diff --git a/api/core/rag/datasource/vdb/tencent/tencent_vector.py b/api/core/rag/datasource/vdb/tencent/tencent_vector.py
index c89748b419..9b1228d9b2 100644
--- a/api/core/rag/datasource/vdb/tencent/tencent_vector.py
+++ b/api/core/rag/datasource/vdb/tencent/tencent_vector.py
@@ -67,7 +67,11 @@ class TencentVector(BaseVector):
     def _create_collection(self, dimension: int) -> None:
         lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            self.delete()
+            collections = self._db.list_collections()
+            for collection in collections:
+                if collection.collection_name == self._collection_name:
+                    self.collection = collection
+                    return
             index_type = None
             for k, v in enum.IndexType.__members__.items():
                 if k == self._client_config.index_type:
@@ -153,12 +157,13 @@ class TencentVector(BaseVector):
                                                                 limit=kwargs.get('top_k', 4),
                                                                 timeout=self._client_config.timeout,
                                                                 )
-        return self._get_search_res(res)
+        score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+        return self._get_search_res(res, score_threshold)
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         return []
 
-    def _get_search_res(self, res):
+    def _get_search_res(self, res, score_threshold):
         docs = []
         if res is None or len(res) == 0:
             return docs
@@ -167,8 +172,11 @@ class TencentVector(BaseVector):
             meta = result.get(self.field_metadata)
             if meta is not None:
                 meta = json.loads(meta)
-            doc = Document(page_content=result.get(self.field_text), metadata=meta)
-            docs.append(doc)
+            score = 1 - result.get("score")
+            if score > score_threshold:
+                meta['score'] = score
+                doc = Document(page_content=result.get(self.field_text), metadata=meta)
+                docs.append(doc)
         return docs
 
     def delete(self) -> None: