v0.3.1的更新，增加了主动回复和记忆系统（海马体），修复一些小bug，改了config位置
v0.3.1的更新，增加了主动回复和记忆系统（海马体），修复一些小bug，改了config位置
This commit is contained in:
SengokuCola
GitHub
2
.gitignore
vendored
2
.gitignore
vendored
@@ -3,7 +3,7 @@ mongodb/
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NapCat.Framework.Windows.Once/
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log/
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src/plugins/memory
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src/plugins/chat/bot_config.toml
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config/bot_config.toml
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/test
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message_queue_content.txt
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message_queue_content.bat
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49
README.md
49
README.md
@@ -4,7 +4,7 @@
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<div align="center">
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@@ -16,11 +16,19 @@
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基于llm、napcat、nonebot和mongodb的专注于群聊天的qqbot
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<div align="center">
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<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
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<img src="https://i0.hdslb.com/bfs/archive/7d9fa0a88e8a1aa01b92b8a5a743a2671c0e1798.jpg" width="500" alt="麦麦演示视频">
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<br>
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👆 点击观看麦麦演示视频 👆
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</a>
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</div>
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> ⚠️ **警告**：代码可能随时更改，目前版本不一定是稳定版本
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> ⚠️ **警告**：请自行了解qqbot的风险，麦麦有时候一天被腾讯肘七八次
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> ⚠️ **警告**：由于麦麦一直在迭代，所以可能存在一些bug，请自行测试，包括胡言乱语（
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关于麦麦的开发和部署相关的讨论群（不建议发布无关消息）这里不会有麦麦发言！
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关于麦麦的开发和建议相关的讨论群（不建议发布无关消息）这里不会有麦麦发言！
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## 开发计划TODO：LIST
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@@ -29,6 +37,10 @@
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- 对思考链长度限制
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- 修复已知bug
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- 完善文档
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- 修复转发
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- config自动生成和检测
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- log别用print
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- 给发送消息写专门的类
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<div align="center">
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@@ -52,11 +64,9 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
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#### 手动运行
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1. **创建Python环境**
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推荐使用conda或其他环境管理来管理你的python环境
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推荐使用conda或其他虚拟环境进行依赖安装，防止出现依赖版本冲突问题
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```bash
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# 安装requirements（还没检查好，可能有包漏了）
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conda activate 你的环境
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cd 对应路径
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# 安装requirements
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pip install -r requirements.txt
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```
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2. **MongoDB设置**
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@@ -68,8 +78,8 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
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- 在Napcat的网络设置中添加ws反向代理:ws://localhost:8080/onebot/v11/ws
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4. **配置文件设置**
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- 把env.example改成.env，并填上你的apikey（硅基流动或deepseekapi）
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- 把bot_config_toml改名为bot_config.toml，并填写相关内容，不然无法正常运行
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- 将.env文件打开，填上你的apikey（硅基流动或deepseekapi）
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- 将bot_config.toml文件打开，并填写相关内容，不然无法正常运行
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#### .env 文件配置说明
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```ini
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@@ -92,14 +102,10 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
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MONGODB_PASSWORD="" # MongoDB密码（可选）
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MONGODB_AUTH_SOURCE="" # MongoDB认证源（可选）
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# API密钥配置
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CHAT_ANY_WHERE_KEY= # ChatAnyWhere API密钥
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SILICONFLOW_KEY= # 硅基流动 API密钥（必填）
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DEEP_SEEK_KEY= # DeepSeek API密钥（必填）
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# API地址配置
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CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
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#api配置项，建议siliconflow必填，识图需要这个
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SILICONFLOW_KEY=
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SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
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DEEP_SEEK_KEY=
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DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
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```
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@@ -158,9 +164,8 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
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```
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5. **运行麦麦**
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在含有bot.py程序的目录下运行（如果使用了虚拟环境需要先进入虚拟环境）
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```bash
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conda activate 你的环境
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cd 对应路径
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nb run
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```
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6. **运行其他组件**
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@@ -205,3 +210,13 @@ NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart
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纯编程外行，面向cursor编程，很多代码史一样多多包涵
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> ⚠️ **警告**：本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。
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## 致谢
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[nonebot2](https://github.com/nonebot/nonebot2): 跨平台 Python 异步聊天机器人框架
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[NapCat](https://github.com/NapNeko/NapCatQQ): 现代化的基于 NTQQ 的 Bot 协议端实现
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### 贡献者
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感谢各位大佬！
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[](https://github.com/SengokuCola/MaiMBot/graphs/contributors)
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@@ -7,8 +7,8 @@ password = "" # 默认空值
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auth_source = "" # 默认空值
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[bot]
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qq = #填入你的机器人QQ
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nickname = "麦麦"
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qq = 123456 #填入你的机器人QQ
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nickname = "麦麦" #你希望bot被称呼的名字
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[message]
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min_text_length = 2 # 与麦麦聊天时麦麦只会回答文本大于等于此数的消息
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@@ -24,11 +24,16 @@ enable_pic_translate = false
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[response]
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api_using = "siliconflow" # 选择大模型API
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api_using = "siliconflow" # 选择大模型API，可选值为siliconflow,deepseek，建议使用siliconflow，因为识图api目前只支持siliconflow的deepseek-vl2模型
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model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率
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model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率
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model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率
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[memory]
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build_memory_interval = 300 # 记忆构建间隔
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[others]
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enable_advance_output = true # 开启后输出更多日志,false关闭true开启
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@@ -36,13 +41,13 @@ enable_advance_output = true # 开启后输出更多日志,false关闭true开启
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[groups]
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talk_allowed = [
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#可以回复消息的群
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]
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123456,12345678
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] #可以回复消息的群
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talk_frequency_down = [
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#降低回复频率的群
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]
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123456,12345678
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] #降低回复频率的群
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ban_user_id = [
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#禁止回复消息的QQ号
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]
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123456,12345678
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] #禁止回复消息的QQ号
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@@ -15,10 +15,8 @@ MONGODB_USERNAME = "" # 默认空值
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MONGODB_PASSWORD = "" # 默认空值
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MONGODB_AUTH_SOURCE = "" # 默认空值
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#key and url
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CHAT_ANY_WHERE_KEY=
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#api配置项
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SILICONFLOW_KEY=
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CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1
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SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/
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DEEP_SEEK_KEY=
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DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1
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@@ -1,5 +0,0 @@
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call conda activate niuniu
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cd "C:\GitHub\MegMeg-bot"
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REM 执行nb run命令
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nb run
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@@ -1,3 +1,4 @@
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from loguru import logger
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from nonebot import on_message, on_command, require, get_driver
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from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment
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from nonebot.typing import T_State
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@@ -10,7 +11,6 @@ from .relationship_manager import relationship_manager
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from ..schedule.schedule_generator import bot_schedule
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from .willing_manager import willing_manager
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# 获取驱动器
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driver = get_driver()
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@@ -19,8 +19,7 @@ Database.initialize(
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global_config.MONGODB_PORT,
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global_config.DATABASE_NAME
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)
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print("\033[1;32m[初始化配置和数据库完成]\033[0m")
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print("\033[1;32m[初始化数据库完成]\033[0m")
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# 导入其他模块
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@@ -28,6 +27,7 @@ from .bot import ChatBot
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from .emoji_manager import emoji_manager
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from .message_send_control import message_sender
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from .relationship_manager import relationship_manager
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from ..memory_system.memory import memory_graph,hippocampus
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# 初始化表情管理器
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emoji_manager.initialize()
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@@ -35,22 +35,27 @@ emoji_manager.initialize()
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print(f"\033[1;32m正在唤醒{global_config.BOT_NICKNAME}......\033[0m")
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# 创建机器人实例
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chat_bot = ChatBot(global_config)
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# 注册消息处理器
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group_msg = on_message()
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# 创建定时任务
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scheduler = require("nonebot_plugin_apscheduler").scheduler
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# 启动后台任务
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@driver.on_startup
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async def start_background_tasks():
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"""启动后台任务"""
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# 只启动表情包管理任务
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asyncio.create_task(emoji_manager.start_periodic_check(interval_MINS=global_config.EMOJI_CHECK_INTERVAL))
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bot_schedule.print_schedule()
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@driver.on_startup
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async def init_relationships():
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"""在 NoneBot2 启动时初始化关系管理器"""
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print("\033[1;32m[初始化]\033[0m 正在加载用户关系数据...")
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await relationship_manager.load_all_relationships()
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asyncio.create_task(relationship_manager._start_relationship_manager())
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@driver.on_bot_connect
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async def _(bot: Bot):
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"""Bot连接成功时的处理"""
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@@ -64,19 +69,23 @@ async def _(bot: Bot):
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print("\033[1;38;5;208m-----------开始偷表情包！-----------\033[0m")
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# 启动消息发送控制任务
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@driver.on_startup
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async def init_relationships():
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"""在 NoneBot2 启动时初始化关系管理器"""
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print("\033[1;32m[初始化]\033[0m 正在加载用户关系数据...")
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await relationship_manager.load_all_relationships()
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asyncio.create_task(relationship_manager._start_relationship_manager())
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@group_msg.handle()
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async def _(bot: Bot, event: GroupMessageEvent, state: T_State):
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await chat_bot.handle_message(event, bot)
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'''
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@scheduler.scheduled_job("interval", seconds=300000, id="monitor_relationships")
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async def monitor_relationships():
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"""每15秒打印一次关系数据"""
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relationship_manager.print_all_relationships()
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'''
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# 添加build_memory定时任务
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@scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval, id="build_memory")
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async def build_memory_task():
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"""每30秒执行一次记忆构建"""
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print("\033[1;32m[记忆构建]\033[0m 开始构建记忆...")
