diff --git a/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py b/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py index 552068c99e..cfa59165a4 100644 --- a/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py +++ b/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py @@ -118,10 +118,22 @@ class TableStoreVector(BaseVector): def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]: top_k = kwargs.get("top_k", 4) - return self._search_by_vector(query_vector, top_k) + document_ids_filter = kwargs.get("document_ids_filter") + filtered_list = None + if document_ids_filter: + filtered_list = ["document_id=" + item for item in document_ids_filter] + score_threshold = float(kwargs.get("score_threshold") or 0.0) + return self._search_by_vector(query_vector, filtered_list, top_k, score_threshold) def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]: - return self._search_by_full_text(query) + top_k = kwargs.get("top_k", 4) + document_ids_filter = kwargs.get("document_ids_filter") + filtered_list = None + if document_ids_filter: + filtered_list = ["document_id=" + item for item in document_ids_filter] + score_threshold = float(kwargs.get("score_threshold") or 0.0) + + return self._search_by_full_text(query, filtered_list, top_k, score_threshold) def delete(self) -> None: self._delete_table_if_exist() @@ -230,32 +242,51 @@ class TableStoreVector(BaseVector): primary_key = [("id", id)] row = tablestore.Row(primary_key) self._tablestore_client.delete_row(self._table_name, row, None) - logging.info("Tablestore delete row successfully. id:%s", id) def _search_by_metadata(self, key: str, value: str) -> list[str]: query = tablestore.SearchQuery( tablestore.TermQuery(self._tags_field, str(key) + "=" + str(value)), - limit=100, + limit=1000, get_total_count=False, ) + rows = [] + next_token = None + while True: + if next_token is not None: + query.next_token = next_token + + search_response = self._tablestore_client.search( + table_name=self._table_name, + index_name=self._index_name, + search_query=query, + columns_to_get=tablestore.ColumnsToGet( + column_names=[Field.PRIMARY_KEY.value], return_type=tablestore.ColumnReturnType.SPECIFIED + ), + ) - search_response = self._tablestore_client.search( - table_name=self._table_name, - index_name=self._index_name, - search_query=query, - columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX), - ) + if search_response is not None: + rows.extend(row[0][0][1] for row in search_response.rows) - return [row[0][0][1] for row in search_response.rows] + if search_response is None or search_response.next_token == b"": + break + else: + next_token = search_response.next_token - def _search_by_vector(self, query_vector: list[float], top_k: int) -> list[Document]: - ots_query = tablestore.KnnVectorQuery( + return rows + + def _search_by_vector( + self, query_vector: list[float], document_ids_filter: list[str], top_k: int, score_threshold: float + ) -> list[Document]: + knn_vector_query = tablestore.KnnVectorQuery( field_name=Field.VECTOR.value, top_k=top_k, float32_query_vector=query_vector, ) + if document_ids_filter: + knn_vector_query.filter = tablestore.TermsQuery(self._tags_field, document_ids_filter) + sort = tablestore.Sort(sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)]) - search_query = tablestore.SearchQuery(ots_query, limit=top_k, get_total_count=False, sort=sort) + search_query = tablestore.SearchQuery(knn_vector_query, limit=top_k, get_total_count=False, sort=sort) search_response = self._tablestore_client.search( table_name=self._table_name, @@ -263,30 +294,22 @@ class TableStoreVector(BaseVector): search_query=search_query, columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX), ) - logging.info( - "Tablestore search successfully. request_id:%s", - search_response.request_id, - ) - return self._to_query_result(search_response) - def _to_query_result(self, search_response: tablestore.SearchResponse) -> list[Document]: - documents = [] - for row in search_response.rows: - documents.append( - Document( - page_content=row[1][2][1], - vector=json.loads(row[1][3][1]), - metadata=json.loads(row[1][0][1]), - ) - ) + return self._to_query_result(search_response, score_threshold) - return documents + def _search_by_full_text( + self, query: str, document_ids_filter: list[str], top_k: int, score_threshold: float + ) -> list[Document]: + bool_query = tablestore.BoolQuery() + bool_query.must_queries.append(tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY.value)) + + if document_ids_filter: + bool_query.filter_queries.append(tablestore.TermsQuery(self._tags_field, document_ids_filter)) - def _search_by_full_text(self, query: str) -> list[Document]: search_query = tablestore.SearchQuery( - query=tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY.value), + query=bool_query, sort=tablestore.Sort(sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)]), - limit=100, + limit=top_k, ) search_response = self._tablestore_client.search( table_name=self._table_name, @@ -295,7 +318,24 @@ class TableStoreVector(BaseVector): columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX), ) - return self._to_query_result(search_response) + return self._to_query_result(search_response, score_threshold) + + @staticmethod + def _to_query_result(search_response: tablestore.SearchResponse, score_threshold: float) -> list[Document]: + documents = [] + for search_hit in search_response.search_hits: + if search_hit.score > score_threshold: + metadata = json.loads(search_hit.row[1][0][1]) + metadata["score"] = search_hit.score + documents.append( + Document( + page_content=search_hit.row[1][2][1], + vector=json.loads(search_hit.row[1][3][1]), + metadata=metadata, + ) + ) + documents = sorted(documents, key=lambda x: x.metadata["score"] if x.metadata else 0, reverse=True) + return documents class TableStoreVectorFactory(AbstractVectorFactory):