ruff

src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py

@@ -122,7 +122,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                 + relationship_score * self.score_weights["relationship"]
                 + mentioned_score * self.score_weights["mentioned"]
             )
-            
+
             # 限制总分上限为1.0，确保分数在合理范围内
             total_score = min(raw_total_score, 1.0)
 
@@ -131,7 +131,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                 f"{relationship_score:.3f}*{self.score_weights['relationship']} + "
                 f"{mentioned_score:.3f}*{self.score_weights['mentioned']} = {raw_total_score:.3f}"
             )
-            
+
             if raw_total_score > 1.0:
                 logger.debug(f"[Affinity兴趣计算] 原始分数 {raw_total_score:.3f} 超过1.0，已限制为 {total_score:.3f}")
 
@@ -217,7 +217,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                 return 0.0
 
         except asyncio.TimeoutError:
-            logger.warning(f"⏱️ 兴趣匹配计算超时(>1.5秒)，返回默认分值0.5以保留其他分数")
+            logger.warning("⏱️ 兴趣匹配计算超时(>1.5秒)，返回默认分值0.5以保留其他分数")
             return 0.5  # 超时时返回默认分值，避免丢失提及分和关系分
         except Exception as e:
             logger.warning(f"智能兴趣匹配失败: {e}")
@@ -251,19 +251,19 @@ class AffinityInterestCalculator(BaseInterestCalculator):
 
     def _calculate_mentioned_score(self, message: "DatabaseMessages", bot_nickname: str) -> float:
         """计算提及分 - 区分强提及和弱提及
-        
+
         强提及（被@、被回复、私聊）: 使用 strong_mention_interest_score
         弱提及（文本匹配名字/别名）: 使用 weak_mention_interest_score
         """
         from src.chat.utils.utils import is_mentioned_bot_in_message
-        
+
         # 使用统一的提及检测函数
         is_mentioned, mention_type = is_mentioned_bot_in_message(message)
-        
+
         if not is_mentioned:
             logger.debug("[提及分计算] 未提及机器人，返回0.0")
             return 0.0
-        
+
         # mention_type: 0=未提及, 1=弱提及, 2=强提及
         if mention_type >= 2:
             # 强提及：被@、被回复、私聊
@@ -281,22 +281,22 @@ class AffinityInterestCalculator(BaseInterestCalculator):
 
     def _apply_no_reply_threshold_adjustment(self) -> tuple[float, float]:
         """应用阈值调整（包括连续不回复和回复后降低机制）
-        
+
         Returns:
             tuple[float, float]: (调整后的回复阈值, 调整后的动作阈值)
         """
         # 基础阈值
         base_reply_threshold = self.reply_threshold
         base_action_threshold = global_config.affinity_flow.non_reply_action_interest_threshold
-        
+
         total_reduction = 0.0
-        
+
         # 1. 连续不回复的阈值降低
         if self.no_reply_count > 0 and self.no_reply_count < self.max_no_reply_count:
             no_reply_reduction = self.no_reply_count * self.probability_boost_per_no_reply
             total_reduction += no_reply_reduction
             logger.debug(f"[阈值调整] 连续不回复降低: {no_reply_reduction:.3f} (计数: {self.no_reply_count})")
-        
+
         # 2. 回复后的阈值降低（使bot更容易连续对话）
         if self.enable_post_reply_boost and self.post_reply_boost_remaining > 0:
             # 计算衰减后的降低值
@@ -309,16 +309,16 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                 f"[阈值调整] 回复后降低: {post_reply_reduction:.3f} "
                 f"(剩余次数: {self.post_reply_boost_remaining}, 衰减: {decay_factor:.2f})"
             )
-        
+
         # 应用总降低量
         adjusted_reply_threshold = max(0.0, base_reply_threshold - total_reduction)
         adjusted_action_threshold = max(0.0, base_action_threshold - total_reduction)
-        
+
         return adjusted_reply_threshold, adjusted_action_threshold
-    
+
     def _apply_no_reply_boost(self, base_score: float) -> float:
         """【已弃用】应用连续不回复的概率提升
-        
+
         注意：此方法已被 _apply_no_reply_threshold_adjustment 替代
         保留用于向后兼容
         """
@@ -388,7 +388,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
             self.no_reply_count = 0
         else:
             self.no_reply_count = min(self.no_reply_count + 1, self.max_no_reply_count)
-    
+
     def on_reply_sent(self):
         """当机器人发送回复后调用，激活回复后阈值降低机制"""
         if self.enable_post_reply_boost:
@@ -399,16 +399,16 @@ class AffinityInterestCalculator(BaseInterestCalculator):
             )
             # 同时重置不回复计数
             self.no_reply_count = 0
-    
+
     def on_message_processed(self, replied: bool):
         """消息处理完成后调用，更新各种计数器
-        
+
         Args:
             replied: 是否回复了此消息
         """
         # 更新不回复计数
         self.update_no_reply_count(replied)
-        
+
         # 如果已回复，激活回复后降低机制
         if replied:
             self.on_reply_sent()

src/plugins/built_in/affinity_flow_chatter/planner/__init__.py

@@ -4,10 +4,10 @@ AffinityFlow Chatter 规划器模块
 包含计划生成、过滤、执行等规划相关功能
 """
 
+from . import planner_prompts
 from .plan_executor import ChatterPlanExecutor
 from .plan_filter import ChatterPlanFilter
 from .plan_generator import ChatterPlanGenerator
 from .planner import ChatterActionPlanner
-from . import planner_prompts
 
-__all__ = ["ChatterActionPlanner", "planner_prompts", "ChatterPlanGenerator", "ChatterPlanFilter", "ChatterPlanExecutor"]
+__all__ = ["ChatterActionPlanner", "ChatterPlanExecutor", "ChatterPlanFilter", "ChatterPlanGenerator", "planner_prompts"]

src/plugins/built_in/affinity_flow_chatter/planner/plan_filter.py

@@ -14,9 +14,7 @@ from json_repair import repair_json
 # 旧的Hippocampus系统已被移除，现在使用增强记忆系统
 # from src.chat.memory_system.enhanced_memory_manager import enhanced_memory_manager
 from src.chat.utils.chat_message_builder import (
-    build_readable_actions,
     build_readable_messages_with_id,
-    get_actions_by_timestamp_with_chat,
 )
 from src.chat.utils.prompt import global_prompt_manager
 from src.common.data_models.info_data_model import ActionPlannerInfo, Plan
@@ -655,7 +653,7 @@ class ChatterPlanFilter:
                 memory_manager = get_memory_manager()
                 if not memory_manager:
                     return "记忆系统未初始化。"
-                    
+
                 # 将关键词转换为查询字符串
                 query = " ".join(keywords)
                 enhanced_memories = await memory_manager.search_memories(

src/plugins/built_in/affinity_flow_chatter/planner/planner.py

@@ -21,7 +21,6 @@ if TYPE_CHECKING:
     from src.common.data_models.message_manager_data_model import StreamContext
 
 # 导入提示词模块以确保其被初始化
-from src.plugins.built_in.affinity_flow_chatter.planner import planner_prompts
 
 logger = get_logger("planner")
 
@@ -159,10 +158,10 @@ class ChatterActionPlanner:
                     action_data={},
                     action_message=None,
                 )
-                
+
                 # 更新连续不回复计数
                 await self._update_interest_calculator_state(replied=False)
-                
+
                 initial_plan = await self.generator.generate(chat_mode)
                 filtered_plan = initial_plan
                 filtered_plan.decided_actions = [no_action]
@@ -270,7 +269,7 @@ class ChatterActionPlanner:
         try:
             # Normal模式开始时，刷新缓存消息到未读列表
             await self._flush_cached_messages_to_unread(context)
-            
+
             unread_messages = context.get_unread_messages() if context else []
 
             if not unread_messages:
@@ -347,7 +346,7 @@ class ChatterActionPlanner:
                 self._update_stats_from_execution_result(execution_result)
 
                 logger.info("Normal模式: 执行reply动作完成")
-                
+
                 # 更新兴趣计算器状态（回复成功，重置不回复计数）
                 await self._update_interest_calculator_state(replied=True)
 
@@ -465,7 +464,7 @@ class ChatterActionPlanner:
 
     async def _update_interest_calculator_state(self, replied: bool) -> None:
         """更新兴趣计算器状态（连续不回复计数和回复后降低机制）
-        
+
         Args:
             replied: 是否回复了消息
         """
@@ -504,36 +503,36 @@ class ChatterActionPlanner:
 
     async def _flush_cached_messages_to_unread(self, context: "StreamContext | None") -> list:
         """在planner开始时将缓存消息刷新到未读消息列表
-        
+
         此方法在动作修改器执行后、生成初始计划前调用，确保计划阶段能看到所有积累的消息。
-        
+
         Args:
             context: 流上下文
-            
+
         Returns:
             list: 刷新的消息列表
         """
         if not context:
             return []
-            
+
         try:
             from src.chat.message_manager.message_manager import message_manager
-            
+
             stream_id = context.stream_id
-            
+
             if message_manager.is_running and message_manager.has_cached_messages(stream_id):
                 # 获取缓存消息
                 cached_messages = message_manager.flush_cached_messages(stream_id)
-                
+
                 if cached_messages:
                     # 直接添加到上下文的未读消息列表
                     for message in cached_messages:
                         context.unread_messages.append(message)
                     logger.info(f"Planner开始前刷新缓存消息到未读列表: stream={stream_id}, 数量={len(cached_messages)}")
                     return cached_messages
-                    
+
             return []
-            
+
         except ImportError:
             logger.debug("MessageManager不可用，跳过缓存刷新")
             return []

src/plugins/built_in/affinity_flow_chatter/proactive/__init__.py

@@ -9,9 +9,9 @@ from .proactive_thinking_executor import execute_proactive_thinking
 from .proactive_thinking_scheduler import ProactiveThinkingScheduler, proactive_thinking_scheduler
 
 __all__ = [
-    "ProactiveThinkingReplyHandler",
     "ProactiveThinkingMessageHandler",
-    "execute_proactive_thinking",
+    "ProactiveThinkingReplyHandler",
     "ProactiveThinkingScheduler",
+    "execute_proactive_thinking",
     "proactive_thinking_scheduler",
 ]

src/plugins/built_in/affinity_flow_chatter/proactive/proactive_thinking_executor.py

@@ -3,7 +3,6 @@
 当定时任务触发时，负责搜集信息、调用LLM决策、并根据决策生成回复
 """
 
-import orjson
 from datetime import datetime
 from typing import Any, Literal
 

src/plugins/built_in/maizone_refactored/services/content_service.py

@@ -14,7 +14,6 @@ from maim_message import UserInfo
 
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.common.logger import get_logger
-from src.config.api_ada_configs import TaskConfig
 from src.llm_models.utils_model import LLMRequest
 from src.plugin_system.apis import config_api, generator_api, llm_api
 
@@ -320,7 +319,7 @@ class ContentService:
             - 禁止在说说中直接、完整地提及当前的年月日，除非日期有特殊含义，但也尽量用节日名/节气名字代替。
 
             2. **严禁重复**：下方会提供你最近发过的说说历史，你必须创作一条全新的、与历史记录内容和主题都不同的说说。
-            
+
             **其他的禁止的内容以及说明**：
             - 绝对禁止提及当下具体几点几分的时间戳。
             - 绝对禁止攻击性内容和过度的负面情绪。

src/plugins/built_in/maizone_refactored/services/qzone_service.py

@@ -136,10 +136,10 @@ class QZoneService:
         logger.info(f"[DEBUG] 准备获取API客户端，qq_account={qq_account}")
         api_client = await self._get_api_client(qq_account, stream_id)
         if not api_client:
-            logger.error(f"[DEBUG] API客户端获取失败，返回错误")
+            logger.error("[DEBUG] API客户端获取失败，返回错误")
             return {"success": False, "message": "获取QZone API客户端失败"}
 
-        logger.info(f"[DEBUG] API客户端获取成功，准备读取说说")
+        logger.info("[DEBUG] API客户端获取成功，准备读取说说")
         num_to_read = self.get_config("read.read_number", 5)
 
         # 尝试执行，如果Cookie失效则自动重试一次
@@ -186,7 +186,7 @@ class QZoneService:
 
                 # 检查是否是Cookie失效（-3000错误）
                 if "错误码: -3000" in error_msg and retry_count == 0:
-                    logger.warning(f"检测到Cookie失效（-3000错误），准备删除缓存并重试...")
+                    logger.warning("检测到Cookie失效（-3000错误），准备删除缓存并重试...")
 
                     # 删除Cookie缓存文件
                     cookie_file = self.cookie_service._get_cookie_file_path(qq_account)
@@ -623,7 +623,7 @@ class QZoneService:
             logger.error(f"获取API客户端失败：Cookie中缺少关键的 'p_skey'。Cookie内容: {cookies}")
             return None
 
-        logger.info(f"[DEBUG] p_skey获取成功")
+        logger.info("[DEBUG] p_skey获取成功")
 
         gtk = self._generate_gtk(p_skey)
         uin = cookies.get("uin", "").lstrip("o")
@@ -1230,7 +1230,7 @@ class QZoneService:
                 logger.error(f"监控好友动态失败: {e}", exc_info=True)
                 return []
 
-        logger.info(f"[DEBUG] API客户端构造完成，返回包含6个方法的字典")
+        logger.info("[DEBUG] API客户端构造完成，返回包含6个方法的字典")
         return {
             "publish": _publish,
             "list_feeds": _list_feeds,

src/plugins/built_in/maizone_refactored/services/reply_tracker_service.py

@@ -3,11 +3,12 @@
 负责记录和管理已回复过的评论ID，避免重复回复
 """
 