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hippocampus.build_memory(chat_size=12)
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print("\033[1;32m[记忆构建]\033[0m 记忆构建完成")
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@@ -5,7 +5,7 @@ from .storage import MessageStorage
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from .llm_generator import LLMResponseGenerator
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from .message_stream import MessageStream, MessageStreamContainer
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from .topic_identifier import topic_identifier
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from random import random
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from random import random, choice
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from .emoji_manager import emoji_manager # 导入表情包管理器
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import time
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import os
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@@ -15,6 +15,7 @@ from .message import Message_Thinking # 导入 Message_Thinking 类
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from .relationship_manager import relationship_manager
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from .willing_manager import willing_manager # 导入意愿管理器
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from .utils import is_mentioned_bot_in_txt, calculate_typing_time
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from ..memory_system.memory import memory_graph
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class ChatBot:
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def __init__(self, config: BotConfig):
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@@ -82,7 +83,7 @@ class ChatBot:
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await relationship_manager.update_relationship(user_id = event.user_id, data = sender_info)
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await relationship_manager.update_relationship_value(user_id = event.user_id, relationship_value = 0.5)
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print(f"\033[1;32m[关系管理]\033[0m 更新关系值: {relationship_manager.get_relationship(event.user_id).relationship_value}")
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# print(f"\033[1;32m[关系管理]\033[0m 更新关系值: {relationship_manager.get_relationship(event.user_id).relationship_value}")
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message = Message(
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@@ -99,11 +100,21 @@ class ChatBot:
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topic = topic_identifier.identify_topic_jieba(message.processed_plain_text)
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print(f"\033[1;32m[主题识别]\033[0m 主题: {topic}")
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all_num = 0
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interested_num = 0
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if topic:
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for current_topic in topic:
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all_num += 1
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first_layer_items, second_layer_items = memory_graph.get_related_item(current_topic, depth=2)
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if first_layer_items:
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interested_num += 1
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print(f"\033[1;32m[前额叶]\033[0m 对|{current_topic}|有印象")
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interested_rate = interested_num / all_num if all_num > 0 else 0
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await self.storage.store_message(message, topic[0] if topic else None)
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is_mentioned = is_mentioned_bot_in_txt(message.processed_plain_text)
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reply_probability = willing_manager.change_reply_willing_received(
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event.group_id,
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@@ -111,7 +122,8 @@ class ChatBot:
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is_mentioned,
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self.config,
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event.user_id,
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message.is_emoji
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message.is_emoji,
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interested_rate
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)
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current_willing = willing_manager.get_willing(event.group_id)
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@@ -182,7 +194,8 @@ class ChatBot:
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user_nickname=global_config.BOT_NICKNAME,
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group_name=message.group_name,
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time=bot_response_time,
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is_emoji=True
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is_emoji=True,
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translate_cq=False
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)
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message_sender.send_temp_container.add_message(bot_message)
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@@ -5,6 +5,9 @@ from nonebot.log import logger, default_format
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import logging
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import configparser
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import tomli
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import sys
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from loguru import logger
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from dotenv import load_dotenv
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@@ -20,7 +23,7 @@ class BotConfig:
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MONGODB_PASSWORD: Optional[str] = None # 默认空值
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MONGODB_AUTH_SOURCE: Optional[str] = None # 默认空值
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|
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BOT_QQ: Optional[int] = None
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BOT_QQ: Optional[int] = 1
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BOT_NICKNAME: Optional[str] = None
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# 消息处理相关配置
|
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@@ -34,6 +37,7 @@ class BotConfig:
|
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talk_frequency_down_groups = set()
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ban_user_id = set()
|
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|
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build_memory_interval: int = 60 # 记忆构建间隔（秒）
|
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EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟）
|
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EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟）
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@@ -44,9 +48,21 @@ class BotConfig:
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enable_advance_output: bool = False # 是否启用高级输出
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@staticmethod
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def get_default_config_path() -> str:
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"""获取默认配置文件路径"""
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current_dir = os.path.dirname(os.path.abspath(__file__))
|
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root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
|
||||
config_dir = os.path.join(root_dir, 'config')
|
||||
return os.path.join(config_dir, 'bot_config.toml')
|
||||
|
||||
@classmethod
|
||||
def load_config(cls, config_path: str = "bot_config.toml") -> "BotConfig":
|
||||
def load_config(cls, config_path: str = None) -> "BotConfig":
|
||||
"""从TOML配置文件加载配置"""
|
||||
if config_path is None:
|
||||
config_path = cls.get_default_config_path()
|
||||
logger.info(f"使用默认配置文件路径: {config_path}")
|
||||
|
||||
config = cls()
|
||||
if os.path.exists(config_path):
|
||||
with open(config_path, "rb") as f:
|
||||
@@ -92,6 +108,10 @@ class BotConfig:
|
||||
config.MAX_CONTEXT_SIZE = msg_config.get("max_context_size", config.MAX_CONTEXT_SIZE)
|
||||
config.emoji_chance = msg_config.get("emoji_chance", config.emoji_chance)
|
||||
|
||||
if "memory" in toml_dict:
|
||||
memory_config = toml_dict["memory"]
|
||||
config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval)
|
||||
|
||||
# 群组配置
|
||||
if "groups" in toml_dict:
|
||||
groups_config = toml_dict["groups"]
|
||||
@@ -103,16 +123,26 @@ class BotConfig:
|
||||
others_config = toml_dict["others"]
|
||||
config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output)
|
||||
|
||||
print(f"\033[1;32m成功加载配置文件: {config_path}\033[0m")
|
||||
logger.success(f"成功加载配置文件: {config_path}")
|
||||
|
||||
return config
|
||||
|
||||
global_config = BotConfig.load_config("./src/plugins/chat/bot_config.toml")
|
||||
# 获取配置文件路径
|
||||
bot_config_path = BotConfig.get_default_config_path()
|
||||
config_dir = os.path.dirname(bot_config_path)
|
||||
env_path = os.path.join(config_dir, '.env')
|
||||
|
||||
from dotenv import load_dotenv
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
|
||||
load_dotenv(os.path.join(root_dir, '.env'))
|
||||
logger.info(f"尝试从 {bot_config_path} 加载机器人配置")
|
||||
global_config = BotConfig.load_config(config_path=bot_config_path)
|
||||
|
||||
# 加载环境变量
|
||||
|
||||
logger.info(f"尝试从 {env_path} 加载环境变量配置")
|
||||
if os.path.exists(env_path):
|
||||
load_dotenv(env_path)
|
||||
logger.success("成功加载环境变量配置")
|
||||
else:
|
||||
logger.error(f"环境变量配置文件不存在: {env_path}")
|
||||
|
||||
@dataclass
|
||||
class LLMConfig:
|
||||
@@ -131,10 +161,5 @@ llm_config.DEEP_SEEK_BASE_URL = os.getenv('DEEP_SEEK_BASE_URL')
|
||||
|
||||
|
||||
if not global_config.enable_advance_output:
|
||||
logger.remove()
|
||||
|
||||
logging.getLogger('nonebot').handlers.clear()
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setLevel(logging.WARNING) # 只输出 WARNING 及以上级别
|
||||
logging.getLogger('nonebot').addHandler(console_handler)
|
||||
logging.getLogger('nonebot').setLevel(logging.WARNING)
|
||||
# logger.remove()
|
||||
pass
|
||||
|
||||
@@ -4,7 +4,7 @@ import asyncio
|
||||
import requests
|
||||
from functools import partial
|
||||
from .message import Message
|
||||
from .config import BotConfig
|
||||
from .config import BotConfig, global_config
|
||||
from ...common.database import Database
|
||||
import random
|
||||
import time
|
||||
@@ -255,4 +255,4 @@ class LLMResponseGenerator:
|
||||
return processed_response, emotion_tags
|
||||
|
||||
# 创建全局实例
|
||||
llm_response = LLMResponseGenerator(config=BotConfig())
|
||||
llm_response = LLMResponseGenerator(global_config)
|
||||
@@ -6,17 +6,13 @@ import os
|
||||
from datetime import datetime
|
||||
from ...common.database import Database
|
||||
from PIL import Image
|
||||
from .config import BotConfig, global_config
|
||||
from .config import global_config
|
||||
import urllib3
|
||||
from .utils_user import get_user_nickname
|
||||
from .utils_cq import parse_cq_code
|
||||
from .cq_code import cq_code_tool,CQCode
|
||||
|
||||
Message = ForwardRef('Message') # 添加这行
|
||||
|
||||
# 加载配置
|
||||
bot_config = BotConfig.load_config()
|
||||
|
||||
# 禁用SSL警告
|
||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||||
|
||||
@@ -49,6 +45,8 @@ class Message:
|
||||
is_emoji: bool = False # 是否是表情包
|
||||
has_emoji: bool = False # 是否包含表情包
|
||||
|
||||
translate_cq: bool = True # 是否翻译cq码
|
||||
|
||||
|
||||
reply_benefits: float = 0.0
|
||||
|
||||
@@ -99,7 +97,7 @@ class Message:
|
||||
- cq_code_list:分割出的聊天对象，包括文本和CQ码
|
||||
- trans_list:翻译后的对象列表
|
||||
"""
|
||||
print(f"\033[1;34m[调试信息]\033[0m 正在处理消息: {message}")
|
||||
# print(f"\033[1;34m[调试信息]\033[0m 正在处理消息: {message}")
|
||||
cq_code_dict_list = []
|
||||
trans_list = []
|
||||
|
||||
|
||||
@@ -184,7 +184,7 @@ class MessageSendControl:
|
||||
message.update_thinking_time()
|
||||
thinking_time = message.thinking_time
|
||||
if thinking_time < 90: # 最少思考2秒
|
||||
if int(thinking_time) % 10 == 0:
|
||||
if int(thinking_time) % 15 == 0:
|
||||
print(f"\033[1;34m[调试]\033[0m 消息正在思考中，已思考{thinking_time:.1f}秒")
|
||||
return
|
||||
else:
|
||||
@@ -208,7 +208,15 @@ class MessageSendControl:
|
||||
print(f"\033[1;34m[调试]\033[0m 消息发送时间: {cost_time}秒")
|
||||
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time))
|
||||
print(f"\033[1;32m群 {group_id} 消息, 用户 {global_config.BOT_NICKNAME}, 时间: {current_time}:\033[0m {str(message.processed_plain_text)}")
|
||||
await self.storage.store_message(message, None)
|
||||
|
||||
if message.is_emoji:
|
||||
message.processed_plain_text = "[表情包]"
|
||||
await self.storage.store_message(message, None)
|
||||
else:
|
||||
await self.storage.store_message(message, None)
|
||||
|
||||
|
||||
|
||||
queue.update_send_time()
|
||||
if queue.has_messages():
|
||||
await asyncio.sleep(
|
||||
|
||||
@@ -6,6 +6,9 @@ import os
|
||||
from .utils import get_embedding, combine_messages, get_recent_group_detailed_plain_text
|
||||
from ...common.database import Database
|
||||
from .config import global_config
|
||||
from .topic_identifier import topic_identifier
|
||||
from ..memory_system.memory import memory_graph
|
||||
from random import choice
|
||||
|
||||
# 获取当前文件的绝对路径
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
@@ -35,6 +38,59 @@ class PromptBuilder:
|
||||
Returns:
|
||||
str: 构建好的prompt
|
||||
"""
|
||||
|
||||
|
||||
memory_prompt = ''
|
||||
start_time = time.time() # 记录开始时间
|
||||
topic = topic_identifier.identify_topic_jieba(message_txt)
|
||||
# print(f"\033[1;32m[pb主题识别]\033[0m 主题: {topic}")
|
||||
|
||||
all_first_layer_items = [] # 存储所有第一层记忆
|
||||
all_second_layer_items = {} # 用字典存储每个topic的第二层记忆
|
||||
overlapping_second_layer = set() # 存储重叠的第二层记忆
|
||||
|
||||
if topic:
|
||||
# 遍历所有topic
|
||||
for current_topic in topic:
|
||||
first_layer_items, second_layer_items = memory_graph.get_related_item(current_topic, depth=2)
|
||||
# if first_layer_items:
|
||||
# print(f"\033[1;32m[前额叶]\033[0m 主题 '{current_topic}' 的第一层记忆: {first_layer_items}")
|
||||
|
||||
# 记录第一层数据
|
||||
all_first_layer_items.extend(first_layer_items)
|
||||
|
||||
# 记录第二层数据
|
||||
all_second_layer_items[current_topic] = second_layer_items
|
||||
|
||||
# 检查是否有重叠的第二层数据
|
||||
for other_topic, other_second_layer in all_second_layer_items.items():
|
||||
if other_topic != current_topic:
|
||||
# 找到重叠的记忆
|
||||
overlap = set(second_layer_items) & set(other_second_layer)
|
||||
if overlap:
|
||||
# print(f"\033[1;32m[前额叶]\033[0m 发现主题 '{current_topic}' 和 '{other_topic}' 有共同的第二层记忆: {overlap}")
|
||||
overlapping_second_layer.update(overlap)
|
||||
|
||||
# 合并所有需要的记忆
|
||||
if all_first_layer_items:
|
||||
print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆1: {all_first_layer_items}")
|
||||
if overlapping_second_layer:
|
||||
print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}")
|
||||
|
||||
all_memories = all_first_layer_items + list(overlapping_second_layer)
|
||||
|
||||
if all_memories: # 只在列表非空时选择随机项
|
||||
random_item = choice(all_memories)
|
||||
memory_prompt = f"看到这些聊天，你想起来{random_item}\n"
|
||||
else:
|
||||
memory_prompt = "" # 如果没有记忆，则返回空字符串
|
||||
|
||||
end_time = time.time() # 记录结束时间
|
||||
print(f"\033[1;32m[回忆耗时]\033[0m 耗时: {(end_time - start_time):.3f}秒") # 输出耗时
|
||||
|
||||
|
||||
|
||||
|
||||
#先禁用关系
|
||||
if 0 > 30:
|
||||
relation_prompt = "关系特别特别好，你很喜欢喜欢他"
|
||||
@@ -54,26 +110,33 @@ class PromptBuilder:
|
||||
bot_schedule_now_time,bot_schedule_now_activity = bot_schedule.get_current_task()
|
||||
prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
|
||||
|
||||
#知识构建（暂时禁用，因为知识库太少了）
|
||||
#知识构建
|
||||
start_time = time.time()
|
||||
|
||||
prompt_info = ''
|
||||
promt_info_prompt = ''
|
||||
prompt_info = self.get_prompt_info(message_txt)
|
||||
prompt_info = self.get_prompt_info(message_txt,threshold=0.5)
|
||||
if prompt_info:
|
||||
prompt_info = f'''\n----------------------------------------------------\n你有以下这些[知识]：
|
||||
\n{prompt_info}\n
|
||||
请你记住上面的[知识]，之后可能会用到\n----------------------------------------------------\n'''
|
||||
prompt_info = f'''\n----------------------------------------------------\n你有以下这些[知识]：\n{prompt_info}\n请你记住上面的[知识]，之后可能会用到\n----------------------------------------------------\n'''
|
||||
promt_info_prompt = '你有一些[知识]，在上面可以参考。'
|
||||
|
||||
print(f"\033[1;34m[调试信息]\033[0m 正在构建聊天上下文")
|
||||
end_time = time.time()
|
||||
print(f"\033[1;32m[知识检索]\033[0m 耗时: {(end_time - start_time):.3f}秒")
|
||||
# print(f"\033[1;34m[调试]\033[0m 获取知识库内容结果: {prompt_info}")
|
||||
|
||||
|
||||
# print(f"\033[1;34m[调试信息]\033[0m 正在构建聊天上下文")
|
||||
|
||||
chat_talking_prompt = ''
|
||||
if group_id:
|
||||
chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
|
||||
print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||
|
||||
chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
|
||||
# print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}")
|
||||
|
||||
#激活prompt构建
|
||||
activate_prompt = ''
|
||||
activate_prompt = f"以上是群里正在进行的聊天，昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}。"
|
||||
activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}。"
|
||||
|
||||
#检测机器人相关词汇
|
||||
bot_keywords = ['人机', 'bot', '机器', '入机', 'robot', '机器人']
|
||||
@@ -87,13 +150,12 @@ class PromptBuilder:
|
||||
prompt_personality = ''
|
||||
personality_choice = random.random()
|
||||
if personality_choice < 4/6: # 第一种人格
|
||||
prompt_personality = f'''你的网名叫{global_config.BOT_NICKNAME}，是一个学习地质的女大学生，喜欢摄影，你会刷贴吧，你正在浏览qq群,{promt_info_prompt},
|
||||
{activate_prompt}
|
||||
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，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，你正在浏览qq群,{promt_info_prompt},
|
||||
现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{is_bot_prompt}
|
||||
请注意把握群里的聊天内容，不要回复的太有条理，可以有个性。'''
|
||||
elif personality_choice < 1: # 第二种人格
|
||||
prompt_personality = f'''你的网名叫{global_config.BOT_NICKNAME}，是一个女大学生，你有黑色头发，你会刷小红书, 你正在浏览qq群,{promt_info_prompt},
|
||||
{activate_prompt}
|
||||
prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，是一个女大学生，你有黑色头发，你会刷小红书, 你正在浏览qq群,{promt_info_prompt},
|
||||
|
||||
现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{is_bot_prompt}
|
||||
请你表达自己的见解和观点。可以有个性。'''
|
||||
|
||||
@@ -108,15 +170,18 @@ class PromptBuilder:
|
||||
|
||||
|
||||
#额外信息要求
|
||||
extra_info = '''但是记得回复平淡一些，简短一些，不要过多提及自身的背景, 记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等),只需要输出回复内容就好，不要输出其他任何内容'''
|
||||
extra_info = '''但是记得回复平淡一些，简短一些,记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情等),只需要输出回复内容就好，不要输出其他任何内容'''
|
||||
|
||||
|
||||
|
||||
#合并prompt
|
||||
prompt = ""
|
||||
# prompt += f"{prompt_info}\n"
|
||||
prompt += f"{prompt_info}\n"
|
||||
prompt += f"{prompt_date}\n"
|
||||
prompt += f"{chat_talking_prompt}\n"
|
||||
|
||||
# prompt += f"{memory_prompt}\n"
|
||||
|
||||
# prompt += f"{activate_prompt}\n"
|
||||
prompt += f"{prompt_personality}\n"
|
||||
prompt += f"{prompt_ger}\n"
|
||||
@@ -124,31 +189,23 @@ class PromptBuilder:
|
||||
|
||||
return prompt
|
||||
|
||||
def get_prompt_info(self,message:str):
|
||||
def get_prompt_info(self,message:str,threshold:float):
|
||||
related_info = ''
|
||||
if len(message) > 10:
|
||||
message_segments = [message[i:i+10] for i in range(0, len(message), 10)]
|
||||
for segment in message_segments:
|
||||
embedding = get_embedding(segment)
|
||||
related_info += self.get_info_from_db(embedding)
|
||||
related_info += self.get_info_from_db(embedding,threshold=threshold)
|
||||
|
||||
else:
|
||||
embedding = get_embedding(message)
|
||||
related_info += self.get_info_from_db(embedding)
|
||||
related_info += self.get_info_from_db(embedding,threshold=threshold)
|
||||
|
||||
return related_info
|
||||
|
||||
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
|
||||
"""
|
||||
从知识库中查找与输入向量最相似的内容
|
||||
Args:
|
||||
query_embedding: 查询向量
|
||||
limit: 返回结果数量，默认为2
|
||||
threshold: 相似度阈值，默认为0.5
|
||||
Returns:
|
||||
str: 找到的相关信息，如果相似度低于阈值则返回空字符串
|
||||
"""
|
||||
if not query_embedding:
|
||||
return ''
|
||||
|
||||
# 使用余弦相似度计算
|
||||
pipeline = [
|
||||
{
|
||||
@@ -206,6 +263,7 @@ class PromptBuilder:
|
||||
]
|
||||
|
||||
results = list(self.db.db.knowledges.aggregate(pipeline))
|
||||
# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
|
||||
|
||||
if not results:
|
||||
return ''
|
||||
|
||||
@@ -7,6 +7,8 @@ import numpy as np
|
||||
from .config import llm_config, global_config
|
||||
import re
|
||||
from typing import Dict
|
||||
from collections import Counter
|
||||
import math
|
||||
|
||||
|
||||
def combine_messages(messages: List[Message]) -> str:
|
||||
@@ -81,6 +83,39 @@ def cosine_similarity(v1, v2):
|
||||
norm2 = np.linalg.norm(v2)
|
||||
return dot_product / (norm1 * norm2)
|
||||
|
||||
def calculate_information_content(text):
|
||||
"""计算文本的信息量（熵）"""
|
||||
# 统计字符频率
|
||||
char_count = Counter(text)
|
||||
total_chars = len(text)
|
||||
|
||||
# 计算熵
|
||||
entropy = 0
|
||||
for count in char_count.values():
|
||||
probability = count / total_chars
|
||||
entropy -= probability * math.log2(probability)
|
||||
|
||||
return entropy
|
||||
|
||||
def get_cloest_chat_from_db(db, length: int, timestamp: str):
|
||||
# 从数据库中根据时间戳获取离其最近的聊天记录
|
||||
chat_text = ''
|
||||
closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
|
||||
# print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
|
||||
|
||||
if closest_record:
|
||||
closest_time = closest_record['time']
|
||||
group_id = closest_record['group_id'] # 获取groupid
|
||||
# 获取该时间戳之后的length条消息，且groupid相同
|
||||
chat_record = list(db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
|
||||
for record in chat_record:
|
||||
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
|
||||
chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息
|
||||
return chat_text
|
||||
|
||||
return [] # 如果没有找到记录，返回空列表
|
||||
|
||||
|
||||
def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
|
||||
"""从数据库获取群组最近的消息记录
|
||||
|
||||
|
||||
@@ -4,11 +4,9 @@ import hashlib
|
||||
import time
|
||||
import os
|
||||
from ...common.database import Database
|
||||
from .config import BotConfig
|
||||
import zlib # 用于 CRC32
|
||||
import base64
|
||||
|
||||
bot_config = BotConfig.load_config()
|
||||
from .config import global_config
|
||||
|
||||
|
||||
def storage_image(image_data: bytes,type: str, max_size: int = 200) -> bytes:
|
||||
@@ -39,12 +37,12 @@ def storage_compress_image(image_data: bytes, max_size: int = 200) -> bytes:
|
||||
|
||||
# 连接数据库
|
||||
db = Database(
|
||||
host=bot_config.MONGODB_HOST,
|
||||
port=bot_config.MONGODB_PORT,
|
||||
db_name=bot_config.DATABASE_NAME,
|
||||
username=bot_config.MONGODB_USERNAME,
|
||||
password=bot_config.MONGODB_PASSWORD,
|
||||
auth_source=bot_config.MONGODB_AUTH_SOURCE
|
||||
host=global_config.MONGODB_HOST,
|
||||
port=global_config.MONGODB_PORT,
|
||||
db_name=global_config.DATABASE_NAME,
|
||||
username=global_config.MONGODB_USERNAME,
|
||||
password=global_config.MONGODB_PASSWORD,
|
||||
auth_source=global_config.MONGODB_AUTH_SOURCE
|
||||
)
|
||||
|
||||
# 检查是否已存在相同哈希值的图片
|
||||
|
||||
@@ -22,22 +22,31 @@ class WillingManager:
|
||||
"""设置指定群组的回复意愿"""
|
||||
self.group_reply_willing[group_id] = willing
|
||||
|
||||
def change_reply_willing_received(self, group_id: int, topic: str, is_mentioned_bot: bool, config, user_id: int = None, is_emoji: bool = False) -> float:
|
||||
def change_reply_willing_received(self, group_id: int, topic: str, is_mentioned_bot: bool, config, user_id: int = None, is_emoji: bool = False, interested_rate: float = 0) -> float:
|
||||
"""改变指定群组的回复意愿并返回回复概率"""
|
||||
current_willing = self.group_reply_willing.get(group_id, 0)
|
||||
|
||||
if topic and current_willing < 1:
|
||||
current_willing += 0.2
|
||||
elif topic:
|
||||
current_willing += 0.05
|
||||
print(f"初始意愿: {current_willing}")
|
||||
|
||||
# if topic and current_willing < 1:
|
||||
# current_willing += 0.2
|
||||
# elif topic:
|
||||
# current_willing += 0.05
|
||||
|
||||
if is_mentioned_bot and current_willing < 1.0:
|
||||
current_willing += 0.9
|
||||
print(f"被提及, 当前意愿: {current_willing}")
|
||||
elif is_mentioned_bot:
|
||||
current_willing += 0.05
|
||||
print(f"被重复提及, 当前意愿: {current_willing}")
|
||||
|
||||
if is_emoji:
|
||||
current_willing *= 0.2
|
||||
current_willing *= 0.15
|
||||
print(f"表情包, 当前意愿: {current_willing}")
|
||||
|
||||
if interested_rate > 0.6:
|
||||
print(f"兴趣度: {interested_rate}, 当前意愿: {current_willing}")
|
||||
current_willing += interested_rate-0.45
|
||||
|
||||
self.group_reply_willing[group_id] = min(current_willing, 3.0)
|
||||
|
||||
@@ -55,15 +64,15 @@ class WillingManager:
|
||||
return reply_probability
|
||||
|
||||
def change_reply_willing_sent(self, group_id: int):
|
||||
"""发送消息后降低群组的回复意愿"""
|
||||
"""开始思考后降低群组的回复意愿"""
|
||||
current_willing = self.group_reply_willing.get(group_id, 0)
|
||||
self.group_reply_willing[group_id] = max(0, current_willing - 1.8)
|
||||
self.group_reply_willing[group_id] = max(0, current_willing - 2)
|
||||
|
||||
def change_reply_willing_after_sent(self, group_id: int):
|
||||
"""发送消息后提高群组的回复意愿"""
|
||||
current_willing = self.group_reply_willing.get(group_id, 0)
|
||||
if current_willing < 1:
|
||||
self.group_reply_willing[group_id] = min(1, current_willing + 0.4)
|
||||
self.group_reply_willing[group_id] = min(1, current_willing + 0.3)
|
||||
|
||||
async def ensure_started(self):
|
||||
"""确保衰减任务已启动"""
|
||||
|
||||
186
src/plugins/knowledege/knowledge_library.py
Normal file
186
src/plugins/knowledege/knowledge_library.py
Normal file
@@ -0,0 +1,186 @@
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
import requests
|
||||
import time
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))
|
||||
sys.path.append(root_path)
|
||||
|
||||
from src.common.database import Database
|
||||
from src.plugins.chat.config import llm_config
|
||||
|
||||
# 直接配置数据库连接信息
|
||||
Database.initialize(
|
||||
"127.0.0.1", # MongoDB 主机
|
||||
27017, # MongoDB 端口
|
||||
"MegBot" # 数据库名称
|
||||
)
|
||||
|
||||
class KnowledgeLibrary:
|
||||
def __init__(self):
|
||||
self.db = Database.get_instance()
|
||||
self.raw_info_dir = "data/raw_info"
|
||||
self._ensure_dirs()
|
||||
|
||||
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 {llm_config.SILICONFLOW_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+300] 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)
|
||||
264
src/plugins/memory_system/draw_memory.py
Normal file
264
src/plugins/memory_system/draw_memory.py
Normal file
@@ -0,0 +1,264 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import sys
|
||||
import jieba
|
||||
from llm_module import LLMModel
|
||||
import networkx as nx
|
||||
import matplotlib.pyplot as plt
|
||||
import math
|
||||
from collections import Counter
|
||||
import datetime
|
||||
import random
|
||||
import time
|
||||
# from chat.config import global_config
|
||||
import sys
|
||||
sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径
|
||||
from src.common.database import Database # 使用正确的导入语法
|
||||
|
||||
class Memory_graph:
|
||||
def __init__(self):
|
||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
||||
self.db = Database.get_instance()
|
||||
|
||||
def connect_dot(self, concept1, concept2):
|
||||
self.G.add_edge(concept1, concept2)
|
||||
|
||||
def add_dot(self, concept, memory):
|
||||
if concept in self.G:
|
||||
# 如果节点已存在，将新记忆添加到现有列表中
|
||||
if 'memory_items' in self.G.nodes[concept]:
|
||||
if not isinstance(self.G.nodes[concept]['memory_items'], list):
|
||||
# 如果当前不是列表，将其转换为列表
|
||||
self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
|
||||
self.G.nodes[concept]['memory_items'].append(memory)
|
||||
else:
|
||||
self.G.nodes[concept]['memory_items'] = [memory]
|
||||
else:
|
||||
# 如果是新节点，创建新的记忆列表
|
||||
self.G.add_node(concept, memory_items=[memory])
|
||||
|
||||
def get_dot(self, concept):
|
||||
# 检查节点是否存在于图中
|
||||
if concept in self.G:
|
||||
# 从图中获取节点数据
|
||||
node_data = self.G.nodes[concept]
|
||||
# print(node_data)
|
||||
# 创建新的Memory_dot对象
|
||||
return concept,node_data
|
||||
return None
|
||||
|
||||
def get_related_item(self, topic, depth=1):
|
||||
if topic not in self.G:
|
||||
return [], []
|
||||
|
||||
first_layer_items = []
|
||||
second_layer_items = []
|
||||
|
||||
# 获取相邻节点
|
||||
neighbors = list(self.G.neighbors(topic))
|
||||
# print(f"第一层: {topic}")
|
||||
|
||||
# 获取当前节点的记忆项
|
||||
node_data = self.get_dot(topic)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
memory_items = data['memory_items']
|
||||
if isinstance(memory_items, list):
|
||||
first_layer_items.extend(memory_items)
|
||||
else:
|
||||
first_layer_items.append(memory_items)
|
||||
|
||||
# 只在depth=2时获取第二层记忆
|
||||
if depth >= 2:
|
||||
# 获取相邻节点的记忆项
|
||||
for neighbor in neighbors:
|
||||
# print(f"第二层: {neighbor}")
|
||||
node_data = self.get_dot(neighbor)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
memory_items = data['memory_items']
|
||||
if isinstance(memory_items, list):
|
||||
second_layer_items.extend(memory_items)
|
||||
else:
|
||||
second_layer_items.append(memory_items)
|
||||
|
||||
return first_layer_items, second_layer_items
|
||||
|
||||
def store_memory(self):
|
||||
for node in self.G.nodes():
|
||||
dot_data = {
|
||||
"concept": node
|
||||
}
|
||||
self.db.db.store_memory_dots.insert_one(dot_data)
|
||||
|
||||
@property
|
||||
def dots(self):
|
||||
# 返回所有节点对应的 Memory_dot 对象
|
||||
return [self.get_dot(node) for node in self.G.nodes()]
|
||||
|
||||
|
||||
def get_random_chat_from_db(self, length: int, timestamp: str):
|
||||
# 从数据库中根据时间戳获取离其最近的聊天记录
|
||||
chat_text = ''
|
||||
closest_record = self.db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
|
||||
print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
|
||||
|
||||
if closest_record:
|
||||
closest_time = closest_record['time']
|
||||
group_id = closest_record['group_id'] # 获取groupid
|
||||
# 获取该时间戳之后的length条消息，且groupid相同
|
||||
chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
|
||||
for record in chat_record:
|
||||
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
|
||||
chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息
|
||||
return chat_text
|
||||
|
||||
return [] # 如果没有找到记录，返回空列表
|
||||
|
||||
def save_graph_to_db(self):
|
||||
# 清空现有的图数据
|
||||
self.db.db.graph_data.delete_many({})
|
||||
# 保存节点
|
||||
for node in self.G.nodes(data=True):
|
||||
node_data = {
|
||||
'concept': node[0],
|
||||
'memory_items': node[1].get('memory_items', []) # 默认为空列表
|
||||
}
|
||||
self.db.db.graph_data.nodes.insert_one(node_data)
|
||||
# 保存边
|
||||
for edge in self.G.edges():
|
||||
edge_data = {
|
||||
'source': edge[0],
|
||||
'target': edge[1]
|
||||
}
|
||||
self.db.db.graph_data.edges.insert_one(edge_data)
|
||||
|
||||
def load_graph_from_db(self):
|
||||
# 清空当前图
|
||||
self.G.clear()
|
||||
# 加载节点
|
||||
nodes = self.db.db.graph_data.nodes.find()
|
||||
for node in nodes:
|
||||
memory_items = node.get('memory_items', [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
self.G.add_node(node['concept'], memory_items=memory_items)
|
||||
# 加载边
|
||||
edges = self.db.db.graph_data.edges.find()
|
||||
for edge in edges:
|
||||
self.G.add_edge(edge['source'], edge['target'])
|
||||
|
||||
|
||||
def main():
|
||||
# 初始化数据库
|
||||
Database.initialize(
|
||||
"127.0.0.1",
|
||||
27017,
|
||||
"MegBot"
|
||||
)
|
||||
|
||||
memory_graph = Memory_graph()
|
||||
# 创建LLM模型实例
|
||||
|
||||
memory_graph.load_graph_from_db()
|
||||
# 展示两种不同的可视化方式
|
||||
print("\n按连接数量着色的图谱：")
|
||||
visualize_graph(memory_graph, color_by_memory=False)
|
||||
|
||||
print("\n按记忆数量着色的图谱：")
|
||||
visualize_graph(memory_graph, color_by_memory=True)
|
||||
|
||||
# memory_graph.save_graph_to_db()
|
||||
|
||||
while True:
|
||||
query = input("请输入新的查询概念（输入'退出'以结束）：")
|
||||
if query.lower() == '退出':
|
||||
break
|
||||
items_list = memory_graph.get_related_item(query)
|
||||
if items_list:
|
||||
# print(items_list)
|
||||
for memory_item in items_list:
|
||||
print(memory_item)
|
||||
else:
|
||||
print("未找到相关记忆。")
|
||||
|
||||
|
||||
def segment_text(text):
|
||||
seg_text = list(jieba.cut(text))
|
||||
return seg_text
|
||||
|
||||
def find_topic(text, topic_num):
|
||||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。'
|
||||
return prompt
|
||||
|
||||
def topic_what(text, topic):
|
||||
prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
|
||||
return prompt
|
||||
|
||||
def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False):
|
||||
# 设置中文字体
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||||
|
||||
G = memory_graph.G
|
||||
|
||||
# 保存图到本地
|
||||
nx.write_gml(G, "memory_graph.gml") # 保存为 GML 格式
|
||||
|
||||
# 根据连接条数或记忆数量设置节点颜色
|
||||
node_colors = []
|
||||
nodes = list(G.nodes()) # 获取图中实际的节点列表
|
||||
|
||||
if color_by_memory:
|
||||
# 计算每个节点的记忆数量
|
||||
memory_counts = []
|
||||
for node in nodes:
|
||||
memory_items = G.nodes[node].get('memory_items', [])
|
||||
if isinstance(memory_items, list):
|
||||
count = len(memory_items)
|
||||
else:
|
||||
count = 1 if memory_items else 0
|
||||
memory_counts.append(count)
|
||||
max_memories = max(memory_counts) if memory_counts else 1
|
||||
|
||||
for count in memory_counts:
|
||||
# 使用不同的颜色方案：红色表示记忆多，蓝色表示记忆少
|
||||
if max_memories > 0:
|
||||
intensity = min(1.0, count / max_memories)
|
||||
color = (intensity, 0, 1.0 - intensity) # 从蓝色渐变到红色
|
||||
else:
|
||||
color = (0, 0, 1) # 如果没有记忆，则为蓝色
|
||||
node_colors.append(color)
|
||||
else:
|
||||
# 使用原来的连接数量着色方案
|
||||
max_degree = max(G.degree(), key=lambda x: x[1])[1] if G.degree() else 1
|
||||
for node in nodes:
|
||||
degree = G.degree(node)
|
||||
if max_degree > 0:
|
||||
red = min(1.0, degree / max_degree)
|
||||
blue = 1.0 - red
|
||||
color = (red, 0, blue)
|
||||
else:
|
||||
color = (0, 0, 1)
|
||||
node_colors.append(color)
|
||||
|
||||
# 绘制图形
|
||||
plt.figure(figsize=(12, 8))
|
||||
pos = nx.spring_layout(G, k=1, iterations=50)
|
||||
nx.draw(G, pos,
|
||||
with_labels=True,
|
||||
node_color=node_colors,
|
||||
node_size=2000,
|
||||
font_size=10,
|
||||
font_family='SimHei',
|
||||
font_weight='bold')
|
||||
|
||||
title = '记忆图谱可视化 - ' + ('按记忆数量着色' if color_by_memory else '按连接数量着色')
|
||||
plt.title(title, fontsize=16, fontfamily='SimHei')
|
||||
plt.show()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
||||
@@ -2,6 +2,7 @@ import os
|
||||
import requests
|
||||
from dotenv import load_dotenv
|
||||
from typing import Tuple, Union
|
||||
import time
|
||||
|
||||
# 加载环境变量
|
||||
load_dotenv()
|
||||
@@ -32,16 +33,34 @@ class LLMModel:
|
||||
# 发送请求到完整的chat/completions端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions"
|
||||
|
||||
try:
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
response.raise_for_status() # 检查响应状态
|
||||
max_retries = 3
|
||||
base_wait_time = 15 # 基础等待时间（秒）
|
||||
|
||||
result = response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content # 返回内容和推理内容
|
||||
return "没有返回结果", "" # 返回两个值
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
return f"请求失败: {str(e)}", "" # 返回错误信息和空字符串
|
||||
if response.status_code == 429:
|
||||
wait_time = base_wait_time * (2 ** retry) # 指数退避
|
||||
print(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
time.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2 ** retry)
|
||||
print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
82
src/plugins/memory_system/llm_module_memory_make.py
Normal file
82
src/plugins/memory_system/llm_module_memory_make.py
Normal file
@@ -0,0 +1,82 @@
|
||||
import os
|
||||
import requests
|
||||
from dotenv import load_dotenv
|
||||
from typing import Tuple, Union
|
||||
import time
|
||||
from ..chat.config import BotConfig
|
||||
|
||||
# 获取当前文件的绝对路径
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
|
||||
env_path = os.path.join(root_dir, 'config', '.env')
|
||||
|
||||
# 加载环境变量
|
||||
print(f"尝试从 {env_path} 加载环境变量配置")
|
||||
if os.path.exists(env_path):
|
||||
load_dotenv(env_path)
|
||||
print("成功加载环境变量配置")
|
||||
else:
|
||||
print(f"环境变量配置文件不存在: {env_path}")
|
||||
|
||||
class LLMModel:
|
||||
# def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", **kwargs):
|
||||
def __init__(self, model_name="Pro/deepseek-ai/DeepSeek-V3", **kwargs):
|
||||
self.model_name = model_name
|
||||
self.params = kwargs
|
||||
self.api_key = os.getenv("SILICONFLOW_KEY")
|
||||
self.base_url = os.getenv("SILICONFLOW_BASE_URL")
|
||||
|
||||
if not self.api_key or not self.base_url:
|
||||
raise ValueError("环境变量未正确加载：SILICONFLOW_KEY 或 SILICONFLOW_BASE_URL 未设置")
|
||||
|
||||
print(f"API URL: {self.base_url}") # 打印 base_url 用于调试
|
||||
|
||||
def generate_response(self, prompt: str) -> Tuple[str, str]:
|
||||
"""根据输入的提示生成模型的响应"""
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
# 构建请求体
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.5,
|
||||
**self.params
|
||||
}
|
||||
|
||||
# 发送请求到完整的chat/completions端点
|
||||
api_url = f"{self.base_url.rstrip('/')}/chat/completions"
|
||||
|
||||
max_retries = 3
|
||||
base_wait_time = 15 # 基础等待时间（秒）
|
||||
|
||||
for retry in range(max_retries):
|
||||
try:
|
||||
response = requests.post(api_url, headers=headers, json=data)
|
||||
|
||||
if response.status_code == 429:
|
||||
wait_time = base_wait_time * (2 ** retry) # 指数退避
|
||||
print(f"遇到请求限制(429)，等待{wait_time}秒后重试...")
|
||||
time.sleep(wait_time)
|
||||
continue
|
||||
|
||||
response.raise_for_status() # 检查其他响应状态
|
||||
|
||||
result = response.json()
|
||||
if "choices" in result and len(result["choices"]) > 0:
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
reasoning_content = result["choices"][0]["message"].get("reasoning_content", "")
|
||||
return content, reasoning_content
|
||||
return "没有返回结果", ""
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
if retry < max_retries - 1: # 如果还有重试机会
|
||||
wait_time = base_wait_time * (2 ** retry)
|
||||
print(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
||||
time.sleep(wait_time)
|
||||
else:
|
||||
return f"请求失败: {str(e)}", ""
|
||||
|
||||
return "达到最大重试次数，请求仍然失败", ""
|
||||
@@ -1,7 +1,6 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import sys
|
||||
import jieba
|
||||
from llm_module import LLMModel
|
||||
from .llm_module import LLMModel
|
||||
import networkx as nx
|
||||
import matplotlib.pyplot as plt
|
||||
import math
|
||||
@@ -9,10 +8,10 @@ from collections import Counter
|
||||
import datetime
|
||||
import random
|
||||
import time
|
||||
|
||||
from ..chat.config import global_config
|
||||
import sys
|
||||
sys.path.append("C:/GitHub/MegMeg-bot") # 添加项目根目录到 Python 路径
|
||||
from src.common.database import Database # 使用正确的导入语法
|
||||
