feat: 整合reasoning模式和hfc模式，统一调控（但不是很统一）

src/config/config.py

@@ -213,8 +213,8 @@ class BotConfig:
 
     # response
     response_mode: str = "heart_flow"  # 回复策略
-    model_reasoning_probability: float = 0.7 # 麦麦回答时选择推理模型(主要)模型概率
-    model_normal_probability: float = 0.3 # 麦麦回答时选择一般模型(次要)模型概率
+    model_reasoning_probability: float = 0.7  # 麦麦回答时选择推理模型(主要)模型概率
+    model_normal_probability: float = 0.3  # 麦麦回答时选择一般模型(次要)模型概率
     # MODEL_R1_DISTILL_PROBABILITY: float = 0.1  # R1蒸馏模型概率
 
     # emoji
@@ -407,9 +407,12 @@ class BotConfig:
 
         def response(parent: dict):
             response_config = parent["response"]
-            config.model_reasoning_probability = response_config.get("model_reasoning_probability", config.model_reasoning_probability)
-            config.model_normal_probability = response_config.get("model_normal_probability", config.model_normal_probability)
-            
+            config.model_reasoning_probability = response_config.get(
+                "model_reasoning_probability", config.model_reasoning_probability
+            )
+            config.model_normal_probability = response_config.get(
+                "model_normal_probability", config.model_normal_probability
+            )
 
             # 添加 enable_heart_flowC 的加载逻辑 (假设它在 [response] 部分)
             if config.INNER_VERSION in SpecifierSet(">=1.4.0"):
@@ -419,7 +422,6 @@ class BotConfig:
             heartflow_config = parent["heartflow"]
             # 加载新增的 heartflowC 参数
 
-
             # 加载原有的 heartflow 参数
             # config.sub_heart_flow_update_interval = heartflow_config.get(
             #     "sub_heart_flow_update_interval", config.sub_heart_flow_update_interval
@@ -442,9 +444,15 @@ class BotConfig:
                     "compress_length_limit", config.compress_length_limit
                 )
             if config.INNER_VERSION in SpecifierSet(">=1.4.0"):
-                config.reply_trigger_threshold = heartflow_config.get("reply_trigger_threshold", config.reply_trigger_threshold)
-                config.probability_decay_factor_per_second = heartflow_config.get("probability_decay_factor_per_second", config.probability_decay_factor_per_second)
-                config.default_decay_rate_per_second = heartflow_config.get("default_decay_rate_per_second", config.default_decay_rate_per_second)
+                config.reply_trigger_threshold = heartflow_config.get(
+                    "reply_trigger_threshold", config.reply_trigger_threshold
+                )
+                config.probability_decay_factor_per_second = heartflow_config.get(
+                    "probability_decay_factor_per_second", config.probability_decay_factor_per_second
+                )
+                config.default_decay_rate_per_second = heartflow_config.get(
+                    "default_decay_rate_per_second", config.default_decay_rate_per_second
+                )
                 config.initial_duration = heartflow_config.get("initial_duration", config.initial_duration)
 
         def willing(parent: dict):

src/heart_flow/heartflow.py

@@ -45,6 +45,8 @@ class CurrentState:
     def __init__(self):
         self.current_state_info = ""
 
+        self.chat_status = "IDLE"
+
         self.mood_manager = MoodManager()
         self.mood = self.mood_manager.get_prompt()
 
@@ -70,7 +72,7 @@ class Heartflow:
         """定期清理不活跃的子心流"""
         while True:
             current_time = time.time()
-            inactive_subheartflows_ids = [] # 修改变量名以清晰表示存储的是ID
+            inactive_subheartflows_ids = []  # 修改变量名以清晰表示存储的是ID
 
             # 检查所有子心流
             # 使用 list(self._subheartflows.items()) 避免在迭代时修改字典
@@ -253,7 +255,7 @@ class Heartflow:
             # 创建并初始化观察对象
             logger.debug(f"为 {subheartflow_id} 创建 observation")
             observation = ChattingObservation(subheartflow_id)
-            await observation.initialize() # 等待初始化完成
+            await observation.initialize()  # 等待初始化完成
             subheartflow.add_observation(observation)
             logger.debug(f"为 {subheartflow_id} 添加 observation 成功")
 
@@ -269,7 +271,7 @@ class Heartflow:
         except Exception as e:
             # 记录详细错误信息
             logger.error(f"创建 subheartflow {subheartflow_id} 失败: {e}")
-            logger.error(traceback.format_exc()) # 记录完整的 traceback
+            logger.error(traceback.format_exc())  # 记录完整的 traceback
             # 考虑是否需要更具体的错误处理或资源清理逻辑
             return None
 

src/heart_flow/sub_heartflow.py

@@ -5,7 +5,6 @@ from src.plugins.models.utils_model import LLMRequest
 from src.config.config import global_config
 import time
 from typing import Optional, List
-from datetime import datetime
 import traceback
 from src.plugins.chat.utils import parse_text_timestamps
 
@@ -78,12 +77,12 @@ class SubHeartflow:
         self.main_heartflow_info = ""
 
         self.last_active_time = time.time()  # 添加最后激活时间
-        self.should_stop = False # 添加停止标志
-        self.task: Optional[asyncio.Task] = None # 添加 task 属性
+        self.should_stop = False  # 添加停止标志
+        self.task: Optional[asyncio.Task] = None  # 添加 task 属性
 
         self.is_active = False
 
-        self.observations: List[ChattingObservation] = [] # 使用 List 类型提示
+        self.observations: List[ChattingObservation] = []  # 使用 List 类型提示
 
         self.running_knowledges = []
 
@@ -98,7 +97,7 @@ class SubHeartflow:
             # 检查是否被主心流标记为停止
             if self.should_stop:
                 logger.info(f"子心流 {self.subheartflow_id} 被标记为停止，正在退出后台任务...")
-                break # 退出循环以停止任务
+                break  # 退出循环以停止任务
 
             await asyncio.sleep(global_config.sub_heart_flow_update_interval)  # 定期检查销毁条件
 

src/main.py

@@ -19,6 +19,7 @@ from .individuality.individuality import Individuality
 from .common.server import global_server
 from .plugins.chat_module.heartFC_chat.interest import InterestManager
 from .plugins.chat_module.heartFC_chat.heartFC_controler import HeartFC_Controller
+from .plugins.chat_module.heartFC_chat.reasoning_chat import ReasoningChat
 
 logger = get_module_logger("main")
 
@@ -117,8 +118,11 @@ class MainSystem:
             await interest_manager.start_background_tasks()
             logger.success("兴趣管理器后台任务启动成功")
 
-            # 初始化并独立启动 HeartFC_Chat
-            HeartFC_Controller()
+            # 初始化 ReasoningChat 单例 (确保它在需要之前被创建)
+            ReasoningChat.get_instance()
+            logger.success("ReasoningChat 单例初始化成功")
+
+            # 初始化并独立启动 HeartFC_Chat 控制器 (使用 get_instance 获取单例)
             heartfc_chat_instance = HeartFC_Controller.get_instance()
             if heartfc_chat_instance:
                 await heartfc_chat_instance.start()

src/plugins/chat/bot.py

@@ -124,8 +124,6 @@ class ChatBot:
                 else:
                     await self.heartFC_processor.process_message(message_data)
 
