diff --git a/src/llm_models/utils.py b/src/llm_models/utils.py
index bf23f144a..f08ce2092 100644
--- a/src/llm_models/utils.py
+++ b/src/llm_models/utils.py
@@ -145,9 +145,9 @@ class LLMUsageRecorder:
     LLM使用情况记录器（SQLAlchemy版本）
     """
 
-    @staticmethod
     async def record_usage_to_database(
-            model_info: ModelInfo,
+        self,
+        model_info: ModelInfo,
         model_usage: UsageRecord,
         user_id: str,
         request_type: str,
diff --git a/src/llm_models/utils_model.py b/src/llm_models/utils_model.py
index 3ea94c1e1..cf2a7cb1c 100644
--- a/src/llm_models/utils_model.py
+++ b/src/llm_models/utils_model.py
@@ -891,7 +891,7 @@ class LLMRequest:
             max_tokens=self.model_for_task.max_tokens if max_tokens is None else max_tokens,
         )
 
-        self._record_usage(model_info, response.usage, time.time() - start_time, "/chat/completions")
+        await self._record_usage(model_info, response.usage, time.time() - start_time, "/chat/completions")
 
         if not response.content and not response.tool_calls:
             if raise_when_empty:
@@ -916,14 +916,14 @@ class LLMRequest:
             embedding_input=embedding_input
         )
         
-        self._record_usage(model_info, response.usage, time.time() - start_time, "/embeddings")
+        await self._record_usage(model_info, response.usage, time.time() - start_time, "/embeddings")
         
         if not response.embedding:
             raise RuntimeError("获取embedding失败")
         
         return response.embedding, model_info.name
 
-    def _record_usage(self, model_info: ModelInfo, usage: Optional[UsageRecord], time_cost: float, endpoint: str):
+    async def _record_usage(self, model_info: ModelInfo, usage: Optional[UsageRecord], time_cost: float, endpoint: str):
         """
         记录模型使用情况。