Merge branch 'dev' into dev

src/chat/chat_loop/heartFC_chat.py

@@ -2,7 +2,7 @@ import asyncio
 import time
 import traceback
 import random
-from typing import List, Optional, Dict, Any
+from typing import List, Optional, Dict, Any, Tuple
 from rich.traceback import install
 
 from src.config.config import global_config
@@ -217,9 +217,11 @@ class HeartFChatting:
                 filter_bot=True,
             )
             if global_config.chat.focus_value != 0:
-                if len(new_messages_data) > 3 / pow(global_config.chat.focus_value,0.5):
+                if len(new_messages_data) > 3 / pow(global_config.chat.focus_value, 0.5):
                     self.loop_mode = ChatMode.FOCUS
-                    self.energy_value = 10 + (len(new_messages_data) / (3 / pow(global_config.chat.focus_value,0.5))) * 10
+                    self.energy_value = (
+                        10 + (len(new_messages_data) / (3 / pow(global_config.chat.focus_value, 0.5))) * 10
+                    )
                     return True
 
                 if self.energy_value >= 30:
@@ -254,20 +256,21 @@ class HeartFChatting:
         )
         person_name = await person_info_manager.get_value(person_id, "person_name")
         return f"{person_name}:{message_data.get('processed_plain_text')}"
-    
+
     async def _send_and_store_reply(
         self,
-        response_set, 
-        reply_to_str, 
-        loop_start_time, 
-        action_message, 
-        cycle_timers, 
+        response_set,
+        reply_to_str,
+        loop_start_time,
+        action_message,
+        cycle_timers: Dict[str, float],
         thinking_id,
-        plan_result):
+        plan_result,
+    ) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
         with Timer("回复发送", cycle_timers):
             reply_text = await self._send_response(response_set, reply_to_str, loop_start_time, action_message)
 
-                # 存储reply action信息
+            # 存储reply action信息
         person_info_manager = get_person_info_manager()
         person_id = person_info_manager.get_person_id(
             action_message.get("chat_info_platform", ""),
@@ -275,7 +278,7 @@ class HeartFChatting:
         )
         person_name = await person_info_manager.get_value(person_id, "person_name")
         action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
-        
+
         await database_api.store_action_info(
             chat_stream=self.chat_stream,
             action_build_into_prompt=False,
@@ -285,10 +288,9 @@ class HeartFChatting:
             action_data={"reply_text": reply_text, "reply_to": reply_to_str},
             action_name="reply",
         )
-        
-        
+
         # 构建循环信息
-        loop_info = {
+        loop_info: Dict[str, Any] = {
             "loop_plan_info": {
                 "action_result": plan_result.get("action_result", {}),
             },
@@ -299,8 +301,8 @@ class HeartFChatting:
                 "taken_time": time.time(),
             },
         }
-        
-        return loop_info, reply_text,cycle_timers
+
+        return loop_info, reply_text, cycle_timers
 
     async def _observe(self, message_data: Optional[Dict[str, Any]] = None):
         # sourcery skip: hoist-statement-from-if, merge-comparisons, reintroduce-else
@@ -310,12 +312,12 @@ class HeartFChatting:
         reply_text = ""  # 初始化reply_text变量，避免UnboundLocalError
         gen_task = None  # 初始化gen_task变量，避免UnboundLocalError
         reply_to_str = ""  # 初始化reply_to_str变量
-        
+
         # 创建新的循环信息
         cycle_timers, thinking_id = self.start_cycle()
 
         logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式：{self.loop_mode}]")
-        
+
         if ENABLE_S4U:
             await send_typing()
 
@@ -337,19 +339,20 @@ class HeartFChatting:
             skip_planner = False
             if self.loop_mode == ChatMode.NORMAL:
                 # 过滤掉reply相关的动作，检查是否还有其他动作
-                non_reply_actions = {k: v for k, v in available_actions.items() 
-                                if k not in ['reply', 'no_reply', 'no_action']}
-                
+                non_reply_actions = {
+                    k: v for k, v in available_actions.items() if k not in ["reply", "no_reply", "no_action"]
+                }
+
                 if not non_reply_actions:
                     skip_planner = True
                     logger.info(f"{self.log_prefix} Normal模式下没有可用动作，直接回复")
-                    
+
                     # 直接设置为reply动作
                     action_type = "reply"
                     reasoning = ""
                     action_data = {"loop_start_time": loop_start_time}
                     is_parallel = False
-                    
+
                     # 构建plan_result用于后续处理
                     plan_result = {
                         "action_result": {
@@ -361,22 +364,25 @@ class HeartFChatting:
                         },
                         "action_prompt": "",
                     }
-                    target_message = message_data            
-                
+                    target_message = message_data
+
                 # 如果normal模式且不跳过规划器，开始一个回复生成进程，先准备好回复（其实是和planer同时进行的）
                 if not skip_planner:
                     reply_to_str = await self.build_reply_to_str(message_data)
-                    gen_task = asyncio.create_task(self._generate_response(
-                        message_data=message_data, 
-                        available_actions=available_actions, 
-                        reply_to=reply_to_str, 
-                        request_type="chat.replyer.normal"))
-                    
+                    gen_task = asyncio.create_task(
+                        self._generate_response(
+                            message_data=message_data,
+                            available_actions=available_actions,
+                            reply_to=reply_to_str,
+                            request_type="chat.replyer.normal",
+                        )
+                    )
+
             if not skip_planner:
                 with Timer("规划器", cycle_timers):
                     plan_result, target_message = await self.action_planner.plan(mode=self.loop_mode)
 
-                action_result: dict = plan_result.get("action_result", {})  # type: ignore
+                action_result: Dict[str, Any] = plan_result.get("action_result", {})  # type: ignore
                 action_type, action_data, reasoning, is_parallel = (
                     action_result.get("action_type", "error"),
                     action_result.get("action_data", {}),
@@ -386,29 +392,27 @@ class HeartFChatting:
 
                 action_data["loop_start_time"] = loop_start_time
 
-
             if action_type == "reply":
                 logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复")
             elif is_parallel:
-                logger.info(
-                    f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作"
-                )
+                logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复, 同时执行{action_type}动作")
             else:
                 # 只有在gen_task存在时才进行相关操作
-                if gen_task is not None:
+                if gen_task:
                     if not gen_task.done():
                         gen_task.cancel()
                         logger.debug(f"{self.log_prefix} 已取消预生成的回复任务")
                         logger.info(
                             f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复，但选择执行{action_type}，不发表回复"
                         )
-                    else:
-                        content = " ".join([item[1] for item in gen_task.result() if item[0] == "text"])
+                    elif generation_result := gen_task.result():
+                        content = " ".join([item[1] for item in generation_result if item[0] == "text"])
                         logger.debug(f"{self.log_prefix} 预生成的回复任务已完成")
                         logger.info(
                             f"{self.log_prefix}{global_config.bot.nickname} 原本想要回复：{content}，但选择执行{action_type}，不发表回复"
                         )
-
+                    else:
+                        logger.warning(f"{self.log_prefix} 预生成的回复任务未生成有效内容")
 
             action_message: Dict[str, Any] = message_data or target_message  # type: ignore
             if action_type == "reply":
@@ -417,11 +421,14 @@ class HeartFChatting:
                     # 只有在gen_task存在时才等待
                     if not gen_task:
                         reply_to_str = await self.build_reply_to_str(message_data)
-                        gen_task = asyncio.create_task(self._generate_response(
-                            message_data=message_data, 
-                            available_actions=available_actions, 
-                            reply_to=reply_to_str, 
-                            request_type="chat.replyer.normal"))
+                        gen_task = asyncio.create_task(
+                            self._generate_response(
+                                message_data=message_data,
+                                available_actions=available_actions,
+                                reply_to=reply_to_str,
+                                request_type="chat.replyer.normal",
+                            )
+                        )
 
                     gather_timeout = global_config.chat.thinking_timeout
                     try:
@@ -436,56 +443,71 @@ class HeartFChatting:
                         return False
                 else:
                     logger.info(f"{self.log_prefix}{global_config.bot.nickname} 决定进行回复 (focus模式)")
-                    
+
                     # 构建reply_to字符串
                     reply_to_str = await self.build_reply_to_str(action_message)
-                    
+
                     # 生成回复
                     with Timer("回复生成", cycle_timers):
                         response_set = await self._generate_response(
-                            message_data=action_message, 
-                            available_actions=available_actions, 
-                            reply_to=reply_to_str, 
-                            request_type="chat.replyer.focus")
-                    
+                            message_data=action_message,
+                            available_actions=available_actions,
+                            reply_to=reply_to_str,
+                            request_type="chat.replyer.focus",
+                        )
+
                     if not response_set:
                         logger.warning(f"{self.log_prefix}模型未生成回复内容")
                         return False
-                    
-                loop_info, reply_text,cycle_timers = await self._send_and_store_reply(response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result)
-                
+
+                loop_info, reply_text, cycle_timers = await self._send_and_store_reply(
+                    response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result
+                )
+
                 return True
 
             else:
                 # 并行执行：同时进行回复发送和动作执行
-                tasks = []
-                
+                # 先置空防止未定义错误
+                background_reply_task = None
+                background_action_task = None
                 # 如果是并行执行且在normal模式下，需要等待预生成的回复任务完成并发送回复
                 if self.loop_mode == ChatMode.NORMAL and is_parallel and gen_task:
-                    async def handle_reply_task():
+
+                    async def handle_reply_task() -> Tuple[Optional[Dict[str, Any]], str, Dict[str, float]]:
                         # 等待预生成的回复任务完成
                         gather_timeout = global_config.chat.thinking_timeout
                         try:
                             response_set = await asyncio.wait_for(gen_task, timeout=gather_timeout)
-                            
+
                         except asyncio.TimeoutError:
-                            logger.warning(f"{self.log_prefix} 并行执行：回复生成超时>{global_config.chat.thinking_timeout}s，已跳过")
+                            logger.warning(
+                                f"{self.log_prefix} 并行执行：回复生成超时>{global_config.chat.thinking_timeout}s，已跳过"
+                            )
                             return None, "", {}
                         except asyncio.CancelledError:
                             logger.debug(f"{self.log_prefix} 并行执行：回复生成任务已被取消")
                             return None, "", {}
-                    
+
                         if not response_set:
                             logger.warning(f"{self.log_prefix} 模型超时或生成回复内容为空")
                             return None, "", {}
-                            
+
                         reply_to_str = await self.build_reply_to_str(action_message)
-                        loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(response_set, reply_to_str, loop_start_time, action_message, cycle_timers, thinking_id, plan_result)
+                        loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
+                            response_set,
+                            reply_to_str,
+                            loop_start_time,
+                            action_message,
+                            cycle_timers,
+                            thinking_id,
+                            plan_result,
+                        )
                         return loop_info, reply_text, cycle_timers_reply
-                    
-                    # 添加回复任务到并行任务列表
-                    tasks.append(asyncio.create_task(handle_reply_task()))
-                
+
+                    # 执行回复任务并赋值到变量
+                    background_reply_task = asyncio.create_task(handle_reply_task())
+
                 # 动作执行任务
                 async def handle_action_task():
                     with Timer("动作执行", cycle_timers):
@@ -493,52 +515,55 @@ class HeartFChatting:
                             action_type, reasoning, action_data, cycle_timers, thinking_id, action_message
                         )
                     return success, reply_text, command
-                
-                # 添加动作执行任务到并行任务列表
-                tasks.append(asyncio.create_task(handle_action_task()))
-                
-                # 并行执行所有任务
-                results = await asyncio.gather(*tasks, return_exceptions=True)
-                
-                # 处理结果
+
+                # 执行动作任务并赋值到变量
+                background_action_task = asyncio.create_task(handle_action_task())
+
                 reply_loop_info = None
                 reply_text_from_reply = ""
                 action_success = False
                 action_reply_text = ""
                 action_command = ""
-                
-                if len(tasks) == 2:  # 有回复任务和动作任务
+
+                # 并行执行所有任务
+                if background_reply_task:
+                    results = await asyncio.gather(
+                        background_reply_task, background_action_task, return_exceptions=True
+                    )
                     # 处理回复任务结果
                     reply_result = results[0]
-                    if isinstance(reply_result, Exception):
+                    if isinstance(reply_result, BaseException):
                         logger.error(f"{self.log_prefix} 回复任务执行异常: {reply_result}")
                     elif reply_result and reply_result[0] is not None:
                         reply_loop_info, reply_text_from_reply, _ = reply_result
-                    
+
                     # 处理动作任务结果
-                    action_result = results[1]
-                    if isinstance(action_result, Exception):
-                        logger.error(f"{self.log_prefix} 动作任务执行异常: {action_result}")
+                    action_task_result = results[1]
+                    if isinstance(action_task_result, BaseException):
+                        logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
                     else:
-                        action_success, action_reply_text, action_command = action_result
-                        
-                else:  # 只有动作任务
-                    action_result = results[0]
-                    if isinstance(action_result, Exception):
-                        logger.error(f"{self.log_prefix} 动作任务执行异常: {action_result}")
+                        action_success, action_reply_text, action_command = action_task_result
+                else:
+                    results = await asyncio.gather(background_action_task, return_exceptions=True)
+                    # 只有动作任务
+                    action_task_result = results[0]
+                    if isinstance(action_task_result, BaseException):
+                        logger.error(f"{self.log_prefix} 动作任务执行异常: {action_task_result}")
                     else:
-                        action_success, action_reply_text, action_command = action_result
+                        action_success, action_reply_text, action_command = action_task_result
 
                 # 构建最终的循环信息
                 if reply_loop_info:
                     # 如果有回复信息，使用回复的loop_info作为基础
                     loop_info = reply_loop_info
                     # 更新动作执行信息
-                    loop_info["loop_action_info"].update({
-                        "action_taken": action_success,
-                        "command": action_command,
-                        "taken_time": time.time(),
-                    })
+                    loop_info["loop_action_info"].update(
+                        {
+                            "action_taken": action_success,
+                            "command": action_command,
+                            "taken_time": time.time(),
+                        }
+                    )
                     reply_text = reply_text_from_reply
                 else:
                     # 没有回复信息，构建纯动作的loop_info
@@ -555,11 +580,10 @@ class HeartFChatting:
                     }
                     reply_text = action_reply_text
 
-
         if ENABLE_S4U:
             await stop_typing()
             await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
-        
+
         self.end_cycle(loop_info, cycle_timers)
         self.print_cycle_info(cycle_timers)
 
@@ -603,7 +627,7 @@ class HeartFChatting:
         action: str,
         reasoning: str,
         action_data: dict,
-        cycle_timers: dict,
+        cycle_timers: Dict[str, float],
         thinking_id: str,
         action_message: dict,
     ) -> tuple[bool, str, str]:
@@ -712,7 +736,11 @@ class HeartFChatting:
         return False
 
     async def _generate_response(
-        self, message_data: dict, available_actions: Optional[Dict[str, ActionInfo]], reply_to: str, request_type: str = "chat.replyer.normal"
+        self,
+        message_data: dict,
+        available_actions: Optional[Dict[str, ActionInfo]],
+        reply_to: str,
+        request_type: str = "chat.replyer.normal",
     ) -> Optional[list]:
         """生成普通回复"""
         try:
@@ -734,7 +762,7 @@ class HeartFChatting:
             logger.error(f"{self.log_prefix}回复生成出现错误：{str(e)} {traceback.format_exc()}")
             return None
 
-    async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data):
+    async def _send_response(self, reply_set, reply_to, thinking_start_time, message_data) -> str:
         current_time = time.time()
         new_message_count = message_api.count_new_messages(
             chat_id=self.chat_stream.stream_id, start_time=thinking_start_time, end_time=current_time
@@ -746,13 +774,9 @@ class HeartFChatting:
         need_reply = new_message_count >= random.randint(2, 4)
 
         if need_reply:
-            logger.info(
-                f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复"
-            )
+            logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复")
         else:
-            logger.info(
-                f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复"
-            )
+            logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，不使用引用回复")
 
         reply_text = ""
         first_replied = False

src/chat/message_receive/message.py

@@ -444,7 +444,7 @@ class MessageSending(MessageProcessBase):
         is_emoji: bool = False,
         thinking_start_time: float = 0,
         apply_set_reply_logic: bool = False,
-        reply_to: str = None,  # type: ignore
+        reply_to: Optional[str] = None,
     ):
         # 调用父类初始化
         super().__init__(

src/chat/planner_actions/planner.py

@@ -211,9 +211,8 @@ class ActionPlanner:
             reasoning = f"Planner 内部处理错误: {outer_e}"
 
         is_parallel = False
-        if mode == ChatMode.NORMAL:
-            if action in current_available_actions:
-                is_parallel = current_available_actions[action].parallel_action
+        if mode == ChatMode.NORMAL and action in current_available_actions:
+            is_parallel = current_available_actions[action].parallel_action
 
         action_result = {
             "action_type": action,
@@ -256,7 +255,7 @@ class ActionPlanner:
 
             actions_before_now = get_actions_by_timestamp_with_chat(
                 chat_id=self.chat_id,
-                timestamp_start=time.time()-3600,
+                timestamp_start=time.time() - 3600,
                 timestamp_end=time.time(),
                 limit=5,
             )
@@ -264,7 +263,7 @@ class ActionPlanner:
             actions_before_now_block = build_readable_actions(
                 actions=actions_before_now,
             )
-            
+
             actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
 
             self.last_obs_time_mark = time.time()
@@ -276,7 +275,6 @@ class ActionPlanner:
                 if global_config.chat.at_bot_inevitable_reply:
                     mentioned_bonus = "\n- 有人提到你，或者at你"
 
-                
                 by_what = "聊天内容"
                 target_prompt = '\n    "target_message_id":"触发action的消息id"'
                 no_action_block = f"""重要说明：

src/chat/replyer/default_generator.py

@@ -39,7 +39,7 @@ def init_prompt():
     Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
     Prompt("在群里聊天", "chat_target_group2")
     Prompt("和{sender_name}聊天", "chat_target_private2")
-    
+
     Prompt(
         """
 {expression_habits_block}
@@ -156,10 +156,18 @@ class DefaultReplyer:
         extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
         enable_tool: bool = True,
-        enable_timeout: bool = False,
     ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
-        回复器 (Replier): 核心逻辑，负责生成回复文本。
+        回复器 (Replier): 负责生成回复文本的核心逻辑。
+
+        Args:
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            extra_info: 额外信息，用于补充上下文
+            available_actions: 可用的动作信息字典
+            enable_tool: 是否启用工具调用
+
+        Returns:
+            Tuple[bool, Optional[str], Optional[str]]: (是否成功, 生成的回复内容, 使用的prompt)
         """
         prompt = None
         if available_actions is None:
@@ -168,43 +176,25 @@ class DefaultReplyer:
             # 3. 构建 Prompt
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_reply_context(
-                    reply_to = reply_to,
+                    reply_to=reply_to,
                     extra_info=extra_info,
                     available_actions=available_actions,
-                    enable_timeout=enable_timeout,
                     enable_tool=enable_tool,
                 )
-                
+
             if not prompt:
                 logger.warning("构建prompt失败，跳过回复生成")
                 return False, None, None
 
             # 4. 调用 LLM 生成回复
             content = None
-            reasoning_content = None
-            model_name = "unknown_model"
+            # TODO: 复活这里
+            # reasoning_content = None
+            # model_name = "unknown_model"
 
             try:
-                with Timer("LLM生成", {}):  # 内部计时器，可选保留
-                    # 加权随机选择一个模型配置
-                    selected_model_config = self._select_weighted_model_config()
-                    logger.info(
-                        f"使用模型生成回复: {selected_model_config.get('name', 'N/A')} (选中概率: {selected_model_config.get('weight', 1.0)})"
-                    )
-
-                    express_model = LLMRequest(
-                        model=selected_model_config,
-                        request_type=self.request_type,
-                    )
-
-                    if global_config.debug.show_prompt:
-                        logger.info(f"\n{prompt}\n")
-                    else:
-                        logger.debug(f"\n{prompt}\n")
-
-                    content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
-
-                    logger.debug(f"replyer生成内容: {content}")
+                content = await self.llm_generate_content(prompt)
+                logger.debug(f"replyer生成内容: {content}")
 
             except Exception as llm_e:
                 # 精简报错信息
@@ -220,62 +210,54 @@ class DefaultReplyer:
 
     async def rewrite_reply_with_context(
         self,
-        reply_data: Dict[str, Any],
         raw_reply: str = "",
         reason: str = "",
         reply_to: str = "",
-        relation_info: str = "",
-    ) -> Tuple[bool, Optional[str]]:
+        return_prompt: bool = False,
+    ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
-        表达器 (Expressor): 核心逻辑，负责生成回复文本。
+        表达器 (Expressor): 负责重写和优化回复文本。
+
+        Args:
+            raw_reply: 原始回复内容
+            reason: 回复原因
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            relation_info: 关系信息
+
+        Returns:
+            Tuple[bool, Optional[str]]: (是否成功, 重写后的回复内容)
         """
         try:
-            if not reply_data:
-                reply_data = {
-                    "reply_to": reply_to,
-                    "relation_info": relation_info,
-                }
-
             with Timer("构建Prompt", {}):  # 内部计时器，可选保留
                 prompt = await self.build_prompt_rewrite_context(
-                    reply_data=reply_data,
+                    raw_reply=raw_reply,
+                    reason=reason,
+                    reply_to=reply_to,
                 )
 
             content = None
-            reasoning_content = None
-            model_name = "unknown_model"
+            # TODO: 复活这里
+            # reasoning_content = None
+            # model_name = "unknown_model"
             if not prompt:
                 logger.error("Prompt 构建失败，无法生成回复。")
-                return False, None
+                return False, None, None
 
             try:
-                with Timer("LLM生成", {}):  # 内部计时器，可选保留
-                    # 加权随机选择一个模型配置
-                    selected_model_config = self._select_weighted_model_config()
-                    logger.info(
-                        f"使用模型重写回复: {selected_model_config.get('name', 'N/A')} (选中概率: {selected_model_config.get('weight', 1.0)})"
-                    )
-
-                    express_model = LLMRequest(
-                        model=selected_model_config,
-                        request_type=self.request_type,
-                    )
-
-                    content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt)
-
-                    logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
+                content = await self.llm_generate_content(prompt)
+                logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
 
             except Exception as llm_e:
                 # 精简报错信息
                 logger.error(f"LLM 生成失败: {llm_e}")
-                return False, None  # LLM 调用失败则无法生成回复
+                return False, None, prompt if return_prompt else None  # LLM 调用失败则无法生成回复
 
-            return True, content
+            return True, content, prompt if return_prompt else None
 
         except Exception as e:
             logger.error(f"回复生成意外失败: {e}")
             traceback.print_exc()
-            return False, None
+            return False, None, prompt if return_prompt else None
 
     async def build_relation_info(self, reply_to: str = ""):
         if not global_config.relationship.enable_relationship:
@@ -297,7 +279,16 @@ class DefaultReplyer:
 
         return await relationship_fetcher.build_relation_info(person_id, points_num=5)
 
-    async def build_expression_habits(self, chat_history, target):
+    async def build_expression_habits(self, chat_history: str, target: str) -> str:
+        """构建表达习惯块
+
+        Args:
+            chat_history: 聊天历史记录
+            target: 目标消息内容
+
+        Returns:
+            str: 表达习惯信息字符串
+        """
         if not global_config.expression.enable_expression:
             return ""
 
@@ -343,11 +334,18 @@ class DefaultReplyer:
         if style_habits_str.strip() and grammar_habits_str.strip():
             expression_habits_title = "你可以参考以下的语言习惯和句法，如果情景合适就使用，不要盲目使用,不要生硬使用，以合理的方式结合到你的回复中："
 
-        expression_habits_block = f"{expression_habits_title}\n{expression_habits_block}"
+        return f"{expression_habits_title}\n{expression_habits_block}"
 
-        return expression_habits_block
+    async def build_memory_block(self, chat_history: str, target: str) -> str:
+        """构建记忆块
 
-    async def build_memory_block(self, chat_history, target):
+        Args:
+            chat_history: 聊天历史记录
+            target: 目标消息内容
+
+        Returns:
+            str: 记忆信息字符串
+        """
         if not global_config.memory.enable_memory:
             return ""
 
@@ -375,12 +373,13 @@ class DefaultReplyer:
 
         return memory_str
 
-    async def build_tool_info(self, chat_history, reply_to: str = "", enable_tool: bool = True):
+    async def build_tool_info(self, chat_history: str, reply_to: str = "", enable_tool: bool = True) -> str:
         """构建工具信息块
 
         Args:
-            reply_data: 回复数据，包含要回复的消息内容
-            chat_history: 聊天历史
+            chat_history: 聊天历史记录
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            enable_tool: 是否启用工具调用
 
         Returns:
             str: 工具信息字符串
@@ -424,7 +423,15 @@ class DefaultReplyer:
             logger.error(f"工具信息获取失败: {e}")
             return ""
 
-    def _parse_reply_target(self, target_message: str) -> tuple:
+    def _parse_reply_target(self, target_message: str) -> Tuple[str, str]:
+        """解析回复目标消息
+
+        Args:
+            target_message: 目标消息，格式为 "发送者:消息内容" 或 "发送者：消息内容"
+
+        Returns:
+            Tuple[str, str]: (发送者名称, 消息内容)
+        """
         sender = ""
         target = ""
         # 添加None检查，防止NoneType错误
@@ -438,7 +445,15 @@ class DefaultReplyer:
                 target = parts[1].strip()
         return sender, target
 
-    async def build_keywords_reaction_prompt(self, target):
+    async def build_keywords_reaction_prompt(self, target: Optional[str]) -> str:
+        """构建关键词反应提示
+
+        Args:
+            target: 目标消息内容
+
+        Returns:
+            str: 关键词反应提示字符串
+        """
         # 关键词检测与反应
         keywords_reaction_prompt = ""
         try:
@@ -472,15 +487,25 @@ class DefaultReplyer:
 
         return keywords_reaction_prompt
 
-    async def _time_and_run_task(self, coroutine, name: str):
-        """一个简单的帮助函数，用于计时和运行异步任务，返回任务名、结果和耗时"""
+    async def _time_and_run_task(self, coroutine, name: str) -> Tuple[str, Any, float]:
+        """计时并运行异步任务的辅助函数
+
+        Args:
+            coroutine: 要执行的协程
+            name: 任务名称
+
+        Returns:
+            Tuple[str, Any, float]: (任务名称, 任务结果, 执行耗时)
+        """
         start_time = time.time()
         result = await coroutine
         end_time = time.time()
         duration = end_time - start_time
         return name, result, duration
 
-    def build_s4u_chat_history_prompts(self, message_list_before_now: list, target_user_id: str) -> tuple[str, str]:
+    def build_s4u_chat_history_prompts(
+        self, message_list_before_now: List[Dict[str, Any]], target_user_id: str
+    ) -> Tuple[str, str]:
         """
         构建 s4u 风格的分离对话 prompt
 
@@ -489,7 +514,7 @@ class DefaultReplyer:
             target_user_id: 目标用户ID（当前对话对象）
 
         Returns:
-            tuple: (核心对话prompt, 背景对话prompt)
+            Tuple[str, str]: (核心对话prompt, 背景对话prompt)
         """
         core_dialogue_list = []
         background_dialogue_list = []
@@ -508,7 +533,7 @@ class DefaultReplyer:
                     # 其他用户的对话
                     background_dialogue_list.append(msg_dict)
             except Exception as e:
-                logger.error(f"![1753364551656](image/default_generator/1753364551656.png)记录: {msg_dict}, 错误: {e}")
+                logger.error(f"处理消息记录时出错: {msg_dict}, 错误: {e}")
 
         # 构建背景对话 prompt
         background_dialogue_prompt = ""
@@ -553,8 +578,25 @@ class DefaultReplyer:
         sender: str,
         target: str,
         chat_info: str,
-    ):
-        """构建 mai_think 上下文信息"""
+    ) -> Any:
+        """构建 mai_think 上下文信息
+
+        Args:
+            chat_id: 聊天ID
+            memory_block: 记忆块内容
+            relation_info: 关系信息
+            time_block: 时间块内容
+            chat_target_1: 聊天目标1
+            chat_target_2: 聊天目标2
+            mood_prompt: 情绪提示
+            identity_block: 身份块内容
+            sender: 发送者名称
+            target: 目标消息内容
+            chat_info: 聊天信息
+
+        Returns:
+            Any: mai_think 实例
+        """
         mai_think = mai_thinking_manager.get_mai_think(chat_id)
         mai_think.memory_block = memory_block
         mai_think.relation_info_block = relation_info
@@ -573,19 +615,17 @@ class DefaultReplyer:
         reply_to: str,
         extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
-        enable_timeout: bool = False,
         enable_tool: bool = True,
     ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         """
         构建回复器上下文
 
         Args:
-            reply_data: 回复数据
-                replay_data 包含以下字段：
-                    structured_info: 结构化信息，一般是工具调用获得的信息
-                    reply_to: 回复对象
-                    extra_info/extra_info_block: 额外信息
+            reply_to: 回复对象，格式为 "发送者:消息内容"
+            extra_info: 额外信息，用于补充上下文
             available_actions: 可用动作
+            enable_timeout: 是否启用超时处理
+            enable_tool: 是否启用工具调用
 
         Returns:
             str: 构建好的上下文
@@ -800,15 +840,14 @@ class DefaultReplyer:
 
     async def build_prompt_rewrite_context(
         self,
-        reply_data: Dict[str, Any],
+        raw_reply: str,
+        reason: str,
+        reply_to: str,
     ) -> str:
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         is_group_chat = bool(chat_stream.group_info)
 
-        reply_to = reply_data.get("reply_to", "none")
-        raw_reply = reply_data.get("raw_reply", "")
-        reason = reply_data.get("reason", "")
         sender, target = self._parse_reply_target(reply_to)
 
         # 添加情绪状态获取
@@ -835,7 +874,7 @@ class DefaultReplyer:
         # 并行执行2个构建任务
         expression_habits_block, relation_info = await asyncio.gather(
             self.build_expression_habits(chat_talking_prompt_half, target),
-            self.build_relation_info(reply_data),
+            self.build_relation_info(reply_to),
         )
 
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
@@ -938,6 +977,30 @@ class DefaultReplyer:
             display_message=display_message,
         )
 
+    async def llm_generate_content(self, prompt: str) -> str:
+        with Timer("LLM生成", {}):  # 内部计时器，可选保留
+            # 加权随机选择一个模型配置
+            selected_model_config = self._select_weighted_model_config()
+            logger.info(
+                f"使用模型生成回复: {selected_model_config.get('name', 'N/A')} (选中概率: {selected_model_config.get('weight', 1.0)})"
+            )
+
+            express_model = LLMRequest(
+                model=selected_model_config,
+                request_type=self.request_type,
+            )
+
+            if global_config.debug.show_prompt:
+                logger.info(f"\n{prompt}\n")
+            else:
+                logger.debug(f"\n{prompt}\n")
+
+            # TODO: 这里的_应该做出替换
+            content, _ = await express_model.generate_response_async(prompt)
+
+            logger.debug(f"replyer生成内容: {content}")
+        return content
+
 
 def weighted_sample_no_replacement(items, weights, k) -> list:
     """
@@ -996,9 +1059,7 @@ async def get_prompt_info(message: str, threshold: float):
             logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
             logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
 
-            # 格式化知识信息
-            formatted_prompt_info = f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
-            return formatted_prompt_info
+            return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
         else:
             logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...")
             return ""