diff --git a/gptcache/manager/vector_data/manager.py b/gptcache/manager/vector_data/manager.py
index 815fb934..fe21b6f9 100644
--- a/gptcache/manager/vector_data/manager.py
+++ b/gptcache/manager/vector_data/manager.py
@@ -76,6 +76,8 @@ class VectorBase:
     :type local_mode: bool
     :param local_data: required when local_mode is True.
     :type local_data: str
+    :param use_partition_key: if true, use partition key feature in milvus.
+    :type use_partition_key: bool
 
     :param url: the connection url for PostgreSQL database, defaults to 'postgresql://postgres@localhost:5432/postgres'
     :type url: str
@@ -125,6 +127,7 @@ def get(name, **kwargs):
             search_params = kwargs.get("search_params", None)
             local_mode = kwargs.get("local_mode", False)
             local_data = kwargs.get("local_data", "./milvus_data")
+            use_partition_key = kwargs.get("use_partition_key", False)
             vector_base = Milvus(
                 host=host,
                 port=port,
@@ -138,6 +141,7 @@ def get(name, **kwargs):
                 search_params=search_params,
                 local_mode=local_mode,
                 local_data=local_data,
+                use_partition_key=use_partition_key
             )
         elif name == "faiss":
             from gptcache.manager.vector_data.faiss import Faiss
diff --git a/gptcache/manager/vector_data/milvus.py b/gptcache/manager/vector_data/milvus.py
index 9f286b3b..d69d974b 100644
--- a/gptcache/manager/vector_data/milvus.py
+++ b/gptcache/manager/vector_data/milvus.py
@@ -74,7 +74,8 @@ def __init__(
         index_params: dict = None,
         search_params: dict = None,
         local_mode: bool = False,
-        local_data: str = "./milvus_data"
+        local_data: str = "./milvus_data",
+        use_partition_key: bool = False
     ):
         if dimension <= 0:
             raise ValueError(
@@ -85,6 +86,7 @@ def __init__(
         self.dimension = dimension
         self.top_k = top_k
         self.index_params = index_params
+        self.use_partition_key = use_partition_key
         if self._local_mode:
             self._create_local(port, local_data)
         self._connect(host, port, user, password, secure)
@@ -131,16 +133,19 @@ def _create_collection(self, collection_name):
                     is_primary=True,
                     auto_id=False,
                 ),
-                FieldSchema(
-                    name="partition_key",
-                    dtype=DataType.VARCHAR,
-                    max_length=256,
-                    is_partition_key=True,
-                ),
                 FieldSchema(
                     name="embedding", dtype=DataType.FLOAT_VECTOR, dim=self.dimension
                 ),
             ]
+            if self.use_partition_key:
+                schema.append(
+                    FieldSchema(
+                        name="partition_key",
+                        dtype=DataType.VARCHAR,
+                        max_length=256,
+                        is_partition_key=True,
+                    )
+                )
             schema = CollectionSchema(schema)
             self.col = Collection(
                 collection_name,
@@ -170,8 +175,11 @@ def _create_collection(self, collection_name):
         self.col.load()
 
     def mul_add(self, datas: List[VectorData], **kwargs):
-        partition_key = kwargs.get("partition_key", "")
-        self.col.insert([{"id": data.id, "embedding": np.array(data.data).astype("float32"), "partition_key": partition_key} for data in datas])
+        if self.use_partition_key:
+            partition_key = kwargs.get("partition_key") or "default"
+            self.col.insert([{"id": data.id, "embedding": np.array(data.data).astype("float32"), "partition_key": partition_key} for data in datas])
+        else:
+            self.col.insert([{"id": data.id, "embedding": np.array(data.data).astype("float32")} for data in datas])
 
     def search(self, data: np.ndarray, top_k: int = -1, **kwargs):
         if top_k == -1:
@@ -182,7 +190,7 @@ def search(self, data: np.ndarray, top_k: int = -1, **kwargs):
             anns_field="embedding",
             param=self.search_params,
             limit=top_k,
-            expr=f'partition_key=="{partition_key}"' if partition_key else None,
+            expr=f'partition_key=="{partition_key}"' if (self.use_partition_key and partition_key) else None,
         )
         return list(zip(search_result[0].distances, search_result[0].ids))