diff --git a/libs/community/langchain_community/vectorstores/tencentvectordb.py b/libs/community/langchain_community/vectorstores/tencentvectordb.py index 4b9b8952cdd..ffa2beb6501 100644 --- a/libs/community/langchain_community/vectorstores/tencentvectordb.py +++ b/libs/community/langchain_community/vectorstores/tencentvectordb.py @@ -375,8 +375,7 @@ class TencentVectorDB(VectorStore): } if embeddings: doc_attrs["vector"] = embeddings[id] - else: - doc_attrs["text"] = texts[id] + doc_attrs["text"] = texts[id] doc_attrs.update(metadata) doc = self.document.Document(**doc_attrs) docs.append(doc) diff --git a/libs/community/tests/integration_tests/vectorstores/test_tencentvectordb.py b/libs/community/tests/integration_tests/vectorstores/test_tencentvectordb.py index 1a637391550..df50beed92f 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_tencentvectordb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_tencentvectordb.py @@ -85,3 +85,27 @@ def test_tencent_vector_db_no_drop() -> None: time.sleep(3) output = docsearch.similarity_search("foo", k=10) assert len(output) == 6 + + +def test_tencent_vector_db_add_texts_and_search_with_score() -> None: + """Test add texts to a new-created db and search with score.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + conn_params = ConnectionParams( + url="http://10.0.X.X", + key="eC4bLRy2va******************************", + username="root", + timeout=20, + ) + docsearch = TencentVectorDB( + embedding=FakeEmbeddings(), + connection_params=conn_params, + ) + docsearch.add_texts(texts, metadatas) + output = docsearch.similarity_search_with_score("foo", k=3) + docs = [o[0] for o in output] + assert docs == [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + Document(page_content="baz", metadata={"page": 2}), + ]