超级Ruff

bot.py

@@ -62,9 +62,10 @@ def easter_egg():
 async def graceful_shutdown():  # sourcery skip: use-named-expression
     try:
         logger.info("正在优雅关闭麦麦...")
-        
+
         from src.plugin_system.core.events_manager import events_manager
         from src.plugin_system.base.component_types import EventType
+
         # 触发 ON_STOP 事件
         await events_manager.handle_mai_events(event_type=EventType.ON_STOP)
 

scripts/expression_stats.py

@@ -5,12 +5,11 @@ from typing import Dict, List
 
 # Add project root to Python path
 from src.common.database.database_model import Expression, ChatStreams
+
 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.insert(0, project_root)
 
 
-
-
 def get_chat_name(chat_id: str) -> str:
     """Get chat name from chat_id by querying ChatStreams table directly"""
     try:
@@ -18,7 +17,7 @@ def get_chat_name(chat_id: str) -> str:
         chat_stream = ChatStreams.get_or_none(ChatStreams.stream_id == chat_id)
         if chat_stream is None:
             return f"未知聊天 ({chat_id})"
-        
+
         # 如果有群组信息，显示群组名称
         if chat_stream.group_name:
             return f"{chat_stream.group_name} ({chat_id})"
@@ -35,117 +34,106 @@ def calculate_time_distribution(expressions) -> Dict[str, int]:
     """Calculate distribution of last active time in days"""
     now = time.time()
     distribution = {
-        '0-1天': 0,
-        '1-3天': 0,
-        '3-7天': 0,
-        '7-14天': 0,
-        '14-30天': 0,
-        '30-60天': 0,
-        '60-90天': 0,
-        '90+天': 0
+        "0-1天": 0,
+        "1-3天": 0,
+        "3-7天": 0,
+        "7-14天": 0,
+        "14-30天": 0,
+        "30-60天": 0,
+        "60-90天": 0,
+        "90+天": 0,
     }
     for expr in expressions:
-        diff_days = (now - expr.last_active_time) / (24*3600)
+        diff_days = (now - expr.last_active_time) / (24 * 3600)
         if diff_days < 1:
-            distribution['0-1天'] += 1
+            distribution["0-1天"] += 1
         elif diff_days < 3:
-            distribution['1-3天'] += 1
+            distribution["1-3天"] += 1
         elif diff_days < 7:
-            distribution['3-7天'] += 1
+            distribution["3-7天"] += 1
         elif diff_days < 14:
-            distribution['7-14天'] += 1
+            distribution["7-14天"] += 1
         elif diff_days < 30:
-            distribution['14-30天'] += 1
+            distribution["14-30天"] += 1
         elif diff_days < 60:
-            distribution['30-60天'] += 1
+            distribution["30-60天"] += 1
         elif diff_days < 90:
-            distribution['60-90天'] += 1
+            distribution["60-90天"] += 1
         else:
-            distribution['90+天'] += 1
+            distribution["90+天"] += 1
     return distribution
 
 
 def calculate_count_distribution(expressions) -> Dict[str, int]:
     """Calculate distribution of count values"""
-    distribution = {
-        '0-1': 0,
-        '1-2': 0,
-        '2-3': 0,
-        '3-4': 0,
-        '4-5': 0,
-        '5-10': 0,
-        '10+': 0
-    }
+    distribution = {"0-1": 0, "1-2": 0, "2-3": 0, "3-4": 0, "4-5": 0, "5-10": 0, "10+": 0}
     for expr in expressions:
         cnt = expr.count
         if cnt < 1:
-            distribution['0-1'] += 1
+            distribution["0-1"] += 1
         elif cnt < 2:
-            distribution['1-2'] += 1
+            distribution["1-2"] += 1
         elif cnt < 3:
-            distribution['2-3'] += 1
+            distribution["2-3"] += 1
         elif cnt < 4:
-            distribution['3-4'] += 1
+            distribution["3-4"] += 1
         elif cnt < 5:
-            distribution['4-5'] += 1
+            distribution["4-5"] += 1
         elif cnt < 10:
-            distribution['5-10'] += 1
+            distribution["5-10"] += 1
         else:
-            distribution['10+'] += 1
+            distribution["10+"] += 1
     return distribution
 
 
 def get_top_expressions_by_chat(chat_id: str, top_n: int = 5) -> List[Expression]:
     """Get top N most used expressions for a specific chat_id"""
-    return (Expression.select()
-                    .where(Expression.chat_id == chat_id)
-                    .order_by(Expression.count.desc())
-                    .limit(top_n))
+    return Expression.select().where(Expression.chat_id == chat_id).order_by(Expression.count.desc()).limit(top_n)
 
 
 def show_overall_statistics(expressions, total: int) -> None:
     """Show overall statistics"""
     time_dist = calculate_time_distribution(expressions)
     count_dist = calculate_count_distribution(expressions)
-    
+
     print("\n=== 总体统计 ===")
     print(f"总表达式数量: {total}")
-    
+
     print("\n上次激活时间分布:")
     for period, count in time_dist.items():
-        print(f"{period}: {count} ({count/total*100:.2f}%)")
-    
+        print(f"{period}: {count} ({count / total * 100:.2f}%)")
+
     print("\ncount分布:")
     for range_, count in count_dist.items():
-        print(f"{range_}: {count} ({count/total*100:.2f}%)")
+        print(f"{range_}: {count} ({count / total * 100:.2f}%)")
 
 
 def show_chat_statistics(chat_id: str, chat_name: str) -> None:
     """Show statistics for a specific chat"""
     chat_exprs = list(Expression.select().where(Expression.chat_id == chat_id))
     chat_total = len(chat_exprs)
-    
+
     print(f"\n=== {chat_name} ===")
     print(f"表达式数量: {chat_total}")
-    
+
     if chat_total == 0:
         print("该聊天没有表达式数据")
         return
-    
+
     # Time distribution for this chat
     time_dist = calculate_time_distribution(chat_exprs)
     print("\n上次激活时间分布:")
     for period, count in time_dist.items():
         if count > 0:
-            print(f"{period}: {count} ({count/chat_total*100:.2f}%)")
-    
+            print(f"{period}: {count} ({count / chat_total * 100:.2f}%)")
+
     # Count distribution for this chat
     count_dist = calculate_count_distribution(chat_exprs)
     print("\ncount分布:")
     for range_, count in count_dist.items():
         if count > 0:
-            print(f"{range_}: {count} ({count/chat_total*100:.2f}%)")
-    
+            print(f"{range_}: {count} ({count / chat_total * 100:.2f}%)")
+
     # Top expressions
     print("\nTop 10使用最多的表达式:")
     top_exprs = get_top_expressions_by_chat(chat_id, 10)
@@ -163,32 +151,32 @@ def interactive_menu() -> None:
     if not expressions:
         print("数据库中没有找到表达式")
         return
-    
+
     total = len(expressions)
-    
+
     # Get unique chat_ids and their names
     chat_ids = list(set(expr.chat_id for expr in expressions))
     chat_info = [(chat_id, get_chat_name(chat_id)) for chat_id in chat_ids]
     chat_info.sort(key=lambda x: x[1])  # Sort by chat name
-    
+
     while True:
-        print("\n" + "="*50)
+        print("\n" + "=" * 50)
         print("表达式统计分析")
-        print("="*50)
+        print("=" * 50)
         print("0. 显示总体统计")
-        
+
         for i, (chat_id, chat_name) in enumerate(chat_info, 1):
             chat_count = sum(1 for expr in expressions if expr.chat_id == chat_id)
             print(f"{i}. {chat_name} ({chat_count}个表达式)")
-        
+
         print("q. 退出")
-        
+
         choice = input("\n请选择要查看的统计 (输入序号): ").strip()
-        
-        if choice.lower() == 'q':
+
+        if choice.lower() == "q":
             print("再见！")
             break
-        
+
         try:
             choice_num = int(choice)
             if choice_num == 0:
@@ -200,9 +188,9 @@ def interactive_menu() -> None:
                 print("无效的选择，请重新输入")
         except ValueError:
             print("请输入有效的数字")
-        
+
         input("\n按回车键继续...")
 
 
 if __name__ == "__main__":
-    interactive_menu() 
+    interactive_menu()

scripts/import_openie.py

@@ -23,6 +23,7 @@ OPENIE_DIR = os.path.join(ROOT_PATH, "data", "openie")
 
 logger = get_logger("OpenIE导入")
 
+
 def ensure_openie_dir():
     """确保OpenIE数据目录存在"""
     if not os.path.exists(OPENIE_DIR):
@@ -253,7 +254,7 @@ def main():
         # 没有运行的事件循环，创建新的
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
-    
+
     try:
         # 在新的事件循环中运行异步主函数
         loop.run_until_complete(main_async())

scripts/info_extraction.py

@@ -12,6 +12,7 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from rich.progress import Progress  # 替换为 rich 进度条
 
 from src.common.logger import get_logger
+
 # from src.chat.knowledge.lpmmconfig import global_config
 from src.chat.knowledge.ie_process import info_extract_from_str
 from src.chat.knowledge.open_ie import OpenIE
@@ -36,6 +37,7 @@ TEMP_DIR = os.path.join(ROOT_PATH, "temp")
 # IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, "data", "imported_lpmm_data")
 OPENIE_OUTPUT_DIR = os.path.join(ROOT_PATH, "data", "openie")
 
+
 def ensure_dirs():
     """确保临时目录和输出目录存在"""
     if not os.path.exists(TEMP_DIR):
@@ -48,6 +50,7 @@ def ensure_dirs():
         os.makedirs(RAW_DATA_PATH)
         logger.info(f"已创建原始数据目录: {RAW_DATA_PATH}")
 
+
 # 创建一个线程安全的锁，用于保护文件操作和共享数据
 file_lock = Lock()
 open_ie_doc_lock = Lock()
@@ -56,13 +59,11 @@ open_ie_doc_lock = Lock()
 shutdown_event = Event()
 
 lpmm_entity_extract_llm = LLMRequest(
-    model_set=model_config.model_task_config.lpmm_entity_extract,
-    request_type="lpmm.entity_extract"
-)
-lpmm_rdf_build_llm = LLMRequest(
-    model_set=model_config.model_task_config.lpmm_rdf_build,
-    request_type="lpmm.rdf_build"
+    model_set=model_config.model_task_config.lpmm_entity_extract, request_type="lpmm.entity_extract"
 )
+lpmm_rdf_build_llm = LLMRequest(model_set=model_config.model_task_config.lpmm_rdf_build, request_type="lpmm.rdf_build")
+
+
 def process_single_text(pg_hash, raw_data):
     """处理单个文本的函数，用于线程池"""
     temp_file_path = f"{TEMP_DIR}/{pg_hash}.json"

scripts/interest_value_analysis.py

@@ -3,12 +3,11 @@ import sys
 import os
 from typing import Dict, List, Tuple, Optional
 from datetime import datetime
+
 # Add project root to Python path
 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.insert(0, project_root)
-from src.common.database.database_model import Messages, ChatStreams #noqa
-
-
+from src.common.database.database_model import Messages, ChatStreams  # noqa
 
 
 def get_chat_name(chat_id: str) -> str:
@@ -17,7 +16,7 @@ def get_chat_name(chat_id: str) -> str:
         chat_stream = ChatStreams.get_or_none(ChatStreams.stream_id == chat_id)
         if chat_stream is None:
             return f"未知聊天 ({chat_id})"
-        
+
         if chat_stream.group_name:
             return f"{chat_stream.group_name} ({chat_id})"
         elif chat_stream.user_nickname:
@@ -39,66 +38,62 @@ def format_timestamp(timestamp: float) -> str:
 def calculate_interest_value_distribution(messages) -> Dict[str, int]:
     """Calculate distribution of interest_value"""
     distribution = {
-        '0.000-0.010': 0,
-        '0.010-0.050': 0,
-        '0.050-0.100': 0,
-        '0.100-0.500': 0,
-        '0.500-1.000': 0,
-        '1.000-2.000': 0,
-        '2.000-5.000': 0,
-        '5.000-10.000': 0,
-        '10.000+': 0
+        "0.000-0.010": 0,
+        "0.010-0.050": 0,
+        "0.050-0.100": 0,
+        "0.100-0.500": 0,
+        "0.500-1.000": 0,
+        "1.000-2.000": 0,
+        "2.000-5.000": 0,
+        "5.000-10.000": 0,
+        "10.000+": 0,
     }
-    
+
     for msg in messages:
         if msg.interest_value is None or msg.interest_value == 0.0:
             continue
-        
+
         value = float(msg.interest_value)
         if value < 0.010:
-            distribution['0.000-0.010'] += 1
+            distribution["0.000-0.010"] += 1
         elif value < 0.050:
-            distribution['0.010-0.050'] += 1
+            distribution["0.010-0.050"] += 1
         elif value < 0.100:
-            distribution['0.050-0.100'] += 1
+            distribution["0.050-0.100"] += 1
         elif value < 0.500:
-            distribution['0.100-0.500'] += 1
+            distribution["0.100-0.500"] += 1
         elif value < 1.000:
-            distribution['0.500-1.000'] += 1
+            distribution["0.500-1.000"] += 1
         elif value < 2.000:
-            distribution['1.000-2.000'] += 1
+            distribution["1.000-2.000"] += 1
         elif value < 5.000:
-            distribution['2.000-5.000'] += 1
+            distribution["2.000-5.000"] += 1
         elif value < 10.000:
-            distribution['5.000-10.000'] += 1
+            distribution["5.000-10.000"] += 1
         else:
-            distribution['10.000+'] += 1
-    
+            distribution["10.000+"] += 1
+
     return distribution
 
 
 def get_interest_value_stats(messages) -> Dict[str, float]:
     """Calculate basic statistics for interest_value"""
-    values = [float(msg.interest_value) for msg in messages if msg.interest_value is not None and msg.interest_value != 0.0]
-    
+    values = [
+        float(msg.interest_value) for msg in messages if msg.interest_value is not None and msg.interest_value != 0.0
+    ]
+
     if not values:
-        return {
-            'count': 0,
-            'min': 0,
-            'max': 0,
-            'avg': 0,
-            'median': 0
-        }
-    
+        return {"count": 0, "min": 0, "max": 0, "avg": 0, "median": 0}
+
     values.sort()
     count = len(values)
-    
+
     return {
-        'count': count,
-        'min': min(values),
-        'max': max(values),
-        'avg': sum(values) / count,
-        'median': values[count // 2] if count % 2 == 1 else (values[count // 2 - 1] + values[count // 2]) / 2
+        "count": count,
+        "min": min(values),
+        "max": max(values),
+        "avg": sum(values) / count,
+        "median": values[count // 2] if count % 2 == 1 else (values[count // 2 - 1] + values[count // 2]) / 2,
     }
 
 
@@ -109,20 +104,24 @@ def get_available_chats() -> List[Tuple[str, str, int]]:
         chat_counts = {}
         for msg in Messages.select(Messages.chat_id).distinct():
             chat_id = msg.chat_id
-            count = Messages.select().where(
-                (Messages.chat_id == chat_id) & 
-                (Messages.interest_value.is_null(False)) &
-                (Messages.interest_value != 0.0)
-            ).count()
+            count = (
+                Messages.select()
+                .where(
+                    (Messages.chat_id == chat_id)
+                    & (Messages.interest_value.is_null(False))
+                    & (Messages.interest_value != 0.0)
+                )
+                .count()
+            )
             if count > 0:
                 chat_counts[chat_id] = count
-        
+
         # 获取聊天名称
         result = []
         for chat_id, count in chat_counts.items():
             chat_name = get_chat_name(chat_id)
             result.append((chat_id, chat_name, count))
-        
+
         # 按消息数量排序
         result.sort(key=lambda x: x[2], reverse=True)
         return result
@@ -135,30 +134,30 @@ def get_time_range_input() -> Tuple[Optional[float], Optional[float]]:
     """Get time range input from user"""
     print("\n时间范围选择:")
     print("1. 最近1天")
-    print("2. 最近3天") 
+    print("2. 最近3天")
     print("3. 最近7天")
     print("4. 最近30天")
     print("5. 自定义时间范围")
     print("6. 不限制时间")
-    
+
     choice = input("请选择时间范围 (1-6): ").strip()
-    
+
     now = time.time()
-    
+
     if choice == "1":
-        return now - 24*3600, now
+        return now - 24 * 3600, now
     elif choice == "2":
-        return now - 3*24*3600, now
+        return now - 3 * 24 * 3600, now
     elif choice == "3":
-        return now - 7*24*3600, now
+        return now - 7 * 24 * 3600, now
     elif choice == "4":
-        return now - 30*24*3600, now
+        return now - 30 * 24 * 3600, now
     elif choice == "5":
         print("请输入开始时间 (格式: YYYY-MM-DD HH:MM:SS):")
         start_str = input().strip()
         print("请输入结束时间 (格式: YYYY-MM-DD HH:MM:SS):")
         end_str = input().strip()
-        
+
         try:
             start_time = datetime.strptime(start_str, "%Y-%m-%d %H:%M:%S").timestamp()
             end_time = datetime.strptime(end_str, "%Y-%m-%d %H:%M:%S").timestamp()
@@ -170,41 +169,40 @@ def get_time_range_input() -> Tuple[Optional[float], Optional[float]]:
         return None, None
 
 
-def analyze_interest_values(chat_id: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None) -> None:
+def analyze_interest_values(
+    chat_id: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None
+) -> None:
     """Analyze interest values with optional filters"""
-    
+
     # 构建查询条件
-    query = Messages.select().where(
-        (Messages.interest_value.is_null(False)) &
-        (Messages.interest_value != 0.0)
-    )
-    
+    query = Messages.select().where((Messages.interest_value.is_null(False)) & (Messages.interest_value != 0.0))
+
     if chat_id:
         query = query.where(Messages.chat_id == chat_id)
-    
+
     if start_time:
         query = query.where(Messages.time >= start_time)
-    
+
     if end_time:
         query = query.where(Messages.time <= end_time)
-    
+
     messages = list(query)
-    
+
     if not messages:
         print("没有找到符合条件的消息")
         return
-    
+
     # 计算统计信息
     distribution = calculate_interest_value_distribution(messages)
     stats = get_interest_value_stats(messages)
-    
+
     # 显示结果
     print("\n=== Interest Value 分析结果 ===")
     if chat_id:
         print(f"聊天: {get_chat_name(chat_id)}")
     else:
         print("聊天: 全部聊天")
-    
+
     if start_time and end_time:
         print(f"时间范围: {format_timestamp(start_time)} 到 {format_timestamp(end_time)}")
     elif start_time:
@@ -213,16 +211,16 @@ def analyze_interest_values(chat_id: Optional[str] = None, start_time: Optional[
         print(f"时间范围: {format_timestamp(end_time)} 之前")
     else:
         print("时间范围: 不限制")
-    
+
     print("\n基本统计:")
     print(f"有效消息数量: {stats['count']} (排除null和0值)")
     print(f"最小值: {stats['min']:.3f}")
     print(f"最大值: {stats['max']:.3f}")
     print(f"平均值: {stats['avg']:.3f}")
     print(f"中位数: {stats['median']:.3f}")
-    
+
     print("\nInterest Value 分布:")
-    total = stats['count']
+    total = stats["count"]
     for range_name, count in distribution.items():
         if count > 0:
             percentage = count / total * 100
@@ -231,34 +229,34 @@ def analyze_interest_values(chat_id: Optional[str] = None, start_time: Optional[
 
 def interactive_menu() -> None:
     """Interactive menu for interest value analysis"""
-    
+
     while True:
-        print("\n" + "="*50)
+        print("\n" + "=" * 50)
         print("Interest Value 分析工具")
-        print("="*50)
+        print("=" * 50)
         print("1. 分析全部聊天")
         print("2. 选择特定聊天分析")
         print("q. 退出")
-        
+
         choice = input("\n请选择分析模式 (1-2, q): ").strip()
-        
-        if choice.lower() == 'q':
+
+        if choice.lower() == "q":
             print("再见！")
             break
-        
+
         chat_id = None
-        
+
         if choice == "2":
             # 显示可用的聊天列表
             chats = get_available_chats()
             if not chats:
                 print("没有找到有interest_value数据的聊天")
                 continue
-            
+
             print(f"\n可用的聊天 (共{len(chats)}个):")
             for i, (_cid, name, count) in enumerate(chats, 1):
                 print(f"{i}. {name} ({count}条有效消息)")
-            
+
             try:
                 chat_choice = int(input(f"\n请选择聊天 (1-{len(chats)}): ").strip())
                 if 1 <= chat_choice <= len(chats):
@@ -269,19 +267,19 @@ def interactive_menu() -> None:
             except ValueError:
                 print("请输入有效数字")
                 continue
-        
+
         elif choice != "1":
             print("无效选择")
             continue
-        
+
         # 获取时间范围
         start_time, end_time = get_time_range_input()
-        
+
         # 执行分析
         analyze_interest_values(chat_id, start_time, end_time)
-        
+
         input("\n按回车键继续...")
 
 
 if __name__ == "__main__":
-    interactive_menu() 
+    interactive_menu()

scripts/log_viewer_optimized.py

@@ -828,7 +828,7 @@ class LogViewer:
                             parts, tags = self.formatter.format_log_entry(log_entry)
                             line_text = " ".join(parts)
                             log_lines.append(line_text)
-                    
+
                     with open(filename, "w", encoding="utf-8") as f:
                         f.write("\n".join(log_lines))
                     messagebox.showinfo("导出成功", f"日志已导出到: {filename}")
@@ -1188,15 +1188,16 @@ class LogViewer:
                         line_count += 1
                     except json.JSONDecodeError:
                         continue
-        
+
         # 如果发现了新模块，在主线程中更新模块集合
         if new_modules:
+
             def update_modules():
                 self.modules.update(new_modules)
                 self.update_module_list()
-            
+
             self.root.after(0, update_modules)
-        
+
         return new_entries
 
     def append_new_logs(self, new_entries):
@@ -1424,4 +1425,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-

scripts/raw_data_preprocessor.py

@@ -2,6 +2,7 @@ import os
 from pathlib import Path
 import sys  # 新增系统模块导入
 from src.chat.knowledge.utils.hash import get_sha256
+
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 from src.common.logger import get_logger
 
@@ -10,6 +11,7 @@ ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 RAW_DATA_PATH = os.path.join(ROOT_PATH, "data/lpmm_raw_data")
 # IMPORTED_DATA_PATH = os.path.join(ROOT_PATH, "data/imported_lpmm_data")
 
