dataset_path=dataset_path, embedding_function=embedding, **kwargs
        )
        deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
        return deeplake_dataset
[docs]    def delete(
        self,
        ids: Any[List[str], None] = None,
        filter: Any[Dict[str, str], None] = None,
        delete_all: Any[bool, None] = None,
    ) -> bool:
        """Delete the entities in the dataset
        Args:
            ids (Optional[List[str]], optional): The document_ids to delete.
                Defaults to None.
            filter (Optional[Dict[str, str]], optional): The filter to delete by.
                Defaults to None.
            delete_all (Optional[bool], optional): Whether to drop the dataset.
                Defaults to None.
        """
        if delete_all:
            self.ds.delete(large_ok=True)
            return True
        view = None
        if ids:
            view = self.ds.filter(lambda x: x["ids"].data()["value"] in ids)
            ids = list(view.sample_indices)
        if filter:
            if view is None:
                view = self.ds
            view = view.filter(partial(dp_filter, filter=filter))
            ids = list(view.sample_indices)
        with self.ds:
            for id in sorted(ids)[::-1]:
                self.ds.pop(id)
            self.ds.commit(f"deleted {len(ids)} samples", allow_empty=True)
        return True
[docs]    @classmethod
    def force_delete_by_path(cls, path: str) -> None:
        """Force delete dataset by path"""
        try:
            import deeplake
        except ImportError:
            raise ValueError(
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/deeplake.html