from ...common.database import Database # 使用正确的导入语法
|
||||
from ..chat.utils import calculate_information_content, get_cloest_chat_from_db
|
||||
|
||||
class Memory_graph:
|
||||
def __init__(self):
|
||||
@@ -23,93 +22,128 @@ class Memory_graph:
|
||||
self.G.add_edge(concept1, concept2)
|
||||
|
||||
def add_dot(self, concept, memory):
|
||||
self.G.add_node(concept, memory_items=memory)
|
||||
if concept in self.G:
|
||||
# 如果节点已存在，将新记忆添加到现有列表中
|
||||
if 'memory_items' in self.G.nodes[concept]:
|
||||
if not isinstance(self.G.nodes[concept]['memory_items'], list):
|
||||
# 如果当前不是列表，将其转换为列表
|
||||
self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
|
||||
self.G.nodes[concept]['memory_items'].append(memory)
|
||||
else:
|
||||
self.G.nodes[concept]['memory_items'] = [memory]
|
||||
else:
|
||||
# 如果是新节点，创建新的记忆列表
|
||||
self.G.add_node(concept, memory_items=[memory])
|
||||
|
||||
def get_dot(self, concept):
|
||||
# 检查节点是否存在于图中
|
||||
if concept in self.G:
|
||||
# 从图中获取节点数据
|
||||
node_data = self.G.nodes[concept]
|
||||
print(node_data)
|
||||
# print(node_data)
|
||||
# 创建新的Memory_dot对象
|
||||
return concept,node_data
|
||||
return None
|
||||
|
||||
def get_related_item(self, topic, depth=1):
|
||||
if topic not in self.G:
|
||||
return set()
|
||||
return [], []
|
||||
|
||||
first_layer_items = []
|
||||
second_layer_items = []
|
||||
|
||||
items_set = set()
|
||||
# 获取相邻节点
|
||||
neighbors = list(self.G.neighbors(topic))
|
||||
print(f"第一层: {topic}")
|
||||
# print(f"第一层: {topic}")
|
||||
|
||||
# 获取当前节点的记忆项
|
||||
node_data = self.get_dot(topic)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
items_set.add(data['memory_items'])
|
||||
memory_items = data['memory_items']
|
||||
if isinstance(memory_items, list):
|
||||
first_layer_items.extend(memory_items)
|
||||
else:
|
||||
first_layer_items.append(memory_items)
|
||||
|
||||
# 获取相邻节点的记忆项
|
||||
for neighbor in neighbors:
|
||||
print(f"第二层: {neighbor}")
|
||||
node_data = self.get_dot(neighbor)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
items_set.add(data['memory_items'])
|
||||
# 只在depth=2时获取第二层记忆
|
||||
if depth >= 2:
|
||||
# 获取相邻节点的记忆项
|
||||
for neighbor in neighbors:
|
||||
# print(f"第二层: {neighbor}")
|
||||
node_data = self.get_dot(neighbor)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
memory_items = data['memory_items']
|
||||
if isinstance(memory_items, list):
|
||||
second_layer_items.extend(memory_items)
|
||||
else:
|
||||
second_layer_items.append(memory_items)
|
||||
|
||||
return items_set
|
||||
|
||||
def store_memory(self):
|
||||
for node in self.G.nodes():
|
||||
dot_data = {
|
||||
"concept": node
|
||||
}
|
||||
self.db.db.store_memory_dots.insert_one(dot_data)
|
||||
return first_layer_items, second_layer_items
|
||||
|
||||
@property
|
||||
def dots(self):
|
||||
# 返回所有节点对应的 Memory_dot 对象
|
||||
return [self.get_dot(node) for node in self.G.nodes()]
|
||||
|
||||
|
||||
def get_random_chat_from_db(self, length: int, timestamp: str):
|
||||
# 从数据库中根据时间戳获取离其最近的聊天记录
|
||||
chat_text = ''
|
||||
closest_record = self.db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
|
||||
print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
|
||||
|
||||
if closest_record:
|
||||
closest_time = closest_record['time']
|
||||
group_id = closest_record['group_id'] # 获取groupid
|
||||
# 获取该时间戳之后的length条消息，且groupid相同
|
||||
chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
|
||||
for record in chat_record:
|
||||
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
|
||||
chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息
|
||||
return chat_text
|
||||
|
||||
return [] # 如果没有找到记录，返回空列表
|
||||
|
||||
def save_graph_to_db(self):
|
||||
# 清空现有的图数据
|
||||
self.db.db.graph_data.delete_many({})
|
||||
# 保存节点
|
||||
for node in self.G.nodes(data=True):
|
||||
node_data = {
|
||||
'concept': node[0],
|
||||
'memory_items': node[1].get('memory_items', None)
|
||||
}
|
||||
self.db.db.graph_data.nodes.insert_one(node_data)
|
||||
concept = node[0]
|
||||
memory_items = node[1].get('memory_items', [])
|
||||
|
||||
# 查找是否存在同名节点
|
||||
existing_node = self.db.db.graph_data.nodes.find_one({'concept': concept})
|
||||
if existing_node:
|
||||
# 如果存在,合并memory_items并去重
|
||||
existing_items = existing_node.get('memory_items', [])
|
||||
if not isinstance(existing_items, list):
|
||||
existing_items = [existing_items] if existing_items else []
|
||||
|
||||
# 合并并去重
|
||||
all_items = list(set(existing_items + memory_items))
|
||||
|
||||
# 更新节点
|
||||
self.db.db.graph_data.nodes.update_one(
|
||||
{'concept': concept},
|
||||
{'$set': {'memory_items': all_items}}
|
||||
)
|
||||
else:
|
||||
# 如果不存在,创建新节点
|
||||
node_data = {
|
||||
'concept': concept,
|
||||
'memory_items': memory_items
|
||||
}
|
||||
self.db.db.graph_data.nodes.insert_one(node_data)
|
||||
|
||||
# 保存边
|
||||
for edge in self.G.edges():
|
||||
edge_data = {
|
||||
'source': edge[0],
|
||||
'target': edge[1]
|
||||
}
|
||||
self.db.db.graph_data.edges.insert_one(edge_data)
|
||||
source, target = edge
|
||||
|
||||
# 查找是否存在同样的边
|
||||
existing_edge = self.db.db.graph_data.edges.find_one({
|
||||
'source': source,
|
||||
'target': target
|
||||
})
|
||||
|
||||
if existing_edge:
|
||||
# 如果存在,增加num属性
|
||||
num = existing_edge.get('num', 1) + 1
|
||||
self.db.db.graph_data.edges.update_one(
|
||||
{'source': source, 'target': target},
|
||||
{'$set': {'num': num}}
|
||||
)
|
||||
else:
|
||||
# 如果不存在,创建新边
|
||||
edge_data = {
|
||||
'source': source,
|
||||
'target': target,
|
||||
'num': 1
|
||||
}
|
||||
self.db.db.graph_data.edges.insert_one(edge_data)
|
||||
|
||||
def load_graph_from_db(self):
|
||||
# 清空当前图
|
||||
@@ -117,127 +151,99 @@ class Memory_graph:
|
||||
# 加载节点
|
||||
nodes = self.db.db.graph_data.nodes.find()
|
||||
for node in nodes:
|
||||
self.G.add_node(node['concept'], memory_items=node['memory_items'])
|
||||
memory_items = node.get('memory_items', [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
self.G.add_node(node['concept'], memory_items=memory_items)
|
||||
# 加载边
|
||||
edges = self.db.db.graph_data.edges.find()
|
||||
for edge in edges:
|
||||
self.G.add_edge(edge['source'], edge['target'])
|
||||
|
||||
def calculate_information_content(text):
|
||||
|
||||
"""计算文本的信息量（熵）"""
|
||||
# 统计字符频率
|
||||
char_count = Counter(text)
|
||||
total_chars = len(text)
|
||||
|
||||
# 计算熵
|
||||
entropy = 0
|
||||
for count in char_count.values():
|
||||
probability = count / total_chars
|
||||
entropy -= probability * math.log2(probability)
|
||||
|
||||
return entropy
|
||||
|
||||
def main():
|
||||
# 初始化数据库
|
||||
Database.initialize(
|
||||
"127.0.0.1",
|
||||
27017,
|
||||
"MegBot"
|
||||
)
|
||||
|
||||
memory_graph = Memory_graph()
|
||||
# 创建LLM模型实例
|
||||
llm_model = LLMModel()
|
||||
llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
|
||||
|
||||
# 使用当前时间戳进行测试
|
||||
current_timestamp = datetime.datetime.now().timestamp()
|
||||
chat_text = []
|
||||
|
||||
chat_size =30
|
||||
|
||||
for _ in range(60): # 循环10次
|
||||
random_time = current_timestamp - random.randint(1, 3600*3) # 随机时间
|
||||
print(f"随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
|
||||
chat_ = memory_graph.get_random_chat_from_db(chat_size, random_time)
|
||||
chat_text.append(chat_) # 拼接所有text
|
||||
self.G.add_edge(edge['source'], edge['target'], num=edge.get('num', 1))
|
||||
|
||||
|
||||
|
||||
for input_text in chat_text:
|
||||
print(input_text)
|
||||
first_memory = set()
|
||||
first_memory = memory_compress(input_text, llm_model_small, llm_model_small, rate=2.5)
|
||||
|
||||
#将记忆加入到图谱中
|
||||
for topic, memory in first_memory:
|
||||
topics = segment_text(topic)
|
||||
print(f"话题: {topic},节点: {topics}, 记忆: {memory}")
|
||||
for split_topic in topics:
|
||||
memory_graph.add_dot(split_topic,memory)
|
||||
for split_topic in topics:
|
||||
for other_split_topic in topics:
|
||||
if split_topic != other_split_topic:
|
||||
memory_graph.connect_dot(split_topic, other_split_topic)
|
||||
|
||||
# memory_graph.store_memory()
|
||||
visualize_graph(memory_graph)
|
||||
# 海马体
|
||||
class Hippocampus:
|
||||
def __init__(self,memory_graph:Memory_graph):
|
||||
self.memory_graph = memory_graph
|
||||
self.llm_model = LLMModel()
|
||||
self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
|
||||
|
||||
memory_graph.save_graph_to_db()
|
||||
# memory_graph.load_graph_from_db()
|
||||
def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}):
|
||||
current_timestamp = datetime.datetime.now().timestamp()
|
||||
chat_text = []
|
||||
#短期：1h 中期：4h 长期：24h
|
||||
for _ in range(time_frequency.get('near')): # 循环10次
|
||||
random_time = current_timestamp - random.randint(1, 3600) # 随机时间
|
||||
# print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
|
||||
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
|
||||
chat_text.append(chat_)
|
||||
for _ in range(time_frequency.get('mid')): # 循环10次
|
||||
random_time = current_timestamp - random.randint(3600, 3600*4) # 随机时间
|
||||
# print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
|
||||
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
|
||||
chat_text.append(chat_)
|
||||
for _ in range(time_frequency.get('far')): # 循环10次
|
||||
random_time = current_timestamp - random.randint(3600*4, 3600*24) # 随机时间
|
||||
# print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
|
||||
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
|
||||
chat_text.append(chat_)
|
||||
return chat_text
|
||||
|
||||
while True:
|
||||
query = input("请输入新的查询概念（输入'退出'以结束）：")
|
||||
if query.lower() == '退出':
|
||||
break
|
||||
items_list = memory_graph.get_related_item(query)
|
||||
if items_list:
|
||||
# print(items_list)
|
||||
for memory_item in items_list:
|
||||
print(memory_item)
|
||||
else:
|
||||
print("未找到相关记忆。")
|
||||
def build_memory(self,chat_size=12):
|
||||
#最近消息获取频率
|
||||
time_frequency = {'near':1,'mid':2,'far':2}
|
||||
memory_sample = self.get_memory_sample(chat_size,time_frequency)
|
||||
# print(f"\033[1;32m[记忆构建]\033[0m 获取记忆样本: {memory_sample}")
|
||||
|
||||
while True:
|
||||
query = input("请输入问题：")
|
||||
|
||||
if query.lower() == '退出':
|
||||
break
|
||||
for i, input_text in enumerate(memory_sample, 1):
|
||||
#加载进度可视化
|
||||
progress = (i / len(memory_sample)) * 100
|
||||
bar_length = 30
|
||||
filled_length = int(bar_length * i // len(memory_sample))
|
||||
bar = '█' * filled_length + '-' * (bar_length - filled_length)
|
||||
print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})")
|
||||
|
||||
topic_prompt = find_topic(query, 3)
|
||||
topic_response = llm_model.generate_response(topic_prompt)
|
||||
# 生成压缩后记忆
|
||||
first_memory = set()
|
||||
first_memory = self.memory_compress(input_text, 2.5)
|
||||
# 延时防止访问超频
|
||||
# time.sleep(5)
|
||||
#将记忆加入到图谱中
|
||||
for topic, memory in first_memory:
|
||||
topics = segment_text(topic)
|
||||
print(f"\033[1;34m话题\033[0m: {topic},节点: {topics}, 记忆: {memory}")
|
||||
for split_topic in topics:
|
||||
self.memory_graph.add_dot(split_topic,memory)
|
||||
for split_topic in topics:
|
||||
for other_split_topic in topics:
|
||||
if split_topic != other_split_topic:
|
||||
self.memory_graph.connect_dot(split_topic, other_split_topic)
|
||||
|
||||
self.memory_graph.save_graph_to_db()
|
||||
|
||||
def memory_compress(self, input_text, rate=1):
|
||||
information_content = calculate_information_content(input_text)
|
||||
print(f"文本的信息量（熵）: {information_content:.4f} bits")
|
||||
topic_num = max(1, min(5, int(information_content * rate / 4)))
|
||||
# print(topic_num)
|
||||
topic_prompt = find_topic(input_text, topic_num)
|
||||
topic_response = self.llm_model.generate_response(topic_prompt)
|
||||
# 检查 topic_response 是否为元组
|
||||
if isinstance(topic_response, tuple):
|
||||
topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串
|
||||
else:
|
||||
topics = topic_response.split(",")
|
||||
print(topics)
|
||||
|
||||
for keyword in topics:
|
||||
items_list = memory_graph.get_related_item(keyword)
|
||||
if items_list:
|
||||
print(items_list)
|
||||
|
||||
def memory_compress(input_text, llm_model, llm_model_small, rate=1):
|
||||
information_content = calculate_information_content(input_text)
|
||||
print(f"文本的信息量（熵）: {information_content:.4f} bits")
|
||||
topic_num = max(1, min(5, int(information_content * rate / 4)))
|
||||
print(topic_num)
|
||||
topic_prompt = find_topic(input_text, topic_num)
|
||||
topic_response = llm_model.generate_response(topic_prompt)
|
||||
# 检查 topic_response 是否为元组
|
||||
if isinstance(topic_response, tuple):
|
||||
topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串
|
||||
else:
|
||||
topics = topic_response.split(",")
|
||||
print(topics)
|
||||
compressed_memory = set()
|
||||
for topic in topics:
|
||||
topic_what_prompt = topic_what(input_text,topic)
|
||||
topic_what_response = llm_model_small.generate_response(topic_what_prompt)
|
||||
compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储
|
||||
return compressed_memory
|
||||
# print(topics)
|
||||
compressed_memory = set()
|
||||
for topic in topics:
|
||||
topic_what_prompt = topic_what(input_text,topic)
|
||||
topic_what_response = self.llm_model_small.generate_response(topic_what_prompt)
|
||||
compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储
|
||||
return compressed_memory
|
||||
|
||||
|
||||
def segment_text(text):
|
||||
@@ -252,48 +258,21 @@ def topic_what(text, topic):
|
||||
prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
|
||||
return prompt
|
||||
|
||||
def visualize_graph(memory_graph: Memory_graph):
|
||||
# 设置中文字体
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||||
|
||||
G = memory_graph.G
|
||||
|
||||
|
||||
# 保存图到本地
|
||||
nx.write_gml(G, "memory_graph.gml") # 保存为 GML 格式
|
||||
|
||||
# 根据连接条数设置节点颜色
|
||||
node_colors = []
|
||||
nodes = list(G.nodes()) # 获取图中实际的节点列表
|
||||
max_degree = max(G.degree(), key=lambda x: x[1])[1] if G.degree() else 1 # 获取最大连接数
|
||||
|
||||
for node in nodes:
|
||||
degree = G.degree(node) # 获取节点的度
|
||||
# 计算颜色，使用渐变效果
|
||||
if max_degree > 0:
|
||||
red = min(1.0, degree / max_degree) # 红色分量随连接数增加而增加
|
||||
blue = 1.0 - red # 蓝色分量随连接数增加而减少
|
||||
color = (red, 0, blue)
|
||||
else:
|
||||
color = (0, 0, 1) # 如果没有连接，则为蓝色
|
||||
node_colors.append(color)
|
||||
|
||||
# 绘制图形
|
||||
plt.figure(figsize=(12, 8))
|
||||
pos = nx.spring_layout(G, k=1, iterations=50) # 使用弹簧布局，调整参数使布局更合理
|
||||
nx.draw(G, pos,
|
||||
with_labels=True,
|
||||
node_color=node_colors,
|
||||
node_size=2000,
|
||||
font_size=10,
|
||||
font_family='SimHei', # 设置节点标签的字体
|
||||
font_weight='bold')
|
||||
|
||||
plt.title('记忆图谱可视化', fontsize=16, fontfamily='SimHei')
|
||||
plt.show()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
start_time = time.time()
|
||||
|
||||
Database.initialize(
|
||||
global_config.MONGODB_HOST,
|
||||
global_config.MONGODB_PORT,
|
||||
global_config.DATABASE_NAME
|
||||
)
|
||||
#创建记忆图
|
||||
memory_graph = Memory_graph()
|
||||
#加载数据库中存储的记忆图
|
||||
memory_graph.load_graph_from_db()
|
||||
#创建海马体
|
||||
hippocampus = Hippocampus(memory_graph)
|
||||
|
||||
end_time = time.time()
|
||||
print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")
|
||||
428
src/plugins/memory_system/memory_make.py
Normal file
428
src/plugins/memory_system/memory_make.py
Normal file
@@ -0,0 +1,428 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import sys
|
||||
import jieba
|
||||
import networkx as nx
|
||||
import matplotlib.pyplot as plt
|
||||
import math
|
||||
from collections import Counter
|
||||
import datetime
|
||||
import random
|
||||
import time
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
# from chat.config import global_config
|
||||
sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径
|
||||
from src.common.database import Database # 使用正确的导入语法
|
||||
from src.plugins.memory_system.llm_module import LLMModel
|
||||
|
||||
class Memory_graph:
|
||||
def __init__(self):
|
||||
self.G = nx.Graph() # 使用 networkx 的图结构
|
||||
self.db = Database.get_instance()
|
||||
|
||||
def connect_dot(self, concept1, concept2):
|
||||
self.G.add_edge(concept1, concept2)
|
||||
|
||||
def add_dot(self, concept, memory):
|
||||
if concept in self.G:
|
||||
# 如果节点已存在，将新记忆添加到现有列表中
|
||||
if 'memory_items' in self.G.nodes[concept]:
|
||||
if not isinstance(self.G.nodes[concept]['memory_items'], list):
|
||||
# 如果当前不是列表，将其转换为列表
|
||||
self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
|
||||
self.G.nodes[concept]['memory_items'].append(memory)
|
||||
else:
|
||||
self.G.nodes[concept]['memory_items'] = [memory]
|
||||
else:
|
||||
# 如果是新节点，创建新的记忆列表
|
||||
self.G.add_node(concept, memory_items=[memory])
|
||||
|
||||
def get_dot(self, concept):
|
||||
# 检查节点是否存在于图中
|
||||
if concept in self.G:
|
||||
# 从图中获取节点数据
|
||||
node_data = self.G.nodes[concept]
|
||||
# print(node_data)
|
||||
# 创建新的Memory_dot对象
|
||||
return concept,node_data
|
||||
return None
|
||||
|
||||
def get_related_item(self, topic, depth=1):
|
||||
if topic not in self.G:
|
||||
return [], []
|
||||
|
||||
first_layer_items = []
|
||||
second_layer_items = []
|
||||
|
||||
# 获取相邻节点
|
||||
neighbors = list(self.G.neighbors(topic))
|
||||
# print(f"第一层: {topic}")
|
||||
|
||||
# 获取当前节点的记忆项
|
||||
node_data = self.get_dot(topic)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
memory_items = data['memory_items']
|
||||
if isinstance(memory_items, list):
|
||||
first_layer_items.extend(memory_items)
|
||||
else:
|
||||
first_layer_items.append(memory_items)
|
||||
|
||||
# 只在depth=2时获取第二层记忆
|
||||
if depth >= 2:
|
||||
# 获取相邻节点的记忆项
|
||||
for neighbor in neighbors:
|
||||
# print(f"第二层: {neighbor}")
|
||||
node_data = self.get_dot(neighbor)
|
||||
if node_data:
|
||||
concept, data = node_data
|
||||
if 'memory_items' in data:
|
||||
memory_items = data['memory_items']
|
||||
if isinstance(memory_items, list):
|
||||
second_layer_items.extend(memory_items)
|
||||
else:
|
||||
second_layer_items.append(memory_items)
|
||||
|
||||
return first_layer_items, second_layer_items
|
||||
|
||||
def store_memory(self):
|
||||
for node in self.G.nodes():
|
||||
dot_data = {
|
||||
"concept": node
|
||||
}
|
||||
self.db.db.store_memory_dots.insert_one(dot_data)
|
||||
|
||||
@property
|
||||
def dots(self):
|
||||
# 返回所有节点对应的 Memory_dot 对象
|
||||
return [self.get_dot(node) for node in self.G.nodes()]
|
||||
|
||||
|
||||
def get_random_chat_from_db(self, length: int, timestamp: str):
|
||||
# 从数据库中根据时间戳获取离其最近的聊天记录
|
||||
chat_text = ''
|
||||
closest_record = self.db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) # 调试输出
|
||||
print(f"距离time最近的消息时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(closest_record['time'])))}")
|
||||
|
||||
if closest_record:
|
||||
closest_time = closest_record['time']
|
||||
group_id = closest_record['group_id'] # 获取groupid
|
||||
# 获取该时间戳之后的length条消息，且groupid相同
|
||||
chat_record = list(self.db.db.messages.find({"time": {"$gt": closest_time}, "group_id": group_id}).sort('time', 1).limit(length))
|
||||
for record in chat_record:
|
||||
time_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(int(record['time'])))
|
||||
chat_text += f'[{time_str}] {record["user_nickname"] or "用户" + str(record["user_id"])}: {record["processed_plain_text"]}\n' # 添加发送者和时间信息
|
||||
return chat_text
|
||||
|
||||
return [] # 如果没有找到记录，返回空列表
|
||||
|
||||
def save_graph_to_db(self):
|
||||
# 保存节点
|
||||
for node in self.G.nodes(data=True):
|
||||
concept = node[0]
|
||||
memory_items = node[1].get('memory_items', [])
|
||||
|
||||
# 查找是否存在同名节点
|
||||
existing_node = self.db.db.graph_data.nodes.find_one({'concept': concept})
|
||||
if existing_node:
|
||||
# 如果存在,合并memory_items并去重
|
||||
existing_items = existing_node.get('memory_items', [])
|
||||
if not isinstance(existing_items, list):
|
||||
existing_items = [existing_items] if existing_items else []
|
||||
|
||||
# 合并并去重
|
||||
all_items = list(set(existing_items + memory_items))
|
||||
|
||||
# 更新节点
|
||||
self.db.db.graph_data.nodes.update_one(
|
||||
{'concept': concept},
|
||||
{'$set': {'memory_items': all_items}}
|
||||
)
|
||||
else:
|
||||
# 如果不存在,创建新节点
|
||||
node_data = {
|
||||
'concept': concept,
|
||||
'memory_items': memory_items
|
||||
}
|
||||
self.db.db.graph_data.nodes.insert_one(node_data)
|
||||
|
||||
# 保存边
|
||||
for edge in self.G.edges():
|
||||
source, target = edge
|
||||
|
||||
# 查找是否存在同样的边
|
||||
existing_edge = self.db.db.graph_data.edges.find_one({
|
||||
'source': source,
|
||||
'target': target
|
||||
})
|
||||
|
||||
if existing_edge:
|
||||
# 如果存在,增加num属性
|
||||
num = existing_edge.get('num', 1) + 1
|
||||
self.db.db.graph_data.edges.update_one(
|
||||
{'source': source, 'target': target},
|
||||
{'$set': {'num': num}}
|
||||
)
|
||||
else:
|
||||
# 如果不存在,创建新边
|
||||
edge_data = {
|
||||
'source': source,
|
||||
'target': target,
|
||||
'num': 1
|
||||
}
|
||||
self.db.db.graph_data.edges.insert_one(edge_data)
|
||||
|
||||
def load_graph_from_db(self):
|
||||
# 清空当前图
|
||||
self.G.clear()
|
||||
# 加载节点
|
||||
nodes = self.db.db.graph_data.nodes.find()
|
||||
for node in nodes:
|
||||
memory_items = node.get('memory_items', [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
self.G.add_node(node['concept'], memory_items=memory_items)
|
||||
# 加载边
|
||||
edges = self.db.db.graph_data.edges.find()
|
||||
for edge in edges:
|
||||
self.G.add_edge(edge['source'], edge['target'], num=edge.get('num', 1))
|
||||
|
||||
def calculate_information_content(text):
|
||||
|
||||
"""计算文本的信息量（熵）"""
|
||||
# 统计字符频率
|
||||
char_count = Counter(text)
|
||||
total_chars = len(text)
|
||||
|
||||
# 计算熵
|
||||
entropy = 0
|
||||
for count in char_count.values():
|
||||
probability = count / total_chars
|
||||
entropy -= probability * math.log2(probability)
|
||||
|
||||
return entropy
|
||||
|
||||
|
||||
# Database.initialize(
|
||||
# global_config.MONGODB_HOST,
|
||||
# global_config.MONGODB_PORT,
|
||||
# global_config.DATABASE_NAME
|
||||
# )
|
||||
# memory_graph = Memory_graph()
|
||||
|
||||
# llm_model = LLMModel()
|
||||
# llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
|
||||
|
||||
# memory_graph.load_graph_from_db()
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
# 获取当前文件的绝对路径
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
|
||||
env_path = os.path.join(root_dir, 'config', '.env')
|
||||
|
||||
# 加载环境变量
|
||||
print(f"尝试从 {env_path} 加载环境变量配置")
|
||||
if os.path.exists(env_path):
|
||||
load_dotenv(env_path)
|
||||
print("成功加载环境变量配置")
|
||||
else:
|
||||
print(f"环境变量配置文件不存在: {env_path}")
|
||||
|
||||
# 初始化数据库
|
||||
Database.initialize(
|
||||
"127.0.0.1",
|
||||
27017,
|
||||
"MegBot"
|
||||
)
|
||||
|
||||
memory_graph = Memory_graph()
|
||||
# 创建LLM模型实例
|
||||
llm_model = LLMModel()
|
||||
llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5")
|
||||
|
||||
# 使用当前时间戳进行测试
|
||||
current_timestamp = datetime.datetime.now().timestamp()
|
||||
chat_text = []
|
||||
|
||||
chat_size =25
|
||||
|
||||
for _ in range(30): # 循环10次
|
||||
random_time = current_timestamp - random.randint(1, 3600*10) # 随机时间
|
||||
print(f"随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
|
||||
chat_ = memory_graph.get_random_chat_from_db(chat_size, random_time)
|
||||
chat_text.append(chat_) # 拼接所有text
|
||||
# time.sleep(1)
|
||||
|
||||
|
||||
|
||||
for i, input_text in enumerate(chat_text, 1):
|
||||
|
||||
progress = (i / len(chat_text)) * 100
|
||||
bar_length = 30
|
||||
filled_length = int(bar_length * i // len(chat_text))
|
||||
bar = '█' * filled_length + '-' * (bar_length - filled_length)
|
||||
print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(chat_text)})")
|
||||
|
||||
# print(input_text)
|
||||
first_memory = set()
|
||||
first_memory = memory_compress(input_text, llm_model_small, llm_model_small, rate=2.5)
|
||||
# time.sleep(5)
|
||||
|
||||
#将记忆加入到图谱中
|
||||
for topic, memory in first_memory:
|
||||
topics = segment_text(topic)
|
||||
print(f"\033[1;34m话题\033[0m: {topic},节点: {topics}, 记忆: {memory}")
|
||||
for split_topic in topics:
|
||||
memory_graph.add_dot(split_topic,memory)
|
||||
for split_topic in topics:
|
||||
for other_split_topic in topics:
|
||||
if split_topic != other_split_topic:
|
||||
memory_graph.connect_dot(split_topic, other_split_topic)
|
||||
|
||||
# memory_graph.store_memory()
|
||||
|
||||
# 展示两种不同的可视化方式
|
||||
print("\n按连接数量着色的图谱：")
|
||||
visualize_graph(memory_graph, color_by_memory=False)
|
||||
|
||||
print("\n按记忆数量着色的图谱：")
|
||||
visualize_graph(memory_graph, color_by_memory=True)
|
||||
|
||||
memory_graph.save_graph_to_db()
|
||||
# memory_graph.load_graph_from_db()
|
||||
|
||||
while True:
|
||||
query = input("请输入新的查询概念（输入'退出'以结束）：")
|
||||
if query.lower() == '退出':
|
||||
break
|
||||
items_list = memory_graph.get_related_item(query)
|
||||
if items_list:
|
||||
# print(items_list)
|
||||
for memory_item in items_list:
|
||||
print(memory_item)
|
||||
else:
|
||||
print("未找到相关记忆。")
|
||||
|
||||
while True:
|
||||
query = input("请输入问题：")
|
||||
|
||||
if query.lower() == '退出':
|
||||
break
|
||||
|
||||
topic_prompt = find_topic(query, 3)
|
||||
topic_response = llm_model.generate_response(topic_prompt)
|
||||
# 检查 topic_response 是否为元组
|
||||
if isinstance(topic_response, tuple):
|
||||
topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串
|
||||
else:
|
||||
topics = topic_response.split(",")
|
||||
print(topics)
|
||||
|
||||
for keyword in topics:
|
||||
items_list = memory_graph.get_related_item(keyword)
|
||||
if items_list:
|
||||
print(items_list)
|
||||
|
||||
def memory_compress(input_text, llm_model, llm_model_small, rate=1):
|
||||
information_content = calculate_information_content(input_text)
|
||||
print(f"文本的信息量（熵）: {information_content:.4f} bits")
|
||||
topic_num = max(1, min(5, int(information_content * rate / 4)))
|
||||
print(topic_num)
|
||||
topic_prompt = find_topic(input_text, topic_num)
|
||||
topic_response = llm_model.generate_response(topic_prompt)
|
||||
# 检查 topic_response 是否为元组
|
||||
if isinstance(topic_response, tuple):
|
||||
topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串
|
||||
else:
|
||||
topics = topic_response.split(",")
|
||||
print(topics)
|
||||
compressed_memory = set()
|
||||
for topic in topics:
|
||||
topic_what_prompt = topic_what(input_text,topic)
|
||||
topic_what_response = llm_model_small.generate_response(topic_what_prompt)
|
||||
compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储
|
||||
return compressed_memory
|
||||
|
||||
|
||||
def segment_text(text):
|
||||
seg_text = list(jieba.cut(text))
|
||||
return seg_text
|
||||
|
||||
def find_topic(text, topic_num):
|
||||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个话题，帮我列出来，用逗号隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要告诉我其他内容。'
|
||||
return prompt
|
||||
|
||||
def topic_what(text, topic):
|
||||
prompt = f'这是一段文字：{text}。我想知道这记忆里有什么关于{topic}的话题，帮我总结成一句自然的话，可以包含时间和人物。只输出这句话就好'
|
||||
return prompt
|
||||
|
||||
def visualize_graph(memory_graph: Memory_graph, color_by_memory: bool = False):
|
||||
# 设置中文字体
|
||||
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
|
||||
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||||
|
||||
G = memory_graph.G
|
||||
|
||||
# 保存图到本地
|
||||
nx.write_gml(G, "memory_graph.gml") # 保存为 GML 格式
|
||||
|
||||
# 根据连接条数或记忆数量设置节点颜色
|
||||
node_colors = []
|
||||
nodes = list(G.nodes()) # 获取图中实际的节点列表
|
||||
|
||||
if color_by_memory:
|
||||
# 计算每个节点的记忆数量
|
||||
memory_counts = []
|
||||
for node in nodes:
|
||||
memory_items = G.nodes[node].get('memory_items', [])
|
||||
if isinstance(memory_items, list):
|
||||
count = len(memory_items)
|
||||
else:
|
||||
count = 1 if memory_items else 0
|
||||
memory_counts.append(count)
|
||||
max_memories = max(memory_counts) if memory_counts else 1
|
||||
|
||||
for count in memory_counts:
|
||||
# 使用不同的颜色方案：红色表示记忆多，蓝色表示记忆少
|
||||
if max_memories > 0:
|
||||
intensity = min(1.0, count / max_memories)
|
||||
color = (intensity, 0, 1.0 - intensity) # 从蓝色渐变到红色
|
||||
else:
|
||||
color = (0, 0, 1) # 如果没有记忆，则为蓝色
|
||||
node_colors.append(color)
|
||||
else:
|
||||
# 使用原来的连接数量着色方案
|
||||
max_degree = max(G.degree(), key=lambda x: x[1])[1] if G.degree() else 1
|
||||
for node in nodes:
|
||||
degree = G.degree(node)
|
||||
if max_degree > 0:
|
||||
red = min(1.0, degree / max_degree)
|
||||
blue = 1.0 - red
|
||||
color = (red, 0, blue)
|
||||
else:
|
||||
color = (0, 0, 1)
|
||||
node_colors.append(color)
|
||||
|
||||
# 绘制图形
|
||||
plt.figure(figsize=(12, 8))
|
||||
pos = nx.spring_layout(G, k=1, iterations=50)
|
||||
nx.draw(G, pos,
|
||||
with_labels=True,
|
||||
node_color=node_colors,
|
||||
node_size=2000,
|
||||
font_size=10,
|
||||
font_family='SimHei',
|
||||
font_weight='bold')
|
||||
|
||||
title = '记忆图谱可视化 - ' + ('按记忆数量着色' if color_by_memory else '按连接数量着色')
|
||||
plt.title(title, fontsize=16, fontfamily='SimHei')
|
||||
plt.show()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
||||
@@ -62,7 +62,7 @@ class ScheduleGenerator:
|
||||
|
||||
elif read_only == False:
|
||||
print(f"{date_str}的日程不存在，准备生成新的日程。")
|
||||
prompt = f"""我是{global_config.BOT_NICKNAME}，一个地质学大二女大学生，喜欢刷qq，贴吧，知乎和小红书，请为我生成{date_str}（{weekday}）的日程安排，包括：
|
||||
prompt = f"""我是{global_config.BOT_NICKNAME}，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，请为我生成{date_str}（{weekday}）的日程安排，包括：
|
||||
1. 早上的学习和工作安排
|
||||
2. 下午的活动和任务
|
||||
3. 晚上的计划和休息时间
|
||||
|
||||
Reference in New Issue
Block a user