diff --git a/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py b/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py
index d79bac8f67..55326fd60f 100644
--- a/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py
+++ b/api/core/rag/datasource/vdb/tablestore/tablestore_vector.py
@@ -248,7 +248,7 @@ class TableStoreVector(BaseVector):
             limit=1000,
             get_total_count=False,
         )
-        rows = []
+        rows: list[str] = []
         next_token = None
         while True:
             if next_token is not None:
@@ -264,7 +264,7 @@ class TableStoreVector(BaseVector):
             )
 
             if search_response is not None:
-                rows.extend(row[0][0][1] for row in search_response.rows)
+                rows.extend([row[0][0][1] for row in search_response.rows])
 
             if search_response is None or search_response.next_token == b"":
                 break
@@ -274,7 +274,7 @@ class TableStoreVector(BaseVector):
         return rows
 
     def _search_by_vector(
-        self, query_vector: list[float], document_ids_filter: list[str], top_k: int, score_threshold: float
+        self, query_vector: list[float], document_ids_filter: list[str] | None, top_k: int, score_threshold: float
     ) -> list[Document]:
         knn_vector_query = tablestore.KnnVectorQuery(
             field_name=Field.VECTOR.value,
@@ -308,7 +308,7 @@ class TableStoreVector(BaseVector):
         documents = sorted(documents, key=lambda x: x.metadata["score"] if x.metadata else 0, reverse=True)
         return documents
 
-    def _search_by_full_text(self, query: str, document_ids_filter: list[str], top_k: int) -> list[Document]:
+    def _search_by_full_text(self, query: str, document_ids_filter: list[str] | None, top_k: int) -> list[Document]:
         bool_query = tablestore.BoolQuery()
         bool_query.must_queries.append(tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY.value))
 
diff --git a/api/tests/integration_tests/vdb/tablestore/test_tablestore.py b/api/tests/integration_tests/vdb/tablestore/test_tablestore.py
index da890d0b7c..898af53ddf 100644
--- a/api/tests/integration_tests/vdb/tablestore/test_tablestore.py
+++ b/api/tests/integration_tests/vdb/tablestore/test_tablestore.py
@@ -1,4 +1,7 @@
 import os
+import uuid
+
+import tablestore
 
 from core.rag.datasource.vdb.tablestore.tablestore_vector import (
     TableStoreConfig,
@@ -7,6 +10,8 @@ from core.rag.datasource.vdb.tablestore.tablestore_vector import (
 from tests.integration_tests.vdb.test_vector_store import (
     AbstractVectorTest,
     setup_mock_redis,
+    get_example_document,
+    get_example_text,
 )
 
 
@@ -29,6 +34,49 @@ class TableStoreVectorTest(AbstractVectorTest):
         assert len(ids) == 1
         assert ids[0] == self.example_doc_id
 
+    def create_vector(self):
+        self.vector.create(
+            texts=[get_example_document(doc_id=self.example_doc_id)],
+            embeddings=[self.example_embedding],
+        )
+        while True:
+            search_response = self.vector._tablestore_client.search(
+                table_name=self.vector._table_name,
+                index_name=self.vector._index_name,
+                search_query=tablestore.SearchQuery(query=tablestore.MatchAllQuery(), get_total_count=True, limit=0),
+                columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX),
+            )
+            if search_response.total_count == 1:
+                break
+
+    def search_by_vector(self):
+        super().search_by_vector()
+        docs = self.vector.search_by_vector(self.example_embedding, document_ids_filter=[self.example_doc_id])
+        assert len(docs) == 1
+        assert docs[0].metadata["doc_id"] == self.example_doc_id
+        assert docs[0].metadata["score"] > 0
+
+        docs = self.vector.search_by_vector(self.example_embedding, document_ids_filter=[str(uuid.uuid4())])
+        assert len(docs) == 0
+
+    def search_by_full_text(self):
+        super().search_by_full_text()
+        docs = self.vector.search_by_full_text(get_example_text(), document_ids_filter=[self.example_doc_id])
+        assert len(docs) == 1
+        assert docs[0].metadata["doc_id"] == self.example_doc_id
+        assert not hasattr(docs[0], "score")
+
+        docs = self.vector.search_by_full_text(get_example_text(), document_ids_filter=[str(uuid.uuid4())])
+        assert len(docs) == 0
+
+    def run_all_tests(self):
+        try:
+            self.vector.delete()
+        except Exception:
+            pass
+
+        return super().run_all_tests()
+
 
 def test_tablestore_vector(setup_mock_redis):
     TableStoreVectorTest().run_all_tests()