remove：移除willing系统，移除reply2，移除能量值,移除reply_to改为message

docs/model_configuration_guide.md

@@ -166,10 +166,10 @@ temperature = 0.7
 max_tokens = 800
 ```
 
-### replyer_1 - 主要回复模型
+### replyer - 主要回复模型
 首要回复模型，也用于表达器和表达方式学习：
 ```toml
-[model_task_config.replyer_1]
+[model_task_config.replyer]
 model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.2
 max_tokens = 800

src/chat/chat_loop/heartFC_chat.py

@@ -11,7 +11,6 @@ from src.common.logger import get_logger
 from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
 from src.chat.utils.prompt_builder import global_prompt_manager
 from src.chat.utils.timer_calculator import Timer
-from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat
 from src.chat.planner_actions.planner import ActionPlanner
 from src.chat.planner_actions.action_modifier import ActionModifier
 from src.chat.planner_actions.action_manager import ActionManager
@@ -22,9 +21,9 @@ from src.person_info.person_info import get_person_info_manager
 from src.plugin_system.base.component_types import ActionInfo, ChatMode, EventType
 from src.plugin_system.core import events_manager
 from src.plugin_system.apis import generator_api, send_api, message_api, database_api
-from src.chat.willing.willing_manager import get_willing_manager
 from src.mais4u.mai_think import mai_thinking_manager
 from src.mais4u.constant_s4u import ENABLE_S4U
+import math
 # no_reply逻辑已集成到heartFC_chat.py中，不再需要导入
 from src.chat.chat_loop.hfc_utils import send_typing, stop_typing
 
@@ -99,7 +98,6 @@ class HeartFChatting:
         # 循环控制内部状态
         self.running: bool = False
         self._loop_task: Optional[asyncio.Task] = None  # 主循环任务
-        self._energy_task: Optional[asyncio.Task] = None
 
         # 添加循环信息管理相关的属性
         self.history_loop: List[CycleDetail] = []
@@ -110,12 +108,6 @@ class HeartFChatting:
         self.plan_timeout_count = 0
 
         self.last_read_time = time.time() - 1
-
-        self.willing_manager = get_willing_manager()
-
-        logger.info(f"{self.log_prefix} HeartFChatting 初始化完成")
-
-        self.energy_value = 5
         
         self.focus_energy = 1
         self.no_reply_consecutive = 0
@@ -134,9 +126,6 @@ class HeartFChatting:
             # 标记为活动状态，防止重复启动
             self.running = True
 
-            self._energy_task = asyncio.create_task(self._energy_loop())
-            self._energy_task.add_done_callback(self._handle_energy_completion)
-
             self._loop_task = asyncio.create_task(self._main_chat_loop())
             self._loop_task.add_done_callback(self._handle_loop_completion)
             logger.info(f"{self.log_prefix} HeartFChatting 启动完成")
@@ -172,19 +161,6 @@ class HeartFChatting:
         self._current_cycle_detail.timers = cycle_timers
         self._current_cycle_detail.end_time = time.time()
 
-    def _handle_energy_completion(self, task: asyncio.Task):
-        if exception := task.exception():
-            logger.error(f"{self.log_prefix} HeartFChatting: 能量循环异常: {exception}")
-            logger.error(traceback.format_exc())
-        else:
-            logger.info(f"{self.log_prefix} HeartFChatting: 能量循环完成")
-
-    async def _energy_loop(self):
-        while self.running:
-            await asyncio.sleep(12)
-            self.energy_value -= 0.5
-            self.energy_value = max(self.energy_value, 0.3)
-
     def print_cycle_info(self, cycle_timers):
         # 记录循环信息和计时器结果
         timer_strings = []
@@ -250,10 +226,8 @@ class HeartFChatting:
         """
         new_message_count = len(new_message)
         
-
-        # talk_frequency = global_config.chat.get_current_talk_frequency(self.stream_id)
         modified_exit_count_threshold = self.focus_energy / global_config.chat.focus_value
-        
+        modified_exit_interest_threshold = 3 / global_config.chat.focus_value
         total_interest = 0.0
         for msg_dict in new_message:
             interest_value = msg_dict.get("interest_value", 0.0)
@@ -261,16 +235,12 @@ class HeartFChatting:
                 total_interest += interest_value
         
         if new_message_count >= modified_exit_count_threshold:
-            # 记录兴趣度到列表
-            
-            
             self.recent_interest_records.append(total_interest)
-            
             logger.info(
                 f"{self.log_prefix} 累计消息数量达到{new_message_count}条(>{modified_exit_count_threshold:.1f})，结束等待"
             )
-            logger.info(self.last_read_time)
+            # logger.info(self.last_read_time)
-            logger.info(new_message)
+            # logger.info(new_message)
             return True,total_interest/new_message_count
 
         # 检查累计兴趣值
@@ -280,12 +250,11 @@ class HeartFChatting:
                 logger.info(f"{self.log_prefix} breaking形式当前累计兴趣值: {total_interest:.2f}, 专注度: {global_config.chat.focus_value:.1f}")
                 self._last_accumulated_interest = total_interest
             
-            if total_interest >= 3 / global_config.chat.focus_value:
+            if total_interest >= modified_exit_interest_threshold:
                 # 记录兴趣度到列表
                 self.recent_interest_records.append(total_interest)
-                
                 logger.info(
-                    f"{self.log_prefix} 累计兴趣值达到{total_interest:.2f}(>{3 / global_config.chat.focus_value})，结束等待"
+                    f"{self.log_prefix} 累计兴趣值达到{total_interest:.2f}(>{modified_exit_interest_threshold:.1f})，结束等待"
                 )
                 return True,total_interest/new_message_count
 
@@ -314,8 +283,6 @@ class HeartFChatting:
         should_process,interest_value = await self._should_process_messages(recent_messages_dict)
         
         if should_process:
-            # earliest_message_data = recent_messages_dict[0]
-            # self.last_read_time = earliest_message_data.get("time")
             self.last_read_time = time.time()
             await self._observe(interest_value = interest_value)
 
@@ -323,38 +290,22 @@ class HeartFChatting:
             # Normal模式：消息数量不足，等待
             await asyncio.sleep(0.5)
             return True
-
         return True
 
-    async def build_reply_to_str(self, message_data: dict):
-        person_info_manager = get_person_info_manager()
-        
-        # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
-        platform = message_data.get("chat_info_platform")
-        if platform is None:
-            platform = getattr(self.chat_stream, "platform", "unknown")
-        
-        person_id = person_info_manager.get_person_id(
-            platform,  # type: ignore
-            message_data.get("user_id"),  # type: ignore
-        )
-        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: Dict[str, float],
         thinking_id,
         actions,
     ) -> 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)
+            reply_text = await self._send_response(response_set, loop_start_time, action_message)
 
-            # 存储reply action信息
+        # 存储reply action信息
         person_info_manager = get_person_info_manager()
         
         # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
@@ -375,7 +326,7 @@ class HeartFChatting:
             action_prompt_display=action_prompt_display,
             action_done=True,
             thinking_id=thinking_id,
-            action_data={"reply_text": reply_text, "reply_to": reply_to_str},
+            action_data={"reply_text": reply_text},
             action_name="reply",
         )
 
@@ -398,12 +349,7 @@ class HeartFChatting:
 
         action_type = "no_action"
         reply_text = ""  # 初始化reply_text变量，避免UnboundLocalError
-        reply_to_str = ""  # 初始化reply_to_str变量
 
-        # 根据interest_value计算概率，决定使用哪种planner模式
-        # interest_value越高，越倾向于使用Normal模式
-        import random
-        import math
         
         # 使用sigmoid函数将interest_value转换为概率
         # 当interest_value为0时，概率接近0（使用Focus模式）
@@ -469,13 +415,6 @@ class HeartFChatting:
                     available_actions=available_actions,
                 )
 
-            # 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", {}),
-            #     action_result.get("reasoning", "未提供理由"),
-            #     action_result.get("is_parallel", True),
-            # )
 
             
             # 3. 并行执行所有动作
@@ -522,32 +461,26 @@ class HeartFChatting:
                             "command": command
                         }
                     else:
-                        # 执行回复动作
-                        reply_to_str = await self.build_reply_to_str(action_info["action_message"])
                         
-                        
-                        # 生成回复
-                        gather_timeout = global_config.chat.thinking_timeout
                         try:
-                            response_set = await asyncio.wait_for(
+                            success, response_set, _ = await generator_api.generate_reply(
-                                self._generate_response(
+                                chat_stream=self.chat_stream,
-                                    message_data=action_info["action_message"],
+                                reply_message = action_info["action_message"],
-                                    available_actions=action_info["available_actions"],
+                                available_actions=available_actions,
-                                    reply_to=reply_to_str,
+                                enable_tool=global_config.tool.enable_tool,
-                                    request_type="chat.replyer",
+                                request_type="chat.replyer",
-                                ),
+                                from_plugin=False,
-                                timeout=gather_timeout
                             )
-                        except asyncio.TimeoutError:
+
-                            logger.warning(
+                            if not success or not response_set:
-                                f"{self.log_prefix} 并行执行：回复生成超时>{global_config.chat.thinking_timeout}s，已跳过"
+                                logger.info(f"对 {action_info['action_message'].get('processed_plain_text')} 的回复生成失败")
-                            )
+                                return {
-                            return {
+                                    "action_type": "reply",
-                                "action_type": "reply",
+                                    "success": False,
-                                "success": False,
+                                    "reply_text": "",
-                                "reply_text": "",
+                                    "loop_info": None
-                                "loop_info": 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 {
@@ -557,18 +490,8 @@ class HeartFChatting:
                                 "loop_info": 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 {
-                                "action_type": "reply",
-                                "success": False,
-                                "reply_text": "",
-                                "loop_info": None
-                            }
-
                         loop_info, reply_text, cycle_timers_reply = await self._send_and_store_reply(
                             response_set,
-                            reply_to_str,
                             loop_start_time,
                             action_info["action_message"],
                             cycle_timers,
@@ -592,8 +515,7 @@ class HeartFChatting:
                         "error": str(e)
                     }
             
-            # 创建所有动作的后台任务
+
-            # print(actions)
             
             action_tasks = [asyncio.create_task(execute_action(action)) for action in actions]
             
@@ -762,42 +684,11 @@ class HeartFChatting:
             traceback.print_exc()
             return False, "", ""
 
-    async def _generate_response(
+    async def _send_response(self, reply_set, thinking_start_time, message_data) -> str:
-        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:
-            success, reply_set, _ = await generator_api.generate_reply(
-                chat_stream=self.chat_stream,
-                reply_to=reply_to,
-                available_actions=available_actions,
-                enable_tool=global_config.tool.enable_tool,
-                request_type=request_type,
-                from_plugin=False,
-            )
-
-            if not success or not reply_set:
-                logger.info(f"对 {message_data.get('processed_plain_text')} 的回复生成失败")
-                return None
-
-            return reply_set
-
-        except Exception as e:
-            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) -> 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
         )
-        platform = message_data.get("user_platform", "")
-        user_id = message_data.get("user_id", "")
-        reply_to_platform_id = f"{platform}:{user_id}"
 
         need_reply = new_message_count >= random.randint(2, 4)
 
@@ -809,27 +700,20 @@ class HeartFChatting:
         for reply_seg in reply_set:
             data = reply_seg[1]
             if not first_replied:
-                if need_reply:
+                await send_api.text_to_stream(
-                    await send_api.text_to_stream(
+                    text=data,
-                        text=data,
+                    stream_id=self.chat_stream.stream_id,
-                        stream_id=self.chat_stream.stream_id,
+                    reply_to_message = message_data,
-                        reply_to=reply_to,
+                    set_reply=need_reply,
-                        reply_to_platform_id=reply_to_platform_id,
+                    typing=False,
-                        typing=False,
+                )
-                    )
-                else:
-                    await send_api.text_to_stream(
-                        text=data,
-                        stream_id=self.chat_stream.stream_id,
-                        reply_to_platform_id=reply_to_platform_id,
-                        typing=False,
-                    )
                 first_replied = True
             else:
                 await send_api.text_to_stream(
                     text=data,
                     stream_id=self.chat_stream.stream_id,
-                    reply_to_platform_id=reply_to_platform_id,
+                    reply_to_message = message_data,
+                    set_reply=need_reply,
                     typing=True,
                 )
             reply_text += data

src/chat/express/expression_learner.py

@@ -81,7 +81,7 @@ def init_prompt() -> None:
 class ExpressionLearner:
     def __init__(self, chat_id: str) -> None:
         self.express_learn_model: LLMRequest = LLMRequest(
-            model_set=model_config.model_task_config.replyer_1, request_type="expressor.learner"
+            model_set=model_config.model_task_config.replyer, request_type="expressor.learner"
         )
         self.chat_id = chat_id
         self.chat_name = get_chat_manager().get_stream_name(chat_id) or chat_id

src/chat/planner_actions/planner.py

@@ -42,6 +42,19 @@ def init_prompt():
 {actions_before_now_block}
 
 {no_action_block}
+
+动作：reply
+动作描述：参与聊天回复，发送文本进行表达
+- 你想要闲聊或者随便附
+- {mentioned_bonus}
+- 如果你刚刚进行了回复，不要对同一个话题重复回应
+- 不要回复自己发送的消息
+{{
+    "action": "reply",
+    "target_message_id":"触发action的消息id",
+    "reason":"回复的原因"
+}}
+
 {action_options_text}
 
 你必须从上面列出的可用action中选择一个，并说明触发action的消息id（不是消息原文）和选择该action的原因。
@@ -82,7 +95,6 @@ class ActionPlanner:
         self.max_plan_retries = 3
 
     def find_message_by_id(self, message_id: str, message_id_list: list) -> Optional[Dict[str, Any]]:
-        # sourcery skip: use-next
         """
         根据message_id从message_id_list中查找对应的原始消息
 
@@ -187,12 +199,11 @@ class ActionPlanner:
                         if key not in ["action", "reasoning"]:
                             action_data[key] = value
 
-                    # 在FOCUS模式下，非no_reply动作需要target_message_id
+                    # 非no_reply动作需要target_message_id
                     if action != "no_reply":
                         if target_message_id := parsed_json.get("target_message_id"):
                             # 根据target_message_id查找原始消息
                             target_message = self.find_message_by_id(target_message_id, message_id_list)
-                            # target_message = None
                             # 如果获取的target_message为None，输出warning并重新plan
                             if target_message is None:
                                 self.plan_retry_count += 1
@@ -205,7 +216,7 @@ class ActionPlanner:
                                     self.plan_retry_count = 0  # 重置计数器
                                 else:
                                     # 递归重新plan
-                                    return await self.plan(mode)
+                                    return await self.plan(mode, loop_start_time, available_actions)
                             else:
                                 # 成功获取到target_message，重置计数器
                                 self.plan_retry_count = 0
@@ -213,9 +224,8 @@ class ActionPlanner:
                             logger.warning(f"{self.log_prefix}动作'{action}'缺少target_message_id")
                     
                     
-                    if action == "no_action":
+
-                        reasoning = "normal决定不使用额外动作"
+                    if action != "no_reply" and action != "reply" and action not in current_available_actions:
-                    elif action != "no_reply" and action != "reply" and action not in current_available_actions:
                         logger.warning(
                             f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
                         )
@@ -301,7 +311,6 @@ class ActionPlanner:
             actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
             if refresh_time:
                 self.last_obs_time_mark = time.time()
-            # logger.info(f"{self.log_prefix}当前时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
             
             mentioned_bonus = ""
             if global_config.chat.mentioned_bot_inevitable_reply:
@@ -311,43 +320,19 @@ class ActionPlanner:
             
 
             if mode == ChatMode.FOCUS:
-                no_action_block = f"""重要说明：
+                no_action_block = """重要说明：
-- 'no_reply' 表示只进行不进行回复，等待合适的回复时机
+- 'no_reply' 表示不进行回复，等待合适的回复时机
 - 当你刚刚发送了消息，没有人回复时，选择no_reply
 - 当你一次发送了太多消息，为了避免打扰聊天节奏，选择no_reply
-
-动作：reply
-动作描述：参与聊天回复，发送文本进行表达
-- 你想要闲聊或者随便附
-- {mentioned_bonus}
-- 如果你刚刚进行了回复，不要对同一个话题重复回应
-- 不要回复自己发送的消息
-{{
-    "action": "reply",
-    "target_message_id":"触发action的消息id",
-    "reason":"回复的原因"
-}}
-
 """
             else:
-                no_action_block = f"""重要说明：
+                no_action_block = """重要说明：
 - 'reply' 表示只进行普通聊天回复，不执行任何额外动作
 - 其他action表示在普通回复的基础上，执行相应的额外动作
-
+"""
-动作：reply
-动作描述：参与聊天回复，发送文本进行表达
-- 你想要闲聊或者随便附
-- {mentioned_bonus}
-- 如果你刚刚进行了回复，不要对同一个话题重复回应
-- 不要回复自己发送的消息
-{{
-    "action": "reply",
-    "target_message_id":"触发action的消息id",
-    "reason":"回复的原因"
-}}"""
 
