🤖 自动格式化代码 [skip ci]

src/chat/focus_chat/planners/actions/emoji_action.py

@@ -23,11 +23,7 @@ class EmojiAction(BaseAction):
     action_parameters: dict[str:str] = {
         "description": "文字描述你想要发送的表情",
     }
-    action_require: list[str] = [
+    action_require: list[str] = ["你想要发送一个表情", "表达情绪时可以选择使用", "一般在你回复之后可以选择性使用"]
-        "你想要发送一个表情",
-        "表达情绪时可以选择使用",
-        "一般在你回复之后可以选择性使用"
-    ]
 
     associated_types: list[str] = ["emoji"]
 
@@ -108,7 +104,7 @@ class EmojiAction(BaseAction):
         #     )
         # else:
         #     anchor_message.update_chat_stream(self.chat_stream)
-        
+
         logger.info(f"{self.log_prefix} 为了表情包创建占位符")
         anchor_message = await create_empty_anchor_message(
             self.chat_stream.platform, self.chat_stream.group_info, self.chat_stream

src/chat/focus_chat/planners/planner_simple.py

@@ -58,7 +58,6 @@ def init_prompt():
 """,
         "simple_planner_prompt",
     )
-    
 
     Prompt(
         """
@@ -192,7 +191,6 @@ class ActionPlanner(BasePlanner):
                 reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}"
                 action = "no_reply"
 
-
             if llm_content:
                 try:
                     fixed_json_string = repair_json(llm_content)
@@ -233,9 +231,7 @@ class ActionPlanner(BasePlanner):
                         reasoning = extracted_reasoning
 
                 except Exception as json_e:
-                    logger.warning(
+                    logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
-                        f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'"
-                    )
                     traceback.print_exc()
                     reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
                     action = "no_reply"
@@ -353,7 +349,7 @@ class ActionPlanner(BasePlanner):
 
             # moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
             moderation_prompt_block = ""
-            
+
             # 获取当前时间
             time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
 

src/chat/focus_chat/replyer/default_replyer.py

@@ -75,7 +75,7 @@ def init_prompt():
 不要浮夸，不要夸张修辞，只输出一条回复就好。
 现在，你说：
 """,
-        "default_replyer_private_prompt", 
+        "default_replyer_private_prompt",
     )
 
 
@@ -181,7 +181,7 @@ class DefaultReplyer:
             return False, None
 
         # --- 回复器 (Replier) 的定义 --- #
-        
+
     async def deal_emoji(
         self,
         anchor_message: MessageRecv,
@@ -192,10 +192,9 @@ class DefaultReplyer:
         """
         表情动作处理类
         """
-        
+
         await self._create_thinking_message(anchor_message, thinking_id)
-        
+
-        
         try:
             has_sent_something = False
             sent_msg_list = []
@@ -208,8 +207,7 @@ class DefaultReplyer:
                     reply.append(("emoji", emoji_base64))
                 else:
                     logger.warning(f"{self.log_prefix} 没有找到合适表情")
-                    
+
-                
             if reply:
                 with Timer("发送表情", cycle_timers):
                     sent_msg_list = await self.send_response_messages(
@@ -230,8 +228,6 @@ class DefaultReplyer:
             logger.error(f"回复失败: {e}")
             traceback.print_exc()
             return False, None
-    
-    
 
     async def reply(
         self,
@@ -293,7 +289,7 @@ class DefaultReplyer:
                     # TODO: API-Adapter修改标记
                     # logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
                     content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt)
-                    
+
                     logger.debug(f"prompt: {prompt}")
                     logger.info(f"最终回复: {content}")
 
@@ -384,8 +380,8 @@ class DefaultReplyer:
 
         style_habbits_str = "\n".join(style_habbits)
         grammar_habbits_str = "\n".join(grammar_habbits)
-        
+
-                # 关键词检测与反应
+        # 关键词检测与反应
         keywords_reaction_prompt = ""
         try:
             # 处理关键词规则
@@ -393,7 +389,7 @@ class DefaultReplyer:
                 if any(keyword in target_message for keyword in rule.keywords):
                     logger.info(f"检测到关键词规则：{rule.keywords}，触发反应：{rule.reaction}")
                     keywords_reaction_prompt += f"{rule.reaction}，"
-            
+
             # 处理正则表达式规则
             for rule in global_config.keyword_reaction.regex_rules:
                 for pattern_str in rule.regex:
@@ -411,7 +407,7 @@ class DefaultReplyer:
                         continue
         except Exception as e:
             logger.error(f"关键词检测与反应时发生异常: {str(e)}", exc_info=True)
-        
+
         time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
 
         # logger.debug("开始构建 focus prompt")

src/chat/focus_chat/working_memory/memory_manager.py

@@ -510,11 +510,15 @@ class MemoryManager:
         # 如果有摘要信息，添加到提示中
         if summary1:
             prompt += f"记忆1主题：{summary1['brief']}\n"
-            prompt += "记忆1关键要点：\n" + "\n".join([f"- {point}" for point in summary1.get("key_points", [])]) + "\n\n"
+            prompt += (
+                "记忆1关键要点：\n" + "\n".join([f"- {point}" for point in summary1.get("key_points", [])]) + "\n\n"
+            )
 
         if summary2:
             prompt += f"记忆2主题：{summary2['brief']}\n"
-            prompt += "记忆2关键要点：\n" + "\n".join([f"- {point}" for point in summary2.get("key_points", [])]) + "\n\n"
+            prompt += (
+                "记忆2关键要点：\n" + "\n".join([f"- {point}" for point in summary2.get("key_points", [])]) + "\n\n"
+            )
 
         # 添加记忆原始内容
         prompt += f"""

src/chat/utils/utils.py

@@ -636,4 +636,4 @@ def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal"
             return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) + ":\n"
     else:  # mode = "lite" or unknown
         # 只返回时分秒格式，喵~
-        return time.strftime("%H:%M:%S", time.localtime(timestamp))
+        return time.strftime("%H:%M:%S", time.localtime(timestamp))

src/chat/utils/utils_image.py

@@ -184,9 +184,7 @@ class ImageManager:
                 return f"[图片：{cached_description}]"
 
             # 调用AI获取描述
-            prompt = (
+            prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来，请留意其主题，直观感受，以及是否有擦边色情内容。最多100个字。"
-                "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来，请留意其主题，直观感受，以及是否有擦边色情内容。最多100个字。"
-            )
             description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
 
             if description is None:

src/common/remote.py

@@ -90,7 +90,9 @@ class TelemetryHeartBeatTask(AsyncTask):
                 else:
                     logger.error("无效的服务端响应")
             else:
-                logger.error(f"请求UUID失败，不过你还是可以正常使用麦麦，状态码: {response.status_code}, 响应内容: {response.text}")
+                logger.error(
+                    f"请求UUID失败，不过你还是可以正常使用麦麦，状态码: {response.status_code}, 响应内容: {response.text}"
+                )
 
             # 请求失败，重试次数+1
             try_count += 1

src/config/config_base.py

@@ -79,7 +79,11 @@ class ConfigBase:
 
             if field_origin_type is list:
                 # 如果列表元素类型是ConfigBase的子类，则对每个元素调用from_dict
-                if field_type_args and isinstance(field_type_args[0], type) and issubclass(field_type_args[0], ConfigBase):
+                if (
+                    field_type_args
+                    and isinstance(field_type_args[0], type)
+                    and issubclass(field_type_args[0], ConfigBase)
+                ):
                     return [field_type_args[0].from_dict(item) for item in value]
                 return [cls._convert_field(item, field_type_args[0]) for item in value]
             elif field_origin_type is set: