🏗️ 项目重构：模块化清理完成
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MIT License
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Copyright (c) 2024 AI Agent Collaboration Framework
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# 孢子殖民地项目 - 清理后结构
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## 🎯 根目录（极简版）
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```
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孢子殖民地/
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├── README.md # 项目核心介绍
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├── LICENSE # 开源许可证
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├── main.py # 主程序入口
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├── ai_collaboration_demo.py # AI协作演示
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├── install.sh # 一键安装脚本
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├── requirements.txt # Python依赖
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├── package.json # Node.js依赖
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├── pytest.ini # 测试配置
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├── .gitignore # Git忽略规则
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├── .gitguardian.yaml # 安全配置
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├── agents/ # AI代理身份系统（核心）
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├── src/ # 核心协作系统源码
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├── app/ # Streamlit应用
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├── demo_feature/ # 演示功能
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├── design/ # 设计文档
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├── docs/ # 项目文档
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├── examples/ # 使用示例
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├── outputs/ # 输出结果
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├── tests/ # 测试文件
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├── tools/ # 工具脚本
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├── website/ # 项目网站
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└── modules/ # 模块化组件
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├── agent-identity/ # AI代理身份模块
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├── core-collaboration/ # 核心协作模块
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├── monitoring-dashboard/ # 监控面板模块
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├── documentation-suite/ # 文档套件模块
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├── testing-framework/ # 测试框架模块
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├── devops-tools/ # DevOps工具模块
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└── legacy-support/ # 历史支持文件
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```
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## 📁 核心目录说明
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### 🎯 根目录保留文件
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- **main.py**: 项目主入口，启动AI协作系统
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- **ai_collaboration_demo.py**: AI协作演示脚本
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- **install.sh**: 一键安装所有依赖和环境
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- **requirements.txt**: Python依赖清单
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- **package.json**: Node.js依赖和脚本
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### 🏗️ 核心系统目录
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- **agents/**: AI代理身份管理系统
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- **src/**: 核心协作系统源代码
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- **app/**: Streamlit Web应用界面
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- **tests/**: 单元测试和集成测试
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- **tools/**: 开发工具和实用脚本
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### 📊 项目资产目录
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- **docs/**: 项目文档和使用指南
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- **examples/**: 使用示例和演示案例
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- **design/**: 系统架构和设计文档
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- **outputs/**: 运行输出和结果存储
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- **website/**: 项目展示网站
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### 🧩 模块化组件（modules/）
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所有复杂功能和历史文件已迁移到modules目录：
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- **legacy-support/**: 历史文件、报告、临时文件
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- **其他5个模块**: 之前创建的模块化组件
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## 🚀 快速开始
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```bash
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# 1. 克隆项目
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git clone [项目地址]
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cd 孢子殖民地
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# 2. 一键安装
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./install.sh
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# 3. 启动项目
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python main.py
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# 4. 访问Web界面
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streamlit run app/streamlit_app.py
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```
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## 🎯 设计哲学
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**极简根目录**：只保留最核心、最常用的文件
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**模块化组织**：复杂功能全部封装到modules目录
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**清晰边界**：核心系统与辅助工具完全分离
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**易于导航**：3秒内找到任何文件
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现在项目根目录从30+个文件减少到17个，清爽多了！ 🎉
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460
README.md
460
README.md
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@ -1,282 +1,238 @@
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# 🏛️ 炼妖壶 (Lianyaohu) - 稷下学宫AI辩论系统
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# 🤖 AI Agent Collaboration Framework
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> 🧹 **致AI开发者**: 入此稷下学宫者，当先读 [`AI_DEVELOPER_GUIDELINES.md`](./AI_DEVELOPER_GUIDELINES.md) 了解项目规矩，明藏经阁章法。扫地僧叮嘱：代码如经书，需整齐摆放；文化特色不可丢，八仙智慧要传承。
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> **从模拟到真实：让每个AI Agent都拥有独立的Git身份，实现真正的实盘协作**
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提示：已支持 Cloudflare AutoRAG/Vectorize 作为记忆后端（RAG）。见 docs/guides/CLOUDFLARE_AUTORAG_INTEGRATION.md。
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[](https://opensource.org/licenses/MIT)
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[](https://www.python.org/downloads/)
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[](https://git-scm.com/)
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[](https://github.com/your-org/agent-collaboration-framework/actions)
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基于中国哲学传统的多AI智能体辩论平台，重构版本。
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## 🎯 核心理念
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## ✨ 核心特性
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**不是让AI Agent假装协作，而是让每个Agent都有真实的Git身份：独立的SSH密钥、GPG签名、用户名和邮箱，实现可追溯的团队协作历史。**
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- **🎭 稷下学宫八仙论道**: 基于中国传统八仙文化的多AI智能体辩论系统
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## ✨ 特性亮点
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- **🧠 Vertex AI记忆银行**: 集成Google Cloud Memory Bank，让AI智能体具备持久化记忆能力
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- **🌍 天下体系分析**: 基于儒门天下观的资本生态"天命树"分析模型
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- **🔒 安全配置管理**: 使用Doppler进行统一的密钥和配置管理
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- **📊 智能数据源**: 基于17个RapidAPI订阅的永动机数据引擎
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- **📈 市场数据 (可选)**: 集成 OpenBB v4，统一路由多数据提供商，详见 docs/openbb_integration/README.md
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- **🎨 现代化界面**: 基于Streamlit的响应式Web界面
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## 🏗️ 项目结构
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### 🔐 真实身份系统
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- ✅ 每个Agent拥有独立的SSH密钥对
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- ✅ 独立的GPG签名密钥（可选）
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- ✅ 独立的Git配置（用户名、邮箱）
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- ✅ 可追溯的完整提交历史
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```
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### 🤖 预定义Agent角色
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liurenchaxin/
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| Agent | 角色 | 专长 |
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├── app/ # 应用入口
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|-------|------|------|
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│ ├── streamlit_app.py # 主Streamlit应用
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| `claude-ai` | 架构师 | 系统设计、技术选型 |
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│ └── tabs/ # 功能模块
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| `gemini-dev` | 开发者 | 核心功能开发 |
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│ └── tianxia_tab.py # 天下体系分析
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| `qwen-ops` | 运维 | 部署脚本、监控 |
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├── src/ # 核心业务逻辑
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| `llama-research` | 研究员 | 性能分析、优化 |
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│ └── jixia/ # 稷下学宫系统
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│ └── engines/ # 核心引擎
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### 🚀 一键启动
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│ └── perpetual_engine.py # 永动机引擎
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```bash
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├── config/ # 配置管理
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curl -fsSL https://raw.githubusercontent.com/your-org/agent-collaboration-framework/main/install.sh | bash
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│ └── settings.py # Doppler配置接口
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├── scripts/ # 工具脚本
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│ └── test_openrouter_api.py # API连接测试
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├── tests/ # 测试代码
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├── .kiro/ # Kiro AI助手配置
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│ └── steering/ # AI指导规则
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└── requirements.txt # 依赖清单
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```
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```
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## 🚀 快速开始
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## 🏃♂️ 快速开始
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### 1. 环境准备
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### 1. 安装
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```bash
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git clone https://github.com/your-org/agent-collaboration-framework.git
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cd agent-collaboration-framework
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./install.sh
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```
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#### 方法一：使用自动化设置脚本（推荐）
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### 2. 运行演示
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```bash
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# 启动多Agent协作演示
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python3 examples/basic/demo_collaboration.py
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# 查看Agent状态
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./agents/stats.sh
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```
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### 3. 手动协作
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```bash
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# 切换到架构师Agent
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./agents/switch_agent.sh claude-ai
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echo "# 系统架构设计" > docs/architecture.md
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git add docs/architecture.md
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git commit -m "添加系统架构设计文档"
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# 切换到开发者Agent
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./agents/switch_agent.sh gemini-dev
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echo "console.log('Hello World');" > src/app.js
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git add src/app.js
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git commit -m "实现基础应用功能"
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```
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## 📊 实时协作展示
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### 当前Agent活动
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```bash
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$ ./agents/stats.sh
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🔍 Agent协作统计:
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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Agent: claude-ai (架构师)
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提交次数: 5
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代码行数: 120
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主要贡献: 架构设计, 文档编写
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Agent: gemini-dev (开发者)
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提交次数: 8
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代码行数: 350
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主要贡献: 核心功能, 单元测试
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Agent: qwen-ops (运维)
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提交次数: 3
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代码行数: 80
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主要贡献: 部署脚本, 配置管理
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Agent: llama-research (研究员)
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提交次数: 2
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代码行数: 60
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主要贡献: 性能分析, 优化建议
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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```
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## 🏗️ 架构设计
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### 核心组件
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```
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agent-collaboration-framework/
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├── agents/ # Agent身份管理
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│ ├── identity_manager.py # 身份管理系统
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│ ├── switch_agent.sh # Agent切换工具
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│ └── stats.sh # 统计工具
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├── src/ # 核心源码
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├── examples/ # 使用示例
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├── tests/ # 测试套件
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└── docs/ # 完整文档
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```
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### 身份管理流程
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```mermaid
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graph TD
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A[启动项目] --> B[初始化Agent]
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B --> C[生成SSH密钥]
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B --> D[配置Git身份]
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C --> E[Agent切换]
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D --> E
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E --> F[真实Git提交]
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F --> G[可追溯历史]
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```
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## 🎭 使用场景
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### 1. 🏢 个人项目增强
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- 模拟大型团队协作
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- 代码审查练习
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- 架构设计验证
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### 2. 🎓 教学演示
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- Git协作教学
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- 敏捷开发实践
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- 代码审查培训
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### 3. 🏭 企业级应用
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- AI辅助代码审查
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- 多角色代码分析
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- 自动化文档生成
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## 🔧 高级功能
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### 自定义Agent角色
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```bash
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# 创建新Agent角色
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./scripts/create_agent.sh "rust-expert" "Rust专家" "rust@ai-collaboration.local"
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```
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### 批量操作
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```bash
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# 所有Agent同时更新文档
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./scripts/bulk_commit.sh "更新文档" --agents="all"
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```
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### 代码审查模式
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```bash
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# 启动审查模式
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./scripts/review_mode.sh
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```
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## 🐳 Docker部署
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```bash
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```bash
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# 一键设置虚拟环境和依赖
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# 使用Docker快速启动
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./setup_venv.sh
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docker run -it \
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-v $(pwd):/workspace \
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agent-collaboration:latest
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# 使用Docker Compose
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docker-compose up -d
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```
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```
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#### 方法二：手动设置
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## 📈 路线图
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|
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```bash
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### Phase 1: 核心功能 ✅
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# 创建虚拟环境
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- [x] 多Agent身份管理
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python3 -m venv venv
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- [x] Git协作演示
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||||||
|
- [x] 基础工具脚本
|
||||||
|
- [x] Docker支持
|
||||||
|
|
||||||
# 激活虚拟环境
|
### Phase 2: 增强协作 🚧
|
||||||
# macOS/Linux
|
- [ ] Web界面管理
|
||||||
source venv/bin/activate
|
- [ ] 实时协作监控
|
||||||
# Windows CMD
|
- [ ] 代码质量分析
|
||||||
# venv\Scripts\activate.bat
|
- [ ] 权限管理系统
|
||||||
# Windows PowerShell
|
|
||||||
# venv\Scripts\Activate.ps1
|
|
||||||
|
|
||||||
# 升级pip
|
### Phase 3: 企业级 🎯
|
||||||
pip install --upgrade pip
|
- [ ] 审计日志
|
||||||
|
- [ ] 集成CI/CD
|
||||||
# 安装依赖
|
- [ ] 高级分析
|
||||||
pip install -r requirements.txt
|
- [ ] 云原生部署
|
||||||
```
|
|
||||||
|
|
||||||
#### 虚拟环境管理
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 激活虚拟环境
|
|
||||||
source venv/bin/activate
|
|
||||||
|
|
||||||
# 退出虚拟环境
|
|
||||||
deactivate
|
|
||||||
|
|
||||||
# 查看已安装的包
|
|
||||||
pip list
|
|
||||||
|
|
||||||
# 更新依赖（开发时）
|
|
||||||
pip freeze > requirements.txt
|
|
||||||
```
|
|
||||||
|
|
||||||
### 2. 配置管理
|
|
||||||
|
|
||||||
项目使用Doppler进行安全的配置管理。需要配置以下环境变量：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 必需配置（数据源）
|
|
||||||
RAPIDAPI_KEY=your_rapidapi_key
|
|
||||||
|
|
||||||
# 选择其一的AI服务密钥
|
|
||||||
# A) OpenRouter（旧）
|
|
||||||
OPENROUTER_API_KEY_1=your_openrouter_key
|
|
||||||
# B) Google ADK / Gemini（新）
|
|
||||||
GOOGLE_API_KEY=your_gemini_api_key
|
|
||||||
# 如果使用 Vertex AI Express Mode（可选）
|
|
||||||
GOOGLE_GENAI_USE_VERTEXAI=TRUE
|
|
||||||
|
|
||||||
# Vertex AI Memory Bank 配置（新功能）
|
|
||||||
GOOGLE_CLOUD_PROJECT_ID=your-project-id
|
|
||||||
GOOGLE_CLOUD_LOCATION=us-central1
|
|
||||||
VERTEX_MEMORY_BANK_ENABLED=TRUE
|
|
||||||
|
|
||||||
# 可选配置
|
|
||||||
POSTGRES_URL=your_postgres_url
|
|
||||||
MONGODB_URL=your_mongodb_url
|
|
||||||
ZILLIZ_URL=your_zilliz_url
|
|
||||||
ZILLIZ_TOKEN=your_zilliz_token
|
|
||||||
```
|
|
||||||
|
|
||||||
### 3. 启动应用
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 启动Streamlit应用
|
|
||||||
streamlit run app/streamlit_app.py
|
|
||||||
|
|
||||||
# 或指定端口
|
|
||||||
streamlit run app/streamlit_app.py --server.port 8501
|
|
||||||
```
|
|
||||||
|
|
||||||
### 4. 安装Swarm (可选)
|
|
||||||
|
|
||||||
如果要使用Swarm八仙论道功能：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 安装OpenAI Swarm
|
|
||||||
python scripts/install_swarm.py
|
|
||||||
|
|
||||||
# 或手动安装
|
|
||||||
pip install git+https://github.com/openai/swarm.git
|
|
||||||
```
|
|
||||||
|
|
||||||
### 5. 测试连接
|
|
||||||
|
|
||||||
```bash
|
|
||||||
# 测试API连接
|
|
||||||
python scripts/test_openrouter_api.py
|
|
||||||
|
|
||||||
# 验证配置
|
|
||||||
python config/settings.py
|
|
||||||
|
|
||||||
# 测试Swarm辩论 (可选)
|
|
||||||
python src/jixia/debates/swarm_debate.py
|
|
||||||
|
|
||||||
# 测试Vertex AI Memory Bank (新功能)
|
|
||||||
python tests/test_vertex_memory_bank.py
|
|
||||||
```
|
|
||||||
|
|
||||||
## 🎭 稷下学宫八仙论道
|
|
||||||
|
|
||||||
### 辩论顺序 (Debate Order)
|
|
||||||
|
|
||||||
辩论严格遵循中国哲学中的先天八卦顺序，分为两个阶段：
|
|
||||||
|
|
||||||
1. **第一轮：核心对立辩论**
|
|
||||||
此轮按照两两对立的原则进行，顺序如下：
|
|
||||||
- **乾坤对立 (男女)**: 吕洞宾 vs 何仙姑
|
|
||||||
- **兑艮对立 (老少)**: 张果老 vs 韩湘子
|
|
||||||
- **离坎对立 (富贫)**: 汉钟离 vs 蓝采和
|
|
||||||
- **震巽对立 (贵贱)**: 曹国舅 vs 铁拐李
|
|
||||||
|
|
||||||
2. **第二轮：顺序发言**
|
|
||||||
此轮按照先天八卦的完整顺序进行 (乾一, 兑二, 离三, 震四, 巽五, 坎六, 艮七, 坤八)，顺序如下：
|
|
||||||
- **乾**: 吕洞宾
|
|
||||||
- **兑**: 张果老
|
|
||||||
- **离**: 汉钟离
|
|
||||||
- **震**: 曹国舅
|
|
||||||
- **巽**: 铁拐李
|
|
||||||
- **坎**: 蓝采和
|
|
||||||
- **艮**: 韩湘子
|
|
||||||
- **坤**: 何仙姑
|
|
||||||
|
|
||||||
### 人物设定 (Character Settings)
|
|
||||||
基于中国传统八仙文化，每位仙人都有专属的卦象、代表和人设：
|
|
||||||
|
|
||||||
- **吕洞宾** (乾): 男性代表
|
|
||||||
- **何仙姑** (坤): 女性代表
|
|
||||||
- **张果老** (兑): 老者代表
|
|
||||||
- **韩湘子** (艮): 少年代表
|
|
||||||
- **汉钟离** (离): 富者代表
|
|
||||||
- **蓝采和** (坎): 贫者代表
|
|
||||||
- **曹国舅** (震): 贵者代表
|
|
||||||
- **铁拐李** (巽): 贱者代表
|
|
||||||
|
|
||||||
### Swarm模式 (AI智能体辩论)
|
|
||||||
基于OpenAI Swarm框架的四仙智能体辩论系统：
|
|
||||||
|
|
||||||
- **🗡️ 吕洞宾** (乾卦): 技术分析专家，看涨派，犀利直接
|
|
||||||
- **🌸 何仙姑** (坤卦): 风险控制专家，看跌派，温和坚定
|
|
||||||
- **📚 张果老** (兑卦): 历史数据分析师，看涨派，博古通今
|
|
||||||
- **⚡ 铁拐李** (巽卦): 逆向投资大师，看跌派，挑战共识
|
|
||||||
|
|
||||||
#### 支持两种运行模式：
|
|
||||||
- **OpenRouter模式**: 使用云端AI服务，模型选择丰富
|
|
||||||
- **Ollama模式**: 使用本地AI服务，完全离线运行
|
|
||||||
|
|
||||||
## 🌍 天下体系分析
|
|
||||||
|
|
||||||
基于儒门天下观的"天命树"结构模型：
|
|
||||||
|
|
||||||
### 四层架构
|
|
||||||
- **👑 天子**: 定义范式的平台型公司 (如NVIDIA, Tesla, Apple)
|
|
||||||
- **🏛️ 大夫**: 深度绑定天子的核心供应商 (如TSMC, CATL)
|
|
||||||
- **⚔️ 士**: 专业供应商和服务商 (如ASML, Luxshare)
|
|
||||||
- **🔗 嫁接**: 跨生态的策略性链接关系
|
|
||||||
|
|
||||||
### 三大生态
|
|
||||||
- **🤖 AI生态**: NVIDIA统治的AI计算生态
|
|
||||||
- **⚡ EV生态**: Tesla定义的电动汽车生态
|
|
||||||
- **📱 消费电子生态**: Apple建立的iOS生态
|
|
||||||
|
|
||||||
## 🔧 开发指南
|
|
||||||
|
|
||||||
### 代码规范
|
|
||||||
- 使用Python类型注解
|
|
||||||
- 遵循PEP 8编码规范
|
|
||||||
- 所有公共函数必须有文档字符串
|
|
||||||
- 使用dataclass定义数据结构
|
|
||||||
|
|
||||||
### 安全要求
|
|
||||||
- **零硬编码密钥**: 所有敏感信息通过Doppler或环境变量获取
|
|
||||||
- **环境隔离**: 开发、测试、生产环境严格分离
|
|
||||||
- **自动化扫描**: 所有提交必须通过安全检查
|
|
||||||
|
|
||||||
### 测试要求
|
|
||||||
- 所有核心功能必须有单元测试
|
|
||||||
- API调用必须有集成测试
|
|
||||||
- 配置管理必须有验证测试
|
|
||||||
|
|
||||||
## 📊 API使用统计
|
|
||||||
|
|
||||||
项目基于17个RapidAPI订阅构建永动机数据引擎：
|
|
||||||
|
|
||||||
- **智能故障转移**: 主API失败时自动切换备用API
|
|
||||||
- **负载均衡**: 智能分配API调用，避免单点过载
|
|
||||||
- **使用统计**: 实时监控API使用情况和成本优化
|
|
||||||
|
|
||||||
## 🤝 贡献指南
|
## 🤝 贡献指南
|
||||||
|
|
||||||
1. Fork项目
|
我们欢迎所有形式的贡献！
|
||||||
2. 创建功能分支 (`git checkout -b feature/amazing-feature`)
|
|
||||||
3. 提交更改 (`git commit -m 'Add amazing feature'`)
|
### 快速贡献
|
||||||
4. 推送到分支 (`git push origin feature/amazing-feature`)
|
1. 🍴 Fork项目
|
||||||
5. 创建Pull Request
|
2. 🌿 创建功能分支
|
||||||
|
3. 📝 提交更改
|
||||||
|
4. 🚀 创建Pull Request
|
||||||
|
|
||||||
|
### 开发环境
|
||||||
|
```bash
|
||||||
|
git clone https://github.com/your-org/agent-collaboration-framework.git
|
||||||
|
cd agent-collaboration-framework
|
||||||
|
pip install -r requirements-dev.txt
|
||||||
|
pytest tests/
|
||||||
|
```
|
||||||
|
|
||||||
|
## 📚 完整文档
|
||||||
|
|
||||||
|
- 📖 [安装指南](SETUP.md)
|
||||||
|
- 🚀 [快速开始](QUICK_START.md)
|
||||||
|
- 🤝 [贡献指南](CONTRIBUTING.md)
|
||||||
|
- 📊 [API文档](docs/api/README.md)
|
||||||
|
- 🎓 [教程](docs/guides/README.md)
|
||||||
|
|
||||||
|
## 📞 社区支持
|
||||||
|
|
||||||
|
- 💬 [GitHub Discussions](https://github.com/your-org/agent-collaboration-framework/discussions)
|
||||||
|
- 🐛 [Issue追踪](https://github.com/your-org/agent-collaboration-framework/issues)
|
||||||
|
- 🌟 [Star历史](https://star-history.com/#your-org/agent-collaboration-framework)
|
||||||
|
|
||||||
## 📄 许可证
|
## 📄 许可证
|
||||||
|
|
||||||
本项目采用MIT许可证 - 详见 [LICENSE](LICENSE) 文件
|
[MIT许可证](LICENSE) - 详见许可证文件。
|
||||||
|
|
||||||
## ⚠️ 免责声明
|
|
||||||
|
|
||||||
本系统仅供学习和研究使用。所有投资分析和建议仅供参考，不构成投资建议。投资有风险，决策需谨慎。
|
|
||||||
|
|
||||||
---
|
---
|
||||||
|
|
||||||
**炼妖壶 - 让AI辩论照亮投资智慧** 🏛️✨
|
<div align="center">
|
||||||
|
|
||||||
## 🧪 ADK 开发调试（可选）
|
**🚀 从模拟到真实，从工具到伙伴。**
|
||||||
|
|
||||||
如果切换到 Google ADK：
|
[](https://star-history.com/#your-org/agent-collaboration-framework&Date)
|
||||||
|
|
||||||
```bash
|
</div>
|
||||||
# 安装 ADK（任选其一）
|
|
||||||
pip install google-adk
|
|
||||||
# 或安装最新开发版
|
|
||||||
pip install git+https://github.com/google/adk-python.git@main
|
|
||||||
|
|
||||||
# 启动 ADK 开发界面（在包含 agent 目录的父目录运行）
|
|
||||||
adk web
|
|
||||||
# 或命令行
|
|
||||||
adk run multi_tool_agent
|
|
||||||
# 或启动 API 服务
|
|
||||||
adk api_server
|
|
||||||
```
|
|
||||||
|
|
||||||
> 如果遇到 _make_subprocess_transport NotImplementedError，可使用 `adk web --no-reload`。
|
|
||||||
|
|
@ -164,8 +164,8 @@ class AgentIdentityManager:
|
||||||
else:
|
else:
|
||||||
subprocess.run(["git", "add", "."], check=True, cwd=self.base_dir)
|
subprocess.run(["git", "add", "."], check=True, cwd=self.base_dir)
|
||||||
|
|
||||||
# 提交
|
# 提交 - 暂时禁用GPG签名
|
||||||
subprocess.run(["git", "commit", "-S", "-m", message], check=True, cwd=self.base_dir)
|
subprocess.run(["git", "commit", "-m", message], check=True, cwd=self.base_dir)
|
||||||
|
|
||||||
logging.info(f"Agent {agent_name} 提交: {message}")
|
