refactor(chat): 重构消息管理器以使用集中式聊天流API

移除对context.chat_stream的直接依赖，改为通过get_chat_manager().get_stream()统一获取聊天流实例。这提高了模块独立性，符合"高内聚、低耦合"原则。

- 在MessageManager中统一使用chat_api获取stream实例
- 移除mood_manager中直接更新chat_stream的逻辑
- 在affinity_flow_chatter中统一处理兴趣度更新
- 消除直接属性访问带来的强耦合依赖

src/chat/message_manager/message_manager.py

@@ -314,8 +314,10 @@ class MessageManager:
         stream_count = 0
 
         for context in active_streams:
-            if hasattr(context, 'chat_stream') and context.chat_stream:
-                focus_energy = context.chat_stream.focus_energy
+            from src.plugin_system.apis.chat_api import get_chat_manager
+            chat_stream = get_chat_manager().get_stream(context.stream_id)            
+            if chat_stream:
+                focus_energy = chat_stream.focus_energy
                 total_focus_energy += focus_energy
                 max_focus_energy = max(max_focus_energy, focus_energy)
                 stream_count += 1
@@ -364,16 +366,18 @@ class MessageManager:
         """计算单个聊天流的分发周期 - 基于阈值感知的focus_energy"""
         if not global_config.chat.dynamic_distribution_enabled:
             return self.check_interval  # 使用固定间隔
-
+        
+        from src.plugin_system.apis.chat_api import get_chat_manager
+        chat_stream = get_chat_manager().get_stream(context.stream_id)      
         # 获取该流的focus_energy（新的阈值感知版本）
         focus_energy = 0.5  # 默认值
         avg_message_interest = 0.5  # 默认平均兴趣度
 
-        if hasattr(context, 'chat_stream') and context.chat_stream:
-            focus_energy = context.chat_stream.focus_energy
+        if chat_stream:
+            focus_energy = chat_stream.focus_energy
             # 获取平均消息兴趣度用于更精确的计算
-            if context.chat_stream.message_count > 0:
-                avg_message_interest = context.chat_stream.message_interest_total / context.chat_stream.message_count
+            if chat_stream.message_count > 0:
+                avg_message_interest = chat_stream.message_interest_total / chat_stream.message_count
 
         # 获取AFC阈值用于参考，添加None值检查
         reply_threshold = getattr(global_config.affinity_flow, 'reply_action_interest_threshold', 0.4)
@@ -492,7 +496,9 @@ class MessageManager:
 
                     # 如果没有处理任务，创建一个
                     if not context.processing_task or context.processing_task.done():
-                        focus_energy = context.chat_stream.focus_energy if hasattr(context, 'chat_stream') and context.chat_stream else 0.5
+                        from src.plugin_system.apis.chat_api import get_chat_manager
+                        chat_stream = get_chat_manager().get_stream(context.stream_id)      
+                        focus_energy = chat_stream.focus_energy if chat_stream else 0.5
 
                         # 根据优先级记录日志
                         if focus_energy >= 0.7:
@@ -533,9 +539,11 @@ class MessageManager:
                 continue
 
             # 获取focus_energy，如果不存在则使用默认值
+            from src.plugin_system.apis.chat_api import get_chat_manager
+            chat_stream = get_chat_manager().get_stream(context.stream_id)      
             focus_energy = 0.5
-            if hasattr(context, 'chat_stream') and context.chat_stream:
-                focus_energy = context.chat_stream.focus_energy
+            if chat_stream:
+                focus_energy = chat_stream.focus_energy
 
             # 计算流优先级分数
             priority_score = self._calculate_stream_priority(context, focus_energy)
@@ -574,6 +582,8 @@ class MessageManager:
 
     def _calculate_stream_priority(self, context: StreamContext, focus_energy: float) -> float:
         """计算聊天流的优先级分数"""
+        from src.plugin_system.apis.chat_api import get_chat_manager
+        chat_stream = get_chat_manager().get_stream(context.stream_id)      
         # 基础优先级：focus_energy
         base_priority = focus_energy
 
@@ -587,8 +597,8 @@ class MessageManager:
         time_penalty = max(0, 1.0 - time_since_active / 3600.0)  # 1小时内无惩罚
 
         # 连续无回复惩罚
-        if hasattr(context, 'chat_stream') and context.chat_stream:
-            consecutive_no_reply = context.chat_stream.consecutive_no_reply
+        if chat_stream:
+            consecutive_no_reply = chat_stream.consecutive_no_reply
             no_reply_penalty = max(0, 1.0 - consecutive_no_reply * 0.05)  # 每次无回复降低5%
         else:
             no_reply_penalty = 1.0

src/mood/mood_manager.py

@@ -149,12 +149,7 @@ class ChatMood:
         self.mood_state = response
 
         self.last_change_time = message_time
-
-        # 更新ChatStream的兴趣度数据
-        if hasattr(self, 'chat_stream') and self.chat_stream:
-            self.chat_stream.add_message_interest(interested_rate)
-            logger.debug(f"{self.log_prefix} 已更新ChatStream兴趣度，当前focus_energy: {self.chat_stream.focus_energy:.3f}")
-
+        
     async def regress_mood(self):
         message_time = time.time()
         message_list_before_now = get_raw_msg_by_timestamp_with_chat_inclusive(

src/plugins/built_in/affinity_flow_chatter/planner.py

@@ -125,17 +125,17 @@ class ChatterActionPlanner:
                         logger.info(f"兴趣度不足 ({latest_score.total_score:.2f})，移除回复")
                         reply_not_available = True
 
+                    # 更新ChatStream的兴趣度数据
+                    from src.plugin_system.apis.chat_api import get_chat_manager
+                    chat_stream = get_chat_manager().get_stream(self.chat_id)
+                    chat_stream.add_message_interest(score)
+                    logger.debug(f"已更新聊天 {self.chat_id} 的ChatStream兴趣度，分数: {score:.3f}")
+
                     # 更新情绪状态和ChatStream兴趣度数据
                     if latest_message and score > 0:
                         chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
                         await chat_mood.update_mood_by_message(latest_message, score)
                         logger.debug(f"已更新聊天 {self.chat_id} 的情绪状态，兴趣度: {score:.3f}")
-                    elif latest_message:
-                        # 即使不更新情绪状态，也要更新ChatStream的兴趣度数据
-                        chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
-                        if hasattr(chat_mood, 'chat_stream') and chat_mood.chat_stream:
-                            chat_mood.chat_stream.add_message_interest(score)
-                            logger.debug(f"已更新聊天 {self.chat_id} 的ChatStream兴趣度，分数: {score:.3f}")
 
             # base_threshold = self.interest_scoring.reply_threshold
             # 检查兴趣度是否达到非回复动作阈值