Merge branch 'dev' of https://github.com/SnowindMe/MaiBot into dev

README.md

@@ -14,7 +14,7 @@
 
 <p align="center">
   <a href="https://github.com/MaiM-with-u/MaiBot/">
-    <img src="depends-data/maimai.png" alt="Logo" width="200">
+    <img src="depends-data/maimai.png" alt="Logo" style="max-width: 200px">
   </a>
   <br />
   <a href="https://space.bilibili.com/1344099355">
@@ -34,7 +34,6 @@
     ·
     <a href="https://github.com/MaiM-with-u/MaiBot/issues">提出新特性</a>
   </p>
-  
 </p>
 
 ## 新版0.6.x部署前先阅读：https://docs.mai-mai.org/manual/usage/mmc_q_a
@@ -53,7 +52,7 @@
 
 <div align="center">
 <a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
-    <img src="depends-data/video.png" width="200" alt="麦麦演示视频">
+    <img src="depends-data/video.png" style="max-width: 200px" alt="麦麦演示视频">
     <br>
     👆 点击观看麦麦演示视频 👆
 </a>
@@ -99,7 +98,7 @@
 
 
 <div align="left">
-<h2>📚 文档</h2>
+<h2>📚 文档 </h2>
 </div>
 
 ### (部分内容可能过时，请注意版本对应)
@@ -186,7 +185,7 @@ MaiCore是一个开源项目，我们非常欢迎你的参与。你的贡献，
 感谢各位大佬！
 
 <a href="https://github.com/MaiM-with-u/MaiBot/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=MaiM-with-u/MaiBot" />
+  <img alt="contributors" src="https://contrib.rocks/image?repo=MaiM-with-u/MaiBot" />
 </a>
 
 **也感谢每一位给麦麦发展提出宝贵意见与建议的用户，感谢陪伴麦麦走到现在的你们**

src/MaiBot0.6roadmap.md

@@ -0,0 +1,16 @@
+MaiCore/MaiBot 0.6路线图 draft
+
+0.6.3：解决0.6.x版本核心问题，改进功能
+主要功能加入
+LPMM全面替代旧知识库
+采用新的HFC回复模式，取代旧心流
+合并推理模式和心流模式，根据麦麦自己决策回复模式
+提供新的表情包系统
+
+0.6.4：提升用户体验，交互优化
+加入webui
+提供麦麦 API
+修复prompt建构的各种问题
+修复各种bug
+调整代码文件结构，重构部分落后设计
+

src/api/__init__.py

@@ -0,0 +1,8 @@
+from fastapi import FastAPI
+from strawberry.fastapi import GraphQLRouter
+
+app = FastAPI()
+
+graphql_router = GraphQLRouter(schema=None, path="/")  # Replace `None` with your actual schema
+
+app.include_router(graphql_router, prefix="/graphql", tags=["GraphQL"])

src/api/config_api.py

@@ -0,0 +1,155 @@
+from typing import Dict, List, Optional
+import strawberry
+
+# from packaging.version import Version, InvalidVersion
+# from packaging.specifiers import SpecifierSet, InvalidSpecifier
+# from ..config.config import global_config
+# import os
+from packaging.version import Version
+
+
+@strawberry.type
+class BotConfig:
+    """机器人配置类"""
+
+    INNER_VERSION: Version
+    MAI_VERSION: str  # 硬编码的版本信息
+
+    # bot
+    BOT_QQ: Optional[int]
+    BOT_NICKNAME: Optional[str]
+    BOT_ALIAS_NAMES: List[str]  # 别名，可以通过这个叫它
+
+    # group
+    talk_allowed_groups: set
+    talk_frequency_down_groups: set
+    ban_user_id: set
+
+    # personality
+    personality_core: str  # 建议20字以内，谁再写3000字小作文敲谁脑袋
+    personality_sides: List[str]
+    # identity
+    identity_detail: List[str]
+    height: int  # 身高 单位厘米
+    weight: int  # 体重 单位千克
+    age: int  # 年龄 单位岁
+    gender: str  # 性别
+    appearance: str  # 外貌特征
+
+    # schedule
+    ENABLE_SCHEDULE_GEN: bool  # 是否启用日程生成
+    PROMPT_SCHEDULE_GEN: str
+    SCHEDULE_DOING_UPDATE_INTERVAL: int  # 日程表更新间隔 单位秒
+    SCHEDULE_TEMPERATURE: float  # 日程表温度，建议0.5-1.0
+    TIME_ZONE: str  # 时区
+
+    # message
+    MAX_CONTEXT_SIZE: int  # 上下文最大消息数
+    emoji_chance: float  # 发送表情包的基础概率
+    thinking_timeout: int  # 思考时间
+    max_response_length: int  # 最大回复长度
+    message_buffer: bool  # 消息缓冲器
+
+    ban_words: set
+    ban_msgs_regex: set
+    # heartflow
+    # enable_heartflow: bool = False  # 是否启用心流
+    sub_heart_flow_update_interval: int  # 子心流更新频率，间隔 单位秒
+    sub_heart_flow_freeze_time: int  # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
+    sub_heart_flow_stop_time: int  # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
+    heart_flow_update_interval: int  # 心流更新频率，间隔 单位秒
+    observation_context_size: int  # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
+    compressed_length: int  # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
+    compress_length_limit: int  # 最多压缩份数，超过该数值的压缩上下文会被删除
+
+    # willing
+    willing_mode: str  # 意愿模式
+    response_willing_amplifier: float  # 回复意愿放大系数
+    response_interested_rate_amplifier: float  # 回复兴趣度放大系数
+    down_frequency_rate: float  # 降低回复频率的群组回复意愿降低系数
+    emoji_response_penalty: float  # 表情包回复惩罚
+    mentioned_bot_inevitable_reply: bool  # 提及 bot 必然回复
+    at_bot_inevitable_reply: bool  # @bot 必然回复
+
+    # response
+    response_mode: str  # 回复策略
+    MODEL_R1_PROBABILITY: float  # R1模型概率
+    MODEL_V3_PROBABILITY: float  # V3模型概率
+    # MODEL_R1_DISTILL_PROBABILITY: float  # R1蒸馏模型概率
+
+    # emoji
+    max_emoji_num: int  # 表情包最大数量
+    max_reach_deletion: bool  # 开启则在达到最大数量时删除表情包，关闭则不会继续收集表情包
+    EMOJI_CHECK_INTERVAL: int  # 表情包检查间隔（分钟）
+    EMOJI_REGISTER_INTERVAL: int  # 表情包注册间隔（分钟）
+    EMOJI_SAVE: bool  # 偷表情包
+    EMOJI_CHECK: bool  # 是否开启过滤
+    EMOJI_CHECK_PROMPT: str  # 表情包过滤要求
+
+    # memory
+    build_memory_interval: int  # 记忆构建间隔（秒）
+    memory_build_distribution: list  # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
+    build_memory_sample_num: int  # 记忆构建采样数量
+    build_memory_sample_length: int  # 记忆构建采样长度
+    memory_compress_rate: float  # 记忆压缩率
+
+    forget_memory_interval: int  # 记忆遗忘间隔（秒）
+    memory_forget_time: int  # 记忆遗忘时间（小时）
+    memory_forget_percentage: float  # 记忆遗忘比例
+
+    memory_ban_words: list  # 添加新的配置项默认值
+
+    # mood
+    mood_update_interval: float  # 情绪更新间隔 单位秒
+    mood_decay_rate: float  # 情绪衰减率
+    mood_intensity_factor: float  # 情绪强度因子
+
+    # keywords
+    keywords_reaction_rules: list  # 关键词回复规则
+
+    # chinese_typo
+    chinese_typo_enable: bool  # 是否启用中文错别字生成器
+    chinese_typo_error_rate: float  # 单字替换概率
+    chinese_typo_min_freq: int  # 最小字频阈值
+    chinese_typo_tone_error_rate: float  # 声调错误概率
+    chinese_typo_word_replace_rate: float  # 整词替换概率
+
+    # response_splitter
+    enable_response_splitter: bool  # 是否启用回复分割器
+    response_max_length: int  # 回复允许的最大长度
+    response_max_sentence_num: int  # 回复允许的最大句子数
+
+    # remote
+    remote_enable: bool  # 是否启用远程控制
+
+    # experimental
+    enable_friend_chat: bool  # 是否启用好友聊天
+    # enable_think_flow: bool  # 是否启用思考流程
+    enable_pfc_chatting: bool  # 是否启用PFC聊天
+
+    # 模型配置
+    llm_reasoning: Dict[str, str]  # LLM推理
+    # llm_reasoning_minor: Dict[str, str]
+    llm_normal: Dict[str, str]  # LLM普通
+    llm_topic_judge: Dict[str, str]  # LLM话题判断
+    llm_summary_by_topic: Dict[str, str]  # LLM话题总结
+    llm_emotion_judge: Dict[str, str]  # LLM情感判断
+    embedding: Dict[str, str]  # 嵌入
+    vlm: Dict[str, str]  # VLM
+    moderation: Dict[str, str]  # 审核
+
+    # 实验性
+    llm_observation: Dict[str, str]  # LLM观察
+    llm_sub_heartflow: Dict[str, str]  # LLM子心流
+    llm_heartflow: Dict[str, str]  # LLM心流
+
+    api_urls: Dict[str, str]  # API URLs
+
+
+@strawberry.type
+class EnvConfig:
+    pass
+
+    @strawberry.field
+    def get_env(self) -> str:
+        return "env"

src/config/config.py

@@ -28,7 +28,7 @@ logger = get_module_logger("config", config=config_config)
 # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
 is_test = True
 mai_version_main = "0.6.3"
-mai_version_fix = "snapshot-2"
+mai_version_fix = "snapshot-3"
 
 if mai_version_fix:
     if is_test:
@@ -186,12 +186,18 @@ class BotConfig:
     ban_words = set()
     ban_msgs_regex = set()
 
-    # heartflow
+    # [heartflow] # 启用启用heart_flowC(心流聊天)模式时生效, 需要填写token消耗量巨大的相关模型
-    # enable_heartflow: bool = False  # 是否启用心流
+    # 启用后麦麦会自主选择进入heart_flowC模式(持续一段时间), 进行长时间高质量的聊天
-    sub_heart_flow_update_interval: int = 60  # 子心流更新频率，间隔 单位秒
+    enable_heart_flowC: bool = True  # 是否启用heart_flowC(心流聊天, HFC)模式
-    sub_heart_flow_freeze_time: int = 120  # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
+    reply_trigger_threshold: float = 3.0  # 心流聊天触发阈值，越低越容易触发
+    probability_decay_factor_per_second: float = 0.2  # 概率衰减因子，越大衰减越快
+    default_decay_rate_per_second: float = 0.98  # 默认衰减率，越大衰减越慢
+    initial_duration: int = 60  # 初始持续时间，越大心流聊天持续的时间越长
+
+    # sub_heart_flow_update_interval: int = 60  # 子心流更新频率，间隔 单位秒
+    # sub_heart_flow_freeze_time: int = 120  # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
     sub_heart_flow_stop_time: int = 600  # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
-    heart_flow_update_interval: int = 300  # 心流更新频率，间隔 单位秒
+    # heart_flow_update_interval: int = 300  # 心流更新频率，间隔 单位秒
     observation_context_size: int = 20  # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
     compressed_length: int = 5  # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
     compress_length_limit: int = 5  # 最多压缩份数，超过该数值的压缩上下文会被删除
@@ -207,8 +213,8 @@ class BotConfig:
 
     # response
     response_mode: str = "heart_flow"  # 回复策略
-    MODEL_R1_PROBABILITY: float = 0.8  # R1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_V3_PROBABILITY: float = 0.1  # V3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
@@ -401,29 +407,34 @@ class BotConfig:
 
         def response(parent: dict):
             response_config = parent["response"]
-            config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
+            config.model_reasoning_probability = response_config.get(
-            config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
+                "model_reasoning_probability", config.model_reasoning_probability
-            # config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
+            )
-            #     "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
+            config.model_normal_probability = response_config.get(
-            # )
+                "model_normal_probability", config.model_normal_probability
-            config.max_response_length = response_config.get("max_response_length", config.max_response_length)
+            )
-            if config.INNER_VERSION in SpecifierSet(">=1.0.4"):
+
-                config.response_mode = response_config.get("response_mode", config.response_mode)
+            # 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"):
+                config.enable_heart_flowC = response_config.get("enable_heart_flowC", config.enable_heart_flowC)
 
         def heartflow(parent: dict):
             heartflow_config = parent["heartflow"]
-            config.sub_heart_flow_update_interval = heartflow_config.get(
+            # 加载新增的 heartflowC 参数
-                "sub_heart_flow_update_interval", config.sub_heart_flow_update_interval
+
-            )
+            # 加载原有的 heartflow 参数
-            config.sub_heart_flow_freeze_time = heartflow_config.get(
+            # config.sub_heart_flow_update_interval = heartflow_config.get(
-                "sub_heart_flow_freeze_time", config.sub_heart_flow_freeze_time
+            #     "sub_heart_flow_update_interval", config.sub_heart_flow_update_interval
-            )
+            # )
+            # config.sub_heart_flow_freeze_time = heartflow_config.get(
+            #     "sub_heart_flow_freeze_time", config.sub_heart_flow_freeze_time
+            # )
             config.sub_heart_flow_stop_time = heartflow_config.get(
                 "sub_heart_flow_stop_time", config.sub_heart_flow_stop_time
             )
-            config.heart_flow_update_interval = heartflow_config.get(
+            # config.heart_flow_update_interval = heartflow_config.get(
-                "heart_flow_update_interval", config.heart_flow_update_interval
+            #     "heart_flow_update_interval", config.heart_flow_update_interval
-            )
+            # )
             if config.INNER_VERSION in SpecifierSet(">=1.3.0"):
                 config.observation_context_size = heartflow_config.get(
                     "observation_context_size", config.observation_context_size
@@ -432,6 +443,17 @@ class BotConfig:
                 config.compress_length_limit = heartflow_config.get(
                     "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.initial_duration = heartflow_config.get("initial_duration", config.initial_duration)
 
         def willing(parent: dict):
             willing_config = parent["willing"]

src/heart_flow/README.md

@@ -79,4 +79,16 @@ await heartflow.heartflow_start_working()
 
 1. 子心流会在长时间不活跃后自动清理
 2. 需要合理配置更新间隔以平衡性能和响应速度
-3. 观察系统会限制消息处理数量以避免过载 
+3. 观察系统会限制消息处理数量以避免过载 
+
+
+更新：
+把聊天控制移动到心流下吧
+首先心流要根据日程以及当前状况判定总体状态MaiStateInfo
+
+然后根据每个子心流的运行情况，给子心流分配聊天资源（ChatStateInfo：ABSENT CHAT 或者 FOCUS）
+
+子心流负责根据状态进行执行
+
+1.将interest.py进行拆分，class InterestChatting 将会在 sub_heartflow中声明，每个sub_heartflow都会所属一个InterestChatting
+class InterestManager 将会在heartflow中声明，成为heartflow的一个组件，伴随heartflow产生

src/heart_flow/Update.md

@@ -0,0 +1,11 @@
+
+更新：
+把聊天控制移动到心流下吧
+首先心流要根据日程以及当前状况判定总体状态MaiStateInfo
+
+然后根据每个子心流的运行情况，给子心流分配聊天资源（ChatStateInfo：ABSENT CHAT 或者 FOCUS）
+
+子心流负责根据状态进行执行
+
+1.将interest.py进行拆分，class InterestChatting 将会在 sub_heartflow中声明，每个sub_heartflow都会所属一个InterestChatting
+class InterestManager 将会在heartflow中声明，成为heartflow的一个组件，伴随heartflow产生

src/heart_flow/heartflow.py

@@ -1,16 +1,20 @@
-from .sub_heartflow import SubHeartflow
+from .sub_heartflow import SubHeartflow, ChattingObservation
-from .observation import ChattingObservation
 from src.plugins.moods.moods import MoodManager
 from src.plugins.models.utils_model import LLMRequest
 from src.config.config import global_config
 from src.plugins.schedule.schedule_generator import bot_schedule
 from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
 import asyncio
-from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG  # noqa: E402
+from src.common.logger import get_module_logger, LogConfig, HEARTFLOW_STYLE_CONFIG  # 修改
 from src.individuality.individuality import Individuality
 import time
 import random
-from typing import Dict, Any
+from typing import Dict, Any, Optional
+import traceback
+import enum
+import os  # 新增
+import json  # 新增
+from src.plugins.chat.chat_stream import chat_manager  # 新增
 
 heartflow_config = LogConfig(
     # 使用海马体专用样式
@@ -41,76 +45,269 @@ def init_prompt():
     Prompt(prompt, "mind_summary_prompt")
 
 
-class CurrentState:
+# --- 新增：从 interest.py 移动过来的常量 ---
+LOG_DIRECTORY = "logs/interest"
+HISTORY_LOG_FILENAME = "interest_history.log"
+CLEANUP_INTERVAL_SECONDS = 1200  # 清理任务运行间隔 (例如：20分钟) - 保持与 interest.py 一致
+INACTIVE_THRESHOLD_SECONDS = 1200  # 不活跃时间阈值 (例如：20分钟) - 保持与 interest.py 一致
+LOG_INTERVAL_SECONDS = 3  # 日志记录间隔 (例如：3秒) - 保持与 interest.py 一致
+# --- 结束新增常量 ---
+
+
+# 新增 ChatStatus 枚举
+class MaiState(enum.Enum):
+    """
+    聊天状态:
+    OFFLINE: 不在线：回复概率极低，不会进行任何聊天
+    PEEKING: 看一眼手机：回复概率较低，会进行一些普通聊天
+    NORMAL_CHAT: 正常聊天：回复概率较高，会进行一些普通聊天和少量的专注聊天
+    FOCUSED_CHAT: 专注聊天：回复概率极高，会进行专注聊天和少量的普通聊天
+    """
+
+    OFFLINE = "不在线"
+    PEEKING = "看一眼手机"
+    NORMAL_CHAT = "正常聊天"
+    FOCUSED_CHAT = "专注聊天"
+
+    def get_normal_chat_max_num(self):
+        if self == MaiState.OFFLINE:
+            return 0
+        elif self == MaiState.PEEKING:
+            return 1
+        elif self == MaiState.NORMAL_CHAT:
+            return 3
+        elif self == MaiState.FOCUSED_CHAT:
+            return 2
+
+    def get_focused_chat_max_num(self):
+        if self == MaiState.OFFLINE:
+            return 0
+        elif self == MaiState.PEEKING:
+            return 0
+        elif self == MaiState.NORMAL_CHAT:
+            return 1
+        elif self == MaiState.FOCUSED_CHAT:
+            return 2
+
+
+class MaiStateInfo:
     def __init__(self):
         self.current_state_info = ""
 
+        # 使用枚举类型初始化状态，默认为不在线
+        self.mai_status: MaiState = MaiState.OFFLINE
+
+        self.normal_chatting = []
+        self.focused_chatting = []
+
         self.mood_manager = MoodManager()
         self.mood = self.mood_manager.get_prompt()
 
-        self.attendance_factor = 0
-        self.engagement_factor = 0
-
     def update_current_state_info(self):
         self.current_state_info = self.mood_manager.get_current_mood()
 
+    # 新增更新聊天状态的方法
+    def update_mai_status(self, new_status: MaiState):
+        """更新聊天状态"""
+        if isinstance(new_status, MaiState):
+            self.mai_status = new_status
+            logger.info(f"麦麦状态更新为: {self.mai_status.value}")
+        else:
+            logger.warning(f"尝试设置无效的麦麦状态: {new_status}")
+
 
 class Heartflow:
     def __init__(self):
         self.current_mind = "你什么也没想"
         self.past_mind = []
-        self.current_state: CurrentState = CurrentState()
+        self.current_state: MaiStateInfo = MaiStateInfo()
         self.llm_model = LLMRequest(
             model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
         )
 
         self._subheartflows: Dict[Any, SubHeartflow] = {}
 
-    async def _cleanup_inactive_subheartflows(self):
+        # --- 新增：日志和清理相关属性 (从 InterestManager 移动) ---
-        """定期清理不活跃的子心流"""
+        self._history_log_file_path = os.path.join(LOG_DIRECTORY, HISTORY_LOG_FILENAME)
+        self._ensure_log_directory()  # 初始化时确保目录存在
+        self._cleanup_task: Optional[asyncio.Task] = None
+        self._logging_task: Optional[asyncio.Task] = None
+        # 注意：衰减任务 (_decay_task) 不再需要，衰减在 SubHeartflow 的 InterestChatting 内部处理
+        # --- 结束新增属性 ---
+
+    def _ensure_log_directory(self):  # 新增方法 (从 InterestManager 移动)
+        """确保日志目录存在"""
+        # 移除 try-except 块，根据用户要求
+        os.makedirs(LOG_DIRECTORY, exist_ok=True)
+        logger.info(f"Log directory '{LOG_DIRECTORY}' ensured.")
+        # except OSError as e:
+        #     logger.error(f"Error creating log directory '{LOG_DIRECTORY}': {e}")
+
+    async def _periodic_cleanup_task(
+        self, interval_seconds: int, max_age_seconds: int
+    ):  # 新增方法 (从 InterestManager 移动和修改)
+        """后台清理任务的异步函数"""
         while True:
-            current_time = time.time()
+            await asyncio.sleep(interval_seconds)
-            inactive_subheartflows = []
+            logger.info(f"[Heartflow] 运行定期清理 (间隔: {interval_seconds}秒)...")
+            self.cleanup_inactive_subheartflows(max_age_seconds=max_age_seconds)  # 调用 Heartflow 自己的清理方法
 
-            # 检查所有子心流
+    async def _periodic_log_task(self, interval_seconds: int):  # 新增方法 (从 InterestManager 移动和修改)
-            for subheartflow_id, subheartflow in self._subheartflows.items():
+        """后台日志记录任务的异步函数 (记录所有子心流的兴趣历史数据)"""
-                if (
+        while True:
-                    current_time - subheartflow.last_active_time > global_config.sub_heart_flow_stop_time
+            await asyncio.sleep(interval_seconds)
-                ):  # 10分钟 = 600秒
+            try:
-                    inactive_subheartflows.append(subheartflow_id)
+                current_timestamp = time.time()
-                    logger.info(f"发现不活跃的子心流: {subheartflow_id}")
+                all_interest_states = self.get_all_interest_states()  # 获取所有子心流的兴趣状态
 
-            # 清理不活跃的子心流
+                # 以追加模式打开历史日志文件
-            for subheartflow_id in inactive_subheartflows:
+                # 移除 try-except IO 块，根据用户要求
-                del self._subheartflows[subheartflow_id]
+                with open(self._history_log_file_path, "a", encoding="utf-8") as f:
-                logger.info(f"已清理不活跃的子心流: {subheartflow_id}")
+                    count = 0
+                    # 创建 items 快照以安全迭代
+                    items_snapshot = list(all_interest_states.items())
+                    for stream_id, state in items_snapshot:
+                        # 从 chat_manager 获取 group_name
+                        group_name = stream_id  # 默认值
+                        try:
+                            chat_stream = chat_manager.get_stream(stream_id)
+                            if chat_stream and chat_stream.group_info:
+                                group_name = chat_stream.group_info.group_name
+                            elif chat_stream and not chat_stream.group_info:  # 处理私聊
+                                group_name = (
+                                    f"私聊_{chat_stream.user_info.user_nickname}"
+                                    if chat_stream.user_info
+                                    else stream_id
+                                )
+                        except Exception:
+                            # 不记录警告，避免刷屏，使用默认 stream_id 即可
+                            # logger.warning(f"Could not get group name for stream_id {stream_id}: {e}")
+                            pass  # 静默处理
 
-            await asyncio.sleep(30)  # 每分钟检查一次
+                        log_entry = {
+                            "timestamp": round(current_timestamp, 2),
+                            "stream_id": stream_id,
+                            "interest_level": state.get("interest_level", 0.0),  # 使用 get 获取，提供默认值
+                            "group_name": group_name,
+                            "reply_probability": state.get("current_reply_probability", 0.0),  # 使用 get 获取
+                            "is_above_threshold": state.get("is_above_threshold", False),  # 使用 get 获取
+                        }
+                        # 将每个条目作为单独的 JSON 行写入
+                        f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")
+                        count += 1
+                # logger.debug(f"[Heartflow] Successfully appended {count} interest history entries to {self._history_log_file_path}")
 
-    async def _sub_heartflow_update(self):
+            # except IOError as e:
+            #     logger.error(f"[Heartflow] Error writing interest history log to {self._history_log_file_path}: {e}")
+            except Exception as e:  # 保留对其他异常的捕获
+                logger.error(f"[Heartflow] Unexpected error during periodic history logging: {e}")
+                logger.error(traceback.format_exc())  # 记录 traceback
+
+    def get_all_interest_states(self) -> Dict[str, Dict]:  # 新增方法
+        """获取所有活跃子心流的当前兴趣状态"""
+        states = {}
+        # 创建副本以避免在迭代时修改字典
+        items_snapshot = list(self._subheartflows.items())
+        for stream_id, subheartflow in items_snapshot:
+            try:
+                # 从 SubHeartflow 获取其 InterestChatting 的状态
+                states[stream_id] = subheartflow.get_interest_state()
+            except Exception as e:
+                logger.warning(f"[Heartflow] Error getting interest state for subheartflow {stream_id}: {e}")
+        return states
+
+    def cleanup_inactive_subheartflows(self, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):  # 修改此方法以使用兴趣时间
+        """
+        清理长时间不活跃的子心流记录 (基于兴趣交互时间)
+        max_age_seconds: 超过此时间未通过兴趣系统交互的将被清理
+        """
+        current_time = time.time()
+        keys_to_remove = []
+        _initial_count = len(self._subheartflows)
+
+        # 创建副本以避免在迭代时修改字典
+        items_snapshot = list(self._subheartflows.items())
+
+        for subheartflow_id, subheartflow in items_snapshot:
+            should_remove = False
+            reason = ""
+            # 检查 InterestChatting 的最后交互时间
+            last_interaction = subheartflow.interest_chatting.last_interaction_time
+            if max_age_seconds is not None and (current_time - last_interaction) > max_age_seconds:
+                should_remove = True
+                reason = (
+                    f"interest inactive time ({current_time - last_interaction:.0f}s) > max age ({max_age_seconds}s)"
+                )
+
+            if should_remove:
+                keys_to_remove.append(subheartflow_id)
+                stream_name = chat_manager.get_stream_name(subheartflow_id) or subheartflow_id  # 获取流名称
+                logger.debug(f"[Heartflow] Marking stream {stream_name} for removal. Reason: {reason}")
+
+                # 标记子心流让其后台任务停止 (如果其后台任务还在运行)
+                subheartflow.should_stop = True
+
+        if keys_to_remove:
+            logger.info(f"[Heartflow] 清理识别到 {len(keys_to_remove)} 个不活跃的流。")
+            for key in keys_to_remove:
+                if key in self._subheartflows:
+                    # 尝试取消子心流的后台任务
+                    task_to_cancel = self._subheartflows[key].task
+                    if task_to_cancel and not task_to_cancel.done():
+                        task_to_cancel.cancel()
+                        logger.debug(f"[Heartflow] Cancelled background task for subheartflow {key}")
+                    # 从字典中删除
+                    del self._subheartflows[key]
+                    stream_name = chat_manager.get_stream_name(key) or key  # 获取流名称
+                    logger.debug(f"[Heartflow] 移除了流: {stream_name}")
+            final_count = len(self._subheartflows)  # 直接获取当前长度
+            logger.info(f"[Heartflow] 清理完成。移除了 {len(keys_to_remove)} 个流。当前数量: {final_count}")
+        else:
+            # logger.info(f"[Heartflow] 清理完成。没有流符合移除条件。当前数量: {initial_count}") # 减少日志噪音
+            pass
+
+    async def _sub_heartflow_update(self):  # 这个任务目前作用不大，可以考虑移除或赋予新职责
         while True:
             # 检查是否存在子心流
             if not self._subheartflows:
                 # logger.info("当前没有子心流，等待新的子心流创建...")
-                await asyncio.sleep(30)  # 每分钟检查一次是否有新的子心流
+                await asyncio.sleep(30)  # 短暂休眠
                 continue
 
-            await self.do_a_thinking()
+            # 当前无实际操作，只是等待
-            await asyncio.sleep(global_config.heart_flow_update_interval)  # 5分钟思考一次
+            await asyncio.sleep(300)
 
     async def heartflow_start_working(self):
-        # 启动清理任务
+        # 启动清理任务 (使用新的 periodic_cleanup_task)
-        asyncio.create_task(self._cleanup_inactive_subheartflows())
+        if self._cleanup_task is None or self._cleanup_task.done():
+            self._cleanup_task = asyncio.create_task(
+                self._periodic_cleanup_task(
+                    interval_seconds=CLEANUP_INTERVAL_SECONDS,
+                    max_age_seconds=INACTIVE_THRESHOLD_SECONDS,
+                )
+            )
+            logger.info(
+                f"[Heartflow] 已创建定期清理任务。间隔: {CLEANUP_INTERVAL_SECONDS}s, 不活跃阈值: {INACTIVE_THRESHOLD_SECONDS}s"
+            )
+        else:
+            logger.warning("[Heartflow] 跳过创建清理任务: 任务已在运行或存在。")
 
-        # 启动子心流更新任务
+        # 启动日志任务 (使用新的 periodic_log_task)
-        asyncio.create_task(self._sub_heartflow_update())
+        if self._logging_task is None or self._logging_task.done():
+            self._logging_task = asyncio.create_task(self._periodic_log_task(interval_seconds=LOG_INTERVAL_SECONDS))
+            logger.info(f"[Heartflow] 已创建定期日志任务。间隔: {LOG_INTERVAL_SECONDS}s")
+        else:
+            logger.warning("[Heartflow] 跳过创建日志任务: 任务已在运行或存在。")
+
+        # (可选) 启动旧的子心流更新任务，如果它还有用的话
+        # asyncio.create_task(self._sub_heartflow_update())
 
     @staticmethod
     async def _update_current_state():
         print("TODO")
 
     async def do_a_thinking(self):
-        logger.debug("麦麦大脑袋转起来了")
+        # logger.debug("麦麦大脑袋转起来了")
         self.current_state.update_current_state_info()
 
         # 开始构建prompt
@@ -122,127 +319,152 @@ class Heartflow:
         prompt_personality += personality_core
 
         personality_sides = individuality.personality.personality_sides
-        random.shuffle(personality_sides)
+        # 检查列表是否为空
-        prompt_personality += f",{personality_sides[0]}"
+        if personality_sides:
+            random.shuffle(personality_sides)
+            prompt_personality += f",{personality_sides[0]}"
 
         identity_detail = individuality.identity.identity_detail
-        random.shuffle(identity_detail)
+        # 检查列表是否为空
-        prompt_personality += f",{identity_detail[0]}"
+        if identity_detail:
+            random.shuffle(identity_detail)
+            prompt_personality += f",{identity_detail[0]}"
 
         personality_info = prompt_personality
 
         current_thinking_info = self.current_mind
         mood_info = self.current_state.mood
-        related_memory_info = "memory"
+        related_memory_info = "memory"  # TODO: 替换为实际的记忆获取逻辑
         try:
-            sub_flows_info = await self.get_all_subheartflows_minds()
+            sub_flows_info = await self.get_all_subheartflows_minds_summary()  # 修改为调用汇总方法
         except Exception as e:
-            logger.error(f"获取子心流的想法失败: {e}")
+            logger.error(f"[Heartflow] 获取子心流想法汇总失败: {e}")
-            return
+            logger.error(traceback.format_exc())
+            sub_flows_info = "(获取子心流想法时出错)"  # 提供默认值
 
         schedule_info = bot_schedule.get_current_num_task(num=4, time_info=True)
 
-        # prompt = ""
-        # prompt += f"你刚刚在做的事情是：{schedule_info}\n"
-        # prompt += f"{personality_info}\n"
-        # prompt += f"你想起来{related_memory_info}。"
-        # prompt += f"刚刚你的主要想法是{current_thinking_info}。"
-        # prompt += f"你还有一些小想法，因为你在参加不同的群聊天，这是你正在做的事情：{sub_flows_info}\n"
-        # prompt += f"你现在{mood_info}。"
-        # prompt += "现在你接下去继续思考，产生新的想法，但是要基于原有的主要想法，不要分点输出，"
-        # prompt += "输出连贯的内心独白，不要太长，但是记得结合上述的消息，关注新内容:"
         prompt = (await global_prompt_manager.get_prompt_async("thinking_prompt")).format(
-            schedule_info, personality_info, related_memory_info, current_thinking_info, sub_flows_info, mood_info
+            schedule_info=schedule_info,  # 使用关键字参数确保正确格式化
+            personality_info=personality_info,
+            related_memory_info=related_memory_info,
+            current_thinking_info=current_thinking_info,
+            sub_flows_info=sub_flows_info,
+            mood_info=mood_info,
         )
 
         try:
             response, reasoning_content = await self.llm_model.generate_response_async(prompt)
+            if not response:
+                logger.warning("[Heartflow] 内心独白 LLM 返回空结果。")
+                response = "(暂时没什么想法...)"  # 提供默认想法
+
+            self.update_current_mind(response)  # 更新主心流想法
+            logger.info(f"麦麦的总体脑内状态：{self.current_mind}")
+
+            # 更新所有子心流的主心流信息
+            items_snapshot = list(self._subheartflows.items())  # 创建快照
+            for _, subheartflow in items_snapshot:
+                subheartflow.main_heartflow_info = response
+
         except Exception as e:
-            logger.error(f"内心独白获取失败: {e}")
+            logger.error(f"[Heartflow] 内心独白获取失败: {e}")
-            return
+            logger.error(traceback.format_exc())
-        self.update_current_mind(response)
+            # 此处不返回，允许程序继续执行，但主心流想法未更新
-
-        self.current_mind = response
-        logger.info(f"麦麦的总体脑内状态：{self.current_mind}")
-        # logger.info("麦麦想了想，当前活动:")
-        # await bot_schedule.move_doing(self.current_mind)
-
-        for _, subheartflow in self._subheartflows.items():
-            subheartflow.main_heartflow_info = response
 
     def update_current_mind(self, response):
         self.past_mind.append(self.current_mind)
         self.current_mind = response
 
-    async def get_all_subheartflows_minds(self):
+    async def get_all_subheartflows_minds_summary(self):  # 重命名并修改
-        sub_minds = ""
+        """获取所有子心流的当前想法，并进行汇总"""
-        for _, subheartflow in self._subheartflows.items():
+        sub_minds_list = []
-            sub_minds += subheartflow.current_mind
+        # 创建快照
+        items_snapshot = list(self._subheartflows.items())
+        for _, subheartflow in items_snapshot:
+            sub_minds_list.append(subheartflow.current_mind)
 
-        return await self.minds_summary(sub_minds)
+        if not sub_minds_list:
+            return "(当前没有活跃的子心流想法)"
+
+        minds_str = "\n".join([f"- {mind}" for mind in sub_minds_list])  # 格式化为列表
+
+        # 调用 LLM 进行汇总
+        return await self.minds_summary(minds_str)
 
     async def minds_summary(self, minds_str):
+        """使用 LLM 汇总子心流的想法字符串"""
         # 开始构建prompt
         prompt_personality = "你"
-        # person
         individuality = Individuality.get_instance()
-
+        prompt_personality += individuality.personality.personality_core
-        personality_core = individuality.personality.personality_core
+        if individuality.personality.personality_sides:
-        prompt_personality += personality_core
+            prompt_personality += f",{random.choice(individuality.personality.personality_sides)}"  # 随机选一个
-
+        if individuality.identity.identity_detail:
-        personality_sides = individuality.personality.personality_sides
+            prompt_personality += f",{random.choice(individuality.identity.identity_detail)}"  # 随机选一个
-        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]}"
 
         personality_info = prompt_personality
         mood_info = self.current_state.mood
+        bot_name = global_config.BOT_NICKNAME  # 使用全局配置中的机器人昵称
 
-        # prompt = ""
-        # prompt += f"{personality_info}\n"
-        # prompt += f"现在{global_config.BOT_NICKNAME}的想法是：{self.current_mind}\n"
-        # prompt += f"现在{global_config.BOT_NICKNAME}在qq群里进行聊天，聊天的话题如下：{minds_str}\n"
-        # prompt += f"你现在{mood_info}\n"
-        # prompt += """现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白
-        # 不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:"""
         prompt = (await global_prompt_manager.get_prompt_async("mind_summary_prompt")).format(
-            personality_info, global_config.BOT_NICKNAME, self.current_mind, minds_str, mood_info
+            personality_info=personality_info,  # 使用关键字参数
+            bot_name=bot_name,
+            current_mind=self.current_mind,
+            minds_str=minds_str,
+            mood_info=mood_info,
         )
 
-        response, reasoning_content = await self.llm_model.generate_response_async(prompt)
-
-        return response
-
-    async def create_subheartflow(self, subheartflow_id):
-        """
-        创建一个新的SubHeartflow实例
-        添加一个SubHeartflow实例到self._subheartflows字典中
-        并根据subheartflow_id为子心流创建一个观察对象
-        """
-
         try:
-            if subheartflow_id not in self._subheartflows:
+            response, reasoning_content = await self.llm_model.generate_response_async(prompt)
-                subheartflow = SubHeartflow(subheartflow_id)
+            if not response:
-                # 创建一个观察对象，目前只可以用chat_id创建观察对象
+                logger.warning("[Heartflow] 想法汇总 LLM 返回空结果。")
-                logger.debug(f"创建 observation: {subheartflow_id}")
+                return "(想法汇总失败...)"
-                observation = ChattingObservation(subheartflow_id)
+            return response
-                await observation.initialize()
-                subheartflow.add_observation(observation)
-                logger.debug("添加 observation 成功")
-                # 创建异步任务
-                asyncio.create_task(subheartflow.subheartflow_start_working())
-                logger.debug("创建异步任务 成功")
-                self._subheartflows[subheartflow_id] = subheartflow
-                logger.info("添加 subheartflow 成功")
-            return self._subheartflows[subheartflow_id]
         except Exception as e:
-            logger.error(f"创建 subheartflow 失败: {e}")
+            logger.error(f"[Heartflow] 想法汇总失败: {e}")
+            logger.error(traceback.format_exc())
+            return "(想法汇总时发生错误...)"
+
+    async def create_subheartflow(self, subheartflow_id: Any) -> Optional[SubHeartflow]:
+        """
+        获取或创建一个新的SubHeartflow实例。
+        (主要逻辑不变，InterestChatting 现在在 SubHeartflow 内部创建)
+        """
+        existing_subheartflow = self._subheartflows.get(subheartflow_id)
+        if existing_subheartflow:
+            # 如果已存在，确保其 last_active_time 更新 (如果需要的话)
+            # existing_subheartflow.last_active_time = time.time() # 移除，活跃时间由实际操作更新
+            # logger.debug(f"[Heartflow] 返回已存在的 subheartflow: {subheartflow_id}")
+            return existing_subheartflow
+
+        logger.info(f"[Heartflow] 尝试创建新的 subheartflow: {subheartflow_id}")
+        try:
+            # 创建 SubHeartflow，它内部会创建 InterestChatting
+            subheartflow = SubHeartflow(subheartflow_id)
+
+            # 创建并初始化观察对象
+            logger.debug(f"[Heartflow] 为 {subheartflow_id} 创建 observation")
+            observation = ChattingObservation(subheartflow_id)
+            await observation.initialize()
+            subheartflow.add_observation(observation)
+            logger.debug(f"[Heartflow] 为 {subheartflow_id} 添加 observation 成功")
+
+            # 创建并存储后台任务 (SubHeartflow 自己的后台任务)
+            subheartflow.task = asyncio.create_task(subheartflow.subheartflow_start_working())
+            logger.debug(f"[Heartflow] 为 {subheartflow_id} 创建后台任务成功")
+
+            # 添加到管理字典
+            self._subheartflows[subheartflow_id] = subheartflow
+            logger.info(f"[Heartflow] 添加 subheartflow {subheartflow_id} 成功")
+            return subheartflow
+
+        except Exception as e:
+            logger.error(f"[Heartflow] 创建 subheartflow {subheartflow_id} 失败: {e}")
+            logger.error(traceback.format_exc())
             return None
 
-    def get_subheartflow(self, observe_chat_id) -> SubHeartflow:
+    def get_subheartflow(self, observe_chat_id: Any) -> Optional[SubHeartflow]:
         """获取指定ID的SubHeartflow实例"""
         return self._subheartflows.get(observe_chat_id)
 

src/heart_flow/observation.py

@@ -139,7 +139,7 @@ class ChattingObservation(Observation):
         #     traceback.print_exc()  # 记录详细堆栈
         # print(f"处理后self.talking_message：{self.talking_message}")
 
-        self.talking_message_str = await build_readable_messages(self.talking_message)
+        self.talking_message_str = await build_readable_messages(messages=self.talking_message, timestamp_mode="normal")
 
         logger.trace(
             f"Chat {self.chat_id} - 压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.talking_message_str}"

src/heart_flow/sub_heartflow.py

@@ -4,22 +4,20 @@ from src.plugins.moods.moods import MoodManager
 from src.plugins.models.utils_model import LLMRequest
 from src.config.config import global_config
 import time
-from typing import Optional
+from typing import Optional, List, Dict
-from datetime import datetime
 import traceback
 from src.plugins.chat.utils import parse_text_timestamps
-
+import enum
-# from src.plugins.schedule.schedule_generator import bot_schedule
-# from src.plugins.memory_system.Hippocampus import HippocampusManager
 from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG  # noqa: E402
-
-# from src.plugins.chat.utils import get_embedding
-# from src.common.database import db
-# from typing import Union
 from src.individuality.individuality import Individuality
 import random
 from src.plugins.person_info.relationship_manager import relationship_manager
 from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
+from src.plugins.chat.message import MessageRecv
+import math
+
+# 定义常量 (从 interest.py 移动过来)
+MAX_INTEREST = 15.0
 
 subheartflow_config = LogConfig(
     # 使用海马体专用样式
@@ -28,6 +26,12 @@ subheartflow_config = LogConfig(
 )
 logger = get_module_logger("subheartflow", config=subheartflow_config)
 
+interest_log_config = LogConfig(
+    console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"],
+    file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"],
+)
+interest_logger = get_module_logger("InterestChatting", config=interest_log_config)
+
 
 def init_prompt():
     prompt = ""
@@ -49,25 +53,178 @@ def init_prompt():
     Prompt(prompt, "sub_heartflow_prompt_before")
 
 
-class CurrentState:
+class ChatState(enum.Enum):
+    ABSENT = "不参与"
+    CHAT = "闲聊"
+    FOCUSED = "专注"
+
+
+class ChatStateInfo:
     def __init__(self):
         self.willing = 0
-        self.current_state_info = ""
+
+        self.chat_status: ChatState = ChatState.ABSENT
 
         self.mood_manager = MoodManager()
         self.mood = self.mood_manager.get_prompt()
 
-    def update_current_state_info(self):
+    def update_chat_state_info(self):
-        self.current_state_info = self.mood_manager.get_current_mood()
+        self.chat_state_info = self.mood_manager.get_current_mood()
+
+
+base_reply_probability = 0.05
+probability_increase_rate_per_second = 0.08
+max_reply_probability = 1
+
+
+class InterestChatting:
+    def __init__(
+        self,
+        decay_rate=global_config.default_decay_rate_per_second,
+        max_interest=MAX_INTEREST,
+        trigger_threshold=global_config.reply_trigger_threshold,
+        base_reply_probability=base_reply_probability,
+        increase_rate=probability_increase_rate_per_second,
+        decay_factor=global_config.probability_decay_factor_per_second,
+        max_probability=max_reply_probability,
+    ):
+        self.interest_level: float = 0.0
+        self.last_update_time: float = time.time()
+        self.decay_rate_per_second: float = decay_rate
+        self.max_interest: float = max_interest
+        self.last_interaction_time: float = self.last_update_time
+
+        self.trigger_threshold: float = trigger_threshold
+        self.base_reply_probability: float = base_reply_probability
+        self.probability_increase_rate: float = increase_rate
+        self.probability_decay_factor: float = decay_factor
+        self.max_reply_probability: float = max_probability
+        self.current_reply_probability: float = 0.0
+        self.is_above_threshold: bool = False
+
+        self.interest_dict: Dict[str, tuple[MessageRecv, float, bool]] = {}
+
+    def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
+        self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
+        self.last_interaction_time = time.time()
+
+    def _calculate_decay(self, current_time: float):
+        time_delta = current_time - self.last_update_time
+        if time_delta > 0:
+            old_interest = self.interest_level
+            if self.interest_level < 1e-9:
+                self.interest_level = 0.0
+            else:
+                if self.decay_rate_per_second <= 0:
+                    interest_logger.warning(
+                        f"InterestChatting encountered non-positive decay rate: {self.decay_rate_per_second}. Setting interest to 0."
+                    )
+                    self.interest_level = 0.0
+                elif self.interest_level < 0:
+                    interest_logger.warning(
+                        f"InterestChatting encountered negative interest level: {self.interest_level}. Setting interest to 0."
+                    )
+                    self.interest_level = 0.0
+                else:
+                    try:
+                        decay_factor = math.pow(self.decay_rate_per_second, time_delta)
+                        self.interest_level *= decay_factor
+                    except ValueError as e:
+                        interest_logger.error(
+                            f"Math error during decay calculation: {e}. Rate: {self.decay_rate_per_second}, Delta: {time_delta}, Level: {self.interest_level}. Setting interest to 0."
+                        )
+                        self.interest_level = 0.0
+
+            if old_interest != self.interest_level:
+                self.last_update_time = current_time
+
+    def _update_reply_probability(self, current_time: float):
+        time_delta = current_time - self.last_update_time
+        if time_delta <= 0:
+            return
+
+        currently_above = self.interest_level >= self.trigger_threshold
+
+        if currently_above:
+            if not self.is_above_threshold:
+                self.current_reply_probability = self.base_reply_probability
+                interest_logger.debug(
+                    f"兴趣跨过阈值 ({self.trigger_threshold}). 概率重置为基础值: {self.base_reply_probability:.4f}"
+                )
+            else:
+                increase_amount = self.probability_increase_rate * time_delta
+                self.current_reply_probability += increase_amount
+
+            self.current_reply_probability = min(self.current_reply_probability, self.max_reply_probability)
+
+        else:
+            if 0 < self.probability_decay_factor < 1:
+                decay_multiplier = math.pow(self.probability_decay_factor, time_delta)
+                self.current_reply_probability *= decay_multiplier
+                if self.current_reply_probability < 1e-6:
+                    self.current_reply_probability = 0.0
+            elif self.probability_decay_factor <= 0:
+                if self.current_reply_probability > 0:
+                    interest_logger.warning(f"无效的衰减因子 ({self.probability_decay_factor}). 设置概率为0.")
+                    self.current_reply_probability = 0.0
+
+            self.current_reply_probability = max(self.current_reply_probability, 0.0)
+
+        self.is_above_threshold = currently_above
+
+    def increase_interest(self, current_time: float, value: float):
+        self._update_reply_probability(current_time)
+        self._calculate_decay(current_time)
+        self.interest_level += value
+        self.interest_level = min(self.interest_level, self.max_interest)
+        self.last_update_time = current_time
+        self.last_interaction_time = current_time
+
+    def decrease_interest(self, current_time: float, value: float):
+        self._update_reply_probability(current_time)
+        self.interest_level -= value
+        self.interest_level = max(self.interest_level, 0.0)
+        self.last_update_time = current_time
+        self.last_interaction_time = current_time
+
+    def get_interest(self) -> float:
+        current_time = time.time()
+        self._update_reply_probability(current_time)
+        self._calculate_decay(current_time)
+        self.last_update_time = current_time
+        return self.interest_level
+
+    def get_state(self) -> dict:
+        interest = self.get_interest()
+        return {
+            "interest_level": round(interest, 2),
+            "last_update_time": self.last_update_time,
+            "current_reply_probability": round(self.current_reply_probability, 4),
+            "is_above_threshold": self.is_above_threshold,
+            "last_interaction_time": self.last_interaction_time,
+        }
+
+    def should_evaluate_reply(self) -> bool:
+        current_time = time.time()
+        self._update_reply_probability(current_time)
+
+        if self.current_reply_probability > 0:
+            trigger = random.random() < self.current_reply_probability
+            return trigger
+        else:
+            return False
 
 
 class SubHeartflow:
     def __init__(self, subheartflow_id):
         self.subheartflow_id = subheartflow_id
 
-        self.current_mind = ""
+        self.current_mind = "你什么也没想"
         self.past_mind = []
-        self.current_state: CurrentState = CurrentState()
+        self.chat_state: ChatStateInfo = ChatStateInfo()
+
+        self.interest_chatting = InterestChatting()
+
         self.llm_model = LLMRequest(
             model=global_config.llm_sub_heartflow,
             temperature=global_config.llm_sub_heartflow["temp"],
@@ -77,15 +234,13 @@ class SubHeartflow:
 
         self.main_heartflow_info = ""
 
-        self.last_reply_time = time.time()
         self.last_active_time = time.time()  # 添加最后激活时间
-
+        self.should_stop = False  # 添加停止标志
-        if not self.current_mind:
+        self.task: Optional[asyncio.Task] = None  # 添加 task 属性
-            self.current_mind = "你什么也没想"
 
         self.is_active = False
 
-        self.observations: list[ChattingObservation] = []
+        self.observations: List[ChattingObservation] = []  # 使用 List 类型提示
 
         self.running_knowledges = []
 
@@ -93,19 +248,13 @@ class SubHeartflow:
 
     async def subheartflow_start_working(self):
         while True:
-            current_time = time.time()
             # --- 调整后台任务逻辑 --- #
             # 这个后台循环现在主要负责检查是否需要自我销毁
             # 不再主动进行思考或状态更新，这些由 HeartFC_Chat 驱动
 
-            # 检查是否超过指定时间没有激活 (例如，没有被调用进行思考)
+            # 检查是否被主心流标记为停止
-            if current_time - self.last_active_time > global_config.sub_heart_flow_stop_time:  # 例如 5 分钟
+            if self.should_stop:
-                logger.info(
+                logger.info(f"子心流 {self.subheartflow_id} 被标记为停止，正在退出后台任务...")
-                    f"子心流 {self.subheartflow_id} 超过 {global_config.sub_heart_flow_stop_time} 秒没有激活，正在销毁..."
-                    f" (Last active: {datetime.fromtimestamp(self.last_active_time).strftime('%Y-%m-%d %H:%M:%S')})"
-                )
-                # 在这里添加实际的销毁逻辑，例如从主 Heartflow 管理器中移除自身
-                # heartflow.remove_subheartflow(self.subheartflow_id) # 假设有这样的方法
                 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
@@ -132,7 +281,7 @@ class SubHeartflow:
         self.last_active_time = time.time()  # 更新最后激活时间戳
 
         current_thinking_info = self.current_mind
-        mood_info = self.current_state.mood
+        mood_info = self.chat_state.mood
         observation = self._get_primary_observation()
 
         # --- 获取观察信息 --- #
@@ -264,6 +413,26 @@ class SubHeartflow:
         logger.warning(f"SubHeartflow {self.subheartflow_id} 没有找到有效的 ChattingObservation")
         return None
 
+    def get_interest_state(self) -> dict:
+        """获取当前兴趣状态"""
+        return self.interest_chatting.get_state()
+
+    def get_interest_level(self) -> float:
+        """获取当前兴趣等级"""
+        return self.interest_chatting.get_interest()
+
+    def should_evaluate_reply(self) -> bool:
+        """判断是否应该评估回复"""
+        return self.interest_chatting.should_evaluate_reply()
+
+    def add_interest_dict_entry(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
+        """添加兴趣字典条目"""
+        self.interest_chatting.add_interest_dict(message, interest_value, is_mentioned)
+
+    def get_interest_dict(self) -> Dict[str, tuple[MessageRecv, float, bool]]:
+        """获取兴趣字典"""
+        return self.interest_chatting.interest_dict
+
 
 init_prompt()
 # subheartflow = SubHeartflow()

src/individuality/individuality.py

@@ -105,3 +105,4 @@ class Individuality:
             return self.personality.agreeableness
         elif factor == "neuroticism":
             return self.personality.neuroticism
+        return None

src/main.py

@@ -17,8 +17,7 @@ from .common.logger import get_module_logger
 from .plugins.remote import heartbeat_thread  # noqa: F401
 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 HeartFCController
-from .plugins.chat_module.heartFC_chat.heartFC_controler import HeartFC_Controller
 
 logger = get_module_logger("main")
 
@@ -112,14 +111,9 @@ class MainSystem:
             asyncio.create_task(heartflow.heartflow_start_working())
             logger.success("心流系统启动成功")
 
-            # 启动 InterestManager 的后台任务
+            # 初始化并独立启动 HeartFCController
-            interest_manager = InterestManager()  # 获取单例
+            HeartFCController()
-            await interest_manager.start_background_tasks()
+            heartfc_chat_instance = HeartFCController.get_instance()
-            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()
-            heartfc_chat_instance = HeartFC_Controller.get_instance()
             if heartfc_chat_instance:
                 await heartfc_chat_instance.start()
                 logger.success("HeartFC_Chat 模块独立启动成功")

src/plugins/PFC/conversation.py

@@ -180,6 +180,7 @@ class Conversation:
                     "time": datetime.datetime.now().strftime("%H:%M:%S"),
                 }
             )
+            return None
 
         elif action == "fetch_knowledge":
             self.waiter.wait_accumulated_time = 0
@@ -193,28 +194,35 @@ class Conversation:
             if knowledge:
                 if topic not in self.conversation_info.knowledge_list:
                     self.conversation_info.knowledge_list.append({"topic": topic, "knowledge": knowledge})
+                    return None
                 else:
                     self.conversation_info.knowledge_list[topic] += knowledge
+                    return None
+            return None
 
         elif action == "rethink_goal":
             self.waiter.wait_accumulated_time = 0
 
             self.state = ConversationState.RETHINKING
             await self.goal_analyzer.analyze_goal(conversation_info, observation_info)
+            return None
 
         elif action == "listening":
             self.state = ConversationState.LISTENING
             logger.info("倾听对方发言...")
             await self.waiter.wait_listening(conversation_info)
+            return None
 
         elif action == "end_conversation":
             self.should_continue = False
             logger.info("决定结束对话...")
+            return None
 
         else:  # wait
             self.state = ConversationState.WAITING
             logger.info("等待更多信息...")
             await self.waiter.wait(self.conversation_info)
+            return None
 
     async def _send_timeout_message(self):
         """发送超时结束消息"""

src/plugins/PFC/reply_generator.py

@@ -151,7 +151,7 @@ class ReplyGenerator:
             return content
 
         except Exception as e:
-            logger.error(f"生成回复时出错: {e}")
+            logger.error(f"生成回复时出错: {str(e)}")
             return "抱歉，我现在有点混乱，让我重新思考一下..."
 
     async def check_reply(self, reply: str, goal: str, retry_count: int = 0) -> Tuple[bool, str, bool]:

src/plugins/chat/bot.py

@@ -7,7 +7,7 @@ from ..chat_module.only_process.only_message_process import MessageProcessor
 
 from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
 from ..chat_module.reasoning_chat.reasoning_chat import ReasoningChat
-from ..chat_module.heartFC_chat.heartFC_processor import HeartFC_Processor
+from ..chat_module.heartFC_chat.heartFC_processor import HeartFCProcessor
 from ..utils.prompt_builder import Prompt, global_prompt_manager
 import traceback
 
@@ -27,9 +27,8 @@ class ChatBot:
         self.bot = None  # bot 实例引用
         self._started = False
         self.mood_manager = MoodManager.get_instance()  # 获取情绪管理器单例
-        self.mood_manager.start_mood_update()  # 启动情绪更新
         self.reasoning_chat = ReasoningChat()
-        self.heartFC_processor = HeartFC_Processor()  # 新增
+        self.heartFC_processor = HeartFCProcessor()  # 新增
 
         # 创建初始化PFC管理器的任务，会在_ensure_started时执行
         self.only_process_chat = MessageProcessor()
@@ -105,53 +104,24 @@ class ChatBot:
                 template_group_name = None
 
             async def preprocess():
-                if global_config.enable_pfc_chatting:
+                if groupinfo is None:
-                    try:
+                    if global_config.enable_friend_chat:
-                        if groupinfo is None:
+                        if global_config.enable_pfc_chatting:
-                            if global_config.enable_friend_chat:
+                            userinfo = message.message_info.user_info
-                                userinfo = message.message_info.user_info
+                            messageinfo = message.message_info
-                                messageinfo = message.message_info
+                            # 创建聊天流
-                                # 创建聊天流
+                            chat = await chat_manager.get_or_create_stream(
-                                chat = await chat_manager.get_or_create_stream(
+                                platform=messageinfo.platform,
-                                    platform=messageinfo.platform,
+                                user_info=userinfo,
-                                    user_info=userinfo,
+                                group_info=groupinfo,
-                                    group_info=groupinfo,
+                            )
-                                )
+                            message.update_chat_stream(chat)
-                                message.update_chat_stream(chat)
+                            await self.only_process_chat.process_message(message)
-                                await self.only_process_chat.process_message(message)
+                            await self._create_pfc_chat(message)
-                                await self._create_pfc_chat(message)
                         else:
-                            if groupinfo.group_id in global_config.talk_allowed_groups:
+                            await self.heartFC_processor.process_message(message_data)
-                                # logger.debug(f"开始群聊模式{str(message_data)[:50]}...")
-                                if global_config.response_mode == "heart_flow":
-                                    # logger.info(f"启动最新最好的思维流FC模式{str(message_data)[:50]}...")
-                                    await self.heartFC_processor.process_message(message_data)
-                                elif global_config.response_mode == "reasoning":
-                                    # logger.debug(f"开始推理模式{str(message_data)[:50]}...")
-                                    await self.reasoning_chat.process_message(message_data)
-                                else:
-                                    logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
-                    except Exception as e:
-                        logger.error(f"处理PFC消息失败: {e}")
                 else:
-                    if groupinfo is None:
+                    await self.heartFC_processor.process_message(message_data)
-                        if global_config.enable_friend_chat:
-                            # 私聊处理流程
-                            # await self._handle_private_chat(message)
-                            if global_config.response_mode == "heart_flow":
-                                await self.heartFC_processor.process_message(message_data)
-                            elif global_config.response_mode == "reasoning":
-                                await self.reasoning_chat.process_message(message_data)
-                            else:
-                                logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
-                    else:  # 群聊处理
-                        if groupinfo.group_id in global_config.talk_allowed_groups:
-                            if global_config.response_mode == "heart_flow":
-                                await self.heartFC_processor.process_message(message_data)
-                            elif global_config.response_mode == "reasoning":
-                                await self.reasoning_chat.process_message(message_data)
-                            else:
-                                logger.error(f"未知的回复模式，请检查配置文件！！: {global_config.response_mode}")
 
             if template_group_name:
                 async with global_prompt_manager.async_message_scope(template_group_name):

src/plugins/chat/message.py

@@ -1,14 +1,13 @@
 import time
 from dataclasses import dataclass
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 import urllib3
 
-from .utils_image import image_manager
-
-from ..message.message_base import Seg, UserInfo, BaseMessageInfo, MessageBase
-from .chat_stream import ChatStream
 from src.common.logger import get_module_logger
+from .chat_stream import ChatStream
+from .utils_image import image_manager
+from ..message.message_base import Seg, UserInfo, BaseMessageInfo, MessageBase
 
 logger = get_module_logger("chat_message")
 
@@ -207,7 +206,7 @@ class MessageProcessBase(Message):
             # 处理单个消息段
             return await self._process_single_segment(segment)
 
-    async def _process_single_segment(self, seg: Seg) -> str:
+    async def _process_single_segment(self, seg: Seg) -> Union[str, None]:
         """处理单个消息段
 
         Args:
@@ -233,6 +232,7 @@ class MessageProcessBase(Message):
             elif seg.type == "reply":
                 if self.reply and hasattr(self.reply, "processed_plain_text"):
                     return f"[回复：{self.reply.processed_plain_text}]"
+                return None
             else:
                 return f"[{seg.type}:{str(seg.data)}]"
         except Exception as e:
@@ -309,10 +309,7 @@ class MessageSending(MessageProcessBase):
 
     def set_reply(self, reply: Optional["MessageRecv"] = None) -> None:
         """设置回复消息"""
-        if (
+        if self.message_info.format_info is not None and "reply" in self.message_info.format_info.accept_format:
-            self.message_info.format_info.accept_format is not None
-            and "reply" in self.message_info.format_info.accept_format
-        ):
             if reply:
                 self.reply = reply
             if self.reply:

src/plugins/chat/utils.py

@@ -2,7 +2,7 @@ import random
 import time
 import re
 from collections import Counter
-from typing import Dict, List
+from typing import Dict, List, Optional
 
 import jieba
 import numpy as np
@@ -76,18 +76,20 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
     else:
         if not is_mentioned:
             # 判断是否被回复
-            if re.match("回复[\s\S]*?\((\d+)\)的消息，说：", message.processed_plain_text):
+            if re.match(
+                f"\[回复 [\s\S]*?\({str(global_config.BOT_QQ)}\)：[\s\S]*?\]，说：", message.processed_plain_text
+            ):
                 is_mentioned = True
-
+            else:
-            # 判断内容中是否被提及
+                # 判断内容中是否被提及
-            message_content = re.sub(r"@[\s\S]*?（(\d+)）", "", message.processed_plain_text)
+                message_content = re.sub(r"@[\s\S]*?（(\d+)）", "", message.processed_plain_text)
-            message_content = re.sub(r"回复[\s\S]*?\((\d+)\)的消息，说： ", "", message_content)
+                message_content = re.sub(r"\[回复 [\s\S]*?\(((\d+)|未知id)\)：[\s\S]*?\]，说：", "", message_content)
-            for keyword in keywords:
+                for keyword in keywords:
-                if keyword in message_content:
+                    if keyword in message_content:
-                    is_mentioned = True
+                        is_mentioned = True
-            for nickname in nicknames:
+                for nickname in nicknames:
-                if nickname in message_content:
+                    if nickname in message_content:
-                    is_mentioned = True
+                        is_mentioned = True
         if is_mentioned and global_config.mentioned_bot_inevitable_reply:
             reply_probability = 1.0
             logger.info("被提及，回复概率设置为100%")
@@ -688,7 +690,7 @@ def count_messages_between(start_time: float, end_time: float, stream_id: str) -
         return 0, 0
 
 
-def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> str:
+def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal") -> Optional[str]:
     """将时间戳转换为人类可读的时间格式
 
     Args:
@@ -716,6 +718,7 @@ def translate_timestamp_to_human_readable(timestamp: float, mode: str = "normal"
             return f"{int(diff / 86400)}天前:\n"
         else:
             return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(timestamp)) + ":\n"
+    return None
 
 
 def parse_text_timestamps(text: str, mode: str = "normal") -> str:

src/plugins/chat_module/heartFC_chat/heartFC_controler.py

@@ -1,82 +1,108 @@
 import traceback
 from typing import Optional, Dict
 import asyncio
-from asyncio import Lock
+import threading  # 导入 threading
 from ...moods.moods import MoodManager
 from ...chat.emoji_manager import emoji_manager
 from .heartFC_generator import ResponseGenerator
 from .messagesender import MessageManager
 from src.heart_flow.heartflow import heartflow
+from src.heart_flow.sub_heartflow import SubHeartflow, ChatState
 from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
 from src.plugins.person_info.relationship_manager import relationship_manager
 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
 
+
 # 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("HeartFC_Controller", config=chat_config)
+logger = get_module_logger("HeartFCController", config=chat_config)
 
 # 检测群聊兴趣的间隔时间
 INTEREST_MONITOR_INTERVAL_SECONDS = 1
 
 
-class HeartFC_Controller:
+# 合并后的版本：使用 __new__ + threading.Lock 实现线程安全单例，类名为 HeartFCController
-    _instance = None  # For potential singleton access if needed by MessageManager
+class HeartFCController:
+    _instance = None
+    _lock = threading.Lock()  # 使用 threading.Lock 保证 __new__ 线程安全
+    _initialized = False
 
-    def __init__(self):
+    def __new__(cls, *args, **kwargs):
-        # --- 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):
         if cls._instance is None:
-            # This might indicate an issue if called before initialization
+            with cls._lock:
-            logger.warning("HeartFC_Controller get_instance called before initialization.")
+                # Double-checked locking
-            # Optionally, initialize here if a strict singleton pattern is desired
+                if cls._instance is None:
-            # cls._instance = cls()
+                    logger.debug("创建 HeartFCController 单例实例...")
+                    cls._instance = super().__new__(cls)
         return cls._instance
 
-    # --- End Added Class Method ---
+    def __init__(self):
+        # 使用 _initialized 标志确保 __init__ 只执行一次
+        if self._initialized:
+            return
+
+        self.gpt = ResponseGenerator()
+        self.mood_manager = MoodManager.get_instance()
+        self.tool_user = ToolUser()
+        self._interest_monitor_task: Optional[asyncio.Task] = None
+
+        self.heartflow = heartflow
+
+        self.pf_chatting_instances: Dict[str, PFChatting] = {}
+        self._pf_chatting_lock = asyncio.Lock()  # 这个是 asyncio.Lock，用于异步上下文
+        self.emoji_manager = emoji_manager  # 假设是全局或已初始化的实例
+        self.relationship_manager = relationship_manager  # 假设是全局或已初始化的实例
+
+        self.MessageManager = MessageManager
+        self._initialized = True
+        logger.info("HeartFCController 单例初始化完成。")
+
+    @classmethod
+    def get_instance(cls):
+        """获取 HeartFCController 的单例实例。"""
+        # 如果实例尚未创建，调用构造函数（这将触发 __new__ 和 __init__）
+        if cls._instance is None:
+            # 在首次调用 get_instance 时创建实例。
+            # __new__ 中的锁会确保线程安全。
+            cls()
+            # 添加日志记录，说明实例是在 get_instance 调用时创建的
+            logger.info("HeartFCController 实例在首次 get_instance 时创建。")
+        elif not cls._initialized:
+            # 实例已创建但可能未初始化完成（理论上不太可能发生，除非 __init__ 异常）
+            logger.warning("HeartFCController 实例存在但尚未完成初始化。")
+        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"""
-        logger.debug("HeartFC_Controller 正在启动异步任务...")
+        logger.debug("HeartFCController 正在启动异步任务...")
         self._initialize_monitor_task()
-        logger.info("HeartFC_Controller 异步任务启动完成")
+        logger.info("HeartFCController 异步任务启动完成")
 
     def _initialize_monitor_task(self):
         """启动后台兴趣监控任务，可以检查兴趣是否足以开启心流对话"""
         if self._interest_monitor_task is None or self._interest_monitor_task.done():
             try:
                 loop = asyncio.get_running_loop()
-                self._interest_monitor_task = loop.create_task(self._interest_monitor_loop())
+                self._interest_monitor_task = loop.create_task(self._response_control_loop())
             except RuntimeError:
                 logger.error("创建兴趣监控任务失败：没有运行中的事件循环。")
                 raise
@@ -89,7 +115,7 @@ class HeartFC_Controller:
         async with self._pf_chatting_lock:
             if stream_id not in self.pf_chatting_instances:
                 logger.info(f"为流 {stream_id} 创建新的PFChatting实例")
-                # 传递 self (HeartFC_Controller 实例) 进行依赖注入
+                # 传递 self (HeartFCController 实例) 进行依赖注入
                 instance = PFChatting(stream_id, self)
                 # 执行异步初始化
                 if not await instance._initialize():
@@ -100,41 +126,41 @@ class HeartFC_Controller:
 
     # --- End Added PFChatting Instance Manager ---
 
-    async def _interest_monitor_loop(self):
+    # async def update_mai_Status(self):
+    #     """后台任务，定期检查更新麦麦状态"""
+    #     logger.info("麦麦状态更新循环开始...")
+    #     while True:
+    #         await asyncio.sleep(0)
+    #         self.heartflow.update_chat_status()
+
+    async def _response_control_loop(self):
         """后台任务，定期检查兴趣度变化并触发回复"""
         logger.info("兴趣监控循环开始...")
         while True:
             await asyncio.sleep(INTEREST_MONITOR_INTERVAL_SECONDS)
+
             try:
                 # 从心流中获取活跃流
-                active_stream_ids = list(heartflow.get_all_subheartflows_streams_ids())
+                active_stream_ids = list(self.heartflow.get_all_subheartflows_streams_ids())
                 for stream_id in active_stream_ids:
                     stream_name = chat_manager.get_stream_name(stream_id) or stream_id  # 获取流名称
-                    sub_hf = heartflow.get_subheartflow(stream_id)
+                    sub_hf = self.heartflow.get_subheartflow(stream_id)
                     if not sub_hf:
                         logger.warning(f"监控循环: 无法获取活跃流 {stream_name} 的 sub_hf")
                         continue
 
-                    should_trigger = False
+                    should_trigger_hfc = False
                     try:
-                        interest_chatting = self.interest_manager.get_interest_chatting(stream_id)
+                        interest_chatting = sub_hf.interest_chatting
-                        if interest_chatting:
+                        should_trigger_hfc = interest_chatting.should_evaluate_reply()
-                            should_trigger = interest_chatting.should_evaluate_reply()
+
-                        else:
-                            logger.trace(
-                                f"[{stream_name}] 没有找到对应的 InterestChatting 实例，跳过基于兴趣的触发检查。"
-                            )
                     except Exception as e:
                         logger.error(f"检查兴趣触发器时出错 流 {stream_name}: {e}")
                         logger.error(traceback.format_exc())
 
-                    if should_trigger:
+                    if should_trigger_hfc:
                         # 启动一次麦麦聊天
-                        pf_instance = await self._get_or_create_pf_chatting(stream_id)
+                        await self._trigger_hfc(sub_hf)
-                        if pf_instance:
-                            asyncio.create_task(pf_instance.add_time())
-                        else:
-                            logger.error(f"[{stream_name}] 无法获取或创建PFChatting实例。跳过触发。")
 
             except asyncio.CancelledError:
                 logger.info("兴趣监控循环已取消。")
@@ -143,3 +169,17 @@ class HeartFC_Controller:
                 logger.error(f"兴趣监控循环错误: {e}")
                 logger.error(traceback.format_exc())
                 await asyncio.sleep(5)  # 发生错误时等待
+
+    async def _trigger_hfc(self, sub_hf: SubHeartflow):
+        chat_state = sub_hf.chat_state
+        if chat_state == ChatState.ABSENT:
+            chat_state = ChatState.CHAT
+        elif chat_state == ChatState.CHAT:
+            chat_state = ChatState.FOCUSED
+
+        # 从 sub_hf 获取 stream_id
+        if chat_state == ChatState.FOCUSED:
+            stream_id = sub_hf.subheartflow_id
+            pf_instance = await self._get_or_create_pf_chatting(stream_id)
+            if pf_instance:  # 确保实例成功获取或创建
+                asyncio.create_task(pf_instance.add_time())

src/plugins/chat_module/heartFC_chat/heartFC_processor.py

@@ -11,8 +11,8 @@ from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
 from ...chat.chat_stream import chat_manager
 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(
@@ -21,15 +21,11 @@ processor_config = LogConfig(
 )
 logger = get_module_logger("heartFC_processor", config=processor_config)
 
-# # 定义兴趣度增加触发回复的阈值 (移至 InterestManager)
-# INTEREST_INCREASE_THRESHOLD = 0.5
 
-
+class HeartFCProcessor:
-class HeartFC_Processor:
     def __init__(self):
         self.storage = MessageStorage()
-        self.interest_manager = InterestManager()
+        self.reasoning_chat = ReasoningChat.get_instance()
-        # self.chat_instance = chat_instance  # 持有 HeartFC_Chat 实例
 
     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。
@@ -72,12 +68,18 @@ class HeartFC_Processor:
                 user_info=userinfo,
                 group_info=groupinfo,
             )
-            if not chat:
+
-                logger.error(
+            # --- 确保 SubHeartflow 存在 ---
-                    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'}"
+            subheartflow = await heartflow.create_subheartflow(chat.stream_id)
-                )
+            if not subheartflow:
+                logger.error(f"无法为 stream_id {chat.stream_id} 创建或获取 SubHeartflow，中止处理")
                 return
 
+            # --- 添加兴趣追踪启动 (现在移动到这里，确保 subheartflow 存在后启动) ---
+            # 在获取到 chat 对象和确认 subheartflow 后，启动对该聊天流的兴趣监控
+            await self.reasoning_chat.start_monitoring_interest(chat)  # start_monitoring_interest 内部需要修改以适应
+            # --- 结束添加 ---
+
             message.update_chat_stream(chat)
 
             await heartflow.create_subheartflow(chat.stream_id)
@@ -90,28 +92,27 @@ 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)
 
             # 处理缓冲器结果 (Bombing logic)
             if not buffer_result:
-                F_type = "seglist"
+                f_type = "seglist"
                 if message.message_segment.type != "seglist":
-                    F_type = message.message_segment.type
+                    f_type = message.message_segment.type
                 else:
                     if (
                         isinstance(message.message_segment.data, list)
                         and all(isinstance(x, Seg) for x in message.message_segment.data)
                         and len(message.message_segment.data) == 1
                     ):
-                        F_type = message.message_segment.data[0].type
+                        f_type = message.message_segment.data[0].type
-                if F_type == "text":
+                if f_type == "text":
                     logger.debug(f"触发缓冲，消息：{message.processed_plain_text}")
-                elif F_type == "image":
+                elif f_type == "image":
                     logger.debug("触发缓冲，表情包/图片等待中")
-                elif F_type == "seglist":
+                elif f_type == "seglist":
                     logger.debug("触发缓冲，消息列表等待中")
                 return  # 被缓冲器拦截，不生成回复
 
@@ -141,21 +142,35 @@ class HeartFC_Processor:
                 logger.error(f"计算记忆激活率失败: {e}")
                 logger.error(traceback.format_exc())
 
+            # --- 修改：兴趣度更新逻辑 --- #
             if is_mentioned:
-                interested_rate += 0.8
+                interest_increase_on_mention = 2
+                mentioned_boost = interest_increase_on_mention  # 从配置获取提及增加值
+                interested_rate += mentioned_boost
+                logger.trace(f"消息提及机器人，额外增加兴趣 {mentioned_boost:.2f}")
 
-            # 更新兴趣度
+            # 更新兴趣度 (调用 SubHeartflow 的方法)
+            current_interest = 0.0  # 初始化
             try:
-                self.interest_manager.increase_interest(chat.stream_id, value=interested_rate)
+                # 获取当前时间，传递给 increase_interest
-                current_interest = self.interest_manager.get_interest(chat.stream_id)  # 获取更新后的值用于日志
+                current_time = time.time()
+                subheartflow.interest_chatting.increase_interest(current_time, value=interested_rate)
+                current_interest = subheartflow.get_interest_level()  # 获取更新后的值
+
                 logger.trace(
-                    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}"
+                    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} (Stream: {chat.stream_id})"
+                )
+
+                # 添加到 SubHeartflow 的 interest_dict
+                subheartflow.add_interest_dict_entry(message, interested_rate, is_mentioned)
+                logger.trace(
+                    f"Message {message.message_info.message_id} added to interest dict for stream {chat.stream_id}"
                 )
 
             except Exception as e:
-                logger.error(f"更新兴趣度失败: {e}")  # 调整日志消息
+                logger.error(f"更新兴趣度失败 (Stream: {chat.stream_id}): {e}")
                 logger.error(traceback.format_exc())
-            # ---- 兴趣度计算和更新结束 ----
+            # --- 结束修改 --- #
 
             # 打印消息接收和处理信息
             mes_name = chat.group_info.group_name if chat.group_info else "私聊"

src/plugins/chat_module/heartFC_chat/interest.py

@@ -1,491 +0,0 @@
-import time
-import math
-import asyncio
-import threading
-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.common.logger import get_module_logger, LogConfig, DEFAULT_CONFIG  # 引入 DEFAULT_CONFIG
-from src.plugins.chat.chat_stream import chat_manager  # *** Import ChatManager ***
-
-# 定义日志配置 (使用 loguru 格式)
-interest_log_config = LogConfig(
-    console_format=DEFAULT_CONFIG["console_format"],  # 使用默认控制台格式
-    file_format=DEFAULT_CONFIG["file_format"],  # 使用默认文件格式
-)
-logger = get_module_logger("InterestManager", config=interest_log_config)
-
-
-# 定义常量
-DEFAULT_DECAY_RATE_PER_SECOND = 0.98  # 每秒衰减率 (兴趣保留 99%)
-MAX_INTEREST = 15.0  # 最大兴趣值
-# MIN_INTEREST_THRESHOLD = 0.1      # 低于此值可能被清理 (可选)
-CLEANUP_INTERVAL_SECONDS = 1200  # 清理任务运行间隔 (例如：20分钟)
-INACTIVE_THRESHOLD_SECONDS = 1200  # 不活跃时间阈值 (例如：20分钟)
-LOG_INTERVAL_SECONDS = 3  # 日志记录间隔 (例如：30秒)
-LOG_DIRECTORY = "logs/interest"  # 日志目录
-# LOG_FILENAME = "interest_log.json"  # 快照日志文件名 (保留，以防其他地方用到)
-HISTORY_LOG_FILENAME = "interest_history.log"  # 新的历史日志文件名
-# 移除阈值，将移至 HeartFC_Chat
-# INTEREST_INCREASE_THRESHOLD = 0.5
-
-# --- 新增：概率回复相关常量 ---
-REPLY_TRIGGER_THRESHOLD = 3.0  # 触发概率回复的兴趣阈值 (示例值)
-BASE_REPLY_PROBABILITY = 0.1  # 首次超过阈值时的基础回复概率 (示例值)
-PROBABILITY_INCREASE_RATE_PER_SECOND = 0.02  # 高于阈值时，每秒概率增加量 (线性增长, 示例值)
-PROBABILITY_DECAY_FACTOR_PER_SECOND = 0.2  # 低于阈值时，每秒概率衰减因子 (指数衰减, 示例值)
-MAX_REPLY_PROBABILITY = 1  # 回复概率上限 (示例值)
-# --- 结束：概率回复相关常量 ---
-
-
-class InterestChatting:
-    def __init__(
-        self,
-        decay_rate=DEFAULT_DECAY_RATE_PER_SECOND,
-        max_interest=MAX_INTEREST,
-        trigger_threshold=REPLY_TRIGGER_THRESHOLD,
-        base_reply_probability=BASE_REPLY_PROBABILITY,
-        increase_rate=PROBABILITY_INCREASE_RATE_PER_SECOND,
-        decay_factor=PROBABILITY_DECAY_FACTOR_PER_SECOND,
-        max_probability=MAX_REPLY_PROBABILITY,
-    ):
-        self.interest_level: float = 0.0
-        self.last_update_time: float = time.time()  # 同时作为兴趣和概率的更新时间基准
-        self.decay_rate_per_second: float = decay_rate
-        self.max_interest: float = max_interest
-        self.last_interaction_time: float = self.last_update_time  # 新增：最后交互时间
-
-        # --- 新增：概率回复相关属性 ---
-        self.trigger_threshold: float = trigger_threshold
-        self.base_reply_probability: float = base_reply_probability
-        self.probability_increase_rate: float = increase_rate
-        self.probability_decay_factor: float = decay_factor
-        self.max_reply_probability: float = max_probability
-        self.current_reply_probability: float = 0.0
-        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 ---
-
-    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
-        if time_delta > 0:
-            # 指数衰减: interest = interest * (decay_rate ^ time_delta)
-            # 添加处理极小兴趣值避免 math domain error
-            old_interest = self.interest_level
-            if self.interest_level < 1e-9:
-                self.interest_level = 0.0
-            else:
-                # 检查 decay_rate_per_second 是否为非正数，避免 math domain error
-                if self.decay_rate_per_second <= 0:
-                    logger.warning(
-                        f"InterestChatting encountered non-positive decay rate: {self.decay_rate_per_second}. Setting interest to 0."
-                    )
-                    self.interest_level = 0.0
-                # 检查 interest_level 是否为负数，虽然理论上不应发生，但以防万一
-                elif self.interest_level < 0:
-                    logger.warning(
-                        f"InterestChatting encountered negative interest level: {self.interest_level}. Setting interest to 0."
-                    )
-                    self.interest_level = 0.0
-                else:
-                    try:
-                        decay_factor = math.pow(self.decay_rate_per_second, time_delta)
-                        self.interest_level *= decay_factor
-                    except ValueError as e:
-                        # 捕获潜在的 math domain error，例如对负数开非整数次方（虽然已加保护）
-                        logger.error(
-                            f"Math error during decay calculation: {e}. Rate: {self.decay_rate_per_second}, Delta: {time_delta}, Level: {self.interest_level}. Setting interest to 0."
-                        )
-                        self.interest_level = 0.0
-
-            # 防止低于阈值 (如果需要)
-            # self.interest_level = max(self.interest_level, MIN_INTEREST_THRESHOLD)
-
-            # 只有在兴趣值发生变化时才更新时间戳
-            if old_interest != self.interest_level:
-                self.last_update_time = current_time
-
-    def _update_reply_probability(self, current_time: float):
-        """根据当前兴趣是否超过阈值及时间差，更新回复概率"""
-        time_delta = current_time - self.last_update_time
-        if time_delta <= 0:
-            return  # 时间未前进，无需更新
-
-        currently_above = self.interest_level >= self.trigger_threshold
-
-        if currently_above:
-            if not self.is_above_threshold:
-                # 刚跨过阈值，重置为基础概率
-                self.current_reply_probability = self.base_reply_probability
-                logger.debug(
-                    f"兴趣跨过阈值 ({self.trigger_threshold}). 概率重置为基础值: {self.base_reply_probability:.4f}"
-                )
-            else:
-                # 持续高于阈值，线性增加概率
-                increase_amount = self.probability_increase_rate * time_delta
-                self.current_reply_probability += increase_amount
-                # logger.debug(f"兴趣高于阈值 ({self.trigger_threshold}) 持续 {time_delta:.2f}秒. 概率增加 {increase_amount:.4f} 到 {self.current_reply_probability:.4f}")
-
-            # 限制概率不超过最大值
-            self.current_reply_probability = min(self.current_reply_probability, self.max_reply_probability)
-
-        else:
-            if 0 < self.probability_decay_factor < 1:
-                decay_multiplier = math.pow(self.probability_decay_factor, time_delta)
-                # old_prob = self.current_reply_probability
-                self.current_reply_probability *= decay_multiplier
-                # 避免因浮点数精度问题导致概率略微大于0，直接设为0
-                if self.current_reply_probability < 1e-6:
-                    self.current_reply_probability = 0.0
-                # logger.debug(f"兴趣低于阈值 ({self.trigger_threshold}) 持续 {time_delta:.2f}秒. 概率从 {old_prob:.4f} 衰减到 {self.current_reply_probability:.4f} (因子: {self.probability_decay_factor})")
-            elif self.probability_decay_factor <= 0:
-                # 如果衰减因子无效或为0，直接清零
-                if self.current_reply_probability > 0:
-                    logger.warning(f"无效的衰减因子 ({self.probability_decay_factor}). 设置概率为0.")
-                    self.current_reply_probability = 0.0
-            # else: decay_factor >= 1, probability will not decay or increase, which might be intended in some cases.
-
-            # 确保概率不低于0
-            self.current_reply_probability = max(self.current_reply_probability, 0.0)
-
-        # 更新状态标记
-        self.is_above_threshold = currently_above
-        # 更新时间戳放在调用者处，确保 interest 和 probability 基于同一点更新
-
-    def increase_interest(self, current_time: float, value: float):
-        """根据传入的值增加兴趣值，并记录增加量"""
-        # 先更新概率和计算衰减（基于上次更新时间）
-        self._update_reply_probability(current_time)
-        self._calculate_decay(current_time)
-        # 应用增加
-        self.interest_level += value
-        self.interest_level = min(self.interest_level, self.max_interest)  # 不超过最大值
-        self.last_update_time = current_time  # 更新时间戳
-        self.last_interaction_time = current_time  # 更新最后交互时间
-
-    def decrease_interest(self, current_time: float, value: float):
-        """降低兴趣值并更新时间 (确保不低于0)"""
-        # 先更新概率（基于上次更新时间）
-        self._update_reply_probability(current_time)
-        # 注意：降低兴趣度是否需要先衰减？取决于具体逻辑，这里假设不衰减直接减
-        self.interest_level -= value
-        self.interest_level = max(self.interest_level, 0.0)  # 确保不低于0
-        self.last_update_time = current_time  # 降低也更新时间戳
-        self.last_interaction_time = current_time  # 更新最后交互时间
-
-    def get_interest(self) -> float:
-        """获取当前兴趣值 (计算衰减后)"""
-        # 注意：这个方法现在会触发概率和兴趣的更新
-        current_time = time.time()
-        self._update_reply_probability(current_time)
-        self._calculate_decay(current_time)
-        self.last_update_time = current_time  # 更新时间戳
-        return self.interest_level
-
-    def get_state(self) -> dict:
-        """获取当前状态字典"""
-        # 调用 get_interest 来确保状态已更新
-        interest = self.get_interest()
-        return {
-            "interest_level": round(interest, 2),
-            "last_update_time": self.last_update_time,
-            "current_reply_probability": round(self.current_reply_probability, 4),  # 添加概率到状态
-            "is_above_threshold": self.is_above_threshold,  # 添加阈值状态
-            "last_interaction_time": self.last_interaction_time,  # 新增：添加最后交互时间到状态
-            # 可以选择性地暴露 last_increase_amount 给状态，方便调试
-            # "last_increase_amount": round(self.last_increase_amount, 2)
-        }
-
-    def should_evaluate_reply(self) -> bool:
-        """
-        判断是否应该触发一次回复评估。
-        首先更新概率状态，然后根据当前概率进行随机判断。
-        """
-        current_time = time.time()
-        # 确保概率是基于最新兴趣值计算的
-        self._update_reply_probability(current_time)
-        # 更新兴趣衰减（如果需要，取决于逻辑，这里保持和 get_interest 一致）
-        # self._calculate_decay(current_time)
-        # self.last_update_time = current_time # 更新时间戳
-
-        if self.current_reply_probability > 0:
-            # 只有在阈值之上且概率大于0时才有可能触发
-            trigger = random.random() < self.current_reply_probability
-            # if trigger:
-            #     logger.info(f"回复概率评估触发! 概率: {self.current_reply_probability:.4f}, 阈值: {self.trigger_threshold}, 兴趣: {self.interest_level:.2f}")
-            #     # 可选：触发后是否重置/降低概率？根据需要决定
-            #     # self.current_reply_probability = self.base_reply_probability # 例如，触发后降回基础概率
-            #     # self.current_reply_probability *= 0.5 # 例如，触发后概率减半
-            # else:
-            #      logger.debug(f"回复概率评估未触发。概率: {self.current_reply_probability:.4f}")
-            return trigger
-        else:
-            # logger.debug(f"Reply evaluation check: Below threshold or zero probability. Probability: {self.current_reply_probability:.4f}")
-            return False
-
-
-class InterestManager:
-    _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):
-        if not self._initialized:
-            with self._lock:
-                # 确保初始化也只执行一次
-                if not self._initialized:
-                    logger.info("Initializing InterestManager singleton...")
-                    # key: stream_id (str), value: InterestChatting instance
-                    self.interest_dict: dict[str, InterestChatting] = {}
-                    # 保留旧的快照文件路径变量，尽管此任务不再写入
-                    # self._snapshot_log_file_path = os.path.join(LOG_DIRECTORY, LOG_FILENAME)
-                    # 定义新的历史日志文件路径
-                    self._history_log_file_path = os.path.join(LOG_DIRECTORY, HISTORY_LOG_FILENAME)
-                    self._ensure_log_directory()
-                    self._cleanup_task = None
-                    self._logging_task = None  # 添加日志任务变量
-                    self._initialized = True
-                    logger.info("InterestManager initialized.")  # 修改日志消息
-                    self._decay_task = None  # 新增：衰减任务变量
-
-    def _ensure_log_directory(self):
-        """确保日志目录存在"""
-        try:
-            os.makedirs(LOG_DIRECTORY, exist_ok=True)
-            logger.info(f"Log directory '{LOG_DIRECTORY}' ensured.")
-        except OSError as e:
-            logger.error(f"Error creating log directory '{LOG_DIRECTORY}': {e}")
-
-    async def _periodic_cleanup_task(self, interval_seconds: int, max_age_seconds: int):
-        """后台清理任务的异步函数"""
-        while True:
-            await asyncio.sleep(interval_seconds)
-            logger.info(f"运行定期清理 (间隔: {interval_seconds}秒)...")
-            self.cleanup_inactive_chats(max_age_seconds=max_age_seconds)
-
-    async def _periodic_log_task(self, interval_seconds: int):
-        """后台日志记录任务的异步函数 (记录历史数据，包含 group_name)"""
-        while True:
-            await asyncio.sleep(interval_seconds)
-            # logger.debug(f"运行定期历史记录 (间隔: {interval_seconds}秒)...")
-            try:
-                current_timestamp = time.time()
-                all_states = self.get_all_interest_states()  # 获取当前所有状态
-
-                # 以追加模式打开历史日志文件
-                with open(self._history_log_file_path, "a", encoding="utf-8") as f:
-                    count = 0
-                    for stream_id, state in all_states.items():
-                        # *** Get group name from ChatManager ***
-                        group_name = stream_id  # Default to stream_id
-                        try:
-                            # Use the imported chat_manager instance
-                            chat_stream = chat_manager.get_stream(stream_id)
-                            if chat_stream and chat_stream.group_info:
-                                group_name = chat_stream.group_info.group_name
-                            elif chat_stream and not chat_stream.group_info:
-                                # Handle private chats - maybe use user nickname?
-                                group_name = (
-                                    f"私聊_{chat_stream.user_info.user_nickname}"
-                                    if chat_stream.user_info
-                                    else stream_id
-                                )
-                        except Exception as e:
-                            logger.warning(f"Could not get group name for stream_id {stream_id}: {e}")
-                            # Fallback to stream_id is already handled by default value
-
-                        log_entry = {
-                            "timestamp": round(current_timestamp, 2),
-                            "stream_id": stream_id,
-                            "interest_level": state.get("interest_level", 0.0),  # 确保有默认值
-                            "group_name": group_name,  # *** Add group_name ***
-                            # --- 新增：记录概率相关信息 ---
-                            "reply_probability": state.get("current_reply_probability", 0.0),
-                            "is_above_threshold": state.get("is_above_threshold", False),
-                            # --- 结束新增 ---
-                        }
-                        # 将每个条目作为单独的 JSON 行写入
-                        f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")
-                        count += 1
-                # logger.debug(f"Successfully appended {count} interest history entries to {self._history_log_file_path}")
-
-                # 注意：不再写入快照文件 interest_log.json
-                # 如果需要快照文件，可以在这里单独写入 self._snapshot_log_file_path
-                # 例如：
-                # with open(self._snapshot_log_file_path, 'w', encoding='utf-8') as snap_f:
-                #     json.dump(all_states, snap_f, indent=4, ensure_ascii=False)
-                # logger.debug(f"Successfully wrote snapshot to {self._snapshot_log_file_path}")
-
-            except IOError as e:
-                logger.error(f"Error writing interest history log to {self._history_log_file_path}: {e}")
-            except Exception as e:
-                logger.error(f"Unexpected error during periodic history logging: {e}")
-
-    async def _periodic_decay_task(self):
-        """后台衰减任务的异步函数，每秒更新一次所有实例的衰减"""
-        while True:
-            await asyncio.sleep(1)  # 每秒运行一次
-            current_time = time.time()
-            # logger.debug("Running periodic decay calculation...") # 调试日志，可能过于频繁
-
-            # 创建字典项的快照进行迭代，避免在迭代时修改字典的问题
-            items_snapshot = list(self.interest_dict.items())
-            count = 0
-            for stream_id, chatting in items_snapshot:
-                try:
-                    # 调用 InterestChatting 实例的衰减方法
-                    chatting._calculate_decay(current_time)
-                    count += 1
-                except Exception as e:
-                    logger.error(f"Error calculating decay for stream_id {stream_id}: {e}")
-            # if count > 0: # 仅在实际处理了项目时记录日志，避免空闲时刷屏
-            #     logger.debug(f"Applied decay to {count} streams.")
-
-    async def start_background_tasks(self):
-        """启动清理，启动衰减，启动记录，启动启动启动启动启动"""
-        if self._cleanup_task is None or self._cleanup_task.done():
-            self._cleanup_task = asyncio.create_task(
-                self._periodic_cleanup_task(
-                    interval_seconds=CLEANUP_INTERVAL_SECONDS, max_age_seconds=INACTIVE_THRESHOLD_SECONDS
-                )
-            )
-            logger.info(
-                f"已创建定期清理任务。间隔时间: {CLEANUP_INTERVAL_SECONDS}秒, 不活跃阈值: {INACTIVE_THRESHOLD_SECONDS}秒"
-            )
-        else:
-            logger.warning("跳过创建清理任务:任务已在运行或存在。")
-
-        if self._logging_task is None or self._logging_task.done():
-            self._logging_task = asyncio.create_task(self._periodic_log_task(interval_seconds=LOG_INTERVAL_SECONDS))
-            logger.info(f"已创建定期日志任务。间隔时间: {LOG_INTERVAL_SECONDS}秒")
-        else:
-            logger.warning("跳过创建日志任务:任务已在运行或存在。")
-
-        # 启动新的衰减任务
-        if self._decay_task is None or self._decay_task.done():
-            self._decay_task = asyncio.create_task(self._periodic_decay_task())
-            logger.info("已创建定期衰减任务。间隔时间: 1秒")
-        else:
-            logger.warning("跳过创建衰减任务:任务已在运行或存在。")
-
-    def get_all_interest_states(self) -> dict[str, dict]:
-        """获取所有聊天流的当前兴趣状态"""
-        # 不再需要 current_time, 因为 get_state 现在不接收它
-        states = {}
-        # 创建副本以避免在迭代时修改字典
-        items_snapshot = list(self.interest_dict.items())
-        for stream_id, chatting in items_snapshot:
-            try:
-                # 直接调用 get_state，它会使用内部的 get_interest 获取已更新的值
-                states[stream_id] = chatting.get_state()
-            except Exception as e:
-                logger.warning(f"Error getting state for stream_id {stream_id}: {e}")
-        return states
-
-    def get_interest_chatting(self, stream_id: str) -> Optional[InterestChatting]:
-        """获取指定流的 InterestChatting 实例，如果不存在则返回 None"""
-        return self.interest_dict.get(stream_id)
-
-    def _get_or_create_interest_chatting(self, stream_id: str) -> InterestChatting:
-        """获取或创建指定流的 InterestChatting 实例 (线程安全)"""
-        if stream_id not in self.interest_dict:
-            logger.debug(f"创建兴趣流: {stream_id}")
-            # --- 修改：创建时传入概率相关参数 (如果需要定制化，否则使用默认值) ---
-            self.interest_dict[stream_id] = InterestChatting(
-                # decay_rate=..., max_interest=..., # 可以从配置读取
-                trigger_threshold=REPLY_TRIGGER_THRESHOLD,  # 使用全局常量
-                base_reply_probability=BASE_REPLY_PROBABILITY,
-                increase_rate=PROBABILITY_INCREASE_RATE_PER_SECOND,
-                decay_factor=PROBABILITY_DECAY_FACTOR_PER_SECOND,
-                max_probability=MAX_REPLY_PROBABILITY,
-            )
-            # --- 结束修改 ---
-            # 首次创建时兴趣为 0，由第一次消息的 activate rate 决定初始值
-        return self.interest_dict[stream_id]
-
-    def get_interest(self, stream_id: str) -> float:
-        """获取指定聊天流当前的兴趣度 (值由后台任务更新)"""
-        # current_time = time.time() # 不再需要获取当前时间
-        interest_chatting = self._get_or_create_interest_chatting(stream_id)
-        # 直接调用修改后的 get_interest，不传入时间
-        return interest_chatting.get_interest()
-
-    def increase_interest(self, stream_id: str, value: float):
-        """当收到消息时，增加指定聊天流的兴趣度"""
-        current_time = time.time()
-        interest_chatting = self._get_or_create_interest_chatting(stream_id)
-        # 调用修改后的 increase_interest，不再传入 message
-        interest_chatting.increase_interest(current_time, value)
-        stream_name = chat_manager.get_stream_name(stream_id) or stream_id  # 获取流名称
-        logger.debug(
-            f"增加了聊天流 {stream_name} 的兴趣度 {value:.2f}，当前值为 {interest_chatting.interest_level:.2f}"
-        )  # 更新日志
-
-    def decrease_interest(self, stream_id: str, value: float):
-        """降低指定聊天流的兴趣度"""
-        current_time = time.time()
-        # 尝试获取，如果不存在则不做任何事
-        interest_chatting = self.get_interest_chatting(stream_id)
-        if interest_chatting:
-            interest_chatting.decrease_interest(current_time, value)
-            stream_name = chat_manager.get_stream_name(stream_id) or stream_id  # 获取流名称
-            logger.debug(
-                f"降低了聊天流 {stream_name} 的兴趣度 {value:.2f}，当前值为 {interest_chatting.interest_level:.2f}"
-            )
-        else:
-            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 cleanup_inactive_chats(self, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):
-        """
-        清理长时间不活跃的聊天流记录
-        max_age_seconds: 超过此时间未更新的将被清理
-        """
-        current_time = time.time()
-        keys_to_remove = []
-        initial_count = len(self.interest_dict)
-        # with self._lock: # 如果需要锁整个迭代过程
-        # 创建副本以避免在迭代时修改字典
-        items_snapshot = list(self.interest_dict.items())
-
-        for stream_id, chatting in items_snapshot:
-            # 先计算当前兴趣，确保是最新的
-            # 加锁保护 chatting 对象状态的读取和可能的修改
-            # with self._lock: # 如果 InterestChatting 内部操作不是原子的
-            last_interaction = chatting.last_interaction_time  # 使用最后交互时间
-            should_remove = False
-            reason = ""
-            # 只有设置了 max_age_seconds 才检查时间
-            if (
-                max_age_seconds is not None and (current_time - last_interaction) > max_age_seconds
-            ):  # 使用 last_interaction
-                should_remove = True
-                reason = f"inactive time ({current_time - last_interaction:.0f}s) > max age ({max_age_seconds}s)"  # 更新日志信息
-
-            if should_remove:
-                keys_to_remove.append(stream_id)
-                stream_name = chat_manager.get_stream_name(stream_id) or stream_id  # 获取流名称
-                logger.debug(f"Marking stream {stream_name} for removal. Reason: {reason}")
-
-        if keys_to_remove:
-            logger.info(f"清理识别到 {len(keys_to_remove)} 个不活跃/低兴趣的流。")
-            # with self._lock: # 确保删除操作的原子性
-            for key in keys_to_remove:
-                # 再次检查 key 是否存在，以防万一在迭代和删除之间状态改变
-                if key in self.interest_dict:
-                    del self.interest_dict[key]
-                    stream_name = chat_manager.get_stream_name(key) or key  # 获取流名称
-                    logger.debug(f"移除了流: {stream_name}")
-            final_count = initial_count - len(keys_to_remove)
-            logger.info(f"清理完成。移除了 {len(keys_to_remove)} 个流。当前数量: {final_count}")
-        else:
-            logger.info(f"清理完成。没有流符合移除条件。当前数量: {initial_count}")

src/plugins/chat_module/heartFC_chat/messagesender.py

@@ -220,9 +220,8 @@ class MessageManager:
                 await asyncio.sleep(typing_time)
                 logger.debug(f"\n{message_earliest.processed_plain_text},{typing_time},等待输入时间结束\n")
 
-                await self.storage.store_message(message_earliest, message_earliest.chat_stream)
-
                 await MessageSender().send_message(message_earliest)
+                await self.storage.store_message(message_earliest, message_earliest.chat_stream)
 
                 container.remove_message(message_earliest)
 

src/plugins/chat_module/heartFC_chat/pf_chatting.py

@@ -15,6 +15,9 @@ from src.config.config import global_config
 from src.plugins.chat.utils_image import image_path_to_base64  # Local import needed after move
 from src.plugins.utils.timer_calculater import Timer  # <--- Import Timer
 
+INITIAL_DURATION = 60.0
+
+
 # 定义日志配置 (使用 loguru 格式)
 interest_log_config = LogConfig(
     console_format=PFC_STYLE_CONFIG["console_format"],  # 使用默认控制台格式
@@ -25,7 +28,7 @@ logger = get_module_logger("PFCLoop", config=interest_log_config)  # Logger Name
 
 # Forward declaration for type hinting
 if TYPE_CHECKING:
-    from .heartFC_controler import HeartFC_Controller
+    from .heartFC_controler import HeartFCController
 
 PLANNER_TOOL_DEFINITION = [
     {
@@ -61,13 +64,13 @@ class PFChatting:
     只要计时器>0，循环就会继续。
     """
 
-    def __init__(self, chat_id: str, heartfc_controller_instance: "HeartFC_Controller"):
+    def __init__(self, chat_id: str, heartfc_controller_instance: "HeartFCController"):
         """
         初始化PFChatting实例。
 
         Args:
             chat_id: The identifier for the chat stream (e.g., stream_id).
-            heartfc_controller_instance: 访问共享资源和方法的主HeartFC_Controller实例。
+            heartfc_controller_instance: 访问共享资源和方法的主HeartFCController实例。
         """
         self.heartfc_controller = heartfc_controller_instance  # Store the controller instance
         self.stream_id: str = chat_id
@@ -91,7 +94,7 @@ class PFChatting:
         self._loop_active: bool = False  # Is the loop currently running?
         self._loop_task: Optional[asyncio.Task] = None  # Stores the main loop task
         self._trigger_count_this_activation: int = 0  # Counts triggers within an active period
-        self._initial_duration: float = 60.0  # 首次触发增加的时间
+        self._initial_duration: float = INITIAL_DURATION  # 首次触发增加的时间
         self._last_added_duration: float = self._initial_duration  # <--- 新增：存储上次增加的时间
 
     def _get_log_prefix(self) -> str:
@@ -374,6 +377,22 @@ class PFChatting:
                             )
                             action_taken_this_cycle = False
 
+                    # --- Print Timer Results --- #
+                    if cycle_timers:  # 先检查cycle_timers是否非空
+                        timer_strings = []
+                        for name, elapsed in cycle_timers.items():
+                            # 直接格式化存储在字典中的浮点数 elapsed
+                            formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒"
+                            timer_strings.append(f"{name}: {formatted_time}")
+
+                        if timer_strings:  # 如果有有效计时器数据才打印
+                            logger.debug(
+                                f"{log_prefix} test testtesttesttesttesttesttesttesttesttest Cycle Timers: {'; '.join(timer_strings)}"
+                            )
+
+                    # --- Timer Decrement --- #
+                    cycle_duration = time.monotonic() - loop_cycle_start_time
+
                 except Exception as e_cycle:
                     logger.error(f"{log_prefix} 循环周期执行时发生错误: {e_cycle}")
                     logger.error(traceback.format_exc())
@@ -387,21 +406,6 @@ class PFChatting:
                         self._processing_lock.release()
                         logger.trace(f"{log_prefix} 循环释放了处理锁.")
 
-                # --- Print Timer Results --- #
-                if cycle_timers:  # 先检查cycle_timers是否非空
-                    timer_strings = []
-                    for name, elapsed in cycle_timers.items():
-                        # 直接格式化存储在字典中的浮点数 elapsed
-                        formatted_time = f"{elapsed * 1000:.2f}毫秒" if elapsed < 1 else f"{elapsed:.2f}秒"
-                        timer_strings.append(f"{name}: {formatted_time}")
-
-                    if timer_strings:  # 如果有有效计时器数据才打印
-                        logger.debug(
-                            f"{log_prefix} test testtesttesttesttesttesttesttesttesttest Cycle Timers: {'; '.join(timer_strings)}"
-                        )
-
-                # --- Timer Decrement --- #
-                cycle_duration = time.monotonic() - loop_cycle_start_time
                 async with self._timer_lock:
                     self._loop_timer -= cycle_duration
                     # Log timer decrement less aggressively
@@ -749,7 +753,7 @@ class PFChatting:
             # --- Generate Response with LLM --- #
             # Access gpt instance via controller
             gpt_instance = self.heartfc_controller.gpt
-            logger.debug(f"{log_prefix}[Replier-{thinking_id}] Calling LLM to generate response...")
+            # logger.debug(f"{log_prefix}[Replier-{thinking_id}] Calling LLM to generate response...")
 
             # Ensure generate_response has access to current_mind if it's crucial context
             response_set = await gpt_instance.generate_response(
@@ -771,7 +775,7 @@ class PFChatting:
             logger.error(traceback.format_exc())
             return None
 
-    # --- Methods moved from HeartFC_Controller start ---
+    # --- Methods moved from HeartFCController start ---
     async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]) -> Optional[str]:
         """创建思考消息 (尝试锚定到 anchor_message)"""
         if not anchor_message or not anchor_message.chat_stream:

src/plugins/chat_module/heartFC_chat/reasoning_chat.py

@@ -0,0 +1,425 @@
+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 src.heart_flow.heartflow import heartflow
+from .heartFC_controler import HeartFCController
+
+# 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()
+            # 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
+            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 = HeartFCController.get_instance()  # 获取控制器实例
+        stream_id = chat.stream_id  # 获取 stream_id
+
+        if not controller:
+            logger.error(f"无法获取 HeartFCController 实例，无法检查 PFChatting 状态。stream: {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?
+
+        logger.info(f"[{stream_id}] 兴趣消息监控任务启动。")  # 增加启动日志
+        while True:
+            await asyncio.sleep(1)  # 每秒检查一次
+
+            # --- 修改：通过 heartflow 获取 subheartflow 和 interest_dict --- #
+            subheartflow = heartflow.get_subheartflow(stream_id)
+
+            # 检查 subheartflow 是否存在以及是否被标记停止
+            if not subheartflow or subheartflow.should_stop:
+                logger.info(f"[{stream_id}] SubHeartflow 不存在或已停止，兴趣消息监控任务退出。")
+                break  # 退出循环，任务结束
+
+            # 从 subheartflow 获取 interest_dict
+            interest_dict = subheartflow.get_interest_dict()
+            # --- 结束修改 --- #
+
+            # 创建 items 快照进行迭代，避免在迭代时修改字典
+            items_to_process = list(interest_dict.items())
+
+            if not items_to_process:
+                continue  # 没有需要处理的消息，继续等待
+
+            # logger.debug(f"[{stream_id}] 发现 {len(items_to_process)} 条待处理兴趣消息。") # 调试日志
+
+            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(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"[{stream_id}] PFChatting 活跃，已跳过并移除兴趣消息 {msg_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"[{stream_id}] 正在处理兴趣消息 {msg_id} (兴趣值: {interest_value:.2f})" )
+                    await self.normal_reasoning_chat(
+                        message=message,
+                        chat=chat,  # chat 对象仍然有效
+                        is_mentioned=is_mentioned,
+                        interested_rate=interest_value,  # 使用从字典获取的原始兴趣值
+                    )
+                    # logger.debug(f"[{stream_id}] 处理完成消息 {msg_id}")
+                except Exception as e:
+                    logger.error(f"[{stream_id}] 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
+                    # 使用 pop(key, None) 避免 Key Error
+                    removed_item = interest_dict.pop(msg_id, None)
+                    if removed_item:
+                        logger.debug(f"[{stream_id}] 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=message,
+                        thinking_id=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(f"[{chat.stream_id}] 模型未生成回复内容")
+                # 如果模型未生成回复，移除思考消息
+                container = message_manager.get_container(chat.stream_id)
+                # thinking_message = None
+                for msg in container.messages[:]:  # Iterate over a copy
+                    if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id:
+                        # thinking_message = msg
+                        container.messages.remove(msg)
+                        logger.debug(f"[{chat.stream_id}] 已移除未产生回复的思考消息 {thinking_id}")
+                        break
+                return  # 不发送回复
+
+            logger.info(f"[{chat.stream_id}] 回复内容: {response_set}")
+
+            # 发送回复
+            with Timer("消息发送", timing_results):
+                first_bot_msg = await self._send_response_messages(message, chat, response_set, thinking_id)
+
+            info_catcher.catch_after_response(timing_results["消息发送"], 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[0])
+
+            # 更新关系情绪
+            with Timer("关系更新", 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 启动兴趣消息监控任务（如果尚未运行）。"""
+        stream_id = chat.stream_id
+        if stream_id not in self._interest_monitoring_tasks or self._interest_monitoring_tasks[stream_id].done():
+            logger.info(f"为聊天流 {stream_id} 启动兴趣消息监控任务...")
+            # 创建新任务
+            task = asyncio.create_task(self._find_interested_message(chat))
+            # 添加完成回调
+            task.add_done_callback(lambda t: self._handle_task_completion(stream_id, t))
+            self._interest_monitoring_tasks[stream_id] = task
+        # else:
+        # logger.debug(f"聊天流 {stream_id} 的兴趣消息监控任务已在运行。")
+
+    def _handle_task_completion(self, stream_id: str, task: asyncio.Task):
+        """兴趣监控任务完成时的回调函数。"""
+        try:
+            # 检查任务是否因异常而结束
+            exception = task.exception()
+            if exception:
+                logger.error(f"聊天流 {stream_id} 的兴趣监控任务因异常结束: {exception}")
+                logger.error(traceback.format_exc())  # 记录完整的 traceback
+            else:
+                logger.info(f"聊天流 {stream_id} 的兴趣监控任务正常结束。")
+        except asyncio.CancelledError:
+            logger.info(f"聊天流 {stream_id} 的兴趣监控任务被取消。")
+        except Exception as e:
+            logger.error(f"处理聊天流 {stream_id} 任务完成回调时出错: {e}")
+        finally:
+            # 从字典中移除已完成或取消的任务
+            if stream_id in self._interest_monitoring_tasks:
+                del self._interest_monitoring_tasks[stream_id]
+                logger.debug(f"已从监控任务字典中移除 {stream_id}")
+
+    async def stop_monitoring_interest(self, stream_id: str):
+        """停止指定聊天流的兴趣监控任务。"""
+        if stream_id in self._interest_monitoring_tasks:
+            task = self._interest_monitoring_tasks[stream_id]
+            if task and not task.done():
+                task.cancel()  # 尝试取消任务
+                logger.info(f"尝试取消聊天流 {stream_id} 的兴趣监控任务。")
+                try:
+                    await task  # 等待任务响应取消
+                except asyncio.CancelledError:
+                    logger.info(f"聊天流 {stream_id} 的兴趣监控任务已成功取消。")
+                except Exception as e:
+                    logger.error(f"等待聊天流 {stream_id} 监控任务取消时出现异常: {e}")
+            # 在回调函数 _handle_task_completion 中移除任务
+        # else:
+        # logger.debug(f"聊天流 {stream_id} 没有正在运行的兴趣监控任务可停止。")

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

@@ -1,25 +1,26 @@
 import time
-from random import random
 import traceback
-from typing import List
+from random import random
-from ...memory_system.Hippocampus import HippocampusManager
+from typing import List, Optional
-from ...moods.moods import MoodManager
+
-from ....config.config import global_config
+from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
-from ...chat.emoji_manager import emoji_manager
+from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
 from .reasoning_generator import ResponseGenerator
+from ...chat.chat_stream import chat_manager
+from ...chat.emoji_manager import emoji_manager
 from ...chat.message import MessageSending, MessageRecv, MessageThinking, MessageSet
+from ...chat.message_buffer import message_buffer
 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 ...memory_system.Hippocampus import HippocampusManager
 from ...message import UserInfo, Seg
-from src.common.logger import get_module_logger, CHAT_STYLE_CONFIG, LogConfig
+from ...moods.moods import MoodManager
-from ...chat.chat_stream import chat_manager
 from ...person_info.relationship_manager import relationship_manager
-from ...chat.message_buffer import message_buffer
+from ...storage.storage import MessageStorage
-from src.plugins.respon_info_catcher.info_catcher import info_catcher_manager
 from ...utils.timer_calculater import Timer
+from ...willing.willing_manager import willing_manager
+from ....config.config import global_config
 
 # 定义日志配置
 chat_config = LogConfig(
@@ -35,7 +36,6 @@ class ReasoningChat:
         self.storage = MessageStorage()
         self.gpt = ResponseGenerator()
         self.mood_manager = MoodManager.get_instance()
-        self.mood_manager.start_mood_update()
 
     @staticmethod
     async def _create_thinking_message(message, chat, userinfo, messageinfo):
@@ -61,7 +61,7 @@ class ReasoningChat:
         return thinking_id
 
     @staticmethod
-    async def _send_response_messages(message, chat, response_set: List[str], thinking_id) -> MessageSending:
+    async def _send_response_messages(message, chat, response_set: List[str], thinking_id) -> Optional[MessageSending]:
         """发送回复消息"""
         container = message_manager.get_container(chat.stream_id)
         thinking_message = None
@@ -74,7 +74,7 @@ class ReasoningChat:
 
         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
+            return None
 
         thinking_start_time = thinking_message.thinking_start_time
         message_set = MessageSet(chat, thinking_id)
@@ -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)
+            # await willing_manager.bombing_buffer_message_handle(message.message_info.message_id)
-            willing_manager.delete(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/manually_alter_memory.py

@@ -5,7 +5,8 @@ import time
 from pathlib import Path
 import datetime
 from rich.console import Console
-from memory_manual_build import Memory_graph, Hippocampus  # 海马体和记忆图
+from Hippocampus import Hippocampus  # 海马体和记忆图
+
 
 from dotenv import load_dotenv
 
@@ -45,13 +46,13 @@ else:
 
 
 # 查询节点信息
-def query_mem_info(memory_graph: Memory_graph):
+def query_mem_info(hippocampus: Hippocampus):
     while True:
         query = input("\n请输入新的查询概念（输入'退出'以结束）：")
         if query.lower() == "退出":
             break
 
-        items_list = memory_graph.get_related_item(query)
+        items_list = hippocampus.memory_graph.get_related_item(query)
         if items_list:
             have_memory = False
             first_layer, second_layer = items_list
@@ -312,14 +313,11 @@ def alter_mem_edge(hippocampus: Hippocampus):
 async def main():
     start_time = time.time()
 
-    # 创建记忆图
-    memory_graph = Memory_graph()
-
     # 创建海马体
-    hippocampus = Hippocampus(memory_graph)
+    hippocampus = Hippocampus()
 
     # 从数据库同步数据
-    hippocampus.sync_memory_from_db()
+    hippocampus.entorhinal_cortex.sync_memory_from_db()
 
     end_time = time.time()
     logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
@@ -338,7 +336,7 @@ async def main():
             query = -1
 
         if query == 0:
-            query_mem_info(memory_graph)
+            query_mem_info(hippocampus.memory_graph)
         elif query == 1:
             add_mem_node(hippocampus)
         elif query == 2:
@@ -355,7 +353,7 @@ async def main():
             print("已结束操作")
             break
 
-        hippocampus.sync_memory_to_db()
+        hippocampus.entorhinal_cortex.sync_memory_to_db()
 
 
 if __name__ == "__main__":

src/plugins/message/message_base.py

@@ -12,7 +12,6 @@ class Seg:
             - 对于 text 类型，data 是字符串
             - 对于 image 类型，data 是 base64 字符串
             - 对于 seglist 类型，data 是 Seg 列表
-        translated_data: 经过翻译处理的数据（可选）
     """
 
     type: str

src/plugins/models/utils_model.py

@@ -2,9 +2,11 @@ import asyncio
 import json
 import re
 from datetime import datetime
-from typing import Tuple, Union
+from typing import Tuple, Union, Dict, Any
 
 import aiohttp
+from aiohttp.client import ClientResponse
+
 from src.common.logger import get_module_logger
 import base64
 from PIL import Image
@@ -16,19 +18,72 @@ from ...config.config import global_config
 logger = get_module_logger("model_utils")
 
 
+class PayLoadTooLargeError(Exception):
+    """自定义异常类，用于处理请求体过大错误"""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return "请求体过大，请尝试压缩图片或减少输入内容。"
+
+
+class RequestAbortException(Exception):
+    """自定义异常类，用于处理请求中断异常"""
+
+    def __init__(self, message: str, response: ClientResponse):
+        super().__init__(message)
+        self.message = message
+        self.response = response
+
+    def __str__(self):
+        return self.message
+
+
+class PermissionDeniedException(Exception):
+    """自定义异常类，用于处理访问拒绝的异常"""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+        self.message = message
+
+    def __str__(self):
+        return self.message
+
+
+# 常见Error Code Mapping
+error_code_mapping = {
+    400: "参数不正确",
+    401: "API key 错误，认证失败，请检查/config/bot_config.toml和.env中的配置是否正确哦~",
+    402: "账号余额不足",
+    403: "需要实名,或余额不足",
+    404: "Not Found",
+    429: "请求过于频繁，请稍后再试",
+    500: "服务器内部故障",
+    503: "服务器负载过高",
+}
+
+
 class LLMRequest:
     # 定义需要转换的模型列表，作为类变量避免重复
     MODELS_NEEDING_TRANSFORMATION = [
-        "o3-mini",
+        "o1",
-        "o1-mini",
-        "o1-preview",
         "o1-2024-12-17",
-        "o1-preview-2024-09-12",
+        "o1-mini",
-        "o3-mini-2025-01-31",
         "o1-mini-2024-09-12",
+        "o1-preview",
+        "o1-preview-2024-09-12",
+        "o1-pro",
+        "o1-pro-2025-03-19",
+        "o3",
+        "o3-2025-04-16",
+        "o3-mini",
+        "o3-mini-2025-01-31o4-mini",
+        "o4-mini-2025-04-16",
     ]
 
-    def __init__(self, model, **kwargs):
+    def __init__(self, model: dict, **kwargs):
         # 将大写的配置键转换为小写并从config中获取实际值
         try:
             self.api_key = os.environ[model["key"]]
@@ -37,7 +92,7 @@ class LLMRequest:
             logger.error(f"原始 model dict 信息：{model}")
             logger.error(f"配置错误：找不到对应的配置项 - {str(e)}")
             raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e
-        self.model_name = model["name"]
+        self.model_name: str = model["name"]
         self.params = kwargs
 
         self.stream = model.get("stream", False)
@@ -123,6 +178,7 @@ class LLMRequest:
         output_cost = (completion_tokens / 1000000) * self.pri_out
         return round(input_cost + output_cost, 6)
 
+    '''
     async def _execute_request(
         self,
         endpoint: str,
@@ -509,6 +565,404 @@ class LLMRequest:
 
         logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败")
         raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败")
+    '''
+
+    async def _prepare_request(
+        self,
+        endpoint: str,
+        prompt: str = None,
+        image_base64: str = None,
+        image_format: str = None,
+        payload: dict = None,
+        retry_policy: dict = None,
+    ) -> Dict[str, Any]:
+        """配置请求参数
+        Args:
+            endpoint: API端点路径 (如 "chat/completions")
+            prompt: prompt文本
+            image_base64: 图片的base64编码
+            image_format: 图片格式
+            payload: 请求体数据
+            retry_policy: 自定义重试策略
+            request_type: 请求类型
+        """
+
+        # 合并重试策略
+        default_retry = {
+            "max_retries": 3,
+            "base_wait": 10,
+            "retry_codes": [429, 413, 500, 503],
+            "abort_codes": [400, 401, 402, 403],
+        }
+        policy = {**default_retry, **(retry_policy or {})}
+
+        api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
+
+        stream_mode = self.stream
+
+        # 构建请求体
+        if image_base64:
+            payload = await self._build_payload(prompt, image_base64, image_format)
+        elif payload is None:
+            payload = await self._build_payload(prompt)
+
+        if stream_mode:
+            payload["stream"] = stream_mode
+
+        return {
+            "policy": policy,
+            "payload": payload,
+            "api_url": api_url,
+            "stream_mode": stream_mode,
+            "image_base64": image_base64,  # 保留必要的exception处理所需的原始数据
+            "image_format": image_format,
+            "prompt": prompt,
+        }
+
+    async def _execute_request(
+        self,
+        endpoint: str,
+        prompt: str = None,
+        image_base64: str = None,
+        image_format: str = None,
+        payload: dict = None,
+        retry_policy: dict = None,
+        response_handler: callable = None,
+        user_id: str = "system",
+        request_type: str = None,
+    ):
+        """统一请求执行入口
+        Args:
+            endpoint: API端点路径 (如 "chat/completions")
+            prompt: prompt文本
+            image_base64: 图片的base64编码
+            image_format: 图片格式
+            payload: 请求体数据
+            retry_policy: 自定义重试策略
+            response_handler: 自定义响应处理器
+            user_id: 用户ID
+            request_type: 请求类型
+        """
+        # 获取请求配置
+        request_content = await self._prepare_request(
+            endpoint, prompt, image_base64, image_format, payload, retry_policy
+        )
+        if request_type is None:
+            request_type = self.request_type
+        for retry in range(request_content["policy"]["max_retries"]):
+            try:
+                # 使用上下文管理器处理会话
+                headers = await self._build_headers()
+                # 似乎是openai流式必须要的东西,不过阿里云的qwq-plus加了这个没有影响
+                if request_content["stream_mode"]:
+                    headers["Accept"] = "text/event-stream"
+                async with aiohttp.ClientSession() as session:
+                    async with session.post(
+                        request_content["api_url"], headers=headers, json=request_content["payload"]
+                    ) as response:
+                        handled_result = await self._handle_response(
+                            response, request_content, retry, response_handler, user_id, request_type, endpoint
+                        )
+                        return handled_result
+            except Exception as e:
+                handled_payload, count_delta = await self._handle_exception(e, retry, request_content)
+                retry += count_delta  # 降级不计入重试次数
+                if handled_payload:
+                    # 如果降级成功，重新构建请求体
+                    request_content["payload"] = handled_payload
+                continue
+
+        logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败")
+        raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败")
+
+    async def _handle_response(
+        self,
+        response: ClientResponse,
+        request_content: Dict[str, Any],
+        retry_count: int,
+        response_handler: callable,
+        user_id,
+        request_type,
+        endpoint,
+    ) -> Union[Dict[str, Any], None]:
+        policy = request_content["policy"]
+        stream_mode = request_content["stream_mode"]
+        if response.status in policy["retry_codes"] or response.status in policy["abort_codes"]:
+            await self._handle_error_response(response, retry_count, policy)
+            return
+
+        response.raise_for_status()
+        result = {}
+        if stream_mode:
+            # 将流式输出转化为非流式输出
+            result = await self._handle_stream_output(response)
+        else:
+            result = await response.json()
+        return (
+            response_handler(result)
+            if response_handler
+            else self._default_response_handler(result, user_id, request_type, endpoint)
+        )
+
+    async def _handle_stream_output(self, response: ClientResponse) -> Dict[str, Any]:
+        flag_delta_content_finished = False
+        accumulated_content = ""
+        usage = None  # 初始化usage变量，避免未定义错误
+        reasoning_content = ""
+        content = ""
+        async for line_bytes in response.content:
+            try:
+                line = line_bytes.decode("utf-8").strip()
+                if not line:
+                    continue
+                if line.startswith("data:"):
+                    data_str = line[5:].strip()
+                    if data_str == "[DONE]":
+                        break
+                    try:
+                        chunk = json.loads(data_str)
+                        if flag_delta_content_finished:
+                            chunk_usage = chunk.get("usage", None)
+                            if chunk_usage:
+                                usage = chunk_usage  # 获取token用量
+                        else:
+                            delta = chunk["choices"][0]["delta"]
+                            delta_content = delta.get("content")
+                            if delta_content is None:
+                                delta_content = ""
+                            accumulated_content += delta_content
+                            # 检测流式输出文本是否结束
+                            finish_reason = chunk["choices"][0].get("finish_reason")
+                            if delta.get("reasoning_content", None):
+                                reasoning_content += delta["reasoning_content"]
+                            if finish_reason == "stop":
+                                chunk_usage = chunk.get("usage", None)
+                                if chunk_usage:
+                                    usage = chunk_usage
+                                    break
+                                # 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
+                                flag_delta_content_finished = True
+                    except Exception as e:
+                        logger.exception(f"模型 {self.model_name} 解析流式输出错误: {str(e)}")
+            except Exception as e:
+                if isinstance(e, GeneratorExit):
+                    log_content = f"模型 {self.model_name} 流式输出被中断，正在清理资源..."
+                else:
+                    log_content = f"模型 {self.model_name} 处理流式输出时发生错误: {str(e)}"
+                logger.warning(log_content)
+                # 确保资源被正确清理
+                try:
+                    await response.release()
+                except Exception as cleanup_error:
+                    logger.error(f"清理资源时发生错误: {cleanup_error}")
+                # 返回已经累积的内容
+                content = accumulated_content
+        if not content:
+            content = accumulated_content
+        think_match = re.search(r"<think>(.*?)</think>", content, re.DOTALL)
+        if think_match:
+            reasoning_content = think_match.group(1).strip()
+        content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
+        result = {
+            "choices": [
+                {
+                    "message": {
+                        "content": content,
+                        "reasoning_content": reasoning_content,
+                        # 流式输出可能没有工具调用，此处不需要添加tool_calls字段
+                    }
+                }
+            ],
+            "usage": usage,
+        }
+        return result
+
+    async def _handle_error_response(
+        self, response: ClientResponse, retry_count: int, policy: Dict[str, Any]
+    ) -> Union[Dict[str, any]]:
+        if response.status in policy["retry_codes"]:
+            wait_time = policy["base_wait"] * (2**retry_count)
+            logger.warning(f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试")
+            if response.status == 413:
+                logger.warning("请求体过大，尝试压缩...")
+                raise PayLoadTooLargeError("请求体过大")
+            elif response.status in [500, 503]:
+                logger.error(
+                    f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
+                )
+                raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
+            else:
+                logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
+                raise RuntimeError("请求限制(429)")
+        elif response.status in policy["abort_codes"]:
+            if response.status != 403:
+                raise RequestAbortException("请求出现错误，中断处理", response)
+            else:
+                raise PermissionDeniedException("模型禁止访问")
+
+    async def _handle_exception(
+        self, exception, retry_count: int, request_content: Dict[str, Any]
+    ) -> Union[Tuple[Dict[str, Any], int], Tuple[None, int]]:
+        policy = request_content["policy"]
+        payload = request_content["payload"]
+        wait_time = policy["base_wait"] * (2**retry_count)
+        if retry_count < policy["max_retries"] - 1:
+            keep_request = True
+        if isinstance(exception, RequestAbortException):
+            response = exception.response
+            logger.error(
+                f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
+            )
+            # 尝试获取并记录服务器返回的详细错误信息
+            try:
+                error_json = await response.json()
+                if error_json and isinstance(error_json, list) and len(error_json) > 0:
+                    # 处理多个错误的情况
+                    for error_item in error_json:
+                        if "error" in error_item and isinstance(error_item["error"], dict):
+                            error_obj: dict = error_item["error"]
+                            error_code = error_obj.get("code")
+                            error_message = error_obj.get("message")
+                            error_status = error_obj.get("status")
+                            logger.error(
+                                f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}"
+                            )
+                elif isinstance(error_json, dict) and "error" in error_json:
+                    # 处理单个错误对象的情况
+                    error_obj = error_json.get("error", {})
+                    error_code = error_obj.get("code")
+                    error_message = error_obj.get("message")
+                    error_status = error_obj.get("status")
+                    logger.error(f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}")
+                else:
+                    # 记录原始错误响应内容
+                    logger.error(f"服务器错误响应: {error_json}")
+            except Exception as e:
+                logger.warning(f"无法解析服务器错误响应: {str(e)}")
+            raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
+
+        elif isinstance(exception, PermissionDeniedException):
+            # 只针对硅基流动的V3和R1进行降级处理
+            if self.model_name.startswith("Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/":
+                old_model_name = self.model_name
+                self.model_name = self.model_name[4:]  # 移除"Pro/"前缀
+                logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
+
+                # 对全局配置进行更新
+                if global_config.llm_normal.get("name") == old_model_name:
+                    global_config.llm_normal["name"] = self.model_name
+                    logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
+                if global_config.llm_reasoning.get("name") == old_model_name:
+                    global_config.llm_reasoning["name"] = self.model_name
+                    logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
+
+                if payload and "model" in payload:
+                    payload["model"] = self.model_name
+
+                await asyncio.sleep(wait_time)
+                return payload, -1
+            raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(403)}")
+
+        elif isinstance(exception, PayLoadTooLargeError):
+            if keep_request:
+                image_base64 = request_content["image_base64"]
+                compressed_image_base64 = compress_base64_image_by_scale(image_base64)
+                new_payload = await self._build_payload(
+                    request_content["prompt"], compressed_image_base64, request_content["image_format"]
+                )
+                return new_payload, 0
+            else:
+                return None, 0
+
+        elif isinstance(exception, aiohttp.ClientError) or isinstance(exception, asyncio.TimeoutError):
+            if keep_request:
+                logger.error(f"模型 {self.model_name} 网络错误，等待{wait_time}秒后重试... 错误: {str(exception)}")
+                await asyncio.sleep(wait_time)
+                return None, 0
+            else:
+                logger.critical(f"模型 {self.model_name} 网络错误达到最大重试次数: {str(exception)}")
+                raise RuntimeError(f"网络请求失败: {str(exception)}")
+
+        elif isinstance(exception, aiohttp.ClientResponseError):
+            # 处理aiohttp抛出的，除了policy中的status的响应错误
+            if keep_request:
+                logger.error(
+                    f"模型 {self.model_name} HTTP响应错误，等待{wait_time}秒后重试... 状态码: {exception.status}, 错误: {exception.message}"
+                )
+                try:
+                    error_text = await exception.response.text()
+                    error_json = json.loads(error_text)
+                    if isinstance(error_json, list) and len(error_json) > 0:
+                        # 处理多个错误的情况
+                        for error_item in error_json:
+                            if "error" in error_item and isinstance(error_item["error"], dict):
+                                error_obj = error_item["error"]
+                                logger.error(
+                                    f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
+                                    f"状态={error_obj.get('status')}, "
+                                    f"消息={error_obj.get('message')}"
+                                )
+                    elif isinstance(error_json, dict) and "error" in error_json:
+                        error_obj = error_json.get("error", {})
+                        logger.error(
+                            f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
+                            f"状态={error_obj.get('status')}, "
+                            f"消息={error_obj.get('message')}"
+                        )
+                    else:
+                        logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
+                except (json.JSONDecodeError, TypeError) as json_err:
+                    logger.warning(
+                        f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}"
+                    )
+                except Exception as parse_err:
+                    logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
+
+                await asyncio.sleep(wait_time)
+                return None, 0
+            else:
+                logger.critical(
+                    f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {exception.status}, 错误: {exception.message}"
+                )
+                # 安全地检查和记录请求详情
+                handled_payload = await self._safely_record(request_content, payload)
+                logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {handled_payload}")
+                raise RuntimeError(
+                    f"模型 {self.model_name} API请求失败: 状态码 {exception.status}, {exception.message}"
+                )
+
+        else:
+            if keep_request:
+                logger.error(f"模型 {self.model_name} 请求失败，等待{wait_time}秒后重试... 错误: {str(exception)}")
+                await asyncio.sleep(wait_time)
+                return None, 0
+            else:
+                logger.critical(f"模型 {self.model_name} 请求失败: {str(exception)}")
+                # 安全地检查和记录请求详情
+                handled_payload = await self._safely_record(request_content, payload)
+                logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {handled_payload}")
+                raise RuntimeError(f"模型 {self.model_name} API请求失败: {str(exception)}")
+
+    async def _safely_record(self, request_content: Dict[str, Any], payload: Dict[str, Any]):
+        image_base64: str = request_content.get("image_base64")
+        image_format: str = request_content.get("image_format")
+        if (
+            image_base64
+            and payload
+            and isinstance(payload, dict)
+            and "messages" in payload
+            and len(payload["messages"]) > 0
+        ):
+            if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
+                content = payload["messages"][0]["content"]
+                if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
+                    payload["messages"][0]["content"][1]["image_url"]["url"] = (
+                        f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,"
+                        f"{image_base64[:10]}...{image_base64[-10:]}"
+                    )
+                # if isinstance(content, str) and len(content) > 100:
+                #     payload["messages"][0]["content"] = content[:100]
+        return payload
 
     async def _transform_parameters(self, params: dict) -> dict:
         """
@@ -532,30 +986,27 @@ class LLMRequest:
         # 复制一份参数，避免直接修改 self.params
         params_copy = await self._transform_parameters(self.params)
         if image_base64:
-            payload = {
+            messages = [
-                "model": self.model_name,
+                {
-                "messages": [
+                    "role": "user",
-                    {
+                    "content": [
-                        "role": "user",
+                        {"type": "text", "text": prompt},
-                        "content": [
+                        {
-                            {"type": "text", "text": prompt},
+                            "type": "image_url",
-                            {
+                            "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"},
-                                "type": "image_url",
+                        },
-                                "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"},
+                    ],
-                            },
+                }
-                        ],
+            ]
-                    }
-                ],
-                "max_tokens": global_config.max_response_length,
-                **params_copy,
-            }
         else:
-            payload = {
+            messages = [{"role": "user", "content": prompt}]
-                "model": self.model_name,
+        payload = {
-                "messages": [{"role": "user", "content": prompt}],
+            "model": self.model_name,
-                "max_tokens": global_config.max_response_length,
+            "messages": messages,
-                **params_copy,
+            **params_copy,
-            }
+        }
+        if "max_tokens" not in payload and "max_completion_tokens" not in payload:
+            payload["max_tokens"] = global_config.max_response_length
         # 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
         if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload:
             payload["max_completion_tokens"] = payload.pop("max_tokens")
@@ -648,11 +1099,10 @@ class LLMRequest:
 
     async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
         """异步方式根据输入的提示生成模型的响应"""
-        # 构建请求体
+        # 构建请求体，不硬编码max_tokens
         data = {
             "model": self.model_name,
             "messages": [{"role": "user", "content": prompt}],
-            "max_tokens": global_config.max_response_length,
             **self.params,
             **kwargs,
         }

src/plugins/person_info/person_info.py

@@ -169,7 +169,7 @@ class PersonInfoManager:
         """给某个用户取名"""
         if not person_id:
             logger.debug("取名失败：person_id不能为空")
-            return
+            return None
 
         old_name = await self.get_value(person_id, "person_name")
         old_reason = await self.get_value(person_id, "name_reason")
@@ -198,9 +198,9 @@ class PersonInfoManager:
                 "nickname": "昵称",
                 "reason": "理由"
             }"""
-            logger.debug(f"取名提示词：{qv_name_prompt}")
+            # logger.debug(f"取名提示词：{qv_name_prompt}")
             response = await self.qv_name_llm.generate_response(qv_name_prompt)
-            logger.debug(f"取名回复：{response}")
+            logger.debug(f"取名提示词：{qv_name_prompt}\n取名回复：{response}")
             result = self._extract_json_from_text(response[0])
 
             if not result["nickname"]:
@@ -217,7 +217,7 @@ class PersonInfoManager:
                 await self.update_one_field(person_id, "name_reason", result["reason"])
 
                 self.person_name_list[person_id] = result["nickname"]
-                logger.debug(f"用户 {person_id} 的名称已更新为 {result['nickname']}，原因：{result['reason']}")
+                # logger.debug(f"用户 {person_id} 的名称已更新为 {result['nickname']}，原因：{result['reason']}")
                 return result
             else:
                 existing_names += f"{result['nickname']}、"

src/plugins/person_info/relationship_manager.py

@@ -89,8 +89,8 @@ class RelationshipManager:
         person_id = person_info_manager.get_person_id(platform, user_id)
         is_qved = await person_info_manager.has_one_field(person_id, "person_name")
         old_name = await person_info_manager.get_value(person_id, "person_name")
-        print(f"old_name: {old_name}")
+        # print(f"old_name: {old_name}")
-        print(f"is_qved: {is_qved}")
+        # print(f"is_qved: {is_qved}")
         if is_qved and old_name is not None:
             return True
         else:

src/plugins/remote/remote.py

@@ -134,3 +134,4 @@ def main():
         heartbeat_thread.start()
 
         return heartbeat_thread  # 返回线程对象，便于外部控制
+    return None

template/bot_config_template.toml

@@ -1,8 +1,7 @@
 [inner]
-version = "1.3.1"
+version = "1.4.0"
 
-
+#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
-#以下是给开发人员阅读的，一般用户不需要阅读
 #如果你想要修改配置文件，请在修改后将version的值进行变更
 #如果新增项目，请在BotConfig类下新增相应的变量
 #1.如果你修改的是[]层级项目，例如你新增了 [memory],那么请在config.py的 load_config函数中的include_configs字典中新增"内容":{
@@ -19,11 +18,12 @@ version = "1.3.1"
 #     次版本号：当你做了向下兼容的功能性新增，
 #     修订号：当你做了向下兼容的问题修正。
 # 先行版本号及版本编译信息可以加到“主版本号.次版本号.修订号”的后面，作为延伸。
+#----以上是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 
 [bot]
-qq = 114514
+qq = 1145141919810
 nickname = "麦麦"
-alias_names = ["麦叠", "牢麦"]
+alias_names = ["麦叠", "牢麦"] #该选项还在调试中，暂时未生效
 
 [groups]
 talk_allowed = [
@@ -41,23 +41,24 @@ personality_sides = [
     "用一句话或几句话描述人格的一些细节",
     "用一句话或几句话描述人格的一些细节",
     "用一句话或几句话描述人格的一些细节",
-]# 条数任意
+]# 条数任意，不能为0, 该选项还在调试中，可能未完全生效
 
 [identity] #アイデンティティがない 生まれないらららら
 # 兴趣爱好 未完善，有些条目未使用
 identity_detail = [
     "身份特点",
     "身份特点",
-]# 条数任意
+]# 条数任意，不能为0, 该选项还在调试中，可能未完全生效
 #外貌特征
-height = 170 # 身高 单位厘米
+height = 170 # 身高 单位厘米 该选项还在调试中，暂时未生效
-weight = 50 # 体重 单位千克
+weight = 50 # 体重 单位千克 该选项还在调试中，暂时未生效
-age = 20 # 年龄 单位岁
+age = 20 # 年龄 单位岁 该选项还在调试中，暂时未生效
-gender = "男" # 性别
+gender = "男" # 性别 该选项还在调试中，暂时未生效
-appearance = "用几句话描述外貌特征" # 外貌特征
+appearance = "用几句话描述外貌特征" # 外貌特征 该选项还在调试中，暂时未生效
 
 [schedule]
-enable_schedule_gen = true # 是否启用日程表(尚未完成)
+enable_schedule_gen = true # 是否启用日程表
+enable_schedule_interaction = true # 日程表是否影响回复模式
 prompt_schedule_gen = "用几句话描述描述性格特点或行动规律，这个特征会用来生成日程表"
 schedule_doing_update_interval = 900 # 日程表更新间隔 单位秒
 schedule_temperature = 0.1 # 日程表温度，建议0.1-0.5
@@ -67,19 +68,25 @@ time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运
 nonebot-qq="http://127.0.0.1:18002/api/message"
 
 [response] #群聊的回复策略
-#reasoning：推理模式，麦麦会根据上下文进行推理，并给出回复
+enable_heart_flowC = true 
-#heart_flow：结合了PFC模式和心流模式，麦麦会进行主动的观察和回复，并给出回复
+# 该功能还在完善中
-response_mode = "heart_flow" # 回复策略，可选值：heart_flow（心流），reasoning（推理)
+# 是否启用heart_flowC(心流聊天,HFC)模式
+# 启用后麦麦会自主选择进入heart_flowC模式(持续一段时间），进行主动的观察和回复，并给出回复，比较消耗token
 
-#推理回复参数
+#一般回复参数
-model_r1_probability = 0.7 # 麦麦回答时选择主要回复模型1 模型的概率
+model_reasoning_probability = 0.7 # 麦麦回答时选择推理模型 模型的概率
-model_v3_probability = 0.3 # 麦麦回答时选择次要回复模型2 模型的概率
+model_normal_probability = 0.3 # 麦麦回答时选择一般模型 模型的概率
+
+[heartflow] #启用启用heart_flowC(心流聊天)模式时生效，需要填写以下参数
+reply_trigger_threshold = 3.0 # 心流聊天触发阈值，越低越容易进入心流聊天
+probability_decay_factor_per_second = 0.2 # 概率衰减因子，越大衰减越快，越高越容易退出心流聊天
+default_decay_rate_per_second = 0.98 # 默认衰减率，越大衰减越快，越高越难进入心流聊天
+initial_duration = 60 # 初始持续时间，越大心流聊天持续的时间越长
 
-[heartflow] # 注意：可能会消耗大量token，请谨慎开启，仅会使用v3模型
-sub_heart_flow_update_interval = 60 # 子心流更新频率，间隔 单位秒
-sub_heart_flow_freeze_time = 100 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
 sub_heart_flow_stop_time = 500 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
-heart_flow_update_interval = 600 # 心流更新频率，间隔 单位秒
+# sub_heart_flow_update_interval = 60 
+# sub_heart_flow_freeze_time = 100 
+# heart_flow_update_interval = 600 
 
 observation_context_size = 20 # 心流观察到的最长上下文大小，超过这个值的上下文会被压缩
 compressed_length = 5 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
@@ -87,11 +94,13 @@ compress_length_limit = 5 #最多压缩份数，超过该数值的压缩上下
 
 
 [message]
-max_context_size = 12 # 麦麦获得的上文数量，建议12，太短太长都会导致脑袋尖尖
+max_context_size = 12 # 麦麦回复时获得的上文数量，建议12，太短太长都会导致脑袋尖尖
-emoji_chance = 0.2 # 麦麦使用表情包的概率，设置为1让麦麦自己决定发不发
+emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发
-thinking_timeout = 60 # 麦麦最长思考时间，超过这个时间的思考会放弃
+thinking_timeout = 100 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢）
-max_response_length = 256 # 麦麦回答的最大token数
+max_response_length = 256 # 麦麦单次回答的最大token数
 message_buffer = true # 启用消息缓冲器？启用此项以解决消息的拆分问题，但会使麦麦的回复延迟
+
+# 以下是消息过滤，可以根据规则过滤特定消息，将不会读取这些消息
 ban_words = [
     # "403","张三"
     ]
@@ -103,22 +112,23 @@ ban_msgs_regex = [
     # "\\[CQ:at,qq=\\d+\\]" # 匹配@
 ]
 
-[willing]
+[willing] # 一般回复模式的回复意愿设置
 willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，动态模式：dynamic，mxp模式：mxp，自定义模式：custom（需要你自己实现）
 response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
 response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数
 down_frequency_rate = 3 # 降低回复频率的群组回复意愿降低系数 除法
-emoji_response_penalty = 0.1 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
+emoji_response_penalty = 0 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
 mentioned_bot_inevitable_reply = false # 提及 bot 必然回复
 at_bot_inevitable_reply = false # @bot 必然回复
 
 [emoji]
-max_emoji_num = 120 # 表情包最大数量
+max_emoji_num = 90 # 表情包最大数量
 max_reach_deletion = true # 开启则在达到最大数量时删除表情包，关闭则达到最大数量时不删除，只是不会继续收集表情包
 check_interval = 30 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
 auto_save = true  # 是否保存表情包和图片
-enable_check = false  # 是否启用表情包过滤
+
-check_prompt = "符合公序良俗" # 表情包过滤要求
+enable_check = false  # 是否启用表情包过滤，只有符合该要求的表情包才会被保存
+check_prompt = "符合公序良俗" # 表情包过滤要求，只有符合该要求的表情包才会被保存
 
 [memory]
 build_memory_interval = 2000 # 记忆构建间隔 单位秒   间隔越低，麦麦学习越多，但是冗余信息也会增多
@@ -131,7 +141,8 @@ forget_memory_interval = 1000 # 记忆遗忘间隔 单位秒   间隔越低，
 memory_forget_time = 24 #多长时间后的记忆会被遗忘 单位小时 
 memory_forget_percentage = 0.01 # 记忆遗忘比例 控制记忆遗忘程度 越大遗忘越多 建议保持默认
 
-memory_ban_words = [ #不希望记忆的词
+#不希望记忆的词，已经记忆的不会受到影响
+memory_ban_words = [ 
     # "403","张三"
 ]
 
@@ -167,7 +178,7 @@ word_replace_rate=0.006 # 整词替换概率
 
 [response_splitter]
 enable_response_splitter = true # 是否启用回复分割器
-response_max_length = 100 # 回复允许的最大长度
+response_max_length = 256 # 回复允许的最大长度
 response_max_sentence_num = 4 # 回复允许的最大句子数
 
 [remote] #发送统计信息，主要是看全球有多少只麦麦

template/template.env

@@ -29,8 +29,18 @@ CHAT_ANY_WHERE_KEY=
 SILICONFLOW_KEY=
 
 # 定义日志相关配置
-SIMPLE_OUTPUT=true # 精简控制台输出格式
+
-CONSOLE_LOG_LEVEL=INFO # 自定义日志的默认控制台输出日志级别
+# 精简控制台输出格式
-FILE_LOG_LEVEL=DEBUG # 自定义日志的默认文件输出日志级别
+SIMPLE_OUTPUT=true
-DEFAULT_CONSOLE_LOG_LEVEL=SUCCESS # 原生日志的控制台输出日志级别（nonebot就是这一类）
+
-DEFAULT_FILE_LOG_LEVEL=DEBUG # 原生日志的默认文件输出日志级别（nonebot就是这一类）
+# 自定义日志的默认控制台输出日志级别
+CONSOLE_LOG_LEVEL=INFO
+
+# 自定义日志的默认文件输出日志级别
+FILE_LOG_LEVEL=DEBUG
+
+# 原生日志的控制台输出日志级别（nonebot就是这一类）
+DEFAULT_CONSOLE_LOG_LEVEL=SUCCESS
+
+# 原生日志的默认文件输出日志级别（nonebot就是这一类）
+DEFAULT_FILE_LOG_LEVEL=DEBUG