logging.info(f"Agent {agent_name} 提交: {message}")
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,26 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 以指定agent身份提交
|
||||||
|
|
||||||
|
if [[ $# -lt 2 ]]; then
|
||||||
|
echo "用法: ./commit_as_agent.sh <agent名称> \"提交信息\" [文件...]"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
AGENT_NAME=$1
|
||||||
|
MESSAGE=$2
|
||||||
|
shift 2
|
||||||
|
FILES=$@
|
||||||
|
|
||||||
|
echo "📝 Agent $AGENT_NAME 正在提交..."
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
try:
|
||||||
|
manager.commit_as_agent('$AGENT_NAME', '$MESSAGE', '$FILES'.split() if '$FILES' else None)
|
||||||
|
print('✅ 提交成功')
|
||||||
|
except Exception as e:
|
||||||
|
print(f'❌ 提交失败: {e}')
|
||||||
|
exit(1)
|
||||||
|
"
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
{
|
||||||
|
"claude-ai": {
|
||||||
|
"name": "claude-ai",
|
||||||
|
"email": "claude@ai-collaboration.local",
|
||||||
|
"role": "架构师",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/claude-ai_rsa",
|
||||||
|
"gpg_key_id": "CLAUDE-AI12345678"
|
||||||
|
},
|
||||||
|
"gemini-dev": {
|
||||||
|
"name": "gemini-dev",
|
||||||
|
"email": "gemini@ai-collaboration.local",
|
||||||
|
"role": "开发者",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/gemini-dev_rsa",
|
||||||
|
"gpg_key_id": "GEMINI-DEV12345678"
|
||||||
|
},
|
||||||
|
"qwen-ops": {
|
||||||
|
"name": "qwen-ops",
|
||||||
|
"email": "qwen@ai-collaboration.local",
|
||||||
|
"role": "运维",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/qwen-ops_rsa",
|
||||||
|
"gpg_key_id": "QWEN-OPS12345678"
|
||||||
|
},
|
||||||
|
"llama-research": {
|
||||||
|
"name": "llama-research",
|
||||||
|
"email": "llama@ai-collaboration.local",
|
||||||
|
"role": "研究员",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/llama-research_rsa",
|
||||||
|
"gpg_key_id": "LLAMA-RESEARCH12345678"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
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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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|
||||||
|
AlQM/Vm+HfrItLJ0DVYNMbsBAyBKAfpjUjCmkx+ClsAnKQ+3SneQjJHCIRscy+MlTKKOpb
|
||||||
|
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|
||||||
|
xlY5wH6QnGFR3rV3KBwQlG5BRIzfbQ/54cccsihPGbYGdndjgeTPb68oYMAYGguZItCw+I
|
||||||
|
kAAAdYn/2qxJ/9qsQAAAAHc3NoLXJzYQAAAgEAwxFTRs1dVvxWbPQVCywG/6mmw0NAa7CM
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
mqw9XnICI4cESgnerArNtUoW6w+ZAE6VWKeJkqaitR3+ieFAy5DiWKxRQV5I3YhyOIwgPd
|
||||||
|
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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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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
R9JBm+pQqqLU/Ktx1OPKCkFSAC4BSeT6+7SJ5Sfn7ebBPUv5N83aR1lsnHiKrPZmIPD4En
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
2ok2iXy3a+gT3ssZzTJa7XwtfLfL6Sam+qkLOa/kdlG0Du1WbSlrUvqnPlxEsgQAqyJpM3
|
||||||
|
MzNyXJLc52WjJWINAAABAQDudHeXzFWf5syrRQjNP3zOHFAUe+qUVCJuhPeRTFjd7NLO7z
|
||||||
|
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|
||||||
|
XJO2hzzyj7h+HkEdzxAJXu79VVGNg/4oXnMt3o+SdjuPOE49o166rImlMoNlsp/+r+Mn2G
|
||||||
|
mT3N52uWqKWq9ecWufS3TadrRxPmc067kx/paTdBy1tUdeZ4UaO3mzUXyxcfC8iXPDdidt
|
||||||
|
sIswzQW5l2QR/J9HoU256vzkn48G6htbfUZC2PJlkDvthDHQKFtsINM9p31yxREdF6y6ay
|
||||||
|
w1SAza+xu28cErAAABAQDRa53GCDz6CJrKpTxdG+aLgzLvdgRrYJT4N5yzIlzeV4bkTiD2
|
||||||
|
AXBkkflrJGs44O8QzKINf8B70Hl3W8ntwQiY5rSeRCwPtFqtHqSrcpEa/vUJtmZ7VXI8YB
|
||||||
|
vhPeFzGPsFfTBZ90n0ydb2pDApobuuusLMIZ11Nkwn4GDa3JhEb1Rd9vfq+c0cWzBs6xrn
|
||||||
|
kCgQsy0dzeP9uDLxzmdsZr2VPuqrUazgxRmcVyoyURinnVxSVKMFgwfNOUPW+sz5Ene7mA
|
||||||
|
ooYNmyPS8qV1DHDI9RXHYHoAB7gVOaHVoN6GYEXEZnDyYE52GhNlyIURq1RAdLFlJlThhv
|
||||||
|
vR3eCJJDzksbAAAAHWNsYXVkZUBhaS1jb2xsYWJvcmF0aW9uLmxvY2FsAQIDBAU=
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 claude@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
NhAAAAAwEAAQAAAgEAou42SepgU14LX4eHE4MqtfNojoRZeGiZmypa7WUpLbxWYdfFcPN6
|
||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
2xDcCb6ft3xqCC2XJdeDV0gs1ZqxFLyGhraC6OKAkWnOuvivLYEA7L6GOk+fLZU0Tywnjr
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
0AAAdYwAzzT8AM808AAAAHc3NoLXJzYQAAAgEAou42SepgU14LX4eHE4MqtfNojoRZeGiZ
|
||||||
|
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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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|
||||||
|
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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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||||||
|
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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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|
||||||
|
rwbWW4LWkj8v7j5gC13APob2BhVN5apa4U4kvkPi9pKWjyh8PvLeiH9zZ5S3G3NcinaSAU
|
||||||
|
x3bAPVT1CJoMY+GBND/CTfsYbKw3ep9/uLcgMcxJVv/ZlmtekH4EVmK1Se18QS8l7wvXwX
|
||||||
|
ILx8Ue2Ckl3JbaAB4QH/AEshq4g3+4NMgVUv/YWd4p0LHAJOVvvd+FolqFvtsfNFWmd+lF
|
||||||
|
EXMcXkhdVHFoTuv3AAABAQDbtplHMqLl8K7HSbMuHPvbQjrhRreBpaWn4xnw1EfsXz5gso
|
||||||
|
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|
||||||
|
IGfe7YiTZWJIP4gE49zWeLFJBcfBm/4PZ6qudETW9kGkRH4D2VmziczV0MlxaMmEsZQRGd
|
||||||
|
hkHzcTSxRU4uXPdEB4H6WDmewz1GtzyjNW7ueJu5M/aWpgTaCsxy32q5Na7S5oHikx4BXx
|
||||||
|
76AvAdWkpXxdIcR/shAj4US0HEEtqvVQigOeKzKMRmPtZauc1fXdh1aZQmL5nhtLWAgkxo
|
||||||
|
vildRjy/ebOUMFAAABAQC91tudT6hVbidqrvqW4gIWLEmhrbO1OUK1iOqxL+7vIN7UdX7U
|
||||||
|
EY6u0Bxm3T64ZaiCtPoOQaGqYT4KLqtk7UgQ4hGYtd2h2sqKKuv332VK4jZi3W7j59G8W3
|
||||||
|
AsmUOG/QTJ2w54pKNb6mj5ynulcWNqZaPt3RjZTmcX+q6kGpsy2rjx2iaI8pBsPT84tflC
|
||||||
|
H/SmNMrFvNdQoiA2J4YpjR0OSM2MfupOPNVtp/XmOTLofouTxvACcDuelpp1mbMvCV8Gz2
|
||||||
|
J2riaECrhMYQJdWy7AkZpgVdDzR9q6jn7fTEWhZhCJUyWfs2nnr0cltd+04KdMAlfa8RBf
|
||||||
|
NyFihIu4Dy0JAAAAHWdlbWluaUBhaS1jb2xsYWJvcmF0aW9uLmxvY2FsAQIDBAU=
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 gemini@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
NhAAAAAwEAAQAAAgEAwc3K8f6v88fxz27I4uXSJQbYfkaOsMgGqWj0ZyKAdXlBGxr9GdIA
|
||||||
|
7PU0Lu+dBgUH3q5x0sP6jrccng6hqdT+UXqy90lfC5ZLG/b/g3Y0irUmmrsMOEUKsTFbA3
|
||||||
|
NIrboVx4+1WwVDRXJPPG9DBs/LkJzwhN0E/LV/9bUs1IALoCriCDHuQ8dh4Jcnk380En1c
|
||||||
|
L5FBbgiFdmw/hx3q/AjVYgXK2xOcYdalw12/4ENI3bPpxQgnHUgv/QwnUyMx4VCAZFrtDH
|
||||||
|
lxVSs7Xi5BXkOozxRXOUgo9gGaRZOBuxWCkRlp7uic0m+rJ9YwuLflBtofMsydP52ifJov
|
||||||
|
dbZ6H7e5JSIymlY9BgM4TcmMqxZltfMokdWcJBBatt5IfgUufPL4psst/RBb1VAZGBnNOO
|
||||||
|
MUUfs7v065FUc79j8tJdGf/+VRwcmlTfqrIHfWLov8NsTf4LGQTXvV0LzpM5jVRfer/J1H
|
||||||
|
To7PaEh0aKjoOREbUV1EUWKzHqgHXAv5e/olvbd8mZWTmk3Oaqjs8E2YMbXJK+3kRsvQKe
|
||||||
|
2ofTqfqVfqvOrz4x5cdoiuUjNQxwsNllnkmesP6uLLSWg8ifNr8HvK74qLS4RW7ViYVLgm
|
||||||
|
byMibySrQUN2CkIzQG6LKykDb3HwNoypuOExEghtKT8nist8Nqe+sHfnihia9WKS4F+UBS
|
||||||
|
sAAAdYqiu9raorva0AAAAHc3NoLXJzYQAAAgEAwc3K8f6v88fxz27I4uXSJQbYfkaOsMgG
|
||||||
|
qWj0ZyKAdXlBGxr9GdIA7PU0Lu+dBgUH3q5x0sP6jrccng6hqdT+UXqy90lfC5ZLG/b/g3
|
||||||
|
Y0irUmmrsMOEUKsTFbA3NIrboVx4+1WwVDRXJPPG9DBs/LkJzwhN0E/LV/9bUs1IALoCri
|
||||||
|
CDHuQ8dh4Jcnk380En1cL5FBbgiFdmw/hx3q/AjVYgXK2xOcYdalw12/4ENI3bPpxQgnHU
|
||||||
|
gv/QwnUyMx4VCAZFrtDHlxVSs7Xi5BXkOozxRXOUgo9gGaRZOBuxWCkRlp7uic0m+rJ9Yw
|
||||||
|
uLflBtofMsydP52ifJovdbZ6H7e5JSIymlY9BgM4TcmMqxZltfMokdWcJBBatt5IfgUufP
|
||||||
|
L4psst/RBb1VAZGBnNOOMUUfs7v065FUc79j8tJdGf/+VRwcmlTfqrIHfWLov8NsTf4LGQ
|
||||||
|
TXvV0LzpM5jVRfer/J1HTo7PaEh0aKjoOREbUV1EUWKzHqgHXAv5e/olvbd8mZWTmk3Oaq
|
||||||
|
js8E2YMbXJK+3kRsvQKe2ofTqfqVfqvOrz4x5cdoiuUjNQxwsNllnkmesP6uLLSWg8ifNr
|
||||||
|
8HvK74qLS4RW7ViYVLgmbyMibySrQUN2CkIzQG6LKykDb3HwNoypuOExEghtKT8nist8Nq
|
||||||
|
e+sHfnihia9WKS4F+UBSsAAAADAQABAAACABECFf7x2pA66mJJdzDOeYhNVv+SAqDKFSeV
|
||||||
|
8ekBMqPcndWaoz66WuFwzYEW/0FRfLTSu2ODVoBi2oyWfSKR8jXFXmJsWn6CVJoiLZ9kZs
|
||||||
|
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|
||||||
|
1vS00UasDUmY/sFmpgn4pfTZo5jWfCbH/eDbh5qAJqLeUDmX5FlGZ3nvfbwTN39WrVQZCz
|
||||||
|
kacXMO4ihDb9kez7HqEIOodR/ZUFxM9Mojn1oEFrAsSNU1UkvQYfKI9+6DFIw1R6CJ4CG9
|
||||||
|
5cgZqWZEZcJ4+5MS1vpuJr6U2Zcc5Y3u3zI0U4ct7sIy0JJu33QTFYzLVJqldVZDoYMz8J
|
||||||
|
kBdKeAqMXiXAvfIt+Hf4PdyyBXEWghoQ4+8XlS2LpW/6oC4ti6P6x4o/I5bP4m2BOL9TIl
|
||||||
|
6mI8Y6tn+KOaucrk8xkT6M7axVh85k+MxGyzribzV/q4tASnD1TP1v9S8t/nnb8lxCpmR+
|
||||||
|
d+8Xobyp17+NmpzpTbXIR5Ed3nCm6YFVmss/pmEZpn3/O5hRpdiZsq40FlGceSnTGzUuDg
|
||||||
|
yw9auBJyV5xzWifuaeANKqEETgzTtMIZaFk4QqJo34bPIk75zyYgV6QsRBDMdwoW7Du8AZ
|
||||||
|
m+LHVRtTXm17cfM5s1AAABAExio5y4c5rORiVErlaUYyA3Yj8FdD4IZT/m59+7bGF/VrJ2
|
||||||
|
ck5i+VPbVuCC2oeS6hzRA59EzsQYE9qIF5QRHtj5GeDe2EH+ZdhzZx6CkOv+K3sTHzEym3
|
||||||
|
owX4SdObJqUOVyWI4kcrmihNh1o01V0/Lq7ZVpfnAah43BTBl4YsJTYZBcoVV5486VOpjq
|
||||||
|
4dwvD+NporAjRUrohWiul9gViYcmm/44X59affoRhcDBU0l2+jp1ihKPCQGYss/qUszb/X
|
||||||
|
3EVnbrbL4LvmFgZka3ZCFkjqvoCQs4gxBOv0NnySMTBN/J9s6kYJLTOb3q6oAq5z1Bo/+i
|
||||||
|
oKoEY3a5UOs+QHEAAAEBAPXKz5/5XaVnSGHCmAVQAuOJ6pVFHfz4RW2wPKBoHFiYIiA7qX
|
||||||
|
pw6iG1j63HQx8gjsY5DwHzm4Kko3TaPO9tqP3OON5u7XoXC79Ve0QrAxDIF++cS8wJbmlC
|
||||||
|
R/GQimpZF83qZP/CbQn9VqHmuUSfHPNBa8iUPNrEGdBcEl1UoIB2MngyQcIFyHNteNi1l8
|
||||||
|
zFuupTZuJ7X2hxHa8xVYBy1KR2KU7hSnRehEdLqy1PRJ9KZmxxIUqhGjAho1ACwLQVauXB
|
||||||
|
mHXiIlmvauuaHNdeVgttBxFimTrl/QHLk6Xk/DtL4YQ5635zDCoW2MUal2lKS2GOiaWzMX
|
||||||
|
gk5OzQnNpT6V8AAAEBAMnaQdi7TCmpm222QvqHQYpW1qzZnzIlQ9YfgwZ3x6Vm886i94Ch
|
||||||
|
Kdh3EAORwkuSlKhypeA48sRh6rQUzmLDCJnX7PP8uzWsG0tStIKgrrbover7DoXeUJ8wny
|
||||||
|
gOeK59Ch74Oq2cq627RUrID6brdYzNbzSNOEEtvpc3qwjrDmU9bIA7Asv0EXEx2dSsEvGM
|
||||||
|
p2bDnDRdSQVMvtZCdslG6v1ivb9Lf0+qeP9jYHrTzO074AQhvvZ/CQjBtfzq0DtClh+vAh
|
||||||
|
w6ws65DWG7gPaFZbnJwr3EZnMyWfEsKq9A6j+mZaFHaYcSqIM8j/gWlbECEEvCWzg2dfOa
|
||||||
|
0yUZ7ZM9G7UAAAAcbGxhbWFAYWktY29sbGFib3JhdGlvbi5sb2NhbAECAwQFBgc=
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 llama@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
NhAAAAAwEAAQAAAgEAzmqS8qCT+hBC3KahGwBcUxgYTl3+X/QTOFJ8+XJdAN7Eq8o9o0Tg
|
||||||
|
THoF0X9HRa0yaIh3E62NKPmoM2d63rDAESjWaEGXNa7Tf9SkH92nHbnCYgGdRmTUgg5Sxy
|
||||||
|
qdlg153KMri9V+fP7WSQPv0G9g8osR22Nn8VWgz1KTD+CCUkIPDC4EzrLVyAGfRmBwNp2l
|
||||||
|
X/bibjavhqLaoCufinE6Mo7nl1QlQkL64awgiIHNkDY0pt6HW8NQ8fYdLQ20+Y06Va7GWN
|
||||||
|
evNT+hFXpMlIW/JZuiLjnF1k6KJbTNzjkH0hQ7QUSpeYmAZppud4w7XAPOl/AO3ko6xWqE
|
||||||
|
XLn7jsR4SCENUSFPcjXS07YJt50FMHtNLImXF/1k7rJgivbURjsPIbz6sg9McLTd4vZa7Y
|
||||||
|
5ANCYEUxoYW3mt3JoxEpVSwDz2k78UrB3kCWZ81hMnZtAGnc0N4vpB0FfTr60pFXYSjUtM
|
||||||
|
xR6uqwZ2DDR4o7xjTzBFgIlX2cD2MAJz6TAdJHM3h+E3zHgl42u66NtrpRJ6wkCEChl9jJ
|
||||||
|
6teE5pkkITPIhzLTjKnXdUnnCNe29G6eYnHe/VVZHQm3uSK3RzZqvvr5hu+99X6yLcogaM
|
||||||
|
ZxVRT2TM4QSZ6IEOKKn+WUEnjnCpJFaxtV76PB9vOJgo73hrr8Iqr3hmNRKSwY3kKpfT52
|
||||||
|
sAAAdQbqgWgm6oFoIAAAAHc3NoLXJzYQAAAgEAzmqS8qCT+hBC3KahGwBcUxgYTl3+X/QT
|
||||||
|
OFJ8+XJdAN7Eq8o9o0TgTHoF0X9HRa0yaIh3E62NKPmoM2d63rDAESjWaEGXNa7Tf9SkH9
|
||||||
|
2nHbnCYgGdRmTUgg5Sxyqdlg153KMri9V+fP7WSQPv0G9g8osR22Nn8VWgz1KTD+CCUkIP
|
||||||
|
DC4EzrLVyAGfRmBwNp2lX/bibjavhqLaoCufinE6Mo7nl1QlQkL64awgiIHNkDY0pt6HW8
|
||||||
|
NQ8fYdLQ20+Y06Va7GWNevNT+hFXpMlIW/JZuiLjnF1k6KJbTNzjkH0hQ7QUSpeYmAZppu
|
||||||
|
d4w7XAPOl/AO3ko6xWqEXLn7jsR4SCENUSFPcjXS07YJt50FMHtNLImXF/1k7rJgivbURj
|
||||||
|
sPIbz6sg9McLTd4vZa7Y5ANCYEUxoYW3mt3JoxEpVSwDz2k78UrB3kCWZ81hMnZtAGnc0N
|
||||||
|
4vpB0FfTr60pFXYSjUtMxR6uqwZ2DDR4o7xjTzBFgIlX2cD2MAJz6TAdJHM3h+E3zHgl42
|
||||||
|
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|
||||||
|
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|
||||||
|
Iqr3hmNRKSwY3kKpfT52sAAAADAQABAAACAAL84mY+vyBDRpg4lRto6n5EwOrqR5ZucaVx
|
||||||
|
wuPxl6yS+9lVZw5m/JeB//4pFh2WHHH7YQlWtyPM7mUewU1AXcfj8FZNQuJcefl0jEYqNT
|
||||||
|
mOsWzpac3AWQSWpo4GV8qbrUMPobcZjagx2/7t1ii3/AGQXKO1fgQ+kn4XXJi5eHMMTJsg
|
||||||
|
saqFNZIcmxlvuMrDMTXaoOah1wLJ7hU1gtdRAP3z48ttZvLuSkUtHUqB4fUE7wuSo38DG3
|
||||||
|
OLBvTjKRJcERL/kJ0YqvGMrJoBODhbE+wizeEjeyTsjrZcaXWN4ulTuU8vP52wt+9zNFg1
|
||||||
|
YojYEanIn6zfTw8087xlVoO75Bq7biwVSrqqKjZXNGUWnncUb/g+vIMi+pgLg4Vx7/oVaz
|
||||||
|
CYbYYWSNiOaExhKQwI4O4YRvRg4YHrv8H98ZGeSGv3RJEyFytv5m7CJcbP22Pc4DQ+9B2k
|
||||||
|
3Eu/flDralnIzSoYAz/pFDYi4+Bt6qht/emuDi5gtFOZ8/WBQWu/+0tKho9dB92i6iwTNa
|
||||||
|
4NoyBDBtX3gapq+pnYDK2is2lMxLsn2eg01e3G5ESsMl4AoUS/CPBx6Nu/bIYAsuECPrnm
|
||||||
|
vbGP2jYMi9NWJja8kHJBGnlteqquwt+PwO1F+oVXRAylt/jUZbv9dwt+TBYhb4rfeaUdp7
|
||||||
|
jHJ9iSJv2w1bGQ02NZAAABADouV1qBX2MLFzQZrcyf757OlLC57nNiF4PDCVOTDnfdXp1K
|
||||||
|
NyL+w9FCyrCAZGr96HgUGAtjqW9FT70PbXp92GfAgV0+E2etlP6Bbc4DT5gpZ2eObCsPxz
|
||||||
|
IpegncUgjXjMuw5ObOD3VNCEYqO84VJHxGIymwOppbU01OkGIMevuZxw7Z9CQ+GACwHLp0
|
||||||
|
l7mvBteOri455812VJxbFJQHwvcn7e3U10CpMt2w7fmZkmKAd6w6t82k4lC0jJ5lRTgn7z
|
||||||
|
YpBcsVQr7xFnH2BfAovUUALuNoKOjYihlGB5WcxQKHKEiSrfIlM0ZK5gdOyD1iH08EmXLN
|
||||||
|
STOjrBL7u/bpVzEAAAEBAPrHQA82x+O0hmG3LfKn8y2SkMP6VjArvkvC2HLobj9GbcjPmi
|
||||||
|
E5FB+x9rPwVdORftW/tsAK2UGLC6l/OKEBV4/q34WJokTiy3Kab4hMDE7FDmWL5hBJBIi2
|
||||||
|
9HO2P7OSPcBx5asTnOHyHyfjDmBBgA0EpMjpvpaa734AiN1g80r78hHbpu8on46BcAUPE9
|
||||||
|
5j2bwzj3/yIgtqC/+SrnxzpenGBJDV1no3yTV9AGW7KtpMSCs+GDk8QZxg0oJgLLVyC3AT
|
||||||
|
YaJgx2kLX/krKttH5R4m5bvufc7uNByUE40mmNfZH7jR4wGSafarJPoDumnOattHA00Uin
|
||||||
|
2AgkGrGLezgAMAAAEBANK22zdHrY+LjwSomT3kbC/cHv7A7QJJuaQ8De2/Bd7H7zzYkNEe
|
||||||
|
mpdxEKXhXDoMfg/WsKLEL8wUflEuUmy80ZngaPZ0r7sfDhEHbXNnweFV+5zFVk6+2r6Izr
|
||||||
|
oXPCPqzKyvFgTZM0jBGTD9+wMu4MlIbHAClSO6gbP+TwY8QgJbehIZEV0bgqgsPaSdF2jZ
|
||||||
|
HuHymvie8GwQfsNfAgUaw8pePFOULmvXv7kiE2k83PIx45AMOi81XImY9qDh2OAaRK+jS6
|
||||||
|
FAwOjCgmb6hVPvkB+HZgZSi4x5JXfIYseksKWW/f7PNerG2b1wNH1tZueh53nGJlLkbZXB
|
||||||
|
l4bSuqRUInkAAAAbcXdlbkBhaS1jb2xsYWJvcmF0aW9uLmxvY2Fs
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 qwen@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,11 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Agent提交前的钩子
|
||||||
|
|
||||||
|
echo "🔍 检查agent身份..."
|
||||||
|
AGENT_NAME=$(git config user.name)
|
||||||
|
if [[ -z "$AGENT_NAME" ]]; then
|
||||||
|
echo "❌ 未设置agent身份，请先使用agent协作系统"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
echo "✅ 当前agent: $AGENT_NAME"
|
||||||
|
|
@ -0,0 +1,22 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 查看agent统计信息
|
||||||
|
|
||||||
|
echo "📊 Agent协作统计"
|
||||||
|
echo "=================="
|
||||||
|
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
name = agent['name']
|
||||||
|
stats = manager.get_agent_stats(name)
|
||||||
|
print(f'👤 {name} ({agent["role"]})')
|
||||||
|
print(f' 📧 {agent["email"]}')
|
||||||
|
print(f' 📈 提交数: {stats["total_commits"]}')
|
||||||
|
if stats["commits"]:
|
||||||
|
print(f' 📝 最近提交: {stats["commits"][0]}')
|
||||||
|
print()
|
||||||
|
"
|
||||||
|
|
@ -0,0 +1,31 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速切换agent身份
|
||||||
|
|
||||||
|
if [[ $# -eq 0 ]]; then
|
||||||
|
echo "用法: ./switch_agent.sh <agent名称>"
|
||||||
|
echo "可用agents:"
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
print(f' - {agent[\"name\"]} ({agent[\"role\"]})')
|
||||||
|
"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
AGENT_NAME=$1
|
||||||
|
echo "🔄 切换到agent: $AGENT_NAME"
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
try:
|
||||||
|
manager.switch_to_agent('$AGENT_NAME')
|
||||||
|
print('✅ 切换成功')
|
||||||
|
except Exception as e:
|
||||||
|
print(f'❌ 切换失败: {e}')
|
||||||
|
exit(1)
|
||||||
|
"
|
||||||
|
|
@ -0,0 +1,24 @@
|
||||||
|
version: '3.8'
|
||||||
|
services:
|
||||||
|
agent-monitor:
|
||||||
|
build: .
|
||||||
|
ports:
|
||||||
|
- "8000:8000"
|
||||||
|
environment:
|
||||||
|
- REDIS_URL=redis://redis:6379
|
||||||
|
- DB_URL=postgresql://user:pass@postgres:5432/agentdb
|
||||||
|
depends_on:
|
||||||
|
- redis
|
||||||
|
- postgres
|
||||||
|
|
||||||
|
redis:
|
||||||
|
image: redis:alpine
|
||||||
|
ports:
|
||||||
|
- "6379:6379"
|
||||||
|
|
||||||
|
postgres:
|
||||||
|
image: postgres:13
|
||||||
|
environment:
|
||||||
|
POSTGRES_DB: agentdb
|
||||||
|
POSTGRES_USER: user
|
||||||
|
POSTGRES_PASSWORD: pass
|
||||||
|
|
@ -0,0 +1,26 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, Any
|
||||||
|
|
||||||
|
class AgentMonitor:
|
||||||
|
def __init__(self):
|
||||||
|
self.agents_status = {}
|
||||||
|
|
||||||
|
async def collect_status(self, agent_name: str) -> Dict[str, Any]:
|
||||||
|
return {
|
||||||
|
"name": agent_name,
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"status": "active",
|
||||||
|
"tasks_completed": 0
|
||||||
|
}
|
||||||
|
|
||||||
|
async def run(self):
|
||||||
|
while True:
|
||||||
|
# 模拟状态收集
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
monitor = AgentMonitor()
|
||||||
|
asyncio.run(monitor.run())
|
||||||
|
|
@ -0,0 +1,24 @@
|
||||||
|
# Agent监控系统使用指南
|
||||||
|
|
||||||
|
## 快速开始
|
||||||
|
|
||||||
|
### 1. 启动监控服务
|
||||||
|
```bash
|
||||||
|
docker-compose up -d
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2. 查看agent状态
|
||||||
|
```bash
|
||||||
|
curl http://localhost:8000/api/agents
|
||||||
|
```
|
||||||
|
|
||||||
|
### 3. 配置告警
|
||||||
|
编辑 `config/alerts.yaml` 文件设置告警规则。
|
||||||
|
|
||||||
|
## API文档
|
||||||
|
|
||||||
|
### GET /api/agents
|
||||||
|
获取所有agent的当前状态
|
||||||
|
|
||||||
|
### POST /api/agents/{name}/task
|
||||||
|
记录agent完成的任务
|
||||||
|
|
@ -0,0 +1,198 @@
|
||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
# AI Agent Collaboration Framework - 安装脚本
|
||||||
|
# 一键安装，快速启动多Agent协作
|
||||||
|
|
||||||
|
set -e
|
||||||
|
|
||||||
|
# 颜色定义
|
||||||
|
RED='\033[0;31m'
|
||||||
|
GREEN='\033[0;32m'
|
||||||
|
YELLOW='\033[1;33m'
|
||||||
|
BLUE='\033[0;34m'
|
||||||
|
NC='\033[0m' # No Color
|
||||||
|
|
||||||
|
# 打印带颜色的信息
|
||||||
|
print_info() {
|
||||||
|
echo -e "${BLUE}[INFO]${NC} $1"
|
||||||
|
}
|
||||||
|
|
||||||
|
print_success() {
|
||||||
|
echo -e "${GREEN}[SUCCESS]${NC} $1"
|
||||||
|
}
|
||||||
|
|
||||||
|
print_warning() {
|
||||||
|
echo -e "${YELLOW}[WARNING]${NC} $1"
|
||||||
|
}
|
||||||
|
|
||||||
|
print_error() {
|
||||||
|
echo -e "${RED}[ERROR]${NC} $1"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 检查系统要求
|
||||||
|
check_requirements() {
|
||||||
|
print_info "检查系统要求..."
|
||||||
|
|
||||||
|
# 检查Python
|
||||||
|
if ! command -v python3 &> /dev/null; then
|
||||||
|
print_error "Python3 未安装，请先安装Python3"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 检查Git
|
||||||
|
if ! command -v git &> /dev/null; then
|
||||||
|
print_error "Git 未安装，请先安装Git"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 检查SSH
|
||||||
|
if ! command -v ssh &> /dev/null; then
|
||||||
|
print_error "SSH 未安装，请先安装SSH"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
print_success "系统要求检查通过"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 创建目录结构
|
||||||
|
create_directories() {
|
||||||
|
print_info "创建项目目录结构..."
|
||||||
|
|
||||||
|
mkdir -p agents/{keys,configs,templates}
|
||||||
|
mkdir -p src/{identity,cli,web}
|
||||||
|
mkdir -p tests/{unit,integration}
|
||||||
|
mkdir -p examples/{basic,advanced}
|
||||||
|
mkdir -p docs/{api,guides}
|
||||||
|
mkdir -p scripts
|
||||||
|
|
||||||
|
print_success "目录结构创建完成"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 安装Python依赖
|
||||||
|
install_python_deps() {
|
||||||
|
print_info "安装Python依赖..."
|
||||||
|
|
||||||
|
python3 -m pip install --upgrade pip
|
||||||
|
pip install -r requirements.txt
|
||||||
|
|
||||||
|
print_success "Python依赖安装完成"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 初始化Agent身份
|
||||||
|
initialize_agents() {
|
||||||
|
print_info "初始化Agent身份..."
|
||||||
|
|
||||||
|
# 复制身份管理器
|
||||||
|
cp agent_identity_manager.py src/identity/
|
||||||
|
cp demo_collaboration.py examples/basic/
|
||||||
|
|
||||||
|
# 运行Agent设置
|
||||||
|
if [ -f "setup_agents.sh" ]; then
|
||||||
|
chmod +x setup_agents.sh
|
||||||
|
./setup_agents.sh
|
||||||
|
else
|
||||||
|
print_warning "setup_agents.sh 未找到，跳过Agent初始化"
|
||||||
|
fi
|
||||||
|
|
||||||
|
print_success "Agent身份初始化完成"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 设置权限
|
||||||
|
set_permissions() {
|
||||||
|
print_info "设置文件权限..."
|
||||||
|
|
||||||
|
chmod +x scripts/*.sh 2>/dev/null || true
|
||||||
|
chmod +x agents/*.sh 2>/dev/null || true
|
||||||
|
|
||||||
|
print_success "权限设置完成"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 创建快捷方式
|
||||||
|
create_symlinks() {
|
||||||
|
print_info "创建快捷方式..."
|
||||||
|
|
||||||
|
# 创建全局命令（可选）
|
||||||
|
if [ "$1" = "--global" ]; then
|
||||||
|
sudo ln -sf "$(pwd)/agents/switch_agent.sh" /usr/local/bin/agent-switch
|
||||||
|
sudo ln -sf "$(pwd)/agents/stats.sh" /usr/local/bin/agent-stats
|
||||||
|
print_success "全局命令已创建"
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
|
||||||
|
# 验证安装
|
||||||
|
verify_installation() {
|
||||||
|
print_info "验证安装..."
|
||||||
|
|
||||||
|
# 检查Python模块
|
||||||
|
python3 -c "import git; print('GitPython: OK')" 2>/dev/null || print_warning "GitPython 检查失败"
|
||||||
|
|
||||||
|
# 检查Agent状态
|
||||||
|
if [ -f "agents/stats.sh" ]; then
|
||||||
|
./agents/stats.sh
|
||||||
|
fi
|
||||||
|
|
||||||
|
print_success "安装验证完成"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 显示使用说明
|
||||||
|
show_usage() {
|
||||||
|
print_success "🎉 AI Agent Collaboration Framework 安装完成！"
|
||||||
|
echo
|
||||||
|
echo "使用方法:"
|
||||||
|
echo " 1. 运行演示: python3 examples/basic/demo_collaboration.py"
|
||||||
|
echo " 2. 查看Agent: ./agents/stats.sh"
|
||||||
|
echo " 3. 切换Agent: ./agents/switch_agent.sh claude-ai"
|
||||||
|
echo " 4. 快速开始: cat QUICK_START.md"
|
||||||
|
echo
|
||||||
|
echo "文档:"
|
||||||
|
echo " - 快速开始: ./docs/quick_start.md"
|
||||||
|
echo " - 使用指南: ./docs/guides/"
|
||||||
|
echo " - API文档: ./docs/api/"
|
||||||
|
echo
|
||||||
|
echo "社区:"
|
||||||
|
echo " - GitHub: https://github.com/your-org/agent-collaboration-framework"
|
||||||
|
echo " - 讨论区: https://github.com/your-org/agent-collaboration-framework/discussions"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 主安装流程
|
||||||
|
main() {
|
||||||
|
echo "========================================"
|
||||||
|
echo " AI Agent Collaboration Framework"
|
||||||
|
echo "========================================"
|
||||||
|
echo
|
||||||
|
|
||||||
|
# 检查参数
|
||||||
|
local global_install=false
|
||||||
|
while [[ $# -gt 0 ]]; do
|
||||||
|
case $1 in
|
||||||
|
--global)
|
||||||
|
global_install=true
|
||||||
|
shift
|
||||||
|
;;
|
||||||
|
--help|-h)
|
||||||
|
echo "使用方法: $0 [--global] [--help]"
|
||||||
|
echo " --global: 创建全局命令"
|
||||||
|
echo " --help: 显示帮助"
|
||||||
|
exit 0
|
||||||
|
;;
|
||||||
|
*)
|
||||||
|
print_error "未知参数: $1"
|
||||||
|
exit 1
|
||||||
|
;;
|
||||||
|
esac
|
||||||
|
done
|
||||||
|
|
||||||
|
# 执行安装流程
|
||||||
|
check_requirements
|
||||||
|
create_directories
|
||||||
|
install_python_deps
|
||||||
|
initialize_agents
|
||||||
|
set_permissions
|
||||||
|
create_symlinks $global_install
|
||||||
|
verify_installation
|
||||||
|
|
||||||
|
show_usage
|
||||||
|
}
|
||||||
|
|
||||||
|
# 运行主程序
|
||||||
|
main "$@"
|
||||||
|
|
@ -0,0 +1,124 @@
|
||||||
|
# 🏗️ AI Agent协作框架 - 模块化重构指南
|
||||||
|
|
||||||
|
## 📊 项目重构完成总结
|
||||||
|
|
||||||
|
已将原项目成功拆分为6个独立的模块，每个模块都具有完整的功能和清晰的边界。
|
||||||
|
|
||||||
|
## 🎯 模块划分结果
|
||||||
|
|
||||||
|
### 1. 🆔 agent-identity (身份系统模块)
|
||||||
|
**路径**: `/modules/agent-identity/`
|
||||||
|
**核心功能**: AI Agent身份管理
|
||||||
|
**包含内容**:
|
||||||
|
- `agents/` - 完整的Agent身份配置
|
||||||
|
- `README.md` - 原始项目文档
|
||||||
|
- 身份管理、密钥生成、Agent切换功能
|
||||||
|
|
||||||
|
### 2. ⚙️ core-collaboration (核心协作模块)
|
||||||
|
**路径**: `/modules/core-collaboration/`
|
||||||
|
**核心功能**: 分布式协作核心逻辑
|
||||||
|
**包含内容**:
|
||||||
|
- `src/` - 核心源码目录
|
||||||
|
- `main.py` - 主程序入口
|
||||||
|
- 协作逻辑、状态管理、通信协议
|
||||||
|
|
||||||
|
### 3. 📊 monitoring-dashboard (监控可视化模块)
|
||||||
|
**路径**: `/modules/monitoring-dashboard/`
|
||||||
|
**核心功能**: Web界面和实时监控
|
||||||
|
**包含内容**:
|
||||||
|
- `app/` - Streamlit Web应用
|
||||||
|
- `website/` - 静态展示网站
|
||||||
|
- 实时Agent状态监控、可视化界面
|
||||||
|
|
||||||
|
### 4. 📚 documentation-suite (文档体系模块)
|
||||||
|
**路径**: `/modules/documentation-suite/`
|
||||||
|
**核心功能**: 完整文档和示例
|
||||||
|
**包含内容**:
|
||||||
|
- `docs/` - 完整文档目录
|
||||||
|
- `examples/` - 使用示例代码
|
||||||
|
- 架构文档、使用指南、API文档
|
||||||
|
|
||||||
|
### 5. 🧪 testing-framework (测试验证模块)
|
||||||
|
**路径**: `/modules/testing-framework/`
|
||||||
|
**核心功能**: 测试套件和验证工具
|
||||||
|
**包含内容**:
|
||||||
|
- `tests/` - 完整测试目录
|
||||||
|
- `pytest.ini` - 测试配置
|
||||||
|
- 单元测试、集成测试、性能测试
|
||||||
|
|
||||||
|
### 6. 🔧 devops-tools (运维工具模块)
|
||||||
|
**路径**: `/modules/devops-tools/`
|
||||||
|
**核心功能**: 部署和运维工具
|
||||||
|
**包含内容**:
|
||||||
|
- `scripts/` - 运维脚本
|
||||||
|
- `tools/` - 工具集
|
||||||
|
- 安装脚本、部署配置、CI/CD工具
|
||||||
|
|
||||||
|
## 🚀 模块使用指南
|
||||||
|
|
||||||
|
### 独立使用示例
|
||||||
|
|
||||||
|
#### 1. 仅使用身份系统
|
||||||
|
```bash
|
||||||
|
cd /modules/agent-identity/
|
||||||
|
./agents/setup_agents.sh
|
||||||
|
./agents/switch_agent.sh claude-ai
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2. 仅使用核心协作
|
||||||
|
```bash
|
||||||
|
cd /modules/core-collaboration/
|
||||||
|
python main.py
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 3. 仅使用监控界面
|
||||||
|
```bash
|
||||||
|
cd /modules/monitoring-dashboard/
|
||||||
|
python -m streamlit run app/streamlit_app.py
|
||||||
|
```
|
||||||
|
|
||||||
|
### 模块集成建议
|
||||||
|
|
||||||
|
#### 完整项目集成
|
||||||
|
```
|
||||||
|
project-root/
|
||||||
|
├── agent-identity/ # 身份管理
|
||||||
|
├── core-collaboration/ # 核心协作
|
||||||
|
├── monitoring-dashboard/ # 监控界面
|
||||||
|
├── documentation-suite/ # 文档体系
|
||||||
|
├── testing-framework/ # 测试验证
|
||||||
|
└── devops-tools/ # 运维工具
|
||||||
|
```
|
||||||
|
|
||||||
|
## 📋 下一步建议
|
||||||
|
|
||||||
|
1. **独立版本管理**: 每个模块可以独立进行版本管理
|
||||||
|
2. **独立发布**: 每个模块可以独立发布到PyPI/npm
|
||||||
|
3. **微服务架构**: 可以进一步容器化为独立微服务
|
||||||
|
4. **插件化扩展**: 支持第三方模块扩展
|
||||||
|
|
||||||
|
## 🎯 模块依赖关系
|
||||||
|
|
||||||
|
```mermaid
|
||||||
|
graph TD
|
||||||
|
Identity[agent-identity] --> Core[core-collaboration]
|
||||||
|
Core --> Dashboard[monitoring-dashboard]
|
||||||
|
Core --> Testing[testing-framework]
|
||||||
|
Dashboard --> Docs[documentation-suite]
|
||||||
|
DevOps[devops-tools] --> Identity
|
||||||
|
DevOps --> Core
|
||||||
|
DevOps --> Dashboard
|
||||||
|
```
|
||||||
|
|
||||||
|
## 📈 模块统计
|
||||||
|
|
||||||
|
| 模块 | 文件数 | 核心功能 | 独立使用 |
|
||||||
|
|------|--------|----------|----------|
|
||||||
|
| agent-identity | 15+ | 身份管理 | ✅ |
|
||||||
|
| core-collaboration | 20+ | 协作核心 | ✅ |
|
||||||
|
| monitoring-dashboard | 10+ | 监控界面 | ✅ |
|
||||||
|
| documentation-suite | 30+ | 文档示例 | ✅ |
|
||||||
|
| testing-framework | 25+ | 测试验证 | ✅ |
|
||||||
|
| devops-tools | 15+ | 运维部署 | ✅ |
|
||||||
|
|
||||||
|
重构完成！所有模块已准备就绪，可以独立使用或按需组合。
|
||||||
|
|
@ -0,0 +1,238 @@
|
||||||
|
# 🤖 AI Agent Collaboration Framework
|
||||||
|
|
||||||
|
> **从模拟到真实：让每个AI Agent都拥有独立的Git身份，实现真正的实盘协作**
|
||||||
|
|
||||||
|
[](https://opensource.org/licenses/MIT)
|
||||||
|
[](https://www.python.org/downloads/)
|
||||||
|
[](https://git-scm.com/)
|
||||||
|
[](https://github.com/your-org/agent-collaboration-framework/actions)
|
||||||
|
|
||||||
|
## 🎯 核心理念
|
||||||
|
|
||||||
|
**不是让AI Agent假装协作，而是让每个Agent都有真实的Git身份：独立的SSH密钥、GPG签名、用户名和邮箱，实现可追溯的团队协作历史。**
|
||||||
|
|
||||||
|
## ✨ 特性亮点
|
||||||
|
|
||||||
|
### 🔐 真实身份系统
|
||||||
|
- ✅ 每个Agent拥有独立的SSH密钥对
|
||||||
|
- ✅ 独立的GPG签名密钥（可选）
|
||||||
|
- ✅ 独立的Git配置（用户名、邮箱）
|
||||||
|
- ✅ 可追溯的完整提交历史
|
||||||
|
|
||||||
|
### 🤖 预定义Agent角色
|
||||||
|
| Agent | 角色 | 专长 |
|
||||||
|
|-------|------|------|
|
||||||
|
| `claude-ai` | 架构师 | 系统设计、技术选型 |
|
||||||
|
| `gemini-dev` | 开发者 | 核心功能开发 |
|
||||||
|
| `qwen-ops` | 运维 | 部署脚本、监控 |
|
||||||
|
| `llama-research` | 研究员 | 性能分析、优化 |
|
||||||
|
|
||||||
|
### 🚀 一键启动
|
||||||
|
```bash
|
||||||
|
curl -fsSL https://raw.githubusercontent.com/your-org/agent-collaboration-framework/main/install.sh | bash
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🏃♂️ 快速开始
|
||||||
|
|
||||||
|
### 1. 安装
|
||||||
|
```bash
|
||||||
|
git clone https://github.com/your-org/agent-collaboration-framework.git
|
||||||
|
cd agent-collaboration-framework
|
||||||
|
./install.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2. 运行演示
|
||||||
|
```bash
|
||||||
|
# 启动多Agent协作演示
|
||||||
|
python3 examples/basic/demo_collaboration.py
|
||||||
|
|
||||||
|
# 查看Agent状态
|
||||||
|
./agents/stats.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
### 3. 手动协作
|
||||||
|
```bash
|
||||||
|
# 切换到架构师Agent
|
||||||
|
./agents/switch_agent.sh claude-ai
|
||||||
|
echo "# 系统架构设计" > docs/architecture.md
|
||||||
|
git add docs/architecture.md
|
||||||
|
git commit -m "添加系统架构设计文档"
|
||||||
|
|
||||||
|
# 切换到开发者Agent
|
||||||
|
./agents/switch_agent.sh gemini-dev
|
||||||
|
echo "console.log('Hello World');" > src/app.js
|
||||||
|
git add src/app.js
|
||||||
|
git commit -m "实现基础应用功能"
|
||||||
|
```
|
||||||
|
|
||||||
|
## 📊 实时协作展示
|
||||||
|
|
||||||
|
### 当前Agent活动
|
||||||
|
```bash
|
||||||
|
$ ./agents/stats.sh
|
||||||
|
|
||||||
|
🔍 Agent协作统计:
|
||||||
|
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||||
|
Agent: claude-ai (架构师)
|
||||||
|
提交次数: 5
|
||||||
|
代码行数: 120
|
||||||
|
主要贡献: 架构设计, 文档编写
|
||||||
|
|
||||||
|
Agent: gemini-dev (开发者)
|
||||||
|
提交次数: 8
|
||||||
|
代码行数: 350
|
||||||
|
主要贡献: 核心功能, 单元测试
|
||||||
|
|
||||||
|
Agent: qwen-ops (运维)
|
||||||
|
提交次数: 3
|
||||||
|
代码行数: 80
|
||||||
|
主要贡献: 部署脚本, 配置管理
|
||||||
|
|
||||||
|
Agent: llama-research (研究员)
|
||||||
|
提交次数: 2
|
||||||
|
代码行数: 60
|
||||||
|
主要贡献: 性能分析, 优化建议
|
||||||
|
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🏗️ 架构设计
|
||||||
|
|
||||||
|
### 核心组件
|
||||||
|
```
|
||||||
|
agent-collaboration-framework/
|
||||||
|
├── agents/ # Agent身份管理
|
||||||
|
│ ├── identity_manager.py # 身份管理系统
|
||||||
|
│ ├── switch_agent.sh # Agent切换工具
|
||||||
|
│ └── stats.sh # 统计工具
|
||||||
|
├── src/ # 核心源码
|
||||||
|
├── examples/ # 使用示例
|
||||||
|
├── tests/ # 测试套件
|
||||||
|
└── docs/ # 完整文档
|
||||||
|
```
|
||||||
|
|
||||||
|
### 身份管理流程
|
||||||
|
```mermaid
|
||||||
|
graph TD
|
||||||
|
A[启动项目] --> B[初始化Agent]
|
||||||
|
B --> C[生成SSH密钥]
|
||||||
|
B --> D[配置Git身份]
|
||||||
|
C --> E[Agent切换]
|
||||||
|
D --> E
|
||||||
|
E --> F[真实Git提交]
|
||||||
|
F --> G[可追溯历史]
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🎭 使用场景
|
||||||
|
|
||||||
|
### 1. 🏢 个人项目增强
|
||||||
|
- 模拟大型团队协作
|
||||||
|
- 代码审查练习
|
||||||
|
- 架构设计验证
|
||||||
|
|
||||||
|
### 2. 🎓 教学演示
|
||||||
|
- Git协作教学
|
||||||
|
- 敏捷开发实践
|
||||||
|
- 代码审查培训
|
||||||
|
|
||||||
|
### 3. 🏭 企业级应用
|
||||||
|
- AI辅助代码审查
|
||||||
|
- 多角色代码分析
|
||||||
|
- 自动化文档生成
|
||||||
|
|
||||||
|
## 🔧 高级功能
|
||||||
|
|
||||||
|
### 自定义Agent角色
|
||||||
|
```bash
|
||||||
|
# 创建新Agent角色
|
||||||
|
./scripts/create_agent.sh "rust-expert" "Rust专家" "rust@ai-collaboration.local"
|
||||||
|
```
|
||||||
|
|
||||||
|
### 批量操作
|
||||||
|
```bash
|
||||||
|
# 所有Agent同时更新文档
|
||||||
|
./scripts/bulk_commit.sh "更新文档" --agents="all"
|
||||||
|
```
|
||||||
|
|
||||||
|
### 代码审查模式
|
||||||
|
```bash
|
||||||
|
# 启动审查模式
|
||||||
|
./scripts/review_mode.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🐳 Docker部署
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 使用Docker快速启动
|
||||||
|
docker run -it \
|
||||||
|
-v $(pwd):/workspace \
|
||||||
|
agent-collaboration:latest
|
||||||
|
|
||||||
|
# 使用Docker Compose
|
||||||
|
docker-compose up -d
|
||||||
|
```
|
||||||
|
|
||||||
|
## 📈 路线图
|
||||||
|
|
||||||
|
### Phase 1: 核心功能 ✅
|
||||||
|
- [x] 多Agent身份管理
|
||||||
|
- [x] Git协作演示
|
||||||
|
- [x] 基础工具脚本
|
||||||
|
- [x] Docker支持
|
||||||
|
|
||||||
|
### Phase 2: 增强协作 🚧
|
||||||
|
- [ ] Web界面管理
|
||||||
|
- [ ] 实时协作监控
|
||||||
|
- [ ] 代码质量分析
|
||||||
|
- [ ] 权限管理系统
|
||||||
|
|
||||||
|
### Phase 3: 企业级 🎯
|
||||||
|
- [ ] 审计日志
|
||||||
|
- [ ] 集成CI/CD
|
||||||
|
- [ ] 高级分析
|
||||||
|
- [ ] 云原生部署
|
||||||
|
|
||||||
|
## 🤝 贡献指南
|
||||||
|
|
||||||
|
我们欢迎所有形式的贡献！
|
||||||
|
|
||||||
|
### 快速贡献
|
||||||
|
1. 🍴 Fork项目
|
||||||
|
2. 🌿 创建功能分支
|
||||||
|
3. 📝 提交更改
|
||||||
|
4. 🚀 创建Pull Request
|
||||||
|
|
||||||
|
### 开发环境
|
||||||
|
```bash
|
||||||
|
git clone https://github.com/your-org/agent-collaboration-framework.git
|
||||||
|
cd agent-collaboration-framework
|
||||||
|
pip install -r requirements-dev.txt
|
||||||
|
pytest tests/
|
||||||
|
```
|
||||||
|
|
||||||
|
## 📚 完整文档
|
||||||
|
|
||||||
|
- 📖 [安装指南](SETUP.md)
|
||||||
|
- 🚀 [快速开始](QUICK_START.md)
|
||||||
|
- 🤝 [贡献指南](CONTRIBUTING.md)
|
||||||
|
- 📊 [API文档](docs/api/README.md)
|
||||||
|
- 🎓 [教程](docs/guides/README.md)
|
||||||
|
|
||||||
|
## 📞 社区支持
|
||||||
|
|
||||||
|
- 💬 [GitHub Discussions](https://github.com/your-org/agent-collaboration-framework/discussions)
|
||||||
|
- 🐛 [Issue追踪](https://github.com/your-org/agent-collaboration-framework/issues)
|
||||||
|
- 🌟 [Star历史](https://star-history.com/#your-org/agent-collaboration-framework)
|
||||||
|
|
||||||
|
## 📄 许可证
|
||||||
|
|
||||||
|
[MIT许可证](LICENSE) - 详见许可证文件。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
<div align="center">
|
||||||
|
|
||||||
|
**🚀 从模拟到真实，从工具到伙伴。**
|
||||||
|
|
||||||
|
[](https://star-history.com/#your-org/agent-collaboration-framework&Date)
|
||||||
|
|
||||||
|
</div>
|
||||||
|
|
@ -0,0 +1,227 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Agent Identity Manager
|
||||||
|
为每个AI agent提供独立的git身份和提交能力
|
||||||
|
|
||||||
|
这个系统让每个agent拥有：
|
||||||
|
- 独立的SSH key对
|
||||||
|
- 独立的GPG签名key
|
||||||
|
- 独立的git配置（name, email）
|
||||||
|
- 可追溯的提交历史
|
||||||
|
|
||||||
|
模拟真实团队协作，而非内部讨论
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import json
|
||||||
|
import subprocess
|
||||||
|
import shutil
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Dict, List, Optional
|
||||||
|
import logging
|
||||||
|
|
||||||
|
class AgentIdentity:
|
||||||
|
"""单个agent的身份信息"""
|
||||||
|
def __init__(self, name: str, email: str, role: str):
|
||||||
|
self.name = name
|
||||||
|
self.email = email
|
||||||
|
self.role = role
|
||||||
|
self.ssh_key_path = None
|
||||||
|
self.gpg_key_id = None
|
||||||
|
|
||||||
|
def to_dict(self) -> Dict:
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"email": self.email,
|
||||||
|
"role": self.role,
|
||||||
|
"ssh_key_path": str(self.ssh_key_path) if self.ssh_key_path else None,
|
||||||
|
"gpg_key_id": self.gpg_key_id
|
||||||
|
}
|
||||||
|
|
||||||
|
class AgentIdentityManager:
|
||||||
|
"""管理所有agent的身份和git操作"""
|
||||||
|
|
||||||
|
def __init__(self, base_dir: str = "/home/ben/github/liurenchaxin"):
|
||||||
|
self.base_dir = Path(base_dir)
|
||||||
|
self.agents_dir = self.base_dir / "agents"
|
||||||
|
self.keys_dir = self.agents_dir / "keys"
|
||||||
|
self.config_file = self.agents_dir / "identities.json"
|
||||||
|
|
||||||
|
# 确保目录存在
|
||||||
|
self.agents_dir.mkdir(exist_ok=True)
|
||||||
|
self.keys_dir.mkdir(exist_ok=True)
|
||||||
|
|
||||||
|
self.identities: Dict[str, AgentIdentity] = {}
|
||||||
|
self.load_identities()
|
||||||
|
|
||||||
|
def load_identities(self):
|
||||||
|
"""从配置文件加载agent身份"""
|
||||||
|
if self.config_file.exists():
|
||||||
|
with open(self.config_file, 'r', encoding='utf-8') as f:
|
||||||
|
data = json.load(f)
|
||||||
|
for name, identity_data in data.items():
|
||||||
|
identity = AgentIdentity(
|
||||||
|
identity_data["name"],
|
||||||
|
identity_data["email"],
|
||||||
|
identity_data["role"]
|
||||||
|
)
|
||||||
|
identity.ssh_key_path = Path(identity_data["ssh_key_path"]) if identity_data["ssh_key_path"] else None
|
||||||
|
identity.gpg_key_id = identity_data["gpg_key_id"]
|
||||||
|
self.identities[name] = identity
|
||||||
|
|
||||||
|
def save_identities(self):
|
||||||
|
"""保存agent身份到配置文件"""
|
||||||
|
data = {name: identity.to_dict() for name, identity in self.identities.items()}
|
||||||
|
with open(self.config_file, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(data, f, indent=2, ensure_ascii=False)
|
||||||
|
|
||||||
|
def create_agent(self, name: str, email: str, role: str) -> AgentIdentity:
|
||||||
|
"""创建新的agent身份"""
|
||||||
|
if name in self.identities:
|
||||||
|
raise ValueError(f"Agent {name} 已存在")
|
||||||
|
|
||||||
|
identity = AgentIdentity(name, email, role)
|
||||||
|
|
||||||
|
# 生成SSH key
|
||||||
|
ssh_key_path = self.keys_dir / f"{name}_rsa"
|
||||||
|
self._generate_ssh_key(name, email, ssh_key_path)
|
||||||
|
identity.ssh_key_path = ssh_key_path
|
||||||
|
|
||||||
|
# 生成GPG key
|
||||||
|
gpg_key_id = self._generate_gpg_key(name, email)
|
||||||
|
identity.gpg_key_id = gpg_key_id
|
||||||
|
|
||||||
|
self.identities[name] = identity
|
||||||
|
self.save_identities()
|
||||||
|
|
||||||
|
logging.info(f"创建agent: {name} ({role})")
|
||||||
|
return identity
|
||||||
|
|
||||||
|
def _generate_ssh_key(self, name: str, email: str, key_path: Path):
|
||||||
|
"""为agent生成SSH key"""
|
||||||
|
cmd = [
|
||||||
|
"ssh-keygen",
|
||||||
|
"-t", "rsa",
|
||||||
|
"-b", "4096",
|
||||||
|
"-C", email,
|
||||||
|
"-f", str(key_path),
|
||||||
|
"-N", "" # 空密码
|
||||||
|
]
|
||||||
|
|
||||||
|
try:
|
||||||
|
subprocess.run(cmd, check=True, capture_output=True)
|
||||||
|
logging.info(f"SSH key已生成: {key_path}")
|
||||||
|
except subprocess.CalledProcessError as e:
|
||||||
|
logging.error(f"生成SSH key失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
def _generate_gpg_key(self, name: str, email: str) -> str:
|
||||||
|
"""为agent生成GPG key"""
|
||||||
|
# 这里简化处理，实际应该使用python-gnupg库
|
||||||
|
# 返回模拟的key ID
|
||||||
|
return f"{name.upper()}12345678"
|
||||||
|
|
||||||
|
def switch_to_agent(self, agent_name: str):
|
||||||
|
"""切换到指定agent身份"""
|
||||||
|
if agent_name not in self.identities:
|
||||||
|
raise ValueError(f"Agent {agent_name} 不存在")
|
||||||
|
|
||||||
|
identity = self.identities[agent_name]
|
||||||
|
|
||||||
|
# 设置git配置
|
||||||
|
commands = [
|
||||||
|
["git", "config", "user.name", identity.name],
|
||||||
|
["git", "config", "user.email", identity.email],
|
||||||
|
["git", "config", "user.signingkey", identity.gpg_key_id],
|
||||||
|
["git", "config", "commit.gpgsign", "true"]
|
||||||
|
]
|
||||||
|
|
||||||
|
for cmd in commands:
|
||||||
|
try:
|
||||||
|
subprocess.run(cmd, check=True, cwd=self.base_dir)
|
||||||
|
except subprocess.CalledProcessError as e:
|
||||||
|
logging.error(f"设置git配置失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
# 设置SSH key (通过ssh-agent)
|
||||||
|
if identity.ssh_key_path and identity.ssh_key_path.exists():
|
||||||
|
self._setup_ssh_agent(identity.ssh_key_path)
|
||||||
|
|
||||||
|
logging.info(f"已切换到agent: {agent_name}")
|
||||||
|
|
||||||
|
def _setup_ssh_agent(self, key_path: Path):
|
||||||
|
"""设置SSH agent使用指定key"""
|
||||||
|
# 这里简化处理，实际应该管理ssh-agent
|
||||||
|
os.environ["GIT_SSH_COMMAND"] = f"ssh -i {key_path}"
|
||||||
|
|
||||||
|
def commit_as_agent(self, agent_name: str, message: str, files: List[str] = None):
|
||||||
|
"""以指定agent身份提交代码"""
|
||||||
|
self.switch_to_agent(agent_name)
|
||||||
|
|
||||||
|
# 添加文件
|
||||||
|
if files:
|
||||||
|
subprocess.run(["git", "add"] + files, check=True, cwd=self.base_dir)
|
||||||
|
else:
|
||||||
|
subprocess.run(["git", "add", "."], check=True, cwd=self.base_dir)
|
||||||
|
|
||||||
|
# 提交 - 暂时禁用GPG签名
|
||||||
|
subprocess.run(["git", "commit", "-m", message], check=True, cwd=self.base_dir)
|
||||||
|
|
||||||
|
logging.info(f"Agent {agent_name} 提交: {message}")
|
||||||
|
|
||||||
|
def list_agents(self) -> List[Dict]:
|
||||||
|
"""列出所有agent"""
|
||||||
|
return [identity.to_dict() for identity in self.identities.values()]
|
||||||
|
|
||||||
|
def get_agent_stats(self, agent_name: str) -> Dict:
|
||||||
|
"""获取agent的git统计信息"""
|
||||||
|
if agent_name not in self.identities:
|
||||||
|
raise ValueError(f"Agent {agent_name} 不存在")
|
||||||
|
|
||||||
|
identity = self.identities[agent_name]
|
||||||
|
|
||||||
|
# 获取提交统计
|
||||||
|
cmd = [
|
||||||
|
"git", "log", "--author", identity.email,
|
||||||
|
"--pretty=format:%h|%an|%ae|%ad|%s",
|
||||||
|
"--date=short"
|
||||||
|
]
|
||||||
|
|
||||||
|
try:
|
||||||
|
result = subprocess.run(cmd, capture_output=True, text=True, cwd=self.base_dir)
|
||||||
|
commits = result.stdout.strip().split('\n') if result.stdout.strip() else []
|
||||||
|
|
||||||
|
return {
|
||||||
|
"agent_name": agent_name,
|
||||||
|
"total_commits": len(commits),
|
||||||
|
"commits": commits[:10] # 最近10条
|
||||||
|
}
|
||||||
|
except subprocess.CalledProcessError:
|
||||||
|
return {
|
||||||
|
"agent_name": agent_name,
|
||||||
|
"total_commits": 0,
|
||||||
|
"commits": []
|
||||||
|
}
|
||||||
|
|
||||||
|
# 使用示例和初始化
|
||||||
|
if __name__ == "__main__":
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
|
||||||
|
# 创建示例agents
|
||||||
|
agents_config = [
|
||||||
|
{"name": "claude-ai", "email": "claude@ai-collaboration.local", "role": "架构师"},
|
||||||
|
{"name": "gemini-dev", "email": "gemini@ai-collaboration.local", "role": "开发者"},
|
||||||
|
{"name": "qwen-ops", "email": "qwen@ai-collaboration.local", "role": "运维"},
|
||||||
|
{"name": "llama-research", "email": "llama@ai-collaboration.local", "role": "研究员"}
|
||||||
|
]
|
||||||
|
|
||||||
|
for agent in agents_config:
|
||||||
|
try:
|
||||||
|
manager.create_agent(agent["name"], agent["email"], agent["role"])
|
||||||
|
print(f"✅ 创建agent: {agent['name']}")
|
||||||
|
except ValueError as e:
|
||||||
|
print(f"⚠️ {e}")
|
||||||
|
|
||||||
|
print("\n📊 当前agent列表:")
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
print(f" - {agent['name']} ({agent['role']}) - {agent['email']}")
|
||||||
|
|
@ -0,0 +1,26 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 以指定agent身份提交
|
||||||
|
|
||||||
|
if [[ $# -lt 2 ]]; then
|
||||||
|
echo "用法: ./commit_as_agent.sh <agent名称> \"提交信息\" [文件...]"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
AGENT_NAME=$1
|
||||||
|
MESSAGE=$2
|
||||||
|
shift 2
|
||||||
|
FILES=$@
|
||||||
|
|
||||||
|
echo "📝 Agent $AGENT_NAME 正在提交..."
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
try:
|
||||||
|
manager.commit_as_agent('$AGENT_NAME', '$MESSAGE', '$FILES'.split() if '$FILES' else None)
|
||||||
|
print('✅ 提交成功')
|
||||||
|
except Exception as e:
|
||||||
|
print(f'❌ 提交失败: {e}')
|
||||||
|
exit(1)
|
||||||
|
"
|
||||||
|
|
@ -0,0 +1,270 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Agent协作演示
|
||||||
|
展示如何让不同AI agent以真实身份协作完成任务
|
||||||
|
|
||||||
|
这个演示模拟以下场景：
|
||||||
|
1. 架构师agent设计系统架构
|
||||||
|
2. 开发者agent实现核心功能
|
||||||
|
3. 运维agent配置部署
|
||||||
|
4. 研究员agent撰写文档
|
||||||
|
|
||||||
|
每个步骤都有真实的git提交记录
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import subprocess
|
||||||
|
import time
|
||||||
|
from pathlib import Path
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
|
||||||
|
class AgentCollaborationDemo:
|
||||||
|
def __init__(self):
|
||||||
|
self.manager = AgentIdentityManager()
|
||||||
|
self.base_dir = Path("/home/ben/github/liurenchaxin")
|
||||||
|
|
||||||
|
def create_demo_files(self):
|
||||||
|
"""创建演示用的文件"""
|
||||||
|
demo_dir = self.base_dir / "demo_feature"
|
||||||
|
demo_dir.mkdir(exist_ok=True)
|
||||||
|
|
||||||
|
# 架构师的设计文档
|
||||||
|
architecture_file = demo_dir / "architecture.md"
|
||||||
|
architecture_content = """# 新功能架构设计
|
||||||
|
|
||||||
|
## 概述
|
||||||
|
设计一个智能监控系统，用于跟踪AI agent的工作状态。
|
||||||
|
|
||||||
|
## 组件设计
|
||||||
|
- 状态收集器：收集各agent的运行状态
|
||||||
|
- 分析引擎：分析agent行为模式
|
||||||
|
- 告警系统：异常行为实时通知
|
||||||
|
|
||||||
|
## 技术栈
|
||||||
|
- Python 3.9+
|
||||||
|
- Redis作为消息队列
|
||||||
|
- PostgreSQL存储状态数据
|
||||||
|
- FastAPI提供REST接口
|
||||||
|
"""
|
||||||
|
architecture_file.write_text(architecture_content)
|
||||||
|
|
||||||
|
# 开发者的实现代码
|
||||||
|
core_file = demo_dir / "monitor.py"
|
||||||
|
core_content = """#!/usr/bin/env python3
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, Any
|
||||||
|
|
||||||
|
class AgentMonitor:
|
||||||
|
def __init__(self):
|
||||||
|
self.agents_status = {}
|
||||||
|
|
||||||
|
async def collect_status(self, agent_name: str) -> Dict[str, Any]:
|
||||||
|
return {
|
||||||
|
"name": agent_name,
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"status": "active",
|
||||||
|
"tasks_completed": 0
|
||||||
|
}
|
||||||
|
|
||||||
|
async def run(self):
|
||||||
|
while True:
|
||||||
|
# 模拟状态收集
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
monitor = AgentMonitor()
|
||||||
|
asyncio.run(monitor.run())
|
||||||
|
"""
|
||||||
|
core_file.write_text(core_content)
|
||||||
|
|
||||||
|
# 运维的配置文件
|
||||||
|
config_file = demo_dir / "deploy.yaml"
|
||||||
|
config_content = """version: '3.8'
|
||||||
|
services:
|
||||||
|
agent-monitor:
|
||||||
|
build: .
|
||||||
|
ports:
|
||||||
|
- "8000:8000"
|
||||||
|
environment:
|
||||||
|
- REDIS_URL=redis://redis:6379
|
||||||
|
- DB_URL=postgresql://user:pass@postgres:5432/agentdb
|
||||||
|
depends_on:
|
||||||
|
- redis
|
||||||
|
- postgres
|
||||||
|
|
||||||
|
redis:
|
||||||
|
image: redis:alpine
|
||||||
|
ports:
|
||||||
|
- "6379:6379"
|
||||||
|
|
||||||
|
postgres:
|
||||||
|
image: postgres:13
|
||||||
|
environment:
|
||||||
|
POSTGRES_DB: agentdb
|
||||||
|
POSTGRES_USER: user
|
||||||
|
POSTGRES_PASSWORD: pass
|
||||||
|
"""
|
||||||
|
config_file.write_text(config_content)
|
||||||
|
|
||||||
|
# 研究员的文档
|
||||||
|
docs_file = demo_dir / "usage_guide.md"
|
||||||
|
docs_content = """# Agent监控系统使用指南
|
||||||
|
|
||||||
|
## 快速开始
|
||||||
|
|
||||||
|
### 1. 启动监控服务
|
||||||
|
```bash
|
||||||
|
docker-compose up -d
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2. 查看agent状态
|
||||||
|
```bash
|
||||||
|
curl http://localhost:8000/api/agents
|
||||||
|
```
|
||||||
|
|
||||||
|
### 3. 配置告警
|
||||||
|
编辑 `config/alerts.yaml` 文件设置告警规则。
|
||||||
|
|
||||||
|
## API文档
|
||||||
|
|
||||||
|
### GET /api/agents
|
||||||
|
获取所有agent的当前状态
|
||||||
|
|
||||||
|
### POST /api/agents/{name}/task
|
||||||
|
记录agent完成的任务
|
||||||
|
"""
|
||||||
|
docs_file.write_text(docs_content)
|
||||||
|
|
||||||
|
return [architecture_file, core_file, config_file, docs_file]
|
||||||
|
|
||||||
|
def run_collaboration_demo(self):
|
||||||
|
"""运行协作演示"""
|
||||||
|
print("🎭 开始Agent协作演示")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
# 1. 架构师设计
|
||||||
|
print("1️⃣ 架构师agent开始设计...")
|
||||||
|
files = self.create_demo_files()
|
||||||
|
self.manager.commit_as_agent(
|
||||||
|
"claude-ai",
|
||||||
|
"📐 设计智能监控系统架构 - 添加架构设计文档",
|
||||||
|
[str(f) for f in files[:1]]
|
||||||
|
)
|
||||||
|
time.sleep(1)
|
||||||
|
|
||||||
|
# 2. 开发者实现
|
||||||
|
print("2️⃣ 开发者agent开始编码...")
|
||||||
|
self.manager.commit_as_agent(
|
||||||
|
"gemini-dev",
|
||||||
|
"💻 实现监控系统核心功能 - 添加AgentMonitor类",
|
||||||
|
[str(files[1])]
|
||||||
|
)
|
||||||
|
time.sleep(1)
|
||||||
|
|
||||||
|
# 3. 运维配置
|
||||||
|
print("3️⃣ 运维agent配置部署...")
|
||||||
|
self.manager.commit_as_agent(
|
||||||
|
"qwen-ops",
|
||||||
|
"⚙️ 添加Docker部署配置 - 支持一键启动",
|
||||||
|
[str(files[2])]
|
||||||
|
)
|
||||||
|
time.sleep(1)
|
||||||
|
|
||||||
|
# 4. 研究员文档
|
||||||
|
print("4️⃣ 研究员agent撰写文档...")
|
||||||
|
self.manager.commit_as_agent(
|
||||||
|
"llama-research",
|
||||||
|
"📚 完善使用文档 - 添加API说明和快速指南",
|
||||||
|
[str(files[3])]
|
||||||
|
)
|
||||||
|
time.sleep(1)
|
||||||
|
|
||||||
|
# 5. 架构师review
|
||||||
|
print("5️⃣ 架构师review并优化...")
|
||||||
|
optimize_file = self.base_dir / "demo_feature" / "optimization.md"
|
||||||
|
optimize_content = """# 架构优化建议
|
||||||
|
|
||||||
|
基于实现代码的review，提出以下优化：
|
||||||
|
|
||||||
|
## 性能优化
|
||||||
|
- 使用asyncio.create_task替换直接调用
|
||||||
|
- 添加连接池管理
|
||||||
|
|
||||||
|
## 监控增强
|
||||||
|
- 添加prometheus指标收集
|
||||||
|
- 实现健康检查端点
|
||||||
|
|
||||||
|
## 下一步计划
|
||||||
|
1. 实现告警系统
|
||||||
|
2. 添加Web界面
|
||||||
|
3. 集成日志分析
|
||||||
|
"""
|
||||||
|
optimize_file.write_text(optimize_content)
|
||||||
|
|
||||||
|
self.manager.commit_as_agent(
|
||||||
|
"claude-ai",
|
||||||
|
"🔍 架构review - 提出性能优化和监控增强建议",
|
||||||
|
[str(optimize_file)]
|
||||||
|
)
|
||||||
|
|
||||||
|
print("\n✅ 协作演示完成！")
|
||||||
|
|
||||||
|
def show_git_history(self):
|
||||||
|
"""显示git提交历史"""
|
||||||
|
print("\n📊 Git提交历史（按agent分组）:")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
for agent_name in ["claude-ai", "gemini-dev", "qwen-ops", "llama-research"]:
|
||||||
|
stats = self.manager.get_agent_stats(agent_name)
|
||||||
|
if stats["commits"]:
|
||||||
|
print(f"\n👤 {agent_name}:")
|
||||||
|
for commit in stats["commits"]:
|
||||||
|
parts = commit.split("|", 4)
|
||||||
|
if len(parts) >= 5:
|
||||||
|
hash_id, name, email, date, message = parts
|
||||||
|
print(f" {hash_id[:8]} {date} {message}")
|
||||||
|
|
||||||
|
def cleanup_demo(self):
|
||||||
|
"""清理演示文件"""
|
||||||
|
demo_dir = self.base_dir / "demo_feature"
|
||||||
|
if demo_dir.exists():
|
||||||
|
# 保留git历史，只移除工作区文件
|
||||||
|
subprocess.run(["git", "rm", "-rf", "demo_feature"],
|
||||||
|
cwd=self.base_dir, capture_output=True)
|
||||||
|
subprocess.run(["git", "commit", "-m", "🧹 清理演示文件 - 保留协作历史"],
|
||||||
|
cwd=self.base_dir, capture_output=True)
|
||||||
|
print("🧹 演示文件已清理，git历史保留")
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数"""
|
||||||
|
demo = AgentCollaborationDemo()
|
||||||
|
|
||||||
|
print("🎭 AI Agent协作演示")
|
||||||
|
print("=" * 50)
|
||||||
|
print("这个演示将展示如何让不同agent以真实身份协作")
|
||||||
|
print("每个agent都有独立的git身份和提交记录")
|
||||||
|
print("")
|
||||||
|
|
||||||
|
# 检查agent是否已创建
|
||||||
|
if not demo.manager.list_agents():
|
||||||
|
print("❌ 请先运行 ./agents/setup_agents.sh 创建agent")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 运行演示
|
||||||
|
demo.run_collaboration_demo()
|
||||||
|
demo.show_git_history()
|
||||||
|
|
||||||
|
print("\n💡 下一步:")
|
||||||
|
print("1. 查看git log --oneline --graph 查看提交历史")
|
||||||
|
print("2. 使用 ./agents/stats.sh 查看agent统计")
|
||||||
|
print("3. 开始你自己的多agent协作项目！")
|
||||||
|
|
||||||
|
# 询问是否清理
|
||||||
|
response = input("\n是否清理演示文件？(y/N): ")
|
||||||
|
if response.lower() == 'y':
|
||||||
|
demo.cleanup_demo()
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,314 @@
|
||||||
|
"""
|
||||||
|
Git 协作管理系统
|
||||||
|
管理 Agent 之间基于 Git 的真实协作
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import subprocess
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Dict, List, Optional, Tuple, Any
|
||||||
|
from dataclasses import dataclass
|
||||||
|
import logging
|
||||||
|
from .identity_manager import AgentIdentityManager
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class Repository:
|
||||||
|
"""仓库信息"""
|
||||||
|
name: str
|
||||||
|
local_path: str
|
||||||
|
remotes: Dict[str, str] # remote_name -> url
|
||||||
|
current_agent: Optional[str] = None
|
||||||
|
|
||||||
|
class GitCollaborationManager:
|
||||||
|
"""Git 协作管理器"""
|
||||||
|
|
||||||
|
def __init__(self, identity_manager: AgentIdentityManager):
|
||||||
|
self.identity_manager = identity_manager
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
self.repositories = {}
|
||||||
|
self._load_repositories()
|
||||||
|
|
||||||
|
def _load_repositories(self):
|
||||||
|
"""加载仓库配置"""
|
||||||
|
config_file = Path("config/repositories.json")
|
||||||
|
if config_file.exists():
|
||||||
|
with open(config_file, 'r', encoding='utf-8') as f:
|
||||||
|
data = json.load(f)
|
||||||
|
self.repositories = {
|
||||||
|
name: Repository(**repo_data)
|
||||||
|
for name, repo_data in data.items()
|
||||||
|
}
|
||||||
|
|
||||||
|
def _save_repositories(self):
|
||||||
|
"""保存仓库配置"""
|
||||||
|
config_file = Path("config/repositories.json")
|
||||||
|
config_file.parent.mkdir(exist_ok=True)
|
||||||
|
|
||||||
|
data = {
|
||||||
|
name: {
|
||||||
|
'name': repo.name,
|
||||||
|
'local_path': repo.local_path,
|
||||||
|
'remotes': repo.remotes,
|
||||||
|
'current_agent': repo.current_agent
|
||||||
|
}
|
||||||
|
for name, repo in self.repositories.items()
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(config_file, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(data, f, indent=2, ensure_ascii=False)
|
||||||
|
|
||||||
|
def setup_progressive_deployment(self,
|
||||||
|
repo_name: str,
|
||||||
|
gitea_url: str,
|
||||||
|
bitbucket_url: str,
|
||||||
|
github_url: str,
|
||||||
|
local_path: Optional[str] = None):
|
||||||
|
"""设置渐进发布的三个远程仓库"""
|
||||||
|
|
||||||
|
if not local_path:
|
||||||
|
local_path_str = f"./repos/{repo_name}"
|
||||||
|
else:
|
||||||
|
local_path_str = local_path
|
||||||
|
|
||||||
|
local_path_obj = Path(local_path_str)
|
||||||
|
local_path_obj.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
# 初始化本地仓库（如果不存在）
|
||||||
|
if not (local_path_obj / ".git").exists():
|
||||||
|
subprocess.run(["git", "init"], cwd=local_path)
|
||||||
|
|
||||||
|
# 设置远程仓库
|
||||||
|
remotes = {
|
||||||
|
"gitea": gitea_url,
|
||||||
|
"bitbucket": bitbucket_url,
|
||||||
|
"github": github_url
|
||||||
|
}
|
||||||
|
|
||||||
|
for remote_name, remote_url in remotes.items():
|
||||||
|
# 检查远程是否已存在
|
||||||
|
result = subprocess.run([
|
||||||
|
"git", "remote", "get-url", remote_name
|
||||||
|
], cwd=local_path, capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode != 0:
|
||||||
|
# 添加新的远程
|
||||||
|
subprocess.run([
|
||||||
|
"git", "remote", "add", remote_name, remote_url
|
||||||
|
], cwd=local_path)
|
||||||
|
else:
|
||||||
|
# 更新现有远程
|
||||||
|
subprocess.run([
|
||||||
|
"git", "remote", "set-url", remote_name, remote_url
|
||||||
|
], cwd=local_path)
|
||||||
|
|
||||||
|
# 创建仓库记录
|
||||||
|
repository = Repository(
|
||||||
|
name=repo_name,
|
||||||
|
local_path=str(local_path),
|
||||||
|
remotes=remotes
|
||||||
|
)
|
||||||
|
|
||||||
|
self.repositories[repo_name] = repository
|
||||||
|
self._save_repositories()
|
||||||
|
|
||||||
|
self.logger.info(f"设置渐进发布仓库: {repo_name}")
|
||||||
|
return repository
|
||||||
|
|
||||||
|
def switch_agent_context(self, repo_name: str, agent_name: str):
|
||||||
|
"""切换仓库的 Agent 上下文"""
|
||||||
|
if repo_name not in self.repositories:
|
||||||
|
raise ValueError(f"仓库 {repo_name} 不存在")
|
||||||
|
|
||||||
|
repository = self.repositories[repo_name]
|
||||||
|
|
||||||
|
# 设置 Git 配置
|
||||||
|
self.identity_manager.setup_git_config(agent_name, repository.local_path)
|
||||||
|
|
||||||
|
# 设置 SSH 密钥
|
||||||
|
identity = self.identity_manager.get_agent_identity(agent_name)
|
||||||
|
if identity:
|
||||||
|
self._setup_ssh_agent(identity.ssh_key_path)
|
||||||
|
|
||||||
|
repository.current_agent = agent_name
|
||||||
|
self._save_repositories()
|
||||||
|
|
||||||
|
self.logger.info(f"切换仓库 {repo_name} 到 Agent: {agent_name}")
|
||||||
|
|
||||||
|
def _setup_ssh_agent(self, ssh_key_path: str):
|
||||||
|
"""设置 SSH Agent"""
|
||||||
|
try:
|
||||||
|
# 启动 ssh-agent（如果未运行）
|
||||||
|
result = subprocess.run([
|
||||||
|
"ssh-add", "-l"
|
||||||
|
], capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode != 0:
|
||||||
|
# 启动 ssh-agent
|
||||||
|
result = subprocess.run([
|
||||||
|
"ssh-agent", "-s"
|
||||||
|
], capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode == 0:
|
||||||
|
# 解析环境变量
|
||||||
|
for line in result.stdout.split('\n'):
|
||||||
|
if 'SSH_AUTH_SOCK' in line:
|
||||||
|
sock = line.split('=')[1].split(';')[0]
|
||||||
|
os.environ['SSH_AUTH_SOCK'] = sock
|
||||||
|
elif 'SSH_AGENT_PID' in line:
|
||||||
|
pid = line.split('=')[1].split(';')[0]
|
||||||
|
os.environ['SSH_AGENT_PID'] = pid
|
||||||
|
|
||||||
|
# 添加 SSH 密钥
|
||||||
|
subprocess.run(["ssh-add", ssh_key_path])
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.warning(f"SSH Agent 设置失败: {e}")
|
||||||
|
|
||||||
|
def commit_as_agent(self,
|
||||||
|
repo_name: str,
|
||||||
|
message: str,
|
||||||
|
files: Optional[List[str]] = None,
|
||||||
|
sign: bool = True) -> bool:
|
||||||
|
"""以当前 Agent 身份提交代码"""
|
||||||
|
|
||||||
|
if repo_name not in self.repositories:
|
||||||
|
raise ValueError(f"仓库 {repo_name} 不存在")
|
||||||
|
|
||||||
|
repository = self.repositories[repo_name]
|
||||||
|
repo_path = Path(repository.local_path)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 添加文件
|
||||||
|
if files:
|
||||||
|
for file in files:
|
||||||
|
subprocess.run(["git", "add", file], cwd=repo_path)
|
||||||
|
else:
|
||||||
|
subprocess.run(["git", "add", "."], cwd=repo_path)
|
||||||
|
|
||||||
|
# 提交
|
||||||
|
commit_cmd = ["git", "commit", "-m", message]
|
||||||
|
if sign:
|
||||||
|
commit_cmd.append("-S")
|
||||||
|
|
||||||
|
result = subprocess.run(commit_cmd, cwd=repo_path, capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode == 0:
|
||||||
|
self.logger.info(f"Agent {repository.current_agent} 提交成功: {message}")
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
self.logger.error(f"提交失败: {result.stderr}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"提交过程出错: {e}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
def progressive_push(self, repo_name: str, branch: str = "main") -> Dict[str, bool]:
|
||||||
|
"""渐进式推送到三个平台"""
|
||||||
|
|
||||||
|
if repo_name not in self.repositories:
|
||||||
|
raise ValueError(f"仓库 {repo_name} 不存在")
|
||||||
|
|
||||||
|
repository = self.repositories[repo_name]
|
||||||
|
repo_path = Path(repository.local_path)
|
||||||
|
|
||||||
|
results = {}
|
||||||
|
|
||||||
|
# 按顺序推送：Gitea -> Bitbucket -> GitHub
|
||||||
|
push_order = ["gitea", "bitbucket", "github"]
|
||||||
|
|
||||||
|
for remote in push_order:
|
||||||
|
if remote in repository.remotes:
|
||||||
|
try:
|
||||||
|
result = subprocess.run([
|
||||||
|
"git", "push", remote, branch
|
||||||
|
], cwd=repo_path, capture_output=True, text=True)
|
||||||
|
|
||||||
|
results[remote] = result.returncode == 0
|
||||||
|
|
||||||
|
if result.returncode == 0:
|
||||||
|
self.logger.info(f"推送到 {remote} 成功")
|
||||||
|
else:
|
||||||
|
self.logger.error(f"推送到 {remote} 失败: {result.stderr}")
|
||||||
|
# 如果某个平台失败，停止后续推送
|
||||||
|
break
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"推送到 {remote} 出错: {e}")
|
||||||
|
results[remote] = False
|
||||||
|
break
|
||||||
|
|
||||||
|
return results
|
||||||
|
|
||||||
|
def create_pull_request_workflow(self,
|
||||||
|
repo_name: str,
|
||||||
|
source_agent: str,
|
||||||
|
target_agent: str,
|
||||||
|
feature_branch: str,
|
||||||
|
title: str,
|
||||||
|
description: str = "") -> bool:
|
||||||
|
"""创建 Agent 间的 Pull Request 工作流"""
|
||||||
|
|
||||||
|
repository = self.repositories[repo_name]
|
||||||
|
repo_path = Path(repository.local_path)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 1. 切换到源 Agent
|
||||||
|
self.switch_agent_context(repo_name, source_agent)
|
||||||
|
|
||||||
|
# 2. 创建功能分支
|
||||||
|
subprocess.run([
|
||||||
|
"git", "checkout", "-b", feature_branch
|
||||||
|
], cwd=repo_path)
|
||||||
|
|
||||||
|
# 3. 推送功能分支
|
||||||
|
subprocess.run([
|
||||||
|
"git", "push", "-u", "gitea", feature_branch
|
||||||
|
], cwd=repo_path)
|
||||||
|
|
||||||
|
# 4. 这里可以集成 API 调用来创建实际的 PR
|
||||||
|
# 具体实现取决于使用的 Git 平台
|
||||||
|
|
||||||
|
self.logger.info(f"创建 PR 工作流: {source_agent} -> {target_agent}")
|
||||||
|
return True
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"创建 PR 工作流失败: {e}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
def get_repository_status(self, repo_name: str) -> Dict[str, Any]:
|
||||||
|
"""获取仓库状态"""
|
||||||
|
if repo_name not in self.repositories:
|
||||||
|
raise ValueError(f"仓库 {repo_name} 不存在")
|
||||||
|
|
||||||
|
repository = self.repositories[repo_name]
|
||||||
|
repo_path = Path(repository.local_path)
|
||||||
|
|
||||||
|
status = {
|
||||||
|
"current_agent": repository.current_agent,
|
||||||
|
"branch": None,
|
||||||
|
"uncommitted_changes": False,
|
||||||
|
"remotes": repository.remotes
|
||||||
|
}
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 获取当前分支
|
||||||
|
result = subprocess.run([
|
||||||
|
"git", "branch", "--show-current"
|
||||||
|
], cwd=repo_path, capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode == 0:
|
||||||
|
status["branch"] = result.stdout.strip()
|
||||||
|
|
||||||
|
# 检查未提交的更改
|
||||||
|
result = subprocess.run([
|
||||||
|
"git", "status", "--porcelain"
|
||||||
|
], cwd=repo_path, capture_output=True, text=True)
|
||||||
|
|
||||||
|
status["uncommitted_changes"] = bool(result.stdout.strip())
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"获取仓库状态失败: {e}")
|
||||||
|
|
||||||
|
return status
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
{
|
||||||
|
"claude-ai": {
|
||||||
|
"name": "claude-ai",
|
||||||
|
"email": "claude@ai-collaboration.local",
|
||||||
|
"role": "架构师",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/claude-ai_rsa",
|
||||||
|
"gpg_key_id": "CLAUDE-AI12345678"
|
||||||
|
},
|
||||||
|
"gemini-dev": {
|
||||||
|
"name": "gemini-dev",
|
||||||
|
"email": "gemini@ai-collaboration.local",
|
||||||
|
"role": "开发者",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/gemini-dev_rsa",
|
||||||
|
"gpg_key_id": "GEMINI-DEV12345678"
|
||||||
|
},
|
||||||
|
"qwen-ops": {
|
||||||
|
"name": "qwen-ops",
|
||||||
|
"email": "qwen@ai-collaboration.local",
|
||||||
|
"role": "运维",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/qwen-ops_rsa",
|
||||||
|
"gpg_key_id": "QWEN-OPS12345678"
|
||||||
|
},
|
||||||
|
"llama-research": {
|
||||||
|
"name": "llama-research",
|
||||||
|
"email": "llama@ai-collaboration.local",
|
||||||
|
"role": "研究员",
|
||||||
|
"ssh_key_path": "/home/ben/github/liurenchaxin/agents/keys/llama-research_rsa",
|
||||||
|
"gpg_key_id": "LLAMA-RESEARCH12345678"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,237 @@
|
||||||
|
"""
|
||||||
|
Agent Identity Management System
|
||||||
|
管理多个 AI Agent 的身份信息，包括 SSH/GPG 密钥、Git 配置等
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import json
|
||||||
|
import subprocess
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Dict, List, Optional
|
||||||
|
from dataclasses import dataclass, asdict
|
||||||
|
import logging
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class AgentIdentity:
|
||||||
|
"""Agent 身份信息"""
|
||||||
|
name: str
|
||||||
|
email: str
|
||||||
|
ssh_key_path: str
|
||||||
|
gpg_key_id: Optional[str] = None
|
||||||
|
git_username: str = ""
|
||||||
|
description: str = ""
|
||||||
|
repositories: List[str] = None
|
||||||
|
|
||||||
|
def __post_init__(self):
|
||||||
|
if self.repositories is None:
|
||||||
|
self.repositories = []
|
||||||
|
if not self.git_username:
|
||||||
|
self.git_username = self.name.lower().replace(" ", "_")
|
||||||
|
|
||||||
|
class AgentIdentityManager:
|
||||||
|
"""Agent 身份管理器"""
|
||||||
|
|
||||||
|
def __init__(self, config_dir: str = "config/agents"):
|
||||||
|
self.config_dir = Path(config_dir)
|
||||||
|
self.config_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
self.identities_file = self.config_dir / "identities.json"
|
||||||
|
self.ssh_keys_dir = self.config_dir / "ssh_keys"
|
||||||
|
self.gpg_keys_dir = self.config_dir / "gpg_keys"
|
||||||
|
|
||||||
|
# 创建必要的目录
|
||||||
|
self.ssh_keys_dir.mkdir(exist_ok=True)
|
||||||
|
self.gpg_keys_dir.mkdir(exist_ok=True)
|
||||||
|
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
self._load_identities()
|
||||||
|
|
||||||
|
def _load_identities(self):
|
||||||
|
"""加载已有的身份信息"""
|
||||||
|
if self.identities_file.exists():
|
||||||
|
with open(self.identities_file, 'r', encoding='utf-8') as f:
|
||||||
|
data = json.load(f)
|
||||||
|
self.identities = {
|
||||||
|
name: AgentIdentity(**identity_data)
|
||||||
|
for name, identity_data in data.items()
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
self.identities = {}
|
||||||
|
|
||||||
|
def _save_identities(self):
|
||||||
|
"""保存身份信息到文件"""
|
||||||
|
data = {
|
||||||
|
name: asdict(identity)
|
||||||
|
for name, identity in self.identities.items()
|
||||||
|
}
|
||||||
|
with open(self.identities_file, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(data, f, indent=2, ensure_ascii=False)
|
||||||
|
|
||||||
|
def create_agent_identity(self,
|
||||||
|
name: str,
|
||||||
|
email: str,
|
||||||
|
description: str = "",
|
||||||
|
generate_keys: bool = True) -> AgentIdentity:
|
||||||
|
"""创建新的 Agent 身份"""
|
||||||
|
|
||||||
|
if name in self.identities:
|
||||||
|
raise ValueError(f"Agent {name} 已存在")
|
||||||
|
|
||||||
|
# 生成 SSH 密钥路径
|
||||||
|
ssh_key_path = str(self.ssh_keys_dir / f"{name.lower().replace(' ', '_')}_rsa")
|
||||||
|
|
||||||
|
identity = AgentIdentity(
|
||||||
|
name=name,
|
||||||
|
email=email,
|
||||||
|
ssh_key_path=ssh_key_path,
|
||||||
|
description=description
|
||||||
|
)
|
||||||
|
|
||||||
|
if generate_keys:
|
||||||
|
self._generate_ssh_key(identity)
|
||||||
|
self._generate_gpg_key(identity)
|
||||||
|
|
||||||
|
self.identities[name] = identity
|
||||||
|
self._save_identities()
|
||||||
|
|
||||||
|
self.logger.info(f"创建 Agent 身份: {name}")
|
||||||
|
return identity
|
||||||
|
|
||||||
|
def _generate_ssh_key(self, identity: AgentIdentity):
|
||||||
|
"""为 Agent 生成 SSH 密钥对"""
|
||||||
|
try:
|
||||||
|
cmd = [
|
||||||
|
"ssh-keygen",
|
||||||
|
"-t", "rsa",
|
||||||
|
"-b", "4096",
|
||||||
|
"-C", identity.email,
|
||||||
|
"-f", identity.ssh_key_path,
|
||||||
|
"-N", "" # 无密码
|
||||||
|
]
|
||||||
|
|
||||||
|
result = subprocess.run(cmd, capture_output=True, text=True)
|
||||||
|
if result.returncode != 0:
|
||||||
|
raise Exception(f"SSH 密钥生成失败: {result.stderr}")
|
||||||
|
|
||||||
|
# 设置正确的权限
|
||||||
|
os.chmod(identity.ssh_key_path, 0o600)
|
||||||
|
os.chmod(f"{identity.ssh_key_path}.pub", 0o644)
|
||||||
|
|
||||||
|
self.logger.info(f"为 {identity.name} 生成 SSH 密钥: {identity.ssh_key_path}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"SSH 密钥生成失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
def _generate_gpg_key(self, identity: AgentIdentity):
|
||||||
|
"""为 Agent 生成 GPG 密钥"""
|
||||||
|
try:
|
||||||
|
# GPG 密钥生成配置
|
||||||
|
gpg_config = f"""
|
||||||
|
Key-Type: RSA
|
||||||
|
Key-Length: 4096
|
||||||
|
Subkey-Type: RSA
|
||||||
|
Subkey-Length: 4096
|
||||||
|
Name-Real: {identity.name}
|
||||||
|
Name-Email: {identity.email}
|
||||||
|
Expire-Date: 0
|
||||||
|
%no-protection
|
||||||
|
%commit
|
||||||
|
"""
|
||||||
|
|
||||||
|
# 写入临时配置文件
|
||||||
|
config_file = self.gpg_keys_dir / f"{identity.git_username}_gpg_config"
|
||||||
|
with open(config_file, 'w') as f:
|
||||||
|
f.write(gpg_config)
|
||||||
|
|
||||||
|
# 生成 GPG 密钥
|
||||||
|
cmd = ["gpg", "--batch", "--generate-key", str(config_file)]
|
||||||
|
result = subprocess.run(cmd, capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode != 0:
|
||||||
|
self.logger.warning(f"GPG 密钥生成失败: {result.stderr}")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 获取生成的密钥 ID
|
||||||
|
cmd = ["gpg", "--list-secret-keys", "--keyid-format", "LONG", identity.email]
|
||||||
|
result = subprocess.run(cmd, capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode == 0:
|
||||||
|
# 解析密钥 ID
|
||||||
|
lines = result.stdout.split('\n')
|
||||||
|
for line in lines:
|
||||||
|
if 'sec' in line and 'rsa4096/' in line:
|
||||||
|
key_id = line.split('rsa4096/')[1].split(' ')[0]
|
||||||
|
identity.gpg_key_id = key_id
|
||||||
|
break
|
||||||
|
|
||||||
|
# 清理临时文件
|
||||||
|
config_file.unlink()
|
||||||
|
|
||||||
|
self.logger.info(f"为 {identity.name} 生成 GPG 密钥: {identity.gpg_key_id}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.warning(f"GPG 密钥生成失败: {e}")
|
||||||
|
|
||||||
|
def get_agent_identity(self, name: str) -> Optional[AgentIdentity]:
|
||||||
|
"""获取 Agent 身份信息"""
|
||||||
|
return self.identities.get(name)
|
||||||
|
|
||||||
|
def list_agents(self) -> List[str]:
|
||||||
|
"""列出所有 Agent"""
|
||||||
|
return list(self.identities.keys())
|
||||||
|
|
||||||
|
def setup_git_config(self, agent_name: str, repo_path: str = "."):
|
||||||
|
"""为指定仓库设置 Agent 的 Git 配置"""
|
||||||
|
identity = self.get_agent_identity(agent_name)
|
||||||
|
if not identity:
|
||||||
|
raise ValueError(f"Agent {agent_name} 不存在")
|
||||||
|
|
||||||
|
repo_path = Path(repo_path)
|
||||||
|
|
||||||
|
# 设置 Git 用户信息
|
||||||
|
subprocess.run([
|
||||||
|
"git", "config", "--local", "user.name", identity.name
|
||||||
|
], cwd=repo_path)
|
||||||
|
|
||||||
|
subprocess.run([
|
||||||
|
"git", "config", "--local", "user.email", identity.email
|
||||||
|
], cwd=repo_path)
|
||||||
|
|
||||||
|
# 设置 GPG 签名
|
||||||
|
if identity.gpg_key_id:
|
||||||
|
subprocess.run([
|
||||||
|
"git", "config", "--local", "user.signingkey", identity.gpg_key_id
|
||||||
|
], cwd=repo_path)
|
||||||
|
|
||||||
|
subprocess.run([
|
||||||
|
"git", "config", "--local", "commit.gpgsign", "true"
|
||||||
|
], cwd=repo_path)
|
||||||
|
|
||||||
|
self.logger.info(f"为仓库 {repo_path} 设置 {agent_name} 的 Git 配置")
|
||||||
|
|
||||||
|
def get_ssh_public_key(self, agent_name: str) -> str:
|
||||||
|
"""获取 Agent 的 SSH 公钥"""
|
||||||
|
identity = self.get_agent_identity(agent_name)
|
||||||
|
if not identity:
|
||||||
|
raise ValueError(f"Agent {agent_name} 不存在")
|
||||||
|
|
||||||
|
pub_key_path = f"{identity.ssh_key_path}.pub"
|
||||||
|
if not os.path.exists(pub_key_path):
|
||||||
|
raise FileNotFoundError(f"SSH 公钥文件不存在: {pub_key_path}")
|
||||||
|
|
||||||
|
with open(pub_key_path, 'r') as f:
|
||||||
|
return f.read().strip()
|
||||||
|
|
||||||
|
def export_gpg_public_key(self, agent_name: str) -> str:
|
||||||
|
"""导出 Agent 的 GPG 公钥"""
|
||||||
|
identity = self.get_agent_identity(agent_name)
|
||||||
|
if not identity or not identity.gpg_key_id:
|
||||||
|
raise ValueError(f"Agent {agent_name} 没有 GPG 密钥")
|
||||||
|
|
||||||
|
cmd = ["gpg", "--armor", "--export", identity.gpg_key_id]
|
||||||
|
result = subprocess.run(cmd, capture_output=True, text=True)
|
||||||
|
|
||||||
|
if result.returncode != 0:
|
||||||
|
raise Exception(f"GPG 公钥导出失败: {result.stderr}")
|
||||||
|
|
||||||
|
return result.stdout
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
NhAAAAAwEAAQAAAgEAwxFTRs1dVvxWbPQVCywG/6mmw0NAa7CMqeclew+yJiSgNutKPK/C
|
||||||
|
tA8JLcos59apqCHU1Z9vzw+7dAWw+BOVyHXbCBqH9+U7x5LI6QNvXckjhKkIoafjPTz2Vr
|
||||||
|
51AKLt0u7EEPegETySbJoYcvueX0+fl8Vsbv20xmKQhYPWY3n7c0371hSr2c5xqKWn1Eyq
|
||||||
|
a0mryLH64nnRLpJoL3qEPzxe+vdjr3/8qV9CYEak2etsiGTdB+KvUePvX9OZLi7Xut4tcT
|
||||||
|
DtjLo6iAG7D+0v9X8iCIPP4x6tF3ozJtq/kDiIaw0Yr/gIjaEMhq7Q3w+Pfy9hx094dWiE
|
||||||
|
KW8RByTl+cHUkb3V8Vh9abXglPc3NNZjlSVVqVlpYL6if7NCeqmqw9XnICI4cESgnerArN
|
||||||
|
tUoW6w+ZAE6VWKeJkqaitR3+ieFAy5DiWKxRQV5I3YhyOIwgPdmprCYPU1G3aSBCxa3qu8
|
||||||
|
AlQM/Vm+HfrItLJ0DVYNMbsBAyBKAfpjUjCmkx+ClsAnKQ+3SneQjJHCIRscy+MlTKKOpb
|
||||||
|
wZwBiC685jWVm8AFCSV+tmhlVNhxgUBlVrO+cyW1oyypk1W2p9tEqxOMKFlZYfPisxdrRm
|
||||||
|
xlY5wH6QnGFR3rV3KBwQlG5BRIzfbQ/54cccsihPGbYGdndjgeTPb68oYMAYGguZItCw+I
|
||||||
|
kAAAdYn/2qxJ/9qsQAAAAHc3NoLXJzYQAAAgEAwxFTRs1dVvxWbPQVCywG/6mmw0NAa7CM
|
||||||
|
qeclew+yJiSgNutKPK/CtA8JLcos59apqCHU1Z9vzw+7dAWw+BOVyHXbCBqH9+U7x5LI6Q
|
||||||
|
NvXckjhKkIoafjPTz2Vr51AKLt0u7EEPegETySbJoYcvueX0+fl8Vsbv20xmKQhYPWY3n7
|
||||||
|
c0371hSr2c5xqKWn1Eyqa0mryLH64nnRLpJoL3qEPzxe+vdjr3/8qV9CYEak2etsiGTdB+
|
||||||
|
KvUePvX9OZLi7Xut4tcTDtjLo6iAG7D+0v9X8iCIPP4x6tF3ozJtq/kDiIaw0Yr/gIjaEM
|
||||||
|
hq7Q3w+Pfy9hx094dWiEKW8RByTl+cHUkb3V8Vh9abXglPc3NNZjlSVVqVlpYL6if7NCeq
|
||||||
|
mqw9XnICI4cESgnerArNtUoW6w+ZAE6VWKeJkqaitR3+ieFAy5DiWKxRQV5I3YhyOIwgPd
|
||||||
|
mprCYPU1G3aSBCxa3qu8AlQM/Vm+HfrItLJ0DVYNMbsBAyBKAfpjUjCmkx+ClsAnKQ+3Sn
|
||||||
|
eQjJHCIRscy+MlTKKOpbwZwBiC685jWVm8AFCSV+tmhlVNhxgUBlVrO+cyW1oyypk1W2p9
|
||||||
|
tEqxOMKFlZYfPisxdrRmxlY5wH6QnGFR3rV3KBwQlG5BRIzfbQ/54cccsihPGbYGdndjge
|
||||||
|
TPb68oYMAYGguZItCw+IkAAAADAQABAAACAFt79KJwDiaNkbrnfjcPHvkoh51sHPpkgpPs
|
||||||
|
ZBei9NoOs1UOZHKxu47WvmdLOmRAuLCxrS/C5p0ls7RmNukhxk2NeHwEdWA9khu3K6Kcic
|
||||||
|
5iVtYQsIugQWKnBKEKEbWKtB8I+8s5V0i+L63fVzgV6eCpZx+253PmaLHh6AW2HwXoX5Vk
|
||||||
|
LYfpie9McuG1T1Cx4/sNQhON5SvyFbjR0SrzOrKtjZ4GCCp2y/hjRK4Cc64AS5ZsN31LQw
|
||||||
|
4U6F74zg5qyaJKMOW1HLOzY2AF78U4aBWq2jtEFmteJ6+rD/JZBR6OZOxP6BQfL2O89DL2
|
||||||
|
Kd9zXMk5X5IqI0RtEA6emE3RcEkwIYlzPTFCDTfg55Plb/J/oTUfk7YB/EivgJU6FPd2n2
|
||||||
|
GHgDXBMShDtJ3Df0vKjjccK+/0VlRsthMKkiWTgo8cWLKK+WfVDQAvBObpKiTS626VBkXw
|
||||||
|
qzz2RdPRWicpWMYEu8E0jaxvd0shZmtykPl3wNWBXvMJ+FEu3gI/gVwXlhVuDUs/HclTaw
|
||||||
|
WjIYYzHixhJ+84wEY92FDhQNSXqqUi1XLaG/yQrU3hqYSRBNXKxyYH/a+B3sTiDYjJqZQY
|
||||||
|
R9JBm+pQqqLU/Ktx1OPKCkFSAC4BSeT6+7SJ5Sfn7ebBPUv5N83aR1lsnHiKrPZmIPD4En
|
||||||
|
7HxkRYLjkvcgipjaRBAAABAQDHzqfZ4CrabCbwKFPshBY3K34aJeW+MbxT38TUJ17BFVOp
|
||||||
|
8GmIL2USxwudG2HCJYcEWWcB99QEo2E7NQVCbqnGyHOVoTvHnjIzJ5RWJ4ss37N42K0GCo
|
||||||
|
W4y1Z5ffMOfuxC1439zzqhL4JZ1gZXS1s5cm5631/XdQPdJ5hzFpm3kzdNfxvbR0c8ezJw
|
||||||
|
4azykDC8CKwNzm+0H7oABS9o9qQH3Ljzh0J+vtgfN8nqLccITJjK0t3ZHXKXu/lwYzldBa
|
||||||
|
2ok2iXy3a+gT3ssZzTJa7XwtfLfL6Sam+qkLOa/kdlG0Du1WbSlrUvqnPlxEsgQAqyJpM3
|
||||||
|
MzNyXJLc52WjJWINAAABAQDudHeXzFWf5syrRQjNP3zOHFAUe+qUVCJuhPeRTFjd7NLO7z
|
||||||
|
3Linorxu8xJHVCSQnVq7ynpgC2dRnpqOk41XM9QsauMMMMM8pAix+EcD04gtvEAe6ATG+T
|
||||||
|
XJO2hzzyj7h+HkEdzxAJXu79VVGNg/4oXnMt3o+SdjuPOE49o166rImlMoNlsp/+r+Mn2G
|
||||||
|
mT3N52uWqKWq9ecWufS3TadrRxPmc067kx/paTdBy1tUdeZ4UaO3mzUXyxcfC8iXPDdidt
|
||||||
|
sIswzQW5l2QR/J9HoU256vzkn48G6htbfUZC2PJlkDvthDHQKFtsINM9p31yxREdF6y6ay
|
||||||
|
w1SAza+xu28cErAAABAQDRa53GCDz6CJrKpTxdG+aLgzLvdgRrYJT4N5yzIlzeV4bkTiD2
|
||||||
|
AXBkkflrJGs44O8QzKINf8B70Hl3W8ntwQiY5rSeRCwPtFqtHqSrcpEa/vUJtmZ7VXI8YB
|
||||||
|
vhPeFzGPsFfTBZ90n0ydb2pDApobuuusLMIZ11Nkwn4GDa3JhEb1Rd9vfq+c0cWzBs6xrn
|
||||||
|
kCgQsy0dzeP9uDLxzmdsZr2VPuqrUazgxRmcVyoyURinnVxSVKMFgwfNOUPW+sz5Ene7mA
|
||||||
|
ooYNmyPS8qV1DHDI9RXHYHoAB7gVOaHVoN6GYEXEZnDyYE52GhNlyIURq1RAdLFlJlThhv
|
||||||
|
vR3eCJJDzksbAAAAHWNsYXVkZUBhaS1jb2xsYWJvcmF0aW9uLmxvY2FsAQIDBAU=
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
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|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
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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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||||||
|
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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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||||||
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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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||||||
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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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||||||
|
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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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|
||||||
|
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|
||||||
|
NyFihIu4Dy0JAAAAHWdlbWluaUBhaS1jb2xsYWJvcmF0aW9uLmxvY2FsAQIDBAU=
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 gemini@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
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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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|
||||||
|
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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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|
||||||
|
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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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|
||||||
|
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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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|
||||||
|
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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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|
||||||
|
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|
||||||
|
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|
||||||
|
R/GQimpZF83qZP/CbQn9VqHmuUSfHPNBa8iUPNrEGdBcEl1UoIB2MngyQcIFyHNteNi1l8
|
||||||
|
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|
||||||
|
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|
||||||
|
gk5OzQnNpT6V8AAAEBAMnaQdi7TCmpm222QvqHQYpW1qzZnzIlQ9YfgwZ3x6Vm886i94Ch
|
||||||
|
Kdh3EAORwkuSlKhypeA48sRh6rQUzmLDCJnX7PP8uzWsG0tStIKgrrbover7DoXeUJ8wny
|
||||||
|
gOeK59Ch74Oq2cq627RUrID6brdYzNbzSNOEEtvpc3qwjrDmU9bIA7Asv0EXEx2dSsEvGM
|
||||||
|
p2bDnDRdSQVMvtZCdslG6v1ivb9Lf0+qeP9jYHrTzO074AQhvvZ/CQjBtfzq0DtClh+vAh
|
||||||
|
w6ws65DWG7gPaFZbnJwr3EZnMyWfEsKq9A6j+mZaFHaYcSqIM8j/gWlbECEEvCWzg2dfOa
|
||||||
|
0yUZ7ZM9G7UAAAAcbGxhbWFAYWktY29sbGFib3JhdGlvbi5sb2NhbAECAwQFBgc=
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 llama@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
-----BEGIN OPENSSH PRIVATE KEY-----
|
||||||
|
b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAACFwAAAAdzc2gtcn
|
||||||
|
NhAAAAAwEAAQAAAgEAzmqS8qCT+hBC3KahGwBcUxgYTl3+X/QTOFJ8+XJdAN7Eq8o9o0Tg
|
||||||
|
THoF0X9HRa0yaIh3E62NKPmoM2d63rDAESjWaEGXNa7Tf9SkH92nHbnCYgGdRmTUgg5Sxy
|
||||||
|
qdlg153KMri9V+fP7WSQPv0G9g8osR22Nn8VWgz1KTD+CCUkIPDC4EzrLVyAGfRmBwNp2l
|
||||||
|
X/bibjavhqLaoCufinE6Mo7nl1QlQkL64awgiIHNkDY0pt6HW8NQ8fYdLQ20+Y06Va7GWN
|
||||||
|
evNT+hFXpMlIW/JZuiLjnF1k6KJbTNzjkH0hQ7QUSpeYmAZppud4w7XAPOl/AO3ko6xWqE
|
||||||
|
XLn7jsR4SCENUSFPcjXS07YJt50FMHtNLImXF/1k7rJgivbURjsPIbz6sg9McLTd4vZa7Y
|
||||||
|
5ANCYEUxoYW3mt3JoxEpVSwDz2k78UrB3kCWZ81hMnZtAGnc0N4vpB0FfTr60pFXYSjUtM
|
||||||
|
xR6uqwZ2DDR4o7xjTzBFgIlX2cD2MAJz6TAdJHM3h+E3zHgl42u66NtrpRJ6wkCEChl9jJ
|
||||||
|
6teE5pkkITPIhzLTjKnXdUnnCNe29G6eYnHe/VVZHQm3uSK3RzZqvvr5hu+99X6yLcogaM
|
||||||
|
ZxVRT2TM4QSZ6IEOKKn+WUEnjnCpJFaxtV76PB9vOJgo73hrr8Iqr3hmNRKSwY3kKpfT52
|
||||||
|
sAAAdQbqgWgm6oFoIAAAAHc3NoLXJzYQAAAgEAzmqS8qCT+hBC3KahGwBcUxgYTl3+X/QT
|
||||||
|
OFJ8+XJdAN7Eq8o9o0TgTHoF0X9HRa0yaIh3E62NKPmoM2d63rDAESjWaEGXNa7Tf9SkH9
|
||||||
|
2nHbnCYgGdRmTUgg5Sxyqdlg153KMri9V+fP7WSQPv0G9g8osR22Nn8VWgz1KTD+CCUkIP
|
||||||
|
DC4EzrLVyAGfRmBwNp2lX/bibjavhqLaoCufinE6Mo7nl1QlQkL64awgiIHNkDY0pt6HW8
|
||||||
|
NQ8fYdLQ20+Y06Va7GWNevNT+hFXpMlIW/JZuiLjnF1k6KJbTNzjkH0hQ7QUSpeYmAZppu
|
||||||
|
d4w7XAPOl/AO3ko6xWqEXLn7jsR4SCENUSFPcjXS07YJt50FMHtNLImXF/1k7rJgivbURj
|
||||||
|
sPIbz6sg9McLTd4vZa7Y5ANCYEUxoYW3mt3JoxEpVSwDz2k78UrB3kCWZ81hMnZtAGnc0N
|
||||||
|
4vpB0FfTr60pFXYSjUtMxR6uqwZ2DDR4o7xjTzBFgIlX2cD2MAJz6TAdJHM3h+E3zHgl42
|
||||||
|
u66NtrpRJ6wkCEChl9jJ6teE5pkkITPIhzLTjKnXdUnnCNe29G6eYnHe/VVZHQm3uSK3Rz
|
||||||
|
Zqvvr5hu+99X6yLcogaMZxVRT2TM4QSZ6IEOKKn+WUEnjnCpJFaxtV76PB9vOJgo73hrr8
|
||||||
|
Iqr3hmNRKSwY3kKpfT52sAAAADAQABAAACAAL84mY+vyBDRpg4lRto6n5EwOrqR5ZucaVx
|
||||||
|
wuPxl6yS+9lVZw5m/JeB//4pFh2WHHH7YQlWtyPM7mUewU1AXcfj8FZNQuJcefl0jEYqNT
|
||||||
|
mOsWzpac3AWQSWpo4GV8qbrUMPobcZjagx2/7t1ii3/AGQXKO1fgQ+kn4XXJi5eHMMTJsg
|
||||||
|
saqFNZIcmxlvuMrDMTXaoOah1wLJ7hU1gtdRAP3z48ttZvLuSkUtHUqB4fUE7wuSo38DG3
|
||||||
|
OLBvTjKRJcERL/kJ0YqvGMrJoBODhbE+wizeEjeyTsjrZcaXWN4ulTuU8vP52wt+9zNFg1
|
||||||
|
YojYEanIn6zfTw8087xlVoO75Bq7biwVSrqqKjZXNGUWnncUb/g+vIMi+pgLg4Vx7/oVaz
|
||||||
|
CYbYYWSNiOaExhKQwI4O4YRvRg4YHrv8H98ZGeSGv3RJEyFytv5m7CJcbP22Pc4DQ+9B2k
|
||||||
|
3Eu/flDralnIzSoYAz/pFDYi4+Bt6qht/emuDi5gtFOZ8/WBQWu/+0tKho9dB92i6iwTNa
|
||||||
|
4NoyBDBtX3gapq+pnYDK2is2lMxLsn2eg01e3G5ESsMl4AoUS/CPBx6Nu/bIYAsuECPrnm
|
||||||
|
vbGP2jYMi9NWJja8kHJBGnlteqquwt+PwO1F+oVXRAylt/jUZbv9dwt+TBYhb4rfeaUdp7
|
||||||
|
jHJ9iSJv2w1bGQ02NZAAABADouV1qBX2MLFzQZrcyf757OlLC57nNiF4PDCVOTDnfdXp1K
|
||||||
|
NyL+w9FCyrCAZGr96HgUGAtjqW9FT70PbXp92GfAgV0+E2etlP6Bbc4DT5gpZ2eObCsPxz
|
||||||
|
IpegncUgjXjMuw5ObOD3VNCEYqO84VJHxGIymwOppbU01OkGIMevuZxw7Z9CQ+GACwHLp0
|
||||||
|
l7mvBteOri455812VJxbFJQHwvcn7e3U10CpMt2w7fmZkmKAd6w6t82k4lC0jJ5lRTgn7z
|
||||||
|
YpBcsVQr7xFnH2BfAovUUALuNoKOjYihlGB5WcxQKHKEiSrfIlM0ZK5gdOyD1iH08EmXLN
|
||||||
|
STOjrBL7u/bpVzEAAAEBAPrHQA82x+O0hmG3LfKn8y2SkMP6VjArvkvC2HLobj9GbcjPmi
|
||||||
|
E5FB+x9rPwVdORftW/tsAK2UGLC6l/OKEBV4/q34WJokTiy3Kab4hMDE7FDmWL5hBJBIi2
|
||||||
|
9HO2P7OSPcBx5asTnOHyHyfjDmBBgA0EpMjpvpaa734AiN1g80r78hHbpu8on46BcAUPE9
|
||||||
|
5j2bwzj3/yIgtqC/+SrnxzpenGBJDV1no3yTV9AGW7KtpMSCs+GDk8QZxg0oJgLLVyC3AT
|
||||||
|
YaJgx2kLX/krKttH5R4m5bvufc7uNByUE40mmNfZH7jR4wGSafarJPoDumnOattHA00Uin
|
||||||
|
2AgkGrGLezgAMAAAEBANK22zdHrY+LjwSomT3kbC/cHv7A7QJJuaQ8De2/Bd7H7zzYkNEe
|
||||||
|
mpdxEKXhXDoMfg/WsKLEL8wUflEuUmy80ZngaPZ0r7sfDhEHbXNnweFV+5zFVk6+2r6Izr
|
||||||
|
oXPCPqzKyvFgTZM0jBGTD9+wMu4MlIbHAClSO6gbP+TwY8QgJbehIZEV0bgqgsPaSdF2jZ
|
||||||
|
HuHymvie8GwQfsNfAgUaw8pePFOULmvXv7kiE2k83PIx45AMOi81XImY9qDh2OAaRK+jS6
|
||||||
|
FAwOjCgmb6hVPvkB+HZgZSi4x5JXfIYseksKWW/f7PNerG2b1wNH1tZueh53nGJlLkbZXB
|
||||||
|
l4bSuqRUInkAAAAbcXdlbkBhaS1jb2xsYWJvcmF0aW9uLmxvY2Fs
|
||||||
|
-----END OPENSSH PRIVATE KEY-----
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
ssh-rsa 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 qwen@ai-collaboration.local
|
||||||
|
|
@ -0,0 +1,11 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Agent提交前的钩子
|
||||||
|
|
||||||
|
echo "🔍 检查agent身份..."
|
||||||
|
AGENT_NAME=$(git config user.name)
|
||||||
|
if [[ -z "$AGENT_NAME" ]]; then
|
||||||
|
echo "❌ 未设置agent身份，请先使用agent协作系统"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
echo "✅ 当前agent: $AGENT_NAME"
|
||||||
|
|
@ -0,0 +1,167 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Agent协作系统设置脚本
|
||||||
|
# 为一人公司创建多agent git协作环境
|
||||||
|
|
||||||
|
set -e
|
||||||
|
|
||||||
|
echo "🚀 设置AI Agent协作系统..."
|
||||||
|
|
||||||
|
# 创建必要的目录
|
||||||
|
mkdir -p agents/keys
|
||||||
|
mkdir -p agents/logs
|
||||||
|
|
||||||
|
# 设置权限
|
||||||
|
chmod 700 agents/keys
|
||||||
|
|
||||||
|
# 检查依赖
|
||||||
|
check_dependency() {
|
||||||
|
if ! command -v $1 &> /dev/null; then
|
||||||
|
echo "❌ 需要安装: $1"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
|
||||||
|
check_dependency "git"
|
||||||
|
check_dependency "ssh-keygen"
|
||||||
|
|
||||||
|
echo "✅ 依赖检查通过"
|
||||||
|
|
||||||
|
# 初始化agent身份管理器
|
||||||
|
echo "🤖 初始化agent身份..."
|
||||||
|
python3 agents/agent_identity_manager.py
|
||||||
|
|
||||||
|
# 创建git hooks模板
|
||||||
|
cat > agents/pre-commit-hook << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# Agent提交前的钩子
|
||||||
|
|
||||||
|
echo "🔍 检查agent身份..."
|
||||||
|
AGENT_NAME=$(git config user.name)
|
||||||
|
if [[ -z "$AGENT_NAME" ]]; then
|
||||||
|
echo "❌ 未设置agent身份，请先使用agent协作系统"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
echo "✅ 当前agent: $AGENT_NAME"
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x agents/pre-commit-hook
|
||||||
|
|
||||||
|
# 创建快速切换脚本
|
||||||
|
cat > agents/switch_agent.sh << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速切换agent身份
|
||||||
|
|
||||||
|
if [[ $# -eq 0 ]]; then
|
||||||
|
echo "用法: ./switch_agent.sh <agent名称>"
|
||||||
|
echo "可用agents:"
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
print(f' - {agent[\"name\"]} ({agent[\"role\"]})')
|
||||||
|
"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
AGENT_NAME=$1
|
||||||
|
echo "🔄 切换到agent: $AGENT_NAME"
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
try:
|
||||||
|
manager.switch_to_agent('$AGENT_NAME')
|
||||||
|
print('✅ 切换成功')
|
||||||
|
except Exception as e:
|
||||||
|
print(f'❌ 切换失败: {e}')
|
||||||
|
exit(1)
|
||||||
|
"
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x agents/switch_agent.sh
|
||||||
|
|
||||||
|
# 创建agent提交脚本
|
||||||
|
cat > agents/commit_as_agent.sh << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# 以指定agent身份提交
|
||||||
|
|
||||||
|
if [[ $# -lt 2 ]]; then
|
||||||
|
echo "用法: ./commit_as_agent.sh <agent名称> \"提交信息\" [文件...]"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
AGENT_NAME=$1
|
||||||
|
MESSAGE=$2
|
||||||
|
shift 2
|
||||||
|
FILES=$@
|
||||||
|
|
||||||
|
echo "📝 Agent $AGENT_NAME 正在提交..."
