diff --git a/pyproject.toml b/pyproject.toml
index 6706a3d4e..2baeb16e3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,8 +39,9 @@ dependencies = [
     "environs",
     "pydantic<v2",
     "scikit-learn",
-    "pymilvus", # with pandas, numpy, ujson
+    "pymilvus", # with pandas, numpy
     "hdrhistogram>=0.10.1",
+    "ujson",
 ]
 dynamic = ["version"]
 
diff --git a/vectordb_bench/backend/clients/polardb/polardb.py b/vectordb_bench/backend/clients/polardb/polardb.py
index f42b6fca5..d53638b0e 100644
--- a/vectordb_bench/backend/clients/polardb/polardb.py
+++ b/vectordb_bench/backend/clients/polardb/polardb.py
@@ -109,14 +109,14 @@ def init(self):
         db_name = self.db_config["database"]
         hint = "/*+ SET_VAR(imci_enable_fast_vector_search=on) */"
 
-        self.insert_sql = f"INSERT INTO {db_name}.{self.table_name} (id, v) VALUES (%s, _binary %s)"  # noqa: S608
+        self.insert_sql = f"INSERT INTO {db_name}.{self.table_name} (id, v) VALUES (%s, _binary %s)"
         self.select_sql = (
-            f"SELECT {hint} id FROM {db_name}.{self.table_name} "  # noqa: S608
+            f"SELECT {hint} id FROM {db_name}.{self.table_name} "
             f"ORDER BY DISTANCE(v, _binary %s, '{metric_type}') "
             f"LIMIT %s"
         )
         self.select_sql_with_filter = (
-            f"SELECT id FROM {db_name}.{self.table_name} "  # noqa: S608
+            f"SELECT id FROM {db_name}.{self.table_name} "
             f"WHERE id >= %s "
             f"ORDER BY DISTANCE(v, _binary %s, '{metric_type}') "
             f"LIMIT %s"
@@ -218,7 +218,7 @@ def _insert_batch(self, embeddings: list[list[float]], metadata: list[int], offs
         conn, cursor = self._create_connection()
         try:
             db_name = self.db_config["database"]
-            insert_sql = f"INSERT INTO {db_name}.{self.table_name} (id, v) VALUES (%s, _binary %s)"  # noqa: S608
+            insert_sql = f"INSERT INTO {db_name}.{self.table_name} (id, v) VALUES (%s, _binary %s)"
             batch_data = []
             for i in range(offset, offset + size):
                 batch_data.append((int(metadata[i]), self.vector_to_hex(embeddings[i])))