-import orjson
 import time
 from pathlib import Path
 from typing import Any
 
+import orjson
+
 from src.common.logger import get_logger
 
 logger = get_logger("MaiZone.ReplyTrackerService")
@@ -117,8 +118,8 @@ class ReplyTrackerService:
             temp_file = self.reply_record_file.with_suffix(".tmp")
 
             # 先写入临时文件
-            with open(temp_file, "w", encoding="utf-8") as f:
-                orjson.dumps(self.replied_comments, option=orjson.OPT_INDENT_2 | orjson.OPT_NON_STR_KEYS).decode('utf-8')
+            with open(temp_file, "w", encoding="utf-8"):
+                orjson.dumps(self.replied_comments, option=orjson.OPT_INDENT_2 | orjson.OPT_NON_STR_KEYS).decode("utf-8")
 
             # 如果写入成功，重命名为正式文件
             if temp_file.stat().st_size > 0:  # 确保写入成功

src/plugins/built_in/napcat_adapter_plugin/src/send_handler.py

@@ -1,6 +1,5 @@
 import json
 import time
-import random
 import websockets as Server
 import uuid
 from maim_message import (
@@ -204,7 +203,7 @@ class SendHandler:
             # 发送响应回MoFox-Bot
             logger.debug(f"[DEBUG handle_adapter_command] 即将调用send_adapter_command_response, request_id={request_id}")
             await self.send_adapter_command_response(raw_message_base, response, request_id)
-            logger.debug(f"[DEBUG handle_adapter_command] send_adapter_command_response调用完成")
+            logger.debug("[DEBUG handle_adapter_command] send_adapter_command_response调用完成")
 
             if response.get("status") == "ok":
                 logger.info(f"适配器命令 {action} 执行成功")

src/plugins/built_in/web_search_tool/engines/metaso_engine.py

@@ -1,10 +1,10 @@
 """
 Metaso Search Engine (Chat Completions Mode)
 """
-import orjson
 from typing import Any
 
 import httpx
+import orjson
 
 from src.common.logger import get_logger
 from src.plugin_system.apis import config_api

src/plugins/built_in/web_search_tool/engines/serper_engine.py

@@ -3,9 +3,10 @@ Serper search engine implementation
 Google Search via Serper.dev API
 """
 
-import aiohttp
 from typing import Any
 
+import aiohttp
+
 from src.common.logger import get_logger
 from src.plugin_system.apis import config_api
 

src/plugins/built_in/web_search_tool/plugin.py

@@ -5,7 +5,7 @@ Web Search Tool Plugin
 """
 
 from src.common.logger import get_logger
-from src.plugin_system import BasePlugin, ComponentInfo, ConfigField, PythonDependency, register_plugin
+from src.plugin_system import BasePlugin, ComponentInfo, ConfigField, register_plugin
 from src.plugin_system.apis import config_api
 
 from .tools.url_parser import URLParserTool

src/plugins/built_in/web_search_tool/tools/web_search.py

@@ -113,7 +113,7 @@ class WebSurfingTool(BaseTool):
                 custom_args["num_results"] = custom_args.get("num_results", 5)
 
                 # 如果启用了answer模式且是Exa引擎，使用answer_search方法
-                if answer_mode and engine_name == "exa" and hasattr(engine, 'answer_search'):
+                if answer_mode and engine_name == "exa" and hasattr(engine, "answer_search"):
                     search_tasks.append(engine.answer_search(custom_args))
                 else:
                     search_tasks.append(engine.search(custom_args))
@@ -162,7 +162,7 @@ class WebSurfingTool(BaseTool):
                 custom_args["num_results"] = custom_args.get("num_results", 5)
 
                 # 如果启用了answer模式且是Exa引擎，使用answer_search方法
-                if answer_mode and engine_name == "exa" and hasattr(engine, 'answer_search'):
+                if answer_mode and engine_name == "exa" and hasattr(engine, "answer_search"):
                     logger.info("使用Exa答案模式进行搜索（fallback策略）")
                     results = await engine.answer_search(custom_args)
                 else:
@@ -195,7 +195,7 @@ class WebSurfingTool(BaseTool):
                 custom_args["num_results"] = custom_args.get("num_results", 5)
 
                 # 如果启用了answer模式且是Exa引擎，使用answer_search方法
-                if answer_mode and engine_name == "exa" and hasattr(engine, 'answer_search'):
+                if answer_mode and engine_name == "exa" and hasattr(engine, "answer_search"):
                     logger.info("使用Exa答案模式进行搜索")
                     results = await engine.answer_search(custom_args)
                 else: