feat: llm统计现已记录模型反应时间

src/llm_models/utils.py

@@ -155,7 +155,7 @@ class LLMUsageRecorder:
             logger.error(f"创建 LLMUsage 表失败: {str(e)}")
 
     def record_usage_to_database(
-        self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str
+        self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str, time_cost: float = 0.0
     ):
         input_cost = (model_usage.prompt_tokens / 1000000) * model_info.price_in
         output_cost = (model_usage.completion_tokens / 1000000) * model_info.price_out
@@ -164,6 +164,8 @@ class LLMUsageRecorder:
             # 使用 Peewee 模型创建记录
             LLMUsage.create(
                 model_name=model_info.model_identifier,
+                model_assign_name=model_info.name,
+                model_api_provider=model_info.api_provider,
                 user_id=user_id,
                 request_type=request_type,
                 endpoint=endpoint,
@@ -171,6 +173,7 @@ class LLMUsageRecorder:
                 completion_tokens=model_usage.completion_tokens or 0,
                 total_tokens=model_usage.total_tokens or 0,
                 cost=total_cost or 0.0,
+                time_cost = round(time_cost or 0.0, 3),
                 status="success",
                 timestamp=datetime.now(),  # Peewee 会处理 DateTimeField
             )