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

.gitignore

@@ -1,4 +1,5 @@
 data/
+data1/
 mongodb/
 NapCat.Framework.Windows.Once/
 log/

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()

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
+```
+

run.py

@@ -128,13 +128,17 @@ if __name__ == "__main__":
     )
     os.system("cls")
     if choice == "1":
+        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")

src/common/database.py

@@ -1,5 +1,4 @@
 from typing import Optional
-
 from pymongo import MongoClient
 from pymongo.database import Database as MongoDatabase
 

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
@@ -38,8 +38,8 @@ 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
 
@@ -91,8 +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)
 
 

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
@@ -42,26 +46,41 @@ 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
+        # 用户屏蔽,不区分私聊/群聊
+        if event.user_id in global_config.ban_user_id:
+            return
 
+        # 处理私聊消息
+        if isinstance(event, PrivateMessageEvent):
+            if not global_config.enable_friend_chat:  # 私聊过滤
+                return
+            else:
+                try:
+                    user_info = UserInfo(
+                        user_id=event.user_id,
+                        user_nickname=(await bot.get_stranger_info(user_id=event.user_id, no_cache=True))["nickname"],
+                        user_cardname=None,
+                        platform="qq",
+                    )
+                except Exception as e:
+                    logger.error(f"获取陌生人信息失败: {e}")
+                    return
+                logger.debug(user_info)
+
+                # group_info = 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
-        # 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
-        if event.user_id in global_config.ban_user_id:
-            return
 
             user_info = UserInfo(
                 user_id=event.user_id,
@@ -70,11 +89,10 @@ class ChatBot:
                 platform="qq",
             )
 
-        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=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,

src/plugins/chat/config.py

@@ -69,6 +69,7 @@ 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
@@ -327,7 +328,9 @@ class BotConfig:
             others_config = parent["others"]
             config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output)
             config.enable_kuuki_read = others_config.get("enable_kuuki_read", config.enable_kuuki_read)
+            if config.INNER_VERSION in SpecifierSet(">=0.0.7"):
                 config.enable_debug_output = others_config.get("enable_debug_output", config.enable_debug_output)
+                config.enable_friend_chat = others_config.get("enable_friend_chat", config.enable_friend_chat)
 
         # 版本表达式：>=1.0.0,<2.0.0
         # 允许字段：func: method, support: str, notice: str, necessary: bool

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,
@@ -302,6 +309,7 @@ class EmojiManager:
                     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,
@@ -389,5 +397,7 @@ class EmojiManager:
 
 
 # 创建全局单例
+
 emoji_manager = EmojiManager()
 
+

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

src/plugins/chat/message.py

@@ -3,14 +3,16 @@ 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)
 
@@ -18,6 +20,7 @@ urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 # 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):
     chat_stream: ChatStream=None
@@ -103,7 +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:
@@ -115,14 +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)
@@ -137,31 +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':
+                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"
@@ -177,7 +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__(
@@ -186,7 +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
@@ -207,14 +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)
@@ -229,39 +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"""
@@ -271,7 +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__(
@@ -279,12 +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"""
@@ -294,10 +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__(
@@ -305,28 +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
@@ -335,8 +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,
@@ -345,17 +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["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
@@ -406,6 +437,3 @@ class MessageSet:
 
     def __len__(self) -> int:
         return len(self.messages)
-        
-
-    

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:

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

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:
@@ -30,18 +29,20 @@ class Message_Sender:
         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)
+            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")
                 except Exception as e:
@@ -49,10 +50,11 @@ class Message_Sender:
                     logger.error(f"[调试] 发送消息{message.processed_plain_text}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")
                 except Exception as e:
@@ -62,6 +64,7 @@ class Message_Sender:
 
 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 +91,7 @@ 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 +130,7 @@ 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 +142,9 @@ 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 +161,11 @@ 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 +173,23 @@ 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'{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 +201,11 @@ 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)

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:

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

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

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
 

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

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:

src/plugins/knowledege/knowledge_library.py

@@ -1,188 +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
-
-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.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.knowledges.update_one(
-                    {"content_hash": content_hash},
-                    {"$set": doc},
-                    upsert=True
-                )
-                
-            # 记录文件已处理
-            self.db.processed_files.insert_one({
-                "file_path": file_path,
-                "processed_time": time.time()
-            })
-                
-            print(f"成功处理文件: {file_path}")
-            
-        except Exception as e:
-            print(f"处理文件 {file_path} 时出错: {str(e)}")
-            
-    def search_similar_segments(self, query: str, limit: int = 5) -> list:
-        """搜索与查询文本相似的片段"""
-        query_embedding = self.get_embedding(query)
-        if not query_embedding:
-            return []
-            
-        # 使用余弦相似度计算
-        pipeline = [
-            {
-                "$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.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)

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

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

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "0.0.6"
+version = "0.0.7"
 
 #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
@@ -101,6 +101,7 @@ 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 = [

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

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

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