diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..adca3069 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,11 @@ +# 请填写以下内容 +（删除掉中括号内的空格，并替换为**小写的x**） +1. - [ ] `main` 分支 **禁止修改**，请确认本次提交的分支 **不是 `main` 分支** +2. - [ ] 本次更新 **包含破坏性变更**（如数据库结构变更、配置文件修改等） +3. - [ ] 本次更新是否经过测试 +4. 请填写破坏性更新的具体内容（如有）: +5. 请简要说明本次更新的内容和目的： +# 其他信息 +- **关联 Issue**：Close # +- **截图/GIF**： +- **附加信息**: diff --git a/.gitignore b/.gitignore index 4e1606a5..6e1be60b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ data/ +data1/ mongodb/ NapCat.Framework.Windows.Once/ log/ @@ -193,9 +194,8 @@ cython_debug/ # jieba jieba.cache - -# vscode -/.vscode +# .vscode +!.vscode/settings.json # direnv /.direnv \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..d30b0e65 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,48 @@ +# MaiMBot 开发指南 + +## 🛠️ 常用命令 + +- **运行机器人**: `python run.py` 或 `python bot.py` +- **安装依赖**: `pip install --upgrade -r requirements.txt` +- **Docker 部署**: `docker-compose up` +- **代码检查**: `ruff check .` +- **代码格式化**: `ruff format .` +- **内存可视化**: `run_memory_vis.bat` 或 `python -m src.plugins.memory_system.draw_memory` +- **推理过程可视化**: `script/run_thingking.bat` + +## 🔧 脚本工具 + +- **运行MongoDB**: `script/run_db.bat` - 在端口27017启动MongoDB +- **Windows完整启动**: `script/run_windows.bat` - 检查Python版本、设置虚拟环境、安装依赖并运行机器人 +- **快速启动**: `script/run_maimai.bat` - 设置UTF-8编码并执行"nb run"命令 + +## 📝 代码风格 + +- **Python版本**: 3.9+ +- **行长度限制**: 88字符 +- **命名规范**: + - `snake_case` 用于函数和变量 + - `PascalCase` 用于类 + - `_prefix` 用于私有成员 +- **导入顺序**: 标准库 → 第三方库 → 本地模块 +- **异步编程**: 对I/O操作使用async/await +- **日志记录**: 使用loguru进行一致的日志记录 +- **错误处理**: 使用带有具体异常的try/except +- **文档**: 为类和公共函数编写docstrings + +## 🧩 系统架构 + +- **框架**: NoneBot2框架与插件架构 +- **数据库**: MongoDB持久化存储 +- **设计模式**: 工厂模式和单例管理器 +- **配置管理**: 使用环境变量和TOML文件 +- **内存系统**: 基于图的记忆结构，支持记忆构建、压缩、检索和遗忘 +- **情绪系统**: 情绪模拟与概率权重 +- **LLM集成**: 支持多个LLM服务提供商(ChatAnywhere, SiliconFlow, DeepSeek) + +## ⚙️ 环境配置 + +- 使用`template.env`作为环境变量模板 +- 使用`template/bot_config_template.toml`作为机器人配置模板 +- MongoDB配置: 主机、端口、数据库名 +- API密钥配置: 各LLM提供商的API密钥 diff --git a/README.md b/README.md index 29e25b2b..ad318aec 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,4 @@ -# 麦麦！MaiMBot (编辑中) - +# 麦麦！MaiMBot (编辑中)
@@ -18,7 +17,11 @@ - MongoDB 提供数据持久化支持 - NapCat 作为QQ协议端支持 -**最新版本: v0.5.*** +**最新版本: v0.5.13** +> [!WARNING] +> 注意，3月12日的v0.5.13, 该版本更新较大，建议单独开文件夹部署，然后转移/data文件 和数据库，数据库可能需要删除messages下的内容（不需要删除记忆） + +
@@ -29,29 +32,35 @@
-> ⚠️ **注意事项** +> [!WARNING] > - 项目处于活跃开发阶段，代码可能随时更改 > - 文档未完善，有问题可以提交 Issue 或者 Discussion > - QQ机器人存在被限制风险，请自行了解，谨慎使用 > - 由于持续迭代，可能存在一些已知或未知的bug > - 由于开发中，可能消耗较多token -**交流群**: 766798517 一群人较多，建议加下面的（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 -**交流群**: 571780722 另一个群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 -**交流群**: 1035228475 另一个群（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 +## 💬交流群 +- [一群](https://qm.qq.com/q/VQ3XZrWgMs) 766798517 ，建议加下面的（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 +- [二群](https://qm.qq.com/q/RzmCiRtHEW) 571780722 （开发和建议相关讨论）不一定有空回复，会优先写文档和代码 +- [三群](https://qm.qq.com/q/wlH5eT8OmQ) 1035228475（开发和建议相关讨论）不一定有空回复，会优先写文档和代码 -**其他平台版本** + + +**📚 有热心网友创作的wiki:** https://maimbot.pages.dev/ + + +**😊 其他平台版本** - (由 [CabLate](https://github.com/cablate) 贡献) [Telegram 与其他平台(未来可能会有)的版本](https://github.com/cablate/MaiMBot/tree/telegram) - [集中讨论串](https://github.com/SengokuCola/MaiMBot/discussions/149) -##

📚 文档 ⬇️ 快速开始使用麦麦 ⬇️

### 部署方式 -- 📦 **Windows 一键傻瓜式部署**：请运行项目根目录中的 ```run.bat```，部署完成后请参照后续配置指南进行配置 +- 📦 **Windows 一键傻瓜式部署**：请运行项目根目录中的 `run.bat`，部署完成后请参照后续配置指南进行配置 + - [📦 Windows 手动部署指南 ](docs/manual_deploy_windows.md) @@ -62,7 +71,9 @@ - [🐳 Docker部署指南](docs/docker_deploy.md) + ### 配置说明 + - [🎀 新手配置指南](docs/installation_cute.md) - 通俗易懂的配置教程，适合初次使用的猫娘 - [⚙️ 标准配置指南](docs/installation_standard.md) - 简明专业的配置说明，适合有经验的用户 @@ -75,6 +86,7 @@ ## 🎯 功能介绍 ### 💬 聊天功能 + - 支持关键词检索主动发言：对消息的话题topic进行识别，如果检测到麦麦存储过的话题就会主动进行发言 - 支持bot名字呼唤发言：检测到"麦麦"会主动发言，可配置 - 支持多模型，多厂商自定义配置 @@ -83,31 +95,33 @@ - 错别字和多条回复功能：麦麦可以随机生成错别字，会多条发送回复以及对消息进行reply ### 😊 表情包功能 + - 支持根据发言内容发送对应情绪的表情包 - 会自动偷群友的表情包 ### 📅 日程功能 + - 麦麦会自动生成一天的日程，实现更拟人的回复 ### 🧠 记忆功能 + - 对聊天记录进行概括存储，在需要时调用，待完善 ### 📚 知识库功能 + - 基于embedding模型的知识库，手动放入txt会自动识别，写完了，暂时禁用 ### 👥 关系功能 + - 针对每个用户创建"关系"，可以对不同用户进行个性化回复，目前只有极其简单的好感度（WIP） - 针对每个群创建"群印象"，可以对不同群进行个性化回复（WIP） - - ## 开发计划TODO：LIST 规划主线 0.6.0：记忆系统更新 0.7.0: 麦麦RunTime - - 人格功能：WIP - 群氛围功能：WIP - 图片发送，转发功能：WIP @@ -127,7 +141,6 @@ - 采用截断生成加快麦麦的反应速度 - 改进发送消息的触发 - ## 设计理念 - **千石可乐说：** @@ -137,13 +150,14 @@ - 如果人类真的需要一个AI来陪伴自己，并不是所有人都需要一个完美的，能解决所有问题的helpful assistant，而是一个会犯错的，拥有自己感知和想法的"生命形式"。 - 代码会保持开源和开放，但个人希望MaiMbot的运行时数据保持封闭，尽量避免以显式命令来对其进行控制和调试.我认为一个你无法完全掌控的个体才更能让你感觉到它的自主性，而视其成为一个对话机器. - ## 📌 注意事项 -SengokuCola纯编程外行，面向cursor编程，很多代码史一样多多包涵 -> ⚠️ **警告**：本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。 +SengokuCola~~纯编程外行，面向cursor编程，很多代码写得不好多多包涵~~已得到大脑升级 +> [!WARNING] +> 本应用生成内容来自人工智能模型，由 AI 生成，请仔细甄别，请勿用于违反法律的用途，AI生成内容不代表本人观点和立场。 ## 致谢 + [nonebot2](https://github.com/nonebot/nonebot2): 跨平台 Python 异步聊天机器人框架 [NapCat](https://github.com/NapNeko/NapCatQQ): 现代化的基于 NTQQ 的 Bot 协议端实现 @@ -155,6 +169,6 @@ SengokuCola纯编程外行，面向cursor编程，很多代码史一样多多包 - ## Stargazers over time + [![Stargazers over time](https://starchart.cc/SengokuCola/MaiMBot.svg?variant=adaptive)](https://starchart.cc/SengokuCola/MaiMBot) diff --git a/bot.py b/bot.py index c2ed3dfd..36d621a6 100644 --- a/bot.py +++ b/bot.py @@ -1,9 +1,12 @@ +import asyncio import os import shutil import sys import nonebot import time + +import uvicorn from dotenv import load_dotenv from loguru import logger from nonebot.adapters.onebot.v11 import Adapter @@ -12,6 +15,8 @@ import platform # 获取没有加载env时的环境变量 env_mask = {key: os.getenv(key) for key in os.environ} +uvicorn_server = None + def easter_egg(): # 彩蛋 @@ -58,7 +63,7 @@ def init_env(): # 首先加载基础环境变量.env if os.path.exists(".env"): - load_dotenv(".env") + load_dotenv(".env",override=True) logger.success("成功加载基础环境变量配置") @@ -94,14 +99,26 @@ def load_env(): def load_logger(): logger.remove() # 移除默认配置 - logger.add( - sys.stderr, - format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <7} | {name:.<8}:{function:.<8}:{line: >4} - {message}", - colorize=True, - level=os.getenv("LOG_LEVEL", "DEBUG") # 根据环境设置日志级别，默认为INFO - ) + if os.getenv("ENVIRONMENT") == "dev": + logger.add( + sys.stderr, + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <7} | {name:.<8}:{function:.<8}:{line: >4} - {message}", + colorize=True, + level=os.getenv("LOG_LEVEL", "DEBUG"), # 根据环境设置日志级别，默认为DEBUG + ) + else: + logger.add( + sys.stderr, + format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <7} | {name:.<8}:{function:.<8}:{line: >4} - {message}", + colorize=True, + level=os.getenv("LOG_LEVEL", "INFO"), # 根据环境设置日志级别，默认为INFO + filter=lambda record: "nonebot" not in record["name"] + ) + def scan_provider(env_config: dict): @@ -138,7 +155,39 @@ def scan_provider(env_config: dict): raise ValueError(f"请检查 '{provider_name}' 提供商配置是否丢失 BASE_URL 或 KEY 环境变量") -if __name__ == "__main__": +async def graceful_shutdown(): + try: + global uvicorn_server + if uvicorn_server: + uvicorn_server.force_exit = True # 强制退出 + await uvicorn_server.shutdown() + + tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()] + for task in tasks: + task.cancel() + await asyncio.gather(*tasks, return_exceptions=True) + + except Exception as e: + logger.error(f"麦麦关闭失败: {e}") + + +async def uvicorn_main(): + global uvicorn_server + config = uvicorn.Config( + app="__main__:app", + host=os.getenv("HOST", "127.0.0.1"), + port=int(os.getenv("PORT", 8080)), + reload=os.getenv("ENVIRONMENT") == "dev", + timeout_graceful_shutdown=5, + log_config=None, + access_log=False + ) + server = uvicorn.Server(config) + uvicorn_server = server + await server.serve() + + +def raw_main(): # 利用 TZ 环境变量设定程序工作的时区 # 仅保证行为一致，不依赖 localtime()，实际对生产环境几乎没有作用 if platform.system().lower() != 'windows': @@ -165,10 +214,30 @@ if __name__ == "__main__": nonebot.init(**base_config, **env_config) # 注册适配器 + global driver driver = nonebot.get_driver() driver.register_adapter(Adapter) # 加载插件 nonebot.load_plugins("src/plugins") - nonebot.run() + +if __name__ == "__main__": + + try: + raw_main() + + global app + app = nonebot.get_asgi() + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(uvicorn_main()) + except KeyboardInterrupt: + logger.warning("麦麦会努力做的更好的！正在停止中......") + except Exception as e: + logger.error(f"主程序异常: {e}") + finally: + loop.run_until_complete(graceful_shutdown()) + loop.close() + logger.info("进程终止完毕，麦麦开始休眠......下次再见哦！") diff --git a/changelog.md b/changelog.md index c68a16ad..b9beed81 100644 --- a/changelog.md +++ b/changelog.md @@ -1,6 +1,84 @@ # Changelog -## [0.5.12] - 2025-3-9 -### Added -- 新增了 我是测试 +## [0.5.13] - 2025-3-12 +AI总结 +### 🌟 核心功能增强 +#### 记忆系统升级 +- 新增了记忆系统的时间戳功能，包括创建时间和最后修改时间 +- 新增了记忆图节点和边的时间追踪功能 +- 新增了自动补充缺失时间字段的功能 +- 新增了记忆遗忘机制，基于时间条件自动遗忘旧记忆 +- 优化了记忆系统的数据同步机制 +- 优化了记忆系统的数据结构，确保所有数据类型的一致性 + +#### 私聊功能完善 +- 新增了完整的私聊功能支持，包括消息处理和回复 +- 新增了聊天流管理器，支持群聊和私聊的上下文管理 +- 新增了私聊过滤开关功能 +- 优化了关系管理系统，支持跨平台用户关系 + +#### 消息处理升级 +- 新增了消息队列管理系统，支持按时间顺序处理消息 +- 新增了消息发送控制器，实现人性化的发送速度和间隔 +- 新增了JSON格式分享卡片读取支持 +- 新增了Base64格式表情包CQ码支持 +- 改进了消息处理流程，支持多种消息类型 + +### 💻 系统架构优化 +#### 配置系统改进 +- 新增了配置文件自动更新和版本检测功能 +- 新增了配置文件热重载API接口 +- 新增了配置文件版本兼容性检查 +- 新增了根据不同环境(dev/prod)显示不同级别的日志功能 +- 优化了配置文件格式和结构 + +#### 部署支持扩展 +- 新增了Linux系统部署指南 +- 新增了Docker部署支持的详细文档 +- 新增了NixOS环境支持（使用venv方式） +- 新增了优雅的shutdown机制 +- 优化了Docker部署文档 + +### 🛠️ 开发体验提升 +#### 工具链升级 +- 新增了ruff代码格式化和检查工具 +- 新增了知识库一键启动脚本 +- 新增了自动保存脚本，定期保存聊天记录和关系数据 +- 新增了表情包自动获取脚本 +- 优化了日志记录（使用logger.debug替代print） +- 精简了日志输出，禁用了Uvicorn/NoneBot默认日志 + +#### 安全性强化 +- 新增了API密钥安全管理机制 +- 新增了数据库完整性检查功能 +- 新增了表情包文件完整性自动检查 +- 新增了异常处理和自动恢复机制 +- 优化了安全性检查机制 + +### 🐛 关键问题修复 +#### 系统稳定性 +- 修复了systemctl强制停止的问题 +- 修复了ENVIRONMENT变量在同一终端下不能被覆盖的问题 +- 修复了libc++.so依赖问题 +- 修复了数据库索引创建失败的问题 +- 修复了MongoDB连接配置相关问题 +- 修复了消息队列溢出问题 +- 修复了配置文件加载时的版本兼容性问题 + +#### 功能完善性 +- 修复了私聊时产生reply消息的bug +- 修复了回复消息无法识别的问题 +- 修复了CQ码解析错误 +- 修复了情绪管理器导入问题 +- 修复了小名无效的问题 +- 修复了表情包发送时的参数缺失问题 +- 修复了表情包重复注册问题 +- 修复了变量拼写错误问题 + +### 主要改进方向 +1. 提升记忆系统的智能性和可靠性 +2. 完善私聊功能的完整生态 +3. 优化系统架构和部署便利性 +4. 提升开发体验和代码质量 +5. 加强系统安全性和稳定性 diff --git a/changelog_config.md b/changelog_config.md index 7101fe82..c4c56064 100644 --- a/changelog_config.md +++ b/changelog_config.md @@ -1,6 +1,12 @@ # Changelog +## [0.0.5] - 2025-3-11 +### Added +- 新增了 `alias_names` 配置项，用于指定麦麦的别名。 + ## [0.0.4] - 2025-3-9 ### Added - 新增了 `memory_ban_words` 配置项，用于指定不希望记忆的词汇。 + + diff --git a/config/auto_update.py b/config/auto_update.py new file mode 100644 index 00000000..28ab108d --- /dev/null +++ b/config/auto_update.py @@ -0,0 +1,59 @@ +import os +import shutil +import tomlkit +from pathlib import Path + +def update_config(): + # 获取根目录路径 + root_dir = Path(__file__).parent.parent + template_dir = root_dir / "template" + config_dir = root_dir / "config" + + # 定义文件路径 + template_path = template_dir / "bot_config_template.toml" + old_config_path = config_dir / "bot_config.toml" + new_config_path = config_dir / "bot_config.toml" + + # 读取旧配置文件 + old_config = {} + if old_config_path.exists(): + with open(old_config_path, "r", encoding="utf-8") as f: + old_config = tomlkit.load(f) + + # 删除旧的配置文件 + if old_config_path.exists(): + os.remove(old_config_path) + + # 复制模板文件到配置目录 + shutil.copy2(template_path, new_config_path) + + # 读取新配置文件 + with open(new_config_path, "r", encoding="utf-8") as f: + new_config = tomlkit.load(f) + + # 递归更新配置 + def update_dict(target, source): + for key, value in source.items(): + # 跳过version字段的更新 + if key == "version": + continue + if key in target: + if isinstance(value, dict) and isinstance(target[key], (dict, tomlkit.items.Table)): + update_dict(target[key], value) + else: + try: + # 直接使用tomlkit的item方法创建新值 + target[key] = tomlkit.item(value) + except (TypeError, ValueError): + # 如果转换失败，直接赋值 + target[key] = value + + # 将旧配置的值更新到新配置中 + update_dict(new_config, old_config) + + # 保存更新后的配置（保留注释和格式） + with open(new_config_path, "w", encoding="utf-8") as f: + f.write(tomlkit.dumps(new_config)) + +if __name__ == "__main__": + update_config() diff --git a/docker-compose.yml b/docker-compose.yml index 51255855..227df606 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -6,8 +6,6 @@ services: - NAPCAT_UID=${NAPCAT_UID} - NAPCAT_GID=${NAPCAT_GID} # 让 NapCat 获取当前用户 GID,UID，防止权限问题 ports: - - 3000:3000 - - 3001:3001 - 6099:6099 restart: unless-stopped volumes: @@ -19,7 +17,7 @@ services: mongodb: container_name: mongodb environment: - - tz=Asia/Shanghai + - TZ=Asia/Shanghai # - MONGO_INITDB_ROOT_USERNAME=your_username # - MONGO_INITDB_ROOT_PASSWORD=your_password expose: diff --git a/docs/Jonathan R.md b/docs/Jonathan R.md new file mode 100644 index 00000000..660caaee --- /dev/null +++ b/docs/Jonathan R.md @@ -0,0 +1,20 @@ +Jonathan R. Wolpaw 在 “Memory in neuroscience: rhetoric versus reality.” 一文中提到，从神经科学的感觉运动假设出发，整个神经系统的功能是将经验与适当的行为联系起来，而不是单纯的信息存储。 +Jonathan R,Wolpaw. (2019). Memory in neuroscience: rhetoric versus reality.. Behavioral and cognitive neuroscience reviews(2). + +1. **单一过程理论** + - 单一过程理论认为，识别记忆主要是基于熟悉性这一单一因素的影响。熟悉性是指对刺激的一种自动的、无意识的感知，它可以使我们在没有回忆起具体细节的情况下，判断一个刺激是否曾经出现过。 + - 例如，在一些实验中，研究者发现被试可以在没有回忆起具体学习情境的情况下，对曾经出现过的刺激做出正确的判断，这被认为是熟悉性在起作用1。 +2. **双重过程理论** + - 双重过程理论则认为，识别记忆是基于两个过程：回忆和熟悉性。回忆是指对过去经验的有意识的回忆，它可以使我们回忆起具体的细节和情境；熟悉性则是一种自动的、无意识的感知。 + - 该理论认为，在识别记忆中，回忆和熟悉性共同作用，使我们能够判断一个刺激是否曾经出现过。例如，在 “记得 / 知道” 范式中，被试被要求判断他们对一个刺激的记忆是基于回忆还是熟悉性。研究发现，被试可以区分这两种不同的记忆过程，这为双重过程理论提供了支持1。 + + + +1. **神经元节点与连接**：借鉴神经网络原理，将每个记忆单元视为一个神经元节点。节点之间通过连接相互关联，连接的强度代表记忆之间的关联程度。在形态学联想记忆中，具有相似形态特征的记忆节点连接强度较高。例如，苹果和橘子的记忆节点，由于在形状、都是水果等形态语义特征上相似，它们之间的连接强度大于苹果与汽车记忆节点间的连接强度。 +2. **记忆聚类与层次结构**：依据形态特征的相似性对记忆进行聚类，形成不同的记忆簇。每个记忆簇内部的记忆具有较高的相似性，而不同记忆簇之间的记忆相似性较低。同时，构建记忆的层次结构，高层次的记忆节点代表更抽象、概括的概念，低层次的记忆节点对应具体的实例。比如，“水果” 作为高层次记忆节点，连接着 “苹果”“橘子”“香蕉” 等低层次具体水果的记忆节点。 +3. **网络的动态更新**：随着新记忆的不断加入，记忆网络动态调整。新记忆节点根据其形态特征与现有网络中的节点建立连接，同时影响相关连接的强度。若新记忆与某个记忆簇的特征高度相似，则被纳入该记忆簇；若具有独特特征，则可能引发新的记忆簇的形成。例如，当系统学习到一种新的水果 “番石榴”，它会根据番石榴的形态、语义等特征，在记忆网络中找到与之最相似的区域（如水果记忆簇），并建立相应连接，同时调整周围节点连接强度以适应这一新记忆。 + + + +- **相似性联想**：该理论认为，当两个或多个事物在形态上具有相似性时，它们在记忆中会形成关联。例如，梨和苹果在形状和都是水果这一属性上有相似性，所以当我们看到梨时，很容易通过形态学联想记忆联想到苹果。这种相似性联想有助于我们对新事物进行分类和理解，当遇到一个新的类似水果时，我们可以通过与已有的水果记忆进行相似性匹配，来推测它的一些特征。 +- **时空关联性联想**：除了相似性联想，MAM 还强调时空关联性联想。如果两个事物在时间或空间上经常同时出现，它们也会在记忆中形成关联。比如，每次在公园里看到花的时候，都能听到鸟儿的叫声，那么花和鸟儿叫声的形态特征（花的视觉形态和鸟叫的听觉形态）就会在记忆中形成关联，以后听到鸟叫可能就会联想到公园里的花。 \ No newline at end of file diff --git a/docs/doc1.md b/docs/doc1.md index 158136b9..e8aa0f0d 100644 --- a/docs/doc1.md +++ b/docs/doc1.md @@ -1,6 +1,7 @@ # 📂 文件及功能介绍 (2025年更新) ## 根目录 + - **README.md**: 项目的概述和使用说明。 - **requirements.txt**: 项目所需的Python依赖包列表。 - **bot.py**: 主启动文件，负责环境配置加载和NoneBot初始化。 @@ -10,6 +11,7 @@ - **run_*.bat**: 各种启动脚本，包括数据库、maimai和thinking功能。 ## `src/` 目录结构 + - **`plugins/` 目录**: 存放不同功能模块的插件。 - **chat/**: 处理聊天相关的功能，如消息发送和接收。 - **memory_system/**: 处理机器人的记忆功能。 @@ -22,94 +24,96 @@ - **`common/` 目录**: 存放通用的工具和库。 - **database.py**: 处理与数据库的交互，负责数据的存储和检索。 - - **__init__.py**: 初始化模块。 + - ****init**.py**: 初始化模块。 ## `config/` 目录 + - **bot_config_template.toml**: 机器人配置模板。 - **auto_format.py**: 自动格式化工具。 ### `src/plugins/chat/` 目录文件详细介绍 -1. **`__init__.py`**: +1. **`__init__.py`**: - 初始化 `chat` 模块，使其可以作为一个包被导入。 -2. **`bot.py`**: +2. **`bot.py`**: - 主要的聊天机器人逻辑实现，处理消息的接收、思考和回复。 - 包含 `ChatBot` 类，负责消息处理流程控制。 - 集成记忆系统和意愿管理。 -3. **`config.py`**: +3. **`config.py`**: - 配置文件，定义了聊天机器人的各种参数和设置。 - 包含 `BotConfig` 和全局配置对象 `global_config`。 -4. **`cq_code.py`**: +4. **`cq_code.py`**: - 处理 CQ 码（CoolQ 码），用于发送和接收特定格式的消息。 -5. **`emoji_manager.py`**: +5. **`emoji_manager.py`**: - 管理表情包的发送和接收，根据情感选择合适的表情。 - 提供根据情绪获取表情的方法。 -6. **`llm_generator.py`**: +6. **`llm_generator.py`**: - 生成基于大语言模型的回复，处理用户输入并生成相应的文本。 - 通过 `ResponseGenerator` 类实现回复生成。 -7. **`message.py`**: +7. **`message.py`**: - 定义消息的结构和处理逻辑，包含多种消息类型： - `Message`: 基础消息类 - `MessageSet`: 消息集合 - `Message_Sending`: 发送中的消息 - `Message_Thinking`: 思考状态的消息 -8. **`message_sender.py`**: +8. **`message_sender.py`**: - 控制消息的发送逻辑，确保消息按照特定规则发送。 - 包含 `message_manager` 对象，用于管理消息队列。 -9. **`prompt_builder.py`**: +9. **`prompt_builder.py`**: - 构建用于生成回复的提示，优化机器人的响应质量。 -10. **`relationship_manager.py`**: +10. **`relationship_manager.py`**: - 管理用户之间的关系，记录用户的互动和偏好。 - 提供更新关系和关系值的方法。 -11. **`Segment_builder.py`**: +11. **`Segment_builder.py`**: - 构建消息片段的工具。 -12. **`storage.py`**: +12. **`storage.py`**: - 处理数据存储，负责将聊天记录和用户信息保存到数据库。 - 实现 `MessageStorage` 类管理消息存储。 -13. **`thinking_idea.py`**: +13. **`thinking_idea.py`**: - 实现机器人的思考机制。 -14. **`topic_identifier.py`**: +14. **`topic_identifier.py`**: - 识别消息中的主题，帮助机器人理解用户的意图。 -15. **`utils.py`** 和 **`utils_*.py`** 系列文件: +15. **`utils.py`** 和 **`utils_*.py`** 系列文件: - 存放各种工具函数，提供辅助功能以支持其他模块。 - 包括 `utils_cq.py`、`utils_image.py`、`utils_user.py` 等专门工具。 -16. **`willing_manager.py`**: +16. **`willing_manager.py`**: - 管理机器人的回复意愿，动态调整回复概率。 - 通过多种因素（如被提及、话题兴趣度）影响回复决策。 ### `src/plugins/memory_system/` 目录文件介绍 -1. **`memory.py`**: +1. **`memory.py`**: - 实现记忆管理核心功能，包含 `memory_graph` 对象。 - 提供相关项目检索，支持多层次记忆关联。 -2. **`draw_memory.py`**: +2. **`draw_memory.py`**: - 记忆可视化工具。 -3. **`memory_manual_build.py`**: +3. **`memory_manual_build.py`**: - 手动构建记忆的工具。 -4. **`offline_llm.py`**: +4. **`offline_llm.py`**: - 离线大语言模型处理功能。 ## 消息处理流程 ### 1. 消息接收与预处理 + - 通过 `ChatBot.handle_message()` 接收群消息。 - 进行用户和群组的权限检查。 - 更新用户关系信息。 @@ -117,12 +121,14 @@ - 对消息进行过滤和敏感词检测。 ### 2. 主题识别与决策 + - 使用 `topic_identifier` 识别消息主题。 - 通过记忆系统检查对主题的兴趣度。 - `willing_manager` 动态计算回复概率。 - 根据概率决定是否回复消息。 ### 3. 回复生成与发送 + - 如需回复，首先创建 `Message_Thinking` 对象表示思考状态。 - 调用 `ResponseGenerator.generate_response()` 生成回复内容和情感状态。 - 删除思考消息，创建 `MessageSet` 准备发送回复。 diff --git a/docs/docker_deploy.md b/docs/docker_deploy.md index 3958d2fc..f78f73dc 100644 --- a/docs/docker_deploy.md +++ b/docs/docker_deploy.md @@ -1,67 +1,93 @@ # 🐳 Docker 部署指南 -## 部署步骤（推荐，但不一定是最新） +## 部署步骤 (推荐，但不一定是最新) + +**"更新镜像与容器"部分在本文档 [Part 6](#6-更新镜像与容器)** + +### 0. 前提说明 + +**本文假设读者已具备一定的 Docker 基础知识。若您对 Docker 不熟悉，建议先参考相关教程或文档进行学习，或选择使用 [📦Linux手动部署指南](./manual_deploy_linux.md) 或 [📦Windows手动部署指南](./manual_deploy_windows.md) 。** -### 1. 获取Docker配置文件: +### 1. 获取Docker配置文件 + +- 建议先单独创建好一个文件夹并进入，作为工作目录 ```bash wget https://raw.githubusercontent.com/SengokuCola/MaiMBot/main/docker-compose.yml -O docker-compose.yml ``` -- 若需要启用MongoDB数据库的用户名和密码，可进入docker-compose.yml，取消MongoDB处的注释并修改变量`=`后方的值为你的用户名和密码\ -修改后请注意在之后配置`.env.prod`文件时指定MongoDB数据库的用户名密码 +- 若需要启用MongoDB数据库的用户名和密码，可进入docker-compose.yml，取消MongoDB处的注释并修改变量旁 `=` 后方的值为你的用户名和密码\ +修改后请注意在之后配置 `.env.prod` 文件时指定MongoDB数据库的用户名密码 +### 2. 启动服务 -### 2. 启动服务: - -- **!!! 请在第一次启动前确保当前工作目录下`.env.prod`与`bot_config.toml`文件存在 !!!**\ +- **!!! 请在第一次启动前确保当前工作目录下 `.env.prod` 与 `bot_config.toml` 文件存在 !!!**\ 由于Docker文件映射行为的特殊性，若宿主机的映射路径不存在，可能导致意外的目录创建，而不会创建文件，由于此处需要文件映射到文件，需提前确保文件存在且路径正确，可使用如下命令: + ```bash touch .env.prod touch bot_config.toml ``` - 启动Docker容器: + ```bash NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d +# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代 +NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose up -d ``` -- 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代 - -### 3. 修改配置并重启Docker: +### 3. 修改配置并重启Docker - 请前往 [🎀 新手配置指南](docs/installation_cute.md) 或 [⚙️ 标准配置指南](docs/installation_standard.md) 完成`.env.prod`与`bot_config.toml`配置文件的编写\ **需要注意`.env.prod`中HOST处IP的填写，Docker中部署和系统中直接安装的配置会有所不同** - 重启Docker容器: + ```bash -docker restart maimbot # 若修改过容器名称则替换maimbot为你自定的名臣 +docker restart maimbot # 若修改过容器名称则替换maimbot为你自定的名称 ``` - 下方命令可以但不推荐，只是同时重启NapCat、MongoDB、MaiMBot三个服务 + ```bash NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose restart +# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代 +NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose restart ``` -- 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代 - - ### 4. 登入NapCat管理页添加反向WebSocket -- 在浏览器地址栏输入`http://<宿主机IP>:6099/`进入NapCat的管理Web页，添加一个Websocket客户端 +- 在浏览器地址栏输入 `http://<宿主机IP>:6099/` 进入NapCat的管理Web页，添加一个Websocket客户端 + > 网络配置 -> 新建 -> Websocket客户端 -- Websocket客户端的名称自定，URL栏填入`ws://maimbot:8080/onebot/v11/ws`，启用并保存即可\ +- Websocket客户端的名称自定，URL栏填入 `ws://maimbot:8080/onebot/v11/ws`，启用并保存即可\ (若修改过容器名称则替换maimbot为你自定的名称) +### 5. 部署完成，愉快地和麦麦对话吧! -### 5. 愉快地和麦麦对话吧! +### 6. 更新镜像与容器 + +- 拉取最新镜像 + +```bash +docker-compose pull +``` + +- 执行启动容器指令，该指令会自动重建镜像有更新的容器并启动 + +```bash +NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker compose up -d +# 旧版Docker中可能找不到docker compose，请使用docker-compose工具替代 +NAPCAT_UID=$(id -u) NAPCAT_GID=$(id -g) docker-compose up -d +``` ## ⚠️ 注意事项 - 目前部署方案仍在测试中，可能存在未知问题 - 配置文件中的API密钥请妥善保管，不要泄露 -- 建议先在测试环境中运行，确认无误后再部署到生产环境 \ No newline at end of file +- 建议先在测试环境中运行，确认无误后再部署到生产环境 diff --git a/docs/installation_cute.md b/docs/installation_cute.md index 3a63988f..e0c03310 100644 --- a/docs/installation_cute.md +++ b/docs/installation_cute.md @@ -1,8 +1,9 @@ # 🔧 配置指南 喵~ -## 👋 你好呀！ +## 👋 你好呀 让咱来告诉你我们要做什么喵： + 1. 我们要一起设置一个可爱的AI机器人 2. 这个机器人可以在QQ上陪你聊天玩耍哦 3. 需要设置两个文件才能让机器人工作呢 @@ -10,16 +11,19 @@ ## 📝 需要设置的文件喵 要设置这两个文件才能让机器人跑起来哦： + 1. `.env.prod` - 这个文件告诉机器人要用哪些AI服务呢 2. `bot_config.toml` - 这个文件教机器人怎么和你聊天喵 ## 🔑 密钥和域名的对应关系 想象一下，你要进入一个游乐园，需要： + 1. 知道游乐园的地址（这就是域名 base_url） 2. 有入场的门票（这就是密钥 key） 在 `.env.prod` 文件里，我们定义了三个游乐园的地址和门票喵： + ```ini # 硅基流动游乐园 SILICONFLOW_KEY=your_key # 硅基流动的门票 @@ -35,6 +39,7 @@ CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # ChatAnyWhere的地 ``` 然后在 `bot_config.toml` 里，机器人会用这些门票和地址去游乐园玩耍： + ```toml [model.llm_reasoning] name = "Pro/deepseek-ai/DeepSeek-R1" @@ -47,9 +52,10 @@ base_url = "SILICONFLOW_BASE_URL" # 还是去硅基流动游乐园 key = "SILICONFLOW_KEY" # 用同一张门票就可以啦 ``` -### 🎪 举个例子喵： +### 🎪 举个例子喵 如果你想用DeepSeek官方的服务，就要这样改： + ```toml [model.llm_reasoning] name = "deepseek-reasoner" # 改成对应的模型名称，这里为DeepseekR1 @@ -62,7 +68,8 @@ base_url = "DEEP_SEEK_BASE_URL" # 也去DeepSeek游乐园 key = "DEEP_SEEK_KEY" # 用同一张DeepSeek门票 ``` -### 🎯 简单来说： +### 🎯 简单来说 + - `.env.prod` 文件就像是你的票夹，存放着各个游乐园的门票和地址 - `bot_config.toml` 就是告诉机器人：用哪张票去哪个游乐园玩 - 所有模型都可以用同一个游乐园的票，也可以去不同的游乐园玩耍 @@ -88,19 +95,25 @@ CHAT_ANY_WHERE_KEY=your_key CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # 如果你不知道这是什么，那么下面这些不用改，保持原样就好啦 -HOST=127.0.0.1 # 如果使用Docker部署，需要改成0.0.0.0喵，不然听不见群友讲话了喵 +# 如果使用Docker部署，需要改成0.0.0.0喵，不然听不见群友讲话了喵 +HOST=127.0.0.1 PORT=8080 # 这些是数据库设置，一般也不用改呢 -MONGODB_HOST=127.0.0.1 # 如果使用Docker部署，需要改成数据库容器的名字喵，默认是mongodb喵 +# 如果使用Docker部署，需要把MONGODB_HOST改成数据库容器的名字喵，默认是mongodb喵 +MONGODB_HOST=127.0.0.1 MONGODB_PORT=27017 DATABASE_NAME=MegBot -MONGODB_USERNAME = "" # 如果数据库需要用户名，就在这里填写喵 -MONGODB_PASSWORD = "" # 如果数据库需要密码，就在这里填写呢 -MONGODB_AUTH_SOURCE = "" # 数据库认证源，一般不用改哦 +# 数据库认证信息，如果需要认证就取消注释并填写下面三行喵 +# MONGODB_USERNAME = "" +# MONGODB_PASSWORD = "" +# MONGODB_AUTH_SOURCE = "" -# 插件设置喵 -PLUGINS=["src2.plugins.chat"] # 这里是机器人的插件列表呢 +# 也可以使用URI连接数据库，取消注释填写在下面这行喵（URI的优先级比上面的高） +# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot + +# 这里是机器人的插件列表呢 +PLUGINS=["src2.plugins.chat"] ``` ### 第二个文件：机器人配置 (bot_config.toml) @@ -110,7 +123,8 @@ PLUGINS=["src2.plugins.chat"] # 这里是机器人的插件列表呢 ```toml [bot] qq = "把这里改成你的机器人QQ号喵" # 填写你的机器人QQ号 -nickname = "麦麦" # 机器人的名字，你可以改成你喜欢的任何名字哦 +nickname = "麦麦" # 机器人的名字，你可以改成你喜欢的任何名字哦，建议和机器人QQ名称/群昵称一样哦 +alias_names = ["小麦", "阿麦"] # 也可以用这个招呼机器人，可以不设置呢 [personality] # 这里可以设置机器人的性格呢，让它更有趣一些喵 @@ -198,10 +212,12 @@ key = "SILICONFLOW_KEY" - `topic`: 负责理解对话主题的能力呢 ## 🌟 小提示 + - 如果你刚开始使用，建议保持默认配置呢 - 不同的模型有不同的特长，可以根据需要调整它们的使用比例哦 ## 🌟 小贴士喵 + - 记得要好好保管密钥（key）哦，不要告诉别人呢 - 配置文件要小心修改，改错了机器人可能就不能和你玩了喵 - 如果想让机器人更聪明，可以调整 personality 里的设置呢 @@ -209,7 +225,8 @@ key = "SILICONFLOW_KEY" - QQ群号和QQ号都要用数字填写，不要加引号哦（除了机器人自己的QQ号） ## ⚠️ 注意事项 + - 这个机器人还在测试中呢，可能会有一些小问题喵 - 如果不知道怎么改某个设置，就保持原样不要动它哦~ - 记得要先有AI服务的密钥，不然机器人就不能和你说话了呢 -- 修改完配置后要重启机器人才能生效喵~ \ No newline at end of file +- 修改完配置后要重启机器人才能生效喵~ diff --git a/docs/installation_standard.md b/docs/installation_standard.md index cf8e7eb9..dfaf0e79 100644 --- a/docs/installation_standard.md +++ b/docs/installation_standard.md @@ -3,14 +3,16 @@ ## 简介 本项目需要配置两个主要文件： + 1. `.env.prod` - 配置API服务和系统环境 2. `bot_config.toml` - 配置机器人行为和模型 ## API配置说明 -`.env.prod`和`bot_config.toml`中的API配置关系如下： +`.env.prod` 和 `bot_config.toml` 中的API配置关系如下： + +### 在.env.prod中定义API凭证 -### 在.env.prod中定义API凭证： ```ini # API凭证配置 SILICONFLOW_KEY=your_key # 硅基流动API密钥 @@ -23,7 +25,8 @@ CHAT_ANY_WHERE_KEY=your_key # ChatAnyWhere API密钥 CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # ChatAnyWhere API地址 ``` -### 在bot_config.toml中引用API凭证： +### 在bot_config.toml中引用API凭证 + ```toml [model.llm_reasoning] name = "Pro/deepseek-ai/DeepSeek-R1" @@ -32,6 +35,7 @@ key = "SILICONFLOW_KEY" # 引用.env.prod中定义的密钥 ``` 如需切换到其他API服务，只需修改引用： + ```toml [model.llm_reasoning] name = "deepseek-reasoner" # 改成对应的模型名称，这里为DeepseekR1 @@ -42,6 +46,7 @@ key = "DEEP_SEEK_KEY" # 使用DeepSeek密钥 ## 配置文件详解 ### 环境配置文件 (.env.prod) + ```ini # API配置 SILICONFLOW_KEY=your_key @@ -52,26 +57,36 @@ CHAT_ANY_WHERE_KEY=your_key CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # 服务配置 + HOST=127.0.0.1 # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入 PORT=8080 # 与反向端口相同 # 数据库配置 MONGODB_HOST=127.0.0.1 # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb MONGODB_PORT=27017 # MongoDB端口 + DATABASE_NAME=MegBot -MONGODB_USERNAME = "" # 数据库用户名 -MONGODB_PASSWORD = "" # 数据库密码 -MONGODB_AUTH_SOURCE = "" # 认证数据库 +# 数据库认证信息，如果需要认证就取消注释并填写下面三行 +# MONGODB_USERNAME = "" +# MONGODB_PASSWORD = "" +# MONGODB_AUTH_SOURCE = "" + +# 也可以使用URI连接数据库，取消注释填写在下面这行（URI的优先级比上面的高） +# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot # 插件配置 PLUGINS=["src2.plugins.chat"] ``` ### 机器人配置文件 (bot_config.toml) + ```toml [bot] qq = "机器人QQ号" # 必填 nickname = "麦麦" # 机器人昵称 +# alias_names: 配置机器人可使用的别名。当机器人在群聊或对话中被调用时，别名可以作为直接命令或提及机器人的关键字使用。 +# 该配置项为字符串数组。例如: ["小麦", "阿麦"] +alias_names = ["小麦", "阿麦"] # 机器人别名 [personality] prompt_personality = [ @@ -151,4 +166,4 @@ key = "SILICONFLOW_KEY" 3. 其他说明： - 项目处于测试阶段，可能存在未知问题 - - 建议初次使用保持默认配置 \ No newline at end of file + - 建议初次使用保持默认配置 diff --git a/docs/linux_deploy_guide_for_beginners.md b/docs/linux_deploy_guide_for_beginners.md new file mode 100644 index 00000000..04601923 --- /dev/null +++ b/docs/linux_deploy_guide_for_beginners.md @@ -0,0 +1,444 @@ +# 面向纯新手的Linux服务器麦麦部署指南 + +## 你得先有一个服务器 + +为了能使麦麦在你的电脑关机之后还能运行，你需要一台不间断开机的主机，也就是我们常说的服务器。 + +华为云、阿里云、腾讯云等等都是在国内可以选择的选择。 + +你可以去租一台最低配置的就足敷需要了，按月租大概十几块钱就能租到了。 + +我们假设你已经租好了一台Linux架构的云服务器。我用的是阿里云ubuntu24.04，其他的原理相似。 + +## 0.我们就从零开始吧 + +### 网络问题 + +为访问github相关界面，推荐去下一款加速器，新手可以试试watttoolkit。 + +### 安装包下载 + +#### MongoDB + +对于ubuntu24.04 x86来说是这个： + +https://repo.mongodb.org/apt/ubuntu/dists/noble/mongodb-org/8.0/multiverse/binary-amd64/mongodb-org-server_8.0.5_amd64.deb + +如果不是就在这里自行选择对应版本 + +https://www.mongodb.com/try/download/community-kubernetes-operator + +#### Napcat + +在这里选择对应版本。 + +https://github.com/NapNeko/NapCatQQ/releases/tag/v4.6.7 + +对于ubuntu24.04 x86来说是这个： + +https://dldir1.qq.com/qqfile/qq/QQNT/ee4bd910/linuxqq_3.2.16-32793_amd64.deb + +#### 麦麦 + +https://github.com/SengokuCola/MaiMBot/archive/refs/tags/0.5.8-alpha.zip + +下载这个官方压缩包。 + +### 路径 + +我把麦麦相关文件放在了/moi/mai里面，你可以凭喜好更改，记得适当调整下面涉及到的部分即可。 + +文件结构： + +``` +moi +└─ mai + ├─ linuxqq_3.2.16-32793_amd64.deb + ├─ mongodb-org-server_8.0.5_amd64.deb + └─ bot + └─ MaiMBot-0.5.8-alpha.zip +``` + +### 网络 + +你可以在你的服务器控制台网页更改防火墙规则，允许6099，8080，27017这几个端口的出入。 + +## 1.正式开始！ + +远程连接你的服务器，你会看到一个黑框框闪着白方格，这就是我们要进行设置的场所——终端了。以下的bash命令都是在这里输入。 + +## 2. Python的安装 + +- 导入 Python 的稳定版 PPA： + +```bash +sudo add-apt-repository ppa:deadsnakes/ppa +``` + +- 导入 PPA 后，更新 APT 缓存： + +```bash +sudo apt update +``` + +- 在「终端」中执行以下命令来安装 Python 3.12： + +```bash +sudo apt install python3.12 +``` + +- 验证安装是否成功： + +```bash +python3.12 --version +``` + +- 在「终端」中，执行以下命令安装 pip： + +```bash +sudo apt install python3-pip +``` + +- 检查Pip是否安装成功： + +```bash +pip --version +``` + +- 安装必要组件 + +``` bash +sudo apt install python-is-python3 +``` + +## 3.MongoDB的安装 + +``` bash +cd /moi/mai +``` + +``` bash +dpkg -i mongodb-org-server_8.0.5_amd64.deb +``` + +``` bash +mkdir -p /root/data/mongodb/{data,log} +``` + +## 4.MongoDB的运行 + +```bash +service mongod start +``` + +```bash +systemctl status mongod #通过这条指令检查运行状态 +``` + +有需要的话可以把这个服务注册成开机自启 + +```bash +sudo systemctl enable mongod +``` + +## 5.napcat的安装 + +``` bash +curl -o napcat.sh https://nclatest.znin.net/NapNeko/NapCat-Installer/main/script/install.sh && sudo bash napcat.sh +``` + +上面的不行试试下面的 + +``` bash +dpkg -i linuxqq_3.2.16-32793_amd64.deb +apt-get install -f +dpkg -i linuxqq_3.2.16-32793_amd64.deb +``` + +成功的标志是输入``` napcat ```出来炫酷的彩虹色界面 + +## 6.napcat的运行 + +此时你就可以根据提示在```napcat```里面登录你的QQ号了。 + +```bash +napcat start <你的QQ号> +napcat status #检查运行状态 +``` + +然后你就可以登录napcat的webui进行设置了： + +```http://<你服务器的公网IP>:6099/webui?token=napcat``` + +第一次是这个，后续改了密码之后token就会对应修改。你也可以使用```napcat log <你的QQ号>```来查看webui地址。把里面的```127.0.0.1```改成<你服务器的公网IP>即可。 + +登录上之后在网络配置界面添加websocket客户端，名称随便输一个，url改成`ws://127.0.0.1:8080/onebot/v11/ws`保存之后点启用，就大功告成了。 + +## 7.麦麦的安装 + +### step 1 安装解压软件 + +``` +sudo apt-get install unzip +``` + +### step 2 解压文件 + +```bash +cd /moi/mai/bot # 注意：要切换到压缩包的目录中去 +unzip MaiMBot-0.5.8-alpha.zip +``` + +### step 3 进入虚拟环境安装库 + +```bash +cd /moi/mai/bot +python -m venv venv +source venv/bin/activate +pip install -r requirements.txt +``` + +### step 4 试运行 + +```bash +cd /moi/mai/bot +python -m venv venv +source venv/bin/activate +python bot.py +``` + +肯定运行不成功，不过你会发现结束之后多了一些文件 + +``` +bot +├─ .env.prod +└─ config + └─ bot_config.toml +``` + +你要会vim直接在终端里修改也行，不过也可以把它们下到本地改好再传上去： + +### step 5 文件配置 + +本项目需要配置两个主要文件： + +1. `.env.prod` - 配置API服务和系统环境 +2. `bot_config.toml` - 配置机器人行为和模型 + +#### API + +你可以注册一个硅基流动的账号，通过邀请码注册有14块钱的免费额度：https://cloud.siliconflow.cn/i/7Yld7cfg。 + +#### 在.env.prod中定义API凭证： + +``` +# API凭证配置 +SILICONFLOW_KEY=your_key # 硅基流动API密钥 +SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ # 硅基流动API地址 + +DEEP_SEEK_KEY=your_key # DeepSeek API密钥 +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 # DeepSeek API地址 + +CHAT_ANY_WHERE_KEY=your_key # ChatAnyWhere API密钥 +CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 # ChatAnyWhere API地址 +``` + +#### 在bot_config.toml中引用API凭证： + +``` +[model.llm_reasoning] +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "SILICONFLOW_BASE_URL" # 引用.env.prod中定义的地址 +key = "SILICONFLOW_KEY" # 引用.env.prod中定义的密钥 +``` + +如需切换到其他API服务，只需修改引用： + +``` +[model.llm_reasoning] +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "DEEP_SEEK_BASE_URL" # 切换为DeepSeek服务 +key = "DEEP_SEEK_KEY" # 使用DeepSeek密钥 +``` + +#### 配置文件详解 + +##### 环境配置文件 (.env.prod) + +``` +# API配置 +SILICONFLOW_KEY=your_key +SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ +DEEP_SEEK_KEY=your_key +DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 +CHAT_ANY_WHERE_KEY=your_key +CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 + +# 服务配置 +HOST=127.0.0.1 # 如果使用Docker部署，需要改成0.0.0.0，否则QQ消息无法传入 +PORT=8080 + +# 数据库配置 +MONGODB_HOST=127.0.0.1 # 如果使用Docker部署，需要改成数据库容器的名字，默认是mongodb +MONGODB_PORT=27017 +DATABASE_NAME=MegBot +MONGODB_USERNAME = "" # 数据库用户名 +MONGODB_PASSWORD = "" # 数据库密码 +MONGODB_AUTH_SOURCE = "" # 认证数据库 + +# 插件配置 +PLUGINS=["src2.plugins.chat"] +``` + +##### 机器人配置文件 (bot_config.toml) + +``` +[bot] +qq = "机器人QQ号" # 必填 +nickname = "麦麦" # 机器人昵称（你希望机器人怎么称呼它自己） + +[personality] +prompt_personality = [ + "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧", + "是一个女大学生，你有黑色头发，你会刷小红书" +] +prompt_schedule = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" + +[message] +min_text_length = 2 # 最小回复长度 +max_context_size = 15 # 上下文记忆条数 +emoji_chance = 0.2 # 表情使用概率 +ban_words = [] # 禁用词列表 + +[emoji] +auto_save = true # 自动保存表情 +enable_check = false # 启用表情审核 +check_prompt = "符合公序良俗" + +[groups] +talk_allowed = [] # 允许对话的群号 +talk_frequency_down = [] # 降低回复频率的群号 +ban_user_id = [] # 禁止回复的用户QQ号 + +[others] +enable_advance_output = true # 启用详细日志 +enable_kuuki_read = true # 启用场景理解 + +# 模型配置 +[model.llm_reasoning] # 推理模型 +name = "Pro/deepseek-ai/DeepSeek-R1" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_reasoning_minor] # 轻量推理模型 +name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_normal] # 对话模型 +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.llm_normal_minor] # 备用对话模型 +name = "deepseek-ai/DeepSeek-V2.5" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.vlm] # 图像识别模型 +name = "deepseek-ai/deepseek-vl2" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + +[model.embedding] # 文本向量模型 +name = "BAAI/bge-m3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" + + +[topic.llm_topic] +name = "Pro/deepseek-ai/DeepSeek-V3" +base_url = "SILICONFLOW_BASE_URL" +key = "SILICONFLOW_KEY" +``` + +**step # 6** 运行 + +现在再运行 + +```bash +cd /moi/mai/bot +python -m venv venv +source venv/bin/activate +python bot.py +``` + +应该就能运行成功了。 + +## 8.事后配置 + +可是现在还有个问题：只要你一关闭终端，bot.py就会停止运行。那该怎么办呢？我们可以把bot.py注册成服务。 + +重启服务器，打开MongoDB和napcat服务。 + +新建一个文件，名为`bot.service`，内容如下 + +``` +[Unit] +Description=maimai bot + +[Service] +WorkingDirectory=/moi/mai/bot +ExecStart=/moi/mai/bot/venv/bin/python /moi/mai/bot/bot.py +Restart=on-failure +User=root + +[Install] +WantedBy=multi-user.target +``` + +里面的路径视自己的情况更改。 + +把它放到`/etc/systemd/system`里面。 + +重新加载 `systemd` 配置： + +```bash +sudo systemctl daemon-reload +``` + +启动服务： + +```bash +sudo systemctl start bot.service # 启动服务 +sudo systemctl restart bot.service # 或者重启服务 +``` + +检查服务状态： + +```bash +sudo systemctl status bot.service +``` + +现在再关闭终端，检查麦麦能不能正常回复QQ信息。如果可以的话就大功告成了！ + +## 9.命令速查 + +```bash +service mongod start # 启动mongod服务 +napcat start <你的QQ号> # 登录napcat +cd /moi/mai/bot # 切换路径 +python -m venv venv # 创建虚拟环境 +source venv/bin/activate # 激活虚拟环境 + +sudo systemctl daemon-reload # 重新加载systemd配置 +sudo systemctl start bot.service # 启动bot服务 +sudo systemctl enable bot.service # 启动bot服务 + +sudo systemctl status bot.service # 检查bot服务状态 +``` + +``` +python bot.py +``` + diff --git a/docs/manual_deploy_linux.md b/docs/manual_deploy_linux.md index d310ffc5..b19f3d6a 100644 --- a/docs/manual_deploy_linux.md +++ b/docs/manual_deploy_linux.md @@ -1,6 +1,7 @@ # 📦 Linux系统如何手动部署MaiMbot麦麦？ ## 准备工作 + - 一台联网的Linux设备（本教程以Ubuntu/Debian系为例） - QQ小号（QQ框架的使用可能导致qq被风控，严重（小概率）可能会导致账号封禁，强烈不推荐使用大号） - 可用的大模型API @@ -20,6 +21,7 @@ - 数据库是什么？如何安装并启动MongoDB - 如何运行一个QQ机器人，以及NapCat框架是什么 + --- ## 环境配置 @@ -33,7 +35,9 @@ python --version # 或 python3 --version ``` + 如果版本低于3.9，请更新Python版本。 + ```bash # Ubuntu/Debian sudo apt update @@ -45,6 +49,7 @@ sudo update-alternatives --config python3 ``` ### 2️⃣ **创建虚拟环境** + ```bash # 方法1：使用venv(推荐) python3 -m venv maimbot @@ -65,32 +70,37 @@ pip install -r requirements.txt --- ## 数据库配置 -### 3️⃣ **安装并启动MongoDB** -- 安装与启动：Debian参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-debian/)，Ubuntu参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/) +### 3️⃣ **安装并启动MongoDB** + +- 安装与启动：Debian参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-debian/)，Ubuntu参考[官方文档](https://docs.mongodb.com/manual/tutorial/install-mongodb-on-ubuntu/) - 默认连接本地27017端口 + --- ## NapCat配置 + ### 4️⃣ **安装NapCat框架** - 参考[NapCat官方文档](https://www.napcat.wiki/guide/boot/Shell#napcat-installer-linux%E4%B8%80%E9%94%AE%E4%BD%BF%E7%94%A8%E8%84%9A%E6%9C%AC-%E6%94%AF%E6%8C%81ubuntu-20-debian-10-centos9)安装 -- 使用QQ小号登录，添加反向WS地址： -`ws://127.0.0.1:8080/onebot/v11/ws` +- 使用QQ小号登录，添加反向WS地址: `ws://127.0.0.1:8080/onebot/v11/ws` --- ## 配置文件设置 + ### 5️⃣ **配置文件设置，让麦麦Bot正常工作** + - 修改环境配置文件：`.env.prod` - 修改机器人配置文件：`bot_config.toml` - --- ## 启动机器人 + ### 6️⃣ **启动麦麦机器人** + ```bash # 在项目目录下操作 nb run @@ -100,17 +110,70 @@ python3 bot.py --- -## **其他组件(可选)** -- 直接运行 knowledge.py生成知识库 +### 7️⃣ **使用systemctl管理maimbot** +使用以下命令添加服务文件： + +```bash +sudo nano /etc/systemd/system/maimbot.service +``` + +输入以下内容： + +``：你的maimbot目录 +``：你的venv环境（就是上文创建环境后，执行的代码`source maimbot/bin/activate`中source后面的路径的绝对路径） + +```ini +[Unit] +Description=MaiMbot 麦麦 +After=network.target mongod.service + +[Service] +Type=simple +WorkingDirectory= +ExecStart=/python3 bot.py +ExecStop=/bin/kill -2 $MAINPID +Restart=always +RestartSec=10s + +[Install] +WantedBy=multi-user.target +``` + +输入以下命令重新加载systemd： + +```bash +sudo systemctl daemon-reload +``` + +启动并设置开机自启： + +```bash +sudo systemctl start maimbot +sudo systemctl enable maimbot +``` + +输入以下命令查看日志： + +```bash +sudo journalctl -xeu maimbot +``` + +--- + +## **其他组件(可选)** + +- 直接运行 knowledge.py生成知识库 --- ## 常见问题 + 🔧 权限问题：在命令前加`sudo` 🔌 端口占用：使用`sudo lsof -i :8080`查看端口占用 🛡️ 防火墙：确保8080/27017端口开放 + ```bash sudo ufw allow 8080/tcp sudo ufw allow 27017/tcp -``` \ No newline at end of file +``` diff --git a/docs/manual_deploy_windows.md b/docs/manual_deploy_windows.md index 86238bcd..37f0a5e3 100644 --- a/docs/manual_deploy_windows.md +++ b/docs/manual_deploy_windows.md @@ -30,12 +30,13 @@ 在创建虚拟环境之前，请确保你的电脑上安装了Python 3.9及以上版本。如果没有，可以按以下步骤安装： -1. 访问Python官网下载页面：https://www.python.org/downloads/release/python-3913/ +1. 访问Python官网下载页面： 2. 下载Windows安装程序 (64-bit): `python-3.9.13-amd64.exe` 3. 运行安装程序，并确保勾选"Add Python 3.9 to PATH"选项 4. 点击"Install Now"开始安装 或者使用PowerShell自动下载安装（需要管理员权限）： + ```powershell # 下载并安装Python 3.9.13 $pythonUrl = "https://www.python.org/ftp/python/3.9.13/python-3.9.13-amd64.exe" @@ -46,7 +47,7 @@ Start-Process -Wait -FilePath $pythonInstaller -ArgumentList "/quiet", "InstallA ### 2️⃣ **创建Python虚拟环境来运行程序** - 你可以选择使用以下两种方法之一来创建Python环境： +> 你可以选择使用以下两种方法之一来创建Python环境： ```bash # ---方法1：使用venv（Python自带） @@ -60,6 +61,7 @@ maimbot\\Scripts\\activate # 安装依赖 pip install -r requirements.txt ``` + ```bash # ---方法2：使用conda # 创建一个新的conda环境（环境名为maimbot） @@ -74,27 +76,35 @@ pip install -r requirements.txt ``` ### 2️⃣ **然后你需要启动MongoDB数据库，来存储信息** + - 安装并启动MongoDB服务 - 默认连接本地27017端口 ### 3️⃣ **配置NapCat，让麦麦bot与qq取得联系** + - 安装并登录NapCat（用你的qq小号） -- 添加反向WS：`ws://127.0.0.1:8080/onebot/v11/ws` +- 添加反向WS: `ws://127.0.0.1:8080/onebot/v11/ws` ### 4️⃣ **配置文件设置，让麦麦Bot正常工作** + - 修改环境配置文件：`.env.prod` - 修改机器人配置文件：`bot_config.toml` ### 5️⃣ **启动麦麦机器人** + - 打开命令行，cd到对应路径 + ```bash nb run ``` + - 或者cd到对应路径后 + ```bash python bot.py ``` ### 6️⃣ **其他组件(可选)** + - `run_thingking.bat`: 启动可视化推理界面（未完善） - 直接运行 knowledge.py生成知识库 diff --git a/flake.lock b/flake.lock index dd215f1c..894acd48 100644 --- a/flake.lock +++ b/flake.lock @@ -1,43 +1,21 @@ { "nodes": { - "flake-utils": { - "inputs": { - "systems": "systems" - }, - "locked": { - "lastModified": 1731533236, - "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b", - "type": "github" - }, - "original": { - "owner": "numtide", - "repo": "flake-utils", - "type": "github" - } - }, "nixpkgs": { "locked": { - "lastModified": 1741196730, - "narHash": "sha256-0Sj6ZKjCpQMfWnN0NURqRCQn2ob7YtXTAOTwCuz7fkA=", - "owner": "NixOS", - "repo": "nixpkgs", - "rev": "48913d8f9127ea6530a2a2f1bd4daa1b8685d8a3", - "type": "github" + "lastModified": 0, + "narHash": "sha256-nJj8f78AYAxl/zqLiFGXn5Im1qjFKU8yBPKoWEeZN5M=", + "path": "/nix/store/f30jn7l0bf7a01qj029fq55i466vmnkh-source", + "type": "path" }, "original": { - "owner": "NixOS", - "ref": "nixos-24.11", - "repo": "nixpkgs", - "type": "github" + "id": "nixpkgs", + "type": "indirect" } }, "root": { "inputs": { - "flake-utils": "flake-utils", - "nixpkgs": "nixpkgs" + "nixpkgs": "nixpkgs", + "utils": "utils" } }, "systems": { @@ -54,6 +32,24 @@ "repo": "default", "type": "github" } + }, + "utils": { + "inputs": { + "systems": "systems" + }, + "locked": { + "lastModified": 1731533236, + "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=", + "owner": "numtide", + "repo": "flake-utils", + "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b", + "type": "github" + }, + "original": { + "owner": "numtide", + "repo": "flake-utils", + "type": "github" + } } }, "root": "root", diff --git a/flake.nix b/flake.nix index 54737d64..404f7555 100644 --- a/flake.nix +++ b/flake.nix @@ -1,61 +1,38 @@ { description = "MaiMBot Nix Dev Env"; - # 本配置仅方便用于开发，但是因为 nb-cli 上游打包中并未包含 nonebot2，因此目前本配置并不能用于运行和调试 inputs = { - nixpkgs.url = "github:NixOS/nixpkgs/nixos-24.11"; - flake-utils.url = "github:numtide/flake-utils"; + utils.url = "github:numtide/flake-utils"; }; - outputs = - { - self, - nixpkgs, - flake-utils, - }: - flake-utils.lib.eachDefaultSystem ( - system: - let - pkgs = import nixpkgs { - inherit system; - }; + outputs = { + self, + nixpkgs, + utils, + ... + }: + utils.lib.eachDefaultSystem (system: let + pkgs = import nixpkgs {inherit system;}; + pythonPackages = pkgs.python3Packages; + in { + devShells.default = pkgs.mkShell { + name = "python-venv"; + venvDir = "./.venv"; + buildInputs = [ + pythonPackages.python + pythonPackages.venvShellHook + pythonPackages.numpy + ]; - pythonEnv = pkgs.python3.withPackages ( - ps: with ps; [ - pymongo - python-dotenv - pydantic - jieba - openai - aiohttp - requests - urllib3 - numpy - pandas - matplotlib - networkx - python-dateutil - APScheduler - loguru - tomli - customtkinter - colorama - pypinyin - pillow - setuptools - ] - ); - in - { - devShell = pkgs.mkShell { - buildInputs = [ - pythonEnv - pkgs.nb-cli - ]; + postVenvCreation = '' + unset SOURCE_DATE_EPOCH + pip install -r requirements.txt + ''; - shellHook = '' - ''; - }; - } - ); -} + postShellHook = '' + # allow pip to install wheels + unset SOURCE_DATE_EPOCH + ''; + }; + }); +} \ No newline at end of file diff --git a/hort --pretty=format-ad -s b/hort --pretty=format-ad -s new file mode 100644 index 00000000..faeacdd5 --- /dev/null +++ b/hort --pretty=format-ad -s @@ -0,0 +1,141 @@ +cbb569e - Create 如果你更新了版本，点我.txt +a91ef7b - 自动升级配置文件脚本 +ed18f2e - 新增了知识库一键启动漂亮脚本 +80ed568 - fix: 删除print调试代码 +c681a82 - 修复小名无效问题 +e54038f - fix: 从 nixpkgs 增加 numpy 依赖，以避免出现 libc++.so 找不到的问题 +26782c9 - fix: 修复 ENVIRONMENT 变量在同一终端下不能被覆盖的问题 +8c34637 - 提高健壮性 +2688a96 - close SengokuCola/MaiMBot#225 让麦麦可以正确读取分享卡片 +cd16e68 - 修复表情包发送时的缺失参数 +b362c35 - feat: 更新 flake.nix ，采用 venv 的方式生成环境，nixos用户也可以本机运行项目了 +3c8c897 - 屏蔽一个臃肿的debug信息 +9d0152a - 修复了合并过程中造成的代码重复 +956135c - 添加一些注释 +a412741 - 将print变为logger.debug +3180426 - 修复了没有改掉的typo字段 +aea3bff - 添加私聊过滤开关,更新config,增加约束 +cda6281 - chore: update emoji_manager.py +baed856 - 修正了私聊屏蔽词输出 +66a0f18 - 修复了私聊时产生reply消息的bug +3bf5cd6 - feat: 新增运行时重载配置文件；新增根据不同环境(dev;prod)显示不同级别的log +33cd83b - 添加私聊功能 +aa41f0d - fix: 放反了 +ef8691c - fix: 修改message继承逻辑，修复回复消息无法识别 +7d017be - fix:模型降级 +e1019ad - fix: 修复变量拼写错误并优化代码可读性 +c24bb70 - fix: 流式输出模式增加结束判断与token用量记录 +60a9376 - 添加logger的debug输出开关,默认为不开启 +bfa9a3c - fix: 添加群信息获取的错误处理 (#173) +4cc5c8e - 修正.env.prod和.env.dev的生成 +dea14c1 - fix: 模型降级目前只对硅基流动的V3和R1生效 +b6edbea - fix: 图片保存路径不正确 +01a6fa8 - fix: 删除神秘test +20f009d - 修复systemctl强制停止maimbot的问题 +af962c2 - 修复了情绪管理器没有正确导入导致发布出消息 +0586700 - 按照Sourcery提供的建议修改systemctl管理指南 +e48b32a - 在手动部署教程中增加使用systemctl管理 +5760412 - fix: 小修 +1c9b0cc - fix: 修复部分cq码解析错误，merge +b6867b9 - fix: 统一使用os.getenv获取数据库连接信息，避免从config对象获取不存在的值时出现KeyError +5e069f7 - 修复记忆保存时无时间信息的bug +73a3e41 - 修复记忆更新bug +52c93ba - refactor: use Base64 for emoji CQ codes +67f6d7c - fix: 保证能运行的小修改 +c32c4fb - refactor: 修改配置文件的版本号 +a54ca8c - Merge remote-tracking branch 'upstream/debug' into feat_regix +8cbf9bb - feat: 史上最好的消息流重构和图片管理 +9e41c4f - feat: 修改 bot_config 0.0.5 版本的变更日志 +eede406 - fix: 修复nonebot无法加载项目的问题 +00e02ed - fix: 0.0.5 版本的增加分层控制项 +0f99d6a - Update docs/docker_deploy.md +c789074 - feat: 增加ruff依赖 +ff65ab8 - feat: 修改默认的ruff配置文件，同时消除config的所有不符合规范的地方 +bf97013 - feat: 精简日志，禁用Uvicorn/NoneBot默认日志；启动方式改为显示加载uvicorn，以便优雅shutdown +d9a2863 - 优化Docker部署文档更新容器部分 +efcf00f - Docker部署文档追加更新部分 +a63ce96 - fix: 更新情感判断模型配置（使配置文件里的 llm_emotion_judge 生效） +1294c88 - feat: 增加标准化格式化设置 +2e8cd47 - fix: 避免可能出现的日程解析错误 +043a724 - 修一下文档跳转，小美化（ +e4b8865 - 支持别名，可以用不同名称召唤机器人 +7b35ddd - ruff 哥又有新点子 +7899e67 - feat: 重构完成开始测试debug +354d6d0 - 记忆系统优化 +6cef8fd - 修复时区，删去napcat用不到的端口 +cd96644 - 添加使用说明 +84495f8 - fix +204744c - 修改配置名与修改过滤对象为raw_message +a03b490 - Update README.md +2b2b342 - feat: 增加 ruff 依赖 +72a6749 - fix: 修复docker部署时区指定问题 +ee579bc - Update README.md +1b611ec - resolve SengokuCola/MaiMBot#167 根据正则表达式过滤消息 +6e2ea82 - refractor: 几乎写完了，进入测试阶段 +2ffdfef - More +e680405 - fix: typo 'discription' +68b3f57 - Minor Doc Update +312f065 - Create linux_deploy_guide_for_beginners.md +ed505a4 - fix: 使用动态路径替换硬编码的项目路径 +8ff7bb6 - docs: 更新文档，修正格式并添加必要的换行符 +6e36a56 - feat: 增加 MONGODB_URI 的配置项，并将所有env文件的注释单独放在一行（python的dotenv有时无法正确处理行内注释） +4baa6c6 - feat: 实现MongoDB URI方式连接，并统一数据库连接代码。 +8a32d18 - feat: 优化willing_manager逻辑，增加回复保底概率 +c9f1244 - docs: 改进README.md文档格式和排版 +e1b484a - docs: 添加CLAUDE.md开发指南文件（用于Claude Code） +a43f949 - fix: remove duplicate message(CR comments) +fddb641 - fix: 修复错误的空值检测逻辑 +8b7876c - fix: 修复没有上传tag的问题 +6b4130e - feat: 增加stable-dev分支的打包 +052e67b - refactor: 日志打印优化（终于改完了，爽了 +a7f9d05 - 修复记忆整理传入格式问题 +536bb1d - fix: 更新情感判断模型配置 +8d99592 - fix: logger初始化顺序 +052802c - refactor: logger promotion +8661d94 - doc: README.md - telegram version information +5746afa - refactor: logger in src\plugins\chat\bot.py +288dbb6 - refactor: logger in src\plugins\chat\__init__.py +8428a06 - fix: memory logger optimization (CR comment) +665c459 - 改进了可视化脚本 +6c35704 - fix: 调用了错误的函数 +3223153 - feat: 一键脚本新增记忆可视化 +3149dd3 - fix: mongodb.zip 无法解压 fix:更换执行命令的方法 fix:当 db 不存在时自动创建 feat: 一键安装完成后启动麦麦 +089d6a6 - feat: 针对硅基流动的Pro模型添加了自动降级功能 +c4b0917 - 一个记忆可视化小脚本 +6a71ea4 - 修复了记忆时间bug,config添加了记忆屏蔽关键词 +1b5344f - fix: 优化bot初始化的日志&格式 +41aa974 - fix: 优化chat/config.py的日志&格式 +980cde7 - fix: 优化scheduler_generator日志&格式 +31a5514 - fix: 调整全局logger加载顺序 +8baef07 - feat: 添加全局logger初始化设置 +5566f17 - refractor: 几乎写完了，进入测试阶段 +6a66933 - feat: 添加开发环境.env.dev初始化 +411ff1a - feat: 安装 MongoDB Compass +0de9eba - feat: 增加实时更新贡献者列表的功能 +f327f45 - fix: 优化src/plugins/chat/__init__.py的import +826daa5 - fix: 当虚拟环境存在时跳过创建 +f54de42 - fix: time.tzset 仅在类 Unix 系统可用 +47c4990 - fix: 修复docker部署场景下时间错误的问题 +e23a371 - docs: 添加 compose 注释 +1002822 - docs: 标注 Python 最低版本 +564350d - feat: 校验 Python 版本 +4cc4482 - docs: 添加傻瓜式脚本 +757173a - 带麦麦看了心理医生，让她没那么容易陷入负面情绪 +39bb99c - 将错别字生成提取到配置，一句一个错别字太烦了！ +fe36847 - feat: 超大型重构 +e304dd7 - Update README.md +b7cfe6d - feat: 发布第 0.0.2 版本配置模板 +ca929d5 - 补充Docker部署文档 +1e97120 - 补充Docker部署文档 +25f7052 - fix: 修复兼容性选项和目前第一个版本之间的版本间隙 0.0.0 版，并将所有的直接退出修改为抛出异常 +c5bdc4f - 防ipv6炸，虽然小概率事件 +d86610d - fix: 修复不能加载环境变量的问题 +2306ebf - feat: 因为判断临界版本范围比较麻烦，增加 notice 字段，删除原本的判断逻辑（存在故障） +dd09576 - fix: 修复 TypeError: BotConfig.convert_to_specifierset() takes 1 positional argument but 2 were given +18f839b - fix: 修复 missing 1 required positional argument: 'INNER_VERSION' +6adb5ed - 调整一些细节，docker部署时可选数据库账密 +07f48e9 - fix: 利用filter来过滤环境变量，避免直接删除key造成的 RuntimeError: dictionary changed size during iteration +5856074 - fix: 修复无法进行基础设置的问题 +32aa032 - feat: 发布 0.0.1 版本的配置文件 +edc07ac - feat: 重构配置加载器，增加配置文件版本控制和程序兼容能力 +0f492ed - fix: 修复 BASE_URL/KEY 组合检查中被 GPG_KEY 干扰的问题 \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index e54dcdac..0a480574 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,23 +1,51 @@ [project] -name = "Megbot" +name = "MaiMaiBot" version = "0.1.0" -description = "New Bot Project" +description = "MaiMaiBot" [tool.nonebot] plugins = ["src.plugins.chat"] -plugin_dirs = ["src/plugins"] +plugin_dirs = ["src/plugins"] [tool.ruff] -# 设置 Python 版本 -target-version = "py39" + +include = ["*.py"] + +# 行长度设置 +line-length = 120 + +[tool.ruff.lint] +fixable = ["ALL"] +unfixable = [] + +# 如果一个变量的名称以下划线开头，即使它未被使用，也不应该被视为错误或警告。 +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" # 启用的规则 select = [ - "E", # pycodestyle 错误 - "F", # pyflakes - "I", # isort - "B", # flake8-bugbear + "E", # pycodestyle 错误 + "F", # pyflakes + "B", # flake8-bugbear ] -# 行长度设置 -line-length = 88 \ No newline at end of file +ignore = ["E711"] + +[tool.ruff.format] +docstring-code-format = true +indent-style = "space" + + +# 使用双引号表示字符串 +quote-style = "double" + +# 尊重魔法尾随逗号 +# 例如： +# items = [ +# "apple", +# "banana", +# "cherry", +# ] +skip-magic-trailing-comma = false + +# 自动检测合适的换行符 +line-ending = "auto" diff --git a/requirements.txt b/requirements.txt index 4f969682..8330c8d0 100644 Binary files a/requirements.txt and b/requirements.txt differ diff --git a/run.py b/run.py index 50e312c3..cfd3a5f1 100644 --- a/run.py +++ b/run.py @@ -128,13 +128,17 @@ if __name__ == "__main__": ) os.system("cls") if choice == "1": - install_napcat() - install_mongodb() + confirm = input("首次安装将下载并配置所需组件\n1.确认\n2.取消\n") + if confirm == "1": + install_napcat() + install_mongodb() + else: + print("已取消安装") elif choice == "2": run_maimbot() - choice = input("是否启动推理可视化？（y/N）").upper() + choice = input("是否启动推理可视化？（未完善）（y/N）").upper() if choice == "Y": run_cmd(r"python src\gui\reasoning_gui.py") - choice = input("是否启动记忆可视化？（y/N）").upper() + choice = input("是否启动记忆可视化？（未完善）（y/N）").upper() if choice == "Y": run_cmd(r"python src/plugins/memory_system/memory_manual_build.py") diff --git a/src/common/database.py b/src/common/database.py index 45ac05da..d592b0f9 100644 --- a/src/common/database.py +++ b/src/common/database.py @@ -1,50 +1,51 @@ from typing import Optional - from pymongo import MongoClient - class Database: _instance: Optional["Database"] = None - def __init__(self, host: str, port: int, db_name: str, username: Optional[str] = None, password: Optional[str] = None, auth_source: Optional[str] = None): - if username and password: + def __init__( + self, + host: str, + port: int, + db_name: str, + username: Optional[str] = None, + password: Optional[str] = None, + auth_source: Optional[str] = None, + uri: Optional[str] = None, + ): + if uri and uri.startswith("mongodb://"): + # 优先使用URI连接 + self.client = MongoClient(uri) + elif username and password: # 如果有用户名和密码，使用认证连接 - # TODO: 复杂情况直接支持URI吧 - self.client = MongoClient(host, port, username=username, password=password, authSource=auth_source) + self.client = MongoClient( + host, port, username=username, password=password, authSource=auth_source + ) else: # 否则使用无认证连接 self.client = MongoClient(host, port) self.db = self.client[db_name] @classmethod - def initialize(cls, host: str, port: int, db_name: str, username: Optional[str] = None, password: Optional[str] = None, auth_source: Optional[str] = None) -> "Database": + def initialize( + cls, + host: str, + port: int, + db_name: str, + username: Optional[str] = None, + password: Optional[str] = None, + auth_source: Optional[str] = None, + uri: Optional[str] = None, + ) -> "Database": if cls._instance is None: - cls._instance = cls(host, port, db_name, username, password, auth_source) + cls._instance = cls( + host, port, db_name, username, password, auth_source, uri + ) return cls._instance @classmethod def get_instance(cls) -> "Database": if cls._instance is None: raise RuntimeError("Database not initialized") - return cls._instance - - - #测试用 - - def get_random_group_messages(self, group_id: str, limit: int = 5): - # 先随机获取一条消息 - random_message = list(self.db.messages.aggregate([ - {"$match": {"group_id": group_id}}, - {"$sample": {"size": 1}} - ]))[0] - - # 获取该消息之后的消息 - subsequent_messages = list(self.db.messages.find({ - "group_id": group_id, - "time": {"$gt": random_message["time"]} - }).sort("time", 1).limit(limit)) - - # 将随机消息和后续消息合并 - messages = [random_message] + subsequent_messages - - return messages \ No newline at end of file + return cls._instance \ No newline at end of file diff --git a/src/gui/reasoning_gui.py b/src/gui/reasoning_gui.py index 514a95df..e131658b 100644 --- a/src/gui/reasoning_gui.py +++ b/src/gui/reasoning_gui.py @@ -7,7 +7,7 @@ from datetime import datetime from typing import Dict, List from loguru import logger from typing import Optional -from pymongo import MongoClient +from ..common.database import Database import customtkinter as ctk from dotenv import load_dotenv @@ -28,38 +28,6 @@ else: logger.error("未找到环境配置文件") sys.exit(1) - -class Database: - _instance: Optional["Database"] = None - - def __init__(self, host: str, port: int, db_name: str, username: str = None, password: str = None, - auth_source: str = None): - if username and password: - self.client = MongoClient( - host=host, - port=port, - username=username, - password=password, - authSource=auth_source or 'admin' - ) - else: - self.client = MongoClient(host, port) - self.db = self.client[db_name] - - @classmethod - def initialize(cls, host: str, port: int, db_name: str, username: str = None, password: str = None, - auth_source: str = None) -> "Database": - if cls._instance is None: - cls._instance = cls(host, port, db_name, username, password, auth_source) - return cls._instance - - @classmethod - def get_instance(cls) -> "Database": - if cls._instance is None: - raise RuntimeError("Database not initialized") - return cls._instance - - class ReasoningGUI: def __init__(self): # 记录启动时间戳，转换为Unix时间戳 @@ -83,11 +51,19 @@ class ReasoningGUI: except RuntimeError: logger.warning("数据库未初始化，正在尝试初始化...") try: - Database.initialize("127.0.0.1", 27017, "maimai_bot") + Database.initialize( + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), + ) self.db = Database.get_instance().db logger.success("数据库初始化成功") except Exception: - logger.exception(f"数据库初始化失败") + logger.exception("数据库初始化失败") sys.exit(1) # 存储群组数据 @@ -342,7 +318,7 @@ class ReasoningGUI: 'group_id': self.selected_group_id }) except Exception: - logger.exception(f"自动更新出错") + logger.exception("自动更新出错") # 每5秒更新一次 time.sleep(5) @@ -359,12 +335,13 @@ class ReasoningGUI: def main(): """主函数""" Database.initialize( - host=os.getenv("MONGODB_HOST"), - port=int(os.getenv("MONGODB_PORT")), - db_name=os.getenv("DATABASE_NAME"), + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), username=os.getenv("MONGODB_USERNAME"), password=os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), ) app = ReasoningGUI() diff --git a/src/plugins/chat/__init__.py b/src/plugins/chat/__init__.py index 8f0e66fa..8b0bba70 100644 --- a/src/plugins/chat/__init__.py +++ b/src/plugins/chat/__init__.py @@ -1,9 +1,10 @@ import asyncio import time +import os from loguru import logger from nonebot import get_driver, on_message, require -from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment +from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent, Message, MessageSegment,MessageEvent from nonebot.typing import T_State from ...common.database import Database @@ -15,6 +16,7 @@ from .config import global_config from .emoji_manager import emoji_manager from .relationship_manager import relationship_manager from .willing_manager import willing_manager +from .chat_stream import chat_manager from ..memory_system.memory import hippocampus, memory_graph from .bot import ChatBot from .message_sender import message_manager, message_sender @@ -31,12 +33,13 @@ driver = get_driver() config = driver.config Database.initialize( - host=config.MONGODB_HOST, - port=int(config.MONGODB_PORT), - db_name=config.DATABASE_NAME, - username=config.MONGODB_USERNAME, - password=config.MONGODB_PASSWORD, - auth_source=config.MONGODB_AUTH_SOURCE + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), ) logger.success("初始化数据库成功") @@ -47,8 +50,8 @@ emoji_manager.initialize() logger.debug(f"正在唤醒{global_config.BOT_NICKNAME}......") # 创建机器人实例 chat_bot = ChatBot() -# 注册群消息处理器 -group_msg = on_message(priority=5) +# 注册消息处理器 +msg_in = on_message(priority=5) # 创建定时任务 scheduler = require("nonebot_plugin_apscheduler").scheduler @@ -96,10 +99,12 @@ async def _(bot: Bot): asyncio.create_task(emoji_manager._periodic_scan(interval_MINS=global_config.EMOJI_REGISTER_INTERVAL)) logger.success("-----------开始偷表情包！-----------") + asyncio.create_task(chat_manager._initialize()) + asyncio.create_task(chat_manager._auto_save_task()) -@group_msg.handle() -async def _(bot: Bot, event: GroupMessageEvent, state: T_State): +@msg_in.handle() +async def _(bot: Bot, event: MessageEvent, state: T_State): await chat_bot.handle_message(event, bot) @@ -121,9 +126,9 @@ async def build_memory_task(): @scheduler.scheduled_job("interval", seconds=global_config.forget_memory_interval, id="forget_memory") async def forget_memory_task(): """每30秒执行一次记忆构建""" - # print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...") - # await hippocampus.operation_forget_topic(percentage=0.1) - # print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成") + print("\033[1;32m[记忆遗忘]\033[0m 开始遗忘记忆...") + await hippocampus.operation_forget_topic(percentage=global_config.memory_forget_percentage) + print("\033[1;32m[记忆遗忘]\033[0m 记忆遗忘完成") @scheduler.scheduled_job("interval", seconds=global_config.build_memory_interval + 10, id="merge_memory") diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py index 34ad7ad2..bfdaa5ed 100644 --- a/src/plugins/chat/bot.py +++ b/src/plugins/chat/bot.py @@ -1,27 +1,35 @@ +import re import time from random import random + import asyncio from loguru import logger -from nonebot.adapters.onebot.v11 import Bot, GroupMessageEvent +from nonebot.adapters.onebot.v11 import ( + Bot, + GroupMessageEvent, + MessageEvent, + PrivateMessageEvent, +) from ..memory_system.memory import hippocampus from ..moods.moods import MoodManager # 导入情绪管理器 from .config import global_config -from .cq_code import CQCode # 导入CQCode模块 from .emoji_manager import emoji_manager # 导入表情包管理器 from .llm_generator import ResponseGenerator -from .message import ( - Message, - Message_Sending, - Message_Thinking, # 导入 Message_Thinking 类 - MessageSet, +from .message import MessageSending, MessageRecv, MessageThinking, MessageSet +from .message_cq import ( + MessageRecvCQ, ) +from .chat_stream import chat_manager + from .message_sender import message_manager # 导入新的消息管理器 from .relationship_manager import relationship_manager from .storage import MessageStorage -from .utils import calculate_typing_time, is_mentioned_bot_in_txt +from .utils import calculate_typing_time, is_mentioned_bot_in_message +from .utils_image import image_path_to_base64 from .willing_manager import willing_manager # 导入意愿管理器 +from .message_base import UserInfo, GroupInfo, Seg class ChatBot: @@ -41,194 +49,266 @@ class ChatBot: if not self._started: self._started = True - async def handle_message(self, event: GroupMessageEvent, bot: Bot) -> None: - """处理收到的群消息""" + async def handle_message(self, event: MessageEvent, bot: Bot) -> None: + """处理收到的消息""" - if event.group_id not in global_config.talk_allowed_groups: - return self.bot = bot # 更新 bot 实例 + # 用户屏蔽,不区分私聊/群聊 if event.user_id in global_config.ban_user_id: return - group_info = await bot.get_group_info(group_id=event.group_id) - sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True) + # 处理私聊消息 + if isinstance(event, PrivateMessageEvent): + if not global_config.enable_friend_chat: # 私聊过滤 + return + else: + try: + user_info = UserInfo( + user_id=event.user_id, + user_nickname=(await bot.get_stranger_info(user_id=event.user_id, no_cache=True))["nickname"], + user_cardname=None, + platform="qq", + ) + except Exception as e: + logger.error(f"获取陌生人信息失败: {e}") + return + logger.debug(user_info) - await relationship_manager.update_relationship(user_id=event.user_id, data=sender_info) - await relationship_manager.update_relationship_value(user_id=event.user_id, relationship_value=0.5) + # group_info = GroupInfo(group_id=0, group_name="私聊", platform="qq") + group_info = None - message = Message( - group_id=event.group_id, - user_id=event.user_id, + # 处理群聊消息 + else: + # 白名单设定由nontbot侧完成 + if event.group_id: + if event.group_id not in global_config.talk_allowed_groups: + return + + user_info = UserInfo( + user_id=event.user_id, + user_nickname=event.sender.nickname, + user_cardname=event.sender.card or None, + platform="qq", + ) + + group_info = GroupInfo(group_id=event.group_id, group_name=None, platform="qq") + + # group_info = await bot.get_group_info(group_id=event.group_id) + # sender_info = await bot.get_group_member_info(group_id=event.group_id, user_id=event.user_id, no_cache=True) + + message_cq = MessageRecvCQ( message_id=event.message_id, - user_cardname=sender_info['card'], + user_info=user_info, raw_message=str(event.original_message), - plain_text=event.get_plaintext(), + group_info=group_info, reply_message=event.reply, + platform="qq", ) - await message.initialize() + message_json = message_cq.to_dict() + # 进入maimbot + message = MessageRecv(message_json) + groupinfo = message.message_info.group_info + userinfo = message.message_info.user_info + messageinfo = message.message_info + + # 消息过滤，涉及到config有待更新 + + chat = await chat_manager.get_or_create_stream( + platform=messageinfo.platform, user_info=userinfo, group_info=groupinfo + ) + message.update_chat_stream(chat) + await relationship_manager.update_relationship( + chat_stream=chat, + ) + await relationship_manager.update_relationship_value(chat_stream=chat, relationship_value=0.5) + + await message.process() # 过滤词 for word in global_config.ban_words: - if word in message.detailed_plain_text: + if word in message.processed_plain_text: logger.info( - f"[{message.group_name}]{message.user_nickname}:{message.processed_plain_text}") + f"[{chat.group_info.group_name if chat.group_info.group_id else '私聊'}]{userinfo.user_nickname}:{message.processed_plain_text}" + ) logger.info(f"[过滤词识别]消息中含有{word}，filtered") return - current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(message.time)) + # 正则表达式过滤 + for pattern in global_config.ban_msgs_regex: + if re.search(pattern, message.raw_message): + logger.info( + f"[{chat.group_info.group_name if chat.group_info.group_id else '私聊'}]{message.user_nickname}:{message.raw_message}" + ) + logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered") + return + + current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(messageinfo.time)) # topic=await topic_identifier.identify_topic_llm(message.processed_plain_text) - topic = '' - interested_rate = 0 + + topic = "" interested_rate = await hippocampus.memory_activate_value(message.processed_plain_text) / 100 - logger.debug(f"对{message.processed_plain_text}" - f"的激活度:{interested_rate}") + logger.debug(f"对{message.processed_plain_text}的激活度:{interested_rate}") # logger.info(f"\033[1;32m[主题识别]\033[0m 使用{global_config.topic_extract}主题: {topic}") - await self.storage.store_message(message, topic[0] if topic else None) - is_mentioned = is_mentioned_bot_in_txt(message.processed_plain_text) + await self.storage.store_message(message, chat, topic[0] if topic else None) + is_mentioned = is_mentioned_bot_in_message(message) + if is_mentioned: #如果被@等待下文10秒 await asyncio.sleep(10) logger.info(f"被@，等待下文") + + reply_probability = await willing_manager.change_reply_willing_received( + chat_stream=chat, + topic=topic[0] if topic else None, + is_mentioned_bot=is_mentioned, + config=global_config, + is_emoji=message.is_emoji, + interested_rate=interested_rate, + - reply_probability = willing_manager.change_reply_willing_received( - event.group_id, - topic[0] if topic else None, - is_mentioned, - global_config, - event.user_id, - message.is_emoji, - interested_rate ) - current_willing = willing_manager.get_willing(event.group_id) + current_willing = willing_manager.get_willing(chat_stream=chat) logger.info( - f"[{current_time}][{message.group_name}]{message.user_nickname}:" - f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]") + f"[{current_time}][{chat.group_info.group_name if chat.group_info.group_id else '私聊'}]{chat.user_info.user_nickname}:" + f"{message.processed_plain_text}[回复意愿:{current_willing:.2f}][概率:{reply_probability * 100:.1f}%]" + ) - response = "" + response = None if random() < reply_probability: - tinking_time_point = round(time.time(), 2) - think_id = 'mt' + str(tinking_time_point) - thinking_message = Message_Thinking(message=message, message_id=think_id) + bot_user_info = UserInfo( + user_id=global_config.BOT_QQ, + user_nickname=global_config.BOT_NICKNAME, + platform=messageinfo.platform, + ) + thinking_time_point = round(time.time(), 2) + think_id = "mt" + str(thinking_time_point) + thinking_message = MessageThinking( + message_id=think_id, + chat_stream=chat, + bot_user_info=bot_user_info, + reply=message, + ) message_manager.add_message(thinking_message) - willing_manager.change_reply_willing_sent(thinking_message.group_id) + willing_manager.change_reply_willing_sent(chat) response, raw_content = await self.gpt.generate_response(message) + # print(f"response: {response}") if response: - container = message_manager.get_container(event.group_id) + # print(f"有response: {response}") + container = message_manager.get_container(chat.stream_id) thinking_message = None # 找到message,删除 + # print(f"开始找思考消息") for msg in container.messages: - if isinstance(msg, Message_Thinking) and msg.message_id == think_id: + if isinstance(msg, MessageThinking) and msg.message_info.message_id == think_id: + # print(f"找到思考消息: {msg}") thinking_message = msg container.messages.remove(msg) - # print(f"\033[1;32m[思考消息删除]\033[0m 已找到思考消息对象，开始删除") break # 如果找不到思考消息，直接返回 if not thinking_message: - logger.warning(f"未找到对应的思考消息，可能已超时被移除") + logger.warning("未找到对应的思考消息，可能已超时被移除") return # 记录开始思考的时间，避免从思考到回复的时间太久 thinking_start_time = thinking_message.thinking_start_time - message_set = MessageSet(event.group_id, global_config.BOT_QQ, - think_id) # 发送消息的id和产生发送消息的message_thinking是一致的 + message_set = MessageSet(chat, think_id) # 计算打字时间，1是为了模拟打字，2是避免多条回复乱序 accu_typing_time = 0 - # print(f"\033[1;32m[开始回复]\033[0m 开始将回复1载入发送容器") mark_head = False for msg in response: # print(f"\033[1;32m[回复内容]\033[0m {msg}") # 通过时间改变时间戳 typing_time = calculate_typing_time(msg) + logger.debug(f"typing_time: {typing_time}") accu_typing_time += typing_time - timepoint = tinking_time_point + accu_typing_time - - bot_message = Message_Sending( - group_id=event.group_id, - user_id=global_config.BOT_QQ, + timepoint = thinking_time_point + accu_typing_time + message_segment = Seg(type="text", data=msg) + # logger.debug(f"message_segment: {message_segment}") + bot_message = MessageSending( message_id=think_id, - raw_message=msg, - plain_text=msg, - processed_plain_text=msg, - user_nickname=global_config.BOT_NICKNAME, - group_name=message.group_name, - time=timepoint, # 记录了回复生成的时间 - thinking_start_time=thinking_start_time, # 记录了思考开始的时间 - reply_message_id=message.message_id + chat_stream=chat, + bot_user_info=bot_user_info, + sender_info=userinfo, + message_segment=message_segment, + reply=message, + is_head=not mark_head, + is_emoji=False, ) - await bot_message.initialize() + print(f"bot_message: {bot_message}") if not mark_head: - bot_message.is_head = True mark_head = True + print(f"添加消息到message_set: {bot_message}") message_set.add_message(bot_message) # message_set 可以直接加入 message_manager # print(f"\033[1;32m[回复]\033[0m 将回复载入发送容器") + + logger.debug("添加message_set到message_manager") + message_manager.add_message(message_set) - bot_response_time = tinking_time_point + bot_response_time = thinking_time_point if random() < global_config.emoji_chance: emoji_raw = await emoji_manager.get_emoji_for_text(response) # 检查是否 <没有找到> emoji if emoji_raw != None: - emoji_path, discription = emoji_raw + emoji_path, description = emoji_raw - emoji_cq = CQCode.create_emoji_cq(emoji_path) + emoji_cq = image_path_to_base64(emoji_path) if random() < 0.5: - bot_response_time = tinking_time_point - 1 + bot_response_time = thinking_time_point - 1 else: bot_response_time = bot_response_time + 1 - bot_message = Message_Sending( - group_id=event.group_id, - user_id=global_config.BOT_QQ, - message_id=0, - raw_message=emoji_cq, - plain_text=emoji_cq, - processed_plain_text=emoji_cq, - detailed_plain_text=discription, - user_nickname=global_config.BOT_NICKNAME, - group_name=message.group_name, - time=bot_response_time, + message_segment = Seg(type="emoji", data=emoji_cq) + bot_message = MessageSending( + message_id=think_id, + chat_stream=chat, + bot_user_info=bot_user_info, + sender_info=userinfo, + message_segment=message_segment, + reply=message, + is_head=False, is_emoji=True, - translate_cq=False, - thinking_start_time=thinking_start_time, - # reply_message_id=message.message_id ) - await bot_message.initialize() message_manager.add_message(bot_message) + emotion = await self.gpt._get_emotion_tags(raw_content) logger.debug(f"为 '{response}' 获取到的情感标签为：{emotion}") valuedict = { - 'happy': 0.5, - 'angry': -1, - 'sad': -0.5, - 'surprised': 0.2, - 'disgusted': -1.5, - 'fearful': -0.7, - 'neutral': 0.1 + "happy": 0.5, + "angry": -1, + "sad": -0.5, + "surprised": 0.2, + "disgusted": -1.5, + "fearful": -0.7, + "neutral": 0.1, } - await relationship_manager.update_relationship_value(message.user_id, - relationship_value=valuedict[emotion[0]]) + await relationship_manager.update_relationship_value( + chat_stream=chat, relationship_value=valuedict[emotion[0]] + ) # 使用情绪管理器更新情绪 self.mood_manager.update_mood_from_emotion(emotion[0], global_config.mood_intensity_factor) - # willing_manager.change_reply_willing_after_sent(event.group_id) + # willing_manager.change_reply_willing_after_sent( + # chat_stream=chat + # ) # 创建全局ChatBot实例 diff --git a/src/plugins/chat/chat_stream.py b/src/plugins/chat/chat_stream.py new file mode 100644 index 00000000..bee67917 --- /dev/null +++ b/src/plugins/chat/chat_stream.py @@ -0,0 +1,226 @@ +import asyncio +import hashlib +import time +import copy +from typing import Dict, Optional + +from loguru import logger + +from ...common.database import Database +from .message_base import GroupInfo, UserInfo + + +class ChatStream: + """聊天流对象，存储一个完整的聊天上下文""" + + def __init__( + self, + stream_id: str, + platform: str, + user_info: UserInfo, + group_info: Optional[GroupInfo] = None, + data: dict = None, + ): + self.stream_id = stream_id + self.platform = platform + self.user_info = user_info + self.group_info = group_info + self.create_time = ( + data.get("create_time", int(time.time())) if data else int(time.time()) + ) + self.last_active_time = ( + data.get("last_active_time", self.create_time) if data else self.create_time + ) + self.saved = False + + def to_dict(self) -> dict: + """转换为字典格式""" + result = { + "stream_id": self.stream_id, + "platform": self.platform, + "user_info": self.user_info.to_dict() if self.user_info else None, + "group_info": self.group_info.to_dict() if self.group_info else None, + "create_time": self.create_time, + "last_active_time": self.last_active_time, + } + return result + + @classmethod + def from_dict(cls, data: dict) -> "ChatStream": + """从字典创建实例""" + user_info = ( + UserInfo(**data.get("user_info", {})) if data.get("user_info") else None + ) + group_info = ( + GroupInfo(**data.get("group_info", {})) if data.get("group_info") else None + ) + + return cls( + stream_id=data["stream_id"], + platform=data["platform"], + user_info=user_info, + group_info=group_info, + data=data, + ) + + def update_active_time(self): + """更新最后活跃时间""" + self.last_active_time = int(time.time()) + self.saved = False + + +class ChatManager: + """聊天管理器，管理所有聊天流""" + + _instance = None + _initialized = False + + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self): + if not self._initialized: + self.streams: Dict[str, ChatStream] = {} # stream_id -> ChatStream + self.db = Database.get_instance() + self._ensure_collection() + self._initialized = True + # 在事件循环中启动初始化 + # asyncio.create_task(self._initialize()) + # # 启动自动保存任务 + # asyncio.create_task(self._auto_save_task()) + + async def _initialize(self): + """异步初始化""" + try: + await self.load_all_streams() + logger.success(f"聊天管理器已启动，已加载 {len(self.streams)} 个聊天流") + except Exception as e: + logger.error(f"聊天管理器启动失败: {str(e)}") + + async def _auto_save_task(self): + """定期自动保存所有聊天流""" + while True: + await asyncio.sleep(300) # 每5分钟保存一次 + try: + await self._save_all_streams() + logger.info("聊天流自动保存完成") + except Exception as e: + logger.error(f"聊天流自动保存失败: {str(e)}") + + def _ensure_collection(self): + """确保数据库集合存在并创建索引""" + if "chat_streams" not in self.db.db.list_collection_names(): + self.db.db.create_collection("chat_streams") + # 创建索引 + self.db.db.chat_streams.create_index([("stream_id", 1)], unique=True) + self.db.db.chat_streams.create_index( + [("platform", 1), ("user_info.user_id", 1), ("group_info.group_id", 1)] + ) + + def _generate_stream_id( + self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None + ) -> str: + """生成聊天流唯一ID""" + if group_info: + # 组合关键信息 + components = [ + platform, + str(group_info.group_id) + ] + else: + components = [ + platform, + str(user_info.user_id), + "private" + ] + + # 使用MD5生成唯一ID + key = "_".join(components) + return hashlib.md5(key.encode()).hexdigest() + + async def get_or_create_stream( + self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None + ) -> ChatStream: + """获取或创建聊天流 + + Args: + platform: 平台标识 + user_info: 用户信息 + group_info: 群组信息（可选） + + Returns: + ChatStream: 聊天流对象 + """ + # 生成stream_id + stream_id = self._generate_stream_id(platform, user_info, group_info) + + # 检查内存中是否存在 + if stream_id in self.streams: + stream = self.streams[stream_id] + # 更新用户信息和群组信息 + stream.update_active_time() + stream=copy.deepcopy(stream) + stream.user_info = user_info + if group_info: + stream.group_info = group_info + return stream + + # 检查数据库中是否存在 + data = self.db.db.chat_streams.find_one({"stream_id": stream_id}) + if data: + stream = ChatStream.from_dict(data) + # 更新用户信息和群组信息 + stream.user_info = user_info + if group_info: + stream.group_info = group_info + stream.update_active_time() + else: + # 创建新的聊天流 + stream = ChatStream( + stream_id=stream_id, + platform=platform, + user_info=user_info, + group_info=group_info, + ) + + # 保存到内存和数据库 + self.streams[stream_id] = stream + await self._save_stream(stream) + return copy.deepcopy(stream) + + def get_stream(self, stream_id: str) -> Optional[ChatStream]: + """通过stream_id获取聊天流""" + return self.streams.get(stream_id) + + def get_stream_by_info( + self, platform: str, user_info: UserInfo, group_info: Optional[GroupInfo] = None + ) -> Optional[ChatStream]: + """通过信息获取聊天流""" + stream_id = self._generate_stream_id(platform, user_info, group_info) + return self.streams.get(stream_id) + + async def _save_stream(self, stream: ChatStream): + """保存聊天流到数据库""" + if not stream.saved: + self.db.db.chat_streams.update_one( + {"stream_id": stream.stream_id}, {"$set": stream.to_dict()}, upsert=True + ) + stream.saved = True + + async def _save_all_streams(self): + """保存所有聊天流""" + for stream in self.streams.values(): + await self._save_stream(stream) + + async def load_all_streams(self): + """从数据库加载所有聊天流""" + all_streams = self.db.db.chat_streams.find({}) + for data in all_streams: + stream = ChatStream.from_dict(data) + self.streams[stream.stream_id] = stream + + +# 创建全局单例 +chat_manager = ChatManager() diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index c37d23a4..88cb31ed 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -1,6 +1,7 @@ import os +import sys from dataclasses import dataclass, field -from typing import Dict, Optional +from typing import Dict, List, Optional import tomli from loguru import logger @@ -12,10 +13,12 @@ from packaging.specifiers import SpecifierSet, InvalidSpecifier @dataclass class BotConfig: """机器人配置类""" + INNER_VERSION: Version = None BOT_QQ: Optional[int] = 1 BOT_NICKNAME: Optional[str] = None + BOT_ALIAS_NAMES: List[str] = field(default_factory=list) # 别名，可以通过这个叫它 # 消息处理相关配置 MIN_TEXT_LENGTH: int = 2 # 最小处理文本长度 @@ -34,8 +37,7 @@ class BotConfig: ban_user_id = set() - build_memory_interval: int = 30 # 记忆构建间隔（秒） - forget_memory_interval: int = 300 # 记忆遗忘间隔（秒） + EMOJI_CHECK_INTERVAL: int = 120 # 表情包检查间隔（分钟） EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） EMOJI_SAVE: bool = True # 偷表情包 @@ -43,6 +45,7 @@ class BotConfig: EMOJI_CHECK_PROMPT: str = "符合公序良俗" # 表情包过滤要求 ban_words = set() + ban_msgs_regex = set() max_response_length: int = 1024 # 最大回复长度 @@ -64,6 +67,8 @@ class BotConfig: enable_advance_output: bool = False # 是否启用高级输出 enable_kuuki_read: bool = True # 是否启用读空气功能 + enable_debug_output: bool = False # 是否启用调试输出 + enable_friend_chat: bool = False # 是否启用好友聊天 mood_update_interval: float = 1.0 # 情绪更新间隔 单位秒 mood_decay_rate: float = 0.95 # 情绪衰减率 @@ -81,23 +86,31 @@ class BotConfig: PROMPT_PERSONALITY = [ "曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧", "是一个女大学生，你有黑色头发，你会刷小红书", - "是一个女大学生，你会刷b站，对ACG文化感兴趣" + "是一个女大学生，你会刷b站，对ACG文化感兴趣", ] - PROMPT_SCHEDULE_GEN="一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" + PROMPT_SCHEDULE_GEN = "一个曾经学习地质,现在学习心理学和脑科学的女大学生，喜欢刷qq，贴吧，知乎和小红书" + + PERSONALITY_1: float = 0.6 # 第一种人格概率 + PERSONALITY_2: float = 0.3 # 第二种人格概率 + PERSONALITY_3: float = 0.1 # 第三种人格概率 - PERSONALITY_1: float = 0.6 # 第一种人格概率 - PERSONALITY_2: float = 0.3 # 第二种人格概率 - PERSONALITY_3: float = 0.1 # 第三种人格概率 - - memory_ban_words: list = field(default_factory=lambda: ['表情包', '图片', '回复', '聊天记录']) # 添加新的配置项默认值 + build_memory_interval: int = 600 # 记忆构建间隔（秒） + forget_memory_interval: int = 600 # 记忆遗忘间隔（秒） + memory_forget_time: int = 24 # 记忆遗忘时间（小时） + memory_forget_percentage: float = 0.01 # 记忆遗忘比例 + memory_compress_rate: float = 0.1 # 记忆压缩率 + memory_ban_words: list = field( + default_factory=lambda: ["表情包", "图片", "回复", "聊天记录"] + ) # 添加新的配置项默认值 + @staticmethod def get_config_dir() -> str: """获取配置文件目录""" current_dir = os.path.dirname(os.path.abspath(__file__)) - root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..')) - config_dir = os.path.join(root_dir, 'config') + root_dir = os.path.abspath(os.path.join(current_dir, "..", "..", "..")) + config_dir = os.path.join(root_dir, "config") if not os.path.exists(config_dir): os.makedirs(config_dir) return config_dir @@ -108,35 +121,32 @@ class BotConfig: Args: value[str]: 版本表达式(字符串) Returns: - SpecifierSet + SpecifierSet """ try: converted = SpecifierSet(value) - except InvalidSpecifier as e: - logger.error( - f"{value} 分类使用了错误的版本约束表达式\n", - "请阅读 https://semver.org/lang/zh-CN/ 修改代码" - ) + except InvalidSpecifier: + logger.error(f"{value} 分类使用了错误的版本约束表达式\n", "请阅读 https://semver.org/lang/zh-CN/ 修改代码") exit(1) return converted @classmethod def get_config_version(cls, toml: dict) -> Version: - """提取配置文件的 SpecifierSet 版本数据 + """提取配置文件的 SpecifierSet 版本数据 Args: toml[dict]: 输入的配置文件字典 Returns: - Version + Version """ - if 'inner' in toml: + if "inner" in toml: try: config_version: str = toml["inner"]["version"] except KeyError as e: - logger.error(f"配置文件中 inner 段 不存在, 这是错误的配置文件") - raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件") + logger.error("配置文件中 inner 段 不存在, 这是错误的配置文件") + raise KeyError(f"配置文件中 inner 段 不存在 {e}, 这是错误的配置文件") from e else: toml["inner"] = {"version": "0.0.0"} config_version = toml["inner"]["version"] @@ -149,7 +159,7 @@ class BotConfig: "请阅读 https://semver.org/lang/zh-CN/ 修改配置，并参考本项目指定的模板进行修改\n" "本项目在不同的版本下有不同的模板，请注意识别" ) - raise InvalidVersion("配置文件中 inner段 的 version 键是错误的版本描述\n") + raise InvalidVersion("配置文件中 inner段 的 version 键是错误的版本描述\n") from e return ver @@ -159,26 +169,26 @@ class BotConfig: config = cls() def personality(parent: dict): - personality_config = parent['personality'] - personality = personality_config.get('prompt_personality') + personality_config = parent["personality"] + personality = personality_config.get("prompt_personality") if len(personality) >= 2: logger.debug(f"载入自定义人格:{personality}") - config.PROMPT_PERSONALITY = personality_config.get('prompt_personality', config.PROMPT_PERSONALITY) + config.PROMPT_PERSONALITY = personality_config.get("prompt_personality", config.PROMPT_PERSONALITY) logger.info(f"载入自定义日程prompt:{personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN)}") - config.PROMPT_SCHEDULE_GEN = personality_config.get('prompt_schedule', config.PROMPT_SCHEDULE_GEN) + config.PROMPT_SCHEDULE_GEN = personality_config.get("prompt_schedule", config.PROMPT_SCHEDULE_GEN) if config.INNER_VERSION in SpecifierSet(">=0.0.2"): - config.PERSONALITY_1 = personality_config.get('personality_1_probability', config.PERSONALITY_1) - config.PERSONALITY_2 = personality_config.get('personality_2_probability', config.PERSONALITY_2) - config.PERSONALITY_3 = personality_config.get('personality_3_probability', config.PERSONALITY_3) + config.PERSONALITY_1 = personality_config.get("personality_1_probability", config.PERSONALITY_1) + config.PERSONALITY_2 = personality_config.get("personality_2_probability", config.PERSONALITY_2) + config.PERSONALITY_3 = personality_config.get("personality_3_probability", config.PERSONALITY_3) def emoji(parent: dict): emoji_config = parent["emoji"] config.EMOJI_CHECK_INTERVAL = emoji_config.get("check_interval", config.EMOJI_CHECK_INTERVAL) config.EMOJI_REGISTER_INTERVAL = emoji_config.get("register_interval", config.EMOJI_REGISTER_INTERVAL) - config.EMOJI_CHECK_PROMPT = emoji_config.get('check_prompt', config.EMOJI_CHECK_PROMPT) - config.EMOJI_SAVE = emoji_config.get('auto_save', config.EMOJI_SAVE) - config.EMOJI_CHECK = emoji_config.get('enable_check', config.EMOJI_CHECK) + config.EMOJI_CHECK_PROMPT = emoji_config.get("check_prompt", config.EMOJI_CHECK_PROMPT) + config.EMOJI_SAVE = emoji_config.get("auto_save", config.EMOJI_SAVE) + config.EMOJI_CHECK = emoji_config.get("enable_check", config.EMOJI_CHECK) def cq_code(parent: dict): cq_code_config = parent["cq_code"] @@ -191,12 +201,16 @@ class BotConfig: config.BOT_QQ = int(bot_qq) config.BOT_NICKNAME = bot_config.get("nickname", config.BOT_NICKNAME) + if config.INNER_VERSION in SpecifierSet(">=0.0.5"): + config.BOT_ALIAS_NAMES = bot_config.get("alias_names", config.BOT_ALIAS_NAMES) + def response(parent: dict): response_config = parent["response"] config.MODEL_R1_PROBABILITY = response_config.get("model_r1_probability", config.MODEL_R1_PROBABILITY) config.MODEL_V3_PROBABILITY = response_config.get("model_v3_probability", config.MODEL_V3_PROBABILITY) - config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", - config.MODEL_R1_DISTILL_PROBABILITY) + config.MODEL_R1_DISTILL_PROBABILITY = response_config.get( + "model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY + ) config.max_response_length = response_config.get("max_response_length", config.max_response_length) def model(parent: dict): @@ -213,7 +227,7 @@ class BotConfig: "llm_emotion_judge", "vlm", "embedding", - "moderation" + "moderation", ] for item in config_list: @@ -222,13 +236,7 @@ class BotConfig: # base_url 的例子： SILICONFLOW_BASE_URL # key 的例子： SILICONFLOW_KEY - cfg_target = { - "name": "", - "base_url": "", - "key": "", - "pri_in": 0, - "pri_out": 0 - } + cfg_target = {"name": "", "base_url": "", "key": "", "pri_in": 0, "pri_out": 0} if config.INNER_VERSION in SpecifierSet("<=0.0.0"): cfg_target = cfg_item @@ -247,7 +255,7 @@ class BotConfig: cfg_target[i] = cfg_item[i] except KeyError as e: logger.error(f"{item} 中的必要字段不存在，请检查") - raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查") + raise KeyError(f"{item} 中的必要字段 {e} 不存在，请检查") from e provider = cfg_item.get("provider") if provider is None: @@ -272,20 +280,30 @@ class BotConfig: if config.INNER_VERSION in SpecifierSet(">=0.0.2"): config.thinking_timeout = msg_config.get("thinking_timeout", config.thinking_timeout) - config.response_willing_amplifier = msg_config.get("response_willing_amplifier", - config.response_willing_amplifier) - config.response_interested_rate_amplifier = msg_config.get("response_interested_rate_amplifier", - config.response_interested_rate_amplifier) + config.response_willing_amplifier = msg_config.get( + "response_willing_amplifier", config.response_willing_amplifier + ) + config.response_interested_rate_amplifier = msg_config.get( + "response_interested_rate_amplifier", config.response_interested_rate_amplifier + ) config.down_frequency_rate = msg_config.get("down_frequency_rate", config.down_frequency_rate) + + if config.INNER_VERSION in SpecifierSet(">=0.0.6"): + config.ban_msgs_regex = msg_config.get("ban_msgs_regex", config.ban_msgs_regex) def memory(parent: dict): memory_config = parent["memory"] config.build_memory_interval = memory_config.get("build_memory_interval", config.build_memory_interval) config.forget_memory_interval = memory_config.get("forget_memory_interval", config.forget_memory_interval) - + # 在版本 >= 0.0.4 时才处理新增的配置项 if config.INNER_VERSION in SpecifierSet(">=0.0.4"): config.memory_ban_words = set(memory_config.get("memory_ban_words", [])) + + if config.INNER_VERSION in SpecifierSet(">=0.0.7"): + config.memory_forget_time = memory_config.get("memory_forget_time", config.memory_forget_time) + config.memory_forget_percentage = memory_config.get("memory_forget_percentage", config.memory_forget_percentage) + config.memory_compress_rate = memory_config.get("memory_compress_rate", config.memory_compress_rate) def mood(parent: dict): mood_config = parent["mood"] @@ -303,10 +321,12 @@ class BotConfig: config.chinese_typo_enable = chinese_typo_config.get("enable", config.chinese_typo_enable) config.chinese_typo_error_rate = chinese_typo_config.get("error_rate", config.chinese_typo_error_rate) config.chinese_typo_min_freq = chinese_typo_config.get("min_freq", config.chinese_typo_min_freq) - config.chinese_typo_tone_error_rate = chinese_typo_config.get("tone_error_rate", - config.chinese_typo_tone_error_rate) - config.chinese_typo_word_replace_rate = chinese_typo_config.get("word_replace_rate", - config.chinese_typo_word_replace_rate) + config.chinese_typo_tone_error_rate = chinese_typo_config.get( + "tone_error_rate", config.chinese_typo_tone_error_rate + ) + config.chinese_typo_word_replace_rate = chinese_typo_config.get( + "word_replace_rate", config.chinese_typo_word_replace_rate + ) def groups(parent: dict): groups_config = parent["groups"] @@ -318,6 +338,9 @@ class BotConfig: others_config = parent["others"] config.enable_advance_output = others_config.get("enable_advance_output", config.enable_advance_output) config.enable_kuuki_read = others_config.get("enable_kuuki_read", config.enable_kuuki_read) + if config.INNER_VERSION in SpecifierSet(">=0.0.7"): + config.enable_debug_output = others_config.get("enable_debug_output", config.enable_debug_output) + config.enable_friend_chat = others_config.get("enable_friend_chat", config.enable_friend_chat) # 版本表达式：>=1.0.0,<2.0.0 # 允许字段：func: method, support: str, notice: str, necessary: bool @@ -325,61 +348,19 @@ class BotConfig: # 例如："notice": "personality 将在 1.3.2 后被移除"，那么在有效版本中的用户就会虽然可以 # 正常执行程序，但是会看到这条自定义提示 include_configs = { - "personality": { - "func": personality, - "support": ">=0.0.0" - }, - "emoji": { - "func": emoji, - "support": ">=0.0.0" - }, - "cq_code": { - "func": cq_code, - "support": ">=0.0.0" - }, - "bot": { - "func": bot, - "support": ">=0.0.0" - }, - "response": { - "func": response, - "support": ">=0.0.0" - }, - "model": { - "func": model, - "support": ">=0.0.0" - }, - "message": { - "func": message, - "support": ">=0.0.0" - }, - "memory": { - "func": memory, - "support": ">=0.0.0", - "necessary": False - }, - "mood": { - "func": mood, - "support": ">=0.0.0" - }, - "keywords_reaction": { - "func": keywords_reaction, - "support": ">=0.0.2", - "necessary": False - }, - "chinese_typo": { - "func": chinese_typo, - "support": ">=0.0.3", - "necessary": False - }, - "groups": { - "func": groups, - "support": ">=0.0.0" - }, - "others": { - "func": others, - "support": ">=0.0.0" - } + "personality": {"func": personality, "support": ">=0.0.0"}, + "emoji": {"func": emoji, "support": ">=0.0.0"}, + "cq_code": {"func": cq_code, "support": ">=0.0.0"}, + "bot": {"func": bot, "support": ">=0.0.0"}, + "response": {"func": response, "support": ">=0.0.0"}, + "model": {"func": model, "support": ">=0.0.0"}, + "message": {"func": message, "support": ">=0.0.0"}, + "memory": {"func": memory, "support": ">=0.0.0", "necessary": False}, + "mood": {"func": mood, "support": ">=0.0.0"}, + "keywords_reaction": {"func": keywords_reaction, "support": ">=0.0.2", "necessary": False}, + "chinese_typo": {"func": chinese_typo, "support": ">=0.0.3", "necessary": False}, + "groups": {"func": groups, "support": ">=0.0.0"}, + "others": {"func": others, "support": ">=0.0.0"}, } # 原地修改，将 字符串版本表达式 转换成 版本对象 @@ -391,7 +372,7 @@ class BotConfig: with open(config_path, "rb") as f: try: toml_dict = tomli.load(f) - except(tomli.TOMLDecodeError) as e: + except tomli.TOMLDecodeError as e: logger.critical(f"配置文件bot_config.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}") exit(1) @@ -406,7 +387,7 @@ class BotConfig: # 检查配置文件版本是否在支持范围内 if config.INNER_VERSION in group_specifierset: # 如果版本在支持范围内，检查是否存在通知 - if 'notice' in include_configs[key]: + if "notice" in include_configs[key]: logger.warning(include_configs[key]["notice"]) include_configs[key]["func"](toml_dict) @@ -420,7 +401,7 @@ class BotConfig: raise InvalidVersion(f"当前程序仅支持以下版本范围: {group_specifierset}") # 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理 - elif "necessary" in include_configs[key] and include_configs[key].get("necessary") == False: + elif "necessary" in include_configs[key] and include_configs[key].get("necessary") is False: # 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理 if key == "keywords_reaction": pass @@ -454,4 +435,8 @@ global_config = BotConfig.load_config(config_path=bot_config_path) if not global_config.enable_advance_output: logger.remove() - pass + +# 调试输出功能 +if global_config.enable_debug_output: + logger.remove() + logger.add(sys.stdout, level="DEBUG") diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index 6016276f..26895006 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -1,24 +1,24 @@ import base64 import html -import os import time from dataclasses import dataclass -from typing import Dict, Optional -from loguru import logger +from typing import Dict, List, Optional, Union import requests # 解析各种CQ码 # 包含CQ码类 import urllib3 +from loguru import logger from nonebot import get_driver from urllib3.util import create_urllib3_context from ..models.utils_model import LLM_request from .config import global_config from .mapper import emojimapper -from .utils_image import storage_emoji, storage_image -from .utils_user import get_user_nickname +from .message_base import Seg +from .utils_user import get_user_nickname,get_groupname +from .message_base import GroupInfo, UserInfo driver = get_driver() config = driver.config @@ -36,65 +36,80 @@ class TencentSSLAdapter(requests.adapters.HTTPAdapter): def init_poolmanager(self, connections, maxsize, block=False): self.poolmanager = urllib3.poolmanager.PoolManager( - num_pools=connections, maxsize=maxsize, - block=block, ssl_context=self.ssl_context) + num_pools=connections, + maxsize=maxsize, + block=block, + ssl_context=self.ssl_context, + ) @dataclass class CQCode: """ CQ码数据类，用于存储和处理CQ码 - + 属性: type: CQ码类型（如'image', 'at', 'face'等） params: CQ码的参数字典 raw_code: 原始CQ码字符串 - translated_plain_text: 经过处理（如AI翻译）后的文本表示 + translated_segments: 经过处理后的Seg对象列表 """ + type: str params: Dict[str, str] - # raw_code: str - group_id: int - user_id: int - group_name: str = "" - user_nickname: str = "" - translated_plain_text: Optional[str] = None + group_info: Optional[GroupInfo] = None + user_info: Optional[UserInfo] = None + translated_segments: Optional[Union[Seg, List[Seg]]] = None reply_message: Dict = None # 存储回复消息 image_base64: Optional[str] = None _llm: Optional[LLM_request] = None def __post_init__(self): """初始化LLM实例""" - self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300) + pass - async def translate(self): - """根据CQ码类型进行相应的翻译处理""" - if self.type == 'text': - self.translated_plain_text = self.params.get('text', '') - elif self.type == 'image': - if self.params.get('sub_type') == '0': - self.translated_plain_text = await self.translate_image() + def translate(self): + """根据CQ码类型进行相应的翻译处理，转换为Seg对象""" + if self.type == "text": + self.translated_segments = Seg( + type="text", data=self.params.get("text", "") + ) + elif self.type == "image": + base64_data = self.translate_image() + if base64_data: + if self.params.get("sub_type") == "0": + self.translated_segments = Seg(type="image", data=base64_data) + else: + self.translated_segments = Seg(type="emoji", data=base64_data) else: - self.translated_plain_text = await self.translate_emoji() - elif self.type == 'at': - user_nickname = get_user_nickname(self.params.get('qq', '')) - if user_nickname: - self.translated_plain_text = f"[@{user_nickname}]" + self.translated_segments = Seg(type="text", data="[图片]") + elif self.type == "at": + user_nickname = get_user_nickname(self.params.get("qq", "")) + self.translated_segments = Seg( + type="text", data=f"[@{user_nickname or '某人'}]" + ) + elif self.type == "reply": + reply_segments = self.translate_reply() + if reply_segments: + self.translated_segments = Seg(type="seglist", data=reply_segments) else: - self.translated_plain_text = "@某人" - elif self.type == 'reply': - self.translated_plain_text = await self.translate_reply() - elif self.type == 'face': - face_id = self.params.get('id', '') - # self.translated_plain_text = f"[表情{face_id}]" - self.translated_plain_text = f"[{emojimapper.get(int(face_id), '表情')}]" - elif self.type == 'forward': - self.translated_plain_text = await self.translate_forward() + self.translated_segments = Seg(type="text", data="[回复某人消息]") + elif self.type == "face": + face_id = self.params.get("id", "") + self.translated_segments = Seg( + type="text", data=f"[{emojimapper.get(int(face_id), '表情')}]" + ) + elif self.type == "forward": + forward_segments = self.translate_forward() + if forward_segments: + self.translated_segments = Seg(type="seglist", data=forward_segments) + else: + self.translated_segments = Seg(type="text", data="[转发消息]") else: - self.translated_plain_text = f"[{self.type}]" + self.translated_segments = Seg(type="text", data=f"[{self.type}]") def get_img(self): - ''' + """ headers = { 'User-Agent': 'QQ/8.9.68.11565 CFNetwork/1220.1 Darwin/20.3.0', 'Accept': 'image/*;q=0.8', @@ -103,18 +118,18 @@ class CQCode: 'Cache-Control': 'no-cache', 'Pragma': 'no-cache' } - ''' + """ # 腾讯专用请求头配置 headers = { - 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.87 Safari/537.36', - 'Accept': 'text/html, application/xhtml xml, */*', - 'Accept-Encoding': 'gbk, GB2312', - 'Accept-Language': 'zh-cn', - 'Content-Type': 'application/x-www-form-urlencoded', - 'Cache-Control': 'no-cache' + "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.87 Safari/537.36", + "Accept": "text/html, application/xhtml xml, */*", + "Accept-Encoding": "gbk, GB2312", + "Accept-Language": "zh-cn", + "Content-Type": "application/x-www-form-urlencoded", + "Cache-Control": "no-cache", } - url = html.unescape(self.params['url']) - if not url.startswith(('http://', 'https://')): + url = html.unescape(self.params["url"]) + if not url.startswith(("http://", "https://")): return None # 创建专用会话 @@ -130,247 +145,215 @@ class CQCode: headers=headers, timeout=15, allow_redirects=True, - stream=True # 流式传输避免大内存问题 + stream=True, # 流式传输避免大内存问题 ) # 腾讯服务器特殊状态码处理 - if response.status_code == 400 and 'multimedia.nt.qq.com.cn' in url: + if response.status_code == 400 and "multimedia.nt.qq.com.cn" in url: return None if response.status_code != 200: raise requests.exceptions.HTTPError(f"HTTP {response.status_code}") # 验证内容类型 - content_type = response.headers.get('Content-Type', '') - if not content_type.startswith('image/'): + content_type = response.headers.get("Content-Type", "") + if not content_type.startswith("image/"): raise ValueError(f"非图片内容类型: {content_type}") # 转换为Base64 - image_base64 = base64.b64encode(response.content).decode('utf-8') + image_base64 = base64.b64encode(response.content).decode("utf-8") self.image_base64 = image_base64 return image_base64 except (requests.exceptions.SSLError, requests.exceptions.HTTPError) as e: if retry == max_retries - 1: logger.error(f"最终请求失败: {str(e)}") - time.sleep(1.5 ** retry) # 指数退避 + time.sleep(1.5**retry) # 指数退避 - except Exception as e: - logger.exception(f"[未知错误]") + except Exception: + logger.exception("[未知错误]") return None return None - async def translate_emoji(self) -> str: - """处理表情包类型的CQ码""" - if 'url' not in self.params: - return '[表情包]' - base64_str = self.get_img() - if base64_str: - # 将 base64 字符串转换为字节类型 - image_bytes = base64.b64decode(base64_str) - storage_emoji(image_bytes) - return await self.get_emoji_description(base64_str) - else: - return '[表情包]' - async def translate_image(self) -> str: - """处理图片类型的CQ码，区分普通图片和表情包""" - # 没有url，直接返回默认文本 - if 'url' not in self.params: - return '[图片]' - base64_str = self.get_img() - if base64_str: - image_bytes = base64.b64decode(base64_str) - storage_image(image_bytes) - return await self.get_image_description(base64_str) - else: - return '[图片]' + def translate_image(self) -> Optional[str]: + """处理图片类型的CQ码，返回base64字符串""" + if "url" not in self.params: + return None + return self.get_img() - async def get_emoji_description(self, image_base64: str) -> str: - """调用AI接口获取表情包描述""" + def translate_forward(self) -> Optional[List[Seg]]: + """处理转发消息，返回Seg列表""" try: - prompt = "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。" - # description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) - description, _ = await self._llm.generate_response_for_image(prompt, image_base64) - return f"[表情包：{description}]" - except Exception as e: - logger.exception(f"AI接口调用失败: {str(e)}") - return "[表情包]" + if "content" not in self.params: + return None - async def get_image_description(self, image_base64: str) -> str: - """调用AI接口获取普通图片描述""" - try: - prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字，可以使用二次元词汇。" - # description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) - description, _ = await self._llm.generate_response_for_image(prompt, image_base64) - return f"[图片：{description}]" - except Exception as e: - logger.exception(f"AI接口调用失败: {str(e)}") - return "[图片]" - - async def translate_forward(self) -> str: - """处理转发消息""" - try: - if 'content' not in self.params: - return '[转发消息]' - - # 解析content内容（需要先反转义） - content = self.unescape(self.params['content']) - # print(f"\033[1;34m[调试信息]\033[0m 转发消息内容: {content}") - # 将字符串形式的列表转换为Python对象 + content = self.unescape(self.params["content"]) import ast + try: messages = ast.literal_eval(content) except ValueError as e: logger.error(f"解析转发消息内容失败: {str(e)}") - return '[转发消息]' + return None - # 处理每条消息 - formatted_messages = [] + formatted_segments = [] for msg in messages: - sender = msg.get('sender', {}) - nickname = sender.get('card') or sender.get('nickname', '未知用户') - - # 获取消息内容并使用Message类处理 - raw_message = msg.get('raw_message', '') - message_array = msg.get('message', []) + sender = msg.get("sender", {}) + nickname = sender.get("card") or sender.get("nickname", "未知用户") + raw_message = msg.get("raw_message", "") + message_array = msg.get("message", []) if message_array and isinstance(message_array, list): - # 检查是否包含嵌套的转发消息 for message_part in message_array: - if message_part.get('type') == 'forward': - content = '[转发消息]' + if message_part.get("type") == "forward": + content_seg = Seg(type="text", data="[转发消息]") break - else: - # 处理普通消息 - if raw_message: - from .message import Message - message_obj = Message( - user_id=msg.get('user_id', 0), - message_id=msg.get('message_id', 0), - raw_message=raw_message, - plain_text=raw_message, - group_id=msg.get('group_id', 0) - ) - await message_obj.initialize() - content = message_obj.processed_plain_text else: - content = '[空消息]' + if raw_message: + from .message_cq import MessageRecvCQ + user_info=UserInfo( + platform='qq', + user_id=msg.get("user_id", 0), + user_nickname=nickname, + ) + group_info=GroupInfo( + platform='qq', + group_id=msg.get("group_id", 0), + group_name=get_groupname(msg.get("group_id", 0)) + ) + + message_obj = MessageRecvCQ( + message_id=msg.get("message_id", 0), + user_info=user_info, + raw_message=raw_message, + plain_text=raw_message, + group_info=group_info, + ) + content_seg = Seg( + type="seglist", data=[message_obj.message_segment] + ) + else: + content_seg = Seg(type="text", data="[空消息]") else: - # 处理普通消息 if raw_message: - from .message import Message - message_obj = Message( - user_id=msg.get('user_id', 0), - message_id=msg.get('message_id', 0), + from .message_cq import MessageRecvCQ + + user_info=UserInfo( + platform='qq', + user_id=msg.get("user_id", 0), + user_nickname=nickname, + ) + group_info=GroupInfo( + platform='qq', + group_id=msg.get("group_id", 0), + group_name=get_groupname(msg.get("group_id", 0)) + ) + message_obj = MessageRecvCQ( + message_id=msg.get("message_id", 0), + user_info=user_info, raw_message=raw_message, plain_text=raw_message, - group_id=msg.get('group_id', 0) + group_info=group_info, + ) + content_seg = Seg( + type="seglist", data=[message_obj.message_segment] ) - await message_obj.initialize() - content = message_obj.processed_plain_text else: - content = '[空消息]' + content_seg = Seg(type="text", data="[空消息]") - formatted_msg = f"{nickname}: {content}" - formatted_messages.append(formatted_msg) + formatted_segments.append(Seg(type="text", data=f"{nickname}: ")) + formatted_segments.append(content_seg) + formatted_segments.append(Seg(type="text", data="\n")) - # 合并所有消息 - combined_messages = '\n'.join(formatted_messages) - logger.debug(f"合并后的转发消息: {combined_messages}") - return f"[转发消息:\n{combined_messages}]" + return formatted_segments except Exception as e: - logger.exception("处理转发消息失败") - return '[转发消息]' + logger.error(f"处理转发消息失败: {str(e)}") + return None - async def translate_reply(self) -> str: - """处理回复类型的CQ码""" + def translate_reply(self) -> Optional[List[Seg]]: + """处理回复类型的CQ码，返回Seg列表""" + from .message_cq import MessageRecvCQ - # 创建Message对象 - from .message import Message - if self.reply_message == None: - # print(f"\033[1;31m[错误]\033[0m 回复消息为空") - return '[回复某人消息]' + if self.reply_message is None: + return None if self.reply_message.sender.user_id: - message_obj = Message( - user_id=self.reply_message.sender.user_id, + + message_obj = MessageRecvCQ( + user_info=UserInfo(user_id=self.reply_message.sender.user_id,user_nickname=self.reply_message.sender.nickname), message_id=self.reply_message.message_id, raw_message=str(self.reply_message.message), - group_id=self.group_id + group_info=GroupInfo(group_id=self.reply_message.group_id), ) - await message_obj.initialize() - if message_obj.user_id == global_config.BOT_QQ: - return f"[回复 {global_config.BOT_NICKNAME} 的消息: {message_obj.processed_plain_text}]" - else: - return f"[回复 {self.reply_message.sender.nickname} 的消息: {message_obj.processed_plain_text}]" + + segments = [] + if message_obj.message_info.user_info.user_id == global_config.BOT_QQ: + segments.append( + Seg( + type="text", data=f"[回复 {global_config.BOT_NICKNAME} 的消息: " + ) + ) + else: + segments.append( + Seg( + type="text", + data=f"[回复 {self.reply_message.sender.nickname} 的消息: ", + ) + ) + + segments.append(Seg(type="seglist", data=[message_obj.message_segment])) + segments.append(Seg(type="text", data="]")) + return segments else: - logger.error("回复消息的sender.user_id为空") - return '[回复某人消息]' + return None @staticmethod def unescape(text: str) -> str: """反转义CQ码中的特殊字符""" - return text.replace(',', ',') \ - .replace('[', '[') \ - .replace(']', ']') \ - .replace('&', '&') - - @staticmethod - def create_emoji_cq(file_path: str) -> str: - """ - 创建表情包CQ码 - Args: - file_path: 本地表情包文件路径 - Returns: - 表情包CQ码字符串 - """ - # 确保使用绝对路径 - abs_path = os.path.abspath(file_path) - # 转义特殊字符 - escaped_path = abs_path.replace('&', '&') \ - .replace('[', '[') \ - .replace(']', ']') \ - .replace(',', ',') - # 生成CQ码，设置sub_type=1表示这是表情包 - return f"[CQ:image,file=file:///{escaped_path},sub_type=1]" - + return ( + text.replace(",", ",") + .replace("[", "[") + .replace("]", "]") + .replace("&", "&") + ) class CQCode_tool: @staticmethod - async def cq_from_dict_to_class(cq_code: Dict, reply: Optional[Dict] = None) -> CQCode: + def cq_from_dict_to_class(cq_code: Dict,msg ,reply: Optional[Dict] = None) -> CQCode: """ 将CQ码字典转换为CQCode对象 - + Args: cq_code: CQ码字典 + msg: MessageCQ对象 reply: 回复消息的字典（可选） - + Returns: CQCode对象 """ # 处理字典形式的CQ码 # 从cq_code字典中获取type字段的值,如果不存在则默认为'text' - cq_type = cq_code.get('type', 'text') + cq_type = cq_code.get("type", "text") params = {} - if cq_type == 'text': - params['text'] = cq_code.get('data', {}).get('text', '') + if cq_type == "text": + params["text"] = cq_code.get("data", {}).get("text", "") else: - params = cq_code.get('data', {}) + params = cq_code.get("data", {}) instance = CQCode( type=cq_type, params=params, - group_id=0, - user_id=0, + group_info=msg.message_info.group_info, + user_info=msg.message_info.user_info, reply_message=reply ) # 进行翻译处理 - await instance.translate() + instance.translate() return instance @staticmethod @@ -384,5 +367,64 @@ class CQCode_tool: """ return f"[CQ:reply,id={message_id}]" + @staticmethod + def create_emoji_cq(file_path: str) -> str: + """ + 创建表情包CQ码 + Args: + file_path: 本地表情包文件路径 + Returns: + 表情包CQ码字符串 + """ + # 确保使用绝对路径 + abs_path = os.path.abspath(file_path) + # 转义特殊字符 + escaped_path = ( + abs_path.replace("&", "&") + .replace("[", "[") + .replace("]", "]") + .replace(",", ",") + ) + # 生成CQ码，设置sub_type=1表示这是表情包 + return f"[CQ:image,file=file:///{escaped_path},sub_type=1]" + + @staticmethod + def create_emoji_cq_base64(base64_data: str) -> str: + """ + 创建表情包CQ码 + Args: + base64_data: base64编码的表情包数据 + Returns: + 表情包CQ码字符串 + """ + # 转义base64数据 + escaped_base64 = ( + base64_data.replace("&", "&") + .replace("[", "[") + .replace("]", "]") + .replace(",", ",") + ) + # 生成CQ码，设置sub_type=1表示这是表情包 + return f"[CQ:image,file=base64://{escaped_base64},sub_type=1]" + + @staticmethod + def create_image_cq_base64(base64_data: str) -> str: + """ + 创建表情包CQ码 + Args: + base64_data: base64编码的表情包数据 + Returns: + 表情包CQ码字符串 + """ + # 转义base64数据 + escaped_base64 = ( + base64_data.replace("&", "&") + .replace("[", "[") + .replace("]", "]") + .replace(",", ",") + ) + # 生成CQ码，设置sub_type=1表示这是表情包 + return f"[CQ:image,file=base64://{escaped_base64},sub_type=0]" + cq_code_tool = CQCode_tool() diff --git a/src/plugins/chat/emoji_manager.py b/src/plugins/chat/emoji_manager.py index 1e8c7a05..a2acb187 100644 --- a/src/plugins/chat/emoji_manager.py +++ b/src/plugins/chat/emoji_manager.py @@ -1,9 +1,11 @@ import asyncio +import base64 +import hashlib import os import random import time import traceback -from typing import Optional +from typing import Optional, Tuple from loguru import logger from nonebot import get_driver @@ -11,11 +13,12 @@ from nonebot import get_driver from ...common.database import Database from ..chat.config import global_config from ..chat.utils import get_embedding -from ..chat.utils_image import image_path_to_base64 +from ..chat.utils_image import ImageManager, image_path_to_base64 from ..models.utils_model import LLM_request driver = get_driver() config = driver.config +image_manager = ImageManager() class EmojiManager: @@ -33,7 +36,7 @@ class EmojiManager: self.db = Database.get_instance() self._scan_task = None self.vlm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000) - self.llm_emotion_judge = LLM_request(model=global_config.llm_normal_minor, max_tokens=60, + self.llm_emotion_judge = LLM_request(model=global_config.llm_emotion_judge, max_tokens=60, temperature=0.8) # 更高的温度，更少的token（后续可以根据情绪来调整温度） @@ -51,8 +54,8 @@ class EmojiManager: self._initialized = True # 启动时执行一次完整性检查 self.check_emoji_file_integrity() - except Exception as e: - logger.exception(f"初始化表情管理器失败") + except Exception: + logger.exception("初始化表情管理器失败") def _ensure_db(self): """确保数据库已初始化""" @@ -76,7 +79,6 @@ class EmojiManager: if 'emoji' not in self.db.db.list_collection_names(): self.db.db.create_collection('emoji') self.db.db.emoji.create_index([('embedding', '2dsphere')]) - self.db.db.emoji.create_index([('tags', 1)]) self.db.db.emoji.create_index([('filename', 1)], unique=True) def record_usage(self, emoji_id: str): @@ -88,9 +90,9 @@ class EmojiManager: {'$inc': {'usage_count': 1}} ) except Exception as e: - logger.exception(f"记录表情使用失败") - - async def get_emoji_for_text(self, text: str) -> Optional[str]: + logger.error(f"记录表情使用失败: {str(e)}") + + async def get_emoji_for_text(self, text: str) -> Optional[Tuple[str,str]]: """根据文本内容获取相关表情包 Args: text: 输入文本 @@ -117,7 +119,7 @@ class EmojiManager: try: # 获取所有表情包 - all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1})) + all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'description': 1})) if not all_emojis: logger.warning("数据库中没有任何表情包") @@ -144,25 +146,26 @@ class EmojiManager: emoji_similarities.sort(key=lambda x: x[1], reverse=True) # 获取前3个最相似的表情包 - top_3_emojis = emoji_similarities[:3] - - if not top_3_emojis: + top_10_emojis = emoji_similarities[:10 if len(emoji_similarities) > 10 else len(emoji_similarities)] + + if not top_10_emojis: logger.warning("未找到匹配的表情包") return None # 从前3个中随机选择一个 - selected_emoji, similarity = random.choice(top_3_emojis) - + selected_emoji, similarity = random.choice(top_10_emojis) + if selected_emoji and 'path' in selected_emoji: # 更新使用次数 self.db.db.emoji.update_one( {'_id': selected_emoji['_id']}, {'$inc': {'usage_count': 1}} ) + logger.success( - f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})") + f"找到匹配的表情包: {selected_emoji.get('description', '无描述')} (相似度: {similarity:.4f})") # 稍微改一下文本描述，不然容易产生幻觉，描述已经包含 表情包 了 - return selected_emoji['path'], "[ %s ]" % selected_emoji.get('discription', '无描述') + return selected_emoji['path'], "[ %s ]" % selected_emoji.get('description', '无描述') except Exception as search_error: logger.error(f"搜索表情包失败: {str(search_error)}") @@ -175,14 +178,15 @@ class EmojiManager: return None async def _get_emoji_discription(self, image_base64: str) -> str: - """获取表情包的标签""" + """获取表情包的标签，使用image_manager的描述生成功能""" + try: - prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容、上面的文字和表情包所表达的情感，可以使用二次元词汇' - - content, _ = await self.vlm.generate_response_for_image(prompt, image_base64) - logger.debug(f"输出描述: {content}") - return content - + # 使用image_manager获取描述，去掉前后的方括号和"表情包："前缀 + description = await image_manager.get_emoji_description(image_base64) + # 去掉[表情包：xxx]的格式，只保留描述内容 + description = description.strip('[]').replace('表情包：', '') + return description + except Exception as e: logger.error(f"获取标签失败: {str(e)}") return None @@ -203,7 +207,7 @@ class EmojiManager: try: prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。' - content, _ = await self.llm_emotion_judge.generate_response_async(prompt) + content, _ = await self.llm_emotion_judge.generate_response_async(prompt,temperature=1.5) logger.info(f"输出描述: {content}") return content @@ -223,49 +227,109 @@ class EmojiManager: for filename in files_to_process: image_path = os.path.join(emoji_dir, filename) - - # 检查是否已经注册过 - existing_emoji = self.db.db['emoji'].find_one({'filename': filename}) - if existing_emoji: - continue - - # 压缩图片并获取base64编码 + + # 获取图片的base64编码和哈希值 image_base64 = image_path_to_base64(image_path) if image_base64 is None: os.remove(image_path) continue + + image_bytes = base64.b64decode(image_base64) + image_hash = hashlib.md5(image_bytes).hexdigest() + + # 检查是否已经注册过 + existing_emoji = self.db.db['emoji'].find_one({'filename': filename}) + description = None + + if existing_emoji: + # 即使表情包已存在，也检查是否需要同步到images集合 + description = existing_emoji.get('discription') + # 检查是否在images集合中存在 + existing_image = image_manager.db.db.images.find_one({'hash': image_hash}) + if not existing_image: + # 同步到images集合 + image_doc = { + 'hash': image_hash, + 'path': image_path, + 'type': 'emoji', + 'description': description, + 'timestamp': int(time.time()) + } + image_manager.db.db.images.update_one( + {'hash': image_hash}, + {'$set': image_doc}, + upsert=True + ) + # 保存描述到image_descriptions集合 + image_manager._save_description_to_db(image_hash, description, 'emoji') + logger.success(f"同步已存在的表情包到images集合: {filename}") + continue + + # 检查是否在images集合中已有描述 + existing_description = image_manager._get_description_from_db(image_hash, 'emoji') + + if existing_description: + description = existing_description + else: + # 获取表情包的描述 + description = await self._get_emoji_discription(image_base64) + + - # 获取表情包的描述 - discription = await self._get_emoji_discription(image_base64) if global_config.EMOJI_CHECK: check = await self._check_emoji(image_base64) if '是' not in check: os.remove(image_path) - logger.info(f"描述: {discription}") + logger.info(f"描述: {description}") + + logger.info(f"描述: {description}") logger.info(f"其不满足过滤规则，被剔除 {check}") continue logger.info(f"check通过 {check}") - if discription is not None: - embedding = await get_embedding(discription) + if description is not None: + embedding = await get_embedding(description) + + if description is not None: + embedding = await get_embedding(description) + # 准备数据库记录 emoji_record = { 'filename': filename, 'path': image_path, 'embedding': embedding, - 'discription': discription, + 'discription': description, + 'hash': image_hash, 'timestamp': int(time.time()) } - - # 保存到数据库 + + # 保存到emoji数据库 self.db.db['emoji'].insert_one(emoji_record) logger.success(f"注册新表情包: {filename}") - logger.info(f"描述: {discription}") + logger.info(f"描述: {description}") + + + # 保存到images数据库 + image_doc = { + 'hash': image_hash, + 'path': image_path, + 'type': 'emoji', + 'description': description, + 'timestamp': int(time.time()) + } + image_manager.db.db.images.update_one( + {'hash': image_hash}, + {'$set': image_doc}, + upsert=True + ) + # 保存描述到image_descriptions集合 + image_manager._save_description_to_db(image_hash, description, 'emoji') + logger.success(f"同步保存到images集合: {filename}") else: logger.warning(f"跳过表情包: {filename}") - except Exception as e: - logger.exception(f"扫描表情包失败") + except Exception: + logger.exception("扫描表情包失败") async def _periodic_scan(self, interval_MINS: int = 10): """定期扫描新表情包""" @@ -332,5 +396,7 @@ class EmojiManager: # 创建全局单例 + emoji_manager = EmojiManager() + diff --git a/src/plugins/chat/llm_generator.py b/src/plugins/chat/llm_generator.py index f40c9a44..46dc34e9 100644 --- a/src/plugins/chat/llm_generator.py +++ b/src/plugins/chat/llm_generator.py @@ -8,7 +8,7 @@ from loguru import logger from ...common.database import Database from ..models.utils_model import LLM_request from .config import global_config -from .message import Message +from .message import MessageRecv, MessageThinking, Message from .prompt_builder import prompt_builder from .relationship_manager import relationship_manager from .utils import process_llm_response @@ -19,58 +19,89 @@ config = driver.config class ResponseGenerator: def __init__(self): - self.model_r1 = LLM_request(model=global_config.llm_reasoning, temperature=0.7,max_tokens=1000,stream=True) - self.model_v3 = LLM_request(model=global_config.llm_normal, temperature=0.7,max_tokens=1000) - self.model_r1_distill = LLM_request(model=global_config.llm_reasoning_minor, temperature=0.7,max_tokens=1000) - self.model_v25 = LLM_request(model=global_config.llm_normal_minor, temperature=0.7,max_tokens=1000) + self.model_r1 = LLM_request( + model=global_config.llm_reasoning, + temperature=0.7, + max_tokens=1000, + stream=True, + ) + self.model_v3 = LLM_request( + model=global_config.llm_normal, temperature=0.7, max_tokens=1000 + ) + self.model_r1_distill = LLM_request( + model=global_config.llm_reasoning_minor, temperature=0.7, max_tokens=1000 + ) + self.model_v25 = LLM_request( + model=global_config.llm_normal_minor, temperature=0.7, max_tokens=1000 + ) self.db = Database.get_instance() - self.current_model_type = 'r1' # 默认使用 R1 + self.current_model_type = "r1" # 默认使用 R1 - async def generate_response(self, message: Message) -> Optional[Union[str, List[str]]]: + async def generate_response( + self, message: MessageThinking + ) -> Optional[Union[str, List[str]]]: """根据当前模型类型选择对应的生成函数""" # 从global_config中获取模型概率值并选择模型 rand = random.random() if rand < global_config.MODEL_R1_PROBABILITY: - self.current_model_type = 'r1' + self.current_model_type = "r1" current_model = self.model_r1 - elif rand < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY: - self.current_model_type = 'v3' + elif ( + rand + < global_config.MODEL_R1_PROBABILITY + global_config.MODEL_V3_PROBABILITY + ): + self.current_model_type = "v3" current_model = self.model_v3 else: - self.current_model_type = 'r1_distill' + self.current_model_type = "r1_distill" current_model = self.model_r1_distill logger.info(f"{global_config.BOT_NICKNAME}{self.current_model_type}思考中") - - model_response = await self._generate_response_with_model(message, current_model) - raw_content=model_response + + model_response = await self._generate_response_with_model( + message, current_model + ) + raw_content = model_response + + # print(f"raw_content: {raw_content}") + # print(f"model_response: {model_response}") if model_response: logger.info(f'{global_config.BOT_NICKNAME}的回复是：{model_response}') model_response = await self._process_response(model_response) if model_response: + return model_response, raw_content + return None, raw_content - return model_response ,raw_content - return None,raw_content - - async def _generate_response_with_model(self, message: Message, model: LLM_request) -> Optional[str]: + async def _generate_response_with_model( + self, message: MessageThinking, model: LLM_request + ) -> Optional[str]: """使用指定的模型生成回复""" - sender_name = message.user_nickname or f"用户{message.user_id}" - if message.user_cardname: - sender_name=f"[({message.user_id}){message.user_nickname}]{message.user_cardname}" - + sender_name = ( + message.chat_stream.user_info.user_nickname + or f"用户{message.chat_stream.user_info.user_id}" + ) + if message.chat_stream.user_info.user_cardname: + sender_name = f"[({message.chat_stream.user_info.user_id}){message.chat_stream.user_info.user_nickname}]{message.chat_stream.user_info.user_cardname}" + # 获取关系值 - relationship_value = relationship_manager.get_relationship(message.user_id).relationship_value if relationship_manager.get_relationship(message.user_id) else 0.0 + relationship_value = ( + relationship_manager.get_relationship( + message.chat_stream + ).relationship_value + if relationship_manager.get_relationship(message.chat_stream) + else 0.0 + ) if relationship_value != 0.0: # print(f"\033[1;32m[关系管理]\033[0m 回复中_当前关系值: {relationship_value}") pass - + # 构建prompt prompt, prompt_check = await prompt_builder._build_prompt( message_txt=message.processed_plain_text, sender_name=sender_name, relationship_value=relationship_value, - group_id=message.group_id + stream_id=message.chat_stream.stream_id, ) # 读空气模块 简化逻辑，先停用 @@ -94,9 +125,9 @@ class ResponseGenerator: try: content, reasoning_content = await model.generate_response(prompt) except Exception: - logger.exception(f"生成回复时出错") + logger.exception("生成回复时出错") return None - + # 保存到数据库 self._save_to_db( message=message, @@ -108,54 +139,73 @@ class ResponseGenerator: reasoning_content=reasoning_content, # reasoning_content_check=reasoning_content_check if global_config.enable_kuuki_read else "" ) - + return content # def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str, # content: str, content_check: str, reasoning_content: str, reasoning_content_check: str): - def _save_to_db(self, message: Message, sender_name: str, prompt: str, prompt_check: str, - content: str, reasoning_content: str,): + def _save_to_db( + self, + message: MessageRecv, + sender_name: str, + prompt: str, + prompt_check: str, + content: str, + reasoning_content: str, + ): """保存对话记录到数据库""" - self.db.db.reasoning_logs.insert_one({ - 'time': time.time(), - 'group_id': message.group_id, - 'user': sender_name, - 'message': message.processed_plain_text, - 'model': self.current_model_type, - # 'reasoning_check': reasoning_content_check, - # 'response_check': content_check, - 'reasoning': reasoning_content, - 'response': content, - 'prompt': prompt, - 'prompt_check': prompt_check - }) + self.db.db.reasoning_logs.insert_one( + { + "time": time.time(), + "chat_id": message.chat_stream.stream_id, + "user": sender_name, + "message": message.processed_plain_text, + "model": self.current_model_type, + # 'reasoning_check': reasoning_content_check, + # 'response_check': content_check, + "reasoning": reasoning_content, + "response": content, + "prompt": prompt, + "prompt_check": prompt_check, + } + ) async def _get_emotion_tags(self, content: str) -> List[str]: """提取情感标签""" try: - prompt = f'''请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出 + prompt = f"""请从以下内容中，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签并输出 只输出标签就好，不要输出其他内容: 内容：{content} 输出： - ''' + """ content, _ = await self.model_v25.generate_response(prompt) - content=content.strip() - if content in ['happy','angry','sad','surprised','disgusted','fearful','neutral']: + content = content.strip() + if content in [ + "happy", + "angry", + "sad", + "surprised", + "disgusted", + "fearful", + "neutral", + ]: return [content] else: return ["neutral"] - - except Exception: - logger.exception(f"获取情感标签时出错") + + except Exception as e: + print(f"获取情感标签时出错: {e}") return ["neutral"] - + async def _process_response(self, content: str) -> Tuple[List[str], List[str]]: """处理响应内容，返回处理后的内容和情感标签""" if not content: return None, [] - + processed_response = process_llm_response(content) + # print(f"得到了处理后的llm返回{processed_response}") + return processed_response diff --git a/src/plugins/chat/message.py b/src/plugins/chat/message.py index f1fc5569..626e7cf4 100644 --- a/src/plugins/chat/message.py +++ b/src/plugins/chat/message.py @@ -1,242 +1,439 @@ import time +import html +import re +import json from dataclasses import dataclass -from typing import Dict, ForwardRef, List, Optional +from typing import Dict, List, Optional import urllib3 +from loguru import logger -from .cq_code import CQCode, cq_code_tool -from .utils_cq import parse_cq_code -from .utils_user import get_groupname, get_user_cardname, get_user_nickname +from .utils_image import image_manager + +from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase +from .chat_stream import ChatStream, chat_manager -Message = ForwardRef('Message') # 添加这行 # 禁用SSL警告 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) -#这个类是消息数据类，用于存储和管理消息数据。 -#它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。 -#它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。 - +# 这个类是消息数据类，用于存储和管理消息数据。 +# 它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。 +# 它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。 @dataclass -class Message: - """消息数据类""" - message_id: int = None - time: float = None +class Message(MessageBase): + chat_stream: ChatStream=None + reply: Optional['Message'] = None + detailed_plain_text: str = "" + processed_plain_text: str = "" - group_id: int = None - group_name: str = None # 群名称 - - user_id: int = None - user_nickname: str = None # 用户昵称 - user_cardname: str = None # 用户群昵称 - - raw_message: str = None # 原始消息，包含未解析的cq码 - plain_text: str = None # 纯文本 - - reply_message: Dict = None # 存储 回复的 源消息 - - # 延迟初始化字段 - _initialized: bool = False - message_segments: List[Dict] = None # 存储解析后的消息片段 - processed_plain_text: str = None # 用于存储处理后的plain_text - detailed_plain_text: str = None # 用于存储详细可读文本 - - # 状态标志 - is_emoji: bool = False - has_emoji: bool = False - translate_cq: bool = True - - async def initialize(self): - """显式异步初始化方法（必须调用）""" - if self._initialized: - return - - # 异步获取补充信息 - self.group_name = self.group_name or get_groupname(self.group_id) - self.user_nickname = self.user_nickname or get_user_nickname(self.user_id) - self.user_cardname = self.user_cardname or get_user_cardname(self.user_id) - - # 消息解析 - if self.raw_message: - if not isinstance(self,Message_Sending): - self.message_segments = await self.parse_message_segments(self.raw_message) - self.processed_plain_text = ' '.join( - seg.translated_plain_text - for seg in self.message_segments - ) - - # 构建详细文本 - if self.time is None: - self.time = int(time.time()) - time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(self.time)) - name = ( - f"{self.user_nickname}(ta的昵称:{self.user_cardname},ta的id:{self.user_id})" - if self.user_cardname - else f"{self.user_nickname or f'用户{self.user_id}'}" + def __init__( + self, + message_id: str, + time: int, + chat_stream: ChatStream, + user_info: UserInfo, + message_segment: Optional[Seg] = None, + reply: Optional['MessageRecv'] = None, + detailed_plain_text: str = "", + processed_plain_text: str = "", + ): + # 构造基础消息信息 + message_info = BaseMessageInfo( + platform=chat_stream.platform, + message_id=message_id, + time=time, + group_info=chat_stream.group_info, + user_info=user_info ) - if isinstance(self,Message_Sending) and self.is_emoji: - self.detailed_plain_text = f"[{time_str}] {name}: {self.detailed_plain_text}\n" - else: - self.detailed_plain_text = f"[{time_str}] {name}: {self.processed_plain_text}\n" - self._initialized = True + # 调用父类初始化 + super().__init__( + message_info=message_info, + message_segment=message_segment, + raw_message=None + ) + + self.chat_stream = chat_stream + # 文本处理相关属性 + self.processed_plain_text = processed_plain_text + self.detailed_plain_text = detailed_plain_text + + # 回复消息 + self.reply = reply + + +@dataclass +class MessageRecv(Message): + """接收消息类，用于处理从MessageCQ序列化的消息""" + + def __init__(self, message_dict: Dict): + """从MessageCQ的字典初始化 + + Args: + message_dict: MessageCQ序列化后的字典 + """ + self.message_info = BaseMessageInfo.from_dict(message_dict.get('message_info', {})) + + message_segment = message_dict.get('message_segment', {}) + + if message_segment.get('data','') == '[json]': + # 提取json消息中的展示信息 + pattern = r'\[CQ:json,data=(?P.+?)\]' + match = re.search(pattern, message_dict.get('raw_message','')) + raw_json = html.unescape(match.group('json_data')) + try: + json_message = json.loads(raw_json) + except json.JSONDecodeError: + json_message = {} + message_segment['data'] = json_message.get('prompt','') + + self.message_segment = Seg.from_dict(message_dict.get('message_segment', {})) + self.raw_message = message_dict.get('raw_message') + + # 处理消息内容 + self.processed_plain_text = "" # 初始化为空字符串 + self.detailed_plain_text = "" # 初始化为空字符串 + self.is_emoji=False - async def parse_message_segments(self, message: str) -> List[CQCode]: - """ - 将消息解析为片段列表，包括纯文本和CQ码 - 返回的列表中每个元素都是字典，包含： - - cq_code_list:分割出的聊天对象，包括文本和CQ码 - - trans_list:翻译后的对象列表 - """ - # print(f"\033[1;34m[调试信息]\033[0m 正在处理消息: {message}") - cq_code_dict_list = [] - trans_list = [] - - start = 0 - while True: - # 查找下一个CQ码的开始位置 - cq_start = message.find('[CQ:', start) - #如果没有cq码，直接返回文本内容 - if cq_start == -1: - # 如果没有找到更多CQ码，添加剩余文本 - if start < len(message): - text = message[start:].strip() - if text: # 只添加非空文本 - cq_code_dict_list.append(parse_cq_code(text)) - break - # 添加CQ码前的文本 - if cq_start > start: - text = message[start:cq_start].strip() - if text: # 只添加非空文本 - cq_code_dict_list.append(parse_cq_code(text)) - # 查找CQ码的结束位置 - cq_end = message.find(']', cq_start) - if cq_end == -1: - # CQ码未闭合，作为普通文本处理 - text = message[cq_start:].strip() - if text: - cq_code_dict_list.append(parse_cq_code(text)) - break - cq_code = message[cq_start:cq_end + 1] - - #将cq_code解析成字典 - cq_code_dict_list.append(parse_cq_code(cq_code)) - # 更新start位置到当前CQ码之后 - start = cq_end + 1 - - # print(f"\033[1;34m[调试信息]\033[0m 提取的消息对象：列表: {cq_code_dict_list}") - - #判定是否是表情包消息，以及是否含有表情包 - if len(cq_code_dict_list) == 1 and cq_code_dict_list[0]['type'] == 'image': - self.is_emoji = True - self.has_emoji_emoji = True - else: - for segment in cq_code_dict_list: - if segment['type'] == 'image' and segment['data'].get('sub_type') == '1': - self.has_emoji_emoji = True - break - - - #翻译作为字典的CQ码 - for _code_item in cq_code_dict_list: - message_obj = await cq_code_tool.cq_from_dict_to_class(_code_item,reply = self.reply_message) - trans_list.append(message_obj) - return trans_list + + def update_chat_stream(self,chat_stream:ChatStream): + self.chat_stream=chat_stream + + async def process(self) -> None: + """处理消息内容，生成纯文本和详细文本 -class Message_Thinking: - """消息思考类""" - def __init__(self, message: Message,message_id: str): - # 复制原始消息的基本属性 - self.group_id = message.group_id - self.user_id = message.user_id - self.user_nickname = message.user_nickname - self.user_cardname = message.user_cardname - self.group_name = message.group_name - - self.message_id = message_id - - # 思考状态相关属性 + 这个方法必须在创建实例后显式调用，因为它包含异步操作。 + """ + self.processed_plain_text = await self._process_message_segments( + self.message_segment + ) + self.detailed_plain_text = self._generate_detailed_text() + + async def _process_message_segments(self, segment: Seg) -> str: + """递归处理消息段，转换为文字描述 + + Args: + segment: 要处理的消息段 + + Returns: + str: 处理后的文本 + """ + if segment.type == "seglist": + # 处理消息段列表 + segments_text = [] + for seg in segment.data: + processed = await self._process_message_segments(seg) + if processed: + segments_text.append(processed) + return " ".join(segments_text) + else: + # 处理单个消息段 + return await self._process_single_segment(segment) + + async def _process_single_segment(self, seg: Seg) -> str: + """处理单个消息段 + + Args: + seg: 要处理的消息段 + + Returns: + str: 处理后的文本 + """ + try: + if seg.type == "text": + return seg.data + elif seg.type == "image": + # 如果是base64图片数据 + if isinstance(seg.data, str): + return await image_manager.get_image_description(seg.data) + return "[图片]" + elif seg.type == "emoji": + self.is_emoji = True + if isinstance(seg.data, str): + return await image_manager.get_emoji_description(seg.data) + return "[表情]" + else: + return f"[{seg.type}:{str(seg.data)}]" + except Exception as e: + logger.error( + f"处理消息段失败: {str(e)}, 类型: {seg.type}, 数据: {seg.data}" + ) + return f"[处理失败的{seg.type}消息]" + + def _generate_detailed_text(self) -> str: + """生成详细文本，包含时间和用户信息""" + time_str = time.strftime( + "%m-%d %H:%M:%S", time.localtime(self.message_info.time) + ) + user_info = self.message_info.user_info + name = ( + f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})" + if user_info.user_cardname != "" + else f"{user_info.user_nickname}(ta的id:{user_info.user_id})" + ) + return f"[{time_str}] {name}: {self.processed_plain_text}\n" + + +@dataclass +class MessageProcessBase(Message): + """消息处理基类，用于处理中和发送中的消息""" + + def __init__( + self, + message_id: str, + chat_stream: ChatStream, + bot_user_info: UserInfo, + message_segment: Optional[Seg] = None, + reply: Optional["MessageRecv"] = None, + ): + # 调用父类初始化 + super().__init__( + message_id=message_id, + time=int(time.time()), + chat_stream=chat_stream, + user_info=bot_user_info, + message_segment=message_segment, + reply=reply, + ) + + # 处理状态相关属性 self.thinking_start_time = int(time.time()) self.thinking_time = 0 - self.interupt=False - - def update_thinking_time(self): - self.thinking_time = round(time.time(), 2) - self.thinking_start_time - -@dataclass -class Message_Sending(Message): - """发送中的消息类""" - thinking_start_time: float = None # 思考开始时间 - thinking_time: float = None # 思考时间 - - reply_message_id: int = None # 存储 回复的 源消息ID - - is_head: bool = False # 是否是头部消息 - - def update_thinking_time(self): - self.thinking_time = round(time.time(), 2) - self.thinking_start_time + def update_thinking_time(self) -> float: + """更新思考时间""" + self.thinking_time = round(time.time() - self.thinking_start_time, 2) return self.thinking_time + async def _process_message_segments(self, segment: Seg) -> str: + """递归处理消息段，转换为文字描述 - + Args: + segment: 要处理的消息段 + + Returns: + str: 处理后的文本 + """ + if segment.type == "seglist": + # 处理消息段列表 + segments_text = [] + for seg in segment.data: + processed = await self._process_message_segments(seg) + if processed: + segments_text.append(processed) + return " ".join(segments_text) + else: + # 处理单个消息段 + return await self._process_single_segment(segment) + + async def _process_single_segment(self, seg: Seg) -> str: + """处理单个消息段 + + Args: + seg: 要处理的消息段 + + Returns: + str: 处理后的文本 + """ + try: + if seg.type == "text": + return seg.data + elif seg.type == "image": + # 如果是base64图片数据 + if isinstance(seg.data, str): + return await image_manager.get_image_description(seg.data) + return "[图片]" + elif seg.type == "emoji": + if isinstance(seg.data, str): + return await image_manager.get_emoji_description(seg.data) + return "[表情]" + elif seg.type == "at": + return f"[@{seg.data}]" + elif seg.type == "reply": + if self.reply and hasattr(self.reply, "processed_plain_text"): + return f"[回复：{self.reply.processed_plain_text}]" + else: + return f"[{seg.type}:{str(seg.data)}]" + except Exception as e: + logger.error( + f"处理消息段失败: {str(e)}, 类型: {seg.type}, 数据: {seg.data}" + ) + return f"[处理失败的{seg.type}消息]" + + def _generate_detailed_text(self) -> str: + """生成详细文本，包含时间和用户信息""" + time_str = time.strftime( + "%m-%d %H:%M:%S", time.localtime(self.message_info.time) + ) + user_info = self.message_info.user_info + name = ( + f"{user_info.user_nickname}(ta的昵称:{user_info.user_cardname},ta的id:{user_info.user_id})" + if user_info.user_cardname != "" + else f"{user_info.user_nickname}(ta的id:{user_info.user_id})" + ) + return f"[{time_str}] {name}: {self.processed_plain_text}\n" + + +@dataclass +class MessageThinking(MessageProcessBase): + """思考状态的消息类""" + + def __init__( + self, + message_id: str, + chat_stream: ChatStream, + bot_user_info: UserInfo, + reply: Optional["MessageRecv"] = None, + ): + # 调用父类初始化 + super().__init__( + message_id=message_id, + chat_stream=chat_stream, + bot_user_info=bot_user_info, + message_segment=None, # 思考状态不需要消息段 + reply=reply, + ) + + # 思考状态特有属性 + self.interrupt = False + + +@dataclass +class MessageSending(MessageProcessBase): + """发送状态的消息类""" + + def __init__( + self, + message_id: str, + chat_stream: ChatStream, + bot_user_info: UserInfo, + sender_info: UserInfo, # 用来记录发送者信息,用于私聊回复 + message_segment: Seg, + reply: Optional["MessageRecv"] = None, + is_head: bool = False, + is_emoji: bool = False, + ): + # 调用父类初始化 + super().__init__( + message_id=message_id, + chat_stream=chat_stream, + bot_user_info=bot_user_info, + message_segment=message_segment, + reply=reply, + ) + + # 发送状态特有属性 + self.sender_info = sender_info + self.reply_to_message_id = reply.message_info.message_id if reply else None + self.is_head = is_head + self.is_emoji = is_emoji + + def set_reply(self, reply: Optional["MessageRecv"]) -> None: + """设置回复消息""" + if reply: + self.reply = reply + self.reply_to_message_id = self.reply.message_info.message_id + self.message_segment = Seg( + type="seglist", + data=[ + Seg(type="reply", data=reply.message_info.message_id), + self.message_segment, + ], + ) + + async def process(self) -> None: + """处理消息内容，生成纯文本和详细文本""" + if self.message_segment: + self.processed_plain_text = await self._process_message_segments( + self.message_segment + ) + self.detailed_plain_text = self._generate_detailed_text() + + @classmethod + def from_thinking( + cls, + thinking: MessageThinking, + message_segment: Seg, + is_head: bool = False, + is_emoji: bool = False, + ) -> "MessageSending": + """从思考状态消息创建发送状态消息""" + return cls( + message_id=thinking.message_info.message_id, + chat_stream=thinking.chat_stream, + message_segment=message_segment, + bot_user_info=thinking.message_info.user_info, + reply=thinking.reply, + is_head=is_head, + is_emoji=is_emoji, + ) + + def to_dict(self): + ret = super().to_dict() + ret["message_info"]["user_info"] = self.chat_stream.user_info.to_dict() + return ret + + def is_private_message(self) -> bool: + """判断是否为私聊消息""" + return ( + self.message_info.group_info is None + or self.message_info.group_info.group_id is None + ) + + +@dataclass class MessageSet: """消息集合类，可以存储多个发送消息""" - def __init__(self, group_id: int, user_id: int, message_id: str): - self.group_id = group_id - self.user_id = user_id + + def __init__(self, chat_stream: ChatStream, message_id: str): + self.chat_stream = chat_stream self.message_id = message_id - self.messages: List[Message_Sending] = [] # 修改类型标注 + self.messages: List[MessageSending] = [] self.time = round(time.time(), 2) - - def add_message(self, message: Message_Sending) -> None: - """添加消息到集合，只接受Message_Sending类型""" - if not isinstance(message, Message_Sending): - raise TypeError("MessageSet只能添加Message_Sending类型的消息") + + def add_message(self, message: MessageSending) -> None: + """添加消息到集合""" + if not isinstance(message, MessageSending): + raise TypeError("MessageSet只能添加MessageSending类型的消息") self.messages.append(message) - # 按时间排序 - self.messages.sort(key=lambda x: x.time) - - def get_message_by_index(self, index: int) -> Optional[Message_Sending]: + self.messages.sort(key=lambda x: x.message_info.time) + + def get_message_by_index(self, index: int) -> Optional[MessageSending]: """通过索引获取消息""" if 0 <= index < len(self.messages): return self.messages[index] return None - - def get_message_by_time(self, target_time: float) -> Optional[Message_Sending]: + + def get_message_by_time(self, target_time: float) -> Optional[MessageSending]: """获取最接近指定时间的消息""" if not self.messages: return None - - # 使用二分查找找到最接近的消息 + left, right = 0, len(self.messages) - 1 while left < right: mid = (left + right) // 2 - if self.messages[mid].time < target_time: + if self.messages[mid].message_info.time < target_time: left = mid + 1 else: right = mid - + return self.messages[left] - - + def clear_messages(self) -> None: """清空所有消息""" self.messages.clear() - - def remove_message(self, message: Message_Sending) -> bool: + + def remove_message(self, message: MessageSending) -> bool: """移除指定消息""" if message in self.messages: self.messages.remove(message) return True return False - + def __str__(self) -> str: return f"MessageSet(id={self.message_id}, count={len(self.messages)})" - + def __len__(self) -> int: return len(self.messages) - - - diff --git a/src/plugins/chat/message_base.py b/src/plugins/chat/message_base.py new file mode 100644 index 00000000..ae7ec387 --- /dev/null +++ b/src/plugins/chat/message_base.py @@ -0,0 +1,186 @@ +from dataclasses import dataclass, asdict +from typing import List, Optional, Union, Dict + +@dataclass +class Seg: + """消息片段类，用于表示消息的不同部分 + + Attributes: + type: 片段类型，可以是 'text'、'image'、'seglist' 等 + data: 片段的具体内容 + - 对于 text 类型，data 是字符串 + - 对于 image 类型，data 是 base64 字符串 + - 对于 seglist 类型，data 是 Seg 列表 + translated_data: 经过翻译处理的数据（可选） + """ + type: str + data: Union[str, List['Seg']] + + + # def __init__(self, type: str, data: Union[str, List['Seg']],): + # """初始化实例，确保字典和属性同步""" + # # 先初始化字典 + # self.type = type + # self.data = data + + @classmethod + def from_dict(cls, data: Dict) -> 'Seg': + """从字典创建Seg实例""" + type=data.get('type') + data=data.get('data') + if type == 'seglist': + data = [Seg.from_dict(seg) for seg in data] + return cls( + type=type, + data=data + ) + + def to_dict(self) -> Dict: + """转换为字典格式""" + result = {'type': self.type} + if self.type == 'seglist': + result['data'] = [seg.to_dict() for seg in self.data] + else: + result['data'] = self.data + return result + +@dataclass +class GroupInfo: + """群组信息类""" + platform: Optional[str] = None + group_id: Optional[int] = None + group_name: Optional[str] = None # 群名称 + + def to_dict(self) -> Dict: + """转换为字典格式""" + return {k: v for k, v in asdict(self).items() if v is not None} + + @classmethod + def from_dict(cls, data: Dict) -> 'GroupInfo': + """从字典创建GroupInfo实例 + + Args: + data: 包含必要字段的字典 + + Returns: + GroupInfo: 新的实例 + """ + return cls( + platform=data.get('platform'), + group_id=data.get('group_id'), + group_name=data.get('group_name',None) + ) + +@dataclass +class UserInfo: + """用户信息类""" + platform: Optional[str] = None + user_id: Optional[int] = None + user_nickname: Optional[str] = None # 用户昵称 + user_cardname: Optional[str] = None # 用户群昵称 + + def to_dict(self) -> Dict: + """转换为字典格式""" + return {k: v for k, v in asdict(self).items() if v is not None} + + @classmethod + def from_dict(cls, data: Dict) -> 'UserInfo': + """从字典创建UserInfo实例 + + Args: + data: 包含必要字段的字典 + + Returns: + UserInfo: 新的实例 + """ + return cls( + platform=data.get('platform'), + user_id=data.get('user_id'), + user_nickname=data.get('user_nickname',None), + user_cardname=data.get('user_cardname',None) + ) + +@dataclass +class BaseMessageInfo: + """消息信息类""" + platform: Optional[str] = None + message_id: Union[str,int,None] = None + time: Optional[int] = None + group_info: Optional[GroupInfo] = None + user_info: Optional[UserInfo] = None + + def to_dict(self) -> Dict: + """转换为字典格式""" + result = {} + for field, value in asdict(self).items(): + if value is not None: + if isinstance(value, (GroupInfo, UserInfo)): + result[field] = value.to_dict() + else: + result[field] = value + return result + @classmethod + def from_dict(cls, data: Dict) -> 'BaseMessageInfo': + """从字典创建BaseMessageInfo实例 + + Args: + data: 包含必要字段的字典 + + Returns: + BaseMessageInfo: 新的实例 + """ + group_info = GroupInfo(**data.get('group_info', {})) + user_info = UserInfo(**data.get('user_info', {})) + return cls( + platform=data.get('platform'), + message_id=data.get('message_id'), + time=data.get('time'), + group_info=group_info, + user_info=user_info + ) + +@dataclass +class MessageBase: + """消息类""" + message_info: BaseMessageInfo + message_segment: Seg + raw_message: Optional[str] = None # 原始消息，包含未解析的cq码 + + def to_dict(self) -> Dict: + """转换为字典格式 + + Returns: + Dict: 包含所有非None字段的字典，其中： + - message_info: 转换为字典格式 + - message_segment: 转换为字典格式 + - raw_message: 如果存在则包含 + """ + result = { + 'message_info': self.message_info.to_dict(), + 'message_segment': self.message_segment.to_dict() + } + if self.raw_message is not None: + result['raw_message'] = self.raw_message + return result + + @classmethod + def from_dict(cls, data: Dict) -> 'MessageBase': + """从字典创建MessageBase实例 + + Args: + data: 包含必要字段的字典 + + Returns: + MessageBase: 新的实例 + """ + message_info = BaseMessageInfo(**data.get('message_info', {})) + message_segment = Seg(**data.get('message_segment', {})) + raw_message = data.get('raw_message',None) + return cls( + message_info=message_info, + message_segment=message_segment, + raw_message=raw_message + ) + + + diff --git a/src/plugins/chat/message_cq.py b/src/plugins/chat/message_cq.py new file mode 100644 index 00000000..59d67a45 --- /dev/null +++ b/src/plugins/chat/message_cq.py @@ -0,0 +1,173 @@ +import time +from dataclasses import dataclass +from typing import Dict, Optional + +import urllib3 + +from .cq_code import cq_code_tool +from .utils_cq import parse_cq_code +from .utils_user import get_groupname +from .message_base import Seg, GroupInfo, UserInfo, BaseMessageInfo, MessageBase +# 禁用SSL警告 +urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) + +#这个类是消息数据类，用于存储和管理消息数据。 +#它定义了消息的属性，包括群组ID、用户ID、消息ID、原始消息内容、纯文本内容和时间戳。 +#它还定义了两个辅助属性：keywords用于提取消息的关键词，is_plain_text用于判断消息是否为纯文本。 + +@dataclass +class MessageCQ(MessageBase): + """QQ消息基类，继承自MessageBase + + 最小必要参数: + - message_id: 消息ID + - user_id: 发送者/接收者ID + - platform: 平台标识（默认为"qq"） + """ + def __init__( + self, + message_id: int, + user_info: UserInfo, + group_info: Optional[GroupInfo] = None, + platform: str = "qq" + ): + # 构造基础消息信息 + message_info = BaseMessageInfo( + platform=platform, + message_id=message_id, + time=int(time.time()), + group_info=group_info, + user_info=user_info + ) + # 调用父类初始化，message_segment 由子类设置 + super().__init__( + message_info=message_info, + message_segment=None, + raw_message=None + ) + +@dataclass +class MessageRecvCQ(MessageCQ): + """QQ接收消息类，用于解析raw_message到Seg对象""" + + def __init__( + self, + message_id: int, + user_info: UserInfo, + raw_message: str, + group_info: Optional[GroupInfo] = None, + platform: str = "qq", + reply_message: Optional[Dict] = None, + ): + # 调用父类初始化 + super().__init__(message_id, user_info, group_info, platform) + + # 私聊消息不携带group_info + if group_info is None: + pass + + elif group_info.group_name is None: + group_info.group_name = get_groupname(group_info.group_id) + + # 解析消息段 + self.message_segment = self._parse_message(raw_message, reply_message) + self.raw_message = raw_message + + def _parse_message(self, message: str, reply_message: Optional[Dict] = None) -> Seg: + """解析消息内容为Seg对象""" + cq_code_dict_list = [] + segments = [] + + start = 0 + while True: + cq_start = message.find('[CQ:', start) + if cq_start == -1: + if start < len(message): + text = message[start:].strip() + if text: + cq_code_dict_list.append(parse_cq_code(text)) + break + + if cq_start > start: + text = message[start:cq_start].strip() + if text: + cq_code_dict_list.append(parse_cq_code(text)) + + cq_end = message.find(']', cq_start) + if cq_end == -1: + text = message[cq_start:].strip() + if text: + cq_code_dict_list.append(parse_cq_code(text)) + break + + cq_code = message[cq_start:cq_end + 1] + cq_code_dict_list.append(parse_cq_code(cq_code)) + start = cq_end + 1 + + # 转换CQ码为Seg对象 + for code_item in cq_code_dict_list: + message_obj = cq_code_tool.cq_from_dict_to_class(code_item,msg=self,reply=reply_message) + if message_obj.translated_segments: + segments.append(message_obj.translated_segments) + + # 如果只有一个segment，直接返回 + if len(segments) == 1: + return segments[0] + + # 否则返回seglist类型的Seg + return Seg(type='seglist', data=segments) + + def to_dict(self) -> Dict: + """转换为字典格式，包含所有必要信息""" + base_dict = super().to_dict() + return base_dict + +@dataclass +class MessageSendCQ(MessageCQ): + """QQ发送消息类，用于将Seg对象转换为raw_message""" + + def __init__( + self, + data: Dict + ): + # 调用父类初始化 + message_info = BaseMessageInfo.from_dict(data.get('message_info', {})) + message_segment = Seg.from_dict(data.get('message_segment', {})) + super().__init__( + message_info.message_id, + message_info.user_info, + message_info.group_info if message_info.group_info else None, + message_info.platform + ) + + self.message_segment = message_segment + self.raw_message = self._generate_raw_message() + + def _generate_raw_message(self, ) -> str: + """将Seg对象转换为raw_message""" + segments = [] + + # 处理消息段 + if self.message_segment.type == 'seglist': + for seg in self.message_segment.data: + segments.append(self._seg_to_cq_code(seg)) + else: + segments.append(self._seg_to_cq_code(self.message_segment)) + + return ''.join(segments) + + def _seg_to_cq_code(self, seg: Seg) -> str: + """将单个Seg对象转换为CQ码字符串""" + if seg.type == 'text': + return str(seg.data) + elif seg.type == 'image': + return cq_code_tool.create_image_cq_base64(seg.data) + elif seg.type == 'emoji': + return cq_code_tool.create_emoji_cq_base64(seg.data) + elif seg.type == 'at': + return f"[CQ:at,qq={seg.data}]" + elif seg.type == 'reply': + return cq_code_tool.create_reply_cq(int(seg.data)) + else: + return f"[{seg.data}]" + diff --git a/src/plugins/chat/message_sender.py b/src/plugins/chat/message_sender.py index 4abbd3b3..584bf9c5 100644 --- a/src/plugins/chat/message_sender.py +++ b/src/plugins/chat/message_sender.py @@ -5,10 +5,10 @@ from typing import Dict, List, Optional, Union from loguru import logger from nonebot.adapters.onebot.v11 import Bot -from .cq_code import cq_code_tool -from .message import Message, Message_Sending, Message_Thinking, MessageSet +from .message_cq import MessageSendCQ +from .message import MessageSending, MessageThinking, MessageRecv, MessageSet + from .storage import MessageStorage -from .utils import calculate_typing_time from .config import global_config @@ -24,64 +24,61 @@ class Message_Sender: """设置当前bot实例""" self._current_bot = bot - async def send_group_message( - self, - group_id: int, - send_text: str, - auto_escape: bool = False, - reply_message_id: int = None, - at_user_id: int = None + async def send_message( + self, + message: MessageSending, ) -> None: + """发送消息""" - if not self._current_bot: - raise RuntimeError("Bot未设置，请先调用set_bot方法设置bot实例") - - message = send_text - - # 如果需要回复 - if reply_message_id: - reply_cq = cq_code_tool.create_reply_cq(reply_message_id) - message = reply_cq + message - - # 如果需要at - # if at_user_id: - # at_cq = cq_code_tool.create_at_cq(at_user_id) - # message = at_cq + " " + message - - typing_time = calculate_typing_time(message) - if typing_time > 10: - typing_time = 10 - await asyncio.sleep(typing_time) - - # 发送消息 - try: - await self._current_bot.send_group_msg( - group_id=group_id, - message=message, - auto_escape=auto_escape - ) - logger.debug(f"发送消息{message}成功") - except Exception as e: - logger.exception(f"发送消息{message}失败") + if isinstance(message, MessageSending): + message_json = message.to_dict() + message_send = MessageSendCQ(data=message_json) + # logger.debug(message_send.message_info,message_send.raw_message) + if ( + message_send.message_info.group_info + and message_send.message_info.group_info.group_id + ): + try: + await self._current_bot.send_group_msg( + group_id=message.message_info.group_info.group_id, + message=message_send.raw_message, + auto_escape=False, + ) + logger.success(f"[调试] 发送消息{message.processed_plain_text}成功") + except Exception as e: + logger.error(f"[调试] 发生错误 {e}") + logger.error(f"[调试] 发送消息{message.processed_plain_text}失败") + else: + try: + logger.debug(message.message_info.user_info) + await self._current_bot.send_private_msg( + user_id=message.sender_info.user_id, + message=message_send.raw_message, + auto_escape=False, + ) + logger.success(f"[调试] 发送消息{message.processed_plain_text}成功") + except Exception as e: + logger.error(f"发生错误 {e}") + logger.error(f"[调试] 发送消息{message.processed_plain_text}失败") class MessageContainer: - """单个群的发送/思考消息容器""" + """单个聊天流的发送/思考消息容器""" - def __init__(self, group_id: int, max_size: int = 100): - self.group_id = group_id + def __init__(self, chat_id: str, max_size: int = 100): + self.chat_id = chat_id self.max_size = max_size self.messages = [] self.last_send_time = 0 self.thinking_timeout = 20 # 思考超时时间（秒） - def get_timeout_messages(self) -> List[Message_Sending]: + def get_timeout_messages(self) -> List[MessageSending]: """获取所有超时的Message_Sending对象（思考时间超过30秒），按thinking_start_time排序""" current_time = time.time() timeout_messages = [] for msg in self.messages: - if isinstance(msg, Message_Sending): + if isinstance(msg, MessageSending): if current_time - msg.thinking_start_time > self.thinking_timeout: timeout_messages.append(msg) @@ -90,11 +87,11 @@ class MessageContainer: return timeout_messages - def get_earliest_message(self) -> Optional[Union[Message_Thinking, Message_Sending]]: + def get_earliest_message(self) -> Optional[Union[MessageThinking, MessageSending]]: """获取thinking_start_time最早的消息对象""" if not self.messages: return None - earliest_time = float('inf') + earliest_time = float("inf") earliest_message = None for msg in self.messages: msg_time = msg.thinking_start_time @@ -103,16 +100,15 @@ class MessageContainer: earliest_message = msg return earliest_message - def add_message(self, message: Union[Message_Thinking, Message_Sending]) -> None: + def add_message(self, message: Union[MessageThinking, MessageSending]) -> None: """添加消息到队列""" - # print(f"\033[1;32m[添加消息]\033[0m 添加消息到对应群") if isinstance(message, MessageSet): for single_message in message.messages: self.messages.append(single_message) else: self.messages.append(message) - def remove_message(self, message: Union[Message_Thinking, Message_Sending]) -> bool: + def remove_message(self, message: Union[MessageThinking, MessageSending]) -> bool: """移除消息，如果消息存在则返回True，否则返回False""" try: if message in self.messages: @@ -120,101 +116,109 @@ class MessageContainer: return True return False except Exception: - logger.exception(f"移除消息时发生错误") + logger.exception("移除消息时发生错误") return False def has_messages(self) -> bool: """检查是否有待发送的消息""" return bool(self.messages) - def get_all_messages(self) -> List[Union[Message, Message_Thinking]]: + def get_all_messages(self) -> List[Union[MessageSending, MessageThinking]]: """获取所有消息""" return list(self.messages) class MessageManager: - """管理所有群的消息容器""" + """管理所有聊天流的消息容器""" def __init__(self): - self.containers: Dict[int, MessageContainer] = {} + self.containers: Dict[str, MessageContainer] = {} # chat_id -> MessageContainer self.storage = MessageStorage() self._running = True - def get_container(self, group_id: int) -> MessageContainer: - """获取或创建群的消息容器""" - if group_id not in self.containers: - self.containers[group_id] = MessageContainer(group_id) - return self.containers[group_id] + def get_container(self, chat_id: str) -> MessageContainer: + """获取或创建聊天流的消息容器""" + if chat_id not in self.containers: + self.containers[chat_id] = MessageContainer(chat_id) + return self.containers[chat_id] - def add_message(self, message: Union[Message_Thinking, Message_Sending, MessageSet]) -> None: - container = self.get_container(message.group_id) + def add_message( + self, message: Union[MessageThinking, MessageSending, MessageSet] + ) -> None: + chat_stream = message.chat_stream + if not chat_stream: + raise ValueError("无法找到对应的聊天流") + container = self.get_container(chat_stream.stream_id) container.add_message(message) - async def process_group_messages(self, group_id: int): - """处理群消息""" - # if int(time.time() / 3) == time.time() / 3: - # print(f"\033[1;34m[调试]\033[0m 开始处理群{group_id}的消息") - container = self.get_container(group_id) + async def process_chat_messages(self, chat_id: str): + """处理聊天流消息""" + container = self.get_container(chat_id) if container.has_messages(): - # 最早的对象，可能是思考消息，也可能是发送消息 - message_earliest = container.get_earliest_message() # 一个message_thinking or message_sending + # print(f"处理有message的容器chat_id: {chat_id}") + message_earliest = container.get_earliest_message() - # 如果是思考消息 - if isinstance(message_earliest, Message_Thinking): - # 优先等待这条消息 + if isinstance(message_earliest, MessageThinking): message_earliest.update_thinking_time() thinking_time = message_earliest.thinking_time - print(f"消息正在思考中，已思考{int(thinking_time)}秒\r", end='', flush=True) + print( + f"消息正在思考中，已思考{int(thinking_time)}秒\r", + end="", + flush=True, + ) # 检查是否超时 if thinking_time > global_config.thinking_timeout: logger.warning(f"消息思考超时({thinking_time}秒)，移除该消息") container.remove_message(message_earliest) - else: # 如果不是message_thinking就只能是message_sending - logger.debug(f"消息'{message_earliest.processed_plain_text}'正在发送中") - # 直接发，等什么呢 - if message_earliest.is_head and message_earliest.update_thinking_time() > 30: - await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, - auto_escape=False, - reply_message_id=message_earliest.reply_message_id) + else: + + if ( + message_earliest.is_head + and message_earliest.update_thinking_time() > 30 + and not message_earliest.is_private_message() # 避免在私聊时插入reply + ): + await message_sender.send_message(message_earliest.set_reply()) else: - await message_sender.send_group_message(group_id, message_earliest.processed_plain_text, - auto_escape=False) - # 移除消息 - if message_earliest.is_emoji: - message_earliest.processed_plain_text = "[表情包]" - await self.storage.store_message(message_earliest, None) + await message_sender.send_message(message_earliest) + await message_earliest.process() + + print( + f"\033[1;34m[调试]\033[0m 消息'{message_earliest.processed_plain_text}'正在发送中" + ) + + await self.storage.store_message( + message_earliest, message_earliest.chat_stream, None + ) container.remove_message(message_earliest) - # 获取并处理超时消息 - message_timeout = container.get_timeout_messages() # 也许是一堆message_sending + message_timeout = container.get_timeout_messages() if message_timeout: logger.warning(f"发现{len(message_timeout)}条超时消息") for msg in message_timeout: if msg == message_earliest: - continue # 跳过已经处理过的消息 + continue try: - # 发送 - if msg.is_head and msg.update_thinking_time() > 30: - await message_sender.send_group_message(group_id, msg.processed_plain_text, - auto_escape=False, - reply_message_id=msg.reply_message_id) + if ( + msg.is_head + and msg.update_thinking_time() > 30 + and not message_earliest.is_private_message() # 避免在私聊时插入reply + ): + await message_sender.send_message(msg.set_reply()) else: - await message_sender.send_group_message(group_id, msg.processed_plain_text, - auto_escape=False) + await message_sender.send_message(msg) - # 如果是表情包，则替换为"[表情包]" - if msg.is_emoji: - msg.processed_plain_text = "[表情包]" - await self.storage.store_message(msg, None) + # if msg.is_emoji: + # msg.processed_plain_text = "[表情包]" + await msg.process() + await self.storage.store_message(msg, msg.chat_stream, None) - # 安全地移除消息 if not container.remove_message(msg): logger.warning("尝试删除不存在的消息") except Exception: - logger.exception(f"处理超时消息时发生错误") + logger.exception("处理超时消息时发生错误") continue async def start_processor(self): @@ -222,8 +226,8 @@ class MessageManager: while self._running: await asyncio.sleep(1) tasks = [] - for group_id in self.containers.keys(): - tasks.append(self.process_group_messages(group_id)) + for chat_id in self.containers.keys(): + tasks.append(self.process_chat_messages(chat_id)) await asyncio.gather(*tasks) diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 8af09e5e..3acf9e1a 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -9,6 +9,7 @@ from ..moods.moods import MoodManager #from ..schedule.schedule_generator import bot_schedule from .config import global_config from .utils import get_embedding, get_recent_group_detailed_plain_text +from .chat_stream import chat_manager class PromptBuilder: @@ -17,11 +18,13 @@ class PromptBuilder: self.activate_messages = '' self.db = Database.get_instance() - async def _build_prompt(self, - message_txt: str, - sender_name: str = "某人", - relationship_value: float = 0.0, - group_id: Optional[int] = None) -> tuple[str, str]: + + + async def _build_prompt(self, + message_txt: str, + sender_name: str = "某人", + relationship_value: float = 0.0, + stream_id: Optional[int] = None) -> tuple[str, str]: """构建prompt Args: @@ -70,14 +73,20 @@ class PromptBuilder: logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒") # 获取聊天上下文 + chat_in_group=True chat_talking_prompt = '' - if group_id: - chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, - limit=global_config.MAX_CONTEXT_SIZE, - combine=True) - - chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}" - + if stream_id: + chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, stream_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True) + chat_stream=chat_manager.get_stream(stream_id) + if chat_stream.group_info: + chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}" + else: + chat_in_group=False + chat_talking_prompt = f"以下是你正在和{sender_name}私聊的内容：\n{chat_talking_prompt}" + # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}") + + + # 使用新的记忆获取方法 memory_prompt = '' start_time = time.time() @@ -108,15 +117,10 @@ class PromptBuilder: # 激活prompt构建 activate_prompt = '' - activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。" - - # 检测机器人相关词汇，改为关键词检测与反应功能了，提取到全局配置中 - # bot_keywords = ['人机', 'bot', '机器', '入机', 'robot', '机器人'] - # is_bot = any(keyword in message_txt.lower() for keyword in bot_keywords) - # if is_bot: - # is_bot_prompt = '有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认' - # else: - # is_bot_prompt = '' + if chat_in_group: + activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。" + else: + activate_prompt = f"以上是你正在和{sender_name}私聊的内容，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和ta{relation_prompt},{mood_prompt},你想要{relation_prompt_2}。" # 关键词检测与反应 keywords_reaction_prompt = '' @@ -125,27 +129,34 @@ class PromptBuilder: if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])): logger.info(f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}") keywords_reaction_prompt += rule.get("reaction", "") + '，' - - # 人格选择 - personality = global_config.PROMPT_PERSONALITY + + #人格选择 + personality=global_config.PROMPT_PERSONALITY probability_1 = global_config.PERSONALITY_1 probability_2 = global_config.PERSONALITY_2 probability_3 = global_config.PERSONALITY_3 - prompt_personality = '' + + prompt_personality = f'{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，你还有很多别名:{"/".join(global_config.BOT_ALIAS_NAMES)}，' personality_choice = random.random() + if chat_in_group: + prompt_in_group=f"你正在浏览{chat_stream.platform}群" + else: + prompt_in_group=f"你正在{chat_stream.platform}上和{sender_name}私聊" if personality_choice < probability_1: # 第一种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}, 你正在浏览qq群,{promt_info_prompt}, + + prompt_personality += f'''{personality[0]}, 你正在浏览qq群,{promt_info_prompt}, 现在请你给出日常且口语化的回复。{keywords_reaction_prompt} 请注意把握群里的聊天内容，回复可以有个性。''' elif personality_choice < probability_1 + probability_2: # 第二种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}, 你正在浏览qq群，{promt_info_prompt}, + prompt_personality += f'''{personality[1]}, 你正在浏览qq群，{promt_info_prompt}, 现在请你给出日常且口语化的回复。{keywords_reaction_prompt} 请注意把握群里的聊天内容，回复可以有个性。''' else: # 第三种人格 - prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}, 你正在浏览qq群，{promt_info_prompt}, + prompt_personality += f'''{personality[2]}, 你正在浏览qq群，{promt_info_prompt}, 现在请你给出日常且口语化的回复。{keywords_reaction_prompt} 请注意把握群里的聊天内容，回复可以有个性。''' + # 中文高手(新加的好玩功能) prompt_ger = '' if random.random() < 0.04: diff --git a/src/plugins/chat/relationship_manager.py b/src/plugins/chat/relationship_manager.py index 4d82184c..90e92e7b 100644 --- a/src/plugins/chat/relationship_manager.py +++ b/src/plugins/chat/relationship_manager.py @@ -1,9 +1,10 @@ import asyncio -from loguru import logger from typing import Optional +from loguru import logger from ...common.database import Database - +from .message_base import UserInfo +from .chat_stream import ChatStream class Impression: traits: str = None @@ -15,92 +16,153 @@ class Impression: class Relationship: user_id: int = None - # impression: Impression = None - # group_id: int = None - # group_name: str = None + platform: str = None gender: str = None age: int = None nickname: str = None relationship_value: float = None saved = False - - def __init__(self, user_id: int, data=None, **kwargs): - if isinstance(data, dict): - # 如果输入是字典，使用字典解析 - self.user_id = data.get('user_id') - self.gender = data.get('gender') - self.age = data.get('age') - self.nickname = data.get('nickname') - self.relationship_value = data.get('relationship_value', 0.0) - self.saved = data.get('saved', False) - else: - # 如果是直接传入属性值 - self.user_id = kwargs.get('user_id') - self.gender = kwargs.get('gender') - self.age = kwargs.get('age') - self.nickname = kwargs.get('nickname') - self.relationship_value = kwargs.get('relationship_value', 0.0) - self.saved = kwargs.get('saved', False) - + + def __init__(self, chat:ChatStream=None,data:dict=None): + self.user_id=chat.user_info.user_id if chat else data.get('user_id',0) + self.platform=chat.platform if chat else data.get('platform','') + self.nickname=chat.user_info.user_nickname if chat else data.get('nickname','') + self.relationship_value=data.get('relationship_value',0) if data else 0 + self.age=data.get('age',0) if data else 0 + self.gender=data.get('gender','') if data else '' + class RelationshipManager: def __init__(self): - self.relationships: dict[int, Relationship] = {} - - async def update_relationship(self, user_id: int, data=None, **kwargs): + self.relationships: dict[tuple[int, str], Relationship] = {} # 修改为使用(user_id, platform)作为键 + + async def update_relationship(self, + chat_stream:ChatStream, + data: dict = None, + **kwargs) -> Optional[Relationship]: + """更新或创建关系 + Args: + chat_stream: 聊天流对象 + data: 字典格式的数据（可选） + **kwargs: 其他参数 + Returns: + Relationship: 关系对象 + """ + # 确定user_id和platform + if chat_stream.user_info is not None: + user_id = chat_stream.user_info.user_id + platform = chat_stream.user_info.platform or 'qq' + else: + platform = platform or 'qq' + + if user_id is None: + raise ValueError("必须提供user_id或user_info") + + # 使用(user_id, platform)作为键 + key = (user_id, platform) + # 检查是否在内存中已存在 - relationship = self.relationships.get(user_id) + relationship = self.relationships.get(key) if relationship: # 如果存在，更新现有对象 if isinstance(data, dict): - for key, value in data.items(): - if hasattr(relationship, key) and value is not None: - setattr(relationship, key, value) - else: - for key, value in kwargs.items(): - if hasattr(relationship, key) and value is not None: - setattr(relationship, key, value) + for k, value in data.items(): + if hasattr(relationship, k) and value is not None: + setattr(relationship, k, value) else: # 如果不存在，创建新对象 - relationship = Relationship(user_id, data=data) if isinstance(data, dict) else Relationship(user_id, - **kwargs) - self.relationships[user_id] = relationship - - # 更新 id_name_nickname_table - # self.id_name_nickname_table[user_id] = [relationship.nickname] # 别称设置为空列表 + if chat_stream.user_info is not None: + relationship = Relationship(chat=chat_stream, **kwargs) + else: + raise ValueError("必须提供user_id或user_info") + self.relationships[key] = relationship # 保存到数据库 await self.storage_relationship(relationship) relationship.saved = True return relationship - - async def update_relationship_value(self, user_id: int, **kwargs): + + async def update_relationship_value(self, + chat_stream:ChatStream, + **kwargs) -> Optional[Relationship]: + """更新关系值 + Args: + user_id: 用户ID（可选，如果提供user_info则不需要） + platform: 平台（可选，如果提供user_info则不需要） + user_info: 用户信息对象（可选） + **kwargs: 其他参数 + Returns: + Relationship: 关系对象 + """ + # 确定user_id和platform + user_info = chat_stream.user_info + if user_info is not None: + user_id = user_info.user_id + platform = user_info.platform or 'qq' + else: + platform = platform or 'qq' + + if user_id is None: + raise ValueError("必须提供user_id或user_info") + + # 使用(user_id, platform)作为键 + key = (user_id, platform) + # 检查是否在内存中已存在 - relationship = self.relationships.get(user_id) + relationship = self.relationships.get(key) if relationship: - for key, value in kwargs.items(): - if key == 'relationship_value': + for k, value in kwargs.items(): + if k == 'relationship_value': relationship.relationship_value += value await self.storage_relationship(relationship) relationship.saved = True return relationship else: - logger.warning(f"用户 {user_id} 不存在，无法更新") + # 如果不存在且提供了user_info，则创建新的关系 + if user_info is not None: + return await self.update_relationship(chat_stream=chat_stream, **kwargs) + logger.warning(f"[关系管理] 用户 {user_id}({platform}) 不存在，无法更新") return None - - def get_relationship(self, user_id: int) -> Optional[Relationship]: - """获取用户关系对象""" - if user_id in self.relationships: - return self.relationships[user_id] + + def get_relationship(self, + chat_stream:ChatStream) -> Optional[Relationship]: + """获取用户关系对象 + Args: + user_id: 用户ID（可选，如果提供user_info则不需要） + platform: 平台（可选，如果提供user_info则不需要） + user_info: 用户信息对象（可选） + Returns: + Relationship: 关系对象 + """ + # 确定user_id和platform + user_info = chat_stream.user_info + platform = chat_stream.user_info.platform or 'qq' + if user_info is not None: + user_id = user_info.user_id + platform = user_info.platform or 'qq' + else: + platform = platform or 'qq' + + if user_id is None: + raise ValueError("必须提供user_id或user_info") + + key = (user_id, platform) + if key in self.relationships: + return self.relationships[key] else: return 0 async def load_relationship(self, data: dict) -> Relationship: """从数据库加载或创建新的关系对象""" - rela = Relationship(user_id=data['user_id'], data=data) + # 确保data中有platform字段，如果没有则默认为'qq' + if 'platform' not in data: + data['platform'] = 'qq' + + rela = Relationship(data=data) rela.saved = True - self.relationships[rela.user_id] = rela + key = (rela.user_id, rela.platform) + self.relationships[key] = rela return rela async def load_all_relationships(self): @@ -117,11 +179,9 @@ class RelationshipManager: all_relationships = db.db.relationships.find({}) # 依次加载每条记录 for data in all_relationships: - user_id = data['user_id'] - relationship = await self.load_relationship(data) - self.relationships[user_id] = relationship - logger.debug(f"已加载 {len(self.relationships)} 条关系记录") - + await self.load_relationship(data) + logger.debug(f"[关系管理] 已加载 {len(self.relationships)} 条关系记录") + while True: logger.debug("正在自动保存关系") await asyncio.sleep(300) # 等待300秒(5分钟) @@ -130,16 +190,15 @@ class RelationshipManager: async def _save_all_relationships(self): """将所有关系数据保存到数据库""" # 保存所有关系数据 - for userid, relationship in self.relationships.items(): + for (userid, platform), relationship in self.relationships.items(): if not relationship.saved: relationship.saved = True await self.storage_relationship(relationship) - + async def storage_relationship(self, relationship: Relationship): - """ - 将关系记录存储到数据库中 - """ + """将关系记录存储到数据库中""" user_id = relationship.user_id + platform = relationship.platform nickname = relationship.nickname relationship_value = relationship.relationship_value gender = relationship.gender @@ -148,8 +207,9 @@ class RelationshipManager: db = Database.get_instance() db.db.relationships.update_one( - {'user_id': user_id}, + {'user_id': user_id, 'platform': platform}, {'$set': { + 'platform': platform, 'nickname': nickname, 'relationship_value': relationship_value, 'gender': gender, @@ -158,13 +218,37 @@ class RelationshipManager: }}, upsert=True ) - - def get_name(self, user_id: int) -> str: + + + def get_name(self, + user_id: int = None, + platform: str = None, + user_info: UserInfo = None) -> str: + """获取用户昵称 + Args: + user_id: 用户ID（可选，如果提供user_info则不需要） + platform: 平台（可选，如果提供user_info则不需要） + user_info: 用户信息对象（可选） + Returns: + str: 用户昵称 + """ + # 确定user_id和platform + if user_info is not None: + user_id = user_info.user_id + platform = user_info.platform or 'qq' + else: + platform = platform or 'qq' + + if user_id is None: + raise ValueError("必须提供user_id或user_info") + # 确保user_id是整数类型 user_id = int(user_id) - if user_id in self.relationships: - - return self.relationships[user_id].nickname + key = (user_id, platform) + if key in self.relationships: + return self.relationships[key].nickname + elif user_info is not None: + return user_info.user_nickname or user_info.user_cardname or "某人" else: return "某人" diff --git a/src/plugins/chat/storage.py b/src/plugins/chat/storage.py index 1c2d0507..c3986a2d 100644 --- a/src/plugins/chat/storage.py +++ b/src/plugins/chat/storage.py @@ -1,50 +1,30 @@ -from typing import Optional +from typing import Optional, Union from ...common.database import Database -from .message import Message +from .message import MessageSending, MessageRecv +from .chat_stream import ChatStream from loguru import logger class MessageStorage: def __init__(self): self.db = Database.get_instance() - - async def store_message(self, message: Message, topic: Optional[str] = None) -> None: + + async def store_message(self, message: Union[MessageSending, MessageRecv],chat_stream:ChatStream, topic: Optional[str] = None) -> None: """存储消息到数据库""" try: - if not message.is_emoji: - message_data = { - "group_id": message.group_id, - "user_id": message.user_id, - "message_id": message.message_id, - "raw_message": message.raw_message, - "plain_text": message.plain_text, + message_data = { + "message_id": message.message_info.message_id, + "time": message.message_info.time, + "chat_id":chat_stream.stream_id, + "chat_info": chat_stream.to_dict(), + "user_info": message.message_info.user_info.to_dict(), "processed_plain_text": message.processed_plain_text, - "time": message.time, - "user_nickname": message.user_nickname, - "user_cardname": message.user_cardname, - "group_name": message.group_name, - "topic": topic, "detailed_plain_text": message.detailed_plain_text, - } - else: - message_data = { - "group_id": message.group_id, - "user_id": message.user_id, - "message_id": message.message_id, - "raw_message": message.raw_message, - "plain_text": message.plain_text, - "processed_plain_text": '[表情包]', - "time": message.time, - "user_nickname": message.user_nickname, - "user_cardname": message.user_cardname, - "group_name": message.group_name, "topic": topic, - "detailed_plain_text": message.detailed_plain_text, } - self.db.db.messages.insert_one(message_data) except Exception: - logger.exception(f"存储消息失败") + logger.exception("存储消息失败") # 如果需要其他存储相关的函数，可以在这里添加 diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py index f174f4fd..b1526446 100644 --- a/src/plugins/chat/utils.py +++ b/src/plugins/chat/utils.py @@ -12,32 +12,15 @@ from loguru import logger from ..models.utils_model import LLM_request from ..utils.typo_generator import ChineseTypoGenerator from .config import global_config -from .message import Message +from .message import MessageRecv,Message +from .message_base import UserInfo +from .chat_stream import ChatStream from ..moods.moods import MoodManager driver = get_driver() config = driver.config -def combine_messages(messages: List[Message]) -> str: - """将消息列表组合成格式化的字符串 - - Args: - messages: Message对象列表 - - Returns: - str: 格式化后的消息字符串 - """ - result = "" - for message in messages: - time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message.time)) - name = message.user_nickname or f"用户{message.user_id}" - content = message.processed_plain_text or message.plain_text - - result += f"[{time_str}] {name}: {content}\n" - - return result - def db_message_to_str(message_dict: Dict) -> str: logger.debug(f"message_dict: {message_dict}") @@ -53,20 +36,15 @@ def db_message_to_str(message_dict: Dict) -> str: return result -def is_mentioned_bot_in_message(message: Message) -> bool: +def is_mentioned_bot_in_message(message: MessageRecv) -> bool: """检查消息是否提到了机器人""" keywords = [global_config.BOT_NICKNAME] + nicknames = global_config.BOT_ALIAS_NAMES for keyword in keywords: if keyword in message.processed_plain_text: return True - return False - - -def is_mentioned_bot_in_txt(message: str) -> bool: - """检查消息是否提到了机器人""" - keywords = [global_config.BOT_NICKNAME] - for keyword in keywords: - if keyword in message: + for nickname in nicknames: + if nickname in message.processed_plain_text: return True return False @@ -99,46 +77,45 @@ def calculate_information_content(text): def get_cloest_chat_from_db(db, length: int, timestamp: str): - """从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数 + """从数据库中获取最接近指定时间戳的聊天记录 + Args: + db: 数据库实例 + length: 要获取的消息数量 + timestamp: 时间戳 + Returns: - list: 消息记录字典列表，每个字典包含消息内容和时间信息 + list: 消息记录列表，每个记录包含时间和文本信息 """ chat_records = [] closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) - if closest_record and closest_record.get('memorized', 0) < 4: + if closest_record: closest_time = closest_record['time'] - group_id = closest_record['group_id'] - # 获取该时间戳之后的length条消息，且groupid相同 - records = list(db.db.messages.find( - {"time": {"$gt": closest_time}, "group_id": group_id} + chat_id = closest_record['chat_id'] # 获取chat_id + # 获取该时间戳之后的length条消息，保持相同的chat_id + chat_records = list(db.db.messages.find( + { + "time": {"$gt": closest_time}, + "chat_id": chat_id # 添加chat_id过滤 + } ).sort('time', 1).limit(length)) - # 更新每条消息的memorized属性 - for record in records: - current_memorized = record.get('memorized', 0) - if current_memorized > 3: - print("消息已读取3次，跳过") - return '' - - # 更新memorized值 - db.db.messages.update_one( - {"_id": record["_id"]}, - {"$set": {"memorized": current_memorized + 1}} - ) - - # 添加到记录列表中 - chat_records.append({ - 'text': record["detailed_plain_text"], + # 转换记录格式 + formatted_records = [] + for record in chat_records: + formatted_records.append({ 'time': record["time"], - 'group_id': record["group_id"] + 'chat_id': record["chat_id"], + 'detailed_plain_text': record.get("detailed_plain_text", "") # 添加文本内容 }) - return chat_records + return formatted_records + + return [] -async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: +async def get_recent_group_messages(db, chat_id:str, limit: int = 12) -> list: """从数据库获取群组最近的消息记录 Args: @@ -152,35 +129,28 @@ async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: # 从数据库获取最近消息 recent_messages = list(db.db.messages.find( - {"group_id": group_id}, - # { - # "time": 1, - # "user_id": 1, - # "user_nickname": 1, - # "message_id": 1, - # "raw_message": 1, - # "processed_text": 1 - # } + {"chat_id": chat_id}, ).sort("time", -1).limit(limit)) if not recent_messages: return [] # 转换为 Message对象列表 - from .message import Message message_objects = [] for msg_data in recent_messages: try: + chat_info=msg_data.get("chat_info",{}) + chat_stream=ChatStream.from_dict(chat_info) + user_info=msg_data.get("user_info",{}) + user_info=UserInfo.from_dict(user_info) msg = Message( - time=msg_data["time"], - user_id=msg_data["user_id"], - user_nickname=msg_data.get("user_nickname", ""), message_id=msg_data["message_id"], - raw_message=msg_data["raw_message"], + chat_stream=chat_stream, + time=msg_data["time"], + user_info=user_info, processed_plain_text=msg_data.get("processed_text", ""), - group_id=group_id + detailed_plain_text=msg_data.get("detailed_plain_text", "") ) - await msg.initialize() message_objects.append(msg) except KeyError: logger.warning("数据库中存在无效的消息") @@ -191,13 +161,14 @@ async def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: return message_objects -def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12, combine=False): +def get_recent_group_detailed_plain_text(db, chat_stream_id: int, limit: int = 12, combine=False): recent_messages = list(db.db.messages.find( - {"group_id": group_id}, + {"chat_id": chat_stream_id}, { "time": 1, # 返回时间字段 - "user_id": 1, # 返回用户ID字段 - "user_nickname": 1, # 返回用户昵称字段 + "chat_id":1, + "chat_info":1, + "user_info": 1, "message_id": 1, # 返回消息ID字段 "detailed_plain_text": 1 # 返回处理后的文本字段 } diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py index 8a8b3ce5..42d5f9ef 100644 --- a/src/plugins/chat/utils_image.py +++ b/src/plugins/chat/utils_image.py @@ -1,296 +1,353 @@ import base64 -import io import os import time -import zlib # 用于 CRC32 +import aiohttp +import hashlib +from typing import Optional, Union from loguru import logger from nonebot import get_driver -from PIL import Image from ...common.database import Database from ..chat.config import global_config - +from ..models.utils_model import LLM_request driver = get_driver() config = driver.config - - -def storage_compress_image(base64_data: str, max_size: int = 200) -> str: - """ - 压缩base64格式的图片到指定大小（单位：KB）并在数据库中记录图片信息 - Args: - base64_data: base64编码的图片数据 - max_size: 最大文件大小（KB） - Returns: - str: 压缩后的base64图片数据 - """ - try: - # 将base64转换为字节数据 - image_data = base64.b64decode(base64_data) - - # 使用 CRC32 计算哈希值 - hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x') - - # 确保图片目录存在 - images_dir = "data/images" - os.makedirs(images_dir, exist_ok=True) - - # 连接数据库 - db = Database( - host=config.mongodb_host, - port=int(config.mongodb_port), - db_name=config.database_name, - username=config.mongodb_username, - password=config.mongodb_password, - auth_source=config.mongodb_auth_source - ) - - # 检查是否已存在相同哈希值的图片 - collection = db.db['images'] - existing_image = collection.find_one({'hash': hash_value}) - - if existing_image: - print(f"\033[1;33m[提示]\033[0m 发现重复图片，使用已存在的文件: {existing_image['path']}") - return base64_data - - # 将字节数据转换为图片对象 - img = Image.open(io.BytesIO(image_data)) - - # 如果是动图，直接返回原图 - if getattr(img, 'is_animated', False): - return base64_data - - # 计算当前大小（KB） - current_size = len(image_data) / 1024 - - # 如果已经小于目标大小，直接使用原图 - if current_size <= max_size: - compressed_data = image_data - else: - # 压缩逻辑 - # 先缩放到50% - new_width = int(img.width * 0.5) - new_height = int(img.height * 0.5) - img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - - # 如果缩放后的最大边长仍然大于400，继续缩放 - max_dimension = 400 - max_current = max(new_width, new_height) - if max_current > max_dimension: - ratio = max_dimension / max_current - new_width = int(new_width * ratio) - new_height = int(new_height * ratio) - img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - - # 转换为RGB模式（去除透明通道） - if img.mode in ('RGBA', 'P'): - img = img.convert('RGB') - - # 使用固定质量参数压缩 - output = io.BytesIO() - img.save(output, format='JPEG', quality=85, optimize=True) - compressed_data = output.getvalue() - - # 生成文件名（使用时间戳和哈希值确保唯一性） - timestamp = int(time.time()) - filename = f"{timestamp}_{hash_value}.jpg" - image_path = os.path.join(images_dir, filename) - - # 保存文件 - with open(image_path, "wb") as f: - f.write(compressed_data) - - print(f"\033[1;32m[成功]\033[0m 保存图片到: {image_path}") - - try: - # 准备数据库记录 - image_record = { - 'filename': filename, - 'path': image_path, - 'size': len(compressed_data) / 1024, - 'timestamp': timestamp, - 'width': img.width, - 'height': img.height, - 'description': '', - 'tags': [], - 'type': 'image', - 'hash': hash_value - } - - # 保存记录 - collection.insert_one(image_record) - print("\033[1;32m[成功]\033[0m 保存图片记录到数据库") - - except Exception as db_error: - print(f"\033[1;31m[错误]\033[0m 数据库操作失败: {str(db_error)}") - - # 将压缩后的数据转换为base64 - compressed_base64 = base64.b64encode(compressed_data).decode('utf-8') - return compressed_base64 - - except Exception as e: - print(f"\033[1;31m[错误]\033[0m 压缩图片失败: {str(e)}") - import traceback - print(traceback.format_exc()) - return base64_data - -def storage_emoji(image_data: bytes) -> bytes: - """ - 存储表情包到本地文件夹 - Args: - image_data: 图片字节数据 - group_id: 群组ID（仅用于日志） - user_id: 用户ID（仅用于日志） - Returns: - bytes: 原始图片数据 - """ - if not global_config.EMOJI_SAVE: - return image_data - try: - # 使用 CRC32 计算哈希值 - hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x') - - # 确保表情包目录存在 - emoji_dir = "data/emoji" - os.makedirs(emoji_dir, exist_ok=True) - - # 检查是否已存在相同哈希值的文件 - for filename in os.listdir(emoji_dir): - if hash_value in filename: - # print(f"\033[1;33m[提示]\033[0m 发现重复表情包: {filename}") - return image_data - - # 生成文件名 - timestamp = int(time.time()) - filename = f"{timestamp}_{hash_value}.jpg" - emoji_path = os.path.join(emoji_dir, filename) - - # 直接保存原始文件 - with open(emoji_path, "wb") as f: - f.write(image_data) - - print(f"\033[1;32m[成功]\033[0m 保存表情包到: {emoji_path}") - return image_data - - except Exception as e: - print(f"\033[1;31m[错误]\033[0m 保存表情包失败: {str(e)}") - return image_data +class ImageManager: + _instance = None + IMAGE_DIR = "data" # 图像存储根目录 + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + cls._instance.db = None + cls._instance._initialized = False + return cls._instance + + def __init__(self): + if not self._initialized: + self.db = Database.get_instance() + self._ensure_image_collection() + self._ensure_description_collection() + self._ensure_image_dir() + self._initialized = True + self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300) + + def _ensure_image_dir(self): + """确保图像存储目录存在""" + os.makedirs(self.IMAGE_DIR, exist_ok=True) + + def _ensure_image_collection(self): + """确保images集合存在并创建索引""" + if 'images' not in self.db.db.list_collection_names(): + self.db.db.create_collection('images') + # 创建索引 + self.db.db.images.create_index([('hash', 1)], unique=True) + self.db.db.images.create_index([('url', 1)]) + self.db.db.images.create_index([('path', 1)]) -def storage_image(image_data: bytes) -> bytes: - """ - 存储图片到本地文件夹 - Args: - image_data: 图片字节数据 - group_id: 群组ID（仅用于日志） - user_id: 用户ID（仅用于日志） - Returns: - bytes: 原始图片数据 - """ - try: - # 使用 CRC32 计算哈希值 - hash_value = format(zlib.crc32(image_data) & 0xFFFFFFFF, 'x') - - # 确保表情包目录存在 - image_dir = "data/image" - os.makedirs(image_dir, exist_ok=True) - - # 检查是否已存在相同哈希值的文件 - for filename in os.listdir(image_dir): - if hash_value in filename: - # print(f"\033[1;33m[提示]\033[0m 发现重复表情包: {filename}") - return image_data - - # 生成文件名 - timestamp = int(time.time()) - filename = f"{timestamp}_{hash_value}.jpg" - image_path = os.path.join(image_dir, filename) - - # 直接保存原始文件 - with open(image_path, "wb") as f: - f.write(image_data) - - print(f"\033[1;32m[成功]\033[0m 保存图片到: {image_path}") - return image_data - - except Exception as e: - print(f"\033[1;31m[错误]\033[0m 保存图片失败: {str(e)}") - return image_data + def _ensure_description_collection(self): + """确保image_descriptions集合存在并创建索引""" + if 'image_descriptions' not in self.db.db.list_collection_names(): + self.db.db.create_collection('image_descriptions') + # 创建索引 + self.db.db.image_descriptions.create_index([('hash', 1)], unique=True) + self.db.db.image_descriptions.create_index([('type', 1)]) -def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str: - """压缩base64格式的图片到指定大小 - Args: - base64_data: base64编码的图片数据 - target_size: 目标文件大小（字节），默认0.8MB - Returns: - str: 压缩后的base64图片数据 - """ - try: - # 将base64转换为字节数据 - image_data = base64.b64decode(base64_data) + def _get_description_from_db(self, image_hash: str, description_type: str) -> Optional[str]: + """从数据库获取图片描述 - # 如果已经小于目标大小，直接返回原图 - if len(image_data) <= 2*1024*1024: - return base64_data + Args: + image_hash: 图片哈希值 + description_type: 描述类型 ('emoji' 或 'image') - # 将字节数据转换为图片对象 - img = Image.open(io.BytesIO(image_data)) + Returns: + Optional[str]: 描述文本，如果不存在则返回None + """ + result= self.db.db.image_descriptions.find_one({ + 'hash': image_hash, + 'type': description_type + }) + return result['description'] if result else None + + def _save_description_to_db(self, image_hash: str, description: str, description_type: str) -> None: + """保存图片描述到数据库 - # 获取原始尺寸 - original_width, original_height = img.size - - # 计算缩放比例 - scale = min(1.0, (target_size / len(image_data)) ** 0.5) - - # 计算新的尺寸 - new_width = int(original_width * scale) - new_height = int(original_height * scale) - - # 创建内存缓冲区 - output_buffer = io.BytesIO() - - # 如果是GIF，处理所有帧 - if getattr(img, "is_animated", False): - frames = [] - for frame_idx in range(img.n_frames): - img.seek(frame_idx) - new_frame = img.copy() - new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折 - frames.append(new_frame) - - # 保存到缓冲区 - frames[0].save( - output_buffer, - format='GIF', - save_all=True, - append_images=frames[1:], - optimize=True, - duration=img.info.get('duration', 100), - loop=img.info.get('loop', 0) - ) - else: - # 处理静态图片 - resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) - - # 保存到缓冲区，保持原始格式 - if img.format == 'PNG' and img.mode in ('RGBA', 'LA'): - resized_img.save(output_buffer, format='PNG', optimize=True) + Args: + image_hash: 图片哈希值 + description: 描述文本 + description_type: 描述类型 ('emoji' 或 'image') + """ + self.db.db.image_descriptions.update_one( + {'hash': image_hash, 'type': description_type}, + { + '$set': { + 'description': description, + 'timestamp': int(time.time()) + } + }, + upsert=True + ) + + async def save_image(self, + image_data: Union[str, bytes], + url: str = None, + description: str = None, + is_base64: bool = False) -> Optional[str]: + """保存图像 + Args: + image_data: 图像数据(base64字符串或字节) + url: 图像URL + description: 图像描述 + is_base64: image_data是否为base64格式 + Returns: + str: 保存后的文件路径,失败返回None + """ + try: + # 转换为字节格式 + if is_base64: + if isinstance(image_data, str): + image_bytes = base64.b64decode(image_data) + else: + return None else: - resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True) + if isinstance(image_data, bytes): + image_bytes = image_data + else: + return None + + # 计算哈希值 + image_hash = hashlib.md5(image_bytes).hexdigest() + + # 查重 + existing = self.db.db.images.find_one({'hash': image_hash}) + if existing: + return existing['path'] + + # 生成文件名和路径 + timestamp = int(time.time()) + filename = f"{timestamp}_{image_hash[:8]}.jpg" + file_path = os.path.join(self.IMAGE_DIR, filename) + + # 保存文件 + with open(file_path, "wb") as f: + f.write(image_bytes) + + # 保存到数据库 + image_doc = { + 'hash': image_hash, + 'path': file_path, + 'url': url, + 'description': description, + 'timestamp': timestamp + } + self.db.db.images.insert_one(image_doc) + + return file_path + + except Exception as e: + logger.error(f"保存图像失败: {str(e)}") + return None + + async def get_image_by_url(self, url: str) -> Optional[str]: + """根据URL获取图像路径(带查重) + Args: + url: 图像URL + Returns: + str: 本地文件路径,不存在返回None + """ + try: + # 先查找是否已存在 + existing = self.db.db.images.find_one({'url': url}) + if existing: + return existing['path'] + + # 下载图像 + async with aiohttp.ClientSession() as session: + async with session.get(url) as resp: + if resp.status == 200: + image_bytes = await resp.read() + return await self.save_image(image_bytes, url=url) + return None + + except Exception as e: + logger.error(f"获取图像失败: {str(e)}") + return None + + async def get_base64_by_url(self, url: str) -> Optional[str]: + """根据URL获取base64(带查重) + Args: + url: 图像URL + Returns: + str: base64字符串,失败返回None + """ + try: + image_path = await self.get_image_by_url(url) + if not image_path: + return None + + with open(image_path, 'rb') as f: + image_bytes = f.read() + return base64.b64encode(image_bytes).decode('utf-8') + + except Exception as e: + logger.error(f"获取base64失败: {str(e)}") + return None + - # 获取压缩后的数据并转换为base64 - compressed_data = output_buffer.getvalue() - logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}") - logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB") + def check_url_exists(self, url: str) -> bool: + """检查URL是否已存在 + Args: + url: 图像URL + Returns: + bool: 是否存在 + """ + return self.db.db.images.find_one({'url': url}) is not None - return base64.b64encode(compressed_data).decode('utf-8') + def check_hash_exists(self, image_data: Union[str, bytes], is_base64: bool = False) -> bool: + """检查图像是否已存在 + Args: + image_data: 图像数据(base64或字节) + is_base64: 是否为base64格式 + Returns: + bool: 是否存在 + """ + try: + if is_base64: + if isinstance(image_data, str): + image_bytes = base64.b64decode(image_data) + else: + return False + else: + if isinstance(image_data, bytes): + image_bytes = image_data + else: + return False + + image_hash = hashlib.md5(image_bytes).hexdigest() + return self.db.db.images.find_one({'hash': image_hash}) is not None + + except Exception as e: + logger.error(f"检查哈希失败: {str(e)}") + return False - except Exception as e: - logger.error(f"压缩图片失败: {str(e)}") - import traceback - logger.error(traceback.format_exc()) - return base64_data + async def get_emoji_description(self, image_base64: str) -> str: + """获取表情包描述，带查重和保存功能""" + try: + # 计算图片哈希 + image_bytes = base64.b64decode(image_base64) + image_hash = hashlib.md5(image_bytes).hexdigest() + + # 查询缓存的描述 + cached_description = self._get_description_from_db(image_hash, 'emoji') + if cached_description: + logger.info(f"缓存表情包描述: {cached_description}") + return f"[表情包：{cached_description}]" + + # 调用AI获取描述 + prompt = "这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感" + description, _ = await self._llm.generate_response_for_image(prompt, image_base64) + + # 根据配置决定是否保存图片 + if global_config.EMOJI_SAVE: + # 生成文件名和路径 + timestamp = int(time.time()) + filename = f"{timestamp}_{image_hash[:8]}.jpg" + file_path = os.path.join(self.IMAGE_DIR, 'emoji',filename) + + try: + # 保存文件 + with open(file_path, "wb") as f: + f.write(image_bytes) + + # 保存到数据库 + image_doc = { + 'hash': image_hash, + 'path': file_path, + 'type': 'emoji', + 'description': description, + 'timestamp': timestamp + } + self.db.db.images.update_one( + {'hash': image_hash}, + {'$set': image_doc}, + upsert=True + ) + logger.success(f"保存表情包: {file_path}") + except Exception as e: + logger.error(f"保存表情包文件失败: {str(e)}") + + # 保存描述到数据库 + self._save_description_to_db(image_hash, description, 'emoji') + + return f"[表情包：{description}]" + except Exception as e: + logger.error(f"获取表情包描述失败: {str(e)}") + return "[表情包]" + + async def get_image_description(self, image_base64: str) -> str: + """获取普通图片描述，带查重和保存功能""" + try: + # 计算图片哈希 + image_bytes = base64.b64decode(image_base64) + image_hash = hashlib.md5(image_bytes).hexdigest() + + # 查询缓存的描述 + cached_description = self._get_description_from_db(image_hash, 'image') + if cached_description: + return f"[图片：{cached_description}]" + + # 调用AI获取描述 + prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。" + description, _ = await self._llm.generate_response_for_image(prompt, image_base64) + + if description is None: + logger.warning("AI未能生成图片描述") + return "[图片]" + + # 根据配置决定是否保存图片 + if global_config.EMOJI_SAVE: + # 生成文件名和路径 + timestamp = int(time.time()) + filename = f"{timestamp}_{image_hash[:8]}.jpg" + file_path = os.path.join(self.IMAGE_DIR,'image', filename) + + try: + # 保存文件 + with open(file_path, "wb") as f: + f.write(image_bytes) + + # 保存到数据库 + image_doc = { + 'hash': image_hash, + 'path': file_path, + 'type': 'image', + 'description': description, + 'timestamp': timestamp + } + self.db.db.images.update_one( + {'hash': image_hash}, + {'$set': image_doc}, + upsert=True + ) + logger.success(f"保存图片: {file_path}") + except Exception as e: + logger.error(f"保存图片文件失败: {str(e)}") + + # 保存描述到数据库 + self._save_description_to_db(image_hash, description, 'image') + + return f"[图片：{description}]" + except Exception as e: + logger.error(f"获取图片描述失败: {str(e)}") + return "[图片]" + + + +# 创建全局单例 +image_manager = ImageManager() + def image_path_to_base64(image_path: str) -> str: """将图片路径转换为base64编码 diff --git a/src/plugins/chat/willing_manager.py b/src/plugins/chat/willing_manager.py index 116ee3f8..f34afb74 100644 --- a/src/plugins/chat/willing_manager.py +++ b/src/plugins/chat/willing_manager.py @@ -1,107 +1,105 @@ import asyncio -from loguru import logger +from typing import Dict + + from .config import global_config +from .chat_stream import ChatStream class WillingManager: def __init__(self): - self.group_reply_willing = {} # 存储每个群的回复意愿 + self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿 + self.chat_reply_willing: Dict[str, float] = {} # 存储每个聊天流的回复意愿 self._decay_task = None self._started = False - self.min_reply_willing = 0.01 - self.attenuation_coefficient = 0.75 - + async def _decay_reply_willing(self): """定期衰减回复意愿""" while True: await asyncio.sleep(5) - for group_id in self.group_reply_willing: - self.group_reply_willing[group_id] = max( - self.min_reply_willing, - self.group_reply_willing[group_id] * self.attenuation_coefficient - ) - - def get_willing(self, group_id: int) -> float: - """获取指定群组的回复意愿""" - return self.group_reply_willing.get(group_id, 0) - - def set_willing(self, group_id: int, willing: float): - """设置指定群组的回复意愿""" - self.group_reply_willing[group_id] = willing - - def 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: - - # 若非目标回复群组，则直接return - if group_id not in config.talk_allowed_groups: - reply_probability = 0 - return reply_probability - - current_willing = self.group_reply_willing.get(group_id, 0) - - logger.debug(f"[{group_id}]的初始回复意愿: {current_willing}") - - # 根据消息类型（被cue/表情包）调控 - if is_mentioned_bot: - current_willing = min( - 3.0, - current_willing + 0.9 - ) - logger.debug(f"被提及, 当前意愿: {current_willing}") - + for chat_id in self.chat_reply_willing: + self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.6) + for chat_id in self.chat_reply_willing: + self.chat_reply_willing[chat_id] = max(0, self.chat_reply_willing[chat_id] * 0.6) + + def get_willing(self,chat_stream:ChatStream) -> float: + """获取指定聊天流的回复意愿""" + stream = chat_stream + if stream: + return self.chat_reply_willing.get(stream.stream_id, 0) + return 0 + + def set_willing(self, chat_id: str, willing: float): + """设置指定聊天流的回复意愿""" + self.chat_reply_willing[chat_id] = willing + def set_willing(self, chat_id: str, willing: float): + """设置指定聊天流的回复意愿""" + self.chat_reply_willing[chat_id] = willing + + async def change_reply_willing_received(self, + chat_stream:ChatStream, + topic: str = None, + is_mentioned_bot: bool = False, + config = None, + is_emoji: bool = False, + interested_rate: float = 0) -> float: + """改变指定聊天流的回复意愿并返回回复概率""" + # 获取或创建聊天流 + stream = chat_stream + chat_id = stream.stream_id + + current_willing = self.chat_reply_willing.get(chat_id, 0) + + # print(f"初始意愿: {current_willing}") + if is_mentioned_bot and current_willing < 1.0: + current_willing += 0.9 + print(f"被提及, 当前意愿: {current_willing}") + elif is_mentioned_bot: + current_willing += 0.05 + print(f"被重复提及, 当前意愿: {current_willing}") + if is_emoji: current_willing *= 0.1 - logger.debug(f"表情包, 当前意愿: {current_willing}") - - # 兴趣放大系数，若兴趣 > 0.4则增加回复概率 - interested_rate_amplifier = global_config.response_interested_rate_amplifier - logger.debug(f"放大系数_interested_rate: {interested_rate_amplifier}") - interested_rate *= interested_rate_amplifier - - current_willing += max( - 0.0, - interested_rate - 0.4 - ) - - # 回复意愿系数调控，独立乘区 - willing_amplifier = max( - global_config.response_willing_amplifier, - self.min_reply_willing - ) - current_willing *= willing_amplifier - logger.debug(f"放大系数_willing: {global_config.response_willing_amplifier}, 当前意愿: {current_willing}") - - # 回复概率迭代，保底0.01回复概率 - reply_probability = max( - (current_willing - 0.45) * 2, - self.min_reply_willing - ) - - # 降低目标低频群组回复概率 - down_frequency_rate = max( - 1.0, - global_config.down_frequency_rate - ) - if group_id in config.talk_frequency_down_groups: - reply_probability = reply_probability / down_frequency_rate + print(f"表情包, 当前意愿: {current_willing}") + + print(f"放大系数_interested_rate: {global_config.response_interested_rate_amplifier}") + interested_rate *= global_config.response_interested_rate_amplifier #放大回复兴趣度 + if interested_rate > 0.4: + # print(f"兴趣度: {interested_rate}, 当前意愿: {current_willing}") + current_willing += interested_rate-0.4 + + current_willing *= global_config.response_willing_amplifier #放大回复意愿 + # print(f"放大系数_willing: {global_config.response_willing_amplifier}, 当前意愿: {current_willing}") + + reply_probability = max((current_willing - 0.45) * 2, 0) + + # 检查群组权限（如果是群聊） + if chat_stream.group_info: + if chat_stream.group_info.group_id in config.talk_frequency_down_groups: + reply_probability = reply_probability / global_config.down_frequency_rate reply_probability = min(reply_probability, 1) - - self.group_reply_willing[group_id] = min(current_willing, 3.0) - logger.debug(f"当前群组{group_id}回复概率：{reply_probability}") + if reply_probability < 0: + reply_probability = 0 + + self.chat_reply_willing[chat_id] = min(current_willing, 3.0) return reply_probability - - def change_reply_willing_sent(self, group_id: int): - """开始思考后降低群组的回复意愿""" - current_willing = self.group_reply_willing.get(group_id, 0) - self.group_reply_willing[group_id] = max(0, current_willing - 2) - - def change_reply_willing_after_sent(self, group_id: int): - """发送消息后提高群组的回复意愿""" - current_willing = self.group_reply_willing.get(group_id, 0) - if current_willing < 1: - self.group_reply_willing[group_id] = min(1, current_willing + 0.2) - + + def change_reply_willing_sent(self, chat_stream:ChatStream): + """开始思考后降低聊天流的回复意愿""" + stream = chat_stream + if stream: + current_willing = self.chat_reply_willing.get(stream.stream_id, 0) + self.chat_reply_willing[stream.stream_id] = max(0, current_willing - 2) + + def change_reply_willing_after_sent(self,chat_stream:ChatStream): + """发送消息后提高聊天流的回复意愿""" + stream = chat_stream + if stream: + current_willing = self.chat_reply_willing.get(stream.stream_id, 0) + if current_willing < 1: + self.chat_reply_willing[stream.stream_id] = min(1, current_willing + 0.2) + async def ensure_started(self): """确保衰减任务已启动""" if not self._started: @@ -109,6 +107,5 @@ class WillingManager: self._decay_task = asyncio.create_task(self._decay_reply_willing()) self._started = True - # 创建全局实例 -willing_manager = WillingManager() +willing_manager = WillingManager() \ No newline at end of file diff --git a/src/plugins/config_reload/__init__.py b/src/plugins/config_reload/__init__.py new file mode 100644 index 00000000..ddb7fa75 --- /dev/null +++ b/src/plugins/config_reload/__init__.py @@ -0,0 +1,10 @@ +from nonebot import get_app +from .api import router +from loguru import logger + +# 获取主应用实例并挂载路由 +app = get_app() +app.include_router(router, prefix="/api") + +# 打印日志，方便确认API已注册 +logger.success("配置重载API已注册，可通过 /api/reload-config 访问") \ No newline at end of file diff --git a/src/plugins/config_reload/api.py b/src/plugins/config_reload/api.py new file mode 100644 index 00000000..4202ba9b --- /dev/null +++ b/src/plugins/config_reload/api.py @@ -0,0 +1,17 @@ +from fastapi import APIRouter, HTTPException +from src.plugins.chat.config import BotConfig +import os + +# 创建APIRouter而不是FastAPI实例 +router = APIRouter() + +@router.post("/reload-config") +async def reload_config(): + try: + bot_config_path = os.path.join(BotConfig.get_config_dir(), "bot_config.toml") + global_config = BotConfig.load_config(config_path=bot_config_path) + return {"message": "配置重载成功", "status": "success"} + except FileNotFoundError as e: + raise HTTPException(status_code=404, detail=str(e)) + except Exception as e: + raise HTTPException(status_code=500, detail=f"重载配置时发生错误: {str(e)}") \ No newline at end of file diff --git a/src/plugins/config_reload/test.py b/src/plugins/config_reload/test.py new file mode 100644 index 00000000..b3b8a9e9 --- /dev/null +++ b/src/plugins/config_reload/test.py @@ -0,0 +1,3 @@ +import requests +response = requests.post("http://localhost:8080/api/reload-config") +print(response.json()) \ No newline at end of file diff --git a/src/plugins/knowledege/knowledge_library.py b/src/plugins/knowledege/knowledge_library.py deleted file mode 100644 index 48107696..00000000 --- a/src/plugins/knowledege/knowledge_library.py +++ /dev/null @@ -1,198 +0,0 @@ -import os -import sys -import time - -import requests -from dotenv import load_dotenv - -# 添加项目根目录到 Python 路径 -root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) -sys.path.append(root_path) - -# 加载根目录下的env.edv文件 -env_path = os.path.join(root_path, ".env.dev") -if not os.path.exists(env_path): - raise FileNotFoundError(f"配置文件不存在: {env_path}") -load_dotenv(env_path) - -from src.common.database import Database - -# 从环境变量获取配置 -Database.initialize( - host=os.getenv("MONGODB_HOST", "127.0.0.1"), - port=int(os.getenv("MONGODB_PORT", "27017")), - db_name=os.getenv("DATABASE_NAME", "maimai"), - username=os.getenv("MONGODB_USERNAME"), - password=os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE", "admin") -) - -class KnowledgeLibrary: - def __init__(self): - self.db = Database.get_instance() - self.raw_info_dir = "data/raw_info" - self._ensure_dirs() - self.api_key = os.getenv("SILICONFLOW_KEY") - if not self.api_key: - raise ValueError("SILICONFLOW_API_KEY 环境变量未设置") - - def _ensure_dirs(self): - """确保必要的目录存在""" - os.makedirs(self.raw_info_dir, exist_ok=True) - - def get_embedding(self, text: str) -> list: - """获取文本的embedding向量""" - url = "https://api.siliconflow.cn/v1/embeddings" - payload = { - "model": "BAAI/bge-m3", - "input": text, - "encoding_format": "float" - } - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - response = requests.post(url, json=payload, headers=headers) - if response.status_code != 200: - print(f"获取embedding失败: {response.text}") - return None - - return response.json()['data'][0]['embedding'] - - def process_files(self): - """处理raw_info目录下的所有txt文件""" - for filename in os.listdir(self.raw_info_dir): - if filename.endswith('.txt'): - file_path = os.path.join(self.raw_info_dir, filename) - self.process_single_file(file_path) - - def process_single_file(self, file_path: str): - """处理单个文件""" - try: - # 检查文件是否已处理 - if self.db.db.processed_files.find_one({"file_path": file_path}): - print(f"文件已处理过，跳过: {file_path}") - return - - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - - # 按1024字符分段 - segments = [content[i:i+600] for i in range(0, len(content), 600)] - - # 处理每个分段 - for segment in segments: - if not segment.strip(): # 跳过空段 - continue - - # 获取embedding - embedding = self.get_embedding(segment) - if not embedding: - continue - - # 存储到数据库 - doc = { - "content": segment, - "embedding": embedding, - "file_path": file_path, - "segment_length": len(segment) - } - - # 使用文本内容的哈希值作为唯一标识 - content_hash = hash(segment) - - # 更新或插入文档 - self.db.db.knowledges.update_one( - {"content_hash": content_hash}, - {"$set": doc}, - upsert=True - ) - - # 记录文件已处理 - self.db.db.processed_files.insert_one({ - "file_path": file_path, - "processed_time": time.time() - }) - - print(f"成功处理文件: {file_path}") - - except Exception as e: - print(f"处理文件 {file_path} 时出错: {str(e)}") - - def search_similar_segments(self, query: str, limit: int = 5) -> list: - """搜索与查询文本相似的片段""" - query_embedding = self.get_embedding(query) - if not query_embedding: - return [] - - # 使用余弦相似度计算 - pipeline = [ - { - "$addFields": { - "dotProduct": { - "$reduce": { - "input": {"$range": [0, {"$size": "$embedding"}]}, - "initialValue": 0, - "in": { - "$add": [ - "$$value", - {"$multiply": [ - {"$arrayElemAt": ["$embedding", "$$this"]}, - {"$arrayElemAt": [query_embedding, "$$this"]} - ]} - ] - } - } - }, - "magnitude1": { - "$sqrt": { - "$reduce": { - "input": "$embedding", - "initialValue": 0, - "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]} - } - } - }, - "magnitude2": { - "$sqrt": { - "$reduce": { - "input": query_embedding, - "initialValue": 0, - "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]} - } - } - } - } - }, - { - "$addFields": { - "similarity": { - "$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}] - } - } - }, - {"$sort": {"similarity": -1}}, - {"$limit": limit}, - {"$project": {"content": 1, "similarity": 1, "file_path": 1}} - ] - - results = list(self.db.db.knowledges.aggregate(pipeline)) - return results - -# 创建单例实例 -knowledge_library = KnowledgeLibrary() - -if __name__ == "__main__": - # 测试知识库功能 - print("开始处理知识库文件...") - knowledge_library.process_files() - - # 测试搜索功能 - test_query = "麦麦评价一下僕と花" - print(f"\n搜索与'{test_query}'相似的内容:") - results = knowledge_library.search_similar_segments(test_query) - for result in results: - print(f"相似度: {result['similarity']:.4f}") - print(f"内容: {result['content'][:100]}...") - print("-" * 50) diff --git a/src/plugins/memory_system/draw_memory.py b/src/plugins/memory_system/draw_memory.py index 6da330d9..9f15164f 100644 --- a/src/plugins/memory_system/draw_memory.py +++ b/src/plugins/memory_system/draw_memory.py @@ -9,7 +9,10 @@ import networkx as nx from dotenv import load_dotenv from loguru import logger -sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径 +# 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 # 使用正确的导入语法 # 加载.env.dev文件 @@ -162,12 +165,13 @@ class Memory_graph: def main(): # 初始化数据库 Database.initialize( + uri=os.getenv("MONGODB_URI"), host=os.getenv("MONGODB_HOST", "127.0.0.1"), port=int(os.getenv("MONGODB_PORT", "27017")), db_name=os.getenv("DATABASE_NAME", "MegBot"), - username=os.getenv("MONGODB_USERNAME", ""), - password=os.getenv("MONGODB_PASSWORD", ""), - auth_source=os.getenv("MONGODB_AUTH_SOURCE", "") + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), ) memory_graph = Memory_graph() diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index 2b6f876b..385e96d7 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -3,11 +3,13 @@ import datetime import math import random import time +import os import jieba import networkx as nx from loguru import logger +from nonebot import get_driver from ...common.database import Database # 使用正确的导入语法 from ..chat.config import global_config from ..chat.utils import ( @@ -18,33 +20,52 @@ from ..chat.utils import ( ) from ..models.utils_model import LLM_request - class Memory_graph: def __init__(self): self.G = nx.Graph() # 使用 networkx 的图结构 self.db = Database.get_instance() def connect_dot(self, concept1, concept2): - # 如果边已存在，增加 strength + # 避免自连接 + if concept1 == concept2: + return + + current_time = datetime.datetime.now().timestamp() + + # 如果边已存在,增加 strength if self.G.has_edge(concept1, concept2): self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1 + # 更新最后修改时间 + self.G[concept1][concept2]['last_modified'] = current_time else: - # 如果是新边，初始化 strength 为 1 - self.G.add_edge(concept1, concept2, strength=1) + # 如果是新边,初始化 strength 为 1 + self.G.add_edge(concept1, concept2, + strength=1, + created_time=current_time, # 添加创建时间 + last_modified=current_time) # 添加最后修改时间 def add_dot(self, concept, memory): + current_time = datetime.datetime.now().timestamp() + if concept in self.G: - # 如果节点已存在，将新记忆添加到现有列表中 if 'memory_items' in self.G.nodes[concept]: if not isinstance(self.G.nodes[concept]['memory_items'], list): - # 如果当前不是列表，将其转换为列表 self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']] self.G.nodes[concept]['memory_items'].append(memory) + # 更新最后修改时间 + self.G.nodes[concept]['last_modified'] = current_time else: self.G.nodes[concept]['memory_items'] = [memory] + # 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time + if 'created_time' not in self.G.nodes[concept]: + self.G.nodes[concept]['created_time'] = current_time + self.G.nodes[concept]['last_modified'] = current_time else: - # 如果是新节点，创建新的记忆列表 - self.G.add_node(concept, memory_items=[memory]) + # 如果是新节点,创建新的记忆列表 + self.G.add_node(concept, + memory_items=[memory], + created_time=current_time, # 添加创建时间 + last_modified=current_time) # 添加最后修改时间 def get_dot(self, concept): # 检查节点是否存在于图中 @@ -130,7 +151,7 @@ class Memory_graph: return None -# 海马体 +# 海马体 class Hippocampus: def __init__(self, memory_graph: Memory_graph): self.memory_graph = memory_graph @@ -191,15 +212,11 @@ class Hippocampus: async def memory_compress(self, messages: list, compress_rate=0.1): """压缩消息记录为记忆 - Args: - messages: 消息记录字典列表，每个字典包含text和time字段 - compress_rate: 压缩率 - Returns: - set: (话题, 记忆) 元组集合 + tuple: (压缩记忆集合, 相似主题字典) """ if not messages: - return set() + return set(), {} # 合并消息文本，同时保留时间信息 input_text = "" @@ -222,7 +239,7 @@ class Hippocampus: time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n" for msg in messages: - input_text += f"{msg['text']}\n" + input_text += f"{msg['detailed_plain_text']}\n" logger.debug(input_text) @@ -246,12 +263,33 @@ class Hippocampus: # 等待所有任务完成 compressed_memory = set() + similar_topics_dict = {} # 存储每个话题的相似主题列表 for topic, task in tasks: response = await task if response: compressed_memory.add((topic, response[0])) + # 为每个话题查找相似的已存在主题 + existing_topics = list(self.memory_graph.G.nodes()) + similar_topics = [] + + for existing_topic in existing_topics: + topic_words = set(jieba.cut(topic)) + existing_words = set(jieba.cut(existing_topic)) + + all_words = topic_words | existing_words + v1 = [1 if word in topic_words else 0 for word in all_words] + v2 = [1 if word in existing_words else 0 for word in all_words] + + similarity = cosine_similarity(v1, v2) + + if similarity >= 0.6: + similar_topics.append((existing_topic, similarity)) + + similar_topics.sort(key=lambda x: x[1], reverse=True) + similar_topics = similar_topics[:5] + similar_topics_dict[topic] = similar_topics - return compressed_memory + return compressed_memory, similar_topics_dict def calculate_topic_num(self, text, compress_rate): """计算文本的话题数量""" @@ -265,33 +303,45 @@ class Hippocampus: return topic_num async def operation_build_memory(self, chat_size=20): - # 最近消息获取频率 - time_frequency = {'near': 2, 'mid': 4, 'far': 2} - memory_sample = self.get_memory_sample(chat_size, time_frequency) - - for i, input_text in enumerate(memory_sample, 1): - # 加载进度可视化 + time_frequency = {'near': 1, 'mid': 4, 'far': 4} + memory_samples = self.get_memory_sample(chat_size, time_frequency) + + for i, messages in enumerate(memory_samples, 1): all_topics = [] - progress = (i / len(memory_sample)) * 100 + # 加载进度可视化 + progress = (i / len(memory_samples)) * 100 bar_length = 30 - filled_length = int(bar_length * i // len(memory_sample)) + filled_length = int(bar_length * i // len(memory_samples)) bar = '█' * filled_length + '-' * (bar_length - filled_length) - logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_sample)})") + logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})") - # 生成压缩后记忆 ,表现为 (话题,记忆) 的元组 - compressed_memory = set() - compress_rate = 0.1 - compressed_memory = await self.memory_compress(input_text, compress_rate) - logger.info(f"压缩后记忆数量: {len(compressed_memory)}") - - # 将记忆加入到图谱中 + compress_rate = global_config.memory_compress_rate + compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate) + logger.info(f"压缩后记忆数量: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}") + + current_time = datetime.datetime.now().timestamp() + for topic, memory in compressed_memory: logger.info(f"添加节点: {topic}") self.memory_graph.add_dot(topic, memory) - all_topics.append(topic) # 收集所有话题 + all_topics.append(topic) + + # 连接相似的已存在主题 + if topic in similar_topics_dict: + similar_topics = similar_topics_dict[topic] + for similar_topic, similarity in similar_topics: + if topic != similar_topic: + strength = int(similarity * 10) + logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})") + self.memory_graph.G.add_edge(topic, similar_topic, + strength=strength, + created_time=current_time, + last_modified=current_time) + + # 连接同批次的相关话题 for i in range(len(all_topics)): for j in range(i + 1, len(all_topics)): - logger.info(f"连接节点: {all_topics[i]} 和 {all_topics[j]}") + logger.info(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}") self.memory_graph.connect_dot(all_topics[i], all_topics[j]) self.sync_memory_to_db() @@ -302,7 +352,7 @@ class Hippocampus: db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find()) memory_nodes = list(self.memory_graph.G.nodes(data=True)) - # 转换数据库节点为字典格式，方便查找 + # 转换数据库节点为字典格式,方便查找 db_nodes_dict = {node['concept']: node for node in db_nodes} # 检查并更新节点 @@ -313,13 +363,19 @@ class Hippocampus: # 计算内存中节点的特征值 memory_hash = self.calculate_node_hash(concept, memory_items) + + # 获取时间信息 + created_time = data.get('created_time', datetime.datetime.now().timestamp()) + last_modified = data.get('last_modified', datetime.datetime.now().timestamp()) if concept not in db_nodes_dict: - # 数据库中缺少的节点，添加 + # 数据库中缺少的节点,添加 node_data = { 'concept': concept, 'memory_items': memory_items, - 'hash': memory_hash + 'hash': memory_hash, + 'created_time': created_time, + 'last_modified': last_modified } self.memory_graph.db.db.graph_data.nodes.insert_one(node_data) else: @@ -327,25 +383,21 @@ class Hippocampus: db_node = db_nodes_dict[concept] db_hash = db_node.get('hash', None) - # 如果特征值不同，则更新节点 + # 如果特征值不同,则更新节点 if db_hash != memory_hash: self.memory_graph.db.db.graph_data.nodes.update_one( {'concept': concept}, {'$set': { 'memory_items': memory_items, - 'hash': memory_hash + 'hash': memory_hash, + 'created_time': created_time, + 'last_modified': last_modified }} ) - # 检查并删除数据库中多余的节点 - memory_concepts = set(node[0] for node in memory_nodes) - for db_node in db_nodes: - if db_node['concept'] not in memory_concepts: - self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']}) - # 处理边的信息 db_edges = list(self.memory_graph.db.db.graph_data.edges.find()) - memory_edges = list(self.memory_graph.G.edges()) + memory_edges = list(self.memory_graph.G.edges(data=True)) # 创建边的哈希值字典 db_edge_dict = {} @@ -357,10 +409,14 @@ class Hippocampus: } # 检查并更新边 - for source, target in memory_edges: + for source, target, data in memory_edges: edge_hash = self.calculate_edge_hash(source, target) edge_key = (source, target) - strength = self.memory_graph.G[source][target].get('strength', 1) + strength = data.get('strength', 1) + + # 获取边的时间信息 + created_time = data.get('created_time', datetime.datetime.now().timestamp()) + last_modified = data.get('last_modified', datetime.datetime.now().timestamp()) if edge_key not in db_edge_dict: # 添加新边 @@ -368,7 +424,9 @@ class Hippocampus: 'source': source, 'target': target, 'strength': strength, - 'hash': edge_hash + 'hash': edge_hash, + 'created_time': created_time, + 'last_modified': last_modified } self.memory_graph.db.db.graph_data.edges.insert_one(edge_data) else: @@ -378,88 +436,168 @@ class Hippocampus: {'source': source, 'target': target}, {'$set': { 'hash': edge_hash, - 'strength': strength + 'strength': strength, + 'created_time': created_time, + 'last_modified': last_modified }} ) - # 删除多余的边 - memory_edge_set = set(memory_edges) - for edge_key in db_edge_dict: - if edge_key not in memory_edge_set: - source, target = edge_key - self.memory_graph.db.db.graph_data.edges.delete_one({ - 'source': source, - 'target': target - }) - def sync_memory_from_db(self): """从数据库同步数据到内存中的图结构""" + current_time = datetime.datetime.now().timestamp() + need_update = False + # 清空当前图 self.memory_graph.G.clear() # 从数据库加载所有节点 - nodes = self.memory_graph.db.db.graph_data.nodes.find() + nodes = list(self.memory_graph.db.db.graph_data.nodes.find()) for node in nodes: concept = node['concept'] memory_items = node.get('memory_items', []) - # 确保memory_items是列表 if not isinstance(memory_items, list): memory_items = [memory_items] if memory_items else [] + + # 检查时间字段是否存在 + if 'created_time' not in node or 'last_modified' not in node: + need_update = True + # 更新数据库中的节点 + update_data = {} + if 'created_time' not in node: + update_data['created_time'] = current_time + if 'last_modified' not in node: + update_data['last_modified'] = current_time + + self.memory_graph.db.db.graph_data.nodes.update_one( + {'concept': concept}, + {'$set': update_data} + ) + logger.info(f"为节点 {concept} 添加缺失的时间字段") + + # 获取时间信息(如果不存在则使用当前时间) + created_time = node.get('created_time', current_time) + last_modified = node.get('last_modified', current_time) + # 添加节点到图中 - self.memory_graph.G.add_node(concept, memory_items=memory_items) + self.memory_graph.G.add_node(concept, + memory_items=memory_items, + created_time=created_time, + last_modified=last_modified) # 从数据库加载所有边 - edges = self.memory_graph.db.db.graph_data.edges.find() + edges = list(self.memory_graph.db.db.graph_data.edges.find()) for edge in edges: source = edge['source'] target = edge['target'] - strength = edge.get('strength', 1) # 获取 strength，默认为 1 + strength = edge.get('strength', 1) + + # 检查时间字段是否存在 + if 'created_time' not in edge or 'last_modified' not in edge: + need_update = True + # 更新数据库中的边 + update_data = {} + if 'created_time' not in edge: + update_data['created_time'] = current_time + if 'last_modified' not in edge: + update_data['last_modified'] = current_time + + self.memory_graph.db.db.graph_data.edges.update_one( + {'source': source, 'target': target}, + {'$set': update_data} + ) + logger.info(f"为边 {source} - {target} 添加缺失的时间字段") + + # 获取时间信息(如果不存在则使用当前时间) + created_time = edge.get('created_time', current_time) + last_modified = edge.get('last_modified', current_time) + # 只有当源节点和目标节点都存在时才添加边 if source in self.memory_graph.G and target in self.memory_graph.G: - self.memory_graph.G.add_edge(source, target, strength=strength) + self.memory_graph.G.add_edge(source, target, + strength=strength, + created_time=created_time, + last_modified=last_modified) + + if need_update: + logger.success("已为缺失的时间字段进行补充") async def operation_forget_topic(self, percentage=0.1): - """随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘""" - # 获取所有节点 + """随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘""" + # 检查数据库是否为空 all_nodes = list(self.memory_graph.G.nodes()) - # 计算要检查的节点数量 - check_count = max(1, int(len(all_nodes) * percentage)) - # 随机选择节点 - nodes_to_check = random.sample(all_nodes, check_count) - - forgotten_nodes = [] + all_edges = list(self.memory_graph.G.edges()) + + if not all_nodes and not all_edges: + logger.info("记忆图为空,无需进行遗忘操作") + return + + check_nodes_count = max(1, int(len(all_nodes) * percentage)) + check_edges_count = max(1, int(len(all_edges) * percentage)) + + nodes_to_check = random.sample(all_nodes, check_nodes_count) + edges_to_check = random.sample(all_edges, check_edges_count) + + edge_changes = {'weakened': 0, 'removed': 0} + node_changes = {'reduced': 0, 'removed': 0} + + current_time = datetime.datetime.now().timestamp() + + # 检查并遗忘连接 + logger.info("开始检查连接...") + for source, target in edges_to_check: + edge_data = self.memory_graph.G[source][target] + last_modified = edge_data.get('last_modified') + # print(source,target) + # print(f"float(last_modified):{float(last_modified)}" ) + # print(f"current_time:{current_time}") + # print(f"current_time - last_modified:{current_time - last_modified}") + if current_time - last_modified > 3600*global_config.memory_forget_time: # test + current_strength = edge_data.get('strength', 1) + new_strength = current_strength - 1 + + if new_strength <= 0: + self.memory_graph.G.remove_edge(source, target) + edge_changes['removed'] += 1 + logger.info(f"\033[1;31m[连接移除]\033[0m {source} - {target}") + else: + edge_data['strength'] = new_strength + edge_data['last_modified'] = current_time + edge_changes['weakened'] += 1 + logger.info(f"\033[1;34m[连接减弱]\033[0m {source} - {target} (强度: {current_strength} -> {new_strength})") + + # 检查并遗忘话题 + logger.info("开始检查节点...") for node in nodes_to_check: - # 获取节点的连接数 - connections = self.memory_graph.G.degree(node) - - # 获取节点的内容条数 - memory_items = self.memory_graph.G.nodes[node].get('memory_items', []) - if not isinstance(memory_items, list): - memory_items = [memory_items] if memory_items else [] - content_count = len(memory_items) - - # 检查连接强度 - weak_connections = True - if connections > 1: # 只有当连接数大于1时才检查强度 - for neighbor in self.memory_graph.G.neighbors(node): - strength = self.memory_graph.G[node][neighbor].get('strength', 1) - if strength > 2: - weak_connections = False - break - - # 如果满足遗忘条件 - if (connections <= 1 and weak_connections) or content_count <= 2: - removed_item = self.memory_graph.forget_topic(node) - if removed_item: - forgotten_nodes.append((node, removed_item)) - logger.debug(f"遗忘节点 {node} 的记忆: {removed_item}") - - # 同步到数据库 - if forgotten_nodes: + node_data = self.memory_graph.G.nodes[node] + last_modified = node_data.get('last_modified', current_time) + + if current_time - last_modified > 3600*24: # test + memory_items = node_data.get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + if memory_items: + current_count = len(memory_items) + removed_item = random.choice(memory_items) + memory_items.remove(removed_item) + + if memory_items: + self.memory_graph.G.nodes[node]['memory_items'] = memory_items + self.memory_graph.G.nodes[node]['last_modified'] = current_time + node_changes['reduced'] += 1 + logger.info(f"\033[1;33m[记忆减少]\033[0m {node} (记忆数量: {current_count} -> {len(memory_items)})") + else: + self.memory_graph.G.remove_node(node) + node_changes['removed'] += 1 + logger.info(f"\033[1;31m[节点移除]\033[0m {node}") + + if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()): self.sync_memory_to_db() - logger.debug(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆") + logger.info("\n遗忘操作统计:") + logger.info(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除") + logger.info(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除") else: - logger.debug("本次检查没有节点满足遗忘条件") + logger.info("\n本次检查没有节点或连接满足遗忘条件") async def merge_memory(self, topic): """ @@ -486,7 +624,7 @@ class Hippocampus: logger.debug(f"选择的记忆:\n{merged_text}") # 使用memory_compress生成新的压缩记忆 - compressed_memories = await self.memory_compress(selected_memories, 0.1) + compressed_memories, _ = await self.memory_compress(selected_memories, 0.1) # 从原记忆列表中移除被选中的记忆 for memory in selected_memories: @@ -749,21 +887,21 @@ def segment_text(text): seg_text = list(jieba.cut(text)) return seg_text - -from nonebot import get_driver - driver = get_driver() config = driver.config start_time = time.time() Database.initialize( - host=config.MONGODB_HOST, - port=int(config.MONGODB_PORT), - db_name=config.DATABASE_NAME, - username=config.MONGODB_USERNAME, - password=config.MONGODB_PASSWORD, - auth_source=config.MONGODB_AUTH_SOURCE + + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), + ) # 创建记忆图 memory_graph = Memory_graph() diff --git a/src/plugins/memory_system/memory_manual_build.py b/src/plugins/memory_system/memory_manual_build.py index 3c120f21..736a50e9 100644 --- a/src/plugins/memory_system/memory_manual_build.py +++ b/src/plugins/memory_system/memory_manual_build.py @@ -10,13 +10,15 @@ from pathlib import Path import matplotlib.pyplot as plt import networkx as nx -import pymongo from dotenv import load_dotenv from loguru import logger import jieba # from chat.config import global_config -sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径 +# 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.memory_system.offline_llm import LLMModel @@ -35,45 +37,6 @@ else: logger.warning(f"未找到环境变量文件: {env_path}") logger.info("将使用默认配置") -class Database: - _instance = None - db = None - - @classmethod - def get_instance(cls): - if cls._instance is None: - cls._instance = cls() - return cls._instance - - def __init__(self): - if not Database.db: - Database.initialize( - host=os.getenv("MONGODB_HOST"), - port=int(os.getenv("MONGODB_PORT")), - db_name=os.getenv("DATABASE_NAME"), - username=os.getenv("MONGODB_USERNAME"), - password=os.getenv("MONGODB_PASSWORD"), - auth_source=os.getenv("MONGODB_AUTH_SOURCE") - ) - - @classmethod - def initialize(cls, host, port, db_name, username=None, password=None, auth_source="admin"): - try: - if username and password: - uri = f"mongodb://{username}:{password}@{host}:{port}/{db_name}?authSource={auth_source}" - else: - uri = f"mongodb://{host}:{port}" - - client = pymongo.MongoClient(uri) - cls.db = client[db_name] - # 测试连接 - client.server_info() - logger.success("MongoDB连接成功!") - - except Exception as e: - logger.error(f"初始化MongoDB失败: {str(e)}") - raise - def calculate_information_content(text): """计算文本的信息量（熵）""" char_count = Counter(text) @@ -202,7 +165,7 @@ class Memory_graph: # 返回所有节点对应的 Memory_dot 对象 return [self.get_dot(node) for node in self.G.nodes()] -# 海马体 +# 海马体 class Hippocampus: def __init__(self, memory_graph: Memory_graph): self.memory_graph = memory_graph @@ -941,59 +904,67 @@ def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = Fal async def main(): # 初始化数据库 logger.info("正在初始化数据库连接...") - db = Database.get_instance() + Database.initialize( + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), + ) start_time = time.time() - + test_pare = {'do_build_memory':False,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False} - + # 创建记忆图 memory_graph = Memory_graph() - + # 创建海马体 hippocampus = Hippocampus(memory_graph) - + # 从数据库同步数据 hippocampus.sync_memory_from_db() - + end_time = time.time() logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m") - + # 构建记忆 if test_pare['do_build_memory']: logger.info("开始构建记忆...") chat_size = 20 await hippocampus.operation_build_memory(chat_size=chat_size) - + end_time = time.time() logger.info(f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m") - + if test_pare['do_forget_topic']: logger.info("开始遗忘记忆...") await hippocampus.operation_forget_topic(percentage=0.1) - + end_time = time.time() logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m") - + if test_pare['do_merge_memory']: logger.info("开始合并记忆...") await hippocampus.operation_merge_memory(percentage=0.1) - + end_time = time.time() logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m") - + if test_pare['do_visualize_graph']: # 展示优化后的图形 logger.info("生成记忆图谱可视化...") print("\n生成优化后的记忆图谱：") visualize_graph_lite(memory_graph) - + if test_pare['do_query']: # 交互式查询 while True: query = input("\n请输入新的查询概念（输入'退出'以结束）：") if query.lower() == '退出': break - + items_list = memory_graph.get_related_item(query) if items_list: first_layer, second_layer = items_list @@ -1008,9 +979,6 @@ async def main(): else: print("未找到相关记忆。") - if __name__ == "__main__": import asyncio asyncio.run(main()) - - diff --git a/src/plugins/memory_system/memory_test1.py b/src/plugins/memory_system/memory_test1.py new file mode 100644 index 00000000..72accc2b --- /dev/null +++ b/src/plugins/memory_system/memory_test1.py @@ -0,0 +1,1209 @@ +# -*- coding: utf-8 -*- +import datetime +import math +import os +import random +import sys +import time +from collections import Counter +from pathlib import Path + +import matplotlib.pyplot as plt +import networkx as nx +import pymongo +from dotenv import load_dotenv +from loguru import logger +import jieba + +''' +该理论认为，当两个或多个事物在形态上具有相似性时， +它们在记忆中会形成关联。 +例如，梨和苹果在形状和都是水果这一属性上有相似性， +所以当我们看到梨时，很容易通过形态学联想记忆联想到苹果。 +这种相似性联想有助于我们对新事物进行分类和理解， +当遇到一个新的类似水果时， +我们可以通过与已有的水果记忆进行相似性匹配， +来推测它的一些特征。 + + + +时空关联性联想： +除了相似性联想，MAM 还强调时空关联性联想。 +如果两个事物在时间或空间上经常同时出现，它们也会在记忆中形成关联。 +比如，每次在公园里看到花的时候，都能听到鸟儿的叫声， +那么花和鸟儿叫声的形态特征（花的视觉形态和鸟叫的听觉形态）就会在记忆中形成关联， +以后听到鸟叫可能就会联想到公园里的花。 + +''' + +# from chat.config import global_config +sys.path.append("C:/GitHub/MaiMBot") # 添加项目根目录到 Python 路径 +from src.common.database import Database +from src.plugins.memory_system.offline_llm import LLMModel + +# 获取当前文件的目录 +current_dir = Path(__file__).resolve().parent +# 获取项目根目录（上三层目录） +project_root = current_dir.parent.parent.parent +# env.dev文件路径 +env_path = project_root / ".env.dev" + +# 加载环境变量 +if env_path.exists(): + logger.info(f"从 {env_path} 加载环境变量") + load_dotenv(env_path) +else: + logger.warning(f"未找到环境变量文件: {env_path}") + logger.info("将使用默认配置") + +class Database: + _instance = None + db = None + + @classmethod + def get_instance(cls): + if cls._instance is None: + cls._instance = cls() + return cls._instance + + def __init__(self): + if not Database.db: + Database.initialize( + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), + ) + + @classmethod + def initialize(cls, host, port, db_name, username=None, password=None, auth_source="admin"): + try: + if username and password: + uri = f"mongodb://{username}:{password}@{host}:{port}/{db_name}?authSource={auth_source}" + else: + uri = f"mongodb://{host}:{port}" + + client = pymongo.MongoClient(uri) + cls.db = client[db_name] + # 测试连接 + client.server_info() + logger.success("MongoDB连接成功!") + + except Exception as e: + logger.error(f"初始化MongoDB失败: {str(e)}") + raise + +def calculate_information_content(text): + """计算文本的信息量（熵）""" + char_count = Counter(text) + total_chars = len(text) + + entropy = 0 + for count in char_count.values(): + probability = count / total_chars + entropy -= probability * math.log2(probability) + + return entropy + +def get_cloest_chat_from_db(db, length: int, timestamp: str): + """从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数 + + Returns: + list: 消息记录字典列表，每个字典包含消息内容和时间信息 + """ + chat_records = [] + closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) + + if closest_record and closest_record.get('memorized', 0) < 4: + closest_time = closest_record['time'] + group_id = closest_record['group_id'] + # 获取该时间戳之后的length条消息，且groupid相同 + records = list(db.db.messages.find( + {"time": {"$gt": closest_time}, "group_id": group_id} + ).sort('time', 1).limit(length)) + + # 更新每条消息的memorized属性 + for record in records: + current_memorized = record.get('memorized', 0) + if current_memorized > 3: + print("消息已读取3次，跳过") + return '' + + # 更新memorized值 + db.db.messages.update_one( + {"_id": record["_id"]}, + {"$set": {"memorized": current_memorized + 1}} + ) + + # 添加到记录列表中 + chat_records.append({ + 'text': record["detailed_plain_text"], + 'time': record["time"], + 'group_id': record["group_id"] + }) + + return chat_records + +class Memory_cortex: + def __init__(self, memory_graph: 'Memory_graph'): + self.memory_graph = memory_graph + + def sync_memory_from_db(self): + """ + 从数据库同步数据到内存中的图结构 + 将清空当前内存中的图，并从数据库重新加载所有节点和边 + """ + # 清空当前图 + self.memory_graph.G.clear() + + # 获取当前时间作为默认时间 + default_time = datetime.datetime.now().timestamp() + + # 从数据库加载所有节点 + nodes = self.memory_graph.db.db.graph_data.nodes.find() + for node in nodes: + concept = node['concept'] + memory_items = node.get('memory_items', []) + # 确保memory_items是列表 + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 获取时间属性,如果不存在则使用默认时间 + created_time = node.get('created_time') + last_modified = node.get('last_modified') + + # 如果时间属性不存在,则更新数据库 + if created_time is None or last_modified is None: + created_time = default_time + last_modified = default_time + # 更新数据库中的节点 + self.memory_graph.db.db.graph_data.nodes.update_one( + {'concept': concept}, + {'$set': { + 'created_time': created_time, + 'last_modified': last_modified + }} + ) + logger.info(f"为节点 {concept} 添加默认时间属性") + + # 添加节点到图中,包含时间属性 + self.memory_graph.G.add_node(concept, + memory_items=memory_items, + created_time=created_time, + last_modified=last_modified) + + # 从数据库加载所有边 + edges = self.memory_graph.db.db.graph_data.edges.find() + for edge in edges: + source = edge['source'] + target = edge['target'] + + # 只有当源节点和目标节点都存在时才添加边 + if source in self.memory_graph.G and target in self.memory_graph.G: + # 获取时间属性,如果不存在则使用默认时间 + created_time = edge.get('created_time') + last_modified = edge.get('last_modified') + + # 如果时间属性不存在,则更新数据库 + if created_time is None or last_modified is None: + created_time = default_time + last_modified = default_time + # 更新数据库中的边 + self.memory_graph.db.db.graph_data.edges.update_one( + {'source': source, 'target': target}, + {'$set': { + 'created_time': created_time, + 'last_modified': last_modified + }} + ) + logger.info(f"为边 {source} - {target} 添加默认时间属性") + + self.memory_graph.G.add_edge(source, target, + strength=edge.get('strength', 1), + created_time=created_time, + last_modified=last_modified) + + logger.success("从数据库同步记忆图谱完成") + + def calculate_node_hash(self, concept, memory_items): + """ + 计算节点的特征值 + """ + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + # 将记忆项排序以确保相同内容生成相同的哈希值 + sorted_items = sorted(memory_items) + # 组合概念和记忆项生成特征值 + content = f"{concept}:{'|'.join(sorted_items)}" + return hash(content) + + def calculate_edge_hash(self, source, target): + """ + 计算边的特征值 + """ + # 对源节点和目标节点排序以确保相同的边生成相同的哈希值 + nodes = sorted([source, target]) + return hash(f"{nodes[0]}:{nodes[1]}") + + def sync_memory_to_db(self): + """ + 检查并同步内存中的图结构与数据库 + 使用特征值(哈希值)快速判断是否需要更新 + """ + current_time = datetime.datetime.now().timestamp() + + # 获取数据库中所有节点和内存中所有节点 + db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find()) + memory_nodes = list(self.memory_graph.G.nodes(data=True)) + + # 转换数据库节点为字典格式，方便查找 + db_nodes_dict = {node['concept']: node for node in db_nodes} + + # 检查并更新节点 + for concept, data in memory_nodes: + memory_items = data.get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 计算内存中节点的特征值 + memory_hash = self.calculate_node_hash(concept, memory_items) + + if concept not in db_nodes_dict: + # 数据库中缺少的节点，添加 + node_data = { + 'concept': concept, + 'memory_items': memory_items, + 'hash': memory_hash, + 'created_time': data.get('created_time', current_time), + 'last_modified': data.get('last_modified', current_time) + } + self.memory_graph.db.db.graph_data.nodes.insert_one(node_data) + else: + # 获取数据库中节点的特征值 + db_node = db_nodes_dict[concept] + db_hash = db_node.get('hash', None) + + # 如果特征值不同，则更新节点 + if db_hash != memory_hash: + self.memory_graph.db.db.graph_data.nodes.update_one( + {'concept': concept}, + {'$set': { + 'memory_items': memory_items, + 'hash': memory_hash, + 'last_modified': current_time + }} + ) + + # 检查并删除数据库中多余的节点 + memory_concepts = set(node[0] for node in memory_nodes) + for db_node in db_nodes: + if db_node['concept'] not in memory_concepts: + self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']}) + + # 处理边的信息 + db_edges = list(self.memory_graph.db.db.graph_data.edges.find()) + memory_edges = list(self.memory_graph.G.edges(data=True)) + + # 创建边的哈希值字典 + db_edge_dict = {} + for edge in db_edges: + edge_hash = self.calculate_edge_hash(edge['source'], edge['target']) + db_edge_dict[(edge['source'], edge['target'])] = { + 'hash': edge_hash, + 'strength': edge.get('strength', 1) + } + + # 检查并更新边 + for source, target, data in memory_edges: + edge_hash = self.calculate_edge_hash(source, target) + edge_key = (source, target) + strength = data.get('strength', 1) + + if edge_key not in db_edge_dict: + # 添加新边 + edge_data = { + 'source': source, + 'target': target, + 'strength': strength, + 'hash': edge_hash, + 'created_time': data.get('created_time', current_time), + 'last_modified': data.get('last_modified', current_time) + } + self.memory_graph.db.db.graph_data.edges.insert_one(edge_data) + else: + # 检查边的特征值是否变化 + if db_edge_dict[edge_key]['hash'] != edge_hash: + self.memory_graph.db.db.graph_data.edges.update_one( + {'source': source, 'target': target}, + {'$set': { + 'hash': edge_hash, + 'strength': strength, + 'last_modified': current_time + }} + ) + + # 删除多余的边 + memory_edge_set = set((source, target) for source, target, _ in memory_edges) + for edge_key in db_edge_dict: + if edge_key not in memory_edge_set: + source, target = edge_key + self.memory_graph.db.db.graph_data.edges.delete_one({ + 'source': source, + 'target': target + }) + + logger.success("完成记忆图谱与数据库的差异同步") + + def remove_node_from_db(self, topic): + """ + 从数据库中删除指定节点及其相关的边 + + Args: + topic: 要删除的节点概念 + """ + # 删除节点 + self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': topic}) + # 删除所有涉及该节点的边 + self.memory_graph.db.db.graph_data.edges.delete_many({ + '$or': [ + {'source': topic}, + {'target': topic} + ] + }) + +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): + # 避免自连接 + if concept1 == concept2: + return + + current_time = datetime.datetime.now().timestamp() + + # 如果边已存在，增加 strength + if self.G.has_edge(concept1, concept2): + self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1 + # 更新最后修改时间 + self.G[concept1][concept2]['last_modified'] = current_time + else: + # 如果是新边，初始化 strength 为 1 + self.G.add_edge(concept1, concept2, + strength=1, + created_time=current_time, + last_modified=current_time) + + def add_dot(self, concept, memory): + current_time = datetime.datetime.now().timestamp() + + if concept in self.G: + # 如果节点已存在，将新记忆添加到现有列表中 + if 'memory_items' in self.G.nodes[concept]: + if not isinstance(self.G.nodes[concept]['memory_items'], list): + # 如果当前不是列表，将其转换为列表 + self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']] + self.G.nodes[concept]['memory_items'].append(memory) + # 更新最后修改时间 + self.G.nodes[concept]['last_modified'] = current_time + else: + self.G.nodes[concept]['memory_items'] = [memory] + self.G.nodes[concept]['last_modified'] = current_time + else: + # 如果是新节点，创建新的记忆列表 + self.G.add_node(concept, + memory_items=[memory], + created_time=current_time, + last_modified=current_time) + + def get_dot(self, concept): + # 检查节点是否存在于图中 + if concept in self.G: + # 从图中获取节点数据 + node_data = self.G.nodes[concept] + return concept, node_data + return None + + def get_related_item(self, topic, depth=1): + if topic not in self.G: + return [], [] + + first_layer_items = [] + second_layer_items = [] + + # 获取相邻节点 + neighbors = list(self.G.neighbors(topic)) + + # 获取当前节点的记忆项 + node_data = self.get_dot(topic) + if node_data: + concept, data = node_data + if 'memory_items' in data: + memory_items = data['memory_items'] + if isinstance(memory_items, list): + first_layer_items.extend(memory_items) + else: + first_layer_items.append(memory_items) + + # 只在depth=2时获取第二层记忆 + if depth >= 2: + # 获取相邻节点的记忆项 + for neighbor in neighbors: + node_data = self.get_dot(neighbor) + if node_data: + concept, data = node_data + if 'memory_items' in data: + memory_items = data['memory_items'] + if isinstance(memory_items, list): + second_layer_items.extend(memory_items) + else: + second_layer_items.append(memory_items) + + return first_layer_items, second_layer_items + + @property + def dots(self): + # 返回所有节点对应的 Memory_dot 对象 + return [self.get_dot(node) for node in self.G.nodes()] + +# 海马体 +class Hippocampus: + def __init__(self, memory_graph: Memory_graph): + self.memory_graph = memory_graph + self.memory_cortex = Memory_cortex(memory_graph) + self.llm_model = LLMModel() + self.llm_model_small = LLMModel(model_name="deepseek-ai/DeepSeek-V2.5") + self.llm_model_get_topic = LLMModel(model_name="Pro/Qwen/Qwen2.5-7B-Instruct") + self.llm_model_summary = LLMModel(model_name="Qwen/Qwen2.5-32B-Instruct") + + def get_memory_sample(self, chat_size=20, time_frequency:dict={'near':2,'mid':4,'far':3}): + """获取记忆样本 + + Returns: + list: 消息记录列表，每个元素是一个消息记录字典列表 + """ + current_timestamp = datetime.datetime.now().timestamp() + chat_samples = [] + + # 短期：1h 中期：4h 长期：24h + for _ in range(time_frequency.get('near')): + random_time = current_timestamp - random.randint(1, 3600*4) + messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) + if messages: + chat_samples.append(messages) + + for _ in range(time_frequency.get('mid')): + random_time = current_timestamp - random.randint(3600*4, 3600*24) + messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) + if messages: + chat_samples.append(messages) + + for _ in range(time_frequency.get('far')): + random_time = current_timestamp - random.randint(3600*24, 3600*24*7) + messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time) + if messages: + chat_samples.append(messages) + + return chat_samples + + def calculate_topic_num(self,text, compress_rate): + """计算文本的话题数量""" + information_content = calculate_information_content(text) + topic_by_length = text.count('\n')*compress_rate + topic_by_information_content = max(1, min(5, int((information_content-3) * 2))) + topic_num = int((topic_by_length + topic_by_information_content)/2) + print(f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, topic_num: {topic_num}") + return topic_num + + async def memory_compress(self, messages: list, compress_rate=0.1): + """压缩消息记录为记忆 + + Args: + messages: 消息记录字典列表，每个字典包含text和time字段 + compress_rate: 压缩率 + + Returns: + tuple: (压缩记忆集合, 相似主题字典) + - 压缩记忆集合: set of (话题, 记忆) 元组 + - 相似主题字典: dict of {话题: [(相似主题, 相似度), ...]} + """ + if not messages: + return set(), {} + + # 合并消息文本，同时保留时间信息 + input_text = "" + time_info = "" + # 计算最早和最晚时间 + earliest_time = min(msg['time'] for msg in messages) + latest_time = max(msg['time'] for msg in messages) + + earliest_dt = datetime.datetime.fromtimestamp(earliest_time) + latest_dt = datetime.datetime.fromtimestamp(latest_time) + + # 如果是同一年 + if earliest_dt.year == latest_dt.year: + earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S") + latest_str = latest_dt.strftime("%m-%d %H:%M:%S") + time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n" + else: + earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S") + latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S") + time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n" + + for msg in messages: + input_text += f"{msg['text']}\n" + + print(input_text) + + topic_num = self.calculate_topic_num(input_text, compress_rate) + topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(input_text, topic_num)) + + # 过滤topics + filter_keywords = ['表情包', '图片', '回复', '聊天记录'] + topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] + filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)] + + print(f"过滤后话题: {filtered_topics}") + + # 为每个话题查找相似的已存在主题 + print("\n检查相似主题:") + similar_topics_dict = {} # 存储每个话题的相似主题列表 + + for topic in filtered_topics: + # 获取所有现有节点 + existing_topics = list(self.memory_graph.G.nodes()) + similar_topics = [] + + # 对每个现有节点计算相似度 + for existing_topic in existing_topics: + # 使用jieba分词并计算余弦相似度 + topic_words = set(jieba.cut(topic)) + existing_words = set(jieba.cut(existing_topic)) + + # 计算词向量 + all_words = topic_words | existing_words + v1 = [1 if word in topic_words else 0 for word in all_words] + v2 = [1 if word in existing_words else 0 for word in all_words] + + # 计算余弦相似度 + similarity = cosine_similarity(v1, v2) + + # 如果相似度超过阈值，添加到结果中 + if similarity >= 0.6: # 设置相似度阈值 + similar_topics.append((existing_topic, similarity)) + + # 按相似度降序排序 + similar_topics.sort(key=lambda x: x[1], reverse=True) + # 只保留前5个最相似的主题 + similar_topics = similar_topics[:5] + + # 存储到字典中 + similar_topics_dict[topic] = similar_topics + + # 输出结果 + if similar_topics: + print(f"\n主题「{topic}」的相似主题:") + for similar_topic, score in similar_topics: + print(f"- {similar_topic} (相似度: {score:.3f})") + else: + print(f"\n主题「{topic}」没有找到相似主题") + + # 创建所有话题的请求任务 + tasks = [] + for topic in filtered_topics: + topic_what_prompt = self.topic_what(input_text, topic , time_info) + # 创建异步任务 + task = self.llm_model_small.generate_response_async(topic_what_prompt) + tasks.append((topic.strip(), task)) + + # 等待所有任务完成 + compressed_memory = set() + for topic, task in tasks: + response = await task + if response: + compressed_memory.add((topic, response[0])) + + return compressed_memory, similar_topics_dict + + async def operation_build_memory(self, chat_size=12): + # 最近消息获取频率 + time_frequency = {'near': 3, 'mid': 8, 'far': 5} + memory_samples = self.get_memory_sample(chat_size, time_frequency) + + all_topics = [] # 用于存储所有话题 + + for i, messages in enumerate(memory_samples, 1): + # 加载进度可视化 + all_topics = [] + progress = (i / len(memory_samples)) * 100 + bar_length = 30 + filled_length = int(bar_length * i // len(memory_samples)) + bar = '█' * filled_length + '-' * (bar_length - filled_length) + print(f"\n进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})") + + # 生成压缩后记忆 + compress_rate = 0.1 + compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate) + print(f"\033[1;33m压缩后记忆数量\033[0m: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}") + + # 将记忆加入到图谱中 + for topic, memory in compressed_memory: + print(f"\033[1;32m添加节点\033[0m: {topic}") + self.memory_graph.add_dot(topic, memory) + all_topics.append(topic) + + # 连接相似的已存在主题 + if topic in similar_topics_dict: + similar_topics = similar_topics_dict[topic] + for similar_topic, similarity in similar_topics: + # 避免自连接 + if topic != similar_topic: + # 根据相似度设置连接强度 + strength = int(similarity * 10) # 将0.3-1.0的相似度映射到3-10的强度 + print(f"\033[1;36m连接相似节点\033[0m: {topic} 和 {similar_topic} (强度: {strength})") + # 使用相似度作为初始连接强度 + self.memory_graph.G.add_edge(topic, similar_topic, strength=strength) + + # 连接同批次的相关话题 + for i in range(len(all_topics)): + for j in range(i + 1, len(all_topics)): + print(f"\033[1;32m连接同批次节点\033[0m: {all_topics[i]} 和 {all_topics[j]}") + self.memory_graph.connect_dot(all_topics[i], all_topics[j]) + + self.memory_cortex.sync_memory_to_db() + + def forget_connection(self, source, target): + """ + 检查并可能遗忘一个连接 + + Args: + source: 连接的源节点 + target: 连接的目标节点 + + Returns: + tuple: (是否有变化, 变化类型, 变化详情) + 变化类型: 0-无变化, 1-强度减少, 2-连接移除 + """ + current_time = datetime.datetime.now().timestamp() + # 获取边的属性 + edge_data = self.memory_graph.G[source][target] + last_modified = edge_data.get('last_modified', current_time) + + # 如果连接超过7天未更新 + if current_time - last_modified > 6000: # test + # 获取当前强度 + current_strength = edge_data.get('strength', 1) + # 减少连接强度 + new_strength = current_strength - 1 + edge_data['strength'] = new_strength + edge_data['last_modified'] = current_time + + # 如果强度降为0,移除连接 + if new_strength <= 0: + self.memory_graph.G.remove_edge(source, target) + return True, 2, f"移除连接: {source} - {target} (强度降至0)" + else: + return True, 1, f"减弱连接: {source} - {target} (强度: {current_strength} -> {new_strength})" + + return False, 0, "" + + def forget_topic(self, topic): + """ + 检查并可能遗忘一个话题的记忆 + + Args: + topic: 要检查的话题 + + Returns: + tuple: (是否有变化, 变化类型, 变化详情) + 变化类型: 0-无变化, 1-记忆减少, 2-节点移除 + """ + current_time = datetime.datetime.now().timestamp() + # 获取节点的最后修改时间 + node_data = self.memory_graph.G.nodes[topic] + last_modified = node_data.get('last_modified', current_time) + + # 如果话题超过7天未更新 + if current_time - last_modified > 3000: # test + memory_items = node_data.get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + if memory_items: + # 获取当前记忆数量 + current_count = len(memory_items) + # 随机选择一条记忆删除 + removed_item = random.choice(memory_items) + memory_items.remove(removed_item) + + if memory_items: + # 更新节点的记忆项和最后修改时间 + self.memory_graph.G.nodes[topic]['memory_items'] = memory_items + self.memory_graph.G.nodes[topic]['last_modified'] = current_time + return True, 1, f"减少记忆: {topic} (记忆数量: {current_count} -> {len(memory_items)})\n被移除的记忆: {removed_item}" + else: + # 如果没有记忆了,删除节点及其所有连接 + self.memory_graph.G.remove_node(topic) + return True, 2, f"移除节点: {topic} (无剩余记忆)\n最后一条记忆: {removed_item}" + + return False, 0, "" + + async def operation_forget_topic(self, percentage=0.1): + """ + 随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘 + + Args: + percentage: 要检查的节点和边的比例,默认为0.1（10%） + """ + # 获取所有节点和边 + all_nodes = list(self.memory_graph.G.nodes()) + all_edges = list(self.memory_graph.G.edges()) + + # 计算要检查的数量 + check_nodes_count = max(1, int(len(all_nodes) * percentage)) + check_edges_count = max(1, int(len(all_edges) * percentage)) + + # 随机选择要检查的节点和边 + nodes_to_check = random.sample(all_nodes, check_nodes_count) + edges_to_check = random.sample(all_edges, check_edges_count) + + # 用于统计不同类型的变化 + edge_changes = {'weakened': 0, 'removed': 0} + node_changes = {'reduced': 0, 'removed': 0} + + # 检查并遗忘连接 + print("\n开始检查连接...") + for source, target in edges_to_check: + changed, change_type, details = self.forget_connection(source, target) + if changed: + if change_type == 1: + edge_changes['weakened'] += 1 + logger.info(f"\033[1;34m[连接减弱]\033[0m {details}") + elif change_type == 2: + edge_changes['removed'] += 1 + logger.info(f"\033[1;31m[连接移除]\033[0m {details}") + + # 检查并遗忘话题 + print("\n开始检查节点...") + for node in nodes_to_check: + changed, change_type, details = self.forget_topic(node) + if changed: + if change_type == 1: + node_changes['reduced'] += 1 + logger.info(f"\033[1;33m[记忆减少]\033[0m {details}") + elif change_type == 2: + node_changes['removed'] += 1 + logger.info(f"\033[1;31m[节点移除]\033[0m {details}") + + # 同步到数据库 + if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()): + self.memory_cortex.sync_memory_to_db() + print("\n遗忘操作统计:") + print(f"连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除") + print(f"节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除") + else: + print("\n本次检查没有节点或连接满足遗忘条件") + + async def merge_memory(self, topic): + """ + 对指定话题的记忆进行合并压缩 + + Args: + topic: 要合并的话题节点 + """ + # 获取节点的记忆项 + memory_items = self.memory_graph.G.nodes[topic].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + + # 如果记忆项不足，直接返回 + if len(memory_items) < 10: + return + + # 随机选择10条记忆 + selected_memories = random.sample(memory_items, 10) + + # 拼接成文本 + merged_text = "\n".join(selected_memories) + print(f"\n[合并记忆] 话题: {topic}") + print(f"选择的记忆:\n{merged_text}") + + # 使用memory_compress生成新的压缩记忆 + compressed_memories, _ = await self.memory_compress(selected_memories, 0.1) + + # 从原记忆列表中移除被选中的记忆 + for memory in selected_memories: + memory_items.remove(memory) + + # 添加新的压缩记忆 + for _, compressed_memory in compressed_memories: + memory_items.append(compressed_memory) + print(f"添加压缩记忆: {compressed_memory}") + + # 更新节点的记忆项 + self.memory_graph.G.nodes[topic]['memory_items'] = memory_items + print(f"完成记忆合并，当前记忆数量: {len(memory_items)}") + + async def operation_merge_memory(self, percentage=0.1): + """ + 随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并 + + Args: + percentage: 要检查的节点比例，默认为0.1（10%） + """ + # 获取所有节点 + all_nodes = list(self.memory_graph.G.nodes()) + # 计算要检查的节点数量 + check_count = max(1, int(len(all_nodes) * percentage)) + # 随机选择节点 + nodes_to_check = random.sample(all_nodes, check_count) + + merged_nodes = [] + for node in nodes_to_check: + # 获取节点的内容条数 + memory_items = self.memory_graph.G.nodes[node].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + + # 如果内容数量超过100，进行合并 + if content_count > 100: + print(f"\n检查节点: {node}, 当前记忆数量: {content_count}") + await self.merge_memory(node) + merged_nodes.append(node) + + # 同步到数据库 + if merged_nodes: + self.memory_cortex.sync_memory_to_db() + print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点") + else: + print("\n本次检查没有需要合并的节点") + + async def _identify_topics(self, text: str) -> list: + """从文本中识别可能的主题""" + topics_response = self.llm_model_get_topic.generate_response(self.find_topic_llm(text, 5)) + topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()] + return topics + + def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list: + """查找与给定主题相似的记忆主题""" + all_memory_topics = list(self.memory_graph.G.nodes()) + all_similar_topics = [] + + for topic in topics: + if debug_info: + pass + + topic_vector = text_to_vector(topic) + has_similar_topic = False + + for memory_topic in all_memory_topics: + memory_vector = text_to_vector(memory_topic) + all_words = set(topic_vector.keys()) | set(memory_vector.keys()) + v1 = [topic_vector.get(word, 0) for word in all_words] + v2 = [memory_vector.get(word, 0) for word in all_words] + similarity = cosine_similarity(v1, v2) + + if similarity >= similarity_threshold: + has_similar_topic = True + all_similar_topics.append((memory_topic, similarity)) + + return all_similar_topics + + def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list: + """获取相似度最高的主题""" + seen_topics = set() + top_topics = [] + + for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True): + if topic not in seen_topics and len(top_topics) < max_topics: + seen_topics.add(topic) + top_topics.append((topic, score)) + + return top_topics + + async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int: + """计算输入文本对记忆的激活程度""" + logger.info(f"[记忆激活]识别主题: {await self._identify_topics(text)}") + + identified_topics = await self._identify_topics(text) + if not identified_topics: + return 0 + + all_similar_topics = self._find_similar_topics( + identified_topics, + similarity_threshold=similarity_threshold, + debug_info="记忆激活" + ) + + if not all_similar_topics: + return 0 + + top_topics = self._get_top_topics(all_similar_topics, max_topics) + + if len(top_topics) == 1: + topic, score = top_topics[0] + memory_items = self.memory_graph.G.nodes[topic].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + penalty = 1.0 / (1 + math.log(content_count + 1)) + + activation = int(score * 50 * penalty) + print(f"\033[1;32m[记忆激活]\033[0m 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}") + return activation + + matched_topics = set() + topic_similarities = {} + + for memory_topic, similarity in top_topics: + memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', []) + if not isinstance(memory_items, list): + memory_items = [memory_items] if memory_items else [] + content_count = len(memory_items) + penalty = 1.0 / (1 + math.log(content_count + 1)) + + for input_topic in identified_topics: + topic_vector = text_to_vector(input_topic) + memory_vector = text_to_vector(memory_topic) + all_words = set(topic_vector.keys()) | set(memory_vector.keys()) + v1 = [topic_vector.get(word, 0) for word in all_words] + v2 = [memory_vector.get(word, 0) for word in all_words] + sim = cosine_similarity(v1, v2) + if sim >= similarity_threshold: + matched_topics.add(input_topic) + adjusted_sim = sim * penalty + topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim) + print(f"\033[1;32m[记忆激活]\033[0m 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})") + + topic_match = len(matched_topics) / len(identified_topics) + average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0 + + activation = int((topic_match + average_similarities) / 2 * 100) + print(f"\033[1;32m[记忆激活]\033[0m 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}") + + return activation + + async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5) -> list: + """根据输入文本获取相关的记忆内容""" + identified_topics = await self._identify_topics(text) + + all_similar_topics = self._find_similar_topics( + identified_topics, + similarity_threshold=similarity_threshold, + debug_info="记忆检索" + ) + + relevant_topics = self._get_top_topics(all_similar_topics, max_topics) + + relevant_memories = [] + for topic, score in relevant_topics: + first_layer, _ = self.memory_graph.get_related_item(topic, depth=1) + if first_layer: + if len(first_layer) > max_memory_num/2: + first_layer = random.sample(first_layer, max_memory_num//2) + for memory in first_layer: + relevant_memories.append({ + 'topic': topic, + 'similarity': score, + 'content': memory + }) + + relevant_memories.sort(key=lambda x: x['similarity'], reverse=True) + + if len(relevant_memories) > max_memory_num: + relevant_memories = random.sample(relevant_memories, max_memory_num) + + return relevant_memories + + def find_topic_llm(self,text, topic_num): + prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。' + return prompt + + def topic_what(self,text, topic, time_info): + prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好' + return prompt + +def segment_text(text): + """使用jieba进行文本分词""" + seg_text = list(jieba.cut(text)) + return seg_text + +def text_to_vector(text): + """将文本转换为词频向量""" + words = segment_text(text) + vector = {} + for word in words: + vector[word] = vector.get(word, 0) + 1 + return vector + +def cosine_similarity(v1, v2): + """计算两个向量的余弦相似度""" + dot_product = sum(a * b for a, b in zip(v1, v2)) + norm1 = math.sqrt(sum(a * a for a in v1)) + norm2 = math.sqrt(sum(b * b for b in v2)) + if norm1 == 0 or norm2 == 0: + return 0 + return dot_product / (norm1 * norm2) + +def visualize_graph_lite(memory_graph: Memory_graph, color_by_memory: bool = False): + # 设置中文字体 + plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 + plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 + + G = memory_graph.G + + # 创建一个新图用于可视化 + H = G.copy() + + # 过滤掉内容数量小于2的节点 + nodes_to_remove = [] + for node in H.nodes(): + memory_items = H.nodes[node].get('memory_items', []) + memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0) + if memory_count < 2: + nodes_to_remove.append(node) + + H.remove_nodes_from(nodes_to_remove) + + # 如果没有符合条件的节点，直接返回 + if len(H.nodes()) == 0: + print("没有找到内容数量大于等于2的节点") + return + + # 计算节点大小和颜色 + node_colors = [] + node_sizes = [] + nodes = list(H.nodes()) + + # 获取最大记忆数用于归一化节点大小 + max_memories = 1 + for node in nodes: + memory_items = H.nodes[node].get('memory_items', []) + memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0) + max_memories = max(max_memories, memory_count) + + # 计算每个节点的大小和颜色 + for node in nodes: + # 计算节点大小（基于记忆数量） + memory_items = H.nodes[node].get('memory_items', []) + memory_count = len(memory_items) if isinstance(memory_items, list) else (1 if memory_items else 0) + # 使用指数函数使变化更明显 + ratio = memory_count / max_memories + size = 400 + 2000 * (ratio ** 2) # 增大节点大小 + node_sizes.append(size) + + # 计算节点颜色（基于连接数） + degree = H.degree(node) + if degree >= 30: + node_colors.append((1.0, 0, 0)) # 亮红色 (#FF0000) + else: + # 将1-10映射到0-1的范围 + color_ratio = (degree - 1) / 29.0 if degree > 1 else 0 + # 使用蓝到红的渐变 + red = min(0.9, color_ratio) + blue = max(0.0, 1.0 - color_ratio) + node_colors.append((red, 0, blue)) + + # 绘制图形 + plt.figure(figsize=(16, 12)) # 减小图形尺寸 + pos = nx.spring_layout(H, + k=1, # 调整节点间斥力 + iterations=100, # 增加迭代次数 + scale=1.5, # 减小布局尺寸 + weight='strength') # 使用边的strength属性作为权重 + + nx.draw(H, pos, + with_labels=True, + node_color=node_colors, + node_size=node_sizes, + font_size=12, # 保持增大的字体大小 + font_family='SimHei', + font_weight='bold', + edge_color='gray', + width=1.5) # 统一的边宽度 + + title = '记忆图谱可视化（仅显示内容≥2的节点）\n节点大小表示记忆数量\n节点颜色：蓝(弱连接)到红(强连接)渐变，边的透明度表示连接强度\n连接强度越大的节点距离越近' + plt.title(title, fontsize=16, fontfamily='SimHei') + plt.show() + +async def main(): + # 初始化数据库 + logger.info("正在初始化数据库连接...") + db = Database.get_instance() + start_time = time.time() + + test_pare = {'do_build_memory':True,'do_forget_topic':False,'do_visualize_graph':True,'do_query':False,'do_merge_memory':False} + + # 创建记忆图 + memory_graph = Memory_graph() + + # 创建海马体 + hippocampus = Hippocampus(memory_graph) + + # 从数据库同步数据 + hippocampus.memory_cortex.sync_memory_from_db() + + end_time = time.time() + logger.info(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m") + + # 构建记忆 + if test_pare['do_build_memory']: + logger.info("开始构建记忆...") + chat_size = 20 + await hippocampus.operation_build_memory(chat_size=chat_size) + + end_time = time.time() + logger.info(f"\033[32m[构建记忆耗时: {end_time - start_time:.2f} 秒,chat_size={chat_size},chat_count = 16]\033[0m") + + if test_pare['do_forget_topic']: + logger.info("开始遗忘记忆...") + await hippocampus.operation_forget_topic(percentage=0.01) + + end_time = time.time() + logger.info(f"\033[32m[遗忘记忆耗时: {end_time - start_time:.2f} 秒]\033[0m") + + if test_pare['do_merge_memory']: + logger.info("开始合并记忆...") + await hippocampus.operation_merge_memory(percentage=0.1) + + end_time = time.time() + logger.info(f"\033[32m[合并记忆耗时: {end_time - start_time:.2f} 秒]\033[0m") + + if test_pare['do_visualize_graph']: + # 展示优化后的图形 + logger.info("生成记忆图谱可视化...") + print("\n生成优化后的记忆图谱：") + visualize_graph_lite(memory_graph) + + if test_pare['do_query']: + # 交互式查询 + while True: + query = input("\n请输入新的查询概念（输入'退出'以结束）：") + if query.lower() == '退出': + break + + items_list = memory_graph.get_related_item(query) + if items_list: + first_layer, second_layer = items_list + if first_layer: + print("\n直接相关的记忆：") + for item in first_layer: + print(f"- {item}") + if second_layer: + print("\n间接相关的记忆：") + for item in second_layer: + print(f"- {item}") + else: + print("未找到相关记忆。") + + +if __name__ == "__main__": + import asyncio + asyncio.run(main()) + + diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index fe8e1a10..3424d662 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -7,10 +7,11 @@ from typing import Tuple, Union import aiohttp from loguru import logger from nonebot import get_driver - +import base64 +from PIL import Image +import io from ...common.database import Database from ..chat.config import global_config -from ..chat.utils_image import compress_base64_image_by_scale driver = get_driver() config = driver.config @@ -44,8 +45,8 @@ class LLM_request: self.db.db.llm_usage.create_index([("model_name", 1)]) self.db.db.llm_usage.create_index([("user_id", 1)]) self.db.db.llm_usage.create_index([("request_type", 1)]) - except Exception as e: - logger.error(f"创建数据库索引失败") + except Exception: + logger.error("创建数据库索引失败") def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int, user_id: str = "system", request_type: str = "chat", @@ -80,7 +81,7 @@ class LLM_request: f"总计: {total_tokens}" ) except Exception: - logger.error(f"记录token使用情况失败") + logger.error("记录token使用情况失败") def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float: """计算API调用成本 @@ -183,18 +184,21 @@ class LLM_request: elif response.status in policy["abort_codes"]: logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}") if response.status == 403: - # 尝试降级Pro模型 - if self.model_name.startswith( - "Pro/") and self.base_url == "https://api.siliconflow.cn/v1/": + #只针对硅基流动的V3和R1进行降级处理 + if self.model_name.startswith( + "Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/": old_model_name = self.model_name self.model_name = self.model_name[4:] # 移除"Pro/"前缀 logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}") # 对全局配置进行更新 - if hasattr(global_config, 'llm_normal') and global_config.llm_normal.get( - 'name') == old_model_name: + if global_config.llm_normal.get('name') == old_model_name: global_config.llm_normal['name'] = self.model_name - logger.warning(f"已将全局配置中的 llm_normal 模型降级") + logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}") + + if global_config.llm_reasoning.get('name') == old_model_name: + global_config.llm_reasoning['name'] = self.model_name + logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}") # 更新payload中的模型名 if payload and 'model' in payload: @@ -210,6 +214,7 @@ class LLM_request: # 将流式输出转化为非流式输出 if stream_mode: + flag_delta_content_finished = False accumulated_content = "" async for line_bytes in response.content: line = line_bytes.decode("utf-8").strip() @@ -221,13 +226,25 @@ class LLM_request: break try: chunk = json.loads(data_str) - delta = chunk["choices"][0]["delta"] - delta_content = delta.get("content") - if delta_content is None: - delta_content = "" - accumulated_content += delta_content + if flag_delta_content_finished: + usage = chunk.get("usage", None) # 获取tokn用量 + else: + delta = chunk["choices"][0]["delta"] + delta_content = delta.get("content") + if delta_content is None: + delta_content = "" + accumulated_content += delta_content + # 检测流式输出文本是否结束 + finish_reason = chunk["choices"][0]["finish_reason"] + if finish_reason == "stop": + usage = chunk.get("usage", None) + if usage: + break + # 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk + flag_delta_content_finished = True + except Exception: - logger.exception(f"解析流式输出错") + logger.exception("解析流式输出错误") content = accumulated_content reasoning_content = "" think_match = re.search(r'(.*?)', content, re.DOTALL) @@ -236,7 +253,7 @@ class LLM_request: content = re.sub(r'.*?', '', content, flags=re.DOTALL).strip() # 构造一个伪result以便调用自定义响应处理器或默认处理器 result = { - "choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]} + "choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}], "usage": usage} return response_handler(result) if response_handler else self._default_response_handler( result, user_id, request_type, endpoint) else: @@ -355,7 +372,7 @@ class LLM_request: """构建请求头""" if no_key: return { - "Authorization": f"Bearer **********", + "Authorization": "Bearer **********", "Content-Type": "application/json" } else: @@ -432,3 +449,78 @@ class LLM_request: response_handler=embedding_handler ) return embedding + +def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str: + """压缩base64格式的图片到指定大小 + Args: + base64_data: base64编码的图片数据 + target_size: 目标文件大小（字节），默认0.8MB + Returns: + str: 压缩后的base64图片数据 + """ + try: + # 将base64转换为字节数据 + image_data = base64.b64decode(base64_data) + + # 如果已经小于目标大小，直接返回原图 + if len(image_data) <= 2*1024*1024: + return base64_data + + # 将字节数据转换为图片对象 + img = Image.open(io.BytesIO(image_data)) + + # 获取原始尺寸 + original_width, original_height = img.size + + # 计算缩放比例 + scale = min(1.0, (target_size / len(image_data)) ** 0.5) + + # 计算新的尺寸 + new_width = int(original_width * scale) + new_height = int(original_height * scale) + + # 创建内存缓冲区 + output_buffer = io.BytesIO() + + # 如果是GIF，处理所有帧 + if getattr(img, "is_animated", False): + frames = [] + for frame_idx in range(img.n_frames): + img.seek(frame_idx) + new_frame = img.copy() + new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折 + frames.append(new_frame) + + # 保存到缓冲区 + frames[0].save( + output_buffer, + format='GIF', + save_all=True, + append_images=frames[1:], + optimize=True, + duration=img.info.get('duration', 100), + loop=img.info.get('loop', 0) + ) + else: + # 处理静态图片 + resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) + + # 保存到缓冲区，保持原始格式 + if img.format == 'PNG' and img.mode in ('RGBA', 'LA'): + resized_img.save(output_buffer, format='PNG', optimize=True) + else: + resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True) + + # 获取压缩后的数据并转换为base64 + compressed_data = output_buffer.getvalue() + logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}") + logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB") + + return base64.b64encode(compressed_data).decode('utf-8') + + except Exception as e: + logger.error(f"压缩图片失败: {str(e)}") + import traceback + logger.error(traceback.format_exc()) + return base64_data + diff --git a/src/plugins/schedule/schedule_generator.py b/src/plugins/schedule/schedule_generator.py index e45940c5..3cf1d473 100644 --- a/src/plugins/schedule/schedule_generator.py +++ b/src/plugins/schedule/schedule_generator.py @@ -1,3 +1,4 @@ +import os import datetime import json from typing import Dict, Union @@ -14,15 +15,15 @@ driver = get_driver() config = driver.config Database.initialize( - host=config.MONGODB_HOST, - port=int(config.MONGODB_PORT), - db_name=config.DATABASE_NAME, - username=config.MONGODB_USERNAME, - password=config.MONGODB_PASSWORD, - auth_source=config.MONGODB_AUTH_SOURCE + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), ) - class ScheduleGenerator: def __init__(self): # 根据global_config.llm_normal这一字典配置指定模型 @@ -68,7 +69,7 @@ class ScheduleGenerator: 1. 早上的学习和工作安排 2. 下午的活动和任务 3. 晚上的计划和休息时间 - 请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用JSON格式返回日程表，仅返回内容，不要返回注释，时间采用24小时制，格式为{"时间": "活动","时间": "活动",...}。""" + 请按照时间顺序列出具体时间点和对应的活动，用一个时间点而不是时间段来表示时间，用JSON格式返回日程表，仅返回内容，不要返回注释，不要添加任何markdown或代码块样式，时间采用24小时制，格式为{"时间": "活动","时间": "活动",...}。""" try: schedule_text, _ = await self.llm_scheduler.generate_response(prompt) @@ -91,7 +92,7 @@ class ScheduleGenerator: try: schedule_dict = json.loads(schedule_text) return schedule_dict - except json.JSONDecodeError as e: + except json.JSONDecodeError: logger.exception("解析日程失败: {}".format(schedule_text)) return False @@ -176,6 +177,6 @@ class ScheduleGenerator: # print(scheduler.tomorrow_schedule) # if __name__ == "__main__": -# main() +# main() bot_schedule = ScheduleGenerator() diff --git a/src/plugins/utils/statistic.py b/src/plugins/utils/statistic.py index a071355a..2974389e 100644 --- a/src/plugins/utils/statistic.py +++ b/src/plugins/utils/statistic.py @@ -155,7 +155,7 @@ class LLMStatistics: all_stats = self._collect_all_statistics() self._save_statistics(all_stats) except Exception: - logger.exception(f"统计数据处理失败") + logger.exception("统计数据处理失败") # 等待1分钟 for _ in range(60): diff --git a/src/plugins/zhishi/knowledge_library.py b/src/plugins/zhishi/knowledge_library.py new file mode 100644 index 00000000..2411e311 --- /dev/null +++ b/src/plugins/zhishi/knowledge_library.py @@ -0,0 +1,383 @@ +import os +import sys +import time +import requests +from dotenv import load_dotenv +import hashlib +from datetime import datetime +from tqdm import tqdm +from rich.console import Console +from rich.table import Table + +# 添加项目根目录到 Python 路径 +root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) +sys.path.append(root_path) + +# 现在可以导入src模块 +from src.common.database import Database + +# 加载根目录下的env.edv文件 +env_path = os.path.join(root_path, ".env.prod") +if not os.path.exists(env_path): + raise FileNotFoundError(f"配置文件不存在: {env_path}") +load_dotenv(env_path) + +class KnowledgeLibrary: + def __init__(self): + # 初始化数据库连接 + if Database._instance is None: + Database.initialize( + uri=os.getenv("MONGODB_URI"), + host=os.getenv("MONGODB_HOST", "127.0.0.1"), + port=int(os.getenv("MONGODB_PORT", "27017")), + db_name=os.getenv("DATABASE_NAME", "MegBot"), + username=os.getenv("MONGODB_USERNAME"), + password=os.getenv("MONGODB_PASSWORD"), + auth_source=os.getenv("MONGODB_AUTH_SOURCE"), + ) + self.db = Database.get_instance() + self.raw_info_dir = "data/raw_info" + self._ensure_dirs() + self.api_key = os.getenv("SILICONFLOW_KEY") + if not self.api_key: + raise ValueError("SILICONFLOW_API_KEY 环境变量未设置") + self.console = Console() + + def _ensure_dirs(self): + """确保必要的目录存在""" + os.makedirs(self.raw_info_dir, exist_ok=True) + + def read_file(self, file_path: str) -> str: + """读取文件内容""" + with open(file_path, 'r', encoding='utf-8') as f: + return f.read() + + def split_content(self, content: str, max_length: int = 512) -> list: + """将内容分割成适当大小的块，保持段落完整性 + + Args: + content: 要分割的文本内容 + max_length: 每个块的最大长度 + + Returns: + list: 分割后的文本块列表 + """ + # 首先按段落分割 + paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()] + chunks = [] + current_chunk = [] + current_length = 0 + + for para in paragraphs: + para_length = len(para) + + # 如果单个段落就超过最大长度 + if para_length > max_length: + # 如果当前chunk不为空，先保存 + if current_chunk: + chunks.append('\n'.join(current_chunk)) + current_chunk = [] + current_length = 0 + + # 将长段落按句子分割 + sentences = [s.strip() for s in para.replace('。', '。\n').replace('！', '！\n').replace('？', '？\n').split('\n') if s.strip()] + temp_chunk = [] + temp_length = 0 + + for sentence in sentences: + sentence_length = len(sentence) + if sentence_length > max_length: + # 如果单个句子超长，强制按长度分割 + if temp_chunk: + chunks.append('\n'.join(temp_chunk)) + temp_chunk = [] + temp_length = 0 + for i in range(0, len(sentence), max_length): + chunks.append(sentence[i:i + max_length]) + elif temp_length + sentence_length + 1 <= max_length: + temp_chunk.append(sentence) + temp_length += sentence_length + 1 + else: + chunks.append('\n'.join(temp_chunk)) + temp_chunk = [sentence] + temp_length = sentence_length + + if temp_chunk: + chunks.append('\n'.join(temp_chunk)) + + # 如果当前段落加上现有chunk不超过最大长度 + elif current_length + para_length + 1 <= max_length: + current_chunk.append(para) + current_length += para_length + 1 + else: + # 保存当前chunk并开始新的chunk + chunks.append('\n'.join(current_chunk)) + current_chunk = [para] + current_length = para_length + + # 添加最后一个chunk + if current_chunk: + chunks.append('\n'.join(current_chunk)) + + return chunks + + def get_embedding(self, text: str) -> list: + """获取文本的embedding向量""" + url = "https://api.siliconflow.cn/v1/embeddings" + payload = { + "model": "BAAI/bge-m3", + "input": text, + "encoding_format": "float" + } + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + response = requests.post(url, json=payload, headers=headers) + if response.status_code != 200: + print(f"获取embedding失败: {response.text}") + return None + + return response.json()['data'][0]['embedding'] + + def process_files(self, knowledge_length:int=512): + """处理raw_info目录下的所有txt文件""" + txt_files = [f for f in os.listdir(self.raw_info_dir) if f.endswith('.txt')] + + if not txt_files: + self.console.print("[red]警告：在 {} 目录下没有找到任何txt文件[/red]".format(self.raw_info_dir)) + self.console.print("[yellow]请将需要处理的文本文件放入该目录后再运行程序[/yellow]") + return + + total_stats = { + "processed_files": 0, + "total_chunks": 0, + "failed_files": [], + "skipped_files": [] + } + + self.console.print(f"\n[bold blue]开始处理知识库文件 - 共{len(txt_files)}个文件[/bold blue]") + + for filename in tqdm(txt_files, desc="处理文件进度"): + file_path = os.path.join(self.raw_info_dir, filename) + result = self.process_single_file(file_path, knowledge_length) + self._update_stats(total_stats, result, filename) + + self._display_processing_results(total_stats) + + def process_single_file(self, file_path: str, knowledge_length: int = 512): + """处理单个文件""" + result = { + "status": "success", + "chunks_processed": 0, + "error": None + } + + try: + current_hash = self.calculate_file_hash(file_path) + processed_record = self.db.db.processed_files.find_one({"file_path": file_path}) + + if processed_record: + if processed_record.get("hash") == current_hash: + if knowledge_length in processed_record.get("split_by", []): + result["status"] = "skipped" + return result + + content = self.read_file(file_path) + chunks = self.split_content(content, knowledge_length) + + for chunk in tqdm(chunks, desc=f"处理 {os.path.basename(file_path)} 的文本块", leave=False): + embedding = self.get_embedding(chunk) + if embedding: + knowledge = { + "content": chunk, + "embedding": embedding, + "source_file": file_path, + "split_length": knowledge_length, + "created_at": datetime.now() + } + self.db.db.knowledges.insert_one(knowledge) + result["chunks_processed"] += 1 + + split_by = processed_record.get("split_by", []) if processed_record else [] + if knowledge_length not in split_by: + split_by.append(knowledge_length) + + self.db.db.processed_files.update_one( + {"file_path": file_path}, + { + "$set": { + "hash": current_hash, + "last_processed": datetime.now(), + "split_by": split_by + } + }, + upsert=True + ) + + except Exception as e: + result["status"] = "failed" + result["error"] = str(e) + + return result + + def _update_stats(self, total_stats, result, filename): + """更新总体统计信息""" + if result["status"] == "success": + total_stats["processed_files"] += 1 + total_stats["total_chunks"] += result["chunks_processed"] + elif result["status"] == "failed": + total_stats["failed_files"].append((filename, result["error"])) + elif result["status"] == "skipped": + total_stats["skipped_files"].append(filename) + + def _display_processing_results(self, stats): + """显示处理结果统计""" + self.console.print("\n[bold green]处理完成！统计信息如下：[/bold green]") + + table = Table(show_header=True, header_style="bold magenta") + table.add_column("统计项", style="dim") + table.add_column("数值") + + table.add_row("成功处理文件数", str(stats["processed_files"])) + table.add_row("处理的知识块总数", str(stats["total_chunks"])) + table.add_row("跳过的文件数", str(len(stats["skipped_files"]))) + table.add_row("失败的文件数", str(len(stats["failed_files"]))) + + self.console.print(table) + + if stats["failed_files"]: + self.console.print("\n[bold red]处理失败的文件：[/bold red]") + for filename, error in stats["failed_files"]: + self.console.print(f"[red]- {filename}: {error}[/red]") + + if stats["skipped_files"]: + self.console.print("\n[bold yellow]跳过的文件（已处理）：[/bold yellow]") + for filename in stats["skipped_files"]: + self.console.print(f"[yellow]- {filename}[/yellow]") + + def calculate_file_hash(self, file_path): + """计算文件的MD5哈希值""" + hash_md5 = hashlib.md5() + with open(file_path, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + return hash_md5.hexdigest() + + def search_similar_segments(self, query: str, limit: int = 5) -> list: + """搜索与查询文本相似的片段""" + query_embedding = self.get_embedding(query) + if not query_embedding: + return [] + + # 使用余弦相似度计算 + pipeline = [ + { + "$addFields": { + "dotProduct": { + "$reduce": { + "input": {"$range": [0, {"$size": "$embedding"}]}, + "initialValue": 0, + "in": { + "$add": [ + "$$value", + {"$multiply": [ + {"$arrayElemAt": ["$embedding", "$$this"]}, + {"$arrayElemAt": [query_embedding, "$$this"]} + ]} + ] + } + } + }, + "magnitude1": { + "$sqrt": { + "$reduce": { + "input": "$embedding", + "initialValue": 0, + "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]} + } + } + }, + "magnitude2": { + "$sqrt": { + "$reduce": { + "input": query_embedding, + "initialValue": 0, + "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]} + } + } + } + } + }, + { + "$addFields": { + "similarity": { + "$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}] + } + } + }, + {"$sort": {"similarity": -1}}, + {"$limit": limit}, + {"$project": {"content": 1, "similarity": 1, "file_path": 1}} + ] + + results = list(self.db.db.knowledges.aggregate(pipeline)) + return results + +# 创建单例实例 +knowledge_library = KnowledgeLibrary() + +if __name__ == "__main__": + console = Console() + console.print("[bold green]知识库处理工具[/bold green]") + + while True: + console.print("\n请选择要执行的操作：") + console.print("[1] 麦麦开始学习") + console.print("[2] 麦麦全部忘光光（仅知识）") + console.print("[q] 退出程序") + + choice = input("\n请输入选项: ").strip() + + if choice.lower() == 'q': + console.print("[yellow]程序退出[/yellow]") + sys.exit(0) + elif choice == '2': + confirm = input("确定要删除所有知识吗？这个操作不可撤销！(y/n): ").strip().lower() + if confirm == 'y': + knowledge_library.db.db.knowledges.delete_many({}) + console.print("[green]已清空所有知识！[/green]") + continue + elif choice == '1': + if not os.path.exists(knowledge_library.raw_info_dir): + console.print(f"[yellow]创建目录：{knowledge_library.raw_info_dir}[/yellow]") + os.makedirs(knowledge_library.raw_info_dir, exist_ok=True) + + # 询问分割长度 + while True: + try: + length_input = input("请输入知识分割长度（默认512，输入q退出，回车使用默认值）: ").strip() + if length_input.lower() == 'q': + break + if not length_input: # 如果直接回车，使用默认值 + knowledge_length = 512 + break + knowledge_length = int(length_input) + if knowledge_length <= 0: + print("分割长度必须大于0，请重新输入") + continue + break + except ValueError: + print("请输入有效的数字") + continue + + if length_input.lower() == 'q': + continue + + # 测试知识库功能 + print(f"开始处理知识库文件，使用分割长度: {knowledge_length}...") + knowledge_library.process_files(knowledge_length=knowledge_length) + else: + console.print("[red]无效的选项，请重新选择[/red]") + continue diff --git a/template.env b/template.env index 09fe6359..d2a76311 100644 --- a/template.env +++ b/template.env @@ -5,13 +5,18 @@ PORT=8080 PLUGINS=["src2.plugins.chat"] # 默认配置 -MONGODB_HOST=127.0.0.1 # 如果工作在Docker下，请改成 MONGODB_HOST=mongodb +# 如果工作在Docker下，请改成 MONGODB_HOST=mongodb +MONGODB_HOST=127.0.0.1 MONGODB_PORT=27017 DATABASE_NAME=MegBot -MONGODB_USERNAME = "" # 默认空值 -MONGODB_PASSWORD = "" # 默认空值 -MONGODB_AUTH_SOURCE = "" # 默认空值 +# 也可以使用 URI 连接数据库（优先级比上面的高） +# MONGODB_URI=mongodb://127.0.0.1:27017/MegBot + +# MongoDB 认证信息，若需要认证，请取消注释以下三行并填写正确的信息 +# MONGODB_USERNAME=user +# MONGODB_PASSWORD=password +# MONGODB_AUTH_SOURCE=admin #key and url CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 @@ -21,4 +26,4 @@ DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 #定义你要用的api的base_url DEEP_SEEK_KEY= CHAT_ANY_WHERE_KEY= -SILICONFLOW_KEY= \ No newline at end of file +SILICONFLOW_KEY= diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index bff64d05..089be69b 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "0.0.4" +version = "0.0.8" #如果你想要修改配置文件，请在修改后将version的值进行变更 #如果新增项目，请在BotConfig类下新增相应的变量 @@ -15,6 +15,7 @@ version = "0.0.4" [bot] qq = 123 nickname = "麦麦" +alias_names = ["小麦", "阿麦"] [personality] prompt_personality = [ @@ -40,6 +41,13 @@ ban_words = [ # "403","张三" ] +ban_msgs_regex = [ + # 需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤（支持CQ码），若不了解正则表达式请勿修改 + #"https?://[^\\s]+", # 匹配https链接 + #"\\d{4}-\\d{2}-\\d{2}", # 匹配日期 + # "\\[CQ:at,qq=\\d+\\]" # 匹配@ +] + [emoji] check_interval = 120 # 检查表情包的时间间隔 register_interval = 10 # 注册表情包的时间间隔 @@ -57,8 +65,13 @@ model_r1_distill_probability = 0.1 # 麦麦回答时选择次要回复模型3 max_response_length = 1024 # 麦麦回答的最大token数 [memory] -build_memory_interval = 300 # 记忆构建间隔 单位秒 -forget_memory_interval = 300 # 记忆遗忘间隔 单位秒 +build_memory_interval = 600 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多 +memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多 + +forget_memory_interval = 600 # 记忆遗忘间隔 单位秒 间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习 +memory_forget_time = 24 #多长时间后的记忆会被遗忘 单位小时 +memory_forget_percentage = 0.01 # 记忆遗忘比例 控制记忆遗忘程度 越大遗忘越多 建议保持默认 + memory_ban_words = [ #不希望记忆的词 # "403","张三" @@ -92,6 +105,8 @@ word_replace_rate=0.006 # 整词替换概率 [others] enable_advance_output = true # 是否启用高级输出 enable_kuuki_read = true # 是否启用读空气功能 +enable_debug_output = false # 是否启用调试输出 +enable_friend_chat = false # 是否启用好友聊天 [groups] talk_allowed = [ diff --git a/如果你更新了版本，点我.txt b/如果你更新了版本，点我.txt new file mode 100644 index 00000000..400e8ae0 --- /dev/null +++ b/如果你更新了版本，点我.txt @@ -0,0 +1,4 @@ +更新版本后，建议删除数据库messages中所有内容，不然会出现报错 +该操作不会影响你的记忆 + +如果显示配置文件版本过低，运行根目录的bat \ No newline at end of file diff --git a/如果你的配置文件版本太老就点我.bat b/如果你的配置文件版本太老就点我.bat new file mode 100644 index 00000000..fec1f4cd --- /dev/null +++ b/如果你的配置文件版本太老就点我.bat @@ -0,0 +1,45 @@ +@echo off +setlocal enabledelayedexpansion +chcp 65001 +cd /d %~dp0 + +echo ===================================== +echo 选择Python环境: +echo 1 - venv (推荐) +echo 2 - conda +echo ===================================== +choice /c 12 /n /m "输入数字(1或2): " + +if errorlevel 2 ( + echo ===================================== + set "CONDA_ENV=" + set /p CONDA_ENV="请输入要激活的 conda 环境名称: " + + :: 检查输入是否为空 + if "!CONDA_ENV!"=="" ( + echo 错误：环境名称不能为空 + pause + exit /b 1 + ) + + call conda activate !CONDA_ENV! + if errorlevel 1 ( + echo 激活 conda 环境失败 + pause + exit /b 1 + ) + + echo Conda 环境 "!CONDA_ENV!" 激活成功 + python config/auto_update.py +) else ( + if exist "venv\Scripts\python.exe" ( + venv\Scripts\python config/auto_update.py + ) else ( + echo ===================================== + echo 错误: venv环境不存在，请先创建虚拟环境 + pause + exit /b 1 + ) +) +endlocal +pause diff --git a/麦麦开始学习.bat b/麦麦开始学习.bat new file mode 100644 index 00000000..f7391150 --- /dev/null +++ b/麦麦开始学习.bat @@ -0,0 +1,45 @@ +@echo off +setlocal enabledelayedexpansion +chcp 65001 +cd /d %~dp0 + +echo ===================================== +echo 选择Python环境: +echo 1 - venv (推荐) +echo 2 - conda +echo ===================================== +choice /c 12 /n /m "输入数字(1或2): " + +if errorlevel 2 ( + echo ===================================== + set "CONDA_ENV=" + set /p CONDA_ENV="请输入要激活的 conda 环境名称: " + + :: 检查输入是否为空 + if "!CONDA_ENV!"=="" ( + echo 错误：环境名称不能为空 + pause + exit /b 1 + ) + + call conda activate !CONDA_ENV! + if errorlevel 1 ( + echo 激活 conda 环境失败 + pause + exit /b 1 + ) + + echo Conda 环境 "!CONDA_ENV!" 激活成功 + python src/plugins/zhishi/knowledge_library.py +) else ( + if exist "venv\Scripts\python.exe" ( + venv\Scripts\python src/plugins/zhishi/knowledge_library.py + ) else ( + echo ===================================== + echo 错误: venv环境不存在，请先创建虚拟环境 + pause + exit /b 1 + ) +) +endlocal +pause