diff --git a/.gitignore b/.gitignore
index e51abc5cc..6e1be60b4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 data/
+data1/
 mongodb/
 NapCat.Framework.Windows.Once/
 log/
diff --git a/bot.py b/bot.py
index 68068a9f9..36d621a6e 100644
--- a/bot.py
+++ b/bot.py
@@ -63,7 +63,7 @@ def init_env():
 
     # 首先加载基础环境变量.env
     if os.path.exists(".env"):
-        load_dotenv(".env")
+        load_dotenv(".env",override=True)
         logger.success("成功加载基础环境变量配置")
 
 
diff --git a/config/auto_update.py b/config/auto_update.py
new file mode 100644
index 000000000..28ab108da
--- /dev/null
+++ b/config/auto_update.py
@@ -0,0 +1,59 @@
+import os
+import shutil
+import tomlkit
+from pathlib import Path
+
+def update_config():
+    # 获取根目录路径
+    root_dir = Path(__file__).parent.parent
+    template_dir = root_dir / "template"
+    config_dir = root_dir / "config"
+    
+    # 定义文件路径
+    template_path = template_dir / "bot_config_template.toml"
+    old_config_path = config_dir / "bot_config.toml"
+    new_config_path = config_dir / "bot_config.toml"
+    
+    # 读取旧配置文件
+    old_config = {}
+    if old_config_path.exists():
+        with open(old_config_path, "r", encoding="utf-8") as f:
+            old_config = tomlkit.load(f)
+    
+    # 删除旧的配置文件
+    if old_config_path.exists():
+        os.remove(old_config_path)
+    
+    # 复制模板文件到配置目录
+    shutil.copy2(template_path, new_config_path)
+    
+    # 读取新配置文件
+    with open(new_config_path, "r", encoding="utf-8") as f:
+        new_config = tomlkit.load(f)
+    
+    # 递归更新配置
+    def update_dict(target, source):
+        for key, value in source.items():
+            # 跳过version字段的更新
+            if key == "version":
+                continue
+            if key in target:
+                if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)):
+                    update_dict(target[key], value)
+                else:
+                    try:
+                        # 直接使用tomlkit的item方法创建新值
+                        target[key] = tomlkit.item(value)
+                    except (TypeError, ValueError):
+                        # 如果转换失败，直接赋值
+                        target[key] = value
+    
+    # 将旧配置的值更新到新配置中
+    update_dict(new_config, old_config)
+    
+    # 保存更新后的配置（保留注释和格式）
+    with open(new_config_path, "w", encoding="utf-8") as f:
+        f.write(tomlkit.dumps(new_config))
+
+if __name__ == "__main__":
+    update_config()
diff --git a/docs/linux_deploy_guide_for_beginners.md b/docs/linux_deploy_guide_for_beginners.md
new file mode 100644
index 000000000..04601923f
--- /dev/null
+++ b/docs/linux_deploy_guide_for_beginners.md
@@ -0,0 +1,444 @@
+# 面向纯新手的Linux服务器麦麦部署指南
+
+## 你得先有一个服务器
+
+为了能使麦麦在你的电脑关机之后还能运行，你需要一台不间断开机的主机，也就是我们常说的服务器。
+
+华为云、阿里云、腾讯云等等都是在国内可以选择的选择。
+
+你可以去租一台最低配置的就足敷需要了，按月租大概十几块钱就能租到了。
+
+我们假设你已经租好了一台Linux架构的云服务器。我用的是阿里云ubuntu24.04，其他的原理相似。
+
+## 0.我们就从零开始吧
+
+### 网络问题
+
+为访问github相关界面，推荐去下一款加速器，新手可以试试watttoolkit。
+
+### 安装包下载
+
+#### MongoDB
+
+对于ubuntu24.04 x86来说是这个：
+
+https://repo.mongodb.org/apt/ubuntu/dists/noble/mongodb-org/8.0/multiverse/binary-amd64/mongodb-org-server_8.0.5_amd64.deb
+
+如果不是就在这里自行选择对应版本
+
+https://www.mongodb.com/try/download/community-kubernetes-operator
+
+#### Napcat
+
+在这里选择对应版本。
+
+https://github.com/NapNeko/NapCatQQ/releases/tag/v4.6.7
+
+对于ubuntu24.04 x86来说是这个：
+
+https://dldir1.qq.com/qqfile/qq/QQNT/ee4bd910/linuxqq_3.2.16-32793_amd64.deb
+
+#### 麦麦
+
+https://github.com/SengokuCola/MaiMBot/archive/refs/tags/0.5.8-alpha.zip
+
+下载这个官方压缩包。
+
+### 路径
+
+我把麦麦相关文件放在了/moi/mai里面，你可以凭喜好更改，记得适当调整下面涉及到的部分即可。
+
+文件结构：
+
+```
+moi
+└─ mai
+   ├─ linuxqq_3.2.16-32793_amd64.deb
+   ├─ mongodb-org-server_8.0.5_amd64.deb
+   └─ bot
+      └─ MaiMBot-0.5.8-alpha.zip
+```
+
+### 网络
+
+你可以在你的服务器控制台网页更改防火墙规则，允许6099，8080，27017这几个端口的出入。
+
+## 1.正式开始！
+
+远程连接你的服务器，你会看到一个黑框框闪着白方格，这就是我们要进行设置的场所——终端了。以下的bash命令都是在这里输入。
+
+## 2. Python的安装
+
+- 导入 Python 的稳定版 PPA：
+
+```bash
+sudo add-apt-repository ppa:deadsnakes/ppa
+```
+
+- 导入 PPA 后，更新 APT 缓存：
+
+```bash
+sudo apt update
+```
+
+- 在「终端」中执行以下命令来安装 Python 3.12：
+
+```bash
+sudo apt install python3.12
+```
+
+- 验证安装是否成功：
+
+```bash
+python3.12 --version
+```
+
+- 在「终端」中，执行以下命令安装 pip：
+
+```bash
+sudo apt install python3-pip
+```
+
+- 检查Pip是否安装成功：
+
+```bash
+pip --version
+```
+
+- 安装必要组件
+
+``` bash
+sudo apt install python-is-python3
+```
+
+## 3.MongoDB的安装
+
+``` bash
+cd /moi/mai
+```
+
+``` bash
+dpkg -i mongodb-org-server_8.0.5_amd64.deb
+```
+
+``` bash
+mkdir -p /root/data/mongodb/{data,log}
+```
+
+## 4.MongoDB的运行
+
+```bash
+service mongod start
+```
+
+```bash
+systemctl status mongod #通过这条指令检查运行状态
+```
+
+有需要的话可以把这个服务注册成开机自启
+
+```bash
+sudo systemctl enable mongod
+```
+
+## 5.napcat的安装
+
+``` bash
+curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && sudo bash napcat.sh
+```
+
+上面的不行试试下面的
+
+``` bash
+dpkg -i linuxqq_3.2.16-32793_amd64.deb
+apt-get install -f
+dpkg -i linuxqq_3.2.16-32793_amd64.deb
+```
+
+成功的标志是输入``` napcat ```出来炫酷的彩虹色界面
+
+## 6.napcat的运行
+
+此时你就可以根据提示在```napcat```里面登录你的QQ号了。
+
+```bash
+napcat start <你的QQ号>
+napcat status #检查运行状态
+```
+
+然后你就可以登录napcat的webui进行设置了：
+
+```http://<你服务器的公网IP>:6099/webui?token=napcat```
+
+第一次是这个，后续改了密码之后token就会对应修改。你也可以使用```napcat log <你的QQ号>```来查看webui地址。把里面的```127.0.0.1```改成<你服务器的公网IP>即可。
+
