() → Any[source]¶
classmethod force_delete_by_path(path: str) → None[source]¶
Force delete dataset by path.
Parameters
path (str) – path of the dataset to delete.
Raises
ValueError – if deeplake is not installed.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, dataset_path: str = './deeplake/', **kwargs: Any) → DeepLake[source]¶
Create a Deep Lake dataset from a raw documents.
If a dataset_path is specified, the dataset will be persisted in that location,
otherwise by default at ./deeplake
Examples:
>>> # Search using an embedding
>>> vector_store = DeepLake.from_texts(
…        texts = <the_texts_that_you_want_to_embed>,
…        embedding_function = <embedding_function_for_query>,
…        k = <number_of_items_to_return>,
…        exec_option = <preferred_exec_option>,