-                    
-
             if template_group_name:
                 async with global_prompt_manager.async_message_scope(template_group_name):
                     await preprocess()

src/plugins/chat_module/heartFC_chat/heartFC_controler.py

@@ -13,6 +13,7 @@ from src.do_tool.tool_use import ToolUser
 from .interest import InterestManager
 from src.plugins.chat.chat_stream import chat_manager
 from .pf_chatting import PFChatting
+import threading  # 导入 threading
 
 # 定义日志配置
 chat_config = LogConfig(
@@ -27,43 +28,58 @@ INTEREST_MONITOR_INTERVAL_SECONDS = 1
 
 
 class HeartFC_Controller:
-    _instance = None  # For potential singleton access if needed by MessageManager
+    _instance = None
+    _lock = threading.Lock()  # 使用 threading.Lock 替代 asyncio.Lock 以兼容 __new__
+    _initialized = False
 
-    def __init__(self):
-        # --- Updated Init ---
-        if HeartFC_Controller._instance is not None:
-            # Prevent re-initialization if used as a singleton
-            return
-        self.gpt = ResponseGenerator()
-        self.mood_manager = MoodManager.get_instance()
-        self.mood_manager.start_mood_update()
-        self.tool_user = ToolUser()
-        self.interest_manager = InterestManager()
-        self._interest_monitor_task: Optional[asyncio.Task] = None
-        # --- New PFChatting Management ---
-        self.pf_chatting_instances: Dict[str, PFChatting] = {}
-        self._pf_chatting_lock = Lock()
-        # --- End New PFChatting Management ---
-        HeartFC_Controller._instance = self  # Register instance
-        # --- End Updated Init ---
-        # --- Make dependencies accessible for PFChatting ---
-        # These are accessed via the passed instance in PFChatting
-        self.emoji_manager = emoji_manager
-        self.relationship_manager = relationship_manager
-        self.MessageManager = MessageManager  # Pass the class/singleton access
-        # --- End dependencies ---
-
-    # --- Added Class Method for Singleton Access ---
-    @classmethod
-    def get_instance(cls):
+    def __new__(cls, *args, **kwargs):
         if cls._instance is None:
-            # This might indicate an issue if called before initialization
-            logger.warning("HeartFC_Controller get_instance called before initialization.")
-            # Optionally, initialize here if a strict singleton pattern is desired
-            # cls._instance = cls()
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
         return cls._instance
 
-    # --- End Added Class Method ---
+    def __init__(self):
+        if self._initialized:
+            return
+        with self.__class__._lock:  # 使用类锁确保初始化线程安全
+            if self._initialized:
+                return
+            logger.info("正在初始化 HeartFC_Controller 单例...")
+            self.gpt = ResponseGenerator()
+            self.mood_manager = MoodManager.get_instance()
+            self.mood_manager.start_mood_update()
+            self.tool_user = ToolUser()
+            self.interest_manager = InterestManager()
+            self._interest_monitor_task: Optional[asyncio.Task] = None
+            self.pf_chatting_instances: Dict[str, PFChatting] = {}
+            self._pf_chatting_lock = Lock()  # 这个可以是 asyncio.Lock，用于异步上下文
+            self.emoji_manager = emoji_manager
+            self.relationship_manager = relationship_manager
+            self.MessageManager = MessageManager
+            self._initialized = True
+            logger.info("HeartFC_Controller 单例初始化完成。")
+
+    @classmethod
+    def get_instance(cls):
+        """获取 HeartFC_Controller 的单例实例。"""
+        if cls._instance is None:
+            logger.warning("HeartFC_Controller 实例在首次 get_instance 时创建，可能未在 main 中正确初始化。")
+            cls()  # 调用构造函数创建
+        return cls._instance
+
+    # --- 新增：检查 PFChatting 状态的方法 --- #
+    def is_pf_chatting_active(self, stream_id: str) -> bool:
+        """检查指定 stream_id 的 PFChatting 循环是否处于活动状态。"""
+        # 注意：这里直接访问字典，不加锁，因为读取通常是安全的，
+        # 并且 PFChatting 实例的 _loop_active 状态由其自身的异步循环管理。
+        # 如果需要更强的保证，可以在访问 pf_instance 前获取 _pf_chatting_lock
+        pf_instance = self.pf_chatting_instances.get(stream_id)
+        if pf_instance and pf_instance._loop_active:  # 直接检查 PFChatting 实例的 _loop_active 属性
+            return True
+        return False
+
+    # --- 结束新增 --- #
 
     async def start(self):
         """启动异步任务,如回复启动器"""

src/plugins/chat_module/heartFC_chat/heartFC_processor.py

@@ -13,6 +13,7 @@ from ...chat.message_buffer import message_buffer
 from ...utils.timer_calculater import Timer
 from .interest import InterestManager
 from src.plugins.person_info.relationship_manager import relationship_manager
+from .reasoning_chat import ReasoningChat
 
 # 定义日志配置
 processor_config = LogConfig(
@@ -29,7 +30,7 @@ class HeartFC_Processor:
     def __init__(self):
         self.storage = MessageStorage()
         self.interest_manager = InterestManager()
-        # self.chat_instance = chat_instance  # 持有 HeartFC_Chat 实例
+        self.reasoning_chat = ReasoningChat.get_instance()
 
     async def process_message(self, message_data: str) -> None:
         """处理接收到的原始消息数据，完成消息解析、缓冲、过滤、存储、兴趣度计算与更新等核心流程。
@@ -72,11 +73,11 @@ class HeartFC_Processor:
                 user_info=userinfo,
                 group_info=groupinfo,
             )
-            if not chat:
-                logger.error(
-                    f"无法为消息创建或获取聊天流: user {userinfo.user_id}, group {groupinfo.group_id if groupinfo else 'None'}"
-                )
-                return
+
+            # --- 添加兴趣追踪启动 ---
+            # 在获取到 chat 对象后，启动对该聊天流的兴趣监控
+            await self.reasoning_chat.start_monitoring_interest(chat)
+            # --- 结束添加 ---
 
             message.update_chat_stream(chat)
 
@@ -90,7 +91,6 @@ class HeartFC_Processor:
                 message.raw_message, chat, userinfo
             ):
                 return
-            logger.trace(f"过滤词/正则表达式过滤成功: {message.processed_plain_text}")
 
             # 查询缓冲器结果
             buffer_result = await message_buffer.query_buffer_result(message)
@@ -152,6 +152,8 @@ class HeartFC_Processor:
                     f"使用激活率 {interested_rate:.2f} 更新后 (通过缓冲后)，当前兴趣度: {current_interest:.2f}"
                 )
 
+                self.interest_manager.add_interest_dict(message, interested_rate, is_mentioned)
+
             except Exception as e:
                 logger.error(f"更新兴趣度失败: {e}")  # 调整日志消息
                 logger.error(traceback.format_exc())

src/plugins/chat_module/heartFC_chat/interest.py

@@ -6,6 +6,7 @@ import json  # 引入 json
 import os  # 引入 os
 from typing import Optional  # <--- 添加导入
 import random  # <--- 添加导入 random
+from src.plugins.chat.message import MessageRecv
 from src.common.logger import get_module_logger, LogConfig, DEFAULT_CONFIG  # 引入 DEFAULT_CONFIG
 from src.plugins.chat.chat_stream import chat_manager  # *** Import ChatManager ***
 
@@ -66,6 +67,13 @@ class InterestChatting:
         self.is_above_threshold: bool = False  # 标记兴趣值是否高于阈值
         # --- 结束：概率回复相关属性 ---
 
+        # 记录激发兴趣对(消息id,激活值)
+        self.interest_dict = {}
+
+    def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
+        # Store the MessageRecv object and the interest value as a tuple
+        self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
+
     def _calculate_decay(self, current_time: float):
         """计算从上次更新到现在的衰减"""
         time_delta = current_time - self.last_update_time
@@ -445,6 +453,10 @@ class InterestManager:
             stream_name = chat_manager.get_stream_name(stream_id) or stream_id  # 获取流名称
             logger.warning(f"尝试降低不存在的聊天流 {stream_name} 的兴趣度")
 
+    def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
+        interest_chatting = self._get_or_create_interest_chatting(message.chat_stream.stream_id)
+        interest_chatting.add_interest_dict(message, interest_value, is_mentioned)
+
     def cleanup_inactive_chats(self, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):
         """
         清理长时间不活跃的聊天流记录

src/plugins/chat_module/heartFC_chat/reasoning_chat.py

@@ -0,0 +1,412 @@
+import time
+import threading  # 导入 threading
+from random import random
+import traceback
+import asyncio
+from typing import List, Dict
+from ...moods.moods import MoodManager
+from ....config.config import global_config
+from ...chat.emoji_manager import emoji_manager
+from .reasoning_generator import ResponseGenerator
+from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
+from ...chat.messagesender import message_manager
+from ...storage.storage import MessageStorage
+from ...chat.utils import is_mentioned_bot_in_message
+from ...chat.utils_image import image_path_to_base64
+from ...willing.willing_manager import willing_manager
+from ...message import UserInfo, Seg
+from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
+from src.plugins.chat.chat_stream import ChatStream
+from src.plugins.person_info.relationship_manager import relationship_manager
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+from src.plugins.utils.timer_calculater import Timer
+from .interest import InterestManager
+from .heartFC_controler import HeartFC_Controller  # 导入 HeartFC_Controller
+
+# 定义日志配置
+chat_config = LogConfig(
+    console_format=CHAT_STYLE_CONFIG["console_format"],
+    file_format=CHAT_STYLE_CONFIG["file_format"],
+)
+
+logger = get_module_logger("reasoning_chat", config=chat_config)
+
+
+class ReasoningChat:
+    _instance = None
+    _lock = threading.Lock()
+    _initialized = False
+
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            with cls._lock:
+                # Double-check locking
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        # 防止重复初始化
+        if self._initialized:
+            return
+        with self.__class__._lock:  # 使用类锁确保线程安全
+            if self._initialized:
+                return
+            logger.info("正在初始化 ReasoningChat 单例...")  # 添加日志
+            self.storage = MessageStorage()
+            self.gpt = ResponseGenerator()
+            self.mood_manager = MoodManager.get_instance()
+            self.mood_manager.start_mood_update()
+            # 用于存储每个 chat stream 的兴趣监控任务
+            self._interest_monitoring_tasks: Dict[str, asyncio.Task] = {}
+            self._initialized = True
+            self.interest_manager = InterestManager()
+            logger.info("ReasoningChat 单例初始化完成。")  # 添加日志
+
+    @classmethod
+    def get_instance(cls):
+        """获取 ReasoningChat 的单例实例。"""
+        if cls._instance is None:
+            # 如果实例还未创建（理论上应该在 main 中初始化，但作为备用）
+            logger.warning("ReasoningChat 实例在首次 get_instance 时创建。")
+            cls()  # 调用构造函数来创建实例
+        return cls._instance
+
+    @staticmethod
+    async def _create_thinking_message(message, chat, userinfo, messageinfo):
+        """创建思考消息"""
+        bot_user_info = UserInfo(
+            user_id=global_config.BOT_QQ,
+            user_nickname=global_config.BOT_NICKNAME,
+            platform=messageinfo.platform,
+        )
+
+        thinking_time_point = round(time.time(), 2)
+        thinking_id = "mt" + str(thinking_time_point)
+        thinking_message = MessageThinking(
+            message_id=thinking_id,
+            chat_stream=chat,
+            bot_user_info=bot_user_info,
+            reply=message,
+            thinking_start_time=thinking_time_point,
+        )
+
+        message_manager.add_message(thinking_message)
+
+        return thinking_id
+
+    @staticmethod
+    async def _send_response_messages(message, chat, response_set: List[str], thinking_id) -> MessageSending:
+        """发送回复消息"""
+        container = message_manager.get_container(chat.stream_id)
+        thinking_message = None
+
+        for msg in container.messages:
+            if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
+                thinking_message = msg
+                container.messages.remove(msg)
+                break
+
+        if not thinking_message:
+            logger.warning("未找到对应的思考消息，可能已超时被移除")
+            return
+
+        thinking_start_time = thinking_message.thinking_start_time
+        message_set = MessageSet(chat, thinking_id)
+
+        mark_head = False
+        first_bot_msg = None
+        for msg in response_set:
+            message_segment = Seg(type="text", data=msg)
+            bot_message = MessageSending(
+                message_id=thinking_id,
+                chat_stream=chat,
+                bot_user_info=UserInfo(
+                    user_id=global_config.BOT_QQ,
+                    user_nickname=global_config.BOT_NICKNAME,
+                    platform=message.message_info.platform,
+                ),
+                sender_info=message.message_info.user_info,
+                message_segment=message_segment,
+                reply=message,
+                is_head=not mark_head,
+                is_emoji=False,
+                thinking_start_time=thinking_start_time,
+            )
+            if not mark_head:
+                mark_head = True
+                first_bot_msg = bot_message
+            message_set.add_message(bot_message)
+        message_manager.add_message(message_set)
+
+        return first_bot_msg
+
+    @staticmethod
+    async def _handle_emoji(message, chat, response):
+        """处理表情包"""
+        if random() < global_config.emoji_chance:
+            emoji_raw = await emoji_manager.get_emoji_for_text(response)
+            if emoji_raw:
+                emoji_path, description = emoji_raw
+                emoji_cq = image_path_to_base64(emoji_path)
+
+                thinking_time_point = round(message.message_info.time, 2)
+
+                message_segment = Seg(type="emoji", data=emoji_cq)
+                bot_message = MessageSending(
+                    message_id="mt" + str(thinking_time_point),
+                    chat_stream=chat,
+                    bot_user_info=UserInfo(
+                        user_id=global_config.BOT_QQ,
+                        user_nickname=global_config.BOT_NICKNAME,
+                        platform=message.message_info.platform,
+                    ),
+                    sender_info=message.message_info.user_info,
+                    message_segment=message_segment,
+                    reply=message,
+                    is_head=False,
+                    is_emoji=True,
+                )
+                message_manager.add_message(bot_message)
+
+    async def _update_relationship(self, message: MessageRecv, response_set):
+        """更新关系情绪"""
+        ori_response = ",".join(response_set)
+        stance, emotion = await self.gpt._get_emotion_tags(ori_response, message.processed_plain_text)
+        await relationship_manager.calculate_update_relationship_value(
+            chat_stream=message.chat_stream, label=emotion, stance=stance
+        )
+        self.mood_manager.update_mood_from_emotion(emotion, global_config.mood_intensity_factor)
+
+    async def _find_interested_message(self, chat: ChatStream) -> None:
+        # 此函数设计为后台任务，轮询指定 chat 的兴趣消息。
+        # 它通常由外部代码在 chat 流活跃时启动。
+        controller = HeartFC_Controller.get_instance()  # 获取控制器实例
+        if not controller:
+            logger.error(f"无法获取 HeartFC_Controller 实例，无法检查 PFChatting 状态。stream: {chat.stream_id}")
+            # 在没有控制器的情况下可能需要决定是继续处理还是完全停止？这里暂时假设继续
+            pass  # 或者 return?
+
+        while True:
+            await asyncio.sleep(1)  # 每秒检查一次
+            interest_chatting = self.interest_manager.get_interest_chatting(chat.stream_id)
+
+            if not interest_chatting:
+                continue
+
+            interest_dict = interest_chatting.interest_dict if interest_chatting.interest_dict else {}
+            items_to_process = list(interest_dict.items())
+
+            if not items_to_process:
+                continue
+
+            for msg_id, (message, interest_value, is_mentioned) in items_to_process:
+                # --- 检查 PFChatting 是否活跃 --- #
+                pf_active = False
+                if controller:
+                    pf_active = controller.is_pf_chatting_active(chat.stream_id)
+
+                if pf_active:
+                    # 如果 PFChatting 活跃，则跳过处理，直接移除消息
+                    removed_item = interest_dict.pop(msg_id, None)
+                    if removed_item:
+                        logger.debug(f"PFChatting 活跃，已跳过并移除兴趣消息 {msg_id} for stream: {chat.stream_id}")
+                    continue  # 处理下一条消息
+                # --- 结束检查 --- #
+
+                # 只有当 PFChatting 不活跃时才执行以下处理逻辑
+                try:
+                    # logger.debug(f"正在处理消息 {msg_id} for stream: {chat.stream_id}") # 可选调试信息
+                    await self.normal_reasoning_chat(
+                        message=message,
+                        chat=chat,
+                        is_mentioned=is_mentioned,
+                        interested_rate=interest_value,
+                    )
+                    # logger.debug(f"处理完成消息 {msg_id}") # 可选调试信息
+                except Exception as e:
+                    logger.error(f"处理兴趣消息 {msg_id} 时出错: {e}\n{traceback.format_exc()}")
+                finally:
+                    # 无论处理成功与否（且PFChatting不活跃），都尝试从原始字典中移除该消息
+                    removed_item = interest_dict.pop(msg_id, None)
+                    if removed_item:
+                        logger.debug(f"已从兴趣字典中移除消息 {msg_id}")
+
+    async def normal_reasoning_chat(
+        self, message: MessageRecv, chat: ChatStream, is_mentioned: bool, interested_rate: float
+    ) -> None:
+        timing_results = {}
+        userinfo = message.message_info.user_info
+        messageinfo = message.message_info
+
+        is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
+        # 意愿管理器：设置当前message信息
+        willing_manager.setup(message, chat, is_mentioned, interested_rate)
+
+        # 获取回复概率
+        is_willing = False
+        if reply_probability != 1:
+            is_willing = True
+            reply_probability = await willing_manager.get_reply_probability(message.message_info.message_id)
+
+            if message.message_info.additional_config:
+                if "maimcore_reply_probability_gain" in message.message_info.additional_config.keys():
+                    reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"]
+
+        # 打印消息信息
+        mes_name = chat.group_info.group_name if chat.group_info else "私聊"
+        current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time))
+        willing_log = f"[回复意愿:{await willing_manager.get_willing(chat.stream_id):.2f}]" if is_willing else ""
+        logger.info(
+            f"[{current_time}][{mes_name}]"
+            f"{chat.user_info.user_nickname}:"
+            f"{message.processed_plain_text}{willing_log}[概率:{reply_probability * 100:.1f}%]"
+        )
+        do_reply = False
+        if random() < reply_probability:
+            do_reply = True
+
+            # 回复前处理
+            await willing_manager.before_generate_reply_handle(message.message_info.message_id)
+
+            # 创建思考消息
+            with Timer("创建思考消息", timing_results):
+                thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
+
+            logger.debug(f"创建捕捉器，thinking_id:{thinking_id}")
+
+            info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+            info_catcher.catch_decide_to_response(message)
+
+            # 生成回复
+            try:
+                with Timer("生成回复", timing_results):
+                    response_set = await self.gpt.generate_response(message, thinking_id)
+
+                info_catcher.catch_after_generate_response(timing_results["生成回复"])
+            except Exception as e:
+                logger.error(f"回复生成出现错误：{str(e)} {traceback.format_exc()}")
+                response_set = None
+
+            if not response_set:
+                logger.info("为什么生成回复失败？")
+                return
+
+            # 发送消息
+            with Timer("发送消息", timing_results):
+                first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
+
+            info_catcher.catch_after_response(timing_results["发送消息"], response_set, first_bot_msg)
+
+            info_catcher.done_catch()
+
+            # 处理表情包
+            with Timer("处理表情包", timing_results):
+                await self._handle_emoji(message, chat, response_set)
+
+            # 更新关系情绪
+            with Timer("更新关系情绪", timing_results):
+                await self._update_relationship(message, response_set)
+
+            # 回复后处理
+            await willing_manager.after_generate_reply_handle(message.message_info.message_id)
+
+        # 输出性能计时结果
+        if do_reply:
+            timing_str = " | ".join([f"{step}: {duration:.2f}秒" for step, duration in timing_results.items()])
+            trigger_msg = message.processed_plain_text
+            response_msg = " ".join(response_set) if response_set else "无回复"
+            logger.info(f"触发消息: {trigger_msg[:20]}... | 推理消息: {response_msg[:20]}... | 性能计时: {timing_str}")
+        else:
+            # 不回复处理
+            await willing_manager.not_reply_handle(message.message_info.message_id)
+
+        # 意愿管理器：注销当前message信息
+        willing_manager.delete(message.message_info.message_id)
+
+    @staticmethod
+    def _check_ban_words(text: str, chat, userinfo) -> bool:
+        """检查消息中是否包含过滤词"""
+        for word in global_config.ban_words:
+            if word in text:
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
+                )
+                logger.info(f"[过滤词识别]消息中含有{word}，filtered")
+                return True
+        return False
+
+    @staticmethod
+    def _check_ban_regex(text: str, chat, userinfo) -> bool:
+        """检查消息是否匹配过滤正则表达式"""
+        for pattern in global_config.ban_msgs_regex:
+            if pattern.search(text):
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{text}"
+                )
+                logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
+                return True
+        return False
+
+    async def start_monitoring_interest(self, chat: ChatStream):
+        """为指定的 ChatStream 启动后台兴趣消息监控任务。"""
+        stream_id = chat.stream_id
+        # 检查任务是否已在运行
+        if stream_id in self._interest_monitoring_tasks and not self._interest_monitoring_tasks[stream_id].done():
+            task = self._interest_monitoring_tasks[stream_id]
+            if not task.cancelled():  # 确保任务未被取消
+                logger.info(f"兴趣监控任务已在运行 stream: {stream_id}")
+                return
+            else:
+                logger.info(f"发现已取消的任务，重新创建 stream: {stream_id}")
+                # 如果任务被取消了，允许重新创建
+
+        logger.info(f"启动兴趣监控任务 stream: {stream_id}...")
+        # 创建新的后台任务来运行 _find_interested_message
+        task = asyncio.create_task(self._find_interested_message(chat))
+        self._interest_monitoring_tasks[stream_id] = task
+
+        # 添加回调，当任务完成（或被取消）时，自动从字典中移除
+        task.add_done_callback(lambda t: self._handle_task_completion(stream_id, t))
+
+    def _handle_task_completion(self, stream_id: str, task: asyncio.Task):
+        """处理监控任务完成的回调。"""
+        try:
+            # 检查任务是否因异常而结束
+            exception = task.exception()
+            if exception:
+                logger.error(f"兴趣监控任务 stream {stream_id} 异常结束: {exception}", exc_info=exception)
+            elif task.cancelled():
+                logger.info(f"兴趣监控任务 stream {stream_id} 已被取消。")
+            else:
+                logger.info(f"兴趣监控任务 stream {stream_id} 正常结束。")  # 理论上 while True 不会正常结束
+        except asyncio.CancelledError:
+            logger.info(f"兴趣监控任务 stream {stream_id} 在完成处理期间被取消。")
+        finally:
+            # 无论如何都从字典中移除
+            removed_task = self._interest_monitoring_tasks.pop(stream_id, None)
+            if removed_task:
+                logger.debug(f"已从监控任务字典移除 stream: {stream_id}")
+
+    async def stop_monitoring_interest(self, stream_id: str):
+        """停止指定 stream_id 的兴趣消息监控任务。"""
+        if stream_id in self._interest_monitoring_tasks:
+            task = self._interest_monitoring_tasks[stream_id]
+            if not task.done():
+                logger.info(f"正在停止兴趣监控任务 stream: {stream_id}...")
+                task.cancel()  # 请求取消任务
+                try:
+                    # 等待任务实际被取消（可选，提供更明确的停止）
+                    # 设置超时以防万一
+                    await asyncio.wait_for(task, timeout=5.0)
+                except asyncio.CancelledError:
+                    logger.info(f"兴趣监控任务 stream {stream_id} 已确认取消。")
+                except asyncio.TimeoutError:
+                    logger.warning(f"停止兴趣监控任务 stream {stream_id} 超时。任务可能仍在运行。")
+                except Exception as e:
+                    # 捕获 task.exception() 可能在取消期间重新引发的错误
+                    logger.error(f"停止兴趣监控任务 stream {stream_id} 时发生错误: {e}")
+            # 任务最终会由 done_callback 移除，或在这里再次确认移除
+            self._interest_monitoring_tasks.pop(stream_id, None)
+        else:
+            logger.warning(f"尝试停止不存在或已停止的监控任务 stream: {stream_id}")

src/plugins/chat_module/heartFC_chat/reasoning_generator.py

@@ -0,0 +1,199 @@
+from typing import List, Optional, Tuple, Union
+import random
+
+from ...models.utils_model import LLMRequest
+from ....config.config import global_config
+from ...chat.message import MessageThinking
+from .reasoning_prompt_builder import prompt_builder
+from ...chat.utils import process_llm_response
+from ...utils.timer_calculater import Timer
+from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
+
+# 定义日志配置
+llm_config = LogConfig(
+    # 使用消息发送专用样式
+    console_format=LLM_STYLE_CONFIG["console_format"],
+    file_format=LLM_STYLE_CONFIG["file_format"],
+)
+
+logger = get_module_logger("llm_generator", config=llm_config)
+
+
+class ResponseGenerator:
+    def __init__(self):
+        self.model_reasoning = LLMRequest(
+            model=global_config.llm_reasoning,
+            temperature=0.7,
+            max_tokens=3000,
+            request_type="response_reasoning",
+        )
+        self.model_normal = LLMRequest(
+            model=global_config.llm_normal,
+            temperature=global_config.llm_normal["temp"],
+            max_tokens=256,
+            request_type="response_reasoning",
+        )
+
+        self.model_sum = LLMRequest(
+            model=global_config.llm_summary_by_topic, temperature=0.7, max_tokens=3000, request_type="relation"
+        )
+        self.current_model_type = "r1"  # 默认使用 R1
+        self.current_model_name = "unknown model"
+
+    async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
+        """根据当前模型类型选择对应的生成函数"""
+        # 从global_config中获取模型概率值并选择模型
+        if random.random() < global_config.model_reasoning_probability:
+            self.current_model_type = "深深地"
+            current_model = self.model_reasoning
+        else:
+            self.current_model_type = "浅浅的"
+            current_model = self.model_normal
+
+        logger.info(
+            f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
+        )  # noqa: E501
+
+        model_response = await self._generate_response_with_model(message, current_model, thinking_id)
+
+        # print(f"raw_content: {model_response}")
+
+        if model_response:
+            logger.info(f"{global_config.BOT_NICKNAME}的回复是：{model_response}")
+            model_response = await self._process_response(model_response)
+
+            return model_response
+        else:
+            logger.info(f"{self.current_model_type}思考，失败")
+            return None
+
+    async def _generate_response_with_model(self, message: MessageThinking, model: LLMRequest, thinking_id: str):
+        info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
+
+        if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname:
+            sender_name = (
+                f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]"
+                f"{message.chat_stream.user_info.user_cardname}"
+            )
+        elif message.chat_stream.user_info.user_nickname:
+            sender_name = f"({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}"
+        else:
+            sender_name = f"用户({message.chat_stream.user_info.user_id})"
+
+        logger.debug("开始使用生成回复-2")
+        # 构建prompt
+        with Timer() as t_build_prompt:
+            prompt = await prompt_builder._build_prompt(
+                message.chat_stream,
+                message_txt=message.processed_plain_text,
+                sender_name=sender_name,
+                stream_id=message.chat_stream.stream_id,
+            )
+        logger.info(f"构建prompt时间: {t_build_prompt.human_readable}")
+
+        try:
+            content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
+
+            info_catcher.catch_after_llm_generated(
+                prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=self.current_model_name
+            )
+
+        except Exception:
+            logger.exception("生成回复时出错")
+            return None
+
+        # 保存到数据库
+        # self._save_to_db(
+        #     message=message,
+        #     sender_name=sender_name,
+        #     prompt=prompt,
+        #     content=content,
+        #     reasoning_content=reasoning_content,
+        #     # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else ""
+        # )
+
+        return content
+
+    # def _save_to_db(
+    #     self,
+    #     message: MessageRecv,
+    #     sender_name: str,
+    #     prompt: str,
+    #     content: str,
+    #     reasoning_content: str,
+    # ):
+    #     """保存对话记录到数据库"""
+    #     db.reasoning_logs.insert_one(
+    #         {
+    #             "time": time.time(),
+    #             "chat_id": message.chat_stream.stream_id,
+    #             "user": sender_name,
+    #             "message": message.processed_plain_text,
+    #             "model": self.current_model_name,
+    #             "reasoning": reasoning_content,
+    #             "response": content,
+    #             "prompt": prompt,
+    #         }
+    #     )
+
+    async def _get_emotion_tags(self, content: str, processed_plain_text: str):
+        """提取情感标签，结合立场和情绪"""
+        try:
+            # 构建提示词，结合回复内容、被回复的内容以及立场分析
+            prompt = f"""
+            请严格根据以下对话内容，完成以下任务：
+            1. 判断回复者对被回复者观点的直接立场：
+            - "支持"：明确同意或强化被回复者观点
+            - "反对"：明确反驳或否定被回复者观点
+            - "中立"：不表达明确立场或无关回应
+            2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签
+            3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒"
+            4. 考虑回复者的人格设定为{global_config.personality_core}
+
+            对话示例：
+            被回复：「A就是笨」
+            回复：「A明明很聪明」 → 反对-愤怒
+
+            当前对话：
+            被回复：「{processed_plain_text}」
+            回复：「{content}」
+
+            输出要求：
+            - 只需输出"立场-情绪"结果，不要解释
+            - 严格基于文字直接表达的对立关系判断
+            """
+
+            # 调用模型生成结果
+            result, _, _ = await self.model_sum.generate_response(prompt)
+            result = result.strip()
+
+            # 解析模型输出的结果
+            if "-" in result:
+                stance, emotion = result.split("-", 1)
+                valid_stances = ["支持", "反对", "中立"]
+                valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"]
+                if stance in valid_stances and emotion in valid_emotions:
+                    return stance, emotion  # 返回有效的立场-情绪组合
+                else:
+                    logger.debug(f"无效立场-情感组合:{result}")
+                    return "中立", "平静"  # 默认返回中立-平静
+            else:
+                logger.debug(f"立场-情感格式错误:{result}")
+                return "中立", "平静"  # 格式错误时返回默认值
+
+        except Exception as e:
+            logger.debug(f"获取情感标签时出错: {e}")
+            return "中立", "平静"  # 出错时返回默认值
+
+    @staticmethod
+    async def _process_response(content: str) -> Tuple[List[str], List[str]]:
+        """处理响应内容，返回处理后的内容和情感标签"""
+        if not content:
+            return None, []
+
+        processed_response = process_llm_response(content)
+
+        # print(f"得到了处理后的llm返回{processed_response}")
+
+        return processed_response

src/plugins/chat_module/heartFC_chat/reasoning_prompt_builder.py

@@ -0,0 +1,445 @@
+import random
+import time
+from typing import Optional, Union
+
+from ....common.database import db
+from ...chat.utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker
+from ...chat.chat_stream import chat_manager
+from ...moods.moods import MoodManager
+from ....individuality.individuality import Individuality
+from ...memory_system.Hippocampus import HippocampusManager
+from ...schedule.schedule_generator import bot_schedule
+from ....config.config import global_config
+from ...person_info.relationship_manager import relationship_manager
+from src.common.logger import get_module_logger
+from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
+
+logger = get_module_logger("prompt")
+
+
+def init_prompt():
+    Prompt(
+        """
+{relation_prompt_all}
+{memory_prompt}
+{prompt_info}
+{schedule_prompt}
+{chat_target}
+{chat_talking_prompt}
+现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
+你的网名叫{bot_name}，有人也叫你{bot_other_names}，{prompt_personality}。
+你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
+尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
+请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 
+请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
+{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""",
+        "reasoning_prompt_main",
+    )
+    Prompt(
+        "{relation_prompt}关系等级越大，关系越好，请分析聊天记录，根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。",
+        "relationship_prompt",
+    )
+    Prompt(
+        "你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n",
+        "memory_prompt",
+    )
+    Prompt("你现在正在做的事情是：{schedule_info}", "schedule_prompt")
+    Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt")
+
+
+class PromptBuilder:
+    def __init__(self):
+        self.prompt_built = ""
+        self.activate_messages = ""
+
+    async def _build_prompt(
+        self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
+    ) -> tuple[str, str]:
+        # 开始构建prompt
+        prompt_personality = "你"
+        # person
+        individuality = Individuality.get_instance()
+
+        personality_core = individuality.personality.personality_core
+        prompt_personality += personality_core
+
+        personality_sides = individuality.personality.personality_sides
+        random.shuffle(personality_sides)
+        prompt_personality += f",{personality_sides[0]}"
+
+        identity_detail = individuality.identity.identity_detail
+        random.shuffle(identity_detail)
+        prompt_personality += f",{identity_detail[0]}"
+
+        # 关系
+        who_chat_in_group = [
+            (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
+        ]
+        who_chat_in_group += get_recent_group_speaker(
+            stream_id,
+            (chat_stream.user_info.platform, chat_stream.user_info.user_id),
+            limit=global_config.MAX_CONTEXT_SIZE,
+        )
+
+        relation_prompt = ""
+        for person in who_chat_in_group:
+            relation_prompt += await relationship_manager.build_relationship_info(person)
+
+        # relation_prompt_all = (
+        #     f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
+        #     f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。"
+        # )
+
+        # 心情
+        mood_manager = MoodManager.get_instance()
+        mood_prompt = mood_manager.get_prompt()
+
+        # logger.info(f"心情prompt: {mood_prompt}")
+
+        # 调取记忆
+        memory_prompt = ""
+        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+            text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
+        )
+        related_memory_info = ""
+        if related_memory:
+            for memory in related_memory:
+                related_memory_info += memory[1]
+            # memory_prompt = f"你想起你之前见过的事情：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n"
+            memory_prompt = await global_prompt_manager.format_prompt(
+                "memory_prompt", related_memory_info=related_memory_info
+            )
+
+        # print(f"相关记忆：{related_memory_info}")
+
+        # 日程构建
+        # schedule_prompt = f"""你现在正在做的事情是：{bot_schedule.get_current_num_task(num=1, time_info=False)}"""
+
+        # 获取聊天上下文
+        chat_in_group = True
+        chat_talking_prompt = ""
+        if stream_id:
+            chat_talking_prompt = get_recent_group_detailed_plain_text(
+                stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
+            )
+            chat_stream = chat_manager.get_stream(stream_id)
+            if chat_stream.group_info:
+                chat_talking_prompt = chat_talking_prompt
+            else:
+                chat_in_group = False
+                chat_talking_prompt = chat_talking_prompt
+                # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
+        # 关键词检测与反应
+        keywords_reaction_prompt = ""
+        for rule in global_config.keywords_reaction_rules:
+            if rule.get("enable", False):
+                if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
+                    logger.info(
+                        f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
+                    )
+                    keywords_reaction_prompt += rule.get("reaction", "") + "，"
+                else:
+                    for pattern in rule.get("regex", []):
+                        result = pattern.search(message_txt)
+                        if result:
+                            reaction = rule.get("reaction", "")
+                            for name, content in result.groupdict().items():
+                                reaction = reaction.replace(f"[{name}]", content)
+                            logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
+                            keywords_reaction_prompt += reaction + "，"
+                            break
+
+        # 中文高手(新加的好玩功能)
+        prompt_ger = ""
+        if random.random() < 0.04:
+            prompt_ger += "你喜欢用倒装句"
+        if random.random() < 0.02:
+            prompt_ger += "你喜欢用反问句"
+        if random.random() < 0.01:
+            prompt_ger += "你喜欢用文言文"
+
+        # 知识构建
+        start_time = time.time()
+        prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
+        if prompt_info:
+            # prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n"""
+            prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
+
+        end_time = time.time()
+        logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
+
+        # moderation_prompt = ""
+        #         moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
+        # 涉及政治敏感以及违法违规的内容请规避。"""
+
+        logger.debug("开始构建prompt")
+
+        #         prompt = f"""
+        # {relation_prompt_all}
+        # {memory_prompt}
+        # {prompt_info}
+        # {schedule_prompt}
+        # {chat_target}
+        # {chat_talking_prompt}
+        # 现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
+        # 你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
+        # 你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
+        # 尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger}
+        # 请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话
+        # 请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
+        # {moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。"""
+
+        prompt = await global_prompt_manager.format_prompt(
+            "reasoning_prompt_main",
+            relation_prompt_all=await global_prompt_manager.get_prompt_async("relationship_prompt"),
+            relation_prompt=relation_prompt,
+            sender_name=sender_name,
+            memory_prompt=memory_prompt,
+            prompt_info=prompt_info,
+            schedule_prompt=await global_prompt_manager.format_prompt(
+                "schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
+            ),
+            chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
+            if chat_in_group
+            else await global_prompt_manager.get_prompt_async("chat_target_private1"),
+            chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
+            if chat_in_group
+            else await global_prompt_manager.get_prompt_async("chat_target_private2"),
+            chat_talking_prompt=chat_talking_prompt,
+            message_txt=message_txt,
+            bot_name=global_config.BOT_NICKNAME,
+            bot_other_names="/".join(
+                global_config.BOT_ALIAS_NAMES,
+            ),
+            prompt_personality=prompt_personality,
+            mood_prompt=mood_prompt,
+            keywords_reaction_prompt=keywords_reaction_prompt,
+            prompt_ger=prompt_ger,
+            moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
+        )
+
+        return prompt
+
+    async def get_prompt_info(self, message: str, threshold: float):
+        start_time = time.time()
+        related_info = ""
+        logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
+
+        # 1. 先从LLM获取主题，类似于记忆系统的做法
+        topics = []
+        # try:
+        #     # 先尝试使用记忆系统的方法获取主题
+        #     hippocampus = HippocampusManager.get_instance()._hippocampus
+        #     topic_num = min(5, max(1, int(len(message) * 0.1)))
+        #     topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
+
+        #     # 提取关键词
+        #     topics = re.findall(r"<([^>]+)>", topics_response[0])
+        #     if not topics:
+        #         topics = []
+        #     else:
+        #         topics = [
+        #             topic.strip()
+        #             for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
+        #             if topic.strip()
+        #         ]
+
+        #     logger.info(f"从LLM提取的主题: {', '.join(topics)}")
+        # except Exception as e:
+        #     logger.error(f"从LLM提取主题失败: {str(e)}")
+        #     # 如果LLM提取失败，使用jieba分词提取关键词作为备选
+        #     words = jieba.cut(message)
+        #     topics = [word for word in words if len(word) > 1][:5]
+        #     logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
+
+        # 如果无法提取到主题，直接使用整个消息
+        if not topics:
+            logger.info("未能提取到任何主题，使用整个消息进行查询")
+            embedding = await get_embedding(message, request_type="prompt_build")
+            if not embedding:
+                logger.error("获取消息嵌入向量失败")
+                return ""
+
+            related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
+            logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
+            return related_info
+
+        # 2. 对每个主题进行知识库查询
+        logger.info(f"开始处理{len(topics)}个主题的知识库查询")
+
+        # 优化：批量获取嵌入向量，减少API调用
+        embeddings = {}
+        topics_batch = [topic for topic in topics if len(topic) > 0]
+        if message:  # 确保消息非空
+            topics_batch.append(message)
+
+        # 批量获取嵌入向量
+        embed_start_time = time.time()
+        for text in topics_batch:
+            if not text or len(text.strip()) == 0:
+                continue
+
+            try:
+                embedding = await get_embedding(text, request_type="prompt_build")
+                if embedding:
+                    embeddings[text] = embedding
+                else:
+                    logger.warning(f"获取'{text}'的嵌入向量失败")
+            except Exception as e:
+                logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
+
+        logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
+
+        if not embeddings:
+            logger.error("所有嵌入向量获取失败")
+            return ""
+
+        # 3. 对每个主题进行知识库查询
+        all_results = []
+        query_start_time = time.time()
+
+        # 首先添加原始消息的查询结果
+        if message in embeddings:
+            original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
+            if original_results:
+                for result in original_results:
+                    result["topic"] = "原始消息"
+                all_results.extend(original_results)
+                logger.info(f"原始消息查询到{len(original_results)}条结果")
+
+        # 然后添加每个主题的查询结果
+        for topic in topics:
+            if not topic or topic not in embeddings:
+                continue
+
+            try:
+                topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
+                if topic_results:
+                    # 添加主题标记
+                    for result in topic_results:
+                        result["topic"] = topic
+                    all_results.extend(topic_results)
+                    logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
+            except Exception as e:
+                logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
+
+        logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
+
+        # 4. 去重和过滤
+        process_start_time = time.time()
+        unique_contents = set()
+        filtered_results = []
+        for result in all_results:
+            content = result["content"]
+            if content not in unique_contents:
+                unique_contents.add(content)
+                filtered_results.append(result)
+
+        # 5. 按相似度排序
+        filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
+
+        # 6. 限制总数量（最多10条）
+        filtered_results = filtered_results[:10]
+        logger.info(
+            f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
+        )
+
+        # 7. 格式化输出
+        if filtered_results:
+            format_start_time = time.time()
+            grouped_results = {}
+            for result in filtered_results:
+                topic = result["topic"]
+                if topic not in grouped_results:
+                    grouped_results[topic] = []
+                grouped_results[topic].append(result)
+
+            # 按主题组织输出
+            for topic, results in grouped_results.items():
+                related_info += f"【主题: {topic}】\n"
+                for _i, result in enumerate(results, 1):
+                    _similarity = result["similarity"]
+                    content = result["content"].strip()
+                    # 调试：为内容添加序号和相似度信息
+                    # related_info += f"{i}. [{similarity:.2f}] {content}\n"
+                    related_info += f"{content}\n"
+                related_info += "\n"
+
+            logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
+
+        logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
+        return related_info
+
+    @staticmethod
+    def get_info_from_db(
+        query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
+    ) -> Union[str, list]:
+        if not query_embedding:
+            return "" if not return_raw else []
+        # 使用余弦相似度计算
+        pipeline = [
+            {
+                "$addFields": {
+                    "dotProduct": {
+                        "$reduce": {
+                            "input": {"$range": [0, {"$size": "$embedding"}]},
+                            "initialValue": 0,
+                            "in": {
+                                "$add": [
+                                    "$$value",
+                                    {
+                                        "$multiply": [
+                                            {"$arrayElemAt": ["$embedding", "$$this"]},
+                                            {"$arrayElemAt": [query_embedding, "$$this"]},
+                                        ]
+                                    },
+                                ]
+                            },
+                        }
+                    },
+                    "magnitude1": {
+                        "$sqrt": {
+                            "$reduce": {
+                                "input": "$embedding",
+                                "initialValue": 0,
+                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
+                            }
+                        }
+                    },
+                    "magnitude2": {
+                        "$sqrt": {
+                            "$reduce": {
+                                "input": query_embedding,
+                                "initialValue": 0,
+                                "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
+                            }
+                        }
+                    },
+                }
+            },
+            {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
+            {
+                "$match": {
+                    "similarity": {"$gte": threshold}  # 只保留相似度大于等于阈值的结果
+                }
+            },
+            {"$sort": {"similarity": -1}},
+            {"$limit": limit},
+            {"$project": {"content": 1, "similarity": 1}},
+        ]
+
+        results = list(db.knowledges.aggregate(pipeline))
+        logger.debug(f"知识库查询结果数量: {len(results)}")
+
+        if not results:
+            return "" if not return_raw else []
+
+        if return_raw:
+            return results
+        else:
+            # 返回所有找到的内容，用换行分隔
+            return "\n".join(str(result["content"]) for result in results)
+
+
+init_prompt()
+prompt_builder = PromptBuilder()

src/plugins/chat_module/reasoning_chat/reasoning_chat.py

@@ -156,17 +156,17 @@ class ReasoningChat:
         # 消息加入缓冲池
         await message_buffer.start_caching_messages(message)
 
-        # logger.info("使用推理聊天模式")
-
         # 创建聊天流
         chat = await chat_manager.get_or_create_stream(
             platform=messageinfo.platform,
             user_info=userinfo,
             group_info=groupinfo,
         )
+
         message.update_chat_stream(chat)
 
         await message.process()
+        logger.trace(f"消息处理成功: {message.processed_plain_text}")
 
         # 过滤词/正则表达式过滤
         if self._check_ban_words(message.processed_plain_text, chat, userinfo) or self._check_ban_regex(
@@ -174,27 +174,13 @@ class ReasoningChat:
         ):
             return
 
-        await self.storage.store_message(message, chat)
-
-        # 记忆激活
-        with Timer("记忆激活", timing_results):
-            interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
-                message.processed_plain_text, fast_retrieval=True
-            )
-
         # 查询缓冲器结果，会整合前面跳过的消息，改变processed_plain_text
         buffer_result = await message_buffer.query_buffer_result(message)
 
-        # 处理提及
-        is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
-
-        # 意愿管理器：设置当前message信息
-        willing_manager.setup(message, chat, is_mentioned, interested_rate)
-
         # 处理缓冲器结果
         if not buffer_result:
-            await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
-            willing_manager.delete(message.message_info.message_id)
+            # await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
+            # willing_manager.delete(message.message_info.message_id)
             f_type = "seglist"
             if message.message_segment.type != "seglist":
                 f_type = message.message_segment.type
@@ -213,6 +199,27 @@ class ReasoningChat:
                 logger.info("触发缓冲，已炸飞消息列")
             return
 
+        try:
+            await self.storage.store_message(message, chat)
+            logger.trace(f"存储成功 (通过缓冲后): {message.processed_plain_text}")
+        except Exception as e:
+            logger.error(f"存储消息失败: {e}")
+            logger.error(traceback.format_exc())
+            # 存储失败可能仍需考虑是否继续，暂时返回
+            return
+
+        is_mentioned, reply_probability = is_mentioned_bot_in_message(message)
+        # 记忆激活
+        with Timer("记忆激活", timing_results):
+            interested_rate = await HippocampusManager.get_instance().get_activate_from_text(
+                message.processed_plain_text, fast_retrieval=True
+            )
+
+        # 处理提及
+
+        # 意愿管理器：设置当前message信息
+        willing_manager.setup(message, chat, is_mentioned, interested_rate)
+
         # 获取回复概率
         is_willing = False
         if reply_probability != 1:

src/plugins/chat_module/reasoning_chat/reasoning_generator.py

@@ -44,7 +44,7 @@ class ResponseGenerator:
     async def generate_response(self, message: MessageThinking, thinking_id: str) -> Optional[Union[str, List[str]]]:
         """根据当前模型类型选择对应的生成函数"""
         # 从global_config中获取模型概率值并选择模型
-        if random.random() < global_config.MODEL_R1_PROBABILITY:
+        if random.random() < global_config.model_reasoning_probability:
             self.current_model_type = "深深地"
             current_model = self.model_reasoning
         else:

src/plugins/memory_system/Hippocampus.py

@@ -1942,11 +1942,7 @@ class HippocampusManager:
         return response
 
     async def get_memory_from_topic(
-        self,
-        valid_keywords: list[str],
-        max_memory_num: int = 3,
-        max_memory_length: int = 2,
-        max_depth: int = 3
+        self, valid_keywords: list[str], max_memory_num: int = 3, max_memory_length: int = 2, max_depth: int = 3
     ) -> list:
         """从文本中获取相关记忆的公共接口"""
         if not self._initialized: