Merge remote-tracking branch 'upstream/debug' into debug

2025-03-12 08:25:42 +08:00
parent 6b11b6ef29 b934d473ab
commit fd40c05bf4
45 changed files with 1842 additions and 659 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 data/
+data1/
 mongodb/
 NapCat.Framework.Windows.Once/
 log/
--- a/README.md
+++ b/README.md
@@ -17,7 +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">
@@ -40,7 +44,12 @@
 - [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)

--- a/bot.py
+++ b/bot.py
@@ -12,6 +12,8 @@ from loguru import logger
 from nonebot.adapters.onebot.v11 import Adapter
 import platform

+from src.common.database import Database
+
 # 获取没有加载env时的环境变量
 env_mask = {key: os.getenv(key) for key in os.environ}

@@ -51,19 +53,19 @@ def init_env():
        with open(".env", "w") as f:
            f.write("ENVIRONMENT=prod")

-    # 检测.env.prod文件是否存在
-    if not os.path.exists(".env.prod"):
-        logger.error("检测到.env.prod文件不存在")
-        shutil.copy("template.env", "./.env.prod")
+        # 检测.env.prod文件是否存在
+        if not os.path.exists(".env.prod"):
+            logger.error("检测到.env.prod文件不存在")
+            shutil.copy("template.env", "./.env.prod")

    # 检测.env.dev文件是否存在，不存在的话直接复制生产环境配置
    if not os.path.exists(".env.dev"):
        logger.error("检测到.env.dev文件不存在")
-        shutil.copy("template.env", "./.env.dev")
+        shutil.copy(".env.prod", "./.env.dev")

    # 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")


@@ -96,18 +98,39 @@ def load_env():
        logger.error(f"ENVIRONMENT 配置错误，请检查 .env 文件中的 ENVIRONMENT 变量及对应 .env.{env} 是否存在")
        RuntimeError(f"ENVIRONMENT 配置错误，请检查 .env 文件中的 ENVIRONMENT 变量及对应 .env.{env} 是否存在")

+def init_database():
+    Database.initialize(
+        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"),
+    )
+    

 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
-        filter=lambda record: "nonebot" not in record["name"]
-    )
+    if os.getenv("ENVIRONMENT") == "dev":
+        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", "DEBUG"),  # 根据环境设置日志级别，默认为DEBUG
+        )
+    else:
+        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
+            filter=lambda record: "nonebot" not in record["name"]
+        )



@@ -188,6 +211,7 @@ def raw_main():
    init_config()
    init_env()
    load_env()
+    init_database() # 加载完成环境后初始化database
    load_logger()

    env_config = {key: os.getenv(key) for key in os.environ}
@@ -213,7 +237,6 @@ def raw_main():


 if __name__ == "__main__":
-
    try:
        raw_main()

--- 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

--- a/config/auto_update.py
+++ 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()
--- a/docs/linux_deploy_guide_for_beginners.md
+++ 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
+```
+
--- a/docs/manual_deploy_linux.md
+++ b/docs/manual_deploy_linux.md
@@ -110,6 +110,57 @@ python3 bot.py

 ---

+### 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
--- 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",
--- a/flake.nix
+++ b/flake.nix
@@ -1,62 +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; [
-            ruff
-            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
+        '';
+      };
+    });
 }
--- a/141
+++ b/141
@@ -0,0 +1,141 @@
+cbb569e - Create 如果你更新了版本，点我.txt
+a91ef7b - 自动升级配置文件脚本
+ed18f2e - 新增了知识库一键启动漂亮脚本
+80ed568 - fix: 删除print调试代码
+c681a82 - 修复小名无效问题
+e54038f - fix: 从 nixpkgs 增加 numpy 依赖，以避免出现 libc++.so 找不到的问题
+26782c9 - fix: 修复 ENVIRONMENT 变量在同一终端下不能被覆盖的问题
+8c34637 - 提高健壮性
+2688a96 - close SengokuCola/MaiMBot#225 让麦麦可以正确读取分享卡片
+cd16e68 - 修复表情包发送时的缺失参数
+b362c35 - feat: 更新 flake.nix ，采用 venv 的方式生成环境，nixos用户也可以本机运行项目了
+3c8c897 - 屏蔽一个臃肿的debug信息
+9d0152a - 修复了合并过程中造成的代码重复
+956135c - 添加一些注释
+a412741 - 将print变为logger.debug
+3180426 - 修复了没有改掉的typo字段
+aea3bff - 添加私聊过滤开关,更新config,增加约束
+cda6281 - chore: update emoji_manager.py
+baed856 - 修正了私聊屏蔽词输出
+66a0f18 - 修复了私聊时产生reply消息的bug
+3bf5cd6 - feat: 新增运行时重载配置文件；新增根据不同环境(dev;prod)显示不同级别的log
+33cd83b - 添加私聊功能
+aa41f0d - fix: 放反了
+ef8691c - fix: 修改message继承逻辑，修复回复消息无法识别
+7d017be - fix:模型降级
+e1019ad - fix: 修复变量拼写错误并优化代码可读性
+c24bb70 - fix: 流式输出模式增加结束判断与token用量记录
+60a9376 - 添加logger的debug输出开关,默认为不开启
+bfa9a3c - fix: 添加群信息获取的错误处理 (#173)
+4cc5c8e - 修正.env.prod和.env.dev的生成
+dea14c1 - fix: 模型降级目前只对硅基流动的V3和R1生效
+b6edbea - fix: 图片保存路径不正确
+01a6fa8 - fix: 删除神秘test
+20f009d - 修复systemctl强制停止maimbot的问题
+af962c2 - 修复了情绪管理器没有正确导入导致发布出消息
+0586700 - 按照Sourcery提供的建议修改systemctl管理指南
+e48b32a - 在手动部署教程中增加使用systemctl管理
+5760412 - fix: 小修
+1c9b0cc - fix: 修复部分cq码解析错误，merge
+b6867b9 - fix: 统一使用os.getenv获取数据库连接信息，避免从config对象获取不存在的值时出现KeyError
+5e069f7 - 修复记忆保存时无时间信息的bug
+73a3e41 - 修复记忆更新bug
+52c93ba - refactor: use Base64 for emoji CQ codes
+67f6d7c - fix: 保证能运行的小修改
+c32c4fb - refactor: 修改配置文件的版本号
+a54ca8c - Merge remote-tracking branch 'upstream/debug' into feat_regix
+8cbf9bb - feat: 史上最好的消息流重构和图片管理
+9e41c4f - feat: 修改 bot_config 0.0.5 版本的变更日志
+eede406 - fix: 修复nonebot无法加载项目的问题
+00e02ed - fix: 0.0.5 版本的增加分层控制项
+0f99d6a - Update docs/docker_deploy.md
+c789074 - feat: 增加ruff依赖
+ff65ab8 - feat: 修改默认的ruff配置文件，同时消除config的所有不符合规范的地方
+bf97013 - feat: 精简日志，禁用Uvicorn/NoneBot默认日志；启动方式改为显示加载uvicorn，以便优雅shutdown
+d9a2863 - 优化Docker部署文档更新容器部分
+efcf00f - Docker部署文档追加更新部分
+a63ce96 - fix: 更新情感判断模型配置（使配置文件里的 llm_emotion_judge 生效）
+1294c88 - feat: 增加标准化格式化设置
+2e8cd47 - fix: 避免可能出现的日程解析错误
+043a724 - 修一下文档跳转，小美化（
+e4b8865 - 支持别名，可以用不同名称召唤机器人
+7b35ddd - ruff 哥又有新点子
+7899e67 - feat: 重构完成开始测试debug
+354d6d0 - 记忆系统优化
+6cef8fd - 修复时区，删去napcat用不到的端口
+cd96644 - 添加使用说明
+84495f8 - fix
+204744c - 修改配置名与修改过滤对象为raw_message
+a03b490 - Update README.md
+2b2b342 - feat: 增加 ruff 依赖
+72a6749 - fix: 修复docker部署时区指定问题
+ee579bc - Update README.md
+1b611ec - resolve SengokuCola/MaiMBot#167 根据正则表达式过滤消息
+6e2ea82 - refractor: 几乎写完了，进入测试阶段
+2ffdfef - More
+e680405 - fix: typo 'discription'
+68b3f57 - Minor Doc Update
+312f065 - Create linux_deploy_guide_for_beginners.md
+ed505a4 - fix: 使用动态路径替换硬编码的项目路径
+8ff7bb6 - docs: 更新文档，修正格式并添加必要的换行符
+6e36a56 - feat: 增加 MONGODB_URI 的配置项，并将所有env文件的注释单独放在一行（python的dotenv有时无法正确处理行内注释）
+4baa6c6 - feat: 实现MongoDB URI方式连接，并统一数据库连接代码。
+8a32d18 - feat: 优化willing_manager逻辑，增加回复保底概率
+c9f1244 - docs: 改进README.md文档格式和排版
+e1b484a - docs: 添加CLAUDE.md开发指南文件（用于Claude Code）
+a43f949 - fix: remove duplicate message(CR comments)
+fddb641 - fix: 修复错误的空值检测逻辑
+8b7876c - fix: 修复没有上传tag的问题
+6b4130e - feat: 增加stable-dev分支的打包
+052e67b - refactor: 日志打印优化（终于改完了，爽了
+a7f9d05 - 修复记忆整理传入格式问题
+536bb1d - fix: 更新情感判断模型配置
+8d99592 - fix: logger初始化顺序
+052802c - refactor: logger promotion
+8661d94 - doc: README.md - telegram version information
+5746afa - refactor: logger in src\plugins\chat\bot.py
+288dbb6 - refactor: logger in src\plugins\chat\__init__.py
+8428a06 - fix: memory logger optimization (CR comment)
+665c459 - 改进了可视化脚本
+6c35704 - fix: 调用了错误的函数
+3223153 - feat: 一键脚本新增记忆可视化
+3149dd3 - fix: mongodb.zip 无法解压 fix:更换执行命令的方法 fix:当 db 不存在时自动创建 feat: 一键安装完成后启动麦麦
+089d6a6 - feat: 针对硅基流动的Pro模型添加了自动降级功能
+c4b0917 - 一个记忆可视化小脚本
+6a71ea4 - 修复了记忆时间bug,config添加了记忆屏蔽关键词
+1b5344f - fix: 优化bot初始化的日志&格式
+41aa974 - fix: 优化chat/config.py的日志&格式
+980cde7 - fix: 优化scheduler_generator日志&格式
+31a5514 - fix: 调整全局logger加载顺序
+8baef07 - feat: 添加全局logger初始化设置
+5566f17 - refractor: 几乎写完了，进入测试阶段
+6a66933 - feat: 添加开发环境.env.dev初始化
+411ff1a - feat: 安装 MongoDB Compass
+0de9eba - feat: 增加实时更新贡献者列表的功能
+f327f45 - fix: 优化src/plugins/chat/__init__.py的import
+826daa5 - fix: 当虚拟环境存在时跳过创建
+f54de42 - fix: time.tzset 仅在类 Unix 系统可用
+47c4990 - fix: 修复docker部署场景下时间错误的问题
+e23a371 - docs: 添加 compose 注释
+1002822 - docs: 标注 Python 最低版本
+564350d - feat: 校验 Python 版本
+4cc4482 - docs: 添加傻瓜式脚本
+757173a - 带麦麦看了心理医生，让她没那么容易陷入负面情绪
+39bb99c - 将错别字生成提取到配置，一句一个错别字太烦了！
+fe36847 - feat: 超大型重构
+e304dd7 - Update README.md
+b7cfe6d - feat: 发布第 0.0.2 版本配置模板
+ca929d5 - 补充Docker部署文档
+1e97120 - 补充Docker部署文档
+25f7052 - fix: 修复兼容性选项和目前第一个版本之间的版本间隙 0.0.0 版，并将所有的直接退出修改为抛出异常
+c5bdc4f - 防ipv6炸，虽然小概率事件
+d86610d - fix: 修复不能加载环境变量的问题
+2306ebf - feat: 因为判断临界版本范围比较麻烦，增加 notice 字段，删除原本的判断逻辑（存在故障）
+dd09576 - fix: 修复 TypeError: BotConfig.convert_to_specifierset() takes 1 positional argument but 2 were given
+18f839b - fix: 修复 missing 1 required positional argument: 'INNER_VERSION'
+6adb5ed - 调整一些细节，docker部署时可选数据库账密
+07f48e9 - fix: 利用filter来过滤环境变量，避免直接删除key造成的 RuntimeError: dictionary changed size during iteration
+5856074 - fix: 修复无法进行基础设置的问题
+32aa032 - feat: 发布 0.0.1 版本的配置文件
+edc07ac - feat: 重构配置加载器，增加配置文件版本控制和程序兼容能力
+0f492ed - fix: 修复 BASE_URL/KEY 组合检查中被 GPG_KEY 干扰的问题
--- a/requirements.txt
+++ b/requirements.txt
--- a/run.py
+++ b/run.py
@@ -128,13 +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")
--- a/src/common/database.py
+++ b/src/common/database.py
@@ -1,7 +1,6 @@
 from typing import Optional
-
 from pymongo import MongoClient
-
+from pymongo.database import Database as MongoDatabase

 class Database:
    _instance: Optional["Database"] = None
@@ -27,7 +26,7 @@ class Database:
        else:
            # 否则使用无认证连接
            self.client = MongoClient(host, port)
-        self.db = self.client[db_name]
+        self.db: MongoDatabase = self.client[db_name]
        
    @classmethod
    def initialize(
@@ -39,18 +38,18 @@ class Database:
        password: Optional[str] = None,
        auth_source: Optional[str] = None,
        uri: Optional[str] = None,
-    ) -> "Database":
+    ) -> MongoDatabase:
        if cls._instance is None:
            cls._instance = cls(
                host, port, db_name, username, password, auth_source, uri
            )
-        return cls._instance
+        return cls._instance.db
        
    @classmethod
-    def get_instance(cls) -> "Database":
+    def get_instance(cls) -> MongoDatabase:
        if cls._instance is None:
            raise RuntimeError("Database not initialized")
-        return cls._instance
+        return cls._instance.db


    #测试用
--- a/src/gui/reasoning_gui.py
+++ b/src/gui/reasoning_gui.py
@@ -46,7 +46,7 @@ class ReasoningGUI:

        # 初始化数据库连接
        try:
-            self.db = Database.get_instance().db
+            self.db = Database.get_instance()
            logger.success("数据库连接成功")
        except RuntimeError:
            logger.warning("数据库未初始化，正在尝试初始化...")
@@ -60,7 +60,7 @@ class ReasoningGUI:
                    password=os.getenv("MONGODB_PASSWORD"),
                    auth_source=os.getenv("MONGODB_AUTH_SOURCE"),
                )
-                self.db = Database.get_instance().db
+                self.db = Database.get_instance()
                logger.success("数据库初始化成功")
            except Exception:
                logger.exception("数据库初始化失败")
--- a/src/plugins/chat/init.py
+++ b/src/plugins/chat/init.py
@@ -4,7 +4,7 @@ import os

 from loguru import logger
 from nonebot import get_driver, on_message, require
-from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment
+from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment,MessageEvent
 from nonebot.typing import T_State

 from ...common.database import Database
@@ -32,26 +32,14 @@ _message_manager_started = False
 driver = get_driver()
 config = driver.config

-Database.initialize(
-    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"),
-)
-logger.success("初始化数据库成功")
-
-
 # 初始化表情管理器
 emoji_manager.initialize()

 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)
 # 创建定时任务
 scheduler = require("nonebot_plugin_apscheduler").scheduler

@@ -103,8 +91,8 @@ async def _(bot: Bot):
    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)


@@ -127,7 +115,7 @@ async def build_memory_task():
 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)
+    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")


--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -2,12 +2,16 @@ 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,
+)

 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, cq_code_tool  # 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 MessageSending, MessageRecv, MessageThinking, MessageSet
@@ -42,39 +46,53 @@ class ChatBot:
        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"""
+    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

-        try:
-            group_info_api = await bot.get_group_info(group_id=event.group_id)
-            logger.info(f"成功获取群信息: {group_info_api}")
-            group_name = group_info_api["group_name"]
-        except Exception as e:
-            logger.error(f"获取群信息失败: {str(e)}")
-            group_name = None
-
-        # 白名单设定由nontbot侧完成
-        # 消息过滤，涉及到config有待更新
-        if event.group_id:
-            if event.group_id not in global_config.talk_allowed_groups:
-                return
+        # 用户屏蔽,不区分私聊/群聊
        if event.user_id in global_config.ban_user_id:
            return

-        user_info = UserInfo(
-            user_id=event.user_id,
-            user_nickname=event.sender.nickname,
-            user_cardname=event.sender.card or None,
-            platform="qq",
-        )
+        # 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 = GroupInfo(
-            group_id=event.group_id,
-            group_name=group_name,  # 使用获取到的群名称或None
-            platform="qq",
-        )
+                # 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,
@@ -88,7 +106,6 @@ class ChatBot:

        # 进入maimbot
        message = MessageRecv(message_json)
-
        groupinfo = message.message_info.group_info
        userinfo = message.message_info.user_info
        messageinfo = message.message_info
@@ -108,7 +125,9 @@ class ChatBot:
        # 过滤词
        for word in global_config.ban_words:
            if word in message.processed_plain_text:
-                logger.info(f"[群{groupinfo.group_id}]{userinfo.user_nickname}:{message.processed_plain_text}")
+                logger.info(
+                    f"[{chat.group_info.group_name if chat.group_info.group_id 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

@@ -116,7 +135,7 @@ class ChatBot:
        for pattern in global_config.ban_msgs_regex:
            if re.search(pattern, message.raw_message):
                logger.info(
-                    f"[群{message.message_info.group_info.group_id}]{message.user_nickname}:{message.raw_message}"
+                    f"[{chat.group_info.group_name if chat.group_info.group_id else '私聊'}]{message.user_nickname}:{message.raw_message}"
                )
                logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered")
                return
@@ -124,8 +143,8 @@ class ChatBot:
        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
        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}")
@@ -144,7 +163,7 @@ class ChatBot:
        current_willing = willing_manager.get_willing(chat_stream=chat)

        logger.info(
-            f"[{current_time}][群{chat.group_info.group_id}]{chat.user_info.user_nickname}:"
+            f"[{current_time}][{chat.group_info.group_name if chat.group_info.group_id 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}%]"
        )

@@ -152,12 +171,17 @@ class ChatBot:

        if random() < reply_probability:
            bot_user_info = UserInfo(
-                user_id=global_config.BOT_QQ, user_nickname=global_config.BOT_NICKNAME, platform=messageinfo.platform
+                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_id=think_id,
+                chat_stream=chat,
+                bot_user_info=bot_user_info,
+                reply=message,
            )

            message_manager.add_message(thinking_message)
@@ -196,15 +220,16 @@ class ChatBot:
                # print(f"\033[1;32m[回复内容]\033[0m {msg}")
                # 通过时间改变时间戳
                typing_time = calculate_typing_time(msg)
-                print(f"typing_time: {typing_time}")
+                logger.debug(f"typing_time: {typing_time}")
                accu_typing_time += typing_time
                timepoint = thinking_time_point + accu_typing_time
                message_segment = Seg(type="text", data=msg)
-                print(f"message_segment: {message_segment}")
+                # logger.debug(f"message_segment: {message_segment}")
                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=not mark_head,
@@ -218,7 +243,9 @@ class ChatBot:

            # 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")
-            print(f"添加message_set到message_manager")
+
+            logger.debug("添加message_set到message_manager")
+
            message_manager.add_message(message_set)

            bot_response_time = thinking_time_point
@@ -242,6 +269,7 @@ class ChatBot:
                        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,
--- a/src/plugins/chat/chat_stream.py
+++ b/src/plugins/chat/chat_stream.py
@@ -111,11 +111,11 @@ class ChatManager:

    def _ensure_collection(self):
        """确保数据库集合存在并创建索引"""
-        if "chat_streams" not in self.db.db.list_collection_names():
-            self.db.db.create_collection("chat_streams")
+        if "chat_streams" not in self.db.list_collection_names():
+            self.db.create_collection("chat_streams")
            # 创建索引
-            self.db.db.chat_streams.create_index([("stream_id", 1)], unique=True)
-            self.db.db.chat_streams.create_index(
+            self.db.chat_streams.create_index([("stream_id", 1)], unique=True)
+            self.db.chat_streams.create_index(
                [("platform", 1), ("user_info.user_id", 1), ("group_info.group_id", 1)]
            )

@@ -168,7 +168,7 @@ class ChatManager:
            return stream

        # 检查数据库中是否存在
-        data = self.db.db.chat_streams.find_one({"stream_id": stream_id})
+        data = self.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
@@ -204,7 +204,7 @@ class ChatManager:
    async def _save_stream(self, stream: ChatStream):
        """保存聊天流到数据库"""
        if not stream.saved:
-            self.db.db.chat_streams.update_one(
+            self.db.chat_streams.update_one(
                {"stream_id": stream.stream_id}, {"$set": stream.to_dict()}, upsert=True
            )
            stream.saved = True
@@ -216,7 +216,7 @@ class ChatManager:

    async def load_all_streams(self):
        """从数据库加载所有聊天流"""
-        all_streams = self.db.db.chat_streams.find({})
+        all_streams = self.db.chat_streams.find({})
        for data in all_streams:
            stream = ChatStream.from_dict(data)
            self.streams[stream.stream_id] = stream
--- a/src/plugins/chat/config.py
+++ b/src/plugins/chat/config.py
@@ -37,8 +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
@@ -69,6 +68,7 @@ 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
@@ -95,6 +95,12 @@ class BotConfig:
    PERSONALITY_2: float = 0.3  # 第二种人格概率
    PERSONALITY_3: float = 0.1  # 第三种人格概率
    
+    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
@@ -294,6 +300,11 @@ class BotConfig:
            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"]
            config.mood_update_interval = mood_config.get("mood_update_interval", config.mood_update_interval)
@@ -327,7 +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)
-            config.enable_debug_output = others_config.get("enable_debug_output", config.enable_debug_output)
+            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
--- a/src/plugins/chat/emoji_manager.py
+++ b/src/plugins/chat/emoji_manager.py
@@ -76,16 +76,16 @@ class EmojiManager:
        
        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([('filename', 1)], unique=True)
+        if 'emoji' not in self.db.list_collection_names():
+            self.db.create_collection('emoji')
+            self.db.emoji.create_index([('embedding', '2dsphere')])
+            self.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(
+            self.db.emoji.update_one(
                {'_id': emoji_id},
                {'$inc': {'usage_count': 1}}
            )
@@ -119,7 +119,7 @@ class EmojiManager:

            try:
                # 获取所有表情包
-                all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'description': 1}))
+                all_emojis = list(self.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")
@@ -157,10 +157,11 @@ class EmojiManager:
                
                if selected_emoji and 'path' in selected_emoji:
                    # 更新使用次数
-                    self.db.db.emoji.update_one(
+                    self.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})")
                    # 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
@@ -176,8 +177,10 @@ class EmojiManager:
            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，使用image_manager的描述生成功能"""
+
        try:
            # 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)
@@ -236,7 +239,7 @@ class EmojiManager:
                image_hash = hashlib.md5(image_bytes).hexdigest()
                
                # 检查是否已经注册过
-                existing_emoji = self.db.db['emoji'].find_one({'filename': filename})
+                existing_emoji = self.db['emoji'].find_one({'filename': filename})
                description = None
                
                if existing_emoji:
@@ -272,11 +275,14 @@ class EmojiManager:
                    # 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:
                        os.remove(image_path)
                        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
@@ -287,6 +293,7 @@ class EmojiManager:
                
                if description is not None:
                    embedding = await get_embedding(description)
+
                    # 准备数据库记录
                    emoji_record = {
                        'filename': filename,
@@ -298,10 +305,11 @@ class EmojiManager:
                    }
                    
                    # 保存到emoji数据库
-                    self.db.db['emoji'].insert_one(emoji_record)
+                    self.db['emoji'].insert_one(emoji_record)
                    logger.success(f"注册新表情包: {filename}")
                    logger.info(f"描述: {description}")

+                    
                    # 保存到images数据库
                    image_doc = {
                        'hash': image_hash,
@@ -338,7 +346,7 @@ 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(self.db.emoji.find())
            removed_count = 0
            total_count = len(all_emojis)

@@ -346,13 +354,13 @@ class EmojiManager:
                try:
                    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']})
+                        self.db.emoji.delete_one({'_id': emoji['_id']})
                        removed_count += 1
                        continue

                    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']})
+                        self.db.emoji.delete_one({'_id': emoji['_id']})
                        removed_count += 1
                        continue

@@ -360,7 +368,7 @@ class EmojiManager:
                    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 = self.db.emoji.delete_one({'_id': emoji['_id']})
                        if result.deleted_count > 0:
                            logger.debug(f"成功删除数据库记录: {emoji['_id']}")
                            removed_count += 1
@@ -371,7 +379,7 @@ class EmojiManager:
                    continue

            # 验证清理结果
-            remaining_count = self.db.db.emoji.count_documents({})
+            remaining_count = self.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}")
@@ -389,5 +397,7 @@ class EmojiManager:


 # 创建全局单例
+
 emoji_manager = EmojiManager()

+
--- a/src/plugins/chat/llm_generator.py
+++ b/src/plugins/chat/llm_generator.py
@@ -8,7 +8,7 @@ from loguru import logger
 from ...common.database import Database
 from ..models.utils_model import LLM_request
 from .config import global_config
-from .message import MessageRecv, MessageThinking, MessageSending,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
@@ -154,7 +154,7 @@ class ResponseGenerator:
        reasoning_content: str,
    ):
        """保存对话记录到数据库"""
-        self.db.db.reasoning_logs.insert_one(
+        self.db.reasoning_logs.insert_one(
            {
                "time": time.time(),
                "chat_id": message.chat_stream.stream_id,
--- a/src/plugins/chat/message.py
+++ b/src/plugins/chat/message.py
@@ -1,19 +1,25 @@
 import time
+import html
+import re
+import json
 from dataclasses import dataclass
-from typing import Dict, ForwardRef, List, Optional, Union
+from typing import Dict, List, Optional

 import urllib3
 from loguru import logger

 from .utils_image import image_manager
+
 from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase
 from .chat_stream import ChatStream, chat_manager
+
 # 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(MessageBase):
@@ -69,6 +75,20 @@ class MessageRecv(Message):
            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')
        
@@ -86,7 +106,9 @@ class MessageRecv(Message):

        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.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:
@@ -98,14 +120,14 @@ class MessageRecv(Message):
        Returns:
            str: 处理后的文本
        """
-        if segment.type == 'seglist':
+        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)
+            return " ".join(segments_text)
        else:
            # 处理单个消息段
            return await self._process_single_segment(segment)
@@ -120,31 +142,35 @@ class MessageRecv(Message):
            str: 处理后的文本
        """
        try:
-            if seg.type == 'text':
+            if seg.type == "text":
                return seg.data
-            elif seg.type == 'image':
+            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
+                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]'
+                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}")
+            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))
+        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!=''
+            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"
@@ -160,7 +186,7 @@ class MessageProcessBase(Message):
        chat_stream: ChatStream,
        bot_user_info: UserInfo,
        message_segment: Optional[Seg] = None,
-        reply: Optional['MessageRecv'] = None
+        reply: Optional["MessageRecv"] = None,
    ):
        # 调用父类初始化
        super().__init__(
@@ -169,7 +195,7 @@ class MessageProcessBase(Message):
            chat_stream=chat_stream,
            user_info=bot_user_info,
            message_segment=message_segment,
-            reply=reply
+            reply=reply,
        )

        # 处理状态相关属性
@@ -190,14 +216,14 @@ class MessageProcessBase(Message):
        Returns:
            str: 处理后的文本
        """
-        if segment.type == 'seglist':
+        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)
+            return " ".join(segments_text)
        else:
            # 处理单个消息段
            return await self._process_single_segment(segment)
@@ -212,39 +238,44 @@ class MessageProcessBase(Message):
            str: 处理后的文本
        """
        try:
-            if seg.type == 'text':
+            if seg.type == "text":
                return seg.data
-            elif seg.type == 'image':
+            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':
+                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 "[表情]"
+            elif seg.type == "at":
                return f"[@{seg.data}]"
-            elif seg.type == 'reply':
-                if self.reply and hasattr(self.reply, 'processed_plain_text'):
+            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}")
+            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))
+        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 != ''
+            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"""
@@ -254,7 +285,7 @@ class MessageThinking(MessageProcessBase):
        message_id: str,
        chat_stream: ChatStream,
        bot_user_info: UserInfo,
-        reply: Optional['MessageRecv'] = None
+        reply: Optional["MessageRecv"] = None,
    ):
        # 调用父类初始化
        super().__init__(
@@ -262,12 +293,13 @@ class MessageThinking(MessageProcessBase):
            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
+            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"""
@@ -277,10 +309,11 @@ class MessageSending(MessageProcessBase):
        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,
+        reply: Optional["MessageRecv"] = None,
        is_head: bool = False,
-        is_emoji: bool = False
+        is_emoji: bool = False,
    ):
        # 调用父类初始化
        super().__init__(
@@ -288,28 +321,34 @@ class MessageSending(MessageProcessBase):
            chat_stream=chat_stream,
            bot_user_info=bot_user_info,
            message_segment=message_segment,
-            reply=reply
+            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:
+    def set_reply(self, reply: Optional["MessageRecv"]) -> None:
        """设置回复消息"""
        if reply:
            self.reply = 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
-                ])
+            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.processed_plain_text = await self._process_message_segments(
+                self.message_segment
+            )
            self.detailed_plain_text = self._generate_detailed_text()

    @classmethod
@@ -318,8 +357,8 @@ class MessageSending(MessageProcessBase):
        thinking: MessageThinking,
        message_segment: Seg,
        is_head: bool = False,
-        is_emoji: bool = False
-    ) -> 'MessageSending':
+        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,
@@ -328,17 +367,26 @@ class MessageSending(MessageProcessBase):
            bot_user_info=thinking.message_info.user_info,
            reply=thinking.reply,
            is_head=is_head,
-            is_emoji=is_emoji
+            is_emoji=is_emoji,
        )

    def to_dict(self):
-        ret= super().to_dict()
-        ret['message_info']['user_info']=self.chat_stream.user_info.to_dict()
+        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, chat_stream: ChatStream, message_id: str):
        self.chat_stream = chat_stream
        self.message_id = message_id
@@ -389,6 +437,3 @@ class MessageSet:

    def __len__(self) -> int:
        return len(self.messages)
-        
-
-    
--- a/src/plugins/chat/message_base.py
+++ b/src/plugins/chat/message_base.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass, asdict
-from typing import List, Optional, Union, Any, Dict
+from typing import List, Optional, Union, Dict

@dataclass
 class Seg:
--- a/src/plugins/chat/message_cq.py
+++ b/src/plugins/chat/message_cq.py
@@ -1,19 +1,21 @@
 import time
 from dataclasses import dataclass
-from typing import Dict, ForwardRef, List, Optional, Union
+from typing import Dict, Optional

 import urllib3

-from .cq_code import CQCode, cq_code_tool
+from .cq_code import cq_code_tool
 from .utils_cq import parse_cq_code
-from .utils_user import get_groupname, get_user_cardname, get_user_nickname
+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。
+# 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):
@@ -24,27 +26,17 @@ class MessageCQ(MessageBase):
    - 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"
+        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
+            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
-        )
+        super().__init__(message_info=message_info, message_segment=None, raw_message=None)
+

@dataclass
 class MessageRecvCQ(MessageCQ):
@@ -62,7 +54,11 @@ class MessageRecvCQ(MessageCQ):
        # 调用父类初始化
        super().__init__(message_id, user_info, group_info, platform)

-        if group_info and group_info.group_name is None:
+        # 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
@@ -76,7 +72,7 @@ class MessageRecvCQ(MessageCQ):

        start = 0
        while True:
-            cq_start = message.find('[CQ:', start)
+            cq_start = message.find("[CQ:", start)
            if cq_start == -1:
                if start < len(message):
                    text = message[start:].strip()
@@ -89,20 +85,20 @@ class MessageRecvCQ(MessageCQ):
                if text:
                    cq_code_dict_list.append(parse_cq_code(text))

-            cq_end = message.find(']', cq_start)
+            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 = 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)
+            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)

@@ -111,59 +107,58 @@ class MessageRecvCQ(MessageCQ):
            return segments[0]

        # 否则返回seglist类型的Seg
-        return Seg(type='seglist', data=segments)
+        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
-    ):
+    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', {}))
+        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
-            )
+            message_info.platform,
+        )

        self.message_segment = message_segment
        self.raw_message = self._generate_raw_message()

-    def _generate_raw_message(self, ) -> str:
+    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':
+        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)
+        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':
+        if seg.type == "text":
            return str(seg.data)
-        elif seg.type == 'image':
+        elif seg.type == "image":
            return cq_code_tool.create_image_cq_base64(seg.data)
-        elif seg.type == 'emoji':
+        elif seg.type == "emoji":
            return cq_code_tool.create_emoji_cq_base64(seg.data)
-        elif seg.type == 'at':
+        elif seg.type == "at":
            return f"[CQ:at,qq={seg.data}]"
-        elif seg.type == 'reply':
+        elif seg.type == "reply":
            return cq_code_tool.create_reply_cq(int(seg.data))
        else:
            return f"[{seg.data}]"
-
--- a/src/plugins/chat/message_sender.py
+++ b/src/plugins/chat/message_sender.py
@@ -5,12 +5,12 @@ 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_cq import MessageSendCQ
-from .message import MessageSending, MessageThinking, MessageRecv,MessageSet
+from .message import MessageSending, MessageThinking, MessageRecv, MessageSet
+
 from .storage import MessageStorage
 from .config import global_config
-from .chat_stream import chat_manager
+from .utils import truncate_message


 class Message_Sender:
@@ -26,42 +26,47 @@ class Message_Sender:
        self._current_bot = bot

    async def send_message(
-            self,
-            message: MessageSending,
+        self,
+        message: MessageSending,
    ) -> None:
        """发送消息"""
+
        if isinstance(message, MessageSending):
            message_json = message.to_dict()
-            message_send=MessageSendCQ(
-                data=message_json
-            )
-
-            if message_send.message_info.group_info:
+            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
+                        auto_escape=False,
                    )
-                    logger.success(f"[调试] 发送消息{message.processed_plain_text}成功")
+                    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.processed_plain_text}失败")
+                    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.message_info.user_info.user_id,
+                        user_id=message.sender_info.user_id,
                        message=message_send.raw_message,
-                        auto_escape=False
+                        auto_escape=False,
                    )
-                    logger.success(f"[调试] 发送消息{message.processed_plain_text}成功")
+                    logger.success(f"[调试] 发送消息“{message_preview}”成功")
                except Exception as e:
-                    logger.error(f"发生错误 {e}")
-                    logger.error(f"[调试] 发送消息{message.processed_plain_text}失败")
+                    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
@@ -88,7 +93,7 @@ class MessageContainer:
        """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:
            msg_time = msg.thinking_start_time
@@ -127,6 +132,7 @@ class MessageContainer:

 class MessageManager:
    """管理所有聊天流的消息容器"""
+
    def __init__(self):
        self.containers: Dict[str, MessageContainer] = {}  # chat_id -> MessageContainer
        self.storage = MessageStorage()
@@ -138,7 +144,9 @@ class MessageManager:
            self.containers[chat_id] = MessageContainer(chat_id)
        return self.containers[chat_id]

-    def add_message(self, message: Union[MessageThinking, MessageSending, MessageSet]) -> None:
+    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")
@@ -155,7 +163,11 @@ class MessageManager:
            if isinstance(message_earliest, MessageThinking):
                message_earliest.update_thinking_time()
                thinking_time = message_earliest.thinking_time
-                print(f"消息正在思考中，已思考{int(thinking_time)}秒\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:
@@ -163,15 +175,23 @@ class MessageManager:
                    container.remove_message(message_earliest)
            else:

-                if message_earliest.is_head and message_earliest.update_thinking_time() > 30:
+                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_message(message_earliest)
                await message_earliest.process()

-                print(f"\033[1;34m[调试]\033[0m 消息'{message_earliest.processed_plain_text}'正在发送中")
+                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)
+                await self.storage.store_message(
+                    message_earliest, message_earliest.chat_stream, None
+                )

                container.remove_message(message_earliest)

@@ -183,7 +203,11 @@ class MessageManager:
                        continue

                    try:
-                        if msg.is_head and msg.update_thinking_time() > 30:
+                        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)
@@ -191,7 +215,7 @@ class MessageManager:
                        # if msg.is_emoji:
                        #     msg.processed_plain_text = "[表情包]"
                        await msg.process()
-                        await self.storage.store_message(msg,msg.chat_stream, None)
+                        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")
--- a/src/plugins/chat/prompt_builder.py
+++ b/src/plugins/chat/prompt_builder.py
@@ -9,7 +9,7 @@ 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 ChatStream, chat_manager
+from .chat_stream import chat_manager


 class PromptBuilder:
@@ -311,7 +311,7 @@ class PromptBuilder:
            {"$project": {"content": 1, "similarity": 1}}
        ]

-        results = list(self.db.db.knowledges.aggregate(pipeline))
+        results = list(self.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:
--- a/src/plugins/chat/relationship_manager.py
+++ b/src/plugins/chat/relationship_manager.py
@@ -1,6 +1,5 @@
 import asyncio
-from typing import Optional, Union
-from typing import Optional, Union
+from typing import Optional
 from loguru import logger

 from ...common.database import Database
@@ -169,7 +168,7 @@ class RelationshipManager:
    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)

@@ -177,7 +176,7 @@ class RelationshipManager:
        """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:
            await self.load_relationship(data)
@@ -207,7 +206,7 @@ class RelationshipManager:
        saved = relationship.saved

        db = Database.get_instance()
-        db.db.relationships.update_one(
+        db.relationships.update_one(
            {'user_id': user_id, 'platform': platform},
            {'$set': {
                'platform': platform,
--- a/src/plugins/chat/storage.py
+++ b/src/plugins/chat/storage.py
@@ -1,8 +1,6 @@
 from typing import Optional, Union
-from typing import Optional, Union

 from ...common.database import Database
-from .message_base import MessageBase
 from .message import MessageSending, MessageRecv
 from .chat_stream import ChatStream
 from loguru import logger
@@ -25,7 +23,7 @@ class MessageStorage:
                    "detailed_plain_text": message.detailed_plain_text,
                    "topic": topic,
                }
-            self.db.db.messages.insert_one(message_data)
+            self.db.messages.insert_one(message_data)
        except Exception:
            logger.exception("存储消息失败")

--- a/src/plugins/chat/utils.py
+++ b/src/plugins/chat/utils.py
@@ -12,8 +12,8 @@ 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 MessageThinking, MessageRecv,MessageSending,MessageProcessBase,Message
-from .message_base import MessageBase,BaseMessageInfo,UserInfo,GroupInfo
+from .message import MessageRecv,Message
+from .message_base import UserInfo
 from .chat_stream import ChatStream
 from ..moods.moods import MoodManager

@@ -39,9 +39,13 @@ def db_message_to_str(message_dict: Dict) -> str:
 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
+    for nickname in nicknames:
+        if nickname in message.processed_plain_text:
+            return True
    return False


@@ -402,3 +406,10 @@ 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
--- a/src/plugins/chat/utils_image.py
+++ b/src/plugins/chat/utils_image.py
@@ -1,16 +1,12 @@
 import base64
-import io
 import os
 import time
-import zlib
 import aiohttp
 import hashlib
-from typing import Optional, Tuple, Union
-from urllib.parse import urlparse
+from typing import Optional, Union

 from loguru import logger
 from nonebot import get_driver
-from PIL import Image

 from ...common.database import Database
 from ..chat.config import global_config
@@ -44,20 +40,20 @@ class ImageManager:
        
    def _ensure_image_collection(self):
        """确保images集合存在并创建索引"""
-        if 'images' not in self.db.db.list_collection_names():
-            self.db.db.create_collection('images')
+        if 'images' not in self.db.list_collection_names():
+            self.db.create_collection('images')
            # 创建索引
-            self.db.db.images.create_index([('hash', 1)], unique=True)
-            self.db.db.images.create_index([('url', 1)])
-            self.db.db.images.create_index([('path', 1)])
+            self.db.images.create_index([('hash', 1)], unique=True)
+            self.db.images.create_index([('url', 1)])
+            self.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 self.db.db.list_collection_names():
-            self.db.db.create_collection('image_descriptions')
+        if 'image_descriptions' not in self.db.list_collection_names():
+            self.db.create_collection('image_descriptions')
            # 创建索引
-            self.db.db.image_descriptions.create_index([('hash', 1)], unique=True)
-            self.db.db.image_descriptions.create_index([('type', 1)])
+            self.db.image_descriptions.create_index([('hash', 1)], unique=True)
+            self.db.image_descriptions.create_index([('type', 1)])

    def _get_description_from_db(self, image_hash: str, description_type: str) -> Optional[str]:
        """从数据库获取图片描述
@@ -69,7 +65,7 @@ class ImageManager:
        Returns:
            Optional[str]: 描述文本，如果不存在则返回None
        """
-        result= self.db.db.image_descriptions.find_one({
+        result= self.db.image_descriptions.find_one({
            'hash': image_hash,
            'type': description_type
        })
@@ -83,7 +79,7 @@ class ImageManager:
            description: 描述文本
            description_type: 描述类型 ('emoji' 或 'image')
        """
-        self.db.db.image_descriptions.update_one(
+        self.db.image_descriptions.update_one(
            {'hash': image_hash, 'type': description_type},
            {
                '$set': {
@@ -125,7 +121,7 @@ class ImageManager:
            image_hash = hashlib.md5(image_bytes).hexdigest()
            
            # 查重
-            existing = self.db.db.images.find_one({'hash': image_hash})
+            existing = self.db.images.find_one({'hash': image_hash})
            if existing:
                return existing['path']
                
@@ -146,7 +142,7 @@ class ImageManager:
                'description': description,
                'timestamp': timestamp
            }
-            self.db.db.images.insert_one(image_doc)
+            self.db.images.insert_one(image_doc)
            
            return file_path
            
@@ -163,7 +159,7 @@ class ImageManager:
        """
        try:
            # 先查找是否已存在
-            existing = self.db.db.images.find_one({'url': url})
+            existing = self.db.images.find_one({'url': url})
            if existing:
                return existing['path']
                
@@ -207,7 +203,7 @@ class ImageManager:
        Returns:
            bool: 是否存在
        """
-        return self.db.db.images.find_one({'url': url}) is not None
+        return self.db.images.find_one({'url': url}) is not None
        
    def check_hash_exists(self, image_data: Union[str, bytes], is_base64: bool = False) -> bool:
        """检查图像是否已存在
@@ -230,7 +226,7 @@ class ImageManager:
                    return False
                    
            image_hash = hashlib.md5(image_bytes).hexdigest()
-            return self.db.db.images.find_one({'hash': image_hash}) is not None
+            return self.db.images.find_one({'hash': image_hash}) is not None
            
        except Exception as e:
            logger.error(f"检查哈希失败: {str(e)}")
@@ -273,7 +269,7 @@ class ImageManager:
                        'description': description,
                        'timestamp': timestamp
                    }
-                    self.db.db.images.update_one(
+                    self.db.images.update_one(
                        {'hash': image_hash},
                        {'$set': image_doc},
                        upsert=True
@@ -330,7 +326,7 @@ class ImageManager:
                        'description': description,
                        'timestamp': timestamp
                    }
-                    self.db.db.images.update_one(
+                    self.db.images.update_one(
                        {'hash': image_hash},
                        {'$set': image_doc},
                        upsert=True
--- a/src/plugins/chat/willing_manager.py
+++ b/src/plugins/chat/willing_manager.py
@@ -1,13 +1,9 @@
 import asyncio
 from typing import Dict
-from loguru import logger

-from typing import Dict
-from loguru import logger

 from .config import global_config
-from .message_base import UserInfo, GroupInfo
-from .chat_stream import chat_manager,ChatStream
+from .chat_stream import ChatStream


 class WillingManager:
--- a/src/plugins/config_reload/init.py
+++ 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")
--- a/src/plugins/config_reload/api.py
+++ 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)}")
--- a/src/plugins/config_reload/test.py
+++ 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())
--- a/src/plugins/knowledege/knowledge_library.py
+++ b/src/plugins/knowledege/knowledge_library.py
@@ -1,199 +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(
-    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"),
-)
-
-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), 300)]
-            
-            # 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)
--- a/src/plugins/memory_system/draw_memory.py
+++ b/src/plugins/memory_system/draw_memory.py
@@ -96,7 +96,7 @@ class Memory_graph:
            dot_data = {
                "concept": node
            }
-            self.db.db.store_memory_dots.insert_one(dot_data)
+            self.db.store_memory_dots.insert_one(dot_data)

    @property
    def dots(self):
@@ -106,7 +106,7 @@ class Memory_graph:
    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": {"$lte": timestamp}}, sort=[('time', -1)])  # 调试输出
+        closest_record = self.db.messages.find_one({"time": {"$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'])))}")

@@ -115,7 +115,7 @@ class Memory_graph:
            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": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(
+                self.db.messages.find({"time": {"$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'])))
@@ -130,34 +130,34 @@ class Memory_graph:

    def save_graph_to_db(self):
        # 清空现有的图数据
-        self.db.db.graph_data.delete_many({})
+        self.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)
+            self.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)
+            self.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 = self.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 = self.db.graph_data.edges.find()
        for edge in edges:
            self.G.add_edge(edge['source'], edge['target'])

--- a/src/plugins/memory_system/memory.py
+++ b/src/plugins/memory_system/memory.py
@@ -303,7 +303,7 @@ class Hippocampus:
        return topic_num

    async def operation_build_memory(self, chat_size=20):
-        time_frequency = {'near': 3, 'mid': 8, 'far': 5}
+        time_frequency = {'near': 1, 'mid': 4, 'far': 4}
        memory_samples = self.get_memory_sample(chat_size, time_frequency)
        
        for i, messages in enumerate(memory_samples, 1):
@@ -315,7 +315,7 @@ class Hippocampus:
            bar = '█' * filled_length + '-' * (bar_length - filled_length)
            logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")

-            compress_rate = 0.1
+            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)}")
            
@@ -523,9 +523,14 @@ class Hippocampus:

    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"""
+        # 检查数据库是否为空
        all_nodes = list(self.memory_graph.G.nodes())
        all_edges = list(self.memory_graph.G.edges())
        
+        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))
        
@@ -546,7 +551,7 @@ class Hippocampus:
            # 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*24:  # test
+            if current_time - last_modified > 3600*global_config.memory_forget_time:  # test
                current_strength = edge_data.get('strength', 1)
                new_strength = current_strength - 1
                
@@ -887,15 +892,6 @@ config = driver.config

 start_time = time.time()

-Database.initialize(
-    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"),
-)
 # 创建记忆图
 memory_graph = Memory_graph()
 # 创建海马体
--- a/src/plugins/memory_system/memory_manual_build.py
+++ b/src/plugins/memory_system/memory_manual_build.py
@@ -10,7 +10,6 @@ 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
--- a/src/plugins/models/utils_model.py
+++ b/src/plugins/models/utils_model.py
@@ -41,10 +41,10 @@ class LLM_request:
        """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)])
+            self.db.llm_usage.create_index([("timestamp", 1)])
+            self.db.llm_usage.create_index([("model_name", 1)])
+            self.db.llm_usage.create_index([("user_id", 1)])
+            self.db.llm_usage.create_index([("request_type", 1)])
        except Exception:
            logger.error("创建数据库索引失败")

@@ -73,7 +73,7 @@ class LLM_request:
                "status": "success",
                "timestamp": datetime.now()
            }
-            self.db.db.llm_usage.insert_one(usage_data)
+            self.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}, "
--- a/src/plugins/schedule/schedule_generator.py
+++ b/src/plugins/schedule/schedule_generator.py
@@ -14,16 +14,6 @@ from ..models.utils_model import LLM_request
 driver = get_driver()
 config = driver.config

-Database.initialize(
-    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"),
-)
-
 class ScheduleGenerator:
    def __init__(self):
        # 根据global_config.llm_normal这一字典配置指定模型
@@ -56,7 +46,7 @@ class ScheduleGenerator:

        schedule_text = str

-        existing_schedule = self.db.db.schedule.find_one({"date": date_str})
+        existing_schedule = self.db.schedule.find_one({"date": date_str})
        if existing_schedule:
            logger.debug(f"{date_str}的日程已存在:")
            schedule_text = existing_schedule["schedule"]
@@ -73,7 +63,7 @@ class ScheduleGenerator:

            try:
                schedule_text, _ = await self.llm_scheduler.generate_response(prompt)
-                self.db.db.schedule.insert_one({"date": date_str, "schedule": schedule_text})
+                self.db.schedule.insert_one({"date": date_str, "schedule": schedule_text})
            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"
@@ -153,7 +143,7 @@ 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")})
+            self.db.schedule.delete_one({"date": datetime.datetime.now().strftime("%Y-%m-%d")})
        else:
            logger.info("=== 今日日程安排 ===")
            for time_str, activity in self.today_schedule.items():
--- a/src/plugins/utils/statistic.py
+++ b/src/plugins/utils/statistic.py
@@ -53,7 +53,7 @@ class LLMStatistics:
            "costs_by_model": defaultdict(float)
        }
        
-        cursor = self.db.db.llm_usage.find({
+        cursor = self.db.llm_usage.find({
            "timestamp": {"$gte": start_time}
        })
        
--- a/src/plugins/zhishi/knowledge_library.py
+++ b/src/plugins/zhishi/knowledge_library.py
@@ -0,0 +1,383 @@
+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 Database
+
+# 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):
+        # 初始化数据库连接
+        if Database._instance is None:
+            Database.initialize(
+                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"),
+            )
+        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")
+        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 = self.db.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()
+                    }
+                    self.db.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)
+            
+            self.db.db.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(self.db.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':
+                knowledge_library.db.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
--- a/template/bot_config_template.toml
+++ b/template/bot_config_template.toml
@@ -1,5 +1,5 @@
 [inner]
-version = "0.0.6"
+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
@@ -65,8 +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"
@@ -101,6 +106,7 @@ word_replace_rate=0.006 # 整词替换概率
 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 = [
--- a/如果你更新了版本，点我.txt
+++ 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
--- 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
@@ -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
--- a/麦麦开始学习.bat
+++ 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