             chat_context_description = "你现在正在一个群聊中"
-            chat_target_name = None  # Only relevant for private
+            chat_target_name = None 
             if not is_group_chat and chat_target_info:
                 chat_target_name = (
                     chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
@@ -399,6 +384,7 @@ class ActionPlanner:
                 chat_content_block=chat_content_block,
                 actions_before_now_block=actions_before_now_block,
                 no_action_block=no_action_block,
+                mentioned_bonus=mentioned_bonus,
                 action_options_text=action_options_block,
                 moderation_prompt=moderation_prompt_block,
                 identity_block=identity_block,

src/chat/replyer/default_generator.py

@@ -121,40 +121,11 @@ class DefaultReplyer:
     def __init__(
         self,
         chat_stream: ChatStream,
-        model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
+        request_type: str = "replyer",
-        request_type: str = "focus.replyer",
     ):
-        self.request_type = request_type
+        self.express_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type=request_type)
-
-        if model_set_with_weight:
-            # self.express_model_configs = model_configs
-            self.model_set: List[Tuple[TaskConfig, float]] = model_set_with_weight
-        else:
-            # 当未提供配置时，使用默认配置并赋予默认权重
-
-            # model_config_1 = global_config.model.replyer_1.copy()
-            # model_config_2 = global_config.model.replyer_2.copy()
-            prob_first = global_config.chat.replyer_random_probability
-
-            # model_config_1["weight"] = prob_first
-            # model_config_2["weight"] = 1.0 - prob_first
-
-            # self.express_model_configs = [model_config_1, model_config_2]
-            self.model_set = [
-                (model_config.model_task_config.replyer_1, prob_first),
-                (model_config.model_task_config.replyer_2, 1.0 - prob_first),
-            ]
-
-        # if not self.express_model_configs:
-        #     logger.warning("未找到有效的模型配置，回复生成可能会失败。")
-        #     # 提供一个最终的回退，以防止在空列表上调用 random.choice
-        #     fallback_config = global_config.model.replyer_1.copy()
-        #     fallback_config.setdefault("weight", 1.0)
-        #     self.express_model_configs = [fallback_config]
-
         self.chat_stream = chat_stream
         self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
-
         self.heart_fc_sender = HeartFCSender()
         self.memory_activator = MemoryActivator()
         self.instant_memory = InstantMemory(chat_id=self.chat_stream.stream_id)
@@ -163,14 +134,6 @@ class DefaultReplyer:
 
         self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3)
 
-    def _select_weighted_models_config(self) -> Tuple[TaskConfig, float]:
-        """使用加权随机选择来挑选一个模型配置"""
-        configs = self.model_set
-        # 提取权重，如果模型配置中没有'weight'键，则默认为1.0
-        weights = [weight for _, weight in configs]
-
-        return random.choices(population=configs, weights=weights, k=1)[0]
-
     async def generate_reply_with_context(
         self,
         reply_to: str = "",
@@ -179,8 +142,8 @@ class DefaultReplyer:
         enable_tool: bool = True,
         from_plugin: bool = True,
         stream_id: Optional[str] = None,
+        reply_message: Optional[Dict[str, Any]] = None,
     ) -> Tuple[bool, Optional[Dict[str, Any]], Optional[str]]:
-        # sourcery skip: merge-nested-ifs
         """
         回复器 (Replier): 负责生成回复文本的核心逻辑。
 
@@ -205,6 +168,7 @@ class DefaultReplyer:
                     extra_info=extra_info,
                     available_actions=available_actions,
                     enable_tool=enable_tool,
+                    reply_message=reply_message,
                 )
 
             if not prompt:
@@ -302,16 +266,11 @@ class DefaultReplyer:
             traceback.print_exc()
             return False, None, prompt if return_prompt else None
 
-    async def build_relation_info(self, reply_to: str = ""):
+    async def build_relation_info(self, sender: str, target: str):
         if not global_config.relationship.enable_relationship:
             return ""
 
         relationship_fetcher = relationship_fetcher_manager.get_fetcher(self.chat_stream.stream_id)
-        if not reply_to:
-            return ""
-        sender, text = self._parse_reply_target(reply_to)
-        if not sender or not text:
-            return ""
 
         # 获取用户ID
         person_info_manager = get_person_info_manager()
@@ -418,7 +377,7 @@ class DefaultReplyer:
 
         return memory_str
 
-    async def build_tool_info(self, chat_history: str, reply_to: str = "", enable_tool: bool = True) -> str:
+    async def build_tool_info(self, chat_history: str, sender: str, target: str, enable_tool: bool = True) -> str:
         """构建工具信息块
 
         Args:
@@ -433,18 +392,11 @@ class DefaultReplyer:
         if not enable_tool:
             return ""
 
-        if not reply_to:
-            return ""
-
-        sender, text = self._parse_reply_target(reply_to)
-
-        if not text:
-            return ""
 
         try:
             # 使用工具执行器获取信息
             tool_results, _, _ = await self.tool_executor.execute_from_chat_message(
-                sender=sender, target_message=text, chat_history=chat_history, return_details=False
+                sender=sender, target_message=target, chat_history=chat_history, return_details=False
             )
 
             if tool_results:
@@ -672,7 +624,8 @@ class DefaultReplyer:
         extra_info: str = "",
         available_actions: Optional[Dict[str, ActionInfo]] = None,
         enable_tool: bool = True,
-    ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
+        reply_message: Optional[Dict[str, Any]] = None,
+    ) -> str:
         """
         构建回复器上下文
 
@@ -682,7 +635,7 @@ class DefaultReplyer:
             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
-
+            reply_message: 回复的原始消息
         Returns:
             str: 构建好的上下文
         """
@@ -698,8 +651,21 @@ class DefaultReplyer:
             mood_prompt = chat_mood.mood_state
         else:
             mood_prompt = ""
-
+        
-        sender, target = self._parse_reply_target(reply_to)
+        if reply_to:
+            #兼容旧的reply_to
+            sender, target = self._parse_reply_target(reply_to)
+        else:
+            # 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
+            platform = reply_message.get("chat_info_platform")
+            person_id = person_info_manager.get_person_id(
+                platform,  # type: ignore
+                reply_message.get("user_id"),  # type: ignore
+            )
+            person_name = await person_info_manager.get_value(person_id, "person_name")
+            sender = person_name
+            target = reply_message.get('processed_plain_text')
+            
         person_info_manager = get_person_info_manager()
         person_id = person_info_manager.get_person_id_by_person_name(sender)
         user_id = person_info_manager.get_value_sync(person_id, "user_id")
@@ -744,12 +710,12 @@ class DefaultReplyer:
             self._time_and_run_task(
                 self.build_expression_habits(chat_talking_prompt_short, target), "expression_habits"
             ),
-            self._time_and_run_task(self.build_relation_info(reply_to), "relation_info"),
+            self._time_and_run_task(self.build_relation_info(sender, target), "relation_info"),
             self._time_and_run_task(self.build_memory_block(chat_talking_prompt_short, target), "memory_block"),
             self._time_and_run_task(
-                self.build_tool_info(chat_talking_prompt_short, reply_to, enable_tool=enable_tool), "tool_info"
+                self.build_tool_info(chat_talking_prompt_short, sender, target, enable_tool=enable_tool), "tool_info"
             ),
-            self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, reply_to), "prompt_info"),
+            self._time_and_run_task(self.get_prompt_info(chat_talking_prompt_short, sender, target), "prompt_info"),
         )
 
         # 任务名称中英文映射
@@ -899,12 +865,17 @@ class DefaultReplyer:
         raw_reply: str,
         reason: str,
         reply_to: str,
+        reply_message: Optional[Dict[str, Any]] = None,
     ) -> str:  # sourcery skip: merge-else-if-into-elif, remove-redundant-if
         chat_stream = self.chat_stream
         chat_id = chat_stream.stream_id
         is_group_chat = bool(chat_stream.group_info)
 
-        sender, target = self._parse_reply_target(reply_to)
+        if reply_message:
+            sender = reply_message.get("sender")
+            target = reply_message.get("target")
+        else:
+            sender, target = self._parse_reply_target(reply_to)
 
         # 添加情绪状态获取
         if global_config.mood.enable_mood:
@@ -930,7 +901,7 @@ 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_to),
+            self.build_relation_info(sender, target),
         )
 
         keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target)
@@ -1035,34 +1006,25 @@ class DefaultReplyer:
 
     async def llm_generate_content(self, prompt: 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
+            # 直接使用已初始化的模型实例
-            selected_model_config, weight = self._select_weighted_models_config()
+            logger.info(f"使用模型集生成回复: {self.express_model.model_for_task}")
-            logger.info(f"使用模型集生成回复: {selected_model_config} (选中概率: {weight})")
-
-            express_model = LLMRequest(model_set=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, tool_calls) = await express_model.generate_response_async(prompt)
+            content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(prompt)
 
             logger.debug(f"replyer生成内容: {content}")
         return content, reasoning_content, model_name, tool_calls
 
-    async def get_prompt_info(self, message: str, reply_to: str):
+    async def get_prompt_info(self, message: str, sender: str, target: str):
         related_info = ""
         start_time = time.time()
         from src.plugins.built_in.knowledge.lpmm_get_knowledge import SearchKnowledgeFromLPMMTool
 
-        if not reply_to:
+
-            logger.debug("没有回复对象，跳过获取知识库内容")
-            return ""
-        sender, content = self._parse_reply_target(reply_to)
-        if not content:
-            logger.debug("回复对象内容为空，跳过获取知识库内容")
-            return ""
         logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
         # 从LPMM知识库获取知识
         try:
@@ -1080,7 +1042,7 @@ class DefaultReplyer:
                 time_now=time_now,
                 chat_history=message,
                 sender=sender,
-                target_message=content,
+                target_message=target,
             )
             _, _, _, _, tool_calls = await llm_api.generate_with_model_with_tools(
                 prompt,

src/chat/replyer/replyer_manager.py

@@ -16,7 +16,6 @@ class ReplyerManager:
         self,
         chat_stream: Optional[ChatStream] = None,
         chat_id: Optional[str] = None,
-        model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
         request_type: str = "replyer",
     ) -> Optional[DefaultReplyer]:
         """
@@ -50,7 +49,6 @@ class ReplyerManager:
         # model_configs 只在此时（初始化时）生效
         replyer = DefaultReplyer(
             chat_stream=target_stream,
-            model_set_with_weight=model_set_with_weight,  # 可以是None，此时使用默认模型
             request_type=request_type,
         )
         self._repliers[stream_id] = replyer

src/chat/willing/mode_classical.py

@@ -1,60 +0,0 @@
-import asyncio
-
-from src.config.config import global_config
-from .willing_manager import BaseWillingManager
-
-
-class ClassicalWillingManager(BaseWillingManager):
-    def __init__(self):
-        super().__init__()
-        self._decay_task: asyncio.Task | None = None
-
-    async def _decay_reply_willing(self):
-        """定期衰减回复意愿"""
-        while True:
-            await asyncio.sleep(1)
-            for chat_id in self.chat_reply_willing:
-                self.chat_reply_willing[chat_id] = max(0.0, self.chat_reply_willing[chat_id] * 0.9)
-
-    async def async_task_starter(self):
-        if self._decay_task is None:
-            self._decay_task = asyncio.create_task(self._decay_reply_willing())
-
-    async def get_reply_probability(self, message_id):
-        willing_info = self.ongoing_messages[message_id]
-        chat_id = willing_info.chat_id
-        current_willing = self.chat_reply_willing.get(chat_id, 0)
-        
-        # print(f"[{chat_id}] 回复意愿: {current_willing}")
-
-        interested_rate = willing_info.interested_rate
-        
-        # print(f"[{chat_id}] 兴趣值: {interested_rate}")
-
-        current_willing += interested_rate
-
-        if willing_info.is_mentioned_bot and global_config.chat.mentioned_bot_inevitable_reply and current_willing < 2:
-            current_willing += 1 if current_willing < 1.0 else 0.2
-
-        self.chat_reply_willing[chat_id] = min(current_willing, 1.0)
-        
-        reply_probability = min(max((current_willing - 0.5), 0.01) * 2, 1.5)
-        
-        # print(f"[{chat_id}] 回复概率: {reply_probability}")
-        
-        return reply_probability
-
-    async def before_generate_reply_handle(self, message_id):
-        pass
-
-    async def after_generate_reply_handle(self, message_id):
-        if message_id not in self.ongoing_messages:
-            return
-
-        chat_id = self.ongoing_messages[message_id].chat_id
-        current_willing = self.chat_reply_willing.get(chat_id, 0)
-        if current_willing < 1:
-            self.chat_reply_willing[chat_id] = min(1.0, current_willing + 0.3)
-
-    async def not_reply_handle(self, message_id):
-        return await super().not_reply_handle(message_id)

src/chat/willing/mode_custom.py

@@ -1,23 +0,0 @@
-from .willing_manager import BaseWillingManager
-
-NOT_IMPLEMENTED_MESSAGE = "\ncustom模式你实现了吗？没自行实现不要选custom。给你退了快点给你麦爹配置\n注：以上内容由gemini生成，如有不满请投诉gemini"
-
-class CustomWillingManager(BaseWillingManager):
-    async def async_task_starter(self) -> None:
-        raise NotImplementedError(NOT_IMPLEMENTED_MESSAGE)
-
-    async def before_generate_reply_handle(self, message_id: str):
-        raise NotImplementedError(NOT_IMPLEMENTED_MESSAGE)
-
-    async def after_generate_reply_handle(self, message_id: str):
-        raise NotImplementedError(NOT_IMPLEMENTED_MESSAGE)
-
-    async def not_reply_handle(self, message_id: str):
-        raise NotImplementedError(NOT_IMPLEMENTED_MESSAGE)
-
-    async def get_reply_probability(self, message_id: str):
-        raise NotImplementedError(NOT_IMPLEMENTED_MESSAGE)
-
-    def __init__(self):
-        super().__init__()
-        raise NotImplementedError(NOT_IMPLEMENTED_MESSAGE)

src/chat/willing/mode_mxp.py

@@ -1,296 +0,0 @@
-"""
-Mxp 模式：梦溪畔独家赞助
-此模式的一些参数不会在配置文件中显示，要修改请在可变参数下修改
-同时一些全局设置对此模式无效
-此模式的可变参数暂时比较草率，需要调参仙人的大手
-此模式的特点：
-1.每个聊天流的每个用户的意愿是独立的
-2.接入关系系统，关系会影响意愿值（已移除，因为关系系统重构）
-3.会根据群聊的热度来调整基础意愿值
-4.限制同时思考的消息数量，防止喷射
-5.拥有单聊增益，无论在群里还是私聊，只要bot一直和你聊，就会增加意愿值
-6.意愿分为衰减意愿+临时意愿
-7.疲劳机制
-
-如果你发现本模式出现了bug
-上上策是询问智慧的小草神（）
-上策是询问万能的千石可乐
-中策是发issue
-下下策是询问一个菜鸟（@梦溪畔）
-"""
-
-from typing import Dict
-import asyncio
-import time
-import math
-
-from src.chat.message_receive.chat_stream import ChatStream
-from .willing_manager import BaseWillingManager
-
-
-class MxpWillingManager(BaseWillingManager):
-    """Mxp意愿管理器"""
-
-    def __init__(self):
-        super().__init__()
-        self.chat_person_reply_willing: Dict[str, Dict[str, float]] = {}  # chat_id: {person_id: 意愿值}
-        self.chat_new_message_time: Dict[str, list[float]] = {}  # 聊天流ID: 消息时间
-        self.last_response_person: Dict[str, tuple[str, int]] = {}  # 上次回复的用户信息
-        self.temporary_willing: float = 0  # 临时意愿值
-        self.chat_bot_message_time: Dict[str, list[float]] = {}  # 聊天流ID: bot已回复消息时间
-        self.chat_fatigue_punishment_list: Dict[
-            str, list[tuple[float, float]]
-        ] = {}  # 聊天流疲劳惩罚列, 聊天流ID: 惩罚时间列(开始时间，持续时间)
-        self.chat_fatigue_willing_attenuation: Dict[str, float] = {}  # 聊天流疲劳意愿衰减值
-
-        # 可变参数
-        self.intention_decay_rate = 0.93  # 意愿衰减率
-
-        self.number_of_message_storage = 12  # 消息存储数量
-        self.expected_replies_per_min = 3  # 每分钟预期回复数
-        self.basic_maximum_willing = 0.5  # 基础最大意愿值
-
-        self.mention_willing_gain = 0.6  # 提及意愿增益
-        self.interest_willing_gain = 0.3  # 兴趣意愿增益
-        self.single_chat_gain = 0.12  # 单聊增益
-
-        self.fatigue_messages_triggered_num = self.expected_replies_per_min  # 疲劳消息触发数量(int)
-        self.fatigue_coefficient = 1.0  # 疲劳系数
-
-        self.is_debug = False  # 是否开启调试模式
-
-    async def async_task_starter(self) -> None:
-        """异步任务启动器"""
-        asyncio.create_task(self._return_to_basic_willing())
-        asyncio.create_task(self._chat_new_message_to_change_basic_willing())
-        asyncio.create_task(self._fatigue_attenuation())
-
-    async def before_generate_reply_handle(self, message_id: str):
-        """回复前处理"""
-        current_time = time.time()
-        async with self.lock:
-            w_info = self.ongoing_messages[message_id]
-            if w_info.chat_id not in self.chat_bot_message_time:
-                self.chat_bot_message_time[w_info.chat_id] = []
-            self.chat_bot_message_time[w_info.chat_id] = [
-                t for t in self.chat_bot_message_time[w_info.chat_id] if current_time - t < 60
-            ]
-            self.chat_bot_message_time[w_info.chat_id].append(current_time)
-            if len(self.chat_bot_message_time[w_info.chat_id]) == int(self.fatigue_messages_triggered_num):
-                time_interval = 60 - (current_time - self.chat_bot_message_time[w_info.chat_id].pop(0))
-                self.chat_fatigue_punishment_list[w_info.chat_id].append((current_time, time_interval * 2))
-
-    async def after_generate_reply_handle(self, message_id: str):
-        """回复后处理"""
-        async with self.lock:
-            w_info = self.ongoing_messages[message_id]
-            # 移除关系值相关代码
-            # rel_value = await w_info.person_info_manager.get_value(w_info.person_id, "relationship_value")
-            # rel_level = self._get_relationship_level_num(rel_value)
-            # self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += rel_level * 0.05
-
-            now_chat_new_person = self.last_response_person.get(w_info.chat_id, (w_info.person_id, 0))
-            if now_chat_new_person[0] == w_info.person_id:
-                if now_chat_new_person[1] < 3:
-                    tmp_list = list(now_chat_new_person)
-                    tmp_list[1] += 1  # type: ignore
-                    self.last_response_person[w_info.chat_id] = tuple(tmp_list)  # type: ignore
-            else:
-                self.last_response_person[w_info.chat_id] = (w_info.person_id, 0)
-
-    async def not_reply_handle(self, message_id: str):
-        """不回复处理"""
-        async with self.lock:
-            w_info = self.ongoing_messages[message_id]
-            if w_info.is_mentioned_bot:
-                self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += self.mention_willing_gain / 2.5
-            if (
-                w_info.chat_id in self.last_response_person
-                and self.last_response_person[w_info.chat_id][0] == w_info.person_id
-                and self.last_response_person[w_info.chat_id][1]
-            ):
-                self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += self.single_chat_gain * (
-                    2 * self.last_response_person[w_info.chat_id][1] - 1
-                )
-            now_chat_new_person = self.last_response_person.get(w_info.chat_id, ("", 0))
-            if now_chat_new_person[0] != w_info.person_id:
-                self.last_response_person[w_info.chat_id] = (w_info.person_id, 0)
-
-    async def get_reply_probability(self, message_id: str):
-        # sourcery skip: merge-duplicate-blocks, remove-redundant-if
-        """获取回复概率"""
-        async with self.lock:
-            w_info = self.ongoing_messages[message_id]
-            current_willing = self.chat_person_reply_willing[w_info.chat_id][w_info.person_id]
-            if self.is_debug:
-                self.logger.debug(f"基础意愿值：{current_willing}")
-
-            if w_info.is_mentioned_bot:
-                willing_gain = self.mention_willing_gain / (int(current_willing) + 1)
-                current_willing += willing_gain
-                if self.is_debug:
-                    self.logger.debug(f"提及增益：{willing_gain}")
-
-            if w_info.interested_rate > 0:
-                willing_gain = math.atan(w_info.interested_rate / 2) / math.pi * 2 * self.interest_willing_gain
-                current_willing += willing_gain
-                if self.is_debug:
-                    self.logger.debug(f"兴趣增益：{willing_gain}")
-
-            self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] = current_willing
-
-            # 添加单聊增益
-            if (
-                w_info.chat_id in self.last_response_person
-                and self.last_response_person[w_info.chat_id][0] == w_info.person_id
-                and self.last_response_person[w_info.chat_id][1]
-            ):
-                current_willing += self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)
-                if self.is_debug:
-                    self.logger.debug(
-                        f"单聊增益：{self.single_chat_gain * (2 * self.last_response_person[w_info.chat_id][1] + 1)}"
-                    )
-
-            current_willing += self.chat_fatigue_willing_attenuation.get(w_info.chat_id, 0)
-            if self.is_debug:
-                self.logger.debug(f"疲劳衰减：{self.chat_fatigue_willing_attenuation.get(w_info.chat_id, 0)}")
-
-            chat_ongoing_messages = [msg for msg in self.ongoing_messages.values() if msg.chat_id == w_info.chat_id]
-            chat_person_ongoing_messages = [msg for msg in chat_ongoing_messages if msg.person_id == w_info.person_id]
-            if len(chat_person_ongoing_messages) >= 2:
-                current_willing = 0
-                if self.is_debug:
-                    self.logger.debug("进行中消息惩罚：归0")
-            elif len(chat_ongoing_messages) == 2:
-                current_willing -= 0.5
-                if self.is_debug:
-                    self.logger.debug("进行中消息惩罚：-0.5")
-            elif len(chat_ongoing_messages) == 3:
-                current_willing -= 1.5
-                if self.is_debug:
-                    self.logger.debug("进行中消息惩罚：-1.5")
-            elif len(chat_ongoing_messages) >= 4:
-                current_willing = 0
-                if self.is_debug:
-                    self.logger.debug("进行中消息惩罚：归0")
-
-            probability = self._willing_to_probability(current_willing)
-
-            self.temporary_willing = current_willing
-
-            return probability
-
-    async def _return_to_basic_willing(self):
-        """使每个人的意愿恢复到chat基础意愿"""
-        while True:
-            await asyncio.sleep(3)
-            async with self.lock:
-                for chat_id, person_willing in self.chat_person_reply_willing.items():
-                    for person_id, willing in person_willing.items():
-                        if chat_id not in self.chat_reply_willing:
-                            self.logger.debug(f"聊天流{chat_id}不存在，错误")
-                            continue
-                        basic_willing = self.chat_reply_willing[chat_id]
-                        person_willing[person_id] = (
-                            basic_willing + (willing - basic_willing) * self.intention_decay_rate
-                        )
-
-    def setup(self, message: dict, chat_stream: ChatStream):
-        super().setup(message, chat_stream)
-        stream_id = chat_stream.stream_id
-        self.chat_reply_willing[stream_id] = self.chat_reply_willing.get(stream_id, self.basic_maximum_willing)
-        self.chat_person_reply_willing[stream_id] = self.chat_person_reply_willing.get(stream_id, {})
-        self.chat_person_reply_willing[stream_id][self.ongoing_messages[message.get("message_id", "")].person_id] = (
-            self.chat_person_reply_willing[stream_id].get(
-                self.ongoing_messages[message.get("message_id", "")].person_id,
-                self.chat_reply_willing[stream_id],
-            )
-        )
-
-        current_time = time.time()
-        if stream_id not in self.chat_new_message_time:
-            self.chat_new_message_time[stream_id] = []
-        self.chat_new_message_time[stream_id].append(current_time)
-        if len(self.chat_new_message_time[stream_id]) > self.number_of_message_storage:
-            self.chat_new_message_time[stream_id].pop(0)
-
-        if stream_id not in self.chat_fatigue_punishment_list:
-            self.chat_fatigue_punishment_list[stream_id] = [
-                (
-                    current_time,
-                    self.number_of_message_storage * self.basic_maximum_willing / self.expected_replies_per_min * 60,
-                )
-            ]
-            self.chat_fatigue_willing_attenuation[stream_id] = (
-                -2 * self.basic_maximum_willing * self.fatigue_coefficient
-            )
-
-    @staticmethod
-    def _willing_to_probability(willing: float) -> float:
-        """意愿值转化为概率"""
-        willing = max(0, willing)
-        if willing < 2:
-            return math.atan(willing * 2) / math.pi * 2
-        elif willing < 2.5:
-            return math.atan(willing * 4) / math.pi * 2
-        else:
-            return 1
-
-    async def _chat_new_message_to_change_basic_willing(self):
-        """聊天流新消息改变基础意愿"""
-        update_time = 20
-        while True:
-            await asyncio.sleep(update_time)
-            async with self.lock:
-                for chat_id, message_times in self.chat_new_message_time.items():
-                    # 清理过期消息
-                    current_time = time.time()
-                    message_times = [
-                        msg_time
-                        for msg_time in message_times
-                        if current_time - msg_time
-                        < self.number_of_message_storage
-                        * self.basic_maximum_willing
-                        / self.expected_replies_per_min
-                        * 60
-                    ]
-                    self.chat_new_message_time[chat_id] = message_times
-
-                    if len(message_times) < self.number_of_message_storage:
-                        self.chat_reply_willing[chat_id] = self.basic_maximum_willing
-                        update_time = 20
-                    elif len(message_times) == self.number_of_message_storage:
-                        time_interval = current_time - message_times[0]
-                        basic_willing = self._basic_willing_calculate(time_interval)
-                        self.chat_reply_willing[chat_id] = basic_willing
-                        update_time = 17 * basic_willing / self.basic_maximum_willing + 3
-                    else:
-                        self.logger.debug(f"聊天流{chat_id}消息时间数量异常，数量：{len(message_times)}")
-                        self.chat_reply_willing[chat_id] = 0
-                if self.is_debug:
-                    self.logger.debug(f"聊天流意愿值更新：{self.chat_reply_willing}")
-
-    def _basic_willing_calculate(self, t: float) -> float:
-        """基础意愿值计算"""
-        return math.tan(t * self.expected_replies_per_min * math.pi / 120 / self.number_of_message_storage) / 2
-
-    async def _fatigue_attenuation(self):
-        """疲劳衰减"""
-        while True:
-            await asyncio.sleep(1)
-            current_time = time.time()
-            async with self.lock:
-                for chat_id, fatigue_list in self.chat_fatigue_punishment_list.items():
-                    fatigue_list = [z for z in fatigue_list if current_time - z[0] < z[1]]
-                    self.chat_fatigue_willing_attenuation[chat_id] = 0
-                    for start_time, duration in fatigue_list:
-                        self.chat_fatigue_willing_attenuation[chat_id] += (
-                            self.chat_reply_willing[chat_id]
-                            * 2
-                            / math.pi
-                            * math.asin(2 * (current_time - start_time) / duration - 1)
-                            - self.chat_reply_willing[chat_id]
-                        ) * self.fatigue_coefficient
-
-    async def get_willing(self, chat_id):
-        return self.temporary_willing

src/chat/willing/willing_manager.py

@@ -1,180 +0,0 @@
-import importlib
-import asyncio
-
-from abc import ABC, abstractmethod
-from typing import Dict, Optional, Any
-from rich.traceback import install
-from dataclasses import dataclass
-
-from src.common.logger import get_logger
-from src.config.config import global_config
-from src.chat.message_receive.chat_stream import ChatStream, GroupInfo
-from src.person_info.person_info import PersonInfoManager, get_person_info_manager
-
-install(extra_lines=3)
-
-"""
-基类方法概览：
-以下8个方法是你必须在子类重写的（哪怕什么都不干）：
-async_task_starter 在程序启动时执行，在其中用asyncio.create_task启动你想要执行的异步任务
-before_generate_reply_handle 确定要回复后，在生成回复前的处理
-after_generate_reply_handle 确定要回复后，在生成回复后的处理
-not_reply_handle 确定不回复后的处理
-get_reply_probability 获取回复概率
-get_variable_parameters 暂不确定
-set_variable_parameters 暂不确定
-以下2个方法根据你的实现可以做调整：
-get_willing 获取某聊天流意愿
-set_willing 设置某聊天流意愿
-规范说明：
-模块文件命名: `mode_{manager_type}.py` 
-示例: 若 `manager_type="aggressive"`，则模块文件应为 `mode_aggressive.py`
-类命名: `{manager_type}WillingManager` (首字母大写)
-示例: 在 `mode_aggressive.py` 中，类名应为 `AggressiveWillingManager`
-"""
-
-
-logger = get_logger("willing")
-
-
-@dataclass
-class WillingInfo:
-    """此类保存意愿模块常用的参数
-
-    Attributes:
-        message (MessageRecv): 原始消息对象
-        chat (ChatStream): 聊天流对象
-        person_info_manager (PersonInfoManager): 用户信息管理对象
-        chat_id (str): 当前聊天流的标识符
-        person_id (str): 发送者的个人信息的标识符
-        group_id (str): 群组ID（如果是私聊则为空）
-        is_mentioned_bot (bool): 是否提及了bot
-        is_emoji (bool): 是否为表情包
-        interested_rate (float): 兴趣度
-    """
-
-    message: Dict[str, Any]  # 原始消息数据
-    chat: ChatStream
-    person_info_manager: PersonInfoManager
-    chat_id: str
-    person_id: str
-    group_info: Optional[GroupInfo]
-    is_mentioned_bot: bool
-    is_emoji: bool
-    is_picid: bool
-    interested_rate: float
-    # current_mood: float  当前心情？
-
-
-class BaseWillingManager(ABC):
-    """回复意愿管理基类"""
-
-    @classmethod
-    def create(cls, manager_type: str) -> "BaseWillingManager":
-        try:
-            module = importlib.import_module(f".mode_{manager_type}", __package__)
-            manager_class = getattr(module, f"{manager_type.capitalize()}WillingManager")
-            if not issubclass(manager_class, cls):
-                raise TypeError(f"Manager class {manager_class.__name__} is not a subclass of {cls.__name__}")
-            else:
-                logger.info(f"普通回复模式：{manager_type}")
-            return manager_class()
-        except (ImportError, AttributeError, TypeError) as e:
-            module = importlib.import_module(".mode_classical", __package__)
-            manager_class = module.ClassicalWillingManager
-            logger.info(f"载入当前意愿模式{manager_type}失败，使用经典配方~~~~")
-            logger.debug(f"加载willing模式{manager_type}失败，原因: {str(e)}。")
-            return manager_class()
-
-    def __init__(self):
-        self.chat_reply_willing: Dict[str, float] = {}  # 存储每个聊天流的回复意愿(chat_id)
-        self.ongoing_messages: Dict[str, WillingInfo] = {}  # 当前正在进行的消息(message_id)
-        self.lock = asyncio.Lock()
-        self.logger = logger
-
-    def setup(self, message: dict, chat: ChatStream):
-        person_id = PersonInfoManager.get_person_id(chat.platform, chat.user_info.user_id)  # type: ignore
-        self.ongoing_messages[message.get("message_id", "")] = WillingInfo(
-            message=message,
-            chat=chat,
-            person_info_manager=get_person_info_manager(),
-            chat_id=chat.stream_id,
-            person_id=person_id,
-            group_info=chat.group_info,
-            is_mentioned_bot=message.get("is_mentioned", False),
-            is_emoji=message.get("is_emoji", False),
-            is_picid=message.get("is_picid", False),
-            interested_rate = message.get("interest_value") or 0.0,
-        )
-
-    def delete(self, message_id: str):
-        del_message = self.ongoing_messages.pop(message_id, None)
-        if not del_message:
-            logger.debug(f"尝试删除不存在的消息 ID: {message_id}，可能已被其他流程处理，喵~")
-
-    @abstractmethod
-    async def async_task_starter(self) -> None:
-        """抽象方法：异步任务启动器"""
-        pass
-
-    @abstractmethod
-    async def before_generate_reply_handle(self, message_id: str):
-        """抽象方法：回复前处理"""
-        pass
-
-    @abstractmethod
-    async def after_generate_reply_handle(self, message_id: str):
-        """抽象方法：回复后处理"""
-        pass
-
-    @abstractmethod
-    async def not_reply_handle(self, message_id: str):
-        """抽象方法：不回复处理"""
-        pass
-
-    @abstractmethod
-    async def get_reply_probability(self, message_id: str):
-        """抽象方法：获取回复概率"""
-        raise NotImplementedError
-
-    async def get_willing(self, chat_id: str):
-        """获取指定聊天流的回复意愿"""
-        async with self.lock:
-            return self.chat_reply_willing.get(chat_id, 0)
-
-    async def set_willing(self, chat_id: str, willing: float):
-        """设置指定聊天流的回复意愿"""
-        async with self.lock:
-            self.chat_reply_willing[chat_id] = willing
-
-    # @abstractmethod
-    # async def get_variable_parameters(self) -> Dict[str, str]:
-    #     """抽象方法：获取可变参数"""
-    #     pass
-
-    # @abstractmethod
-    # async def set_variable_parameters(self, parameters: Dict[str, any]):
-    #     """抽象方法：设置可变参数"""
-    #     pass
-
-
-def init_willing_manager() -> BaseWillingManager:
-    """
-    根据配置初始化并返回对应的WillingManager实例
-
-    Returns:
-        对应mode的WillingManager实例
-    """
-    mode = global_config.normal_chat.willing_mode.lower()
-    return BaseWillingManager.create(mode)
-
-
-# 全局willing_manager对象
-willing_manager = None
-
-
-def get_willing_manager():
-    global willing_manager
-    if willing_manager is None:
-        willing_manager = init_willing_manager()
-    return willing_manager

src/config/api_ada_configs.py

@@ -99,12 +99,9 @@ class ModelTaskConfig(ConfigBase):
     utils_small: TaskConfig
     """组件小模型配置"""
 
-    replyer_1: TaskConfig
+    replyer: TaskConfig
     """normal_chat首要回复模型模型配置"""
 
-    replyer_2: TaskConfig
-    """normal_chat次要回复模型配置"""
-
     emotion: TaskConfig
     """情绪模型配置"""
 

src/main.py

@@ -6,7 +6,6 @@ from src.common.remote import TelemetryHeartBeatTask
 from src.manager.async_task_manager import async_task_manager
 from src.chat.utils.statistic import OnlineTimeRecordTask, StatisticOutputTask
 from src.chat.emoji_system.emoji_manager import get_emoji_manager
-from src.chat.willing.willing_manager import get_willing_manager
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.config.config import global_config
 from src.chat.message_receive.bot import chat_bot
@@ -31,8 +30,6 @@ if global_config.memory.enable_memory:
 
 install(extra_lines=3)
 
-willing_manager = get_willing_manager()
-
 logger = get_logger("main")
 
 
@@ -91,11 +88,6 @@ class MainSystem:
         get_emoji_manager().initialize()
         logger.info("表情包管理器初始化成功")
 
-        # 启动愿望管理器
-        await willing_manager.async_task_starter()
-
-        logger.info("willing管理器初始化成功")
-
         # 启动情绪管理器
         await mood_manager.start()
         logger.info("情绪管理器初始化成功")

src/mais4u/mai_think.py

@@ -60,7 +60,7 @@ class MaiThinking:
         self.sender = ""
         self.target = ""
 
-        self.thinking_model = LLMRequest(model_set=model_config.model_task_config.replyer_1, request_type="thinking")
+        self.thinking_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type="thinking")
 
     async def do_think_before_response(self):
         pass

src/mais4u/mais4u_chat/s4u_stream_generator.py

@@ -13,8 +13,8 @@ logger = get_logger("s4u_stream_generator")
 
 class S4UStreamGenerator:
     def __init__(self):
-        replyer_1_config = model_config.model_task_config.replyer_1
+        replyer_config = model_config.model_task_config.replyer
-        model_to_use = replyer_1_config.model_list[0]
+        model_to_use = replyer_config.model_list[0]
         model_info = model_config.get_model_info(model_to_use)
         if not model_info:
             logger.error(f"模型 {model_to_use} 在配置中未找到")
@@ -22,8 +22,8 @@ class S4UStreamGenerator:
         provider_name = model_info.api_provider
         provider_info = model_config.get_provider(provider_name)
         if not provider_info:
-            logger.error("`replyer_1` 找不到对应的Provider")
+            logger.error("`replyer` 找不到对应的Provider")
-            raise ValueError("`replyer_1` 找不到对应的Provider")
+            raise ValueError("`replyer` 找不到对应的Provider")
 
         api_key = provider_info.api_key
         base_url = provider_info.base_url
@@ -34,7 +34,7 @@ class S4UStreamGenerator:
 
         self.client_1 = AsyncOpenAIClient(api_key=api_key, base_url=base_url)
         self.model_1_name = model_to_use
-        self.replyer_1_config = replyer_1_config
+        self.replyer_config = replyer_config
 
         self.current_model_name = "unknown model"
         self.partial_response = ""
@@ -104,10 +104,10 @@ class S4UStreamGenerator:
         self.current_model_name = self.model_1_name
 
         extra_kwargs = {}
-        if self.replyer_1_config.get("enable_thinking") is not None:
+        if self.replyer_config.get("enable_thinking") is not None:
-            extra_kwargs["enable_thinking"] = self.replyer_1_config.get("enable_thinking")
+            extra_kwargs["enable_thinking"] = self.replyer_config.get("enable_thinking")
-        if self.replyer_1_config.get("thinking_budget") is not None:
+        if self.replyer_config.get("thinking_budget") is not None:
-            extra_kwargs["thinking_budget"] = self.replyer_1_config.get("thinking_budget")
+            extra_kwargs["thinking_budget"] = self.replyer_config.get("thinking_budget")
 
         async for chunk in self._generate_response_with_model(
             prompt, current_client, self.current_model_name, **extra_kwargs

src/plugin_system/apis/generator_api.py

@@ -32,7 +32,6 @@ logger = get_logger("generator_api")
 def get_replyer(
     chat_stream: Optional[ChatStream] = None,
     chat_id: Optional[str] = None,
-    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
     request_type: str = "replyer",
 ) -> Optional[DefaultReplyer]:
     """获取回复器对象
@@ -43,7 +42,6 @@ def get_replyer(
     Args:
         chat_stream: 聊天流对象（优先）
         chat_id: 聊天ID（实际上就是stream_id）
-        model_set_with_weight: 模型配置列表，每个元素为 (TaskConfig, weight) 元组
         request_type: 请求类型
 
     Returns:
@@ -59,7 +57,6 @@ def get_replyer(
         return replyer_manager.get_replyer(
             chat_stream=chat_stream,
             chat_id=chat_id,
-            model_set_with_weight=model_set_with_weight,
             request_type=request_type,
         )
     except Exception as e:
@@ -78,13 +75,13 @@ async def generate_reply(
     chat_id: Optional[str] = None,
     action_data: Optional[Dict[str, Any]] = None,
     reply_to: str = "",
+    reply_message: Optional[Dict[str, Any]] = None,
     extra_info: str = "",
     available_actions: Optional[Dict[str, ActionInfo]] = None,
     enable_tool: bool = False,
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
     return_prompt: bool = False,
-    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
     request_type: str = "generator_api",
     from_plugin: bool = True,
 ) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
@@ -95,6 +92,7 @@ async def generate_reply(
         chat_id: 聊天ID（备用）
         action_data: 动作数据（向下兼容，包含reply_to和extra_info）
         reply_to: 回复对象，格式为 "发送者:消息内容"
+        reply_message: 回复的原始消息
         extra_info: 额外信息，用于补充上下文
         available_actions: 可用动作
         enable_tool: 是否启用工具调用
@@ -110,7 +108,7 @@ async def generate_reply(
     try:
         # 获取回复器
         replyer = get_replyer(
-            chat_stream, chat_id, model_set_with_weight=model_set_with_weight, request_type=request_type
+            chat_stream, chat_id, request_type=request_type
         )
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
@@ -131,6 +129,7 @@ async def generate_reply(
             enable_tool=enable_tool,
             from_plugin=from_plugin,
             stream_id=chat_stream.stream_id if chat_stream else chat_id,
+            reply_message=reply_message,
         )
         if not success:
             logger.warning("[GeneratorAPI] 回复生成失败")
@@ -166,11 +165,11 @@ async def rewrite_reply(
     chat_id: Optional[str] = None,
     enable_splitter: bool = True,
     enable_chinese_typo: bool = True,
-    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
     raw_reply: str = "",
     reason: str = "",
     reply_to: str = "",
     return_prompt: bool = False,
+    request_type: str = "generator_api",
 ) -> Tuple[bool, List[Tuple[str, Any]], Optional[str]]:
     """重写回复
 
@@ -191,7 +190,7 @@ async def rewrite_reply(
     """
     try:
         # 获取回复器
-        replyer = get_replyer(chat_stream, chat_id, model_set_with_weight=model_set_with_weight)
+        replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
         if not replyer:
             logger.error("[GeneratorAPI] 无法获取回复器")
             return False, [], None
@@ -258,10 +257,10 @@ def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo:
 async def generate_response_custom(
     chat_stream: Optional[ChatStream] = None,
     chat_id: Optional[str] = None,
-    model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
+    request_type: str = "generator_api",
     prompt: str = "",
 ) -> Optional[str]:
-    replyer = get_replyer(chat_stream, chat_id, model_set_with_weight=model_set_with_weight)
+    replyer = get_replyer(chat_stream, chat_id, request_type=request_type)
     if not replyer:
         logger.error("[GeneratorAPI] 无法获取回复器")
         return None

src/plugin_system/apis/send_api.py

@@ -22,7 +22,7 @@
 import traceback
 import time
 import difflib
-from typing import Optional, Union
+from typing import Optional, Union, Dict, Any
 from src.common.logger import get_logger
 
 # 导入依赖
@@ -49,7 +49,8 @@ async def _send_to_target(
     display_message: str = "",
     typing: bool = False,
     reply_to: str = "",
-    reply_to_platform_id: Optional[str] = None,
+    set_reply: bool = False,
+    reply_to_message: Optional[Dict[str, Any]] = None,
     storage_message: bool = True,
     show_log: bool = True,
 ) -> bool:
@@ -62,7 +63,6 @@ async def _send_to_target(
         display_message: 显示消息
         typing: 是否模拟打字等待。
         reply_to: 回复消息，格式为"发送者:消息内容"
-        reply_to_platform_id: 回复消息，格式为"平台:用户ID"，如果不提供则自动查找（插件开发者禁用！）
         storage_message: 是否存储消息到数据库
         show_log: 发送是否显示日志
 
@@ -70,6 +70,9 @@ async def _send_to_target(
         bool: 是否发送成功
     """
     try:
+        if reply_to:
+            logger.warning("[SendAPI] 在0.10.0, reply_to 参数已弃用，请使用 reply_to_message 参数")
+        
         if show_log:
             logger.debug(f"[SendAPI] 发送{message_type}消息到 {stream_id}")
 
@@ -96,14 +99,14 @@ async def _send_to_target(
         # 创建消息段
         message_segment = Seg(type=message_type, data=content)  # type: ignore
 
-        # 处理回复消息
+        if reply_to_message:
-        anchor_message = None
+            anchor_message = MessageRecv(message_dict=reply_to_message)
-        if reply_to:
+            anchor_message.update_chat_stream(target_stream)
-            anchor_message = await _find_reply_message(target_stream, reply_to)
+            reply_to_platform_id = (
-            if anchor_message and anchor_message.message_info.user_info and not reply_to_platform_id:
+                f"{anchor_message.message_info.platform}:{anchor_message.message_info.user_info.user_id}"
-                reply_to_platform_id = (
+            )            
-                    f"{anchor_message.message_info.platform}:{anchor_message.message_info.user_info.user_id}"
+        else:
-                )
+            anchor_message = None
 
         # 构建发送消息对象
         bot_message = MessageSending(
@@ -124,7 +127,7 @@ async def _send_to_target(
         sent_msg = await heart_fc_sender.send_message(
             bot_message,
             typing=typing,
-            set_reply=(anchor_message is not None),
+            set_reply=set_reply,
             storage_message=storage_message,
             show_log=show_log,
         )
@@ -259,7 +262,8 @@ async def text_to_stream(
     stream_id: str,
     typing: bool = False,
     reply_to: str = "",
-    reply_to_platform_id: str = "",
+    reply_to_message: Optional[Dict[str, Any]] = None,
+    set_reply: bool = False,
     storage_message: bool = True,
 ) -> bool:
     """向指定流发送文本消息
@@ -269,7 +273,6 @@ async def text_to_stream(
         stream_id: 聊天流ID
         typing: 是否显示正在输入
         reply_to: 回复消息，格式为"发送者:消息内容"
-        reply_to_platform_id: 回复消息，格式为"平台:用户ID"，如果不提供则自动查找（插件开发者禁用！）
         storage_message: 是否存储消息到数据库
 
     Returns:
@@ -282,12 +285,13 @@ async def text_to_stream(
         "",
         typing,
         reply_to,
-        reply_to_platform_id=reply_to_platform_id,
+        set_reply=set_reply,
+        reply_to_message=reply_to_message,
         storage_message=storage_message,
     )
 
 
-async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bool = True) -> bool:
+async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bool = True, set_reply: bool = False) -> bool:
     """向指定流发送表情包
 
     Args:
@@ -298,10 +302,10 @@ async def emoji_to_stream(emoji_base64: str, stream_id: str, storage_message: bo
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message)
+    return await _send_to_target("emoji", emoji_base64, stream_id, "", typing=False, storage_message=storage_message, set_reply=set_reply)
 
 
-async def image_to_stream(image_base64: str, stream_id: str, storage_message: bool = True) -> bool:
+async def image_to_stream(image_base64: str, stream_id: str, storage_message: bool = True, set_reply: bool = False) -> bool:
     """向指定流发送图片
 
     Args:
@@ -312,11 +316,11 @@ async def image_to_stream(image_base64: str, stream_id: str, storage_message: bo
     Returns:
         bool: 是否发送成功
     """
-    return await _send_to_target("image", image_base64, stream_id, "", typing=False, storage_message=storage_message)
+    return await _send_to_target("image", image_base64, stream_id, "", typing=False, storage_message=storage_message, set_reply=set_reply)
 
 
 async def command_to_stream(
-    command: Union[str, dict], stream_id: str, storage_message: bool = True, display_message: str = ""
+    command: Union[str, dict], stream_id: str, storage_message: bool = True, display_message: str = "", set_reply: bool = False
 ) -> bool:
     """向指定流发送命令
 
@@ -329,7 +333,7 @@ async def command_to_stream(
         bool: 是否发送成功
     """
     return await _send_to_target(
-        "command", command, stream_id, display_message, typing=False, storage_message=storage_message
+        "command", command, stream_id, display_message, typing=False, storage_message=storage_message, set_reply=set_reply
     )
 
 
@@ -340,6 +344,8 @@ async def custom_to_stream(
     display_message: str = "",
     typing: bool = False,
     reply_to: str = "",
+    reply_to_message: Optional[Dict[str, Any]] = None,
+    set_reply: bool = False,
     storage_message: bool = True,
     show_log: bool = True,
 ) -> bool:
@@ -364,6 +370,8 @@ async def custom_to_stream(
         display_message=display_message,
         typing=typing,
         reply_to=reply_to,
+        reply_to_message=reply_to_message,
+        set_reply=set_reply,
         storage_message=storage_message,
         show_log=show_log,
     )

template/model_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "1.2.0"
+version = "1.3.0"
 
 # 配置文件版本号迭代规则同bot_config.toml
 
@@ -112,16 +112,11 @@ model_list = ["qwen3-8b"]
 temperature = 0.7
 max_tokens = 800
 
-[model_task_config.replyer_1] # 首要回复模型，还用于表达器和表达方式学习
+[model_task_config.replyer] # 首要回复模型，还用于表达器和表达方式学习
 model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.2                        # 模型温度，新V3建议0.1-0.3
 max_tokens = 800
 
-[model_task_config.replyer_2] # 次要回复模型
-model_list = ["siliconflow-deepseek-v3"]
-temperature = 0.7
-max_tokens = 800
-
 [model_task_config.planner] #决策：负责决定麦麦该做什么的模型
 model_list = ["siliconflow-deepseek-v3"]
 temperature = 0.3