diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
new file mode 100644
index 000000000..0d1e50c5a
--- /dev/null
+++ b/.github/workflows/ruff.yml
@@ -0,0 +1,8 @@
+name: Ruff
+on: [ push, pull_request ]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v3
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 4e1606a54..b4c7154de 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,9 @@
 data/
+data1/
 mongodb/
 NapCat.Framework.Windows.Once/
 log/
+logs/
 /test
 /src/test
 message_queue_content.txt
@@ -188,14 +190,17 @@ cython_debug/
 
 # PyPI configuration file
 .pypirc
-.env
 
 # jieba
 jieba.cache
 
-
-# vscode
-/.vscode
+# .vscode
+!.vscode/settings.json
 
 # direnv
-/.direnv
\ No newline at end of file
+/.direnv
+
+# JetBrains
+.idea
+*.iml
+*.ipr
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 000000000..8a04e2d84
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,10 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  # Ruff version.
+  rev: v0.9.10
+  hooks:
+    # Run the linter.
+    - id: ruff
+      args: [ --fix ]
+    # Run the formatter.
+    - id: ruff-format
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 000000000..d30b0e651
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,48 @@
+# MaiMBot 开发指南
+
+## 🛠️ 常用命令
+
+- **运行机器人**: `python run.py` 或 `python bot.py`
+- **安装依赖**: `pip install --upgrade -r requirements.txt`
+- **Docker 部署**: `docker-compose up`
+- **代码检查**: `ruff check .`
+- **代码格式化**: `ruff format .`
+- **内存可视化**: `run_memory_vis.bat` 或 `python -m src.plugins.memory_system.draw_memory`
+- **推理过程可视化**: `script/run_thingking.bat`
+
+## 🔧 脚本工具
+
+- **运行MongoDB**: `script/run_db.bat` - 在端口27017启动MongoDB
+- **Windows完整启动**: `script/run_windows.bat` - 检查Python版本、设置虚拟环境、安装依赖并运行机器人
+- **快速启动**: `script/run_maimai.bat` - 设置UTF-8编码并执行"nb run"命令
+
+## 📝 代码风格
+
+- **Python版本**: 3.9+
+- **行长度限制**: 88字符
+- **命名规范**:
+  - `snake_case` 用于函数和变量
+  - `PascalCase` 用于类
+  - `_prefix` 用于私有成员
+- **导入顺序**: 标准库 → 第三方库 → 本地模块
+- **异步编程**: 对I/O操作使用async/await
+- **日志记录**: 使用loguru进行一致的日志记录
+- **错误处理**: 使用带有具体异常的try/except
+- **文档**: 为类和公共函数编写docstrings
+
+## 🧩 系统架构
+
+- **框架**: NoneBot2框架与插件架构
+- **数据库**: MongoDB持久化存储
+- **设计模式**: 工厂模式和单例管理器
+- **配置管理**: 使用环境变量和TOML文件
+- **内存系统**: 基于图的记忆结构，支持记忆构建、压缩、检索和遗忘
+- **情绪系统**: 情绪模拟与概率权重
+- **LLM集成**: 支持多个LLM服务提供商(ChatAnywhere, SiliconFlow, DeepSeek)
+
+## ⚙️ 环境配置
+
+- 使用`template.env`作为环境变量模板
+- 使用`template/bot_config_template.toml`作为机器人配置模板
+- MongoDB配置: 主机、端口、数据库名
+- API密钥配置: 各LLM提供商的API密钥
diff --git a/README.md b/README.md
index 0c02d1cba..a7394c7cf 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,4 @@
-# 麦麦！MaiMBot (编辑中) 
-
+# 麦麦！MaiMBot (编辑中)
 
 <div align="center">
 
@@ -18,7 +17,11 @@
 - MongoDB 提供数据持久化支持
 - NapCat 作为QQ协议端支持
 
-**最新版本: v0.5.***
+**最新版本: v0.5.13**
+> [!WARNING]
+> 注意，3月12日的v0.5.13, 该版本更新较大，建议单独开文件夹部署，然后转移/data文件 和数据库，数据库可能需要删除messages下的内容（不需要删除记忆）
+
+
 
 <div align="center">
 <a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
@@ -29,44 +32,56 @@
 </a>
 </div>
 
-> ⚠️ **注意事项**
+> [!WARNING]
 > - 项目处于活跃开发阶段，代码可能随时更改
 > - 文档未完善，有问题可以提交 Issue 或者 Discussion
 > - QQ机器人存在被限制风险，请自行了解，谨慎使用
 > - 由于持续迭代，可能存在一些已知或未知的bug
 > - 由于开发中，可能消耗较多token
 
-**交流群**: 766798517 一群人较多，建议加下面的（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
-**交流群**: 571780722 另一个群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
-**交流群**: 1035228475 另一个群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
+## 💬交流群
+- [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 ，建议加下面的（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
+- [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722 （开发和建议相关讨论）不一定有空回复，会优先写文档和代码
+- [三群](https://qm.qq.com/q/wlH5eT8OmQ) 1035228475（开发和建议相关讨论）不一定有空回复，会优先写文档和代码
 
-**其他平台版本**
+
+
+**📚 有热心网友创作的wiki:** https://maimbot.pages.dev/
+
+
+**😊 其他平台版本** 
 
 - (由 [CabLate](https://github.com/cablate) 贡献) [Telegram 与其他平台(未来可能会有)的版本](https://github.com/cablate/MaiMBot/tree/telegram) - [集中讨论串](https://github.com/SengokuCola/MaiMBot/discussions/149)
 
-## 
 <div align="left">
 <h2>📚 文档        ⬇️ 快速开始使用麦麦 ⬇️</h2>
 </div>
 
 ### 部署方式
 
-如果你不知道Docker是什么，建议寻找相关教程或使用手动部署（现在不建议使用docker，更新慢，可能不适配）
+-  📦 **Windows 一键傻瓜式部署**：请运行项目根目录中的 `run.bat`，部署完成后请参照后续配置指南进行配置
+
+-  📦 Linux 自动部署（实验） ：请下载并运行项目根目录中的`run.sh`并按照提示安装，部署完成后请参照后续配置指南进行配置
+
+- [📦 Windows 手动部署指南 ](docs/manual_deploy_windows.md)
+
+- [📦 Linux 手动部署指南 ](docs/manual_deploy_linux.md)
+
+如果你不知道Docker是什么，建议寻找相关教程或使用手动部署 **（现在不建议使用docker，更新慢，可能不适配）**
 
 - [🐳 Docker部署指南](docs/docker_deploy.md)
 
 
-- [📦 手动部署指南 Windows](docs/manual_deploy_windows.md)
-
-
-- [📦 手动部署指南 Linux](docs/manual_deploy_linux.md)
-
--  📦 Windows 一键傻瓜式部署，请运行项目根目录中的 ```run.bat```，部署完成后请参照后续配置指南进行配置
 
 ### 配置说明
+
 - [🎀 新手配置指南](docs/installation_cute.md) - 通俗易懂的配置教程，适合初次使用的猫娘
 - [⚙️ 标准配置指南](docs/installation_standard.md) - 简明专业的配置说明，适合有经验的用户
 
+### 常见问题
+
+- [❓ 快速 Q & A ](docs/fast_q_a.md) - 针对新手的疑难解答，适合完全没接触过编程的新手
+
 <div align="left">
 <h3>了解麦麦 </h3>
 </div>
@@ -76,6 +91,7 @@
 ## 🎯 功能介绍
 
 ### 💬 聊天功能
+
 - 支持关键词检索主动发言：对消息的话题topic进行识别，如果检测到麦麦存储过的话题就会主动进行发言
 - 支持bot名字呼唤发言：检测到"麦麦"会主动发言，可配置
 - 支持多模型，多厂商自定义配置
@@ -84,31 +100,33 @@
 - 错别字和多条回复功能：麦麦可以随机生成错别字，会多条发送回复以及对消息进行reply
 
 ### 😊 表情包功能
+
 - 支持根据发言内容发送对应情绪的表情包
 - 会自动偷群友的表情包
 
 ### 📅 日程功能
+
 - 麦麦会自动生成一天的日程，实现更拟人的回复
 
 ### 🧠 记忆功能
+
 - 对聊天记录进行概括存储，在需要时调用，待完善
 
 ### 📚 知识库功能
+
 - 基于embedding模型的知识库，手动放入txt会自动识别，写完了，暂时禁用
 
 ### 👥 关系功能
+
 - 针对每个用户创建"关系"，可以对不同用户进行个性化回复，目前只有极其简单的好感度（WIP）
 - 针对每个群创建"群印象"，可以对不同群进行个性化回复（WIP）
 
-
-
 ## 开发计划TODO：LIST
 
 规划主线
 0.6.0：记忆系统更新
 0.7.0: 麦麦RunTime
 
-
 - 人格功能：WIP
 - 群氛围功能：WIP
 - 图片发送，转发功能：WIP
@@ -128,7 +146,6 @@
 - 采用截断生成加快麦麦的反应速度
 - 改进发送消息的触发
 
-
 ## 设计理念
 
 - **千石可乐说：**
@@ -138,13 +155,14 @@
 - 如果人类真的需要一个AI来陪伴自己，并不是所有人都需要一个完美的，能解决所有问题的helpful assistant，而是一个会犯错的，拥有自己感知和想法的"生命形式"。
 - 代码会保持开源和开放，但个人希望MaiMbot的运行时数据保持封闭，尽量避免以显式命令来对其进行控制和调试.我认为一个你无法完全掌控的个体才更能让你感觉到它的自主性，而视其成为一个对话机器.
 
-
 ## 📌 注意事项
-SengokuCola纯编程外行，面向cursor编程，很多代码史一样多多包涵
 
-> ⚠️ **警告**：本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。
+SengokuCola~~纯编程外行，面向cursor编程，很多代码写得不好多多包涵~~已得到大脑升级
+> [!WARNING]
+> 本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。
 
 ## 致谢
+
 [nonebot2](https://github.com/nonebot/nonebot2): 跨平台 Python 异步聊天机器人框架  
 [NapCat](https://github.com/NapNeko/NapCatQQ): 现代化的基于 NTQQ 的 Bot 协议端实现  
 
@@ -156,6 +174,6 @@ SengokuCola纯编程外行，面向cursor编程，很多代码史一样多多包
   <img src="https://contrib.rocks/image?repo=SengokuCola/MaiMBot" />
 </a>
 
-
 ## Stargazers over time
+
 [![Stargazers over time](https://starchart.cc/SengokuCola/MaiMBot.svg?variant=adaptive)](https://starchart.cc/SengokuCola/MaiMBot)
diff --git a/bot.py b/bot.py
index 84ce5067b..a3a844a15 100644
--- a/bot.py
+++ b/bot.py
@@ -1,9 +1,12 @@
+import asyncio
 import os
 import shutil
 import sys
 
 import nonebot
 import time
+
+import uvicorn
 from dotenv import load_dotenv
 from loguru import logger
 from nonebot.adapters.onebot.v11 import Adapter
@@ -12,6 +15,8 @@ import platform
 # 获取没有加载env时的环境变量
 env_mask = {key: os.getenv(key) for key in os.environ}
 
+uvicorn_server = None
+
 
 def easter_egg():
     # 彩蛋
@@ -58,7 +63,7 @@ def init_env():
 
     # 首先加载基础环境变量.env
     if os.path.exists(".env"):
-        load_dotenv(".env")
+        load_dotenv(".env", override=True)
         logger.success("成功加载基础环境变量配置")
 
 
@@ -72,10 +77,7 @@ def load_env():
         logger.success("加载开发环境变量配置")
         load_dotenv(".env.dev", override=True)  # override=True 允许覆盖已存在的环境变量
 
-    fn_map = {
-        "prod": prod,
-        "dev": dev
-    }
+    fn_map = {"prod": prod, "dev": dev}
 
     env = os.getenv("ENVIRONMENT")
     logger.info(f"[load_env] 当前的 ENVIRONMENT 变量值：{env}")
@@ -93,15 +95,43 @@ def load_env():
 
 
 def load_logger():
-    logger.remove()  # 移除默认配置
-    logger.add(
-        sys.stderr,
-        format="<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> <fg #777777>|</> <level>{level: <7}</level> <fg "
-               "#777777>|</> <cyan>{name:.<8}</cyan>:<cyan>{function:.<8}</cyan>:<cyan>{line: >4}</cyan> <fg "
-               "#777777>-</> <level>{message}</level>",
-        colorize=True,
-        level=os.getenv("LOG_LEVEL", "INFO")  # 根据环境设置日志级别，默认为INFO
+    logger.remove()
+
+    # 配置日志基础路径
+    log_path = os.path.join(os.getcwd(), "logs")
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+
+    current_env = os.getenv("ENVIRONMENT", "dev")
+
+    # 公共配置参数
+    log_level = os.getenv("LOG_LEVEL", "INFO" if current_env == "prod" else "DEBUG")
+    log_filter = lambda record: (
+        ("nonebot" not in record["name"] or record["level"].no >= logger.level("ERROR").no)
+        if current_env == "prod"
+        else True
     )
+    log_format = (
+        "<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> "
+        "<fg #777777>|</> <level>{level: <7}</level> "
+        "<fg #777777>|</> <cyan>{name:.<8}</cyan>:<cyan>{function:.<8}</cyan>:<cyan>{line: >4}</cyan> "
+        "<fg #777777>-</> <level>{message}</level>"
+    )
+
+    # 日志文件储存至/logs
+    logger.add(
+        os.path.join(log_path, "maimbot_{time:YYYY-MM-DD}.log"),
+        rotation="00:00",
+        retention="30 days",
+        format=log_format,
+        colorize=False,
+        level=log_level,
+        filter=log_filter,
+        encoding="utf-8",
+    )
+
+    # 终端输出
+    logger.add(sys.stderr, format=log_format, colorize=True, level=log_level, filter=log_filter)
 
 
 def scan_provider(env_config: dict):
@@ -131,24 +161,53 @@ def scan_provider(env_config: dict):
     # 检查每个 provider 是否同时存在 url 和 key
     for provider_name, config in provider.items():
         if config["url"] is None or config["key"] is None:
-            logger.error(
-                f"provider 内容：{config}\n"
-                f"env_config 内容：{env_config}"
-            )
+            logger.error(f"provider 内容：{config}\nenv_config 内容：{env_config}")
             raise ValueError(f"请检查 '{provider_name}' 提供商配置是否丢失 BASE_URL 或 KEY 环境变量")
 
 
-if __name__ == "__main__":
+async def graceful_shutdown():
+    try:
+        global uvicorn_server
+        if uvicorn_server:
+            uvicorn_server.force_exit = True  # 强制退出
+            await uvicorn_server.shutdown()
+
+        tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()]
+        for task in tasks:
+            task.cancel()
+        await asyncio.gather(*tasks, return_exceptions=True)
+
+    except Exception as e:
+        logger.error(f"麦麦关闭失败: {e}")
+
+
+async def uvicorn_main():
+    global uvicorn_server
+    config = uvicorn.Config(
+        app="__main__:app",
+        host=os.getenv("HOST", "127.0.0.1"),
+        port=int(os.getenv("PORT", 8080)),
+        reload=os.getenv("ENVIRONMENT") == "dev",
+        timeout_graceful_shutdown=5,
+        log_config=None,
+        access_log=False,
+    )
+    server = uvicorn.Server(config)
+    uvicorn_server = server
+    await server.serve()
+
+
+def raw_main():
     # 利用 TZ 环境变量设定程序工作的时区
     # 仅保证行为一致，不依赖 localtime()，实际对生产环境几乎没有作用
-    if platform.system().lower() != 'windows':
+    if platform.system().lower() != "windows":
         time.tzset()
 
     easter_egg()
-    load_logger()
     init_config()
     init_env()
     load_env()
+    load_logger()
 
     env_config = {key: os.getenv(key) for key in os.environ}
     scan_provider(env_config)
@@ -164,10 +223,29 @@ if __name__ == "__main__":
     nonebot.init(**base_config, **env_config)
 
     # 注册适配器
+    global driver
     driver = nonebot.get_driver()
     driver.register_adapter(Adapter)
 
     # 加载插件
     nonebot.load_plugins("src/plugins")
 
-    nonebot.run()
+
+if __name__ == "__main__":
+    try:
+        raw_main()
+
+        global app
+        app = nonebot.get_asgi()
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        loop.run_until_complete(uvicorn_main())
+    except KeyboardInterrupt:
+        logger.warning("麦麦会努力做的更好的！正在停止中......")
+    except Exception as e:
+        logger.error(f"主程序异常: {e}")
+    finally:
+        loop.run_until_complete(graceful_shutdown())
+        loop.close()
+        logger.info("进程终止完毕，麦麦开始休眠......下次再见哦！")
diff --git a/changelog.md b/changelog.md
index c68a16ad9..b9beed81e 100644
--- a/changelog.md
+++ b/changelog.md
@@ -1,6 +1,84 @@
 # Changelog
 
-## [0.5.12] - 2025-3-9
-### Added
-- 新增了 我是测试
+## [0.5.13] - 2025-3-12
+AI总结
+### 🌟 核心功能增强
+#### 记忆系统升级
+- 新增了记忆系统的时间戳功能，包括创建时间和最后修改时间
+- 新增了记忆图节点和边的时间追踪功能
+- 新增了自动补充缺失时间字段的功能
+- 新增了记忆遗忘机制，基于时间条件自动遗忘旧记忆
+- 优化了记忆系统的数据同步机制
+- 优化了记忆系统的数据结构，确保所有数据类型的一致性
+
+#### 私聊功能完善
+- 新增了完整的私聊功能支持，包括消息处理和回复
+- 新增了聊天流管理器，支持群聊和私聊的上下文管理
+- 新增了私聊过滤开关功能
+- 优化了关系管理系统，支持跨平台用户关系
+
+#### 消息处理升级
+- 新增了消息队列管理系统，支持按时间顺序处理消息
+- 新增了消息发送控制器，实现人性化的发送速度和间隔
+- 新增了JSON格式分享卡片读取支持
+- 新增了Base64格式表情包CQ码支持
+- 改进了消息处理流程，支持多种消息类型
+
+### 💻 系统架构优化
+#### 配置系统改进
+- 新增了配置文件自动更新和版本检测功能
+- 新增了配置文件热重载API接口
+- 新增了配置文件版本兼容性检查
+- 新增了根据不同环境(dev/prod)显示不同级别的日志功能
+- 优化了配置文件格式和结构
+
+#### 部署支持扩展
+- 新增了Linux系统部署指南
+- 新增了Docker部署支持的详细文档
+- 新增了NixOS环境支持（使用venv方式）
+- 新增了优雅的shutdown机制
+- 优化了Docker部署文档
+
+### 🛠️ 开发体验提升
+#### 工具链升级
+- 新增了ruff代码格式化和检查工具
+- 新增了知识库一键启动脚本
+- 新增了自动保存脚本，定期保存聊天记录和关系数据
+- 新增了表情包自动获取脚本
+- 优化了日志记录（使用logger.debug替代print）
+- 精简了日志输出，禁用了Uvicorn/NoneBot默认日志
+
+#### 安全性强化
+- 新增了API密钥安全管理机制
+- 新增了数据库完整性检查功能
+- 新增了表情包文件完整性自动检查
+- 新增了异常处理和自动恢复机制
+- 优化了安全性检查机制
+
+### 🐛 关键问题修复
+#### 系统稳定性
+- 修复了systemctl强制停止的问题
+- 修复了ENVIRONMENT变量在同一终端下不能被覆盖的问题
+- 修复了libc++.so依赖问题
+- 修复了数据库索引创建失败的问题
+- 修复了MongoDB连接配置相关问题
+- 修复了消息队列溢出问题
+- 修复了配置文件加载时的版本兼容性问题
+
+#### 功能完善性
+- 修复了私聊时产生reply消息的bug
+- 修复了回复消息无法识别的问题
+- 修复了CQ码解析错误
+- 修复了情绪管理器导入问题
+- 修复了小名无效的问题
+- 修复了表情包发送时的参数缺失问题
+- 修复了表情包重复注册问题
+- 修复了变量拼写错误问题
+
+### 主要改进方向
+1. 提升记忆系统的智能性和可靠性
+2. 完善私聊功能的完整生态
+3. 优化系统架构和部署便利性
+4. 提升开发体验和代码质量
+5. 加强系统安全性和稳定性
 
diff --git a/changelog_config.md b/changelog_config.md
index 7101fe828..c4c560644 100644
--- a/changelog_config.md
+++ b/changelog_config.md
@@ -1,6 +1,12 @@
 # Changelog
 
+## [0.0.5] - 2025-3-11
+### Added
+- 新增了 `alias_names` 配置项，用于指定麦麦的别名。
+
 ## [0.0.4] - 2025-3-9
 ### Added
 - 新增了 `memory_ban_words` 配置项，用于指定不希望记忆的词汇。
 
+
+
diff --git a/config/auto_update.py b/config/auto_update.py
new file mode 100644
index 000000000..28ab108da
--- /dev/null
+++ b/config/auto_update.py
@@ -0,0 +1,59 @@
+import os
+import shutil
+import tomlkit
+from pathlib import Path
+
+def update_config():
+    # 获取根目录路径
+    root_dir = Path(__file__).parent.parent
+    template_dir = root_dir / "template"
+    config_dir = root_dir / "config"
+    
+    # 定义文件路径
+    template_path = template_dir / "bot_config_template.toml"
+    old_config_path = config_dir / "bot_config.toml"
+    new_config_path = config_dir / "bot_config.toml"
+    
+    # 读取旧配置文件
+    old_config = {}
+    if old_config_path.exists():
+        with open(old_config_path, "r", encoding="utf-8") as f:
+            old_config = tomlkit.load(f)
+    
+    # 删除旧的配置文件
+    if old_config_path.exists():
+        os.remove(old_config_path)
+    
+    # 复制模板文件到配置目录
+    shutil.copy2(template_path, new_config_path)
+    
+    # 读取新配置文件
+    with open(new_config_path, "r", encoding="utf-8") as f:
+        new_config = tomlkit.load(f)
+    
+    # 递归更新配置
+    def update_dict(target, source):
+        for key, value in source.items():
+            # 跳过version字段的更新
+            if key == "version":
+                continue
+            if key in target:
+                if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)):
+                    update_dict(target[key], value)
+                else:
+                    try:
+                        # 直接使用tomlkit的item方法创建新值
+                        target[key] = tomlkit.item(value)
+                    except (TypeError, ValueError):
+                        # 如果转换失败，直接赋值
+                        target[key] = value
+    
+    # 将旧配置的值更新到新配置中
+    update_dict(new_config, old_config)
+    
+    # 保存更新后的配置（保留注释和格式）
+    with open(new_config_path, "w", encoding="utf-8") as f:
+        f.write(tomlkit.dumps(new_config))
+
+if __name__ == "__main__":
+    update_config()
diff --git a/docker-compose.yml b/docker-compose.yml
index 512558558..227df606b 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -6,8 +6,6 @@ services:
       - NAPCAT_UID=${NAPCAT_UID}
       - NAPCAT_GID=${NAPCAT_GID} # 让 NapCat 获取当前用户 GID,UID，防止权限问题
     ports:
-      - 3000:3000
-      - 3001:3001
       - 6099:6099
     restart: unless-stopped
     volumes:
@@ -19,7 +17,7 @@ services:
   mongodb:
     container_name: mongodb
     environment:
-      - tz=Asia/Shanghai
+      - TZ=Asia/Shanghai
       # - MONGO_INITDB_ROOT_USERNAME=your_username
       # - MONGO_INITDB_ROOT_PASSWORD=your_password
     expose:
diff --git a/docs/API_KEY.png b/docs/API_KEY.png
new file mode 100644
index 000000000..901d1d137
Binary files /dev/null and b/docs/API_KEY.png differ
diff --git a/docs/Jonathan R.md b/docs/Jonathan R.md
new file mode 100644
index 000000000..660caaeec
--- /dev/null
+++ b/docs/Jonathan R.md	
@@ -0,0 +1,20 @@
+Jonathan R. Wolpaw 在 “Memory in neuroscience: rhetoric versus reality.” 一文中提到，从神经科学的感觉运动假设出发，整个神经系统的功能是将经验与适当的行为联系起来，而不是单纯的信息存储。
+Jonathan R,Wolpaw. (2019). Memory in neuroscience: rhetoric versus reality.. Behavioral and cognitive neuroscience reviews(2).
+
+1. **单一过程理论**
+   - 单一过程理论认为，识别记忆主要是基于熟悉性这一单一因素的影响。熟悉性是指对刺激的一种自动的、无意识的感知，它可以使我们在没有回忆起具体细节的情况下，判断一个刺激是否曾经出现过。
+   - 例如，在一些实验中，研究者发现被试可以在没有回忆起具体学习情境的情况下，对曾经出现过的刺激做出正确的判断，这被认为是熟悉性在起作用1。
+2. **双重过程理论**
+   - 双重过程理论则认为，识别记忆是基于两个过程：回忆和熟悉性。回忆是指对过去经验的有意识的回忆，它可以使我们回忆起具体的细节和情境；熟悉性则是一种自动的、无意识的感知。
+   - 该理论认为，在识别记忆中，回忆和熟悉性共同作用，使我们能够判断一个刺激是否曾经出现过。例如，在 “记得 / 知道” 范式中，被试被要求判断他们对一个刺激的记忆是基于回忆还是熟悉性。研究发现，被试可以区分这两种不同的记忆过程，这为双重过程理论提供了支持1。
+
+
+
+1. **神经元节点与连接**：借鉴神经网络原理，将每个记忆单元视为一个神经元节点。节点之间通过连接相互关联，连接的强度代表记忆之间的关联程度。在形态学联想记忆中，具有相似形态特征的记忆节点连接强度较高。例如，苹果和橘子的记忆节点，由于在形状、都是水果等形态语义特征上相似，它们之间的连接强度大于苹果与汽车记忆节点间的连接强度。
+2. **记忆聚类与层次结构**：依据形态特征的相似性对记忆进行聚类，形成不同的记忆簇。每个记忆簇内部的记忆具有较高的相似性，而不同记忆簇之间的记忆相似性较低。同时，构建记忆的层次结构，高层次的记忆节点代表更抽象、概括的概念，低层次的记忆节点对应具体的实例。比如，“水果” 作为高层次记忆节点，连接着 “苹果”“橘子”“香蕉” 等低层次具体水果的记忆节点。
+3. **网络的动态更新**：随着新记忆的不断加入，记忆网络动态调整。新记忆节点根据其形态特征与现有网络中的节点建立连接，同时影响相关连接的强度。若新记忆与某个记忆簇的特征高度相似，则被纳入该记忆簇；若具有独特特征，则可能引发新的记忆簇的形成。例如，当系统学习到一种新的水果 “番石榴”，它会根据番石榴的形态、语义等特征，在记忆网络中找到与之最相似的区域（如水果记忆簇），并建立相应连接，同时调整周围节点连接强度以适应这一新记忆。
+
+
+
+- **相似性联想**：该理论认为，当两个或多个事物在形态上具有相似性时，它们在记忆中会形成关联。例如，梨和苹果在形状和都是水果这一属性上有相似性，所以当我们看到梨时，很容易通过形态学联想记忆联想到苹果。这种相似性联想有助于我们对新事物进行分类和理解，当遇到一个新的类似水果时，我们可以通过与已有的水果记忆进行相似性匹配，来推测它的一些特征。
+- **时空关联性联想**：除了相似性联想，MAM 还强调时空关联性联想。如果两个事物在时间或空间上经常同时出现，它们也会在记忆中形成关联。比如，每次在公园里看到花的时候，都能听到鸟儿的叫声，那么花和鸟儿叫声的形态特征（花的视觉形态和鸟叫的听觉形态）就会在记忆中形成关联，以后听到鸟叫可能就会联想到公园里的花。
\ No newline at end of file
diff --git a/docs/MONGO_DB_0.png b/docs/MONGO_DB_0.png
new file mode 100644
index 000000000..8d91d37d8
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diff --git a/docs/MONGO_DB_1.png b/docs/MONGO_DB_1.png
new file mode 100644
index 000000000..0ef3b5590
Binary files /dev/null and b/docs/MONGO_DB_1.png differ
diff --git a/docs/MONGO_DB_2.png b/docs/MONGO_DB_2.png
new file mode 100644
index 000000000..e59cc8793
Binary files /dev/null and b/docs/MONGO_DB_2.png differ
diff --git a/docs/avatars/SengokuCola.jpg b/docs/avatars/SengokuCola.jpg
new file mode 100644
index 000000000..deebf5ed5
Binary files /dev/null and b/docs/avatars/SengokuCola.jpg differ
diff --git a/docs/avatars/default.png b/docs/avatars/default.png
new file mode 100644
index 000000000..5b561dac4
Binary files /dev/null and b/docs/avatars/default.png differ
diff --git a/docs/avatars/run.bat b/docs/avatars/run.bat
new file mode 100644
index 000000000..6b9ca9f2b
--- /dev/null
+++ b/docs/avatars/run.bat
@@ -0,0 +1 @@
+gource gource.log --user-image-dir docs/avatars/ --default-user-image docs/avatars/default.png
\ No newline at end of file
diff --git a/docs/doc1.md b/docs/doc1.md
index 158136b9c..e8aa0f0d6 100644
--- a/docs/doc1.md
+++ b/docs/doc1.md
@@ -1,6 +1,7 @@
 # 📂 文件及功能介绍 (2025年更新)
 
 ## 根目录
+
 - **README.md**: 项目的概述和使用说明。
 - **requirements.txt**: 项目所需的Python依赖包列表。
 - **bot.py**: 主启动文件，负责环境配置加载和NoneBot初始化。
@@ -10,6 +11,7 @@
 - **run_*.bat**: 各种启动脚本，包括数据库、maimai和thinking功能。
 
 ## `src/` 目录结构
+
 - **`plugins/` 目录**: 存放不同功能模块的插件。
   - **chat/**: 处理聊天相关的功能，如消息发送和接收。
   - **memory_system/**: 处理机器人的记忆功能。
@@ -22,94 +24,96 @@
 
 - **`common/` 目录**: 存放通用的工具和库。
   - **database.py**: 处理与数据库的交互，负责数据的存储和检索。
-  - **__init__.py**: 初始化模块。
+  - ****init**.py**: 初始化模块。
 
 ## `config/` 目录
+
 - **bot_config_template.toml**: 机器人配置模板。
 - **auto_format.py**: 自动格式化工具。
 
 ### `src/plugins/chat/` 目录文件详细介绍
 
-1. **`__init__.py`**: 
+1. **`__init__.py`**:
    - 初始化 `chat` 模块，使其可以作为一个包被导入。
 
-2. **`bot.py`**: 
+2. **`bot.py`**:
    - 主要的聊天机器人逻辑实现，处理消息的接收、思考和回复。
    - 包含 `ChatBot` 类，负责消息处理流程控制。
    - 集成记忆系统和意愿管理。
 
-3. **`config.py`**: 
+3. **`config.py`**:
    - 配置文件，定义了聊天机器人的各种参数和设置。
    - 包含 `BotConfig` 和全局配置对象 `global_config`。
 
-4. **`cq_code.py`**: 
+4. **`cq_code.py`**:
    - 处理 CQ 码（CoolQ 码），用于发送和接收特定格式的消息。
 
-5. **`emoji_manager.py`**: 
+5. **`emoji_manager.py`**:
    - 管理表情包的发送和接收，根据情感选择合适的表情。
    - 提供根据情绪获取表情的方法。
 
-6. **`llm_generator.py`**: 
+6. **`llm_generator.py`**:
    - 生成基于大语言模型的回复，处理用户输入并生成相应的文本。
    - 通过 `ResponseGenerator` 类实现回复生成。
 
-7. **`message.py`**: 
+7. **`message.py`**:
    - 定义消息的结构和处理逻辑，包含多种消息类型：
      - `Message`: 基础消息类
      - `MessageSet`: 消息集合
      - `Message_Sending`: 发送中的消息
      - `Message_Thinking`: 思考状态的消息
 
-8. **`message_sender.py`**: 
+8. **`message_sender.py`**:
    - 控制消息的发送逻辑，确保消息按照特定规则发送。
    - 包含 `message_manager` 对象，用于管理消息队列。
 
-9. **`prompt_builder.py`**: 
+9. **`prompt_builder.py`**:
    - 构建用于生成回复的提示，优化机器人的响应质量。
 
-10. **`relationship_manager.py`**: 
+10. **`relationship_manager.py`**:
     - 管理用户之间的关系，记录用户的互动和偏好。
     - 提供更新关系和关系值的方法。
 
-11. **`Segment_builder.py`**: 
+11. **`Segment_builder.py`**:
     - 构建消息片段的工具。
 
-12. **`storage.py`**: 
+12. **`storage.py`**:
     - 处理数据存储，负责将聊天记录和用户信息保存到数据库。
     - 实现 `MessageStorage` 类管理消息存储。
 
-13. **`thinking_idea.py`**: 
+13. **`thinking_idea.py`**:
     - 实现机器人的思考机制。
 
-14. **`topic_identifier.py`**: 
+14. **`topic_identifier.py`**:
     - 识别消息中的主题，帮助机器人理解用户的意图。
 
-15. **`utils.py`** 和 **`utils_*.py`** 系列文件: 
+15. **`utils.py`** 和 **`utils_*.py`** 系列文件:
     - 存放各种工具函数，提供辅助功能以支持其他模块。
     - 包括 `utils_cq.py`、`utils_image.py`、`utils_user.py` 等专门工具。
 
-16. **`willing_manager.py`**: 
+16. **`willing_manager.py`**:
     - 管理机器人的回复意愿，动态调整回复概率。
     - 通过多种因素（如被提及、话题兴趣度）影响回复决策。
 
 ### `src/plugins/memory_system/` 目录文件介绍
 
-1. **`memory.py`**: 
+1. **`memory.py`**:
    - 实现记忆管理核心功能，包含 `memory_graph` 对象。
    - 提供相关项目检索，支持多层次记忆关联。
 
-2. **`draw_memory.py`**: 
+2. **`draw_memory.py`**:
    - 记忆可视化工具。
 
-3. **`memory_manual_build.py`**: 
+3. **`memory_manual_build.py`**:
    - 手动构建记忆的工具。
 
-4. **`offline_llm.py`**: 
+4. **`offline_llm.py`**:
    - 离线大语言模型处理功能。
 
 ## 消息处理流程
 
 ### 1. 消息接收与预处理
+
 - 通过 `ChatBot.handle_message()` 接收群消息。
 - 进行用户和群组的权限检查。
 - 更新用户关系信息。
@@ -117,12 +121,14 @@
 - 对消息进行过滤和敏感词检测。
 
 ### 2. 主题识别与决策
+
 - 使用 `topic_identifier` 识别消息主题。
 - 通过记忆系统检查对主题的兴趣度。
 - `willing_manager` 动态计算回复概率。
 - 根据概率决定是否回复消息。
 
 ### 3. 回复生成与发送
+
 - 如需回复，首先创建 `Message_Thinking` 对象表示思考状态。
 - 调用 `ResponseGenerator.generate_response()` 生成回复内容和情感状态。
 - 删除思考消息，创建 `MessageSet` 准备发送回复。
diff --git a/docs/docker_deploy.md b/docs/docker_deploy.md
index 3958d2fc4..f78f73dca 100644
--- a/docs/docker_deploy.md
+++ b/docs/docker_deploy.md
@@ -1,67 +1,93 @@
 # 🐳 Docker 部署指南
 
-## 部署步骤（推荐，但不一定是最新）
+## 部署步骤 (推荐，但不一定是最新)
+
+**"更新镜像与容器"部分在本文档 [Part 6](#6-更新镜像与容器)**
+
+### 0. 前提说明
+
+**本文假设读者已具备一定的 Docker 基础知识。若您对 Docker 不熟悉，建议先参考相关教程或文档进行学习，或选择使用 [📦Linux手动部署指南](./manual_deploy_linux.md) 或 [📦Windows手动部署指南](./manual_deploy_windows.md) 。**
 
 
-### 1. 获取Docker配置文件:
+### 1. 获取Docker配置文件
+
+- 建议先单独创建好一个文件夹并进入，作为工作目录
 
 ```bash
 wget https://raw.githubusercontent.com/SengokuCola/MaiMBot/main/docker-compose.yml -O docker-compose.yml
 ```
 
-- 若需要启用MongoDB数据库的用户名和密码，可进入docker-compose.yml，取消MongoDB处的注释并修改变量`=`后方的值为你的用户名和密码\
-修改后请注意在之后配置`.env.prod`文件时指定MongoDB数据库的用户名密码
+- 若需要启用MongoDB数据库的用户名和密码，可进入docker-compose.yml，取消MongoDB处的注释并修改变量旁 `=` 后方的值为你的用户名和密码\
+修改后请注意在之后配置 `.env.prod` 文件时指定MongoDB数据库的用户名密码
 
+### 2. 启动服务
 
-### 2. 启动服务:
-
-- **!!! 请在第一次启动前确保当前工作目录下`.env.prod`与`bot_config.toml`文件存在 !!!**\
+- **!!! 请在第一次启动前确保当前工作目录下 `.env.prod` 与 `bot_config.toml` 文件存在 !!!**\
 由于Docker文件映射行为的特殊性，若宿主机的映射路径不存在，可能导致意外的目录创建，而不会创建文件，由于此处需要文件映射到文件，需提前确保文件存在且路径正确，可使用如下命令:
+
 ```bash
 touch .env.prod
 touch bot_config.toml
 ```
 
 - 启动Docker容器:
+
 ```bash
 NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d
+# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
+NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose up -d
 ```
 
-- 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
 
-
-### 3. 修改配置并重启Docker:
+### 3. 修改配置并重启Docker
 
 - 请前往 [🎀 新手配置指南](docs/installation_cute.md) 或 [⚙️ 标准配置指南](docs/installation_standard.md) 完成`.env.prod`与`bot_config.toml`配置文件的编写\
 **需要注意`.env.prod`中HOST处IP的填写，Docker中部署和系统中直接安装的配置会有所不同**
 
 - 重启Docker容器:
+
 ```bash
-docker restart maimbot  # 若修改过容器名称则替换maimbot为你自定的名臣
+docker restart maimbot  # 若修改过容器名称则替换maimbot为你自定的名称
 ```
 
 - 下方命令可以但不推荐，只是同时重启NapCat、MongoDB、MaiMBot三个服务
+
 ```bash
 NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
+# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
+NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose restart
 ```
 
-- 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
-
-
 ### 4. 登入NapCat管理页添加反向WebSocket
 
-- 在浏览器地址栏输入`http://<宿主机IP>:6099/`进入NapCat的管理Web页，添加一个Websocket客户端
+- 在浏览器地址栏输入 `http://<宿主机IP>:6099/` 进入NapCat的管理Web页，添加一个Websocket客户端
+
 > 网络配置 -> 新建 -> Websocket客户端
 
-- Websocket客户端的名称自定，URL栏填入`ws://maimbot:8080/onebot/v11/ws`，启用并保存即可\
+- Websocket客户端的名称自定，URL栏填入 `ws://maimbot:8080/onebot/v11/ws`，启用并保存即可\
 (若修改过容器名称则替换maimbot为你自定的名称)
 
+### 5. 部署完成，愉快地和麦麦对话吧!
 
-### 5. 愉快地和麦麦对话吧!
 
+### 6. 更新镜像与容器
+
+- 拉取最新镜像
+
+```bash
+docker-compose pull
+```
+
+- 执行启动容器指令，该指令会自动重建镜像有更新的容器并启动
+
+```bash
+NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d
+# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代
+NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose up -d
+```
 
 ## ⚠️ 注意事项
 
 - 目前部署方案仍在测试中，可能存在未知问题
 - 配置文件中的API密钥请妥善保管，不要泄露
-- 建议先在测试环境中运行，确认无误后再部署到生产环境 
\ No newline at end of file
+- 建议先在测试环境中运行，确认无误后再部署到生产环境
diff --git a/docs/fast_q_a.md b/docs/fast_q_a.md
new file mode 100644
index 000000000..3b995e24a
--- /dev/null
+++ b/docs/fast_q_a.md
@@ -0,0 +1,149 @@
+## 快速更新Q&A❓
+
+<br>
+
+- 这个文件用来记录一些常见的新手问题。
+
+<br>
+
+### 完整安装教程
+
+<br>
+
+[MaiMbot简易配置教程](https://www.bilibili.com/video/BV1zsQ5YCEE6)
+
+<br>
+
+### Api相关问题
+
+<br>
+
+<br>
+
+- 为什么显示:"缺失必要的API KEY" ❓
+
+<br>
+  
+
+<img src="API_KEY.png" width=650>
+  
+
+---
+
+<br>
+
+><br>
+>
+>你需要在 [Silicon Flow Api](https://cloud.siliconflow.cn/account/ak) 
+>网站上注册一个账号，然后点击这个链接打开API KEY获取页面。
+>
+>点击 "新建API密钥" 按钮新建一个给MaiMBot使用的API KEY。不要忘了点击复制。
+>
+>之后打开MaiMBot在你电脑上的文件根目录，使用记事本或者其他文本编辑器打开 [.env.prod](../.env.prod) 
+>这个文件。把你刚才复制的API KEY填入到 "SILICONFLOW_KEY=" 这个等号的右边。
+>
+>在默认情况下，MaiMBot使用的默认Api都是硅基流动的。
+>
+><br>
+
+<br>
+
+<br>
+
+
+- 我想使用硅基流动之外的Api网站，我应该怎么做 ❓
+
+---
+
+<br>
+
+><br>
+>
+>你需要使用记事本或者其他文本编辑器打开config目录下的 [bot_config.toml](../config/bot_config.toml) 
+>然后修改其中的 "provider = " 字段。同时不要忘记模仿 [.env.prod](../.env.prod) 
+>文件的写法添加 Api Key 和 Base URL。
+>
+>举个例子，如果你写了 " provider = \"ABC\" "，那你需要相应的在 [.env.prod](../.env.prod) 
+>文件里添加形如 " ABC_BASE_URL = https://api.abc.com/v1 " 和 " ABC_KEY = sk-1145141919810 " 的字段。
+>
+>**如果你对AI没有较深的了解，修改识图模型和嵌入模型的provider字段可能会产生bug，因为你从Api网站调用了一个并不存在的模型**
+>
+>这个时候，你需要把字段的值改回 "provider = \"SILICONFLOW\" " 以此解决bug。
+>
+><br>
+
+
+<br>
+
+### MongoDB相关问题
+
+<br>
+
+- 我应该怎么清空bot内存储的表情包 ❓
+
+---
+
+<br>
+
+><br>
+>
+>打开你的MongoDB Compass软件，你会在左上角看到这样的一个界面：
+>
+><br>
+>
+><img src="MONGO_DB_0.png" width=250>
+>
+><br>
+>
+>点击 "CONNECT" 之后，点击展开 MegBot 标签栏
+>
+><br>
+>
+><img src="MONGO_DB_1.png" width=250>
+>
+><br>
+>
+>点进 "emoji" 再点击 "DELETE" 删掉所有条目，如图所示
+>
+><br>
+>
+><img src="MONGO_DB_2.png" width=450>
+>
+><br>
+>
+>你可以用类似的方式手动清空MaiMBot的所有服务器数据。
+>
+>MaiMBot的所有图片均储存在 [data](../data) 文件夹内，按类型分为 [emoji](../data/emoji) 和 [image](../data/image)
+>
+>在删除服务器数据时不要忘记清空这些图片。
+>
+><br>
+
+<br>
+
+- 为什么我连接不上MongoDB服务器 ❓
+
+---
+
+
+><br>
+>
+>这个问题比较复杂，但是你可以按照下面的步骤检查，看看具体是什么问题
+>
+><br>
+>
+> 1. 检查有没有把 mongod.exe 所在的目录添加到 path。 具体可参照
+>
+><br>
+>
+>&emsp;&emsp;[CSDN-windows10设置环境变量Path详细步骤](https://blog.csdn.net/flame_007/article/details/106401215)
+>
+><br>
+>
+>&emsp;&emsp;**需要往path里填入的是 exe 所在的完整目录！不带 exe 本体**
+>
+><br>
+>
+> 2. 待完成
+>
+><br>
\ No newline at end of file
diff --git a/docs/installation_cute.md b/docs/installation_cute.md
index e7541f7d3..e0c03310f 100644
--- a/docs/installation_cute.md
+++ b/docs/installation_cute.md
@@ -1,8 +1,9 @@
 # 🔧 配置指南 喵~
 
-## 👋 你好呀！
+## 👋 你好呀
 
 让咱来告诉你我们要做什么喵：
+
 1. 我们要一起设置一个可爱的AI机器人
 2. 这个机器人可以在QQ上陪你聊天玩耍哦
 3. 需要设置两个文件才能让机器人工作呢
@@ -10,16 +11,19 @@
 ## 📝 需要设置的文件喵
 
 要设置这两个文件才能让机器人跑起来哦：
+
 1. `.env.prod` - 这个文件告诉机器人要用哪些AI服务呢
 2. `bot_config.toml` - 这个文件教机器人怎么和你聊天喵
 
 ## 🔑 密钥和域名的对应关系
 
 想象一下，你要进入一个游乐园，需要：
+
 1. 知道游乐园的地址（这就是域名 base_url）
 2. 有入场的门票（这就是密钥 key）
 
 在 `.env.prod` 文件里，我们定义了三个游乐园的地址和门票喵：
+
 ```ini
 # 硅基流动游乐园
 SILICONFLOW_KEY=your_key        # 硅基流动的门票
@@ -35,6 +39,7 @@ CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1  # ChatAnyWhere的地
 ```
 
 然后在 `bot_config.toml` 里，机器人会用这些门票和地址去游乐园玩耍：
+
 ```toml
 [model.llm_reasoning]
 name = "Pro/deepseek-ai/DeepSeek-R1"
@@ -47,22 +52,24 @@ base_url = "SILICONFLOW_BASE_URL"  # 还是去硅基流动游乐园
 key = "SILICONFLOW_KEY"            # 用同一张门票就可以啦
 ```
 
-### 🎪 举个例子喵：
+### 🎪 举个例子喵
 
 如果你想用DeepSeek官方的服务，就要这样改：
+
 ```toml
 [model.llm_reasoning]
-name = "Pro/deepseek-ai/DeepSeek-R1"
+name = "deepseek-reasoner"       # 改成对应的模型名称，这里为DeepseekR1
 base_url = "DEEP_SEEK_BASE_URL"  # 改成去DeepSeek游乐园
 key = "DEEP_SEEK_KEY"            # 用DeepSeek的门票
 
 [model.llm_normal]
-name = "Pro/deepseek-ai/DeepSeek-V3"
+name = "deepseek-chat"           # 改成对应的模型名称，这里为DeepseekV3
 base_url = "DEEP_SEEK_BASE_URL"  # 也去DeepSeek游乐园
 key = "DEEP_SEEK_KEY"            # 用同一张DeepSeek门票
 ```
 
-### 🎯 简单来说：
+### 🎯 简单来说
+
 - `.env.prod` 文件就像是你的票夹，存放着各个游乐园的门票和地址
 - `bot_config.toml` 就是告诉机器人：用哪张票去哪个游乐园玩
 - 所有模型都可以用同一个游乐园的票，也可以去不同的游乐园玩耍
@@ -88,19 +95,25 @@ CHAT_ANY_WHERE_KEY=your_key
 CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
 
 # 如果你不知道这是什么，那么下面这些不用改，保持原样就好啦
-HOST=127.0.0.1  # 如果使用Docker部署，需要改成0.0.0.0喵，不然听不见群友讲话了喵
+# 如果使用Docker部署，需要改成0.0.0.0喵，不然听不见群友讲话了喵
+HOST=127.0.0.1
 PORT=8080
 
 # 这些是数据库设置，一般也不用改呢
-MONGODB_HOST=127.0.0.1  # 如果使用Docker部署，需要改成数据库容器的名字喵，默认是mongodb喵
+# 如果使用Docker部署，需要把MONGODB_HOST改成数据库容器的名字喵，默认是mongodb喵
+MONGODB_HOST=127.0.0.1
 MONGODB_PORT=27017
 DATABASE_NAME=MegBot
-MONGODB_USERNAME = ""  # 如果数据库需要用户名，就在这里填写喵
-MONGODB_PASSWORD = ""  # 如果数据库需要密码，就在这里填写呢
-MONGODB_AUTH_SOURCE = ""  # 数据库认证源，一般不用改哦
+# 数据库认证信息，如果需要认证就取消注释并填写下面三行喵
+# MONGODB_USERNAME = ""
+# MONGODB_PASSWORD = ""
+# MONGODB_AUTH_SOURCE = ""
 
-# 插件设置喵
-PLUGINS=["src2.plugins.chat"]  # 这里是机器人的插件列表呢
+# 也可以使用URI连接数据库，取消注释填写在下面这行喵（URI的优先级比上面的高）
+# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot
+
+# 这里是机器人的插件列表呢
+PLUGINS=["src2.plugins.chat"]
 ```
 
 ### 第二个文件：机器人配置 (bot_config.toml)
@@ -110,7 +123,8 @@ PLUGINS=["src2.plugins.chat"]  # 这里是机器人的插件列表呢
 ```toml
 [bot]
 qq = "把这里改成你的机器人QQ号喵"  # 填写你的机器人QQ号
-nickname = "麦麦"  # 机器人的名字，你可以改成你喜欢的任何名字哦
+nickname = "麦麦"  # 机器人的名字，你可以改成你喜欢的任何名字哦，建议和机器人QQ名称/群昵称一样哦
+alias_names = ["小麦", "阿麦"] # 也可以用这个招呼机器人，可以不设置呢
 
 [personality]
 # 这里可以设置机器人的性格呢，让它更有趣一些喵
@@ -198,10 +212,12 @@ key = "SILICONFLOW_KEY"
    - `topic`: 负责理解对话主题的能力呢
 
 ## 🌟 小提示
+
 - 如果你刚开始使用，建议保持默认配置呢
 - 不同的模型有不同的特长，可以根据需要调整它们的使用比例哦
 
 ## 🌟 小贴士喵
+
 - 记得要好好保管密钥（key）哦，不要告诉别人呢
 - 配置文件要小心修改，改错了机器人可能就不能和你玩了喵
 - 如果想让机器人更聪明，可以调整 personality 里的设置呢
@@ -209,7 +225,8 @@ key = "SILICONFLOW_KEY"
 - QQ群号和QQ号都要用数字填写，不要加引号哦（除了机器人自己的QQ号）
 
 ## ⚠️ 注意事项
+
 - 这个机器人还在测试中呢，可能会有一些小问题喵
 - 如果不知道怎么改某个设置，就保持原样不要动它哦~
 - 记得要先有AI服务的密钥，不然机器人就不能和你说话了呢
-- 修改完配置后要重启机器人才能生效喵~
\ No newline at end of file
+- 修改完配置后要重启机器人才能生效喵~
diff --git a/docs/installation_standard.md b/docs/installation_standard.md
index 5f52676d1..dfaf0e797 100644
--- a/docs/installation_standard.md
+++ b/docs/installation_standard.md
@@ -3,14 +3,16 @@
 ## 简介
 
 本项目需要配置两个主要文件：
+
 1. `.env.prod` - 配置API服务和系统环境
 2. `bot_config.toml` - 配置机器人行为和模型
 
 ## API配置说明
 
-`.env.prod`和`bot_config.toml`中的API配置关系如下：
+`.env.prod` 和 `bot_config.toml` 中的API配置关系如下：
+
+### 在.env.prod中定义API凭证
 
-### 在.env.prod中定义API凭证：
 ```ini
 # API凭证配置
 SILICONFLOW_KEY=your_key        # 硅基流动API密钥
@@ -23,7 +25,8 @@ CHAT_ANY_WHERE_KEY=your_key     # ChatAnyWhere API密钥
 CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1  # ChatAnyWhere API地址
 ```
 
-### 在bot_config.toml中引用API凭证：
+### 在bot_config.toml中引用API凭证
+
 ```toml
 [model.llm_reasoning]
 name = "Pro/deepseek-ai/DeepSeek-R1"
@@ -32,9 +35,10 @@ key = "SILICONFLOW_KEY"            # 引用.env.prod中定义的密钥
 ```
 
 如需切换到其他API服务，只需修改引用：
+
 ```toml
 [model.llm_reasoning]
-name = "Pro/deepseek-ai/DeepSeek-R1"
+name = "deepseek-reasoner"       # 改成对应的模型名称，这里为DeepseekR1
 base_url = "DEEP_SEEK_BASE_URL"  # 切换为DeepSeek服务
 key = "DEEP_SEEK_KEY"            # 使用DeepSeek密钥
 ```
@@ -42,6 +46,7 @@ key = "DEEP_SEEK_KEY"            # 使用DeepSeek密钥
 ## 配置文件详解
 
 ### 环境配置文件 (.env.prod)
+
 ```ini
 # API配置
 SILICONFLOW_KEY=your_key
@@ -52,26 +57,36 @@ CHAT_ANY_WHERE_KEY=your_key
 CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
 
 # 服务配置
+
 HOST=127.0.0.1  # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入
-PORT=8080
+PORT=8080       # 与反向端口相同
 
 # 数据库配置
 MONGODB_HOST=127.0.0.1  # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb
-MONGODB_PORT=27017
+MONGODB_PORT=27017      # MongoDB端口
+
 DATABASE_NAME=MegBot
-MONGODB_USERNAME = ""  # 数据库用户名
-MONGODB_PASSWORD = ""  # 数据库密码
-MONGODB_AUTH_SOURCE = ""  # 认证数据库
+# 数据库认证信息，如果需要认证就取消注释并填写下面三行
+# MONGODB_USERNAME = ""
+# MONGODB_PASSWORD = ""
+# MONGODB_AUTH_SOURCE = ""
+
+# 也可以使用URI连接数据库，取消注释填写在下面这行（URI的优先级比上面的高）
+# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot
 
 # 插件配置
 PLUGINS=["src2.plugins.chat"]
 ```
 
 ### 机器人配置文件 (bot_config.toml)
+
 ```toml
 [bot]
 qq = "机器人QQ号"  # 必填
 nickname = "麦麦"  # 机器人昵称
+# alias_names: 配置机器人可使用的别名。当机器人在群聊或对话中被调用时，别名可以作为直接命令或提及机器人的关键字使用。
+# 该配置项为字符串数组。例如: ["小麦", "阿麦"]
+alias_names = ["小麦", "阿麦"]  # 机器人别名
 
 [personality]
 prompt_personality = [
@@ -151,4 +166,4 @@ key = "SILICONFLOW_KEY"
 
 3. 其他说明：
    - 项目处于测试阶段，可能存在未知问题
-   - 建议初次使用保持默认配置 
\ No newline at end of file
+   - 建议初次使用保持默认配置
diff --git a/docs/linux_deploy_guide_for_beginners.md b/docs/linux_deploy_guide_for_beginners.md
new file mode 100644
index 000000000..04601923f
--- /dev/null
+++ b/docs/linux_deploy_guide_for_beginners.md
@@ -0,0 +1,444 @@
+# 面向纯新手的Linux服务器麦麦部署指南
+
+## 你得先有一个服务器
+
+为了能使麦麦在你的电脑关机之后还能运行，你需要一台不间断开机的主机，也就是我们常说的服务器。
+
+华为云、阿里云、腾讯云等等都是在国内可以选择的选择。
+
+你可以去租一台最低配置的就足敷需要了，按月租大概十几块钱就能租到了。
+
+我们假设你已经租好了一台Linux架构的云服务器。我用的是阿里云ubuntu24.04，其他的原理相似。
+
+## 0.我们就从零开始吧
+
+### 网络问题
+
+为访问github相关界面，推荐去下一款加速器，新手可以试试watttoolkit。
+
+### 安装包下载
+
+#### MongoDB
+
+对于ubuntu24.04 x86来说是这个：
+
+https://repo.mongodb.org/apt/ubuntu/dists/noble/mongodb-org/8.0/multiverse/binary-amd64/mongodb-org-server_8.0.5_amd64.deb
+
+如果不是就在这里自行选择对应版本
+
+https://www.mongodb.com/try/download/community-kubernetes-operator
+
+#### Napcat
+
+在这里选择对应版本。
+
+https://github.com/NapNeko/NapCatQQ/releases/tag/v4.6.7
+
+对于ubuntu24.04 x86来说是这个：
+
+https://dldir1.qq.com/qqfile/qq/QQNT/ee4bd910/linuxqq_3.2.16-32793_amd64.deb
+
+#### 麦麦
+
+https://github.com/SengokuCola/MaiMBot/archive/refs/tags/0.5.8-alpha.zip
+
+下载这个官方压缩包。
+
+### 路径
+
+我把麦麦相关文件放在了/moi/mai里面，你可以凭喜好更改，记得适当调整下面涉及到的部分即可。
+
+文件结构：
+
+```
+moi
+└─ mai
+   ├─ linuxqq_3.2.16-32793_amd64.deb
+   ├─ mongodb-org-server_8.0.5_amd64.deb
+   └─ bot
+      └─ MaiMBot-0.5.8-alpha.zip
+```
+
+### 网络
+
+你可以在你的服务器控制台网页更改防火墙规则，允许6099，8080，27017这几个端口的出入。
+
+## 1.正式开始！
+
+远程连接你的服务器，你会看到一个黑框框闪着白方格，这就是我们要进行设置的场所——终端了。以下的bash命令都是在这里输入。
+
+## 2. Python的安装
+
+- 导入 Python 的稳定版 PPA：
+
+```bash
+sudo add-apt-repository ppa:deadsnakes/ppa
+```
+
+- 导入 PPA 后，更新 APT 缓存：
+
+```bash
+sudo apt update
+```
+
+- 在「终端」中执行以下命令来安装 Python 3.12：
+
+```bash
+sudo apt install python3.12
+```
+
+- 验证安装是否成功：
+
+```bash
+python3.12 --version
+```
+
+- 在「终端」中，执行以下命令安装 pip：
+
+```bash
+sudo apt install python3-pip
+```
+
+- 检查Pip是否安装成功：
+
+```bash
+pip --version
+```
+
+- 安装必要组件
+
+``` bash
+sudo apt install python-is-python3
+```
+
+## 3.MongoDB的安装
+
+``` bash
+cd /moi/mai
+```
+
+``` bash
+dpkg -i mongodb-org-server_8.0.5_amd64.deb
+```
+
+``` bash
+mkdir -p /root/data/mongodb/{data,log}
+```
+
+## 4.MongoDB的运行
+
+```bash
+service mongod start
+```
+
+```bash
+systemctl status mongod #通过这条指令检查运行状态
+```
+
+有需要的话可以把这个服务注册成开机自启
+
+```bash
+sudo systemctl enable mongod
+```
+
+## 5.napcat的安装
+
+``` bash
+curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && sudo bash napcat.sh
+```
+
+上面的不行试试下面的
+
+``` bash
+dpkg -i linuxqq_3.2.16-32793_amd64.deb
+apt-get install -f
+dpkg -i linuxqq_3.2.16-32793_amd64.deb
+```
+
+成功的标志是输入``` napcat ```出来炫酷的彩虹色界面
+
+## 6.napcat的运行
+
+此时你就可以根据提示在```napcat```里面登录你的QQ号了。
+
+```bash
+napcat start <你的QQ号>
+napcat status #检查运行状态
+```
+
+然后你就可以登录napcat的webui进行设置了：
+
+```http://<你服务器的公网IP>:6099/webui?token=napcat```
+
+第一次是这个，后续改了密码之后token就会对应修改。你也可以使用```napcat log <你的QQ号>```来查看webui地址。把里面的```127.0.0.1```改成<你服务器的公网IP>即可。
+
+登录上之后在网络配置界面添加websocket客户端，名称随便输一个，url改成`ws://127.0.0.1:8080/onebot/v11/ws`保存之后点启用，就大功告成了。
+
+## 7.麦麦的安装
+
+### step 1 安装解压软件
+
+```
+sudo apt-get install unzip
+```
+
+### step 2 解压文件
+
+```bash
+cd /moi/mai/bot # 注意：要切换到压缩包的目录中去
+unzip MaiMBot-0.5.8-alpha.zip
+```
+
+### step 3 进入虚拟环境安装库
+
+```bash
+cd /moi/mai/bot
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+### step 4 试运行
+
+```bash
+cd /moi/mai/bot
+python -m venv venv
+source venv/bin/activate
+python bot.py
+```
+
+肯定运行不成功，不过你会发现结束之后多了一些文件
+
+```
+bot
+├─ .env.prod
+└─ config
+   └─ bot_config.toml
+```
+
+你要会vim直接在终端里修改也行，不过也可以把它们下到本地改好再传上去：
+
+### step 5 文件配置
+
+本项目需要配置两个主要文件：
+
+1. `.env.prod` - 配置API服务和系统环境
+2. `bot_config.toml` - 配置机器人行为和模型
+
+#### API
+
+你可以注册一个硅基流动的账号，通过邀请码注册有14块钱的免费额度：https://cloud.siliconflow.cn/i/7Yld7cfg。
+
+#### 在.env.prod中定义API凭证：
+
+```
+# API凭证配置
+SILICONFLOW_KEY=your_key        # 硅基流动API密钥
+SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/  # 硅基流动API地址
+
+DEEP_SEEK_KEY=your_key          # DeepSeek API密钥
+DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1  # DeepSeek API地址
+
+CHAT_ANY_WHERE_KEY=your_key     # ChatAnyWhere API密钥
+CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1  # ChatAnyWhere API地址
+```
+
+#### 在bot_config.toml中引用API凭证：
+
+```
+[model.llm_reasoning]
+name = "Pro/deepseek-ai/DeepSeek-R1"
+base_url = "SILICONFLOW_BASE_URL"  # 引用.env.prod中定义的地址
+key = "SILICONFLOW_KEY"            # 引用.env.prod中定义的密钥
+```
+
+如需切换到其他API服务，只需修改引用：
+
+```
+[model.llm_reasoning]
+name = "Pro/deepseek-ai/DeepSeek-R1"
+base_url = "DEEP_SEEK_BASE_URL"  # 切换为DeepSeek服务
+key = "DEEP_SEEK_KEY"            # 使用DeepSeek密钥
+```
+
+#### 配置文件详解
+
+##### 环境配置文件 (.env.prod)
+
+```
+# API配置
+SILICONFLOW_KEY=your_key
+SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
+DEEP_SEEK_KEY=your_key
+DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
+CHAT_ANY_WHERE_KEY=your_key
+CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
+
+# 服务配置
+HOST=127.0.0.1  # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入
+PORT=8080
+
+# 数据库配置
+MONGODB_HOST=127.0.0.1  # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb
+MONGODB_PORT=27017
+DATABASE_NAME=MegBot
+MONGODB_USERNAME = ""  # 数据库用户名
+MONGODB_PASSWORD = ""  # 数据库密码
+MONGODB_AUTH_SOURCE = ""  # 认证数据库
+
+# 插件配置
+PLUGINS=["src2.plugins.chat"]
+```
+
+##### 机器人配置文件 (bot_config.toml)
+
+```
+[bot]
+qq = "机器人QQ号"  # 必填
+nickname = "麦麦"  # 机器人昵称（你希望机器人怎么称呼它自己）
+
+[personality]
+prompt_personality = [
+    "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
+    "是一个女大学生，你有黑色头发，你会刷小红书"
+]
+prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
+
+[message]
+min_text_length = 2  # 最小回复长度
+max_context_size = 15  # 上下文记忆条数
+emoji_chance = 0.2  # 表情使用概率
+ban_words = []  # 禁用词列表
+
+[emoji]
+auto_save = true  # 自动保存表情
+enable_check = false  # 启用表情审核
+check_prompt = "符合公序良俗"
+
+[groups]
+talk_allowed = []      # 允许对话的群号
+talk_frequency_down = []   # 降低回复频率的群号
+ban_user_id = []      # 禁止回复的用户QQ号
+
+[others]
+enable_advance_output = true  # 启用详细日志
+enable_kuuki_read = true  # 启用场景理解
+
+# 模型配置
+[model.llm_reasoning]  # 推理模型
+name = "Pro/deepseek-ai/DeepSeek-R1"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.llm_reasoning_minor]  # 轻量推理模型
+name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.llm_normal]  # 对话模型
+name = "Pro/deepseek-ai/DeepSeek-V3"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.llm_normal_minor]  # 备用对话模型
+name = "deepseek-ai/DeepSeek-V2.5"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.vlm]  # 图像识别模型
+name = "deepseek-ai/deepseek-vl2"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.embedding]  # 文本向量模型
+name = "BAAI/bge-m3"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+
+[topic.llm_topic]
+name = "Pro/deepseek-ai/DeepSeek-V3"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+```
+
+**step # 6** 运行
+
+现在再运行
+
+```bash
+cd /moi/mai/bot
+python -m venv venv
+source venv/bin/activate
+python bot.py
+```
+
+应该就能运行成功了。
+
+## 8.事后配置
+
+可是现在还有个问题：只要你一关闭终端，bot.py就会停止运行。那该怎么办呢？我们可以把bot.py注册成服务。
+
+重启服务器，打开MongoDB和napcat服务。
+
+新建一个文件，名为`bot.service`，内容如下
+
+```
+[Unit]
+Description=maimai bot
+
+[Service]
+WorkingDirectory=/moi/mai/bot
+ExecStart=/moi/mai/bot/venv/bin/python /moi/mai/bot/bot.py
+Restart=on-failure
+User=root
+
+[Install]
+WantedBy=multi-user.target
+```
+
+里面的路径视自己的情况更改。
+
+把它放到`/etc/systemd/system`里面。
+
+重新加载 `systemd` 配置：
+
+```bash
+sudo systemctl daemon-reload
+```
+
+启动服务：
+
+```bash
+sudo systemctl start bot.service # 启动服务
+sudo systemctl restart bot.service # 或者重启服务
+```
+
+检查服务状态：
+
+```bash
+sudo systemctl status bot.service
+```
+
+现在再关闭终端，检查麦麦能不能正常回复QQ信息。如果可以的话就大功告成了！
+
+## 9.命令速查
+
+```bash
+service mongod start # 启动mongod服务
+napcat start <你的QQ号> # 登录napcat
+cd /moi/mai/bot # 切换路径
+python -m venv venv # 创建虚拟环境
+source venv/bin/activate # 激活虚拟环境
+
+sudo systemctl daemon-reload # 重新加载systemd配置
+sudo systemctl start bot.service # 启动bot服务
+sudo systemctl enable bot.service # 启动bot服务
+
+sudo systemctl status bot.service # 检查bot服务状态
+```
+
+```
+python bot.py
+```
+
diff --git a/docs/manual_deploy_linux.md b/docs/manual_deploy_linux.md
index d310ffc59..a5c91d6e2 100644
--- a/docs/manual_deploy_linux.md
+++ b/docs/manual_deploy_linux.md
@@ -1,6 +1,7 @@
 # 📦 Linux系统如何手动部署MaiMbot麦麦？
 
 ## 准备工作
+
 - 一台联网的Linux设备（本教程以Ubuntu/Debian系为例）
 - QQ小号（QQ框架的使用可能导致qq被风控，严重（小概率）可能会导致账号封禁，强烈不推荐使用大号）
 - 可用的大模型API
@@ -20,6 +21,7 @@
 - 数据库是什么？如何安装并启动MongoDB
 
 - 如何运行一个QQ机器人，以及NapCat框架是什么
+
 ---
 
 ## 环境配置
@@ -33,7 +35,9 @@ python --version
 # 或
 python3 --version
 ```
+
 如果版本低于3.9，请更新Python版本。
+
 ```bash
 # Ubuntu/Debian
 sudo apt update
@@ -45,6 +49,7 @@ sudo update-alternatives --config python3
 ```
 
 ### 2️⃣ **创建虚拟环境**
+
 ```bash
 # 方法1：使用venv(推荐)
 python3 -m venv maimbot
@@ -65,32 +70,37 @@ pip install -r requirements.txt
 ---
 
 ## 数据库配置
-### 3️⃣ **安装并启动MongoDB**
-- 安装与启动：Debian参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-debian/)，Ubuntu参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/)
 
+### 3️⃣ **安装并启动MongoDB**
+
+- 安装与启动：Debian参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-debian/)，Ubuntu参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/)
 - 默认连接本地27017端口
+
 ---
 
 ## NapCat配置
+
 ### 4️⃣ **安装NapCat框架**
 
 - 参考[NapCat官方文档](https://www.napcat.wiki/guide/boot/Shell#napcat-installer-linux%E4%B8%80%E9%94%AE%E4%BD%BF%E7%94%A8%E8%84%9A%E6%9C%AC-%E6%94%AF%E6%8C%81ubuntu-20-debian-10-centos9)安装
 
--  使用QQ小号登录，添加反向WS地址：
-`ws://127.0.0.1:8080/onebot/v11/ws`
+-  使用QQ小号登录，添加反向WS地址: `ws://127.0.0.1:8080/onebot/v11/ws`
 
 ---
 
 ## 配置文件设置
+
 ### 5️⃣ **配置文件设置，让麦麦Bot正常工作**
+
 - 修改环境配置文件：`.env.prod`
 - 修改机器人配置文件：`bot_config.toml`
 
-
 ---
 
 ## 启动机器人
+
 ### 6️⃣ **启动麦麦机器人**
+
 ```bash
 # 在项目目录下操作
 nb run
@@ -100,17 +110,71 @@ python3 bot.py
 
 ---
 
-## **其他组件(可选)**
-- 直接运行 knowledge.py生成知识库
+### 7️⃣ **使用systemctl管理maimbot**
 
+使用以下命令添加服务文件：
+
+```bash
+sudo nano /etc/systemd/system/maimbot.service
+```
+
+输入以下内容：
+
+`<maimbot_directory>`：你的maimbot目录
+
+`<venv_directory>`：你的venv环境（就是上文创建环境后，执行的代码`source maimbot/bin/activate`中source后面的路径的绝对路径）
+
+```ini
+[Unit]
+Description=MaiMbot 麦麦
+After=network.target mongod.service
+
+[Service]
+Type=simple
+WorkingDirectory=<maimbot_directory>
+ExecStart=<venv_directory>/python3 bot.py
+ExecStop=/bin/kill -2 $MAINPID
+Restart=always
+RestartSec=10s
+
+[Install]
+WantedBy=multi-user.target
+```
+
+输入以下命令重新加载systemd：
+
+```bash
+sudo systemctl daemon-reload
+```
+
+启动并设置开机自启：
+
+```bash
+sudo systemctl start maimbot
+sudo systemctl enable maimbot
+```
+
+输入以下命令查看日志：
+
+```bash
+sudo journalctl -xeu maimbot
+```
+
+---
+
+## **其他组件(可选)**
+
+- 直接运行 knowledge.py生成知识库
 
 ---
 
 ## 常见问题
+
 🔧 权限问题：在命令前加`sudo`  
 🔌 端口占用：使用`sudo lsof -i :8080`查看端口占用  
 🛡️ 防火墙：确保8080/27017端口开放  
+
 ```bash
 sudo ufw allow 8080/tcp
 sudo ufw allow 27017/tcp
-```
\ No newline at end of file
+```
diff --git a/docs/manual_deploy_windows.md b/docs/manual_deploy_windows.md
index 86238bcd4..37f0a5e31 100644
--- a/docs/manual_deploy_windows.md
+++ b/docs/manual_deploy_windows.md
@@ -30,12 +30,13 @@
 
 在创建虚拟环境之前，请确保你的电脑上安装了Python 3.9及以上版本。如果没有，可以按以下步骤安装：
 
-1. 访问Python官网下载页面：https://www.python.org/downloads/release/python-3913/
+1. 访问Python官网下载页面：<https://www.python.org/downloads/release/python-3913/>
 2. 下载Windows安装程序 (64-bit): `python-3.9.13-amd64.exe`
 3. 运行安装程序，并确保勾选"Add Python 3.9 to PATH"选项
 4. 点击"Install Now"开始安装
 
 或者使用PowerShell自动下载安装（需要管理员权限）：
+
 ```powershell
 # 下载并安装Python 3.9.13
 $pythonUrl = "https://www.python.org/ftp/python/3.9.13/python-3.9.13-amd64.exe"
@@ -46,7 +47,7 @@ Start-Process -Wait -FilePath $pythonInstaller -ArgumentList "/quiet", "InstallA
 
 ### 2️⃣ **创建Python虚拟环境来运行程序**
 
-    你可以选择使用以下两种方法之一来创建Python环境：
+> 你可以选择使用以下两种方法之一来创建Python环境：
 
 ```bash
 # ---方法1：使用venv（Python自带）
@@ -60,6 +61,7 @@ maimbot\\Scripts\\activate
 # 安装依赖
 pip install -r requirements.txt
 ```
+
 ```bash
 # ---方法2：使用conda
 # 创建一个新的conda环境（环境名为maimbot）
@@ -74,27 +76,35 @@ pip install -r requirements.txt
 ```
 
 ### 2️⃣ **然后你需要启动MongoDB数据库，来存储信息**
+
 - 安装并启动MongoDB服务
 - 默认连接本地27017端口
 
 ### 3️⃣ **配置NapCat，让麦麦bot与qq取得联系**
+
 - 安装并登录NapCat（用你的qq小号）
-- 添加反向WS：`ws://127.0.0.1:8080/onebot/v11/ws`
+- 添加反向WS: `ws://127.0.0.1:8080/onebot/v11/ws`
 
 ### 4️⃣ **配置文件设置，让麦麦Bot正常工作**
+
 - 修改环境配置文件：`.env.prod`
 - 修改机器人配置文件：`bot_config.toml`
 
 ### 5️⃣ **启动麦麦机器人**
+
 - 打开命令行，cd到对应路径
+
 ```bash
 nb run
 ```
+
 - 或者cd到对应路径后
+
 ```bash
 python bot.py
 ```
 
 ### 6️⃣ **其他组件(可选)**
+
 - `run_thingking.bat`: 启动可视化推理界面（未完善）
 - 直接运行 knowledge.py生成知识库
diff --git a/docs/synology_.env.prod.png b/docs/synology_.env.prod.png
new file mode 100644
index 000000000..0bdcacdf3
Binary files /dev/null and b/docs/synology_.env.prod.png differ
diff --git a/docs/synology_create_project.png b/docs/synology_create_project.png
new file mode 100644
index 000000000..f716d4605
Binary files /dev/null and b/docs/synology_create_project.png differ
diff --git a/docs/synology_deploy.md b/docs/synology_deploy.md
new file mode 100644
index 000000000..23e24e704
--- /dev/null
+++ b/docs/synology_deploy.md
@@ -0,0 +1,67 @@
+# 群晖 NAS 部署指南
+
+**笔者使用的是 DSM 7.2.2，其他 DSM 版本的操作可能不完全一样**
+**需要使用 Container Manager，群晖的部分部分入门级 NAS 可能不支持**
+
+## 部署步骤
+
+### 创建配置文件目录
+
+打开 `DSM ➡️ 控制面板 ➡️ 共享文件夹`，点击 `新增` ，创建一个共享文件夹
+只需要设置名称，其他设置均保持默认即可。如果你已经有 docker 专用的共享文件夹了，就跳过这一步
+
+打开 `DSM ➡️ FileStation`， 在共享文件夹中创建一个 `MaiMBot` 文件夹
+
+### 准备配置文件
+
+docker-compose.yml: https://github.com/SengokuCola/MaiMBot/blob/main/docker-compose.yml
+下载后打开，将 `services-mongodb-image` 修改为 `mongo:4.4.24`。这是因为最新的 MongoDB 强制要求 AVX 指令集，而群晖似乎不支持这个指令集
+![](https://raw.githubusercontent.com/ProperSAMA/MaiMBot/refs/heads/debug/docs/synology_docker-compose.png)
+
+bot_config.toml: https://github.com/SengokuCola/MaiMBot/blob/main/template/bot_config_template.toml
+下载后，重命名为 `bot_config.toml`
+打开它，按自己的需求填写配置文件
+
+.env.prod: https://github.com/SengokuCola/MaiMBot/blob/main/template.env
+下载后，重命名为 `.env.prod`
+按下图修改 mongodb 设置，使用  `MONGODB_URI`
+![](https://raw.githubusercontent.com/ProperSAMA/MaiMBot/refs/heads/debug/docs/synology_.env.prod.png)
+
+把 `bot_config.toml` 和 `.env.prod` 放入之前创建的 `MaiMBot`文件夹
+
+#### 如何下载？
+
+点这里！![](https://raw.githubusercontent.com/ProperSAMA/MaiMBot/refs/heads/debug/docs/synology_how_to_download.png)
+
+### 创建项目
+
+打开 `DSM ➡️ ContainerManager ➡️ 项目`，点击 `新增` 创建项目，填写以下内容：
+
+- 项目名称： `maimbot`
+- 路径：之前创建的 `MaiMBot` 文件夹
+- 来源： `上传 docker-compose.yml`
+- 文件：之前下载的 `docker-compose.yml` 文件
+
+图例：
+
+![](https://raw.githubusercontent.com/ProperSAMA/MaiMBot/refs/heads/debug/docs/synology_create_project.png)
+
+一路点下一步，等待项目创建完成
+
+### 设置 Napcat
+
+1. 登陆 napcat
+   打开 napcat： `http://<你的nas地址>:6099` ，输入token登陆
+   token可以打开 `DSM ➡️ ContainerManager ➡️ 项目 ➡️ MaiMBot ➡️ 容器 ➡️ Napcat ➡️ 日志`，找到类似 `[WebUi] WebUi Local Panel Url: http://127.0.0.1:6099/webui?token=xxxx` 的日志
+   这个 `token=` 后面的就是你的 napcat token
+
+2. 按提示，登陆你给麦麦准备的QQ小号
+
+3. 设置 websocket 客户端
+   `网络配置 -> 新建 -> Websocket客户端`，名称自定，URL栏填入 `ws://maimbot:8080/onebot/v11/ws`，启用并保存即可。
+   若修改过容器名称，则替换 `maimbot` 为你自定的名称
+
+### 部署完成
+
+找个群，发送 `麦麦，你在吗` 之类的
+如果一切正常，应该能正常回复了
\ No newline at end of file
diff --git a/docs/synology_docker-compose.png b/docs/synology_docker-compose.png
new file mode 100644
index 000000000..f70003e29
Binary files /dev/null and b/docs/synology_docker-compose.png differ
diff --git a/docs/synology_how_to_download.png b/docs/synology_how_to_download.png
new file mode 100644
index 000000000..011f98876
Binary files /dev/null and b/docs/synology_how_to_download.png differ
diff --git a/flake.lock b/flake.lock
index dd215f1c6..894acd486 100644
--- a/flake.lock
+++ b/flake.lock
@@ -1,43 +1,21 @@
 {
   "nodes": {
-    "flake-utils": {
-      "inputs": {
-        "systems": "systems"
-      },
-      "locked": {
-        "lastModified": 1731533236,
-        "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=",
-        "owner": "numtide",
-        "repo": "flake-utils",
-        "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b",
-        "type": "github"
-      },
-      "original": {
-        "owner": "numtide",
-        "repo": "flake-utils",
-        "type": "github"
-      }
-    },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1741196730,
-        "narHash": "sha256-0Sj6ZKjCpQMfWnN0NURqRCQn2ob7YtXTAOTwCuz7fkA=",
-        "owner": "NixOS",
-        "repo": "nixpkgs",
-        "rev": "48913d8f9127ea6530a2a2f1bd4daa1b8685d8a3",
-        "type": "github"
+        "lastModified": 0,
+        "narHash": "sha256-nJj8f78AYAxl/zqLiFGXn5Im1qjFKU8yBPKoWEeZN5M=",
+        "path": "/nix/store/f30jn7l0bf7a01qj029fq55i466vmnkh-source",
+        "type": "path"
       },
       "original": {
-        "owner": "NixOS",
-        "ref": "nixos-24.11",
-        "repo": "nixpkgs",
-        "type": "github"
+        "id": "nixpkgs",
+        "type": "indirect"
       }
     },
     "root": {
       "inputs": {
-        "flake-utils": "flake-utils",
-        "nixpkgs": "nixpkgs"
+        "nixpkgs": "nixpkgs",
+        "utils": "utils"
       }
     },
     "systems": {
@@ -54,6 +32,24 @@
         "repo": "default",
         "type": "github"
       }
+    },
+    "utils": {
+      "inputs": {
+        "systems": "systems"
+      },
+      "locked": {
+        "lastModified": 1731533236,
+        "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
     }
   },
   "root": "root",
diff --git a/flake.nix b/flake.nix
index 54737d640..404f7555c 100644
--- a/flake.nix
+++ b/flake.nix
@@ -1,61 +1,38 @@
 {
   description = "MaiMBot Nix Dev Env";
-  # 本配置仅方便用于开发，但是因为 nb-cli 上游打包中并未包含 nonebot2，因此目前本配置并不能用于运行和调试
 
   inputs = {
-    nixpkgs.url = "github:NixOS/nixpkgs/nixos-24.11";
-    flake-utils.url = "github:numtide/flake-utils";
+    utils.url = "github:numtide/flake-utils";
   };
 
-  outputs =
-    {
-      self,
-      nixpkgs,
-      flake-utils,
-    }:
-    flake-utils.lib.eachDefaultSystem (
-      system:
-      let
-        pkgs = import nixpkgs {
-          inherit system;
-        };
+  outputs = {
+    self,
+    nixpkgs,
+    utils,
+    ...
+  }:
+    utils.lib.eachDefaultSystem (system: let
+      pkgs = import nixpkgs {inherit system;};
+      pythonPackages = pkgs.python3Packages;
+    in {
+      devShells.default = pkgs.mkShell {
+        name = "python-venv";
+        venvDir = "./.venv";
+        buildInputs = [
+          pythonPackages.python
+          pythonPackages.venvShellHook
+          pythonPackages.numpy
+        ];
 
-        pythonEnv = pkgs.python3.withPackages (
-          ps: with ps; [
-            pymongo
-            python-dotenv
-            pydantic
-            jieba
-            openai
-            aiohttp
-            requests
-            urllib3
-            numpy
-            pandas
-            matplotlib
-            networkx
-            python-dateutil
-            APScheduler
-            loguru
-            tomli
-            customtkinter
-            colorama
-            pypinyin
-            pillow
-            setuptools
-          ]
-        );
-      in
-      {
-        devShell = pkgs.mkShell {
-          buildInputs = [
-            pythonEnv
-            pkgs.nb-cli
-          ];
+        postVenvCreation = ''
+          unset SOURCE_DATE_EPOCH
+          pip install -r requirements.txt
+        '';
 
-          shellHook = ''
-          '';
-        };
-      }
-    );
-}
+        postShellHook = ''
+          # allow pip to install wheels
+          unset SOURCE_DATE_EPOCH
+        '';
+      };
+    });
+}
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index e54dcdacd..0a4805744 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,23 +1,51 @@
 [project]
-name = "Megbot"
+name = "MaiMaiBot"
 version = "0.1.0"
-description = "New Bot Project"
+description = "MaiMaiBot"
 
 [tool.nonebot]
 plugins = ["src.plugins.chat"]
-plugin_dirs = ["src/plugins"] 
+plugin_dirs = ["src/plugins"]
 
 [tool.ruff]
-# 设置 Python 版本
-target-version = "py39"
+
+include = ["*.py"]
+
+# 行长度设置
+line-length = 120
+
+[tool.ruff.lint]
+fixable = ["ALL"]
+unfixable = []
+
+# 如果一个变量的名称以下划线开头，即使它未被使用，也不应该被视为错误或警告。
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
 
 # 启用的规则
 select = [
-    "E",  # pycodestyle 错误
-    "F",  # pyflakes
-    "I",  # isort
-    "B",  # flake8-bugbear
+    "E", # pycodestyle 错误
+    "F", # pyflakes
+    "B", # flake8-bugbear
 ]
 
-# 行长度设置
-line-length = 88
\ No newline at end of file
+ignore = ["E711"]
+
+[tool.ruff.format]
+docstring-code-format = true
+indent-style = "space"
+
+
+# 使用双引号表示字符串
+quote-style = "double"
+
+# 尊重魔法尾随逗号
+# 例如：
+# items = [
+#     "apple",
+#     "banana",
+#     "cherry",
+# ]
+skip-magic-trailing-comma = false
+
+# 自动检测合适的换行符
+line-ending = "auto"
diff --git a/requirements.txt b/requirements.txt
index 4f969682f..8330c8d06 100644
Binary files a/requirements.txt and b/requirements.txt differ
diff --git a/run.bat b/run.bat
index 659a7545a..91904bc34 100644
--- a/run.bat
+++ b/run.bat
@@ -3,7 +3,7 @@ chcp 65001
 if not exist "venv" (
   python -m venv venv
   call venv\Scripts\activate.bat
-  pip install -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple --upgrade -r requirements.txt
+  pip install -i https://mirrors.aliyun.com/pypi/simple --upgrade -r requirements.txt
   ) else (
   call venv\Scripts\activate.bat
 )
diff --git a/run.py b/run.py
index baea4d13c..cfd3a5f14 100644
--- a/run.py
+++ b/run.py
@@ -107,6 +107,8 @@ def install_napcat():
     napcat_filename = input(
         "下载完成后请把文件复制到此文件夹，并将**不包含后缀的文件名**输入至此窗口，如 NapCat.32793.Shell："
     )
+    if(napcat_filename[-4:] == ".zip"):
+        napcat_filename = napcat_filename[:-4]
     extract_files(napcat_filename + ".zip", "napcat")
     print("NapCat 安装完成")
     os.remove(napcat_filename + ".zip")
@@ -126,13 +128,17 @@ if __name__ == "__main__":
     )
     os.system("cls")
     if choice == "1":
-        install_napcat()
-        install_mongodb()
+        confirm = input("首次安装将下载并配置所需组件\n1.确认\n2.取消\n")
+        if confirm == "1":
+            install_napcat()
+            install_mongodb()
+        else:
+            print("已取消安装")
     elif choice == "2":
         run_maimbot()
-        choice = input("是否启动推理可视化？（y/N）").upper()
+        choice = input("是否启动推理可视化？（未完善）（y/N）").upper()
         if choice == "Y":
             run_cmd(r"python src\gui\reasoning_gui.py")
-        choice = input("是否启动记忆可视化？（y/N）").upper()
+        choice = input("是否启动记忆可视化？（未完善）（y/N）").upper()
         if choice == "Y":
             run_cmd(r"python src/plugins/memory_system/memory_manual_build.py")
diff --git a/run.sh b/run.sh
new file mode 100644
index 000000000..c3f6969b6
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,278 @@
+#!/bin/bash
+
+# Maimbot 一键安装脚本 by Cookie987
+# 适用于Debian系
+# 请小心使用任何一键脚本！
+
+# 如无法访问GitHub请修改此处镜像地址
+
+LANG=C.UTF-8
+
+GITHUB_REPO="https://ghfast.top/https://github.com/SengokuCola/MaiMBot.git"
+
+# 颜色输出
+GREEN="\e[32m"
+RED="\e[31m"
+RESET="\e[0m"
+
+# 需要的基本软件包
+REQUIRED_PACKAGES=("git" "sudo" "python3" "python3-venv" "curl" "gnupg" "python3-pip")
+
+# 默认项目目录
+DEFAULT_INSTALL_DIR="/opt/maimbot"
+
+# 服务名称
+SERVICE_NAME="maimbot"
+
+IS_INSTALL_MONGODB=false
+IS_INSTALL_NAPCAT=false
+
+# 1/6: 检测是否安装 whiptail
+if ! command -v whiptail &>/dev/null; then
+    echo -e "${RED}[1/6] whiptail 未安装，正在安装...${RESET}"
+    apt update && apt install -y whiptail
+fi
+
+get_os_info() {
+    if command -v lsb_release &>/dev/null; then
+        OS_INFO=$(lsb_release -d | cut -f2)
+    elif [[ -f /etc/os-release ]]; then
+        OS_INFO=$(grep "^PRETTY_NAME=" /etc/os-release | cut -d '"' -f2)
+    else
+        OS_INFO="Unknown OS"
+    fi
+    echo "$OS_INFO"
+}
+
+# 检查系统
+check_system() {
+    # 检查是否为 root 用户
+    if [[ "$(id -u)" -ne 0 ]]; then
+        whiptail --title "🚫 权限不足" --msgbox "请使用 root 用户运行此脚本！\n执行方式: sudo bash $0" 10 60
+        exit 1
+    fi
+
+    if [[ -f /etc/os-release ]]; then
+        source /etc/os-release
+        if [[ "$ID" != "debian" || "$VERSION_ID" != "12" ]]; then
+            whiptail --title "🚫 不支持的系统" --msgbox "此脚本仅支持 Debian 12 (Bookworm)！\n当前系统: $PRETTY_NAME\n安装已终止。" 10 60
+            exit 1
+        fi
+    else
+        whiptail --title "⚠️ 无法检测系统" --msgbox "无法识别系统版本，安装已终止。" 10 60
+        exit 1
+    fi
+}
+
+# 3/6: 询问用户是否安装缺失的软件包
+install_packages() {
+    missing_packages=()
+    for package in "${REQUIRED_PACKAGES[@]}"; do
+        if ! dpkg -s "$package" &>/dev/null; then
+            missing_packages+=("$package")
+        fi
+    done
+
+    if [[ ${#missing_packages[@]} -gt 0 ]]; then
+        whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到以下必须的依赖项目缺失:\n${missing_packages[*]}\n\n是否要自动安装？" 12 60
+        if [[ $? -eq 0 ]]; then
+            return 0
+        else
+            whiptail --title "⚠️ 注意" --yesno "某些必要的依赖项未安装，可能会影响运行！\n是否继续？" 10 60 || exit 1
+        fi
+    fi
+}
+
+# 4/6: Python 版本检查
+check_python() {
+    PYTHON_VERSION=$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')
+
+    python3 -c "import sys; exit(0) if sys.version_info >= (3,9) else exit(1)"
+    if [[ $? -ne 0 ]]; then
+        whiptail --title "⚠️ [4/6] Python 版本过低" --msgbox "检测到 Python 版本为 $PYTHON_VERSION，需要 3.9 或以上！\n请升级 Python 后重新运行本脚本。" 10 60
+        exit 1
+    fi
+}
+
+# 5/6: 选择分支
+choose_branch() {
+    BRANCH=$(whiptail --title "🔀 [5/6] 选择 Maimbot 分支" --menu "请选择要安装的 Maimbot 分支：" 15 60 2 \
+        "main" "稳定版本（推荐）" \
+        "debug" "开发版本（可能不稳定）" 3>&1 1>&2 2>&3)
+
+    if [[ -z "$BRANCH" ]]; then
+        BRANCH="main"
+        whiptail --title "🔀 默认选择" --msgbox "未选择分支，默认安装稳定版本（main）" 10 60
+    fi
+}
+
+# 6/6: 选择安装路径
+choose_install_dir() {
+    INSTALL_DIR=$(whiptail --title "📂 [6/6] 选择安装路径" --inputbox "请输入 Maimbot 的安装目录：" 10 60 "$DEFAULT_INSTALL_DIR" 3>&1 1>&2 2>&3)
+
+    if [[ -z "$INSTALL_DIR" ]]; then
+        whiptail --title "⚠️ 取消输入" --yesno "未输入安装路径，是否退出安装？" 10 60
+        if [[ $? -ne 0 ]]; then
+            INSTALL_DIR="$DEFAULT_INSTALL_DIR"
+        else
+            exit 1
+        fi
+    fi
+}
+
+# 显示确认界面
+confirm_install() {
+    local confirm_message="请确认以下更改:\n\n"
+
+    if [[ ${#missing_packages[@]} -gt 0 ]]; then
+        confirm_message+="📦 安装缺失的依赖项: ${missing_packages[*]}\n"
+    else
+        confirm_message+="✅ 所有依赖项已安装\n"
+    fi
+
+    confirm_message+="📂 安装麦麦Bot到: $INSTALL_DIR\n"
+    confirm_message+="🔀 分支: $BRANCH\n"
+
+    if [[ "$MONGODB_INSTALLED" == "true" ]]; then
+        confirm_message+="✅ MongoDB 已安装\n"
+    else
+        if [[ "$IS_INSTALL_MONGODB" == "true" ]]; then
+            confirm_message+="📦 安装 MongoDB\n"
+        fi
+    fi
+
+    if [[ "$NAPCAT_INSTALLED" == "true" ]]; then
+        confirm_message+="✅ NapCat 已安装\n"
+    else
+        if [[ "$IS_INSTALL_NAPCAT" == "true" ]]; then
+            confirm_message+="📦 安装 NapCat\n"
+        fi
+    fi
+
+    confirm_message+="🛠️ 添加麦麦Bot作为系统服务 ($SERVICE_NAME.service)\n"
+
+    confitm_message+="\n\n注意：本脚本默认使用ghfast.top为GitHub进行加速，如不想使用请手动修改脚本开头的GITHUB_REPO变量。"
+    whiptail --title "🔧 安装确认" --yesno "$confirm_message\n\n是否继续安装？" 15 60
+    if [[ $? -ne 0 ]]; then
+        whiptail --title "🚫 取消安装" --msgbox "安装已取消。" 10 60
+        exit 1
+    fi
+}
+
+check_mongodb() {
+    if command -v mongod &>/dev/null; then
+        MONGO_INSTALLED=true
+    else
+        MONGO_INSTALLED=false
+    fi
+}
+
+# 安装 MongoDB
+install_mongodb() {
+    if [[ "$MONGO_INSTALLED" == "true" ]]; then
+        return 0
+    fi
+
+    whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到未安装MongoDB，是否安装？\n如果您想使用远程数据库，请跳过此步。" 10 60
+    if [[ $? -ne 0 ]]; then
+        return 1
+    fi
+    IS_INSTALL_MONGODB=true
+}
+
+check_napcat() {
+    if command -v napcat &>/dev/null; then
+        NAPCAT_INSTALLED=true
+    else
+        NAPCAT_INSTALLED=false
+    fi
+}
+
+install_napcat() {
+    if [[ "$NAPCAT_INSTALLED" == "true" ]]; then
+        return 0
+    fi
+
+    whiptail --title "📦 [3/6] 软件包检查" --yesno "检测到未安装NapCat，是否安装？\n如果您想使用远程NapCat，请跳过此步。" 10 60
+    if [[ $? -ne 0 ]]; then
+        return 1
+    fi
+    IS_INSTALL_NAPCAT=true
+}
+
+# 运行安装步骤
+check_system
+check_mongodb
+check_napcat
+install_packages
+install_mongodb
+install_napcat
+check_python
+choose_branch
+choose_install_dir
+confirm_install
+
+# 开始安装
+whiptail --title "🚀 开始安装" --msgbox "所有环境检查完毕，即将开始安装麦麦Bot！" 10 60
+
+echo -e "${GREEN}安装依赖项...${RESET}"
+
+apt update && apt install -y "${missing_packages[@]}"
+
+
+if [[ "$IS_INSTALL_MONGODB" == "true" ]]; then
+    echo -e "${GREEN}安装 MongoDB...${RESET}"
+    curl -fsSL https://www.mongodb.org/static/pgp/server-8.0.asc | gpg -o /usr/share/keyrings/mongodb-server-8.0.gpg --dearmor
+    echo "deb [ signed-by=/usr/share/keyrings/mongodb-server-8.0.gpg ] http://repo.mongodb.org/apt/debian bookworm/mongodb-org/8.0 main" | sudo tee /etc/apt/sources.list.d/mongodb-org-8.0.list
+    apt-get update
+    apt-get install -y mongodb-org
+
+    systemctl enable mongod
+    systemctl start mongod
+fi
+
+if [[ "$IS_INSTALL_NAPCAT" == "true" ]]; then
+    echo -e "${GREEN}安装 NapCat...${RESET}"
+    curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && bash napcat.sh
+fi
+
+echo -e "${GREEN}创建 Python 虚拟环境...${RESET}"
+mkdir -p "$INSTALL_DIR"
+cd "$INSTALL_DIR" || exit
+python3 -m venv venv
+source venv/bin/activate
+
+echo -e "${GREEN}克隆仓库...${RESET}"
+# 安装 Maimbot
+mkdir -p "$INSTALL_DIR/repo"
+cd "$INSTALL_DIR/repo" || exit 1
+git clone -b "$BRANCH" $GITHUB_REPO .
+
+echo -e "${GREEN}安装 Python 依赖...${RESET}"
+pip install -r requirements.txt
+
+echo -e "${GREEN}设置服务...${RESET}"
+
+# 设置 Maimbot 服务
+cat <<EOF | tee /etc/systemd/system/$SERVICE_NAME.service
+[Unit]
+Description=MaiMbot 麦麦
+After=network.target mongod.service
+
+[Service]
+Type=simple
+WorkingDirectory=$INSTALL_DIR/repo/
+ExecStart=$INSTALL_DIR/venv/bin/python3 bot.py
+ExecStop=/bin/kill -2 $MAINPID
+Restart=always
+RestartSec=10s
+
+[Install]
+WantedBy=multi-user.target
+EOF
+
+systemctl daemon-reload
+systemctl enable maimbot
+systemctl start maimbot
+
+whiptail --title "🎉 安装完成" --msgbox "麦麦Bot安装完成！\n已经启动麦麦Bot服务。\n\n安装路径: $INSTALL_DIR\n分支: $BRANCH" 12 60
diff --git a/src/common/database.py b/src/common/database.py
index 45ac05dac..cd149e526 100644
--- a/src/common/database.py
+++ b/src/common/database.py
@@ -1,50 +1,51 @@
-from typing import Optional
-
+import os
+from typing import cast
 from pymongo import MongoClient
+from pymongo.database import Database
+
+_client = None
+_db = None
 
 
-class Database:
-    _instance: Optional["Database"] = None
-    
-    def __init__(self, host: str, port: int, db_name: str, username: Optional[str] = None, password: Optional[str] = None, auth_source: Optional[str] = None):
-        if username and password:
-            # 如果有用户名和密码，使用认证连接
-            # TODO: 复杂情况直接支持URI吧
-            self.client = MongoClient(host, port, username=username, password=password, authSource=auth_source)
-        else:
-            # 否则使用无认证连接
-            self.client = MongoClient(host, port)
-        self.db = self.client[db_name]
-        
-    @classmethod
-    def initialize(cls, host: str, port: int, db_name: str, username: Optional[str] = None, password: Optional[str] = None, auth_source: Optional[str] = None) -> "Database":
-        if cls._instance is None:
-            cls._instance = cls(host, port, db_name, username, password, auth_source)
-        return cls._instance
-        
-    @classmethod
-    def get_instance(cls) -> "Database":
-        if cls._instance is None:
-            raise RuntimeError("Database not initialized")
-        return cls._instance
+def __create_database_instance():
+    uri = os.getenv("MONGODB_URI")
+    host = os.getenv("MONGODB_HOST", "127.0.0.1")
+    port = int(os.getenv("MONGODB_PORT", "27017"))
+    db_name = os.getenv("DATABASE_NAME", "MegBot")
+    username = os.getenv("MONGODB_USERNAME")
+    password = os.getenv("MONGODB_PASSWORD")
+    auth_source = os.getenv("MONGODB_AUTH_SOURCE")
+
+    if uri and uri.startswith("mongodb://"):
+        # 优先使用URI连接
+        return MongoClient(uri)
+
+    if username and password:
+        # 如果有用户名和密码，使用认证连接
+        return MongoClient(host, port, username=username, password=password, authSource=auth_source)
+
+    # 否则使用无认证连接
+    return MongoClient(host, port)
 
 
-    #测试用
-    
-    def get_random_group_messages(self, group_id: str, limit: int = 5):
-        # 先随机获取一条消息
-        random_message = list(self.db.messages.aggregate([
-            {"$match": {"group_id": group_id}},
-            {"$sample": {"size": 1}}
-        ]))[0]
-        
-        # 获取该消息之后的消息
-        subsequent_messages = list(self.db.messages.find({
-            "group_id": group_id,
-            "time": {"$gt": random_message["time"]}
-        }).sort("time", 1).limit(limit))
-        
-        # 将随机消息和后续消息合并
-        messages = [random_message] + subsequent_messages
-        
-        return messages
\ No newline at end of file
+def get_db():
+    """获取数据库连接实例，延迟初始化。"""
+    global _client, _db
+    if _client is None:
+        _client = __create_database_instance()
+        _db = _client[os.getenv("DATABASE_NAME", "MegBot")]
+    return _db
+
+
+class DBWrapper:
+    """数据库代理类，保持接口兼容性同时实现懒加载。"""
+
+    def __getattr__(self, name):
+        return getattr(get_db(), name)
+
+    def __getitem__(self, key):
+        return get_db()[key]
+
+
+# 全局数据库访问点
+db: Database = DBWrapper()
diff --git a/src/gui/reasoning_gui.py b/src/gui/reasoning_gui.py
index 572e4ece9..c577ba3ae 100644
--- a/src/gui/reasoning_gui.py
+++ b/src/gui/reasoning_gui.py
@@ -5,6 +5,9 @@ import threading
 import time
 from datetime import datetime
 from typing import Dict, List
+from loguru import logger
+from typing import Optional
+
 
 import customtkinter as ctk
 from dotenv import load_dotenv
@@ -13,128 +16,81 @@ from dotenv import load_dotenv
 current_dir = os.path.dirname(os.path.abspath(__file__))
 # 获取项目根目录
 root_dir = os.path.abspath(os.path.join(current_dir, '..', '..'))
+sys.path.insert(0, root_dir)
+from src.common.database import db
 
 # 加载环境变量
 if os.path.exists(os.path.join(root_dir, '.env.dev')):
     load_dotenv(os.path.join(root_dir, '.env.dev'))
-    print("成功加载开发环境配置")
+    logger.info("成功加载开发环境配置")
 elif os.path.exists(os.path.join(root_dir, '.env.prod')):
     load_dotenv(os.path.join(root_dir, '.env.prod'))
-    print("成功加载生产环境配置")
+    logger.info("成功加载生产环境配置")
 else:
-    print("未找到环境配置文件")
+    logger.error("未找到环境配置文件")
     sys.exit(1)
 
-from typing import Optional
-
-from pymongo import MongoClient
-
-
-class Database:
-    _instance: Optional["Database"] = None
-    
-    def __init__(self, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None):
-        if username and password:
-            self.client = MongoClient(
-                host=host,
-                port=port,
-                username=username,
-                password=password,
-                authSource=auth_source or 'admin'
-            )
-        else:
-            self.client = MongoClient(host, port)
-        self.db = self.client[db_name]
-        
-    @classmethod
-    def initialize(cls, host: str, port: int, db_name: str, username: str = None, password: str = None, auth_source: str = None) -> "Database":
-        if cls._instance is None:
-            cls._instance = cls(host, port, db_name, username, password, auth_source)
-        return cls._instance
-        
-    @classmethod
-    def get_instance(cls) -> "Database":
-        if cls._instance is None:
-            raise RuntimeError("Database not initialized")
-        return cls._instance 
-    
-
-
 class ReasoningGUI:
     def __init__(self):
         # 记录启动时间戳，转换为Unix时间戳
         self.start_timestamp = datetime.now().timestamp()
-        print(f"程序启动时间戳: {self.start_timestamp}")
-        
+        logger.info(f"程序启动时间戳: {self.start_timestamp}")
+
         # 设置主题
         ctk.set_appearance_mode("dark")
         ctk.set_default_color_theme("blue")
-        
+
         # 创建主窗口
         self.root = ctk.CTk()
         self.root.title('麦麦推理')
         self.root.geometry('800x600')
         self.root.protocol("WM_DELETE_WINDOW", self._on_closing)
-        
-        # 初始化数据库连接
-        try:
-            self.db = Database.get_instance().db
-            print("数据库连接成功")
-        except RuntimeError:
-            print("数据库未初始化，正在尝试初始化...")
-            try:
-                Database.initialize("127.0.0.1", 27017, "maimai_bot")
-                self.db = Database.get_instance().db
-                print("数据库初始化成功")
-            except Exception as e:
-                print(f"数据库初始化失败: {e}")
-                sys.exit(1)
-        
+
         # 存储群组数据
         self.group_data: Dict[str, List[dict]] = {}
-        
+
         # 创建更新队列
         self.update_queue = queue.Queue()
-        
+
         # 创建主框架
         self.frame = ctk.CTkFrame(self.root)
         self.frame.pack(pady=20, padx=20, fill="both", expand=True)
-        
+
         # 添加标题
         self.title = ctk.CTkLabel(self.frame, text="麦麦的脑内所想", font=("Arial", 24))
         self.title.pack(pady=10, padx=10)
-        
+
         # 创建左右分栏
         self.paned = ctk.CTkFrame(self.frame)
         self.paned.pack(fill="both", expand=True, padx=10, pady=10)
-        
+
         # 左侧群组列表
         self.left_frame = ctk.CTkFrame(self.paned, width=200)
         self.left_frame.pack(side="left", fill="y", padx=5, pady=5)
-        
+
         self.group_label = ctk.CTkLabel(self.left_frame, text="群组列表", font=("Arial", 16))
         self.group_label.pack(pady=5)
-        
+
         # 创建可滚动框架来容纳群组按钮
         self.group_scroll_frame = ctk.CTkScrollableFrame(self.left_frame, width=180, height=400)
         self.group_scroll_frame.pack(pady=5, padx=5, fill="both", expand=True)
-        
+
         # 存储群组按钮的字典
         self.group_buttons: Dict[str, ctk.CTkButton] = {}
         # 当前选中的群组ID
         self.selected_group_id: Optional[str] = None
-        
+
         # 右侧内容显示
         self.right_frame = ctk.CTkFrame(self.paned)
         self.right_frame.pack(side="right", fill="both", expand=True, padx=5, pady=5)
-        
+
         self.content_label = ctk.CTkLabel(self.right_frame, text="推理内容", font=("Arial", 16))
         self.content_label.pack(pady=5)
-        
+
         # 创建富文本显示框
         self.content_text = ctk.CTkTextbox(self.right_frame, width=500, height=400)
         self.content_text.pack(pady=5, padx=5, fill="both", expand=True)
-        
+
         # 配置文本标签 - 只使用颜色
         self.content_text.tag_config("timestamp", foreground="#888888")  # 时间戳使用灰色
         self.content_text.tag_config("user", foreground="#4CAF50")  # 用户名使用绿色
@@ -144,11 +100,11 @@ class ReasoningGUI:
         self.content_text.tag_config("reasoning", foreground="#FF9800")  # 推理过程使用橙色
         self.content_text.tag_config("response", foreground="#E91E63")  # 回复使用粉色
         self.content_text.tag_config("separator", foreground="#666666")  # 分隔符使用深灰色
-        
+
         # 底部控制栏
         self.control_frame = ctk.CTkFrame(self.frame)
         self.control_frame.pack(fill="x", padx=10, pady=5)
-        
+
         self.clear_button = ctk.CTkButton(
             self.control_frame,
             text="清除显示",
@@ -156,19 +112,19 @@ class ReasoningGUI:
             width=120
         )
         self.clear_button.pack(side="left", padx=5)
-        
+
         # 启动自动更新线程
         self.update_thread = threading.Thread(target=self._auto_update, daemon=True)
         self.update_thread.start()
-        
+
         # 启动GUI更新检查
         self.root.after(100, self._process_queue)
-    
+
     def _on_closing(self):
         """处理窗口关闭事件"""
         self.root.quit()
         sys.exit(0)
-    
+
     def _process_queue(self):
         """处理更新队列中的任务"""
         try:
@@ -183,14 +139,14 @@ class ReasoningGUI:
         finally:
             # 继续检查队列
             self.root.after(100, self._process_queue)
-    
+
     def _update_group_list_gui(self):
         """在主线程中更新群组列表"""
         # 清除现有按钮
         for button in self.group_buttons.values():
             button.destroy()
         self.group_buttons.clear()
-        
+
         # 创建新的群组按钮
         for group_id in self.group_data.keys():
             button = ctk.CTkButton(
@@ -203,16 +159,16 @@ class ReasoningGUI:
             )
             button.pack(pady=2, padx=5)
             self.group_buttons[group_id] = button
-            
+
         # 如果有选中的群组，保持其高亮状态
         if self.selected_group_id and self.selected_group_id in self.group_buttons:
             self._highlight_selected_group(self.selected_group_id)
-    
+
     def _on_group_select(self, group_id: str):
         """处理群组选择事件"""
         self._highlight_selected_group(group_id)
         self._update_display_gui(group_id)
-    
+
     def _highlight_selected_group(self, group_id: str):
         """高亮显示选中的群组按钮"""
         # 重置所有按钮的颜色
@@ -223,9 +179,9 @@ class ReasoningGUI:
             else:
                 # 恢复其他按钮的默认颜色
                 button.configure(fg_color="#2B2B2B", hover_color="#404040")
-        
+
         self.selected_group_id = group_id
-    
+
     def _update_display_gui(self, group_id: str):
         """在主线程中更新显示内容"""
         if group_id in self.group_data:
@@ -234,19 +190,19 @@ class ReasoningGUI:
                 # 时间戳
                 time_str = item['time'].strftime("%Y-%m-%d %H:%M:%S")
                 self.content_text.insert("end", f"[{time_str}]\n", "timestamp")
-                
+
                 # 用户信息
                 self.content_text.insert("end", "用户: ", "timestamp")
                 self.content_text.insert("end", f"{item.get('user', '未知')}\n", "user")
-                
+
                 # 消息内容
                 self.content_text.insert("end", "消息: ", "timestamp")
                 self.content_text.insert("end", f"{item.get('message', '')}\n", "message")
-                
+
                 # 模型信息
                 self.content_text.insert("end", "模型: ", "timestamp")
                 self.content_text.insert("end", f"{item.get('model', '')}\n", "model")
-                
+
                 # Prompt内容
                 self.content_text.insert("end", "Prompt内容:\n", "timestamp")
                 prompt_text = item.get('prompt', '')
@@ -257,7 +213,7 @@ class ReasoningGUI:
                             self.content_text.insert("end", "    " + line + "\n", "prompt")
                 else:
                     self.content_text.insert("end", "    无Prompt内容\n", "prompt")
-                
+
                 # 推理过程
                 self.content_text.insert("end", "推理过程:\n", "timestamp")
                 reasoning_text = item.get('reasoning', '')
@@ -268,53 +224,53 @@ class ReasoningGUI:
                             self.content_text.insert("end", "    " + line + "\n", "reasoning")
                 else:
                     self.content_text.insert("end", "    无推理过程\n", "reasoning")
-                
+
                 # 回复内容
                 self.content_text.insert("end", "回复: ", "timestamp")
                 self.content_text.insert("end", f"{item.get('response', '')}\n", "response")
-                
+
                 # 分隔符
-                self.content_text.insert("end", f"\n{'='*50}\n\n", "separator")
-                
+                self.content_text.insert("end", f"\n{'=' * 50}\n\n", "separator")
+
             # 滚动到顶部
             self.content_text.see("1.0")
-    
+
     def _auto_update(self):
         """自动更新函数"""
         while True:
             try:
                 # 从数据库获取最新数据，只获取启动时间之后的记录
                 query = {"time": {"$gt": self.start_timestamp}}
-                print(f"查询条件: {query}")
-                
+                logger.debug(f"查询条件: {query}")
+
                 # 先获取一条记录检查时间格式
-                sample = self.db.reasoning_logs.find_one()
+                sample = db.reasoning_logs.find_one()
                 if sample:
-                    print(f"样本记录时间格式: {type(sample['time'])} 值: {sample['time']}")
-                
-                cursor = self.db.reasoning_logs.find(query).sort("time", -1)
+                    logger.debug(f"样本记录时间格式: {type(sample['time'])} 值: {sample['time']}")
+
+                cursor = db.reasoning_logs.find(query).sort("time", -1)
                 new_data = {}
                 total_count = 0
-                
+
                 for item in cursor:
                     # 调试输出
                     if total_count == 0:
-                        print(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
-                    
+                        logger.debug(f"记录时间: {item['time']}, 类型: {type(item['time'])}")
+
                     total_count += 1
                     group_id = str(item.get('group_id', 'unknown'))
                     if group_id not in new_data:
                         new_data[group_id] = []
-                    
+
                     # 转换时间戳为datetime对象
                     if isinstance(item['time'], (int, float)):
                         time_obj = datetime.fromtimestamp(item['time'])
                     elif isinstance(item['time'], datetime):
                         time_obj = item['time']
                     else:
-                        print(f"未知的时间格式: {type(item['time'])}")
+                        logger.warning(f"未知的时间格式: {type(item['time'])}")
                         time_obj = datetime.now()  # 使用当前时间作为后备
-                    
+
                     new_data[group_id].append({
                         'time': time_obj,
                         'user': item.get('user', '未知'),
@@ -324,13 +280,13 @@ class ReasoningGUI:
                         'response': item.get('response', ''),
                         'prompt': item.get('prompt', '')  # 添加prompt字段
                     })
-                
-                print(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
-                
+
+                logger.info(f"从数据库加载了 {total_count} 条记录，分布在 {len(new_data)} 个群组中")
+
                 # 更新数据
                 if new_data != self.group_data:
                     self.group_data = new_data
-                    print("数据已更新，正在刷新显示...")
+                    logger.info("数据已更新，正在刷新显示...")
                     # 将更新任务添加到队列
                     self.update_queue.put({'type': 'update_group_list'})
                     if self.group_data:
@@ -341,36 +297,25 @@ class ReasoningGUI:
                             'type': 'update_display',
                             'group_id': self.selected_group_id
                         })
-            except Exception as e:
-                print(f"自动更新出错: {e}")
-            
+            except Exception:
+                logger.exception("自动更新出错")
+
             # 每5秒更新一次
             time.sleep(5)
-    
+
     def clear_display(self):
         """清除显示内容"""
         self.content_text.delete("1.0", "end")
-    
+
     def run(self):
         """运行GUI"""
         self.root.mainloop()
 
 
 def main():
-    """主函数"""
-    Database.initialize(
-        host= os.getenv("MONGODB_HOST"),
-        port= int(os.getenv("MONGODB_PORT")),
-        db_name=  os.getenv("DATABASE_NAME"),
-        username= os.getenv("MONGODB_USERNAME"),
-        password= os.getenv("MONGODB_PASSWORD"),
-        auth_source=os.getenv("MONGODB_AUTH_SOURCE")
-    )
-    
     app = ReasoningGUI()
     app.run()
 
 
-
 if __name__ == "__main__":
     main()
diff --git a/src/plugins/chat/__init__.py b/src/plugins/chat/__init__.py
index 485d9d759..6dde80d24 100644
--- a/src/plugins/chat/__init__.py
+++ b/src/plugins/chat/__init__.py
@@ -1,13 +1,13 @@
 import asyncio
 import time
+import os
 
 from loguru import logger
-from nonebot import get_driver, on_command, on_message, require
-from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment
+from nonebot import get_driver, on_message, on_notice, require
 from nonebot.rule import to_me
+from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment, MessageEvent, NoticeEvent
 from nonebot.typing import T_State
 
-from ...common.database import Database
 from ..moods.moods import MoodManager  # 导入情绪管理器
 from ..schedule.schedule_generator import bot_schedule
 from ..utils.statistic import LLMStatistics
@@ -16,6 +16,11 @@ from .config import global_config
 from .emoji_manager import emoji_manager
 from .relationship_manager import relationship_manager
 from .willing_manager import willing_manager
+from .chat_stream import chat_manager
+from ..memory_system.memory import hippocampus, memory_graph
+from .bot import ChatBot
+from .message_sender import message_manager, message_sender
+
 
 # 创建LLM统计实例
 llm_stats = LLMStatistics("llm_statistics.txt")
@@ -27,31 +32,16 @@ _message_manager_started = False
 driver = get_driver()
 config = driver.config
 
-Database.initialize(
-    host=config.MONGODB_HOST,
-    port=int(config.MONGODB_PORT),
-    db_name=config.DATABASE_NAME,
-    username=config.MONGODB_USERNAME,
-    password=config.MONGODB_PASSWORD,
-    auth_source=config.MONGODB_AUTH_SOURCE
-)
-print("\033[1;32m[初始化数据库完成]\033[0m")
-
-# 导入其他模块
-from ..memory_system.memory import hippocampus, memory_graph
-from .bot import ChatBot
-
-# from .message_send_control import message_sender
-from .message_sender import message_manager, message_sender
-
 # 初始化表情管理器
 emoji_manager.initialize()
 
-print(f"\033[1;32m正在唤醒{global_config.BOT_NICKNAME}......\033[0m")
+logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......")
 # 创建机器人实例
 chat_bot = ChatBot()
-# 注册群消息处理器
-group_msg = on_message(priority=5)
+# 注册消息处理器
+msg_in = on_message(priority=5)
+# 注册和bot相关的通知处理器
+notice_matcher = on_notice(priority=1)
 # 创建定时任务
 scheduler = require("nonebot_plugin_apscheduler").scheduler
 
@@ -61,12 +51,12 @@ async def start_background_tasks():
     """启动后台任务"""
     # 启动LLM统计
     llm_stats.start()
-    logger.success("[初始化]LLM统计功能已启动")
+    logger.success("LLM统计功能启动成功")
 
     # 初始化并启动情绪管理器
     mood_manager = MoodManager.get_instance()
     mood_manager.start_mood_update(update_interval=global_config.mood_update_interval)
-    logger.success("[初始化]情绪管理器已启动")
+    logger.success("情绪管理器启动成功")
 
     # 只启动表情包管理任务
     asyncio.create_task(emoji_manager.start_periodic_check(interval_MINS=global_config.EMOJI_CHECK_INTERVAL))
@@ -77,7 +67,7 @@ async def start_background_tasks():
 @driver.on_startup
 async def init_relationships():
     """在 NoneBot2 启动时初始化关系管理器"""
-    print("\033[1;32m[初始化]\033[0m 正在加载用户关系数据...")
+    logger.debug("正在加载用户关系数据...")
     await relationship_manager.load_all_relationships()
     asyncio.create_task(relationship_manager._start_relationship_manager())
 
@@ -86,45 +76,54 @@ async def init_relationships():
 async def _(bot: Bot):
     """Bot连接成功时的处理"""
     global _message_manager_started
-    print(f"\033[1;38;5;208m-----------{global_config.BOT_NICKNAME}成功连接！-----------\033[0m")
+    logger.debug(f"-----------{global_config.BOT_NICKNAME}成功连接！-----------")
     await willing_manager.ensure_started()
 
     message_sender.set_bot(bot)
-    print("\033[1;38;5;208m-----------消息发送器已启动！-----------\033[0m")
+    logger.success("-----------消息发送器已启动！-----------")
 
     if not _message_manager_started:
         asyncio.create_task(message_manager.start_processor())
         _message_manager_started = True
-        print("\033[1;38;5;208m-----------消息处理器已启动！-----------\033[0m")
+        logger.success("-----------消息处理器已启动！-----------")
 
     asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL))
-    print("\033[1;38;5;208m-----------开始偷表情包！-----------\033[0m")
+    logger.success("-----------开始偷表情包！-----------")
+    asyncio.create_task(chat_manager._initialize())
+    asyncio.create_task(chat_manager._auto_save_task())
 
 
-@group_msg.handle()
-async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
+@msg_in.handle()
+async def _(bot: Bot, event: MessageEvent, state: T_State):
     await chat_bot.handle_message(event, bot)
 
 
+@notice_matcher.handle()
+async def _(bot: Bot, event: NoticeEvent, state: T_State):
+    logger.debug(f"收到通知：{event}")
+    await chat_bot.handle_notice(event, bot)
+
+
 # 添加build_memory定时任务
 @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
 async def build_memory_task():
     """每build_memory_interval秒执行一次记忆构建"""
-    print(
-        "\033[1;32m[记忆构建]\033[0m -------------------------------------------开始构建记忆-------------------------------------------")
+    logger.debug("[记忆构建]------------------------------------开始构建记忆--------------------------------------")
     start_time = time.time()
     await hippocampus.operation_build_memory(chat_size=20)
     end_time = time.time()
-    print(
-        f"\033[1;32m[记忆构建]\033[0m -------------------------------------------记忆构建完成：耗时: {end_time - start_time:.2f} 秒-------------------------------------------")
+    logger.success(
+        f"[记忆构建]--------------------------记忆构建完成：耗时: {end_time - start_time:.2f} "
+        "秒-------------------------------------------"
+    )
 
 
 @scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory")
 async def forget_memory_task():
     """每30秒执行一次记忆构建"""
-    # print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
-    # await hippocampus.operation_forget_topic(percentage=0.1)
-    # print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
+    print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...")
+    await hippocampus.operation_forget_topic(percentage=global_config.memory_forget_percentage)
+    print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成")
 
 
 @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="merge_memory")
@@ -140,3 +139,12 @@ async def print_mood_task():
     """每30秒打印一次情绪状态"""
     mood_manager = MoodManager.get_instance()
     mood_manager.print_mood_status()
+
+
+@scheduler.scheduled_job("interval", seconds=7200, id="generate_schedule")
+async def generate_schedule_task():
+    """每2小时尝试生成一次日程"""
+    logger.debug("尝试生成日程")
+    await bot_schedule.initialize()
+    if not bot_schedule.enable_output:
+        bot_schedule.print_schedule()
diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py
index a02c4a059..b8624dae0 100644
--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -1,26 +1,35 @@
+import re
 import time
 from random import random
-
 from loguru import logger
-from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent
+from nonebot.adapters.onebot.v11 import (
+    Bot,
+    GroupMessageEvent,
+    MessageEvent,
+    PrivateMessageEvent,
+    NoticeEvent,
+    PokeNotifyEvent,
+)
 
 from ..memory_system.memory import hippocampus
 from ..moods.moods import MoodManager  # 导入情绪管理器
 from .config import global_config
-from .cq_code import CQCode  # 导入CQCode模块
 from .emoji_manager import emoji_manager  # 导入表情包管理器
 from .llm_generator import ResponseGenerator
-from .message import (
-    Message,
-    Message_Sending,
-    Message_Thinking,  # 导入 Message_Thinking 类
-    MessageSet,
+from .message import MessageSending, MessageRecv, MessageThinking, MessageSet
+from .message_cq import (
+    MessageRecvCQ,
 )
+from .chat_stream import chat_manager
+
 from .message_sender import message_manager  # 导入新的消息管理器
 from .relationship_manager import relationship_manager
 from .storage import MessageStorage
-from .utils import calculate_typing_time, is_mentioned_bot_in_txt
+from .utils import calculate_typing_time, is_mentioned_bot_in_message
+from .utils_image import image_path_to_base64
+from .utils_user import get_user_nickname, get_user_cardname, get_groupname
 from .willing_manager import willing_manager  # 导入意愿管理器
+from .message_base import UserInfo, GroupInfo, Seg
 
 
 class ChatBot:
@@ -31,196 +40,337 @@ class ChatBot:
         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.emoji_chance = 0.2  # 发送表情包的基础概率
         # self.message_streams = MessageStreamContainer()
-        
+
     async def _ensure_started(self):
         """确保所有任务已启动"""
         if not self._started:
             self._started = True
 
-    async def handle_message(self, event: GroupMessageEvent, bot: Bot) -> None:
-        """处理收到的群消息"""
-        
-        if event.group_id not in global_config.talk_allowed_groups:
-            return
+    async def handle_notice(self, event: NoticeEvent, bot: Bot) -> None:
+        """处理收到的通知"""
+        # 戳一戳通知
+        if isinstance(event, PokeNotifyEvent):
+            # 用户屏蔽,不区分私聊/群聊
+            if event.user_id in global_config.ban_user_id:
+                return
+            reply_poke_probability = 1  # 回复戳一戳的概率
+
+            if random() < reply_poke_probability:
+                user_info = UserInfo(
+                    user_id=event.user_id,
+                    user_nickname=get_user_nickname(event.user_id) or None,
+                    user_cardname=get_user_cardname(event.user_id) or None,
+                    platform="qq",
+                )
+                group_info = GroupInfo(group_id=event.group_id, group_name=None, platform="qq")
+                message_cq = MessageRecvCQ(
+                    message_id=None,
+                    user_info=user_info,
+                    raw_message=str("[戳了戳]你"),
+                    group_info=group_info,
+                    reply_message=None,
+                    platform="qq",
+                )
+                message_json = message_cq.to_dict()
+
+                # 进入maimbot
+                message = MessageRecv(message_json)
+                groupinfo = message.message_info.group_info
+                userinfo = message.message_info.user_info
+                messageinfo = message.message_info
+
+                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()
+
+                bot_user_info = UserInfo(
+                    user_id=global_config.BOT_QQ,
+                    user_nickname=global_config.BOT_NICKNAME,
+                    platform=messageinfo.platform,
+                )
+
+                response, raw_content = await self.gpt.generate_response(message)
+
+                if response:
+                    for msg in response:
+                        message_segment = Seg(type="text", data=msg)
+
+                        bot_message = MessageSending(
+                            message_id=None,
+                            chat_stream=chat,
+                            bot_user_info=bot_user_info,
+                            sender_info=userinfo,
+                            message_segment=message_segment,
+                            reply=None,
+                            is_head=False,
+                            is_emoji=False,
+                        )
+                        message_manager.add_message(bot_message)
+
+    async def handle_message(self, event: MessageEvent, bot: Bot) -> None:
+        """处理收到的消息"""
+
         self.bot = bot  # 更新 bot 实例
-        
+
+        # 用户屏蔽,不区分私聊/群聊
         if event.user_id in global_config.ban_user_id:
             return
+        
+        if event.reply and hasattr(event.reply, 'sender') and hasattr(event.reply.sender, 'user_id') and event.reply.sender.user_id in global_config.ban_user_id:
+            logger.debug(f"跳过处理回复来自被ban用户 {event.reply.sender.user_id} 的消息")
+            return
+        # 处理私聊消息
+        if isinstance(event, PrivateMessageEvent):
+            if not global_config.enable_friend_chat:  # 私聊过滤
+                return
+            else:
+                try:
+                    user_info = UserInfo(
+                        user_id=event.user_id,
+                        user_nickname=(await bot.get_stranger_info(user_id=event.user_id, no_cache=True))["nickname"],
+                        user_cardname=None,
+                        platform="qq",
+                    )
+                except Exception as e:
+                    logger.error(f"获取陌生人信息失败: {e}")
+                    return
+                logger.debug(user_info)
 
-        group_info = await bot.get_group_info(group_id=event.group_id)
-        sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True)
-        
-        await relationship_manager.update_relationship(user_id = event.user_id, data = sender_info)
-        await relationship_manager.update_relationship_value(user_id = event.user_id, relationship_value = 0.5)
-        
-        message = Message(
-            group_id=event.group_id,
-            user_id=event.user_id,
+                # group_info = GroupInfo(group_id=0, group_name="私聊", platform="qq")
+                group_info = None
+
+        # 处理群聊消息
+        else:
+            # 白名单设定由nontbot侧完成
+            if event.group_id:
+                if event.group_id not in global_config.talk_allowed_groups:
+                    return
+
+            user_info = UserInfo(
+                user_id=event.user_id,
+                user_nickname=event.sender.nickname,
+                user_cardname=event.sender.card or None,
+                platform="qq",
+            )
+
+            group_info = GroupInfo(group_id=event.group_id, group_name=None, platform="qq")
+
+        # group_info = await bot.get_group_info(group_id=event.group_id)
+        # sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True)
+
+        message_cq = MessageRecvCQ(
             message_id=event.message_id,
-            user_cardname=sender_info['card'],
-            raw_message=str(event.original_message), 
-            plain_text=event.get_plaintext(),
+            user_info=user_info,
+            raw_message=str(event.original_message),
+            group_info=group_info,
             reply_message=event.reply,
+            platform="qq",
         )
-        await message.initialize()
+        message_json = message_cq.to_dict()
 
+        # 进入maimbot
+        message = MessageRecv(message_json)
+        groupinfo = message.message_info.group_info
+        userinfo = message.message_info.user_info
+        messageinfo = message.message_info
+
+        # 消息过滤，涉及到config有待更新
+
+        chat = await chat_manager.get_or_create_stream(
+            platform=messageinfo.platform, user_info=userinfo, group_info=groupinfo
+        )
+        message.update_chat_stream(chat)
+        await relationship_manager.update_relationship(
+            chat_stream=chat,
+        )
+        await relationship_manager.update_relationship_value(chat_stream=chat, relationship_value=0.5)
+
+        await message.process()
         # 过滤词
         for word in global_config.ban_words:
-            if word in message.detailed_plain_text:
-                logger.info(f"\033[1;32m[{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}")
-                logger.info(f"\033[1;32m[过滤词识别]\033[0m 消息中含有{word}，filtered")
+            if word in message.processed_plain_text:
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{message.processed_plain_text}"
+                )
+                logger.info(f"[过滤词识别]消息中含有{word}，filtered")
                 return
-        
-        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time))
 
+        # 正则表达式过滤
+        for pattern in global_config.ban_msgs_regex:
+            if re.search(pattern, message.raw_message):
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info else '私聊'}]{userinfo.user_nickname}:{message.raw_message}"
+                )
+                logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
+                return
 
+        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time))
 
         # topic=await topic_identifier.identify_topic_llm(message.processed_plain_text)
-        topic = ''
-        interested_rate = 0
-        interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text)/100
-        print(f"\033[1;32m[记忆激活]\033[0m 对{message.processed_plain_text}的激活度:---------------------------------------{interested_rate}\n")
+
+        topic = ""
+        interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100
+        logger.debug(f"对{message.processed_plain_text}的激活度:{interested_rate}")
         # logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}")
-        
-        await self.storage.store_message(message, topic[0] if topic else None)
 
-        is_mentioned = is_mentioned_bot_in_txt(message.processed_plain_text)
-        reply_probability = willing_manager.change_reply_willing_received(
-            event.group_id, 
-            topic[0] if topic else None,
-            is_mentioned,
-            global_config,
-            event.user_id,
-            message.is_emoji,
-            interested_rate
+        await self.storage.store_message(message, chat, topic[0] if topic else None)
+
+        is_mentioned = is_mentioned_bot_in_message(message)
+        reply_probability = await willing_manager.change_reply_willing_received(
+            chat_stream=chat,
+            topic=topic[0] if topic else None,
+            is_mentioned_bot=is_mentioned,
+            config=global_config,
+            is_emoji=message.is_emoji,
+            interested_rate=interested_rate,
+            sender_id=str(message.message_info.user_info.user_id),
         )
-        current_willing = willing_manager.get_willing(event.group_id)
-        
-        
-        print(f"\033[1;32m[{current_time}][{message.group_name}]{message.user_nickname}:\033[0m {message.processed_plain_text}\033[1;36m[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]\033[0m")
+        current_willing = willing_manager.get_willing(chat_stream=chat)
+
+        logger.info(
+            f"[{current_time}][{chat.group_info.group_name if chat.group_info else '私聊'}]{chat.user_info.user_nickname}:"
+            f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]"
+        )
+
+        response = None
 
-        response = ""
-        
         if random() < reply_probability:
-            
-            
-            tinking_time_point = round(time.time(), 2)
-            think_id = 'mt' + str(tinking_time_point)
-            thinking_message = Message_Thinking(message=message,message_id=think_id)
-            
+            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)
+            think_id = "mt" + str(thinking_time_point)
+            thinking_message = MessageThinking(
+                message_id=think_id,
+                chat_stream=chat,
+                bot_user_info=bot_user_info,
+                reply=message,
+            )
+
             message_manager.add_message(thinking_message)
 
-            willing_manager.change_reply_willing_sent(thinking_message.group_id)
-            
-            response,raw_content = await self.gpt.generate_response(message)
-            
+            willing_manager.change_reply_willing_sent(chat)
+
+            response, raw_content = await self.gpt.generate_response(message)
+        else:
+            # 决定不回复时，也更新回复意愿
+            willing_manager.change_reply_willing_not_sent(chat)
+
+        # print(f"response: {response}")
         if response:
-            container = message_manager.get_container(event.group_id)
+            # print(f"有response: {response}")
+            container = message_manager.get_container(chat.stream_id)
             thinking_message = None
             # 找到message,删除
+            # print(f"开始找思考消息")
             for msg in container.messages:
-                if isinstance(msg, Message_Thinking) and msg.message_id == think_id:
+                if isinstance(msg, MessageThinking) and msg.message_info.message_id == think_id:
+                    # print(f"找到思考消息: {msg}")
                     thinking_message = msg
                     container.messages.remove(msg)
-                    # print(f"\033[1;32m[思考消息删除]\033[0m 已找到思考消息对象，开始删除")
                     break
-                    
+
             # 如果找不到思考消息，直接返回
             if not thinking_message:
-                print(f"\033[1;33m[警告]\033[0m 未找到对应的思考消息，可能已超时被移除")
+                logger.warning("未找到对应的思考消息，可能已超时被移除")
                 return
-            
-            #记录开始思考的时间，避免从思考到回复的时间太久
+
+            # 记录开始思考的时间，避免从思考到回复的时间太久
             thinking_start_time = thinking_message.thinking_start_time
-            message_set = MessageSet(event.group_id, global_config.BOT_QQ, think_id) # 发送消息的id和产生发送消息的message_thinking是一致的
-            #计算打字时间，1是为了模拟打字，2是避免多条回复乱序
+            message_set = MessageSet(chat, think_id)
+            # 计算打字时间，1是为了模拟打字，2是避免多条回复乱序
             accu_typing_time = 0
-            
-            # print(f"\033[1;32m[开始回复]\033[0m 开始将回复1载入发送容器")
+
             mark_head = False
             for msg in response:
                 # print(f"\033[1;32m[回复内容]\033[0m {msg}")
-                #通过时间改变时间戳
+                # 通过时间改变时间戳
                 typing_time = calculate_typing_time(msg)
+                logger.debug(f"typing_time: {typing_time}")
                 accu_typing_time += typing_time
-                timepoint = tinking_time_point + accu_typing_time
-                
-                bot_message = Message_Sending(
-                    group_id=event.group_id,
-                    user_id=global_config.BOT_QQ,
+                timepoint = thinking_time_point + accu_typing_time
+                message_segment = Seg(type="text", data=msg)
+                # logger.debug(f"message_segment: {message_segment}")
+                bot_message = MessageSending(
                     message_id=think_id,
-                    raw_message=msg,
-                    plain_text=msg,
-                    processed_plain_text=msg,
-                    user_nickname=global_config.BOT_NICKNAME,
-                    group_name=message.group_name,
-                    time=timepoint, #记录了回复生成的时间
-                    thinking_start_time=thinking_start_time, #记录了思考开始的时间
-                    reply_message_id=message.message_id
+                    chat_stream=chat,
+                    bot_user_info=bot_user_info,
+                    sender_info=userinfo,
+                    message_segment=message_segment,
+                    reply=message,
+                    is_head=not mark_head,
+                    is_emoji=False,
                 )
-                await bot_message.initialize()
+                logger.debug(f"bot_message: {bot_message}")
                 if not mark_head:
-                    bot_message.is_head = True
                     mark_head = True
+                logger.debug(f"添加消息到message_set: {bot_message}")
                 message_set.add_message(bot_message)
-                
-            #message_set 可以直接加入 message_manager
+
+            # message_set 可以直接加入 message_manager
             # print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器")
+
+            logger.debug("添加message_set到message_manager")
+
             message_manager.add_message(message_set)
-            
-            bot_response_time = tinking_time_point
+
+            bot_response_time = thinking_time_point
 
             if random() < global_config.emoji_chance:
                 emoji_raw = await emoji_manager.get_emoji_for_text(response)
-                
+
                 # 检查是否 <没有找到> emoji
                 if emoji_raw != None:
-                    emoji_path,discription = emoji_raw
+                    emoji_path, description = emoji_raw
+
+                    emoji_cq = image_path_to_base64(emoji_path)
 
-                    emoji_cq = CQCode.create_emoji_cq(emoji_path)
-                    
                     if random() < 0.5:
-                        bot_response_time = tinking_time_point - 1
+                        bot_response_time = thinking_time_point - 1
                     else:
                         bot_response_time = bot_response_time + 1
-                        
-                    bot_message = Message_Sending(
-                        group_id=event.group_id,
-                        user_id=global_config.BOT_QQ,
-                        message_id=0,
-                        raw_message=emoji_cq,
-                        plain_text=emoji_cq,
-                        processed_plain_text=emoji_cq,
-                        detailed_plain_text=discription,
-                        user_nickname=global_config.BOT_NICKNAME,
-                        group_name=message.group_name,
-                        time=bot_response_time,
+
+                    message_segment = Seg(type="emoji", data=emoji_cq)
+                    bot_message = MessageSending(
+                        message_id=think_id,
+                        chat_stream=chat,
+                        bot_user_info=bot_user_info,
+                        sender_info=userinfo,
+                        message_segment=message_segment,
+                        reply=message,
+                        is_head=False,
                         is_emoji=True,
-                        translate_cq=False,
-                        thinking_start_time=thinking_start_time,
-                        # reply_message_id=message.message_id
                     )
-                    await bot_message.initialize()
                     message_manager.add_message(bot_message)
+
             emotion = await self.gpt._get_emotion_tags(raw_content)
-            print(f"为 '{response}' 获取到的情感标签为：{emotion}")
-            valuedict={
-                'happy': 0.5,
-                'angry': -1,
-                'sad': -0.5,
-                'surprised': 0.2,
-                'disgusted': -1.5,
-                'fearful': -0.7,
-                'neutral': 0.1
+            logger.debug(f"为 '{response}' 获取到的情感标签为：{emotion}")
+            valuedict = {
+                "happy": 0.5,
+                "angry": -1,
+                "sad": -0.5,
+                "surprised": 0.2,
+                "disgusted": -1.5,
+                "fearful": -0.7,
+                "neutral": 0.1,
             }
-            await relationship_manager.update_relationship_value(message.user_id, relationship_value=valuedict[emotion[0]])
+            await relationship_manager.update_relationship_value(
+                chat_stream=chat, relationship_value=valuedict[emotion[0]]
+            )
             # 使用情绪管理器更新情绪
             self.mood_manager.update_mood_from_emotion(emotion[0], global_config.mood_intensity_factor)
-        
-        # willing_manager.change_reply_willing_after_sent(event.group_id)
+
+            # willing_manager.change_reply_willing_after_sent(
+            #     chat_stream=chat
+            # )
+
 
 # 创建全局ChatBot实例
-chat_bot = ChatBot()
\ No newline at end of file
+chat_bot = ChatBot()
diff --git a/src/plugins/chat/chat_stream.py b/src/plugins/chat/chat_stream.py
new file mode 100644
index 000000000..60b0af493
--- /dev/null
+++ b/src/plugins/chat/chat_stream.py
@@ -0,0 +1,225 @@
+import asyncio
+import hashlib
+import time
+import copy
+from typing import Dict, Optional
+
+from loguru import logger
+
+from ...common.database import db
+from .message_base import GroupInfo, UserInfo
+
+
+class ChatStream:
+    """聊天流对象，存储一个完整的聊天上下文"""
+
+    def __init__(
+        self,
+        stream_id: str,
+        platform: str,
+        user_info: UserInfo,
+        group_info: Optional[GroupInfo] = None,
+        data: dict = None,
+    ):
+        self.stream_id = stream_id
+        self.platform = platform
+        self.user_info = user_info
+        self.group_info = group_info
+        self.create_time = (
+            data.get("create_time", int(time.time())) if data else int(time.time())
+        )
+        self.last_active_time = (
+            data.get("last_active_time", self.create_time) if data else self.create_time
+        )
+        self.saved = False
+
+    def to_dict(self) -> dict:
+        """转换为字典格式"""
+        result = {
+            "stream_id": self.stream_id,
+            "platform": self.platform,
+            "user_info": self.user_info.to_dict() if self.user_info else None,
+            "group_info": self.group_info.to_dict() if self.group_info else None,
+            "create_time": self.create_time,
+            "last_active_time": self.last_active_time,
+        }
+        return result
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "ChatStream":
+        """从字典创建实例"""
+        user_info = (
+            UserInfo(**data.get("user_info", {})) if data.get("user_info") else None
+        )
+        group_info = (
+            GroupInfo(**data.get("group_info", {})) if data.get("group_info") else None
+        )
+
+        return cls(
+            stream_id=data["stream_id"],
+            platform=data["platform"],
+            user_info=user_info,
+            group_info=group_info,
+            data=data,
+        )
+
+    def update_active_time(self):
+        """更新最后活跃时间"""
+        self.last_active_time = int(time.time())
+        self.saved = False
+
+
+class ChatManager:
+    """聊天管理器，管理所有聊天流"""
+
+    _instance = None
+    _initialized = False
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        if not self._initialized:
+            self.streams: Dict[str, ChatStream] = {}  # stream_id -> ChatStream
+            self._ensure_collection()
+            self._initialized = True
+            # 在事件循环中启动初始化
+            # asyncio.create_task(self._initialize())
+            # # 启动自动保存任务
+            # asyncio.create_task(self._auto_save_task())
+
+    async def _initialize(self):
+        """异步初始化"""
+        try:
+            await self.load_all_streams()
+            logger.success(f"聊天管理器已启动，已加载 {len(self.streams)} 个聊天流")
+        except Exception as e:
+            logger.error(f"聊天管理器启动失败: {str(e)}")
+
+    async def _auto_save_task(self):
+        """定期自动保存所有聊天流"""
+        while True:
+            await asyncio.sleep(300)  # 每5分钟保存一次
+            try:
+                await self._save_all_streams()
+                logger.info("聊天流自动保存完成")
+            except Exception as e:
+                logger.error(f"聊天流自动保存失败: {str(e)}")
+
+    def _ensure_collection(self):
+        """确保数据库集合存在并创建索引"""
+        if "chat_streams" not in db.list_collection_names():
+            db.create_collection("chat_streams")
+            # 创建索引
+            db.chat_streams.create_index([("stream_id", 1)], unique=True)
+            db.chat_streams.create_index(
+                [("platform", 1), ("user_info.user_id", 1), ("group_info.group_id", 1)]
+            )
+
+    def _generate_stream_id(
+        self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None
+    ) -> str:
+        """生成聊天流唯一ID"""
+        if group_info:
+            # 组合关键信息
+            components = [
+                platform,
+                str(group_info.group_id)
+            ]
+        else:
+            components = [
+                platform,
+                str(user_info.user_id),
+                "private"
+            ]
+
+        # 使用MD5生成唯一ID
+        key = "_".join(components)
+        return hashlib.md5(key.encode()).hexdigest()
+
+    async def get_or_create_stream(
+        self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None
+    ) -> ChatStream:
+        """获取或创建聊天流
+
+        Args:
+            platform: 平台标识
+            user_info: 用户信息
+            group_info: 群组信息（可选）
+
+        Returns:
+            ChatStream: 聊天流对象
+        """
+        # 生成stream_id
+        stream_id = self._generate_stream_id(platform, user_info, group_info)
+
+        # 检查内存中是否存在
+        if stream_id in self.streams:
+            stream = self.streams[stream_id]
+            # 更新用户信息和群组信息
+            stream.update_active_time()
+            stream=copy.deepcopy(stream)
+            stream.user_info = user_info
+            if group_info:
+                stream.group_info = group_info
+            return stream
+
+        # 检查数据库中是否存在
+        data = db.chat_streams.find_one({"stream_id": stream_id})
+        if data:
+            stream = ChatStream.from_dict(data)
+            # 更新用户信息和群组信息
+            stream.user_info = user_info
+            if group_info:
+                stream.group_info = group_info
+            stream.update_active_time()
+        else:
+            # 创建新的聊天流
+            stream = ChatStream(
+                stream_id=stream_id,
+                platform=platform,
+                user_info=user_info,
+                group_info=group_info,
+            )
+
+        # 保存到内存和数据库
+        self.streams[stream_id] = stream
+        await self._save_stream(stream)
+        return copy.deepcopy(stream)
+
+    def get_stream(self, stream_id: str) -> Optional[ChatStream]:
+        """通过stream_id获取聊天流"""
+        return self.streams.get(stream_id)
+
+    def get_stream_by_info(
+        self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None
+    ) -> Optional[ChatStream]:
+        """通过信息获取聊天流"""
+        stream_id = self._generate_stream_id(platform, user_info, group_info)
+        return self.streams.get(stream_id)
+
+    async def _save_stream(self, stream: ChatStream):
+        """保存聊天流到数据库"""
+        if not stream.saved:
+            db.chat_streams.update_one(
+                {"stream_id": stream.stream_id}, {"$set": stream.to_dict()}, upsert=True
+            )
+            stream.saved = True
+
+    async def _save_all_streams(self):
+        """保存所有聊天流"""
+        for stream in self.streams.values():
+            await self._save_stream(stream)
+
+    async def load_all_streams(self):
+        """从数据库加载所有聊天流"""
+        all_streams = db.chat_streams.find({})
+        for data in all_streams:
+            stream = ChatStream.from_dict(data)
+            self.streams[stream.stream_id] = stream
+
+
+# 创建全局单例
+chat_manager = ChatManager()
diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py
index 49963ad3b..88cb31ed5 100644
--- a/src/plugins/chat/config.py
+++ b/src/plugins/chat/config.py
@@ -1,6 +1,7 @@
 import os
+import sys
 from dataclasses import dataclass, field
-from typing import Dict, Optional
+from typing import Dict, List, Optional
 
 import tomli
 from loguru import logger
@@ -12,10 +13,12 @@ from packaging.specifiers import SpecifierSet, InvalidSpecifier
 @dataclass
 class BotConfig:
     """机器人配置类"""
+
     INNER_VERSION: Version = None
 
     BOT_QQ: Optional[int] = 1
     BOT_NICKNAME: Optional[str] = None
+    BOT_ALIAS_NAMES: List[str] = field(default_factory=list)  # 别名，可以通过这个叫它
 
     # 消息处理相关配置
     MIN_TEXT_LENGTH: int = 2  # 最小处理文本长度
@@ -34,8 +37,7 @@ class BotConfig:
 
     ban_user_id = set()
 
-    build_memory_interval: int = 30  # 记忆构建间隔（秒）
-    forget_memory_interval: int = 300  # 记忆遗忘间隔（秒）
+    
     EMOJI_CHECK_INTERVAL: int = 120  # 表情包检查间隔（分钟）
     EMOJI_REGISTER_INTERVAL: int = 10  # 表情包注册间隔（分钟）
     EMOJI_SAVE: bool = True  # 偷表情包
@@ -43,6 +45,7 @@ class BotConfig:
     EMOJI_CHECK_PROMPT: str = "符合公序良俗"  # 表情包过滤要求
 
     ban_words = set()
+    ban_msgs_regex = set()
 
     max_response_length: int = 1024  # 最大回复长度
 
@@ -64,6 +67,8 @@ class BotConfig:
 
     enable_advance_output: bool = False  # 是否启用高级输出
     enable_kuuki_read: bool = True  # 是否启用读空气功能
+    enable_debug_output: bool = False  # 是否启用调试输出
+    enable_friend_chat: bool = False  # 是否启用好友聊天
 
     mood_update_interval: float = 1.0  # 情绪更新间隔 单位秒
     mood_decay_rate: float = 0.95  # 情绪衰减率
@@ -81,23 +86,31 @@ class BotConfig:
     PROMPT_PERSONALITY = [
         "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
         "是一个女大学生，你有黑色头发，你会刷小红书",
-        "是一个女大学生，你会刷b站，对ACG文化感兴趣"
+        "是一个女大学生，你会刷b站，对ACG文化感兴趣",
     ]
 
-    PROMPT_SCHEDULE_GEN="一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
+    PROMPT_SCHEDULE_GEN = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
+
+    PERSONALITY_1: float = 0.6  # 第一种人格概率
+    PERSONALITY_2: float = 0.3  # 第二种人格概率
+    PERSONALITY_3: float = 0.1  # 第三种人格概率
     
-    PERSONALITY_1: float = 0.6 # 第一种人格概率
-    PERSONALITY_2: float = 0.3 # 第二种人格概率
-    PERSONALITY_3: float = 0.1 # 第三种人格概率
-    
-    memory_ban_words: list = field(default_factory=lambda: ['表情包', '图片', '回复', '聊天记录'])  # 添加新的配置项默认值
+    build_memory_interval: int = 600  # 记忆构建间隔（秒）
     
+    forget_memory_interval: int = 600  # 记忆遗忘间隔（秒）
+    memory_forget_time: int = 24  # 记忆遗忘时间（小时）
+    memory_forget_percentage: float = 0.01  # 记忆遗忘比例
+    memory_compress_rate: float = 0.1  # 记忆压缩率
+    memory_ban_words: list = field(
+        default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"]
+    )  # 添加新的配置项默认值
+
     @staticmethod
     def get_config_dir() -> str:
         """获取配置文件目录"""
         current_dir = os.path.dirname(os.path.abspath(__file__))
-        root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
-        config_dir = os.path.join(root_dir, 'config')
+        root_dir = os.path.abspath(os.path.join(current_dir, "..", "..", ".."))
+        config_dir = os.path.join(root_dir, "config")
         if not os.path.exists(config_dir):
             os.makedirs(config_dir)
         return config_dir
@@ -108,35 +121,32 @@ class BotConfig:
         Args:
             value[str]: 版本表达式(字符串)
         Returns:
-            SpecifierSet 
+            SpecifierSet
         """
 
         try:
             converted = SpecifierSet(value)
-        except InvalidSpecifier as e:
-            logger.error(
-                f"{value} 分类使用了错误的版本约束表达式\n",
-                "请阅读 https://semver.org/lang/zh-CN/ 修改代码"
-            )
+        except InvalidSpecifier:
+            logger.error(f"{value} 分类使用了错误的版本约束表达式\n", "请阅读 https://semver.org/lang/zh-CN/ 修改代码")
             exit(1)
 
         return converted
 
     @classmethod
     def get_config_version(cls, toml: dict) -> Version:
-        """提取配置文件的 SpecifierSet 版本数据 
+        """提取配置文件的 SpecifierSet 版本数据
         Args:
             toml[dict]: 输入的配置文件字典
         Returns:
-            Version 
+            Version
         """
 
-        if 'inner' in toml:
+        if "inner" in toml:
             try:
                 config_version: str = toml["inner"]["version"]
             except KeyError as e:
-                logger.error(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件")
-                raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件")
+                logger.error("配置文件中 inner 段 不存在, 这是错误的配置文件")
+                raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件") from e
         else:
             toml["inner"] = {"version": "0.0.0"}
             config_version = toml["inner"]["version"]
@@ -149,7 +159,7 @@ class BotConfig:
                 "请阅读 https://semver.org/lang/zh-CN/ 修改配置，并参考本项目指定的模板进行修改\n"
                 "本项目在不同的版本下有不同的模板，请注意识别"
             )
-            raise InvalidVersion("配置文件中 inner段 的 version 键是错误的版本描述\n")
+            raise InvalidVersion("配置文件中 inner段 的 version 键是错误的版本描述\n") from e
 
         return ver
 
@@ -159,26 +169,26 @@ class BotConfig:
         config = cls()
 
         def personality(parent: dict):
-            personality_config = parent['personality']
-            personality = personality_config.get('prompt_personality')
+            personality_config = parent["personality"]
+            personality = personality_config.get("prompt_personality")
             if len(personality) >= 2:
-                logger.info(f"载入自定义人格:{personality}")
-                config.PROMPT_PERSONALITY = personality_config.get('prompt_personality', config.PROMPT_PERSONALITY)
+                logger.debug(f"载入自定义人格:{personality}")
+                config.PROMPT_PERSONALITY = personality_config.get("prompt_personality", config.PROMPT_PERSONALITY)
             logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)}")
-            config.PROMPT_SCHEDULE_GEN = personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)
+            config.PROMPT_SCHEDULE_GEN = personality_config.get("prompt_schedule", config.PROMPT_SCHEDULE_GEN)
 
             if config.INNER_VERSION in SpecifierSet(">=0.0.2"):
-                config.PERSONALITY_1 = personality_config.get('personality_1_probability', config.PERSONALITY_1)
-                config.PERSONALITY_2 = personality_config.get('personality_2_probability', config.PERSONALITY_2)
-                config.PERSONALITY_3 = personality_config.get('personality_3_probability', config.PERSONALITY_3)
+                config.PERSONALITY_1 = personality_config.get("personality_1_probability", config.PERSONALITY_1)
+                config.PERSONALITY_2 = personality_config.get("personality_2_probability", config.PERSONALITY_2)
+                config.PERSONALITY_3 = personality_config.get("personality_3_probability", config.PERSONALITY_3)
 
         def emoji(parent: dict):
             emoji_config = parent["emoji"]
             config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL)
             config.EMOJI_REGISTER_INTERVAL = emoji_config.get("register_interval", config.EMOJI_REGISTER_INTERVAL)
-            config.EMOJI_CHECK_PROMPT = emoji_config.get('check_prompt', config.EMOJI_CHECK_PROMPT)
-            config.EMOJI_SAVE = emoji_config.get('auto_save', config.EMOJI_SAVE)
-            config.EMOJI_CHECK = emoji_config.get('enable_check', config.EMOJI_CHECK)
+            config.EMOJI_CHECK_PROMPT = emoji_config.get("check_prompt", config.EMOJI_CHECK_PROMPT)
+            config.EMOJI_SAVE = emoji_config.get("auto_save", config.EMOJI_SAVE)
+            config.EMOJI_CHECK = emoji_config.get("enable_check", config.EMOJI_CHECK)
 
         def cq_code(parent: dict):
             cq_code_config = parent["cq_code"]
@@ -191,12 +201,16 @@ class BotConfig:
             config.BOT_QQ = int(bot_qq)
             config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME)
 
+            if config.INNER_VERSION in SpecifierSet(">=0.0.5"):
+                config.BOT_ALIAS_NAMES = bot_config.get("alias_names", config.BOT_ALIAS_NAMES)
+
         def response(parent: dict):
             response_config = parent["response"]
             config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY)
             config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY)
-            config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability",
-                                                                      config.MODEL_R1_DISTILL_PROBABILITY)
+            config.MODEL_R1_DISTILL_PROBABILITY = response_config.get(
+                "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY
+            )
             config.max_response_length = response_config.get("max_response_length", config.max_response_length)
 
         def model(parent: dict):
@@ -213,7 +227,7 @@ class BotConfig:
                 "llm_emotion_judge",
                 "vlm",
                 "embedding",
-                "moderation"
+                "moderation",
             ]
 
             for item in config_list:
@@ -222,13 +236,7 @@ class BotConfig:
 
                     # base_url 的例子： SILICONFLOW_BASE_URL
                     # key 的例子： SILICONFLOW_KEY
-                    cfg_target = {
-                        "name": "",
-                        "base_url": "",
-                        "key": "",
-                        "pri_in": 0,
-                        "pri_out": 0
-                    }
+                    cfg_target = {"name": "", "base_url": "", "key": "", "pri_in": 0, "pri_out": 0}
 
                     if config.INNER_VERSION in SpecifierSet("<=0.0.0"):
                         cfg_target = cfg_item
@@ -246,11 +254,11 @@ class BotConfig:
                                 try:
                                     cfg_target[i] = cfg_item[i]
                                 except KeyError as e:
-                                    logger.error(f"{item} 中的必要字段 {e} 不存在，请检查")
-                                    raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查")
+                                    logger.error(f"{item} 中的必要字段不存在，请检查")
+                                    raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查") from e
 
                         provider = cfg_item.get("provider")
-                        if provider == None:
+                        if provider is None:
                             logger.error(f"provider 字段在模型配置 {item} 中不存在，请检查")
                             raise KeyError(f"provider 字段在模型配置 {item} 中不存在，请检查")
 
@@ -272,20 +280,30 @@ class BotConfig:
 
             if config.INNER_VERSION in SpecifierSet(">=0.0.2"):
                 config.thinking_timeout = msg_config.get("thinking_timeout", config.thinking_timeout)
-                config.response_willing_amplifier = msg_config.get("response_willing_amplifier",
-                                                                   config.response_willing_amplifier)
-                config.response_interested_rate_amplifier = msg_config.get("response_interested_rate_amplifier",
-                                                                           config.response_interested_rate_amplifier)
+                config.response_willing_amplifier = msg_config.get(
+                    "response_willing_amplifier", config.response_willing_amplifier
+                )
+                config.response_interested_rate_amplifier = msg_config.get(
+                    "response_interested_rate_amplifier", config.response_interested_rate_amplifier
+                )
                 config.down_frequency_rate = msg_config.get("down_frequency_rate", config.down_frequency_rate)
+            
+            if config.INNER_VERSION in SpecifierSet(">=0.0.6"):
+                config.ban_msgs_regex = msg_config.get("ban_msgs_regex", config.ban_msgs_regex)
 
         def memory(parent: dict):
             memory_config = parent["memory"]
             config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval)
             config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval)
-            
+
             # 在版本 >= 0.0.4 时才处理新增的配置项
             if config.INNER_VERSION in SpecifierSet(">=0.0.4"):
                 config.memory_ban_words = set(memory_config.get("memory_ban_words", []))
+                
+            if config.INNER_VERSION in SpecifierSet(">=0.0.7"):
+                config.memory_forget_time = memory_config.get("memory_forget_time", config.memory_forget_time)
+                config.memory_forget_percentage = memory_config.get("memory_forget_percentage", config.memory_forget_percentage)
+                config.memory_compress_rate = memory_config.get("memory_compress_rate", config.memory_compress_rate)
 
         def mood(parent: dict):
             mood_config = parent["mood"]
@@ -303,10 +321,12 @@ class BotConfig:
             config.chinese_typo_enable = chinese_typo_config.get("enable", config.chinese_typo_enable)
             config.chinese_typo_error_rate = chinese_typo_config.get("error_rate", config.chinese_typo_error_rate)
             config.chinese_typo_min_freq = chinese_typo_config.get("min_freq", config.chinese_typo_min_freq)
-            config.chinese_typo_tone_error_rate = chinese_typo_config.get("tone_error_rate",
-                                                                          config.chinese_typo_tone_error_rate)
-            config.chinese_typo_word_replace_rate = chinese_typo_config.get("word_replace_rate",
-                                                                            config.chinese_typo_word_replace_rate)
+            config.chinese_typo_tone_error_rate = chinese_typo_config.get(
+                "tone_error_rate", config.chinese_typo_tone_error_rate
+            )
+            config.chinese_typo_word_replace_rate = chinese_typo_config.get(
+                "word_replace_rate", config.chinese_typo_word_replace_rate
+            )
 
         def groups(parent: dict):
             groups_config = parent["groups"]
@@ -318,6 +338,9 @@ class BotConfig:
             others_config = parent["others"]
             config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output)
             config.enable_kuuki_read = others_config.get("enable_kuuki_read", config.enable_kuuki_read)
+            if config.INNER_VERSION in SpecifierSet(">=0.0.7"):
+                config.enable_debug_output = others_config.get("enable_debug_output", config.enable_debug_output)
+                config.enable_friend_chat = others_config.get("enable_friend_chat", config.enable_friend_chat)
 
         # 版本表达式：>=1.0.0,<2.0.0
         # 允许字段：func: method, support: str, notice: str, necessary: bool
@@ -325,61 +348,19 @@ class BotConfig:
         # 例如："notice": "personality 将在 1.3.2 后被移除"，那么在有效版本中的用户就会虽然可以
         # 正常执行程序，但是会看到这条自定义提示
         include_configs = {
-            "personality": {
-                "func": personality,
-                "support": ">=0.0.0"
-            },
-            "emoji": {
-                "func": emoji,
-                "support": ">=0.0.0"
-            },
-            "cq_code": {
-                "func": cq_code,
-                "support": ">=0.0.0"
-            },
-            "bot": {
-                "func": bot,
-                "support": ">=0.0.0"
-            },
-            "response": {
-                "func": response,
-                "support": ">=0.0.0"
-            },
-            "model": {
-                "func": model,
-                "support": ">=0.0.0"
-            },
-            "message": {
-                "func": message,
-                "support": ">=0.0.0"
-            },
-            "memory": {
-                "func": memory,
-                "support": ">=0.0.0",
-                "necessary": False
-            },
-            "mood": {
-                "func": mood,
-                "support": ">=0.0.0"
-            },
-            "keywords_reaction": {
-                "func": keywords_reaction,
-                "support": ">=0.0.2",
-                "necessary": False
-            },
-            "chinese_typo": {
-                "func": chinese_typo,
-                "support": ">=0.0.3",
-                "necessary": False
-            },
-            "groups": {
-                "func": groups,
-                "support": ">=0.0.0"
-            },
-            "others": {
-                "func": others,
-                "support": ">=0.0.0"
-            }
+            "personality": {"func": personality, "support": ">=0.0.0"},
+            "emoji": {"func": emoji, "support": ">=0.0.0"},
+            "cq_code": {"func": cq_code, "support": ">=0.0.0"},
+            "bot": {"func": bot, "support": ">=0.0.0"},
+            "response": {"func": response, "support": ">=0.0.0"},
+            "model": {"func": model, "support": ">=0.0.0"},
+            "message": {"func": message, "support": ">=0.0.0"},
+            "memory": {"func": memory, "support": ">=0.0.0", "necessary": False},
+            "mood": {"func": mood, "support": ">=0.0.0"},
+            "keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False},
+            "chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False},
+            "groups": {"func": groups, "support": ">=0.0.0"},
+            "others": {"func": others, "support": ">=0.0.0"},
         }
 
         # 原地修改，将 字符串版本表达式 转换成 版本对象
@@ -391,7 +372,7 @@ class BotConfig:
             with open(config_path, "rb") as f:
                 try:
                     toml_dict = tomli.load(f)
-                except(tomli.TOMLDecodeError) as e:
+                except tomli.TOMLDecodeError as e:
                     logger.critical(f"配置文件bot_config.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}")
                     exit(1)
 
@@ -406,7 +387,7 @@ class BotConfig:
                         # 检查配置文件版本是否在支持范围内
                         if config.INNER_VERSION in group_specifierset:
                             # 如果版本在支持范围内，检查是否存在通知
-                            if 'notice' in include_configs[key]:
+                            if "notice" in include_configs[key]:
                                 logger.warning(include_configs[key]["notice"])
 
                             include_configs[key]["func"](toml_dict)
@@ -420,7 +401,7 @@ class BotConfig:
                             raise InvalidVersion(f"当前程序仅支持以下版本范围: {group_specifierset}")
 
                     # 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理
-                    elif "necessary" in include_configs[key] and include_configs[key].get("necessary") == False:
+                    elif "necessary" in include_configs[key] and include_configs[key].get("necessary") is False:
                         # 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理
                         if key == "keywords_reaction":
                             pass
@@ -454,4 +435,8 @@ global_config = BotConfig.load_config(config_path=bot_config_path)
 
 if not global_config.enable_advance_output:
     logger.remove()
-    pass
+    
+# 调试输出功能
+if global_config.enable_debug_output:
+    logger.remove()
+    logger.add(sys.stdout, level="DEBUG")
diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py
index 4a295e3d5..049419f1c 100644
--- a/src/plugins/chat/cq_code.py
+++ b/src/plugins/chat/cq_code.py
@@ -1,23 +1,26 @@
 import base64
 import html
-import os
 import time
 from dataclasses import dataclass
-from typing import Dict, Optional
+from typing import Dict, List, Optional, Union
+
+import os
 
 import requests
 
 # 解析各种CQ码
 # 包含CQ码类
 import urllib3
+from loguru import logger
 from nonebot import get_driver
 from urllib3.util import create_urllib3_context
 
 from ..models.utils_model import LLM_request
 from .config import global_config
 from .mapper import emojimapper
-from .utils_image import storage_emoji, storage_image
-from .utils_user import get_user_nickname
+from .message_base import Seg
+from .utils_user import get_user_nickname,get_groupname
+from .message_base import GroupInfo, UserInfo
 
 driver = get_driver()
 config = driver.config
@@ -35,65 +38,83 @@ class TencentSSLAdapter(requests.adapters.HTTPAdapter):
 
     def init_poolmanager(self, connections, maxsize, block=False):
         self.poolmanager = urllib3.poolmanager.PoolManager(
-            num_pools=connections, maxsize=maxsize,
-            block=block, ssl_context=self.ssl_context)
+            num_pools=connections,
+            maxsize=maxsize,
+            block=block,
+            ssl_context=self.ssl_context,
+        )
 
 
 @dataclass
 class CQCode:
     """
     CQ码数据类，用于存储和处理CQ码
-    
+
     属性:
         type: CQ码类型（如'image', 'at', 'face'等）
         params: CQ码的参数字典
         raw_code: 原始CQ码字符串
-        translated_plain_text: 经过处理（如AI翻译）后的文本表示
+        translated_segments: 经过处理后的Seg对象列表
     """
+
     type: str
     params: Dict[str, str]
-    # raw_code: str
-    group_id: int
-    user_id: int
-    group_name: str = ""
-    user_nickname: str = ""
-    translated_plain_text: Optional[str] = None
+    group_info: Optional[GroupInfo] = None
+    user_info: Optional[UserInfo] = None
+    translated_segments: Optional[Union[Seg, List[Seg]]] = None
     reply_message: Dict = None  # 存储回复消息
     image_base64: Optional[str] = None
     _llm: Optional[LLM_request] = None
 
     def __post_init__(self):
         """初始化LLM实例"""
-        self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300)
+        pass
 
-    async def translate(self):
-        """根据CQ码类型进行相应的翻译处理"""
-        if self.type == 'text':
-            self.translated_plain_text = self.params.get('text', '')
-        elif self.type == 'image':
-            if self.params.get('sub_type') == '0':
-                self.translated_plain_text = await self.translate_image()
+    def translate(self):
+        """根据CQ码类型进行相应的翻译处理，转换为Seg对象"""
+        if self.type == "text":
+            self.translated_segments = Seg(
+                type="text", data=self.params.get("text", "")
+            )
+        elif self.type == "image":
+            base64_data = self.translate_image()
+            if base64_data:
+                if self.params.get("sub_type") == "0":
+                    self.translated_segments = Seg(type="image", data=base64_data)
+                else:
+                    self.translated_segments = Seg(type="emoji", data=base64_data)
             else:
-                self.translated_plain_text = await self.translate_emoji()
-        elif self.type == 'at':
-            user_nickname = get_user_nickname(self.params.get('qq', ''))
-            if user_nickname:
-                self.translated_plain_text = f"[@{user_nickname}]"
+                self.translated_segments = Seg(type="text", data="[图片]")
+        elif self.type == "at":
+            if self.params.get("qq") == "all":
+                self.translated_segments = Seg(type="text", data="@[全体成员]")
+            else:  
+                user_nickname = get_user_nickname(self.params.get("qq", ""))
+                self.translated_segments = Seg(
+                    type="text", data=f"[@{user_nickname or '某人'}]"
+            )
+        elif self.type == "reply":
+            reply_segments = self.translate_reply()
+            if reply_segments:
+                self.translated_segments = Seg(type="seglist", data=reply_segments)
             else:
-                self.translated_plain_text = "@某人"
-        elif self.type == 'reply':
-            self.translated_plain_text = await self.translate_reply()
-        elif self.type == 'face':
-            face_id = self.params.get('id', '')
-            # self.translated_plain_text = f"[表情{face_id}]"
-            self.translated_plain_text = f"[{emojimapper.get(int(face_id), '表情')}]"
-        elif self.type == 'forward':
-            self.translated_plain_text = await self.translate_forward()
+                self.translated_segments = Seg(type="text", data="[回复某人消息]")
+        elif self.type == "face":
+            face_id = self.params.get("id", "")
+            self.translated_segments = Seg(
+                type="text", data=f"[{emojimapper.get(int(face_id), '表情')}]"
+            )
+        elif self.type == "forward":
+            forward_segments = self.translate_forward()
+            if forward_segments:
+                self.translated_segments = Seg(type="seglist", data=forward_segments)
+            else:
+                self.translated_segments = Seg(type="text", data="[转发消息]")
         else:
-            self.translated_plain_text = f"[{self.type}]"
+            self.translated_segments = Seg(type="text", data=f"[{self.type}]")
 
     def get_img(self):
-        '''
+        """
         headers = {
             'User-Agent': 'QQ/8.9.68.11565 CFNetwork/1220.1 Darwin/20.3.0',
             'Accept': 'image/*;q=0.8',
@@ -102,18 +123,18 @@ class CQCode:
             'Cache-Control': 'no-cache',
             'Pragma': 'no-cache'
         }
-        '''
+        """
         # 腾讯专用请求头配置
         headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.87 Safari/537.36',
-            'Accept': 'text/html, application/xhtml xml, */*',
-            'Accept-Encoding': 'gbk, GB2312',
-            'Accept-Language': 'zh-cn',
-            'Content-Type': 'application/x-www-form-urlencoded',
-            'Cache-Control': 'no-cache'
+            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.87 Safari/537.36",
+            "Accept": "text/html, application/xhtml xml, */*",
+            "Accept-Encoding": "gbk, GB2312",
+            "Accept-Language": "zh-cn",
+            "Content-Type": "application/x-www-form-urlencoded",
+            "Cache-Control": "no-cache",
         }
-        url = html.unescape(self.params['url'])
-        if not url.startswith(('http://', 'https://')):
+        url = html.unescape(self.params["url"])
+        if not url.startswith(("http://", "https://")):
             return None
 
         # 创建专用会话
@@ -129,247 +150,214 @@ class CQCode:
                     headers=headers,
                     timeout=15,
                     allow_redirects=True,
-                    stream=True  # 流式传输避免大内存问题
+                    stream=True,  # 流式传输避免大内存问题
                 )
 
                 # 腾讯服务器特殊状态码处理
-                if response.status_code == 400 and 'multimedia.nt.qq.com.cn' in url:
+                if response.status_code == 400 and "multimedia.nt.qq.com.cn" in url:
                     return None
 
                 if response.status_code != 200:
                     raise requests.exceptions.HTTPError(f"HTTP {response.status_code}")
 
                 # 验证内容类型
-                content_type = response.headers.get('Content-Type', '')
-                if not content_type.startswith('image/'):
+                content_type = response.headers.get("Content-Type", "")
+                if not content_type.startswith("image/"):
                     raise ValueError(f"非图片内容类型: {content_type}")
 
                 # 转换为Base64
-                image_base64 = base64.b64encode(response.content).decode('utf-8')
+                image_base64 = base64.b64encode(response.content).decode("utf-8")
                 self.image_base64 = image_base64
                 return image_base64
 
             except (requests.exceptions.SSLError, requests.exceptions.HTTPError) as e:
                 if retry == max_retries - 1:
-                    print(f"\033[1;31m[致命错误]\033[0m 最终请求失败: {str(e)}")
-                time.sleep(1.5 ** retry)  # 指数退避
+                    logger.error(f"最终请求失败: {str(e)}")
+                time.sleep(1.5**retry)  # 指数退避
 
-            except Exception as e:
-                print(f"\033[1;33m[未知错误]\033[0m {str(e)}")
+            except Exception:
+                logger.exception("[未知错误]")
                 return None
 
         return None
 
-    async def translate_emoji(self) -> str:
-        """处理表情包类型的CQ码"""
-        if 'url' not in self.params:
-            return '[表情包]'
-        base64_str = self.get_img()
-        if base64_str:
-            # 将 base64 字符串转换为字节类型
-            image_bytes = base64.b64decode(base64_str)
-            storage_emoji(image_bytes)
-            return await self.get_emoji_description(base64_str)
-        else:
-            return '[表情包]'
+    def translate_image(self) -> Optional[str]:
+        """处理图片类型的CQ码，返回base64字符串"""
+        if "url" not in self.params:
+            return None
+        return self.get_img()
 
-    async def translate_image(self) -> str:
-        """处理图片类型的CQ码，区分普通图片和表情包"""
-        # 没有url，直接返回默认文本
-        if 'url' not in self.params:
-            return '[图片]'
-        base64_str = self.get_img()
-        if base64_str:
-            image_bytes = base64.b64decode(base64_str)
-            storage_image(image_bytes)
-            return await self.get_image_description(base64_str)
-        else:
-            return '[图片]'
-
-    async def get_emoji_description(self, image_base64: str) -> str:
-        """调用AI接口获取表情包描述"""
+    def translate_forward(self) -> Optional[List[Seg]]:
+        """处理转发消息，返回Seg列表"""
         try:
-            prompt = "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。"
-            # description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64)
-            description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
-            return f"[表情包：{description}]"
-        except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}")
-            return "[表情包]"
+            if "content" not in self.params:
+                return None
 
-    async def get_image_description(self, image_base64: str) -> str:
-        """调用AI接口获取普通图片描述"""
-        try:
-            prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。"
-            # description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64)
-            description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
-            return f"[图片：{description}]"
-        except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}")
-            return "[图片]"
-
-    async def translate_forward(self) -> str:
-        """处理转发消息"""
-        try:
-            if 'content' not in self.params:
-                return '[转发消息]'
-
-            # 解析content内容（需要先反转义）
-            content = self.unescape(self.params['content'])
-            # print(f"\033[1;34m[调试信息]\033[0m 转发消息内容: {content}")
-            # 将字符串形式的列表转换为Python对象
+            content = self.unescape(self.params["content"])
             import ast
+
             try:
                 messages = ast.literal_eval(content)
             except ValueError as e:
-                print(f"\033[1;31m[错误]\033[0m 解析转发消息内容失败: {str(e)}")
-                return '[转发消息]'
+                logger.error(f"解析转发消息内容失败: {str(e)}")
+                return None
 
-            # 处理每条消息
-            formatted_messages = []
+            formatted_segments = []
             for msg in messages:
-                sender = msg.get('sender', {})
-                nickname = sender.get('card') or sender.get('nickname', '未知用户')
-
-                # 获取消息内容并使用Message类处理
-                raw_message = msg.get('raw_message', '')
-                message_array = msg.get('message', [])
+                sender = msg.get("sender", {})
+                nickname = sender.get("card") or sender.get("nickname", "未知用户")
+                raw_message = msg.get("raw_message", "")
+                message_array = msg.get("message", [])
 
                 if message_array and isinstance(message_array, list):
-                    # 检查是否包含嵌套的转发消息
                     for message_part in message_array:
-                        if message_part.get('type') == 'forward':
-                            content = '[转发消息]'
+                        if message_part.get("type") == "forward":
+                            content_seg = Seg(type="text", data="[转发消息]")
                             break
-                    else:
-                        # 处理普通消息
-                        if raw_message:
-                            from .message import Message
-                            message_obj = Message(
-                                user_id=msg.get('user_id', 0),
-                                message_id=msg.get('message_id', 0),
-                                raw_message=raw_message,
-                                plain_text=raw_message,
-                                group_id=msg.get('group_id', 0)
-                            )
-                            await message_obj.initialize()
-                            content = message_obj.processed_plain_text
                         else:
-                            content = '[空消息]'
+                            if raw_message:
+                                from .message_cq import MessageRecvCQ
+                                user_info=UserInfo(
+                                    platform='qq',
+                                    user_id=msg.get("user_id", 0),
+                                    user_nickname=nickname,
+                                )
+                                group_info=GroupInfo(
+                                    platform='qq',
+                                    group_id=msg.get("group_id", 0),
+                                    group_name=get_groupname(msg.get("group_id", 0))
+                                )
+
+                                message_obj = MessageRecvCQ(
+                                    message_id=msg.get("message_id", 0),
+                                    user_info=user_info,
+                                    raw_message=raw_message,
+                                    plain_text=raw_message,
+                                    group_info=group_info,
+                                )
+                                content_seg = Seg(
+                                    type="seglist", data=[message_obj.message_segment]                             
+                                )
+                            else:
+                                content_seg = Seg(type="text", data="[空消息]")
                 else:
-                    # 处理普通消息
                     if raw_message:
-                        from .message import Message
-                        message_obj = Message(
-                            user_id=msg.get('user_id', 0),
-                            message_id=msg.get('message_id', 0),
+                        from .message_cq import MessageRecvCQ
+
+                        user_info=UserInfo(
+                            platform='qq',
+                            user_id=msg.get("user_id", 0),
+                            user_nickname=nickname,
+                        )
+                        group_info=GroupInfo(
+                            platform='qq',
+                            group_id=msg.get("group_id", 0),
+                            group_name=get_groupname(msg.get("group_id", 0))
+                        )
+                        message_obj = MessageRecvCQ(
+                            message_id=msg.get("message_id", 0),
+                            user_info=user_info,
                             raw_message=raw_message,
                             plain_text=raw_message,
-                            group_id=msg.get('group_id', 0)
+                            group_info=group_info,
+                        )
+                        content_seg = Seg(
+                            type="seglist", data=[message_obj.message_segment]
                         )
-                        await message_obj.initialize()
-                        content = message_obj.processed_plain_text
                     else:
-                        content = '[空消息]'
+                        content_seg = Seg(type="text", data="[空消息]")
 
-                formatted_msg = f"{nickname}: {content}"
-                formatted_messages.append(formatted_msg)
+                formatted_segments.append(Seg(type="text", data=f"{nickname}: "))
+                formatted_segments.append(content_seg)
+                formatted_segments.append(Seg(type="text", data="\n"))
 
-            # 合并所有消息
-            combined_messages = '\n'.join(formatted_messages)
-            print(f"\033[1;34m[调试信息]\033[0m 合并后的转发消息: {combined_messages}")
-            return f"[转发消息:\n{combined_messages}]"
+            return formatted_segments
 
         except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 处理转发消息失败: {str(e)}")
-            return '[转发消息]'
+            logger.error(f"处理转发消息失败: {str(e)}")
+            return None
 
-    async def translate_reply(self) -> str:
-        """处理回复类型的CQ码"""
+    def translate_reply(self) -> Optional[List[Seg]]:
+        """处理回复类型的CQ码，返回Seg列表"""
+        from .message_cq import MessageRecvCQ
 
-        # 创建Message对象
-        from .message import Message
-        if self.reply_message == None:
-            # print(f"\033[1;31m[错误]\033[0m 回复消息为空")
-            return '[回复某人消息]'
+        if self.reply_message is None:
+            return None
 
         if self.reply_message.sender.user_id:
-            message_obj = Message(
-                user_id=self.reply_message.sender.user_id,
+            
+            message_obj = MessageRecvCQ(
+                user_info=UserInfo(user_id=self.reply_message.sender.user_id,user_nickname=self.reply_message.sender.nickname),
                 message_id=self.reply_message.message_id,
                 raw_message=str(self.reply_message.message),
-                group_id=self.group_id
+                group_info=GroupInfo(group_id=self.reply_message.group_id),
             )
-            await message_obj.initialize()
-            if message_obj.user_id == global_config.BOT_QQ:
-                return f"[回复 {global_config.BOT_NICKNAME} 的消息: {message_obj.processed_plain_text}]"
-            else:
-                return f"[回复 {self.reply_message.sender.nickname} 的消息: {message_obj.processed_plain_text}]"
+            
 
+            segments = []
+            if message_obj.message_info.user_info.user_id == global_config.BOT_QQ:
+                segments.append(
+                    Seg(
+                        type="text", data=f"[回复 {global_config.BOT_NICKNAME} 的消息: "
+                    )
+                )
+            else:
+                segments.append(
+                    Seg(
+                        type="text",
+                        data=f"[回复 {self.reply_message.sender.nickname} 的消息: ",
+                    )
+                )
+
+            segments.append(Seg(type="seglist", data=[message_obj.message_segment]))
+            segments.append(Seg(type="text", data="]"))
+            return segments
         else:
-            print("\033[1;31m[错误]\033[0m 回复消息的sender.user_id为空")
-            return '[回复某人消息]'
+            return None
 
     @staticmethod
     def unescape(text: str) -> str:
         """反转义CQ码中的特殊字符"""
-        return text.replace('&#44;', ',') \
-            .replace('&#91;', '[') \
-            .replace('&#93;', ']') \
-            .replace('&amp;', '&')
-
-    @staticmethod
-    def create_emoji_cq(file_path: str) -> str:
-        """
-        创建表情包CQ码
-        Args:
-            file_path: 本地表情包文件路径
-        Returns:
-            表情包CQ码字符串
-        """
-        # 确保使用绝对路径
-        abs_path = os.path.abspath(file_path)
-        # 转义特殊字符
-        escaped_path = abs_path.replace('&', '&amp;') \
-            .replace('[', '&#91;') \
-            .replace(']', '&#93;') \
-            .replace(',', '&#44;')
-        # 生成CQ码，设置sub_type=1表示这是表情包
-        return f"[CQ:image,file=file:///{escaped_path},sub_type=1]"
-
+        return (
+            text.replace("&#44;", ",")
+            .replace("&#91;", "[")
+            .replace("&#93;", "]")
+            .replace("&amp;", "&")
+        )
 
 class CQCode_tool:
     @staticmethod
-    async def cq_from_dict_to_class(cq_code: Dict, reply: Optional[Dict] = None) -> CQCode:
+    def cq_from_dict_to_class(cq_code: Dict,msg ,reply: Optional[Dict] = None) -> CQCode:
         """
         将CQ码字典转换为CQCode对象
-        
+
         Args:
             cq_code: CQ码字典
+            msg: MessageCQ对象
             reply: 回复消息的字典（可选）
-            
+
         Returns:
             CQCode对象
         """
         # 处理字典形式的CQ码
         # 从cq_code字典中获取type字段的值,如果不存在则默认为'text'
-        cq_type = cq_code.get('type', 'text')
+        cq_type = cq_code.get("type", "text")
         params = {}
-        if cq_type == 'text':
-            params['text'] = cq_code.get('data', {}).get('text', '')
+        if cq_type == "text":
+            params["text"] = cq_code.get("data", {}).get("text", "")
         else:
-            params = cq_code.get('data', {})
+            params = cq_code.get("data", {})
 
         instance = CQCode(
             type=cq_type,
             params=params,
-            group_id=0,
-            user_id=0,
+            group_info=msg.message_info.group_info,
+            user_info=msg.message_info.user_info,
             reply_message=reply
         )
 
         # 进行翻译处理
-        await instance.translate()
+        instance.translate()
         return instance
 
     @staticmethod
@@ -383,5 +371,64 @@ class CQCode_tool:
         """
         return f"[CQ:reply,id={message_id}]"
 
+    @staticmethod
+    def create_emoji_cq(file_path: str) -> str:
+        """
+        创建表情包CQ码
+        Args:
+            file_path: 本地表情包文件路径
+        Returns:
+            表情包CQ码字符串
+        """
+        # 确保使用绝对路径
+        abs_path = os.path.abspath(file_path)
+        # 转义特殊字符
+        escaped_path = (
+            abs_path.replace("&", "&amp;")
+            .replace("[", "&#91;")
+            .replace("]", "&#93;")
+            .replace(",", "&#44;")
+        )
+        # 生成CQ码，设置sub_type=1表示这是表情包
+        return f"[CQ:image,file=file:///{escaped_path},sub_type=1]"
+
+    @staticmethod
+    def create_emoji_cq_base64(base64_data: str) -> str:
+        """
+        创建表情包CQ码
+        Args:
+            base64_data: base64编码的表情包数据
+        Returns:
+            表情包CQ码字符串
+        """
+        # 转义base64数据
+        escaped_base64 = (
+            base64_data.replace("&", "&amp;")
+            .replace("[", "&#91;")
+            .replace("]", "&#93;")
+            .replace(",", "&#44;")
+        )
+        # 生成CQ码，设置sub_type=1表示这是表情包
+        return f"[CQ:image,file=base64://{escaped_base64},sub_type=1]"
+    
+    @staticmethod
+    def create_image_cq_base64(base64_data: str) -> str:
+        """
+        创建表情包CQ码
+        Args:
+            base64_data: base64编码的表情包数据
+        Returns:
+            表情包CQ码字符串
+        """
+        # 转义base64数据
+        escaped_base64 = (
+            base64_data.replace("&", "&amp;")
+            .replace("[", "&#91;")
+            .replace("]", "&#93;")
+            .replace(",", "&#44;")
+        )
+        # 生成CQ码，设置sub_type=1表示这是表情包
+        return f"[CQ:image,file=base64://{escaped_base64},sub_type=0]"
+
 
 cq_code_tool = CQCode_tool()
diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py
index 708454a1e..e3342d1a7 100644
--- a/src/plugins/chat/emoji_manager.py
+++ b/src/plugins/chat/emoji_manager.py
@@ -1,110 +1,110 @@
 import asyncio
+import base64
+import hashlib
 import os
 import random
 import time
 import traceback
-from typing import Optional
+from typing import Optional, Tuple
+from PIL import Image
+import io
 
 from loguru import logger
 from nonebot import get_driver
 
-from ...common.database import Database
+from ...common.database import db
 from ..chat.config import global_config
 from ..chat.utils import get_embedding
-from ..chat.utils_image import image_path_to_base64
+from ..chat.utils_image import ImageManager, image_path_to_base64
 from ..models.utils_model import LLM_request
 
 driver = get_driver()
 config = driver.config
+image_manager = ImageManager()
 
 
 class EmojiManager:
     _instance = None
-    EMOJI_DIR = "data/emoji"  # 表情包存储目录
-    
+    EMOJI_DIR = os.path.join("data", "emoji")  # 表情包存储目录
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
-            cls._instance.db = None
             cls._instance._initialized = False
         return cls._instance
-    
+
     def __init__(self):
-        self.db = Database.get_instance()
         self._scan_task = None
         self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
-        self.llm_emotion_judge = LLM_request(model=global_config.llm_emotion_judge, max_tokens=60,temperature=0.8) #更高的温度，更少的token（后续可以根据情绪来调整温度）
-        
+        self.llm_emotion_judge = LLM_request(
+            model=global_config.llm_emotion_judge, max_tokens=60, temperature=0.8
+        )  # 更高的温度，更少的token（后续可以根据情绪来调整温度）
+
     def _ensure_emoji_dir(self):
         """确保表情存储目录存在"""
         os.makedirs(self.EMOJI_DIR, exist_ok=True)
-    
+
     def initialize(self):
         """初始化数据库连接和表情目录"""
         if not self._initialized:
             try:
-                self.db = Database.get_instance()
                 self._ensure_emoji_collection()
                 self._ensure_emoji_dir()
                 self._initialized = True
                 # 启动时执行一次完整性检查
                 self.check_emoji_file_integrity()
-            except Exception as e:
-                logger.error(f"初始化表情管理器失败: {str(e)}")
-                
+            except Exception:
+                logger.exception("初始化表情管理器失败")
+
     def _ensure_db(self):
         """确保数据库已初始化"""
         if not self._initialized:
             self.initialize()
         if not self._initialized:
             raise RuntimeError("EmojiManager not initialized")
-        
+
     def _ensure_emoji_collection(self):
         """确保emoji集合存在并创建索引
-        
+
         这个函数用于确保MongoDB数据库中存在emoji集合,并创建必要的索引。
-        
+
         索引的作用是加快数据库查询速度:
         - embedding字段的2dsphere索引: 用于加速向量相似度搜索,帮助快速找到相似的表情包
         - tags字段的普通索引: 加快按标签搜索表情包的速度
         - filename字段的唯一索引: 确保文件名不重复,同时加快按文件名查找的速度
-        
+
         没有索引的话,数据库每次查询都需要扫描全部数据,建立索引后可以大大提高查询效率。
         """
-        if 'emoji' not in self.db.db.list_collection_names():
-            self.db.db.create_collection('emoji')
-            self.db.db.emoji.create_index([('embedding', '2dsphere')])
-            self.db.db.emoji.create_index([('tags', 1)])
-            self.db.db.emoji.create_index([('filename', 1)], unique=True)
-            
+        if "emoji" not in db.list_collection_names():
+            db.create_collection("emoji")
+            db.emoji.create_index([("embedding", "2dsphere")])
+            db.emoji.create_index([("filename", 1)], unique=True)
+
     def record_usage(self, emoji_id: str):
         """记录表情使用次数"""
         try:
             self._ensure_db()
-            self.db.db.emoji.update_one(
-                {'_id': emoji_id},
-                {'$inc': {'usage_count': 1}}
-            )
+            db.emoji.update_one({"_id": emoji_id}, {"$inc": {"usage_count": 1}})
         except Exception as e:
             logger.error(f"记录表情使用失败: {str(e)}")
-            
-    async def get_emoji_for_text(self, text: str) -> Optional[str]:
+
+    async def get_emoji_for_text(self, text: str) -> Optional[Tuple[str, str]]:
         """根据文本内容获取相关表情包
         Args:
             text: 输入文本
         Returns:
             Optional[str]: 表情包文件路径，如果没有找到则返回None
-            
-        
+
+
         可不可以通过 配置文件中的指令 来自定义使用表情包的逻辑？
-        我觉得可行    
+        我觉得可行
 
         """
         try:
             self._ensure_db()
-            
+
             # 获取文本的embedding
-            text_for_search= await self._get_kimoji_for_text(text)
+            text_for_search = await self._get_kimoji_for_text(text)
             if not text_for_search:
                 logger.error("无法获取文本的情绪")
                 return None
@@ -112,15 +112,15 @@ class EmojiManager:
             if not text_embedding:
                 logger.error("无法获取文本的embedding")
                 return None
-            
+
             try:
                 # 获取所有表情包
-                all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1}))
-                
+                all_emojis = list(db.emoji.find({}, {"_id": 1, "path": 1, "embedding": 1, "description": 1}))
+
                 if not all_emojis:
                     logger.warning("数据库中没有任何表情包")
                     return None
-                
+
                 # 计算余弦相似度并排序
                 def cosine_similarity(v1, v2):
                     if not v1 or not v2:
@@ -131,147 +131,196 @@ class EmojiManager:
                     if norm_v1 == 0 or norm_v2 == 0:
                         return 0
                     return dot_product / (norm_v1 * norm_v2)
-                
+
                 # 计算所有表情包与输入文本的相似度
                 emoji_similarities = [
-                    (emoji, cosine_similarity(text_embedding, emoji.get('embedding', [])))
-                    for emoji in all_emojis
+                    (emoji, cosine_similarity(text_embedding, emoji.get("embedding", []))) for emoji in all_emojis
                 ]
-                
+
                 # 按相似度降序排序
                 emoji_similarities.sort(key=lambda x: x[1], reverse=True)
-                
+
                 # 获取前3个最相似的表情包
-                top_3_emojis = emoji_similarities[:3]
-                
-                if not top_3_emojis:
+                top_10_emojis = emoji_similarities[: 10 if len(emoji_similarities) > 10 else len(emoji_similarities)]
+
+                if not top_10_emojis:
                     logger.warning("未找到匹配的表情包")
                     return None
-                
+
                 # 从前3个中随机选择一个
-                selected_emoji, similarity = random.choice(top_3_emojis)
-                
-                if selected_emoji and 'path' in selected_emoji:
+                selected_emoji, similarity = random.choice(top_10_emojis)
+
+                if selected_emoji and "path" in selected_emoji:
                     # 更新使用次数
-                    self.db.db.emoji.update_one(
-                        {'_id': selected_emoji['_id']},
-                        {'$inc': {'usage_count': 1}}
+                    db.emoji.update_one({"_id": selected_emoji["_id"]}, {"$inc": {"usage_count": 1}})
+
+                    logger.success(
+                        f"找到匹配的表情包: {selected_emoji.get('description', '无描述')} (相似度: {similarity:.4f})"
                     )
-                    logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
                     # 稍微改一下文本描述，不然容易产生幻觉，描述已经包含 表情包 了
-                    return selected_emoji['path'],"[ %s ]" % selected_emoji.get('discription', '无描述')
-                    
+                    return selected_emoji["path"], "[ %s ]" % selected_emoji.get("description", "无描述")
+
             except Exception as search_error:
                 logger.error(f"搜索表情包失败: {str(search_error)}")
                 return None
-            
+
             return None
-            
+
         except Exception as e:
             logger.error(f"获取表情包失败: {str(e)}")
             return None
 
     async def _get_emoji_discription(self, image_base64: str) -> str:
-        """获取表情包的标签"""
+        """获取表情包的标签，使用image_manager的描述生成功能"""
+
         try:
-            prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感'
-            
-            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
-            logger.debug(f"输出描述: {content}")
-            return content
-            
+            # 使用image_manager获取描述，去掉前后的方括号和"表情包："前缀
+            description = await image_manager.get_emoji_description(image_base64)
+            # 去掉[表情包：xxx]的格式，只保留描述内容
+            description = description.strip("[]").replace("表情包：", "")
+            return description
+
         except Exception as e:
             logger.error(f"获取标签失败: {str(e)}")
             return None
-    
-    async def _check_emoji(self, image_base64: str) -> str:
+
+    async def _check_emoji(self, image_base64: str, image_format: str) -> str:
         try:
-            prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容'
-            
-            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
+            prompt = f'这是一个表情包，请回答这个表情包是否满足"{global_config.EMOJI_CHECK_PROMPT}"的要求，是则回答是，否则回答否，不要出现任何其他内容'
+
+            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
             logger.debug(f"输出描述: {content}")
             return content
-            
+
         except Exception as e:
             logger.error(f"获取标签失败: {str(e)}")
             return None
-        
-    async def _get_kimoji_for_text(self, text:str):
+
+    async def _get_kimoji_for_text(self, text: str):
         try:
-            prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。'
-            
-            content, _ = await self.llm_emotion_judge.generate_response_async(prompt)
+            prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出"一种什么样的感觉"中间的形容词部分。'
+
+            content, _ = await self.llm_emotion_judge.generate_response_async(prompt, temperature=1.5)
             logger.info(f"输出描述: {content}")
             return content
-            
+
         except Exception as e:
             logger.error(f"获取标签失败: {str(e)}")
             return None
-                    
+
     async def scan_new_emojis(self):
         """扫描新的表情包"""
         try:
-            emoji_dir = "data/emoji"
+            emoji_dir = self.EMOJI_DIR
             os.makedirs(emoji_dir, exist_ok=True)
 
             # 获取所有支持的图片文件
-            files_to_process = [f for f in os.listdir(emoji_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]
-            
+            files_to_process = [
+                f for f in os.listdir(emoji_dir) if f.lower().endswith((".jpg", ".jpeg", ".png", ".gif"))
+            ]
+
             for filename in files_to_process:
                 image_path = os.path.join(emoji_dir, filename)
-                
-                # 检查是否已经注册过
-                existing_emoji = self.db.db['emoji'].find_one({'filename': filename})
-                if existing_emoji:
-                    continue
-                
-                # 压缩图片并获取base64编码
+
+                # 获取图片的base64编码和哈希值
                 image_base64 = image_path_to_base64(image_path)
                 if image_base64 is None:
                     os.remove(image_path)
                     continue
-                
-                # 获取表情包的描述
-                discription = await self._get_emoji_discription(image_base64)
+
+                image_bytes = base64.b64decode(image_base64)
+                image_hash = hashlib.md5(image_bytes).hexdigest()
+                image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
+                # 检查是否已经注册过
+                existing_emoji = db["emoji"].find_one({"hash": image_hash})
+                description = None
+
+                if existing_emoji:
+                    # 即使表情包已存在，也检查是否需要同步到images集合
+                    description = existing_emoji.get("discription")
+                    # 检查是否在images集合中存在
+                    existing_image = db.images.find_one({"hash": image_hash})
+                    if not existing_image:
+                        # 同步到images集合
+                        image_doc = {
+                            "hash": image_hash,
+                            "path": image_path,
+                            "type": "emoji",
+                            "description": description,
+                            "timestamp": int(time.time()),
+                        }
+                        db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
+                        # 保存描述到image_descriptions集合
+                        image_manager._save_description_to_db(image_hash, description, "emoji")
+                        logger.success(f"同步已存在的表情包到images集合: {filename}")
+                    continue
+
+                # 检查是否在images集合中已有描述
+                existing_description = image_manager._get_description_from_db(image_hash, "emoji")
+
+                if existing_description:
+                    description = existing_description
+                else:
+                    # 获取表情包的描述
+                    description = await self._get_emoji_discription(image_base64)
+
                 if global_config.EMOJI_CHECK:
-                    check = await self._check_emoji(image_base64)
-                    if '是' not in check:
+                    check = await self._check_emoji(image_base64, image_format)
+                    if "是" not in check:
                         os.remove(image_path)
-                        logger.info(f"描述: {discription}")
+                        logger.info(f"描述: {description}")
+
+                        logger.info(f"描述: {description}")
                         logger.info(f"其不满足过滤规则，被剔除 {check}")
                         continue
                     logger.info(f"check通过 {check}")
-                
-                if discription is not None:
-                    embedding = await get_embedding(discription)
+
+                if description is not None:
+                    embedding = await get_embedding(description)
+
+                if description is not None:
+                    embedding = await get_embedding(description)
+
                     # 准备数据库记录
                     emoji_record = {
-                        'filename': filename,
-                        'path': image_path,
-                        'embedding':embedding,
-                        'discription': discription,
-                        'timestamp': int(time.time())
+                        "filename": filename,
+                        "path": image_path,
+                        "embedding": embedding,
+                        "discription": description,
+                        "hash": image_hash,
+                        "timestamp": int(time.time()),
                     }
-                    
-                    # 保存到数据库
-                    self.db.db['emoji'].insert_one(emoji_record)
+
+                    # 保存到emoji数据库
+                    db["emoji"].insert_one(emoji_record)
                     logger.success(f"注册新表情包: {filename}")
-                    logger.info(f"描述: {discription}")
+                    logger.info(f"描述: {description}")
+
+                    # 保存到images数据库
+                    image_doc = {
+                        "hash": image_hash,
+                        "path": image_path,
+                        "type": "emoji",
+                        "description": description,
+                        "timestamp": int(time.time()),
+                    }
+                    db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
+                    # 保存描述到image_descriptions集合
+                    image_manager._save_description_to_db(image_hash, description, "emoji")
+                    logger.success(f"同步保存到images集合: {filename}")
                 else:
                     logger.warning(f"跳过表情包: {filename}")
-                
-        except Exception as e:
-            logger.error(f"扫描表情包失败: {str(e)}")
-            logger.error(traceback.format_exc())
-    
+
+        except Exception:
+            logger.exception("扫描表情包失败")
+
     async def _periodic_scan(self, interval_MINS: int = 10):
         """定期扫描新表情包"""
         while True:
-            print("\033[1;36m[表情包]\033[0m 开始扫描新表情包...")
+            logger.info("开始扫描新表情包...")
             await self.scan_new_emojis()
             await asyncio.sleep(interval_MINS * 60)  # 每600秒扫描一次
 
-
     def check_emoji_file_integrity(self):
         """检查表情包文件完整性
         如果文件已被删除，则从数据库中移除对应记录
@@ -279,46 +328,53 @@ class EmojiManager:
         try:
             self._ensure_db()
             # 获取所有表情包记录
-            all_emojis = list(self.db.db.emoji.find())
+            all_emojis = list(db.emoji.find())
             removed_count = 0
             total_count = len(all_emojis)
-            
+
             for emoji in all_emojis:
                 try:
-                    if 'path' not in emoji:
+                    if "path" not in emoji:
                         logger.warning(f"发现无效记录（缺少path字段），ID: {emoji.get('_id', 'unknown')}")
-                        self.db.db.emoji.delete_one({'_id': emoji['_id']})
+                        db.emoji.delete_one({"_id": emoji["_id"]})
                         removed_count += 1
                         continue
-                    
-                    if 'embedding' not in emoji:
+
+                    if "embedding" not in emoji:
                         logger.warning(f"发现过时记录（缺少embedding字段），ID: {emoji.get('_id', 'unknown')}")
-                        self.db.db.emoji.delete_one({'_id': emoji['_id']})
+                        db.emoji.delete_one({"_id": emoji["_id"]})
                         removed_count += 1
                         continue
-                        
+
                     # 检查文件是否存在
-                    if not os.path.exists(emoji['path']):
+                    if not os.path.exists(emoji["path"]):
                         logger.warning(f"表情包文件已被删除: {emoji['path']}")
                         # 从数据库中删除记录
-                        result = self.db.db.emoji.delete_one({'_id': emoji['_id']})
+                        result = db.emoji.delete_one({"_id": emoji["_id"]})
                         if result.deleted_count > 0:
-                            logger.success(f"成功删除数据库记录: {emoji['_id']}")
+                            logger.debug(f"成功删除数据库记录: {emoji['_id']}")
                             removed_count += 1
                         else:
                             logger.error(f"删除数据库记录失败: {emoji['_id']}")
+                        continue
+
+                    if "hash" not in emoji:
+                        logger.warning(f"发现缺失记录（缺少hash字段），ID: {emoji.get('_id', 'unknown')}")
+                        hash = hashlib.md5(open(emoji["path"], "rb").read()).hexdigest()
+                        db.emoji.update_one({"_id": emoji["_id"]}, {"$set": {"hash": hash}})
+
                 except Exception as item_error:
                     logger.error(f"处理表情包记录时出错: {str(item_error)}")
                     continue
-            
+
             # 验证清理结果
-            remaining_count = self.db.db.emoji.count_documents({})
+            remaining_count = db.emoji.count_documents({})
             if removed_count > 0:
                 logger.success(f"已清理 {removed_count} 个失效的表情包记录")
                 logger.info(f"清理前总数: {total_count} | 清理后总数: {remaining_count}")
             else:
                 logger.info(f"已检查 {total_count} 个表情包记录")
-                
+
         except Exception as e:
             logger.error(f"检查表情包完整性失败: {str(e)}")
             logger.error(traceback.format_exc())
@@ -329,6 +385,6 @@ class EmojiManager:
             await asyncio.sleep(interval_MINS * 60)
 
 
-
 # 创建全局单例
-emoji_manager = EmojiManager() 
+
+emoji_manager = EmojiManager()
diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py
index 1ac421e6b..2e0c0eb1f 100644
--- a/src/plugins/chat/llm_generator.py
+++ b/src/plugins/chat/llm_generator.py
@@ -3,11 +3,12 @@ import time
 from typing import List, Optional, Tuple, Union
 
 from nonebot import get_driver
+from loguru import logger
 
-from ...common.database import Database
+from ...common.database import db
 from ..models.utils_model import LLM_request
 from .config import global_config
-from .message import Message
+from .message import MessageRecv, MessageThinking, Message
 from .prompt_builder import prompt_builder
 from .relationship_manager import relationship_manager
 from .utils import process_llm_response
@@ -18,58 +19,88 @@ config = driver.config
 
 class ResponseGenerator:
     def __init__(self):
-        self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000,stream=True)
-        self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000)
-        self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000)
-        self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7,max_tokens=1000)
-        self.db = Database.get_instance()
-        self.current_model_type = 'r1'  # 默认使用 R1
+        self.model_r1 = LLM_request(
+            model=global_config.llm_reasoning,
+            temperature=0.7,
+            max_tokens=1000,
+            stream=True,
+        )
+        self.model_v3 = LLM_request(
+            model=global_config.llm_normal, temperature=0.7, max_tokens=1000
+        )
+        self.model_r1_distill = LLM_request(
+            model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=1000
+        )
+        self.model_v25 = LLM_request(
+            model=global_config.llm_normal_minor, temperature=0.7, max_tokens=1000
+        )
+        self.current_model_type = "r1"  # 默认使用 R1
 
-    async def generate_response(self, message: Message) -> Optional[Union[str, List[str]]]:
+    async def generate_response(
+        self, message: MessageThinking
+    ) -> 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
         rand = random.random()
         if rand < global_config.MODEL_R1_PROBABILITY:
-            self.current_model_type = 'r1'
+            self.current_model_type = "r1"
             current_model = self.model_r1
-        elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY:
-            self.current_model_type = 'v3'
+        elif (
+            rand
+            < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY
+        ):
+            self.current_model_type = "v3"
             current_model = self.model_v3
         else:
-            self.current_model_type = 'r1_distill'
+            self.current_model_type = "r1_distill"
             current_model = self.model_r1_distill
 
-        print(f"+++++++++++++++++{global_config.BOT_NICKNAME}{self.current_model_type}思考中+++++++++++++++++")
-        
-        model_response = await self._generate_response_with_model(message, current_model)
-        raw_content=model_response
+        logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中")
+
+        model_response = await self._generate_response_with_model(
+            message, current_model
+        )
+        raw_content = model_response
+
+        # print(f"raw_content: {raw_content}")
+        # print(f"model_response: {model_response}")
         
         if model_response:
-            print(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
+            logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}')
             model_response = await self._process_response(model_response)
             if model_response:
+                return model_response, raw_content
+        return None, raw_content
 
-                return model_response ,raw_content
-        return None,raw_content
-
-    async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]:
+    async def _generate_response_with_model(
+        self, message: MessageThinking, model: LLM_request
+    ) -> Optional[str]:
         """使用指定的模型生成回复"""
-        sender_name = message.user_nickname or f"用户{message.user_id}"
-        if message.user_cardname:
-            sender_name=f"[({message.user_id}){message.user_nickname}]{message.user_cardname}"
-        
+        sender_name = (
+            message.chat_stream.user_info.user_nickname
+            or f"用户{message.chat_stream.user_info.user_id}"
+        )
+        if message.chat_stream.user_info.user_cardname:
+            sender_name = f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]{message.chat_stream.user_info.user_cardname}"
+
         # 获取关系值
-        relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0
+        relationship_value = (
+            relationship_manager.get_relationship(
+                message.chat_stream
+            ).relationship_value
+            if relationship_manager.get_relationship(message.chat_stream)
+            else 0.0
+        )
         if relationship_value != 0.0:
             # print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}")
             pass
-        
+
         # 构建prompt
         prompt, prompt_check = await prompt_builder._build_prompt(
             message_txt=message.processed_plain_text,
             sender_name=sender_name,
             relationship_value=relationship_value,
-            group_id=message.group_id
+            stream_id=message.chat_stream.stream_id,
         )
 
         # 读空气模块 简化逻辑，先停用
@@ -92,10 +123,10 @@ class ResponseGenerator:
         # 生成回复
         try:
             content, reasoning_content = await model.generate_response(prompt)
-        except Exception as e:
-            print(f"生成回复时出错: {e}")
+        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,
@@ -107,60 +138,78 @@ class ResponseGenerator:
             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: Message, sender_name: str, prompt: str, prompt_check: str,
     #                 content: str, content_check: str, reasoning_content: str, reasoning_content_check: str):
-    def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str,
-                content: str, reasoning_content: str,):
+    def _save_to_db(
+        self,
+        message: MessageRecv,
+        sender_name: str,
+        prompt: str,
+        prompt_check: str,
+        content: str,
+        reasoning_content: str,
+    ):
         """保存对话记录到数据库"""
-        self.db.db.reasoning_logs.insert_one({
-            'time': time.time(),
-            'group_id': message.group_id,
-            'user': sender_name,
-            'message': message.processed_plain_text,
-            'model': self.current_model_type,
-            # 'reasoning_check': reasoning_content_check,
-            # 'response_check': content_check,
-            'reasoning': reasoning_content,
-            'response': content,
-            'prompt': prompt,
-            'prompt_check': prompt_check
-        })
+        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_type,
+                # 'reasoning_check': reasoning_content_check,
+                # 'response_check': content_check,
+                "reasoning": reasoning_content,
+                "response": content,
+                "prompt": prompt,
+                "prompt_check": prompt_check,
+            }
+        )
 
     async def _get_emotion_tags(self, content: str) -> List[str]:
         """提取情感标签"""
         try:
-            prompt = f'''请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出
+            prompt = f"""请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出
             只输出标签就好，不要输出其他内容:
             内容：{content}
             输出：
-            '''
+            """
             content, _ = await self.model_v25.generate_response(prompt)
-            content=content.strip()
-            if content in ['happy','angry','sad','surprised','disgusted','fearful','neutral']:
+            content = content.strip()
+            if content in [
+                "happy",
+                "angry",
+                "sad",
+                "surprised",
+                "disgusted",
+                "fearful",
+                "neutral",
+            ]:
                 return [content]
             else:
                 return ["neutral"]
-            
+
         except Exception as e:
             print(f"获取情感标签时出错: {e}")
             return ["neutral"]
-    
+
     async def _process_response(self, 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
 
 
 class InitiativeMessageGenerate:
     def __init__(self):
-        self.db = Database.get_instance()
         self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7)
         self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7)
         self.model_r1_distill = LLM_request(
@@ -172,7 +221,7 @@ class InitiativeMessageGenerate:
             prompt_builder._build_initiative_prompt_select(message.group_id)
         )
         content_select, reasoning = self.model_v3.generate_response(topic_select_prompt)
-        print(f"[DEBUG] {content_select} {reasoning}")
+        logger.debug(f"{content_select} {reasoning}")
         topics_list = [dot[0] for dot in dots_for_select]
         if content_select:
             if content_select in topics_list:
@@ -185,12 +234,12 @@ class InitiativeMessageGenerate:
             select_dot[1], prompt_template
         )
         content_check, reasoning_check = self.model_v3.generate_response(prompt_check)
-        print(f"[DEBUG] {content_check} {reasoning_check}")
+        logger.info(f"{content_check} {reasoning_check}")
         if "yes" not in content_check.lower():
             return None
         prompt = prompt_builder._build_initiative_prompt(
             select_dot, prompt_template, memory
         )
         content, reasoning = self.model_r1.generate_response_async(prompt)
-        print(f"[DEBUG] {content} {reasoning}")
+        logger.debug(f"[DEBUG] {content} {reasoning}")
         return content
diff --git a/src/plugins/chat/message.py b/src/plugins/chat/message.py
index f1fc5569d..96308c50b 100644
--- a/src/plugins/chat/message.py
+++ b/src/plugins/chat/message.py
@@ -1,242 +1,420 @@
 import time
+import html
+import re
+import json
 from dataclasses import dataclass
-from typing import Dict, ForwardRef, List, Optional
+from typing import Dict, List, Optional
 
 import urllib3
+from loguru import logger
 
-from .cq_code import CQCode, cq_code_tool
-from .utils_cq import parse_cq_code
-from .utils_user import get_groupname, get_user_cardname, get_user_nickname
+from .utils_image import image_manager
+
+from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
+from .chat_stream import ChatStream, chat_manager
 
-Message = ForwardRef('Message')  # 添加这行
 # 禁用SSL警告
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
-#这个类是消息数据类，用于存储和管理消息数据。
-#它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。
-#它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。
-
+# 这个类是消息数据类，用于存储和管理消息数据。
+# 它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。
+# 它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。
 
 
 @dataclass
-class Message:
-    """消息数据类"""
-    message_id: int = None
-    time: float = None
+class Message(MessageBase):
+    chat_stream: ChatStream = None
+    reply: Optional["Message"] = None
+    detailed_plain_text: str = ""
+    processed_plain_text: str = ""
 
-    group_id: int = None
-    group_name: str = None  # 群名称
-
-    user_id: int = None
-    user_nickname: str = None  # 用户昵称
-    user_cardname: str = None  # 用户群昵称
-
-    raw_message: str = None  # 原始消息，包含未解析的cq码
-    plain_text: str = None  # 纯文本
-
-    reply_message: Dict = None  # 存储 回复的 源消息
-
-    # 延迟初始化字段
-    _initialized: bool = False
-    message_segments: List[Dict] = None  # 存储解析后的消息片段
-    processed_plain_text: str = None  # 用于存储处理后的plain_text
-    detailed_plain_text: str = None  # 用于存储详细可读文本
-
-    # 状态标志
-    is_emoji: bool = False
-    has_emoji: bool = False
-    translate_cq: bool = True
-
-    async def initialize(self):
-        """显式异步初始化方法（必须调用）"""
-        if self._initialized:
-            return
-
-        # 异步获取补充信息
-        self.group_name = self.group_name or get_groupname(self.group_id)
-        self.user_nickname = self.user_nickname or get_user_nickname(self.user_id)
-        self.user_cardname = self.user_cardname or get_user_cardname(self.user_id)
-
-        # 消息解析
-        if self.raw_message:
-            if not isinstance(self,Message_Sending):
-                self.message_segments = await self.parse_message_segments(self.raw_message)
-                self.processed_plain_text = ' '.join(
-                    seg.translated_plain_text
-                    for seg in self.message_segments
-                )
-
-        # 构建详细文本
-        if self.time is None:
-            self.time = int(time.time())
-        time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(self.time))
-        name = (
-            f"{self.user_nickname}(ta的昵称:{self.user_cardname},ta的id:{self.user_id})"
-            if self.user_cardname
-            else f"{self.user_nickname or f'用户{self.user_id}'}"
+    def __init__(
+        self,
+        message_id: str,
+        time: int,
+        chat_stream: ChatStream,
+        user_info: UserInfo,
+        message_segment: Optional[Seg] = None,
+        reply: Optional["MessageRecv"] = None,
+        detailed_plain_text: str = "",
+        processed_plain_text: str = "",
+    ):
+        # 构造基础消息信息
+        message_info = BaseMessageInfo(
+            platform=chat_stream.platform,
+            message_id=message_id,
+            time=time,
+            group_info=chat_stream.group_info,
+            user_info=user_info,
         )
-        if isinstance(self,Message_Sending) and self.is_emoji:
-            self.detailed_plain_text = f"[{time_str}] {name}: {self.detailed_plain_text}\n"
-        else:
-            self.detailed_plain_text = f"[{time_str}] {name}: {self.processed_plain_text}\n"
 
-        self._initialized = True
-    
-    async def parse_message_segments(self, message: str) -> List[CQCode]:
-        """
-        将消息解析为片段列表，包括纯文本和CQ码
-        返回的列表中每个元素都是字典，包含：
-        - cq_code_list:分割出的聊天对象，包括文本和CQ码
-        - trans_list:翻译后的对象列表
-        """
-        # print(f"\033[1;34m[调试信息]\033[0m 正在处理消息: {message}")
-        cq_code_dict_list = []
-        trans_list = []
-        
-        start = 0
-        while True:
-            # 查找下一个CQ码的开始位置
-            cq_start = message.find('[CQ:', start)
-            #如果没有cq码，直接返回文本内容
-            if cq_start == -1:
-                # 如果没有找到更多CQ码，添加剩余文本
-                if start < len(message):
-                    text = message[start:].strip()
-                    if text:  # 只添加非空文本
-                        cq_code_dict_list.append(parse_cq_code(text))
-                break
-            # 添加CQ码前的文本
-            if cq_start > start:
-                text = message[start:cq_start].strip()
-                if text:  # 只添加非空文本
-                    cq_code_dict_list.append(parse_cq_code(text))
-            # 查找CQ码的结束位置
-            cq_end = message.find(']', cq_start)
-            if cq_end == -1:
-                # CQ码未闭合，作为普通文本处理
-                text = message[cq_start:].strip()
-                if text:
-                    cq_code_dict_list.append(parse_cq_code(text))
-                break
-            cq_code = message[cq_start:cq_end + 1]
-            
-            #将cq_code解析成字典
-            cq_code_dict_list.append(parse_cq_code(cq_code))
-            # 更新start位置到当前CQ码之后
-            start = cq_end + 1
-            
-        # print(f"\033[1;34m[调试信息]\033[0m 提取的消息对象：列表: {cq_code_dict_list}")
-        
-        #判定是否是表情包消息，以及是否含有表情包
-        if len(cq_code_dict_list) == 1 and cq_code_dict_list[0]['type'] == 'image':
-            self.is_emoji = True
-            self.has_emoji_emoji = True
-        else:
-            for segment in cq_code_dict_list:
-                if segment['type'] == 'image' and segment['data'].get('sub_type') == '1':
-                    self.has_emoji_emoji = True
-                    break
-                
-        
-        #翻译作为字典的CQ码  
-        for _code_item in cq_code_dict_list:
-            message_obj = await cq_code_tool.cq_from_dict_to_class(_code_item,reply = self.reply_message)
-            trans_list.append(message_obj)       
-        return trans_list
+        # 调用父类初始化
+        super().__init__(message_info=message_info, message_segment=message_segment, raw_message=None)
 
-class Message_Thinking:
-    """消息思考类"""
-    def __init__(self, message: Message,message_id: str):
-        # 复制原始消息的基本属性
-        self.group_id = message.group_id
-        self.user_id = message.user_id
-        self.user_nickname = message.user_nickname
-        self.user_cardname = message.user_cardname
-        self.group_name = message.group_name
-        
-        self.message_id = message_id
-        
-        # 思考状态相关属性
+        self.chat_stream = chat_stream
+        # 文本处理相关属性
+        self.processed_plain_text = processed_plain_text
+        self.detailed_plain_text = detailed_plain_text
+
+        # 回复消息
+        self.reply = reply
+
+
+@dataclass
+class MessageRecv(Message):
+    """接收消息类，用于处理从MessageCQ序列化的消息"""
+
+    def __init__(self, message_dict: Dict):
+        """从MessageCQ的字典初始化
+
+        Args:
+            message_dict: MessageCQ序列化后的字典
+        """
+        self.message_info = BaseMessageInfo.from_dict(message_dict.get("message_info", {}))
+
+        message_segment = message_dict.get("message_segment", {})
+
+        if message_segment.get("data", "") == "[json]":
+            # 提取json消息中的展示信息
+            pattern = r"\[CQ:json,data=(?P<json_data>.+?)\]"
+            match = re.search(pattern, message_dict.get("raw_message", ""))
+            raw_json = html.unescape(match.group("json_data"))
+            try:
+                json_message = json.loads(raw_json)
+            except json.JSONDecodeError:
+                json_message = {}
+            message_segment["data"] = json_message.get("prompt", "")
+
+        self.message_segment = Seg.from_dict(message_dict.get("message_segment", {}))
+        self.raw_message = message_dict.get("raw_message")
+
+        # 处理消息内容
+        self.processed_plain_text = ""  # 初始化为空字符串
+        self.detailed_plain_text = ""  # 初始化为空字符串
+        self.is_emoji = False
+
+    def update_chat_stream(self, chat_stream: ChatStream):
+        self.chat_stream = chat_stream
+
+    async def process(self) -> None:
+        """处理消息内容，生成纯文本和详细文本
+
+        这个方法必须在创建实例后显式调用，因为它包含异步操作。
+        """
+        self.processed_plain_text = await self._process_message_segments(self.message_segment)
+        self.detailed_plain_text = self._generate_detailed_text()
+
+    async def _process_message_segments(self, segment: Seg) -> str:
+        """递归处理消息段，转换为文字描述
+
+        Args:
+            segment: 要处理的消息段
+
+        Returns:
+            str: 处理后的文本
+        """
+        if segment.type == "seglist":
+            # 处理消息段列表
+            segments_text = []
+            for seg in segment.data:
+                processed = await self._process_message_segments(seg)
+                if processed:
+                    segments_text.append(processed)
+            return " ".join(segments_text)
+        else:
+            # 处理单个消息段
+            return await self._process_single_segment(segment)
+
+    async def _process_single_segment(self, seg: Seg) -> str:
+        """处理单个消息段
+
+        Args:
+            seg: 要处理的消息段
+
+        Returns:
+            str: 处理后的文本
+        """
+        try:
+            if seg.type == "text":
+                return seg.data
+            elif seg.type == "image":
+                # 如果是base64图片数据
+                if isinstance(seg.data, str):
+                    return await image_manager.get_image_description(seg.data)
+                return "[图片]"
+            elif seg.type == "emoji":
+                self.is_emoji = True
+                if isinstance(seg.data, str):
+                    return await image_manager.get_emoji_description(seg.data)
+                return "[表情]"
+            else:
+                return f"[{seg.type}:{str(seg.data)}]"
+        except Exception as e:
+            logger.error(f"处理消息段失败: {str(e)}, 类型: {seg.type}, 数据: {seg.data}")
+            return f"[处理失败的{seg.type}消息]"
+
+    def _generate_detailed_text(self) -> str:
+        """生成详细文本，包含时间和用户信息"""
+        time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(self.message_info.time))
+        user_info = self.message_info.user_info
+        name = (
+            f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})"
+            if user_info.user_cardname != ""
+            else f"{user_info.user_nickname}(ta的id:{user_info.user_id})"
+        )
+        return f"[{time_str}] {name}: {self.processed_plain_text}\n"
+
+
+@dataclass
+class MessageProcessBase(Message):
+    """消息处理基类，用于处理中和发送中的消息"""
+
+    def __init__(
+        self,
+        message_id: str,
+        chat_stream: ChatStream,
+        bot_user_info: UserInfo,
+        message_segment: Optional[Seg] = None,
+        reply: Optional["MessageRecv"] = None,
+    ):
+        # 调用父类初始化
+        super().__init__(
+            message_id=message_id,
+            time=int(time.time()),
+            chat_stream=chat_stream,
+            user_info=bot_user_info,
+            message_segment=message_segment,
+            reply=reply,
+        )
+
+        # 处理状态相关属性
         self.thinking_start_time = int(time.time())
         self.thinking_time = 0
-        self.interupt=False
-    
-    def update_thinking_time(self):
-        self.thinking_time = round(time.time(), 2) - self.thinking_start_time
-    
 
-@dataclass
-class Message_Sending(Message):
-    """发送中的消息类"""
-    thinking_start_time: float = None  # 思考开始时间
-    thinking_time: float = None  # 思考时间
-    
-    reply_message_id: int = None  # 存储 回复的 源消息ID
-    
-    is_head: bool = False  # 是否是头部消息
-    
-    def update_thinking_time(self):
-        self.thinking_time = round(time.time(), 2) - self.thinking_start_time
+    def update_thinking_time(self) -> float:
+        """更新思考时间"""
+        self.thinking_time = round(time.time() - self.thinking_start_time, 2)
         return self.thinking_time
 
+    async def _process_message_segments(self, segment: Seg) -> str:
+        """递归处理消息段，转换为文字描述
 
-       
+        Args:
+            segment: 要处理的消息段
+
+        Returns:
+            str: 处理后的文本
+        """
+        if segment.type == "seglist":
+            # 处理消息段列表
+            segments_text = []
+            for seg in segment.data:
+                processed = await self._process_message_segments(seg)
+                if processed:
+                    segments_text.append(processed)
+            return " ".join(segments_text)
+        else:
+            # 处理单个消息段
+            return await self._process_single_segment(segment)
+
+    async def _process_single_segment(self, seg: Seg) -> str:
+        """处理单个消息段
+
+        Args:
+            seg: 要处理的消息段
+
+        Returns:
+            str: 处理后的文本
+        """
+        try:
+            if seg.type == "text":
+                return seg.data
+            elif seg.type == "image":
+                # 如果是base64图片数据
+                if isinstance(seg.data, str):
+                    return await image_manager.get_image_description(seg.data)
+                return "[图片]"
+            elif seg.type == "emoji":
+                if isinstance(seg.data, str):
+                    return await image_manager.get_emoji_description(seg.data)
+                return "[表情]"
+            elif seg.type == "at":
+                return f"[@{seg.data}]"
+            elif seg.type == "reply":
+                if self.reply and hasattr(self.reply, "processed_plain_text"):
+                    return f"[回复：{self.reply.processed_plain_text}]"
+            else:
+                return f"[{seg.type}:{str(seg.data)}]"
+        except Exception as e:
+            logger.error(f"处理消息段失败: {str(e)}, 类型: {seg.type}, 数据: {seg.data}")
+            return f"[处理失败的{seg.type}消息]"
+
+    def _generate_detailed_text(self) -> str:
+        """生成详细文本，包含时间和用户信息"""
+        time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(self.message_info.time))
+        user_info = self.message_info.user_info
+        name = (
+            f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})"
+            if user_info.user_cardname != ""
+            else f"{user_info.user_nickname}(ta的id:{user_info.user_id})"
+        )
+        return f"[{time_str}] {name}: {self.processed_plain_text}\n"
+
+
+@dataclass
+class MessageThinking(MessageProcessBase):
+    """思考状态的消息类"""
+
+    def __init__(
+        self,
+        message_id: str,
+        chat_stream: ChatStream,
+        bot_user_info: UserInfo,
+        reply: Optional["MessageRecv"] = None,
+    ):
+        # 调用父类初始化
+        super().__init__(
+            message_id=message_id,
+            chat_stream=chat_stream,
+            bot_user_info=bot_user_info,
+            message_segment=None,  # 思考状态不需要消息段
+            reply=reply,
+        )
+
+        # 思考状态特有属性
+        self.interrupt = False
+
+
+@dataclass
+class MessageSending(MessageProcessBase):
+    """发送状态的消息类"""
+
+    def __init__(
+        self,
+        message_id: str,
+        chat_stream: ChatStream,
+        bot_user_info: UserInfo,
+        sender_info: UserInfo,  # 用来记录发送者信息,用于私聊回复
+        message_segment: Seg,
+        reply: Optional["MessageRecv"] = None,
+        is_head: bool = False,
+        is_emoji: bool = False,
+    ):
+        # 调用父类初始化
+        super().__init__(
+            message_id=message_id,
+            chat_stream=chat_stream,
+            bot_user_info=bot_user_info,
+            message_segment=message_segment,
+            reply=reply,
+        )
+
+        # 发送状态特有属性
+        self.sender_info = sender_info
+        self.reply_to_message_id = reply.message_info.message_id if reply else None
+        self.is_head = is_head
+        self.is_emoji = is_emoji
+
+    def set_reply(self, reply: Optional["MessageRecv"] = None) -> None:
+        """设置回复消息"""
+        if reply:
+            self.reply = reply
+        if self.reply:
+            self.reply_to_message_id = self.reply.message_info.message_id
+            self.message_segment = Seg(
+                type="seglist",
+                data=[
+                    Seg(type="reply", data=reply.message_info.message_id),
+                    self.message_segment,
+                ],
+            )
+
+    async def process(self) -> None:
+        """处理消息内容，生成纯文本和详细文本"""
+        if self.message_segment:
+            self.processed_plain_text = await self._process_message_segments(self.message_segment)
+            self.detailed_plain_text = self._generate_detailed_text()
+
+    @classmethod
+    def from_thinking(
+        cls,
+        thinking: MessageThinking,
+        message_segment: Seg,
+        is_head: bool = False,
+        is_emoji: bool = False,
+    ) -> "MessageSending":
+        """从思考状态消息创建发送状态消息"""
+        return cls(
+            message_id=thinking.message_info.message_id,
+            chat_stream=thinking.chat_stream,
+            message_segment=message_segment,
+            bot_user_info=thinking.message_info.user_info,
+            reply=thinking.reply,
+            is_head=is_head,
+            is_emoji=is_emoji,
+        )
+
+    def to_dict(self):
+        ret = super().to_dict()
+        ret["message_info"]["user_info"] = self.chat_stream.user_info.to_dict()
+        return ret
+
+    def is_private_message(self) -> bool:
+        """判断是否为私聊消息"""
+        return self.message_info.group_info is None or self.message_info.group_info.group_id is None
+
+
+@dataclass
 class MessageSet:
     """消息集合类，可以存储多个发送消息"""
-    def __init__(self, group_id: int, user_id: int, message_id: str):
-        self.group_id = group_id
-        self.user_id = user_id
+
+    def __init__(self, chat_stream: ChatStream, message_id: str):
+        self.chat_stream = chat_stream
         self.message_id = message_id
-        self.messages: List[Message_Sending] = []  # 修改类型标注
+        self.messages: List[MessageSending] = []
         self.time = round(time.time(), 2)
-        
-    def add_message(self, message: Message_Sending) -> None:
-        """添加消息到集合，只接受Message_Sending类型"""
-        if not isinstance(message, Message_Sending):
-            raise TypeError("MessageSet只能添加Message_Sending类型的消息")
+
+    def add_message(self, message: MessageSending) -> None:
+        """添加消息到集合"""
+        if not isinstance(message, MessageSending):
+            raise TypeError("MessageSet只能添加MessageSending类型的消息")
         self.messages.append(message)
-        # 按时间排序
-        self.messages.sort(key=lambda x: x.time)
-        
-    def get_message_by_index(self, index: int) -> Optional[Message_Sending]:
+        self.messages.sort(key=lambda x: x.message_info.time)
+
+    def get_message_by_index(self, index: int) -> Optional[MessageSending]:
         """通过索引获取消息"""
         if 0 <= index < len(self.messages):
             return self.messages[index]
         return None
-        
-    def get_message_by_time(self, target_time: float) -> Optional[Message_Sending]:
+
+    def get_message_by_time(self, target_time: float) -> Optional[MessageSending]:
         """获取最接近指定时间的消息"""
         if not self.messages:
             return None
-            
-        # 使用二分查找找到最接近的消息
+
         left, right = 0, len(self.messages) - 1
         while left < right:
             mid = (left + right) // 2
-            if self.messages[mid].time < target_time:
+            if self.messages[mid].message_info.time < target_time:
                 left = mid + 1
             else:
                 right = mid
-                
+
         return self.messages[left]
-        
-        
+
     def clear_messages(self) -> None:
         """清空所有消息"""
         self.messages.clear()
-        
-    def remove_message(self, message: Message_Sending) -> bool:
+
+    def remove_message(self, message: MessageSending) -> bool:
         """移除指定消息"""
         if message in self.messages:
             self.messages.remove(message)
             return True
         return False
-        
+
     def __str__(self) -> str:
         return f"MessageSet(id={self.message_id}, count={len(self.messages)})"
-        
+
     def __len__(self) -> int:
         return len(self.messages)
-        
-
-    
diff --git a/src/plugins/chat/message_base.py b/src/plugins/chat/message_base.py
new file mode 100644
index 000000000..80b8b6618
--- /dev/null
+++ b/src/plugins/chat/message_base.py
@@ -0,0 +1,188 @@
+from dataclasses import dataclass, asdict
+from typing import List, Optional, Union, Dict
+
+@dataclass
+class Seg:
+    """消息片段类，用于表示消息的不同部分
+    
+    Attributes:
+        type: 片段类型，可以是 'text'、'image'、'seglist' 等
+        data: 片段的具体内容
+            - 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
+    data: Union[str, List['Seg']]
+    
+
+    # def __init__(self, type: str, data: Union[str, List['Seg']],):
+    #     """初始化实例，确保字典和属性同步"""
+    #     # 先初始化字典
+    #     self.type = type
+    #     self.data = data
+        
+    @classmethod  
+    def from_dict(cls, data: Dict) -> 'Seg':
+        """从字典创建Seg实例"""
+        type=data.get('type')
+        data=data.get('data')
+        if type == 'seglist':
+            data = [Seg.from_dict(seg) for seg in data]
+        return cls(
+            type=type,
+            data=data
+        )
+
+    def to_dict(self) -> Dict:
+        """转换为字典格式"""
+        result = {'type': self.type}
+        if self.type == 'seglist':
+            result['data'] = [seg.to_dict() for seg in self.data]
+        else:
+            result['data'] = self.data
+        return result
+
+@dataclass
+class GroupInfo:
+    """群组信息类"""
+    platform: Optional[str] = None
+    group_id: Optional[int] = None
+    group_name: Optional[str] = None  # 群名称
+
+    def to_dict(self) -> Dict:
+        """转换为字典格式"""
+        return {k: v for k, v in asdict(self).items() if v is not None}
+    
+    @classmethod
+    def from_dict(cls, data: Dict) -> 'GroupInfo':
+        """从字典创建GroupInfo实例
+        
+        Args:
+            data: 包含必要字段的字典
+            
+        Returns:
+            GroupInfo: 新的实例
+        """
+        if data.get('group_id') is None:
+            return None
+        return cls(
+            platform=data.get('platform'),
+            group_id=data.get('group_id'),
+            group_name=data.get('group_name',None)
+        )
+
+@dataclass
+class UserInfo:
+    """用户信息类"""
+    platform: Optional[str] = None
+    user_id: Optional[int] = None
+    user_nickname: Optional[str] = None  # 用户昵称
+    user_cardname: Optional[str] = None  # 用户群昵称
+
+    def to_dict(self) -> Dict:
+        """转换为字典格式"""
+        return {k: v for k, v in asdict(self).items() if v is not None}
+    
+    @classmethod
+    def from_dict(cls, data: Dict) -> 'UserInfo':
+        """从字典创建UserInfo实例
+        
+        Args:
+            data: 包含必要字段的字典
+            
+        Returns:
+            UserInfo: 新的实例
+        """
+        return cls(
+            platform=data.get('platform'),
+            user_id=data.get('user_id'),
+            user_nickname=data.get('user_nickname',None),
+            user_cardname=data.get('user_cardname',None)
+        )
+
+@dataclass
+class BaseMessageInfo:
+    """消息信息类"""
+    platform: Optional[str] = None
+    message_id: Union[str,int,None] = None
+    time: Optional[int] = None
+    group_info: Optional[GroupInfo] = None
+    user_info: Optional[UserInfo] = None
+
+    def to_dict(self) -> Dict:
+        """转换为字典格式"""
+        result = {}
+        for field, value in asdict(self).items():
+            if value is not None:
+                if isinstance(value, (GroupInfo, UserInfo)):
+                    result[field] = value.to_dict()
+                else:
+                    result[field] = value
+        return result
+    @classmethod
+    def from_dict(cls, data: Dict) -> 'BaseMessageInfo':
+        """从字典创建BaseMessageInfo实例
+        
+        Args:
+            data: 包含必要字段的字典
+            
+        Returns:
+            BaseMessageInfo: 新的实例
+        """
+        group_info = GroupInfo.from_dict(data.get('group_info', {}))
+        user_info = UserInfo.from_dict(data.get('user_info', {}))
+        return cls(
+            platform=data.get('platform'),
+            message_id=data.get('message_id'),
+            time=data.get('time'),
+            group_info=group_info,
+            user_info=user_info
+        )
+
+@dataclass
+class MessageBase:
+    """消息类"""
+    message_info: BaseMessageInfo
+    message_segment: Seg
+    raw_message: Optional[str] = None  # 原始消息，包含未解析的cq码
+
+    def to_dict(self) -> Dict:
+        """转换为字典格式
+        
+        Returns:
+            Dict: 包含所有非None字段的字典，其中：
+                - message_info: 转换为字典格式
+                - message_segment: 转换为字典格式
+                - raw_message: 如果存在则包含
+        """
+        result = {
+            'message_info': self.message_info.to_dict(),
+            'message_segment': self.message_segment.to_dict()
+        }
+        if self.raw_message is not None:
+            result['raw_message'] = self.raw_message
+        return result
+
+    @classmethod
+    def from_dict(cls, data: Dict) -> 'MessageBase':
+        """从字典创建MessageBase实例
+        
+        Args:
+            data: 包含必要字段的字典
+            
+        Returns:
+            MessageBase: 新的实例
+        """
+        message_info = BaseMessageInfo.from_dict(data.get('message_info', {}))
+        message_segment = Seg(**data.get('message_segment', {}))
+        raw_message = data.get('raw_message',None)
+        return cls(
+            message_info=message_info,
+            message_segment=message_segment,
+            raw_message=raw_message
+        )
+
+    
+    
diff --git a/src/plugins/chat/message_cq.py b/src/plugins/chat/message_cq.py
new file mode 100644
index 000000000..4c46d3bf2
--- /dev/null
+++ b/src/plugins/chat/message_cq.py
@@ -0,0 +1,164 @@
+import time
+from dataclasses import dataclass
+from typing import Dict, Optional
+
+import urllib3
+
+from .cq_code import cq_code_tool
+from .utils_cq import parse_cq_code
+from .utils_user import get_groupname
+from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
+
+# 禁用SSL警告
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+# 这个类是消息数据类，用于存储和管理消息数据。
+# 它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。
+# 它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。
+
+
+@dataclass
+class MessageCQ(MessageBase):
+    """QQ消息基类，继承自MessageBase
+
+    最小必要参数:
+    - message_id: 消息ID
+    - user_id: 发送者/接收者ID
+    - platform: 平台标识（默认为"qq"）
+    """
+
+    def __init__(
+        self, message_id: int, user_info: UserInfo, group_info: Optional[GroupInfo] = None, platform: str = "qq"
+    ):
+        # 构造基础消息信息
+        message_info = BaseMessageInfo(
+            platform=platform, message_id=message_id, time=int(time.time()), group_info=group_info, user_info=user_info
+        )
+        # 调用父类初始化，message_segment 由子类设置
+        super().__init__(message_info=message_info, message_segment=None, raw_message=None)
+
+
+@dataclass
+class MessageRecvCQ(MessageCQ):
+    """QQ接收消息类，用于解析raw_message到Seg对象"""
+
+    def __init__(
+        self,
+        message_id: int,
+        user_info: UserInfo,
+        raw_message: str,
+        group_info: Optional[GroupInfo] = None,
+        platform: str = "qq",
+        reply_message: Optional[Dict] = None,
+    ):
+        # 调用父类初始化
+        super().__init__(message_id, user_info, group_info, platform)
+
+        # 私聊消息不携带group_info
+        if group_info is None:
+            pass
+
+        elif group_info.group_name is None:
+            group_info.group_name = get_groupname(group_info.group_id)
+
+        # 解析消息段
+        self.message_segment = self._parse_message(raw_message, reply_message)
+        self.raw_message = raw_message
+
+    def _parse_message(self, message: str, reply_message: Optional[Dict] = None) -> Seg:
+        """解析消息内容为Seg对象"""
+        cq_code_dict_list = []
+        segments = []
+
+        start = 0
+        while True:
+            cq_start = message.find("[CQ:", start)
+            if cq_start == -1:
+                if start < len(message):
+                    text = message[start:].strip()
+                    if text:
+                        cq_code_dict_list.append(parse_cq_code(text))
+                break
+
+            if cq_start > start:
+                text = message[start:cq_start].strip()
+                if text:
+                    cq_code_dict_list.append(parse_cq_code(text))
+
+            cq_end = message.find("]", cq_start)
+            if cq_end == -1:
+                text = message[cq_start:].strip()
+                if text:
+                    cq_code_dict_list.append(parse_cq_code(text))
+                break
+
+            cq_code = message[cq_start : cq_end + 1]
+            cq_code_dict_list.append(parse_cq_code(cq_code))
+            start = cq_end + 1
+
+        # 转换CQ码为Seg对象
+        for code_item in cq_code_dict_list:
+            message_obj = cq_code_tool.cq_from_dict_to_class(code_item, msg=self, reply=reply_message)
+            if message_obj.translated_segments:
+                segments.append(message_obj.translated_segments)
+
+        # 如果只有一个segment，直接返回
+        if len(segments) == 1:
+            return segments[0]
+
+        # 否则返回seglist类型的Seg
+        return Seg(type="seglist", data=segments)
+
+    def to_dict(self) -> Dict:
+        """转换为字典格式，包含所有必要信息"""
+        base_dict = super().to_dict()
+        return base_dict
+
+
+@dataclass
+class MessageSendCQ(MessageCQ):
+    """QQ发送消息类，用于将Seg对象转换为raw_message"""
+
+    def __init__(self, data: Dict):
+        # 调用父类初始化
+        message_info = BaseMessageInfo.from_dict(data.get("message_info", {}))
+        message_segment = Seg.from_dict(data.get("message_segment", {}))
+        super().__init__(
+            message_info.message_id,
+            message_info.user_info,
+            message_info.group_info if message_info.group_info else None,
+            message_info.platform,
+        )
+
+        self.message_segment = message_segment
+        self.raw_message = self._generate_raw_message()
+
+    def _generate_raw_message(
+        self,
+    ) -> str:
+        """将Seg对象转换为raw_message"""
+        segments = []
+
+        # 处理消息段
+        if self.message_segment.type == "seglist":
+            for seg in self.message_segment.data:
+                segments.append(self._seg_to_cq_code(seg))
+        else:
+            segments.append(self._seg_to_cq_code(self.message_segment))
+
+        return "".join(segments)
+
+    def _seg_to_cq_code(self, seg: Seg) -> str:
+        """将单个Seg对象转换为CQ码字符串"""
+        if seg.type == "text":
+            return str(seg.data)
+        elif seg.type == "image":
+            return cq_code_tool.create_image_cq_base64(seg.data)
+        elif seg.type == "emoji":
+            return cq_code_tool.create_emoji_cq_base64(seg.data)
+        elif seg.type == "at":
+            return f"[CQ:at,qq={seg.data}]"
+        elif seg.type == "reply":
+            return cq_code_tool.create_reply_cq(int(seg.data))
+        else:
+            return f"[{seg.data}]"
diff --git a/src/plugins/chat/message_sender.py b/src/plugins/chat/message_sender.py
index 050c59d74..5b580f244 100644
--- a/src/plugins/chat/message_sender.py
+++ b/src/plugins/chat/message_sender.py
@@ -2,224 +2,238 @@ import asyncio
 import time
 from typing import Dict, List, Optional, Union
 
+from loguru import logger
 from nonebot.adapters.onebot.v11 import Bot
 
-from .cq_code import cq_code_tool
-from .message import Message, Message_Sending, Message_Thinking, MessageSet
+from .message_cq import MessageSendCQ
+from .message import MessageSending, MessageThinking, MessageRecv, MessageSet
+
 from .storage import MessageStorage
-from .utils import calculate_typing_time
 from .config import global_config
+from .utils import truncate_message
 
 
 class Message_Sender:
     """发送器"""
+
     def __init__(self):
         self.message_interval = (0.5, 1)  # 消息间隔时间范围(秒)
         self.last_send_time = 0
         self._current_bot = None
-        
+
     def set_bot(self, bot: Bot):
         """设置当前bot实例"""
         self._current_bot = bot
-        
-    async def send_group_message(
-        self, 
-        group_id: int, 
-        send_text: str, 
-        auto_escape: bool = False,
-        reply_message_id: int = None,
-        at_user_id: int = None
-    ) -> None:
 
-        if not self._current_bot:
-            raise RuntimeError("Bot未设置，请先调用set_bot方法设置bot实例")
-            
-        message = send_text
-        
-        # 如果需要回复
-        if reply_message_id:
-            reply_cq = cq_code_tool.create_reply_cq(reply_message_id)
-            message = reply_cq + message
-            
-        # 如果需要at
-        # if at_user_id:
-        #     at_cq = cq_code_tool.create_at_cq(at_user_id)
-        #     message = at_cq + " " + message
-        
-        
-        typing_time = calculate_typing_time(message)
-        if typing_time > 10:
-            typing_time = 10
-        await asyncio.sleep(typing_time)
-        
-        # 发送消息
-        try:
-            await self._current_bot.send_group_msg(
-                group_id=group_id,
-                message=message,
-                auto_escape=auto_escape
-            )
-            print(f"\033[1;34m[调试]\033[0m 发送消息{message}成功")
-        except Exception as e:
-            print(f"发生错误 {e}")
-            print(f"\033[1;34m[调试]\033[0m 发送消息{message}失败")
+    async def send_message(
+        self,
+        message: MessageSending,
+    ) -> None:
+        """发送消息"""
+
+        if isinstance(message, MessageSending):
+            message_json = message.to_dict()
+            message_send = MessageSendCQ(data=message_json)
+            # logger.debug(message_send.message_info,message_send.raw_message)
+            message_preview = truncate_message(message.processed_plain_text)
+            if (
+                message_send.message_info.group_info
+                and message_send.message_info.group_info.group_id
+            ):
+                try:
+                    await self._current_bot.send_group_msg(
+                        group_id=message.message_info.group_info.group_id,
+                        message=message_send.raw_message,
+                        auto_escape=False,
+                    )
+                    logger.success(f"[调试] 发送消息“{message_preview}”成功")
+                except Exception as e:
+                    logger.error(f"[调试] 发生错误 {e}")
+                    logger.error(f"[调试] 发送消息“{message_preview}”失败")
+            else:
+                try:
+                    logger.debug(message.message_info.user_info)
+                    await self._current_bot.send_private_msg(
+                        user_id=message.sender_info.user_id,
+                        message=message_send.raw_message,
+                        auto_escape=False,
+                    )
+                    logger.success(f"[调试] 发送消息“{message_preview}”成功")
+                except Exception as e:
+                    logger.error(f"[调试] 发生错误 {e}")
+                    logger.error(f"[调试] 发送消息“{message_preview}”失败")
 
 
 class MessageContainer:
-    """单个群的发送/思考消息容器"""
-    def __init__(self, group_id: int, max_size: int = 100):
-        self.group_id = group_id
+    """单个聊天流的发送/思考消息容器"""
+
+    def __init__(self, chat_id: str, max_size: int = 100):
+        self.chat_id = chat_id
         self.max_size = max_size
         self.messages = []
         self.last_send_time = 0
         self.thinking_timeout = 20  # 思考超时时间（秒）
-        
-    def get_timeout_messages(self) -> List[Message_Sending]:
+
+    def get_timeout_messages(self) -> List[MessageSending]:
         """获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序"""
         current_time = time.time()
         timeout_messages = []
-        
+
         for msg in self.messages:
-            if isinstance(msg, Message_Sending):
+            if isinstance(msg, MessageSending):
                 if current_time - msg.thinking_start_time > self.thinking_timeout:
                     timeout_messages.append(msg)
-                    
+
         # 按thinking_start_time排序，时间早的在前面
         timeout_messages.sort(key=lambda x: x.thinking_start_time)
-                    
+
         return timeout_messages
-        
-    def get_earliest_message(self) -> Optional[Union[Message_Thinking, Message_Sending]]:
+
+    def get_earliest_message(self) -> Optional[Union[MessageThinking, MessageSending]]:
         """获取thinking_start_time最早的消息对象"""
         if not self.messages:
             return None
-        earliest_time = float('inf')
+        earliest_time = float("inf")
         earliest_message = None
-        for msg in self.messages:            
+        for msg in self.messages:
             msg_time = msg.thinking_start_time
             if msg_time < earliest_time:
                 earliest_time = msg_time
-                earliest_message = msg     
+                earliest_message = msg
         return earliest_message
-        
-    def add_message(self, message: Union[Message_Thinking, Message_Sending]) -> None:
+
+    def add_message(self, message: Union[MessageThinking, MessageSending]) -> None:
         """添加消息到队列"""
-        # print(f"\033[1;32m[添加消息]\033[0m 添加消息到对应群")
         if isinstance(message, MessageSet):
             for single_message in message.messages:
                 self.messages.append(single_message)
         else:
             self.messages.append(message)
-            
-    def remove_message(self, message: Union[Message_Thinking, Message_Sending]) -> bool:
+
+    def remove_message(self, message: Union[MessageThinking, MessageSending]) -> bool:
         """移除消息，如果消息存在则返回True，否则返回False"""
         try:
             if message in self.messages:
                 self.messages.remove(message)
                 return True
             return False
-        except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 移除消息时发生错误: {e}")
+        except Exception:
+            logger.exception("移除消息时发生错误")
             return False
-        
+
     def has_messages(self) -> bool:
         """检查是否有待发送的消息"""
         return bool(self.messages)
-        
-    def get_all_messages(self) -> List[Union[Message, Message_Thinking]]:
+
+    def get_all_messages(self) -> List[Union[MessageSending, MessageThinking]]:
         """获取所有消息"""
         return list(self.messages)
-        
+
 
 class MessageManager:
-    """管理所有群的消息容器"""
+    """管理所有聊天流的消息容器"""
+
     def __init__(self):
-        self.containers: Dict[int, MessageContainer] = {}
+        self.containers: Dict[str, MessageContainer] = {}  # chat_id -> MessageContainer
         self.storage = MessageStorage()
         self._running = True
-        
-    def get_container(self, group_id: int) -> MessageContainer:
-        """获取或创建群的消息容器"""
-        if group_id not in self.containers:
-            self.containers[group_id] = MessageContainer(group_id)
-        return self.containers[group_id]
-        
-    def add_message(self, message: Union[Message_Thinking, Message_Sending, MessageSet]) -> None:
-        container = self.get_container(message.group_id)
+
+    def get_container(self, chat_id: str) -> MessageContainer:
+        """获取或创建聊天流的消息容器"""
+        if chat_id not in self.containers:
+            self.containers[chat_id] = MessageContainer(chat_id)
+        return self.containers[chat_id]
+
+    def add_message(
+        self, message: Union[MessageThinking, MessageSending, MessageSet]
+    ) -> None:
+        chat_stream = message.chat_stream
+        if not chat_stream:
+            raise ValueError("无法找到对应的聊天流")
+        container = self.get_container(chat_stream.stream_id)
         container.add_message(message)
-        
-    async def process_group_messages(self, group_id: int):
-        """处理群消息"""
-        # if int(time.time() / 3) == time.time() / 3:
-            # print(f"\033[1;34m[调试]\033[0m 开始处理群{group_id}的消息")
-        container = self.get_container(group_id)
+
+    async def process_chat_messages(self, chat_id: str):
+        """处理聊天流消息"""
+        container = self.get_container(chat_id)
         if container.has_messages():
-            #最早的对象，可能是思考消息，也可能是发送消息
-            message_earliest = container.get_earliest_message() #一个message_thinking or message_sending
-            
-            #如果是思考消息
-            if isinstance(message_earliest, Message_Thinking):
-                #优先等待这条消息
+            # print(f"处理有message的容器chat_id: {chat_id}")
+            message_earliest = container.get_earliest_message()
+
+            if isinstance(message_earliest, MessageThinking):
                 message_earliest.update_thinking_time()
                 thinking_time = message_earliest.thinking_time
-                print(f"\033[1;34m[调试]\033[0m 消息正在思考中，已思考{int(thinking_time)}秒\033[K\r", end='', flush=True)
-                
+                print(
+                    f"消息正在思考中，已思考{int(thinking_time)}秒\r",
+                    end="",
+                    flush=True,
+                )
+
                 # 检查是否超时
                 if thinking_time > global_config.thinking_timeout:
-                    print(f"\033[1;33m[警告]\033[0m 消息思考超时({thinking_time}秒)，移除该消息")
+                    logger.warning(f"消息思考超时({thinking_time}秒)，移除该消息")
                     container.remove_message(message_earliest)
-            else:# 如果不是message_thinking就只能是message_sending    
-                print(f"\033[1;34m[调试]\033[0m 消息'{message_earliest.processed_plain_text}'正在发送中")
-                #直接发，等什么呢
-                if message_earliest.is_head and message_earliest.update_thinking_time() >30:
-                    await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False, reply_message_id=message_earliest.reply_message_id)
+            else:
+
+                if (
+                    message_earliest.is_head
+                    and message_earliest.update_thinking_time() > 30
+                    and not message_earliest.is_private_message()  # 避免在私聊时插入reply
+                ):
+                    await message_sender.send_message(message_earliest.set_reply())
                 else:
-                    await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, auto_escape=False)
-        #移除消息
-                if message_earliest.is_emoji:
-                    message_earliest.processed_plain_text = "[表情包]"
-                await self.storage.store_message(message_earliest, None)
-                
+                    await message_sender.send_message(message_earliest)
+                await message_earliest.process()
+
+                print(
+                    f"\033[1;34m[调试]\033[0m 消息“{truncate_message(message_earliest.processed_plain_text)}”正在发送中"
+                )
+
+                await self.storage.store_message(
+                    message_earliest, message_earliest.chat_stream, None
+                )
+
                 container.remove_message(message_earliest)
-            
-            #获取并处理超时消息
-            message_timeout = container.get_timeout_messages() #也许是一堆message_sending
+
+            message_timeout = container.get_timeout_messages()
             if message_timeout:
-                print(f"\033[1;34m[调试]\033[0m 发现{len(message_timeout)}条超时消息")
+                logger.warning(f"发现{len(message_timeout)}条超时消息")
                 for msg in message_timeout:
                     if msg == message_earliest:
-                        continue  # 跳过已经处理过的消息
-                        
-                    try:
-                        #发送
-                        if msg.is_head and msg.update_thinking_time() >30:
-                            await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False, reply_message_id=msg.reply_message_id)
-                        else:
-                            await message_sender.send_group_message(group_id, msg.processed_plain_text, auto_escape=False)
-                            
-                        
-                        #如果是表情包，则替换为"[表情包]"
-                        if msg.is_emoji:
-                            msg.processed_plain_text = "[表情包]"
-                        await self.storage.store_message(msg, None)
-                        
-                        # 安全地移除消息
-                        if not container.remove_message(msg):
-                            print("\033[1;33m[警告]\033[0m 尝试删除不存在的消息")
-                    except Exception as e:
-                        print(f"\033[1;31m[错误]\033[0m 处理超时消息时发生错误: {e}")
                         continue
-            
+
+                    try:
+                        if (
+                            msg.is_head
+                            and msg.update_thinking_time() > 30
+                            and not message_earliest.is_private_message()  # 避免在私聊时插入reply
+                        ):
+                            await message_sender.send_message(msg.set_reply())
+                        else:
+                            await message_sender.send_message(msg)
+
+                        # if msg.is_emoji:
+                        #     msg.processed_plain_text = "[表情包]"
+                        await msg.process()
+                        await self.storage.store_message(msg, msg.chat_stream, None)
+
+                        if not container.remove_message(msg):
+                            logger.warning("尝试删除不存在的消息")
+                    except Exception:
+                        logger.exception("处理超时消息时发生错误")
+                        continue
+
     async def start_processor(self):
         """启动消息处理器"""
         while self._running:
             await asyncio.sleep(1)
             tasks = []
-            for group_id in self.containers.keys():
-                tasks.append(self.process_group_messages(group_id))
-            
+            for chat_id in self.containers.keys():
+                tasks.append(self.process_chat_messages(chat_id))
+
             await asyncio.gather(*tasks)
 
+
 # 创建全局消息管理器实例
 message_manager = MessageManager()
 # 创建全局发送器实例
diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py
index fdb887af5..a41ed51e2 100644
--- a/src/plugins/chat/prompt_builder.py
+++ b/src/plugins/chat/prompt_builder.py
@@ -1,20 +1,21 @@
 import random
 import time
 from typing import Optional
+from loguru import logger
 
-from ...common.database import Database
+from ...common.database import db
 from ..memory_system.memory import hippocampus, memory_graph
 from ..moods.moods import MoodManager
 from ..schedule.schedule_generator import bot_schedule
 from .config import global_config
 from .utils import get_embedding, get_recent_group_detailed_plain_text
+from .chat_stream import chat_manager
 
 
 class PromptBuilder:
     def __init__(self):
         self.prompt_built = ''
         self.activate_messages = ''
-        self.db = Database.get_instance()
 
 
 
@@ -22,7 +23,7 @@ class PromptBuilder:
                     message_txt: str, 
                     sender_name: str = "某人",
                     relationship_value: float = 0.0,
-                    group_id: Optional[int] = None) -> tuple[str, str]:
+                    stream_id: Optional[int] = None) -> tuple[str, str]:
         """构建prompt
         
         Args:
@@ -33,57 +34,62 @@ class PromptBuilder:
             
         Returns:
             str: 构建好的prompt
-        """        
-        #先禁用关系
+        """
+        # 先禁用关系
         if 0 > 30:
             relation_prompt = "关系特别特别好，你很喜欢喜欢他"
             relation_prompt_2 = "热情发言或者回复"
-        elif 0 <-20:
+        elif 0 < -20:
             relation_prompt = "关系很差，你很讨厌他"
             relation_prompt_2 = "骂他"
         else:
             relation_prompt = "关系一般"
             relation_prompt_2 = "发言或者回复"
-        
-        #开始构建prompt
-        
-        
-        #心情
+
+        # 开始构建prompt
+
+        # 心情
         mood_manager = MoodManager.get_instance()
         mood_prompt = mood_manager.get_prompt()
-        
-        
-        #日程构建
+
+        # 日程构建
         current_date = time.strftime("%Y-%m-%d", time.localtime())
         current_time = time.strftime("%H:%M:%S", time.localtime())
-        bot_schedule_now_time,bot_schedule_now_activity = bot_schedule.get_current_task()
+        bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
         prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
 
-        #知识构建
+        # 知识构建
         start_time = time.time()
-        
+
         prompt_info = ''
         promt_info_prompt = ''
-        prompt_info = await self.get_prompt_info(message_txt,threshold=0.5)
+        prompt_info = await self.get_prompt_info(message_txt, threshold=0.5)
         if prompt_info:
-            prompt_info = f'''\n----------------------------------------------------\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----------------------------------------------------\n'''
-            
+            prompt_info = f'''你有以下这些[知识]：{prompt_info}请你记住上面的[
+            知识]，之后可能会用到-'''
+
         end_time = time.time()
-        print(f"\033[1;32m[知识检索]\033[0m 耗时: {(end_time - start_time):.3f}秒")
-            
+        logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
+
         # 获取聊天上下文
+        chat_in_group=True
         chat_talking_prompt = ''
-        if group_id:
-            chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
-        
-        chat_talking_prompt = f"以下是群里正在聊天的内容：\n{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 = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
+            else:
+                chat_in_group=False
+                chat_talking_prompt = f"以下是你正在和{sender_name}私聊的内容：\n{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}")
         
         
         
         # 使用新的记忆获取方法
         memory_prompt = ''
         start_time = time.time()
-        
+
         # 调用 hippocampus 的 get_relevant_memories 方法
         relevant_memories = await hippocampus.get_relevant_memories(
             text=message_txt,
@@ -91,67 +97,64 @@ class PromptBuilder:
             similarity_threshold=0.4,
             max_memory_num=5
         )
-        
+
         if relevant_memories:
             # 格式化记忆内容
             memory_items = []
             for memory in relevant_memories:
                 memory_items.append(f"关于「{memory['topic']}」的记忆：{memory['content']}")
-            
+
             memory_prompt = "看到这些聊天，你想起来：\n" + "\n".join(memory_items) + "\n"
-            
+
             # 打印调试信息
-            print("\n\033[1;32m[记忆检索]\033[0m 找到以下相关记忆：")
+            logger.debug("[记忆检索]找到以下相关记忆：")
             for memory in relevant_memories:
-                print(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
-        
+                logger.debug(f"- 主题「{memory['topic']}」[相似度: {memory['similarity']:.2f}]: {memory['content']}")
+
         end_time = time.time()
-        print(f"\033[1;32m[回忆耗时]\033[0m 耗时: {(end_time - start_time):.3f}秒")
-        
-        
-            
-        #激活prompt构建
+        logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒")
+
+        # 激活prompt构建
         activate_prompt = ''
-        activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"          
-        
-        #检测机器人相关词汇，改为关键词检测与反应功能了，提取到全局配置中
-        # bot_keywords = ['人机', 'bot', '机器', '入机', 'robot', '机器人']
-        # is_bot = any(keyword in message_txt.lower() for keyword in bot_keywords)
-        # if is_bot:
-        #     is_bot_prompt = '有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认'
-        # else:
-        #     is_bot_prompt = ''
+        if chat_in_group:
+            activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"          
+        else:
+            activate_prompt = f"以上是你正在和{sender_name}私聊的内容，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。"
 
         # 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", [])):
-                    print(f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}")
+                    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", "") + '，'
-
         
         #人格选择
         personality=global_config.PROMPT_PERSONALITY
         probability_1 = global_config.PERSONALITY_1
         probability_2 = global_config.PERSONALITY_2
         probability_3 = global_config.PERSONALITY_3
-        prompt_personality = ''
+        
+        prompt_personality = f'{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{"/".join(global_config.BOT_ALIAS_NAMES)}，'
         personality_choice = random.random()
+        if chat_in_group:
+            prompt_in_group=f"你正在浏览{chat_stream.platform}群"
+        else:
+            prompt_in_group=f"你正在{chat_stream.platform}上和{sender_name}私聊"
         if personality_choice < probability_1:  # 第一种人格
-            prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群,{promt_info_prompt},
+            prompt_personality += f'''{personality[0]}, 你正在浏览qq群,{promt_info_prompt},
             现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{keywords_reaction_prompt}
             请注意把握群里的聊天内容，不要刻意突出自身学科背景，不要回复的太有条理，可以有个性。'''
         elif personality_choice < probability_1 + probability_2:  # 第二种人格
-            prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt},
+            prompt_personality += f'''{personality[1]}, 你正在浏览qq群，{promt_info_prompt},
             现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
             请你表达自己的见解和观点。可以有个性。'''
         else:  # 第三种人格
-            prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt},
+            prompt_personality += f'''{personality[2]}, 你正在浏览qq群，{promt_info_prompt},
             现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{keywords_reaction_prompt}
             请你表达自己的见解和观点。可以有个性。'''
-        
-        #中文高手(新加的好玩功能)
+
+        # 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'
@@ -159,23 +162,23 @@ class PromptBuilder:
             prompt_ger += '你喜欢用反问句'
         if random.random() < 0.01:
             prompt_ger += '你喜欢用文言文'
-        
-        #额外信息要求
-        extra_info = '''但是记得回复平淡一些，简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只需要输出回复内容就好，不要输出其他任何内容''' 
-        
-        #合并prompt
+
+        # 额外信息要求
+        extra_info = '''但是记得回复平淡一些，简短一些，尤其注意在没明确提到时不要过多提及自身的背景, 不要直接回复别人发的表情包，记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只需要输出回复内容就好，不要输出其他任何内容'''
+
+        # 合并prompt
         prompt = ""
         prompt += f"{prompt_info}\n"
         prompt += f"{prompt_date}\n"
-        prompt += f"{chat_talking_prompt}\n"  
+        prompt += f"{chat_talking_prompt}\n"
         prompt += f"{prompt_personality}\n"
         prompt += f"{prompt_ger}\n"
-        prompt += f"{extra_info}\n"    
-        
-        '''读空气prompt处理''' 
-        activate_prompt_check=f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}，但是这不一定是合适的时机，请你决定是否要回应这条消息。"     
+        prompt += f"{extra_info}\n"
+
+        '''读空气prompt处理'''
+        activate_prompt_check = f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}，但是这不一定是合适的时机，请你决定是否要回应这条消息。"
         prompt_personality_check = ''
-        extra_check_info=f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
+        extra_check_info = f"请注意把握群里的聊天内容的基础上，综合群内的氛围，例如，和{global_config.BOT_NICKNAME}相关的话题要积极回复,如果是at自己的消息一定要回复，如果自己正在和别人聊天一定要回复，其他话题如果合适搭话也可以回复，如果认为应该回复请输出yes，否则输出no，请注意是决定是否需要回复，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
         if personality_choice < probability_1:  # 第一种人格
             prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
         elif personality_choice < probability_1 + probability_2:  # 第二种人格
@@ -183,34 +186,36 @@ class PromptBuilder:
         else:  # 第三种人格
             prompt_personality_check = f'''你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt} {activate_prompt_check} {extra_check_info}'''
 
-        prompt_check_if_response=f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}"
-        
-        return prompt,prompt_check_if_response
-    
-    def _build_initiative_prompt_select(self,group_id): 
+        prompt_check_if_response = f"{prompt_info}\n{prompt_date}\n{chat_talking_prompt}\n{prompt_personality_check}"
+
+        return prompt, prompt_check_if_response
+
+    def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1):
         current_date = time.strftime("%Y-%m-%d", time.localtime())
         current_time = time.strftime("%H:%M:%S", time.localtime())
-        bot_schedule_now_time,bot_schedule_now_activity = bot_schedule.get_current_task()
+        bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task()
         prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
 
         chat_talking_prompt = ''
         if group_id:
-            chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
-        
+            chat_talking_prompt = get_recent_group_detailed_plain_text(group_id,
+                                                                       limit=global_config.MAX_CONTEXT_SIZE,
+                                                                       combine=True)
+
         chat_talking_prompt = f"以下是群里正在聊天的内容：\n{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}")
+        # 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}")
 
         # 获取主动发言的话题
-        all_nodes=memory_graph.dots
-        all_nodes=filter(lambda dot:len(dot[1]['memory_items'])>3,all_nodes)
-        nodes_for_select=random.sample(all_nodes,5)
-        topics=[info[0] for info in nodes_for_select]
-        infos=[info[1] for info in nodes_for_select]
+        all_nodes = memory_graph.dots
+        all_nodes = filter(lambda dot: len(dot[1]['memory_items']) > 3, all_nodes)
+        nodes_for_select = random.sample(all_nodes, 5)
+        topics = [info[0] for info in nodes_for_select]
+        infos = [info[1] for info in nodes_for_select]
 
-        #激活prompt构建
+        # 激活prompt构建
         activate_prompt = ''
         activate_prompt = "以上是群里正在进行的聊天。"
-        personality=global_config.PROMPT_PERSONALITY
+        personality = global_config.PROMPT_PERSONALITY
         prompt_personality = ''
         personality_choice = random.random()
         if personality_choice < probability_1:  # 第一种人格
@@ -219,32 +224,31 @@ class PromptBuilder:
             prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}'''
         else:  # 第三种人格
             prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}'''
-        
-        topics_str=','.join(f"\"{topics}\"")
-        prompt_for_select=f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
-        
-        prompt_initiative_select=f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
-        prompt_regular=f"{prompt_date}\n{prompt_personality}"
 
-        return prompt_initiative_select,nodes_for_select,prompt_regular
-    
-    def _build_initiative_prompt_check(self,selected_node,prompt_regular):
-        memory=random.sample(selected_node['memory_items'],3)
-        memory='\n'.join(memory)
-        prompt_for_check=f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
-        return prompt_for_check,memory
-    
-    def _build_initiative_prompt(self,selected_node,prompt_regular,memory):
-        prompt_for_initiative=f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)"
+        topics_str = ','.join(f"\"{topics}\"")
+        prompt_for_select = f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)"
+
+        prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}"
+        prompt_regular = f"{prompt_date}\n{prompt_personality}"
+
+        return prompt_initiative_select, nodes_for_select, prompt_regular
+
+    def _build_initiative_prompt_check(self, selected_node, prompt_regular):
+        memory = random.sample(selected_node['memory_items'], 3)
+        memory = '\n'.join(memory)
+        prompt_for_check = f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，除了yes和no不要输出任何回复内容。"
+        return prompt_for_check, memory
+
+    def _build_initiative_prompt(self, selected_node, prompt_regular, memory):
+        prompt_for_initiative = f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)"
         return prompt_for_initiative
-        
 
-    async def get_prompt_info(self,message:str,threshold:float):
+    async def get_prompt_info(self, message: str, threshold: float):
         related_info = ''
-        print(f"\033[1;34m[调试]\033[0m 获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
+        logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
         embedding = await get_embedding(message)
-        related_info += self.get_info_from_db(embedding,threshold=threshold)
-            
+        related_info += self.get_info_from_db(embedding, threshold=threshold)
+
         return related_info
 
     def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
@@ -305,14 +309,15 @@ class PromptBuilder:
             {"$limit": limit},
             {"$project": {"content": 1, "similarity": 1}}
         ]
-        
-        results = list(self.db.db.knowledges.aggregate(pipeline))
+
+        results = list(db.knowledges.aggregate(pipeline))
         # print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
-        
+
         if not results:
             return ''
-            
+
         # 返回所有找到的内容，用换行分隔
         return '\n'.join(str(result['content']) for result in results)
-    
-prompt_builder = PromptBuilder()
\ No newline at end of file
+
+
+prompt_builder = PromptBuilder()
diff --git a/src/plugins/chat/relationship_manager.py b/src/plugins/chat/relationship_manager.py
index 4ed7a2f11..d604e6734 100644
--- a/src/plugins/chat/relationship_manager.py
+++ b/src/plugins/chat/relationship_manager.py
@@ -1,155 +1,212 @@
 import asyncio
 from typing import Optional
+from loguru import logger
 
-from ...common.database import Database
-
+from ...common.database import db
+from .message_base import UserInfo
+from .chat_stream import ChatStream
 
 class Impression:
     traits: str = None
     called: str = None
     know_time: float = None
-    
+
     relationship_value: float = None
 
+
 class Relationship:
     user_id: int = None
-    # impression: Impression = None
-    # group_id: int = None
-    # group_name: str = None
+    platform: str = None
     gender: str = None
     age: int = None
     nickname: str = None
     relationship_value: float = None
     saved = False
     
-    def __init__(self, user_id: int, data=None, **kwargs):
-        if isinstance(data, dict):
-            # 如果输入是字典，使用字典解析
-            self.user_id = data.get('user_id')
-            self.gender = data.get('gender')
-            self.age = data.get('age')
-            self.nickname = data.get('nickname')
-            self.relationship_value = data.get('relationship_value', 0.0)
-            self.saved = data.get('saved', False)
-        else:
-            # 如果是直接传入属性值
-            self.user_id = kwargs.get('user_id')
-            self.gender = kwargs.get('gender')
-            self.age = kwargs.get('age')
-            self.nickname = kwargs.get('nickname')
-            self.relationship_value = kwargs.get('relationship_value', 0.0)
-            self.saved = kwargs.get('saved', False)
+    def __init__(self, chat:ChatStream=None,data:dict=None):
+        self.user_id=chat.user_info.user_id if chat else data.get('user_id',0)
+        self.platform=chat.platform if chat else data.get('platform','')
+        self.nickname=chat.user_info.user_nickname if chat else data.get('nickname','')
+        self.relationship_value=data.get('relationship_value',0) if data else 0
+        self.age=data.get('age',0) if data else 0
+        self.gender=data.get('gender','') if data else ''
     
 
-        
-
 class RelationshipManager:
     def __init__(self):
-        self.relationships: dict[int, Relationship] = {}  
+        self.relationships: dict[tuple[int, str], Relationship] = {}  # 修改为使用(user_id, platform)作为键
     
-    async def update_relationship(self, user_id: int, data=None, **kwargs):
+    async def update_relationship(self, 
+                                chat_stream:ChatStream,
+                                data: dict = None, 
+                                **kwargs) -> Optional[Relationship]:
+        """更新或创建关系
+        Args:
+            chat_stream: 聊天流对象
+            data: 字典格式的数据（可选）
+            **kwargs: 其他参数
+        Returns:
+            Relationship: 关系对象
+        """
+        # 确定user_id和platform
+        if chat_stream.user_info is not None:
+            user_id = chat_stream.user_info.user_id
+            platform = chat_stream.user_info.platform or 'qq'
+        else:
+            platform = platform or 'qq'
+            
+        if user_id is None:
+            raise ValueError("必须提供user_id或user_info")
+            
+        # 使用(user_id, platform)作为键
+        key = (user_id, platform)
+        
         # 检查是否在内存中已存在
-        relationship = self.relationships.get(user_id)
+        relationship = self.relationships.get(key)
         if relationship:
             # 如果存在，更新现有对象
             if isinstance(data, dict):
-                for key, value in data.items():
-                    if hasattr(relationship, key) and value is not None:
-                        setattr(relationship, key, value)
-            else:
-                for key, value in kwargs.items():
-                    if hasattr(relationship, key) and value is not None:
-                        setattr(relationship, key, value)
+                for k, value in data.items():
+                    if hasattr(relationship, k) and value is not None:
+                        setattr(relationship, k, value)
         else:
             # 如果不存在，创建新对象
-            relationship = Relationship(user_id, data=data) if isinstance(data, dict) else Relationship(user_id, **kwargs)
-            self.relationships[user_id] = relationship
-
-        # 更新 id_name_nickname_table
-        # self.id_name_nickname_table[user_id] = [relationship.nickname]  # 别称设置为空列表
+            if chat_stream.user_info is not None:
+                relationship = Relationship(chat=chat_stream, **kwargs)
+            else:
+                raise ValueError("必须提供user_id或user_info")
+            self.relationships[key] = relationship
 
         # 保存到数据库
         await self.storage_relationship(relationship)
         relationship.saved = True
-        
+
         return relationship
     
-    async def update_relationship_value(self, user_id: int, **kwargs):
+    async def update_relationship_value(self, 
+                                      chat_stream:ChatStream,
+                                      **kwargs) -> Optional[Relationship]:
+        """更新关系值
+        Args:
+            user_id: 用户ID（可选，如果提供user_info则不需要）
+            platform: 平台（可选，如果提供user_info则不需要）
+            user_info: 用户信息对象（可选）
+            **kwargs: 其他参数
+        Returns:
+            Relationship: 关系对象
+        """
+        # 确定user_id和platform
+        user_info = chat_stream.user_info
+        if user_info is not None:
+            user_id = user_info.user_id
+            platform = user_info.platform or 'qq'
+        else:
+            platform = platform or 'qq'
+            
+        if user_id is None:
+            raise ValueError("必须提供user_id或user_info")
+            
+        # 使用(user_id, platform)作为键
+        key = (user_id, platform)
+        
         # 检查是否在内存中已存在
-        relationship = self.relationships.get(user_id)
+        relationship = self.relationships.get(key)
         if relationship:
-            for key, value in kwargs.items():
-                if key == 'relationship_value':
+            for k, value in kwargs.items():
+                if k == 'relationship_value':
                     relationship.relationship_value += value
             await self.storage_relationship(relationship)
             relationship.saved = True
             return relationship
         else:
-            print(f"\033[1;31m[关系管理]\033[0m 用户 {user_id} 不存在，无法更新")
+            # 如果不存在且提供了user_info，则创建新的关系
+            if user_info is not None:
+                return await self.update_relationship(chat_stream=chat_stream, **kwargs)
+            logger.warning(f"[关系管理] 用户 {user_id}({platform}) 不存在，无法更新")
             return None
     
-    
-    def get_relationship(self, user_id: int) -> Optional[Relationship]:
-        """获取用户关系对象"""
-        if user_id in self.relationships:
-            return self.relationships[user_id]
+    def get_relationship(self, 
+                        chat_stream:ChatStream) -> Optional[Relationship]:
+        """获取用户关系对象
+        Args:
+            user_id: 用户ID（可选，如果提供user_info则不需要）
+            platform: 平台（可选，如果提供user_info则不需要）
+            user_info: 用户信息对象（可选）
+        Returns:
+            Relationship: 关系对象
+        """
+        # 确定user_id和platform
+        user_info = chat_stream.user_info
+        platform = chat_stream.user_info.platform or 'qq'
+        if user_info is not None:
+            user_id = user_info.user_id
+            platform = user_info.platform or 'qq'
+        else:
+            platform = platform or 'qq'
+            
+        if user_id is None:
+            raise ValueError("必须提供user_id或user_info")
+            
+        key = (user_id, platform)
+        if key in self.relationships:
+            return self.relationships[key]
         else:
             return 0
-    
+
     async def load_relationship(self, data: dict) -> Relationship:
-        """从数据库加载或创建新的关系对象"""       
-        rela = Relationship(user_id=data['user_id'], data=data)
+        """从数据库加载或创建新的关系对象"""
+        # 确保data中有platform字段，如果没有则默认为'qq'
+        if 'platform' not in data:
+            data['platform'] = 'qq'
+            
+        rela = Relationship(data=data)
         rela.saved = True
-        self.relationships[rela.user_id] = rela
+        key = (rela.user_id, rela.platform)
+        self.relationships[key] = rela
         return rela
-    
+
     async def load_all_relationships(self):
         """加载所有关系对象"""
-        db = Database.get_instance()
-        all_relationships = db.db.relationships.find({})
+        all_relationships = db.relationships.find({})
         for data in all_relationships:
             await self.load_relationship(data)
-    
+
     async def _start_relationship_manager(self):
         """每5分钟自动保存一次关系数据"""
-        db = Database.get_instance()
         # 获取所有关系记录
-        all_relationships = db.db.relationships.find({})
+        all_relationships = db.relationships.find({})
         # 依次加载每条记录
         for data in all_relationships:
-            user_id = data['user_id']
-            relationship = await self.load_relationship(data)
-            self.relationships[user_id] = relationship  
-        print(f"\033[1;32m[关系管理]\033[0m 已加载 {len(self.relationships)} 条关系记录")
+            await self.load_relationship(data)
+        logger.debug(f"[关系管理] 已加载 {len(self.relationships)} 条关系记录")
         
         while True:
-            print("\033[1;32m[关系管理]\033[0m 正在自动保存关系")
+            logger.debug("正在自动保存关系")
             await asyncio.sleep(300)  # 等待300秒(5分钟)
             await self._save_all_relationships()
-    
+
     async def _save_all_relationships(self):
-        """将所有关系数据保存到数据库"""         
+        """将所有关系数据保存到数据库"""
         # 保存所有关系数据
-        for userid, relationship in self.relationships.items():
+        for (userid, platform), relationship in self.relationships.items():
             if not relationship.saved:
                 relationship.saved = True
                 await self.storage_relationship(relationship)
     
-    async def storage_relationship(self,relationship: Relationship):
-        """
-        将关系记录存储到数据库中
-        """
+    async def storage_relationship(self, relationship: Relationship):
+        """将关系记录存储到数据库中"""
         user_id = relationship.user_id
+        platform = relationship.platform
         nickname = relationship.nickname
         relationship_value = relationship.relationship_value
         gender = relationship.gender
         age = relationship.age
         saved = relationship.saved
-        
-        db = Database.get_instance()
-        db.db.relationships.update_one(
-            {'user_id': user_id},
+
+        db.relationships.update_one(
+            {'user_id': user_id, 'platform': platform},
             {'$set': {
+                'platform': platform,
                 'nickname': nickname,
                 'relationship_value': relationship_value,
                 'gender': gender,
@@ -159,14 +216,38 @@ class RelationshipManager:
             upsert=True
         )
         
-    def get_name(self, user_id: int) -> str:
+            
+    def get_name(self, 
+                 user_id: int = None, 
+                 platform: str = None,
+                 user_info: UserInfo = None) -> str:
+        """获取用户昵称
+        Args:
+            user_id: 用户ID（可选，如果提供user_info则不需要）
+            platform: 平台（可选，如果提供user_info则不需要）
+            user_info: 用户信息对象（可选）
+        Returns:
+            str: 用户昵称
+        """
+        # 确定user_id和platform
+        if user_info is not None:
+            user_id = user_info.user_id
+            platform = user_info.platform or 'qq'
+        else:
+            platform = platform or 'qq'
+            
+        if user_id is None:
+            raise ValueError("必须提供user_id或user_info")
+            
         # 确保user_id是整数类型
         user_id = int(user_id)
-        if user_id in self.relationships:
-
-            return self.relationships[user_id].nickname
+        key = (user_id, platform)
+        if key in self.relationships:
+            return self.relationships[key].nickname
+        elif user_info is not None:
+            return user_info.user_nickname or user_info.user_cardname or "某人"
         else:
             return "某人"
 
 
-relationship_manager = RelationshipManager()
\ No newline at end of file
+relationship_manager = RelationshipManager()
diff --git a/src/plugins/chat/storage.py b/src/plugins/chat/storage.py
index 6a87480b7..ad6662f2b 100644
--- a/src/plugins/chat/storage.py
+++ b/src/plugins/chat/storage.py
@@ -1,49 +1,27 @@
-from typing import Optional
+from typing import Optional, Union
 
-from ...common.database import Database
-from .message import Message
+from ...common.database import db
+from .message import MessageSending, MessageRecv
+from .chat_stream import ChatStream
+from loguru import logger
 
 
 class MessageStorage:
-    def __init__(self):
-        self.db = Database.get_instance()
-        
-    async def store_message(self, message: Message, topic: Optional[str] = None) -> None:
+    async def store_message(self, message: Union[MessageSending, MessageRecv],chat_stream:ChatStream, topic: Optional[str] = None) -> None:
         """存储消息到数据库"""
         try:
-            if not message.is_emoji:
-                message_data = {
-                    "group_id": message.group_id,
-                    "user_id": message.user_id,
-                    "message_id": message.message_id,
-                    "raw_message": message.raw_message,
-                    "plain_text": message.plain_text,
+            message_data = {
+                    "message_id": message.message_info.message_id,
+                    "time": message.message_info.time,
+                    "chat_id":chat_stream.stream_id,
+                    "chat_info": chat_stream.to_dict(),
+                    "user_info": message.message_info.user_info.to_dict(),
                     "processed_plain_text": message.processed_plain_text,
-                    "time": message.time,
-                    "user_nickname": message.user_nickname,
-                    "user_cardname": message.user_cardname,
-                    "group_name": message.group_name,
-                    "topic": topic,
                     "detailed_plain_text": message.detailed_plain_text,
-                }
-            else:
-                message_data = {
-                    "group_id": message.group_id,
-                    "user_id": message.user_id,
-                    "message_id": message.message_id,
-                    "raw_message": message.raw_message,
-                    "plain_text": message.plain_text,
-                    "processed_plain_text": '[表情包]',
-                    "time": message.time,
-                    "user_nickname": message.user_nickname,
-                    "user_cardname": message.user_cardname,
-                    "group_name": message.group_name,
                     "topic": topic,
-                    "detailed_plain_text": message.detailed_plain_text,
                 }
-                
-            self.db.db.messages.insert_one(message_data)
-        except Exception as e:
-            print(f"\033[1;31m[错误]\033[0m 存储消息失败: {e}") 
+            db.messages.insert_one(message_data)
+        except Exception:
+            logger.exception("存储消息失败")
 
-# 如果需要其他存储相关的函数，可以在这里添加 
\ No newline at end of file
+# 如果需要其他存储相关的函数，可以在这里添加
diff --git a/src/plugins/chat/topic_identifier.py b/src/plugins/chat/topic_identifier.py
index 3296d0895..a0c5bae30 100644
--- a/src/plugins/chat/topic_identifier.py
+++ b/src/plugins/chat/topic_identifier.py
@@ -4,9 +4,11 @@ from nonebot import get_driver
 
 from ..models.utils_model import LLM_request
 from .config import global_config
+from loguru import logger
 
 driver = get_driver()
-config = driver.config  
+config = driver.config
+
 
 class TopicIdentifier:
     def __init__(self):
@@ -23,19 +25,20 @@ class TopicIdentifier:
 
         # 使用 LLM_request 类进行请求
         topic, _ = await self.llm_topic_judge.generate_response(prompt)
-        
+
         if not topic:
-            print("\033[1;31m[错误]\033[0m LLM API 返回为空")
+            logger.error("LLM API 返回为空")
             return None
-            
+
         # 直接在这里处理主题解析
         if not topic or topic == "无主题":
             return None
-            
+
         # 解析主题字符串为列表
         topic_list = [t.strip() for t in topic.split(",") if t.strip()]
-        
-        print(f"\033[1;32m[主题识别]\033[0m 主题: {topic_list}")
+
+        logger.info(f"主题: {topic_list}")
         return topic_list if topic_list else None
 
-topic_identifier = TopicIdentifier()
\ No newline at end of file
+
+topic_identifier = TopicIdentifier()
diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py
index f8331a148..f28d0e192 100644
--- a/src/plugins/chat/utils.py
+++ b/src/plugins/chat/utils.py
@@ -7,65 +7,45 @@ from typing import Dict, List
 import jieba
 import numpy as np
 from nonebot import get_driver
+from loguru import logger
 
 from ..models.utils_model import LLM_request
 from ..utils.typo_generator import ChineseTypoGenerator
 from .config import global_config
-from .message import Message
+from .message import MessageRecv,Message
+from .message_base import UserInfo
+from .chat_stream import ChatStream
 from ..moods.moods import MoodManager
+from ...common.database import db
 
 driver = get_driver()
 config = driver.config
 
 
-def combine_messages(messages: List[Message]) -> str:
-    """将消息列表组合成格式化的字符串
-    
-    Args:
-        messages: Message对象列表
-        
-    Returns:
-        str: 格式化后的消息字符串
-    """
-    result = ""
-    for message in messages:
-        time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message.time))
-        name = message.user_nickname or f"用户{message.user_id}"
-        content = message.processed_plain_text or message.plain_text
-
-        result += f"[{time_str}] {name}: {content}\n"
-
-    return result
-
 
 def db_message_to_str(message_dict: Dict) -> str:
-    print(f"message_dict: {message_dict}")
+    logger.debug(f"message_dict: {message_dict}")
     time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
     try:
         name = "[(%s)%s]%s" % (
-        message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
+            message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
     except:
         name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
     content = message_dict.get("processed_plain_text", "")
     result = f"[{time_str}] {name}: {content}\n"
-    print(f"result: {result}")
+    logger.debug(f"result: {result}")
     return result
 
 
-def is_mentioned_bot_in_message(message: Message) -> bool:
+def is_mentioned_bot_in_message(message: MessageRecv) -> bool:
     """检查消息是否提到了机器人"""
     keywords = [global_config.BOT_NICKNAME]
+    nicknames = global_config.BOT_ALIAS_NAMES
     for keyword in keywords:
         if keyword in message.processed_plain_text:
             return True
-    return False
-
-
-def is_mentioned_bot_in_txt(message: str) -> bool:
-    """检查消息是否提到了机器人"""
-    keywords = [global_config.BOT_NICKNAME]
-    for keyword in keywords:
-        if keyword in message:
+    for nickname in nicknames:
+        if nickname in message.processed_plain_text:
             return True
     return False
 
@@ -97,51 +77,48 @@ def calculate_information_content(text):
     return entropy
 
 
-def get_cloest_chat_from_db(db, length: int, timestamp: str):
-    """从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数
+def get_closest_chat_from_db(length: int, timestamp: str):
+    """从数据库中获取最接近指定时间戳的聊天记录
     
+    Args:
+        length: 要获取的消息数量
+        timestamp: 时间戳
+        
     Returns:
-        list: 消息记录字典列表，每个字典包含消息内容和时间信息
+        list: 消息记录列表，每个记录包含时间和文本信息
     """
     chat_records = []
-    closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
+    closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
     
-    if closest_record and closest_record.get('memorized', 0) < 4:            
+    if closest_record:            
         closest_time = closest_record['time']
-        group_id = closest_record['group_id']
-        # 获取该时间戳之后的length条消息，且groupid相同
-        records = list(db.db.messages.find(
-            {"time": {"$gt": closest_time}, "group_id": group_id}
+        chat_id = closest_record['chat_id']  # 获取chat_id
+        # 获取该时间戳之后的length条消息，保持相同的chat_id
+        chat_records = list(db.messages.find(
+            {
+                "time": {"$gt": closest_time},
+                "chat_id": chat_id  # 添加chat_id过滤
+            }
         ).sort('time', 1).limit(length))
         
-        # 更新每条消息的memorized属性
-        for record in records:
-            current_memorized = record.get('memorized', 0)
-            if current_memorized > 3:
-                print("消息已读取3次，跳过")
-                return ''
-                
-            # 更新memorized值
-            db.db.messages.update_one(
-                {"_id": record["_id"]},
-                {"$set": {"memorized": current_memorized + 1}}
-            )
-            
-            # 添加到记录列表中
-            chat_records.append({
-                'text': record["detailed_plain_text"],
+        # 转换记录格式
+        formatted_records = []
+        for record in chat_records:
+            formatted_records.append({
                 'time': record["time"],
-                'group_id': record["group_id"]
+                'chat_id': record["chat_id"],
+                'detailed_plain_text': record.get("detailed_plain_text", "")  # 添加文本内容
             })
             
-    return chat_records
+        return formatted_records
+            
+    return []
 
 
-async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
+async def get_recent_group_messages(chat_id:str, limit: int = 12) -> list:
     """从数据库获取群组最近的消息记录
     
     Args:
-        db: Database实例
         group_id: 群组ID
         limit: 获取消息数量，默认12条
         
@@ -150,39 +127,32 @@ async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
     """
 
     # 从数据库获取最近消息
-    recent_messages = list(db.db.messages.find(
-        {"group_id": group_id},
-        # {
-        #     "time": 1,
-        #     "user_id": 1,
-        #     "user_nickname": 1,
-        #     "message_id": 1,
-        #     "raw_message": 1,
-        #     "processed_text": 1
-        # }
+    recent_messages = list(db.messages.find(
+        {"chat_id": chat_id},
     ).sort("time", -1).limit(limit))
 
     if not recent_messages:
         return []
 
     # 转换为 Message对象列表
-    from .message import Message
     message_objects = []
     for msg_data in recent_messages:
         try:
+            chat_info=msg_data.get("chat_info",{})
+            chat_stream=ChatStream.from_dict(chat_info)
+            user_info=msg_data.get("user_info",{})
+            user_info=UserInfo.from_dict(user_info)
             msg = Message(
-                time=msg_data["time"],
-                user_id=msg_data["user_id"],
-                user_nickname=msg_data.get("user_nickname", ""),
                 message_id=msg_data["message_id"],
-                raw_message=msg_data["raw_message"],
+                chat_stream=chat_stream,
+                time=msg_data["time"],
+                user_info=user_info,
                 processed_plain_text=msg_data.get("processed_text", ""),
-                group_id=group_id
+                detailed_plain_text=msg_data.get("detailed_plain_text", "")
             )
-            await msg.initialize()
             message_objects.append(msg)
         except KeyError:
-            print("[WARNING] 数据库中存在无效的消息")
+            logger.warning("数据库中存在无效的消息")
             continue
 
     # 按时间正序排列
@@ -190,13 +160,14 @@ async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
     return message_objects
 
 
-def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12, combine=False):
-    recent_messages = list(db.db.messages.find(
-        {"group_id": group_id},
+def get_recent_group_detailed_plain_text(chat_stream_id: int, limit: int = 12, combine=False):
+    recent_messages = list(db.messages.find(
+        {"chat_id": chat_stream_id},
         {
             "time": 1,  # 返回时间字段
-            "user_id": 1,  # 返回用户ID字段
-            "user_nickname": 1,  # 返回用户昵称字段
+            "chat_id":1,
+            "chat_info":1,
+            "user_info": 1,
             "message_id": 1,  # 返回消息ID字段
             "detailed_plain_text": 1  # 返回处理后的文本字段
         }
@@ -298,11 +269,10 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
             sentence = sentence.replace('，', ' ').replace(',', ' ')
         sentences_done.append(sentence)
 
-    print(f"处理后的句子: {sentences_done}")
+    logger.info(f"处理后的句子: {sentences_done}")
     return sentences_done
 
 
-
 def random_remove_punctuation(text: str) -> str:
     """随机处理标点符号，模拟人类打字习惯
     
@@ -330,11 +300,10 @@ def random_remove_punctuation(text: str) -> str:
     return result
 
 
-
 def process_llm_response(text: str) -> List[str]:
     # processed_response = process_text_with_typos(content)
     if len(text) > 200:
-        print(f"回复过长 ({len(text)} 字符)，返回默认回复")
+        logger.warning(f"回复过长 ({len(text)} 字符)，返回默认回复")
         return ['懒得说']
     # 处理长消息
     typo_generator = ChineseTypoGenerator(
@@ -354,9 +323,9 @@ def process_llm_response(text: str) -> List[str]:
         else:
             sentences.append(sentence)
     # 检查分割后的消息数量是否过多（超过3条）
-    
+
     if len(sentences) > 5:
-        print(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
+        logger.warning(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
         return [f'{global_config.BOT_NICKNAME}不知道哦']
 
     return sentences
@@ -378,15 +347,15 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.4, english_
     mood_arousal = mood_manager.current_mood.arousal
     # 映射到0.5到2倍的速度系数
     typing_speed_multiplier = 1.5 ** mood_arousal  # 唤醒度为1时速度翻倍,为-1时速度减半
-    chinese_time *= 1/typing_speed_multiplier
-    english_time *= 1/typing_speed_multiplier
+    chinese_time *= 1 / typing_speed_multiplier
+    english_time *= 1 / typing_speed_multiplier
     # 计算中文字符数
     chinese_chars = sum(1 for char in input_string if '\u4e00' <= char <= '\u9fff')
-    
+
     # 如果只有一个中文字符，使用3倍时间
     if chinese_chars == 1 and len(input_string.strip()) == 1:
         return chinese_time * 3 + 0.3  # 加上回车时间
-        
+
     # 正常计算所有字符的输入时间
     total_time = 0.0
     for char in input_string:
@@ -436,3 +405,10 @@ def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list:
 
     # 按相似度降序排序并返回前k个
     return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]
+
+
+def truncate_message(message: str, max_length=20) -> str:
+    """截断消息，使其不超过指定长度"""
+    if len(message) > max_length:
+        return message[:max_length] + "..."
+    return message
diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py
index 8a8b3ce5a..dd6d7d4d1 100644
--- a/src/plugins/chat/utils_image.py
+++ b/src/plugins/chat/utils_image.py
@@ -1,296 +1,232 @@
 import base64
-import io
 import os
 import time
-import zlib  # 用于 CRC32
+import aiohttp
+import hashlib
+from typing import Optional, Union
+from PIL import Image
+import io
 
 from loguru import logger
 from nonebot import get_driver
-from PIL import Image
 
-from ...common.database import Database
+from ...common.database import db
 from ..chat.config import global_config
+from ..models.utils_model import LLM_request
 
 driver = get_driver()
 config = driver.config
 
 
+class ImageManager:
+    _instance = None
+    IMAGE_DIR = "data"  # 图像存储根目录
 
-def storage_compress_image(base64_data: str, max_size: int = 200) -> str:
-    """
-    压缩base64格式的图片到指定大小（单位：KB）并在数据库中记录图片信息
-    Args:
-        base64_data: base64编码的图片数据
-        max_size: 最大文件大小（KB）
-    Returns:
-        str: 压缩后的base64图片数据
-    """
-    try:
-        # 将base64转换为字节数据
-        image_data = base64.b64decode(base64_data)
-        
-        # 使用 CRC32 计算哈希值
-        hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x')
-        
-        # 确保图片目录存在
-        images_dir = "data/images"
-        os.makedirs(images_dir, exist_ok=True)
-        
-        # 连接数据库
-        db = Database(
-            host=config.mongodb_host,
-            port=int(config.mongodb_port),
-            db_name=config.database_name,
-            username=config.mongodb_username,
-            password=config.mongodb_password,
-            auth_source=config.mongodb_auth_source
-        )
-        
-        # 检查是否已存在相同哈希值的图片
-        collection = db.db['images']
-        existing_image = collection.find_one({'hash': hash_value})
-        
-        if existing_image:
-            print(f"\033[1;33m[提示]\033[0m 发现重复图片，使用已存在的文件: {existing_image['path']}")
-            return base64_data
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
 
-        # 将字节数据转换为图片对象
-        img = Image.open(io.BytesIO(image_data))
-        
-        # 如果是动图，直接返回原图
-        if getattr(img, 'is_animated', False):
-            return base64_data
-            
-        # 计算当前大小（KB）
-        current_size = len(image_data) / 1024
-        
-        # 如果已经小于目标大小，直接使用原图
-        if current_size <= max_size:
-            compressed_data = image_data
-        else:
-            # 压缩逻辑
-            # 先缩放到50%
-            new_width = int(img.width * 0.5)
-            new_height = int(img.height * 0.5)
-            img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
-            
-            # 如果缩放后的最大边长仍然大于400，继续缩放
-            max_dimension = 400
-            max_current = max(new_width, new_height)
-            if max_current > max_dimension:
-                ratio = max_dimension / max_current
-                new_width = int(new_width * ratio)
-                new_height = int(new_height * ratio)
-                img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
-            
-            # 转换为RGB模式（去除透明通道）
-            if img.mode in ('RGBA', 'P'):
-                img = img.convert('RGB')
-            
-            # 使用固定质量参数压缩
-            output = io.BytesIO()
-            img.save(output, format='JPEG', quality=85, optimize=True)
-            compressed_data = output.getvalue()
-        
-        # 生成文件名（使用时间戳和哈希值确保唯一性）
-        timestamp = int(time.time())
-        filename = f"{timestamp}_{hash_value}.jpg"
-        image_path = os.path.join(images_dir, filename)
-        
-        # 保存文件
-        with open(image_path, "wb") as f:
-            f.write(compressed_data)
-            
-        print(f"\033[1;32m[成功]\033[0m 保存图片到: {image_path}")
-        
+    def __init__(self):
+        if not self._initialized:
+            self._ensure_image_collection()
+            self._ensure_description_collection()
+            self._ensure_image_dir()
+            self._initialized = True
+            self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300)
+
+    def _ensure_image_dir(self):
+        """确保图像存储目录存在"""
+        os.makedirs(self.IMAGE_DIR, exist_ok=True)
+
+    def _ensure_image_collection(self):
+        """确保images集合存在并创建索引"""
+        if "images" not in db.list_collection_names():
+            db.create_collection("images")
+
+        # 删除旧索引
+        db.images.drop_indexes()
+        # 创建新的复合索引
+        db.images.create_index([("hash", 1), ("type", 1)], unique=True)
+        db.images.create_index([("url", 1)])
+        db.images.create_index([("path", 1)])
+
+    def _ensure_description_collection(self):
+        """确保image_descriptions集合存在并创建索引"""
+        if "image_descriptions" not in db.list_collection_names():
+            db.create_collection("image_descriptions")
+
+        # 删除旧索引
+        db.image_descriptions.drop_indexes()
+        # 创建新的复合索引
+        db.image_descriptions.create_index([("hash", 1), ("type", 1)], unique=True)
+
+    def _get_description_from_db(self, image_hash: str, description_type: str) -> Optional[str]:
+        """从数据库获取图片描述
+
+        Args:
+            image_hash: 图片哈希值
+            description_type: 描述类型 ('emoji' 或 'image')
+
+        Returns:
+            Optional[str]: 描述文本，如果不存在则返回None
+        """
+        result = db.image_descriptions.find_one({"hash": image_hash, "type": description_type})
+        return result["description"] if result else None
+
+    def _save_description_to_db(self, image_hash: str, description: str, description_type: str) -> None:
+        """保存图片描述到数据库
+
+        Args:
+            image_hash: 图片哈希值
+            description: 描述文本
+            description_type: 描述类型 ('emoji' 或 'image')
+        """
         try:
-            # 准备数据库记录
-            image_record = {
-                'filename': filename,
-                'path': image_path,
-                'size': len(compressed_data) / 1024,
-                'timestamp': timestamp,
-                'width': img.width,
-                'height': img.height,
-                'description': '',
-                'tags': [],
-                'type': 'image',
-                'hash': hash_value
-            }
-            
-            # 保存记录
-            collection.insert_one(image_record)
-            print("\033[1;32m[成功]\033[0m 保存图片记录到数据库")
-            
-        except Exception as db_error:
-            print(f"\033[1;31m[错误]\033[0m 数据库操作失败: {str(db_error)}")
-        
-        # 将压缩后的数据转换为base64
-        compressed_base64 = base64.b64encode(compressed_data).decode('utf-8')
-        return compressed_base64
-        
-    except Exception as e:
-        print(f"\033[1;31m[错误]\033[0m 压缩图片失败: {str(e)}")
-        import traceback
-        print(traceback.format_exc())
-        return base64_data
-
-def storage_emoji(image_data: bytes) -> bytes:
-    """
-    存储表情包到本地文件夹
-    Args:
-        image_data: 图片字节数据
-        group_id: 群组ID（仅用于日志）
-        user_id: 用户ID（仅用于日志）
-    Returns:
-        bytes: 原始图片数据
-    """
-    if not global_config.EMOJI_SAVE:
-        return image_data
-    try:
-        # 使用 CRC32 计算哈希值
-        hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x')
-        
-        # 确保表情包目录存在
-        emoji_dir = "data/emoji"
-        os.makedirs(emoji_dir, exist_ok=True)
-        
-        # 检查是否已存在相同哈希值的文件
-        for filename in os.listdir(emoji_dir):
-            if hash_value in filename:
-                # print(f"\033[1;33m[提示]\033[0m 发现重复表情包: {filename}")
-                return image_data
-        
-        # 生成文件名
-        timestamp = int(time.time())
-        filename = f"{timestamp}_{hash_value}.jpg"
-        emoji_path = os.path.join(emoji_dir, filename)
-        
-        # 直接保存原始文件
-        with open(emoji_path, "wb") as f:
-            f.write(image_data)
-            
-        print(f"\033[1;32m[成功]\033[0m 保存表情包到: {emoji_path}")
-        return image_data
-        
-    except Exception as e:
-        print(f"\033[1;31m[错误]\033[0m 保存表情包失败: {str(e)}")
-        return image_data 
-    
-
-def storage_image(image_data: bytes) -> bytes:
-    """
-    存储图片到本地文件夹
-    Args:
-        image_data: 图片字节数据
-        group_id: 群组ID（仅用于日志）
-        user_id: 用户ID（仅用于日志）
-    Returns:
-        bytes: 原始图片数据
-    """
-    try:
-        # 使用 CRC32 计算哈希值
-        hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x')
-        
-        # 确保表情包目录存在
-        image_dir = "data/image"
-        os.makedirs(image_dir, exist_ok=True)
-        
-        # 检查是否已存在相同哈希值的文件
-        for filename in os.listdir(image_dir):
-            if hash_value in filename:
-                # print(f"\033[1;33m[提示]\033[0m 发现重复表情包: {filename}")
-                return image_data
-        
-        # 生成文件名
-        timestamp = int(time.time())
-        filename = f"{timestamp}_{hash_value}.jpg"
-        image_path = os.path.join(image_dir, filename)
-        
-        # 直接保存原始文件
-        with open(image_path, "wb") as f:
-            f.write(image_data)
-            
-        print(f"\033[1;32m[成功]\033[0m 保存图片到: {image_path}")
-        return image_data
-        
-    except Exception as e:
-        print(f"\033[1;31m[错误]\033[0m 保存图片失败: {str(e)}")
-        return image_data 
-
-def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
-    """压缩base64格式的图片到指定大小
-    Args:
-        base64_data: base64编码的图片数据
-        target_size: 目标文件大小（字节），默认0.8MB
-    Returns:
-        str: 压缩后的base64图片数据
-    """
-    try:
-        # 将base64转换为字节数据
-        image_data = base64.b64decode(base64_data)
-        
-        # 如果已经小于目标大小，直接返回原图
-        if len(image_data) <= 2*1024*1024:
-            return base64_data
-            
-        # 将字节数据转换为图片对象
-        img = Image.open(io.BytesIO(image_data))
-        
-        # 获取原始尺寸
-        original_width, original_height = img.size
-        
-        # 计算缩放比例
-        scale = min(1.0, (target_size / len(image_data)) ** 0.5)
-        
-        # 计算新的尺寸
-        new_width = int(original_width * scale)
-        new_height = int(original_height * scale)
-        
-        # 创建内存缓冲区
-        output_buffer = io.BytesIO()
-        
-        # 如果是GIF，处理所有帧
-        if getattr(img, "is_animated", False):
-            frames = []
-            for frame_idx in range(img.n_frames):
-                img.seek(frame_idx)
-                new_frame = img.copy()
-                new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折
-                frames.append(new_frame)
-            
-            # 保存到缓冲区
-            frames[0].save(
-                output_buffer,
-                format='GIF',
-                save_all=True,
-                append_images=frames[1:],
-                optimize=True,
-                duration=img.info.get('duration', 100),
-                loop=img.info.get('loop', 0)
+            db.image_descriptions.update_one(
+                {"hash": image_hash, "type": description_type},
+                {
+                    "$set": {
+                        "description": description,
+                        "timestamp": int(time.time()),
+                        "hash": image_hash,  # 确保hash字段存在
+                        "type": description_type,  # 确保type字段存在
+                    }
+                },
+                upsert=True,
             )
-        else:
-            # 处理静态图片
-            resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
-            
-            # 保存到缓冲区，保持原始格式
-            if img.format == 'PNG' and img.mode in ('RGBA', 'LA'):
-                resized_img.save(output_buffer, format='PNG', optimize=True)
-            else:
-                resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True)
-        
-        # 获取压缩后的数据并转换为base64
-        compressed_data = output_buffer.getvalue()
-        logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
-        logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB")
-        
-        return base64.b64encode(compressed_data).decode('utf-8')
-        
-    except Exception as e:
-        logger.error(f"压缩图片失败: {str(e)}")
-        import traceback
-        logger.error(traceback.format_exc())
-        return base64_data 
+        except Exception as e:
+            logger.error(f"保存描述到数据库失败: {str(e)}")
+
+    async def get_emoji_description(self, image_base64: str) -> str:
+        """获取表情包描述，带查重和保存功能"""
+        try:
+            # 计算图片哈希
+            image_bytes = base64.b64decode(image_base64)
+            image_hash = hashlib.md5(image_bytes).hexdigest()
+            image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
+
+            # 查询缓存的描述
+            cached_description = self._get_description_from_db(image_hash, "emoji")
+            if cached_description:
+                logger.info(f"缓存表情包描述: {cached_description}")
+                return f"[表情包：{cached_description}]"
+
+            # 调用AI获取描述
+            prompt = "这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感"
+            description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
+
+            cached_description = self._get_description_from_db(image_hash, "emoji")
+            if cached_description:
+                logger.warning(f"虽然生成了描述，但是找到缓存表情包描述: {cached_description}")
+                return f"[表情包：{cached_description}]"
+
+            # 根据配置决定是否保存图片
+            if global_config.EMOJI_SAVE:
+                # 生成文件名和路径
+                timestamp = int(time.time())
+                filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
+                if not os.path.exists(os.path.join(self.IMAGE_DIR, "emoji")):
+                    os.makedirs(os.path.join(self.IMAGE_DIR, "emoji"))
+                file_path = os.path.join(self.IMAGE_DIR, "emoji", filename)
+
+                try:
+                    # 保存文件
+                    with open(file_path, "wb") as f:
+                        f.write(image_bytes)
+
+                    # 保存到数据库
+                    image_doc = {
+                        "hash": image_hash,
+                        "path": file_path,
+                        "type": "emoji",
+                        "description": description,
+                        "timestamp": timestamp,
+                    }
+                    db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
+                    logger.success(f"保存表情包: {file_path}")
+                except Exception as e:
+                    logger.error(f"保存表情包文件失败: {str(e)}")
+
+            # 保存描述到数据库
+            self._save_description_to_db(image_hash, description, "emoji")
+
+            return f"[表情包：{description}]"
+        except Exception as e:
+            logger.error(f"获取表情包描述失败: {str(e)}")
+            return "[表情包]"
+
+    async def get_image_description(self, image_base64: str) -> str:
+        """获取普通图片描述，带查重和保存功能"""
+        try:
+            # 计算图片哈希
+            image_bytes = base64.b64decode(image_base64)
+            image_hash = hashlib.md5(image_bytes).hexdigest()
+            image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
+
+            # 查询缓存的描述
+            cached_description = self._get_description_from_db(image_hash, "image")
+            if cached_description:
+                logger.info(f"图片描述缓存中 {cached_description}")
+                return f"[图片：{cached_description}]"
+
+            # 调用AI获取描述
+            prompt = (
+                "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。"
+            )
+            description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
+
+            cached_description = self._get_description_from_db(image_hash, "image")
+            if cached_description:
+                logger.warning(f"虽然生成了描述，但是找到缓存图片描述 {cached_description}")
+                return f"[图片：{cached_description}]"
+
+            logger.info(f"描述是{description}")
+
+            if description is None:
+                logger.warning("AI未能生成图片描述")
+                return "[图片]"
+
+            # 根据配置决定是否保存图片
+            if global_config.EMOJI_SAVE:
+                # 生成文件名和路径
+                timestamp = int(time.time())
+                filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
+                if not os.path.exists(os.path.join(self.IMAGE_DIR, "image")):
+                    os.makedirs(os.path.join(self.IMAGE_DIR, "image"))
+                file_path = os.path.join(self.IMAGE_DIR, "image", filename)
+
+                try:
+                    # 保存文件
+                    with open(file_path, "wb") as f:
+                        f.write(image_bytes)
+
+                    # 保存到数据库
+                    image_doc = {
+                        "hash": image_hash,
+                        "path": file_path,
+                        "type": "image",
+                        "description": description,
+                        "timestamp": timestamp,
+                    }
+                    db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
+                    logger.success(f"保存图片: {file_path}")
+                except Exception as e:
+                    logger.error(f"保存图片文件失败: {str(e)}")
+
+            # 保存描述到数据库
+            self._save_description_to_db(image_hash, description, "image")
+
+            return f"[图片：{description}]"
+        except Exception as e:
+            logger.error(f"获取图片描述失败: {str(e)}")
+            return "[图片]"
+
+
+# 创建全局单例
+image_manager = ImageManager()
+
 
 def image_path_to_base64(image_path: str) -> str:
     """将图片路径转换为base64编码
@@ -300,9 +236,9 @@ def image_path_to_base64(image_path: str) -> str:
         str: base64编码的图片数据
     """
     try:
-        with open(image_path, 'rb') as f:
+        with open(image_path, "rb") as f:
             image_data = f.read()
-            return base64.b64encode(image_data).decode('utf-8')
+            return base64.b64encode(image_data).decode("utf-8")
     except Exception as e:
         logger.error(f"读取图片失败: {image_path}, 错误: {str(e)}")
-        return None 
\ No newline at end of file
+        return None
diff --git a/src/plugins/chat/utils_user.py b/src/plugins/chat/utils_user.py
index 489eb7a1d..90c93eeb2 100644
--- a/src/plugins/chat/utils_user.py
+++ b/src/plugins/chat/utils_user.py
@@ -5,14 +5,16 @@ from .relationship_manager import relationship_manager
 def get_user_nickname(user_id: int) -> str:
     if int(user_id) == int(global_config.BOT_QQ):
         return global_config.BOT_NICKNAME
-#     print(user_id)
+    #     print(user_id)
     return relationship_manager.get_name(user_id)
 
+
 def get_user_cardname(user_id: int) -> str:
     if int(user_id) == int(global_config.BOT_QQ):
         return global_config.BOT_NICKNAME
-#     print(user_id)
-    return ''
+    #     print(user_id)
+    return ""
+
 
 def get_groupname(group_id: int) -> str:
-    return f"群{group_id}"
\ No newline at end of file
+    return f"群{group_id}"
diff --git a/src/plugins/chat/willing_manager.py b/src/plugins/chat/willing_manager.py
index 001b66207..6df27f3a4 100644
--- a/src/plugins/chat/willing_manager.py
+++ b/src/plugins/chat/willing_manager.py
@@ -1,86 +1,259 @@
 import asyncio
+import random
+import time
+from typing import Dict
+from loguru import logger
+
+
 from .config import global_config
+from .chat_stream import ChatStream
 
 
 class WillingManager:
     def __init__(self):
-        self.group_reply_willing = {}  # 存储每个群的回复意愿
+        self.chat_reply_willing: Dict[str, float] = {}  # 存储每个聊天流的回复意愿
+        self.chat_high_willing_mode: Dict[str, bool] = {}  # 存储每个聊天流是否处于高回复意愿期
+        self.chat_msg_count: Dict[str, int] = {}  # 存储每个聊天流接收到的消息数量
+        self.chat_last_mode_change: Dict[str, float] = {}  # 存储每个聊天流上次模式切换的时间
+        self.chat_high_willing_duration: Dict[str, int] = {}  # 高意愿期持续时间(秒)
+        self.chat_low_willing_duration: Dict[str, int] = {}  # 低意愿期持续时间(秒)
+        self.chat_last_reply_time: Dict[str, float] = {}  # 存储每个聊天流上次回复的时间
+        self.chat_last_sender_id: Dict[str, str] = {}  # 存储每个聊天流上次回复的用户ID
+        self.chat_conversation_context: Dict[str, bool] = {}  # 标记是否处于对话上下文中
         self._decay_task = None
+        self._mode_switch_task = None
         self._started = False
         
     async def _decay_reply_willing(self):
         """定期衰减回复意愿"""
         while True:
             await asyncio.sleep(5)
-            for group_id in self.group_reply_willing:
-                self.group_reply_willing[group_id] = max(0, self.group_reply_willing[group_id] * 0.6)
+            for chat_id in self.chat_reply_willing:
+                is_high_mode = self.chat_high_willing_mode.get(chat_id, False)
+                if is_high_mode:
+                    # 高回复意愿期内轻微衰减
+                    self.chat_reply_willing[chat_id] = max(0.5, self.chat_reply_willing[chat_id] * 0.95)
+                else:
+                    # 低回复意愿期内正常衰减
+                    self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.8)
                 
-    def get_willing(self, group_id: int) -> float:
-        """获取指定群组的回复意愿"""
-        return self.group_reply_willing.get(group_id, 0)
+    async def _mode_switch_check(self):
+        """定期检查是否需要切换回复意愿模式"""
+        while True:
+            current_time = time.time()
+            await asyncio.sleep(10)  # 每10秒检查一次
+            
+            for chat_id in self.chat_high_willing_mode:
+                last_change_time = self.chat_last_mode_change.get(chat_id, 0)
+                is_high_mode = self.chat_high_willing_mode.get(chat_id, False)
+                
+                # 获取当前模式的持续时间
+                duration = 0
+                if is_high_mode:
+                    duration = self.chat_high_willing_duration.get(chat_id, 180)  # 默认3分钟
+                else:
+                    duration = self.chat_low_willing_duration.get(chat_id, random.randint(300, 1200))  # 默认5-20分钟
+                
+                # 检查是否需要切换模式
+                if current_time - last_change_time > duration:
+                    self._switch_willing_mode(chat_id)
+                elif not is_high_mode and random.random() < 0.1:
+                    # 低回复意愿期有10%概率随机切换到高回复期
+                    self._switch_willing_mode(chat_id)
+                
+                # 检查对话上下文状态是否需要重置
+                last_reply_time = self.chat_last_reply_time.get(chat_id, 0)
+                if current_time - last_reply_time > 300:  # 5分钟无交互，重置对话上下文
+                    self.chat_conversation_context[chat_id] = False
     
-    def set_willing(self, group_id: int, willing: float):
-        """设置指定群组的回复意愿"""
-        self.group_reply_willing[group_id] = willing
+    def _switch_willing_mode(self, chat_id: str):
+        """切换聊天流的回复意愿模式"""
+        is_high_mode = self.chat_high_willing_mode.get(chat_id, False)
         
-    def change_reply_willing_received(self, group_id: int, topic: str, is_mentioned_bot: bool, config, user_id: int = None, is_emoji: bool = False, interested_rate: float = 0) -> float:
-        """改变指定群组的回复意愿并返回回复概率"""
-        current_willing = self.group_reply_willing.get(group_id, 0)
+        if is_high_mode:
+            # 从高回复期切换到低回复期
+            self.chat_high_willing_mode[chat_id] = False
+            self.chat_reply_willing[chat_id] = 0.1  # 设置为最低回复意愿
+            self.chat_low_willing_duration[chat_id] = random.randint(600, 1200)  # 10-20分钟
+            logger.debug(f"聊天流 {chat_id} 切换到低回复意愿期，持续 {self.chat_low_willing_duration[chat_id]} 秒")
+        else:
+            # 从低回复期切换到高回复期
+            self.chat_high_willing_mode[chat_id] = True
+            self.chat_reply_willing[chat_id] = 1.0  # 设置为较高回复意愿
+            self.chat_high_willing_duration[chat_id] = random.randint(180, 240)  # 3-4分钟
+            logger.debug(f"聊天流 {chat_id} 切换到高回复意愿期，持续 {self.chat_high_willing_duration[chat_id]} 秒")
         
-        # print(f"初始意愿: {current_willing}")
-        if is_mentioned_bot and current_willing < 1.0:
-            current_willing += 0.9
-            print(f"被提及, 当前意愿: {current_willing}")
-        elif is_mentioned_bot:
-            current_willing += 0.05
-            print(f"被重复提及, 当前意愿: {current_willing}")
+        self.chat_last_mode_change[chat_id] = time.time()
+        self.chat_msg_count[chat_id] = 0  # 重置消息计数
+                
+    def get_willing(self, chat_stream: ChatStream) -> float:
+        """获取指定聊天流的回复意愿"""
+        stream = chat_stream
+        if stream:
+            return self.chat_reply_willing.get(stream.stream_id, 0)
+        return 0
+    
+    def set_willing(self, chat_id: str, willing: float):
+        """设置指定聊天流的回复意愿"""
+        self.chat_reply_willing[chat_id] = willing
+        
+    def _ensure_chat_initialized(self, chat_id: str):
+        """确保聊天流的所有数据已初始化"""
+        if chat_id not in self.chat_reply_willing:
+            self.chat_reply_willing[chat_id] = 0.1
+        
+        if chat_id not in self.chat_high_willing_mode:
+            self.chat_high_willing_mode[chat_id] = False
+            self.chat_last_mode_change[chat_id] = time.time()
+            self.chat_low_willing_duration[chat_id] = random.randint(300, 1200)  # 5-20分钟
+        
+        if chat_id not in self.chat_msg_count:
+            self.chat_msg_count[chat_id] = 0
+            
+        if chat_id not in self.chat_conversation_context:
+            self.chat_conversation_context[chat_id] = False
+        
+    async def change_reply_willing_received(self, 
+                                          chat_stream: ChatStream,
+                                          topic: str = None,
+                                          is_mentioned_bot: bool = False,
+                                          config = None,
+                                          is_emoji: bool = False,
+                                          interested_rate: float = 0,
+                                          sender_id: str = None) -> float:
+        """改变指定聊天流的回复意愿并返回回复概率"""
+        # 获取或创建聊天流
+        stream = chat_stream
+        chat_id = stream.stream_id
+        current_time = time.time()
+        
+        self._ensure_chat_initialized(chat_id)
+        
+        # 增加消息计数
+        self.chat_msg_count[chat_id] = self.chat_msg_count.get(chat_id, 0) + 1
+        
+        current_willing = self.chat_reply_willing.get(chat_id, 0)
+        is_high_mode = self.chat_high_willing_mode.get(chat_id, False)
+        msg_count = self.chat_msg_count.get(chat_id, 0)
+        in_conversation_context = self.chat_conversation_context.get(chat_id, False)
+        
+        # 检查是否是对话上下文中的追问
+        last_reply_time = self.chat_last_reply_time.get(chat_id, 0)
+        last_sender = self.chat_last_sender_id.get(chat_id, "")
+        is_follow_up_question = False
+        
+        # 如果是同一个人在短时间内（2分钟内）发送消息，且消息数量较少（<=5条），视为追问
+        if sender_id and sender_id == last_sender and current_time - last_reply_time < 120 and msg_count <= 5:
+            is_follow_up_question = True
+            in_conversation_context = True
+            self.chat_conversation_context[chat_id] = True
+            logger.debug(f"检测到追问 (同一用户), 提高回复意愿")
+            current_willing += 0.3
+        
+        # 特殊情况处理
+        if is_mentioned_bot:
+            current_willing += 0.5
+            in_conversation_context = True
+            self.chat_conversation_context[chat_id] = True
+            logger.debug(f"被提及, 当前意愿: {current_willing}")
         
         if is_emoji:
             current_willing *= 0.1
-            print(f"表情包, 当前意愿: {current_willing}")
+            logger.debug(f"表情包, 当前意愿: {current_willing}")
         
-        print(f"放大系数_interested_rate: {global_config.response_interested_rate_amplifier}")
-        interested_rate *= global_config.response_interested_rate_amplifier #放大回复兴趣度
-        if interested_rate > 0.4:
-            # print(f"兴趣度: {interested_rate}, 当前意愿: {current_willing}")
-            current_willing += interested_rate-0.4
-        
-        current_willing *= global_config.response_willing_amplifier #放大回复意愿
-        # print(f"放大系数_willing: {global_config.response_willing_amplifier}, 当前意愿: {current_willing}")
-        
-        reply_probability = max((current_willing - 0.45) * 2, 0)
-        if group_id not in config.talk_allowed_groups:
-            current_willing = 0
-            reply_probability = 0
+        # 根据话题兴趣度适当调整
+        if interested_rate > 0.5:
+            current_willing += (interested_rate - 0.5) * 0.5
             
-        if group_id in config.talk_frequency_down_groups:
-            reply_probability = reply_probability / global_config.down_frequency_rate
+        # 根据当前模式计算回复概率
+        base_probability = 0.0
+        
+        if in_conversation_context:
+            # 在对话上下文中，降低基础回复概率
+            base_probability = 0.5 if is_high_mode else 0.25
+            logger.debug(f"处于对话上下文中，基础回复概率: {base_probability}")
+        elif is_high_mode:
+            # 高回复周期：4-8句话有50%的概率会回复一次
+            base_probability = 0.50 if 4 <= msg_count <= 8 else 0.2
+        else:
+            # 低回复周期：需要最少15句才有30%的概率会回一句
+            base_probability = 0.30 if msg_count >= 15 else 0.03 * min(msg_count, 10)
+            
+        # 考虑回复意愿的影响
+        reply_probability = base_probability * current_willing
+        
+        # 检查群组权限（如果是群聊）
+        if chat_stream.group_info and config:               
+            if chat_stream.group_info.group_id in config.talk_frequency_down_groups:
+                reply_probability = reply_probability / global_config.down_frequency_rate
 
-        reply_probability = min(reply_probability, 1)
+        # 限制最大回复概率
+        reply_probability = min(reply_probability, 0.75)  # 设置最大回复概率为75%
         if reply_probability < 0:
             reply_probability = 0
+        
+        # 记录当前发送者ID以便后续追踪
+        if sender_id:
+            self.chat_last_sender_id[chat_id] = sender_id
             
-            
-        self.group_reply_willing[group_id] = min(current_willing, 3.0)
+        self.chat_reply_willing[chat_id] = min(current_willing, 3.0)
         return reply_probability
     
-    def change_reply_willing_sent(self, group_id: int):
-        """开始思考后降低群组的回复意愿"""
-        current_willing = self.group_reply_willing.get(group_id, 0)
-        self.group_reply_willing[group_id] = max(0, current_willing - 2)
+    def change_reply_willing_sent(self, chat_stream: ChatStream):
+        """开始思考后降低聊天流的回复意愿"""
+        stream = chat_stream
+        if stream:
+            chat_id = stream.stream_id
+            self._ensure_chat_initialized(chat_id)
+            is_high_mode = self.chat_high_willing_mode.get(chat_id, False)
+            current_willing = self.chat_reply_willing.get(chat_id, 0)
+            
+            # 回复后减少回复意愿
+            self.chat_reply_willing[chat_id] = max(0, current_willing - 0.3)
+            
+            # 标记为对话上下文中
+            self.chat_conversation_context[chat_id] = True
+            
+            # 记录最后回复时间
+            self.chat_last_reply_time[chat_id] = time.time()
+            
+            # 重置消息计数
+            self.chat_msg_count[chat_id] = 0
         
-    def change_reply_willing_after_sent(self, group_id: int):
-        """发送消息后提高群组的回复意愿"""
-        current_willing = self.group_reply_willing.get(group_id, 0)
-        if current_willing < 1:
-            self.group_reply_willing[group_id] = min(1, current_willing + 0.2)
+    def change_reply_willing_not_sent(self, chat_stream: ChatStream):
+        """决定不回复后提高聊天流的回复意愿"""
+        stream = chat_stream
+        if stream:
+            chat_id = stream.stream_id
+            self._ensure_chat_initialized(chat_id)
+            is_high_mode = self.chat_high_willing_mode.get(chat_id, False)
+            current_willing = self.chat_reply_willing.get(chat_id, 0)
+            in_conversation_context = self.chat_conversation_context.get(chat_id, False)
+            
+            # 根据当前模式调整不回复后的意愿增加
+            if is_high_mode:
+                willing_increase = 0.1
+            elif in_conversation_context:
+                # 在对话上下文中但决定不回复，小幅增加回复意愿
+                willing_increase = 0.15
+            else:
+                willing_increase = random.uniform(0.05, 0.1)
+                
+            self.chat_reply_willing[chat_id] = min(2.0, current_willing + willing_increase)
+        
+    def change_reply_willing_after_sent(self, chat_stream: ChatStream):
+        """发送消息后提高聊天流的回复意愿"""
+        # 由于已经在sent中处理，这个方法保留但不再需要额外调整
+        pass
         
     async def ensure_started(self):
-        """确保衰减任务已启动"""
+        """确保所有任务已启动"""
         if not self._started:
             if self._decay_task is None:
                 self._decay_task = asyncio.create_task(self._decay_reply_willing())
+            if self._mode_switch_task is None:
+                self._mode_switch_task = asyncio.create_task(self._mode_switch_check())
             self._started = True
 
 # 创建全局实例
-willing_manager = WillingManager() 
+willing_manager = WillingManager() 
\ No newline at end of file
diff --git a/src/plugins/config_reload/__init__.py b/src/plugins/config_reload/__init__.py
new file mode 100644
index 000000000..ddb7fa754
--- /dev/null
+++ b/src/plugins/config_reload/__init__.py
@@ -0,0 +1,10 @@
+from nonebot import get_app
+from .api import router
+from loguru import logger
+
+# 获取主应用实例并挂载路由
+app = get_app()
+app.include_router(router, prefix="/api")
+
+# 打印日志，方便确认API已注册
+logger.success("配置重载API已注册，可通过 /api/reload-config 访问")
\ No newline at end of file
diff --git a/src/plugins/config_reload/api.py b/src/plugins/config_reload/api.py
new file mode 100644
index 000000000..4202ba9bd
--- /dev/null
+++ b/src/plugins/config_reload/api.py
@@ -0,0 +1,17 @@
+from fastapi import APIRouter, HTTPException
+from src.plugins.chat.config import BotConfig
+import os
+
+# 创建APIRouter而不是FastAPI实例
+router = APIRouter()
+
+@router.post("/reload-config")
+async def reload_config():
+    try:
+        bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml")
+        global_config = BotConfig.load_config(config_path=bot_config_path)
+        return {"message": "配置重载成功", "status": "success"}
+    except FileNotFoundError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"重载配置时发生错误: {str(e)}")
\ No newline at end of file
diff --git a/src/plugins/config_reload/test.py b/src/plugins/config_reload/test.py
new file mode 100644
index 000000000..b3b8a9e92
--- /dev/null
+++ b/src/plugins/config_reload/test.py
@@ -0,0 +1,3 @@
+import requests
+response = requests.post("http://localhost:8080/api/reload-config")
+print(response.json())
\ No newline at end of file
diff --git a/src/plugins/knowledege/knowledge_library.py b/src/plugins/knowledege/knowledge_library.py
deleted file mode 100644
index 481076961..000000000
--- a/src/plugins/knowledege/knowledge_library.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import os
-import sys
-import time
-
-import requests
-from dotenv import load_dotenv
-
-# 添加项目根目录到 Python 路径
-root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
-sys.path.append(root_path)
-
-# 加载根目录下的env.edv文件
-env_path = os.path.join(root_path, ".env.dev")
-if not os.path.exists(env_path):
-    raise FileNotFoundError(f"配置文件不存在: {env_path}")
-load_dotenv(env_path)
-
-from src.common.database import Database
-
-# 从环境变量获取配置
-Database.initialize(
-    host=os.getenv("MONGODB_HOST", "127.0.0.1"),
-    port=int(os.getenv("MONGODB_PORT", "27017")),
-    db_name=os.getenv("DATABASE_NAME", "maimai"),
-    username=os.getenv("MONGODB_USERNAME"),
-    password=os.getenv("MONGODB_PASSWORD"),
-    auth_source=os.getenv("MONGODB_AUTH_SOURCE", "admin")
-)
-
-class KnowledgeLibrary:
-    def __init__(self):
-        self.db = Database.get_instance()
-        self.raw_info_dir = "data/raw_info"
-        self._ensure_dirs()
-        self.api_key = os.getenv("SILICONFLOW_KEY")
-        if not self.api_key:
-            raise ValueError("SILICONFLOW_API_KEY 环境变量未设置")
-        
-    def _ensure_dirs(self):
-        """确保必要的目录存在"""
-        os.makedirs(self.raw_info_dir, exist_ok=True)
-        
-    def get_embedding(self, text: str) -> list:
-        """获取文本的embedding向量"""
-        url = "https://api.siliconflow.cn/v1/embeddings"
-        payload = {
-            "model": "BAAI/bge-m3",
-            "input": text,
-            "encoding_format": "float"
-        }
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json"
-        }
-        
-        response = requests.post(url, json=payload, headers=headers)
-        if response.status_code != 200:
-            print(f"获取embedding失败: {response.text}")
-            return None
-            
-        return response.json()['data'][0]['embedding']
-        
-    def process_files(self):
-        """处理raw_info目录下的所有txt文件"""
-        for filename in os.listdir(self.raw_info_dir):
-            if filename.endswith('.txt'):
-                file_path = os.path.join(self.raw_info_dir, filename)
-                self.process_single_file(file_path)
-                
-    def process_single_file(self, file_path: str):
-        """处理单个文件"""
-        try:
-            # 检查文件是否已处理
-            if self.db.db.processed_files.find_one({"file_path": file_path}):
-                print(f"文件已处理过，跳过: {file_path}")
-                return
-                
-            with open(file_path, 'r', encoding='utf-8') as f:
-                content = f.read()
-                
-            # 按1024字符分段
-            segments = [content[i:i+600] for i in range(0, len(content), 600)]
-            
-            # 处理每个分段
-            for segment in segments:
-                if not segment.strip():  # 跳过空段
-                    continue
-                    
-                # 获取embedding
-                embedding = self.get_embedding(segment)
-                if not embedding:
-                    continue
-                    
-                # 存储到数据库
-                doc = {
-                    "content": segment,
-                    "embedding": embedding,
-                    "file_path": file_path,
-                    "segment_length": len(segment)
-                }
-                
-                # 使用文本内容的哈希值作为唯一标识
-                content_hash = hash(segment)
-                
-                # 更新或插入文档
-                self.db.db.knowledges.update_one(
-                    {"content_hash": content_hash},
-                    {"$set": doc},
-                    upsert=True
-                )
-                
-            # 记录文件已处理
-            self.db.db.processed_files.insert_one({
-                "file_path": file_path,
-                "processed_time": time.time()
-            })
-                
-            print(f"成功处理文件: {file_path}")
-            
-        except Exception as e:
-            print(f"处理文件 {file_path} 时出错: {str(e)}")
-            
-    def search_similar_segments(self, query: str, limit: int = 5) -> list:
-        """搜索与查询文本相似的片段"""
-        query_embedding = self.get_embedding(query)
-        if not query_embedding:
-            return []
-            
-        # 使用余弦相似度计算
-        pipeline = [
-            {
-                "$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"]}]
-                    }
-                }
-            },
-            {"$sort": {"similarity": -1}},
-            {"$limit": limit},
-            {"$project": {"content": 1, "similarity": 1, "file_path": 1}}
-        ]
-        
-        results = list(self.db.db.knowledges.aggregate(pipeline))
-        return results
-
-# 创建单例实例
-knowledge_library = KnowledgeLibrary()
-
-if __name__ == "__main__":
-    # 测试知识库功能
-    print("开始处理知识库文件...")
-    knowledge_library.process_files()
-    
-    # 测试搜索功能
-    test_query = "麦麦评价一下僕と花"
-    print(f"\n搜索与'{test_query}'相似的内容:")
-    results = knowledge_library.search_similar_segments(test_query)
-    for result in results:
-        print(f"相似度: {result['similarity']:.4f}")
-        print(f"内容: {result['content'][:100]}...")
-        print("-" * 50)
diff --git a/src/plugins/memory_system/draw_memory.py b/src/plugins/memory_system/draw_memory.py
index c2d04064d..df699f459 100644
--- a/src/plugins/memory_system/draw_memory.py
+++ b/src/plugins/memory_system/draw_memory.py
@@ -7,23 +7,26 @@ import jieba
 import matplotlib.pyplot as plt
 import networkx as nx
 from dotenv import load_dotenv
+from loguru import logger
 
-sys.path.append("C:/GitHub/MaiMBot")  # 添加项目根目录到 Python 路径
-from src.common.database import Database  # 使用正确的导入语法
+# 添加项目根目录到 Python 路径
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.common.database import db  # 使用正确的导入语法
 
 # 加载.env.dev文件
 env_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), '.env.dev')
 load_dotenv(env_path)
 
-   
+
 class Memory_graph:
     def __init__(self):
         self.G = nx.Graph()  # 使用 networkx 的图结构
-        self.db = Database.get_instance()
-        
+
     def connect_dot(self, concept1, concept2):
         self.G.add_edge(concept1, concept2)
-    
+
     def add_dot(self, concept, memory):
         if concept in self.G:
             # 如果节点已存在，将新记忆添加到现有列表中
@@ -37,7 +40,7 @@ class Memory_graph:
         else:
             # 如果是新节点，创建新的记忆列表
             self.G.add_node(concept, memory_items=[memory])
-        
+
     def get_dot(self, concept):
         # 检查节点是否存在于图中
         if concept in self.G:
@@ -45,20 +48,20 @@ class Memory_graph:
             node_data = self.G.nodes[concept]
             # print(node_data)
             # 创建新的Memory_dot对象
-            return concept,node_data
+            return concept, node_data
         return None
 
     def get_related_item(self, topic, depth=1):
         if topic not in self.G:
             return [], []
-            
+
         first_layer_items = []
         second_layer_items = []
-        
+
         # 获取相邻节点
         neighbors = list(self.G.neighbors(topic))
         # print(f"第一层: {topic}")
-        
+
         # 获取当前节点的记忆项
         node_data = self.get_dot(topic)
         if node_data:
@@ -69,7 +72,7 @@ class Memory_graph:
                     first_layer_items.extend(memory_items)
                 else:
                     first_layer_items.append(memory_items)
-        
+
         # 只在depth=2时获取第二层记忆
         if depth >= 2:
             # 获取相邻节点的记忆项
@@ -84,135 +87,128 @@ class Memory_graph:
                             second_layer_items.extend(memory_items)
                         else:
                             second_layer_items.append(memory_items)
-        
+
         return first_layer_items, second_layer_items
-    
+
     def store_memory(self):
         for node in self.G.nodes():
             dot_data = {
                 "concept": node
             }
-            self.db.db.store_memory_dots.insert_one(dot_data)
-    
+            db.store_memory_dots.insert_one(dot_data)
+
     @property
     def dots(self):
         # 返回所有节点对应的 Memory_dot 对象
         return [self.get_dot(node) for node in self.G.nodes()]
-    
-    
+
     def get_random_chat_from_db(self, length: int, timestamp: str):
         # 从数据库中根据时间戳获取离其最近的聊天记录
         chat_text = ''
-        closest_record = self.db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])  # 调试输出
-        print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
-        
+        closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])  # 调试输出
+        logger.info(
+            f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
+
         if closest_record:
             closest_time = closest_record['time']
             group_id = closest_record['group_id']  # 获取groupid
             # 获取该时间戳之后的length条消息，且groupid相同
-            chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
+            chat_record = list(
+                db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(
+                    length))
             for record in chat_record:
                 time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
                 try:
-                    displayname="[(%s)%s]%s" % (record["user_id"],record["user_nickname"],record["user_cardname"])
+                    displayname = "[(%s)%s]%s" % (record["user_id"], record["user_nickname"], record["user_cardname"])
                 except:
-                    displayname=record["user_nickname"] or "用户" + str(record["user_id"])
+                    displayname = record["user_nickname"] or "用户" + str(record["user_id"])
                 chat_text += f'[{time_str}] {displayname}: {record["processed_plain_text"]}\n'  # 添加发送者和时间信息
             return chat_text
-        
+
         return []  # 如果没有找到记录，返回空列表
 
     def save_graph_to_db(self):
         # 清空现有的图数据
-        self.db.db.graph_data.delete_many({})
+        db.graph_data.delete_many({})
         # 保存节点
         for node in self.G.nodes(data=True):
             node_data = {
                 'concept': node[0],
                 'memory_items': node[1].get('memory_items', [])  # 默认为空列表
             }
-            self.db.db.graph_data.nodes.insert_one(node_data)
+            db.graph_data.nodes.insert_one(node_data)
         # 保存边
         for edge in self.G.edges():
             edge_data = {
                 'source': edge[0],
                 'target': edge[1]
             }
-            self.db.db.graph_data.edges.insert_one(edge_data)
+            db.graph_data.edges.insert_one(edge_data)
 
     def load_graph_from_db(self):
         # 清空当前图
         self.G.clear()
         # 加载节点
-        nodes = self.db.db.graph_data.nodes.find()
+        nodes = db.graph_data.nodes.find()
         for node in nodes:
             memory_items = node.get('memory_items', [])
             if not isinstance(memory_items, list):
                 memory_items = [memory_items] if memory_items else []
             self.G.add_node(node['concept'], memory_items=memory_items)
         # 加载边
-        edges = self.db.db.graph_data.edges.find()
+        edges = db.graph_data.edges.find()
         for edge in edges:
             self.G.add_edge(edge['source'], edge['target'])
 
 
 def main():
-    # 初始化数据库
-    Database.initialize(
-        host=os.getenv("MONGODB_HOST", "127.0.0.1"),
-        port=int(os.getenv("MONGODB_PORT", "27017")),
-        db_name=os.getenv("DATABASE_NAME", "MegBot"),
-        username=os.getenv("MONGODB_USERNAME", ""),
-        password=os.getenv("MONGODB_PASSWORD", ""),
-        auth_source=os.getenv("MONGODB_AUTH_SOURCE", "")
-    )
-    
     memory_graph = Memory_graph()
     memory_graph.load_graph_from_db()
-    
+
     # 只显示一次优化后的图形
     visualize_graph_lite(memory_graph)
-    
+
     while True:
         query = input("请输入新的查询概念（输入'退出'以结束）：")
         if query.lower() == '退出':
             break
         first_layer_items, second_layer_items = memory_graph.get_related_item(query)
         if first_layer_items or second_layer_items:
-            print("\n第一层记忆：")
+            logger.debug("第一层记忆：")
             for item in first_layer_items:
-                print(item)
-            print("\n第二层记忆：")
+                logger.debug(item)
+            logger.debug("第二层记忆：")
             for item in second_layer_items:
-                print(item)
+                logger.debug(item)
         else:
-            print("未找到相关记忆。")
-            
+            logger.debug("未找到相关记忆。")
+
 
 def segment_text(text):
     seg_text = list(jieba.cut(text))
-    return seg_text    
+    return seg_text
+
 
 def find_topic(text, topic_num):
     prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。'
     return prompt
 
+
 def topic_what(text, topic):
     prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
     return prompt
 
 
-
 def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
     # 设置中文字体
     plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
     plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
-    
+
     G = memory_graph.G
-    
+
     # 创建一个新图用于可视化
     H = G.copy()
-    
+
     # 移除只有一条记忆的节点和连接数少于3的节点
     nodes_to_remove = []
     for node in H.nodes():
@@ -221,14 +217,14 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
         degree = H.degree(node)
         if memory_count < 3 or degree < 2:  # 改为小于2而不是小于等于2
             nodes_to_remove.append(node)
-    
+
     H.remove_nodes_from(nodes_to_remove)
-    
+
     # 如果过滤后没有节点，则返回
     if len(H.nodes()) == 0:
-        print("过滤后没有符合条件的节点可显示")
+        logger.debug("过滤后没有符合条件的节点可显示")
         return
-    
+
     # 保存图到本地
     # nx.write_gml(H, "memory_graph.gml")  # 保存为 GML 格式
 
@@ -236,7 +232,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
     node_colors = []
     node_sizes = []
     nodes = list(H.nodes())
-    
+
     # 获取最大记忆数和最大度数用于归一化
     max_memories = 1
     max_degree = 1
@@ -246,7 +242,7 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
         degree = H.degree(node)
         max_memories = max(max_memories, memory_count)
         max_degree = max(max_degree, degree)
-    
+
     # 计算每个节点的大小和颜色
     for node in nodes:
         # 计算节点大小（基于记忆数量）
@@ -254,9 +250,9 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
         memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
         # 使用指数函数使变化更明显
         ratio = memory_count / max_memories
-        size = 500 + 5000 * (ratio )  # 使用1.5次方函数使差异不那么明显
+        size = 500 + 5000 * (ratio)  # 使用1.5次方函数使差异不那么明显
         node_sizes.append(size)
-        
+
         # 计算节点颜色（基于连接数）
         degree = H.degree(node)
         # 红色分量随着度数增加而增加
@@ -267,26 +263,25 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
         # blue = 1
         color = (red, 0.1, blue)
         node_colors.append(color)
-    
+
     # 绘制图形
     plt.figure(figsize=(12, 8))
     pos = nx.spring_layout(H, k=1, iterations=50)  # 增加k值使节点分布更开
-    nx.draw(H, pos, 
-           with_labels=True, 
-           node_color=node_colors,
-           node_size=node_sizes,
-           font_size=10,
-           font_family='SimHei',
-           font_weight='bold',
-           edge_color='gray',
-           width=0.5,
-           alpha=0.9)
-    
+    nx.draw(H, pos,
+            with_labels=True,
+            node_color=node_colors,
+            node_size=node_sizes,
+            font_size=10,
+            font_family='SimHei',
+            font_weight='bold',
+            edge_color='gray',
+            width=0.5,
+            alpha=0.9)
+
     title = '记忆图谱可视化 - 节点大小表示记忆数量，颜色表示连接数'
     plt.title(title, fontsize=16, fontfamily='SimHei')
     plt.show()
-    
-    
-    
+
+
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py
index 08fc6d30c..f87f037d5 100644
--- a/src/plugins/memory_system/memory.py
+++ b/src/plugins/memory_system/memory.py
@@ -3,48 +3,69 @@ import datetime
 import math
 import random
 import time
+import os
 
 import jieba
 import networkx as nx
 
-from ...common.database import Database  # 使用正确的导入语法
+from loguru import logger
+from nonebot import get_driver
+from ...common.database import db  # 使用正确的导入语法
 from ..chat.config import global_config
 from ..chat.utils import (
     calculate_information_content,
     cosine_similarity,
-    get_cloest_chat_from_db,
+    get_closest_chat_from_db,
     text_to_vector,
 )
 from ..models.utils_model import LLM_request
 
-
 class Memory_graph:
     def __init__(self):
         self.G = nx.Graph()  # 使用 networkx 的图结构
-        self.db = Database.get_instance()
-        
+
     def connect_dot(self, concept1, concept2):
-        # 如果边已存在，增加 strength
+        # 避免自连接
+        if concept1 == concept2:
+            return
+        
+        current_time = datetime.datetime.now().timestamp()
+        
+        # 如果边已存在,增加 strength
         if self.G.has_edge(concept1, concept2):
             self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
+            # 更新最后修改时间
+            self.G[concept1][concept2]['last_modified'] = current_time
         else:
-            # 如果是新边，初始化 strength 为 1
-            self.G.add_edge(concept1, concept2, strength=1)
-    
+            # 如果是新边,初始化 strength 为 1
+            self.G.add_edge(concept1, concept2, 
+                          strength=1,
+                          created_time=current_time,  # 添加创建时间
+                          last_modified=current_time) # 添加最后修改时间
+
     def add_dot(self, concept, memory):
+        current_time = datetime.datetime.now().timestamp()
+        
         if concept in self.G:
-            # 如果节点已存在，将新记忆添加到现有列表中
             if 'memory_items' in self.G.nodes[concept]:
                 if not isinstance(self.G.nodes[concept]['memory_items'], list):
-                    # 如果当前不是列表，将其转换为列表
                     self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
                 self.G.nodes[concept]['memory_items'].append(memory)
+                # 更新最后修改时间
+                self.G.nodes[concept]['last_modified'] = current_time
             else:
                 self.G.nodes[concept]['memory_items'] = [memory]
+                # 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
+                if 'created_time' not in self.G.nodes[concept]:
+                    self.G.nodes[concept]['created_time'] = current_time
+                self.G.nodes[concept]['last_modified'] = current_time
         else:
-            # 如果是新节点，创建新的记忆列表
-            self.G.add_node(concept, memory_items=[memory])
-        
+            # 如果是新节点,创建新的记忆列表
+            self.G.add_node(concept, 
+                          memory_items=[memory],
+                          created_time=current_time,  # 添加创建时间
+                          last_modified=current_time) # 添加最后修改时间
+
     def get_dot(self, concept):
         # 检查节点是否存在于图中
         if concept in self.G:
@@ -56,13 +77,13 @@ class Memory_graph:
     def get_related_item(self, topic, depth=1):
         if topic not in self.G:
             return [], []
-            
+
         first_layer_items = []
         second_layer_items = []
-        
+
         # 获取相邻节点
         neighbors = list(self.G.neighbors(topic))
-        
+
         # 获取当前节点的记忆项
         node_data = self.get_dot(topic)
         if node_data:
@@ -73,7 +94,7 @@ class Memory_graph:
                     first_layer_items.extend(memory_items)
                 else:
                     first_layer_items.append(memory_items)
-        
+
         # 只在depth=2时获取第二层记忆
         if depth >= 2:
             # 获取相邻节点的记忆项
@@ -87,9 +108,9 @@ class Memory_graph:
                             second_layer_items.extend(memory_items)
                         else:
                             second_layer_items.append(memory_items)
-        
+
         return first_layer_items, second_layer_items
-    
+
     @property
     def dots(self):
         # 返回所有节点对应的 Memory_dot 对象
@@ -99,43 +120,43 @@ class Memory_graph:
         """随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点"""
         if topic not in self.G:
             return None
-            
+
         # 获取话题节点数据
         node_data = self.G.nodes[topic]
-        
+
         # 如果节点存在memory_items
         if 'memory_items' in node_data:
             memory_items = node_data['memory_items']
-            
+
             # 确保memory_items是列表
             if not isinstance(memory_items, list):
                 memory_items = [memory_items] if memory_items else []
-                
+
             # 如果有记忆项可以删除
             if memory_items:
                 # 随机选择一个记忆项删除
                 removed_item = random.choice(memory_items)
                 memory_items.remove(removed_item)
-                
+
                 # 更新节点的记忆项
                 if memory_items:
                     self.G.nodes[topic]['memory_items'] = memory_items
                 else:
                     # 如果没有记忆项了，删除整个节点
                     self.G.remove_node(topic)
-                    
+
                 return removed_item
-        
+
         return None
 
 
-# 海马体 
+# 海马体
 class Hippocampus:
-    def __init__(self,memory_graph:Memory_graph):
+    def __init__(self, memory_graph: Memory_graph):
         self.memory_graph = memory_graph
-        self.llm_topic_judge = LLM_request(model = global_config.llm_topic_judge,temperature=0.5)
-        self.llm_summary_by_topic = LLM_request(model = global_config.llm_summary_by_topic,temperature=0.5)
-        
+        self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, temperature=0.5)
+        self.llm_summary_by_topic = LLM_request(model=global_config.llm_summary_by_topic, temperature=0.5)
+
     def get_all_node_names(self) -> list:
         """获取记忆图中所有节点的名字列表
         
@@ -156,8 +177,8 @@ class Hippocampus:
         """计算边的特征值"""
         nodes = sorted([source, target])
         return hash(f"{nodes[0]}:{nodes[1]}")
-        
-    def get_memory_sample(self, chat_size=20, time_frequency:dict={'near':2,'mid':4,'far':3}):
+
+    def get_memory_sample(self, chat_size=20, time_frequency: dict = {'near': 2, 'mid': 4, 'far': 3}):
         """获取记忆样本
         
         Returns:
@@ -165,51 +186,47 @@ class Hippocampus:
         """
         current_timestamp = datetime.datetime.now().timestamp()
         chat_samples = []
-        
+
         # 短期：1h   中期：4h   长期：24h
         for _ in range(time_frequency.get('near')):
             random_time = current_timestamp - random.randint(1, 3600)
-            messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
             if messages:
                 chat_samples.append(messages)
-                
+
         for _ in range(time_frequency.get('mid')):
-            random_time = current_timestamp - random.randint(3600, 3600*4)
-            messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
+            random_time = current_timestamp - random.randint(3600, 3600 * 4)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
             if messages:
                 chat_samples.append(messages)
-                
+
         for _ in range(time_frequency.get('far')):
-            random_time = current_timestamp - random.randint(3600*4, 3600*24)
-            messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
+            random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
             if messages:
                 chat_samples.append(messages)
-                
+
         return chat_samples
 
     async def memory_compress(self, messages: list, compress_rate=0.1):
         """压缩消息记录为记忆
         
-        Args:
-            messages: 消息记录字典列表，每个字典包含text和time字段
-            compress_rate: 压缩率
-            
         Returns:
-            set: (话题, 记忆) 元组集合
+            tuple: (压缩记忆集合, 相似主题字典)
         """
         if not messages:
-            return set()
-            
+            return set(), {}
+
         # 合并消息文本，同时保留时间信息
         input_text = ""
         time_info = ""
         # 计算最早和最晚时间
         earliest_time = min(msg['time'] for msg in messages)
         latest_time = max(msg['time'] for msg in messages)
-        
+
         earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
         latest_dt = datetime.datetime.fromtimestamp(latest_time)
-        
+
         # 如果是同一年
         if earliest_dt.year == latest_dt.year:
             earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
@@ -217,132 +234,170 @@ class Hippocampus:
             time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
         else:
             earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
-            latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S") 
+            latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
             time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
-        
+
         for msg in messages:
-            input_text += f"{msg['text']}\n"
-            
-        print(input_text)
-        
+            input_text += f"{msg['detailed_plain_text']}\n"
+
+        logger.debug(input_text)
+
         topic_num = self.calculate_topic_num(input_text, compress_rate)
         topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
-        
+
         # 过滤topics
         filter_keywords = global_config.memory_ban_words
-        topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
+        topics = [topic.strip() for topic in
+                  topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
         filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
-        
-        print(f"过滤后话题: {filtered_topics}")
-        
+
+        logger.info(f"过滤后话题: {filtered_topics}")
+
         # 创建所有话题的请求任务
         tasks = []
         for topic in filtered_topics:
             topic_what_prompt = self.topic_what(input_text, topic, time_info)
             task = self.llm_summary_by_topic.generate_response_async(topic_what_prompt)
             tasks.append((topic.strip(), task))
-            
+
         # 等待所有任务完成
         compressed_memory = set()
+        similar_topics_dict = {}  # 存储每个话题的相似主题列表
         for topic, task in tasks:
             response = await task
             if response:
                 compressed_memory.add((topic, response[0]))
+                # 为每个话题查找相似的已存在主题
+                existing_topics = list(self.memory_graph.G.nodes())
+                similar_topics = []
                 
-        return compressed_memory
+                for existing_topic in existing_topics:
+                    topic_words = set(jieba.cut(topic))
+                    existing_words = set(jieba.cut(existing_topic))
+                    
+                    all_words = topic_words | existing_words
+                    v1 = [1 if word in topic_words else 0 for word in all_words]
+                    v2 = [1 if word in existing_words else 0 for word in all_words]
+                    
+                    similarity = cosine_similarity(v1, v2)
+                    
+                    if similarity >= 0.6:
+                        similar_topics.append((existing_topic, similarity))
+                
+                similar_topics.sort(key=lambda x: x[1], reverse=True)
+                similar_topics = similar_topics[:5]
+                similar_topics_dict[topic] = similar_topics
 
-    def calculate_topic_num(self,text, compress_rate):
+        return compressed_memory, similar_topics_dict
+
+    def calculate_topic_num(self, text, compress_rate):
         """计算文本的话题数量"""
         information_content = calculate_information_content(text)
-        topic_by_length = text.count('\n')*compress_rate
-        topic_by_information_content = max(1, min(5, int((information_content-3) * 2)))
-        topic_num = int((topic_by_length + topic_by_information_content)/2)
-        print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}")
+        topic_by_length = text.count('\n') * compress_rate
+        topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
+        topic_num = int((topic_by_length + topic_by_information_content) / 2)
+        logger.debug(
+            f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
+            f"topic_num: {topic_num}")
         return topic_num
 
-    async def operation_build_memory(self,chat_size=20):
-        # 最近消息获取频率
-        time_frequency = {'near':2,'mid':4,'far':2}
-        memory_sample = self.get_memory_sample(chat_size,time_frequency)
+    async def operation_build_memory(self, chat_size=20):
+        time_frequency = {'near': 1, 'mid': 4, 'far': 4}
+        memory_samples = self.get_memory_sample(chat_size, time_frequency)
         
-        for i, input_text in enumerate(memory_sample, 1):
-            # 加载进度可视化
+        for i, messages in enumerate(memory_samples, 1):
             all_topics = []
-            progress = (i / len(memory_sample)) * 100
+            # 加载进度可视化
+            progress = (i / len(memory_samples)) * 100
             bar_length = 30
-            filled_length = int(bar_length * i // len(memory_sample))
+            filled_length = int(bar_length * i // len(memory_samples))
             bar = '█' * filled_length + '-' * (bar_length - filled_length)
-            print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})")
+            logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
 
-            # 生成压缩后记忆 ,表现为 (话题,记忆) 的元组
-            compressed_memory = set()
-            compress_rate = 0.1
-            compressed_memory = await self.memory_compress(input_text, compress_rate)
-            print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}")
+            compress_rate = global_config.memory_compress_rate
+            compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
+            logger.info(f"压缩后记忆数量: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}")
+            
+            current_time = datetime.datetime.now().timestamp()
             
-            # 将记忆加入到图谱中
             for topic, memory in compressed_memory:
-                print(f"\033[1;32m添加节点\033[0m: {topic}")
+                logger.info(f"添加节点: {topic}")
                 self.memory_graph.add_dot(topic, memory)
-                all_topics.append(topic)  # 收集所有话题
+                all_topics.append(topic)
+                
+                # 连接相似的已存在主题
+                if topic in similar_topics_dict:
+                    similar_topics = similar_topics_dict[topic]
+                    for similar_topic, similarity in similar_topics:
+                        if topic != similar_topic:
+                            strength = int(similarity * 10)
+                            logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
+                            self.memory_graph.G.add_edge(topic, similar_topic, 
+                                                       strength=strength,
+                                                       created_time=current_time,
+                                                       last_modified=current_time)
+            
+            # 连接同批次的相关话题
             for i in range(len(all_topics)):
                 for j in range(i + 1, len(all_topics)):
-                    print(f"\033[1;32m连接节点\033[0m: {all_topics[i]} 和 {all_topics[j]}")
+                    logger.info(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
                     self.memory_graph.connect_dot(all_topics[i], all_topics[j])
-                
+
         self.sync_memory_to_db()
 
     def sync_memory_to_db(self):
         """检查并同步内存中的图结构与数据库"""
         # 获取数据库中所有节点和内存中所有节点
-        db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find())
+        db_nodes = list(db.graph_data.nodes.find())
         memory_nodes = list(self.memory_graph.G.nodes(data=True))
-        
-        # 转换数据库节点为字典格式，方便查找
+
+        # 转换数据库节点为字典格式,方便查找
         db_nodes_dict = {node['concept']: node for node in db_nodes}
-        
+
         # 检查并更新节点
         for concept, data in memory_nodes:
             memory_items = data.get('memory_items', [])
             if not isinstance(memory_items, list):
                 memory_items = [memory_items] if memory_items else []
-            
+
             # 计算内存中节点的特征值
             memory_hash = self.calculate_node_hash(concept, memory_items)
-                
+            
+            # 获取时间信息
+            created_time = data.get('created_time', datetime.datetime.now().timestamp())
+            last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
+
             if concept not in db_nodes_dict:
-                # 数据库中缺少的节点，添加
+                # 数据库中缺少的节点,添加
                 node_data = {
                     'concept': concept,
                     'memory_items': memory_items,
-                    'hash': memory_hash
+                    'hash': memory_hash,
+                    'created_time': created_time,
+                    'last_modified': last_modified
                 }
-                self.memory_graph.db.db.graph_data.nodes.insert_one(node_data)
+                db.graph_data.nodes.insert_one(node_data)
             else:
                 # 获取数据库中节点的特征值
                 db_node = db_nodes_dict[concept]
                 db_hash = db_node.get('hash', None)
-                
-                # 如果特征值不同，则更新节点
+
+                # 如果特征值不同,则更新节点
                 if db_hash != memory_hash:
-                    self.memory_graph.db.db.graph_data.nodes.update_one(
+                    db.graph_data.nodes.update_one(
                         {'concept': concept},
                         {'$set': {
                             'memory_items': memory_items,
-                            'hash': memory_hash
+                            'hash': memory_hash,
+                            'created_time': created_time,
+                            'last_modified': last_modified
                         }}
                     )
-                    
-        # 检查并删除数据库中多余的节点
-        memory_concepts = set(node[0] for node in memory_nodes)
-        for db_node in db_nodes:
-            if db_node['concept'] not in memory_concepts:
-                self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']})
-                
+
         # 处理边的信息
-        db_edges = list(self.memory_graph.db.db.graph_data.edges.find())
-        memory_edges = list(self.memory_graph.G.edges())
-        
+        db_edges = list(db.graph_data.edges.find())
+        memory_edges = list(self.memory_graph.G.edges(data=True))
+
         # 创建边的哈希值字典
         db_edge_dict = {}
         for edge in db_edges:
@@ -351,111 +406,197 @@ class Hippocampus:
                 'hash': edge_hash,
                 'strength': edge.get('strength', 1)
             }
-            
+
         # 检查并更新边
-        for source, target in memory_edges:
+        for source, target, data in memory_edges:
             edge_hash = self.calculate_edge_hash(source, target)
             edge_key = (source, target)
-            strength = self.memory_graph.G[source][target].get('strength', 1)
+            strength = data.get('strength', 1)
             
+            # 获取边的时间信息
+            created_time = data.get('created_time', datetime.datetime.now().timestamp())
+            last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
+
             if edge_key not in db_edge_dict:
                 # 添加新边
                 edge_data = {
                     'source': source,
                     'target': target,
                     'strength': strength,
-                    'hash': edge_hash
+                    'hash': edge_hash,
+                    'created_time': created_time,
+                    'last_modified': last_modified
                 }
-                self.memory_graph.db.db.graph_data.edges.insert_one(edge_data)
+                db.graph_data.edges.insert_one(edge_data)
             else:
                 # 检查边的特征值是否变化
                 if db_edge_dict[edge_key]['hash'] != edge_hash:
-                    self.memory_graph.db.db.graph_data.edges.update_one(
+                    db.graph_data.edges.update_one(
                         {'source': source, 'target': target},
                         {'$set': {
                             'hash': edge_hash,
-                            'strength': strength
+                            'strength': strength,
+                            'created_time': created_time,
+                            'last_modified': last_modified
                         }}
                     )
-                    
-        # 删除多余的边
-        memory_edge_set = set(memory_edges)
-        for edge_key in db_edge_dict:
-            if edge_key not in memory_edge_set:
-                source, target = edge_key
-                self.memory_graph.db.db.graph_data.edges.delete_one({
-                    'source': source,
-                    'target': target
-                })
 
     def sync_memory_from_db(self):
         """从数据库同步数据到内存中的图结构"""
+        current_time = datetime.datetime.now().timestamp()
+        need_update = False
+        
         # 清空当前图
         self.memory_graph.G.clear()
-        
+
         # 从数据库加载所有节点
-        nodes = self.memory_graph.db.db.graph_data.nodes.find()
+        nodes = list(db.graph_data.nodes.find())
         for node in nodes:
             concept = node['concept']
             memory_items = node.get('memory_items', [])
-            # 确保memory_items是列表
             if not isinstance(memory_items, list):
                 memory_items = [memory_items] if memory_items else []
-            # 添加节点到图中
-            self.memory_graph.G.add_node(concept, memory_items=memory_items)
             
+            # 检查时间字段是否存在
+            if 'created_time' not in node or 'last_modified' not in node:
+                need_update = True
+                # 更新数据库中的节点
+                update_data = {}
+                if 'created_time' not in node:
+                    update_data['created_time'] = current_time
+                if 'last_modified' not in node:
+                    update_data['last_modified'] = current_time
+                
+                db.graph_data.nodes.update_one(
+                    {'concept': concept},
+                    {'$set': update_data}
+                )
+                logger.info(f"为节点 {concept} 添加缺失的时间字段")
+            
+            # 获取时间信息(如果不存在则使用当前时间)
+            created_time = node.get('created_time', current_time)
+            last_modified = node.get('last_modified', current_time)
+            
+            # 添加节点到图中
+            self.memory_graph.G.add_node(concept, 
+                                       memory_items=memory_items,
+                                       created_time=created_time,
+                                       last_modified=last_modified)
+
         # 从数据库加载所有边
-        edges = self.memory_graph.db.db.graph_data.edges.find()
+        edges = list(db.graph_data.edges.find())
         for edge in edges:
             source = edge['source']
             target = edge['target']
-            strength = edge.get('strength', 1)  # 获取 strength，默认为 1
+            strength = edge.get('strength', 1)
+            
+            # 检查时间字段是否存在
+            if 'created_time' not in edge or 'last_modified' not in edge:
+                need_update = True
+                # 更新数据库中的边
+                update_data = {}
+                if 'created_time' not in edge:
+                    update_data['created_time'] = current_time
+                if 'last_modified' not in edge:
+                    update_data['last_modified'] = current_time
+                
+                db.graph_data.edges.update_one(
+                    {'source': source, 'target': target},
+                    {'$set': update_data}
+                )
+                logger.info(f"为边 {source} - {target} 添加缺失的时间字段")
+            
+            # 获取时间信息(如果不存在则使用当前时间)
+            created_time = edge.get('created_time', current_time)
+            last_modified = edge.get('last_modified', current_time)
+            
             # 只有当源节点和目标节点都存在时才添加边
             if source in self.memory_graph.G and target in self.memory_graph.G:
-                self.memory_graph.G.add_edge(source, target, strength=strength)
+                self.memory_graph.G.add_edge(source, target, 
+                                           strength=strength,
+                                           created_time=created_time,
+                                           last_modified=last_modified)
         
+        if need_update:
+            logger.success("已为缺失的时间字段进行补充")
+
     async def operation_forget_topic(self, percentage=0.1):
-        """随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘"""
-        # 获取所有节点
+        """随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘"""
+        # 检查数据库是否为空
         all_nodes = list(self.memory_graph.G.nodes())
-        # 计算要检查的节点数量
-        check_count = max(1, int(len(all_nodes) * percentage))
-        # 随机选择节点
-        nodes_to_check = random.sample(all_nodes, check_count)
+        all_edges = list(self.memory_graph.G.edges())
         
-        forgotten_nodes = []
+        if not all_nodes and not all_edges:
+            logger.info("记忆图为空,无需进行遗忘操作")
+            return
+            
+        check_nodes_count = max(1, int(len(all_nodes) * percentage))
+        check_edges_count = max(1, int(len(all_edges) * percentage))
+        
+        nodes_to_check = random.sample(all_nodes, check_nodes_count)
+        edges_to_check = random.sample(all_edges, check_edges_count)
+        
+        edge_changes = {'weakened': 0, 'removed': 0}
+        node_changes = {'reduced': 0, 'removed': 0}
+        
+        current_time = datetime.datetime.now().timestamp()
+        
+        # 检查并遗忘连接
+        logger.info("开始检查连接...")
+        for source, target in edges_to_check:
+            edge_data = self.memory_graph.G[source][target]
+            last_modified = edge_data.get('last_modified')
+            # print(source,target)
+            # print(f"float(last_modified):{float(last_modified)}"    )
+            # print(f"current_time:{current_time}")
+            # print(f"current_time - last_modified:{current_time - last_modified}")
+            if current_time - last_modified > 3600*global_config.memory_forget_time:  # test
+                current_strength = edge_data.get('strength', 1)
+                new_strength = current_strength - 1
+                
+                if new_strength <= 0:
+                    self.memory_graph.G.remove_edge(source, target)
+                    edge_changes['removed'] += 1
+                    logger.info(f"\033[1;31m[连接移除]\033[0m {source} - {target}")
+                else:
+                    edge_data['strength'] = new_strength
+                    edge_data['last_modified'] = current_time
+                    edge_changes['weakened'] += 1
+                    logger.info(f"\033[1;34m[连接减弱]\033[0m {source} - {target} (强度: {current_strength} -> {new_strength})")
+        
+        # 检查并遗忘话题
+        logger.info("开始检查节点...")
         for node in nodes_to_check:
-            # 获取节点的连接数
-            connections = self.memory_graph.G.degree(node)
+            node_data = self.memory_graph.G.nodes[node]
+            last_modified = node_data.get('last_modified', current_time)
             
-            # 获取节点的内容条数
-            memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
-            if not isinstance(memory_items, list):
-                memory_items = [memory_items] if memory_items else []
-            content_count = len(memory_items)
-            
-            # 检查连接强度
-            weak_connections = True
-            if connections > 1:  # 只有当连接数大于1时才检查强度
-                for neighbor in self.memory_graph.G.neighbors(node):
-                    strength = self.memory_graph.G[node][neighbor].get('strength', 1)
-                    if strength > 2:
-                        weak_connections = False
-                        break
-            
-            # 如果满足遗忘条件
-            if (connections <= 1 and weak_connections) or content_count <= 2:
-                removed_item = self.memory_graph.forget_topic(node)
-                if removed_item:
-                    forgotten_nodes.append((node, removed_item))
-                    print(f"遗忘节点 {node} 的记忆: {removed_item}")
+            if current_time - last_modified > 3600*24:  # test
+                memory_items = node_data.get('memory_items', [])
+                if not isinstance(memory_items, list):
+                    memory_items = [memory_items] if memory_items else []
+                
+                if memory_items:
+                    current_count = len(memory_items)
+                    removed_item = random.choice(memory_items)
+                    memory_items.remove(removed_item)
+                    
+                    if memory_items:
+                        self.memory_graph.G.nodes[node]['memory_items'] = memory_items
+                        self.memory_graph.G.nodes[node]['last_modified'] = current_time
+                        node_changes['reduced'] += 1
+                        logger.info(f"\033[1;33m[记忆减少]\033[0m {node} (记忆数量: {current_count} -> {len(memory_items)})")
+                    else:
+                        self.memory_graph.G.remove_node(node)
+                        node_changes['removed'] += 1
+                        logger.info(f"\033[1;31m[节点移除]\033[0m {node}")
         
-        # 同步到数据库
-        if forgotten_nodes:
+        if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
             self.sync_memory_to_db()
-            print(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
+            logger.info("\n遗忘操作统计:")
+            logger.info(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
+            logger.info(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
         else:
-            print("本次检查没有节点满足遗忘条件")
+            logger.info("\n本次检查没有节点或连接满足遗忘条件")
 
     async def merge_memory(self, topic):
         """
@@ -468,35 +609,35 @@ class Hippocampus:
         memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
         if not isinstance(memory_items, list):
             memory_items = [memory_items] if memory_items else []
-            
+
         # 如果记忆项不足，直接返回
         if len(memory_items) < 10:
             return
-            
+
         # 随机选择10条记忆
         selected_memories = random.sample(memory_items, 10)
-        
+
         # 拼接成文本
         merged_text = "\n".join(selected_memories)
-        print(f"\n[合并记忆] 话题: {topic}")
-        print(f"选择的记忆:\n{merged_text}")
-        
+        logger.debug(f"\n[合并记忆] 话题: {topic}")
+        logger.debug(f"选择的记忆:\n{merged_text}")
+
         # 使用memory_compress生成新的压缩记忆
-        compressed_memories = await self.memory_compress(selected_memories, 0.1)
-        
+        compressed_memories, _ = await self.memory_compress(selected_memories, 0.1)
+
         # 从原记忆列表中移除被选中的记忆
         for memory in selected_memories:
             memory_items.remove(memory)
-            
+
         # 添加新的压缩记忆
         for _, compressed_memory in compressed_memories:
             memory_items.append(compressed_memory)
-            print(f"添加压缩记忆: {compressed_memory}")
-            
+            logger.info(f"添加压缩记忆: {compressed_memory}")
+
         # 更新节点的记忆项
         self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
-        print(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
-        
+        logger.debug(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
+
     async def operation_merge_memory(self, percentage=0.1):
         """
         随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
@@ -510,7 +651,7 @@ class Hippocampus:
         check_count = max(1, int(len(all_nodes) * percentage))
         # 随机选择节点
         nodes_to_check = random.sample(all_nodes, check_count)
-        
+
         merged_nodes = []
         for node in nodes_to_check:
             # 获取节点的内容条数
@@ -518,25 +659,25 @@ class Hippocampus:
             if not isinstance(memory_items, list):
                 memory_items = [memory_items] if memory_items else []
             content_count = len(memory_items)
-            
+
             # 如果内容数量超过100，进行合并
             if content_count > 100:
-                print(f"\n检查节点: {node}, 当前记忆数量: {content_count}")
+                logger.debug(f"检查节点: {node}, 当前记忆数量: {content_count}")
                 await self.merge_memory(node)
                 merged_nodes.append(node)
-        
+
         # 同步到数据库
         if merged_nodes:
             self.sync_memory_to_db()
-            print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
+            logger.debug(f"完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
         else:
-            print("\n本次检查没有需要合并的节点")
+            logger.debug("本次检查没有需要合并的节点")
 
-    def find_topic_llm(self,text, topic_num):
+    def find_topic_llm(self, text, topic_num):
         prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
         return prompt
 
-    def topic_what(self,text, topic, time_info):
+    def topic_what(self, text, topic, time_info):
         prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
         return prompt
 
@@ -551,11 +692,12 @@ class Hippocampus:
         """
         topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
         # print(f"话题: {topics_response[0]}")
-        topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
+        topics = [topic.strip() for topic in
+                  topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
         # print(f"话题: {topics}")
-                    
+
         return topics
-        
+
     def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
         """查找与给定主题相似的记忆主题
         
@@ -569,16 +711,16 @@ class Hippocampus:
         """
         all_memory_topics = self.get_all_node_names()
         all_similar_topics = []
-        
+
         # 计算每个识别出的主题与记忆主题的相似度
         for topic in topics:
             if debug_info:
                 # print(f"\033[1;32m[{debug_info}]\033[0m 正在思考有没有见过: {topic}")
                 pass
-                
+
             topic_vector = text_to_vector(topic)
             has_similar_topic = False
-            
+
             for memory_topic in all_memory_topics:
                 memory_vector = text_to_vector(memory_topic)
                 # 获取所有唯一词
@@ -588,20 +730,20 @@ class Hippocampus:
                 v2 = [memory_vector.get(word, 0) for word in all_words]
                 # 计算相似度
                 similarity = cosine_similarity(v1, v2)
-                
+
                 if similarity >= similarity_threshold:
                     has_similar_topic = True
                     if debug_info:
                         # print(f"\033[1;32m[{debug_info}]\033[0m 找到相似主题: {topic} -> {memory_topic} (相似度: {similarity:.2f})")
                         pass
                     all_similar_topics.append((memory_topic, similarity))
-                    
+
             if not has_similar_topic and debug_info:
                 # print(f"\033[1;31m[{debug_info}]\033[0m 没有见过: {topic}  ，呃呃")
                 pass
-                
+
         return all_similar_topics
-        
+
     def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
         """获取相似度最高的主题
         
@@ -614,36 +756,36 @@ class Hippocampus:
         """
         seen_topics = set()
         top_topics = []
-        
+
         for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True):
             if topic not in seen_topics and len(top_topics) < max_topics:
                 seen_topics.add(topic)
                 top_topics.append((topic, score))
-                
+
         return top_topics
 
     async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
         """计算输入文本对记忆的激活程度"""
-        print(f"\033[1;32m[记忆激活]\033[0m 识别主题: {await self._identify_topics(text)}")
-        
+        logger.info(f"识别主题: {await self._identify_topics(text)}")
+
         # 识别主题
         identified_topics = await self._identify_topics(text)
         if not identified_topics:
             return 0
-            
+
         # 查找相似主题
         all_similar_topics = self._find_similar_topics(
-            identified_topics, 
+            identified_topics,
             similarity_threshold=similarity_threshold,
             debug_info="记忆激活"
         )
-        
+
         if not all_similar_topics:
             return 0
-            
+
         # 获取最相关的主题
         top_topics = self._get_top_topics(all_similar_topics, max_topics)
-        
+
         # 如果只找到一个主题，进行惩罚
         if len(top_topics) == 1:
             topic, score = top_topics[0]
@@ -653,15 +795,16 @@ class Hippocampus:
                 memory_items = [memory_items] if memory_items else []
             content_count = len(memory_items)
             penalty = 1.0 / (1 + math.log(content_count + 1))
-            
+
             activation = int(score * 50 * penalty)
-            print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
+            logger.info(
+                f"[记忆激活]单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
             return activation
-            
+
         # 计算关键词匹配率，同时考虑内容数量
         matched_topics = set()
         topic_similarities = {}
-        
+
         for memory_topic, similarity in top_topics:
             # 计算内容数量惩罚
             memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', [])
@@ -669,7 +812,7 @@ class Hippocampus:
                 memory_items = [memory_items] if memory_items else []
             content_count = len(memory_items)
             penalty = 1.0 / (1 + math.log(content_count + 1))
-            
+
             # 对每个记忆主题，检查它与哪些输入主题相似
             for input_topic in identified_topics:
                 topic_vector = text_to_vector(input_topic)
@@ -682,33 +825,36 @@ class Hippocampus:
                     matched_topics.add(input_topic)
                     adjusted_sim = sim * penalty
                     topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
-                    print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
-        
+                    logger.info(
+                        f"[记忆激活]主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
+
         # 计算主题匹配率和平均相似度
         topic_match = len(matched_topics) / len(identified_topics)
         average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0
-        
+
         # 计算最终激活值
         activation = int((topic_match + average_similarities) / 2 * 100)
-        print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
-        
+        logger.info(
+            f"[记忆激活]匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
+
         return activation
 
-    async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list:
+    async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4,
+                                    max_memory_num: int = 5) -> list:
         """根据输入文本获取相关的记忆内容"""
         # 识别主题
         identified_topics = await self._identify_topics(text)
-        
+
         # 查找相似主题
         all_similar_topics = self._find_similar_topics(
-            identified_topics, 
+            identified_topics,
             similarity_threshold=similarity_threshold,
             debug_info="记忆检索"
         )
-        
+
         # 获取最相关的主题
         relevant_topics = self._get_top_topics(all_similar_topics, max_topics)
-        
+
         # 获取相关记忆内容
         relevant_memories = []
         for topic, score in relevant_topics:
@@ -716,8 +862,8 @@ class Hippocampus:
             first_layer, _ = self.memory_graph.get_related_item(topic, depth=1)
             if first_layer:
                 # 如果记忆条数超过限制，随机选择指定数量的记忆
-                if len(first_layer) > max_memory_num/2:
-                    first_layer = random.sample(first_layer, max_memory_num//2)
+                if len(first_layer) > max_memory_num / 2:
+                    first_layer = random.sample(first_layer, max_memory_num // 2)
                 # 为每条记忆添加来源主题和相似度信息
                 for memory in first_layer:
                     relevant_memories.append({
@@ -725,43 +871,32 @@ class Hippocampus:
                         'similarity': score,
                         'content': memory
                     })
-                    
+
         # 如果记忆数量超过5个,随机选择5个
         # 按相似度排序
         relevant_memories.sort(key=lambda x: x['similarity'], reverse=True)
-        
+
         if len(relevant_memories) > max_memory_num:
             relevant_memories = random.sample(relevant_memories, max_memory_num)
-        
+
         return relevant_memories
 
 
 def segment_text(text):
     seg_text = list(jieba.cut(text))
-    return seg_text    
-
-
-from nonebot import get_driver
+    return seg_text
 
 driver = get_driver()
 config = driver.config
 
 start_time = time.time()
 
-Database.initialize(
-    host= config.MONGODB_HOST,
-    port= config.MONGODB_PORT,
-    db_name=  config.DATABASE_NAME,
-    username= config.MONGODB_USERNAME,
-    password= config.MONGODB_PASSWORD,
-    auth_source=config.MONGODB_AUTH_SOURCE
-)
-#创建记忆图
+# 创建记忆图
 memory_graph = Memory_graph()
-#创建海马体
+# 创建海马体
 hippocampus = Hippocampus(memory_graph)
-#从数据库加载记忆图
+# 从数据库加载记忆图
 hippocampus.sync_memory_from_db()
 
 end_time = time.time()
-print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
\ No newline at end of file
+logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")
diff --git a/src/plugins/memory_system/memory_manual_build.py b/src/plugins/memory_system/memory_manual_build.py
index db88febf2..2d16998e0 100644
--- a/src/plugins/memory_system/memory_manual_build.py
+++ b/src/plugins/memory_system/memory_manual_build.py
@@ -10,14 +10,16 @@ from pathlib import Path
 
 import matplotlib.pyplot as plt
 import networkx as nx
-import pymongo
 from dotenv import load_dotenv
 from loguru import logger
 import jieba
 
 # from chat.config import global_config
-sys.path.append("C:/GitHub/MaiMBot")  # 添加项目根目录到 Python 路径
-from src.common.database import Database
+# 添加项目根目录到 Python 路径
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+from src.common.database import db
 from src.plugins.memory_system.offline_llm import LLMModel
 
 # 获取当前文件的目录
@@ -35,45 +37,6 @@ else:
     logger.warning(f"未找到环境变量文件: {env_path}")
     logger.info("将使用默认配置")
 
-class Database:
-    _instance = None
-    db = None
-    
-    @classmethod
-    def get_instance(cls):
-        if cls._instance is None:
-            cls._instance = cls()
-        return cls._instance
-    
-    def __init__(self):
-        if not Database.db:
-            Database.initialize(
-                host=os.getenv("MONGODB_HOST"),
-                port=int(os.getenv("MONGODB_PORT")),
-                db_name=os.getenv("DATABASE_NAME"),
-                username=os.getenv("MONGODB_USERNAME"),
-                password=os.getenv("MONGODB_PASSWORD"),
-                auth_source=os.getenv("MONGODB_AUTH_SOURCE")
-            )
-            
-    @classmethod
-    def initialize(cls, host, port, db_name, username=None, password=None, auth_source="admin"):
-        try:
-            if username and password:
-                uri = f"mongodb://{username}:{password}@{host}:{port}/{db_name}?authSource={auth_source}"
-            else:
-                uri = f"mongodb://{host}:{port}"
-                
-            client = pymongo.MongoClient(uri)
-            cls.db = client[db_name]
-            # 测试连接
-            client.server_info()
-            logger.success("MongoDB连接成功!")
-            
-        except Exception as e:
-            logger.error(f"初始化MongoDB失败: {str(e)}")
-            raise
-
 def calculate_information_content(text):
     """计算文本的信息量（熵）"""
     char_count = Counter(text)
@@ -86,20 +49,20 @@ def calculate_information_content(text):
     
     return entropy
 
-def get_cloest_chat_from_db(db, length: int, timestamp: str):
+def get_closest_chat_from_db(length: int, timestamp: str):
     """从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数
     
     Returns:
         list: 消息记录字典列表，每个字典包含消息内容和时间信息
     """
     chat_records = []
-    closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
+    closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
     
     if closest_record and closest_record.get('memorized', 0) < 4:            
         closest_time = closest_record['time']
         group_id = closest_record['group_id']
         # 获取该时间戳之后的length条消息，且groupid相同
-        records = list(db.db.messages.find(
+        records = list(db.messages.find(
             {"time": {"$gt": closest_time}, "group_id": group_id}
         ).sort('time', 1).limit(length))
         
@@ -111,7 +74,7 @@ def get_cloest_chat_from_db(db, length: int, timestamp: str):
                 return ''
                 
             # 更新memorized值
-            db.db.messages.update_one(
+            db.messages.update_one(
                 {"_id": record["_id"]},
                 {"$set": {"memorized": current_memorized + 1}}
             )
@@ -128,7 +91,6 @@ def get_cloest_chat_from_db(db, length: int, timestamp: str):
 class Memory_graph:
     def __init__(self):
         self.G = nx.Graph()  # 使用 networkx 的图结构
-        self.db = Database.get_instance()
         
     def connect_dot(self, concept1, concept2):
         # 如果边已存在，增加 strength
@@ -202,7 +164,7 @@ class Memory_graph:
         # 返回所有节点对应的 Memory_dot 对象
         return [self.get_dot(node) for node in self.G.nodes()]
 
-# 海马体 
+# 海马体
 class Hippocampus:
     def __init__(self, memory_graph: Memory_graph):
         self.memory_graph = memory_graph
@@ -223,19 +185,19 @@ class Hippocampus:
         # 短期：1h   中期：4h   长期：24h
         for _ in range(time_frequency.get('near')):
             random_time = current_timestamp - random.randint(1, 3600*4)
-            messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
             if messages:
                 chat_samples.append(messages)
                 
         for _ in range(time_frequency.get('mid')):
             random_time = current_timestamp - random.randint(3600*4, 3600*24)
-            messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
             if messages:
                 chat_samples.append(messages)
                 
         for _ in range(time_frequency.get('far')):
             random_time = current_timestamp - random.randint(3600*24, 3600*24*7)
-            messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
             if messages:
                 chat_samples.append(messages)
                 
@@ -360,7 +322,7 @@ class Hippocampus:
         self.memory_graph.G.clear()
         
         # 从数据库加载所有节点
-        nodes = self.memory_graph.db.db.graph_data.nodes.find()
+        nodes = db.graph_data.nodes.find()
         for node in nodes:
             concept = node['concept']
             memory_items = node.get('memory_items', [])
@@ -371,7 +333,7 @@ class Hippocampus:
             self.memory_graph.G.add_node(concept, memory_items=memory_items)
             
         # 从数据库加载所有边
-        edges = self.memory_graph.db.db.graph_data.edges.find()
+        edges = db.graph_data.edges.find()
         for edge in edges:
             source = edge['source']
             target = edge['target']
@@ -408,7 +370,7 @@ class Hippocampus:
         使用特征值(哈希值)快速判断是否需要更新
         """
         # 获取数据库中所有节点和内存中所有节点
-        db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find())
+        db_nodes = list(db.graph_data.nodes.find())
         memory_nodes = list(self.memory_graph.G.nodes(data=True))
         
         # 转换数据库节点为字典格式，方便查找
@@ -431,7 +393,7 @@ class Hippocampus:
                     'memory_items': memory_items,
                     'hash': memory_hash
                 }
-                self.memory_graph.db.db.graph_data.nodes.insert_one(node_data)
+                db.graph_data.nodes.insert_one(node_data)
             else:
                 # 获取数据库中节点的特征值
                 db_node = db_nodes_dict[concept]
@@ -440,7 +402,7 @@ class Hippocampus:
                 # 如果特征值不同，则更新节点
                 if db_hash != memory_hash:
                     # logger.info(f"更新节点内容: {concept}")
-                    self.memory_graph.db.db.graph_data.nodes.update_one(
+                    db.graph_data.nodes.update_one(
                         {'concept': concept},
                         {'$set': {
                             'memory_items': memory_items,
@@ -453,10 +415,10 @@ class Hippocampus:
         for db_node in db_nodes:
             if db_node['concept'] not in memory_concepts:
                 # logger.info(f"删除多余节点: {db_node['concept']}")
-                self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']})
+                db.graph_data.nodes.delete_one({'concept': db_node['concept']})
                 
         # 处理边的信息
-        db_edges = list(self.memory_graph.db.db.graph_data.edges.find())
+        db_edges = list(db.graph_data.edges.find())
         memory_edges = list(self.memory_graph.G.edges())
         
         # 创建边的哈希值字典
@@ -482,12 +444,12 @@ class Hippocampus:
                     'num': 1,
                     'hash': edge_hash
                 }
-                self.memory_graph.db.db.graph_data.edges.insert_one(edge_data)
+                db.graph_data.edges.insert_one(edge_data)
             else:
                 # 检查边的特征值是否变化
                 if db_edge_dict[edge_key]['hash'] != edge_hash:
                     logger.info(f"更新边: {source} - {target}")
-                    self.memory_graph.db.db.graph_data.edges.update_one(
+                    db.graph_data.edges.update_one(
                         {'source': source, 'target': target},
                         {'$set': {'hash': edge_hash}}
                     )
@@ -498,7 +460,7 @@ class Hippocampus:
             if edge_key not in memory_edge_set:
                 source, target = edge_key
                 logger.info(f"删除多余边: {source} - {target}")
-                self.memory_graph.db.db.graph_data.edges.delete_one({
+                db.graph_data.edges.delete_one({
                     'source': source,
                     'target': target
                 })
@@ -524,9 +486,9 @@ class Hippocampus:
             topic: 要删除的节点概念
         """
         # 删除节点
-        self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': topic})
+        db.graph_data.nodes.delete_one({'concept': topic})
         # 删除所有涉及该节点的边
-        self.memory_graph.db.db.graph_data.edges.delete_many({
+        db.graph_data.edges.delete_many({
             '$or': [
                 {'source': topic},
                 {'target': topic}
@@ -743,7 +705,7 @@ class Hippocampus:
 
     async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
         """计算输入文本对记忆的激活程度"""
-        print(f"\033[1;32m[记忆激活]\033[0m 识别主题: {await self._identify_topics(text)}")
+        logger.info(f"[记忆激活]识别主题: {await self._identify_topics(text)}")
         
         identified_topics = await self._identify_topics(text)
         if not identified_topics:
@@ -939,61 +901,58 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal
     plt.show()
 
 async def main():
-    # 初始化数据库
-    logger.info("正在初始化数据库连接...")
-    db = Database.get_instance()
     start_time = time.time()
-    
+
     test_pare = {'do_build_memory':False,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False}
-    
+
     # 创建记忆图
     memory_graph = Memory_graph()
-    
+
     # 创建海马体
     hippocampus = Hippocampus(memory_graph)
-    
+
     # 从数据库同步数据
     hippocampus.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")
-    
+
     # 构建记忆
     if test_pare['do_build_memory']:
         logger.info("开始构建记忆...")
         chat_size = 20
         await hippocampus.operation_build_memory(chat_size=chat_size)
-        
+
         end_time = time.time()
         logger.info(f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m")
-        
+
     if test_pare['do_forget_topic']:
         logger.info("开始遗忘记忆...")
         await hippocampus.operation_forget_topic(percentage=0.1)
-        
+
         end_time = time.time()
         logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
-        
+
     if test_pare['do_merge_memory']:
         logger.info("开始合并记忆...")
         await hippocampus.operation_merge_memory(percentage=0.1)
-        
+
         end_time = time.time()
         logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
-    
+
     if test_pare['do_visualize_graph']:
         # 展示优化后的图形
         logger.info("生成记忆图谱可视化...")
         print("\n生成优化后的记忆图谱：")
         visualize_graph_lite(memory_graph)
-    
+
     if test_pare['do_query']:
         # 交互式查询
         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)
             if items_list:
                 first_layer, second_layer = items_list
@@ -1008,9 +967,6 @@ async def main():
             else:
                 print("未找到相关记忆。")
 
-
 if __name__ == "__main__":
     import asyncio
     asyncio.run(main())
-
-    
diff --git a/src/plugins/memory_system/memory_test1.py b/src/plugins/memory_system/memory_test1.py
new file mode 100644
index 000000000..245eb9b26
--- /dev/null
+++ b/src/plugins/memory_system/memory_test1.py
@@ -0,0 +1,1168 @@
+# -*- coding: utf-8 -*-
+import datetime
+import math
+import os
+import random
+import sys
+import time
+from collections import Counter
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import networkx as nx
+import pymongo
+from dotenv import load_dotenv
+from loguru import logger
+import jieba
+
+'''
+该理论认为，当两个或多个事物在形态上具有相似性时，
+它们在记忆中会形成关联。
+例如，梨和苹果在形状和都是水果这一属性上有相似性，
+所以当我们看到梨时，很容易通过形态学联想记忆联想到苹果。
+这种相似性联想有助于我们对新事物进行分类和理解，
+当遇到一个新的类似水果时，
+我们可以通过与已有的水果记忆进行相似性匹配，
+来推测它的一些特征。
+
+
+
+时空关联性联想：
+除了相似性联想，MAM 还强调时空关联性联想。
+如果两个事物在时间或空间上经常同时出现，它们也会在记忆中形成关联。
+比如，每次在公园里看到花的时候，都能听到鸟儿的叫声，
+那么花和鸟儿叫声的形态特征（花的视觉形态和鸟叫的听觉形态）就会在记忆中形成关联，
+以后听到鸟叫可能就会联想到公园里的花。
+
+'''
+
+# from chat.config import global_config
+sys.path.append("C:/GitHub/MaiMBot")  # 添加项目根目录到 Python 路径
+from src.common.database import db
+from src.plugins.memory_system.offline_llm import LLMModel
+
+# 获取当前文件的目录
+current_dir = Path(__file__).resolve().parent
+# 获取项目根目录（上三层目录）
+project_root = current_dir.parent.parent.parent
+# env.dev文件路径
+env_path = project_root / ".env.dev"
+
+# 加载环境变量
+if env_path.exists():
+    logger.info(f"从 {env_path} 加载环境变量")
+    load_dotenv(env_path)
+else:
+    logger.warning(f"未找到环境变量文件: {env_path}")
+    logger.info("将使用默认配置")
+
+
+def calculate_information_content(text):
+    """计算文本的信息量（熵）"""
+    char_count = Counter(text)
+    total_chars = len(text)
+    
+    entropy = 0
+    for count in char_count.values():
+        probability = count / total_chars
+        entropy -= probability * math.log2(probability)
+    
+    return entropy
+
+def get_closest_chat_from_db(length: int, timestamp: str):
+    """从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数
+    
+    Returns:
+        list: 消息记录字典列表，每个字典包含消息内容和时间信息
+    """
+    chat_records = []
+    closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
+    
+    if closest_record and closest_record.get('memorized', 0) < 4:            
+        closest_time = closest_record['time']
+        group_id = closest_record['group_id']
+        # 获取该时间戳之后的length条消息，且groupid相同
+        records = list(db.messages.find(
+            {"time": {"$gt": closest_time}, "group_id": group_id}
+        ).sort('time', 1).limit(length))
+        
+        # 更新每条消息的memorized属性
+        for record in records:
+            current_memorized = record.get('memorized', 0)
+            if current_memorized > 3:
+                print("消息已读取3次，跳过")
+                return ''
+                
+            # 更新memorized值
+            db.messages.update_one(
+                {"_id": record["_id"]},
+                {"$set": {"memorized": current_memorized + 1}}
+            )
+            
+            # 添加到记录列表中
+            chat_records.append({
+                'text': record["detailed_plain_text"],
+                'time': record["time"],
+                'group_id': record["group_id"]
+            })
+            
+    return chat_records
+
+class Memory_cortex:
+    def __init__(self, memory_graph: 'Memory_graph'):
+        self.memory_graph = memory_graph
+        
+    def sync_memory_from_db(self):
+        """
+        从数据库同步数据到内存中的图结构
+        将清空当前内存中的图，并从数据库重新加载所有节点和边
+        """
+        # 清空当前图
+        self.memory_graph.G.clear()
+        
+        # 获取当前时间作为默认时间
+        default_time = datetime.datetime.now().timestamp()
+        
+        # 从数据库加载所有节点
+        nodes = db.graph_data.nodes.find()
+        for node in nodes:
+            concept = node['concept']
+            memory_items = node.get('memory_items', [])
+            # 确保memory_items是列表
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+                
+            # 获取时间属性,如果不存在则使用默认时间
+            created_time = node.get('created_time')
+            last_modified = node.get('last_modified')
+            
+            # 如果时间属性不存在,则更新数据库
+            if created_time is None or last_modified is None:
+                created_time = default_time
+                last_modified = default_time
+                # 更新数据库中的节点
+                db.graph_data.nodes.update_one(
+                    {'concept': concept},
+                    {'$set': {
+                        'created_time': created_time,
+                        'last_modified': last_modified
+                    }}
+                )
+                logger.info(f"为节点 {concept} 添加默认时间属性")
+                
+            # 添加节点到图中,包含时间属性
+            self.memory_graph.G.add_node(concept, 
+                                       memory_items=memory_items,
+                                       created_time=created_time,
+                                       last_modified=last_modified)
+            
+        # 从数据库加载所有边
+        edges = db.graph_data.edges.find()
+        for edge in edges:
+            source = edge['source']
+            target = edge['target']
+            
+            # 只有当源节点和目标节点都存在时才添加边
+            if source in self.memory_graph.G and target in self.memory_graph.G:
+                # 获取时间属性,如果不存在则使用默认时间
+                created_time = edge.get('created_time')
+                last_modified = edge.get('last_modified')
+                
+                # 如果时间属性不存在,则更新数据库
+                if created_time is None or last_modified is None:
+                    created_time = default_time
+                    last_modified = default_time
+                    # 更新数据库中的边
+                    db.graph_data.edges.update_one(
+                        {'source': source, 'target': target},
+                        {'$set': {
+                            'created_time': created_time,
+                            'last_modified': last_modified
+                        }}
+                    )
+                    logger.info(f"为边 {source} - {target} 添加默认时间属性")
+                
+                self.memory_graph.G.add_edge(source, target,
+                                           strength=edge.get('strength', 1),
+                                           created_time=created_time,
+                                           last_modified=last_modified)
+        
+        logger.success("从数据库同步记忆图谱完成")
+        
+    def calculate_node_hash(self, concept, memory_items):
+        """
+        计算节点的特征值
+        """
+        if not isinstance(memory_items, list):
+            memory_items = [memory_items] if memory_items else []
+        # 将记忆项排序以确保相同内容生成相同的哈希值
+        sorted_items = sorted(memory_items)
+        # 组合概念和记忆项生成特征值
+        content = f"{concept}:{'|'.join(sorted_items)}"
+        return hash(content)
+
+    def calculate_edge_hash(self, source, target):
+        """
+        计算边的特征值
+        """
+        # 对源节点和目标节点排序以确保相同的边生成相同的哈希值
+        nodes = sorted([source, target])
+        return hash(f"{nodes[0]}:{nodes[1]}")
+
+    def sync_memory_to_db(self):
+        """
+        检查并同步内存中的图结构与数据库
+        使用特征值(哈希值)快速判断是否需要更新
+        """
+        current_time = datetime.datetime.now().timestamp()
+        
+        # 获取数据库中所有节点和内存中所有节点
+        db_nodes = list(db.graph_data.nodes.find())
+        memory_nodes = list(self.memory_graph.G.nodes(data=True))
+        
+        # 转换数据库节点为字典格式，方便查找
+        db_nodes_dict = {node['concept']: node for node in db_nodes}
+        
+        # 检查并更新节点
+        for concept, data in memory_nodes:
+            memory_items = data.get('memory_items', [])
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+            
+            # 计算内存中节点的特征值
+            memory_hash = self.calculate_node_hash(concept, memory_items)
+                
+            if concept not in db_nodes_dict:
+                # 数据库中缺少的节点，添加
+                node_data = {
+                    'concept': concept,
+                    'memory_items': memory_items,
+                    'hash': memory_hash,
+                    'created_time': data.get('created_time', current_time),
+                    'last_modified': data.get('last_modified', current_time)
+                }
+                db.graph_data.nodes.insert_one(node_data)
+            else:
+                # 获取数据库中节点的特征值
+                db_node = db_nodes_dict[concept]
+                db_hash = db_node.get('hash', None)
+                
+                # 如果特征值不同，则更新节点
+                if db_hash != memory_hash:
+                    db.graph_data.nodes.update_one(
+                        {'concept': concept},
+                        {'$set': {
+                            'memory_items': memory_items,
+                            'hash': memory_hash,
+                            'last_modified': current_time
+                        }}
+                    )
+                    
+        # 检查并删除数据库中多余的节点
+        memory_concepts = set(node[0] for node in memory_nodes)
+        for db_node in db_nodes:
+            if db_node['concept'] not in memory_concepts:
+                db.graph_data.nodes.delete_one({'concept': db_node['concept']})
+                
+        # 处理边的信息
+        db_edges = list(db.graph_data.edges.find())
+        memory_edges = list(self.memory_graph.G.edges(data=True))
+        
+        # 创建边的哈希值字典
+        db_edge_dict = {}
+        for edge in db_edges:
+            edge_hash = self.calculate_edge_hash(edge['source'], edge['target'])
+            db_edge_dict[(edge['source'], edge['target'])] = {
+                'hash': edge_hash,
+                'strength': edge.get('strength', 1)
+            }
+            
+        # 检查并更新边
+        for source, target, data in memory_edges:
+            edge_hash = self.calculate_edge_hash(source, target)
+            edge_key = (source, target)
+            strength = data.get('strength', 1)
+            
+            if edge_key not in db_edge_dict:
+                # 添加新边
+                edge_data = {
+                    'source': source,
+                    'target': target,
+                    'strength': strength,
+                    'hash': edge_hash,
+                    'created_time': data.get('created_time', current_time),
+                    'last_modified': data.get('last_modified', current_time)
+                }
+                db.graph_data.edges.insert_one(edge_data)
+            else:
+                # 检查边的特征值是否变化
+                if db_edge_dict[edge_key]['hash'] != edge_hash:
+                    db.graph_data.edges.update_one(
+                        {'source': source, 'target': target},
+                        {'$set': {
+                            'hash': edge_hash,
+                            'strength': strength,
+                            'last_modified': current_time
+                        }}
+                    )
+                    
+        # 删除多余的边
+        memory_edge_set = set((source, target) for source, target, _ in memory_edges)
+        for edge_key in db_edge_dict:
+            if edge_key not in memory_edge_set:
+                source, target = edge_key
+                db.graph_data.edges.delete_one({
+                    'source': source,
+                    'target': target
+                })
+        
+        logger.success("完成记忆图谱与数据库的差异同步")
+        
+    def remove_node_from_db(self, topic):
+        """
+        从数据库中删除指定节点及其相关的边
+        
+        Args:
+            topic: 要删除的节点概念
+        """
+        # 删除节点
+        db.graph_data.nodes.delete_one({'concept': topic})
+        # 删除所有涉及该节点的边
+        db.graph_data.edges.delete_many({
+            '$or': [
+                {'source': topic},
+                {'target': topic}
+            ]
+        })
+
+class Memory_graph:
+    def __init__(self):
+        self.G = nx.Graph()  # 使用 networkx 的图结构
+        
+    def connect_dot(self, concept1, concept2):
+        # 避免自连接
+        if concept1 == concept2:
+            return
+            
+        current_time = datetime.datetime.now().timestamp()
+            
+        # 如果边已存在，增加 strength
+        if self.G.has_edge(concept1, concept2):
+            self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
+            # 更新最后修改时间
+            self.G[concept1][concept2]['last_modified'] = current_time
+        else:
+            # 如果是新边，初始化 strength 为 1
+            self.G.add_edge(concept1, concept2, 
+                          strength=1,
+                          created_time=current_time,
+                          last_modified=current_time)
+    
+    def add_dot(self, concept, memory):
+        current_time = datetime.datetime.now().timestamp()
+        
+        if concept in self.G:
+            # 如果节点已存在，将新记忆添加到现有列表中
+            if 'memory_items' in self.G.nodes[concept]:
+                if not isinstance(self.G.nodes[concept]['memory_items'], list):
+                    # 如果当前不是列表，将其转换为列表
+                    self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
+                self.G.nodes[concept]['memory_items'].append(memory)
+                # 更新最后修改时间
+                self.G.nodes[concept]['last_modified'] = current_time
+            else:
+                self.G.nodes[concept]['memory_items'] = [memory]
+                self.G.nodes[concept]['last_modified'] = current_time
+        else:
+            # 如果是新节点，创建新的记忆列表
+            self.G.add_node(concept, 
+                          memory_items=[memory],
+                          created_time=current_time,
+                          last_modified=current_time)
+        
+    def get_dot(self, concept):
+        # 检查节点是否存在于图中
+        if concept in self.G:
+            # 从图中获取节点数据
+            node_data = self.G.nodes[concept]
+            return concept, node_data
+        return None
+
+    def get_related_item(self, topic, depth=1):
+        if topic not in self.G:
+            return [], []
+            
+        first_layer_items = []
+        second_layer_items = []
+        
+        # 获取相邻节点
+        neighbors = list(self.G.neighbors(topic))
+        
+        # 获取当前节点的记忆项
+        node_data = self.get_dot(topic)
+        if node_data:
+            concept, data = node_data
+            if 'memory_items' in data:
+                memory_items = data['memory_items']
+                if isinstance(memory_items, list):
+                    first_layer_items.extend(memory_items)
+                else:
+                    first_layer_items.append(memory_items)
+        
+        # 只在depth=2时获取第二层记忆
+        if depth >= 2:
+            # 获取相邻节点的记忆项
+            for neighbor in neighbors:
+                node_data = self.get_dot(neighbor)
+                if node_data:
+                    concept, data = node_data
+                    if 'memory_items' in data:
+                        memory_items = data['memory_items']
+                        if isinstance(memory_items, list):
+                            second_layer_items.extend(memory_items)
+                        else:
+                            second_layer_items.append(memory_items)
+        
+        return first_layer_items, second_layer_items
+    
+    @property
+    def dots(self):
+        # 返回所有节点对应的 Memory_dot 对象
+        return [self.get_dot(node) for node in self.G.nodes()]
+
+# 海马体 
+class Hippocampus:
+    def __init__(self, memory_graph: Memory_graph):
+        self.memory_graph = memory_graph
+        self.memory_cortex = Memory_cortex(memory_graph)
+        self.llm_model = LLMModel()
+        self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
+        self.llm_model_get_topic = LLMModel(model_name="Pro/Qwen/Qwen2.5-7B-Instruct")
+        self.llm_model_summary = LLMModel(model_name="Qwen/Qwen2.5-32B-Instruct")
+        
+    def get_memory_sample(self, chat_size=20, time_frequency:dict={'near':2,'mid':4,'far':3}):
+        """获取记忆样本
+        
+        Returns:
+            list: 消息记录列表，每个元素是一个消息记录字典列表
+        """
+        current_timestamp = datetime.datetime.now().timestamp()
+        chat_samples = []
+        
+        # 短期：1h   中期：4h   长期：24h
+        for _ in range(time_frequency.get('near')):
+            random_time = current_timestamp - random.randint(1, 3600*4)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
+            if messages:
+                chat_samples.append(messages)
+                
+        for _ in range(time_frequency.get('mid')):
+            random_time = current_timestamp - random.randint(3600*4, 3600*24)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
+            if messages:
+                chat_samples.append(messages)
+                
+        for _ in range(time_frequency.get('far')):
+            random_time = current_timestamp - random.randint(3600*24, 3600*24*7)
+            messages = get_closest_chat_from_db(length=chat_size, timestamp=random_time)
+            if messages:
+                chat_samples.append(messages)
+                
+        return chat_samples
+    
+    def calculate_topic_num(self,text, compress_rate):
+        """计算文本的话题数量"""
+        information_content = calculate_information_content(text)
+        topic_by_length = text.count('\n')*compress_rate
+        topic_by_information_content = max(1, min(5, int((information_content-3) * 2)))
+        topic_num = int((topic_by_length + topic_by_information_content)/2)
+        print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}")
+        return topic_num
+    
+    async def memory_compress(self, messages: list, compress_rate=0.1):
+        """压缩消息记录为记忆
+        
+        Args:
+            messages: 消息记录字典列表，每个字典包含text和time字段
+            compress_rate: 压缩率
+            
+        Returns:
+            tuple: (压缩记忆集合, 相似主题字典)
+                - 压缩记忆集合: set of (话题, 记忆) 元组
+                - 相似主题字典: dict of {话题: [(相似主题, 相似度), ...]}
+        """
+        if not messages:
+            return set(), {}
+            
+        # 合并消息文本，同时保留时间信息
+        input_text = ""
+        time_info = ""
+        # 计算最早和最晚时间
+        earliest_time = min(msg['time'] for msg in messages)
+        latest_time = max(msg['time'] for msg in messages)
+        
+        earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
+        latest_dt = datetime.datetime.fromtimestamp(latest_time)
+        
+        # 如果是同一年
+        if earliest_dt.year == latest_dt.year:
+            earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
+            latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
+            time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
+        else:
+            earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
+            latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S") 
+            time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
+        
+        for msg in messages:
+            input_text += f"{msg['text']}\n"
+            
+        print(input_text)
+        
+        topic_num = self.calculate_topic_num(input_text, compress_rate)
+        topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num))
+        
+        # 过滤topics
+        filter_keywords = ['表情包', '图片', '回复', '聊天记录']
+        topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
+        filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
+        
+        print(f"过滤后话题: {filtered_topics}")
+        
+        # 为每个话题查找相似的已存在主题
+        print("\n检查相似主题:")
+        similar_topics_dict = {}  # 存储每个话题的相似主题列表
+        
+        for topic in filtered_topics:
+            # 获取所有现有节点
+            existing_topics = list(self.memory_graph.G.nodes())
+            similar_topics = []
+            
+            # 对每个现有节点计算相似度
+            for existing_topic in existing_topics:
+                # 使用jieba分词并计算余弦相似度
+                topic_words = set(jieba.cut(topic))
+                existing_words = set(jieba.cut(existing_topic))
+                
+                # 计算词向量
+                all_words = topic_words | existing_words
+                v1 = [1 if word in topic_words else 0 for word in all_words]
+                v2 = [1 if word in existing_words else 0 for word in all_words]
+                
+                # 计算余弦相似度
+                similarity = cosine_similarity(v1, v2)
+                
+                # 如果相似度超过阈值，添加到结果中
+                if similarity >= 0.6:  # 设置相似度阈值
+                    similar_topics.append((existing_topic, similarity))
+            
+            # 按相似度降序排序
+            similar_topics.sort(key=lambda x: x[1], reverse=True)
+            # 只保留前5个最相似的主题
+            similar_topics = similar_topics[:5]
+            
+            # 存储到字典中
+            similar_topics_dict[topic] = similar_topics
+            
+            # 输出结果
+            if similar_topics:
+                print(f"\n主题「{topic}」的相似主题:")
+                for similar_topic, score in similar_topics:
+                    print(f"- {similar_topic} (相似度: {score:.3f})")
+            else:
+                print(f"\n主题「{topic}」没有找到相似主题")
+        
+        # 创建所有话题的请求任务
+        tasks = []
+        for topic in filtered_topics:
+            topic_what_prompt = self.topic_what(input_text, topic , time_info)
+            # 创建异步任务
+            task = self.llm_model_small.generate_response_async(topic_what_prompt)
+            tasks.append((topic.strip(), task))
+            
+        # 等待所有任务完成
+        compressed_memory = set()
+        for topic, task in tasks:
+            response = await task
+            if response:
+                compressed_memory.add((topic, response[0]))
+                
+        return compressed_memory, similar_topics_dict
+    
+    async def operation_build_memory(self, chat_size=12):
+        # 最近消息获取频率
+        time_frequency = {'near': 3, 'mid': 8, 'far': 5}
+        memory_samples = self.get_memory_sample(chat_size, time_frequency)
+        
+        all_topics = []  # 用于存储所有话题
+
+        for i, messages in enumerate(memory_samples, 1):
+            # 加载进度可视化
+            all_topics = []
+            progress = (i / len(memory_samples)) * 100
+            bar_length = 30
+            filled_length = int(bar_length * i // len(memory_samples))
+            bar = '█' * filled_length + '-' * (bar_length - filled_length)
+            print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
+
+            # 生成压缩后记忆
+            compress_rate = 0.1
+            compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
+            print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}")
+            
+            # 将记忆加入到图谱中
+            for topic, memory in compressed_memory:
+                print(f"\033[1;32m添加节点\033[0m: {topic}")
+                self.memory_graph.add_dot(topic, memory)
+                all_topics.append(topic)
+                
+                # 连接相似的已存在主题
+                if topic in similar_topics_dict:
+                    similar_topics = similar_topics_dict[topic]
+                    for similar_topic, similarity in similar_topics:
+                        # 避免自连接
+                        if topic != similar_topic:
+                            # 根据相似度设置连接强度
+                            strength = int(similarity * 10)  # 将0.3-1.0的相似度映射到3-10的强度
+                            print(f"\033[1;36m连接相似节点\033[0m: {topic} 和 {similar_topic} (强度: {strength})")
+                            # 使用相似度作为初始连接强度
+                            self.memory_graph.G.add_edge(topic, similar_topic, strength=strength)
+                
+            # 连接同批次的相关话题
+            for i in range(len(all_topics)):
+                for j in range(i + 1, len(all_topics)):
+                    print(f"\033[1;32m连接同批次节点\033[0m: {all_topics[i]} 和 {all_topics[j]}")
+                    self.memory_graph.connect_dot(all_topics[i], all_topics[j])
+                    
+        self.memory_cortex.sync_memory_to_db()
+
+    def forget_connection(self, source, target):
+        """
+        检查并可能遗忘一个连接
+        
+        Args:
+            source: 连接的源节点
+            target: 连接的目标节点
+            
+        Returns:
+            tuple: (是否有变化, 变化类型, 变化详情)
+                变化类型: 0-无变化, 1-强度减少, 2-连接移除
+        """
+        current_time = datetime.datetime.now().timestamp()
+        # 获取边的属性
+        edge_data = self.memory_graph.G[source][target]
+        last_modified = edge_data.get('last_modified', current_time)
+        
+        # 如果连接超过7天未更新
+        if current_time - last_modified > 6000:  # test
+            # 获取当前强度
+            current_strength = edge_data.get('strength', 1)
+            # 减少连接强度
+            new_strength = current_strength - 1
+            edge_data['strength'] = new_strength
+            edge_data['last_modified'] = current_time
+            
+            # 如果强度降为0,移除连接
+            if new_strength <= 0:
+                self.memory_graph.G.remove_edge(source, target)
+                return True, 2, f"移除连接: {source} - {target} (强度降至0)"
+            else:
+                return True, 1, f"减弱连接: {source} - {target} (强度: {current_strength} -> {new_strength})"
+            
+        return False, 0, ""
+
+    def forget_topic(self, topic):
+        """
+        检查并可能遗忘一个话题的记忆
+        
+        Args:
+            topic: 要检查的话题
+            
+        Returns:
+            tuple: (是否有变化, 变化类型, 变化详情)
+                变化类型: 0-无变化, 1-记忆减少, 2-节点移除
+        """
+        current_time = datetime.datetime.now().timestamp()
+        # 获取节点的最后修改时间
+        node_data = self.memory_graph.G.nodes[topic]
+        last_modified = node_data.get('last_modified', current_time)
+        
+        # 如果话题超过7天未更新
+        if current_time - last_modified > 3000:  # test
+            memory_items = node_data.get('memory_items', [])
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+            
+            if memory_items:
+                # 获取当前记忆数量
+                current_count = len(memory_items)
+                # 随机选择一条记忆删除
+                removed_item = random.choice(memory_items)
+                memory_items.remove(removed_item)
+                
+                if memory_items:
+                    # 更新节点的记忆项和最后修改时间
+                    self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
+                    self.memory_graph.G.nodes[topic]['last_modified'] = current_time
+                    return True, 1, f"减少记忆: {topic} (记忆数量: {current_count} -> {len(memory_items)})\n被移除的记忆: {removed_item}"
+                else:
+                    # 如果没有记忆了,删除节点及其所有连接
+                    self.memory_graph.G.remove_node(topic)
+                    return True, 2, f"移除节点: {topic} (无剩余记忆)\n最后一条记忆: {removed_item}"
+                
+        return False, 0, ""
+
+    async def operation_forget_topic(self, percentage=0.1):
+        """
+        随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘
+        
+        Args:
+            percentage: 要检查的节点和边的比例,默认为0.1（10%）
+        """
+        # 获取所有节点和边
+        all_nodes = list(self.memory_graph.G.nodes())
+        all_edges = list(self.memory_graph.G.edges())
+        
+        # 计算要检查的数量
+        check_nodes_count = max(1, int(len(all_nodes) * percentage))
+        check_edges_count = max(1, int(len(all_edges) * percentage))
+        
+        # 随机选择要检查的节点和边
+        nodes_to_check = random.sample(all_nodes, check_nodes_count)
+        edges_to_check = random.sample(all_edges, check_edges_count)
+        
+        # 用于统计不同类型的变化
+        edge_changes = {'weakened': 0, 'removed': 0}
+        node_changes = {'reduced': 0, 'removed': 0}
+        
+        # 检查并遗忘连接
+        print("\n开始检查连接...")
+        for source, target in edges_to_check:
+            changed, change_type, details = self.forget_connection(source, target)
+            if changed:
+                if change_type == 1:
+                    edge_changes['weakened'] += 1
+                    logger.info(f"\033[1;34m[连接减弱]\033[0m {details}")
+                elif change_type == 2:
+                    edge_changes['removed'] += 1
+                    logger.info(f"\033[1;31m[连接移除]\033[0m {details}")
+            
+        # 检查并遗忘话题
+        print("\n开始检查节点...")
+        for node in nodes_to_check:
+            changed, change_type, details = self.forget_topic(node)
+            if changed:
+                if change_type == 1:
+                    node_changes['reduced'] += 1
+                    logger.info(f"\033[1;33m[记忆减少]\033[0m {details}")
+                elif change_type == 2:
+                    node_changes['removed'] += 1
+                    logger.info(f"\033[1;31m[节点移除]\033[0m {details}")
+        
+        # 同步到数据库
+        if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
+            self.memory_cortex.sync_memory_to_db()
+            print("\n遗忘操作统计:")
+            print(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
+            print(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
+        else:
+            print("\n本次检查没有节点或连接满足遗忘条件")
+
+    async def merge_memory(self, topic):
+        """
+        对指定话题的记忆进行合并压缩
+        
+        Args:
+            topic: 要合并的话题节点
+        """
+        # 获取节点的记忆项
+        memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
+        if not isinstance(memory_items, list):
+            memory_items = [memory_items] if memory_items else []
+            
+        # 如果记忆项不足，直接返回
+        if len(memory_items) < 10:
+            return
+            
+        # 随机选择10条记忆
+        selected_memories = random.sample(memory_items, 10)
+        
+        # 拼接成文本
+        merged_text = "\n".join(selected_memories)
+        print(f"\n[合并记忆] 话题: {topic}")
+        print(f"选择的记忆:\n{merged_text}")
+        
+        # 使用memory_compress生成新的压缩记忆
+        compressed_memories, _ = await self.memory_compress(selected_memories, 0.1)
+        
+        # 从原记忆列表中移除被选中的记忆
+        for memory in selected_memories:
+            memory_items.remove(memory)
+            
+        # 添加新的压缩记忆
+        for _, compressed_memory in compressed_memories:
+            memory_items.append(compressed_memory)
+            print(f"添加压缩记忆: {compressed_memory}")
+            
+        # 更新节点的记忆项
+        self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
+        print(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
+        
+    async def operation_merge_memory(self, percentage=0.1):
+        """
+        随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
+        
+        Args:
+            percentage: 要检查的节点比例，默认为0.1（10%）
+        """
+        # 获取所有节点
+        all_nodes = list(self.memory_graph.G.nodes())
+        # 计算要检查的节点数量
+        check_count = max(1, int(len(all_nodes) * percentage))
+        # 随机选择节点
+        nodes_to_check = random.sample(all_nodes, check_count)
+        
+        merged_nodes = []
+        for node in nodes_to_check:
+            # 获取节点的内容条数
+            memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+            content_count = len(memory_items)
+            
+            # 如果内容数量超过100，进行合并
+            if content_count > 100:
+                print(f"\n检查节点: {node}, 当前记忆数量: {content_count}")
+                await self.merge_memory(node)
+                merged_nodes.append(node)
+        
+        # 同步到数据库
+        if merged_nodes:
+            self.memory_cortex.sync_memory_to_db()
+            print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
+        else:
+            print("\n本次检查没有需要合并的节点")
+        
+    async def _identify_topics(self, text: str) -> list:
+        """从文本中识别可能的主题"""
+        topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5))
+        topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
+        return topics
+        
+    def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
+        """查找与给定主题相似的记忆主题"""
+        all_memory_topics = list(self.memory_graph.G.nodes())
+        all_similar_topics = []
+        
+        for topic in topics:
+            if debug_info:
+                pass
+                
+            topic_vector = text_to_vector(topic)
+            has_similar_topic = False
+            
+            for memory_topic in all_memory_topics:
+                memory_vector = text_to_vector(memory_topic)
+                all_words = set(topic_vector.keys()) | set(memory_vector.keys())
+                v1 = [topic_vector.get(word, 0) for word in all_words]
+                v2 = [memory_vector.get(word, 0) for word in all_words]
+                similarity = cosine_similarity(v1, v2)
+                
+                if similarity >= similarity_threshold:
+                    has_similar_topic = True
+                    all_similar_topics.append((memory_topic, similarity))
+                    
+        return all_similar_topics
+        
+    def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
+        """获取相似度最高的主题"""
+        seen_topics = set()
+        top_topics = []
+        
+        for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True):
+            if topic not in seen_topics and len(top_topics) < max_topics:
+                seen_topics.add(topic)
+                top_topics.append((topic, score))
+                
+        return top_topics
+
+    async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
+        """计算输入文本对记忆的激活程度"""
+        logger.info(f"[记忆激活]识别主题: {await self._identify_topics(text)}")
+        
+        identified_topics = await self._identify_topics(text)
+        if not identified_topics:
+            return 0
+            
+        all_similar_topics = self._find_similar_topics(
+            identified_topics, 
+            similarity_threshold=similarity_threshold,
+            debug_info="记忆激活"
+        )
+        
+        if not all_similar_topics:
+            return 0
+            
+        top_topics = self._get_top_topics(all_similar_topics, max_topics)
+        
+        if len(top_topics) == 1:
+            topic, score = top_topics[0]
+            memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+            content_count = len(memory_items)
+            penalty = 1.0 / (1 + math.log(content_count + 1))
+            
+            activation = int(score * 50 * penalty)
+            print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
+            return activation
+            
+        matched_topics = set()
+        topic_similarities = {}
+        
+        for memory_topic, similarity in top_topics:
+            memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', [])
+            if not isinstance(memory_items, list):
+                memory_items = [memory_items] if memory_items else []
+            content_count = len(memory_items)
+            penalty = 1.0 / (1 + math.log(content_count + 1))
+            
+            for input_topic in identified_topics:
+                topic_vector = text_to_vector(input_topic)
+                memory_vector = text_to_vector(memory_topic)
+                all_words = set(topic_vector.keys()) | set(memory_vector.keys())
+                v1 = [topic_vector.get(word, 0) for word in all_words]
+                v2 = [memory_vector.get(word, 0) for word in all_words]
+                sim = cosine_similarity(v1, v2)
+                if sim >= similarity_threshold:
+                    matched_topics.add(input_topic)
+                    adjusted_sim = sim * penalty
+                    topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
+                    print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
+        
+        topic_match = len(matched_topics) / len(identified_topics)
+        average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0
+        
+        activation = int((topic_match + average_similarities) / 2 * 100)
+        print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
+        
+        return activation
+
+    async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list:
+        """根据输入文本获取相关的记忆内容"""
+        identified_topics = await self._identify_topics(text)
+        
+        all_similar_topics = self._find_similar_topics(
+            identified_topics, 
+            similarity_threshold=similarity_threshold,
+            debug_info="记忆检索"
+        )
+        
+        relevant_topics = self._get_top_topics(all_similar_topics, max_topics)
+        
+        relevant_memories = []
+        for topic, score in relevant_topics:
+            first_layer, _ = self.memory_graph.get_related_item(topic, depth=1)
+            if first_layer:
+                if len(first_layer) > max_memory_num/2:
+                    first_layer = random.sample(first_layer, max_memory_num//2)
+                for memory in first_layer:
+                    relevant_memories.append({
+                        'topic': topic,
+                        'similarity': score,
+                        'content': memory
+                    })
+                    
+        relevant_memories.sort(key=lambda x: x['similarity'], reverse=True)
+        
+        if len(relevant_memories) > max_memory_num:
+            relevant_memories = random.sample(relevant_memories, max_memory_num)
+        
+        return relevant_memories
+
+    def find_topic_llm(self,text, topic_num):
+        prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
+        return prompt
+
+    def topic_what(self,text, topic, time_info):
+        prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
+        return prompt
+
+def segment_text(text):
+    """使用jieba进行文本分词"""
+    seg_text = list(jieba.cut(text))
+    return seg_text
+
+def text_to_vector(text):
+    """将文本转换为词频向量"""
+    words = segment_text(text)
+    vector = {}
+    for word in words:
+        vector[word] = vector.get(word, 0) + 1
+    return vector
+
+def cosine_similarity(v1, v2):
+    """计算两个向量的余弦相似度"""
+    dot_product = sum(a * b for a, b in zip(v1, v2))
+    norm1 = math.sqrt(sum(a * a for a in v1))
+    norm2 = math.sqrt(sum(b * b for b in v2))
+    if norm1 == 0 or norm2 == 0:
+        return 0
+    return dot_product / (norm1 * norm2)
+
+def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False):
+    # 设置中文字体
+    plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
+    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
+    
+    G = memory_graph.G
+    
+    # 创建一个新图用于可视化
+    H = G.copy()
+    
+    # 过滤掉内容数量小于2的节点
+    nodes_to_remove = []
+    for node in H.nodes():
+        memory_items = H.nodes[node].get('memory_items', [])
+        memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
+        if memory_count < 2:
+            nodes_to_remove.append(node)
+    
+    H.remove_nodes_from(nodes_to_remove)
+    
+    # 如果没有符合条件的节点，直接返回
+    if len(H.nodes()) == 0:
+        print("没有找到内容数量大于等于2的节点")
+        return
+
+    # 计算节点大小和颜色
+    node_colors = []
+    node_sizes = []
+    nodes = list(H.nodes())
+    
+    # 获取最大记忆数用于归一化节点大小
+    max_memories = 1
+    for node in nodes:
+        memory_items = H.nodes[node].get('memory_items', [])
+        memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
+        max_memories = max(max_memories, memory_count)
+    
+    # 计算每个节点的大小和颜色
+    for node in nodes:
+        # 计算节点大小（基于记忆数量）
+        memory_items = H.nodes[node].get('memory_items', [])
+        memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0)
+        # 使用指数函数使变化更明显
+        ratio = memory_count / max_memories
+        size = 400 + 2000 * (ratio ** 2)  # 增大节点大小
+        node_sizes.append(size)
+        
+        # 计算节点颜色（基于连接数）
+        degree = H.degree(node)
+        if degree >= 30:
+            node_colors.append((1.0, 0, 0))  # 亮红色 (#FF0000)
+        else:
+            # 将1-10映射到0-1的范围
+            color_ratio = (degree - 1) / 29.0 if degree > 1 else 0
+            # 使用蓝到红的渐变
+            red = min(0.9, color_ratio)
+            blue = max(0.0, 1.0 - color_ratio)
+            node_colors.append((red, 0, blue))
+    
+    # 绘制图形
+    plt.figure(figsize=(16, 12))  # 减小图形尺寸
+    pos = nx.spring_layout(H, 
+                          k=1,        # 调整节点间斥力
+                          iterations=100,  # 增加迭代次数
+                          scale=1.5,    # 减小布局尺寸
+                          weight='strength')  # 使用边的strength属性作为权重
+    
+    nx.draw(H, pos, 
+           with_labels=True, 
+           node_color=node_colors,
+           node_size=node_sizes,
+           font_size=12,  # 保持增大的字体大小
+           font_family='SimHei',
+           font_weight='bold',
+           edge_color='gray',
+           width=1.5)  # 统一的边宽度
+    
+    title = '记忆图谱可视化（仅显示内容≥2的节点）\n节点大小表示记忆数量\n节点颜色：蓝(弱连接)到红(强连接)渐变，边的透明度表示连接强度\n连接强度越大的节点距离越近'
+    plt.title(title, fontsize=16, fontfamily='SimHei')
+    plt.show()
+
+async def main():
+    # 初始化数据库
+    logger.info("正在初始化数据库连接...")
+    start_time = time.time()
+    
+    test_pare = {'do_build_memory':True,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False}
+    
+    # 创建记忆图
+    memory_graph = Memory_graph()
+    
+    # 创建海马体
+    hippocampus = Hippocampus(memory_graph)
+    
+    # 从数据库同步数据
+    hippocampus.memory_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")
+    
+    # 构建记忆
+    if test_pare['do_build_memory']:
+        logger.info("开始构建记忆...")
+        chat_size = 20
+        await hippocampus.operation_build_memory(chat_size=chat_size)
+        
+        end_time = time.time()
+        logger.info(f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m")
+        
+    if test_pare['do_forget_topic']:
+        logger.info("开始遗忘记忆...")
+        await hippocampus.operation_forget_topic(percentage=0.01)
+        
+        end_time = time.time()
+        logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
+        
+    if test_pare['do_merge_memory']:
+        logger.info("开始合并记忆...")
+        await hippocampus.operation_merge_memory(percentage=0.1)
+        
+        end_time = time.time()
+        logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m")
+    
+    if test_pare['do_visualize_graph']:
+        # 展示优化后的图形
+        logger.info("生成记忆图谱可视化...")
+        print("\n生成优化后的记忆图谱：")
+        visualize_graph_lite(memory_graph)
+    
+    if test_pare['do_query']:
+        # 交互式查询
+        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)
+            if items_list:
+                first_layer, second_layer = items_list
+                if first_layer:
+                    print("\n直接相关的记忆：")
+                    for item in first_layer:
+                        print(f"- {item}")
+                if second_layer:
+                    print("\n间接相关的记忆：")
+                    for item in second_layer:
+                        print(f"- {item}")
+            else:
+                print("未找到相关记忆。")
+
+
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main())
+
+    
diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py
index bd06fd6dd..0f5bb335c 100644
--- a/src/plugins/models/utils_model.py
+++ b/src/plugins/models/utils_model.py
@@ -7,10 +7,11 @@ from typing import Tuple, Union
 import aiohttp
 from loguru import logger
 from nonebot import get_driver
-
-from ...common.database import Database
+import base64
+from PIL import Image
+import io
+from ...common.database import db
 from ..chat.config import global_config
-from ..chat.utils_image import compress_base64_image_by_scale
 
 driver = get_driver()
 config = driver.config
@@ -28,28 +29,27 @@ class LLM_request:
             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.params = kwargs
-        
+
         self.pri_in = model.get("pri_in", 0)
         self.pri_out = model.get("pri_out", 0)
-        
+
         # 获取数据库实例
-        self.db = Database.get_instance()
         self._init_database()
 
     def _init_database(self):
         """初始化数据库集合"""
         try:
             # 创建llm_usage集合的索引
-            self.db.db.llm_usage.create_index([("timestamp", 1)])
-            self.db.db.llm_usage.create_index([("model_name", 1)])
-            self.db.db.llm_usage.create_index([("user_id", 1)])
-            self.db.db.llm_usage.create_index([("request_type", 1)])
-        except Exception as e:
-            logger.error(f"创建数据库索引失败: {e}")
+            db.llm_usage.create_index([("timestamp", 1)])
+            db.llm_usage.create_index([("model_name", 1)])
+            db.llm_usage.create_index([("user_id", 1)])
+            db.llm_usage.create_index([("request_type", 1)])
+        except Exception:
+            logger.error("创建数据库索引失败")
 
-    def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int, 
-                     user_id: str = "system", request_type: str = "chat", 
-                     endpoint: str = "/chat/completions"):
+    def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int,
+                      user_id: str = "system", request_type: str = "chat",
+                      endpoint: str = "/chat/completions"):
         """记录模型使用情况到数据库
         Args:
             prompt_tokens: 输入token数
@@ -72,15 +72,15 @@ class LLM_request:
                 "status": "success",
                 "timestamp": datetime.now()
             }
-            self.db.db.llm_usage.insert_one(usage_data)
+            db.llm_usage.insert_one(usage_data)
             logger.info(
                 f"Token使用情况 - 模型: {self.model_name}, "
                 f"用户: {user_id}, 类型: {request_type}, "
                 f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
                 f"总计: {total_tokens}"
             )
-        except Exception as e:
-            logger.error(f"记录token使用情况失败: {e}")
+        except Exception:
+            logger.error("记录token使用情况失败")
 
     def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
         """计算API调用成本
@@ -103,6 +103,7 @@ class LLM_request:
             endpoint: str,
             prompt: str = None,
             image_base64: str = None,
+            image_format: str = None,
             payload: dict = None,
             retry_policy: dict = None,
             response_handler: callable = None,
@@ -114,6 +115,7 @@ class LLM_request:
             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
@@ -130,7 +132,7 @@ class LLM_request:
         # 常见Error Code Mapping
         error_code_mapping = {
             400: "参数不正确",
-            401: "API key 错误，认证失败",
+            401: "API key 错误，认证失败，请检查/config/bot_config.toml和.env.prod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",
@@ -140,17 +142,17 @@ class LLM_request:
         }
 
         api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
-        #判断是否为流式
+        # 判断是否为流式
         stream_mode = self.params.get("stream", False)
         if self.params.get("stream", False) is True:
-            logger.info(f"进入流式输出模式，发送请求到URL: {api_url}")
+            logger.debug(f"进入流式输出模式，发送请求到URL: {api_url}")
         else:
-            logger.info(f"发送请求到URL: {api_url}")
+            logger.debug(f"发送请求到URL: {api_url}")
         logger.info(f"使用模型: {self.model_name}")
 
         # 构建请求体
         if image_base64:
-            payload = await self._build_payload(prompt, image_base64)
+            payload = await self._build_payload(prompt, image_base64, image_format)
         elif payload is None:
             payload = await self._build_payload(prompt)
 
@@ -158,7 +160,7 @@ class LLM_request:
             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
+                # 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 stream_mode:
                     headers["Accept"] = "text/event-stream"
 
@@ -171,7 +173,7 @@ class LLM_request:
                             if response.status == 413:
                                 logger.warning("请求体过大，尝试压缩...")
                                 image_base64 = compress_base64_image_by_scale(image_base64)
-                                payload = await self._build_payload(prompt, image_base64)
+                                payload = await self._build_payload(prompt, image_base64, image_format)
                             elif response.status in [500, 503]:
                                 logger.error(f"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")
@@ -183,31 +185,37 @@ class LLM_request:
                         elif response.status in policy["abort_codes"]:
                             logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
                             if response.status == 403:
-                                # 尝试降级Pro模型
-                                if self.model_name.startswith("Pro/") and self.base_url == "https://api.siliconflow.cn/v1/":
+                                #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 hasattr(global_config, 'llm_normal') and global_config.llm_normal.get('name') == old_model_name:
+                                    if global_config.llm_normal.get('name') == old_model_name:
                                         global_config.llm_normal['name'] = self.model_name
-                                        logger.warning(f"已将全局配置中的 llm_normal 模型降级")
-                                    
+                                        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}")
+
                                     # 更新payload中的模型名
                                     if payload and 'model' in payload:
                                         payload['model'] = self.model_name
-                                    
+
                                     # 重新尝试请求
                                     retry -= 1  # 不计入重试次数
                                     continue
-                            
+
                             raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
-                            
+
                         response.raise_for_status()
-                        
-                        #将流式输出转化为非流式输出
+
+                        # 将流式输出转化为非流式输出
                         if stream_mode:
+                            flag_delta_content_finished = False
                             accumulated_content = ""
                             async for line_bytes in response.content:
                                 line = line_bytes.decode("utf-8").strip()
@@ -219,13 +227,25 @@ class LLM_request:
                                         break
                                     try:
                                         chunk = json.loads(data_str)
-                                        delta = chunk["choices"][0]["delta"]
-                                        delta_content = delta.get("content")
-                                        if delta_content is None:
-                                            delta_content = ""
-                                        accumulated_content += delta_content
-                                    except Exception as e:
-                                        logger.error(f"解析流式输出错误: {e}")
+                                        if flag_delta_content_finished:
+                                            usage = chunk.get("usage", None) # 获取tokn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 finish_reason == "stop":
+                                                usage = chunk.get("usage", None)
+                                                if 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:
+                                        logger.exception("解析流式输出错误")
                             content = accumulated_content
                             reasoning_content = ""
                             think_match = re.search(r'<think>(.*?)</think>', content, re.DOTALL)
@@ -233,12 +253,15 @@ class LLM_request:
                                 reasoning_content = think_match.group(1).strip()
                             content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
                             # 构造一个伪result以便调用自定义响应处理器或默认处理器
-                            result = {"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]}
-                            return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
+                            result = {
+                                "choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}],  "usage": usage}
+                            return response_handler(result) if response_handler else self._default_response_handler(
+                                result, user_id, request_type, endpoint)
                         else:
                             result = await response.json()
                             # 使用自定义处理器或默认处理
-                            return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
+                            return response_handler(result) if response_handler else self._default_response_handler(
+                                result, user_id, request_type, endpoint)
 
             except Exception as e:
                 if retry < policy["max_retries"] - 1:
@@ -252,8 +275,8 @@ class LLM_request:
 
         logger.error("达到最大重试次数，请求仍然失败")
         raise RuntimeError("达到最大重试次数，API请求仍然失败")
-        
-    async def _transform_parameters(self, params: dict) ->dict:
+
+    async def _transform_parameters(self, params: dict) -> dict:
         """
         根据模型名称转换参数：
         - 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temprature' 参数，
@@ -262,7 +285,8 @@ class LLM_request:
         # 复制一份参数，避免直接修改原始数据
         new_params = dict(params)
         # 定义需要转换的模型列表
-        models_needing_transformation = ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12", "o3-mini-2025-01-31", "o1-mini-2024-09-12"]
+        models_needing_transformation = ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12",
+                                         "o3-mini-2025-01-31", "o1-mini-2024-09-12"]
         if self.model_name.lower() in models_needing_transformation:
             # 删除 'temprature' 参数（如果存在）
             new_params.pop("temperature", None)
@@ -271,7 +295,7 @@ class LLM_request:
                 new_params["max_completion_tokens"] = new_params.pop("max_tokens")
         return new_params
 
-    async def _build_payload(self, prompt: str, image_base64: str = None) -> dict:
+    async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
         """构建请求体"""
         # 复制一份参数，避免直接修改 self.params
         params_copy = await self._transform_parameters(self.params)
@@ -283,7 +307,7 @@ class LLM_request:
                         "role": "user",
                         "content": [
                             {"type": "text", "text": prompt},
-                            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
+                            {"type": "image_url", "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}}
                         ]
                     }
                 ],
@@ -298,13 +322,13 @@ class LLM_request:
                 **params_copy
             }
         # 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 ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12", "o3-mini-2025-01-31", "o1-mini-2024-09-12"] and "max_tokens" in payload:
+        if self.model_name.lower() in ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12",
+                                       "o3-mini-2025-01-31", "o1-mini-2024-09-12"] and "max_tokens" in payload:
             payload["max_completion_tokens"] = payload.pop("max_tokens")
         return payload
-        
 
-    def _default_response_handler(self, result: dict, user_id: str = "system", 
-                                request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:
+    def _default_response_handler(self, result: dict, user_id: str = "system",
+                                  request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:
         """默认响应解析"""
         if "choices" in result and result["choices"]:
             message = result["choices"][0]["message"]
@@ -349,15 +373,15 @@ class LLM_request:
         """构建请求头"""
         if no_key:
             return {
-                "Authorization": f"Bearer **********",
+                "Authorization": "Bearer **********",
                 "Content-Type": "application/json"
             }
         else:
             return {
                 "Authorization": f"Bearer {self.api_key}",
                 "Content-Type": "application/json"
-            } 
-        # 防止小朋友们截图自己的key
+            }
+            # 防止小朋友们截图自己的key
 
     async def generate_response(self, prompt: str) -> Tuple[str, str]:
         """根据输入的提示生成模型的异步响应"""
@@ -368,13 +392,14 @@ class LLM_request:
         )
         return content, reasoning_content
 
-    async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]:
+    async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple[str, str]:
         """根据输入的提示和图片生成模型的异步响应"""
 
         content, reasoning_content = await self._execute_request(
             endpoint="/chat/completions",
             prompt=prompt,
-            image_base64=image_base64
+            image_base64=image_base64,
+            image_format=image_format
         )
         return content, reasoning_content
 
@@ -404,6 +429,7 @@ class LLM_request:
         Returns:
             list: embedding向量，如果失败则返回None
         """
+
         def embedding_handler(result):
             """处理响应"""
             if "data" in result and len(result["data"]) > 0:
@@ -426,3 +452,77 @@ class LLM_request:
         )
         return embedding
 
+def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
+    """压缩base64格式的图片到指定大小
+    Args:
+        base64_data: base64编码的图片数据
+        target_size: 目标文件大小（字节），默认0.8MB
+    Returns:
+        str: 压缩后的base64图片数据
+    """
+    try:
+        # 将base64转换为字节数据
+        image_data = base64.b64decode(base64_data)
+        
+        # 如果已经小于目标大小，直接返回原图
+        if len(image_data) <= 2*1024*1024:
+            return base64_data
+            
+        # 将字节数据转换为图片对象
+        img = Image.open(io.BytesIO(image_data))
+        
+        # 获取原始尺寸
+        original_width, original_height = img.size
+        
+        # 计算缩放比例
+        scale = min(1.0, (target_size / len(image_data)) ** 0.5)
+        
+        # 计算新的尺寸
+        new_width = int(original_width * scale)
+        new_height = int(original_height * scale)
+        
+        # 创建内存缓冲区
+        output_buffer = io.BytesIO()
+        
+        # 如果是GIF，处理所有帧
+        if getattr(img, "is_animated", False):
+            frames = []
+            for frame_idx in range(img.n_frames):
+                img.seek(frame_idx)
+                new_frame = img.copy()
+                new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折
+                frames.append(new_frame)
+            
+            # 保存到缓冲区
+            frames[0].save(
+                output_buffer,
+                format='GIF',
+                save_all=True,
+                append_images=frames[1:],
+                optimize=True,
+                duration=img.info.get('duration', 100),
+                loop=img.info.get('loop', 0)
+            )
+        else:
+            # 处理静态图片
+            resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+            
+            # 保存到缓冲区，保持原始格式
+            if img.format == 'PNG' and img.mode in ('RGBA', 'LA'):
+                resized_img.save(output_buffer, format='PNG', optimize=True)
+            else:
+                resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True)
+        
+        # 获取压缩后的数据并转换为base64
+        compressed_data = output_buffer.getvalue()
+        logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
+        logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB")
+        
+        return base64.b64encode(compressed_data).decode('utf-8')
+        
+    except Exception as e:
+        logger.error(f"压缩图片失败: {str(e)}")
+        import traceback
+        logger.error(traceback.format_exc())
+        return base64_data 
+
diff --git a/src/plugins/moods/moods.py b/src/plugins/moods/moods.py
index c35779f84..c37bfc81d 100644
--- a/src/plugins/moods/moods.py
+++ b/src/plugins/moods/moods.py
@@ -4,7 +4,7 @@ import time
 from dataclasses import dataclass
 
 from ..chat.config import global_config
-
+from loguru import logger
 
 @dataclass
 class MoodState:
@@ -210,7 +210,7 @@ class MoodManager:
 
     def print_mood_status(self) -> None:
         """打印当前情绪状态"""
-        print(f"\033[1;35m[情绪状态]\033[0m 愉悦度: {self.current_mood.valence:.2f}, " 
+        logger.info(f"[情绪状态]愉悦度: {self.current_mood.valence:.2f}, " 
               f"唤醒度: {self.current_mood.arousal:.2f}, "
               f"心情: {self.current_mood.text}")
 
diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py
index ffe99a2da..2f96f3531 100644
--- a/src/plugins/schedule/schedule_generator.py
+++ b/src/plugins/schedule/schedule_generator.py
@@ -1,3 +1,4 @@
+import os
 import datetime
 import json
 from typing import Dict, Union
@@ -7,28 +8,20 @@ from nonebot import get_driver
 
 from src.plugins.chat.config import global_config
 
-from ...common.database import Database  # 使用正确的导入语法
+from ...common.database import db  # 使用正确的导入语法
 from ..models.utils_model import LLM_request
 
 driver = get_driver()
 config = driver.config
 
-Database.initialize(
-    host=config.MONGODB_HOST,
-    port=int(config.MONGODB_PORT),
-    db_name=config.DATABASE_NAME,
-    username=config.MONGODB_USERNAME,
-    password=config.MONGODB_PASSWORD,
-    auth_source=config.MONGODB_AUTH_SOURCE
-)
-
 
 class ScheduleGenerator:
+    enable_output: bool = True
+
     def __init__(self):
         # 根据global_config.llm_normal这一字典配置指定模型
         # self.llm_scheduler = LLMModel(model = global_config.llm_normal,temperature=0.9)
         self.llm_scheduler = LLM_request(model=global_config.llm_normal, temperature=0.9)
-        self.db = Database.get_instance()
         self.today_schedule_text = ""
         self.today_schedule = {}
         self.tomorrow_schedule_text = ""
@@ -42,43 +35,50 @@ class ScheduleGenerator:
         yesterday = datetime.datetime.now() - datetime.timedelta(days=1)
 
         self.today_schedule_text, self.today_schedule = await self.generate_daily_schedule(target_date=today)
-        self.tomorrow_schedule_text, self.tomorrow_schedule = await self.generate_daily_schedule(target_date=tomorrow,
-                                                                                                 read_only=True)
+        self.tomorrow_schedule_text, self.tomorrow_schedule = await self.generate_daily_schedule(
+            target_date=tomorrow, read_only=True
+        )
         self.yesterday_schedule_text, self.yesterday_schedule = await self.generate_daily_schedule(
-            target_date=yesterday, read_only=True)
-
-    async def generate_daily_schedule(self, target_date: datetime.datetime = None, read_only: bool = False) -> Dict[
-        str, str]:
+            target_date=yesterday, read_only=True
+        )
 
+    async def generate_daily_schedule(
+        self, target_date: datetime.datetime = None, read_only: bool = False
+    ) -> Dict[str, str]:
         date_str = target_date.strftime("%Y-%m-%d")
         weekday = target_date.strftime("%A")
 
         schedule_text = str
 
-        existing_schedule = self.db.db.schedule.find_one({"date": date_str})
+        existing_schedule = db.schedule.find_one({"date": date_str})
         if existing_schedule:
-            logger.info(f"{date_str}的日程已存在:")
+            if self.enable_output:
+                logger.debug(f"{date_str}的日程已存在:")
             schedule_text = existing_schedule["schedule"]
             # print(self.schedule_text)
 
-        elif read_only == False:
-            logger.info(f"{date_str}的日程不存在，准备生成新的日程。")
-            prompt = f"""我是{global_config.BOT_NICKNAME}，{global_config.PROMPT_SCHEDULE_GEN}，请为我生成{date_str}（{weekday}）的日程安排，包括：""" + \
-                     """
+        elif not read_only:
+            logger.debug(f"{date_str}的日程不存在，准备生成新的日程。")
+            prompt = (
+                f"""我是{global_config.BOT_NICKNAME}，{global_config.PROMPT_SCHEDULE_GEN}，请为我生成{date_str}（{weekday}）的日程安排，包括："""
+                + """
             1. 早上的学习和工作安排
             2. 下午的活动和任务
             3. 晚上的计划和休息时间
-            请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用JSON格式返回日程表，仅返回内容，不要返回注释，时间采用24小时制，格式为{"时间": "活动","时间": "活动",...}。"""
+            请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用JSON格式返回日程表，仅返回内容，不要返回注释，不要添加任何markdown或代码块样式，时间采用24小时制，格式为{"时间": "活动","时间": "活动",...}。"""
+            )
 
             try:
                 schedule_text, _ = await self.llm_scheduler.generate_response(prompt)
-                self.db.db.schedule.insert_one({"date": date_str, "schedule": schedule_text})
+                db.schedule.insert_one({"date": date_str, "schedule": schedule_text})
+                self.enable_output = True
             except Exception as e:
                 logger.error(f"生成日程失败: {str(e)}")
                 schedule_text = "生成日程时出错了"
             # print(self.schedule_text)
         else:
-            logger.info(f"{date_str}的日程不存在。")
+            if self.enable_output:
+                logger.debug(f"{date_str}的日程不存在。")
             schedule_text = "忘了"
 
             return schedule_text, None
@@ -91,7 +91,7 @@ class ScheduleGenerator:
         try:
             schedule_dict = json.loads(schedule_text)
             return schedule_dict
-        except json.JSONDecodeError as e:
+        except json.JSONDecodeError:
             logger.exception("解析日程失败: {}".format(schedule_text))
             return False
 
@@ -105,7 +105,7 @@ class ScheduleGenerator:
 
         # 找到最接近当前时间的任务
         closest_time = None
-        min_diff = float('inf')
+        min_diff = float("inf")
 
         # 检查今天的日程
         if not self.today_schedule:
@@ -152,12 +152,13 @@ class ScheduleGenerator:
         """打印完整的日程安排"""
         if not self._parse_schedule(self.today_schedule_text):
             logger.warning("今日日程有误，将在下次运行时重新生成")
-            self.db.db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
+            db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
         else:
-            logger.info("\n=== 今日日程安排 ===")
+            logger.info("=== 今日日程安排 ===")
             for time_str, activity in self.today_schedule.items():
                 logger.info(f"时间[{time_str}]: 活动[{activity}]")
-            logger.info("==================\n")
+            logger.info("==================")
+            self.enable_output = False
 
 
 # def main():
@@ -176,6 +177,6 @@ class ScheduleGenerator:
 #     print(scheduler.tomorrow_schedule)
 
 # if __name__ == "__main__":
-#     main() 
+#     main()
 
 bot_schedule = ScheduleGenerator()
diff --git a/src/plugins/utils/statistic.py b/src/plugins/utils/statistic.py
index d7248e869..e812bce4b 100644
--- a/src/plugins/utils/statistic.py
+++ b/src/plugins/utils/statistic.py
@@ -3,8 +3,9 @@ import time
 from collections import defaultdict
 from datetime import datetime, timedelta
 from typing import Any, Dict
+from loguru import logger
 
-from ...common.database import Database
+from ...common.database import db
 
 
 class LLMStatistics:
@@ -14,7 +15,6 @@ class LLMStatistics:
         Args:
             output_file: 统计结果输出文件路径
         """
-        self.db = Database.get_instance()
         self.output_file = output_file
         self.running = False
         self.stats_thread = None
@@ -52,7 +52,7 @@ class LLMStatistics:
             "costs_by_model": defaultdict(float)
         }
         
-        cursor = self.db.db.llm_usage.find({
+        cursor = db.llm_usage.find({
             "timestamp": {"$gte": start_time}
         })
         
@@ -153,8 +153,8 @@ class LLMStatistics:
             try:
                 all_stats = self._collect_all_statistics()
                 self._save_statistics(all_stats)
-            except Exception as e:
-                print(f"\033[1;31m[错误]\033[0m 统计数据处理失败: {e}")
+            except Exception:
+                logger.exception("统计数据处理失败")
             
             # 等待1分钟
             for _ in range(60):
diff --git a/src/plugins/utils/typo_generator.py b/src/plugins/utils/typo_generator.py
index aa72c387f..f99a7ab20 100644
--- a/src/plugins/utils/typo_generator.py
+++ b/src/plugins/utils/typo_generator.py
@@ -13,6 +13,8 @@ from pathlib import Path
 import jieba
 from pypinyin import Style, pinyin
 
+from loguru import logger
+
 
 class ChineseTypoGenerator:
     def __init__(self, 
@@ -38,7 +40,9 @@ class ChineseTypoGenerator:
         self.max_freq_diff = max_freq_diff
         
         # 加载数据
-        print("正在加载汉字数据库，请稍候...")
+        # print("正在加载汉字数据库，请稍候...")
+        logger.info("正在加载汉字数据库，请稍候...")
+        
         self.pinyin_dict = self._create_pinyin_dict()
         self.char_frequency = self._load_or_create_char_frequency()
     
diff --git a/src/plugins/zhishi/knowledge_library.py b/src/plugins/zhishi/knowledge_library.py
new file mode 100644
index 000000000..a049394fe
--- /dev/null
+++ b/src/plugins/zhishi/knowledge_library.py
@@ -0,0 +1,371 @@
+import os
+import sys
+import time
+import requests
+from dotenv import load_dotenv
+import hashlib
+from datetime import datetime
+from tqdm import tqdm
+from rich.console import Console
+from rich.table import Table
+
+# 添加项目根目录到 Python 路径
+root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
+sys.path.append(root_path)
+
+# 现在可以导入src模块
+from src.common.database import db
+
+# 加载根目录下的env.edv文件
+env_path = os.path.join(root_path, ".env.prod")
+if not os.path.exists(env_path):
+    raise FileNotFoundError(f"配置文件不存在: {env_path}")
+load_dotenv(env_path)
+
+class KnowledgeLibrary:
+    def __init__(self):
+        self.raw_info_dir = "data/raw_info"
+        self._ensure_dirs()
+        self.api_key = os.getenv("SILICONFLOW_KEY")
+        if not self.api_key:
+            raise ValueError("SILICONFLOW_API_KEY 环境变量未设置")
+        self.console = Console()
+        
+    def _ensure_dirs(self):
+        """确保必要的目录存在"""
+        os.makedirs(self.raw_info_dir, exist_ok=True)
+        
+    def read_file(self, file_path: str) -> str:
+        """读取文件内容"""
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read()
+            
+    def split_content(self, content: str, max_length: int = 512) -> list:
+        """将内容分割成适当大小的块，保持段落完整性
+        
+        Args:
+            content: 要分割的文本内容
+            max_length: 每个块的最大长度
+            
+        Returns:
+            list: 分割后的文本块列表
+        """
+        # 首先按段落分割
+        paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()]
+        chunks = []
+        current_chunk = []
+        current_length = 0
+        
+        for para in paragraphs:
+            para_length = len(para)
+            
+            # 如果单个段落就超过最大长度
+            if para_length > max_length:
+                # 如果当前chunk不为空，先保存
+                if current_chunk:
+                    chunks.append('\n'.join(current_chunk))
+                    current_chunk = []
+                    current_length = 0
+                
+                # 将长段落按句子分割
+                sentences = [s.strip() for s in para.replace('。', '。\n').replace('！', '！\n').replace('？', '？\n').split('\n') if s.strip()]
+                temp_chunk = []
+                temp_length = 0
+                
+                for sentence in sentences:
+                    sentence_length = len(sentence)
+                    if sentence_length > max_length:
+                        # 如果单个句子超长，强制按长度分割
+                        if temp_chunk:
+                            chunks.append('\n'.join(temp_chunk))
+                            temp_chunk = []
+                            temp_length = 0
+                        for i in range(0, len(sentence), max_length):
+                            chunks.append(sentence[i:i + max_length])
+                    elif temp_length + sentence_length + 1 <= max_length:
+                        temp_chunk.append(sentence)
+                        temp_length += sentence_length + 1
+                    else:
+                        chunks.append('\n'.join(temp_chunk))
+                        temp_chunk = [sentence]
+                        temp_length = sentence_length
+                
+                if temp_chunk:
+                    chunks.append('\n'.join(temp_chunk))
+                
+            # 如果当前段落加上现有chunk不超过最大长度
+            elif current_length + para_length + 1 <= max_length:
+                current_chunk.append(para)
+                current_length += para_length + 1
+            else:
+                # 保存当前chunk并开始新的chunk
+                chunks.append('\n'.join(current_chunk))
+                current_chunk = [para]
+                current_length = para_length
+        
+        # 添加最后一个chunk
+        if current_chunk:
+            chunks.append('\n'.join(current_chunk))
+        
+        return chunks
+        
+    def get_embedding(self, text: str) -> list:
+        """获取文本的embedding向量"""
+        url = "https://api.siliconflow.cn/v1/embeddings"
+        payload = {
+            "model": "BAAI/bge-m3",
+            "input": text,
+            "encoding_format": "float"
+        }
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        response = requests.post(url, json=payload, headers=headers)
+        if response.status_code != 200:
+            print(f"获取embedding失败: {response.text}")
+            return None
+            
+        return response.json()['data'][0]['embedding']
+        
+    def process_files(self, knowledge_length:int=512):
+        """处理raw_info目录下的所有txt文件"""
+        txt_files = [f for f in os.listdir(self.raw_info_dir) if f.endswith('.txt')]
+        
+        if not txt_files:
+            self.console.print("[red]警告：在 {} 目录下没有找到任何txt文件[/red]".format(self.raw_info_dir))
+            self.console.print("[yellow]请将需要处理的文本文件放入该目录后再运行程序[/yellow]")
+            return
+            
+        total_stats = {
+            "processed_files": 0,
+            "total_chunks": 0,
+            "failed_files": [],
+            "skipped_files": []
+        }
+        
+        self.console.print(f"\n[bold blue]开始处理知识库文件 - 共{len(txt_files)}个文件[/bold blue]")
+        
+        for filename in tqdm(txt_files, desc="处理文件进度"):
+            file_path = os.path.join(self.raw_info_dir, filename)
+            result = self.process_single_file(file_path, knowledge_length)
+            self._update_stats(total_stats, result, filename)
+            
+        self._display_processing_results(total_stats)
+                
+    def process_single_file(self, file_path: str, knowledge_length: int = 512):
+        """处理单个文件"""
+        result = {
+            "status": "success",
+            "chunks_processed": 0,
+            "error": None
+        }
+        
+        try:
+            current_hash = self.calculate_file_hash(file_path)
+            processed_record = db.processed_files.find_one({"file_path": file_path})
+            
+            if processed_record:
+                if processed_record.get("hash") == current_hash:
+                    if knowledge_length in processed_record.get("split_by", []):
+                        result["status"] = "skipped"
+                        return result
+            
+            content = self.read_file(file_path)
+            chunks = self.split_content(content, knowledge_length)
+            
+            for chunk in tqdm(chunks, desc=f"处理 {os.path.basename(file_path)} 的文本块", leave=False):
+                embedding = self.get_embedding(chunk)
+                if embedding:
+                    knowledge = {
+                        "content": chunk,
+                        "embedding": embedding,
+                        "source_file": file_path,
+                        "split_length": knowledge_length,
+                        "created_at": datetime.now()
+                    }
+                    db.knowledges.insert_one(knowledge)
+                    result["chunks_processed"] += 1
+            
+            split_by = processed_record.get("split_by", []) if processed_record else []
+            if knowledge_length not in split_by:
+                split_by.append(knowledge_length)
+            
+            db.knowledges.processed_files.update_one(
+                {"file_path": file_path},
+                {
+                    "$set": {
+                        "hash": current_hash,
+                        "last_processed": datetime.now(),
+                        "split_by": split_by
+                    }
+                },
+                upsert=True
+            )
+            
+        except Exception as e:
+            result["status"] = "failed"
+            result["error"] = str(e)
+            
+        return result
+        
+    def _update_stats(self, total_stats, result, filename):
+        """更新总体统计信息"""
+        if result["status"] == "success":
+            total_stats["processed_files"] += 1
+            total_stats["total_chunks"] += result["chunks_processed"]
+        elif result["status"] == "failed":
+            total_stats["failed_files"].append((filename, result["error"]))
+        elif result["status"] == "skipped":
+            total_stats["skipped_files"].append(filename)
+            
+    def _display_processing_results(self, stats):
+        """显示处理结果统计"""
+        self.console.print("\n[bold green]处理完成！统计信息如下：[/bold green]")
+        
+        table = Table(show_header=True, header_style="bold magenta")
+        table.add_column("统计项", style="dim")
+        table.add_column("数值")
+        
+        table.add_row("成功处理文件数", str(stats["processed_files"]))
+        table.add_row("处理的知识块总数", str(stats["total_chunks"]))
+        table.add_row("跳过的文件数", str(len(stats["skipped_files"])))
+        table.add_row("失败的文件数", str(len(stats["failed_files"])))
+        
+        self.console.print(table)
+        
+        if stats["failed_files"]:
+            self.console.print("\n[bold red]处理失败的文件：[/bold red]")
+            for filename, error in stats["failed_files"]:
+                self.console.print(f"[red]- {filename}: {error}[/red]")
+                
+        if stats["skipped_files"]:
+            self.console.print("\n[bold yellow]跳过的文件（已处理）：[/bold yellow]")
+            for filename in stats["skipped_files"]:
+                self.console.print(f"[yellow]- {filename}[/yellow]")
+            
+    def calculate_file_hash(self, file_path):
+        """计算文件的MD5哈希值"""
+        hash_md5 = hashlib.md5()
+        with open(file_path, "rb") as f:
+            for chunk in iter(lambda: f.read(4096), b""):
+                hash_md5.update(chunk)
+        return hash_md5.hexdigest()
+
+    def search_similar_segments(self, query: str, limit: int = 5) -> list:
+        """搜索与查询文本相似的片段"""
+        query_embedding = self.get_embedding(query)
+        if not query_embedding:
+            return []
+            
+        # 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"]}]
+                    }
+                }
+            },
+            {"$sort": {"similarity": -1}},
+            {"$limit": limit},
+            {"$project": {"content": 1, "similarity": 1, "file_path": 1}}
+        ]
+        
+        results = list(db.knowledges.aggregate(pipeline))
+        return results
+
+# 创建单例实例
+knowledge_library = KnowledgeLibrary()
+
+if __name__ == "__main__":
+    console = Console()
+    console.print("[bold green]知识库处理工具[/bold green]")
+    
+    while True:
+        console.print("\n请选择要执行的操作：")
+        console.print("[1] 麦麦开始学习")
+        console.print("[2] 麦麦全部忘光光（仅知识）")
+        console.print("[q] 退出程序")
+        
+        choice = input("\n请输入选项: ").strip()
+        
+        if choice.lower() == 'q':
+            console.print("[yellow]程序退出[/yellow]")
+            sys.exit(0)
+        elif choice == '2':
+            confirm = input("确定要删除所有知识吗？这个操作不可撤销！(y/n): ").strip().lower()
+            if confirm == 'y':
+                db.knowledges.delete_many({})
+                console.print("[green]已清空所有知识！[/green]")
+            continue
+        elif choice == '1':
+            if not os.path.exists(knowledge_library.raw_info_dir):
+                console.print(f"[yellow]创建目录：{knowledge_library.raw_info_dir}[/yellow]")
+                os.makedirs(knowledge_library.raw_info_dir, exist_ok=True)
+            
+            # 询问分割长度
+            while True:
+                try:
+                    length_input = input("请输入知识分割长度（默认512，输入q退出，回车使用默认值）: ").strip()
+                    if length_input.lower() == 'q':
+                        break
+                    if not length_input:  # 如果直接回车，使用默认值
+                        knowledge_length = 512
+                        break
+                    knowledge_length = int(length_input)
+                    if knowledge_length <= 0:
+                        print("分割长度必须大于0，请重新输入")
+                        continue
+                    break
+                except ValueError:
+                    print("请输入有效的数字")
+                    continue
+            
+            if length_input.lower() == 'q':
+                continue
+                
+            # 测试知识库功能
+            print(f"开始处理知识库文件，使用分割长度: {knowledge_length}...")
+            knowledge_library.process_files(knowledge_length=knowledge_length)
+        else:
+            console.print("[red]无效的选项，请重新选择[/red]")
+            continue
diff --git a/src/test/typo.py b/src/test/typo.py
index 16834200f..1378eae7d 100644
--- a/src/test/typo.py
+++ b/src/test/typo.py
@@ -11,12 +11,14 @@ from pathlib import Path
 import random
 import math
 import time
+from loguru import logger
+
 
 class ChineseTypoGenerator:
-    def __init__(self, 
-                 error_rate=0.3, 
-                 min_freq=5, 
-                 tone_error_rate=0.2, 
+    def __init__(self,
+                 error_rate=0.3,
+                 min_freq=5,
+                 tone_error_rate=0.2,
                  word_replace_rate=0.3,
                  max_freq_diff=200):
         """
@@ -34,27 +36,27 @@ class ChineseTypoGenerator:
         self.tone_error_rate = tone_error_rate
         self.word_replace_rate = word_replace_rate
         self.max_freq_diff = max_freq_diff
-        
+
         # 加载数据
-        print("正在加载汉字数据库，请稍候...")
+        logger.debug("正在加载汉字数据库，请稍候...")
         self.pinyin_dict = self._create_pinyin_dict()
         self.char_frequency = self._load_or_create_char_frequency()
-    
+
     def _load_or_create_char_frequency(self):
         """
         加载或创建汉字频率字典
         """
         cache_file = Path("char_frequency.json")
-        
+
         # 如果缓存文件存在，直接加载
         if cache_file.exists():
             with open(cache_file, 'r', encoding='utf-8') as f:
                 return json.load(f)
-        
+
         # 使用内置的词频文件
         char_freq = defaultdict(int)
         dict_path = os.path.join(os.path.dirname(jieba.__file__), 'dict.txt')
-        
+
         # 读取jieba的词典文件
         with open(dict_path, 'r', encoding='utf-8') as f:
             for line in f:
@@ -63,15 +65,15 @@ class ChineseTypoGenerator:
                 for char in word:
                     if self._is_chinese_char(char):
                         char_freq[char] += int(freq)
-        
+
         # 归一化频率值
         max_freq = max(char_freq.values())
-        normalized_freq = {char: freq/max_freq * 1000 for char, freq in char_freq.items()}
-        
+        normalized_freq = {char: freq / max_freq * 1000 for char, freq in char_freq.items()}
+
         # 保存到缓存文件
         with open(cache_file, 'w', encoding='utf-8') as f:
             json.dump(normalized_freq, f, ensure_ascii=False, indent=2)
-        
+
         return normalized_freq
 
     def _create_pinyin_dict(self):
@@ -81,7 +83,7 @@ class ChineseTypoGenerator:
         # 常用汉字范围
         chars = [chr(i) for i in range(0x4e00, 0x9fff)]
         pinyin_dict = defaultdict(list)
-        
+
         # 为每个汉字建立拼音映射
         for char in chars:
             try:
@@ -89,7 +91,7 @@ class ChineseTypoGenerator:
                 pinyin_dict[py].append(char)
             except Exception:
                 continue
-        
+
         return pinyin_dict
 
     def _is_chinese_char(self, char):
@@ -107,7 +109,7 @@ class ChineseTypoGenerator:
         """
         # 将句子拆分成单个字符
         characters = list(sentence)
-        
+
         # 获取每个字符的拼音
         result = []
         for char in characters:
@@ -117,7 +119,7 @@ class ChineseTypoGenerator:
             # 获取拼音（数字声调）
             py = pinyin(char, style=Style.TONE3)[0][0]
             result.append((char, py))
-        
+
         return result
 
     def _get_similar_tone_pinyin(self, py):
@@ -127,19 +129,19 @@ class ChineseTypoGenerator:
         # 检查拼音是否为空或无效
         if not py or len(py) < 1:
             return py
-            
+
         # 如果最后一个字符不是数字，说明可能是轻声或其他特殊情况
         if not py[-1].isdigit():
             # 为非数字结尾的拼音添加数字声调1
             return py + '1'
-        
+
         base = py[:-1]  # 去掉声调
         tone = int(py[-1])  # 获取声调
-        
+
         # 处理轻声（通常用5表示）或无效声调
         if tone not in [1, 2, 3, 4]:
             return base + str(random.choice([1, 2, 3, 4]))
-        
+
         # 正常处理声调
         possible_tones = [1, 2, 3, 4]
         possible_tones.remove(tone)  # 移除原声调
@@ -152,11 +154,11 @@ class ChineseTypoGenerator:
         """
         if target_freq > orig_freq:
             return 1.0  # 如果替换字频率更高，保持原有概率
-        
+
         freq_diff = orig_freq - target_freq
         if freq_diff > self.max_freq_diff:
             return 0.0  # 频率差太大，不替换
-        
+
         # 使用指数衰减函数计算概率
         # 频率差为0时概率为1，频率差为max_freq_diff时概率接近0
         return math.exp(-3 * freq_diff / self.max_freq_diff)
@@ -166,42 +168,42 @@ class ChineseTypoGenerator:
         获取与给定字频率相近的同音字，可能包含声调错误
         """
         homophones = []
-        
+
         # 有一定概率使用错误声调
         if random.random() < self.tone_error_rate:
             wrong_tone_py = self._get_similar_tone_pinyin(py)
             homophones.extend(self.pinyin_dict[wrong_tone_py])
-        
+
         # 添加正确声调的同音字
         homophones.extend(self.pinyin_dict[py])
-        
+
         if not homophones:
             return None
-            
+
         # 获取原字的频率
         orig_freq = self.char_frequency.get(char, 0)
-        
+
         # 计算所有同音字与原字的频率差，并过滤掉低频字
-        freq_diff = [(h, self.char_frequency.get(h, 0)) 
-                    for h in homophones 
-                    if h != char and self.char_frequency.get(h, 0) >= self.min_freq]
-        
+        freq_diff = [(h, self.char_frequency.get(h, 0))
+                     for h in homophones
+                     if h != char and self.char_frequency.get(h, 0) >= self.min_freq]
+
         if not freq_diff:
             return None
-        
+
         # 计算每个候选字的替换概率
         candidates_with_prob = []
         for h, freq in freq_diff:
             prob = self._calculate_replacement_probability(orig_freq, freq)
             if prob > 0:  # 只保留有效概率的候选字
                 candidates_with_prob.append((h, prob))
-        
+
         if not candidates_with_prob:
             return None
-        
+
         # 根据概率排序
         candidates_with_prob.sort(key=lambda x: x[1], reverse=True)
-        
+
         # 返回概率最高的几个字
         return [char for char, _ in candidates_with_prob[:num_candidates]]
 
@@ -223,10 +225,10 @@ class ChineseTypoGenerator:
         """
         if len(word) == 1:
             return []
-            
+
         # 获取词的拼音
         word_pinyin = self._get_word_pinyin(word)
-        
+
         # 遍历所有可能的同音字组合
         candidates = []
         for py in word_pinyin:
@@ -234,11 +236,11 @@ class ChineseTypoGenerator:
             if not chars:
                 return []
             candidates.append(chars)
-        
+
         # 生成所有可能的组合
         import itertools
         all_combinations = itertools.product(*candidates)
-        
+
         # 获取jieba词典和词频信息
         dict_path = os.path.join(os.path.dirname(jieba.__file__), 'dict.txt')
         valid_words = {}  # 改用字典存储词语及其频率
@@ -249,11 +251,11 @@ class ChineseTypoGenerator:
                     word_text = parts[0]
                     word_freq = float(parts[1])  # 获取词频
                     valid_words[word_text] = word_freq
-        
+
         # 获取原词的词频作为参考
         original_word_freq = valid_words.get(word, 0)
         min_word_freq = original_word_freq * 0.1  # 设置最小词频为原词频的10%
-        
+
         # 过滤和计算频率
         homophones = []
         for combo in all_combinations:
@@ -268,7 +270,7 @@ class ChineseTypoGenerator:
                     combined_score = (new_word_freq * 0.7 + char_avg_freq * 0.3)
                     if combined_score >= self.min_freq:
                         homophones.append((new_word, combined_score))
-        
+
         # 按综合分数排序并限制返回数量
         sorted_homophones = sorted(homophones, key=lambda x: x[1], reverse=True)
         return [word for word, _ in sorted_homophones[:5]]  # 限制返回前5个结果
@@ -286,19 +288,19 @@ class ChineseTypoGenerator:
         """
         result = []
         typo_info = []
-        
+
         # 分词
         words = self._segment_sentence(sentence)
-        
+
         for word in words:
             # 如果是标点符号或空格，直接添加
             if all(not self._is_chinese_char(c) for c in word):
                 result.append(word)
                 continue
-                
+
             # 获取词语的拼音
             word_pinyin = self._get_word_pinyin(word)
-            
+
             # 尝试整词替换
             if len(word) > 1 and random.random() < self.word_replace_rate:
                 word_homophones = self._get_word_homophones(word)
@@ -307,15 +309,15 @@ class ChineseTypoGenerator:
                     # 计算词的平均频率
                     orig_freq = sum(self.char_frequency.get(c, 0) for c in word) / len(word)
                     typo_freq = sum(self.char_frequency.get(c, 0) for c in typo_word) / len(typo_word)
-                    
+
                     # 添加到结果中
                     result.append(typo_word)
-                    typo_info.append((word, typo_word, 
-                                    ' '.join(word_pinyin), 
-                                    ' '.join(self._get_word_pinyin(typo_word)), 
-                                    orig_freq, typo_freq))
+                    typo_info.append((word, typo_word,
+                                      ' '.join(word_pinyin),
+                                      ' '.join(self._get_word_pinyin(typo_word)),
+                                      orig_freq, typo_freq))
                     continue
-            
+
             # 如果不进行整词替换，则进行单字替换
             if len(word) == 1:
                 char = word
@@ -339,7 +341,7 @@ class ChineseTypoGenerator:
                 for i, (char, py) in enumerate(zip(word, word_pinyin)):
                     # 词中的字替换概率降低
                     word_error_rate = self.error_rate * (0.7 ** (len(word) - 1))
-                    
+
                     if random.random() < word_error_rate:
                         similar_chars = self._get_similar_frequency_chars(char, py)
                         if similar_chars:
@@ -354,7 +356,7 @@ class ChineseTypoGenerator:
                                 continue
                     word_result.append(char)
                 result.append(''.join(word_result))
-        
+
         return ''.join(result), typo_info
 
     def format_typo_info(self, typo_info):
@@ -369,7 +371,7 @@ class ChineseTypoGenerator:
         """
         if not typo_info:
             return "未生成错别字"
-            
+
         result = []
         for orig, typo, orig_py, typo_py, orig_freq, typo_freq in typo_info:
             # 判断是否为词语替换
@@ -379,12 +381,12 @@ class ChineseTypoGenerator:
             else:
                 tone_error = orig_py[:-1] == typo_py[:-1] and orig_py[-1] != typo_py[-1]
                 error_type = "声调错误" if tone_error else "同音字替换"
-            
+
             result.append(f"原文：{orig}({orig_py}) [频率：{orig_freq:.2f}] -> "
-                        f"替换：{typo}({typo_py}) [频率：{typo_freq:.2f}] [{error_type}]")
-        
+                          f"替换：{typo}({typo_py}) [频率：{typo_freq:.2f}] [{error_type}]")
+
         return "\n".join(result)
-    
+
     def set_params(self, **kwargs):
         """
         设置参数
@@ -399,9 +401,10 @@ class ChineseTypoGenerator:
         for key, value in kwargs.items():
             if hasattr(self, key):
                 setattr(self, key, value)
-                print(f"参数 {key} 已设置为 {value}")
+                logger.debug(f"参数 {key} 已设置为 {value}")
             else:
-                print(f"警告: 参数 {key} 不存在")
+                logger.warning(f"警告: 参数 {key} 不存在")
+
 
 def main():
     # 创建错别字生成器实例
@@ -411,27 +414,27 @@ def main():
         tone_error_rate=0.02,
         word_replace_rate=0.3
     )
-    
+
     # 获取用户输入
     sentence = input("请输入中文句子：")
-    
+
     # 创建包含错别字的句子
     start_time = time.time()
     typo_sentence, typo_info = typo_generator.create_typo_sentence(sentence)
-    
+
     # 打印结果
-    print("\n原句：", sentence)
-    print("错字版：", typo_sentence)
-    
+    logger.debug("原句：", sentence)
+    logger.debug("错字版：", typo_sentence)
+
     # 打印错别字信息
     if typo_info:
-        print("\n错别字信息：")
-        print(typo_generator.format_typo_info(typo_info))
-    
+        logger.debug(f"错别字信息：{typo_generator.format_typo_info(typo_info)})")
+
     # 计算并打印总耗时
     end_time = time.time()
     total_time = end_time - start_time
-    print(f"\n总耗时：{total_time:.2f}秒")
+    logger.debug(f"总耗时：{total_time:.2f}秒")
+
 
 if __name__ == "__main__":
     main()
diff --git a/template.env b/template.env
index 09fe63597..322776ce7 100644
--- a/template.env
+++ b/template.env
@@ -5,20 +5,25 @@ PORT=8080
 PLUGINS=["src2.plugins.chat"]
 
 # 默认配置
-MONGODB_HOST=127.0.0.1 # 如果工作在Docker下，请改成 MONGODB_HOST=mongodb
+# 如果工作在Docker下，请改成 MONGODB_HOST=mongodb
+MONGODB_HOST=127.0.0.1
 MONGODB_PORT=27017
 DATABASE_NAME=MegBot
 
-MONGODB_USERNAME = ""  # 默认空值
-MONGODB_PASSWORD = ""  # 默认空值
-MONGODB_AUTH_SOURCE = ""  # 默认空值
+# 也可以使用 URI 连接数据库（优先级比上面的高）
+# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot
+
+# MongoDB 认证信息，若需要认证，请取消注释以下三行并填写正确的信息
+# MONGODB_USERNAME=user
+# MONGODB_PASSWORD=password
+# MONGODB_AUTH_SOURCE=admin
 
 #key and url
 CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
 SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
 DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
 
-#定义你要用的api的base_url
+#定义你要用的api的key(需要去对应网站申请哦)
 DEEP_SEEK_KEY=
 CHAT_ANY_WHERE_KEY=
-SILICONFLOW_KEY=
\ No newline at end of file
+SILICONFLOW_KEY=
diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml
index bff64d05f..089be69b0 100644
--- a/template/bot_config_template.toml
+++ b/template/bot_config_template.toml
@@ -1,5 +1,5 @@
 [inner]
-version = "0.0.4"
+version = "0.0.8"
 
 #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
@@ -15,6 +15,7 @@ version = "0.0.4"
 [bot]
 qq = 123
 nickname = "麦麦"
+alias_names = ["小麦", "阿麦"]
 
 [personality]
 prompt_personality = [
@@ -40,6 +41,13 @@ ban_words = [
     # "403","张三"
     ]
 
+ban_msgs_regex = [
+    # 需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤（支持CQ码），若不了解正则表达式请勿修改
+    #"https?://[^\\s]+", # 匹配https链接
+    #"\\d{4}-\\d{2}-\\d{2}", # 匹配日期
+    # "\\[CQ:at,qq=\\d+\\]" # 匹配@
+]
+
 [emoji]
 check_interval = 120 # 检查表情包的时间间隔
 register_interval = 10 # 注册表情包的时间间隔
@@ -57,8 +65,13 @@ model_r1_distill_probability = 0.1 # 麦麦回答时选择次要回复模型3 
 max_response_length = 1024 # 麦麦回答的最大token数
 
 [memory]
-build_memory_interval = 300 # 记忆构建间隔 单位秒
-forget_memory_interval = 300 # 记忆遗忘间隔 单位秒
+build_memory_interval = 600 # 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
+memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多
+
+forget_memory_interval = 600 # 记忆遗忘间隔 单位秒   间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习
+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
     # "403","张三"
@@ -92,6 +105,8 @@ word_replace_rate=0.006 # 整词替换概率
 [others]
 enable_advance_output = true # 是否启用高级输出
 enable_kuuki_read = true # 是否启用读空气功能
+enable_debug_output = false # 是否启用调试输出
+enable_friend_chat = false # 是否启用好友聊天
 
 [groups]
 talk_allowed = [
diff --git a/如果你更新了版本，点我.txt b/如果你更新了版本，点我.txt
new file mode 100644
index 000000000..400e8ae0c
--- /dev/null
+++ b/如果你更新了版本，点我.txt
@@ -0,0 +1,4 @@
+更新版本后，建议删除数据库messages中所有内容，不然会出现报错
+该操作不会影响你的记忆
+
+如果显示配置文件版本过低，运行根目录的bat
\ No newline at end of file
diff --git a/如果你的配置文件版本太老就点我.bat b/如果你的配置文件版本太老就点我.bat
new file mode 100644
index 000000000..fec1f4cdb
--- /dev/null
+++ b/如果你的配置文件版本太老就点我.bat
@@ -0,0 +1,45 @@
+@echo off
+setlocal enabledelayedexpansion
+chcp 65001
+cd /d %~dp0
+
+echo =====================================
+echo 选择Python环境:
+echo 1 - venv (推荐)
+echo 2 - conda
+echo =====================================
+choice /c 12 /n /m "输入数字(1或2): "
+
+if errorlevel 2 (
+    echo =====================================
+    set "CONDA_ENV="
+    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
+    
+    :: 检查输入是否为空
+    if "!CONDA_ENV!"=="" (
+        echo 错误：环境名称不能为空
+        pause
+        exit /b 1
+    )
+    
+    call conda activate !CONDA_ENV!
+    if errorlevel 1 (
+        echo 激活 conda 环境失败
+        pause
+        exit /b 1
+    )
+    
+    echo Conda 环境 "!CONDA_ENV!" 激活成功
+    python config/auto_update.py
+) else (
+    if exist "venv\Scripts\python.exe" (
+        venv\Scripts\python config/auto_update.py
+    ) else (
+        echo =====================================
+        echo 错误: venv环境不存在，请先创建虚拟环境
+        pause
+        exit /b 1
+    )
+)
+endlocal
+pause
diff --git a/麦麦开始学习.bat b/麦麦开始学习.bat
new file mode 100644
index 000000000..f7391150f
--- /dev/null
+++ b/麦麦开始学习.bat
@@ -0,0 +1,45 @@
+@echo off
+setlocal enabledelayedexpansion
+chcp 65001
+cd /d %~dp0
+
+echo =====================================
+echo 选择Python环境:
+echo 1 - venv (推荐)
+echo 2 - conda
+echo =====================================
+choice /c 12 /n /m "输入数字(1或2): "
+
+if errorlevel 2 (
+    echo =====================================
+    set "CONDA_ENV="
+    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
+    
+    :: 检查输入是否为空
+    if "!CONDA_ENV!"=="" (
+        echo 错误：环境名称不能为空
+        pause
+        exit /b 1
+    )
+    
+    call conda activate !CONDA_ENV!
+    if errorlevel 1 (
+        echo 激活 conda 环境失败
+        pause
+        exit /b 1
+    )
+    
+    echo Conda 环境 "!CONDA_ENV!" 激活成功
+    python src/plugins/zhishi/knowledge_library.py
+) else (
+    if exist "venv\Scripts\python.exe" (
+        venv\Scripts\python src/plugins/zhishi/knowledge_library.py
+    ) else (
+        echo =====================================
+        echo 错误: venv环境不存在，请先创建虚拟环境
+        pause
+        exit /b 1
+    )
+)
+endlocal
+pause