+
 def _process_text_file(file_path):
     """处理单个文本文件，返回段落列表"""
     with open(file_path, "r", encoding="utf-8") as f:
@@ -44,6 +46,7 @@ def _process_multi_files() -> list:
         all_paragraphs.extend(paragraphs)
     return all_paragraphs
 
+
 def load_raw_data() -> tuple[list[str], list[str]]:
     """加载原始数据文件
 
@@ -72,4 +75,4 @@ def load_raw_data() -> tuple[list[str], list[str]]:
         raw_data.append(item)
     logger.info(f"共读取到{len(raw_data)}条数据")
 
-    return sha256_list, raw_data
+    return sha256_list, raw_data

scripts/text_length_analysis.py

@@ -4,21 +4,22 @@ import os
 import re
 from typing import Dict, List, Tuple, Optional
 from datetime import datetime
+
 # Add project root to Python path
 project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.insert(0, project_root)
-from src.common.database.database_model import Messages, ChatStreams #noqa
+from src.common.database.database_model import Messages, ChatStreams  # noqa
 
 
 def contains_emoji_or_image_tags(text: str) -> bool:
     """Check if text contains [表情包xxxxx] or [图片xxxxx] tags"""
     if not text:
         return False
-    
+
     # 检查是否包含 [表情包] 或 [图片] 标记
-    emoji_pattern = r'\[表情包[^\]]*\]'
-    image_pattern = r'\[图片[^\]]*\]'
-    
+    emoji_pattern = r"\[表情包[^\]]*\]"
+    image_pattern = r"\[图片[^\]]*\]"
+
     return bool(re.search(emoji_pattern, text) or re.search(image_pattern, text))
 
 
@@ -26,14 +27,14 @@ def clean_reply_text(text: str) -> str:
     """Remove reply references like [回复 xxxx...] from text"""
     if not text:
         return text
-    
+
     # 匹配 [回复 xxxx...] 格式的内容
     # 使用非贪婪匹配，匹配到第一个 ] 就停止
-    cleaned_text = re.sub(r'\[回复[^\]]*\]', '', text)
-    
+    cleaned_text = re.sub(r"\[回复[^\]]*\]", "", text)
+
     # 去除多余的空白字符
     cleaned_text = cleaned_text.strip()
-    
+
     return cleaned_text
 
 
@@ -43,7 +44,7 @@ def get_chat_name(chat_id: str) -> str:
         chat_stream = ChatStreams.get_or_none(ChatStreams.stream_id == chat_id)
         if chat_stream is None:
             return f"未知聊天 ({chat_id})"
-        
+
         if chat_stream.group_name:
             return f"{chat_stream.group_name} ({chat_id})"
         elif chat_stream.user_nickname:
@@ -65,63 +66,63 @@ def format_timestamp(timestamp: float) -> str:
 def calculate_text_length_distribution(messages) -> Dict[str, int]:
     """Calculate distribution of processed_plain_text length"""
     distribution = {
-        '0': 0,           # 空文本
-        '1-5': 0,         # 极短文本
-        '6-10': 0,        # 很短文本
-        '11-20': 0,       # 短文本
-        '21-30': 0,       # 较短文本
-        '31-50': 0,       # 中短文本
-        '51-70': 0,       # 中等文本
-        '71-100': 0,      # 较长文本
-        '101-150': 0,     # 长文本
-        '151-200': 0,     # 很长文本
-        '201-300': 0,     # 超长文本
-        '301-500': 0,     # 极长文本
-        '501-1000': 0,    # 巨长文本
-        '1000+': 0        # 超巨长文本
+        "0": 0,  # 空文本
+        "1-5": 0,  # 极短文本
+        "6-10": 0,  # 很短文本
+        "11-20": 0,  # 短文本
+        "21-30": 0,  # 较短文本
+        "31-50": 0,  # 中短文本
+        "51-70": 0,  # 中等文本
+        "71-100": 0,  # 较长文本
+        "101-150": 0,  # 长文本
+        "151-200": 0,  # 很长文本
+        "201-300": 0,  # 超长文本
+        "301-500": 0,  # 极长文本
+        "501-1000": 0,  # 巨长文本
+        "1000+": 0,  # 超巨长文本
     }
-    
+
     for msg in messages:
         if msg.processed_plain_text is None:
             continue
-        
+
         # 排除包含表情包或图片标记的消息
         if contains_emoji_or_image_tags(msg.processed_plain_text):
             continue
-        
+
         # 清理文本中的回复引用
         cleaned_text = clean_reply_text(msg.processed_plain_text)
         length = len(cleaned_text)
-        
+
         if length == 0:
-            distribution['0'] += 1
+            distribution["0"] += 1
         elif length <= 5:
-            distribution['1-5'] += 1
+            distribution["1-5"] += 1
         elif length <= 10:
-            distribution['6-10'] += 1
+            distribution["6-10"] += 1
         elif length <= 20:
-            distribution['11-20'] += 1
+            distribution["11-20"] += 1
         elif length <= 30:
-            distribution['21-30'] += 1
+            distribution["21-30"] += 1
         elif length <= 50:
-            distribution['31-50'] += 1
+            distribution["31-50"] += 1
         elif length <= 70:
-            distribution['51-70'] += 1
+            distribution["51-70"] += 1
         elif length <= 100:
-            distribution['71-100'] += 1
+            distribution["71-100"] += 1
         elif length <= 150:
-            distribution['101-150'] += 1
+            distribution["101-150"] += 1
         elif length <= 200:
-            distribution['151-200'] += 1
+            distribution["151-200"] += 1
         elif length <= 300:
-            distribution['201-300'] += 1
+            distribution["201-300"] += 1
         elif length <= 500:
-            distribution['301-500'] += 1
+            distribution["301-500"] += 1
         elif length <= 1000:
-            distribution['501-1000'] += 1
+            distribution["501-1000"] += 1
         else:
-            distribution['1000+'] += 1
-    
+            distribution["1000+"] += 1
+
     return distribution
 
 
@@ -130,7 +131,7 @@ def get_text_length_stats(messages) -> Dict[str, float]:
     lengths = []
     null_count = 0
     excluded_count = 0  # 被排除的消息数量
-    
+
     for msg in messages:
         if msg.processed_plain_text is None:
             null_count += 1
@@ -141,29 +142,29 @@ def get_text_length_stats(messages) -> Dict[str, float]:
             # 清理文本中的回复引用
             cleaned_text = clean_reply_text(msg.processed_plain_text)
             lengths.append(len(cleaned_text))
-    
+
     if not lengths:
         return {
-            'count': 0,
-            'null_count': null_count,
-            'excluded_count': excluded_count,
-            'min': 0,
-            'max': 0,
-            'avg': 0,
-            'median': 0
+            "count": 0,
+            "null_count": null_count,
+            "excluded_count": excluded_count,
+            "min": 0,
+            "max": 0,
+            "avg": 0,
+            "median": 0,
         }
-    
+
     lengths.sort()
     count = len(lengths)
-    
+
     return {
-        'count': count,
-        'null_count': null_count,
-        'excluded_count': excluded_count,
-        'min': min(lengths),
-        'max': max(lengths),
-        'avg': sum(lengths) / count,
-        'median': lengths[count // 2] if count % 2 == 1 else (lengths[count // 2 - 1] + lengths[count // 2]) / 2
+        "count": count,
+        "null_count": null_count,
+        "excluded_count": excluded_count,
+        "min": min(lengths),
+        "max": max(lengths),
+        "avg": sum(lengths) / count,
+        "median": lengths[count // 2] if count % 2 == 1 else (lengths[count // 2 - 1] + lengths[count // 2]) / 2,
     }
 
 
@@ -174,21 +175,25 @@ def get_available_chats() -> List[Tuple[str, str, int]]:
         chat_counts = {}
         for msg in Messages.select(Messages.chat_id).distinct():
             chat_id = msg.chat_id
-            count = Messages.select().where(
-                (Messages.chat_id == chat_id) &
-                (Messages.is_emoji != 1) &
-                (Messages.is_picid != 1) &
-                (Messages.is_command != 1)
-            ).count()
+            count = (
+                Messages.select()
+                .where(
+                    (Messages.chat_id == chat_id)
+                    & (Messages.is_emoji != 1)
+                    & (Messages.is_picid != 1)
+                    & (Messages.is_command != 1)
+                )
+                .count()
+            )
             if count > 0:
                 chat_counts[chat_id] = count
-        
+
         # 获取聊天名称
         result = []
         for chat_id, count in chat_counts.items():
             chat_name = get_chat_name(chat_id)
             result.append((chat_id, chat_name, count))
-        
+
         # 按消息数量排序
         result.sort(key=lambda x: x[2], reverse=True)
         return result
@@ -201,30 +206,30 @@ def get_time_range_input() -> Tuple[Optional[float], Optional[float]]:
     """Get time range input from user"""
     print("\n时间范围选择:")
     print("1. 最近1天")
-    print("2. 最近3天") 
+    print("2. 最近3天")
     print("3. 最近7天")
     print("4. 最近30天")
     print("5. 自定义时间范围")
     print("6. 不限制时间")
-    
+
     choice = input("请选择时间范围 (1-6): ").strip()
-    
+
     now = time.time()
-    
+
     if choice == "1":
-        return now - 24*3600, now
+        return now - 24 * 3600, now
     elif choice == "2":
-        return now - 3*24*3600, now
+        return now - 3 * 24 * 3600, now
     elif choice == "3":
-        return now - 7*24*3600, now
+        return now - 7 * 24 * 3600, now
     elif choice == "4":
-        return now - 30*24*3600, now
+        return now - 30 * 24 * 3600, now
     elif choice == "5":
         print("请输入开始时间 (格式: YYYY-MM-DD HH:MM:SS):")
         start_str = input().strip()
         print("请输入结束时间 (格式: YYYY-MM-DD HH:MM:SS):")
         end_str = input().strip()
-        
+
         try:
             start_time = datetime.strptime(start_str, "%Y-%m-%d %H:%M:%S").timestamp()
             end_time = datetime.strptime(end_str, "%Y-%m-%d %H:%M:%S").timestamp()
@@ -239,13 +244,13 @@ def get_time_range_input() -> Tuple[Optional[float], Optional[float]]:
 def get_top_longest_messages(messages, top_n: int = 10) -> List[Tuple[str, int, str, str]]:
     """Get top N longest messages"""
     message_lengths = []
-    
+
     for msg in messages:
         if msg.processed_plain_text is not None:
             # 排除包含表情包或图片标记的消息
             if contains_emoji_or_image_tags(msg.processed_plain_text):
                 continue
-            
+
             # 清理文本中的回复引用
             cleaned_text = clean_reply_text(msg.processed_plain_text)
             length = len(cleaned_text)
@@ -254,42 +259,40 @@ def get_top_longest_messages(messages, top_n: int = 10) -> List[Tuple[str, int,
             # 截取前100个字符作为预览
             preview = cleaned_text[:100] + "..." if len(cleaned_text) > 100 else cleaned_text
             message_lengths.append((chat_name, length, time_str, preview))
-    
+
     # 按长度排序，取前N个
     message_lengths.sort(key=lambda x: x[1], reverse=True)
     return message_lengths[:top_n]
 
 
-def analyze_text_lengths(chat_id: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None) -> None:
+def analyze_text_lengths(
+    chat_id: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None
+) -> None:
     """Analyze processed_plain_text lengths with optional filters"""
-    
+
     # 构建查询条件，排除特殊类型的消息
-    query = Messages.select().where(
-        (Messages.is_emoji != 1) &
-        (Messages.is_picid != 1) &
-        (Messages.is_command != 1)
-    )
-    
+    query = Messages.select().where((Messages.is_emoji != 1) & (Messages.is_picid != 1) & (Messages.is_command != 1))
+
     if chat_id:
         query = query.where(Messages.chat_id == chat_id)
-    
+
     if start_time:
         query = query.where(Messages.time >= start_time)
-    
+
     if end_time:
         query = query.where(Messages.time <= end_time)
-    
+
     messages = list(query)
-    
+
     if not messages:
         print("没有找到符合条件的消息")
         return
-    
+
     # 计算统计信息
     distribution = calculate_text_length_distribution(messages)
     stats = get_text_length_stats(messages)
     top_longest = get_top_longest_messages(messages, 10)
-    
+
     # 显示结果
     print("\n=== Processed Plain Text 长度分析结果 ===")
     print("(已排除表情、图片ID、命令类型消息，已排除[表情包]和[图片]标记消息，已清理回复引用)")
@@ -297,7 +300,7 @@ def analyze_text_lengths(chat_id: Optional[str] = None, start_time: Optional[flo
         print(f"聊天: {get_chat_name(chat_id)}")
     else:
         print("聊天: 全部聊天")
-    
+
     if start_time and end_time:
         print(f"时间范围: {format_timestamp(start_time)} 到 {format_timestamp(end_time)}")
     elif start_time:
@@ -306,26 +309,26 @@ def analyze_text_lengths(chat_id: Optional[str] = None, start_time: Optional[flo
         print(f"时间范围: {format_timestamp(end_time)} 之前")
     else:
         print("时间范围: 不限制")
-    
+
     print("\n基本统计:")
     print(f"总消息数量: {len(messages)}")
     print(f"有文本消息数量: {stats['count']}")
     print(f"空文本消息数量: {stats['null_count']}")
     print(f"被排除的消息数量: {stats['excluded_count']}")
-    if stats['count'] > 0:
+    if stats["count"] > 0:
         print(f"最短长度: {stats['min']} 字符")
         print(f"最长长度: {stats['max']} 字符")
         print(f"平均长度: {stats['avg']:.2f} 字符")
         print(f"中位数长度: {stats['median']:.2f} 字符")
-    
+
     print("\n文本长度分布:")
-    total = stats['count']
+    total = stats["count"]
     if total > 0:
         for range_name, count in distribution.items():
             if count > 0:
                 percentage = count / total * 100
                 print(f"{range_name} 字符: {count} ({percentage:.2f}%)")
-    
+
     # 显示最长的消息
     if top_longest:
         print(f"\n最长的 {len(top_longest)} 条消息:")
@@ -338,34 +341,34 @@ def analyze_text_lengths(chat_id: Optional[str] = None, start_time: Optional[flo
 
 def interactive_menu() -> None:
     """Interactive menu for text length analysis"""
-    
+
     while True:
-        print("\n" + "="*50)
+        print("\n" + "=" * 50)
         print("Processed Plain Text 长度分析工具")
-        print("="*50)
+        print("=" * 50)
         print("1. 分析全部聊天")
         print("2. 选择特定聊天分析")
         print("q. 退出")
-        
+
         choice = input("\n请选择分析模式 (1-2, q): ").strip()
-        
-        if choice.lower() == 'q':
+
+        if choice.lower() == "q":
             print("再见！")
             break
-        
+
         chat_id = None
-        
+
         if choice == "2":
             # 显示可用的聊天列表
             chats = get_available_chats()
             if not chats:
                 print("没有找到聊天数据")
                 continue
-            
+
             print(f"\n可用的聊天 (共{len(chats)}个):")
             for i, (_cid, name, count) in enumerate(chats, 1):
                 print(f"{i}. {name} ({count}条消息)")
-            
+
             try:
                 chat_choice = int(input(f"\n请选择聊天 (1-{len(chats)}): ").strip())
                 if 1 <= chat_choice <= len(chats):
@@ -376,19 +379,19 @@ def interactive_menu() -> None:
             except ValueError:
                 print("请输入有效数字")
                 continue
-        
+
         elif choice != "1":
             print("无效选择")
             continue
-        
+
         # 获取时间范围
         start_time, end_time = get_time_range_input()
-        
+
         # 执行分析
         analyze_text_lengths(chat_id, start_time, end_time)
-        
+
         input("\n按回车键继续...")
 
 
 if __name__ == "__main__":
-    interactive_menu() 
+    interactive_menu()

src/chat/emoji_system/emoji_manager.py

@@ -708,7 +708,7 @@ class EmojiManager:
             if not emoji.is_deleted and emoji.hash == emoji_hash:
                 return emoji
         return None  # 如果循环结束还没找到，则返回 None
-    
+
     async def get_emoji_tag_by_hash(self, emoji_hash: str) -> Optional[List[str]]:
         """根据哈希值获取已注册表情包的情感标签列表
 
@@ -731,7 +731,7 @@ class EmojiManager:
                 emoji_record = Emoji.get_or_none(Emoji.emoji_hash == emoji_hash)
                 if emoji_record and emoji_record.emotion:
                     logger.info(f"[缓存命中] 从数据库获取表情包情感标签: {emoji_record.emotion[:50]}...")
-                    return emoji_record.emotion.split(',')
+                    return emoji_record.emotion.split(",")
             except Exception as e:
                 logger.error(f"从数据库查询表情包情感标签时出错: {e}")
 

src/chat/express/expression_selector.py

@@ -77,10 +77,10 @@ class ExpressionSelector:
     def can_use_expression_for_chat(self, chat_id: str) -> bool:
         """
         检查指定聊天流是否允许使用表达
-        
+
         Args:
             chat_id: 聊天流ID
-            
+
         Returns:
             bool: 是否允许使用表达
         """
@@ -123,9 +123,7 @@ class ExpressionSelector:
                 return group_chat_ids
         return [chat_id]
 
-    def get_random_expressions(
-        self, chat_id: str, total_num: int
-    ) -> List[Dict[str, Any]]:
+    def get_random_expressions(self, chat_id: str, total_num: int) -> List[Dict[str, Any]]:
         # sourcery skip: extract-duplicate-method, move-assign
         # 支持多chat_id合并抽选
         related_chat_ids = self.get_related_chat_ids(chat_id)
@@ -200,7 +198,7 @@ class ExpressionSelector:
     ) -> Tuple[List[Dict[str, Any]], List[int]]:
         # sourcery skip: inline-variable, list-comprehension
         """使用LLM选择适合的表达方式"""
-        
+
         # 检查是否允许在此聊天流中使用表达
         if not self.can_use_expression_for_chat(chat_id):
             logger.debug(f"聊天流 {chat_id} 不允许使用表达，返回空列表")
@@ -208,7 +206,7 @@ class ExpressionSelector:
 
         # 1. 获取20个随机表达方式（现在按权重抽取）
         style_exprs = self.get_random_expressions(chat_id, 10)
-        
+
         if len(style_exprs) < 10:
             logger.info(f"聊天流 {chat_id} 表达方式正在积累中")
             return [], []
@@ -248,7 +246,6 @@ class ExpressionSelector:
 
         # 4. 调用LLM
         try:
-            
             # start_time = time.time()
             content, (reasoning_content, model_name, _) = await self.llm_model.generate_response_async(prompt=prompt)
             # logger.info(f"LLM请求时间: {model_name}  {time.time() - start_time} \n{prompt}")
@@ -295,7 +292,6 @@ class ExpressionSelector:
         except Exception as e:
             logger.error(f"LLM处理表达方式选择时出错: {e}")
             return [], []
-    
 
 
 init_prompt()

src/chat/frequency_control/focus_value_control.py

@@ -119,4 +119,3 @@ def get_global_focus_value() -> Optional[float]:
             return get_time_based_focus_value(config_item[1:])
 
     return None
-

src/chat/frequency_control/talk_frequency_control.py

@@ -124,5 +124,3 @@ def get_global_frequency() -> Optional[float]:
             return get_time_based_frequency(config_item[1:])
 
     return None
-
-

src/chat/frequency_control/utils.py

@@ -34,4 +34,4 @@ def parse_stream_config_to_chat_id(stream_config_str: str) -> Optional[str]:
         return hashlib.md5(key.encode()).hexdigest()
 
     except (ValueError, IndexError):
-        return None
+        return None

src/chat/heart_flow/heartFC_chat.py

@@ -155,21 +155,21 @@ class HeartFChatting:
             timer_strings.append(f"{name}: {formatted_time}")
 
         # 获取动作类型，兼容新旧格式
-        action_type = "未知动作"
+        _action_type = "未知动作"
         if hasattr(self, "_current_cycle_detail") and self._current_cycle_detail:
             loop_plan_info = self._current_cycle_detail.loop_plan_info
             if isinstance(loop_plan_info, dict):
                 action_result = loop_plan_info.get("action_result", {})
                 if isinstance(action_result, dict):
                     # 旧格式：action_result是字典
-                    action_type = action_result.get("action_type", "未知动作")
+                    _action_type = action_result.get("action_type", "未知动作")
                 elif isinstance(action_result, list) and action_result:
                     # 新格式：action_result是actions列表
                     # TODO: 把这里写明白
-                    action_type = action_result[0].action_type or "未知动作"
+                    _action_type = action_result[0].action_type or "未知动作"
             elif isinstance(loop_plan_info, list) and loop_plan_info:
                 # 直接是actions列表的情况
-                action_type = loop_plan_info[0].get("action_type", "未知动作")
+                _action_type = loop_plan_info[0].get("action_type", "未知动作")
 
         logger.info(
             f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
@@ -258,7 +258,11 @@ class HeartFChatting:
 
         return loop_info, reply_text, cycle_timers
 
-    async def _observe(self, interest_value: float = 0.0, recent_messages_list: List["DatabaseMessages"] = []) -> bool:
+    async def _observe(
+        self, interest_value: float = 0.0, recent_messages_list: Optional[List["DatabaseMessages"]] = None
+    ) -> bool:
+        if recent_messages_list is None:
+            recent_messages_list = []
         reply_text = ""  # 初始化reply_text变量，避免UnboundLocalError
 
         # 使用sigmoid函数将interest_value转换为概率

src/chat/heart_flow/heartflow.py

@@ -3,14 +3,16 @@ from typing import Any, Optional, Dict
 
 from src.common.logger import get_logger
 from src.chat.heart_flow.heartFC_chat import HeartFChatting
+
 logger = get_logger("heartflow")
 
+
 class Heartflow:
     """主心流协调器，负责初始化并协调聊天"""
 
     def __init__(self):
         self.heartflow_chat_list: Dict[Any, HeartFChatting] = {}
-        
+
     async def get_or_create_heartflow_chat(self, chat_id: Any) -> Optional[HeartFChatting]:
         """获取或创建一个新的HeartFChatting实例"""
         try:
@@ -18,7 +20,7 @@ class Heartflow:
                 if chat := self.heartflow_chat_list.get(chat_id):
                     return chat
             else:
-                new_chat = HeartFChatting(chat_id = chat_id)
+                new_chat = HeartFChatting(chat_id=chat_id)
                 await new_chat.start()
                 self.heartflow_chat_list[chat_id] = new_chat
                 return new_chat
@@ -27,4 +29,5 @@ class Heartflow:
             traceback.print_exc()
             return None
 
+
 heartflow = Heartflow()

src/chat/heart_flow/heartflow_message_processor.py

@@ -23,6 +23,7 @@ if TYPE_CHECKING:
 
 logger = get_logger("chat")
 
+
 async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
     """计算消息的兴趣度
 
@@ -34,14 +35,14 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
     """
     if message.is_picid or message.is_emoji:
         return 0.0, []
-    
-    is_mentioned,is_at,reply_probability_boost = is_mentioned_bot_in_message(message)
+
+    is_mentioned, is_at, reply_probability_boost = is_mentioned_bot_in_message(message)
     interested_rate = 0.0
 
     with Timer("记忆激活"):
-        interested_rate, keywords,keywords_lite = await hippocampus_manager.get_activate_from_text(
+        interested_rate, keywords, keywords_lite = await hippocampus_manager.get_activate_from_text(
             message.processed_plain_text,
-            max_depth= 4,
+            max_depth=4,
             fast_retrieval=global_config.chat.interest_rate_mode == "fast",
         )
         message.key_words = keywords
@@ -51,7 +52,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
     text_len = len(message.processed_plain_text)
     # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
     # 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
-    
+
     if text_len == 0:
         base_interest = 0.01  # 空消息最低兴趣度
     elif text_len <= 5:
@@ -75,16 +76,15 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
     else:
         # 100+字符：对数增长 0.26 -> 0.3，增长率递减
         base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901))  # 1000-99=901
-    
+
     # 确保在范围内
     base_interest = min(max(base_interest, 0.01), 0.3)
 
-        
     message.interest_value = base_interest
     message.is_mentioned = is_mentioned
     message.is_at = is_at
     message.reply_probability_boost = reply_probability_boost
-    
+
     return base_interest, keywords
 
 
@@ -115,14 +115,13 @@ class HeartFCMessageReceiver:
 
             # 2. 兴趣度计算与更新
             interested_rate, keywords = await _calculate_interest(message)
-            
 
             await self.storage.store_message(message, chat)
 
             heartflow_chat: HeartFChatting = await heartflow.get_or_create_heartflow_chat(chat.stream_id)  # type: ignore
 
             # subheartflow.add_message_to_normal_chat_cache(message, interested_rate, is_mentioned)
-            if global_config.mood.enable_mood:  
+            if global_config.mood.enable_mood:
                 chat_mood = mood_manager.get_mood_by_chat_id(heartflow_chat.stream_id)
                 asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
 
@@ -132,7 +131,7 @@ class HeartFCMessageReceiver:
             # 用这个pattern截取出id部分，picid是一个list，并替换成对应的图片描述
             picid_pattern = r"\[picid:([^\]]+)\]"
             picid_list = re.findall(picid_pattern, message.processed_plain_text)
-            
+
             # 创建替换后的文本
             processed_text = message.processed_plain_text
             if picid_list:
@@ -145,18 +144,20 @@ class HeartFCMessageReceiver:
                         # 如果没有找到图片描述，则移除[picid:xxxx]标记
                         processed_text = processed_text.replace(f"[picid:{picid}]", "[图片：网络不好，图片无法加载]")
 
-            
             # 应用用户引用格式替换，将回复<aaa:bbb>和@<aaa:bbb>格式转换为可读格式
             processed_plain_text = replace_user_references(
                 processed_text,
-                message.message_info.platform, # type: ignore
-                replace_bot_name=True
+                message.message_info.platform,  # type: ignore
+                replace_bot_name=True,
             )
 
-
             logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[{interested_rate:.2f}]")  # type: ignore
 
-            _ = Person.register_person(platform=message.message_info.platform, user_id=message.message_info.user_info.user_id,nickname=userinfo.user_nickname) # type: ignore
+            _ = Person.register_person(
+                platform=message.message_info.platform,
+                user_id=message.message_info.user_info.user_id,
+                nickname=userinfo.user_nickname,
+            )  # type: ignore
 
         except Exception as e:
             logger.error(f"消息处理失败: {e}")

src/chat/heart_flow/hfc_utils.py

@@ -124,6 +124,7 @@ async def send_typing():
         message_type="state", content="typing", stream_id=chat.stream_id, storage_message=False
     )
 
+
 async def stop_typing():
     group_info = GroupInfo(platform="amaidesu_default", group_id="114514", group_name="内心")
 
@@ -135,4 +136,4 @@ async def stop_typing():
 
     await send_api.custom_to_stream(
         message_type="state", content="stop_typing", stream_id=chat.stream_id, storage_message=False
-    )
+    )

src/chat/knowledge/__init__.py

@@ -30,6 +30,7 @@ DATA_PATH = os.path.join(ROOT_PATH, "data")
 qa_manager = None
 inspire_manager = None
 
+
 def lpmm_start_up():  # sourcery skip: extract-duplicate-method
     # 检查LPMM知识库是否启用
     if global_config.lpmm_knowledge.enable:

src/chat/knowledge/embedding_store.py

@@ -32,11 +32,11 @@ install(extra_lines=3)
 
 # 多线程embedding配置常量
 DEFAULT_MAX_WORKERS = 10  # 默认最大线程数
-DEFAULT_CHUNK_SIZE = 10   # 默认每个线程处理的数据块大小
-MIN_CHUNK_SIZE = 1        # 最小分块大小
-MAX_CHUNK_SIZE = 50       # 最大分块大小
-MIN_WORKERS = 1           # 最小线程数
-MAX_WORKERS = 20          # 最大线程数
+DEFAULT_CHUNK_SIZE = 10  # 默认每个线程处理的数据块大小
+MIN_CHUNK_SIZE = 1  # 最小分块大小
+MAX_CHUNK_SIZE = 50  # 最大分块大小
+MIN_WORKERS = 1  # 最小线程数
+MAX_WORKERS = 20  # 最大线程数
 
 ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
 EMBEDDING_DATA_DIR = os.path.join(ROOT_PATH, "data", "embedding")
@@ -93,7 +93,13 @@ class EmbeddingStoreItem:
 
 
 class EmbeddingStore:
-    def __init__(self, namespace: str, dir_path: str, max_workers: int = DEFAULT_MAX_WORKERS, chunk_size: int = DEFAULT_CHUNK_SIZE):
+    def __init__(
+        self,
+        namespace: str,
+        dir_path: str,
+        max_workers: int = DEFAULT_MAX_WORKERS,
+        chunk_size: int = DEFAULT_CHUNK_SIZE,
+    ):
         self.namespace = namespace
         self.dir = dir_path
         self.embedding_file_path = f"{dir_path}/{namespace}.parquet"
@@ -103,12 +109,16 @@ class EmbeddingStore:
         # 多线程配置参数验证和设置
         self.max_workers = max(MIN_WORKERS, min(MAX_WORKERS, max_workers))
         self.chunk_size = max(MIN_CHUNK_SIZE, min(MAX_CHUNK_SIZE, chunk_size))
-        
+
         # 如果配置值被调整，记录日志
         if self.max_workers != max_workers:
-            logger.warning(f"max_workers 已从 {max_workers} 调整为 {self.max_workers} (范围: {MIN_WORKERS}-{MAX_WORKERS})")
+            logger.warning(
+                f"max_workers 已从 {max_workers} 调整为 {self.max_workers} (范围: {MIN_WORKERS}-{MAX_WORKERS})"
+            )
         if self.chunk_size != chunk_size:
-            logger.warning(f"chunk_size 已从 {chunk_size} 调整为 {self.chunk_size} (范围: {MIN_CHUNK_SIZE}-{MAX_CHUNK_SIZE})")
+            logger.warning(
+                f"chunk_size 已从 {chunk_size} 调整为 {self.chunk_size} (范围: {MIN_CHUNK_SIZE}-{MAX_CHUNK_SIZE})"
+            )
 
         self.store = {}
 
@@ -120,23 +130,23 @@ class EmbeddingStore:
         # 创建新的事件循环并在完成后立即关闭
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
-        
+
         try:
             # 创建新的LLMRequest实例
             from src.llm_models.utils_model import LLMRequest
             from src.config.config import model_config
-            
+
             llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
-            
+
             # 使用新的事件循环运行异步方法
             embedding, _ = loop.run_until_complete(llm.get_embedding(s))
-            
+
             if embedding and len(embedding) > 0:
                 return embedding
             else:
                 logger.error(f"获取嵌入失败: {s}")
                 return []
-                
+
         except Exception as e:
             logger.error(f"获取嵌入时发生异常: {s}, 错误: {e}")
             return []
@@ -147,43 +157,45 @@ class EmbeddingStore:
             except Exception:
                 pass
 
-    def _get_embeddings_batch_threaded(self, strs: List[str], chunk_size: int = 10, max_workers: int = 10, progress_callback=None) -> List[Tuple[str, List[float]]]:
+    def _get_embeddings_batch_threaded(
+        self, strs: List[str], chunk_size: int = 10, max_workers: int = 10, progress_callback=None
+    ) -> List[Tuple[str, List[float]]]:
         """使用多线程批量获取嵌入向量
-        
+
         Args:
             strs: 要获取嵌入的字符串列表
             chunk_size: 每个线程处理的数据块大小
             max_workers: 最大线程数
             progress_callback: 进度回调函数，接收一个参数表示完成的数量
-            
+
         Returns:
             包含(原始字符串, 嵌入向量)的元组列表，保持与输入顺序一致
         """
         if not strs:
             return []
-            
+
         # 分块
         chunks = []
         for i in range(0, len(strs), chunk_size):
-            chunk = strs[i:i + chunk_size]
+            chunk = strs[i : i + chunk_size]
             chunks.append((i, chunk))  # 保存起始索引以维持顺序
-        
+
         # 结果存储，使用字典按索引存储以保证顺序
         results = {}
-        
+
         def process_chunk(chunk_data):
             """处理单个数据块的函数"""
             start_idx, chunk_strs = chunk_data
             chunk_results = []
-            
+
             # 为每个线程创建独立的LLMRequest实例
             from src.llm_models.utils_model import LLMRequest
             from src.config.config import model_config
-            
+
             try:
                 # 创建线程专用的LLM实例
                 llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
-                
+
                 for i, s in enumerate(chunk_strs):
                     try:
                         # 在线程中创建独立的事件循环
@@ -193,25 +205,25 @@ class EmbeddingStore:
                             embedding = loop.run_until_complete(llm.get_embedding(s))
                         finally:
                             loop.close()
-                            
+
                         if embedding and len(embedding) > 0:
                             chunk_results.append((start_idx + i, s, embedding[0]))  # embedding[0] 是实际的向量
                         else:
                             logger.error(f"获取嵌入失败: {s}")
                             chunk_results.append((start_idx + i, s, []))
-                        
+
                         # 每完成一个嵌入立即更新进度
                         if progress_callback:
                             progress_callback(1)
-                            
+
                     except Exception as e:
                         logger.error(f"获取嵌入时发生异常: {s}, 错误: {e}")
                         chunk_results.append((start_idx + i, s, []))
-                        
+
                         # 即使失败也要更新进度
                         if progress_callback:
                             progress_callback(1)
-                        
+
             except Exception as e:
                 logger.error(f"创建LLM实例失败: {e}")
                 # 如果创建LLM实例失败，返回空结果
@@ -220,14 +232,14 @@ class EmbeddingStore:
                     # 即使失败也要更新进度
                     if progress_callback:
                         progress_callback(1)
-                    
+
             return chunk_results
-        
+
         # 使用线程池处理
         with ThreadPoolExecutor(max_workers=max_workers) as executor:
             # 提交所有任务
             future_to_chunk = {executor.submit(process_chunk, chunk): chunk for chunk in chunks}
-            
+
             # 收集结果（进度已在process_chunk中实时更新）
             for future in as_completed(future_to_chunk):
                 try:
@@ -241,7 +253,7 @@ class EmbeddingStore:
                     start_idx, chunk_strs = chunk
                     for i, s in enumerate(chunk_strs):
                         results[start_idx + i] = (s, [])
-        
+
         # 按原始顺序返回结果
         ordered_results = []
         for i in range(len(strs)):
@@ -250,7 +262,7 @@ class EmbeddingStore:
             else:
                 # 防止遗漏
                 ordered_results.append((strs[i], []))
-                
+
         return ordered_results
 
     def get_test_file_path(self):
@@ -259,14 +271,14 @@ class EmbeddingStore:
     def save_embedding_test_vectors(self):
         """保存测试字符串的嵌入到本地（使用多线程优化）"""
         logger.info("开始保存测试字符串的嵌入向量...")
-        
+
         # 使用多线程批量获取测试字符串的嵌入
         embedding_results = self._get_embeddings_batch_threaded(
             EMBEDDING_TEST_STRINGS,
             chunk_size=min(self.chunk_size, len(EMBEDDING_TEST_STRINGS)),
-            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS))
+            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS)),
         )
-        
+
         # 构建测试向量字典
         test_vectors = {}
         for idx, (s, embedding) in enumerate(embedding_results):
@@ -276,10 +288,10 @@ class EmbeddingStore:
                 logger.error(f"获取测试字符串嵌入失败: {s}")
                 # 使用原始单线程方法作为后备
                 test_vectors[str(idx)] = self._get_embedding(s)
-        
+
         with open(self.get_test_file_path(), "w", encoding="utf-8") as f:
             json.dump(test_vectors, f, ensure_ascii=False, indent=2)
-        
+
         logger.info("测试字符串嵌入向量保存完成")
 
     def load_embedding_test_vectors(self):
@@ -297,35 +309,35 @@ class EmbeddingStore:
             logger.warning("未检测到本地嵌入模型测试文件，将保存当前模型的测试嵌入。")
             self.save_embedding_test_vectors()
             return True
-            
+
         # 检查本地向量完整性
         for idx in range(len(EMBEDDING_TEST_STRINGS)):
             if local_vectors.get(str(idx)) is None:
                 logger.warning("本地嵌入模型测试文件缺失部分测试字符串，将重新保存。")
                 self.save_embedding_test_vectors()
                 return True
-        
+
         logger.info("开始检验嵌入模型一致性...")
-        
+
         # 使用多线程批量获取当前模型的嵌入
         embedding_results = self._get_embeddings_batch_threaded(
             EMBEDDING_TEST_STRINGS,
             chunk_size=min(self.chunk_size, len(EMBEDDING_TEST_STRINGS)),
-            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS))
+            max_workers=min(self.max_workers, len(EMBEDDING_TEST_STRINGS)),
         )
-        
+
         # 检查一致性
         for idx, (s, new_emb) in enumerate(embedding_results):
             local_emb = local_vectors.get(str(idx))
             if not new_emb:
                 logger.error(f"获取测试字符串嵌入失败: {s}")
                 return False
-                
+
             sim = cosine_similarity(local_emb, new_emb)
             if sim < EMBEDDING_SIM_THRESHOLD:
                 logger.error(f"嵌入模型一致性校验失败，字符串: {s}, 相似度: {sim:.4f}")
                 return False
-                
+
         logger.info("嵌入模型一致性校验通过。")
         return True
 
@@ -333,22 +345,22 @@ class EmbeddingStore:
         """向库中存入字符串（使用多线程优化）"""
         if not strs:
             return
-            
+
         total = len(strs)
-        
+
         # 过滤已存在的字符串
         new_strs = []
         for s in strs:
             item_hash = self.namespace + "-" + get_sha256(s)
             if item_hash not in self.store:
                 new_strs.append(s)
-        
+
         if not new_strs:
             logger.info(f"所有字符串已存在于{self.namespace}嵌入库中，跳过处理")
             return
-            
+
         logger.info(f"需要处理 {len(new_strs)}/{total} 个新字符串")
-        
+
         with Progress(
             SpinnerColumn(),
             TextColumn("[progress.description]{task.description}"),
@@ -362,31 +374,39 @@ class EmbeddingStore:
             transient=False,
         ) as progress:
             task = progress.add_task(f"存入嵌入库：({times}/{TOTAL_EMBEDDING_TIMES})", total=total)
-            
+
             # 首先更新已存在项的进度
             already_processed = total - len(new_strs)
             if already_processed > 0:
                 progress.update(task, advance=already_processed)
-            
+
             if new_strs:
                 # 使用实例配置的参数，智能调整分块和线程数
-                optimal_chunk_size = max(MIN_CHUNK_SIZE, min(self.chunk_size, len(new_strs) // self.max_workers if self.max_workers > 0 else self.chunk_size))
-                optimal_max_workers = min(self.max_workers, max(MIN_WORKERS, len(new_strs) // optimal_chunk_size if optimal_chunk_size > 0 else 1))
-                
+                optimal_chunk_size = max(
+                    MIN_CHUNK_SIZE,
+                    min(
+                        self.chunk_size, len(new_strs) // self.max_workers if self.max_workers > 0 else self.chunk_size
+                    ),
+                )
+                optimal_max_workers = min(
+                    self.max_workers,
+                    max(MIN_WORKERS, len(new_strs) // optimal_chunk_size if optimal_chunk_size > 0 else 1),
+                )
+
                 logger.debug(f"使用多线程处理: chunk_size={optimal_chunk_size}, max_workers={optimal_max_workers}")
-                
+
                 # 定义进度更新回调函数
                 def update_progress(count):
                     progress.update(task, advance=count)
-                
+
                 # 批量获取嵌入，并实时更新进度
                 embedding_results = self._get_embeddings_batch_threaded(
-                    new_strs, 
-                    chunk_size=optimal_chunk_size, 
+                    new_strs,
+                    chunk_size=optimal_chunk_size,
                     max_workers=optimal_max_workers,
-                    progress_callback=update_progress
+                    progress_callback=update_progress,
                 )
-                
+
                 # 存入结果（不再需要在这里更新进度，因为已经在回调中更新了）
                 for s, embedding in embedding_results:
                     item_hash = self.namespace + "-" + get_sha256(s)
@@ -519,7 +539,7 @@ class EmbeddingManager:
     def __init__(self, max_workers: int = DEFAULT_MAX_WORKERS, chunk_size: int = DEFAULT_CHUNK_SIZE):
         """
         初始化EmbeddingManager
-        
+
         Args:
             max_workers: 最大线程数
             chunk_size: 每个线程处理的数据块大小

src/chat/knowledge/kg_manager.py

@@ -426,9 +426,7 @@ class KGManager:
         # 获取最终结果
         # 从搜索结果中提取文段节点的结果
         passage_node_res = [
-            (node_key, score)
-            for node_key, score in ppr_res.items()
-            if node_key.startswith("paragraph")
+            (node_key, score) for node_key, score in ppr_res.items() if node_key.startswith("paragraph")
         ]
         del ppr_res
 

src/chat/knowledge/mem_active_manager.py

@@ -1,8 +1,8 @@
 raise DeprecationWarning("MemoryActiveManager is not used yet, please do not import it")
-from .lpmmconfig import global_config
-from .embedding_store import EmbeddingManager
-from .llm_client import LLMClient
-from .utils.dyn_topk import dyn_select_top_k
+from .lpmmconfig import global_config  # noqa
+from .embedding_store import EmbeddingManager  # noqa
+from .llm_client import LLMClient  # noqa
+from .utils.dyn_topk import dyn_select_top_k  # noqa
 
 
 class MemoryActiveManager:

src/chat/knowledge/utils/dyn_topk.py

@@ -8,7 +8,7 @@ def dyn_select_top_k(
     # 检查输入列表是否为空
     if not score:
         return []
-    
+
     # 按照分数排序（降序）
     sorted_score = sorted(score, key=lambda x: x[1], reverse=True)
 

src/chat/message_receive/storage.py

@@ -18,7 +18,7 @@ class MessageStorage:
         if isinstance(keywords, list):
             return json.dumps(keywords, ensure_ascii=False)
         return "[]"
-    
+
     @staticmethod
     def _deserialize_keywords(keywords_str: str) -> list:
         """将JSON字符串反序列化为关键词列表"""
@@ -85,7 +85,7 @@ class MessageStorage:
                 key_words = MessageStorage._serialize_keywords(message.key_words)
                 key_words_lite = MessageStorage._serialize_keywords(message.key_words_lite)
                 selected_expressions = ""
-                
+
             chat_info_dict = chat_stream.to_dict()
             user_info_dict = message.message_info.user_info.to_dict()  # type: ignore
 

src/chat/planner_actions/action_manager.py

@@ -124,4 +124,4 @@ class ActionManager:
         """恢复到默认动作集"""
         actions_to_restore = list(self._using_actions.keys())
         self._using_actions = component_registry.get_default_actions()
-        logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")
+        logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")

src/chat/planner_actions/action_modifier.py

@@ -103,25 +103,23 @@ class ActionModifier:
             self.action_manager.remove_action_from_using(action_name)
             logger.debug(f"{self.log_prefix}阶段二移除动作: {action_name}，原因: {reason}")
 
-
-
         # === 第三阶段：激活类型判定 ===
         # if chat_content is not None:
-            # logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
+        # logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
 
-            # 获取当前使用的动作集（经过第一阶段处理）
-            # current_using_actions = self.action_manager.get_using_actions()
+        # 获取当前使用的动作集（经过第一阶段处理）
+        # current_using_actions = self.action_manager.get_using_actions()
 
-            # 获取因激活类型判定而需要移除的动作
-            # removals_s3 = await self._get_deactivated_actions_by_type(
-                # current_using_actions,
-                # chat_content,
-            # )
+        # 获取因激活类型判定而需要移除的动作
+        # removals_s3 = await self._get_deactivated_actions_by_type(
+        # current_using_actions,
+        # chat_content,
+        # )
 
-            # 应用第三阶段的移除
-            # for action_name, reason in removals_s3:
-                # self.action_manager.remove_action_from_using(action_name)
-                # logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name}，原因: {reason}")
+        # 应用第三阶段的移除
+        # for action_name, reason in removals_s3:
+        # self.action_manager.remove_action_from_using(action_name)
+        # logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name}，原因: {reason}")
 
         # === 统一日志记录 ===
         all_removals = removals_s1 + removals_s2
@@ -131,9 +129,7 @@ class ActionModifier:
 
         available_actions = list(self.action_manager.get_using_actions().keys())
         available_actions_text = "、".join(available_actions) if available_actions else "无"
-        logger.debug(
-            f"{self.log_prefix} 当前可用动作: {available_actions_text}||移除: {removals_summary}"
-        )
+        logger.debug(f"{self.log_prefix} 当前可用动作: {available_actions_text}||移除: {removals_summary}")
 
     def _check_action_associated_types(self, all_actions: Dict[str, ActionInfo], chat_context: ChatMessageContext):
         type_mismatched_actions: List[Tuple[str, str]] = []

src/chat/utils/statistic.py

@@ -385,18 +385,18 @@ class StatisticOutputTask(AsyncTask):
                 time_cost_key = f"time_costs_by_{category.split('_')[-1]}"
                 avg_key = f"avg_time_costs_by_{category.split('_')[-1]}"
                 std_key = f"std_time_costs_by_{category.split('_')[-1]}"
-                
+
                 for item_name in stats[period_key][category]:
                     time_costs = stats[period_key][time_cost_key].get(item_name, [])
                     if time_costs:
                         # 计算平均耗时
                         avg_time_cost = sum(time_costs) / len(time_costs)
                         stats[period_key][avg_key][item_name] = round(avg_time_cost, 3)
-                        
+
                         # 计算标准差
                         if len(time_costs) > 1:
                             variance = sum((x - avg_time_cost) ** 2 for x in time_costs) / len(time_costs)
-                            std_time_cost = variance ** 0.5
+                            std_time_cost = variance**0.5
                             stats[period_key][std_key][item_name] = round(std_time_cost, 3)
                         else:
                             stats[period_key][std_key][item_name] = 0.0
@@ -506,8 +506,6 @@ class StatisticOutputTask(AsyncTask):
                     break
         return stats
 
-    
-
     def _collect_all_statistics(self, now: datetime) -> Dict[str, Dict[str, Any]]:
         """
         收集各时间段的统计数据
@@ -639,7 +637,9 @@ class StatisticOutputTask(AsyncTask):
             cost = stats[COST_BY_MODEL][model_name]
             avg_time_cost = stats[AVG_TIME_COST_BY_MODEL][model_name]
             std_time_cost = stats[STD_TIME_COST_BY_MODEL][model_name]
-            output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost, avg_time_cost, std_time_cost))
+            output.append(
+                data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost, avg_time_cost, std_time_cost)
+            )
 
         output.append("")
         return "\n".join(output)
@@ -728,7 +728,9 @@ class StatisticOutputTask(AsyncTask):
                     f"<td>{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.1f} 秒</td>"
                     f"</tr>"
                     for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
-                ] if stat_data[REQ_CNT_BY_MODEL] else ["<tr><td colspan='8' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
+                ]
+                if stat_data[REQ_CNT_BY_MODEL]
+                else ["<tr><td colspan='8' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
             )
             # 按请求类型分类统计
             type_rows = "\n".join(
@@ -744,7 +746,9 @@ class StatisticOutputTask(AsyncTask):
                     f"<td>{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.1f} 秒</td>"
                     f"</tr>"
                     for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
-                ] if stat_data[REQ_CNT_BY_TYPE] else ["<tr><td colspan='8' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
+                ]
+                if stat_data[REQ_CNT_BY_TYPE]
+                else ["<tr><td colspan='8' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
             )
             # 按模块分类统计
             module_rows = "\n".join(
@@ -760,7 +764,9 @@ class StatisticOutputTask(AsyncTask):
                     f"<td>{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.1f} 秒</td>"
                     f"</tr>"
                     for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
-                ] if stat_data[REQ_CNT_BY_MODULE] else ["<tr><td colspan='8' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
+                ]
+                if stat_data[REQ_CNT_BY_MODULE]
+                else ["<tr><td colspan='8' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
             )
 
             # 聊天消息统计
@@ -768,7 +774,9 @@ class StatisticOutputTask(AsyncTask):
                 [
                     f"<tr><td>{self.name_mapping[chat_id][0]}</td><td>{count}</td></tr>"
                     for chat_id, count in sorted(stat_data[MSG_CNT_BY_CHAT].items())
-                ] if stat_data[MSG_CNT_BY_CHAT] else ["<tr><td colspan='2' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
+                ]
+                if stat_data[MSG_CNT_BY_CHAT]
+                else ["<tr><td colspan='2' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
             )
             # 生成HTML
             return f"""

src/chat/utils/utils.py

@@ -49,9 +49,9 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, bool, float
     reply_probability = 0.0
     is_at = False
     is_mentioned = False
-    
+
     # 这部分怎么处理啊啊啊啊
-    #我觉得可以给消息加一个 reply_probability_boost字段
+    # 我觉得可以给消息加一个 reply_probability_boost字段
     if (
         message.message_info.additional_config is not None
         and message.message_info.additional_config.get("is_mentioned") is not None
@@ -826,20 +826,48 @@ def parse_keywords_string(keywords_input) -> list[str]:
     return [keywords_str] if keywords_str else []
 
 
-
-
 def cut_key_words(concept_name: str) -> list[str]:
     """对概念名称进行jieba分词，并过滤掉关键词列表中的关键词"""
     concept_name_tokens = list(jieba.cut(concept_name))
 
     # 定义常见连词、停用词与标点
-    conjunctions = {
-        "和", "与", "及", "跟", "以及", "并且", "而且", "或", "或者", "并"
-    }
+    conjunctions = {"和", "与", "及", "跟", "以及", "并且", "而且", "或", "或者", "并"}
     stop_words = {
-        "的", "了", "呢", "吗", "吧", "啊", "哦", "恩", "嗯", "呀", "嘛", "哇",
-        "在", "是", "很", "也", "又", "就", "都", "还", "更", "最", "被", "把",
-        "给", "对", "和", "与", "及", "跟", "并", "而且", "或者", "或", "以及"
+        "的",
+        "了",
+        "呢",
+        "吗",
+        "吧",
+        "啊",
+        "哦",
+        "恩",
+        "嗯",
+        "呀",
+        "嘛",
+        "哇",
+        "在",
+        "是",
+        "很",
+        "也",
+        "又",
+        "就",
+        "都",
+        "还",
+        "更",
+        "最",
+        "被",
+        "把",
+        "给",
+        "对",
+        "和",
+        "与",
+        "及",
+        "跟",
+        "并",
+        "而且",
+        "或者",
+        "或",
+        "以及",
     }
     chinese_punctuations = set("，。！？、；：（）【】《》“”‘’—…·-——,.!?;:()[]<>'\"/\\")
 
@@ -864,11 +892,16 @@ def cut_key_words(concept_name: str) -> list[str]:
             left = merged_tokens[-1]
             right = cleaned_tokens[i + 1]
             # 左右都需要是有效词
-            if left and right \
-                and left not in conjunctions and right not in conjunctions \
-                and left not in stop_words and right not in stop_words \
-                and not all(ch in chinese_punctuations for ch in left) \
-                and not all(ch in chinese_punctuations for ch in right):
+            if (
+                left
+                and right
+                and left not in conjunctions
+                and right not in conjunctions
+                and left not in stop_words
+                and right not in stop_words
+                and not all(ch in chinese_punctuations for ch in left)
+                and not all(ch in chinese_punctuations for ch in right)
+            ):
                 # 合并为一个新词，并替换掉左侧与跳过右侧
                 combined = f"{left}{tok}{right}"
                 merged_tokens[-1] = combined
@@ -889,7 +922,7 @@ def cut_key_words(concept_name: str) -> list[str]:
         if tok in stop_words:
             continue
         # if tok in ban_words:
-            # continue
+        # continue
         if all(ch in chinese_punctuations for ch in tok):
             continue
         if tok.strip() == "":
@@ -899,4 +932,4 @@ def cut_key_words(concept_name: str) -> list[str]:
             result_tokens.append(tok)
 
     filtered_concept_name_tokens = result_tokens
-    return filtered_concept_name_tokens
+    return filtered_concept_name_tokens

src/chat/utils/utils_image.py

@@ -91,9 +91,10 @@ class ImageManager:
                 desc_obj.save()
         except Exception as e:
             logger.error(f"保存描述到数据库失败 (Peewee): {str(e)}")
-            
+
     async def get_emoji_tag(self, image_base64: str) -> str:
         from src.chat.emoji_system.emoji_manager import get_emoji_manager
+
         emoji_manager = get_emoji_manager()
         if isinstance(image_base64, str):
             image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
@@ -120,6 +121,7 @@ class ImageManager:
             # 优先使用EmojiManager查询已注册表情包的描述
             try:
                 from src.chat.emoji_system.emoji_manager import get_emoji_manager
+
                 emoji_manager = get_emoji_manager()
                 tags = await emoji_manager.get_emoji_tag_by_hash(image_hash)
                 if tags:

src/common/data_models/__init__.py

@@ -6,6 +6,7 @@ class BaseDataModel:
     def deepcopy(self):
         return copy.deepcopy(self)
 
+
 def temporarily_transform_class_to_dict(obj: Any) -> Any:
     # sourcery skip: assign-if-exp, reintroduce-else
     """

src/common/data_models/database_data_model.py

@@ -205,6 +205,7 @@ class DatabaseMessages(BaseDataModel):
             "chat_info_user_cardname": self.chat_info.user_info.user_cardname,
         }
 
+
 @dataclass(init=False)
 class DatabaseActionRecords(BaseDataModel):
     def __init__(
@@ -232,4 +233,4 @@ class DatabaseActionRecords(BaseDataModel):
         self.action_prompt_display = action_prompt_display
         self.chat_id = chat_id
         self.chat_info_stream_id = chat_info_stream_id
-        self.chat_info_platform = chat_info_platform
+        self.chat_info_platform = chat_info_platform

src/common/data_models/llm_data_model.py

@@ -2,9 +2,11 @@ from dataclasses import dataclass
 from typing import Optional, List, Tuple, TYPE_CHECKING, Any
 
 from . import BaseDataModel
+
 if TYPE_CHECKING:
     from src.llm_models.payload_content.tool_option import ToolCall
 
+
 @dataclass
 class LLMGenerationDataModel(BaseDataModel):
     content: Optional[str] = None
@@ -13,4 +15,4 @@ class LLMGenerationDataModel(BaseDataModel):
     tool_calls: Optional[List["ToolCall"]] = None
     prompt: Optional[str] = None
     selected_expressions: Optional[List[int]] = None
-    reply_set: Optional[List[Tuple[str, Any]]] = None
+    reply_set: Optional[List[Tuple[str, Any]]] = None

src/common/database/database_model.py

@@ -135,7 +135,7 @@ class Messages(BaseModel):
     interest_value = DoubleField(null=True)
     key_words = TextField(null=True)
     key_words_lite = TextField(null=True)
-    
+
     is_mentioned = BooleanField(null=True)
     is_at = BooleanField(null=True)
     reply_probability_boost = DoubleField(null=True)
@@ -169,7 +169,7 @@ class Messages(BaseModel):
     is_picid = BooleanField(default=False)
     is_command = BooleanField(default=False)
     is_notify = BooleanField(default=False)
-    
+
     selected_expressions = TextField(null=True)
 
     class Meta:
@@ -267,13 +267,10 @@ class PersonInfo(BaseModel):
     know_times = FloatField(null=True)  # 认识时间 (时间戳)
     know_since = FloatField(null=True)  # 首次印象总结时间
     last_know = FloatField(null=True)  # 最后一次印象总结时间
-    
+
     attitude_to_me = TextField(null=True)  # 对bot的态度
     attitude_to_me_confidence = FloatField(null=True)  # 对bot的态度置信度
 
-
-
-
     class Meta:
         # database = db # 继承自 BaseModel
         table_name = "person_info"
@@ -299,6 +296,7 @@ class GroupInfo(BaseModel):
         # database = db # 继承自 BaseModel
         table_name = "group_info"
 
+
 class Expression(BaseModel):
     """
     用于存储表达风格的模型。
@@ -315,6 +313,7 @@ class Expression(BaseModel):
     class Meta:
         table_name = "expression"
 
+
 class GraphNodes(BaseModel):
     """
     用于存储记忆图节点的模型
@@ -374,7 +373,7 @@ def initialize_database(sync_constraints=False):
     """
     检查所有定义的表是否存在，如果不存在则创建它们。
     检查所有表的所有字段是否存在，如果缺失则自动添加。
-    
+
     Args:
         sync_constraints (bool): 是否同步字段约束。默认为 False。
                                如果为 True，会检查并修复字段的 NULL 约束不一致问题。
@@ -456,13 +455,13 @@ def initialize_database(sync_constraints=False):
                         logger.info(f"字段 '{field_name}' 删除成功")
                     except Exception as e:
                         logger.error(f"删除字段 '{field_name}' 失败: {e}")
-                        
+
         # 如果启用了约束同步，执行约束检查和修复
         if sync_constraints:
             logger.debug("开始同步数据库字段约束...")
             sync_field_constraints()
             logger.debug("数据库字段约束同步完成")
-            
+
     except Exception as e:
         logger.exception(f"检查表或字段是否存在时出错: {e}")
         # 如果检查失败（例如数据库不可用），则退出
@@ -476,7 +475,7 @@ def sync_field_constraints():
     同步数据库字段约束，确保现有数据库字段的 NULL 约束与模型定义一致。
     如果发现不一致，会自动修复字段约束。
     """
-    
+
     models = [
         ChatStreams,
         LLMUsage,
@@ -501,50 +500,55 @@ def sync_field_constraints():
                     continue
 
                 logger.debug(f"检查表 '{table_name}' 的字段约束...")
-                
+
                 # 获取当前表结构信息
                 cursor = db.execute_sql(f"PRAGMA table_info('{table_name}')")
-                current_schema = {row[1]: {'type': row[2], 'notnull': bool(row[3]), 'default': row[4]} 
-                                for row in cursor.fetchall()}
-                
+                current_schema = {
+                    row[1]: {"type": row[2], "notnull": bool(row[3]), "default": row[4]} for row in cursor.fetchall()
+                }
+
                 # 检查每个模型字段的约束
                 constraints_to_fix = []
                 for field_name, field_obj in model._meta.fields.items():
                     if field_name not in current_schema:
                         continue  # 字段不存在，跳过
-                    
-                    current_notnull = current_schema[field_name]['notnull']
+
+                    current_notnull = current_schema[field_name]["notnull"]
                     model_allows_null = field_obj.null
-                    
+
                     # 如果模型允许 null 但数据库字段不允许 null，需要修复
                     if model_allows_null and current_notnull:
-                        constraints_to_fix.append({
-                            'field_name': field_name,
-                            'field_obj': field_obj,
-                            'action': 'allow_null',
-                            'current_constraint': 'NOT NULL',
-                            'target_constraint': 'NULL'
-                        })
+                        constraints_to_fix.append(
+                            {
+                                "field_name": field_name,
+                                "field_obj": field_obj,
+                                "action": "allow_null",
+                                "current_constraint": "NOT NULL",
+                                "target_constraint": "NULL",
+                            }
+                        )
                         logger.warning(f"字段 '{field_name}' 约束不一致: 模型允许NULL，但数据库为NOT NULL")
-                    
+
                     # 如果模型不允许 null 但数据库字段允许 null，也需要修复（但要小心）
                     elif not model_allows_null and not current_notnull:
-                        constraints_to_fix.append({
-                            'field_name': field_name,
-                            'field_obj': field_obj,
-                            'action': 'disallow_null',
-                            'current_constraint': 'NULL',
-                            'target_constraint': 'NOT NULL'
-                        })
+                        constraints_to_fix.append(
+                            {
+                                "field_name": field_name,
+                                "field_obj": field_obj,
+                                "action": "disallow_null",
+                                "current_constraint": "NULL",
+                                "target_constraint": "NOT NULL",
+                            }
+                        )
                         logger.warning(f"字段 '{field_name}' 约束不一致: 模型不允许NULL，但数据库允许NULL")
-                
+
                 # 修复约束不一致的字段
                 if constraints_to_fix:
                     logger.info(f"表 '{table_name}' 需要修复 {len(constraints_to_fix)} 个字段约束")
                     _fix_table_constraints(table_name, model, constraints_to_fix)
                 else:
                     logger.debug(f"表 '{table_name}' 的字段约束已同步")
-                    
+
     except Exception as e:
         logger.exception(f"同步字段约束时出错: {e}")
 
@@ -557,40 +561,39 @@ def _fix_table_constraints(table_name, model, constraints_to_fix):
     try:
         # 备份表名
         backup_table = f"{table_name}_backup_{int(datetime.datetime.now().timestamp())}"
-        
+
         logger.info(f"开始修复表 '{table_name}' 的字段约束...")
-        
+
         # 1. 创建备份表
         db.execute_sql(f"CREATE TABLE {backup_table} AS SELECT * FROM {table_name}")
         logger.info(f"已创建备份表 '{backup_table}'")
-        
+
         # 2. 删除原表
         db.execute_sql(f"DROP TABLE {table_name}")
         logger.info(f"已删除原表 '{table_name}'")
-        
+
         # 3. 重新创建表（使用当前模型定义）
         db.create_tables([model])
         logger.info(f"已重新创建表 '{table_name}' 使用新的约束")
-        
+
         # 4. 从备份表恢复数据
         # 获取字段列表
         fields = list(model._meta.fields.keys())
-        fields_str = ', '.join(fields)
-        
+        fields_str = ", ".join(fields)
+
         # 对于需要从 NOT NULL 改为 NULL 的字段，直接复制数据
         # 对于需要从 NULL 改为 NOT NULL 的字段，需要处理 NULL 值
         insert_sql = f"INSERT INTO {table_name} ({fields_str}) SELECT {fields_str} FROM {backup_table}"
-        
+
         # 检查是否有字段需要从 NULL 改为 NOT NULL
         null_to_notnull_fields = [
-            constraint['field_name'] for constraint in constraints_to_fix 
-            if constraint['action'] == 'disallow_null'
+            constraint["field_name"] for constraint in constraints_to_fix if constraint["action"] == "disallow_null"
         ]
-        
+
         if null_to_notnull_fields:
             # 需要处理 NULL 值，为这些字段设置默认值
             logger.warning(f"字段 {null_to_notnull_fields} 将从允许NULL改为不允许NULL，需要处理现有的NULL值")
-            
+
             # 构建更复杂的 SELECT 语句来处理 NULL 值
             select_fields = []
             for field_name in fields:
@@ -607,21 +610,21 @@ def _fix_table_constraints(table_name, model, constraints_to_fix):
                         default_value = f"'{datetime.datetime.now()}'"
                     else:
                         default_value = "''"
-                    
+
                     select_fields.append(f"COALESCE({field_name}, {default_value}) as {field_name}")
                 else:
                     select_fields.append(field_name)
-            
-            select_str = ', '.join(select_fields)
+
+            select_str = ", ".join(select_fields)
             insert_sql = f"INSERT INTO {table_name} ({fields_str}) SELECT {select_str} FROM {backup_table}"
-        
+
         db.execute_sql(insert_sql)
         logger.info(f"已从备份表恢复数据到 '{table_name}'")
-        
+
         # 5. 验证数据完整性
         original_count = db.execute_sql(f"SELECT COUNT(*) FROM {backup_table}").fetchone()[0]
         new_count = db.execute_sql(f"SELECT COUNT(*) FROM {table_name}").fetchone()[0]
-        
+
         if original_count == new_count:
             logger.info(f"数据完整性验证通过: {original_count} 行数据")
             # 删除备份表
@@ -630,12 +633,14 @@ def _fix_table_constraints(table_name, model, constraints_to_fix):
         else:
             logger.error(f"数据完整性验证失败: 原始 {original_count} 行，新表 {new_count} 行")
             logger.error(f"备份表 '{backup_table}' 已保留，请手动检查")
-        
+
         # 记录修复的约束
         for constraint in constraints_to_fix:
-            logger.info(f"已修复字段 '{constraint['field_name']}': "
-                       f"{constraint['current_constraint']} -> {constraint['target_constraint']}")
-                       
+            logger.info(
+                f"已修复字段 '{constraint['field_name']}': "
+                f"{constraint['current_constraint']} -> {constraint['target_constraint']}"
+            )
+
     except Exception as e:
         logger.exception(f"修复表 '{table_name}' 约束时出错: {e}")
         # 尝试恢复
@@ -654,7 +659,7 @@ def check_field_constraints():
     检查但不修复字段约束，返回不一致的字段信息。
     用于在修复前预览需要修复的内容。
     """
-    
+
     models = [
         ChatStreams,
         LLMUsage,
@@ -669,9 +674,9 @@ def check_field_constraints():
         GraphEdges,
         ActionRecords,
     ]
-    
+
     inconsistencies = {}
-    
+
     try:
         with db:
             for model in models:
@@ -681,49 +686,49 @@ def check_field_constraints():
 
                 # 获取当前表结构信息
                 cursor = db.execute_sql(f"PRAGMA table_info('{table_name}')")
-                current_schema = {row[1]: {'type': row[2], 'notnull': bool(row[3]), 'default': row[4]} 
-                                for row in cursor.fetchall()}
-                
+                current_schema = {
+                    row[1]: {"type": row[2], "notnull": bool(row[3]), "default": row[4]} for row in cursor.fetchall()
+                }
+
                 table_inconsistencies = []
-                
+
                 # 检查每个模型字段的约束
                 for field_name, field_obj in model._meta.fields.items():
                     if field_name not in current_schema:
                         continue
-                    
-                    current_notnull = current_schema[field_name]['notnull']
+
+                    current_notnull = current_schema[field_name]["notnull"]
                     model_allows_null = field_obj.null
-                    
+
                     if model_allows_null and current_notnull:
-                        table_inconsistencies.append({
-                            'field_name': field_name,
-                            'issue': 'model_allows_null_but_db_not_null',
-                            'model_constraint': 'NULL',
-                            'db_constraint': 'NOT NULL',
-                            'recommended_action': 'allow_null'
-                        })
+                        table_inconsistencies.append(
+                            {
+                                "field_name": field_name,
+                                "issue": "model_allows_null_but_db_not_null",
+                                "model_constraint": "NULL",
+                                "db_constraint": "NOT NULL",
+                                "recommended_action": "allow_null",
+                            }
+                        )
                     elif not model_allows_null and not current_notnull:
-                        table_inconsistencies.append({
-                            'field_name': field_name,
-                            'issue': 'model_not_null_but_db_allows_null',
-                            'model_constraint': 'NOT NULL',
-                            'db_constraint': 'NULL',
-                            'recommended_action': 'disallow_null'
-                        })
-                
+                        table_inconsistencies.append(
+                            {
+                                "field_name": field_name,
+                                "issue": "model_not_null_but_db_allows_null",
+                                "model_constraint": "NOT NULL",
+                                "db_constraint": "NULL",
+                                "recommended_action": "disallow_null",
+                            }
+                        )
+
                 if table_inconsistencies:
                     inconsistencies[table_name] = table_inconsistencies
-                    
+
     except Exception as e:
         logger.exception(f"检查字段约束时出错: {e}")
-    
-    return inconsistencies
 
+    return inconsistencies
 
 
 # 模块加载时调用初始化函数
 initialize_database(sync_constraints=True)
-
-
-
-

src/common/logger.py

@@ -339,24 +339,18 @@ MODULE_COLORS = {
     #   67         ：具体的颜色编号（0-255），这里是较暗的蓝色
     "sender": "\033[38;5;24m",  # 67号色，较暗的蓝色，适合不显眼的日志
     "send_api": "\033[38;5;24m",  # 208号色，橙色，适合突出显示
-    
     # 生成
     "replyer": "\033[38;5;208m",  # 橙色
     "llm_api": "\033[38;5;208m",  # 橙色
-    
     # 消息处理
     "chat": "\033[38;5;82m",  # 亮蓝色
     "chat_image": "\033[38;5;68m",  # 浅蓝色
-    
-    #emoji
+    # emoji
     "emoji": "\033[38;5;214m",  # 橙黄色，偏向橙色
     "emoji_api": "\033[38;5;214m",  # 橙黄色，偏向橙色
-    
     # 核心模块
     "main": "\033[1;97m",  # 亮白色+粗体 (主程序)
-
     "memory": "\033[38;5;34m",  # 天蓝色
-    
     "config": "\033[93m",  # 亮黄色
     "common": "\033[95m",  # 亮紫色
     "tools": "\033[96m",  # 亮青色
@@ -367,9 +361,6 @@ MODULE_COLORS = {
     "llm_models": "\033[36m",  # 青色
     "remote": "\033[38;5;242m",  # 深灰色，更不显眼
     "planner": "\033[36m",
-   
-
-
     "relation": "\033[38;5;139m",  # 柔和的紫色，不刺眼
     # 聊天相关模块
     "normal_chat": "\033[38;5;81m",  # 亮蓝绿色
@@ -379,11 +370,9 @@ MODULE_COLORS = {
     "background_tasks": "\033[38;5;240m",  # 灰色
     "chat_message": "\033[38;5;45m",  # 青色
     "chat_stream": "\033[38;5;51m",  # 亮青色
-
     "message_storage": "\033[38;5;33m",  # 深蓝色
     "expressor": "\033[38;5;166m",  # 橙色
     # 专注聊天模块
-    
     "memory_activator": "\033[38;5;117m",  # 天蓝色
     # 插件系统
     "plugins": "\033[31m",  # 红色
@@ -412,7 +401,6 @@ MODULE_COLORS = {
     # 工具和实用模块
     "prompt_build": "\033[38;5;105m",  # 紫色
     "chat_utils": "\033[38;5;111m",  # 蓝色
-    
     "maibot_statistic": "\033[38;5;129m",  # 紫色
     # 特殊功能插件
     "mute_plugin": "\033[38;5;240m",  # 灰色
@@ -447,10 +435,8 @@ MODULE_ALIASES = {
     "llm_api": "生成API",
     "emoji": "表情包",
     "emoji_api": "表情包API",
-    
     "chat": "所见",
     "chat_image": "识图",
-    
     "action_manager": "动作",
     "memory_activator": "记忆",
     "tool_use": "工具",
@@ -460,7 +446,6 @@ MODULE_ALIASES = {
     "memory": "记忆",
     "tool_executor": "工具",
     "hfc": "聊天节奏",
-    
     "plugin_manager": "插件",
     "relationship_builder": "关系",
     "llm_models": "模型",

src/config/config.py

@@ -114,7 +114,7 @@ def set_value_by_path(d, path, value):
         if k not in d or not isinstance(d[k], dict):
             d[k] = {}
         d = d[k]
-    
+
     # 使用 tomlkit.item 来保持 TOML 格式
     try:
         d[path[-1]] = tomlkit.item(value)
@@ -253,7 +253,7 @@ def _update_config_generic(config_name: str, template_name: str):
                         f"已自动将{config_name}配置 {'.'.join(path)} 的值从旧默认值 {old_default} 更新为新默认值 {new_default}"
                     )
                     config_updated = True
-            
+
             # 如果配置有更新，立即保存到文件
             if config_updated:
                 with open(old_config_path, "w", encoding="utf-8") as f:

src/config/official_configs.py

@@ -43,10 +43,11 @@ class PersonalityConfig(ConfigBase):
 
     reply_style: str = ""
     """表达风格"""
-    
+
     interest: str = ""
     """兴趣"""
 
+
 @dataclass
 class RelationshipConfig(ConfigBase):
     """关系配置类"""
@@ -61,31 +62,30 @@ class ChatConfig(ConfigBase):
 
     max_context_size: int = 18
     """上下文长度"""
-    
+
     interest_rate_mode: Literal["fast", "accurate"] = "fast"
     """兴趣值计算模式，fast为快速计算，accurate为精确计算"""
 
     mentioned_bot_reply: float = 1
     """提及 bot 必然回复，1为100%回复，0为不额外增幅"""
-    
+
     planner_size: float = 1.5
     """副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误"""
 
     at_bot_inevitable_reply: float = 1
     """@bot 必然回复，1为100%回复，0为不额外增幅"""
-    
+
     talk_frequency: float = 0.5
     """回复频率阈值"""
 
     # 合并后的时段频率配置
     talk_frequency_adjust: list[list[str]] = field(default_factory=lambda: [])
 
-
     focus_value: float = 0.5
     """麦麦的专注思考能力，越低越容易专注，消耗token也越多"""
-    
+
     focus_value_adjust: list[list[str]] = field(default_factory=lambda: [])
-    
+
     """
     统一的活跃度和专注度配置
     格式：[["platform:chat_id:type", "HH:MM,frequency", "HH:MM,frequency", ...], ...]
@@ -110,7 +110,6 @@ class ChatConfig(ConfigBase):
     - talk_frequency_adjust 控制回复频率，数值越高回复越频繁
     - focus_value_adjust 控制专注思考能力，数值越低越容易专注，消耗token也越多
     """
-    
 
 
 @dataclass
@@ -123,6 +122,7 @@ class MessageReceiveConfig(ConfigBase):
     ban_msgs_regex: set[str] = field(default_factory=lambda: set())
     """过滤正则表达式列表"""
 
+
 @dataclass
 class ExpressionConfig(ConfigBase):
     """表达配置类"""

src/llm_models/model_client/base_client.py

@@ -174,7 +174,7 @@ class ClientRegistry:
                 return client_class(api_provider)
             else:
                 raise KeyError(f"'{api_provider.client_type}' 类型的 Client 未注册")
-        
+
         # 正常的缓存逻辑
         if api_provider.name not in self.client_instance_cache:
             if client_class := self.client_registry.get(api_provider.client_type):

src/llm_models/model_client/openai_client.py

@@ -531,7 +531,7 @@ class OpenaiClient(BaseClient):
             # 添加详细的错误信息以便调试
             logger.error(f"OpenAI API连接错误（嵌入模型）: {str(e)}")
             logger.error(f"错误类型: {type(e)}")
-            if hasattr(e, '__cause__') and e.__cause__:
+            if hasattr(e, "__cause__") and e.__cause__:
                 logger.error(f"底层错误: {str(e.__cause__)}")
             raise NetworkConnectionError() from e
         except APIStatusError as e:

src/llm_models/payload_content/__init__.py

@@ -1,3 +1,3 @@
 from .tool_option import ToolCall
 
-__all__ = ["ToolCall"]
+__all__ = ["ToolCall"]

src/llm_models/payload_content/resp_format.py

@@ -48,8 +48,7 @@ def _json_schema_type_check(instance) -> str | None:
     elif not isinstance(instance["name"], str) or instance["name"].strip() == "":
         return "schema的'name'字段必须是非空字符串"
     if "description" in instance and (
-        not isinstance(instance["description"], str)
-        or instance["description"].strip() == ""
+        not isinstance(instance["description"], str) or instance["description"].strip() == ""
     ):
         return "schema的'description'字段只能填入非空字符串"
     if "schema" not in instance:
@@ -101,9 +100,7 @@ def _link_definitions(schema: dict[str, Any]) -> dict[str, Any]:
             # 如果当前Schema是列表，则遍历每个元素
             for i in range(len(sub_schema)):
                 if isinstance(sub_schema[i], dict):
-                    sub_schema[i] = link_definitions_recursive(
-                        f"{path}/{str(i)}", sub_schema[i], defs
-                    )
+                    sub_schema[i] = link_definitions_recursive(f"{path}/{str(i)}", sub_schema[i], defs)
         else:
             # 否则为字典
             if "$defs" in sub_schema:
@@ -125,9 +122,7 @@ def _link_definitions(schema: dict[str, Any]) -> dict[str, Any]:
             for key, value in sub_schema.items():
                 if isinstance(value, (dict, list)):
                     # 如果当前值是字典或列表，则递归调用
-                    sub_schema[key] = link_definitions_recursive(
-                        f"{path}/{key}", value, defs
-                    )
+                    sub_schema[key] = link_definitions_recursive(f"{path}/{key}", value, defs)
 
         return sub_schema
 
@@ -163,9 +158,7 @@ class RespFormat:
     def _generate_schema_from_model(schema):
         json_schema = {
             "name": schema.__name__,
-            "schema": _remove_defs(
-                _link_definitions(_remove_title(schema.model_json_schema()))
-            ),
+            "schema": _remove_defs(_link_definitions(_remove_title(schema.model_json_schema()))),
             "strict": False,
         }
         if schema.__doc__:

src/llm_models/utils.py

@@ -155,7 +155,13 @@ class LLMUsageRecorder:
             logger.error(f"创建 LLMUsage 表失败: {str(e)}")
 
     def record_usage_to_database(
-        self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str, time_cost: float = 0.0
+        self,
+        model_info: ModelInfo,
+        model_usage: UsageRecord,
+        user_id: str,
+        request_type: str,
+        endpoint: str,
+        time_cost: float = 0.0,
     ):
         input_cost = (model_usage.prompt_tokens / 1000000) * model_info.price_in
         output_cost = (model_usage.completion_tokens / 1000000) * model_info.price_out
@@ -173,7 +179,7 @@ class LLMUsageRecorder:
                 completion_tokens=model_usage.completion_tokens or 0,
                 total_tokens=model_usage.total_tokens or 0,
                 cost=total_cost or 0.0,
-                time_cost = round(time_cost or 0.0, 3),
+                time_cost=round(time_cost or 0.0, 3),
                 status="success",
                 timestamp=datetime.now(),  # Peewee 会处理 DateTimeField
             )
@@ -186,4 +192,5 @@ class LLMUsageRecorder:
         except Exception as e:
             logger.error(f"记录token使用情况失败: {str(e)}")
 
-llm_usage_recorder = LLMUsageRecorder()
+
+llm_usage_recorder = LLMUsageRecorder()

src/mais4u/mais4u_chat/context_web_manager.py

@@ -14,31 +14,31 @@ logger = get_logger("context_web")
 
 class ContextMessage:
     """上下文消息类"""
-    
+
     def __init__(self, message: MessageRecv):
         self.user_name = message.message_info.user_info.user_nickname
         self.user_id = message.message_info.user_info.user_id
         self.content = message.processed_plain_text
         self.timestamp = datetime.now()
         self.group_name = message.message_info.group_info.group_name if message.message_info.group_info else "私聊"
-        
+
         # 识别消息类型
-        self.is_gift = getattr(message, 'is_gift', False)
-        self.is_superchat = getattr(message, 'is_superchat', False)
-        
+        self.is_gift = getattr(message, "is_gift", False)
+        self.is_superchat = getattr(message, "is_superchat", False)
+
         # 添加礼物和SC相关信息
         if self.is_gift:
-            self.gift_name = getattr(message, 'gift_name', '')
-            self.gift_count = getattr(message, 'gift_count', '1')
+            self.gift_name = getattr(message, "gift_name", "")
+            self.gift_count = getattr(message, "gift_count", "1")
             self.content = f"送出了 {self.gift_name} x{self.gift_count}"
         elif self.is_superchat:
-            self.superchat_price = getattr(message, 'superchat_price', '0')
-            self.superchat_message = getattr(message, 'superchat_message_text', '')
+            self.superchat_price = getattr(message, "superchat_price", "0")
+            self.superchat_message = getattr(message, "superchat_message_text", "")
             if self.superchat_message:
                 self.content = f"[¥{self.superchat_price}] {self.superchat_message}"
             else:
                 self.content = f"[¥{self.superchat_price}] {self.content}"
-        
+
     def to_dict(self):
         return {
             "user_name": self.user_name,
@@ -47,13 +47,13 @@ class ContextMessage:
             "timestamp": self.timestamp.strftime("%m-%d %H:%M:%S"),
             "group_name": self.group_name,
             "is_gift": self.is_gift,
-            "is_superchat": self.is_superchat
+            "is_superchat": self.is_superchat,
         }
 
 
 class ContextWebManager:
     """上下文网页管理器"""
-    
+
     def __init__(self, max_messages: int = 10, port: int = 8765):
         self.max_messages = max_messages
         self.port = port
@@ -63,53 +63,53 @@ class ContextWebManager:
         self.runner = None
         self.site = None
         self._server_starting = False  # 添加启动标志防止并发
-        
+
     async def start_server(self):
         """启动web服务器"""
         if self.site is not None:
             logger.debug("Web服务器已经启动，跳过重复启动")
             return
-            
+
         if self._server_starting:
             logger.debug("Web服务器正在启动中，等待启动完成...")
             # 等待启动完成
             while self._server_starting and self.site is None:
                 await asyncio.sleep(0.1)
             return
-            
+
         self._server_starting = True
-        
+
         try:
             self.app = web.Application()
-            
+
             # 设置CORS
-            cors = aiohttp_cors.setup(self.app, defaults={
-                "*": aiohttp_cors.ResourceOptions(
-                    allow_credentials=True,
-                    expose_headers="*",
-                    allow_headers="*",
-                    allow_methods="*"
-                )
-            })
-            
+            cors = aiohttp_cors.setup(
+                self.app,
+                defaults={
+                    "*": aiohttp_cors.ResourceOptions(
+                        allow_credentials=True, expose_headers="*", allow_headers="*", allow_methods="*"
+                    )
+                },
+            )
+
             # 添加路由
-            self.app.router.add_get('/', self.index_handler)
-            self.app.router.add_get('/ws', self.websocket_handler)
-            self.app.router.add_get('/api/contexts', self.get_contexts_handler)
-            self.app.router.add_get('/debug', self.debug_handler)
-            
+            self.app.router.add_get("/", self.index_handler)
+            self.app.router.add_get("/ws", self.websocket_handler)
+            self.app.router.add_get("/api/contexts", self.get_contexts_handler)
+            self.app.router.add_get("/debug", self.debug_handler)
+
             # 为所有路由添加CORS
             for route in list(self.app.router.routes()):
                 cors.add(route)
-            
+
             self.runner = web.AppRunner(self.app)
             await self.runner.setup()
-            
-            self.site = web.TCPSite(self.runner, 'localhost', self.port)
+
+            self.site = web.TCPSite(self.runner, "localhost", self.port)
             await self.site.start()
-            
+
             logger.info(f"🌐 上下文网页服务器启动成功在 http://localhost:{self.port}")
-            
+
         except Exception as e:
             logger.error(f"❌ 启动Web服务器失败: {e}")
             # 清理部分启动的资源
@@ -121,7 +121,7 @@ class ContextWebManager:
             raise
         finally:
             self._server_starting = False
-        
+
     async def stop_server(self):
         """停止web服务器"""
         if self.site:
@@ -132,10 +132,11 @@ class ContextWebManager:
         self.runner = None
         self.site = None
         self._server_starting = False
-        
+
     async def index_handler(self, request):
         """主页处理器"""
-        html_content = '''
+        html_content = (
+            """
 <!DOCTYPE html>
 <html>
 <head>
@@ -286,7 +287,9 @@ class ContextWebManager:
         
                  function connectWebSocket() {
              console.log('正在连接WebSocket...');
-             ws = new WebSocket('ws://localhost:''' + str(self.port) + '''/ws');
+             ws = new WebSocket('ws://localhost:"""
+            + str(self.port)
+            + """/ws');
              
              ws.onopen = function() {
                  console.log('WebSocket连接已建立');
@@ -470,47 +473,48 @@ class ContextWebManager:
     </script>
 </body>
 </html>
-        '''
-        return web.Response(text=html_content, content_type='text/html')
-        
+        """
+        )
+        return web.Response(text=html_content, content_type="text/html")
+
     async def websocket_handler(self, request):
         """WebSocket处理器"""
         ws = web.WebSocketResponse()
         await ws.prepare(request)
-        
+
         self.websockets.append(ws)
         logger.debug(f"WebSocket连接建立，当前连接数: {len(self.websockets)}")
-        
+
         # 发送初始数据
         await self.send_contexts_to_websocket(ws)
-        
+
         async for msg in ws:
             if msg.type == WSMsgType.ERROR:
-                logger.error(f'WebSocket错误: {ws.exception()}')
+                logger.error(f"WebSocket错误: {ws.exception()}")
                 break
-                
+
         # 清理断开的连接
         if ws in self.websockets:
             self.websockets.remove(ws)
         logger.debug(f"WebSocket连接断开，当前连接数: {len(self.websockets)}")
-        
+
         return ws
-        
+
     async def get_contexts_handler(self, request):
         """获取上下文API"""
         all_context_msgs = []
         for _chat_id, contexts in self.contexts.items():
             all_context_msgs.extend(list(contexts))
-        
+
         # 按时间排序，最新的在最后
         all_context_msgs.sort(key=lambda x: x.timestamp)
-        
+
         # 转换为字典格式
-        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages:]]
-        
+        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages :]]
+
         logger.debug(f"返回上下文数据，共 {len(contexts_data)} 条消息")
         return web.json_response({"contexts": contexts_data})
-        
+
     async def debug_handler(self, request):
         """调试信息处理器"""
         debug_info = {
@@ -519,7 +523,7 @@ class ContextWebManager:
             "total_chats": len(self.contexts),
             "total_messages": sum(len(contexts) for contexts in self.contexts.values()),
         }
-        
+
         # 构建聊天详情HTML
         chats_html = ""
         for chat_id, contexts in self.contexts.items():
@@ -528,15 +532,15 @@ class ContextWebManager:
                 timestamp = msg.timestamp.strftime("%H:%M:%S")
                 content = msg.content[:50] + "..." if len(msg.content) > 50 else msg.content
                 messages_html += f'<div class="message">[{timestamp}] {msg.user_name}: {content}</div>'
-            
-            chats_html += f'''
+
+            chats_html += f"""
             <div class="chat">
                 <h3>聊天 {chat_id} ({len(contexts)} 条消息)</h3>
                 {messages_html}
             </div>
-            '''
-        
-        html_content = f'''
+            """
+
+        html_content = f"""
 <!DOCTYPE html>
 <html>
 <head>
@@ -578,74 +582,78 @@ class ContextWebManager:
     </script>
 </body>
 </html>
-        '''
-        
-        return web.Response(text=html_content, content_type='text/html')
-        
+        """
+
+        return web.Response(text=html_content, content_type="text/html")
+
     async def add_message(self, chat_id: str, message: MessageRecv):
         """添加新消息到上下文"""
         if chat_id not in self.contexts:
             self.contexts[chat_id] = deque(maxlen=self.max_messages)
             logger.debug(f"为聊天 {chat_id} 创建新的上下文队列")
-            
+
         context_msg = ContextMessage(message)
         self.contexts[chat_id].append(context_msg)
-        
+
         # 统计当前总消息数
         total_messages = sum(len(contexts) for contexts in self.contexts.values())
-        
-        logger.info(f"✅ 添加消息到上下文 [总数: {total_messages}]: [{context_msg.group_name}] {context_msg.user_name}: {context_msg.content}")
-        
+
+        logger.info(
+            f"✅ 添加消息到上下文 [总数: {total_messages}]: [{context_msg.group_name}] {context_msg.user_name}: {context_msg.content}"
+        )
+
         # 调试：打印当前所有消息
         logger.info("📝 当前上下文中的所有消息：")
         for cid, contexts in self.contexts.items():
             logger.info(f"  聊天 {cid}: {len(contexts)} 条消息")
             for i, msg in enumerate(contexts):
-                logger.info(f"    {i+1}. [{msg.timestamp.strftime('%H:%M:%S')}] {msg.user_name}: {msg.content[:30]}...")
-        
+                logger.info(
+                    f"    {i + 1}. [{msg.timestamp.strftime('%H:%M:%S')}] {msg.user_name}: {msg.content[:30]}..."
+                )
+
         # 广播更新给所有WebSocket连接
         await self.broadcast_contexts()
-        
+
     async def send_contexts_to_websocket(self, ws: web.WebSocketResponse):
         """向单个WebSocket发送上下文数据"""
         all_context_msgs = []
         for _chat_id, contexts in self.contexts.items():
             all_context_msgs.extend(list(contexts))
-        
+
         # 按时间排序，最新的在最后
         all_context_msgs.sort(key=lambda x: x.timestamp)
-        
+
         # 转换为字典格式
-        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages:]]
-        
+        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages :]]
+
         data = {"contexts": contexts_data}
         await ws.send_str(json.dumps(data, ensure_ascii=False))
-        
+
     async def broadcast_contexts(self):
         """向所有WebSocket连接广播上下文更新"""
         if not self.websockets:
             logger.debug("没有WebSocket连接，跳过广播")
             return
-            
+
         all_context_msgs = []
         for _chat_id, contexts in self.contexts.items():
             all_context_msgs.extend(list(contexts))
-        
+
         # 按时间排序，最新的在最后
         all_context_msgs.sort(key=lambda x: x.timestamp)
-        
+
         # 转换为字典格式
-        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages:]]
-        
+        contexts_data = [msg.to_dict() for msg in all_context_msgs[-self.max_messages :]]
+
         data = {"contexts": contexts_data}
         message = json.dumps(data, ensure_ascii=False)
-        
+
         logger.info(f"广播 {len(contexts_data)} 条消息到 {len(self.websockets)} 个WebSocket连接")
-        
+
         # 创建WebSocket列表的副本，避免在遍历时修改
         websockets_copy = self.websockets.copy()
         removed_count = 0
-        
+
         for ws in websockets_copy:
             if ws.closed:
                 if ws in self.websockets:
@@ -660,7 +668,7 @@ class ContextWebManager:
                     if ws in self.websockets:
                         self.websockets.remove(ws)
                         removed_count += 1
-        
+
         if removed_count > 0:
             logger.debug(f"清理了 {removed_count} 个断开的WebSocket连接")
 
@@ -681,5 +689,4 @@ async def init_context_web_manager():
     """初始化上下文网页管理器"""
     manager = get_context_web_manager()
     await manager.start_server()
-    return manager 
-
+    return manager

src/mais4u/mais4u_chat/gift_manager.py

@@ -11,6 +11,7 @@ logger = get_logger("gift_manager")
 @dataclass
 class PendingGift:
     """等待中的礼物消息"""
+
     message: MessageRecvS4U
     total_count: int
     timer_task: asyncio.Task
@@ -19,71 +20,68 @@ class PendingGift:
 
 class GiftManager:
     """礼物管理器，提供防抖功能"""
-    
+
     def __init__(self):
         """初始化礼物管理器"""
         self.pending_gifts: Dict[Tuple[str, str], PendingGift] = {}
         self.debounce_timeout = 5.0  # 3秒防抖时间
-    
-    async def handle_gift(self, message: MessageRecvS4U, callback: Optional[Callable[[MessageRecvS4U], None]] = None) -> bool:
+
+    async def handle_gift(
+        self, message: MessageRecvS4U, callback: Optional[Callable[[MessageRecvS4U], None]] = None
+    ) -> bool:
         """处理礼物消息，返回是否应该立即处理
-        
+
         Args:
             message: 礼物消息
             callback: 防抖完成后的回调函数
-            
+
         Returns:
             bool: False表示消息被暂存等待防抖，True表示应该立即处理
         """
         if not message.is_gift:
             return True
-            
+
         # 构建礼物的唯一键：(发送人ID, 礼物名称)
         gift_key = (message.message_info.user_info.user_id, message.gift_name)
-        
+
         # 如果已经有相同的礼物在等待中，则合并
         if gift_key in self.pending_gifts:
             await self._merge_gift(gift_key, message)
             return False
-        
+
         # 创建新的等待礼物
         await self._create_pending_gift(gift_key, message, callback)
         return False
-    
+
     async def _merge_gift(self, gift_key: Tuple[str, str], new_message: MessageRecvS4U) -> None:
         """合并礼物消息"""
         pending_gift = self.pending_gifts[gift_key]
-        
+
         # 取消之前的定时器
         if not pending_gift.timer_task.cancelled():
             pending_gift.timer_task.cancel()
-        
+
         # 累加礼物数量
         try:
             new_count = int(new_message.gift_count)
             pending_gift.total_count += new_count
-            
+
             # 更新消息为最新的（保留最新的消息，但累加数量）
             pending_gift.message = new_message
             pending_gift.message.gift_count = str(pending_gift.total_count)
             pending_gift.message.gift_info = f"{pending_gift.message.gift_name}:{pending_gift.total_count}"
-            
+
         except ValueError:
             logger.warning(f"无法解析礼物数量: {new_message.gift_count}")
             # 如果无法解析数量，保持原有数量不变
-        
+
         # 重新创建定时器
-        pending_gift.timer_task = asyncio.create_task(
-            self._gift_timeout(gift_key)
-        )
-        
+        pending_gift.timer_task = asyncio.create_task(self._gift_timeout(gift_key))
+
         logger.debug(f"合并礼物: {gift_key}, 总数量: {pending_gift.total_count}")
-    
+
     async def _create_pending_gift(
-        self, 
-        gift_key: Tuple[str, str], 
-        message: MessageRecvS4U, 
-        callback: Optional[Callable[[MessageRecvS4U], None]]
+        self, gift_key: Tuple[str, str], message: MessageRecvS4U, callback: Optional[Callable[[MessageRecvS4U], None]]
     ) -> None:
         """创建新的等待礼物"""
         try:
@@ -91,56 +89,51 @@ class GiftManager:
         except ValueError:
             initial_count = 1
             logger.warning(f"无法解析礼物数量: {message.gift_count}，默认设为1")
-        
+
         # 创建定时器任务
         timer_task = asyncio.create_task(self._gift_timeout(gift_key))
-        
+
         # 创建等待礼物对象
-        pending_gift = PendingGift(
-            message=message,
-            total_count=initial_count,
-            timer_task=timer_task,
-            callback=callback
-        )
-        
+        pending_gift = PendingGift(message=message, total_count=initial_count, timer_task=timer_task, callback=callback)
+
         self.pending_gifts[gift_key] = pending_gift
-        
+
         logger.debug(f"创建等待礼物: {gift_key}, 初始数量: {initial_count}")
-    
+
     async def _gift_timeout(self, gift_key: Tuple[str, str]) -> None:
         """礼物防抖超时处理"""
         try:
             # 等待防抖时间
             await asyncio.sleep(self.debounce_timeout)
-            
+
             # 获取等待中的礼物
             if gift_key not in self.pending_gifts:
                 return
-                
+
             pending_gift = self.pending_gifts.pop(gift_key)
-            
+
             logger.info(f"礼物防抖完成: {gift_key}, 最终数量: {pending_gift.total_count}")
-            
+
             message = pending_gift.message
             message.processed_plain_text = f"用户{message.message_info.user_info.user_nickname}送出了礼物{message.gift_name} x{pending_gift.total_count}"
-            
+
             # 执行回调
             if pending_gift.callback:
                 try:
                     pending_gift.callback(message)
                 except Exception as e:
                     logger.error(f"礼物回调执行失败: {e}", exc_info=True)
-            
+
         except asyncio.CancelledError:
             # 定时器被取消，不需要处理
             pass
         except Exception as e:
             logger.error(f"礼物防抖处理异常: {e}", exc_info=True)
-    
+
     def get_pending_count(self) -> int:
         """获取当前等待中的礼物数量"""
         return len(self.pending_gifts)
-    
+
     async def flush_all(self) -> None:
         """立即处理所有等待中的礼物"""
         for gift_key in list(self.pending_gifts.keys()):
@@ -152,4 +145,3 @@ class GiftManager:
 
 # 创建全局礼物管理器实例
 gift_manager = GiftManager()
-    

src/mais4u/mais4u_chat/internal_manager.py

@@ -1,14 +1,15 @@
 class InternalManager:
     def __init__(self):
         self.now_internal_state = str()
-        
-    def set_internal_state(self,internal_state:str):
+
+    def set_internal_state(self, internal_state: str):
         self.now_internal_state = internal_state
-        
+
     def get_internal_state(self):
         return self.now_internal_state
-    
+
     def get_internal_state_str(self):
         return f"你今天的直播内容是直播QQ水群，你正在一边回复弹幕，一边在QQ群聊天，你在QQ群聊天中产生的想法是：{self.now_internal_state}"
 
-internal_manager = InternalManager()
+
+internal_manager = InternalManager()

src/mais4u/mais4u_chat/s4u_chat.py

@@ -16,7 +16,6 @@ import json
 from .s4u_mood_manager import mood_manager
 from src.mais4u.s4u_config import s4u_config
 from src.person_info.person_info import get_person_id
-from .super_chat_manager import get_super_chat_manager
 from .yes_or_no import yes_or_no_head
 
 logger = get_logger("S4U_chat")
@@ -33,15 +32,12 @@ class MessageSenderContainer:
         self._task: Optional[asyncio.Task] = None
         self._paused_event = asyncio.Event()
         self._paused_event.set()  # 默认设置为非暂停状态
-        
-        self.msg_id = ""
-        
-        self.last_msg_id = ""
-        
-        self.voice_done = ""
-        
 
-        
+        self.msg_id = ""
+
+        self.last_msg_id = ""
+
+        self.voice_done = ""
 
     async def add_message(self, chunk: str):
         """向队列中添加一个消息块。"""
@@ -131,7 +127,7 @@ class MessageSenderContainer:
                     reply_to=f"{self.original_message.message_info.user_info.platform}:{self.original_message.message_info.user_info.user_id}",
                 )
                 await bot_message.process()
-                
+
                 await self.storage.store_message(bot_message, self.chat_stream)
 
             except Exception as e:
@@ -198,12 +194,12 @@ class S4UChat:
         self.gpt = S4UStreamGenerator()
         self.gpt.chat_stream = self.chat_stream
         self.interest_dict: Dict[str, float] = {}  # 用户兴趣分
-        
-        self.internal_message :List[MessageRecvS4U] = []
-        
+
+        self.internal_message: List[MessageRecvS4U] = []
+
         self.msg_id = ""
         self.voice_done = ""
-        
+
         logger.info(f"[{self.stream_name}] S4UChat with two-queue system initialized.")
 
     def _get_priority_info(self, message: MessageRecv) -> dict:
@@ -226,7 +222,7 @@ class S4UChat:
     def _get_interest_score(self, user_id: str) -> float:
         """获取用户的兴趣分，默认为1.0"""
         return self.interest_dict.get(user_id, 1.0)
-    
+
     def go_processing(self):
         if self.voice_done == self.last_msg_id:
             return True
@@ -237,14 +233,14 @@ class S4UChat:
         为消息计算基础优先级分数。分数越高，优先级越高。
         """
         score = 0.0
-        
+
         # 加上消息自带的优先级
         score += priority_info.get("message_priority", 0.0)
 
         # 加上用户的固有兴趣分
         score += self._get_interest_score(message.message_info.user_info.user_id)
         return score
-    
+
     def decay_interest_score(self):
         for person_id, score in self.interest_dict.items():
             if score > 0:
@@ -252,15 +248,14 @@ class S4UChat:
             else:
                 self.interest_dict[person_id] = 0
 
-    async def add_message(self, message: MessageRecvS4U|MessageRecv) -> None:
-        
+    async def add_message(self, message: MessageRecvS4U | MessageRecv) -> None:
         self.decay_interest_score()
-        
+
         """根据VIP状态和中断逻辑将消息放入相应队列。"""
         user_id = message.message_info.user_info.user_id
         platform = message.message_info.platform
-        person_id = get_person_id(platform, user_id)
-        
+        _person_id = get_person_id(platform, user_id)
+
         # try:
         #     is_gift = message.is_gift
         #     is_superchat = message.is_superchat
@@ -276,7 +271,7 @@ class S4UChat:
         #         # 安全地增加兴趣分，如果person_id不存在则先初始化为1.0
         #         current_score = self.interest_dict.get(person_id, 1.0)
         #         self.interest_dict[person_id] = current_score + 0.1 * float(message.superchat_price)
-                
+
         #         # 添加SuperChat到管理器
         #         super_chat_manager = get_super_chat_manager()
         #         await super_chat_manager.add_superchat(message)
@@ -284,16 +279,19 @@ class S4UChat:
         #         await self.relationship_builder.build_relation(20)
         # except Exception:
         #     traceback.print_exc()
-            
+
         logger.info(f"[{self.stream_name}] 消息处理完毕，消息内容：{message.processed_plain_text}")
-        
+
         priority_info = self._get_priority_info(message)
         is_vip = self._is_vip(priority_info)
         new_priority_score = self._calculate_base_priority_score(message, priority_info)
 
         should_interrupt = False
-        if (s4u_config.enable_message_interruption and 
-            self._current_generation_task and not self._current_generation_task.done()):
+        if (
+            s4u_config.enable_message_interruption
+            and self._current_generation_task
+            and not self._current_generation_task.done()
+        ):
             if self._current_message_being_replied:
                 current_queue, current_priority, _, current_msg = self._current_message_being_replied
 
@@ -344,39 +342,45 @@ class S4UChat:
         """清理普通队列中不在最近N条消息范围内的消息"""
         if not s4u_config.enable_old_message_cleanup or self._normal_queue.empty():
             return
-        
+
         # 计算阈值：保留最近 recent_message_keep_count 条消息
         cutoff_counter = max(0, self._entry_counter - s4u_config.recent_message_keep_count)
-        
+
         # 临时存储需要保留的消息
         temp_messages = []
         removed_count = 0
-        
+
         # 取出所有普通队列中的消息
         while not self._normal_queue.empty():
             try:
                 item = self._normal_queue.get_nowait()
                 neg_priority, entry_count, timestamp, message = item
-                
+
                 # 如果消息在最近N条消息范围内，保留它
-                logger.info(f"检查消息:{message.processed_plain_text},entry_count:{entry_count} cutoff_counter:{cutoff_counter}")
-                
+                logger.info(
+                    f"检查消息:{message.processed_plain_text},entry_count:{entry_count} cutoff_counter:{cutoff_counter}"
+                )
+
                 if entry_count >= cutoff_counter:
                     temp_messages.append(item)
                 else:
                     removed_count += 1
                     self._normal_queue.task_done()  # 标记被移除的任务为完成
-                    
+
             except asyncio.QueueEmpty:
                 break
-        
+
         # 将保留的消息重新放入队列
         for item in temp_messages:
             self._normal_queue.put_nowait(item)
-        
+
         if removed_count > 0:
-            logger.info(f"消息{message.processed_plain_text}超过{s4u_config.recent_message_keep_count}条，现在counter:{self._entry_counter}被移除")
-            logger.info(f"[{self.stream_name}] Cleaned up {removed_count} old normal messages outside recent {s4u_config.recent_message_keep_count} range.")
+            logger.info(
+                f"消息{message.processed_plain_text}超过{s4u_config.recent_message_keep_count}条，现在counter:{self._entry_counter}被移除"
+            )
+            logger.info(
+                f"[{self.stream_name}] Cleaned up {removed_count} old normal messages outside recent {s4u_config.recent_message_keep_count} range."
+            )
 
     async def _message_processor(self):
         """调度器：优先处理VIP队列，然后处理普通队列。"""
@@ -385,7 +389,7 @@ class S4UChat:
                 # 等待有新消息的信号，避免空转
                 await self._new_message_event.wait()
                 self._new_message_event.clear()
-                
+
                 # 清理普通队列中的过旧消息
                 self._cleanup_old_normal_messages()
 
@@ -396,7 +400,6 @@ class S4UChat:
                     queue_name = "vip"
                 # 其次处理普通队列
                 elif not self._normal_queue.empty():
-
                     neg_priority, entry_count, timestamp, message = self._normal_queue.get_nowait()
                     priority = -neg_priority
                     # 检查普通消息是否超时
@@ -411,13 +414,15 @@ class S4UChat:
                     if self.internal_message:
                         message = self.internal_message[-1]
                         self.internal_message = []
-                        
+
                         priority = 0
                         neg_priority = 0
                         entry_count = 0
                         queue_name = "internal"
 
-                        logger.info(f"[{self.stream_name}] normal/vip 队列都空，触发 internal_message 回复: {getattr(message, 'processed_plain_text', str(message))[:20]}...")
+                        logger.info(
+                            f"[{self.stream_name}] normal/vip 队列都空，触发 internal_message 回复: {getattr(message, 'processed_plain_text', str(message))[:20]}..."
+                        )
                     else:
                         continue  # 没有消息了，回去等事件
 
@@ -457,23 +462,21 @@ class S4UChat:
             except Exception as e:
                 logger.error(f"[{self.stream_name}] Message processor main loop error: {e}", exc_info=True)
                 await asyncio.sleep(1)
-                
-        
+
     def get_processing_message_id(self):
         self.last_msg_id = self.msg_id
         self.msg_id = f"{time.time()}_{random.randint(1000, 9999)}"
 
-
     async def _generate_and_send(self, message: MessageRecv):
         """为单个消息生成文本回复。整个过程可以被中断。"""
         self._is_replying = True
         total_chars_sent = 0  # 跟踪发送的总字符数
-        
+
         self.get_processing_message_id()
-        
+
         # 视线管理：开始生成回复时切换视线状态
         chat_watching = watching_manager.get_watching_by_chat_id(self.stream_id)
-        
+
         if message.is_internal:
             await chat_watching.on_internal_message_start()
         else:
@@ -516,16 +519,19 @@ class S4UChat:
                 total_chars_sent = len("麦麦不知道哦")
 
             mood = mood_manager.get_mood_by_chat_id(self.stream_id)
-            await yes_or_no_head(text = total_chars_sent,emotion = mood.mood_state,chat_history=message.processed_plain_text,chat_id=self.stream_id)
+            await yes_or_no_head(
+                text=total_chars_sent,
+                emotion=mood.mood_state,
+                chat_history=message.processed_plain_text,
+                chat_id=self.stream_id,
+            )
 
             # 等待所有文本消息发送完成
             await sender_container.close()
             await sender_container.join()
-            
+
             await chat_watching.on_thinking_finished()
-            
-            
-            
+
             start_time = time.time()
             logged = False
             while not self.go_processing():
@@ -536,7 +542,7 @@ class S4UChat:
                     logger.info(f"[{self.stream_name}] 等待消息发送完成...")
                     logged = True
                 await asyncio.sleep(0.2)
-            
+
             logger.info(f"[{self.stream_name}] 所有文本块处理完毕。")
 
         except asyncio.CancelledError:
@@ -548,11 +554,11 @@ class S4UChat:
             # 回复生成实时展示：清空内容（出错时）
         finally:
             self._is_replying = False
-            
+
             # 视线管理：回复结束时切换视线状态
             chat_watching = watching_manager.get_watching_by_chat_id(self.stream_id)
             await chat_watching.on_reply_finished()
-            
+
             # 确保发送器被妥善关闭（即使已关闭，再次调用也是安全的）
             sender_container.resume()
             if not sender_container._task.done():
@@ -576,4 +582,3 @@ class S4UChat:
             await self._processing_task
         except asyncio.CancelledError:
             logger.info(f"处理任务已成功取消: {self.stream_name}")
-        

src/mais4u/mais4u_chat/s4u_msg_processor.py

@@ -40,7 +40,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
 
     if global_config.memory.enable_memory:
         with Timer("记忆激活"):
-            interested_rate,_ ,_= await hippocampus_manager.get_activate_from_text(
+            interested_rate, _, _ = await hippocampus_manager.get_activate_from_text(
                 message.processed_plain_text,
                 fast_retrieval=True,
             )
@@ -49,7 +49,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
     text_len = len(message.processed_plain_text)
     # 根据文本长度分布调整兴趣度，采用分段函数实现更精确的兴趣度计算
     # 基于实际分布：0-5字符(26.57%), 6-10字符(27.18%), 11-20字符(22.76%), 21-30字符(10.33%), 31+字符(13.86%)
-    
+
     if text_len == 0:
         base_interest = 0.01  # 空消息最低兴趣度
     elif text_len <= 5:
@@ -73,7 +73,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]:
     else:
         # 100+字符：对数增长 0.26 -> 0.3，增长率递减
         base_interest = 0.26 + (0.3 - 0.26) * (math.log10(text_len - 99) / math.log10(901))  # 1000-99=901
-    
+
     # 确保在范围内
     base_interest = min(max(base_interest, 0.01), 0.3)
 
@@ -117,36 +117,32 @@ class S4UMessageProcessor:
             user_info=userinfo,
             group_info=groupinfo,
         )
-        
+
         if await self.handle_internal_message(message):
             return
-        
+
         if await self.hadle_if_voice_done(message):
             return
-        
+
         # 处理礼物消息，如果消息被暂存则停止当前处理流程
         if not skip_gift_debounce and not await self.handle_if_gift(message):
             return
         await self.check_if_fake_gift(message)
-        
+
         # 处理屏幕消息
         if await self.handle_screen_message(message):
             return
-        
 
         await self.storage.store_message(message, chat)
 
         s4u_chat = get_s4u_chat_manager().get_or_create_chat(chat)
 
-
         await s4u_chat.add_message(message)
 
         _interested_rate, _ = await _calculate_interest(message)
-        
+
         await mood_manager.start()
 
-
-
         # 一系列llm驱动的前处理
         chat_mood = mood_manager.get_mood_by_chat_id(chat.stream_id)
         asyncio.create_task(chat_mood.update_mood_by_message(message))
@@ -164,61 +160,56 @@ class S4UMessageProcessor:
             logger.info(f"[S4U-礼物] {userinfo.user_nickname} 送出了 {message.gift_name} x{message.gift_count}")
         else:
             logger.info(f"[S4U]{userinfo.user_nickname}:{message.processed_plain_text}")
-    
+
     async def handle_internal_message(self, message: MessageRecvS4U):
         if message.is_internal:
-            
-            group_info = GroupInfo(platform = "amaidesu_default",group_id = 660154,group_name = "内心")
-            
-            chat =  await get_chat_manager().get_or_create_stream(
-                platform = "amaidesu_default",
-                user_info = message.message_info.user_info,
-                group_info = group_info
+            group_info = GroupInfo(platform="amaidesu_default", group_id=660154, group_name="内心")
+
+            chat = await get_chat_manager().get_or_create_stream(
+                platform="amaidesu_default", user_info=message.message_info.user_info, group_info=group_info
             )
             s4u_chat = get_s4u_chat_manager().get_or_create_chat(chat)
             message.message_info.group_info = s4u_chat.chat_stream.group_info
             message.message_info.platform = s4u_chat.chat_stream.platform
-            
-            
+
             s4u_chat.internal_message.append(message)
             s4u_chat._new_message_event.set()
-            
-            
-            logger.info(f"[{s4u_chat.stream_name}] 添加内部消息-------------------------------------------------------: {message.processed_plain_text}")
-            
-            
+
+            logger.info(
+                f"[{s4u_chat.stream_name}] 添加内部消息-------------------------------------------------------: {message.processed_plain_text}"
+            )
+
             return True
         return False
-    
-    
+
     async def handle_screen_message(self, message: MessageRecvS4U):
         if message.is_screen:
             screen_manager.set_screen(message.screen_info)
             return True
         return False
-    
+
     async def hadle_if_voice_done(self, message: MessageRecvS4U):
         if message.voice_done:
             s4u_chat = get_s4u_chat_manager().get_or_create_chat(message.chat_stream)
             s4u_chat.voice_done = message.voice_done
             return True
         return False
-    
+
     async def check_if_fake_gift(self, message: MessageRecvS4U) -> bool:
         """检查消息是否为假礼物"""
         if message.is_gift:
             return False
-        
-        gift_keywords = ["送出了礼物", "礼物", "送出了","投喂"]
+
+        gift_keywords = ["送出了礼物", "礼物", "送出了", "投喂"]
         if any(keyword in message.processed_plain_text for keyword in gift_keywords):
             message.is_fake_gift = True
             return True
 
         return False
-    
+
     async def handle_if_gift(self, message: MessageRecvS4U) -> bool:
         """处理礼物消息
-        
+
         Returns:
             bool: True表示应该继续处理消息，False表示消息已被暂存不需要继续处理
         """
@@ -228,37 +219,37 @@ class S4UMessageProcessor:
                 """礼物防抖完成后的回调"""
                 # 创建异步任务来处理合并后的礼物消息，跳过防抖处理
                 asyncio.create_task(self.process_message(merged_message, skip_gift_debounce=True))
-            
+
             # 交给礼物管理器处理，并传入回调函数
             # 对于礼物消息，handle_gift 总是返回 False（消息被暂存）
             await gift_manager.handle_gift(message, gift_callback)
             return False  # 消息被暂存，不继续处理
-        
+
         return True  # 非礼物消息，继续正常处理
 
     async def _handle_context_web_update(self, chat_id: str, message: MessageRecv):
         """处理上下文网页更新的独立task
-        
+
         Args:
             chat_id: 聊天ID
             message: 消息对象
         """
         try:
             logger.debug(f"🔄 开始处理上下文网页更新: {message.message_info.user_info.user_nickname}")
-            
+
             context_manager = get_context_web_manager()
-            
+
             # 只在服务器未启动时启动（避免重复启动）
             if context_manager.site is None:
                 logger.info("🚀 首次启动上下文网页服务器...")
                 await context_manager.start_server()
-            
+
             # 添加消息到上下文并更新网页
             await asyncio.sleep(1.5)
-            
+
             await context_manager.add_message(chat_id, message)
-            
+
             logger.debug(f"✅ 上下文网页更新完成: {message.message_info.user_info.user_nickname}")
-            
+
         except Exception as e:
             logger.error(f"❌ 处理上下文网页更新失败: {e}", exc_info=True)

src/mais4u/mais4u_chat/s4u_prompt.py

@@ -176,7 +176,7 @@ class PromptBuilder:
         message_list_before_now = get_raw_msg_before_timestamp_with_chat(
             chat_id=chat_stream.stream_id,
             timestamp=time.time(),
-        # sourcery skip: lift-duplicated-conditional, merge-duplicate-blocks, remove-redundant-if
+            # sourcery skip: lift-duplicated-conditional, merge-duplicate-blocks, remove-redundant-if
             limit=300,
         )
 
@@ -228,13 +228,17 @@ class PromptBuilder:
                 last_speaking_user_id = start_speaking_user_id
                 msg_seg_str = "对方的发言：\n"
 
-            msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(first_msg.time))}: {first_msg.processed_plain_text}\n"
+            msg_seg_str += (
+                f"{time.strftime('%H:%M:%S', time.localtime(first_msg.time))}: {first_msg.processed_plain_text}\n"
+            )
 
             all_msg_seg_list = []
             for msg in core_dialogue_list[1:]:
                 speaker = msg.user_info.user_id
                 if speaker == last_speaking_user_id:
-                    msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.time))}: {msg.processed_plain_text}\n"
+                    msg_seg_str += (
+                        f"{time.strftime('%H:%M:%S', time.localtime(msg.time))}: {msg.processed_plain_text}\n"
+                    )
                 else:
                     msg_seg_str = f"{msg_seg_str}\n"
                     all_msg_seg_list.append(msg_seg_str)

src/mais4u/mais4u_chat/s4u_stream_generator.py

@@ -14,11 +14,8 @@ logger = get_logger("s4u_stream_generator")
 class S4UStreamGenerator:
     def __init__(self):
         # 使用LLMRequest替代AsyncOpenAIClient
-        self.llm_request = LLMRequest(
-            model_set=model_config.model_task_config.replyer, 
-            request_type="s4u_replyer"
-        )
-        
+        self.llm_request = LLMRequest(model_set=model_config.model_task_config.replyer, request_type="s4u_replyer")
+
         self.current_model_name = "unknown model"
         self.partial_response = ""
 
@@ -89,16 +86,16 @@ class S4UStreamGenerator:
 
     async def _generate_response_with_llm_request(self, prompt: str) -> AsyncGenerator[str, None]:
         """使用LLMRequest进行流式响应生成"""
-        
+
         # 构建消息
         message_builder = MessageBuilder()
         message_builder.add_text_content(prompt)
         messages = [message_builder.build()]
-        
+
         # 选择模型
         model_info, api_provider, client = self.llm_request._select_model()
         self.current_model_name = model_info.name
-        
+
         # 如果模型支持强制流式模式，使用真正的流式处理
         if model_info.force_stream_mode:
             # 简化流式处理：直接使用LLMRequest的流式功能
@@ -111,14 +108,14 @@ class S4UStreamGenerator:
                     model_info=model_info,
                     message_list=messages,
                 )
-                
+
                 # 处理响应内容
                 content = response.content or ""
                 if content:
                     # 将内容按句子分割并输出
                     async for chunk in self._process_content_streaming(content):
                         yield chunk
-                        
+
             except Exception as e:
                 logger.error(f"流式请求执行失败: {e}")
                 # 如果流式请求失败，回退到普通模式
@@ -132,7 +129,7 @@ class S4UStreamGenerator:
                 content = response.content or ""
                 async for chunk in self._process_content_streaming(content):
                     yield chunk
-            
+
         else:
             # 如果不支持流式，使用普通方式然后模拟流式输出
             response = await self.llm_request._execute_request(
@@ -142,7 +139,7 @@ class S4UStreamGenerator:
                 model_info=model_info,
                 message_list=messages,
             )
-            
+
             content = response.content or ""
             async for chunk in self._process_content_streaming(content):
                 yield chunk
@@ -163,7 +160,7 @@ class S4UStreamGenerator:
         """处理内容进行流式输出（用于非流式模型的模拟流式输出）"""
         buffer = content
         punctuation_buffer = ""
-        
+
         # 使用正则表达式匹配句子
         last_match_end = 0
         for match in self.sentence_split_pattern.finditer(buffer):

src/mais4u/mais4u_chat/s4u_watching_manager.py

@@ -1,4 +1,3 @@
-
 from src.common.logger import get_logger
 from src.plugin_system.apis import send_api
 
@@ -47,6 +46,7 @@ HEAD_CODE = {
     "看向正前方": "(0,0,0)",
 }
 
+
 class ChatWatching:
     def __init__(self, chat_id: str):
         self.chat_id: str = chat_id
@@ -56,13 +56,13 @@ class ChatWatching:
         await send_api.custom_to_stream(
             message_type="state", content="start_thinking", stream_id=self.chat_id, storage_message=False
         )
-        
+
     async def on_reply_finished(self):
         """生成回复完毕时调用"""
         await send_api.custom_to_stream(
             message_type="state", content="finish_reply", stream_id=self.chat_id, storage_message=False
         )
-        
+
     async def on_thinking_finished(self):
         """思考完毕时调用"""
         await send_api.custom_to_stream(
@@ -74,14 +74,14 @@ class ChatWatching:
         await send_api.custom_to_stream(
             message_type="state", content="start_viewing", stream_id=self.chat_id, storage_message=False
         )
-        
-    
+
     async def on_internal_message_start(self):
         """收到消息时调用"""
         await send_api.custom_to_stream(
             message_type="state", content="start_internal_thinking", stream_id=self.chat_id, storage_message=False
         )
 
+
 class WatchingManager:
     def __init__(self):
         self.watching_list: list[ChatWatching] = []
@@ -100,6 +100,7 @@ class WatchingManager:
 
         return new_watching
 
+
 # 全局视线管理器实例
 watching_manager = WatchingManager()
 """全局视线管理器"""

src/mais4u/mais4u_chat/screen_manager.py

@@ -1,14 +1,15 @@
 class ScreenManager:
     def __init__(self):
         self.now_screen = str()
-        
-    def set_screen(self,screen_str:str):
+
+    def set_screen(self, screen_str: str):
         self.now_screen = screen_str
-        
+
     def get_screen(self):
         return self.now_screen
-    
+
     def get_screen_str(self):
         return f"你可以看见面前的屏幕，目前屏幕的内容是:现在千石可乐在和你一起直播，这是他正在操作的屏幕内容：{self.now_screen}"
 
-screen_manager = ScreenManager()
+
+screen_manager = ScreenManager()

src/mais4u/mais4u_chat/super_chat_manager.py

@@ -4,6 +4,7 @@ from dataclasses import dataclass
 from typing import Dict, List, Optional
 from src.common.logger import get_logger
 from src.chat.message_receive.message import MessageRecvS4U
+
 # 全局SuperChat管理器实例
 from src.mais4u.s4u_config import s4u_config
 
@@ -13,7 +14,7 @@ logger = get_logger("super_chat_manager")
 @dataclass
 class SuperChatRecord:
     """SuperChat记录数据类"""
-    
+
     user_id: str
     user_nickname: str
     platform: str
@@ -23,15 +24,15 @@ class SuperChatRecord:
     timestamp: float
     expire_time: float
     group_name: Optional[str] = None
-    
+
     def is_expired(self) -> bool:
         """检查SuperChat是否已过期"""
         return time.time() > self.expire_time
-    
+
     def remaining_time(self) -> float:
         """获取剩余时间（秒）"""
         return max(0, self.expire_time - time.time())
-    
+
     def to_dict(self) -> dict:
         """转换为字典格式"""
         return {
@@ -44,19 +45,19 @@ class SuperChatRecord:
             "timestamp": self.timestamp,
             "expire_time": self.expire_time,
             "group_name": self.group_name,
-            "remaining_time": self.remaining_time()
+            "remaining_time": self.remaining_time(),
         }
 
 
 class SuperChatManager:
     """SuperChat管理器，负责管理和跟踪SuperChat消息"""
-    
+
     def __init__(self):
         self.super_chats: Dict[str, List[SuperChatRecord]] = {}  # chat_id -> SuperChat列表
         self._cleanup_task: Optional[asyncio.Task] = None
         self._is_initialized = False
         logger.info("SuperChat管理器已初始化")
-    
+
     def _ensure_cleanup_task_started(self):
         """确保清理任务已启动（延迟启动）"""
         if self._cleanup_task is None or self._cleanup_task.done():
@@ -68,7 +69,7 @@ class SuperChatManager:
             except RuntimeError:
                 # 没有运行的事件循环，稍后再启动
                 logger.debug("当前没有运行的事件循环，将在需要时启动清理任务")
-    
+
     def _start_cleanup_task(self):
         """启动清理任务（已弃用，保留向后兼容）"""
         self._ensure_cleanup_task_started()
@@ -78,39 +79,36 @@ class SuperChatManager:
         while True:
             try:
                 total_removed = 0
-                
+
                 for chat_id in list(self.super_chats.keys()):
                     original_count = len(self.super_chats[chat_id])
                     # 移除过期的SuperChat
-                    self.super_chats[chat_id] = [
-                        sc for sc in self.super_chats[chat_id] 
-                        if not sc.is_expired()
-                    ]
-                    
+                    self.super_chats[chat_id] = [sc for sc in self.super_chats[chat_id] if not sc.is_expired()]
+
                     removed_count = original_count - len(self.super_chats[chat_id])
                     total_removed += removed_count
-                    
+
                     if removed_count > 0:
                         logger.info(f"从聊天 {chat_id} 中清理了 {removed_count} 个过期的SuperChat")
-                    
+
                     # 如果列表为空，删除该聊天的记录
                     if not self.super_chats[chat_id]:
                         del self.super_chats[chat_id]
-                
+
                 if total_removed > 0:
                     logger.info(f"总共清理了 {total_removed} 个过期的SuperChat")
-                
+
                 # 每30秒检查一次
                 await asyncio.sleep(30)
-                
+
             except Exception as e:
                 logger.error(f"清理过期SuperChat时出错: {e}", exc_info=True)
                 await asyncio.sleep(60)  # 出错时等待更长时间
-    
+
     def _calculate_expire_time(self, price: float) -> float:
         """根据SuperChat金额计算过期时间"""
         current_time = time.time()
-        
+
         # 根据金额阶梯设置不同的存活时间
         if price >= 500:
             # 500元以上：保持4小时
@@ -133,27 +131,27 @@ class SuperChatManager:
         else:
             # 10元以下：保持5分钟
             duration = 5 * 60
-        
+
         return current_time + duration
-    
+
     async def add_superchat(self, message: MessageRecvS4U) -> None:
         """添加新的SuperChat记录"""
         # 确保清理任务已启动
         self._ensure_cleanup_task_started()
-        
+
         if not message.is_superchat or not message.superchat_price:
             logger.warning("尝试添加非SuperChat消息到SuperChat管理器")
             return
-        
+
         try:
             price = float(message.superchat_price)
         except (ValueError, TypeError):
             logger.error(f"无效的SuperChat价格: {message.superchat_price}")
             return
-        
+
         user_info = message.message_info.user_info
         group_info = message.message_info.group_info
-        chat_id = getattr(message, 'chat_stream', None)
+        chat_id = getattr(message, "chat_stream", None)
         if chat_id:
             chat_id = chat_id.stream_id
         else:
@@ -161,9 +159,9 @@ class SuperChatManager:
             chat_id = f"{message.message_info.platform}_{user_info.user_id}"
             if group_info:
                 chat_id = f"{message.message_info.platform}_{group_info.group_id}"
-        
+
         expire_time = self._calculate_expire_time(price)
-        
+
         record = SuperChatRecord(
             user_id=user_info.user_id,
             user_nickname=user_info.user_nickname,
@@ -173,44 +171,44 @@ class SuperChatManager:
             message_text=message.superchat_message_text or "",
             timestamp=message.message_info.time,
             expire_time=expire_time,
-            group_name=group_info.group_name if group_info else None
+            group_name=group_info.group_name if group_info else None,
         )
-        
+
         # 添加到对应聊天的SuperChat列表
         if chat_id not in self.super_chats:
             self.super_chats[chat_id] = []
-        
+
         self.super_chats[chat_id].append(record)
-        
+
         # 按价格降序排序（价格高的在前）
         self.super_chats[chat_id].sort(key=lambda x: x.price, reverse=True)
-        
+
         logger.info(f"添加SuperChat记录: {user_info.user_nickname} - {price}元 - {message.superchat_message_text}")
-    
+
     def get_superchats_by_chat(self, chat_id: str) -> List[SuperChatRecord]:
         """获取指定聊天的所有有效SuperChat"""
         # 确保清理任务已启动
         self._ensure_cleanup_task_started()
-        
+
         if chat_id not in self.super_chats:
             return []
-        
+
         # 过滤掉过期的SuperChat
         valid_superchats = [sc for sc in self.super_chats[chat_id] if not sc.is_expired()]
         return valid_superchats
-    
+
     def get_all_valid_superchats(self) -> Dict[str, List[SuperChatRecord]]:
         """获取所有有效的SuperChat"""
         # 确保清理任务已启动
         self._ensure_cleanup_task_started()
-        
+
         result = {}
         for chat_id, superchats in self.super_chats.items():
             valid_superchats = [sc for sc in superchats if not sc.is_expired()]
             if valid_superchats:
                 result[chat_id] = valid_superchats
         return result
-    
+
     def build_superchat_display_string(self, chat_id: str, max_count: int = 10) -> str:
         """构建SuperChat显示字符串"""
         superchats = self.get_superchats_by_chat(chat_id)
@@ -226,7 +224,9 @@ class SuperChatManager:
             remaining_minutes = int(sc.remaining_time() / 60)
             remaining_seconds = int(sc.remaining_time() % 60)
 
-            time_display = f"{remaining_minutes}分{remaining_seconds}秒" if remaining_minutes > 0 else f"{remaining_seconds}秒"
+            time_display = (
+                f"{remaining_minutes}分{remaining_seconds}秒" if remaining_minutes > 0 else f"{remaining_seconds}秒"
+            )
 
             line = f"{i}. 【{sc.price}元】{sc.user_nickname}: {sc.message_text}"
             if len(line) > 100:  # 限制单行长度
@@ -238,7 +238,7 @@ class SuperChatManager:
             lines.append(f"... 还有{len(superchats) - max_count}条SuperChat")
 
         return "\n".join(lines)
-    
+
     def build_superchat_summary_string(self, chat_id: str) -> str:
         """构建SuperChat摘要字符串"""
         superchats = self.get_superchats_by_chat(chat_id)
@@ -261,30 +261,24 @@ class SuperChatManager:
         if lines:
             final_str += "\n" + "\n".join(lines)
         return final_str
-    
+
     def get_superchat_statistics(self, chat_id: str) -> dict:
         """获取SuperChat统计信息"""
         superchats = self.get_superchats_by_chat(chat_id)
-        
+
         if not superchats:
-            return {
-                "count": 0,
-                "total_amount": 0,
-                "average_amount": 0,
-                "highest_amount": 0,
-                "lowest_amount": 0
-            }
-        
+            return {"count": 0, "total_amount": 0, "average_amount": 0, "highest_amount": 0, "lowest_amount": 0}
+
         amounts = [sc.price for sc in superchats]
-        
+
         return {
             "count": len(superchats),
             "total_amount": sum(amounts),
             "average_amount": sum(amounts) / len(amounts),
             "highest_amount": max(amounts),
-            "lowest_amount": min(amounts)
+            "lowest_amount": min(amounts),
         }
-    
+
     async def shutdown(self):  # sourcery skip: use-contextlib-suppress
         """关闭管理器，清理资源"""
         if self._cleanup_task and not self._cleanup_task.done():
@@ -296,15 +290,14 @@ class SuperChatManager:
         logger.info("SuperChat管理器已关闭")
 
 
-
-
 # sourcery skip: assign-if-exp
 if s4u_config.enable_s4u:
     super_chat_manager = SuperChatManager()
 else:
     super_chat_manager = None
 
+
 def get_super_chat_manager() -> SuperChatManager:
     """获取全局SuperChat管理器实例"""
 
-    return super_chat_manager
+    return super_chat_manager

src/mais4u/s4u_config.py

@@ -10,10 +10,12 @@ from src.common.logger import get_logger
 
 logger = get_logger("s4u_config")
 
+
 # 新增：兼容dict和tomlkit Table
 def is_dict_like(obj):
     return isinstance(obj, (dict, Table))
 
+
 # 新增：递归将Table转为dict
 def table_to_dict(obj):
     if isinstance(obj, Table):
@@ -25,6 +27,7 @@ def table_to_dict(obj):
     else:
         return obj
 
+
 # 获取mais4u模块目录
 MAIS4U_ROOT = os.path.dirname(__file__)
 CONFIG_DIR = os.path.join(MAIS4U_ROOT, "config")
@@ -190,7 +193,7 @@ class S4UModelConfig(S4UConfigBase):
 @dataclass
 class S4UConfig(S4UConfigBase):
     """S4U聊天系统配置类"""
-    
+
     enable_s4u: bool = False
     """是否启用S4U聊天系统"""
 
@@ -229,12 +232,12 @@ class S4UConfig(S4UConfigBase):
 
     enable_streaming_output: bool = True
     """是否启用流式输出，false时全部生成后一次性发送"""
-    
+
     max_context_message_length: int = 20
     """上下文消息最大长度"""
-    
+
     max_core_message_length: int = 30
-    """核心消息最大长度"""  
+    """核心消息最大长度"""
 
     # 模型配置
     models: S4UModelConfig = field(default_factory=S4UModelConfig)
@@ -243,7 +246,6 @@ class S4UConfig(S4UConfigBase):
     # 兼容性字段，保持向后兼容
 
 
-
 @dataclass
 class S4UGlobalConfig(S4UConfigBase):
     """S4U总配置类"""
@@ -256,7 +258,7 @@ def update_s4u_config():
     """更新S4U配置文件"""
     # 创建配置目录（如果不存在）
     os.makedirs(CONFIG_DIR, exist_ok=True)
-    
+
     # 检查模板文件是否存在
     if not os.path.exists(TEMPLATE_PATH):
         logger.error(f"S4U配置模板文件不存在: {TEMPLATE_PATH}")
@@ -354,13 +356,13 @@ def load_s4u_config(config_path: str) -> S4UGlobalConfig:
         logger.critical("S4U配置文件解析失败")
         raise e
 
-
-
     # 初始化S4U配置
+
+
 logger.info(f"S4U当前版本: {S4U_VERSION}")
 update_s4u_config()
 
 s4u_config_main = load_s4u_config(config_path=CONFIG_PATH)
 logger.info("S4U配置文件加载完成！")
 
-s4u_config: S4UConfig = s4u_config_main.s4u
+s4u_config: S4UConfig = s4u_config_main.s4u

src/migrate_helper/migrate.py

@@ -13,7 +13,7 @@ async def migrate_memory_items_to_string():
     并根据原始list的项目数量设置weight值
     """
     logger.info("开始迁移记忆节点格式...")
-    
+
     migration_stats = {
         "total_nodes": 0,
         "converted_nodes": 0,
@@ -21,72 +21,74 @@ async def migrate_memory_items_to_string():
         "empty_nodes": 0,
         "error_nodes": 0,
         "weight_updated_nodes": 0,
-        "truncated_nodes": 0
+        "truncated_nodes": 0,
     }
-    
+
     try:
         # 获取所有图节点
         all_nodes = GraphNodes.select()
         migration_stats["total_nodes"] = all_nodes.count()
-        
+
         logger.info(f"找到 {migration_stats['total_nodes']} 个记忆节点")
-        
+
         for node in all_nodes:
             try:
                 concept = node.concept
                 memory_items_raw = node.memory_items.strip() if node.memory_items else ""
-                original_weight = node.weight if hasattr(node, 'weight') and node.weight is not None else 1.0
-                
+                original_weight = node.weight if hasattr(node, "weight") and node.weight is not None else 1.0
+
                 # 如果为空，跳过
                 if not memory_items_raw:
                     migration_stats["empty_nodes"] += 1
                     logger.debug(f"跳过空节点: {concept}")
                     continue
-                
+
                 try:
                     # 尝试解析JSON
                     parsed_data = json.loads(memory_items_raw)
-                    
+
                     if isinstance(parsed_data, list):
                         # 如果是list格式，需要转换
                         if parsed_data:
                             # 转换为字符串格式
                             new_memory_items = " | ".join(str(item) for item in parsed_data)
                             original_length = len(new_memory_items)
-                            
+
                             # 检查长度并截断
                             if len(new_memory_items) > 100:
                                 new_memory_items = new_memory_items[:100]
                                 migration_stats["truncated_nodes"] += 1
                                 logger.debug(f"节点 '{concept}' 内容过长，从 {original_length} 字符截断到 100 字符")
-                            
+
                             new_weight = float(len(parsed_data))  # weight = list项目数量
-                            
+
                             # 更新数据库
                             node.memory_items = new_memory_items
                             node.weight = new_weight
                             node.save()
-                            
+
                             migration_stats["converted_nodes"] += 1
                             migration_stats["weight_updated_nodes"] += 1
-                            
+
                             length_info = f" (截断: {original_length}→100)" if original_length > 100 else ""
-                            logger.info(f"转换节点 '{concept}': {len(parsed_data)} 项 -> 字符串{length_info}, weight: {original_weight} -> {new_weight}")
+                            logger.info(
+                                f"转换节点 '{concept}': {len(parsed_data)} 项 -> 字符串{length_info}, weight: {original_weight} -> {new_weight}"
+                            )
                         else:
                             # 空list，设置为空字符串
                             node.memory_items = ""
                             node.weight = 1.0
                             node.save()
-                            
+
                             migration_stats["converted_nodes"] += 1
                             logger.debug(f"转换空list节点: {concept}")
-                    
+
                     elif isinstance(parsed_data, str):
                         # 已经是字符串格式，检查长度和weight
                         current_content = parsed_data
                         original_length = len(current_content)
                         content_truncated = False
-                        
+
                         # 检查长度并截断
                         if len(current_content) > 100:
                             current_content = current_content[:100]
@@ -94,19 +96,21 @@ async def migrate_memory_items_to_string():
                             migration_stats["truncated_nodes"] += 1
                             node.memory_items = current_content
                             logger.debug(f"节点 '{concept}' 字符串内容过长，从 {original_length} 字符截断到 100 字符")
-                        
+
                         # 检查weight是否需要更新
                         update_needed = False
                         if original_weight == 1.0:
                             # 如果weight还是默认值，可以根据内容复杂度估算
-                            content_parts = current_content.split(" | ") if " | " in current_content else [current_content]
+                            content_parts = (
+                                current_content.split(" | ") if " | " in current_content else [current_content]
+                            )
                             estimated_weight = max(1.0, float(len(content_parts)))
-                            
+
                             if estimated_weight != original_weight:
                                 node.weight = estimated_weight
                                 update_needed = True
                                 logger.debug(f"更新字符串节点权重 '{concept}': {original_weight} -> {estimated_weight}")
-                        
+
                         # 如果内容被截断或权重需要更新，保存到数据库
                         if content_truncated or update_needed:
                             node.save()
@@ -118,26 +122,26 @@ async def migrate_memory_items_to_string():
                                 migration_stats["already_string_nodes"] += 1
                         else:
                             migration_stats["already_string_nodes"] += 1
-                    
+
                     else:
                         # 其他JSON类型，转换为字符串
                         new_memory_items = str(parsed_data) if parsed_data else ""
                         original_length = len(new_memory_items)
-                        
+
                         # 检查长度并截断
                         if len(new_memory_items) > 100:
                             new_memory_items = new_memory_items[:100]
                             migration_stats["truncated_nodes"] += 1
                             logger.debug(f"节点 '{concept}' 其他类型内容过长，从 {original_length} 字符截断到 100 字符")
-                        
+
                         node.memory_items = new_memory_items
                         node.weight = 1.0
                         node.save()
-                        
+
                         migration_stats["converted_nodes"] += 1
                         length_info = f" (截断: {original_length}→100)" if original_length > 100 else ""
                         logger.debug(f"转换其他类型节点: {concept}{length_info}")
-                
+
                 except json.JSONDecodeError:
                     # 不是JSON格式，假设已经是纯字符串
                     # 检查是否是带引号的字符串
@@ -145,16 +149,16 @@ async def migrate_memory_items_to_string():
                         # 去掉引号
                         clean_content = memory_items_raw[1:-1]
                         original_length = len(clean_content)
-                        
+
                         # 检查长度并截断
                         if len(clean_content) > 100:
                             clean_content = clean_content[:100]
                             migration_stats["truncated_nodes"] += 1
                             logger.debug(f"节点 '{concept}' 去引号内容过长，从 {original_length} 字符截断到 100 字符")
-                        
+
                         node.memory_items = clean_content
                         node.save()
-                        
+
                         migration_stats["converted_nodes"] += 1
                         length_info = f" (截断: {original_length}→100)" if original_length > 100 else ""
                         logger.debug(f"去除引号节点: {concept}{length_info}")
@@ -162,29 +166,29 @@ async def migrate_memory_items_to_string():
                         # 已经是纯字符串格式，检查长度
                         current_content = memory_items_raw
                         original_length = len(current_content)
-                        
+
                         # 检查长度并截断
                         if len(current_content) > 100:
                             current_content = current_content[:100]
                             node.memory_items = current_content
                             node.save()
-                            
+
                             migration_stats["converted_nodes"] += 1  # 算作转换节点
                             migration_stats["truncated_nodes"] += 1
                             logger.debug(f"节点 '{concept}' 纯字符串内容过长，从 {original_length} 字符截断到 100 字符")
                         else:
                             migration_stats["already_string_nodes"] += 1
                             logger.debug(f"已是字符串格式节点: {concept}")
-            
+
             except Exception as e:
                 migration_stats["error_nodes"] += 1
                 logger.error(f"处理节点 {concept} 时发生错误: {e}")
                 continue
-    
+
     except Exception as e:
         logger.error(f"迁移过程中发生严重错误: {e}")
         raise
-    
+
     # 输出迁移统计
     logger.info("=== 记忆节点迁移完成 ===")
     logger.info(f"总节点数: {migration_stats['total_nodes']}")
@@ -194,101 +198,105 @@ async def migrate_memory_items_to_string():
     logger.info(f"错误节点: {migration_stats['error_nodes']}")
     logger.info(f"权重更新节点: {migration_stats['weight_updated_nodes']}")
     logger.info(f"内容截断节点: {migration_stats['truncated_nodes']}")
-    
-    success_rate = (migration_stats['converted_nodes'] + migration_stats['already_string_nodes']) / migration_stats['total_nodes'] * 100 if migration_stats['total_nodes'] > 0 else 0
+
+    success_rate = (
+        (migration_stats["converted_nodes"] + migration_stats["already_string_nodes"])
+        / migration_stats["total_nodes"]
+        * 100
+        if migration_stats["total_nodes"] > 0
+        else 0
+    )
     logger.info(f"迁移成功率: {success_rate:.1f}%")
-    
+
     return migration_stats
 
 
-
-
 async def set_all_person_known():
     """
     将person_info库中所有记录的is_known字段设置为True
     在设置之前，先清理掉user_id或platform为空的记录
     """
     logger.info("开始设置所有person_info记录为已认识...")
-    
+
     try:
         from src.common.database.database_model import PersonInfo
-        
+
         # 获取所有PersonInfo记录
         all_persons = PersonInfo.select()
         total_count = all_persons.count()
-        
+
         logger.info(f"找到 {total_count} 个人员记录")
-        
+
         if total_count == 0:
             logger.info("没有找到任何人员记录")
             return {"total": 0, "deleted": 0, "updated": 0, "known_count": 0}
-        
+
         # 删除user_id或platform为空的记录
         deleted_count = 0
         invalid_records = PersonInfo.select().where(
-            (PersonInfo.user_id.is_null()) | 
-            (PersonInfo.user_id == '') |
-            (PersonInfo.platform.is_null()) |
-            (PersonInfo.platform == '')
+            (PersonInfo.user_id.is_null())
+            | (PersonInfo.user_id == "")
+            | (PersonInfo.platform.is_null())
+            | (PersonInfo.platform == "")
         )
-        
+
         # 记录要删除的记录信息
         for record in invalid_records:
             user_id_info = f"'{record.user_id}'" if record.user_id else "NULL"
             platform_info = f"'{record.platform}'" if record.platform else "NULL"
             person_name_info = f"'{record.person_name}'" if record.person_name else "无名称"
-            logger.debug(f"删除无效记录: person_id={record.person_id}, user_id={user_id_info}, platform={platform_info}, person_name={person_name_info}")
-        
+            logger.debug(
+                f"删除无效记录: person_id={record.person_id}, user_id={user_id_info}, platform={platform_info}, person_name={person_name_info}"
+            )
+
         # 执行删除操作
-        deleted_count = PersonInfo.delete().where(
-            (PersonInfo.user_id.is_null()) | 
-            (PersonInfo.user_id == '') |
-            (PersonInfo.platform.is_null()) |
-            (PersonInfo.platform == '')
-        ).execute()
-        
+        deleted_count = (
+            PersonInfo.delete()
+            .where(
+                (PersonInfo.user_id.is_null())
+                | (PersonInfo.user_id == "")
+                | (PersonInfo.platform.is_null())
+                | (PersonInfo.platform == "")
+            )
+            .execute()
+        )
+
         if deleted_count > 0:
             logger.info(f"删除了 {deleted_count} 个user_id或platform为空的记录")
         else:
             logger.info("没有发现user_id或platform为空的记录")
-        
+
         # 重新获取剩余记录数量
         remaining_count = PersonInfo.select().count()
         logger.info(f"清理后剩余 {remaining_count} 个有效记录")
-        
+
         if remaining_count == 0:
             logger.info("清理后没有剩余记录")
             return {"total": total_count, "deleted": deleted_count, "updated": 0, "known_count": 0}
-        
+
         # 批量更新剩余记录的is_known字段为True
         updated_count = PersonInfo.update(is_known=True).execute()
-        
+
         logger.info(f"成功更新 {updated_count} 个人员记录的is_known字段为True")
-        
+
         # 验证更新结果
         known_count = PersonInfo.select().where(PersonInfo.is_known).count()
-        
-        result = {
-            "total": total_count,
-            "deleted": deleted_count,
-            "updated": updated_count,
-            "known_count": known_count
-        }
-        
+
+        result = {"total": total_count, "deleted": deleted_count, "updated": updated_count, "known_count": known_count}
+
         logger.info("=== person_info更新完成 ===")
         logger.info(f"原始记录数: {result['total']}")
         logger.info(f"删除记录数: {result['deleted']}")
         logger.info(f"更新记录数: {result['updated']}")
         logger.info(f"已认识记录数: {result['known_count']}")
-        
+
         return result
-        
+
     except Exception as e:
         logger.error(f"更新person_info过程中发生错误: {e}")
         raise
 
 
-
 async def check_and_run_migrations():
     # 获取根目录
     project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
@@ -309,4 +317,3 @@ async def check_and_run_migrations():
         # 创建done.mem文件
         with open(done_file, "w", encoding="utf-8") as f:
             f.write("done")
-        

src/person_info/person_info.py

@@ -282,7 +282,7 @@ class Person:
 
             memory_category = parts[0].strip()
             memory_text = parts[1].strip()
-            memory_weight = parts[2].strip()
+            _memory_weight = parts[2].strip()
 
             # 检查分类是否匹配
             if memory_category != category:

src/person_info/relationship_manager.py

@@ -1,11 +1,5 @@
-import json
-from json_repair import repair_json
-from datetime import datetime
 from src.common.logger import get_logger
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config, model_config
-from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
-from .person_info import Person
+from src.chat.utils.prompt_builder import Prompt
 
 
 logger = get_logger("relation")
@@ -43,4 +37,3 @@ def init_prompt():
 """,
         "attitude_to_me_prompt",
     )
-

src/plugin_system/__init__.py

@@ -121,5 +121,5 @@ __all__ = [
     "DatabaseChatInfo",
     "TargetPersonInfo",
     "ActionPlannerInfo",
-    "LLMGenerationDataModel"
+    "LLMGenerationDataModel",
 ]

src/plugin_system/apis/frequency_api.py

@@ -7,22 +7,24 @@ logger = get_logger("frequency_api")
 def get_current_focus_value(chat_id: str) -> float:
     return frequency_control_manager.get_or_create_frequency_control(chat_id).get_final_focus_value()
 
+
 def get_current_talk_frequency(chat_id: str) -> float:
     return frequency_control_manager.get_or_create_frequency_control(chat_id).get_final_talk_frequency()
 
+
 def set_focus_value_adjust(chat_id: str, focus_value_adjust: float) -> None:
     frequency_control_manager.get_or_create_frequency_control(chat_id).focus_value_external_adjust = focus_value_adjust
-    
+
+
 def set_talk_frequency_adjust(chat_id: str, talk_frequency_adjust: float) -> None:
-    frequency_control_manager.get_or_create_frequency_control(chat_id).talk_frequency_external_adjust = talk_frequency_adjust
+    frequency_control_manager.get_or_create_frequency_control(
+        chat_id
+    ).talk_frequency_external_adjust = talk_frequency_adjust
+
 
 def get_focus_value_adjust(chat_id: str) -> float:
     return frequency_control_manager.get_or_create_frequency_control(chat_id).focus_value_external_adjust
-    
+
+
 def get_talk_frequency_adjust(chat_id: str) -> float:
     return frequency_control_manager.get_or_create_frequency_control(chat_id).talk_frequency_external_adjust
-
-
-
-
-

src/plugin_system/apis/generator_api.py

@@ -159,6 +159,7 @@ async def generate_reply(
         logger.error(traceback.format_exc())
         return False, None
 
+
 async def rewrite_reply(
     chat_stream: Optional[ChatStream] = None,
     reply_data: Optional[Dict[str, Any]] = None,

src/plugin_system/apis/llm_api.py

@@ -72,7 +72,9 @@ async def generate_with_model(
 
         llm_request = LLMRequest(model_set=model_config, request_type=request_type)
 
-        response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(prompt, temperature=temperature, max_tokens=max_tokens)
+        response, (reasoning_content, model_name, _) = await llm_request.generate_response_async(
+            prompt, temperature=temperature, max_tokens=max_tokens
+        )
         return True, response, reasoning_content, model_name
 
     except Exception as e:
@@ -80,6 +82,7 @@ async def generate_with_model(
         logger.error(f"[LLMAPI] {error_msg}")
         return False, error_msg, "", ""
 
+
 async def generate_with_model_with_tools(
     prompt: str,
     model_config: TaskConfig,
@@ -109,10 +112,7 @@ async def generate_with_model_with_tools(
         llm_request = LLMRequest(model_set=model_config, request_type=request_type)
 
         response, (reasoning_content, model_name, tool_call) = await llm_request.generate_response_async(
-            prompt,
-            tools=tool_options,
-            temperature=temperature,
-            max_tokens=max_tokens
+            prompt, tools=tool_options, temperature=temperature, max_tokens=max_tokens
         )
         return True, response, reasoning_content, model_name, tool_call
 

src/plugin_system/apis/message_api.py

@@ -435,9 +435,7 @@ def build_readable_messages_to_str(
     Returns:
         格式化后的可读字符串
     """
-    return build_readable_messages(
-        messages, replace_bot_name, timestamp_mode, read_mark, truncate, show_actions
-    )
+    return build_readable_messages(messages, replace_bot_name, timestamp_mode, read_mark, truncate, show_actions)
 
 
 async def build_readable_messages_with_details(
@@ -491,8 +489,6 @@ def filter_mai_messages(messages: List[DatabaseMessages]) -> List[DatabaseMessag
     return [msg for msg in messages if msg.user_info.user_id != str(global_config.bot.qq_account)]
 
 
-
-
 def translate_pid_to_description(pid: str) -> str:
     image = Images.get_or_none(Images.image_id == pid)
     description = ""
@@ -500,4 +496,4 @@ def translate_pid_to_description(pid: str) -> str:
         description = image.description
     else:
         description = "[图片]"
-    return description
+    return description

src/plugin_system/apis/plugin_manage_api.py

@@ -34,7 +34,7 @@ def get_plugin_path(plugin_name: str) -> str:
 
     Returns:
         str: 插件目录的绝对路径。
-        
+
     Raises:
         ValueError: 如果插件不存在。
     """

src/plugin_system/apis/plugin_register_api.py

@@ -2,7 +2,7 @@ from pathlib import Path
 
 from src.common.logger import get_logger
 
-logger = get_logger("plugin_manager") # 复用plugin_manager名称
+logger = get_logger("plugin_manager")  # 复用plugin_manager名称
 
 
 def register_plugin(cls):

src/plugin_system/core/global_announcement_manager.py

@@ -88,7 +88,7 @@ class GlobalAnnouncementManager:
             return False
         self._user_disabled_tools[chat_id].append(tool_name)
         return True
-    
+
     def enable_specific_chat_tool(self, chat_id: str, tool_name: str) -> bool:
         """启用特定聊天的某个工具"""
         if chat_id in self._user_disabled_tools:
@@ -111,7 +111,7 @@ class GlobalAnnouncementManager:
     def get_disabled_chat_event_handlers(self, chat_id: str) -> List[str]:
         """获取特定聊天禁用的所有事件处理器"""
         return self._user_disabled_event_handlers.get(chat_id, []).copy()
-    
+
     def get_disabled_chat_tools(self, chat_id: str) -> List[str]:
         """获取特定聊天禁用的所有工具"""
         return self._user_disabled_tools.get(chat_id, []).copy()

src/plugin_system/core/plugin_manager.py

@@ -224,7 +224,7 @@ class PluginManager:
             list: 已注册的插件类名称列表。
         """
         return list(self.plugin_classes.keys())
-    
+
     def get_plugin_path(self, plugin_name: str) -> Optional[str]:
         """
         获取指定插件的路径。
@@ -401,9 +401,7 @@ class PluginManager:
                         command_components = [
                             c for c in plugin_info.components if c.component_type == ComponentType.COMMAND
                         ]
-                        tool_components = [
-                            c for c in plugin_info.components if c.component_type == ComponentType.TOOL
-                        ]
+                        tool_components = [c for c in plugin_info.components if c.component_type == ComponentType.TOOL]
                         event_handler_components = [
                             c for c in plugin_info.components if c.component_type == ComponentType.EVENT_HANDLER
                         ]

src/plugin_system/core/tool_use.py

@@ -149,10 +149,10 @@ class ToolExecutor:
         if not tool_calls:
             logger.debug(f"{self.log_prefix}无需执行工具")
             return [], []
-        
+
         # 提取tool_calls中的函数名称
         func_names = [call.func_name for call in tool_calls if call.func_name]
-        
+
         logger.info(f"{self.log_prefix}开始执行工具调用: {func_names}")
 
         # 执行每个工具调用
@@ -195,7 +195,9 @@ class ToolExecutor:
 
         return tool_results, used_tools
 
-    async def execute_tool_call(self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None) -> Optional[Dict[str, Any]]:
+    async def execute_tool_call(
+        self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None
+    ) -> Optional[Dict[str, Any]]:
         # sourcery skip: use-assigned-variable
         """执行单个工具调用
 

src/plugins/built_in/emoji_plugin/plugin.py

@@ -63,5 +63,4 @@ class CoreActionsPlugin(BasePlugin):
         if self.get_config("components.enable_emoji", True):
             components.append((EmojiAction.get_action_info(), EmojiAction))
 
-
         return components

src/plugins/built_in/memory/build_memory.py

@@ -74,7 +74,9 @@ class BuildMemoryAction(BaseAction):
 
     # 动作基本信息
     action_name = "build_memory"
-    action_description = "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息"
+    action_description = (
+        "了解对于某个概念或者某件事的记忆，并存储下来，在之后的聊天中，你可以根据这条记忆来获取相关信息"
+    )
 
     # 动作参数定义
     action_parameters = {
@@ -103,31 +105,34 @@ class BuildMemoryAction(BaseAction):
             concept_name = self.action_data.get("concept_name", "")
             # 2. 获取目标用户信息
 
-
-
             # 对 concept_name 进行jieba分词
             concept_name_tokens = cut_key_words(concept_name)
             # logger.info(f"{self.log_prefix} 对 concept_name 进行分词结果: {concept_name_tokens}")
-            
+
             filtered_concept_name_tokens = [
-                token for token in concept_name_tokens if all(keyword not in token for keyword in global_config.memory.memory_ban_words)
+                token
+                for token in concept_name_tokens
+                if all(keyword not in token for keyword in global_config.memory.memory_ban_words)
             ]
-            
+
             if not filtered_concept_name_tokens:
                 logger.warning(f"{self.log_prefix} 过滤后的概念名称列表为空，跳过添加记忆")
                 return False, "过滤后的概念名称列表为空，跳过添加记忆"
-            
-            similar_topics_dict = hippocampus_manager.get_hippocampus().parahippocampal_gyrus.get_similar_topics_from_keywords(filtered_concept_name_tokens)
-            await hippocampus_manager.get_hippocampus().parahippocampal_gyrus.add_memory_with_similar(concept_description, similar_topics_dict)
-            
-            
-            
+
+            similar_topics_dict = (
+                hippocampus_manager.get_hippocampus().parahippocampal_gyrus.get_similar_topics_from_keywords(
+                    filtered_concept_name_tokens
+                )
+            )
+            await hippocampus_manager.get_hippocampus().parahippocampal_gyrus.add_memory_with_similar(
+                concept_description, similar_topics_dict
+            )
+
             return True, f"成功添加记忆: {concept_name}"
-            
+
         except Exception as e:
             logger.error(f"{self.log_prefix} 构建记忆时出错: {e}")
             return False, f"构建记忆时出错: {e}"
-        
 
 
 # 还缺一个关系的太多遗忘和对应的提取

src/plugins/built_in/plugin_management/plugin.py

@@ -425,7 +425,7 @@ class ManagementCommand(BaseCommand):
             await self._send_message(f"本地禁用组件成功: {component_name}")
         else:
             await self._send_message(f"本地禁用组件失败: {component_name}")
-    
+
     async def _send_message(self, message: str):
         await send_api.text_to_stream(message, self.stream_id, typing=False, storage_message=False)
 

src/plugins/built_in/relation/relation.py

@@ -107,7 +107,7 @@ class BuildRelationAction(BaseAction):
             if not person.is_known:
                 logger.warning(f"{self.log_prefix} 用户 {person_name} 不存在，跳过添加记忆")
                 return False, f"用户 {person_name} 不存在，跳过添加记忆"
-            
+
             person.last_know = time.time()
             person.know_times += 1
             person.sync_to_database()
@@ -178,7 +178,9 @@ class BuildRelationAction(BaseAction):
 
             chat_model_config = models.get("utils")
             success, update_memory, _, _ = await llm_api.generate_with_model(
-                prompt, model_config=chat_model_config, request_type="relation.category.update" # type: ignore
+                prompt,
+                model_config=chat_model_config,
+                request_type="relation.category.update",  # type: ignore
             )
 
             update_memory_data = json.loads(repair_json(update_memory))
@@ -190,7 +192,7 @@ class BuildRelationAction(BaseAction):
                 # 新记忆
                 person.memory_points.append(f"{category}:{new_memory}:1.0")
                 person.sync_to_database()
-                
+
                 logger.info(f"{self.log_prefix} 为{person.person_name}新增记忆点: {new_memory}")
 
                 return True, f"为{person.person_name}新增记忆点: {new_memory}"
@@ -207,14 +209,15 @@ class BuildRelationAction(BaseAction):
                     person.memory_points.append(f"{category}:{integrate_memory}:{memory_weight + 1.0}")
                     person.sync_to_database()
 
-                    logger.info(f"{self.log_prefix} 更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}")
+                    logger.info(
+                        f"{self.log_prefix} 更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}"
+                    )
 
                     return True, f"更新{person.person_name}的记忆点: {memory_content} -> {integrate_memory}"
 
                 else:
                     logger.warning(f"{self.log_prefix} 删除记忆点失败: {memory_content}")
                     return False, f"删除{person.person_name}的记忆点失败: {memory_content}"
-                
 
             return True, "关系动作执行成功"