+登录上之后在网络配置界面添加websocket客户端，名称随便输一个，url改成`ws://127.0.0.1:8080/onebot/v11/ws`保存之后点启用，就大功告成了。
+
+## 7.麦麦的安装
+
+### step 1 安装解压软件
+
+```
+sudo apt-get install unzip
+```
+
+### step 2 解压文件
+
+```bash
+cd /moi/mai/bot # 注意：要切换到压缩包的目录中去
+unzip MaiMBot-0.5.8-alpha.zip
+```
+
+### step 3 进入虚拟环境安装库
+
+```bash
+cd /moi/mai/bot
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+### step 4 试运行
+
+```bash
+cd /moi/mai/bot
+python -m venv venv
+source venv/bin/activate
+python bot.py
+```
+
+肯定运行不成功，不过你会发现结束之后多了一些文件
+
+```
+bot
+├─ .env.prod
+└─ config
+   └─ bot_config.toml
+```
+
+你要会vim直接在终端里修改也行，不过也可以把它们下到本地改好再传上去：
+
+### step 5 文件配置
+
+本项目需要配置两个主要文件：
+
+1. `.env.prod` - 配置API服务和系统环境
+2. `bot_config.toml` - 配置机器人行为和模型
+
+#### API
+
+你可以注册一个硅基流动的账号，通过邀请码注册有14块钱的免费额度：https://cloud.siliconflow.cn/i/7Yld7cfg。
+
+#### 在.env.prod中定义API凭证：
+
+```
+# API凭证配置
+SILICONFLOW_KEY=your_key        # 硅基流动API密钥
+SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/  # 硅基流动API地址
+
+DEEP_SEEK_KEY=your_key          # DeepSeek API密钥
+DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1  # DeepSeek API地址
+
+CHAT_ANY_WHERE_KEY=your_key     # ChatAnyWhere API密钥
+CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1  # ChatAnyWhere API地址
+```
+
+#### 在bot_config.toml中引用API凭证：
+
+```
+[model.llm_reasoning]
+name = "Pro/deepseek-ai/DeepSeek-R1"
+base_url = "SILICONFLOW_BASE_URL"  # 引用.env.prod中定义的地址
+key = "SILICONFLOW_KEY"            # 引用.env.prod中定义的密钥
+```
+
+如需切换到其他API服务，只需修改引用：
+
+```
+[model.llm_reasoning]
+name = "Pro/deepseek-ai/DeepSeek-R1"
+base_url = "DEEP_SEEK_BASE_URL"  # 切换为DeepSeek服务
+key = "DEEP_SEEK_KEY"            # 使用DeepSeek密钥
+```
+
+#### 配置文件详解
+
+##### 环境配置文件 (.env.prod)
+
+```
+# API配置
+SILICONFLOW_KEY=your_key
+SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
+DEEP_SEEK_KEY=your_key
+DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
+CHAT_ANY_WHERE_KEY=your_key
+CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
+
+# 服务配置
+HOST=127.0.0.1  # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入
+PORT=8080
+
+# 数据库配置
+MONGODB_HOST=127.0.0.1  # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb
+MONGODB_PORT=27017
+DATABASE_NAME=MegBot
+MONGODB_USERNAME = ""  # 数据库用户名
+MONGODB_PASSWORD = ""  # 数据库密码
+MONGODB_AUTH_SOURCE = ""  # 认证数据库
+
+# 插件配置
+PLUGINS=["src2.plugins.chat"]
+```
+
+##### 机器人配置文件 (bot_config.toml)
+
+```
+[bot]
+qq = "机器人QQ号"  # 必填
+nickname = "麦麦"  # 机器人昵称（你希望机器人怎么称呼它自己）
+
+[personality]
+prompt_personality = [
+    "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧",
+    "是一个女大学生，你有黑色头发，你会刷小红书"
+]
+prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书"
+
+[message]
+min_text_length = 2  # 最小回复长度
+max_context_size = 15  # 上下文记忆条数
+emoji_chance = 0.2  # 表情使用概率
+ban_words = []  # 禁用词列表
+
+[emoji]
+auto_save = true  # 自动保存表情
+enable_check = false  # 启用表情审核
+check_prompt = "符合公序良俗"
+
+[groups]
+talk_allowed = []      # 允许对话的群号
+talk_frequency_down = []   # 降低回复频率的群号
+ban_user_id = []      # 禁止回复的用户QQ号
+
+[others]
+enable_advance_output = true  # 启用详细日志
+enable_kuuki_read = true  # 启用场景理解
+
+# 模型配置
+[model.llm_reasoning]  # 推理模型
+name = "Pro/deepseek-ai/DeepSeek-R1"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.llm_reasoning_minor]  # 轻量推理模型
+name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.llm_normal]  # 对话模型
+name = "Pro/deepseek-ai/DeepSeek-V3"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.llm_normal_minor]  # 备用对话模型
+name = "deepseek-ai/DeepSeek-V2.5"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.vlm]  # 图像识别模型
+name = "deepseek-ai/deepseek-vl2"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+[model.embedding]  # 文本向量模型
+name = "BAAI/bge-m3"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+
+
+[topic.llm_topic]
+name = "Pro/deepseek-ai/DeepSeek-V3"
+base_url = "SILICONFLOW_BASE_URL"
+key = "SILICONFLOW_KEY"
+```
+
+**step # 6** 运行
+
+现在再运行
+
+```bash
+cd /moi/mai/bot
+python -m venv venv
+source venv/bin/activate
+python bot.py
+```
+
+应该就能运行成功了。
+
+## 8.事后配置
+
+可是现在还有个问题：只要你一关闭终端，bot.py就会停止运行。那该怎么办呢？我们可以把bot.py注册成服务。
+
+重启服务器，打开MongoDB和napcat服务。
+
+新建一个文件，名为`bot.service`，内容如下
+
+```
+[Unit]
+Description=maimai bot
+
+[Service]
+WorkingDirectory=/moi/mai/bot
+ExecStart=/moi/mai/bot/venv/bin/python /moi/mai/bot/bot.py
+Restart=on-failure
+User=root
+
+[Install]
+WantedBy=multi-user.target
+```
+
+里面的路径视自己的情况更改。
+
+把它放到`/etc/systemd/system`里面。
+
+重新加载 `systemd` 配置：
+
+```bash
+sudo systemctl daemon-reload
+```
+
+启动服务：
+
+```bash
+sudo systemctl start bot.service # 启动服务
+sudo systemctl restart bot.service # 或者重启服务
+```
+
+检查服务状态：
+
+```bash
+sudo systemctl status bot.service
+```
+
+现在再关闭终端，检查麦麦能不能正常回复QQ信息。如果可以的话就大功告成了！
+
+## 9.命令速查
+
+```bash
+service mongod start # 启动mongod服务
+napcat start <你的QQ号> # 登录napcat
+cd /moi/mai/bot # 切换路径
+python -m venv venv # 创建虚拟环境
+source venv/bin/activate # 激活虚拟环境
+
+sudo systemctl daemon-reload # 重新加载systemd配置
+sudo systemctl start bot.service # 启动bot服务
+sudo systemctl enable bot.service # 启动bot服务
+
+sudo systemctl status bot.service # 检查bot服务状态
+```
+
+```
+python bot.py
+```
+
diff --git a/flake.nix b/flake.nix
index 7022dd68e..404f7555c 100644
--- a/flake.nix
+++ b/flake.nix
@@ -21,13 +21,14 @@
         buildInputs = [
           pythonPackages.python
           pythonPackages.venvShellHook
+          pythonPackages.numpy
         ];
 
         postVenvCreation = ''
           unset SOURCE_DATE_EPOCH
           pip install -r requirements.txt
         '';
-        
+
         postShellHook = ''
           # allow pip to install wheels
           unset SOURCE_DATE_EPOCH
diff --git a/requirements.txt b/requirements.txt
index 0acaade5e..8330c8d06 100644
Binary files a/requirements.txt and b/requirements.txt differ
diff --git a/run.py b/run.py
index 50e312c37..cfd3a5f14 100644
--- 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")
diff --git a/src/common/database.py b/src/common/database.py
index f0954b07c..d592b0f90 100644
--- a/src/common/database.py
+++ b/src/common/database.py
@@ -1,8 +1,6 @@
 from typing import Optional
-
 from pymongo import MongoClient
 
-
 class Database:
     _instance: Optional["Database"] = None
     
@@ -50,25 +48,4 @@ class Database:
     def get_instance(cls) -> "Database":
         if cls._instance is None:
             raise RuntimeError("Database not initialized")
-        return cls._instance
-
-
-    #测试用
-    
-    def get_random_group_messages(self, group_id: str, limit: int = 5):
-        # 先随机获取一条消息
-        random_message = list(self.db.messages.aggregate([
-            {"$match": {"group_id": group_id}},
-            {"$sample": {"size": 1}}
-        ]))[0]
-        
-        # 获取该消息之后的消息
-        subsequent_messages = list(self.db.messages.find({
-            "group_id": group_id,
-            "time": {"$gt": random_message["time"]}
-        }).sort("time", 1).limit(limit))
-        
-        # 将随机消息和后续消息合并
-        messages = [random_message] + subsequent_messages
-        
-        return messages
\ No newline at end of file
+        return cls._instance
\ No newline at end of file
diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py
index 3bc491171..f335a2ba3 100644
--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -12,7 +12,6 @@ from nonebot.adapters.onebot.v11 import (
 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
@@ -244,7 +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")
+
             logger.debug("添加message_set到message_manager")
+
             message_manager.add_message(message_set)
 
             bot_response_time = thinking_time_point
diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py
index 3adb952d3..98987c3e9 100644
--- a/src/plugins/chat/emoji_manager.py
+++ b/src/plugins/chat/emoji_manager.py
@@ -161,6 +161,7 @@ class EmojiManager:
                         {'_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)
@@ -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,
@@ -301,6 +308,7 @@ class EmojiManager:
                     self.db.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 = {
@@ -389,5 +397,7 @@ class EmojiManager:
 
 
 # 创建全局单例
+
 emoji_manager = EmojiManager()
 
+
diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py
index af7334afe..46dc34e92 100644
--- 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
diff --git a/src/plugins/chat/message.py b/src/plugins/chat/message.py
index ec0aada85..626e7cf4e 100644
--- a/src/plugins/chat/message.py
+++ b/src/plugins/chat/message.py
@@ -3,12 +3,13 @@ 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
 
diff --git a/src/plugins/chat/message_base.py b/src/plugins/chat/message_base.py
index d17c2c357..ae7ec3872 100644
--- 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:
diff --git a/src/plugins/chat/message_cq.py b/src/plugins/chat/message_cq.py
index dc65b65ea..59d67a455 100644
--- a/src/plugins/chat/message_cq.py
+++ b/src/plugins/chat/message_cq.py
@@ -1,12 +1,12 @@
 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)
diff --git a/src/plugins/chat/message_sender.py b/src/plugins/chat/message_sender.py
index 55272953c..584bf9c5d 100644
--- a/src/plugins/chat/message_sender.py
+++ b/src/plugins/chat/message_sender.py
@@ -5,12 +5,11 @@ 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 .storage import MessageStorage
 from .config import global_config
-from .chat_stream import chat_manager
 
 
 class Message_Sender:
diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py
index fec6c7926..b97666763 100644
--- 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:
diff --git a/src/plugins/chat/relationship_manager.py b/src/plugins/chat/relationship_manager.py
index 9e7cafda0..90e92e7b6 100644
--- 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
diff --git a/src/plugins/chat/storage.py b/src/plugins/chat/storage.py
index f403b2c8b..c3986a2d0 100644
--- 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
diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py
index 55fb9eb43..186f2ab79 100644
--- 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
 
 
diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py
index 25f23359b..42d5f9efc 100644
--- 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
diff --git a/src/plugins/chat/willing_manager.py b/src/plugins/chat/willing_manager.py
index 39083f0b8..f34afb746 100644
--- 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:
diff --git a/src/plugins/knowledege/knowledge_library.py b/src/plugins/knowledege/knowledge_library.py
deleted file mode 100644
index e9d7167fd..000000000
--- a/src/plugins/knowledege/knowledge_library.py
+++ /dev/null
@@ -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)
diff --git a/src/plugins/memory_system/memory_manual_build.py b/src/plugins/memory_system/memory_manual_build.py
index 9c1d43ce9..736a50e97 100644
--- 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
diff --git a/src/plugins/zhishi/knowledge_library.py b/src/plugins/zhishi/knowledge_library.py
new file mode 100644
index 000000000..2411e3112
--- /dev/null
+++ 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
diff --git a/如果你更新了版本，点我.txt b/如果你更新了版本，点我.txt
new file mode 100644
index 000000000..400e8ae0c
--- /dev/null
+++ b/如果你更新了版本，点我.txt
@@ -0,0 +1,4 @@
+更新版本后，建议删除数据库messages中所有内容，不然会出现报错
+该操作不会影响你的记忆
+
+如果显示配置文件版本过低，运行根目录的bat
\ No newline at end of file
diff --git a/如果你的配置文件版本太老就点我.bat b/如果你的配置文件版本太老就点我.bat
new file mode 100644
index 000000000..fec1f4cdb
--- /dev/null
+++ b/如果你的配置文件版本太老就点我.bat
@@ -0,0 +1,45 @@
+@echo off
+setlocal enabledelayedexpansion
+chcp 65001
+cd /d %~dp0
+
+echo =====================================
+echo 选择Python环境:
+echo 1 - venv (推荐)
+echo 2 - conda
+echo =====================================
+choice /c 12 /n /m "输入数字(1或2): "
+
+if errorlevel 2 (
+    echo =====================================
+    set "CONDA_ENV="
+    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
+    
+    :: 检查输入是否为空
+    if "!CONDA_ENV!"=="" (
+        echo 错误：环境名称不能为空
+        pause
+        exit /b 1
+    )
+    
+    call conda activate !CONDA_ENV!
+    if errorlevel 1 (
+        echo 激活 conda 环境失败
+        pause
+        exit /b 1
+    )
+    
+    echo Conda 环境 "!CONDA_ENV!" 激活成功
+    python config/auto_update.py
+) else (
+    if exist "venv\Scripts\python.exe" (
+        venv\Scripts\python config/auto_update.py
+    ) else (
+        echo =====================================
+        echo 错误: venv环境不存在，请先创建虚拟环境
+        pause
+        exit /b 1
+    )
+)
+endlocal
+pause
diff --git a/麦麦开始学习.bat b/麦麦开始学习.bat
new file mode 100644
index 000000000..f7391150f
--- /dev/null
+++ b/麦麦开始学习.bat
@@ -0,0 +1,45 @@
+@echo off
+setlocal enabledelayedexpansion
+chcp 65001
+cd /d %~dp0
+
+echo =====================================
+echo 选择Python环境:
+echo 1 - venv (推荐)
+echo 2 - conda
+echo =====================================
+choice /c 12 /n /m "输入数字(1或2): "
+
+if errorlevel 2 (
+    echo =====================================
+    set "CONDA_ENV="
+    set /p CONDA_ENV="请输入要激活的 conda 环境名称: "
+    
+    :: 检查输入是否为空
+    if "!CONDA_ENV!"=="" (
+        echo 错误：环境名称不能为空
+        pause
+        exit /b 1
+    )
+    
+    call conda activate !CONDA_ENV!
+    if errorlevel 1 (
+        echo 激活 conda 环境失败
+        pause
+        exit /b 1
+    )
+    
+    echo Conda 环境 "!CONDA_ENV!" 激活成功
+    python src/plugins/zhishi/knowledge_library.py
+) else (
+    if exist "venv\Scripts\python.exe" (
+        venv\Scripts\python src/plugins/zhishi/knowledge_library.py
+    ) else (
+        echo =====================================
+        echo 错误: venv环境不存在，请先创建虚拟环境
+        pause
+        exit /b 1
+    )
+)
+endlocal
+pause