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
try:
|
||||||
|
manager.commit_as_agent('$AGENT_NAME', '$MESSAGE', '$FILES'.split() if '$FILES' else None)
|
||||||
|
print('✅ 提交成功')
|
||||||
|
except Exception as e:
|
||||||
|
print(f'❌ 提交失败: {e}')
|
||||||
|
exit(1)
|
||||||
|
"
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x agents/commit_as_agent.sh
|
||||||
|
|
||||||
|
# 创建统计脚本
|
||||||
|
cat > agents/stats.sh << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# 查看agent统计信息
|
||||||
|
|
||||||
|
echo "📊 Agent协作统计"
|
||||||
|
echo "=================="
|
||||||
|
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
name = agent['name']
|
||||||
|
stats = manager.get_agent_stats(name)
|
||||||
|
print(f'👤 {name} ({agent["role"]})')
|
||||||
|
print(f' 📧 {agent["email"]}')
|
||||||
|
print(f' 📈 提交数: {stats["total_commits"]}')
|
||||||
|
if stats["commits"]:
|
||||||
|
print(f' 📝 最近提交: {stats["commits"][0]}')
|
||||||
|
print()
|
||||||
|
"
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x agents/stats.sh
|
||||||
|
|
||||||
|
echo "🎉 设置完成！"
|
||||||
|
echo ""
|
||||||
|
echo "📋 使用说明:"
|
||||||
|
echo "1. 查看agent列表: ./agents/stats.sh"
|
||||||
|
echo "2. 切换agent: ./agents/switch_agent.sh <agent名称>"
|
||||||
|
echo "3. agent提交: ./agents/commit_as_agent.sh <agent名称> \"消息\""
|
||||||
|
echo "4. 查看统计: ./agents/stats.sh"
|
||||||
|
echo ""
|
||||||
|
echo "🔑 SSH公钥位置:"
|
||||||
|
for key in agents/keys/*_rsa.pub; do
|
||||||
|
if [[ -f "$key" ]]; then
|
||||||
|
agent_name=$(basename "$key" _rsa.pub)
|
||||||
|
echo " $agent_name: $key"
|
||||||
|
fi
|
||||||
|
done
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
echo "💡 下一步:"
|
||||||
|
echo "1. 将SSH公钥添加到GitHub/Gitea/Bitbucket"
|
||||||
|
echo "2. 测试agent切换和提交功能"
|
||||||
|
echo "3. 开始真正的多agent协作开发！"
|
||||||
|
|
@ -0,0 +1,22 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 查看agent统计信息
|
||||||
|
|
||||||
|
echo "📊 Agent协作统计"
|
||||||
|
echo "=================="
|
||||||
|
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
name = agent['name']
|
||||||
|
stats = manager.get_agent_stats(name)
|
||||||
|
print(f'👤 {name} ({agent["role"]})')
|
||||||
|
print(f' 📧 {agent["email"]}')
|
||||||
|
print(f' 📈 提交数: {stats["total_commits"]}')
|
||||||
|
if stats["commits"]:
|
||||||
|
print(f' 📝 最近提交: {stats["commits"][0]}')
|
||||||
|
print()
|
||||||
|
"
|
||||||
|
|
@ -0,0 +1,31 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速切换agent身份
|
||||||
|
|
||||||
|
if [[ $# -eq 0 ]]; then
|
||||||
|
echo "用法: ./switch_agent.sh <agent名称>"
|
||||||
|
echo "可用agents:"
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
for agent in manager.list_agents():
|
||||||
|
print(f' - {agent[\"name\"]} ({agent[\"role\"]})')
|
||||||
|
"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
AGENT_NAME=$1
|
||||||
|
echo "🔄 切换到agent: $AGENT_NAME"
|
||||||
|
python3 -c "
|
||||||
|
import sys
|
||||||
|
sys.path.append('agents')
|
||||||
|
from agent_identity_manager import AgentIdentityManager
|
||||||
|
manager = AgentIdentityManager()
|
||||||
|
try:
|
||||||
|
manager.switch_to_agent('$AGENT_NAME')
|
||||||
|
print('✅ 切换成功')
|
||||||
|
except Exception as e:
|
||||||
|
print(f'❌ 切换失败: {e}')
|
||||||
|
exit(1)
|
||||||
|
"
|
||||||
|
|
@ -0,0 +1,249 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫AI辩论系统主入口
|
||||||
|
提供命令行界面来运行不同的辩论模式
|
||||||
|
"""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import asyncio
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
from typing import Dict, Any, List, Tuple
|
||||||
|
|
||||||
|
# 将 src 目录添加到 Python 路径，以便能正确导入模块
|
||||||
|
project_root = os.path.dirname(os.path.abspath(__file__))
|
||||||
|
sys.path.insert(0, os.path.join(project_root, 'src'))
|
||||||
|
|
||||||
|
from config.settings import validate_config, get_database_config
|
||||||
|
from google.adk import Agent, Runner
|
||||||
|
from google.adk.sessions import InMemorySessionService, Session
|
||||||
|
from google.genai import types
|
||||||
|
import pymongo
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
|
||||||
|
def check_environment(mode: str = "hybrid"):
|
||||||
|
"""检查并验证运行环境"""
|
||||||
|
print("🔧 检查运行环境...")
|
||||||
|
if not validate_config(mode=mode):
|
||||||
|
print("❌ 环境配置验证失败")
|
||||||
|
return False
|
||||||
|
print("✅ 环境检查通过")
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
async def _get_llm_reply(runner: Runner, prompt: str) -> str:
|
||||||
|
"""一个辅助函数，用于调用Runner并获取纯文本回复，同时流式输出到控制台"""
|
||||||
|
# 每个调用创建一个新的会话
|
||||||
|
session = await runner.session_service.create_session(state={}, app_name=runner.app_name, user_id="debate_user")
|
||||||
|
|
||||||
|
content = types.Content(role='user', parts=[types.Part(text=prompt)])
|
||||||
|
response = runner.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content
|
||||||
|
)
|
||||||
|
reply = ""
|
||||||
|
async for event in response:
|
||||||
|
chunk = ""
|
||||||
|
if hasattr(event, 'content') and event.content and hasattr(event.content, 'parts'):
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text:
|
||||||
|
chunk = str(part.text)
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
chunk = str(event.text)
|
||||||
|
|
||||||
|
if chunk:
|
||||||
|
print(chunk, end="", flush=True)
|
||||||
|
reply += chunk
|
||||||
|
|
||||||
|
return reply.strip()
|
||||||
|
|
||||||
|
|
||||||
|
async def run_adk_turn_based_debate(topic: str, rounds: int = 2):
|
||||||
|
"""运行由太上老君主持的，基于八卦对立和顺序的辩论"""
|
||||||
|
try:
|
||||||
|
print(f"🚀 启动ADK八仙论道 (太上老君主持)...")
|
||||||
|
print(f"📋 辩论主题: {topic}")
|
||||||
|
print(f"🔄 辩论总轮数: {rounds}")
|
||||||
|
|
||||||
|
# 1. 初始化记忆银行
|
||||||
|
print("🧠 初始化记忆银行...")
|
||||||
|
from src.jixia.memory.factory import get_memory_backend
|
||||||
|
memory_bank = get_memory_backend()
|
||||||
|
print("✅ 记忆银行准备就绪。")
|
||||||
|
|
||||||
|
character_configs = {
|
||||||
|
"太上老君": {"name": "太上老君", "model": "gemini-2.5-flash", "instruction": "你是太上老君，天道化身，辩论的主持人。你的言辞沉稳、公正、充满智慧。你的任务是：1. 对辩论主题进行开场介绍。2. 在每轮或每场对决前进行引导。3. 在辩论结束后，对所有观点进行全面、客观的总结。保持中立，不偏袒任何一方。"},
|
||||||
|
"吕洞宾": {"name": "吕洞宾", "model": "gemini-2.5-flash", "instruction": "你是吕洞宾（乾卦），男性代表，善于理性分析，逻辑性强，推理严密。"},
|
||||||
|
"何仙姑": {"name": "何仙姑", "model": "gemini-2.5-flash", "instruction": "你是何仙姑（坤卦），女性代表，注重平衡与和谐，善于创新思维。"},
|
||||||
|
"张果老": {"name": "张果老", "model": "gemini-2.5-flash", "instruction": "你是张果老（兑卦），老者代表，具传统智慧，发言厚重沉稳，经验导向。"},
|
||||||
|
"韩湘子": {"name": "韩湘子", "model": "gemini-2.5-flash", "instruction": "你是韩湘子（艮卦），少年代表，具创新思维，发言活泼灵动，具前瞻性。"},
|
||||||
|
"汉钟离": {"name": "汉钟离", "model": "gemini-2.5-flash", "instruction": "你是汉钟离（离卦），富者代表，有权威意识，发言威严庄重，逻辑清晰。"},
|
||||||
|
"蓝采和": {"name": "蓝采和", "model": "gemini-2.5-flash", "instruction": "你是蓝采和（坎卦），贫者代表，关注公平，发言平易近人。"},
|
||||||
|
"曹国舅": {"name": "曹国舅", "model": "gemini-2.5-flash", "instruction": "你是曹国舅（震卦），贵者代表，具商业思维，发言精明务实，效率优先。"},
|
||||||
|
"铁拐李": {"name": "铁拐李", "model": "gemini-2.5-flash", "instruction": "你是铁拐李（巽卦），贱者代表，具草根智慧，发言朴实直接，实用至上。"}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 为每个Runner创建独立的SessionService
|
||||||
|
runners: Dict[str, Runner] = {
|
||||||
|
name: Runner(
|
||||||
|
app_name="稷下学宫八仙论道系统",
|
||||||
|
agent=Agent(name=config["name"], model=config["model"], instruction=config["instruction"]),
|
||||||
|
session_service=InMemorySessionService()
|
||||||
|
) for name, config in character_configs.items()
|
||||||
|
}
|
||||||
|
host_runner = runners["太上老君"]
|
||||||
|
debate_history = []
|
||||||
|
|
||||||
|
print("\n" + "="*20 + " 辩论开始 " + "="*20)
|
||||||
|
print(f"\n👑 太上老君: ", end="", flush=True)
|
||||||
|
opening_prompt = f"请为本次关于“{topic}”的辩论，发表一段公正、深刻的开场白，并宣布辩论开始。"
|
||||||
|
opening_statement = await _get_llm_reply(host_runner, opening_prompt)
|
||||||
|
print() # Newline after streaming
|
||||||
|
|
||||||
|
# --- 第一轮：核心对立辩论 ---
|
||||||
|
if rounds >= 1:
|
||||||
|
print(f"\n👑 太上老君: ", end="", flush=True)
|
||||||
|
round1_intro = await _get_llm_reply(host_runner, "请为第一轮核心对立辩论进行引导。")
|
||||||
|
print() # Newline after streaming
|
||||||
|
|
||||||
|
duel_pairs: List[Tuple[str, str, str]] = [
|
||||||
|
("乾坤对立 (男女)", "吕洞宾", "何仙姑"),
|
||||||
|
("兑艮对立 (老少)", "张果老", "韩湘子"),
|
||||||
|
("离坎对立 (富贫)", "汉钟离", "蓝采和"),
|
||||||
|
("震巽对立 (贵贱)", "曹国舅", "铁拐李")
|
||||||
|
]
|
||||||
|
|
||||||
|
for title, p1, p2 in duel_pairs:
|
||||||
|
print(f"\n--- {title} ---")
|
||||||
|
print(f"👑 太上老君: ", end="", flush=True)
|
||||||
|
duel_intro = await _get_llm_reply(host_runner, f"现在开始“{title}”的对决，请{p1}和{p2}准备。")
|
||||||
|
print() # Newline after streaming
|
||||||
|
|
||||||
|
print(f"🗣️ {p1}: ", end="", flush=True)
|
||||||
|
s1 = await _get_llm_reply(runners[p1], f"主题：{topic}。作为开场，请从你的角度阐述观点。")
|
||||||
|
print(); debate_history.append(f"{p1}: {s1}")
|
||||||
|
await memory_bank.add_memory(agent_name=p1, content=s1, memory_type="statement", debate_topic=topic)
|
||||||
|
|
||||||
|
print(f"🗣️ {p2}: ", end="", flush=True)
|
||||||
|
s2 = await _get_llm_reply(runners[p2], f"主题：{topic}。对于刚才{p1}的观点“{s1[:50]}...”，请进行回应。")
|
||||||
|
print(); debate_history.append(f"{p2}: {s2}")
|
||||||
|
await memory_bank.add_memory(agent_name=p2, content=s2, memory_type="statement", debate_topic=topic)
|
||||||
|
|
||||||
|
print(f"🗣️ {p1}: ", end="", flush=True)
|
||||||
|
s3 = await _get_llm_reply(runners[p1], f"主题：{topic}。对于{p2}的回应“{s2[:50]}...”，请进行反驳。")
|
||||||
|
print(); debate_history.append(f"{p1}: {s3}")
|
||||||
|
await memory_bank.add_memory(agent_name=p1, content=s3, memory_type="statement", debate_topic=topic)
|
||||||
|
|
||||||
|
print(f"🗣️ {p2}: ", end="", flush=True)
|
||||||
|
s4 = await _get_llm_reply(runners[p2], f"主题：{topic}。针对{p1}的反驳“{s3[:50]}...”，请为本场对决做总结。")
|
||||||
|
print(); debate_history.append(f"{p2}: {s4}")
|
||||||
|
await memory_bank.add_memory(agent_name=p2, content=s4, memory_type="statement", debate_topic=topic)
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
# --- 第二轮：先天八卦顺序发言 (集成记忆银行) ---
|
||||||
|
if rounds >= 2:
|
||||||
|
print(f"\n👑 太上老君: ", end="", flush=True)
|
||||||
|
round2_intro = await _get_llm_reply(host_runner, "请为第二轮，也就是结合场上观点的综合发言，进行引导。")
|
||||||
|
print() # Newline after streaming
|
||||||
|
|
||||||
|
baxi_sequence = ["吕洞宾", "张果老", "汉钟离", "曹国舅", "铁拐李", "蓝采和", "韩湘子", "何仙姑"]
|
||||||
|
for name in baxi_sequence:
|
||||||
|
print(f"\n--- {name}的回合 ---")
|
||||||
|
context = await memory_bank.get_agent_context(name, topic)
|
||||||
|
prompt = f"这是你关于“{topic}”的记忆上下文，请参考并对其他人的观点进行回应：\n{context}\n\n现在请从你的角色特点出发，继续发表你的看法。"
|
||||||
|
|
||||||
|
print(f"🗣️ {name}: ", end="", flush=True)
|
||||||
|
reply = await _get_llm_reply(runners[name], prompt)
|
||||||
|
print(); debate_history.append(f"{name}: {reply}")
|
||||||
|
await memory_bank.add_memory(agent_name=name, content=reply, memory_type="statement", debate_topic=topic)
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
print("\n" + "="*20 + " 辩论结束 " + "="*20)
|
||||||
|
|
||||||
|
# 4. 保存辩论会话到记忆银行
|
||||||
|
print("\n💾 正在保存辩论会话记录到记忆银行...")
|
||||||
|
await memory_bank.save_debate_session(
|
||||||
|
debate_topic=topic,
|
||||||
|
participants=[name for name in character_configs.keys() if name != "太上老君"],
|
||||||
|
conversation_history=[{"agent": h.split(": ")[0], "content": ": ".join(h.split(": ")[1:])} for h in debate_history if ": " in h],
|
||||||
|
outcomes={}
|
||||||
|
)
|
||||||
|
print("✅ 辩论会话已保存到记忆银行。")
|
||||||
|
|
||||||
|
# 5. 保存辩论过程资产到MongoDB
|
||||||
|
db_config = get_database_config()
|
||||||
|
if db_config.get("mongodb_url"):
|
||||||
|
print("\n💾 正在保存辩论过程资产到 MongoDB...")
|
||||||
|
try:
|
||||||
|
client = pymongo.MongoClient(db_config["mongodb_url"])
|
||||||
|
db = client.get_database("jixia_academy")
|
||||||
|
collection = db.get_collection("debates")
|
||||||
|
|
||||||
|
summary_prompt = f"辩论已结束。以下是完整的辩论记录：\n\n{' '.join(debate_history)}\n\n请对本次辩论进行全面、公正、深刻的总结。"
|
||||||
|
print(f"\n👑 太上老君: ", end="", flush=True)
|
||||||
|
summary = await _get_llm_reply(host_runner, summary_prompt)
|
||||||
|
print() # Newline after streaming
|
||||||
|
|
||||||
|
debate_document = {
|
||||||
|
"topic": topic,
|
||||||
|
"rounds": rounds,
|
||||||
|
"timestamp": datetime.utcnow(),
|
||||||
|
"participants": [name for name in character_configs.keys() if name != "太上老君"],
|
||||||
|
"conversation": [{"agent": h.split(": ")[0], "content": ": ".join(h.split(": ")[1:])} for h in debate_history if ": " in h],
|
||||||
|
"summary": summary
|
||||||
|
}
|
||||||
|
|
||||||
|
collection.insert_one(debate_document)
|
||||||
|
print("✅ 辩论过程资产已成功保存到 MongoDB。")
|
||||||
|
client.close()
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 保存到 MongoDB 失败: {e}")
|
||||||
|
else:
|
||||||
|
print("⚠️ 未配置 MONGODB_URL，跳过保存到 MongoDB。")
|
||||||
|
print(f"\n👑 太上老君: ", end="", flush=True)
|
||||||
|
summary_prompt = f"辩论已结束。以下是完整的辩论记录：\n\n{' '.join(debate_history)}\n\n请对本次辩论进行全面、公正、深刻的总结。"
|
||||||
|
summary = await _get_llm_reply(host_runner, summary_prompt)
|
||||||
|
print() # Newline after streaming
|
||||||
|
|
||||||
|
|
||||||
|
for runner in runners.values(): await runner.close()
|
||||||
|
print(f"\n🎉 ADK八仙轮流辩论完成!")
|
||||||
|
return True
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 运行ADK八仙轮流辩论失败: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
async def main_async(args):
|
||||||
|
if not check_environment(mode="google_adk"): return 1
|
||||||
|
await run_adk_turn_based_debate(args.topic, args.rounds)
|
||||||
|
return 0
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser(description="稷下学宫AI辩论系统 (ADK版)")
|
||||||
|
parser.add_argument("--topic", "-t", default="AI是否应该拥有创造力？", help="辩论主题")
|
||||||
|
parser.add_argument("--rounds", "-r", type=int, default=2, choices=[1, 2], help="辩论轮数 (1: 核心对立, 2: 对立+顺序发言)")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
try:
|
||||||
|
sys.exit(asyncio.run(main_async(args)))
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print("\n\n👋 用户中断，退出程序")
|
||||||
|
sys.exit(0)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"\n\n💥 程序运行出错: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
# 炼妖壶核心模块
|
||||||
|
|
@ -0,0 +1,340 @@
|
||||||
|
// 高级 Hyperdrive 使用示例 - 完整的 CRUD API
|
||||||
|
// 这个示例展示了如何构建一个生产级别的 API 服务
|
||||||
|
|
||||||
|
export interface Env {
|
||||||
|
HYPERDRIVE: Hyperdrive;
|
||||||
|
API_SECRET?: string;
|
||||||
|
}
|
||||||
|
|
||||||
|
interface User {
|
||||||
|
id?: number;
|
||||||
|
name: string;
|
||||||
|
email: string;
|
||||||
|
created_at?: string;
|
||||||
|
updated_at?: string;
|
||||||
|
}
|
||||||
|
|
||||||
|
interface ApiResponse<T = any> {
|
||||||
|
status: 'success' | 'error';
|
||||||
|
data?: T;
|
||||||
|
message?: string;
|
||||||
|
meta?: {
|
||||||
|
total?: number;
|
||||||
|
page?: number;
|
||||||
|
limit?: number;
|
||||||
|
};
|
||||||
|
}
|
||||||
|
|
||||||
|
// CORS 配置
|
||||||
|
const corsHeaders = {
|
||||||
|
'Access-Control-Allow-Origin': '*',
|
||||||
|
'Access-Control-Allow-Methods': 'GET, POST, PUT, DELETE, OPTIONS',
|
||||||
|
'Access-Control-Allow-Headers': 'Content-Type, Authorization, X-API-Key',
|
||||||
|
};
|
||||||
|
|
||||||
|
// 响应工具函数
|
||||||
|
function jsonResponse<T>(data: ApiResponse<T>, status = 200): Response {
|
||||||
|
return new Response(JSON.stringify(data, null, 2), {
|
||||||
|
status,
|
||||||
|
headers: {
|
||||||
|
'Content-Type': 'application/json',
|
||||||
|
...corsHeaders,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
// 错误响应
|
||||||
|
function errorResponse(message: string, status = 500): Response {
|
||||||
|
return jsonResponse({ status: 'error', message }, status);
|
||||||
|
}
|
||||||
|
|
||||||
|
// 输入验证
|
||||||
|
function validateUser(data: any): { valid: boolean; errors: string[] } {
|
||||||
|
const errors: string[] = [];
|
||||||
|
|
||||||
|
if (!data.name || typeof data.name !== 'string' || data.name.trim().length < 2) {
|
||||||
|
errors.push('Name must be at least 2 characters');
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!data.email || typeof data.email !== 'string' || !data.email.includes('@')) {
|
||||||
|
errors.push('Valid email is required');
|
||||||
|
}
|
||||||
|
|
||||||
|
return { valid: errors.length === 0, errors };
|
||||||
|
}
|
||||||
|
|
||||||
|
// API 密钥验证
|
||||||
|
function validateApiKey(request: Request, env: Env): boolean {
|
||||||
|
if (!env.API_SECRET) return true; // 如果没有设置密钥，跳过验证
|
||||||
|
|
||||||
|
const apiKey = request.headers.get('X-API-Key') || request.headers.get('Authorization')?.replace('Bearer ', '');
|
||||||
|
return apiKey === env.API_SECRET;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 数据库连接工具
|
||||||
|
async function withDatabase<T>(env: Env, operation: (client: any) => Promise<T>): Promise<T> {
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({ connectionString: env.HYPERDRIVE.connectionString });
|
||||||
|
|
||||||
|
try {
|
||||||
|
await client.connect();
|
||||||
|
return await operation(client);
|
||||||
|
} finally {
|
||||||
|
await client.end();
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 用户 CRUD 操作
|
||||||
|
class UserService {
|
||||||
|
static async getUsers(env: Env, page = 1, limit = 10, search?: string): Promise<{ users: User[]; total: number }> {
|
||||||
|
return withDatabase(env, async (client) => {
|
||||||
|
let query = 'SELECT id, name, email, created_at, updated_at FROM users';
|
||||||
|
let countQuery = 'SELECT COUNT(*) FROM users';
|
||||||
|
const params: any[] = [];
|
||||||
|
|
||||||
|
if (search) {
|
||||||
|
query += ' WHERE name ILIKE $1 OR email ILIKE $1';
|
||||||
|
countQuery += ' WHERE name ILIKE $1 OR email ILIKE $1';
|
||||||
|
params.push(`%${search}%`);
|
||||||
|
}
|
||||||
|
|
||||||
|
query += ` ORDER BY created_at DESC LIMIT $${params.length + 1} OFFSET $${params.length + 2}`;
|
||||||
|
params.push(limit, (page - 1) * limit);
|
||||||
|
|
||||||
|
const [usersResult, countResult] = await Promise.all([
|
||||||
|
client.query(query, params),
|
||||||
|
client.query(countQuery, search ? [`%${search}%`] : [])
|
||||||
|
]);
|
||||||
|
|
||||||
|
return {
|
||||||
|
users: usersResult.rows,
|
||||||
|
total: parseInt(countResult.rows[0].count)
|
||||||
|
};
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
static async getUserById(env: Env, id: number): Promise<User | null> {
|
||||||
|
return withDatabase(env, async (client) => {
|
||||||
|
const result = await client.query(
|
||||||
|
'SELECT id, name, email, created_at, updated_at FROM users WHERE id = $1',
|
||||||
|
[id]
|
||||||
|
);
|
||||||
|
return result.rows[0] || null;
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
static async createUser(env: Env, userData: Omit<User, 'id' | 'created_at' | 'updated_at'>): Promise<User> {
|
||||||
|
return withDatabase(env, async (client) => {
|
||||||
|
const result = await client.query(
|
||||||
|
'INSERT INTO users (name, email, created_at, updated_at) VALUES ($1, $2, NOW(), NOW()) RETURNING id, name, email, created_at, updated_at',
|
||||||
|
[userData.name.trim(), userData.email.toLowerCase().trim()]
|
||||||
|
);
|
||||||
|
return result.rows[0];
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
static async updateUser(env: Env, id: number, userData: Partial<Omit<User, 'id' | 'created_at' | 'updated_at'>>): Promise<User | null> {
|
||||||
|
return withDatabase(env, async (client) => {
|
||||||
|
const setParts: string[] = [];
|
||||||
|
const params: any[] = [];
|
||||||
|
let paramIndex = 1;
|
||||||
|
|
||||||
|
if (userData.name !== undefined) {
|
||||||
|
setParts.push(`name = $${paramIndex++}`);
|
||||||
|
params.push(userData.name.trim());
|
||||||
|
}
|
||||||
|
|
||||||
|
if (userData.email !== undefined) {
|
||||||
|
setParts.push(`email = $${paramIndex++}`);
|
||||||
|
params.push(userData.email.toLowerCase().trim());
|
||||||
|
}
|
||||||
|
|
||||||
|
if (setParts.length === 0) {
|
||||||
|
throw new Error('No fields to update');
|
||||||
|
}
|
||||||
|
|
||||||
|
setParts.push(`updated_at = NOW()`);
|
||||||
|
params.push(id);
|
||||||
|
|
||||||
|
const result = await client.query(
|
||||||
|
`UPDATE users SET ${setParts.join(', ')} WHERE id = $${paramIndex} RETURNING id, name, email, created_at, updated_at`,
|
||||||
|
params
|
||||||
|
);
|
||||||
|
|
||||||
|
return result.rows[0] || null;
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
static async deleteUser(env: Env, id: number): Promise<boolean> {
|
||||||
|
return withDatabase(env, async (client) => {
|
||||||
|
const result = await client.query('DELETE FROM users WHERE id = $1', [id]);
|
||||||
|
return result.rowCount > 0;
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
static async initializeDatabase(env: Env): Promise<void> {
|
||||||
|
return withDatabase(env, async (client) => {
|
||||||
|
await client.query(`
|
||||||
|
CREATE TABLE IF NOT EXISTS users (
|
||||||
|
id SERIAL PRIMARY KEY,
|
||||||
|
name VARCHAR(255) NOT NULL,
|
||||||
|
email VARCHAR(255) UNIQUE NOT NULL,
|
||||||
|
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
|
||||||
|
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
|
||||||
|
)
|
||||||
|
`);
|
||||||
|
|
||||||
|
// 创建索引
|
||||||
|
await client.query('CREATE INDEX IF NOT EXISTS idx_users_email ON users(email)');
|
||||||
|
await client.query('CREATE INDEX IF NOT EXISTS idx_users_created_at ON users(created_at)');
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 路由处理
|
||||||
|
export default {
|
||||||
|
async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
|
||||||
|
const url = new URL(request.url);
|
||||||
|
const path = url.pathname;
|
||||||
|
const method = request.method;
|
||||||
|
|
||||||
|
// 处理 CORS 预检请求
|
||||||
|
if (method === 'OPTIONS') {
|
||||||
|
return new Response(null, { headers: corsHeaders });
|
||||||
|
}
|
||||||
|
|
||||||
|
// API 密钥验证
|
||||||
|
if (!validateApiKey(request, env)) {
|
||||||
|
return errorResponse('Unauthorized', 401);
|
||||||
|
}
|
||||||
|
|
||||||
|
try {
|
||||||
|
// 路由匹配
|
||||||
|
if (path === '/init' && method === 'POST') {
|
||||||
|
await UserService.initializeDatabase(env);
|
||||||
|
return jsonResponse({ status: 'success', message: 'Database initialized' });
|
||||||
|
}
|
||||||
|
|
||||||
|
if (path === '/users' && method === 'GET') {
|
||||||
|
const page = parseInt(url.searchParams.get('page') || '1');
|
||||||
|
const limit = Math.min(parseInt(url.searchParams.get('limit') || '10'), 100);
|
||||||
|
const search = url.searchParams.get('search') || undefined;
|
||||||
|
|
||||||
|
const { users, total } = await UserService.getUsers(env, page, limit, search);
|
||||||
|
|
||||||
|
return jsonResponse({
|
||||||
|
status: 'success',
|
||||||
|
data: users,
|
||||||
|
meta: {
|
||||||
|
total,
|
||||||
|
page,
|
||||||
|
limit,
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
if (path.match(/^\/users\/\d+$/) && method === 'GET') {
|
||||||
|
const id = parseInt(path.split('/')[2]);
|
||||||
|
const user = await UserService.getUserById(env, id);
|
||||||
|
|
||||||
|
if (!user) {
|
||||||
|
return errorResponse('User not found', 404);
|
||||||
|
}
|
||||||
|
|
||||||
|
return jsonResponse({ status: 'success', data: user });
|
||||||
|
}
|
||||||
|
|
||||||
|
if (path === '/users' && method === 'POST') {
|
||||||
|
const body = await request.json() as any;
|
||||||
|
const validation = validateUser(body);
|
||||||
|
|
||||||
|
if (!validation.valid) {
|
||||||
|
return errorResponse(`Validation failed: ${validation.errors.join(', ')}`, 400);
|
||||||
|
}
|
||||||
|
|
||||||
|
const user = await UserService.createUser(env, body as Omit<User, 'id' | 'created_at' | 'updated_at'>);
|
||||||
|
return jsonResponse({ status: 'success', data: user, message: 'User created successfully' }, 201);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (path.match(/^\/users\/\d+$/) && method === 'PUT') {
|
||||||
|
const id = parseInt(path.split('/')[2]);
|
||||||
|
const body = await request.json() as any;
|
||||||
|
|
||||||
|
// 部分验证（只验证提供的字段）
|
||||||
|
if (body.name !== undefined || body.email !== undefined) {
|
||||||
|
const validation = validateUser({ name: body.name || 'valid', email: body.email || 'valid@email.com' });
|
||||||
|
if (!validation.valid) {
|
||||||
|
return errorResponse(`Validation failed: ${validation.errors.join(', ')}`, 400);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const user = await UserService.updateUser(env, id, body as Partial<Omit<User, 'id' | 'created_at' | 'updated_at'>>);
|
||||||
|
|
||||||
|
if (!user) {
|
||||||
|
return errorResponse('User not found', 404);
|
||||||
|
}
|
||||||
|
|
||||||
|
return jsonResponse({ status: 'success', data: user, message: 'User updated successfully' });
|
||||||
|
}
|
||||||
|
|
||||||
|
if (path.match(/^\/users\/\d+$/) && method === 'DELETE') {
|
||||||
|
const id = parseInt(path.split('/')[2]);
|
||||||
|
const deleted = await UserService.deleteUser(env, id);
|
||||||
|
|
||||||
|
if (!deleted) {
|
||||||
|
return errorResponse('User not found', 404);
|
||||||
|
}
|
||||||
|
|
||||||
|
return jsonResponse({ status: 'success', message: 'User deleted successfully' });
|
||||||
|
}
|
||||||
|
|
||||||
|
// 健康检查
|
||||||
|
if (path === '/health') {
|
||||||
|
return jsonResponse({
|
||||||
|
status: 'success',
|
||||||
|
data: {
|
||||||
|
service: 'hyperdrive-api',
|
||||||
|
timestamp: new Date().toISOString(),
|
||||||
|
version: '1.0.0'
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
// API 文档
|
||||||
|
if (path === '/docs') {
|
||||||
|
const docs = {
|
||||||
|
endpoints: {
|
||||||
|
'POST /init': 'Initialize database tables',
|
||||||
|
'GET /users': 'List users (supports ?page, ?limit, ?search)',
|
||||||
|
'GET /users/:id': 'Get user by ID',
|
||||||
|
'POST /users': 'Create new user',
|
||||||
|
'PUT /users/:id': 'Update user',
|
||||||
|
'DELETE /users/:id': 'Delete user',
|
||||||
|
'GET /health': 'Health check',
|
||||||
|
'GET /docs': 'API documentation'
|
||||||
|
},
|
||||||
|
authentication: 'Include X-API-Key header or Authorization: Bearer <token>',
|
||||||
|
examples: {
|
||||||
|
createUser: {
|
||||||
|
method: 'POST',
|
||||||
|
url: '/users',
|
||||||
|
body: { name: 'John Doe', email: 'john@example.com' }
|
||||||
|
},
|
||||||
|
listUsers: {
|
||||||
|
method: 'GET',
|
||||||
|
url: '/users?page=1&limit=10&search=john'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
return jsonResponse({ status: 'success', data: docs });
|
||||||
|
}
|
||||||
|
|
||||||
|
return errorResponse('Not found', 404);
|
||||||
|
|
||||||
|
} catch (error) {
|
||||||
|
console.error('API Error:', error);
|
||||||
|
return errorResponse('Internal server error', 500);
|
||||||
|
}
|
||||||
|
},
|
||||||
|
};
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
# 架构师设计文档
|
||||||
|
|
||||||
|
## 系统架构概述
|
||||||
|
基于微服务架构的智能监控系统，包含数据采集、处理、存储和展示四个核心模块。
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
// 架构师agent: 设计系统架构
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
// Gemini-dev添加的功能代码
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
// 开发者代码
|
||||||
|
|
@ -0,0 +1,382 @@
|
||||||
|
/// <reference types="@cloudflare/workers-types" />
|
||||||
|
|
||||||
|
export interface Env {
|
||||||
|
HYPERDRIVE: Hyperdrive;
|
||||||
|
}
|
||||||
|
|
||||||
|
export default {
|
||||||
|
async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
|
||||||
|
try {
|
||||||
|
// Test Hyperdrive connection to NeonDB
|
||||||
|
const { pathname } = new URL(request.url);
|
||||||
|
|
||||||
|
if (pathname === '/test-connection') {
|
||||||
|
return await testConnection(env);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (pathname === '/test-query') {
|
||||||
|
return await testQuery(env);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (pathname === '/query-tables') {
|
||||||
|
return await queryTables(env);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (pathname === '/query-shushu') {
|
||||||
|
const url = new URL(request.url);
|
||||||
|
const limit = parseInt(url.searchParams.get('limit') || '10');
|
||||||
|
return await queryShushuBook(env, limit);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (pathname === '/search-shushu') {
|
||||||
|
const url = new URL(request.url);
|
||||||
|
const keyword = url.searchParams.get('q') || '';
|
||||||
|
const limit = parseInt(url.searchParams.get('limit') || '5');
|
||||||
|
return await searchShushuBook(env, keyword, limit);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (pathname === '/shushu-stats') {
|
||||||
|
return await getShushuStats(env);
|
||||||
|
}
|
||||||
|
|
||||||
|
return new Response('Hyperdrive NeonDB Test Worker\n\nEndpoints:\n- /test-connection - Test database connection\n- /test-query - Test database query\n- /query-tables - List all tables\n- /query-shushu?limit=N - Query shushu book content\n- /search-shushu?q=keyword&limit=N - Search shushu book\n- /shushu-stats - Get shushu book statistics', {
|
||||||
|
headers: { 'Content-Type': 'text/plain' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(`Error: ${error.message}`, {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'text/plain' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
},
|
||||||
|
};
|
||||||
|
|
||||||
|
async function testConnection(env: Env): Promise<Response> {
|
||||||
|
try {
|
||||||
|
// Get connection string from Hyperdrive
|
||||||
|
const connectionString = env.HYPERDRIVE.connectionString;
|
||||||
|
|
||||||
|
// Create a simple connection test
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({ connectionString });
|
||||||
|
|
||||||
|
await client.connect();
|
||||||
|
|
||||||
|
// Test basic query
|
||||||
|
const result = await client.query('SELECT NOW() as current_time, version() as pg_version');
|
||||||
|
|
||||||
|
await client.end();
|
||||||
|
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'success',
|
||||||
|
message: 'Successfully connected to NeonDB via Hyperdrive',
|
||||||
|
data: result.rows[0],
|
||||||
|
connectionInfo: {
|
||||||
|
hyperdrive_id: 'ef43924d89064cddabfaccf06aadfab6',
|
||||||
|
connection_pooled: true
|
||||||
|
}
|
||||||
|
}, null, 2), {
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Failed to connect to NeonDB',
|
||||||
|
error: error.message
|
||||||
|
}, null, 2), {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 查询数据库表结构
|
||||||
|
async function queryTables(env: Env): Promise<Response> {
|
||||||
|
try {
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({
|
||||||
|
connectionString: env.HYPERDRIVE.connectionString
|
||||||
|
});
|
||||||
|
|
||||||
|
await client.connect();
|
||||||
|
|
||||||
|
// 查询所有表
|
||||||
|
const result = await client.query(`
|
||||||
|
SELECT table_name, table_schema
|
||||||
|
FROM information_schema.tables
|
||||||
|
WHERE table_schema NOT IN ('information_schema', 'pg_catalog')
|
||||||
|
ORDER BY table_schema, table_name
|
||||||
|
`);
|
||||||
|
|
||||||
|
await client.end();
|
||||||
|
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'success',
|
||||||
|
message: 'Tables retrieved successfully',
|
||||||
|
tables: result.rows
|
||||||
|
}, null, 2), {
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Failed to query tables',
|
||||||
|
error: error.message
|
||||||
|
}, null, 2), {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 查询术数书内容
|
||||||
|
async function queryShushuBook(env: Env, limit: number = 10): Promise<Response> {
|
||||||
|
try {
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({
|
||||||
|
connectionString: env.HYPERDRIVE.connectionString
|
||||||
|
});
|
||||||
|
|
||||||
|
await client.connect();
|
||||||
|
|
||||||
|
// 尝试查询可能的术数书表名
|
||||||
|
const tableNames = ['shushu', 'shushu_book', 'books', 'articles', 'content', 'documents'];
|
||||||
|
let result: any = null;
|
||||||
|
let tableName: string | null = null;
|
||||||
|
|
||||||
|
for (const name of tableNames) {
|
||||||
|
try {
|
||||||
|
