Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox_Bot into dev

src/chat/replyer/default_generator.py

@@ -1468,18 +1468,15 @@ class DefaultReplyer:
                         if now < start_time:
                             now += timedelta(days=1)
 
-                        if start_time <= now < end_time:
+                        duration_minutes = (now - start_time).total_seconds() / 60
-                            duration_minutes = (now - start_time).total_seconds() / 60
+                        remaining_minutes = (end_time - now).total_seconds() / 60
-                            remaining_minutes = (end_time - now).total_seconds() / 60
+                        schedule_block = (
-                            schedule_block = (
                                 f"你当前正在进行“{activity}”，"
                                 f"计划时间从{start_time.strftime('%H:%M')}到{end_time.strftime('%H:%M')}。"
                                 f"这项活动已经开始了{duration_minutes:.0f}分钟，"
                                 f"预计还有{remaining_minutes:.0f}分钟结束。"
                                 "（此为你的当前状态，仅供参考。除非被直接询问，否则不要在对话中主动提及。）"
                             )
-                        else:
-                            schedule_block = f"你当前正在进行“{activity}”。(此为你的当前状态，仅供参考。除非被直接询问，否则不要在对话中主动提及。)"
 
                     except (ValueError, AttributeError):
                         schedule_block = f"你当前正在进行“{activity}”。(此为你的当前状态，仅供参考。除非被直接询问，否则不要在对话中主动提及。)"

src/llm_models/model_client/openai_client.py

@@ -350,10 +350,10 @@ def _default_normal_response_parser(
         api_response.tool_calls = []
         for call in message_part.tool_calls:
             try:
-                arguments = orjson.loads(repair_json(call.function.arguments))
+                arguments = orjson.loads(repair_json(call.function.arguments)) # type: ignore
                 if not isinstance(arguments, dict):
                     raise RespParseException(resp, "响应解析失败，工具调用参数无法解析为字典类型")
-                api_response.tool_calls.append(ToolCall(call.id, call.function.name, arguments))
+                api_response.tool_calls.append(ToolCall(call.id, call.function.name, arguments)) # type: ignore
             except orjson.JSONDecodeError as e:
                 raise RespParseException(resp, "响应解析失败，无法解析工具调用参数") from e
 

src/main.py

@@ -218,14 +218,6 @@ class MainSystem:
 
         cleanup_tasks = []
 
-        # 停止数据库服务
-        try:
-            from src.common.database.core import close_engine as stop_database
-
-            cleanup_tasks.append(("数据库服务", stop_database()))
-        except Exception as e:
-            logger.error(f"准备停止数据库服务时出错: {e}")
-
         # 停止消息批处理器
         try:
             from src.chat.message_receive.storage import get_message_storage_batcher, get_message_update_batcher
@@ -329,6 +321,18 @@ class MainSystem:
         else:
             logger.warning("没有需要清理的任务")
 
+        # 停止数据库服务 (在所有其他任务完成后最后停止)
+        try:
+            from src.common.database.core import close_engine as stop_database
+
+            logger.info("正在停止数据库服务...")
+            await asyncio.wait_for(stop_database(), timeout=15.0)
+            logger.info("🛑 数据库服务已停止")
+        except asyncio.TimeoutError:
+            logger.error("停止数据库服务超时")
+        except Exception as e:
+            logger.error(f"停止数据库服务时出错: {e}")
+
     def _cleanup(self) -> None:
         """同步清理资源（向后兼容）"""
         try:

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.3.6"
+version = "1.3.7"
 
 # 配置文件版本号迭代规则同bot_config.toml
 
@@ -53,8 +53,8 @@ price_out = 8.0                    # 输出价格（用于API调用统计，单
 #use_anti_truncation = true        # [可选] 启用反截断功能。当模型输出不完整时，系统会自动重试。建议只为有需要的模型（如Gemini）开启。
 
 [[models]]
-model_identifier = "deepseek-ai/DeepSeek-V3.1-Terminus"
+model_identifier = "deepseek-ai/DeepSeek-V3.2-Exp"
-name = "siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"
+name = "siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"
 api_provider = "SiliconFlow"
 price_in = 2.0
 price_out = 8.0
@@ -122,7 +122,7 @@ price_in = 4.0
 price_out = 16.0
 
 [model_task_config.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，是麦麦必须的模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"] # 使用的模型列表，每个子项对应上面的模型名称(name)
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"] # 使用的模型列表，每个子项对应上面的模型名称(name)
 temperature = 0.2                        # 模型温度，新V3建议0.1-0.3
 max_tokens = 800                         # 最大输出token数
 #concurrency_count = 2                   # 并发请求数量，默认为1（不并发），设置为2或更高启用并发
@@ -133,28 +133,28 @@ temperature = 0.7
 max_tokens = 800
 
 [model_task_config.replyer] # 首要回复模型，还用于表达器和表达方式学习
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.2                        # 模型温度，新V3建议0.1-0.3
 max_tokens = 800
 
 [model_task_config.planner] #决策：负责决定麦麦该做什么的模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.3
 max_tokens = 800
 
 
 [model_task_config.emotion] #负责麦麦的情绪变化
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.3
 max_tokens = 800
 
 [model_task_config.mood] #负责麦麦的心情变化
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.3
 max_tokens = 800
 
 [model_task_config.maizone] # maizone模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.7
 max_tokens = 800
 
@@ -181,22 +181,22 @@ temperature = 0.7
 max_tokens = 800
 
 [model_task_config.schedule_generator]#日程表生成模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.7
 max_tokens = 1000
 
 [model_task_config.anti_injection] # 反注入检测专用模型
-model_list = ["moonshotai-Kimi-K2-Instruct"]           # 使用快速的小模型进行检测
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]           # 使用快速的小模型进行检测
 temperature = 0.1                   # 低温度确保检测结果稳定
 max_tokens = 200                    # 检测结果不需要太长的输出
 
 [model_task_config.monthly_plan_generator] # 月层计划生成模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.7
 max_tokens = 1000
 
 [model_task_config.relationship_tracker] # 用户关系追踪模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.7
 max_tokens = 1000
 
@@ -210,12 +210,12 @@ embedding_dimension = 1024
 #------------LPMM知识库模型------------
 
 [model_task_config.lpmm_entity_extract] # 实体提取模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.2
 max_tokens = 800
 
 [model_task_config.lpmm_rdf_build] # RDF构建模型
-model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.1-Terminus"]
+model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
 temperature = 0.2
 max_tokens = 800