embedding (Optional[Embeddings]): Embedding function. Defaults to None.
                Note, in other places, it is called embedding_function.
            metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
            ids (Optional[List[str]]): List of document IDs. Defaults to None.
            **kwargs: Additional keyword arguments.
        Returns:
            DeepLake: Deep Lake dataset.
        """
        deeplake_dataset = cls(dataset_path=dataset_path, embedding=embedding, **kwargs)
        deeplake_dataset.add_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
        )
        return deeplake_dataset
[docs]    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool:
        """Delete the entities in the dataset.
        Args:
            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:
            bool: Whether the delete operation was successful.
        """
        filter = kwargs.get("filter")
        delete_all = kwargs.get("delete_all")
        self.vectorstore.delete(ids=ids, filter=filter, delete_all=delete_all)
        return True
[docs]    @classmethod
    def force_delete_by_path(cls, path: str) -> None:
        """Force delete dataset by path.
        Args:
            path (str): path of the dataset to delete.
        Raises:
            ValueError: if deeplake is not installed.