fix：修复表达学习出错

src/chat/chat_loop/heartFC_chat.py

@@ -627,8 +627,9 @@ class HeartFChatting:
             # 设置了关闭标志位后被取消是正常流程
             logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
         except Exception:
-            logger.error(f"{self.log_prefix} 麦麦聊天意外错误，尝试重新启动")
+            logger.error(f"{self.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动")
             print(traceback.format_exc())
+            await asyncio.sleep(3)
             self._loop_task = asyncio.create_task(self._main_chat_loop())
         logger.error(f"{self.log_prefix} 结束了当前聊天循环")
 

src/chat/express/expression_learner.py

@@ -10,7 +10,7 @@ from src.common.logger import get_logger
 from src.common.database.database_model import Expression
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import model_config, global_config
-from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_random, build_anonymous_messages
+from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat_inclusive, build_anonymous_messages
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
 
@@ -146,8 +146,10 @@ class ExpressionLearner:
             return False
             
         # 检查消息数量（只检查指定聊天流的消息）
-        recent_messages = get_raw_msg_by_timestamp_random(
+        recent_messages = get_raw_msg_by_timestamp_with_chat_inclusive(
-            self.last_learning_time, current_time, limit=self.min_messages_for_learning + 1, chat_id=self.chat_id
+            chat_id=self.chat_id,
+            timestamp_start=self.last_learning_time,
+            timestamp_end=time.time(),
         )
         
         if not recent_messages or len(recent_messages) < self.min_messages_for_learning:
@@ -404,9 +406,11 @@ class ExpressionLearner:
         current_time = time.time()
         
         # 获取上次学习时间
-        last_time = self.last_learning_time.get(self.chat_id, current_time - 3600 * 24)
+        random_msg: Optional[List[Dict[str, Any]]] = get_raw_msg_by_timestamp_with_chat_inclusive(
-        random_msg: Optional[List[Dict[str, Any]]] = get_raw_msg_by_timestamp_random(
+            chat_id=self.chat_id,
-            last_time, current_time, limit=num, chat_id=self.chat_id
+            timestamp_start=self.last_learning_time,
+            timestamp_end=current_time,
+            limit=num,
         )
             
         # print(random_msg)

src/llm_models/utils_model.py

@@ -165,7 +165,7 @@ class LLMRequest:
         model_info, api_provider, client = self._select_model()
         
         # 请求并处理返回值
-        logger.info(f"LLM选择耗时: {model_info.name} {time.time() - start_time}")
+        logger.debug(f"LLM选择耗时: {model_info.name} {time.time() - start_time}")
         
         response = await self._execute_request(
             api_provider=api_provider,

src/mood/mood_manager.py

@@ -196,7 +196,7 @@ class MoodRegressionTask(AsyncTask):
         self.mood_manager = mood_manager
 
     async def run(self):
-        logger.debug("Running mood regression task...")
+        logger.debug("开始情绪回归任务...")
         now = time.time()
         for mood in self.mood_manager.mood_list:
             if mood.last_change_time == 0:
@@ -206,7 +206,7 @@ class MoodRegressionTask(AsyncTask):
                 if mood.regression_count >= 3:
                     continue
 
-                logger.info(f"{mood.log_prefix} 开始情绪回归, 这是第 {mood.regression_count + 1} 次")
+                logger.info(f"{mood.log_prefix} 开始情绪回归, 第 {mood.regression_count + 1} 次")
                 await mood.regress_mood()