Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

src/chat/express/expression_selector.py

@@ -145,8 +145,10 @@ class ExpressionSelector:
 
         except Exception as e:
             logger.error(f"更新表达方式count失败: {e}")
-    
-    async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str, max_num: int = 10, min_num: int = 5) -> List[Dict[str, str]]:
+
+    async def select_suitable_expressions_llm(
+        self, chat_id: str, chat_info: str, max_num: int = 10, min_num: int = 5
+    ) -> List[Dict[str, str]]:
         """使用LLM选择适合的表达方式"""
 
         # 1. 获取35个随机表达方式（现在按权重抽取）
@@ -195,7 +197,6 @@ class ExpressionSelector:
             max_num=max_num,
         )
 
-
         # 4. 调用LLM
         try:
             content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt)

src/chat/focus_chat/heartflow_message_processor.py

@@ -181,7 +181,9 @@ class HeartFCMessageReceiver:
             mes_name = chat.group_info.group_name if chat.group_info else "私聊"
             # current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
             current_talk_frequency = global_config.chat.get_current_talk_frequency(chat.stream_id)
-            logger.info(f"[{mes_name}]{userinfo.user_nickname}:{message.processed_plain_text}[当前回复频率: {current_talk_frequency}]")
+            logger.info(
+                f"[{mes_name}]{userinfo.user_nickname}:{message.processed_plain_text}[当前回复频率: {current_talk_frequency}]"
+            )
 
             # 8. 关系处理
             if global_config.relationship.enable_relationship:

src/chat/focus_chat/info_processors/expression_selector_processor.py

@@ -71,7 +71,9 @@ class ExpressionSelectorProcessor(BaseProcessor):
 
         try:
             # LLM模式：调用LLM选择5-10个，然后随机选5个
-            selected_expressions = await expression_selector.select_suitable_expressions_llm(self.subheartflow_id, chat_info, max_num=12, min_num=2)
+            selected_expressions = await expression_selector.select_suitable_expressions_llm(
+                self.subheartflow_id, chat_info, max_num=12, min_num=2
+            )
             cache_size = len(selected_expressions) if selected_expressions else 0
             mode_desc = f"LLM模式（已缓存{cache_size}个）"
 

src/chat/focus_chat/info_processors/relationship_processor.py

@@ -292,8 +292,7 @@ class PersonImpressionpProcessor(BaseProcessor):
                 "message_count": self._count_messages_in_timerange(potential_start_time, message_time),
             }
             segments.append(new_segment)
-            
-            
+
             person_name = get_person_info_manager().get_value(person_id, "person_name")
             logger.info(
                 f"{self.log_prefix} 眼熟用户 {person_name} 在 {time.strftime('%H:%M:%S', time.localtime(potential_start_time))} - {time.strftime('%H:%M:%S', time.localtime(message_time))} 之间有 {new_segment['message_count']} 条消息"

src/chat/normal_chat/normal_chat.py

@@ -1000,7 +1000,9 @@ class NormalChat:
         """
         # --- 1. 定义参数 ---
         evaluation_minutes = 10.0
-        target_replies_per_min = global_config.chat.get_current_talk_frequency(self.stream_id)  # 目标频率：e.g. 1条/分钟
+        target_replies_per_min = global_config.chat.get_current_talk_frequency(
+            self.stream_id
+        )  # 目标频率：e.g. 1条/分钟
         target_replies_in_window = target_replies_per_min * evaluation_minutes  # 10分钟内的目标回复数
 
         if target_replies_in_window <= 0:

src/chat/normal_chat/normal_prompt.py

@@ -162,8 +162,10 @@ class PromptBuilder:
             read_mark=0.0,
             show_actions=True,
         )
-        
-        expressions = await expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half, max_num=8, min_num=3)
+
+        expressions = await expression_selector.select_suitable_expressions_llm(
+            chat_stream.stream_id, chat_talking_prompt_half, max_num=8, min_num=3
+        )
         style_habbits = []
         grammar_habbits = []
         if expressions: