mirror of https://github.com/Mai-with-u/MaiBot.git
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README.md
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README.md
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<img src="depends-data/maimai.png" alt="MaiBot" title="作者:略nd" width="300">
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<div align="center">
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<h1>麦麦 MaiBot <sub><small>MaiCore</small></sub></h1>
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|
||||
# 麦麦！MaiCore-MaiBot
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||||
|
||||
|
||||
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||||
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||||
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||||
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||||
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||||
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[](https://deepwiki.com/DrSmoothl/MaiBot)
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<div style="text-align: center">
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<strong>
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<a href="https://www.bilibili.com/video/BV1amAneGE3P">🌟 演示视频</a> |
|
||||
<a href="#-更新和安装">🚀 快速入门</a> |
|
||||
<a href="#-文档">📃 教程</a> |
|
||||
<a href="#-讨论">💬 讨论</a> |
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<a href="#-贡献和致谢">🙋 贡献指南</a>
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</strong>
|
||||
<!-- Badges Row -->
|
||||
<p>
|
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<img src="https://img.shields.io/badge/Python-3.10+-blue" alt="Python Version">
|
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<img src="https://img.shields.io/github/license/Mai-with-u/MaiBot?label=%E5%8D%8F%E8%AE%AE" alt="License">
|
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<img src="https://img.shields.io/badge/状态-开发中-yellow" alt="Status">
|
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<img src="https://img.shields.io/github/contributors/Mai-with-u/MaiBot.svg?style=flat&label=%E8%B4%A1%E7%8C%AE%E8%80%85" alt="Contributors">
|
||||
<img src="https://img.shields.io/github/forks/Mai-with-u/MaiBot.svg?style=flat&label=%E5%88%86%E6%94%AF%E6%95%B0" alt="Forks">
|
||||
<img src="https://img.shields.io/github/stars/Mai-with-u/MaiBot?style=flat&label=%E6%98%9F%E6%A0%87%E6%95%B0" alt="Stars">
|
||||
<a href="https://deepwiki.com/DrSmoothl/MaiBot"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
|
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</p>
|
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</div>
|
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|
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<br>
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|
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<!-- Mascot on the Right (Float) -->
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<img src="depends-data/maimai-v2.png" align="right" width="40%" alt="MaiBot Character" style="margin-left: 20px; margin-bottom: 20px;">
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## 🎉 介绍
|
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|
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**🍔MaiCore 是一个基于大语言模型的可交互智能体**
|
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**🍔 MaiCore 是一个基于大语言模型的可交互智能体**
|
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|
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- 💭 **拟人构建的prompt**：使用自然语言风格构建回复器的prompt，实现近似人类言语习惯的回复。
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- 💭 **行为规划**：在合适的时间说话，使用合适的动作
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- 🧠 **表达学习**：学习群友的说话风格和表达方式，学会真实人类的说话风格
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- 🤔 **黑话学习**：自主的学习没有见过的词语，尝试理解并认知含义
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- 🔌 **插件系统**：提供API和事件系统，可编写丰富插件。
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- 💝 **情感表达**：情绪系统和表情包系统。
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MaiBot 不仅仅是一个机器人，她致力于成为一个活跃在 QQ 群聊中的“生命体”。她不追求完美，但追求真实。
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<div style="text-align: center">
|
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<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
|
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- 💭 **拟人构建**：使用自然语言风格构建 Prompt，回复贴近人类习惯。
|
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- 🎭 **行为规划**：懂得在合适的时间说话，使用合适的动作。
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- 🧠 **表达学习**：模仿群友的说话风格，学习黑话，不断进化。
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- 🔌 **插件系统**：提供强大的 API 和事件系统，无限扩展可能。
|
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- 💝 **情感表达**：拥有独立的情绪系统和表情包互动能力。
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### 🚀 快速导航
|
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<p>
|
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<a href="https://www.bilibili.com/video/BV1amAneGE3P">🌟 演示视频</a> |
|
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<a href="#-更新和安装">📦 快速入门</a> |
|
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<a href="#-部署教程">📃 核心文档</a> |
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<a href="#-讨论与社区">💬 加入社区</a>
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</p>
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<!-- Clear float to ensure subsequent content starts below the image area if text is short -->
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<br clear="both">
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<div align="center">
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<br>
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<h3>🎥 精彩演示</h3>
|
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<a href="https://www.bilibili.com/video/BV1amAneGE3P" target="_blank">
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<picture>
|
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<source media="(max-width: 600px)" srcset="depends-data/video.png" width="100%">
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<img src="depends-data/video.png" width="30%" alt="麦麦演示视频">
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<img src="depends-data/video.png" width="60%" alt="麦麦演示视频" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
|
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</picture>
|
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<br />
|
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👆 点击观看麦麦演示视频 👆
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</a>
|
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<br>
|
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<small>👆 点击观看麦麦演示视频 👆</small>
|
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</a>
|
||||
</div>
|
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|
||||
---
|
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## 🔥 更新和安装
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> **最新版本: v0.12.2** ([📄 更新日志](changelogs/changelog.md))
|
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**最新版本: v0.12.0** ([更新日志](changelogs/changelog.md))
|
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- **下载**: 前往 [Release](https://github.com/MaiM-with-u/MaiBot/releases/) 页面下载最新版本
|
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- **启动器**: [Mailauncher](https://github.com/MaiM-with-u/mailauncher/releases/) (仅支持 MacOS, 早期开发中)
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| 分支 | 说明 |
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| :--- | :--- |
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| `main` | ✅ **稳定发布版本 (推荐)** |
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| `dev` | 🚧 开发测试版本 (不稳定) |
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| `classical` | 🛑 经典版本 (停止维护) |
|
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|
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可前往 [Release](https://github.com/MaiM-with-u/MaiBot/releases/) 页面下载最新版本
|
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可前往 [启动器发布页面](https://github.com/MaiM-with-u/mailauncher/releases/)下载最新启动器
|
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注意，启动器处于早期开发版本，仅支持MacOS
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**GitHub 分支说明：**
|
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- `main`: 稳定发布版本(推荐)
|
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- `dev`: 开发测试版本(不稳定)
|
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|
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- `classical`: 经典版本(停止维护)
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|
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### 最新版本部署教程
|
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- [🚀 最新版本部署教程](https://docs.mai-mai.org/manual/deployment/mmc_deploy_windows.html) - 基于 MaiCore 的新版本部署方式(与旧版本不兼容)
|
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### 📚 部署教程
|
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👉 **[🚀 最新版本部署教程](https://docs.mai-mai.org/manual/deployment/mmc_deploy_windows.html)**
|
||||
*(注意：MaiCore 新版本部署方式与旧版本不兼容)*
|
||||
|
||||
> [!WARNING]
|
||||
> - 项目处于活跃开发阶段，功能和 API 可能随时调整。
|
||||
> - 有问题可以提交 Issue 。
|
||||
> - QQ 机器人存在被限制风险，请自行了解，谨慎使用。
|
||||
> - 由于程序处于开发中，可能消耗较多 token。
|
||||
> - ⚠️ 项目处于活跃开发阶段，API 可能随时调整。
|
||||
> - ⚠️ QQ 机器人存在风控风险，请谨慎使用。
|
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> - ⚠️ AI 模型运行可能消耗较多 Token。
|
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|
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## 💬 讨论
|
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---
|
||||
|
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**技术交流群/答疑群：**
|
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[麦麦脑电图](https://qm.qq.com/q/RzmCiRtHEW) |
|
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[麦麦大脑磁共振](https://qm.qq.com/q/VQ3XZrWgMs) |
|
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[麦麦要当VTB](https://qm.qq.com/q/wGePTl1UyY) |
|
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## 💬 讨论与社区
|
||||
|
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为了维持技术交流和互帮互助的氛围，请不要在技术交流群讨论过多无关内容~
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我们欢迎所有对 MaiBot 感兴趣的朋友加入！
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|
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**聊天吹水群：**
|
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- [麦麦之闲聊群](https://qm.qq.com/q/JxvHZnxyec)
|
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| 类别 | 群组 | 说明 |
|
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| :--- | :--- | :--- |
|
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| **技术交流** | [麦麦脑电图](https://qm.qq.com/q/RzmCiRtHEW) | 技术交流/答疑 |
|
||||
| **技术交流** | [麦麦大脑磁共振](https://qm.qq.com/q/VQ3XZrWgMs) | 技术交流/答疑 |
|
||||
| **技术交流** | [麦麦要当VTB](https://qm.qq.com/q/wGePTl1UyY) | 技术交流/答疑 |
|
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| **闲聊吹水** | [麦麦之闲聊群](https://qm.qq.com/q/JxvHZnxyec) | 仅限闲聊，不答疑 |
|
||||
| **插件开发** | [插件开发群](https://qm.qq.com/q/1036092828) | 进阶开发与测试 |
|
||||
|
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麦麦相关闲聊群，此群仅用于聊天，提问部署/技术问题可能不会快速得到答案
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||||
|
||||
**插件开发/测试版讨论群：**
|
||||
- [插件开发群](https://qm.qq.com/q/1036092828)
|
||||
|
||||
进阶内容，包括插件开发，测试版使用等等
|
||||
---
|
||||
|
||||
## 📚 文档
|
||||
|
||||
**部分内容可能更新不够及时，请注意版本对应**
|
||||
> [!NOTE]
|
||||
> 部分内容可能更新不够及时，请注意版本对应。
|
||||
|
||||
- [📚 核心 Wiki 文档](https://docs.mai-mai.org) - 项目最全面的文档中心，你可以了解麦麦有关的一切。
|
||||
- **[📚 核心 Wiki 文档](https://docs.mai-mai.org)**: 最全面的文档中心，了解麦麦的一切。
|
||||
|
||||
### 🧩 衍生项目
|
||||
|
||||
## 📚 衍生项目
|
||||
- **[MaiCraft](https://github.com/MaiM-with-u/Maicraft)**: 让麦麦陪你玩 Minecraft (早期开发中)。
|
||||
- **[MoFox_Bot](https://github.com/MoFox-Studio/MoFox-Core)**: 基于 MaiCore 0.10.0 的增强型 Fork，更稳定更有趣。
|
||||
|
||||
### MaiCraft（早期开发）
|
||||
[MaiCraft](https://github.com/MaiM-with-u/Maicraft)
|
||||
> 让麦麦具有玩MC能力的项目
|
||||
> 交流群：1058573197
|
||||
---
|
||||
|
||||
### MoFox_Bot
|
||||
[MoFox - 仓库地址](https://github.com/MoFox-Studio/MoFox-Core)
|
||||
> MoFox_Bot 是一个基于 MaiCore 0.10.0 snapshot.5 的增强型 fork 项目
|
||||
> 我们保留了原项目几乎所有核心功能，并在此基础上进行了深度优化与功能扩展，致力于打造一个更稳定、更智能、更具趣味性的 AI 智能体。
|
||||
|
||||
|
||||
|
||||
## 设计理念(原始时代的火花)
|
||||
## 💡 设计理念 (原始时代的火花)
|
||||
|
||||
> **千石可乐说：**
|
||||
> - 这个项目最初只是为了给牛牛 bot 添加一点额外的功能，但是功能越写越多，最后决定重写。其目的是为了创造一个活跃在 QQ 群聊的"生命体"。目的并不是为了写一个功能齐全的机器人，而是一个尽可能让人感知到真实的类人存在。
|
||||
|
|
@ -119,41 +116,39 @@
|
|||
> - 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。
|
||||
> - 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。
|
||||
> *Code is open, but the soul is yours.*
|
||||
|
||||
---
|
||||
|
||||
## 🙋 贡献和致谢
|
||||
你可以阅读[开发文档](https://docs.mai-mai.org/develop/)来更好的了解麦麦!
|
||||
MaiCore 是一个开源项目，我们非常欢迎你的参与。你的贡献，无论是提交 bug 报告、功能需求还是代码 pr，都对项目非常宝贵。我们非常感谢你的支持！🎉
|
||||
但无序的讨论会降低沟通效率，进而影响问题的解决速度，因此在提交任何贡献前，请务必先阅读本项目的[贡献指南](docs-src/CONTRIBUTE.md)。(待补完)
|
||||
|
||||
### 贡献者
|
||||
欢迎参与贡献！请先阅读 [贡献指南](docs-src/CONTRIBUTE.md)。
|
||||
|
||||
感谢各位大佬！
|
||||
### 🌟 贡献者
|
||||
|
||||
<a href="https://github.com/MaiM-with-u/MaiBot/graphs/contributors">
|
||||
<img alt="contributors" src="https://contrib.rocks/image?repo=MaiM-with-u/MaiBot" />
|
||||
</a>
|
||||
|
||||
### 致谢
|
||||
### ❤️ 特别致谢
|
||||
|
||||
- [略nd](https://space.bilibili.com/1344099355): 为麦麦绘制人设。
|
||||
- [NapCat](https://github.com/NapNeko/NapCatQQ): 现代化的基于 NTQQ 的 Bot 协议端实现。
|
||||
- **[略nd](https://space.bilibili.com/1344099355)**: 🎨 为麦麦绘制精美人设。
|
||||
- **[NapCat](https://github.com/NapNeko/NapCatQQ)**: 🚀 现代化的基于 NTQQ 的 Bot 协议实现。
|
||||
|
||||
**也感谢每一位给麦麦发展提出宝贵意见与建议的用户，感谢陪伴麦麦走到现在的你们！**
|
||||
---
|
||||
|
||||
## 📌 注意事项
|
||||
|
||||
> [!WARNING]
|
||||
> 使用本项目前必须阅读和同意[用户协议](EULA.md)和[隐私协议](PRIVACY.md)。
|
||||
> 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 生成内容不代表本项目团队的观点和立场。
|
||||
|
||||
## 麦麦仓库状态
|
||||
## 📊 仓库状态
|
||||
|
||||
|
||||
|
||||
### Star 趋势
|
||||
|
||||
[](https://starchart.cc/MaiM-with-u/MaiBot)
|
||||
|
||||
## License
|
||||
---
|
||||
|
||||
GPL-3.0
|
||||
## 📌 注意事项 & License
|
||||
|
||||
> [!IMPORTANT]
|
||||
> 使用前请阅读 [用户协议 (EULA)](EULA.md) 和 [隐私协议](PRIVACY.md)。AI 生成内容请仔细甄别。
|
||||
|
||||
**License**: GPL-3.0
|
||||
|
|
|
|||
|
|
@ -1,4 +1,13 @@
|
|||
# Changelog
|
||||
## [0.12.2] - 2025-1-11
|
||||
### 功能更改
|
||||
- 优化私聊wait逻辑
|
||||
- 超时时强制引用回复
|
||||
- 修复部分适配器断联问题
|
||||
- 修复表达反思配置未生效
|
||||
- 优化记忆检索逻辑
|
||||
- 更新readme
|
||||
|
||||
## [0.12.1] - 2025-12-31
|
||||
### 🌟 主要更新
|
||||
- 添加年度总结！可以在webui查看
|
||||
|
|
|
|||
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|
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|
|
@ -1,507 +0,0 @@
|
|||
import argparse
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import json
|
||||
import importlib
|
||||
from typing import Dict, Any
|
||||
from datetime import datetime
|
||||
|
||||
# 强制使用 utf-8，避免控制台编码报错
|
||||
try:
|
||||
if hasattr(sys.stdout, "reconfigure"):
|
||||
sys.stdout.reconfigure(encoding="utf-8")
|
||||
if hasattr(sys.stderr, "reconfigure"):
|
||||
sys.stderr.reconfigure(encoding="utf-8")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 确保能导入 src.*
|
||||
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
|
||||
|
||||
from src.common.logger import initialize_logging, get_logger
|
||||
from src.common.database.database import db
|
||||
from src.common.database.database_model import LLMUsage
|
||||
|
||||
logger = get_logger("compare_finish_search_token")
|
||||
|
||||
|
||||
def get_token_usage_since(start_time: float) -> Dict[str, Any]:
|
||||
"""获取从指定时间开始的token使用情况
|
||||
|
||||
Args:
|
||||
start_time: 开始时间戳
|
||||
|
||||
Returns:
|
||||
包含token使用统计的字典
|
||||
"""
|
||||
try:
|
||||
start_datetime = datetime.fromtimestamp(start_time)
|
||||
|
||||
# 查询从开始时间到现在的所有memory相关的token使用记录
|
||||
records = (
|
||||
LLMUsage.select()
|
||||
.where(
|
||||
(LLMUsage.timestamp >= start_datetime)
|
||||
& (
|
||||
(LLMUsage.request_type.like("%memory%"))
|
||||
| (LLMUsage.request_type == "memory.question")
|
||||
| (LLMUsage.request_type == "memory.react")
|
||||
| (LLMUsage.request_type == "memory.react.final")
|
||||
)
|
||||
)
|
||||
.order_by(LLMUsage.timestamp.asc())
|
||||
)
|
||||
|
||||
total_prompt_tokens = 0
|
||||
total_completion_tokens = 0
|
||||
total_tokens = 0
|
||||
total_cost = 0.0
|
||||
request_count = 0
|
||||
model_usage = {} # 按模型统计
|
||||
|
||||
for record in records:
|
||||
total_prompt_tokens += record.prompt_tokens or 0
|
||||
total_completion_tokens += record.completion_tokens or 0
|
||||
total_tokens += record.total_tokens or 0
|
||||
total_cost += record.cost or 0.0
|
||||
request_count += 1
|
||||
|
||||
# 按模型统计
|
||||
model_name = record.model_name or "unknown"
|
||||
if model_name not in model_usage:
|
||||
model_usage[model_name] = {
|
||||
"prompt_tokens": 0,
|
||||
"completion_tokens": 0,
|
||||
"total_tokens": 0,
|
||||
"cost": 0.0,
|
||||
"request_count": 0,
|
||||
}
|
||||
model_usage[model_name]["prompt_tokens"] += record.prompt_tokens or 0
|
||||
model_usage[model_name]["completion_tokens"] += record.completion_tokens or 0
|
||||
model_usage[model_name]["total_tokens"] += record.total_tokens or 0
|
||||
model_usage[model_name]["cost"] += record.cost or 0.0
|
||||
model_usage[model_name]["request_count"] += 1
|
||||
|
||||
return {
|
||||
"total_prompt_tokens": total_prompt_tokens,
|
||||
"total_completion_tokens": total_completion_tokens,
|
||||
"total_tokens": total_tokens,
|
||||
"total_cost": total_cost,
|
||||
"request_count": request_count,
|
||||
"model_usage": model_usage,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"获取token使用情况失败: {e}")
|
||||
return {
|
||||
"total_prompt_tokens": 0,
|
||||
"total_completion_tokens": 0,
|
||||
"total_tokens": 0,
|
||||
"total_cost": 0.0,
|
||||
"request_count": 0,
|
||||
"model_usage": {},
|
||||
}
|
||||
|
||||
|
||||
def _import_memory_retrieval():
|
||||
"""使用 importlib 动态导入 memory_retrieval 模块，避免循环导入"""
|
||||
try:
|
||||
# 先导入 prompt_builder，检查 prompt 是否已经初始化
|
||||
from src.chat.utils.prompt_builder import global_prompt_manager
|
||||
|
||||
# 检查 memory_retrieval 相关的 prompt 是否已经注册
|
||||
# 如果已经注册，说明模块可能已经通过其他路径初始化过了
|
||||
prompt_already_init = "memory_retrieval_question_prompt" in global_prompt_manager._prompts
|
||||
|
||||
module_name = "src.memory_system.memory_retrieval"
|
||||
|
||||
# 如果 prompt 已经初始化，尝试直接使用已加载的模块
|
||||
if prompt_already_init and module_name in sys.modules:
|
||||
existing_module = sys.modules[module_name]
|
||||
if hasattr(existing_module, 'init_memory_retrieval_prompt'):
|
||||
return (
|
||||
existing_module.init_memory_retrieval_prompt,
|
||||
existing_module._react_agent_solve_question,
|
||||
)
|
||||
|
||||
# 如果模块已经在 sys.modules 中但部分初始化，先移除它
|
||||
if module_name in sys.modules:
|
||||
existing_module = sys.modules[module_name]
|
||||
if not hasattr(existing_module, 'init_memory_retrieval_prompt'):
|
||||
# 模块部分初始化，移除它
|
||||
logger.warning(f"检测到部分初始化的模块 {module_name}，尝试重新导入")
|
||||
del sys.modules[module_name]
|
||||
# 清理可能相关的部分初始化模块
|
||||
keys_to_remove = []
|
||||
for key in sys.modules.keys():
|
||||
if key.startswith('src.memory_system.') and key != 'src.memory_system':
|
||||
keys_to_remove.append(key)
|
||||
for key in keys_to_remove:
|
||||
try:
|
||||
del sys.modules[key]
|
||||
except KeyError:
|
||||
pass
|
||||
|
||||
# 在导入 memory_retrieval 之前，先确保所有可能触发循环导入的模块都已完全加载
|
||||
# 这些模块在导入时可能会触发 memory_retrieval 的导入，所以我们需要先加载它们
|
||||
try:
|
||||
# 先导入可能触发循环导入的模块，让它们完成初始化
|
||||
import src.config.config
|
||||
import src.chat.utils.prompt_builder
|
||||
# 尝试导入可能触发循环导入的模块（这些模块可能在模块级别导入了 memory_retrieval）
|
||||
# 如果它们已经导入，就确保它们完全初始化
|
||||
try:
|
||||
import src.chat.replyer.group_generator # noqa: F401
|
||||
except (ImportError, AttributeError):
|
||||
pass # 如果导入失败，继续
|
||||
try:
|
||||
import src.chat.replyer.private_generator # noqa: F401
|
||||
except (ImportError, AttributeError):
|
||||
pass # 如果导入失败，继续
|
||||
except Exception as e:
|
||||
logger.warning(f"预加载依赖模块时出现警告: {e}")
|
||||
|
||||
# 现在尝试导入 memory_retrieval
|
||||
# 如果此时仍然触发循环导入，说明有其他模块在模块级别导入了 memory_retrieval
|
||||
memory_retrieval_module = importlib.import_module(module_name)
|
||||
|
||||
return (
|
||||
memory_retrieval_module.init_memory_retrieval_prompt,
|
||||
memory_retrieval_module._react_agent_solve_question,
|
||||
)
|
||||
except (ImportError, AttributeError) as e:
|
||||
logger.error(f"导入 memory_retrieval 模块失败: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
def _init_tools_without_finish_search():
|
||||
"""初始化工具但不注册 finish_search"""
|
||||
from src.memory_system.retrieval_tools import (
|
||||
register_query_chat_history,
|
||||
register_query_person_info,
|
||||
register_query_words,
|
||||
)
|
||||
from src.memory_system.retrieval_tools.tool_registry import get_tool_registry
|
||||
from src.config.config import global_config
|
||||
|
||||
# 清空工具注册器
|
||||
tool_registry = get_tool_registry()
|
||||
tool_registry.tools.clear()
|
||||
|
||||
# 注册除 finish_search 外的所有工具
|
||||
register_query_chat_history()
|
||||
register_query_person_info()
|
||||
register_query_words()
|
||||
|
||||
# 如果启用 LPMM agent 模式，也注册 LPMM 工具
|
||||
if global_config.lpmm_knowledge.lpmm_mode == "agent":
|
||||
from src.memory_system.retrieval_tools.query_lpmm_knowledge import register_tool as register_lpmm_knowledge
|
||||
register_lpmm_knowledge()
|
||||
|
||||
logger.info("已初始化工具（不包含 finish_search）")
|
||||
|
||||
|
||||
def _init_tools_with_finish_search():
|
||||
"""初始化工具并注册 finish_search"""
|
||||
from src.memory_system.retrieval_tools.tool_registry import get_tool_registry
|
||||
from src.memory_system.retrieval_tools import init_all_tools
|
||||
|
||||
# 清空工具注册器
|
||||
tool_registry = get_tool_registry()
|
||||
tool_registry.tools.clear()
|
||||
|
||||
# 初始化所有工具（包括 finish_search）
|
||||
init_all_tools()
|
||||
logger.info("已初始化工具（包含 finish_search）")
|
||||
|
||||
|
||||
async def get_prompt_tokens_for_tools(
|
||||
question: str,
|
||||
chat_id: str,
|
||||
use_finish_search: bool,
|
||||
) -> Dict[str, Any]:
|
||||
"""获取使用不同工具配置时的prompt token消耗
|
||||
|
||||
Args:
|
||||
question: 要查询的问题
|
||||
chat_id: 聊天ID
|
||||
use_finish_search: 是否使用 finish_search 工具
|
||||
|
||||
Returns:
|
||||
包含prompt token信息的字典
|
||||
"""
|
||||
# 先初始化 prompt（如果还未初始化）
|
||||
# 注意：init_memory_retrieval_prompt 会调用 init_all_tools，所以我们需要在它之后重新设置工具
|
||||
from src.chat.utils.prompt_builder import global_prompt_manager
|
||||
if "memory_retrieval_question_prompt" not in global_prompt_manager._prompts:
|
||||
init_memory_retrieval_prompt, _ = _import_memory_retrieval()
|
||||
init_memory_retrieval_prompt()
|
||||
|
||||
# 初始化工具（根据参数决定是否包含 finish_search）
|
||||
# 必须在 init_memory_retrieval_prompt 之后调用，因为它会调用 init_all_tools
|
||||
if use_finish_search:
|
||||
_init_tools_with_finish_search()
|
||||
else:
|
||||
_init_tools_without_finish_search()
|
||||
|
||||
# 获取工具注册器
|
||||
from src.memory_system.retrieval_tools.tool_registry import get_tool_registry
|
||||
tool_registry = get_tool_registry()
|
||||
tool_definitions = tool_registry.get_tool_definitions()
|
||||
|
||||
# 验证工具列表（调试用）
|
||||
tool_names = [tool["name"] for tool in tool_definitions]
|
||||
if use_finish_search:
|
||||
if "finish_search" not in tool_names:
|
||||
logger.warning("期望包含 finish_search 工具，但工具列表中未找到")
|
||||
else:
|
||||
if "finish_search" in tool_names:
|
||||
logger.warning("期望不包含 finish_search 工具，但工具列表中找到了，将移除")
|
||||
# 移除 finish_search 工具
|
||||
tool_registry.tools.pop("finish_search", None)
|
||||
tool_definitions = tool_registry.get_tool_definitions()
|
||||
tool_names = [tool["name"] for tool in tool_definitions]
|
||||
|
||||
# 构建第一次调用的prompt（模拟_react_agent_solve_question的第一次调用）
|
||||
from src.config.config import global_config
|
||||
bot_name = global_config.bot.nickname
|
||||
time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||||
|
||||
# 构建head_prompt
|
||||
head_prompt = await global_prompt_manager.format_prompt(
|
||||
"memory_retrieval_react_prompt_head",
|
||||
bot_name=bot_name,
|
||||
time_now=time_now,
|
||||
question=question,
|
||||
collected_info="",
|
||||
current_iteration=1,
|
||||
remaining_iterations=global_config.memory.max_agent_iterations - 1,
|
||||
max_iterations=global_config.memory.max_agent_iterations,
|
||||
)
|
||||
|
||||
# 构建消息列表（只包含system message，模拟第一次调用）
|
||||
from src.llm_models.payload_content.message import MessageBuilder, RoleType
|
||||
messages = []
|
||||
system_builder = MessageBuilder()
|
||||
system_builder.set_role(RoleType.System)
|
||||
system_builder.add_text_content(head_prompt)
|
||||
messages.append(system_builder.build())
|
||||
|
||||
# 调用LLM API来计算token（只调用一次，不实际执行）
|
||||
from src.llm_models.utils_model import LLMRequest, RequestType
|
||||
from src.config.config import model_config
|
||||
|
||||
# 创建LLM请求对象
|
||||
llm_request = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="memory.react.compare")
|
||||
|
||||
# 构建工具选项
|
||||
tool_built = llm_request._build_tool_options(tool_definitions)
|
||||
|
||||
# 直接调用 _execute_request 以获取完整的响应对象（包含 usage）
|
||||
response, model_info = await llm_request._execute_request(
|
||||
request_type=RequestType.RESPONSE,
|
||||
message_factory=lambda _client, *, _messages=messages: _messages,
|
||||
temperature=None,
|
||||
max_tokens=None,
|
||||
tool_options=tool_built,
|
||||
)
|
||||
|
||||
# 从响应中获取token使用情况
|
||||
prompt_tokens = 0
|
||||
completion_tokens = 0
|
||||
total_tokens = 0
|
||||
|
||||
if response and hasattr(response, 'usage') and response.usage:
|
||||
prompt_tokens = response.usage.prompt_tokens or 0
|
||||
completion_tokens = response.usage.completion_tokens or 0
|
||||
total_tokens = response.usage.total_tokens or 0
|
||||
|
||||
return {
|
||||
"use_finish_search": use_finish_search,
|
||||
"prompt_tokens": prompt_tokens,
|
||||
"completion_tokens": completion_tokens,
|
||||
"total_tokens": total_tokens,
|
||||
"tool_count": len(tool_definitions),
|
||||
"tool_names": [tool["name"] for tool in tool_definitions],
|
||||
}
|
||||
|
||||
|
||||
async def compare_prompt_tokens(
|
||||
question: str,
|
||||
chat_id: str = "compare_finish_search",
|
||||
) -> Dict[str, Any]:
|
||||
"""对比使用 finish_search 工具与否的输入 token 差异
|
||||
|
||||
只运行一次，只计算输入 token 的差异，确保除了工具定义外其他内容一致
|
||||
|
||||
Args:
|
||||
question: 要查询的问题
|
||||
chat_id: 聊天ID
|
||||
|
||||
Returns:
|
||||
包含对比结果的字典
|
||||
"""
|
||||
print("\n" + "=" * 80)
|
||||
print("finish_search 工具 输入 Token 消耗对比测试")
|
||||
print("=" * 80)
|
||||
print(f"\n[测试问题] {question}")
|
||||
print(f"[聊天ID] {chat_id}")
|
||||
print("\n注意: 只对比第一次LLM调用的输入token差异，不运行完整迭代流程")
|
||||
|
||||
# 第一次测试：不使用 finish_search
|
||||
print("\n" + "-" * 80)
|
||||
print("[测试 1/2] 不使用 finish_search 工具")
|
||||
print("-" * 80)
|
||||
result_without = await get_prompt_tokens_for_tools(
|
||||
question=question,
|
||||
chat_id=f"{chat_id}_without",
|
||||
use_finish_search=False,
|
||||
)
|
||||
|
||||
print(f"\n[结果]")
|
||||
print(f" 工具数量: {result_without['tool_count']}")
|
||||
print(f" 工具列表: {', '.join(result_without['tool_names'])}")
|
||||
print(f" 输入 Prompt Tokens: {result_without['prompt_tokens']:,}")
|
||||
|
||||
# 等待一下，确保数据库记录已写入
|
||||
await asyncio.sleep(1)
|
||||
|
||||
# 第二次测试：使用 finish_search
|
||||
print("\n" + "-" * 80)
|
||||
print("[测试 2/2] 使用 finish_search 工具")
|
||||
print("-" * 80)
|
||||
result_with = await get_prompt_tokens_for_tools(
|
||||
question=question,
|
||||
chat_id=f"{chat_id}_with",
|
||||
use_finish_search=True,
|
||||
)
|
||||
|
||||
print(f"\n[结果]")
|
||||
print(f" 工具数量: {result_with['tool_count']}")
|
||||
print(f" 工具列表: {', '.join(result_with['tool_names'])}")
|
||||
print(f" 输入 Prompt Tokens: {result_with['prompt_tokens']:,}")
|
||||
|
||||
# 对比结果
|
||||
print("\n" + "=" * 80)
|
||||
print("[对比结果]")
|
||||
print("=" * 80)
|
||||
|
||||
prompt_token_diff = result_with['prompt_tokens'] - result_without['prompt_tokens']
|
||||
prompt_token_diff_percent = (prompt_token_diff / result_without['prompt_tokens'] * 100) if result_without['prompt_tokens'] > 0 else 0
|
||||
|
||||
tool_count_diff = result_with['tool_count'] - result_without['tool_count']
|
||||
|
||||
print(f"\n[输入 Prompt Token 对比]")
|
||||
print(f" 不使用 finish_search: {result_without['prompt_tokens']:,} tokens")
|
||||
print(f" 使用 finish_search: {result_with['prompt_tokens']:,} tokens")
|
||||
print(f" 差异: {prompt_token_diff:+,} tokens ({prompt_token_diff_percent:+.2f}%)")
|
||||
|
||||
print(f"\n[工具数量对比]")
|
||||
print(f" 不使用 finish_search: {result_without['tool_count']} 个工具")
|
||||
print(f" 使用 finish_search: {result_with['tool_count']} 个工具")
|
||||
print(f" 差异: {tool_count_diff:+d} 个工具")
|
||||
|
||||
print(f"\n[工具列表对比]")
|
||||
without_tools = set(result_without['tool_names'])
|
||||
with_tools = set(result_with['tool_names'])
|
||||
only_with = with_tools - without_tools
|
||||
only_without = without_tools - with_tools
|
||||
|
||||
if only_with:
|
||||
print(f" 仅在 '使用 finish_search' 中的工具: {', '.join(only_with)}")
|
||||
if only_without:
|
||||
print(f" 仅在 '不使用 finish_search' 中的工具: {', '.join(only_without)}")
|
||||
if not only_with and not only_without:
|
||||
print(f" 工具列表相同（除了 finish_search）")
|
||||
|
||||
# 显示其他token信息
|
||||
print(f"\n[其他 Token 信息]")
|
||||
print(f" Completion Tokens (不使用 finish_search): {result_without.get('completion_tokens', 0):,}")
|
||||
print(f" Completion Tokens (使用 finish_search): {result_with.get('completion_tokens', 0):,}")
|
||||
print(f" 总 Tokens (不使用 finish_search): {result_without.get('total_tokens', 0):,}")
|
||||
print(f" 总 Tokens (使用 finish_search): {result_with.get('total_tokens', 0):,}")
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
|
||||
return {
|
||||
"question": question,
|
||||
"without_finish_search": result_without,
|
||||
"with_finish_search": result_with,
|
||||
"comparison": {
|
||||
"prompt_token_diff": prompt_token_diff,
|
||||
"prompt_token_diff_percent": prompt_token_diff_percent,
|
||||
"tool_count_diff": tool_count_diff,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="对比使用 finish_search 工具与否的 token 消耗差异"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--chat-id",
|
||||
default="compare_finish_search",
|
||||
help="测试用的聊天ID（默认: compare_finish_search）",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
"-o",
|
||||
help="将结果保存到JSON文件（可选）",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 初始化日志（使用较低的详细程度，避免输出过多日志）
|
||||
initialize_logging(verbose=False)
|
||||
|
||||
# 交互式输入问题
|
||||
print("\n" + "=" * 80)
|
||||
print("finish_search 工具 Token 消耗对比测试工具")
|
||||
print("=" * 80)
|
||||
question = input("\n请输入要查询的问题: ").strip()
|
||||
if not question:
|
||||
print("错误: 问题不能为空")
|
||||
return
|
||||
|
||||
# 连接数据库
|
||||
try:
|
||||
db.connect(reuse_if_open=True)
|
||||
except Exception as e:
|
||||
logger.error(f"数据库连接失败: {e}")
|
||||
print(f"错误: 数据库连接失败: {e}")
|
||||
return
|
||||
|
||||
# 运行对比测试
|
||||
try:
|
||||
result = asyncio.run(
|
||||
compare_prompt_tokens(
|
||||
question=question,
|
||||
chat_id=args.chat_id,
|
||||
)
|
||||
)
|
||||
|
||||
# 如果指定了输出文件，保存结果
|
||||
if args.output:
|
||||
# 将thinking_steps转换为可序列化的格式
|
||||
output_result = result.copy()
|
||||
with open(args.output, "w", encoding="utf-8") as f:
|
||||
json.dump(output_result, f, ensure_ascii=False, indent=2)
|
||||
print(f"\n[结果已保存] {args.output}")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n\n[中断] 用户中断测试")
|
||||
except Exception as e:
|
||||
logger.error(f"测试失败: {e}", exc_info=True)
|
||||
print(f"\n[错误] 测试失败: {e}")
|
||||
finally:
|
||||
try:
|
||||
db.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
|
@ -1,476 +0,0 @@
|
|||
"""
|
||||
表达方式评估脚本
|
||||
|
||||
功能：
|
||||
1. 随机读取指定数量的表达方式，获取其situation和style
|
||||
2. 先进行人工评估（逐条手动评估）
|
||||
3. 然后使用LLM进行评估
|
||||
4. 对比人工评估和LLM评估的正确率、精确率、召回率、F1分数等指标（以人工评估为标准）
|
||||
5. 不真正修改数据库，只是做评估
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import random
|
||||
import json
|
||||
import sys
|
||||
import os
|
||||
from typing import List, Dict
|
||||
|
||||
# 添加项目根目录到路径
|
||||
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
|
||||
sys.path.insert(0, project_root)
|
||||
|
||||
from src.common.database.database_model import Expression
|
||||
from src.common.database.database import db
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import model_config
|
||||
from src.common.logger import get_logger
|
||||
|
||||
logger = get_logger("expression_evaluator_comparison")
|
||||
|
||||
|
||||
def get_random_expressions(count: int = 10) -> List[Expression]:
|
||||
"""
|
||||
随机读取指定数量的表达方式
|
||||
|
||||
Args:
|
||||
count: 要读取的数量，默认10条
|
||||
|
||||
Returns:
|
||||
表达方式列表
|
||||
"""
|
||||
try:
|
||||
# 查询所有表达方式
|
||||
all_expressions = list(Expression.select())
|
||||
|
||||
if not all_expressions:
|
||||
logger.warning("数据库中没有表达方式记录")
|
||||
return []
|
||||
|
||||
# 如果总数少于请求数量，返回所有
|
||||
if len(all_expressions) <= count:
|
||||
logger.info(f"数据库中共有 {len(all_expressions)} 条表达方式，全部返回")
|
||||
return all_expressions
|
||||
|
||||
# 随机选择指定数量
|
||||
selected = random.sample(all_expressions, count)
|
||||
logger.info(f"从 {len(all_expressions)} 条表达方式中随机选择了 {len(selected)} 条")
|
||||
return selected
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"随机读取表达方式失败: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
return []
|
||||
|
||||
|
||||
def manual_evaluate_expression(expression: Expression, index: int, total: int) -> Dict:
|
||||
"""
|
||||
人工评估单个表达方式
|
||||
|
||||
Args:
|
||||
expression: 表达方式对象
|
||||
index: 当前索引（从1开始）
|
||||
total: 总数
|
||||
|
||||
Returns:
|
||||
评估结果字典，包含：
|
||||
- expression_id: 表达方式ID
|
||||
- situation: 情境
|
||||
- style: 风格
|
||||
- suitable: 是否合适（人工评估）
|
||||
- reason: 评估理由（始终为None）
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print(f"人工评估 [{index}/{total}]")
|
||||
print("=" * 60)
|
||||
print(f"Situation: {expression.situation}")
|
||||
print(f"Style: {expression.style}")
|
||||
print("\n请评估该表达方式是否合适：")
|
||||
print(" 输入 'y' 或 'yes' 或 '1' 表示合适（通过）")
|
||||
print(" 输入 'n' 或 'no' 或 '0' 表示不合适（不通过）")
|
||||
print(" 输入 'q' 或 'quit' 退出评估")
|
||||
|
||||
while True:
|
||||
user_input = input("\n您的评估 (y/n/q): ").strip().lower()
|
||||
|
||||
if user_input in ['q', 'quit']:
|
||||
print("退出评估")
|
||||
return None
|
||||
|
||||
if user_input in ['y', 'yes', '1', '是', '通过']:
|
||||
suitable = True
|
||||
break
|
||||
elif user_input in ['n', 'no', '0', '否', '不通过']:
|
||||
suitable = False
|
||||
break
|
||||
else:
|
||||
print("输入无效，请重新输入 (y/n/q)")
|
||||
|
||||
result = {
|
||||
"expression_id": expression.id,
|
||||
"situation": expression.situation,
|
||||
"style": expression.style,
|
||||
"suitable": suitable,
|
||||
"reason": None,
|
||||
"evaluator": "manual"
|
||||
}
|
||||
|
||||
print(f"\n✓ 已记录：{'通过' if suitable else '不通过'}")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def create_evaluation_prompt(situation: str, style: str) -> str:
|
||||
"""
|
||||
创建评估提示词
|
||||
|
||||
Args:
|
||||
situation: 情境
|
||||
style: 风格
|
||||
|
||||
Returns:
|
||||
评估提示词
|
||||
"""
|
||||
prompt = f"""请评估以下表达方式是否合适：
|
||||
|
||||
情境（situation）：{situation}
|
||||
风格（style）：{style}
|
||||
|
||||
请从以下方面进行评估：
|
||||
1. 情境描述是否清晰、准确
|
||||
2. 风格表达是否合理、自然
|
||||
3. 情境和风格是否匹配
|
||||
4. 允许部分语法错误出现
|
||||
5. 允许口头化或缺省表达
|
||||
6. 允许部分上下文缺失
|
||||
|
||||
请以JSON格式输出评估结果：
|
||||
{{
|
||||
"suitable": true/false,
|
||||
"reason": "评估理由（如果不合适，请说明原因）"
|
||||
}}
|
||||
|
||||
如果合适，suitable设为true；如果不合适，suitable设为false，并在reason中说明原因。
|
||||
请严格按照JSON格式输出，不要包含其他内容。"""
|
||||
|
||||
return prompt
|
||||
|
||||
|
||||
async def _single_llm_evaluation(expression: Expression, llm: LLMRequest) -> tuple[bool, str, str | None]:
|
||||
"""
|
||||
执行单次LLM评估
|
||||
|
||||
Args:
|
||||
expression: 表达方式对象
|
||||
llm: LLM请求实例
|
||||
|
||||
Returns:
|
||||
(suitable, reason, error) 元组，如果出错则 suitable 为 False，error 包含错误信息
|
||||
"""
|
||||
try:
|
||||
prompt = create_evaluation_prompt(expression.situation, expression.style)
|
||||
logger.debug(f"正在评估表达方式 ID: {expression.id}")
|
||||
|
||||
response, (reasoning, model_name, _) = await llm.generate_response_async(
|
||||
prompt=prompt,
|
||||
temperature=0.6,
|
||||
max_tokens=1024
|
||||
)
|
||||
|
||||
logger.debug(f"LLM响应: {response}")
|
||||
|
||||
# 解析JSON响应
|
||||
try:
|
||||
evaluation = json.loads(response)
|
||||
except json.JSONDecodeError:
|
||||
import re
|
||||
json_match = re.search(r'\{[^{}]*"suitable"[^{}]*\}', response, re.DOTALL)
|
||||
if json_match:
|
||||
evaluation = json.loads(json_match.group())
|
||||
else:
|
||||
raise ValueError("无法从响应中提取JSON格式的评估结果")
|
||||
|
||||
suitable = evaluation.get("suitable", False)
|
||||
reason = evaluation.get("reason", "未提供理由")
|
||||
|
||||
logger.debug(f"评估结果: {'通过' if suitable else '不通过'}")
|
||||
return suitable, reason, None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"评估表达方式 ID: {expression.id} 时出错: {e}")
|
||||
return False, f"评估过程出错: {str(e)}", str(e)
|
||||
|
||||
|
||||
async def evaluate_expression_llm(expression: Expression, llm: LLMRequest) -> Dict:
|
||||
"""
|
||||
使用LLM评估单个表达方式
|
||||
|
||||
Args:
|
||||
expression: 表达方式对象
|
||||
llm: LLM请求实例
|
||||
|
||||
Returns:
|
||||
评估结果字典
|
||||
"""
|
||||
logger.info(f"开始评估表达方式 ID: {expression.id}")
|
||||
|
||||
suitable, reason, error = await _single_llm_evaluation(expression, llm)
|
||||
|
||||
if error:
|
||||
suitable = False
|
||||
|
||||
logger.info(f"评估完成: {'通过' if suitable else '不通过'}")
|
||||
|
||||
return {
|
||||
"expression_id": expression.id,
|
||||
"situation": expression.situation,
|
||||
"style": expression.style,
|
||||
"suitable": suitable,
|
||||
"reason": reason,
|
||||
"error": error,
|
||||
"evaluator": "llm"
|
||||
}
|
||||
|
||||
|
||||
def compare_evaluations(manual_results: List[Dict], llm_results: List[Dict], method_name: str) -> Dict:
|
||||
"""
|
||||
对比人工评估和LLM评估的结果
|
||||
|
||||
Args:
|
||||
manual_results: 人工评估结果列表
|
||||
llm_results: LLM评估结果列表
|
||||
method_name: 评估方法名称（用于标识）
|
||||
|
||||
Returns:
|
||||
对比分析结果字典
|
||||
"""
|
||||
# 按expression_id建立映射
|
||||
llm_dict = {r["expression_id"]: r for r in llm_results}
|
||||
|
||||
total = len(manual_results)
|
||||
matched = 0
|
||||
true_positives = 0
|
||||
true_negatives = 0
|
||||
false_positives = 0
|
||||
false_negatives = 0
|
||||
|
||||
for manual_result in manual_results:
|
||||
llm_result = llm_dict.get(manual_result["expression_id"])
|
||||
if llm_result is None:
|
||||
continue
|
||||
|
||||
manual_suitable = manual_result["suitable"]
|
||||
llm_suitable = llm_result["suitable"]
|
||||
|
||||
if manual_suitable == llm_suitable:
|
||||
matched += 1
|
||||
|
||||
if manual_suitable and llm_suitable:
|
||||
true_positives += 1
|
||||
elif not manual_suitable and not llm_suitable:
|
||||
true_negatives += 1
|
||||
elif not manual_suitable and llm_suitable:
|
||||
false_positives += 1
|
||||
elif manual_suitable and not llm_suitable:
|
||||
false_negatives += 1
|
||||
|
||||
accuracy = (matched / total * 100) if total > 0 else 0
|
||||
precision = (true_positives / (true_positives + false_positives) * 100) if (true_positives + false_positives) > 0 else 0
|
||||
recall = (true_positives / (true_positives + false_negatives) * 100) if (true_positives + false_negatives) > 0 else 0
|
||||
f1_score = (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0
|
||||
specificity = (true_negatives / (true_negatives + false_positives) * 100) if (true_negatives + false_positives) > 0 else 0
|
||||
|
||||
random_baseline = 50.0
|
||||
accuracy_above_random = accuracy - random_baseline
|
||||
accuracy_improvement_ratio = (accuracy / random_baseline) if random_baseline > 0 else 0
|
||||
|
||||
return {
|
||||
"method": method_name,
|
||||
"total": total,
|
||||
"matched": matched,
|
||||
"accuracy": accuracy,
|
||||
"accuracy_above_random": accuracy_above_random,
|
||||
"accuracy_improvement_ratio": accuracy_improvement_ratio,
|
||||
"true_positives": true_positives,
|
||||
"true_negatives": true_negatives,
|
||||
"false_positives": false_positives,
|
||||
"false_negatives": false_negatives,
|
||||
"precision": precision,
|
||||
"recall": recall,
|
||||
"f1_score": f1_score,
|
||||
"specificity": specificity
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
async def main():
|
||||
"""主函数"""
|
||||
logger.info("=" * 60)
|
||||
logger.info("开始表达方式评估")
|
||||
logger.info("=" * 60)
|
||||
|
||||
# 初始化数据库连接
|
||||
try:
|
||||
db.connect(reuse_if_open=True)
|
||||
logger.info("数据库连接成功")
|
||||
except Exception as e:
|
||||
logger.error(f"数据库连接失败: {e}")
|
||||
return
|
||||
|
||||
# 1. 随机读取表达方式
|
||||
logger.info("\n步骤1: 随机读取表达方式")
|
||||
expressions = get_random_expressions(10)
|
||||
if not expressions:
|
||||
logger.error("没有可用的表达方式，退出")
|
||||
return
|
||||
logger.info(f"成功读取 {len(expressions)} 条表达方式")
|
||||
|
||||
# 2. 人工评估
|
||||
print("\n" + "=" * 60)
|
||||
print("开始人工评估")
|
||||
print("=" * 60)
|
||||
print(f"共需要评估 {len(expressions)} 条表达方式")
|
||||
print("请逐条进行评估...\n")
|
||||
|
||||
manual_results = []
|
||||
for i, expression in enumerate(expressions, 1):
|
||||
manual_result = manual_evaluate_expression(expression, i, len(expressions))
|
||||
if manual_result is None:
|
||||
print("\n评估已中断")
|
||||
return
|
||||
manual_results.append(manual_result)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("人工评估完成")
|
||||
print("=" * 60)
|
||||
|
||||
# 3. 创建LLM实例并评估
|
||||
logger.info("\n步骤3: 创建LLM实例")
|
||||
try:
|
||||
llm = LLMRequest(
|
||||
model_set=model_config.model_task_config.tool_use,
|
||||
request_type="expression_evaluator_comparison"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"创建LLM实例失败: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
return
|
||||
|
||||
logger.info("\n步骤4: 开始LLM评估")
|
||||
llm_results = []
|
||||
for i, expression in enumerate(expressions, 1):
|
||||
logger.info(f"LLM评估进度: {i}/{len(expressions)}")
|
||||
llm_results.append(await evaluate_expression_llm(expression, llm))
|
||||
await asyncio.sleep(0.3)
|
||||
|
||||
# 4. 对比分析并输出结果
|
||||
comparison = compare_evaluations(manual_results, llm_results, "LLM评估")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("评估结果（以人工评估为标准）")
|
||||
print("=" * 60)
|
||||
print("\n评估目标：")
|
||||
print(" 1. 核心能力：将不合适的项目正确提取出来（特定负类召回率）")
|
||||
print(" 2. 次要能力：尽可能少的误删合适的项目（召回率）")
|
||||
|
||||
# 详细评估结果（核心指标优先）
|
||||
print("\n【详细对比】")
|
||||
print(f"\n--- {comparison['method']} ---")
|
||||
print(f" 总数: {comparison['total']} 条")
|
||||
print()
|
||||
print(" 【核心能力指标】")
|
||||
print(f" ⭐ 特定负类召回率: {comparison['specificity']:.2f}% (将不合适项目正确提取出来的能力)")
|
||||
print(f" - 计算: TN / (TN + FP) = {comparison['true_negatives']} / ({comparison['true_negatives']} + {comparison['false_positives']})")
|
||||
print(f" - 含义: 在 {comparison['true_negatives'] + comparison['false_positives']} 个实际不合适的项目中，正确识别出 {comparison['true_negatives']} 个")
|
||||
print(f" - 随机水平: 50.00% (当前高于随机: {comparison['specificity'] - 50.0:+.2f}%)")
|
||||
print()
|
||||
print(f" ⭐ 召回率: {comparison['recall']:.2f}% (尽可能少的误删合适项目的能力)")
|
||||
print(f" - 计算: TP / (TP + FN) = {comparison['true_positives']} / ({comparison['true_positives']} + {comparison['false_negatives']})")
|
||||
print(f" - 含义: 在 {comparison['true_positives'] + comparison['false_negatives']} 个实际合适的项目中，正确识别出 {comparison['true_positives']} 个")
|
||||
print(f" - 随机水平: 50.00% (当前高于随机: {comparison['recall'] - 50.0:+.2f}%)")
|
||||
print()
|
||||
print(" 【其他指标】")
|
||||
print(f" 准确率: {comparison['accuracy']:.2f}% (整体判断正确率)")
|
||||
print(f" 精确率: {comparison['precision']:.2f}% (判断为合适的项目中，实际合适的比例)")
|
||||
print(f" F1分数: {comparison['f1_score']:.2f} (精确率和召回率的调和平均)")
|
||||
print(f" 匹配数: {comparison['matched']}/{comparison['total']}")
|
||||
print()
|
||||
print(" 【分类统计】")
|
||||
print(f" TP (正确识别为合适): {comparison['true_positives']}")
|
||||
print(f" TN (正确识别为不合适): {comparison['true_negatives']} ⭐")
|
||||
print(f" FP (误判为合适): {comparison['false_positives']} ⚠️")
|
||||
print(f" FN (误删合适项目): {comparison['false_negatives']} ⚠️")
|
||||
|
||||
# 5. 输出人工评估不通过但LLM误判为通过的详细信息
|
||||
print("\n" + "=" * 60)
|
||||
print("人工评估不通过但LLM误判为通过的项目（FP - False Positive）")
|
||||
print("=" * 60)
|
||||
|
||||
# 按expression_id建立映射
|
||||
llm_dict = {r["expression_id"]: r for r in llm_results}
|
||||
|
||||
fp_items = []
|
||||
for manual_result in manual_results:
|
||||
llm_result = llm_dict.get(manual_result["expression_id"])
|
||||
if llm_result is None:
|
||||
continue
|
||||
|
||||
# 人工评估不通过，但LLM评估通过（FP情况）
|
||||
if not manual_result["suitable"] and llm_result["suitable"]:
|
||||
fp_items.append({
|
||||
"expression_id": manual_result["expression_id"],
|
||||
"situation": manual_result["situation"],
|
||||
"style": manual_result["style"],
|
||||
"manual_suitable": manual_result["suitable"],
|
||||
"llm_suitable": llm_result["suitable"],
|
||||
"llm_reason": llm_result.get("reason", "未提供理由"),
|
||||
"llm_error": llm_result.get("error")
|
||||
})
|
||||
|
||||
if fp_items:
|
||||
print(f"\n共找到 {len(fp_items)} 条误判项目：\n")
|
||||
for idx, item in enumerate(fp_items, 1):
|
||||
print(f"--- [{idx}] 项目 ID: {item['expression_id']} ---")
|
||||
print(f"Situation: {item['situation']}")
|
||||
print(f"Style: {item['style']}")
|
||||
print("人工评估: 不通过 ❌")
|
||||
print("LLM评估: 通过 ✅ (误判)")
|
||||
if item.get('llm_error'):
|
||||
print(f"LLM错误: {item['llm_error']}")
|
||||
print(f"LLM理由: {item['llm_reason']}")
|
||||
print()
|
||||
else:
|
||||
print("\n✓ 没有误判项目（所有人工评估不通过的项目都被LLM正确识别为不通过）")
|
||||
|
||||
# 6. 保存结果到JSON文件
|
||||
output_file = os.path.join(project_root, "data", "expression_evaluation_comparison.json")
|
||||
try:
|
||||
os.makedirs(os.path.dirname(output_file), exist_ok=True)
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
json.dump({
|
||||
"manual_results": manual_results,
|
||||
"llm_results": llm_results,
|
||||
"comparison": comparison
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
logger.info(f"\n评估结果已保存到: {output_file}")
|
||||
except Exception as e:
|
||||
logger.warning(f"保存结果到文件失败: {e}")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("评估完成")
|
||||
print("=" * 60)
|
||||
|
||||
# 关闭数据库连接
|
||||
try:
|
||||
db.close()
|
||||
logger.info("数据库连接已关闭")
|
||||
except Exception as e:
|
||||
logger.warning(f"关闭数据库连接时出错: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
||||
|
|
@ -28,7 +28,7 @@ class ExpressionReflector:
|
|||
try:
|
||||
logger.debug(f"[Expression Reflection] 开始检查是否需要提问 (stream_id: {self.chat_id})")
|
||||
|
||||
if not global_config.expression.expression_self_reflect:
|
||||
if not global_config.expression.expression_manual_reflect:
|
||||
logger.debug("[Expression Reflection] 表达反思功能未启用，跳过")
|
||||
return False
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
import time
|
||||
from typing import Tuple, Optional # 增加了 Optional
|
||||
from src.common.logger_manager import get_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
from ...config.config import global_config
|
||||
from src.common.logger import get_logger
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import random
|
||||
from .chat_observer import ChatObserver
|
||||
from .pfc_utils import get_items_from_json
|
||||
from src.individuality.individuality import Individuality
|
||||
from .observation_info import ObservationInfo
|
||||
from .conversation_info import ConversationInfo
|
||||
from src.plugins.utils.chat_message_builder import build_readable_messages
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages
|
||||
|
||||
|
||||
logger = get_logger("pfc_action_planner")
|
||||
|
|
@ -113,12 +113,27 @@ class ActionPlanner:
|
|||
max_tokens=1500,
|
||||
request_type="action_planning",
|
||||
)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
|
||||
self.personality_info = self._get_personality_prompt()
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.private_name = private_name
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
|
||||
# self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
|
||||
|
||||
def _get_personality_prompt(self) -> str:
|
||||
"""获取个性提示信息"""
|
||||
prompt_personality = global_config.personality.personality
|
||||
|
||||
# 检查是否需要随机替换为状态
|
||||
if (
|
||||
global_config.personality.states
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
bot_name = global_config.BOT_NICKNAME
|
||||
return f"你的名字是{bot_name},你{prompt_personality};"
|
||||
|
||||
# 修改 plan 方法签名，增加 last_successful_reply_action 参数
|
||||
async def plan(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -3,22 +3,22 @@ import asyncio
|
|||
import datetime
|
||||
|
||||
# from .message_storage import MongoDBMessageStorage
|
||||
from src.plugins.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat
|
||||
|
||||
# from ...config.config import global_config
|
||||
# from src.config.config import global_config
|
||||
from typing import Dict, Any, Optional
|
||||
from ..chat.message import Message
|
||||
from src.chat.message_receive.message import Message
|
||||
from .pfc_types import ConversationState
|
||||
from .pfc import ChatObserver, GoalAnalyzer
|
||||
from .message_sender import DirectMessageSender
|
||||
from src.common.logger_manager import get_logger
|
||||
from src.common.logger import get_logger
|
||||
from .action_planner import ActionPlanner
|
||||
from .observation_info import ObservationInfo
|
||||
from .conversation_info import ConversationInfo # 确保导入 ConversationInfo
|
||||
from .reply_generator import ReplyGenerator
|
||||
from ..chat.chat_stream import ChatStream
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from maim_message import UserInfo
|
||||
from src.plugins.chat.chat_stream import chat_manager
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
from .pfc_KnowledgeFetcher import KnowledgeFetcher
|
||||
from .waiter import Waiter
|
||||
|
||||
|
|
@ -60,7 +60,7 @@ class Conversation:
|
|||
self.direct_sender = DirectMessageSender(self.private_name)
|
||||
|
||||
# 获取聊天流信息
|
||||
self.chat_stream = chat_manager.get_stream(self.stream_id)
|
||||
self.chat_stream = get_chat_manager().get_stream(self.stream_id)
|
||||
|
||||
self.stop_action_planner = False
|
||||
except Exception as e:
|
||||
|
|
@ -248,14 +248,14 @@ class Conversation:
|
|||
def _convert_to_message(self, msg_dict: Dict[str, Any]) -> Message:
|
||||
"""将消息字典转换为Message对象"""
|
||||
try:
|
||||
# 尝试从 msg_dict 直接获取 chat_stream，如果失败则从全局 chat_manager 获取
|
||||
# 尝试从 msg_dict 直接获取 chat_stream，如果失败则从全局 get_chat_manager 获取
|
||||
chat_info = msg_dict.get("chat_info")
|
||||
if chat_info and isinstance(chat_info, dict):
|
||||
chat_stream = ChatStream.from_dict(chat_info)
|
||||
elif self.chat_stream: # 使用实例变量中的 chat_stream
|
||||
chat_stream = self.chat_stream
|
||||
else: # Fallback: 尝试从 manager 获取 (可能需要 stream_id)
|
||||
chat_stream = chat_manager.get_stream(self.stream_id)
|
||||
chat_stream = get_chat_manager().get_stream(self.stream_id)
|
||||
if not chat_stream:
|
||||
raise ValueError(f"无法确定 ChatStream for stream_id {self.stream_id}")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,11 @@
|
|||
import time
|
||||
from typing import Optional
|
||||
from src.common.logger import get_module_logger
|
||||
from ..chat.chat_stream import ChatStream
|
||||
from ..chat.message import Message
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
from src.chat.message_receive.message import Message, MessageSending
|
||||
from maim_message import UserInfo, Seg
|
||||
from src.plugins.chat.message import MessageSending, MessageSet
|
||||
from src.plugins.chat.message_sender import message_manager
|
||||
from ..storage.storage import MessageStorage
|
||||
from ...config.config import global_config
|
||||
from src.chat.message_receive.storage import MessageStorage
|
||||
from src.config.config import global_config
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
|
@ -66,15 +64,18 @@ class DirectMessageSender:
|
|||
# 处理消息
|
||||
await message.process()
|
||||
|
||||
# 不知道有什么用，先留下来了，和之前那套sender一样
|
||||
_message_json = message.to_dict()
|
||||
# 发送消息（直接调用底层 API）
|
||||
from src.chat.message_receive.uni_message_sender import _send_message
|
||||
|
||||
# 发送消息
|
||||
message_set = MessageSet(chat_stream, message_id)
|
||||
message_set.add_message(message)
|
||||
await message_manager.add_message(message_set)
|
||||
sent = await _send_message(message, show_log=True)
|
||||
|
||||
if sent:
|
||||
# 存储消息
|
||||
await self.storage.store_message(message, chat_stream)
|
||||
logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
|
||||
else:
|
||||
logger.error(f"[私聊][{self.private_name}]PFC消息发送失败")
|
||||
raise RuntimeError("消息发送失败")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ import time
|
|||
from src.common.logger import get_module_logger
|
||||
from .chat_observer import ChatObserver
|
||||
from .chat_states import NotificationHandler, NotificationType, Notification
|
||||
from src.plugins.utils.chat_message_builder import build_readable_messages
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages
|
||||
import traceback # 导入 traceback 用于调试
|
||||
|
||||
logger = get_module_logger("observation_info")
|
||||
|
|
|
|||
|
|
@ -1,13 +1,13 @@
|
|||
from typing import List, Tuple, TYPE_CHECKING
|
||||
from src.common.logger import get_module_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
from ...config.config import global_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import random
|
||||
from .chat_observer import ChatObserver
|
||||
from .pfc_utils import get_items_from_json
|
||||
from src.individuality.individuality import Individuality
|
||||
from .conversation_info import ConversationInfo
|
||||
from .observation_info import ObservationInfo
|
||||
from src.plugins.utils.chat_message_builder import build_readable_messages
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages
|
||||
from rich.traceback import install
|
||||
|
||||
install(extra_lines=3)
|
||||
|
|
@ -46,7 +46,7 @@ class GoalAnalyzer:
|
|||
model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="conversation_goal"
|
||||
)
|
||||
|
||||
self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
|
||||
self.personality_info = self._get_personality_prompt()
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.nick_name = global_config.BOT_ALIAS_NAMES
|
||||
self.private_name = private_name
|
||||
|
|
@ -57,6 +57,21 @@ class GoalAnalyzer:
|
|||
self.max_goals = 3 # 同时保持的最大目标数量
|
||||
self.current_goal_and_reason = None
|
||||
|
||||
def _get_personality_prompt(self) -> str:
|
||||
"""获取个性提示信息"""
|
||||
prompt_personality = global_config.personality.personality
|
||||
|
||||
# 检查是否需要随机替换为状态
|
||||
if (
|
||||
global_config.personality.states
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
bot_name = global_config.bot.nickname
|
||||
return f"你的名字是{bot_name},你{prompt_personality};"
|
||||
|
||||
async def analyze_goal(self, conversation_info: ConversationInfo, observation_info: ObservationInfo):
|
||||
"""分析对话历史并设定目标
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
from typing import List, Tuple
|
||||
from src.common.logger import get_module_logger
|
||||
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||
from ..models.utils_model import LLMRequest
|
||||
from ...config.config import global_config
|
||||
from ..chat.message import Message
|
||||
from ..knowledge.knowledge_lib import qa_manager
|
||||
from ..utils.chat_message_builder import build_readable_messages
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from src.chat.message_receive.message import Message
|
||||
from src.chat.knowledge.knowledge_lib import qa_manager
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages
|
||||
|
||||
logger = get_module_logger("knowledge_fetcher")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
import json
|
||||
from typing import Tuple, List, Dict, Any
|
||||
from src.common.logger import get_module_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
from ...config.config import global_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
from .chat_observer import ChatObserver
|
||||
from maim_message import UserInfo
|
||||
|
||||
|
|
|
|||
|
|
@ -1,15 +1,15 @@
|
|||
from typing import Tuple, List, Dict, Any
|
||||
from src.common.logger import get_module_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
from ...config.config import global_config
|
||||
from src.common.logger import get_logger
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config
|
||||
import random
|
||||
from .chat_observer import ChatObserver
|
||||
from .reply_checker import ReplyChecker
|
||||
from src.individuality.individuality import Individuality
|
||||
from .observation_info import ObservationInfo
|
||||
from .conversation_info import ConversationInfo
|
||||
from src.plugins.utils.chat_message_builder import build_readable_messages
|
||||
from src.chat.utils.chat_message_builder import build_readable_messages
|
||||
|
||||
logger = get_module_logger("reply_generator")
|
||||
logger = get_logger("reply_generator")
|
||||
|
||||
# --- 定义 Prompt 模板 ---
|
||||
|
||||
|
|
@ -92,12 +92,27 @@ class ReplyGenerator:
|
|||
max_tokens=300,
|
||||
request_type="reply_generation",
|
||||
)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(x_person=2, level=3)
|
||||
self.personality_info = self._get_personality_prompt()
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.private_name = private_name
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
|
||||
self.reply_checker = ReplyChecker(stream_id, private_name)
|
||||
|
||||
def _get_personality_prompt(self) -> str:
|
||||
"""获取个性提示信息"""
|
||||
prompt_personality = global_config.personality.personality
|
||||
|
||||
# 检查是否需要随机替换为状态
|
||||
if (
|
||||
global_config.personality.states
|
||||
and global_config.personality.state_probability > 0
|
||||
and random.random() < global_config.personality.state_probability
|
||||
):
|
||||
prompt_personality = random.choice(global_config.personality.states)
|
||||
|
||||
bot_name = global_config.BOT_NICKNAME
|
||||
return f"你的名字是{bot_name},你{prompt_personality};"
|
||||
|
||||
# 修改 generate 方法签名，增加 action_type 参数
|
||||
async def generate(
|
||||
self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from .chat_observer import ChatObserver
|
|||
from .conversation_info import ConversationInfo
|
||||
|
||||
# from src.individuality.individuality import Individuality # 不再需要
|
||||
from ...config.config import global_config
|
||||
from src.config.config import global_config
|
||||
import time
|
||||
import asyncio
|
||||
|
||||
|
|
|
|||
|
|
@ -87,6 +87,7 @@ class BrainChatting:
|
|||
# 循环控制内部状态
|
||||
self.running: bool = False
|
||||
self._loop_task: Optional[asyncio.Task] = None # 主循环任务
|
||||
self._new_message_event = asyncio.Event() # 新消息事件，用于打断 wait
|
||||
|
||||
# 添加循环信息管理相关的属性
|
||||
self.history_loop: List[CycleDetail] = []
|
||||
|
|
@ -173,9 +174,10 @@ class BrainChatting:
|
|||
filter_intercept_message_level=1,
|
||||
)
|
||||
|
||||
# 如果有新消息，更新 last_read_time
|
||||
# 如果有新消息，更新 last_read_time 并触发事件以打断正在进行的 wait
|
||||
if len(recent_messages_list) >= 1:
|
||||
self.last_read_time = time.time()
|
||||
self._new_message_event.set() # 触发新消息事件，打断 wait
|
||||
|
||||
# 总是执行一次思考迭代（不管有没有新消息）
|
||||
# wait 动作会在其内部等待，不需要在这里处理
|
||||
|
|
@ -434,6 +436,9 @@ class BrainChatting:
|
|||
last_check_time = self.last_read_time
|
||||
check_interval = 1.0 # 每秒检查一次
|
||||
|
||||
# 清除事件状态，准备等待新消息
|
||||
self._new_message_event.clear()
|
||||
|
||||
while self.running:
|
||||
# 检查是否有新消息
|
||||
recent_messages_list = message_api.get_messages_by_time_in_chat(
|
||||
|
|
@ -453,8 +458,15 @@ class BrainChatting:
|
|||
logger.info(f"{self.log_prefix} 检测到新消息，恢复循环")
|
||||
return
|
||||
|
||||
# 等待一段时间后再次检查
|
||||
await asyncio.sleep(check_interval)
|
||||
# 等待新消息事件或超时后再次检查
|
||||
try:
|
||||
await asyncio.wait_for(self._new_message_event.wait(), timeout=check_interval)
|
||||
# 事件被触发，说明有新消息
|
||||
logger.info(f"{self.log_prefix} 检测到新消息事件，恢复循环")
|
||||
return
|
||||
except asyncio.TimeoutError:
|
||||
# 超时后继续检查
|
||||
continue
|
||||
|
||||
async def _handle_action(
|
||||
self,
|
||||
|
|
@ -674,7 +686,10 @@ class BrainChatting:
|
|||
logger.warning(f"{self.log_prefix} wait_seconds 参数格式错误，使用默认值 5 秒")
|
||||
wait_seconds = 5
|
||||
|
||||
logger.info(f"{self.log_prefix} 执行 wait 动作，等待 {wait_seconds} 秒")
|
||||
logger.info(f"{self.log_prefix} 执行 wait 动作，等待 {wait_seconds} 秒（可被新消息打断）")
|
||||
|
||||
# 清除事件状态，准备等待新消息
|
||||
self._new_message_event.clear()
|
||||
|
||||
# 记录动作信息
|
||||
await database_api.store_action_info(
|
||||
|
|
@ -687,8 +702,17 @@ class BrainChatting:
|
|||
action_name="wait",
|
||||
)
|
||||
|
||||
# 等待指定时间
|
||||
await asyncio.sleep(wait_seconds)
|
||||
# 等待指定时间，但可被新消息打断
|
||||
try:
|
||||
await asyncio.wait_for(
|
||||
self._new_message_event.wait(),
|
||||
timeout=wait_seconds
|
||||
)
|
||||
# 如果事件被触发，说明有新消息到达
|
||||
logger.info(f"{self.log_prefix} wait 动作被新消息打断，提前结束等待")
|
||||
except asyncio.TimeoutError:
|
||||
# 超时正常完成
|
||||
pass
|
||||
|
||||
logger.info(f"{self.log_prefix} wait 动作完成，继续下一次思考")
|
||||
|
||||
|
|
@ -707,7 +731,10 @@ class BrainChatting:
|
|||
# 使用默认等待时间
|
||||
wait_seconds = 3
|
||||
|
||||
logger.info(f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒")
|
||||
logger.info(f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒（可被新消息打断）")
|
||||
|
||||
# 清除事件状态，准备等待新消息
|
||||
self._new_message_event.clear()
|
||||
|
||||
# 记录动作信息
|
||||
await database_api.store_action_info(
|
||||
|
|
@ -720,8 +747,17 @@ class BrainChatting:
|
|||
action_name="listening",
|
||||
)
|
||||
|
||||
# 等待指定时间
|
||||
await asyncio.sleep(wait_seconds)
|
||||
# 等待指定时间，但可被新消息打断
|
||||
try:
|
||||
await asyncio.wait_for(
|
||||
self._new_message_event.wait(),
|
||||
timeout=wait_seconds
|
||||
)
|
||||
# 如果事件被触发，说明有新消息到达
|
||||
logger.info(f"{self.log_prefix} listening 动作被新消息打断，提前结束等待")
|
||||
except asyncio.TimeoutError:
|
||||
# 超时正常完成
|
||||
pass
|
||||
|
||||
logger.info(f"{self.log_prefix} listening 动作完成，继续下一次思考")
|
||||
|
||||
|
|
|
|||
|
|
@ -402,7 +402,7 @@ class BrainPlanner:
|
|||
moderation_prompt=moderation_prompt_block,
|
||||
name_block=name_block,
|
||||
interest=interest,
|
||||
plan_style=global_config.personality.private_plan_style,
|
||||
plan_style=global_config.experimental.private_plan_style,
|
||||
)
|
||||
|
||||
return prompt, message_id_list
|
||||
|
|
|
|||
|
|
@ -545,9 +545,9 @@ class HeartFChatting:
|
|||
new_message_count = message_api.count_new_messages(
|
||||
chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time()
|
||||
)
|
||||
need_reply = new_message_count >= random.randint(2, 3)
|
||||
need_reply = new_message_count >= random.randint(2, 3) or time.time() - self.last_read_time > 90
|
||||
if need_reply:
|
||||
logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复")
|
||||
logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复，或者上次回复时间超过90秒")
|
||||
|
||||
reply_text = ""
|
||||
first_replied = False
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import time
|
||||
import asyncio
|
||||
import urllib3
|
||||
|
||||
from abc import abstractmethod
|
||||
|
|
@ -20,6 +21,9 @@ logger = get_logger("chat_message")
|
|||
# 禁用SSL警告
|
||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||||
|
||||
# VLM 处理并发限制（避免同时处理太多图片导致卡死）
|
||||
_vlm_semaphore = asyncio.Semaphore(3)
|
||||
|
||||
# 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。
|
||||
|
|
@ -73,20 +77,35 @@ class Message(MessageBase):
|
|||
str: 处理后的文本
|
||||
"""
|
||||
if segment.type == "seglist":
|
||||
# 处理消息段列表
|
||||
# 处理消息段列表 - 使用并行处理提升性能
|
||||
tasks = [self._process_message_segments(seg) for seg in segment.data] # type: ignore
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
segments_text = []
|
||||
for seg in segment.data:
|
||||
processed = await self._process_message_segments(seg) # type: ignore
|
||||
if processed:
|
||||
segments_text.append(processed)
|
||||
for result in results:
|
||||
if isinstance(result, Exception):
|
||||
logger.error(f"处理消息段时出错: {result}")
|
||||
continue
|
||||
if result:
|
||||
segments_text.append(result)
|
||||
return " ".join(segments_text)
|
||||
elif segment.type == "forward":
|
||||
segments_text = []
|
||||
for node_dict in segment.data:
|
||||
# 处理转发消息 - 使用并行处理
|
||||
async def process_forward_node(node_dict):
|
||||
message = MessageBase.from_dict(node_dict) # type: ignore
|
||||
processed_text = await self._process_message_segments(message.message_segment)
|
||||
if processed_text:
|
||||
segments_text.append(f"{global_config.bot.nickname}: {processed_text}")
|
||||
return f"{global_config.bot.nickname}: {processed_text}"
|
||||
return None
|
||||
|
||||
tasks = [process_forward_node(node_dict) for node_dict in segment.data]
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
segments_text = []
|
||||
for result in results:
|
||||
if isinstance(result, Exception):
|
||||
logger.error(f"处理转发节点时出错: {result}")
|
||||
continue
|
||||
if result:
|
||||
segments_text.append(result)
|
||||
return "[合并消息]: " + "\n-- ".join(segments_text)
|
||||
else:
|
||||
# 处理单个消息段
|
||||
|
|
@ -173,7 +192,8 @@ class MessageRecv(Message):
|
|||
self.is_picid = True
|
||||
self.is_emoji = False
|
||||
image_manager = get_image_manager()
|
||||
# print(f"segment.data: {segment.data}")
|
||||
# 使用 semaphore 限制 VLM 并发，避免同时处理太多图片
|
||||
async with _vlm_semaphore:
|
||||
_, processed_text = await image_manager.process_image(segment.data)
|
||||
return processed_text
|
||||
return "[发了一张图片，网卡了加载不出来]"
|
||||
|
|
@ -183,6 +203,8 @@ class MessageRecv(Message):
|
|||
self.is_picid = False
|
||||
self.is_voice = False
|
||||
if isinstance(segment.data, str):
|
||||
# 使用 semaphore 限制 VLM 并发
|
||||
async with _vlm_semaphore:
|
||||
return await get_image_manager().get_emoji_description(segment.data)
|
||||
return "[发了一个表情包，网卡了加载不出来]"
|
||||
elif segment.type == "voice":
|
||||
|
|
|
|||
|
|
@ -50,7 +50,15 @@ class Server:
|
|||
async def run(self):
|
||||
"""启动服务器"""
|
||||
# 禁用 uvicorn 默认日志和访问日志
|
||||
config = Config(app=self.app, host=self._host, port=self._port, log_config=None, access_log=False)
|
||||
# 设置 ws_max_size 为 100MB，支持大消息（如包含多张图片的转发消息）
|
||||
config = Config(
|
||||
app=self.app,
|
||||
host=self._host,
|
||||
port=self._port,
|
||||
log_config=None,
|
||||
access_log=False,
|
||||
ws_max_size=104_857_600, # 100MB
|
||||
)
|
||||
self._server = UvicornServer(config=config)
|
||||
try:
|
||||
await self._server.serve()
|
||||
|
|
|
|||
|
|
@ -57,7 +57,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
|
|||
|
||||
# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
|
||||
# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
|
||||
MMC_VERSION = "0.12.1"
|
||||
MMC_VERSION = "0.12.2"
|
||||
|
||||
|
||||
def get_key_comment(toml_table, key):
|
||||
|
|
|
|||
|
|
@ -57,9 +57,6 @@ class PersonalityConfig(ConfigBase):
|
|||
visual_style: str = ""
|
||||
"""图片提示词"""
|
||||
|
||||
private_plan_style: str = ""
|
||||
"""私聊说话规则，行为风格"""
|
||||
|
||||
states: list[str] = field(default_factory=lambda: [])
|
||||
"""状态列表，用于随机替换personality"""
|
||||
|
||||
|
|
@ -713,8 +710,8 @@ class DebugConfig(ConfigBase):
|
|||
class ExperimentalConfig(ConfigBase):
|
||||
"""实验功能配置类"""
|
||||
|
||||
enable_friend_chat: bool = False
|
||||
"""是否启用好友聊天"""
|
||||
private_plan_style: str = ""
|
||||
"""私聊说话规则，行为风格（实验性功能）"""
|
||||
|
||||
chat_prompts: list[str] = field(default_factory=lambda: [])
|
||||
"""
|
||||
|
|
@ -904,6 +901,13 @@ class DreamConfig(ConfigBase):
|
|||
|
||||
return False
|
||||
|
||||
dream_visible: bool = False
|
||||
"""
|
||||
做梦结果是否存储到上下文
|
||||
- True: 将梦境发送给配置的用户后，也会存储到聊天上下文中，在后续对话中可见
|
||||
- False: 仅发送梦境但不存储，不在后续对话上下文中出现
|
||||
"""
|
||||
|
||||
def __post_init__(self):
|
||||
"""验证配置值"""
|
||||
if self.interval_minutes < 1:
|
||||
|
|
|
|||
|
|
@ -215,11 +215,12 @@ async def generate_dream_summary(
|
|||
f"platform={platform!r}, user_id={user_id!r}"
|
||||
)
|
||||
else:
|
||||
dream_visible = global_config.dream.dream_visible
|
||||
ok = await send_api.text_to_stream(
|
||||
dream_content,
|
||||
stream_id=stream_id,
|
||||
typing=False,
|
||||
storage_message=True,
|
||||
storage_message=dream_visible,
|
||||
)
|
||||
if ok:
|
||||
logger.info(
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import time
|
||||
import json
|
||||
import asyncio
|
||||
import re
|
||||
from typing import List, Dict, Any, Optional, Tuple
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
|
|
@ -108,7 +107,7 @@ def init_memory_retrieval_prompt():
|
|||
- 你可以对查询思路给出简短的思考：思考要简短，直接切入要点
|
||||
- 先思考当前信息是否足够回答问题
|
||||
- 如果信息不足，则需要使用tool查询信息，你必须给出使用什么工具进行查询
|
||||
- 如果当前已收集的信息足够或信息不足确定无法找到答案，你必须调用finish_search工具结束查询
|
||||
- 如果当前已收集的信息足够或信息不足确定无法找到答案，你必须调用found_answer工具结束查询
|
||||
""",
|
||||
name="memory_retrieval_react_prompt_head",
|
||||
)
|
||||
|
|
@ -312,7 +311,7 @@ async def _react_agent_solve_question(
|
|||
|
||||
return None
|
||||
|
||||
# 正常迭代：使用head_prompt决定调用哪些工具（包含finish_search工具）
|
||||
# 正常迭代：使用head_prompt决定调用哪些工具（包含found_answer工具）
|
||||
tool_definitions = tool_registry.get_tool_definitions()
|
||||
# tool_names = [tool_def["name"] for tool_def in tool_definitions]
|
||||
# logger.debug(f"ReAct Agent 第 {iteration + 1} 次迭代，问题: {question}|可用工具: {', '.join(tool_names)} (共{len(tool_definitions)}个)")
|
||||
|
|
@ -373,7 +372,7 @@ async def _react_agent_solve_question(
|
|||
logger.error(f"ReAct Agent LLM调用失败: {response}")
|
||||
break
|
||||
|
||||
# 注意：这里会检查finish_search工具调用，如果检测到finish_search工具，会根据found_answer参数决定返回答案或退出查询
|
||||
# 注意：这里会检查found_answer工具调用，如果检测到found_answer工具，会根据answer参数决定返回答案或退出查询
|
||||
|
||||
assistant_message: Optional[Message] = None
|
||||
if tool_calls:
|
||||
|
|
@ -403,81 +402,117 @@ async def _react_agent_solve_question(
|
|||
|
||||
# 处理工具调用
|
||||
if not tool_calls:
|
||||
# 如果没有工具调用，检查响应文本中是否包含finish_search函数调用格式
|
||||
# 如果没有工具调用，检查响应文本中是否包含found_answer函数调用格式或JSON格式
|
||||
if response and response.strip():
|
||||
# 尝试从文本中解析finish_search函数调用
|
||||
def parse_finish_search_from_text(text: str):
|
||||
"""从文本中解析finish_search函数调用，返回(found_answer, answer)元组，如果未找到则返回(None, None)"""
|
||||
# 首先尝试解析JSON格式的found_answer
|
||||
def parse_json_found_answer(text: str):
|
||||
"""从文本中解析JSON格式的found_answer，返回(found_answer, answer)元组，如果未找到则返回(None, None)"""
|
||||
if not text:
|
||||
return None, None
|
||||
|
||||
# 查找finish_search函数调用位置（不区分大小写）
|
||||
func_pattern = "finish_search"
|
||||
try:
|
||||
# 尝试提取JSON对象（可能包含在代码块中或直接是JSON）
|
||||
json_text = text.strip()
|
||||
|
||||
# 如果包含代码块标记，提取JSON部分
|
||||
if "```json" in json_text:
|
||||
start = json_text.find("```json") + 7
|
||||
end = json_text.find("```", start)
|
||||
if end != -1:
|
||||
json_text = json_text[start:end].strip()
|
||||
elif "```" in json_text:
|
||||
start = json_text.find("```") + 3
|
||||
end = json_text.find("```", start)
|
||||
if end != -1:
|
||||
json_text = json_text[start:end].strip()
|
||||
|
||||
# 尝试解析JSON
|
||||
data = json.loads(json_text)
|
||||
|
||||
# 检查是否包含found_answer字段
|
||||
if isinstance(data, dict) and "found_answer" in data:
|
||||
found_answer = bool(data.get("found_answer", False))
|
||||
answer = data.get("answer", "")
|
||||
return found_answer, answer
|
||||
except (json.JSONDecodeError, ValueError, TypeError):
|
||||
# 如果JSON解析失败，尝试在文本中查找JSON对象
|
||||
try:
|
||||
# 查找第一个 { 和最后一个 } 之间的内容（更健壮的JSON提取）
|
||||
first_brace = text.find('{')
|
||||
if first_brace != -1:
|
||||
# 从第一个 { 开始，找到匹配的 }
|
||||
brace_count = 0
|
||||
json_end = -1
|
||||
for i in range(first_brace, len(text)):
|
||||
if text[i] == '{':
|
||||
brace_count += 1
|
||||
elif text[i] == '}':
|
||||
brace_count -= 1
|
||||
if brace_count == 0:
|
||||
json_end = i + 1
|
||||
break
|
||||
|
||||
if json_end != -1:
|
||||
json_text = text[first_brace:json_end]
|
||||
data = json.loads(json_text)
|
||||
if isinstance(data, dict) and "found_answer" in data:
|
||||
found_answer = bool(data.get("found_answer", False))
|
||||
answer = data.get("answer", "")
|
||||
return found_answer, answer
|
||||
except (json.JSONDecodeError, ValueError, TypeError):
|
||||
pass
|
||||
|
||||
return None, None
|
||||
|
||||
# 尝试从文本中解析found_answer函数调用
|
||||
def parse_found_answer_from_text(text: str):
|
||||
"""从文本中解析found_answer函数调用，返回answer字符串，如果未找到则返回None
|
||||
如果answer存在且非空，表示找到答案；如果answer为空或不存在，表示未找到答案"""
|
||||
if not text:
|
||||
return None
|
||||
|
||||
# 查找found_answer函数调用位置（不区分大小写）
|
||||
func_pattern = "found_answer"
|
||||
text_lower = text.lower()
|
||||
func_pos = text_lower.find(func_pattern)
|
||||
if func_pos == -1:
|
||||
return None, None
|
||||
|
||||
# 查找函数调用的开始和结束位置
|
||||
# 从func_pos开始向后查找左括号
|
||||
start_pos = text.find("(", func_pos)
|
||||
if start_pos == -1:
|
||||
return None, None
|
||||
|
||||
# 查找匹配的右括号（考虑嵌套）
|
||||
paren_count = 0
|
||||
end_pos = start_pos
|
||||
for i in range(start_pos, len(text)):
|
||||
if text[i] == "(":
|
||||
paren_count += 1
|
||||
elif text[i] == ")":
|
||||
paren_count -= 1
|
||||
if paren_count == 0:
|
||||
end_pos = i
|
||||
break
|
||||
else:
|
||||
# 没有找到匹配的右括号
|
||||
return None, None
|
||||
|
||||
# 提取函数参数部分
|
||||
params_text = text[start_pos + 1 : end_pos]
|
||||
|
||||
# 解析found_answer参数（布尔值，可能是true/false/True/False）
|
||||
found_answer = None
|
||||
found_answer_patterns = [
|
||||
r"found_answer\s*=\s*true",
|
||||
r"found_answer\s*=\s*True",
|
||||
r"found_answer\s*=\s*false",
|
||||
r"found_answer\s*=\s*False",
|
||||
]
|
||||
for pattern in found_answer_patterns:
|
||||
match = re.search(pattern, params_text, re.IGNORECASE)
|
||||
if match:
|
||||
found_answer = "true" in match.group(0).lower()
|
||||
break
|
||||
return None
|
||||
|
||||
# 解析answer参数（字符串，使用extract_quoted_content）
|
||||
answer = extract_quoted_content(text, "finish_search", "answer")
|
||||
answer = extract_quoted_content(text, "found_answer", "answer")
|
||||
|
||||
return found_answer, answer
|
||||
# 如果answer存在（即使是空字符串），也返回它（空字符串表示未找到答案）
|
||||
return answer
|
||||
|
||||
parsed_found_answer, parsed_answer = parse_finish_search_from_text(response)
|
||||
# 首先尝试解析JSON格式
|
||||
parsed_found_answer_json, parsed_answer_json = parse_json_found_answer(response)
|
||||
is_json_format = parsed_found_answer_json is not None
|
||||
|
||||
if parsed_found_answer is not None:
|
||||
# 检测到finish_search函数调用格式
|
||||
if parsed_found_answer:
|
||||
# 如果JSON解析成功，使用JSON结果
|
||||
if is_json_format:
|
||||
parsed_answer = parsed_answer_json
|
||||
has_answer = parsed_found_answer_json and parsed_answer and parsed_answer.strip()
|
||||
else:
|
||||
# 如果JSON解析失败，尝试解析函数调用格式
|
||||
parsed_answer = parse_found_answer_from_text(response)
|
||||
# 如果answer存在且非空，表示找到答案；否则表示未找到答案
|
||||
has_answer = parsed_answer is not None and parsed_answer.strip() != ""
|
||||
|
||||
if parsed_answer is not None or is_json_format:
|
||||
# 检测到found_answer格式（可能是JSON格式或函数调用格式）
|
||||
format_type = "JSON格式" if is_json_format else "函数调用格式"
|
||||
if has_answer:
|
||||
# 找到了答案
|
||||
if parsed_answer:
|
||||
step["actions"].append(
|
||||
{
|
||||
"action_type": "finish_search",
|
||||
"action_params": {"found_answer": True, "answer": parsed_answer},
|
||||
"action_type": "found_answer",
|
||||
"action_params": {"answer": parsed_answer},
|
||||
}
|
||||
)
|
||||
step["observations"] = ["检测到finish_search文本格式调用，找到答案"]
|
||||
step["observations"] = [f"检测到found_answer{format_type}调用，找到答案"]
|
||||
thinking_steps.append(step)
|
||||
logger.info(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过finish_search文本格式找到关于问题{question}的答案: {parsed_answer}"
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过found_answer{format_type}找到关于问题{question}的答案: {parsed_answer[:100]}..."
|
||||
)
|
||||
|
||||
_log_conversation_messages(
|
||||
|
|
@ -487,31 +522,26 @@ async def _react_agent_solve_question(
|
|||
)
|
||||
|
||||
return True, parsed_answer, thinking_steps, False
|
||||
else:
|
||||
# found_answer为True但没有提供answer，视为错误，继续迭代
|
||||
logger.warning(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 finish_search文本格式found_answer为True但未提供answer"
|
||||
)
|
||||
else:
|
||||
# 未找到答案，直接退出查询
|
||||
step["actions"].append(
|
||||
{"action_type": "finish_search", "action_params": {"found_answer": False}}
|
||||
{"action_type": "found_answer", "action_params": {"answer": ""}}
|
||||
)
|
||||
step["observations"] = ["检测到finish_search文本格式调用，未找到答案"]
|
||||
step["observations"] = [f"检测到found_answer{format_type}调用，未找到答案"]
|
||||
thinking_steps.append(step)
|
||||
logger.info(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过finish_search文本格式判断未找到答案"
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过found_answer{format_type}判断未找到答案"
|
||||
)
|
||||
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status="未找到答案：通过finish_search文本格式判断未找到答案",
|
||||
final_status="未找到答案：通过found_answer文本格式判断未找到答案",
|
||||
)
|
||||
|
||||
return False, "", thinking_steps, False
|
||||
|
||||
# 如果没有检测到finish_search格式，记录思考过程，继续下一轮迭代
|
||||
# 如果没有检测到found_answer格式，记录思考过程，继续下一轮迭代
|
||||
step["observations"] = [f"思考完成，但未调用工具。响应: {response}"]
|
||||
logger.info(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 思考完成但未调用工具: {response}"
|
||||
|
|
@ -525,62 +555,55 @@ async def _react_agent_solve_question(
|
|||
continue
|
||||
|
||||
# 处理工具调用
|
||||
# 首先检查是否有finish_search工具调用，如果有则立即返回，不再处理其他工具
|
||||
finish_search_found = None
|
||||
finish_search_answer = None
|
||||
# 首先检查是否有found_answer工具调用，如果有则立即返回，不再处理其他工具
|
||||
found_answer_answer = None
|
||||
for tool_call in tool_calls:
|
||||
tool_name = tool_call.func_name
|
||||
tool_args = tool_call.args or {}
|
||||
|
||||
if tool_name == "finish_search":
|
||||
finish_search_found = tool_args.get("found_answer", False)
|
||||
finish_search_answer = tool_args.get("answer", "")
|
||||
if tool_name == "found_answer":
|
||||
found_answer_answer = tool_args.get("answer", "")
|
||||
|
||||
if finish_search_found:
|
||||
# 如果answer存在且非空，表示找到答案；否则表示未找到答案
|
||||
if found_answer_answer and found_answer_answer.strip():
|
||||
# 找到了答案
|
||||
if finish_search_answer:
|
||||
step["actions"].append(
|
||||
{
|
||||
"action_type": "finish_search",
|
||||
"action_params": {"found_answer": True, "answer": finish_search_answer},
|
||||
"action_type": "found_answer",
|
||||
"action_params": {"answer": found_answer_answer},
|
||||
}
|
||||
)
|
||||
step["observations"] = ["检测到finish_search工具调用，找到答案"]
|
||||
step["observations"] = ["检测到found_answer工具调用，找到答案"]
|
||||
thinking_steps.append(step)
|
||||
logger.info(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过finish_search工具找到关于问题{question}的答案: {finish_search_answer}"
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过found_answer工具找到关于问题{question}的答案: {found_answer_answer}"
|
||||
)
|
||||
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status=f"找到答案：{finish_search_answer}",
|
||||
final_status=f"找到答案：{found_answer_answer}",
|
||||
)
|
||||
|
||||
return True, finish_search_answer, thinking_steps, False
|
||||
else:
|
||||
# found_answer为True但没有提供answer，视为错误
|
||||
logger.warning(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 finish_search工具found_answer为True但未提供answer"
|
||||
)
|
||||
return True, found_answer_answer, thinking_steps, False
|
||||
else:
|
||||
# 未找到答案，直接退出查询
|
||||
step["actions"].append({"action_type": "finish_search", "action_params": {"found_answer": False}})
|
||||
step["observations"] = ["检测到finish_search工具调用，未找到答案"]
|
||||
step["actions"].append({"action_type": "found_answer", "action_params": {"answer": ""}})
|
||||
step["observations"] = ["检测到found_answer工具调用，未找到答案"]
|
||||
thinking_steps.append(step)
|
||||
logger.info(
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过finish_search工具判断未找到答案"
|
||||
f"{react_log_prefix}第 {iteration + 1} 次迭代 通过found_answer工具判断未找到答案"
|
||||
)
|
||||
|
||||
_log_conversation_messages(
|
||||
conversation_messages,
|
||||
head_prompt=first_head_prompt,
|
||||
final_status="未找到答案：通过finish_search工具判断未找到答案",
|
||||
final_status="未找到答案：通过found_answer工具判断未找到答案",
|
||||
)
|
||||
|
||||
return False, "", thinking_steps, False
|
||||
|
||||
# 如果没有finish_search工具调用，继续处理其他工具
|
||||
# 如果没有found_answer工具调用，继续处理其他工具
|
||||
tool_tasks = []
|
||||
for i, tool_call in enumerate(tool_calls):
|
||||
tool_name = tool_call.func_name
|
||||
|
|
@ -590,8 +613,8 @@ async def _react_agent_solve_question(
|
|||
f"{react_log_prefix}第 {iteration + 1} 次迭代 工具调用 {i + 1}/{len(tool_calls)}: {tool_name}({tool_args})"
|
||||
)
|
||||
|
||||
# 跳过finish_search工具调用（已经在上面处理过了）
|
||||
if tool_name == "finish_search":
|
||||
# 跳过found_answer工具调用（已经在上面处理过了）
|
||||
if tool_name == "found_answer":
|
||||
continue
|
||||
|
||||
# 记录最后一次使用的工具名称（用于判断是否需要额外迭代）
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ from .query_chat_history import register_tool as register_query_chat_history
|
|||
from .query_lpmm_knowledge import register_tool as register_lpmm_knowledge
|
||||
from .query_person_info import register_tool as register_query_person_info
|
||||
from .query_words import register_tool as register_query_words
|
||||
from .found_answer import register_tool as register_finish_search
|
||||
from .found_answer import register_tool as register_found_answer
|
||||
from src.config.config import global_config
|
||||
|
||||
|
||||
|
|
@ -24,7 +24,7 @@ def init_all_tools():
|
|||
register_query_chat_history()
|
||||
register_query_person_info()
|
||||
register_query_words() # 注册query_words工具
|
||||
register_finish_search() # 注册finish_search工具
|
||||
register_found_answer() # 注册found_answer工具
|
||||
|
||||
if global_config.lpmm_knowledge.lpmm_mode == "agent":
|
||||
register_lpmm_knowledge()
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
"""
|
||||
finish_search工具 - 用于在记忆检索过程中结束查询
|
||||
found_answer工具 - 用于在记忆检索过程中结束查询
|
||||
"""
|
||||
|
||||
from src.common.logger import get_logger
|
||||
|
|
@ -8,17 +8,16 @@ from .tool_registry import register_memory_retrieval_tool
|
|||
logger = get_logger("memory_retrieval_tools")
|
||||
|
||||
|
||||
async def finish_search(found_answer: bool, answer: str = "") -> str:
|
||||
async def found_answer(answer: str = "") -> str:
|
||||
"""结束查询
|
||||
|
||||
Args:
|
||||
found_answer: 是否找到了答案
|
||||
answer: 如果找到了答案，提供答案内容；如果未找到，可以为空
|
||||
answer: 如果找到了答案，提供答案内容；如果未找到答案，可以为空或不提供此参数
|
||||
|
||||
Returns:
|
||||
str: 确认信息
|
||||
"""
|
||||
if found_answer:
|
||||
if answer and answer.strip():
|
||||
logger.info(f"找到答案: {answer}")
|
||||
return f"已确认找到答案: {answer}"
|
||||
else:
|
||||
|
|
@ -27,23 +26,17 @@ async def finish_search(found_answer: bool, answer: str = "") -> str:
|
|||
|
||||
|
||||
def register_tool():
|
||||
"""注册finish_search工具"""
|
||||
"""注册found_answer工具"""
|
||||
register_memory_retrieval_tool(
|
||||
name="finish_search",
|
||||
description="当你决定结束查询时，调用此工具。如果找到了明确答案，设置found_answer为true并在answer中提供答案；如果未找到答案，设置found_answer为false。只有在检索到明确、具体的答案时才设置found_answer为true，不要编造信息。",
|
||||
name="found_answer",
|
||||
description="当你决定结束查询时，调用此工具。如果找到了明确答案，在answer参数中提供答案内容；如果未找到答案，可以不提供answer参数或提供空字符串。只有在检索到明确、具体的答案时才提供answer，不要编造信息。",
|
||||
parameters=[
|
||||
{
|
||||
"name": "found_answer",
|
||||
"type": "boolean",
|
||||
"description": "是否找到了答案",
|
||||
"required": True,
|
||||
},
|
||||
{
|
||||
"name": "answer",
|
||||
"type": "string",
|
||||
"description": "如果found_answer为true，提供找到的答案内容，必须基于已收集的信息，不要编造；如果found_answer为false，可以为空",
|
||||
"description": "如果找到了答案，提供找到的答案内容，必须基于已收集的信息，不要编造；如果未找到答案，可以不提供此参数或提供空字符串",
|
||||
"required": False,
|
||||
},
|
||||
],
|
||||
execute_func=finish_search,
|
||||
execute_func=found_answer,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "7.3.3"
|
||||
version = "7.3.5"
|
||||
|
||||
#----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
|
||||
# 如果你想要修改配置文件，请递增version的值
|
||||
|
|
@ -40,20 +40,11 @@ multiple_reply_style = [
|
|||
multiple_probability = 0.3
|
||||
|
||||
# 麦麦的说话规则和行为规则:
|
||||
plan_style = """
|
||||
1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用
|
||||
2.如果相同的内容已经被执行，请不要重复执行
|
||||
3.你对技术相关话题，游戏和动漫相关话题感兴趣，也对日常话题感兴趣，不喜欢太过沉重严肃的话题
|
||||
4.请控制你的发言频率，不要太过频繁的发言
|
||||
5.如果有人对你感到厌烦，请减少回复
|
||||
6.如果有人在追问你，或者话题没有说完，请你继续回复"""
|
||||
|
||||
|
||||
# 麦麦私聊的说话规则，行为风格:
|
||||
private_plan_style = """
|
||||
1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用
|
||||
2.如果相同的内容已经被执行，请不要重复执行
|
||||
3.某句话如果已经被回复过，不要重复回复"""
|
||||
plan_style = """1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用
|
||||
2.如果相同的action已经被执行，请不要重复执行该action
|
||||
3.如果有人对你感到厌烦，请减少回复
|
||||
4.如果有人在追问你，或者话题没有说完，请你继续回复
|
||||
5.请分析哪些对话是和你说的，哪些是其他人之间的互动，不要误认为其他人之间的互动是和你说的"""
|
||||
|
||||
# 多重人格，根据概率随机选择人格
|
||||
states = [
|
||||
|
|
@ -153,6 +144,7 @@ first_delay_seconds = 1800 # 程序启动后首次做梦前的延迟时间（秒
|
|||
# 为空字符串时不推送。
|
||||
dream_send = ""
|
||||
|
||||
dream_visible = false # 做梦结果是否存储到上下文，True: 将梦境发送给配置的用户后，也会存储到聊天上下文中，在后续对话中可见；False: 仅发送梦境但不存储，不在后续对话上下文中出现
|
||||
# 做梦时间段配置，格式：["HH:MM-HH:MM", ...]
|
||||
# 如果列表为空，则表示全天允许做梦。
|
||||
# 如果配置了时间段，则只有在这些时间段内才会实际执行做梦流程。
|
||||
|
|
@ -298,6 +290,12 @@ trust_xff = false # 是否启用X-Forwarded-For代理解析（默认false）
|
|||
secure_cookie = false # 是否启用安全Cookie（仅通过HTTPS传输，默认false）
|
||||
|
||||
[experimental] #实验性功能
|
||||
# 麦麦私聊的说话规则，行为风格（实验性功能）
|
||||
private_plan_style = """
|
||||
1.思考**所有**的可用的action中的**每个动作**是否符合当下条件，如果动作使用条件符合聊天内容就使用
|
||||
2.如果相同的内容已经被执行，请不要重复执行
|
||||
3.某句话如果已经被回复过，不要重复回复"""
|
||||
|
||||
# 为指定聊天添加额外的prompt配置
|
||||
# 格式: ["platform:id:type:prompt内容", ...]
|
||||
# 示例:
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
Binary file not shown.
|
|
@ -11,21 +11,21 @@
|
|||
<link rel="icon" type="image/x-icon" href="/maimai.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>MaiBot Dashboard</title>
|
||||
<script type="module" crossorigin src="/assets/index-DC6giT2e.js"></script>
|
||||
<script type="module" crossorigin src="/assets/index-DD4VGX3W.js"></script>
|
||||
<link rel="modulepreload" crossorigin href="/assets/react-vendor-BmxF9s7Q.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/router-9vIXuQkh.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/utils-BqoaXoQ1.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/radix-core-DyJi0yyw.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/radix-extra-DmmnfeQE.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/charts-simvewUa.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/icons-9Z4kBNLK.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/icons-8bdCaZgy.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/codemirror-TZqPU532.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/dnd-BiPfFtVp.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/misc-CJqnlRwD.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/reactflow-DtsZHOR4.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/uppy-DFP_VzYR.js">
|
||||
<link rel="modulepreload" crossorigin href="/assets/markdown-CKA5gBQ9.js">
|
||||
<link rel="stylesheet" crossorigin href="/assets/index-DseBW7cm.css">
|
||||
<link rel="stylesheet" crossorigin href="/assets/index-RB5cYCSR.css">
|
||||
</head>
|
||||
<body>
|
||||
<div id="root" class="notranslate"></div>
|
||||
|
|
|
|||
Loading…
Reference in New Issue