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

src/plugins/chat_module/reasoning_chat/reasoning_chat.py

@@ -37,7 +37,8 @@ class ReasoningChat:
         self.mood_manager = MoodManager.get_instance()
         self.mood_manager.start_mood_update()
 
-    async def _create_thinking_message(self, message, chat, userinfo, messageinfo):
+    @staticmethod
+    async def _create_thinking_message(message, chat, userinfo, messageinfo):
         """创建思考消息"""
         bot_user_info = UserInfo(
             user_id=global_config.BOT_QQ,
@@ -59,7 +60,8 @@ class ReasoningChat:
 
         return thinking_id
 
-    async def _send_response_messages(self, message, chat, response_set: List[str], thinking_id) -> MessageSending:
+    @staticmethod
+    async def _send_response_messages(message, chat, response_set: List[str], thinking_id) -> MessageSending:
         """发送回复消息"""
         container = message_manager.get_container(chat.stream_id)
         thinking_message = None
@@ -104,7 +106,8 @@ class ReasoningChat:
 
         return first_bot_msg
 
-    async def _handle_emoji(self, message, chat, response):
+    @staticmethod
+    async def _handle_emoji(message, chat, response):
         """处理表情包"""
         if random() < global_config.emoji_chance:
             emoji_raw = await emoji_manager.get_emoji_for_text(response)
@@ -192,21 +195,21 @@ class ReasoningChat:
         if not buffer_result:
             await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
             willing_manager.delete(message.message_info.message_id)
-            F_type = "seglist"
+            f_type = "seglist"
             if message.message_segment.type != "seglist":
-                F_type = message.message_segment.type
+                f_type = message.message_segment.type
             else:
                 if (
                     isinstance(message.message_segment.data, list)
                     and all(isinstance(x, Seg) for x in message.message_segment.data)
                     and len(message.message_segment.data) == 1
                 ):
-                    F_type = message.message_segment.data[0].type
-            if F_type == "text":
+                    f_type = message.message_segment.data[0].type
+            if f_type == "text":
                 logger.info(f"触发缓冲，已炸飞消息：{message.processed_plain_text}")
-            elif F_type == "image":
+            elif f_type == "image":
                 logger.info("触发缓冲，已炸飞表情包/图片")
-            elif F_type == "seglist":
+            elif f_type == "seglist":
                 logger.info("触发缓冲，已炸飞消息列")
             return
 
@@ -291,7 +294,8 @@ class ReasoningChat:
         # 意愿管理器：注销当前message信息
         willing_manager.delete(message.message_info.message_id)
 
-    def _check_ban_words(self, text: str, chat, userinfo) -> bool:
+    @staticmethod
+    def _check_ban_words(text: str, chat, userinfo) -> bool:
         """检查消息中是否包含过滤词"""
         for word in global_config.ban_words:
             if word in text:
@@ -302,7 +306,8 @@ class ReasoningChat:
                 return True
         return False
 
-    def _check_ban_regex(self, text: str, chat, userinfo) -> bool:
+    @staticmethod
+    def _check_ban_regex(text: str, chat, userinfo) -> bool:
         """检查消息是否匹配过滤正则表达式"""
         for pattern in global_config.ban_msgs_regex:
             if pattern.search(text):

src/plugins/chat_module/reasoning_chat/reasoning_generator.py

@@ -69,8 +69,6 @@ class ResponseGenerator:
             return None
 
     async def _generate_response_with_model(self, message: MessageThinking, model: LLMRequest, thinking_id: str):
-        sender_name = ""
-
         info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
 
         if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
@@ -188,7 +186,8 @@ class ResponseGenerator:
             logger.debug(f"获取情感标签时出错: {e}")
             return "中立", "平静"  # 出错时返回默认值
 
-    async def _process_response(self, content: str) -> Tuple[List[str], List[str]]:
+    @staticmethod
+    async def _process_response(content: str) -> Tuple[List[str], List[str]]:
         """处理响应内容，返回处理后的内容和情感标签"""
         if not content:
             return None, []

src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py

@@ -101,16 +101,14 @@ class PromptBuilder:
         related_memory = await HippocampusManager.get_instance().get_memory_from_text(
             text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
         )
+        related_memory_info = ""
         if related_memory:
-            related_memory_info = ""
             for memory in related_memory:
                 related_memory_info += memory[1]
             # memory_prompt = f"你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
             memory_prompt = await global_prompt_manager.format_prompt(
                 "memory_prompt", related_memory_info=related_memory_info
             )
-        else:
-            related_memory_info = ""
 
         # print(f"相关记忆：{related_memory_info}")
 
@@ -162,7 +160,6 @@ class PromptBuilder:
 
         # 知识构建
         start_time = time.time()
-        prompt_info = ""
         prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
         if prompt_info:
             # prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
@@ -373,8 +370,9 @@ class PromptBuilder:
         logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
         return related_info
 
+    @staticmethod
     def get_info_from_db(
-        self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
+        query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
     ) -> Union[str, list]:
         if not query_embedding:
             return "" if not return_raw else []