tmp_res = (Document(page_content=content, metadata=meta_data), score)
            results.append(tmp_res)
        return results
    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        return self.similarity_search_with_score(query, k, **kwargs)
[docs]    def delete(
        self,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Optional[bool]:
        """Delete the documents which have the specified ids.
        Args:
            ids: The ids of the embedding vectors.
            **kwargs: Other keyword arguments that subclasses might use.
        Returns:
            Optional[bool]: True if deletion is successful.
            False otherwise, None if not implemented.
        """
        ret: Optional[bool] = None
        tmp_res = []
        if ids is None or ids.__len__() == 0:
            return ret
        for _id in ids:
            if self.flag:
                ret = self.vearch.delete(self.using_db_name, self.using_table_name, _id)
            else:
                ret = self.vearch.del_doc(_id)
            tmp_res.append(ret)
        ret = all(i == 0 for i in tmp_res)
        return ret
[docs]    def get(
        self,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Dict[str, Document]:
        """Return docs according ids.
        Args:
            ids: The ids of the embedding vectors.
        Returns:
            Documents which satisfy the input conditions.
        """