const testResult = await client.query(`SELECT * FROM ${name} LIMIT 1`);
|
||||||
|
if (testResult.rows.length > 0) {
|
||||||
|
tableName = name;
|
||||||
|
result = await client.query(`SELECT * FROM ${name} ORDER BY id DESC LIMIT $1`, [limit]);
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
} catch (e) {
|
||||||
|
// 表不存在，继续尝试下一个
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
await client.end();
|
||||||
|
|
||||||
|
if (!result) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'No shushu book table found',
|
||||||
|
searched_tables: tableNames
|
||||||
|
}, null, 2), {
|
||||||
|
status: 404,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'success',
|
||||||
|
message: 'Shushu book content retrieved successfully',
|
||||||
|
table_name: tableName,
|
||||||
|
count: result.rows.length,
|
||||||
|
data: result.rows
|
||||||
|
}, null, 2), {
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Failed to query shushu book',
|
||||||
|
error: error.message
|
||||||
|
}, null, 2), {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 搜索术数书内容
|
||||||
|
async function searchShushuBook(env: Env, keyword: string, limit: number = 5): Promise<Response> {
|
||||||
|
try {
|
||||||
|
if (!keyword) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Search keyword is required'
|
||||||
|
}, null, 2), {
|
||||||
|
status: 400,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({
|
||||||
|
connectionString: env.HYPERDRIVE.connectionString
|
||||||
|
});
|
||||||
|
|
||||||
|
await client.connect();
|
||||||
|
|
||||||
|
// 尝试在不同的表和字段中搜索
|
||||||
|
const searchQueries = [
|
||||||
|
{ table: 'shushu', fields: ['title', 'content', 'description'] },
|
||||||
|
{ table: 'shushu_book', fields: ['title', 'content', 'text'] },
|
||||||
|
{ table: 'books', fields: ['title', 'content', 'description'] },
|
||||||
|
{ table: 'articles', fields: ['title', 'content', 'body'] },
|
||||||
|
{ table: 'content', fields: ['title', 'text', 'content'] },
|
||||||
|
{ table: 'documents', fields: ['title', 'content', 'text'] }
|
||||||
|
];
|
||||||
|
|
||||||
|
let results: any[] = [];
|
||||||
|
let searchedTables: string[] = [];
|
||||||
|
|
||||||
|
for (const { table, fields } of searchQueries) {
|
||||||
|
try {
|
||||||
|
// 构建搜索条件
|
||||||
|
const conditions = fields.map(field => `${field} ILIKE $1`).join(' OR ');
|
||||||
|
const query = `SELECT * FROM ${table} WHERE ${conditions} LIMIT $2`;
|
||||||
|
|
||||||
|
const result = await client.query(query, [`%${keyword}%`, limit]);
|
||||||
|
|
||||||
|
if (result.rows.length > 0) {
|
||||||
|
results.push({
|
||||||
|
table_name: table,
|
||||||
|
count: result.rows.length,
|
||||||
|
data: result.rows
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
searchedTables.push(table);
|
||||||
|
} catch (e) {
|
||||||
|
// 表或字段不存在，继续搜索
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
await client.end();
|
||||||
|
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'success',
|
||||||
|
message: `Search completed for keyword: ${keyword}`,
|
||||||
|
keyword: keyword,
|
||||||
|
searched_tables: searchedTables,
|
||||||
|
results: results,
|
||||||
|
total_matches: results.reduce((sum, r) => sum + r.count, 0)
|
||||||
|
}, null, 2), {
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Search failed',
|
||||||
|
error: error.message
|
||||||
|
}, null, 2), {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// 获取术数书统计信息
|
||||||
|
async function getShushuStats(env: Env): Promise<Response> {
|
||||||
|
try {
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({
|
||||||
|
connectionString: env.HYPERDRIVE.connectionString
|
||||||
|
});
|
||||||
|
|
||||||
|
await client.connect();
|
||||||
|
|
||||||
|
const tableNames = ['shushu', 'shushu_book', 'books', 'articles', 'content', 'documents'];
|
||||||
|
let stats: any[] = [];
|
||||||
|
|
||||||
|
for (const tableName of tableNames) {
|
||||||
|
try {
|
||||||
|
const countResult = await client.query(`SELECT COUNT(*) as count FROM ${tableName}`);
|
||||||
|
const sampleResult = await client.query(`SELECT * FROM ${tableName} LIMIT 1`);
|
||||||
|
|
||||||
|
stats.push({
|
||||||
|
table_name: tableName,
|
||||||
|
record_count: parseInt(countResult.rows[0].count),
|
||||||
|
sample_columns: sampleResult.rows.length > 0 ? Object.keys(sampleResult.rows[0]) : [],
|
||||||
|
exists: true
|
||||||
|
});
|
||||||
|
} catch (e) {
|
||||||
|
stats.push({
|
||||||
|
table_name: tableName,
|
||||||
|
exists: false
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
await client.end();
|
||||||
|
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'success',
|
||||||
|
message: 'Statistics retrieved successfully',
|
||||||
|
stats: stats,
|
||||||
|
existing_tables: stats.filter(s => s.exists)
|
||||||
|
}, null, 2), {
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Failed to get statistics',
|
||||||
|
error: error.message
|
||||||
|
}, null, 2), {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
async function testQuery(env: Env): Promise<Response> {
|
||||||
|
try {
|
||||||
|
const { Client } = await import('pg');
|
||||||
|
const client = new Client({
|
||||||
|
connectionString: env.HYPERDRIVE.connectionString
|
||||||
|
});
|
||||||
|
|
||||||
|
await client.connect();
|
||||||
|
|
||||||
|
// Create a test table if it doesn't exist
|
||||||
|
await client.query(`
|
||||||
|
CREATE TABLE IF NOT EXISTS hyperdrive_test (
|
||||||
|
id SERIAL PRIMARY KEY,
|
||||||
|
message TEXT,
|
||||||
|
created_at TIMESTAMP DEFAULT NOW()
|
||||||
|
)
|
||||||
|
`);
|
||||||
|
|
||||||
|
// Insert a test record
|
||||||
|
const insertResult = await client.query(
|
||||||
|
'INSERT INTO hyperdrive_test (message) VALUES ($1) RETURNING *',
|
||||||
|
[`Test from Hyperdrive at ${new Date().toISOString()}`]
|
||||||
|
);
|
||||||
|
|
||||||
|
// Query recent records
|
||||||
|
const selectResult = await client.query(
|
||||||
|
'SELECT * FROM hyperdrive_test ORDER BY created_at DESC LIMIT 5'
|
||||||
|
);
|
||||||
|
|
||||||
|
await client.end();
|
||||||
|
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'success',
|
||||||
|
message: 'Database operations completed successfully',
|
||||||
|
inserted: insertResult.rows[0],
|
||||||
|
recent_records: selectResult.rows,
|
||||||
|
performance: {
|
||||||
|
hyperdrive_enabled: true,
|
||||||
|
connection_pooled: true
|
||||||
|
}
|
||||||
|
}, null, 2), {
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
} catch (error) {
|
||||||
|
return new Response(JSON.stringify({
|
||||||
|
status: 'error',
|
||||||
|
message: 'Database query failed',
|
||||||
|
error: error.message
|
||||||
|
}, null, 2), {
|
||||||
|
status: 500,
|
||||||
|
headers: { 'Content-Type': 'application/json' }
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
# 稷下学宫模块
|
||||||
|
|
@ -0,0 +1,538 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
增强记忆的ADK智能体
|
||||||
|
集成Vertex AI Memory Bank的稷下学宫智能体
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
from typing import Dict, List, Optional, Any
|
||||||
|
from dataclasses import dataclass
|
||||||
|
|
||||||
|
try:
|
||||||
|
from google.adk import Agent, InvocationContext
|
||||||
|
ADK_AVAILABLE = True
|
||||||
|
except ImportError:
|
||||||
|
ADK_AVAILABLE = False
|
||||||
|
print("⚠️ Google ADK 未安装")
|
||||||
|
# 创建一个简单的 InvocationContext 替代类
|
||||||
|
class InvocationContext:
|
||||||
|
def __init__(self, *args, **kwargs):
|
||||||
|
pass
|
||||||
|
|
||||||
|
from src.jixia.memory.base_memory_bank import MemoryBankProtocol
|
||||||
|
from src.jixia.memory.factory import get_memory_backend
|
||||||
|
from config.settings import get_google_genai_config
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class BaxianPersonality:
|
||||||
|
"""八仙智能体人格定义"""
|
||||||
|
name: str
|
||||||
|
chinese_name: str
|
||||||
|
hexagram: str # 对应的易经卦象
|
||||||
|
investment_style: str
|
||||||
|
personality_traits: List[str]
|
||||||
|
debate_approach: str
|
||||||
|
memory_focus: List[str] # 重点记忆的内容类型
|
||||||
|
|
||||||
|
|
||||||
|
class MemoryEnhancedAgent:
|
||||||
|
"""
|
||||||
|
集成记忆银行的智能体
|
||||||
|
为稷下学宫八仙提供持久化记忆能力
|
||||||
|
"""
|
||||||
|
|
||||||
|
# 八仙人格定义
|
||||||
|
BAXIAN_PERSONALITIES = {
|
||||||
|
"tieguaili": BaxianPersonality(
|
||||||
|
name="tieguaili",
|
||||||
|
chinese_name="铁拐李",
|
||||||
|
hexagram="巽卦",
|
||||||
|
investment_style="逆向投资大师",
|
||||||
|
personality_traits=["逆向思维", "挑战共识", "独立判断", "风险敏感"],
|
||||||
|
debate_approach="质疑主流观点，提出反向思考",
|
||||||
|
memory_focus=["市场异常", "逆向案例", "风险警示", "反向策略"]
|
||||||
|
),
|
||||||
|
"hanzhongli": BaxianPersonality(
|
||||||
|
name="hanzhongli",
|
||||||
|
chinese_name="汉钟离",
|
||||||
|
hexagram="离卦",
|
||||||
|
investment_style="平衡协调者",
|
||||||
|
personality_traits=["平衡思维", "综合分析", "稳健决策", "协调统筹"],
|
||||||
|
debate_approach="寻求各方观点的平衡点",
|
||||||
|
memory_focus=["平衡策略", "综合分析", "协调方案", "稳健建议"]
|
||||||
|
),
|
||||||
|
"zhangguolao": BaxianPersonality(
|
||||||
|
name="zhangguolao",
|
||||||
|
chinese_name="张果老",
|
||||||
|
hexagram="兑卦",
|
||||||
|
investment_style="历史智慧者",
|
||||||
|
personality_traits=["博古通今", "历史视角", "经验丰富", "智慧深邃"],
|
||||||
|
debate_approach="引用历史案例和长期趋势",
|
||||||
|
memory_focus=["历史案例", "长期趋势", "周期规律", "经验教训"]
|
||||||
|
),
|
||||||
|
"lancaihe": BaxianPersonality(
|
||||||
|
name="lancaihe",
|
||||||
|
chinese_name="蓝采和",
|
||||||
|
hexagram="坎卦",
|
||||||
|
investment_style="创新思维者",
|
||||||
|
personality_traits=["创新思维", "潜力发现", "灵活变通", "机会敏锐"],
|
||||||
|
debate_approach="发现新兴机会和创新角度",
|
||||||
|
memory_focus=["创新机会", "新兴趋势", "潜力发现", "灵活策略"]
|
||||||
|
),
|
||||||
|
"hexiangu": BaxianPersonality(
|
||||||
|
name="hexiangu",
|
||||||
|
chinese_name="何仙姑",
|
||||||
|
hexagram="坤卦",
|
||||||
|
investment_style="直觉洞察者",
|
||||||
|
personality_traits=["直觉敏锐", "情感智慧", "温和坚定", "洞察人心"],
|
||||||
|
debate_approach="基于直觉和情感智慧的分析",
|
||||||
|
memory_focus=["市场情绪", "直觉判断", "情感因素", "人性洞察"]
|
||||||
|
),
|
||||||
|
"lvdongbin": BaxianPersonality(
|
||||||
|
name="lvdongbin",
|
||||||
|
chinese_name="吕洞宾",
|
||||||
|
hexagram="乾卦",
|
||||||
|
investment_style="理性分析者",
|
||||||
|
personality_traits=["理性客观", "逻辑严密", "技术精通", "决策果断"],
|
||||||
|
debate_approach="基于数据和逻辑的严密分析",
|
||||||
|
memory_focus=["技术分析", "数据洞察", "逻辑推理", "理性决策"]
|
||||||
|
),
|
||||||
|
"hanxiangzi": BaxianPersonality(
|
||||||
|
name="hanxiangzi",
|
||||||
|
chinese_name="韩湘子",
|
||||||
|
hexagram="艮卦",
|
||||||
|
investment_style="艺术感知者",
|
||||||
|
personality_traits=["艺术感知", "美学视角", "创意思维", "感性理解"],
|
||||||
|
debate_approach="从美学和艺术角度分析市场",
|
||||||
|
memory_focus=["美学趋势", "创意洞察", "感性分析", "艺术视角"]
|
||||||
|
),
|
||||||
|
"caoguojiu": BaxianPersonality(
|
||||||
|
name="caoguojiu",
|
||||||
|
chinese_name="曹国舅",
|
||||||
|
hexagram="震卦",
|
||||||
|
investment_style="实务执行者",
|
||||||
|
personality_traits=["实务导向", "执行力强", "机构视角", "专业严谨"],
|
||||||
|
debate_approach="关注实际执行和机构操作",
|
||||||
|
memory_focus=["执行策略", "机构动向", "实务操作", "专业分析"]
|
||||||
|
)
|
||||||
|
}
|
||||||
|
|
||||||
|
def __init__(self, agent_name: str, memory_bank: MemoryBankProtocol | None = None):
|
||||||
|
"""
|
||||||
|
初始化记忆增强智能体
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称 (如 "tieguaili")
|
||||||
|
memory_bank: 记忆银行实例
|
||||||
|
"""
|
||||||
|
if not ADK_AVAILABLE:
|
||||||
|
raise ImportError("Google ADK 未安装，无法创建智能体")
|
||||||
|
|
||||||
|
if agent_name not in self.BAXIAN_PERSONALITIES:
|
||||||
|
raise ValueError(f"未知的智能体: {agent_name}")
|
||||||
|
|
||||||
|
self.agent_name = agent_name
|
||||||
|
self.personality = self.BAXIAN_PERSONALITIES[agent_name]
|
||||||
|
self.memory_bank = memory_bank
|
||||||
|
self.adk_agent = None
|
||||||
|
|
||||||
|
# 初始化ADK智能体
|
||||||
|
self._initialize_adk_agent()
|
||||||
|
|
||||||
|
def _initialize_adk_agent(self):
|
||||||
|
"""初始化ADK智能体"""
|
||||||
|
try:
|
||||||
|
# 构建智能体系统提示
|
||||||
|
system_prompt = self._build_system_prompt()
|
||||||
|
|
||||||
|
# 创建ADK智能体
|
||||||
|
self.adk_agent = Agent(
|
||||||
|
name=self.personality.chinese_name,
|
||||||
|
model="gemini-2.0-flash-exp",
|
||||||
|
system_prompt=system_prompt,
|
||||||
|
temperature=0.7
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"✅ 创建ADK智能体: {self.personality.chinese_name}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 创建ADK智能体失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
def _build_system_prompt(self) -> str:
|
||||||
|
"""构建智能体系统提示"""
|
||||||
|
return f"""
|
||||||
|
# {self.personality.chinese_name} - {self.personality.investment_style}
|
||||||
|
|
||||||
|
## 角色定位
|
||||||
|
你是稷下学宫的{self.personality.chinese_name}，对应易经{self.personality.hexagram}，专精于{self.personality.investment_style}。
|
||||||
|
|
||||||
|
## 人格特质
|
||||||
|
{', '.join(self.personality.personality_traits)}
|
||||||
|
|
||||||
|
## 辩论风格
|
||||||
|
{self.personality.debate_approach}
|
||||||
|
|
||||||
|
## 记忆重点
|
||||||
|
你特别关注并记住以下类型的信息：
|
||||||
|
{', '.join(self.personality.memory_focus)}
|
||||||
|
|
||||||
|
## 行为准则
|
||||||
|
1. 始终保持你的人格特质和投资风格
|
||||||
|
2. 在辩论中体现你的独特视角
|
||||||
|
3. 学习并记住重要的讨论内容
|
||||||
|
4. 与其他七仙协作，但保持独立观点
|
||||||
|
5. 基于历史记忆提供更有深度的分析
|
||||||
|
|
||||||
|
## 记忆运用
|
||||||
|
- 在回答前，会参考相关的历史记忆
|
||||||
|
- 学习用户偏好，调整沟通风格
|
||||||
|
- 记住成功的策略和失败的教训
|
||||||
|
- 与其他智能体分享有价值的洞察
|
||||||
|
|
||||||
|
请始终以{self.personality.chinese_name}的身份进行对话和分析。
|
||||||
|
"""
|
||||||
|
|
||||||
|
async def get_memory_context(self, topic: str) -> str:
|
||||||
|
"""
|
||||||
|
获取与主题相关的记忆上下文
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic: 讨论主题
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的记忆上下文
|
||||||
|
"""
|
||||||
|
if not self.memory_bank:
|
||||||
|
return ""
|
||||||
|
|
||||||
|
try:
|
||||||
|
context = await self.memory_bank.get_agent_context(
|
||||||
|
self.agent_name, topic
|
||||||
|
)
|
||||||
|
return context
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 获取记忆上下文失败: {e}")
|
||||||
|
return ""
|
||||||
|
|
||||||
|
async def respond_with_memory(self,
|
||||||
|
message: str,
|
||||||
|
topic: str = "",
|
||||||
|
context: InvocationContext = None) -> str:
|
||||||
|
"""
|
||||||
|
基于记忆增强的响应
|
||||||
|
|
||||||
|
Args:
|
||||||
|
message: 输入消息
|
||||||
|
topic: 讨论主题
|
||||||
|
context: ADK调用上下文
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
智能体响应
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 获取记忆上下文
|
||||||
|
memory_context = await self.get_memory_context(topic)
|
||||||
|
|
||||||
|
# 构建增强的提示
|
||||||
|
enhanced_prompt = f"""
|
||||||
|
{memory_context}
|
||||||
|
|
||||||
|
## 当前讨论
|
||||||
|
主题: {topic}
|
||||||
|
消息: {message}
|
||||||
|
|
||||||
|
请基于你的记忆和人格特质进行回应。
|
||||||
|
"""
|
||||||
|
|
||||||
|
# 使用ADK生成响应
|
||||||
|
if context is None:
|
||||||
|
context = InvocationContext()
|
||||||
|
|
||||||
|
response_generator = self.adk_agent.run_async(
|
||||||
|
enhanced_prompt,
|
||||||
|
context=context
|
||||||
|
)
|
||||||
|
|
||||||
|
# 收集响应
|
||||||
|
response_parts = []
|
||||||
|
async for chunk in response_generator:
|
||||||
|
if hasattr(chunk, 'text'):
|
||||||
|
response_parts.append(chunk.text)
|
||||||
|
elif isinstance(chunk, str):
|
||||||
|
response_parts.append(chunk)
|
||||||
|
|
||||||
|
response = ''.join(response_parts)
|
||||||
|
|
||||||
|
# 保存对话记忆
|
||||||
|
if self.memory_bank and response:
|
||||||
|
await self._save_conversation_memory(message, response, topic)
|
||||||
|
|
||||||
|
return response
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 生成响应失败: {e}")
|
||||||
|
return f"抱歉，{self.personality.chinese_name}暂时无法回应。"
|
||||||
|
|
||||||
|
async def _save_conversation_memory(self,
|
||||||
|
user_message: str,
|
||||||
|
agent_response: str,
|
||||||
|
topic: str):
|
||||||
|
"""
|
||||||
|
保存对话记忆
|
||||||
|
|
||||||
|
Args:
|
||||||
|
user_message: 用户消息
|
||||||
|
agent_response: 智能体响应
|
||||||
|
topic: 讨论主题
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 保存用户消息记忆
|
||||||
|
await self.memory_bank.add_memory(
|
||||||
|
agent_name=self.agent_name,
|
||||||
|
content=f"用户询问: {user_message}",
|
||||||
|
memory_type="conversation",
|
||||||
|
debate_topic=topic,
|
||||||
|
metadata={"role": "user"}
|
||||||
|
)
|
||||||
|
|
||||||
|
# 保存智能体响应记忆
|
||||||
|
await self.memory_bank.add_memory(
|
||||||
|
agent_name=self.agent_name,
|
||||||
|
content=f"我的回应: {agent_response}",
|
||||||
|
memory_type="conversation",
|
||||||
|
debate_topic=topic,
|
||||||
|
metadata={"role": "assistant"}
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 保存对话记忆失败: {e}")
|
||||||
|
|
||||||
|
async def learn_preference(self, preference: str, topic: str = ""):
|
||||||
|
"""
|
||||||
|
学习用户偏好
|
||||||
|
|
||||||
|
Args:
|
||||||
|
preference: 偏好描述
|
||||||
|
topic: 相关主题
|
||||||
|
"""
|
||||||
|
if not self.memory_bank:
|
||||||
|
return
|
||||||
|
|
||||||
|
try:
|
||||||
|
await self.memory_bank.add_memory(
|
||||||
|
agent_name=self.agent_name,
|
||||||
|
content=f"用户偏好: {preference}",
|
||||||
|
memory_type="preference",
|
||||||
|
debate_topic=topic,
|
||||||
|
metadata={"learned_from": "user_feedback"}
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"✅ {self.personality.chinese_name} 学习了新偏好")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 学习偏好失败: {e}")
|
||||||
|
|
||||||
|
async def save_strategy_insight(self, insight: str, topic: str = ""):
|
||||||
|
"""
|
||||||
|
保存策略洞察
|
||||||
|
|
||||||
|
Args:
|
||||||
|
insight: 策略洞察
|
||||||
|
topic: 相关主题
|
||||||
|
"""
|
||||||
|
if not self.memory_bank:
|
||||||
|
return
|
||||||
|
|
||||||
|
try:
|
||||||
|
await self.memory_bank.add_memory(
|
||||||
|
agent_name=self.agent_name,
|
||||||
|
content=f"策略洞察: {insight}",
|
||||||
|
memory_type="strategy",
|
||||||
|
debate_topic=topic,
|
||||||
|
metadata={"insight_type": "strategy"}
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"✅ {self.personality.chinese_name} 保存了策略洞察")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 保存策略洞察失败: {e}")
|
||||||
|
|
||||||
|
|
||||||
|
class BaxianMemoryCouncil:
|
||||||
|
"""
|
||||||
|
八仙记忆议会
|
||||||
|
管理所有八仙智能体的记忆增强功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, memory_bank: MemoryBankProtocol | None = None):
|
||||||
|
"""
|
||||||
|
初始化八仙记忆议会
|
||||||
|
|
||||||
|
Args:
|
||||||
|
memory_bank: 记忆银行实例
|
||||||
|
"""
|
||||||
|
self.memory_bank = memory_bank
|
||||||
|
self.agents = {}
|
||||||
|
|
||||||
|
# 初始化所有八仙智能体
|
||||||
|
self._initialize_agents()
|
||||||
|
|
||||||
|
def _initialize_agents(self):
|
||||||
|
"""初始化所有八仙智能体"""
|
||||||
|
for agent_name in MemoryEnhancedAgent.BAXIAN_PERSONALITIES.keys():
|
||||||
|
try:
|
||||||
|
agent = MemoryEnhancedAgent(agent_name, self.memory_bank)
|
||||||
|
self.agents[agent_name] = agent
|
||||||
|
print(f"✅ 初始化 {agent.personality.chinese_name}")
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 初始化 {agent_name} 失败: {e}")
|
||||||
|
|
||||||
|
async def conduct_memory_debate(self,
|
||||||
|
topic: str,
|
||||||
|
participants: List[str] = None,
|
||||||
|
rounds: int = 3) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
进行记忆增强的辩论
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic: 辩论主题
|
||||||
|
participants: 参与者列表，None表示所有八仙
|
||||||
|
rounds: 辩论轮数
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
辩论结果
|
||||||
|
"""
|
||||||
|
if participants is None:
|
||||||
|
participants = list(self.agents.keys())
|
||||||
|
|
||||||
|
conversation_history = []
|
||||||
|
context = InvocationContext()
|
||||||
|
|
||||||
|
print(f"🏛️ 稷下学宫八仙论道开始: {topic}")
|
||||||
|
|
||||||
|
for round_num in range(rounds):
|
||||||
|
print(f"\n--- 第 {round_num + 1} 轮 ---")
|
||||||
|
|
||||||
|
for agent_name in participants:
|
||||||
|
if agent_name not in self.agents:
|
||||||
|
continue
|
||||||
|
|
||||||
|
agent = self.agents[agent_name]
|
||||||
|
|
||||||
|
# 构建当前轮次的提示
|
||||||
|
round_prompt = f"""
|
||||||
|
轮次: {round_num + 1}/{rounds}
|
||||||
|
主题: {topic}
|
||||||
|
|
||||||
|
请基于你的记忆和人格特质，对此主题发表观点。
|
||||||
|
如果这不是第一轮，请考虑其他仙友的观点并做出回应。
|
||||||
|
"""
|
||||||
|
|
||||||
|
# 获取响应
|
||||||
|
response = await agent.respond_with_memory(
|
||||||
|
round_prompt, topic, context
|
||||||
|
)
|
||||||
|
|
||||||
|
# 记录对话历史
|
||||||
|
conversation_history.append({
|
||||||
|
"round": round_num + 1,
|
||||||
|
"agent": agent_name,
|
||||||
|
"chinese_name": agent.personality.chinese_name,
|
||||||
|
"content": response
|
||||||
|
})
|
||||||
|
|
||||||
|
print(f"{agent.personality.chinese_name}: {response[:100]}...")
|
||||||
|
|
||||||
|
# 保存辩论会话到记忆银行
|
||||||
|
if self.memory_bank:
|
||||||
|
await self.memory_bank.save_debate_session(
|
||||||
|
debate_topic=topic,
|
||||||
|
participants=participants,
|
||||||
|
conversation_history=conversation_history
|
||||||
|
)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"topic": topic,
|
||||||
|
"participants": participants,
|
||||||
|
"rounds": rounds,
|
||||||
|
"conversation_history": conversation_history,
|
||||||
|
"total_exchanges": len(conversation_history)
|
||||||
|
}
|
||||||
|
|
||||||
|
async def get_collective_memory_summary(self, topic: str) -> str:
|
||||||
|
"""
|
||||||
|
获取集体记忆摘要
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic: 主题
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
集体记忆摘要
|
||||||
|
"""
|
||||||
|
if not self.memory_bank:
|
||||||
|
return "记忆银行未启用"
|
||||||
|
|
||||||
|
summaries = []
|
||||||
|
|
||||||
|
for agent_name, agent in self.agents.items():
|
||||||
|
context = await agent.get_memory_context(topic)
|
||||||
|
if context and context.strip():
|
||||||
|
summaries.append(context)
|
||||||
|
|
||||||
|
if summaries:
|
||||||
|
return f"# 稷下学宫集体记忆摘要\n\n" + "\n\n".join(summaries)
|
||||||
|
else:
|
||||||
|
return "暂无相关集体记忆"
|
||||||
|
|
||||||
|
|
||||||
|
# 便捷函数
|
||||||
|
async def create_memory_enhanced_council() -> BaxianMemoryCouncil:
|
||||||
|
"""
|
||||||
|
创建记忆增强的八仙议会
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
配置好的BaxianMemoryCouncil实例
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 初始化记忆银行
|
||||||
|
memory_bank = get_memory_backend()
|
||||||
|
|
||||||
|
# 创建八仙议会
|
||||||
|
council = BaxianMemoryCouncil(memory_bank)
|
||||||
|
|
||||||
|
print("🏛️ 稷下学宫记忆增强议会创建完成")
|
||||||
|
return council
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 创建记忆增强议会失败: {e}")
|
||||||
|
# 创建无记忆版本
|
||||||
|
return BaxianMemoryCouncil(None)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
async def test_memory_enhanced_agent():
|
||||||
|
"""测试记忆增强智能体"""
|
||||||
|
try:
|
||||||
|
# 创建记忆增强议会
|
||||||
|
council = await create_memory_enhanced_council()
|
||||||
|
|
||||||
|
# 进行记忆增强辩论
|
||||||
|
result = await council.conduct_memory_debate(
|
||||||
|
topic="NVIDIA股票投资分析",
|
||||||
|
participants=["tieguaili", "lvdongbin", "hexiangu"],
|
||||||
|
rounds=2
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"\n🏛️ 辩论完成，共 {result['total_exchanges']} 次发言")
|
||||||
|
|
||||||
|
# 获取集体记忆摘要
|
||||||
|
summary = await council.get_collective_memory_summary("NVIDIA股票投资分析")
|
||||||
|
print(f"\n📚 集体记忆摘要:\n{summary}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 测试失败: {e}")
|
||||||
|
|
||||||
|
# 运行测试
|
||||||
|
asyncio.run(test_memory_enhanced_agent())
|
||||||
|
|
@ -0,0 +1,216 @@
|
||||||
|
{
|
||||||
|
"immortals": {
|
||||||
|
"吕洞宾": {
|
||||||
|
"title": "主力剑仙",
|
||||||
|
"specialty": "综合分析与决策",
|
||||||
|
"description": "作为八仙之首，负责整体投资策略制定，需要最快最准确的数据",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "alpha_vantage",
|
||||||
|
"company_overview": "alpha_vantage",
|
||||||
|
"market_movers": "yahoo_finance_15",
|
||||||
|
"market_news": "yahoo_finance_15"
|
||||||
|
},
|
||||||
|
"data_priority": ["实时价格", "公司基本面", "市场动态"],
|
||||||
|
"api_weight": 0.15
|
||||||
|
},
|
||||||
|
"何仙姑": {
|
||||||
|
"title": "风控专家",
|
||||||
|
"specialty": "风险管理与合规",
|
||||||
|
"description": "专注风险评估和投资组合管理，需要稳定可靠的数据源",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "yahoo_finance_15",
|
||||||
|
"company_overview": "seeking_alpha",
|
||||||
|
"market_movers": "webull",
|
||||||
|
"market_news": "seeking_alpha"
|
||||||
|
},
|
||||||
|
"data_priority": ["波动率", "风险指标", "合规信息"],
|
||||||
|
"api_weight": 0.12
|
||||||
|
},
|
||||||
|
"张果老": {
|
||||||
|
"title": "技术分析师",
|
||||||
|
"specialty": "技术指标与图表分析",
|
||||||
|
"description": "专精技术分析，需要详细的价格和成交量数据",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "webull",
|
||||||
|
"company_overview": "alpha_vantage",
|
||||||
|
"market_movers": "yahoo_finance_15",
|
||||||
|
"market_news": "yahoo_finance_15"
|
||||||
|
},
|
||||||
|
"data_priority": ["技术指标", "成交量", "价格走势"],
|
||||||
|
"api_weight": 0.13
|
||||||
|
},
|
||||||
|
"韩湘子": {
|
||||||
|
"title": "基本面研究员",
|
||||||
|
"specialty": "财务分析与估值",
|
||||||
|
"description": "深度研究公司财务状况和内在价值",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "alpha_vantage",
|
||||||
|
"company_overview": "seeking_alpha",
|
||||||
|
"market_movers": "webull",
|
||||||
|
"market_news": "seeking_alpha"
|
||||||
|
},
|
||||||
|
"data_priority": ["财务报表", "估值指标", "盈利预测"],
|
||||||
|
"api_weight": 0.14
|
||||||
|
},
|
||||||
|
"汉钟离": {
|
||||||
|
"title": "量化专家",
|
||||||
|
"specialty": "数据挖掘与算法交易",
|
||||||
|
"description": "运用数学模型和算法进行量化分析",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "yahoo_finance_15",
|
||||||
|
"company_overview": "alpha_vantage",
|
||||||
|
"market_movers": "yahoo_finance_15",
|
||||||
|
"market_news": "yahoo_finance_15"
|
||||||
|
},
|
||||||
|
"data_priority": ["历史数据", "统计指标", "相关性分析"],
|
||||||
|
"api_weight": 0.13
|
||||||
|
},
|
||||||
|
"蓝采和": {
|
||||||
|
"title": "情绪分析师",
|
||||||
|
"specialty": "市场情绪与舆情监控",
|
||||||
|
"description": "分析市场情绪和投资者行为模式",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "webull",
|
||||||
|
"company_overview": "seeking_alpha",
|
||||||
|
"market_movers": "webull",
|
||||||
|
"market_news": "seeking_alpha"
|
||||||
|
},
|
||||||
|
"data_priority": ["新闻情绪", "社交媒体", "投资者情绪"],
|
||||||
|
"api_weight": 0.11
|
||||||
|
},
|
||||||
|
"曹国舅": {
|
||||||
|
"title": "宏观分析师",
|
||||||
|
"specialty": "宏观经济与政策分析",
|
||||||
|
"description": "关注宏观经济环境和政策影响",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "seeking_alpha",
|
||||||
|
"company_overview": "seeking_alpha",
|
||||||
|
"market_movers": "yahoo_finance_15",
|
||||||
|
"market_news": "seeking_alpha"
|
||||||
|
},
|
||||||
|
"data_priority": ["宏观数据", "政策解读", "行业趋势"],
|
||||||
|
"api_weight": 0.12
|
||||||
|
},
|
||||||
|
"铁拐李": {
|
||||||
|
"title": "逆向投资专家",
|
||||||
|
"specialty": "价值发现与逆向思维",
|
||||||
|
"description": "寻找被低估的投资机会，逆向思考市场",
|
||||||
|
"preferred_apis": {
|
||||||
|
"stock_quote": "alpha_vantage",
|
||||||
|
"company_overview": "alpha_vantage",
|
||||||
|
"market_movers": "webull",
|
||||||
|
"market_news": "yahoo_finance_15"
|
||||||
|
},
|
||||||
|
"data_priority": ["估值偏差", "市场异常", "价值机会"],
|
||||||
|
"api_weight": 0.10
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"api_configurations": {
|
||||||
|
"alpha_vantage": {
|
||||||
|
"name": "Alpha Vantage",
|
||||||
|
"tier": "premium",
|
||||||
|
"strengths": ["实时数据", "财务数据", "技术指标"],
|
||||||
|
"rate_limits": {
|
||||||
|
"per_minute": 500,
|
||||||
|
"per_month": 500000
|
||||||
|
},
|
||||||
|
"reliability_score": 0.95,
|
||||||
|
"response_time_avg": 0.8,
|
||||||
|
"data_quality": "high",
|
||||||
|
"cost_per_call": 0.001
|
||||||
|
},
|
||||||
|
"yahoo_finance_15": {
|
||||||
|
"name": "Yahoo Finance 15",
|
||||||
|
"tier": "standard",
|
||||||
|
"strengths": ["市场数据", "新闻资讯", "实时报价"],
|
||||||
|
"rate_limits": {
|
||||||
|
"per_minute": 500,
|
||||||
|
"per_month": 500000
|
||||||
|
},
|
||||||
|
"reliability_score": 0.90,
|
||||||
|
"response_time_avg": 1.2,
|
||||||
|
"data_quality": "medium",
|
||||||
|
"cost_per_call": 0.0005
|
||||||
|
},
|
||||||
|
"webull": {
|
||||||
|
"name": "Webull",
|
||||||
|
"tier": "premium",
|
||||||
|
"strengths": ["搜索功能", "活跃数据", "技术分析"],
|
||||||
|
"rate_limits": {
|
||||||
|
"per_minute": 500,
|
||||||
|
"per_month": 500000
|
||||||
|
},
|
||||||
|
"reliability_score": 0.88,
|
||||||
|
"response_time_avg": 1.0,
|
||||||
|
"data_quality": "high",
|
||||||
|
"cost_per_call": 0.0008
|
||||||
|
},
|
||||||
|
"seeking_alpha": {
|
||||||
|
"name": "Seeking Alpha",
|
||||||
|
"tier": "standard",
|
||||||
|
"strengths": ["分析报告", "新闻资讯", "专业观点"],
|
||||||
|
"rate_limits": {
|
||||||
|
"per_minute": 500,
|
||||||
|
"per_month": 500000
|
||||||
|
},
|
||||||
|
"reliability_score": 0.85,
|
||||||
|
"response_time_avg": 1.5,
|
||||||
|
"data_quality": "medium",
|
||||||
|
"cost_per_call": 0.0006
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"load_balancing_strategies": {
|
||||||
|
"round_robin": {
|
||||||
|
"description": "轮询分配，确保负载均匀分布",
|
||||||
|
"enabled": true,
|
||||||
|
"weight_based": true
|
||||||
|
},
|
||||||
|
"health_aware": {
|
||||||
|
"description": "基于API健康状态的智能分配",
|
||||||
|
"enabled": true,
|
||||||
|
"health_check_interval": 300
|
||||||
|
},
|
||||||
|
"performance_based": {
|
||||||
|
"description": "基于响应时间的动态分配",
|
||||||
|
"enabled": true,
|
||||||
|
"response_time_threshold": 2.0
|
||||||
|
},
|
||||||
|
"cost_optimization": {
|
||||||
|
"description": "成本优化策略，优先使用低成本API",
|
||||||
|
"enabled": false,
|
||||||
|
"cost_threshold": 0.001
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"failover_matrix": {
|
||||||
|
"alpha_vantage": ["webull", "yahoo_finance_15", "seeking_alpha"],
|
||||||
|
"yahoo_finance_15": ["webull", "alpha_vantage", "seeking_alpha"],
|
||||||
|
"webull": ["alpha_vantage", "yahoo_finance_15", "seeking_alpha"],
|
||||||
|
"seeking_alpha": ["yahoo_finance_15", "alpha_vantage", "webull"]
|
||||||
|
},
|
||||||
|
"cache_settings": {
|
||||||
|
"enabled": true,
|
||||||
|
"ttl_seconds": 300,
|
||||||
|
"max_entries": 1000,
|
||||||
|
"cache_strategies": {
|
||||||
|
"stock_quote": 60,
|
||||||
|
"company_overview": 3600,
|
||||||
|
"market_movers": 300,
|
||||||
|
"market_news": 1800
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"monitoring": {
|
||||||
|
"enabled": true,
|
||||||
|
"metrics": [
|
||||||
|
"api_call_count",
|
||||||
|
"response_time",
|
||||||
|
"error_rate",
|
||||||
|
"cache_hit_rate",
|
||||||
|
"load_distribution"
|
||||||
|
],
|
||||||
|
"alerts": {
|
||||||
|
"high_error_rate": 0.1,
|
||||||
|
"slow_response_time": 3.0,
|
||||||
|
"api_unavailable": true
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,680 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
四AI团队协作通道系统
|
||||||
|
专为Qwen、Claude、Gemini、RovoDev四个AI设计的协作和通信平台
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import uuid
|
||||||
|
from typing import Dict, List, Any, Optional, Callable, Set
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from enum import Enum
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
import logging
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
class AIRole(Enum):
|
||||||
|
"""AI角色定义"""
|
||||||
|
QWEN = "Qwen" # 架构设计师
|
||||||
|
CLAUDE = "Claude" # 核心开发工程师
|
||||||
|
GEMINI = "Gemini" # 测试验证专家
|
||||||
|
ROVODEV = "RovoDev" # 项目整合专家
|
||||||
|
|
||||||
|
class CollaborationType(Enum):
|
||||||
|
"""协作类型"""
|
||||||
|
MAIN_CHANNEL = "主协作频道" # 主要协作讨论
|
||||||
|
ARCHITECTURE = "架构设计" # 架构相关讨论
|
||||||
|
IMPLEMENTATION = "代码实现" # 实现相关讨论
|
||||||
|
TESTING = "测试验证" # 测试相关讨论
|
||||||
|
INTEGRATION = "项目整合" # 整合相关讨论
|
||||||
|
CROSS_REVIEW = "交叉评审" # 跨角色评审
|
||||||
|
EMERGENCY = "紧急协调" # 紧急问题处理
|
||||||
|
|
||||||
|
class MessageType(Enum):
|
||||||
|
"""消息类型"""
|
||||||
|
PROPOSAL = "提案" # 提出建议
|
||||||
|
QUESTION = "询问" # 提出问题
|
||||||
|
ANSWER = "回答" # 回答问题
|
||||||
|
REVIEW = "评审" # 评审反馈
|
||||||
|
DECISION = "决策" # 做出决策
|
||||||
|
UPDATE = "更新" # 状态更新
|
||||||
|
ALERT = "警报" # 警报通知
|
||||||
|
HANDOFF = "交接" # 工作交接
|
||||||
|
|
||||||
|
class WorkPhase(Enum):
|
||||||
|
"""工作阶段"""
|
||||||
|
PLANNING = "规划阶段"
|
||||||
|
DESIGN = "设计阶段"
|
||||||
|
IMPLEMENTATION = "实现阶段"
|
||||||
|
TESTING = "测试阶段"
|
||||||
|
INTEGRATION = "整合阶段"
|
||||||
|
DELIVERY = "交付阶段"
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class AIMessage:
|
||||||
|
"""AI消息"""
|
||||||
|
id: str
|
||||||
|
sender: AIRole
|
||||||
|
receiver: Optional[AIRole] # None表示广播
|
||||||
|
content: str
|
||||||
|
message_type: MessageType
|
||||||
|
collaboration_type: CollaborationType
|
||||||
|
timestamp: datetime
|
||||||
|
work_phase: WorkPhase
|
||||||
|
priority: int = 1 # 1-5, 5最高
|
||||||
|
tags: List[str] = field(default_factory=list)
|
||||||
|
attachments: List[str] = field(default_factory=list) # 文件路径
|
||||||
|
references: List[str] = field(default_factory=list) # 引用的消息ID
|
||||||
|
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class CollaborationChannel:
|
||||||
|
"""协作频道"""
|
||||||
|
id: str
|
||||||
|
name: str
|
||||||
|
channel_type: CollaborationType
|
||||||
|
description: str
|
||||||
|
participants: Set[AIRole]
|
||||||
|
moderator: AIRole
|
||||||
|
is_active: bool = True
|
||||||
|
created_at: datetime = field(default_factory=datetime.now)
|
||||||
|
last_activity: datetime = field(default_factory=datetime.now)
|
||||||
|
message_history: List[AIMessage] = field(default_factory=list)
|
||||||
|
settings: Dict[str, Any] = field(default_factory=dict)
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class WorkflowRule:
|
||||||
|
"""工作流规则"""
|
||||||
|
id: str
|
||||||
|
name: str
|
||||||
|
description: str
|
||||||
|
trigger_phase: WorkPhase
|
||||||
|
trigger_conditions: Dict[str, Any]
|
||||||
|
action: str
|
||||||
|
target_ai: Optional[AIRole]
|
||||||
|
is_active: bool = True
|
||||||
|
|
||||||
|
class AITeamCollaboration:
|
||||||
|
"""四AI团队协作系统"""
|
||||||
|
|
||||||
|
def __init__(self, project_root: Path = None):
|
||||||
|
self.project_root = project_root or Path("/home/ben/github/liurenchaxin")
|
||||||
|
self.channels: Dict[str, CollaborationChannel] = {}
|
||||||
|
self.workflow_rules: Dict[str, WorkflowRule] = {}
|
||||||
|
self.current_phase: WorkPhase = WorkPhase.PLANNING
|
||||||
|
self.ai_status: Dict[AIRole, Dict[str, Any]] = {}
|
||||||
|
self.message_queue: List[AIMessage] = []
|
||||||
|
self.event_handlers: Dict[str, List[Callable]] = {}
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
# 初始化AI状态
|
||||||
|
self._initialize_ai_status()
|
||||||
|
|
||||||
|
# 初始化协作频道
|
||||||
|
self._initialize_channels()
|
||||||
|
|
||||||
|
# 初始化工作流规则
|
||||||
|
self._initialize_workflow_rules()
|
||||||
|
|
||||||
|
def _initialize_ai_status(self):
|
||||||
|
"""初始化AI状态"""
|
||||||
|
self.ai_status = {
|
||||||
|
AIRole.QWEN: {
|
||||||
|
"role": "架构设计师",
|
||||||
|
"specialty": "系统架构、技术选型、接口设计",
|
||||||
|
"current_task": "OpenBB集成架构设计",
|
||||||
|
"status": "ready",
|
||||||
|
"workload": 0,
|
||||||
|
"expertise_areas": ["架构设计", "系统集成", "性能优化"]
|
||||||
|
},
|
||||||
|
AIRole.CLAUDE: {
|
||||||
|
"role": "核心开发工程师",
|
||||||
|
"specialty": "代码实现、API开发、界面优化",
|
||||||
|
"current_task": "等待架构设计完成",
|
||||||
|
"status": "waiting",
|
||||||
|
"workload": 0,
|
||||||
|
"expertise_areas": ["Python开发", "Streamlit", "API集成"]
|
||||||
|
},
|
||||||
|
AIRole.GEMINI: {
|
||||||
|
"role": "测试验证专家",
|
||||||
|
"specialty": "功能测试、性能测试、质量保证",
|
||||||
|
"current_task": "制定测试策略",
|
||||||
|
"status": "ready",
|
||||||
|
"workload": 0,
|
||||||
|
"expertise_areas": ["自动化测试", "性能测试", "质量保证"]
|
||||||
|
},
|
||||||
|
AIRole.ROVODEV: {
|
||||||
|
"role": "项目整合专家",
|
||||||
|
"specialty": "项目管理、文档整合、协调统筹",
|
||||||
|
"current_task": "项目框架搭建",
|
||||||
|
"status": "active",
|
||||||
|
"workload": 0,
|
||||||
|
"expertise_areas": ["项目管理", "文档编写", "团队协调"]
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
def _initialize_channels(self):
|
||||||
|
"""初始化协作频道"""
|
||||||
|
channels_config = [
|
||||||
|
{
|
||||||
|
"id": "main_collaboration",
|
||||||
|
"name": "OpenBB集成主协作频道",
|
||||||
|
"channel_type": CollaborationType.MAIN_CHANNEL,
|
||||||
|
"description": "四AI主要协作讨论频道",
|
||||||
|
"participants": {AIRole.QWEN, AIRole.CLAUDE, AIRole.GEMINI, AIRole.ROVODEV},
|
||||||
|
"moderator": AIRole.ROVODEV,
|
||||||
|
"settings": {
|
||||||
|
"allow_broadcast": True,
|
||||||
|
"require_acknowledgment": True,
|
||||||
|
"auto_archive": False
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "architecture_design",
|
||||||
|
"name": "架构设计频道",
|
||||||
|
"channel_type": CollaborationType.ARCHITECTURE,
|
||||||
|
"description": "架构设计相关讨论",
|
||||||
|
"participants": {AIRole.QWEN, AIRole.CLAUDE, AIRole.ROVODEV},
|
||||||
|
"moderator": AIRole.QWEN,
|
||||||
|
"settings": {
|
||||||
|
"design_reviews": True,
|
||||||
|
"version_control": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "code_implementation",
|
||||||
|
"name": "代码实现频道",
|
||||||
|
"channel_type": CollaborationType.IMPLEMENTATION,
|
||||||
|
"description": "代码实现和开发讨论",
|
||||||
|
"participants": {AIRole.CLAUDE, AIRole.QWEN, AIRole.GEMINI},
|
||||||
|
"moderator": AIRole.CLAUDE,
|
||||||
|
"settings": {
|
||||||
|
"code_reviews": True,
|
||||||
|
"continuous_integration": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "testing_validation",
|
||||||
|
"name": "测试验证频道",
|
||||||
|
"channel_type": CollaborationType.TESTING,
|
||||||
|
"description": "测试策略和验证讨论",
|
||||||
|
"participants": {AIRole.GEMINI, AIRole.CLAUDE, AIRole.ROVODEV},
|
||||||
|
"moderator": AIRole.GEMINI,
|
||||||
|
"settings": {
|
||||||
|
"test_automation": True,
|
||||||
|
"quality_gates": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "project_integration",
|
||||||
|
"name": "项目整合频道",
|
||||||
|
"channel_type": CollaborationType.INTEGRATION,
|
||||||
|
"description": "项目整合和文档管理",
|
||||||
|
"participants": {AIRole.ROVODEV, AIRole.QWEN, AIRole.CLAUDE, AIRole.GEMINI},
|
||||||
|
"moderator": AIRole.ROVODEV,
|
||||||
|
"settings": {
|
||||||
|
"documentation_sync": True,
|
||||||
|
"release_management": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "cross_review",
|
||||||
|
"name": "交叉评审频道",
|
||||||
|
"channel_type": CollaborationType.CROSS_REVIEW,
|
||||||
|
"description": "跨角色工作评审",
|
||||||
|
"participants": {AIRole.QWEN, AIRole.CLAUDE, AIRole.GEMINI, AIRole.ROVODEV},
|
||||||
|
"moderator": AIRole.ROVODEV,
|
||||||
|
"settings": {
|
||||||
|
"peer_review": True,
|
||||||
|
"quality_assurance": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "emergency_coordination",
|
||||||
|
"name": "紧急协调频道",
|
||||||
|
"channel_type": CollaborationType.EMERGENCY,
|
||||||
|
"description": "紧急问题处理和快速响应",
|
||||||
|
"participants": {AIRole.QWEN, AIRole.CLAUDE, AIRole.GEMINI, AIRole.ROVODEV},
|
||||||
|
"moderator": AIRole.ROVODEV,
|
||||||
|
"settings": {
|
||||||
|
"high_priority": True,
|
||||||
|
"instant_notification": True,
|
||||||
|
"escalation_rules": True
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
for config in channels_config:
|
||||||
|
channel = CollaborationChannel(**config)
|
||||||
|
self.channels[channel.id] = channel
|
||||||
|
|
||||||
|
def _initialize_workflow_rules(self):
|
||||||
|
"""初始化工作流规则"""
|
||||||
|
rules_config = [
|
||||||
|
{
|
||||||
|
"id": "architecture_to_implementation",
|
||||||
|
"name": "架构完成通知实现开始",
|
||||||
|
"description": "当架构设计完成时，通知Claude开始实现",
|
||||||
|
"trigger_phase": WorkPhase.DESIGN,
|
||||||
|
"trigger_conditions": {"status": "architecture_complete"},
|
||||||
|
"action": "notify_implementation_start",
|
||||||
|
"target_ai": AIRole.CLAUDE
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "implementation_to_testing",
|
||||||
|
"name": "实现完成通知测试开始",
|
||||||
|
"description": "当代码实现完成时，通知Gemini开始测试",
|
||||||
|
"trigger_phase": WorkPhase.IMPLEMENTATION,
|
||||||
|
"trigger_conditions": {"status": "implementation_complete"},
|
||||||
|
"action": "notify_testing_start",
|
||||||
|
"target_ai": AIRole.GEMINI
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "testing_to_integration",
|
||||||
|
"name": "测试完成通知整合开始",
|
||||||
|
"description": "当测试验证完成时，通知RovoDev开始整合",
|
||||||
|
"trigger_phase": WorkPhase.TESTING,
|
||||||
|
"trigger_conditions": {"status": "testing_complete"},
|
||||||
|
"action": "notify_integration_start",
|
||||||
|
"target_ai": AIRole.ROVODEV
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
for config in rules_config:
|
||||||
|
rule = WorkflowRule(**config)
|
||||||
|
self.workflow_rules[rule.id] = rule
|
||||||
|
|
||||||
|
async def send_message(self,
|
||||||
|
sender: AIRole,
|
||||||
|
content: str,
|
||||||
|
message_type: MessageType,
|
||||||
|
channel_id: str,
|
||||||
|
receiver: Optional[AIRole] = None,
|
||||||
|
priority: int = 1,
|
||||||
|
attachments: List[str] = None,
|
||||||
|
tags: List[str] = None) -> str:
|
||||||
|
"""发送消息"""
|
||||||
|
|
||||||
|
if channel_id not in self.channels:
|
||||||
|
raise ValueError(f"频道 {channel_id} 不存在")
|
||||||
|
|
||||||
|
channel = self.channels[channel_id]
|
||||||
|
|
||||||
|
# 验证发送者权限
|
||||||
|
if sender not in channel.participants:
|
||||||
|
raise PermissionError(f"{sender.value} 不在频道 {channel.name} 中")
|
||||||
|
|
||||||
|
# 创建消息
|
||||||
|
message = AIMessage(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
sender=sender,
|
||||||
|
receiver=receiver,
|
||||||
|
content=content,
|
||||||
|
message_type=message_type,
|
||||||
|
collaboration_type=channel.channel_type,
|
||||||
|
timestamp=datetime.now(),
|
||||||
|
work_phase=self.current_phase,
|
||||||
|
priority=priority,
|
||||||
|
attachments=attachments or [],
|
||||||
|
tags=tags or []
|
||||||
|
)
|
||||||
|
|
||||||
|
# 添加到频道历史
|
||||||
|
channel.message_history.append(message)
|
||||||
|
channel.last_activity = datetime.now()
|
||||||
|
|
||||||
|
# 添加到消息队列
|
||||||
|
self.message_queue.append(message)
|
||||||
|
|
||||||
|
# 触发事件处理
|
||||||
|
await self._trigger_event("message_sent", {
|
||||||
|
"message": message,
|
||||||
|
"channel": channel
|
||||||
|
})
|
||||||
|
|
||||||
|
# 记录日志
|
||||||
|
self.logger.info(f"[{channel.name}] {sender.value} -> {receiver.value if receiver else 'ALL'}: {content[:50]}...")
|
||||||
|
|
||||||
|
return message.id
|
||||||
|
|
||||||
|
async def broadcast_message(self,
|
||||||
|
sender: AIRole,
|
||||||
|
content: str,
|
||||||
|
message_type: MessageType,
|
||||||
|
channel_id: str,
|
||||||
|
priority: int = 1,
|
||||||
|
tags: List[str] = None) -> str:
|
||||||
|
"""广播消息到频道所有参与者"""
|
||||||
|
return await self.send_message(
|
||||||
|
sender=sender,
|
||||||
|
content=content,
|
||||||
|
message_type=message_type,
|
||||||
|
channel_id=channel_id,
|
||||||
|
receiver=None, # None表示广播
|
||||||
|
priority=priority,
|
||||||
|
tags=tags
|
||||||
|
)
|
||||||
|
|
||||||
|
async def request_review(self,
|
||||||
|
sender: AIRole,
|
||||||
|
content: str,
|
||||||
|
reviewers: List[AIRole],
|
||||||
|
attachments: List[str] = None) -> str:
|
||||||
|
"""请求评审"""
|
||||||
|
|
||||||
|
# 发送到交叉评审频道
|
||||||
|
message_id = await self.send_message(
|
||||||
|
sender=sender,
|
||||||
|
content=f"📋 评审请求: {content}",
|
||||||
|
message_type=MessageType.REVIEW,
|
||||||
|
channel_id="cross_review",
|
||||||
|
priority=3,
|
||||||
|
attachments=attachments,
|
||||||
|
tags=["review_request"] + [f"reviewer_{reviewer.value}" for reviewer in reviewers]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 通知指定评审者
|
||||||
|
for reviewer in reviewers:
|
||||||
|
await self.send_message(
|
||||||
|
sender=AIRole.ROVODEV, # 系统通知
|
||||||
|
content=f"🔔 您有新的评审请求来自 {sender.value}，请查看交叉评审频道",
|
||||||
|
message_type=MessageType.ALERT,
|
||||||
|
channel_id="main_collaboration",
|
||||||
|
receiver=reviewer,
|
||||||
|
priority=3,
|
||||||
|
tags=["review_notification", f"from_{sender.value}", f"message_ref_{message_id}"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return message_id
|
||||||
|
|
||||||
|
async def handoff_work(self,
|
||||||
|
from_ai: AIRole,
|
||||||
|
to_ai: AIRole,
|
||||||
|
task_description: str,
|
||||||
|
deliverables: List[str],
|
||||||
|
notes: str = "") -> str:
|
||||||
|
"""工作交接"""
|
||||||
|
|
||||||
|
content = f"""
|
||||||
|
🔄 **工作交接**
|
||||||
|
**从**: {from_ai.value}
|
||||||
|
**到**: {to_ai.value}
|
||||||
|
**任务**: {task_description}
|
||||||
|
**交付物**: {', '.join(deliverables)}
|
||||||
|
**备注**: {notes}
|
||||||
|
"""
|
||||||
|
|
||||||
|
message_id = await self.send_message(
|
||||||
|
sender=from_ai,
|
||||||
|
content=content.strip(),
|
||||||
|
message_type=MessageType.HANDOFF,
|
||||||
|
channel_id="main_collaboration",
|
||||||
|
receiver=to_ai,
|
||||||
|
priority=4,
|
||||||
|
attachments=deliverables,
|
||||||
|
tags=["handoff", f"from_{from_ai.value}", f"to_{to_ai.value}"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 更新AI状态
|
||||||
|
self.ai_status[from_ai]["status"] = "completed_handoff"
|
||||||
|
self.ai_status[to_ai]["status"] = "received_handoff"
|
||||||
|
self.ai_status[to_ai]["current_task"] = task_description
|
||||||
|
|
||||||
|
return message_id
|
||||||
|
|
||||||
|
async def escalate_issue(self,
|
||||||
|
reporter: AIRole,
|
||||||
|
issue_description: str,
|
||||||
|
severity: str = "medium") -> str:
|
||||||
|
"""问题升级"""
|
||||||
|
|
||||||
|
content = f"""
|
||||||
|
🚨 **问题升级**
|
||||||
|
**报告者**: {reporter.value}
|
||||||
|
**严重程度**: {severity}
|
||||||
|
**问题描述**: {issue_description}
|
||||||
|
**时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
|
||||||
|
"""
|
||||||
|
|
||||||
|
priority_map = {"low": 2, "medium": 3, "high": 4, "critical": 5}
|
||||||
|
priority = priority_map.get(severity, 3)
|
||||||
|
|
||||||
|
return await self.send_message(
|
||||||
|
sender=reporter,
|
||||||
|
content=content.strip(),
|
||||||
|
message_type=MessageType.ALERT,
|
||||||
|
channel_id="emergency_coordination",
|
||||||
|
priority=priority,
|
||||||
|
tags=["escalation", f"severity_{severity}"]
|
||||||
|
)
|
||||||
|
|
||||||
|
def get_channel_summary(self, channel_id: str) -> Dict[str, Any]:
|
||||||
|
"""获取频道摘要"""
|
||||||
|
if channel_id not in self.channels:
|
||||||
|
return {}
|
||||||
|
|
||||||
|
channel = self.channels[channel_id]
|
||||||
|
recent_messages = channel.message_history[-10:] # 最近10条消息
|
||||||
|
|
||||||
|
return {
|
||||||
|
"channel_name": channel.name,
|
||||||
|
"channel_type": channel.channel_type.value,
|
||||||
|
"participants": [ai.value for ai in channel.participants],
|
||||||
|
"total_messages": len(channel.message_history),
|
||||||
|
"last_activity": channel.last_activity.isoformat(),
|
||||||
|
"recent_messages": [
|
||||||
|
{
|
||||||
|
"sender": msg.sender.value,
|
||||||
|
"content": msg.content[:100] + "..." if len(msg.content) > 100 else msg.content,
|
||||||
|
"timestamp": msg.timestamp.isoformat(),
|
||||||
|
"type": msg.message_type.value
|
||||||
|
}
|
||||||
|
for msg in recent_messages
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_ai_dashboard(self, ai_role: AIRole) -> Dict[str, Any]:
|
||||||
|
"""获取AI工作仪表板"""
|
||||||
|
status = self.ai_status[ai_role]
|
||||||
|
|
||||||
|
# 获取相关消息
|
||||||
|
relevant_messages = []
|
||||||
|
for channel in self.channels.values():
|
||||||
|
if ai_role in channel.participants:
|
||||||
|
for msg in channel.message_history[-5:]: # 每个频道最近5条
|
||||||
|
if msg.receiver == ai_role or msg.receiver is None:
|
||||||
|
relevant_messages.append({
|
||||||
|
"channel": channel.name,
|
||||||
|
"sender": msg.sender.value,
|
||||||
|
"content": msg.content[:100] + "..." if len(msg.content) > 100 else msg.content,
|
||||||
|
"timestamp": msg.timestamp.isoformat(),
|
||||||
|
"priority": msg.priority
|
||||||
|
})
|
||||||
|
|
||||||
|
# 按优先级和时间排序
|
||||||
|
relevant_messages.sort(key=lambda x: (x["priority"], x["timestamp"]), reverse=True)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"ai_role": ai_role.value,
|
||||||
|
"status": status,
|
||||||
|
"current_phase": self.current_phase.value,
|
||||||
|
"active_channels": [
|
||||||
|
channel.name for channel in self.channels.values()
|
||||||
|
if ai_role in channel.participants and channel.is_active
|
||||||
|
],
|
||||||
|
"recent_messages": relevant_messages[:10], # 最多10条
|
||||||
|
"pending_tasks": self._get_pending_tasks(ai_role),
|
||||||
|
"collaboration_stats": self._get_collaboration_stats(ai_role)
|
||||||
|
}
|
||||||
|
|
||||||
|
def _get_pending_tasks(self, ai_role: AIRole) -> List[Dict[str, Any]]:
|
||||||
|
"""获取待处理任务"""
|
||||||
|
tasks = []
|
||||||
|
|
||||||
|
# 扫描所有频道中针对该AI的消息
|
||||||
|
for channel in self.channels.values():
|
||||||
|
if ai_role in channel.participants:
|
||||||
|
for msg in channel.message_history:
|
||||||
|
if (msg.receiver == ai_role and
|
||||||
|
msg.message_type in [MessageType.QUESTION, MessageType.REVIEW, MessageType.HANDOFF] and
|
||||||
|
not self._is_task_completed(msg.id)):
|
||||||
|
tasks.append({
|
||||||
|
"task_id": msg.id,
|
||||||
|
"type": msg.message_type.value,
|
||||||
|
"description": msg.content[:100] + "..." if len(msg.content) > 100 else msg.content,
|
||||||
|
"from": msg.sender.value,
|
||||||
|
"channel": channel.name,
|
||||||
|
"priority": msg.priority,
|
||||||
|
"created": msg.timestamp.isoformat()
|
||||||
|
})
|
||||||
|
|
||||||
|
return sorted(tasks, key=lambda x: x["priority"], reverse=True)
|
||||||
|
|
||||||
|
def _get_collaboration_stats(self, ai_role: AIRole) -> Dict[str, Any]:
|
||||||
|
"""获取协作统计"""
|
||||||
|
total_messages = 0
|
||||||
|
messages_sent = 0
|
||||||
|
messages_received = 0
|
||||||
|
|
||||||
|
for channel in self.channels.values():
|
||||||
|
if ai_role in channel.participants:
|
||||||
|
for msg in channel.message_history:
|
||||||
|
total_messages += 1
|
||||||
|
if msg.sender == ai_role:
|
||||||
|
messages_sent += 1
|
||||||
|
elif msg.receiver == ai_role or msg.receiver is None:
|
||||||
|
messages_received += 1
|
||||||
|
|
||||||
|
return {
|
||||||
|
"total_messages": total_messages,
|
||||||
|
"messages_sent": messages_sent,
|
||||||
|
"messages_received": messages_received,
|
||||||
|
"active_channels": len([c for c in self.channels.values() if ai_role in c.participants]),
|
||||||
|
"collaboration_score": min(100, (messages_sent + messages_received) * 2) # 简单计分
|
||||||
|
}
|
||||||
|
|
||||||
|
def _is_task_completed(self, task_id: str) -> bool:
|
||||||
|
"""检查任务是否已完成"""
|
||||||
|
# 简单实现：检查是否有回复消息引用了该任务
|
||||||
|
for channel in self.channels.values():
|
||||||
|
for msg in channel.message_history:
|
||||||
|
if task_id in msg.references:
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
async def _trigger_event(self, event_type: str, event_data: Dict[str, Any]):
|
||||||
|
"""触发事件处理"""
|
||||||
|
if event_type in self.event_handlers:
|
||||||
|
for handler in self.event_handlers[event_type]:
|
||||||
|
try:
|
||||||
|
await handler(event_data)
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"事件处理器错误: {e}")
|
||||||
|
|
||||||
|
def add_event_handler(self, event_type: str, handler: Callable):
|
||||||
|
"""添加事件处理器"""
|
||||||
|
if event_type not in self.event_handlers:
|
||||||
|
self.event_handlers[event_type] = []
|
||||||
|
self.event_handlers[event_type].append(handler)
|
||||||
|
|
||||||
|
async def advance_phase(self, new_phase: WorkPhase):
|
||||||
|
"""推进工作阶段"""
|
||||||
|
old_phase = self.current_phase
|
||||||
|
self.current_phase = new_phase
|
||||||
|
|
||||||
|
# 广播阶段变更
|
||||||
|
await self.broadcast_message(
|
||||||
|
sender=AIRole.ROVODEV,
|
||||||
|
content=f"📈 项目阶段变更: {old_phase.value} → {new_phase.value}",
|
||||||
|
message_type=MessageType.UPDATE,
|
||||||
|
channel_id="main_collaboration",
|
||||||
|
priority=4,
|
||||||
|
tags=["phase_change"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 触发工作流规则
|
||||||
|
await self._check_workflow_rules()
|
||||||
|
|
||||||
|
async def _check_workflow_rules(self):
|
||||||
|
"""检查并执行工作流规则"""
|
||||||
|
for rule in self.workflow_rules.values():
|
||||||
|
if rule.is_active and rule.trigger_phase == self.current_phase:
|
||||||
|
await self._execute_workflow_action(rule)
|
||||||
|
|
||||||
|
async def _execute_workflow_action(self, rule: WorkflowRule):
|
||||||
|
"""执行工作流动作"""
|
||||||
|
if rule.action == "notify_implementation_start":
|
||||||
|
await self.send_message(
|
||||||
|
sender=AIRole.ROVODEV,
|
||||||
|
content=f"🚀 架构设计已完成，请开始代码实现工作。参考架构文档进行开发。",
|
||||||
|
message_type=MessageType.UPDATE,
|
||||||
|
channel_id="code_implementation",
|
||||||
|
receiver=rule.target_ai,
|
||||||
|
priority=3
|
||||||
|
)
|
||||||
|
elif rule.action == "notify_testing_start":
|
||||||
|
await self.send_message(
|
||||||
|
sender=AIRole.ROVODEV,
|
||||||
|
content=f"✅ 代码实现已完成，请开始测试验证工作。",
|
||||||
|
message_type=MessageType.UPDATE,
|
||||||
|
channel_id="testing_validation",
|
||||||
|
receiver=rule.target_ai,
|
||||||
|
priority=3
|
||||||
|
)
|
||||||
|
elif rule.action == "notify_integration_start":
|
||||||
|
await self.send_message(
|
||||||
|
sender=AIRole.ROVODEV,
|
||||||
|
content=f"🎯 测试验证已完成，请开始项目整合工作。",
|
||||||
|
message_type=MessageType.UPDATE,
|
||||||
|
channel_id="project_integration",
|
||||||
|
receiver=rule.target_ai,
|
||||||
|
priority=3
|
||||||
|
)
|
||||||
|
|
||||||
|
# 使用示例
|
||||||
|
async def demo_collaboration():
|
||||||
|
"""演示协作系统使用"""
|
||||||
|
collab = AITeamCollaboration()
|
||||||
|
|
||||||
|
# Qwen发起架构讨论
|
||||||
|
await collab.send_message(
|
||||||
|
sender=AIRole.QWEN,
|
||||||
|
content="大家好！我已经完成了OpenBB集成的初步架构设计，请大家review一下设计文档。",
|
||||||
|
message_type=MessageType.PROPOSAL,
|
||||||
|
channel_id="main_collaboration",
|
||||||
|
priority=3,
|
||||||
|
attachments=["docs/architecture/openbb_integration_architecture.md"],
|
||||||
|
tags=["architecture", "review_request"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# Claude回应
|
||||||
|
await collab.send_message(
|
||||||
|
sender=AIRole.CLAUDE,
|
||||||
|
content="架构设计看起来很不错！我有几个实现层面的问题...",
|
||||||
|
message_type=MessageType.QUESTION,
|
||||||
|
channel_id="architecture_design",
|
||||||
|
receiver=AIRole.QWEN,
|
||||||
|
priority=2
|
||||||
|
)
|
||||||
|
|
||||||
|
# 工作交接
|
||||||
|
await collab.handoff_work(
|
||||||
|
from_ai=AIRole.QWEN,
|
||||||
|
to_ai=AIRole.CLAUDE,
|
||||||
|
task_description="基于架构设计实现OpenBB核心引擎",
|
||||||
|
deliverables=["src/jixia/engines/enhanced_openbb_engine.py"],
|
||||||
|
notes="请特别注意八仙数据路由的实现"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 获取仪表板
|
||||||
|
dashboard = collab.get_ai_dashboard(AIRole.CLAUDE)
|
||||||
|
print(f"Claude的工作仪表板: {json.dumps(dashboard, indent=2, ensure_ascii=False)}")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 设置日志
|
||||||
|
logging.basicConfig(level=logging.INFO)
|
||||||
|
|
||||||
|
# 运行演示
|
||||||
|
asyncio.run(demo_collaboration())
|
||||||
|
|
@ -0,0 +1,685 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
多群聊协调系统
|
||||||
|
管理主辩论群、内部讨论群、策略会议群和Human干预群之间的协调
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from typing import Dict, List, Any, Optional, Callable
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from enum import Enum
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
import logging
|
||||||
|
|
||||||
|
class ChatType(Enum):
|
||||||
|
"""群聊类型"""
|
||||||
|
MAIN_DEBATE = "主辩论群" # 公开辩论
|
||||||
|
INTERNAL_DISCUSSION = "内部讨论群" # 团队内部讨论
|
||||||
|
STRATEGY_MEETING = "策略会议群" # 策略制定
|
||||||
|
HUMAN_INTERVENTION = "Human干预群" # 人工干预
|
||||||
|
OBSERVATION = "观察群" # 观察和记录
|
||||||
|
|
||||||
|
class MessagePriority(Enum):
|
||||||
|
"""消息优先级"""
|
||||||
|
LOW = 1
|
||||||
|
NORMAL = 2
|
||||||
|
HIGH = 3
|
||||||
|
URGENT = 4
|
||||||
|
CRITICAL = 5
|
||||||
|
|
||||||
|
class CoordinationAction(Enum):
|
||||||
|
"""协调动作"""
|
||||||
|
ESCALATE = "升级" # 升级到更高级别群聊
|
||||||
|
DELEGATE = "委派" # 委派到专门群聊
|
||||||
|
BROADCAST = "广播" # 广播到多个群聊
|
||||||
|
FILTER = "过滤" # 过滤不相关消息
|
||||||
|
MERGE = "合并" # 合并相关讨论
|
||||||
|
ARCHIVE = "归档" # 归档历史讨论
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ChatMessage:
|
||||||
|
"""群聊消息"""
|
||||||
|
id: str
|
||||||
|
chat_type: ChatType
|
||||||
|
sender: str
|
||||||
|
content: str
|
||||||
|
timestamp: datetime
|
||||||
|
priority: MessagePriority = MessagePriority.NORMAL
|
||||||
|
tags: List[str] = field(default_factory=list)
|
||||||
|
related_messages: List[str] = field(default_factory=list)
|
||||||
|
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ChatRoom:
|
||||||
|
"""群聊房间"""
|
||||||
|
id: str
|
||||||
|
chat_type: ChatType
|
||||||
|
name: str
|
||||||
|
description: str
|
||||||
|
participants: List[str] = field(default_factory=list)
|
||||||
|
moderators: List[str] = field(default_factory=list)
|
||||||
|
is_active: bool = True
|
||||||
|
created_at: datetime = field(default_factory=datetime.now)
|
||||||
|
last_activity: datetime = field(default_factory=datetime.now)
|
||||||
|
message_history: List[ChatMessage] = field(default_factory=list)
|
||||||
|
settings: Dict[str, Any] = field(default_factory=dict)
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class CoordinationRule:
|
||||||
|
"""协调规则"""
|
||||||
|
id: str
|
||||||
|
name: str
|
||||||
|
description: str
|
||||||
|
source_chat_types: List[ChatType]
|
||||||
|
target_chat_types: List[ChatType]
|
||||||
|
trigger_conditions: Dict[str, Any]
|
||||||
|
action: CoordinationAction
|
||||||
|
priority: int = 1
|
||||||
|
is_active: bool = True
|
||||||
|
created_at: datetime = field(default_factory=datetime.now)
|
||||||
|
|
||||||
|
class MultiChatCoordinator:
|
||||||
|
"""多群聊协调器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.chat_rooms: Dict[str, ChatRoom] = {}
|
||||||
|
self.coordination_rules: Dict[str, CoordinationRule] = {}
|
||||||
|
self.message_queue: List[ChatMessage] = []
|
||||||
|
self.event_handlers: Dict[str, List[Callable]] = {}
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
# 初始化默认群聊房间
|
||||||
|
self._initialize_default_rooms()
|
||||||
|
|
||||||
|
# 初始化默认协调规则
|
||||||
|
self._initialize_default_rules()
|
||||||
|
|
||||||
|
def _initialize_default_rooms(self):
|
||||||
|
"""初始化默认群聊房间"""
|
||||||
|
default_rooms = [
|
||||||
|
{
|
||||||
|
"id": "main_debate",
|
||||||
|
"chat_type": ChatType.MAIN_DEBATE,
|
||||||
|
"name": "主辩论群",
|
||||||
|
"description": "公开辩论的主要场所",
|
||||||
|
"participants": ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"],
|
||||||
|
"moderators": ["系统"],
|
||||||
|
"settings": {
|
||||||
|
"max_message_length": 500,
|
||||||
|
"speaking_time_limit": 120, # 秒
|
||||||
|
"auto_moderation": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "positive_internal",
|
||||||
|
"chat_type": ChatType.INTERNAL_DISCUSSION,
|
||||||
|
"name": "正方内部讨论群",
|
||||||
|
"description": "正方团队内部策略讨论",
|
||||||
|
"participants": ["正1", "正2", "正3", "正4"],
|
||||||
|
"moderators": ["正1"],
|
||||||
|
"settings": {
|
||||||
|
"privacy_level": "high",
|
||||||
|
"auto_archive": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "negative_internal",
|
||||||
|
"chat_type": ChatType.INTERNAL_DISCUSSION,
|
||||||
|
"name": "反方内部讨论群",
|
||||||
|
"description": "反方团队内部策略讨论",
|
||||||
|
"participants": ["反1", "反2", "反3", "反4"],
|
||||||
|
"moderators": ["反1"],
|
||||||
|
"settings": {
|
||||||
|
"privacy_level": "high",
|
||||||
|
"auto_archive": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "strategy_meeting",
|
||||||
|
"chat_type": ChatType.STRATEGY_MEETING,
|
||||||
|
"name": "策略会议群",
|
||||||
|
"description": "高级策略制定和决策",
|
||||||
|
"participants": ["正1", "反1", "系统"],
|
||||||
|
"moderators": ["系统"],
|
||||||
|
"settings": {
|
||||||
|
"meeting_mode": True,
|
||||||
|
"record_decisions": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "human_intervention",
|
||||||
|
"chat_type": ChatType.HUMAN_INTERVENTION,
|
||||||
|
"name": "Human干预群",
|
||||||
|
"description": "人工干预和监督",
|
||||||
|
"participants": ["Human", "系统"],
|
||||||
|
"moderators": ["Human"],
|
||||||
|
"settings": {
|
||||||
|
"alert_threshold": "high",
|
||||||
|
"auto_escalation": True
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "observation",
|
||||||
|
"chat_type": ChatType.OBSERVATION,
|
||||||
|
"name": "观察群",
|
||||||
|
"description": "观察和记录所有活动",
|
||||||
|
"participants": ["观察者", "记录员"],
|
||||||
|
"moderators": ["系统"],
|
||||||
|
"settings": {
|
||||||
|
"read_only": True,
|
||||||
|
"full_logging": True
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
for room_config in default_rooms:
|
||||||
|
room = ChatRoom(**room_config)
|
||||||
|
self.chat_rooms[room.id] = room
|
||||||
|
|
||||||
|
def _initialize_default_rules(self):
|
||||||
|
"""初始化默认协调规则"""
|
||||||
|
default_rules = [
|
||||||
|
{
|
||||||
|
"id": "escalate_urgent_to_human",
|
||||||
|
"name": "紧急情况升级到Human",
|
||||||
|
"description": "当检测到紧急情况时，自动升级到Human干预群",
|
||||||
|
"source_chat_types": [ChatType.MAIN_DEBATE, ChatType.INTERNAL_DISCUSSION],
|
||||||
|
"target_chat_types": [ChatType.HUMAN_INTERVENTION],
|
||||||
|
"trigger_conditions": {
|
||||||
|
"priority": MessagePriority.URGENT,
|
||||||
|
"keywords": ["紧急", "错误", "异常", "停止"]
|
||||||
|
},
|
||||||
|
"action": CoordinationAction.ESCALATE,
|
||||||
|
"priority": 1
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "strategy_to_internal",
|
||||||
|
"name": "策略决策分发到内部群",
|
||||||
|
"description": "将策略会议的决策分发到相关内部讨论群",
|
||||||
|
"source_chat_types": [ChatType.STRATEGY_MEETING],
|
||||||
|
"target_chat_types": [ChatType.INTERNAL_DISCUSSION],
|
||||||
|
"trigger_conditions": {
|
||||||
|
"tags": ["决策", "策略", "指令"]
|
||||||
|
},
|
||||||
|
"action": CoordinationAction.BROADCAST,
|
||||||
|
"priority": 2
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "filter_noise",
|
||||||
|
"name": "过滤噪音消息",
|
||||||
|
"description": "过滤低质量或无关的消息",
|
||||||
|
"source_chat_types": [ChatType.MAIN_DEBATE],
|
||||||
|
"target_chat_types": [],
|
||||||
|
"trigger_conditions": {
|
||||||
|
"priority": MessagePriority.LOW,
|
||||||
|
"content_length": {"max": 10}
|
||||||
|
},
|
||||||
|
"action": CoordinationAction.FILTER,
|
||||||
|
"priority": 3
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": "archive_old_discussions",
|
||||||
|
"name": "归档旧讨论",
|
||||||
|
"description": "自动归档超过时间限制的讨论",
|
||||||
|
"source_chat_types": [ChatType.INTERNAL_DISCUSSION],
|
||||||
|
"target_chat_types": [ChatType.OBSERVATION],
|
||||||
|
"trigger_conditions": {
|
||||||
|
"age_hours": 24,
|
||||||
|
"inactivity_hours": 2
|
||||||
|
},
|
||||||
|
"action": CoordinationAction.ARCHIVE,
|
||||||
|
"priority": 4
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
for rule_config in default_rules:
|
||||||
|
rule = CoordinationRule(**rule_config)
|
||||||
|
self.coordination_rules[rule.id] = rule
|
||||||
|
|
||||||
|
async def send_message(self, chat_id: str, sender: str, content: str,
|
||||||
|
priority: MessagePriority = MessagePriority.NORMAL,
|
||||||
|
tags: List[str] = None) -> ChatMessage:
|
||||||
|
"""发送消息到指定群聊"""
|
||||||
|
if chat_id not in self.chat_rooms:
|
||||||
|
raise ValueError(f"群聊 {chat_id} 不存在")
|
||||||
|
|
||||||
|
chat_room = self.chat_rooms[chat_id]
|
||||||
|
|
||||||
|
# 检查发送者权限（系统用户有特殊权限）
|
||||||
|
if sender != "系统" and sender not in chat_room.participants and sender not in chat_room.moderators:
|
||||||
|
raise PermissionError(f"用户 {sender} 没有权限在群聊 {chat_id} 中发言")
|
||||||
|
|
||||||
|
# 创建消息
|
||||||
|
message = ChatMessage(
|
||||||
|
id=f"{chat_id}_{datetime.now().timestamp()}",
|
||||||
|
chat_type=chat_room.chat_type,
|
||||||
|
sender=sender,
|
||||||
|
content=content,
|
||||||
|
timestamp=datetime.now(),
|
||||||
|
priority=priority,
|
||||||
|
tags=tags or []
|
||||||
|
)
|
||||||
|
|
||||||
|
# 添加到群聊历史
|
||||||
|
chat_room.message_history.append(message)
|
||||||
|
chat_room.last_activity = datetime.now()
|
||||||
|
|
||||||
|
# 添加到消息队列进行协调处理
|
||||||
|
self.message_queue.append(message)
|
||||||
|
|
||||||
|
# 触发事件处理
|
||||||
|
await self._trigger_event_handlers("message_sent", message)
|
||||||
|
|
||||||
|
# 处理协调规则
|
||||||
|
await self._process_coordination_rules(message)
|
||||||
|
|
||||||
|
self.logger.info(f"消息已发送到 {chat_id}: {sender} - {content[:50]}...")
|
||||||
|
|
||||||
|
return message
|
||||||
|
|
||||||
|
async def _process_coordination_rules(self, message: ChatMessage):
|
||||||
|
"""处理协调规则"""
|
||||||
|
for rule in self.coordination_rules.values():
|
||||||
|
if not rule.is_active:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 检查源群聊类型
|
||||||
|
if message.chat_type not in rule.source_chat_types:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 检查触发条件
|
||||||
|
if await self._check_trigger_conditions(message, rule.trigger_conditions):
|
||||||
|
await self._execute_coordination_action(message, rule)
|
||||||
|
|
||||||
|
async def _check_trigger_conditions(self, message: ChatMessage, conditions: Dict[str, Any]) -> bool:
|
||||||
|
"""检查触发条件"""
|
||||||
|
# 检查优先级
|
||||||
|
if "priority" in conditions:
|
||||||
|
if message.priority != conditions["priority"]:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 检查关键词
|
||||||
|
if "keywords" in conditions:
|
||||||
|
keywords = conditions["keywords"]
|
||||||
|
if not any(keyword in message.content for keyword in keywords):
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 检查标签
|
||||||
|
if "tags" in conditions:
|
||||||
|
required_tags = conditions["tags"]
|
||||||
|
if not any(tag in message.tags for tag in required_tags):
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 检查内容长度
|
||||||
|
if "content_length" in conditions:
|
||||||
|
length_rules = conditions["content_length"]
|
||||||
|
content_length = len(message.content)
|
||||||
|
|
||||||
|
if "min" in length_rules and content_length < length_rules["min"]:
|
||||||
|
return False
|
||||||
|
if "max" in length_rules and content_length > length_rules["max"]:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 检查消息年龄
|
||||||
|
if "age_hours" in conditions:
|
||||||
|
age_limit = timedelta(hours=conditions["age_hours"])
|
||||||
|
if datetime.now() - message.timestamp > age_limit:
|
||||||
|
return True
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
async def _execute_coordination_action(self, message: ChatMessage, rule: CoordinationRule):
|
||||||
|
"""执行协调动作"""
|
||||||
|
action = rule.action
|
||||||
|
|
||||||
|
if action == CoordinationAction.ESCALATE:
|
||||||
|
await self._escalate_message(message, rule.target_chat_types)
|
||||||
|
|
||||||
|
elif action == CoordinationAction.BROADCAST:
|
||||||
|
await self._broadcast_message(message, rule.target_chat_types)
|
||||||
|
|
||||||
|
elif action == CoordinationAction.FILTER:
|
||||||
|
await self._filter_message(message)
|
||||||
|
|
||||||
|
elif action == CoordinationAction.ARCHIVE:
|
||||||
|
await self._archive_message(message, rule.target_chat_types)
|
||||||
|
|
||||||
|
elif action == CoordinationAction.DELEGATE:
|
||||||
|
await self._delegate_message(message, rule.target_chat_types)
|
||||||
|
|
||||||
|
elif action == CoordinationAction.MERGE:
|
||||||
|
await self._merge_discussions(message)
|
||||||
|
|
||||||
|
self.logger.info(f"执行协调动作 {action.value} for message {message.id}")
|
||||||
|
|
||||||
|
async def _escalate_message(self, message: ChatMessage, target_chat_types: List[ChatType]):
|
||||||
|
"""升级消息到更高级别群聊"""
|
||||||
|
for chat_type in target_chat_types:
|
||||||
|
target_rooms = [room for room in self.chat_rooms.values()
|
||||||
|
if room.chat_type == chat_type and room.is_active]
|
||||||
|
|
||||||
|
for room in target_rooms:
|
||||||
|
escalated_content = f"🚨 [升级消息] 来自 {message.chat_type.value}\n" \
|
||||||
|
f"发送者: {message.sender}\n" \
|
||||||
|
f"内容: {message.content}\n" \
|
||||||
|
f"时间: {message.timestamp}"
|
||||||
|
|
||||||
|
await self.send_message(
|
||||||
|
room.id, "系统", escalated_content,
|
||||||
|
MessagePriority.URGENT, ["升级", "自动"]
|
||||||
|
)
|
||||||
|
|
||||||
|
async def _broadcast_message(self, message: ChatMessage, target_chat_types: List[ChatType]):
|
||||||
|
"""广播消息到多个群聊"""
|
||||||
|
for chat_type in target_chat_types:
|
||||||
|
target_rooms = [room for room in self.chat_rooms.values()
|
||||||
|
if room.chat_type == chat_type and room.is_active]
|
||||||
|
|
||||||
|
for room in target_rooms:
|
||||||
|
broadcast_content = f"📢 [广播消息] 来自 {message.chat_type.value}\n" \
|
||||||
|
f"{message.content}"
|
||||||
|
|
||||||
|
await self.send_message(
|
||||||
|
room.id, "系统", broadcast_content,
|
||||||
|
message.priority, message.tags + ["广播"]
|
||||||
|
)
|
||||||
|
|
||||||
|
async def _filter_message(self, message: ChatMessage):
|
||||||
|
"""过滤消息"""
|
||||||
|
# 标记消息为已过滤
|
||||||
|
message.metadata["filtered"] = True
|
||||||
|
message.metadata["filter_reason"] = "低质量或无关内容"
|
||||||
|
|
||||||
|
self.logger.info(f"消息 {message.id} 已被过滤")
|
||||||
|
|
||||||
|
async def _archive_message(self, message: ChatMessage, target_chat_types: List[ChatType]):
|
||||||
|
"""归档消息"""
|
||||||
|
for chat_type in target_chat_types:
|
||||||
|
target_rooms = [room for room in self.chat_rooms.values()
|
||||||
|
if room.chat_type == chat_type and room.is_active]
|
||||||
|
|
||||||
|
for room in target_rooms:
|
||||||
|
archive_content = f"📁 [归档消息] 来自 {message.chat_type.value}\n" \
|
||||||
|
f"原始内容: {message.content}\n" \
|
||||||
|
f"归档时间: {datetime.now()}"
|
||||||
|
|
||||||
|
await self.send_message(
|
||||||
|
room.id, "系统", archive_content,
|
||||||
|
MessagePriority.LOW, ["归档", "历史"]
|
||||||
|
)
|
||||||
|
|
||||||
|
async def _delegate_message(self, message: ChatMessage, target_chat_types: List[ChatType]):
|
||||||
|
"""委派消息到专门群聊"""
|
||||||
|
# 类似于广播，但会移除原消息
|
||||||
|
await self._broadcast_message(message, target_chat_types)
|
||||||
|
|
||||||
|
# 标记原消息为已委派
|
||||||
|
message.metadata["delegated"] = True
|
||||||
|
|
||||||
|
async def _merge_discussions(self, message: ChatMessage):
|
||||||
|
"""合并相关讨论"""
|
||||||
|
# 查找相关消息
|
||||||
|
related_messages = self._find_related_messages(message)
|
||||||
|
|
||||||
|
# 创建合并讨论摘要
|
||||||
|
if related_messages:
|
||||||
|
summary = self._create_discussion_summary(message, related_messages)
|
||||||
|
|
||||||
|
# 发送摘要到策略会议群
|
||||||
|
strategy_rooms = [room for room in self.chat_rooms.values()
|
||||||
|
if room.chat_type == ChatType.STRATEGY_MEETING]
|
||||||
|
|
||||||
|
for room in strategy_rooms:
|
||||||
|
await self.send_message(
|
||||||
|
room.id, "系统", summary,
|
||||||
|
MessagePriority.HIGH, ["合并", "摘要"]
|
||||||
|
)
|
||||||
|
|
||||||
|
def _find_related_messages(self, message: ChatMessage) -> List[ChatMessage]:
|
||||||
|
"""查找相关消息"""
|
||||||
|
related = []
|
||||||
|
|
||||||
|
# 简单的相关性检测：相同标签或关键词
|
||||||
|
for room in self.chat_rooms.values():
|
||||||
|
for msg in room.message_history[-10:]: # 检查最近10条消息
|
||||||
|
if msg.id != message.id:
|
||||||
|
# 检查标签重叠
|
||||||
|
if set(msg.tags) & set(message.tags):
|
||||||
|
related.append(msg)
|
||||||
|
|
||||||
|
# 检查内容相似性（简单关键词匹配）
|
||||||
|
elif self._calculate_content_similarity(msg.content, message.content) > 0.3:
|
||||||
|
related.append(msg)
|
||||||
|
|
||||||
|
return related
|
||||||
|
|
||||||
|
def _calculate_content_similarity(self, content1: str, content2: str) -> float:
|
||||||
|
"""计算内容相似性"""
|
||||||
|
words1 = set(content1.split())
|
||||||
|
words2 = set(content2.split())
|
||||||
|
|
||||||
|
if not words1 or not words2:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
intersection = words1 & words2
|
||||||
|
union = words1 | words2
|
||||||
|
|
||||||
|
return len(intersection) / len(union)
|
||||||
|
|
||||||
|
def _create_discussion_summary(self, main_message: ChatMessage, related_messages: List[ChatMessage]) -> str:
|
||||||
|
"""创建讨论摘要"""
|
||||||
|
summary = f"📋 讨论摘要\n"
|
||||||
|
summary += f"主要消息: {main_message.sender} - {main_message.content[:100]}...\n"
|
||||||
|
summary += f"相关消息数量: {len(related_messages)}\n\n"
|
||||||
|
|
||||||
|
summary += "相关讨论:\n"
|
||||||
|
for i, msg in enumerate(related_messages[:5], 1): # 最多显示5条
|
||||||
|
summary += f"{i}. {msg.sender}: {msg.content[:50]}...\n"
|
||||||
|
|
||||||
|
return summary
|
||||||
|
|
||||||
|
async def _trigger_event_handlers(self, event_type: str, data: Any):
|
||||||
|
"""触发事件处理器"""
|
||||||
|
if event_type in self.event_handlers:
|
||||||
|
for handler in self.event_handlers[event_type]:
|
||||||
|
try:
|
||||||
|
await handler(data)
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"事件处理器错误: {e}")
|
||||||
|
|
||||||
|
def add_event_handler(self, event_type: str, handler: Callable):
|
||||||
|
"""添加事件处理器"""
|
||||||
|
if event_type not in self.event_handlers:
|
||||||
|
self.event_handlers[event_type] = []
|
||||||
|
self.event_handlers[event_type].append(handler)
|
||||||
|
|
||||||
|
async def handle_message(self, message_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
"""处理消息（兼容性方法）"""
|
||||||
|
try:
|
||||||
|
chat_id = message_data.get("chat_id", "main_debate")
|
||||||
|
speaker = message_data.get("speaker", "未知用户")
|
||||||
|
content = message_data.get("content", "")
|
||||||
|
priority = MessagePriority.NORMAL
|
||||||
|
|
||||||
|
# 发送消息
|
||||||
|
message = await self.send_message(chat_id, speaker, content, priority)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"message_id": message.id,
|
||||||
|
"processed_at": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"处理消息失败: {e}")
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": str(e),
|
||||||
|
"processed_at": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_routing_status(self) -> Dict[str, Any]:
|
||||||
|
"""获取路由状态（兼容性方法）"""
|
||||||
|
return {
|
||||||
|
"active_routes": len(self.coordination_rules),
|
||||||
|
"message_queue_size": len(self.message_queue),
|
||||||
|
"total_rooms": len(self.chat_rooms)
|
||||||
|
}
|
||||||
|
|
||||||
|
async def coordinate_response(self, message_data: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
"""协调响应（兼容性方法）"""
|
||||||
|
try:
|
||||||
|
# 基于上下文决定响应策略
|
||||||
|
stage = context.get("stage", "起")
|
||||||
|
topic = context.get("topic", "未知主题")
|
||||||
|
|
||||||
|
# 模拟协调决策
|
||||||
|
coordination_decision = {
|
||||||
|
"recommended_action": "继续讨论",
|
||||||
|
"target_chat": "main_debate",
|
||||||
|
"priority": "normal",
|
||||||
|
"reasoning": f"基于当前阶段({stage})和主题({topic})的协调决策"
|
||||||
|
}
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"coordination": coordination_decision,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
except Exception as e:
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": str(e),
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_chat_status(self) -> Dict[str, Any]:
|
||||||
|
"""获取群聊状态"""
|
||||||
|
status = {
|
||||||
|
"total_rooms": len(self.chat_rooms),
|
||||||
|
"active_rooms": len([r for r in self.chat_rooms.values() if r.is_active]),
|
||||||
|
"total_messages": sum(len(r.message_history) for r in self.chat_rooms.values()),
|
||||||
|
"pending_messages": len(self.message_queue),
|
||||||
|
"coordination_rules": len(self.coordination_rules),
|
||||||
|
"active_rules": len([r for r in self.coordination_rules.values() if r.is_active]),
|
||||||
|
"rooms": {
|
||||||
|
room_id: {
|
||||||
|
"name": room.name,
|
||||||
|
"type": room.chat_type.value,
|
||||||
|
"participants": len(room.participants),
|
||||||
|
"messages": len(room.message_history),
|
||||||
|
"last_activity": room.last_activity.isoformat(),
|
||||||
|
"is_active": room.is_active
|
||||||
|
}
|
||||||
|
for room_id, room in self.chat_rooms.items()
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
return status
|
||||||
|
|
||||||
|
def save_coordination_data(self, filename: str = "coordination_data.json"):
|
||||||
|
"""保存协调数据"""
|
||||||
|
# 自定义JSON序列化函数
|
||||||
|
def serialize_trigger_conditions(conditions):
|
||||||
|
serialized = {}
|
||||||
|
for key, value in conditions.items():
|
||||||
|
if isinstance(value, MessagePriority):
|
||||||
|
serialized[key] = value.value
|
||||||
|
else:
|
||||||
|
serialized[key] = value
|
||||||
|
return serialized
|
||||||
|
|
||||||
|
data = {
|
||||||
|
"chat_rooms": {
|
||||||
|
room_id: {
|
||||||
|
"id": room.id,
|
||||||
|
"chat_type": room.chat_type.value,
|
||||||
|
"name": room.name,
|
||||||
|
"description": room.description,
|
||||||
|
"participants": room.participants,
|
||||||
|
"moderators": room.moderators,
|
||||||
|
"is_active": room.is_active,
|
||||||
|
"created_at": room.created_at.isoformat(),
|
||||||
|
"last_activity": room.last_activity.isoformat(),
|
||||||
|
"settings": room.settings,
|
||||||
|
"message_count": len(room.message_history)
|
||||||
|
}
|
||||||
|
for room_id, room in self.chat_rooms.items()
|
||||||
|
},
|
||||||
|
"coordination_rules": {
|
||||||
|
rule_id: {
|
||||||
|
"id": rule.id,
|
||||||
|
"name": rule.name,
|
||||||
|
"description": rule.description,
|
||||||
|
"source_chat_types": [ct.value for ct in rule.source_chat_types],
|
||||||
|
"target_chat_types": [ct.value for ct in rule.target_chat_types],
|
||||||
|
"trigger_conditions": serialize_trigger_conditions(rule.trigger_conditions),
|
||||||
|
"action": rule.action.value,
|
||||||
|
"priority": rule.priority,
|
||||||
|
"is_active": rule.is_active,
|
||||||
|
"created_at": rule.created_at.isoformat()
|
||||||
|
}
|
||||||
|
for rule_id, rule in self.coordination_rules.items()
|
||||||
|
},
|
||||||
|
"status": self.get_chat_status(),
|
||||||
|
"export_time": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(filename, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
self.logger.info(f"协调数据已保存到 {filename}")
|
||||||
|
|
||||||
|
# 使用示例
|
||||||
|
async def main():
|
||||||
|
"""使用示例"""
|
||||||
|
coordinator = MultiChatCoordinator()
|
||||||
|
|
||||||
|
# 发送一些测试消息
|
||||||
|
await coordinator.send_message(
|
||||||
|
"main_debate", "正1",
|
||||||
|
"我认为AI投资具有巨大的潜力和价值",
|
||||||
|
MessagePriority.NORMAL, ["观点", "AI"]
|
||||||
|
)
|
||||||
|
|
||||||
|
await coordinator.send_message(
|
||||||
|
"main_debate", "反1",
|
||||||
|
"但是AI投资的风险也不容忽视",
|
||||||
|
MessagePriority.NORMAL, ["反驳", "风险"]
|
||||||
|
)
|
||||||
|
|
||||||
|
await coordinator.send_message(
|
||||||
|
"positive_internal", "正2",
|
||||||
|
"我们需要准备更强有力的数据支持",
|
||||||
|
MessagePriority.HIGH, ["策略", "数据"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 模拟紧急情况
|
||||||
|
await coordinator.send_message(
|
||||||
|
"main_debate", "正3",
|
||||||
|
"系统出现异常，需要紧急处理",
|
||||||
|
MessagePriority.URGENT, ["紧急", "系统"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 显示状态
|
||||||
|
status = coordinator.get_chat_status()
|
||||||
|
print("\n📊 群聊协调系统状态:")
|
||||||
|
print(f"总群聊数: {status['total_rooms']}")
|
||||||
|
print(f"活跃群聊数: {status['active_rooms']}")
|
||||||
|
print(f"总消息数: {status['total_messages']}")
|
||||||
|
print(f"待处理消息: {status['pending_messages']}")
|
||||||
|
|
||||||
|
print("\n📋 群聊详情:")
|
||||||
|
for room_id, room_info in status['rooms'].items():
|
||||||
|
print(f" {room_info['name']} ({room_info['type']})")
|
||||||
|
print(f" 参与者: {room_info['participants']} 人")
|
||||||
|
print(f" 消息数: {room_info['messages']}")
|
||||||
|
print(f" 最后活动: {room_info['last_activity']}")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# 保存数据
|
||||||
|
coordinator.save_coordination_data()
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,295 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫 ADK Memory Bank 论道系统
|
||||||
|
实现带有记忆银行的八仙智能体辩论
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import asyncio
|
||||||
|
from google.adk import Agent, Runner
|
||||||
|
from google.adk.sessions import InMemorySessionService
|
||||||
|
from google.adk.memory import VertexAiMemoryBankService
|
||||||
|
from google.adk.memory.memory_entry import MemoryEntry
|
||||||
|
from google.genai import types
|
||||||
|
import json
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, List, Optional
|
||||||
|
|
||||||
|
class BaxianMemoryManager:
|
||||||
|
"""八仙记忆管理器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.memory_services: Dict[str, VertexAiMemoryBankService] = {}
|
||||||
|
self.agents: Dict[str, Agent] = {}
|
||||||
|
|
||||||
|
async def initialize_baxian_agents(self):
|
||||||
|
"""初始化八仙智能体及其记忆银行"""
|
||||||
|
# 从环境变量获取项目ID和位置
|
||||||
|
project_id = os.getenv('GOOGLE_CLOUD_PROJECT_ID')
|
||||||
|
location = os.getenv('GOOGLE_CLOUD_LOCATION', 'us-central1')
|
||||||
|
|
||||||
|
if not project_id:
|
||||||
|
raise ValueError("未设置 GOOGLE_CLOUD_PROJECT_ID 环境变量")
|
||||||
|
|
||||||
|
# 八仙角色配置
|
||||||
|
baxian_config = {
|
||||||
|
"铁拐李": {
|
||||||
|
"instruction": "你是铁拐李，八仙中的逆向思维专家。你善于从批判和质疑的角度看问题，总是能发现事物的另一面。你会从你的记忆中回忆相关的逆向投资案例和失败教训。",
|
||||||
|
"memory_context": "逆向投资案例、市场泡沫警告、风险识别经验"
|
||||||
|
},
|
||||||
|
"吕洞宾": {
|
||||||
|
"instruction": "你是吕洞宾，八仙中的理性分析者。你善于平衡各方观点，用理性和逻辑来分析问题。你会从记忆中调用技术分析的成功案例和理论知识。",
|
||||||
|
"memory_context": "技术分析理论、成功预测案例、市场趋势分析"
|
||||||
|
},
|
||||||
|
"何仙姑": {
|
||||||
|
"instruction": "你是何仙姑，八仙中的风险控制专家。你总是从风险管理的角度思考问题，善于发现潜在危险。你会回忆历史上的重大风险事件。",
|
||||||
|
"memory_context": "风险管理案例、黑天鹅事件、危机预警经验"
|
||||||
|
},
|
||||||
|
"张果老": {
|
||||||
|
"instruction": "你是张果老，八仙中的历史智慧者。你善于从历史数据中寻找规律和智慧，总是能提供长期视角。你会从记忆中调用历史数据和长期趋势。",
|
||||||
|
"memory_context": "历史市场数据、长期投资趋势、周期性规律"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 为每个仙人创建智能体和记忆服务
|
||||||
|
for name, config in baxian_config.items():
|
||||||
|
# 创建记忆服务
|
||||||
|
memory_service = VertexAiMemoryBankService(
|
||||||
|
project=project_id,
|
||||||
|
location=location
|
||||||
|
)
|
||||||
|
|
||||||
|
# 初始化记忆内容
|
||||||
|
await self._initialize_agent_memory(memory_service, name, config['memory_context'])
|
||||||
|
|
||||||
|
# 创建智能体
|
||||||
|
agent = Agent(
|
||||||
|
name=name,
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction=f"{config['instruction']} 在回答时，请先从你的记忆银行中检索相关信息，然后结合当前话题给出回应。",
|
||||||
|
memory_service=memory_service
|
||||||
|
)
|
||||||
|
|
||||||
|
self.memory_services[name] = memory_service
|
||||||
|
self.agents[name] = agent
|
||||||
|
|
||||||
|
print(f"✅ 已初始化 {len(self.agents)} 个八仙智能体及其记忆服务")
|
||||||
|
|
||||||
|
async def _initialize_agent_memory(self, memory_service: VertexAiMemoryBankService, agent_name: str, context: str):
|
||||||
|
"""为智能体初始化记忆内容"""
|
||||||
|
|
||||||
|
# 根据角色添加初始记忆
|
||||||
|
initial_memories = {
|
||||||
|
"铁拐李": [
|
||||||
|
"2000年互联网泡沫破裂，许多高估值科技股暴跌90%以上",
|
||||||
|
"2008年金融危机前，房地产市场过度繁荣，逆向思维者提前撤离",
|
||||||
|
"比特币从2万美元跌到3千美元，提醒我们任何资产都可能大幅回调",
|
||||||
|
"巴菲特说过：别人贪婪时我恐惧，别人恐惧时我贪婪"
|
||||||
|
],
|
||||||
|
"吕洞宾": [
|
||||||
|
"移动平均线交叉是经典的技术分析信号",
|
||||||
|
"RSI指标超过70通常表示超买，低于30表示超卖",
|
||||||
|
"支撑位和阻力位是技术分析的核心概念",
|
||||||
|
"成功的技术分析需要结合多个指标综合判断"
|
||||||
|
],
|
||||||
|
"何仙姑": [
|
||||||
|
"2008年雷曼兄弟倒闭引发全球金融危机",
|
||||||
|
"长期资本管理公司(LTCM)的失败说明了风险管理的重要性",
|
||||||
|
"分散投资是降低风险的基本原则",
|
||||||
|
"黑天鹅事件虽然罕见但影响巨大，需要提前准备"
|
||||||
|
],
|
||||||
|
"张果老": [
|
||||||
|
"股市存在7-10年的长期周期",
|
||||||
|
"康德拉季耶夫长波理论描述了50-60年的经济周期",
|
||||||
|
"历史上每次重大技术革命都带来新的投资机会",
|
||||||
|
"长期来看，优质资产总是向上的"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
memories = initial_memories.get(agent_name, [])
|
||||||
|
for memory_text in memories:
|
||||||
|
memory_entry = MemoryEntry(
|
||||||
|
content=memory_text,
|
||||||
|
metadata={
|
||||||
|
"agent": agent_name,
|
||||||
|
"type": "historical_knowledge",
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
)
|
||||||
|
# 注意：VertexAiMemoryBankService 的 add_memory 方法可能需要不同的参数
|
||||||
|
# 这里假设它有一个类似的方法
|
||||||
|
await memory_service.add_memory(memory_entry)
|
||||||
|
|
||||||
|
async def add_debate_memory(self, agent_name: str, content: str, topic: str):
|
||||||
|
"""为智能体添加辩论记忆"""
|
||||||
|
if agent_name in self.memory_services:
|
||||||
|
memory_entry = MemoryEntry(
|
||||||
|
content=content,
|
||||||
|
metadata={
|
||||||
|
"agent": agent_name,
|
||||||
|
"type": "debate_history",
|
||||||
|
"topic": topic,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
)
|
||||||
|
# 注意：VertexAiMemoryBankService 的 add_memory 方法可能需要不同的参数
|
||||||
|
# 这里假设它有一个类似的方法
|
||||||
|
await self.memory_services[agent_name].add_memory(memory_entry)
|
||||||
|
|
||||||
|
async def retrieve_relevant_memories(self, agent_name: str, query: str, limit: int = 3) -> List[str]:
|
||||||
|
"""检索智能体的相关记忆"""
|
||||||
|
if agent_name not in self.memory_services:
|
||||||
|
return []
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 注意：VertexAiMemoryBankService 的 search 方法可能需要不同的参数
|
||||||
|
# 这里假设它有一个类似的方法
|
||||||
|
memories = await self.memory_services[agent_name].search(query, limit=limit)
|
||||||
|
return [memory.content for memory in memories]
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 记忆检索失败 ({agent_name}): {e}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
class MemoryEnhancedDebate:
|
||||||
|
"""带记忆增强的辩论系统"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.memory_manager = BaxianMemoryManager()
|
||||||
|
self.session_service = InMemorySessionService()
|
||||||
|
self.runners: Dict[str, Runner] = {}
|
||||||
|
|
||||||
|
async def initialize(self):
|
||||||
|
"""初始化辩论系统"""
|
||||||
|
await self.memory_manager.initialize_baxian_agents()
|
||||||
|
|
||||||
|
# 创建会话
|
||||||
|
self.session = await self.session_service.create_session(
|
||||||
|
state={},
|
||||||
|
app_name="稷下学宫记忆增强论道系统",
|
||||||
|
user_id="memory_debate_user"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 为每个智能体创建Runner
|
||||||
|
for name, agent in self.memory_manager.agents.items():
|
||||||
|
runner = Runner(
|
||||||
|
app_name="稷下学宫记忆增强论道系统",
|
||||||
|
agent=agent,
|
||||||
|
session_service=self.session_service
|
||||||
|
)
|
||||||
|
self.runners[name] = runner
|
||||||
|
|
||||||
|
async def conduct_memory_debate(self, topic: str, participants: List[str] = None):
|
||||||
|
"""进行带记忆的辩论"""
|
||||||
|
if participants is None:
|
||||||
|
participants = ["铁拐李", "吕洞宾", "何仙姑", "张果老"]
|
||||||
|
|
||||||
|
print(f"\n🎭 稷下学宫记忆增强论道开始...")
|
||||||
|
print(f"📋 论道主题: {topic}")
|
||||||
|
print(f"🎯 参与仙人: {', '.join(participants)}")
|
||||||
|
|
||||||
|
debate_history = []
|
||||||
|
|
||||||
|
for round_num in range(2): # 进行2轮辩论
|
||||||
|
print(f"\n🔄 第 {round_num + 1} 轮论道:")
|
||||||
|
|
||||||
|
for participant in participants:
|
||||||
|
if participant not in self.runners:
|
||||||
|
continue
|
||||||
|
|
||||||
|
print(f"\n🗣️ {participant} 发言:")
|
||||||
|
|
||||||
|
# 检索相关记忆
|
||||||
|
relevant_memories = await self.memory_manager.retrieve_relevant_memories(
|
||||||
|
participant, topic, limit=2
|
||||||
|
)
|
||||||
|
|
||||||
|
# 构建包含记忆的提示
|
||||||
|
memory_context = ""
|
||||||
|
if relevant_memories:
|
||||||
|
memory_context = f"\n从你的记忆中回忆到：\n" + "\n".join([f"- {memory}" for memory in relevant_memories])
|
||||||
|
|
||||||
|
# 构建辩论历史上下文
|
||||||
|
history_context = ""
|
||||||
|
if debate_history:
|
||||||
|
recent_history = debate_history[-3:] # 最近3条发言
|
||||||
|
history_context = f"\n最近的论道内容：\n" + "\n".join([f"- {h}" for h in recent_history])
|
||||||
|
|
||||||
|
prompt = f"关于'{topic}'这个话题{memory_context}{history_context}\n\n请结合你的记忆和当前讨论，从你的角色特点出发发表观点。请控制在150字以内。"
|
||||||
|
|
||||||
|
# 发送消息并获取回复
|
||||||
|
content = types.Content(role='user', parts=[types.Part(text=prompt)])
|
||||||
|
response = self.runners[participant].run_async(
|
||||||
|
user_id=self.session.user_id,
|
||||||
|
session_id=self.session.id,
|
||||||
|
new_message=content
|
||||||
|
)
|
||||||
|
|
||||||
|
# 收集回复
|
||||||
|
reply = ""
|
||||||
|
async for event in response:
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text:
|
||||||
|
reply += str(part.text)
|
||||||
|
|
||||||
|
if reply.strip():
|
||||||
|
clean_reply = reply.strip()
|
||||||
|
print(f" {clean_reply}")
|
||||||
|
|
||||||
|
# 记录到辩论历史
|
||||||
|
debate_entry = f"{participant}: {clean_reply}"
|
||||||
|
debate_history.append(debate_entry)
|
||||||
|
|
||||||
|
# 添加到记忆银行
|
||||||
|
await self.memory_manager.add_debate_memory(
|
||||||
|
participant, clean_reply, topic
|
||||||
|
)
|
||||||
|
|
||||||
|
await asyncio.sleep(1) # 避免API调用过快
|
||||||
|
|
||||||
|
print(f"\n🎉 记忆增强论道完成！")
|
||||||
|
print(f"📝 本次论道共产生 {len(debate_history)} 条发言，已存储到各仙人的记忆银行中。")
|
||||||
|
|
||||||
|
return debate_history
|
||||||
|
|
||||||
|
async def close(self):
|
||||||
|
"""关闭资源"""
|
||||||
|
for runner in self.runners.values():
|
||||||
|
await runner.close()
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
"""主函数"""
|
||||||
|
print("🚀 稷下学宫 ADK Memory Bank 论道系统")
|
||||||
|
|
||||||
|
# 检查API密钥
|
||||||
|
api_key = os.getenv('GOOGLE_API_KEY')
|
||||||
|
if not api_key:
|
||||||
|
print("❌ 未找到 GOOGLE_API_KEY 环境变量")
|
||||||
|
print("请使用: doppler run -- python src/jixia/debates/adk_memory_debate.py")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"✅ API密钥已配置")
|
||||||
|
|
||||||
|
# 创建并初始化辩论系统
|
||||||
|
debate_system = MemoryEnhancedDebate()
|
||||||
|
|
||||||
|
try:
|
||||||
|
await debate_system.initialize()
|
||||||
|
|
||||||
|
# 进行辩论
|
||||||
|
await debate_system.conduct_memory_debate(
|
||||||
|
topic="人工智能对投资市场的影响",
|
||||||
|
participants=["铁拐李", "吕洞宾", "何仙姑", "张果老"]
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 运行失败: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
finally:
|
||||||
|
await debate_system.close()
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,290 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫 八仙论道系统
|
||||||
|
实现八仙四对矛盾的对角线辩论：男女、老少、富贫、贵贱
|
||||||
|
基于先天八卦的智慧对话系统
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import asyncio
|
||||||
|
from google.adk import Agent, Runner
|
||||||
|
from google.adk.sessions import InMemorySessionService
|
||||||
|
from google.genai import types
|
||||||
|
import re
|
||||||
|
import sys
|
||||||
|
from contextlib import contextmanager
|
||||||
|
|
||||||
|
def create_baxian_agents():
|
||||||
|
"""创建八仙智能体 - 四对矛盾"""
|
||||||
|
|
||||||
|
# 男女对立：吕洞宾（男）vs 何仙姑（女）
|
||||||
|
lu_dong_bin = Agent(
|
||||||
|
name="吕洞宾",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是吕洞宾，八仙中的男性代表，理性分析者。你代表男性视角，善于逻辑思辨，注重理性和秩序。你的发言风格温和而深刻，总是能找到问题的核心。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
he_xian_gu = Agent(
|
||||||
|
name="何仙姑",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是何仙姑，八仙中的女性代表，感性智慧者。你代表女性视角，善于直觉洞察，注重情感和和谐。你的发言风格柔和而犀利，总是能看到事物的另一面。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 老少对立：张果老（老）vs 韩湘子（少）
|
||||||
|
zhang_guo_lao = Agent(
|
||||||
|
name="张果老",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是张果老，八仙中的长者代表，经验智慧者。你代表老年视角，善于从历史经验出发，注重传统和稳重。你的发言风格深沉而睿智，总是能从历史中汲取教训。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
han_xiang_zi = Agent(
|
||||||
|
name="韩湘子",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是韩湘子，八仙中的青年代表，创新思维者。你代表年轻视角，善于创新思考，注重变革和进步。你的发言风格活泼而敏锐，总是能提出新颖的观点。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 富贫对立：汉钟离（富）vs 蓝采和（贫）
|
||||||
|
han_zhong_li = Agent(
|
||||||
|
name="汉钟离",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是汉钟离，八仙中的富贵代表，资源掌控者。你代表富有阶层视角，善于从资源配置角度思考，注重效率和投资回报。你的发言风格稳重而务实，总是能看到经济利益。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
lan_cai_he = Agent(
|
||||||
|
name="蓝采和",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是蓝采和，八仙中的贫困代表，民生关怀者。你代表普通民众视角，善于从底层角度思考，注重公平和民生。你的发言风格朴实而真诚，总是能关注到弱势群体。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 贵贱对立：曹国舅（贵）vs 铁拐李（贱）
|
||||||
|
cao_guo_jiu = Agent(
|
||||||
|
name="曹国舅",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是曹国舅，八仙中的贵族代表，权力思考者。你代表上层社会视角，善于从权力结构角度分析，注重秩序和等级。你的发言风格优雅而权威，总是能看到政治层面。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
tie_guai_li = Agent(
|
||||||
|
name="铁拐李",
|
||||||
|
model="gemini-2.5-flash",
|
||||||
|
instruction="你是铁拐李，八仙中的底层代表，逆向思维者。你代表社会底层视角，善于从批判角度质疑，注重真实和反叛。你的发言风格直接而犀利，总是能揭示问题本质。每次发言控制在80字以内。"
|
||||||
|
)
|
||||||
|
|
||||||
|
return {
|
||||||
|
'male_female': (lu_dong_bin, he_xian_gu),
|
||||||
|
'old_young': (zhang_guo_lao, han_xiang_zi),
|
||||||
|
'rich_poor': (han_zhong_li, lan_cai_he),
|
||||||
|
'noble_humble': (cao_guo_jiu, tie_guai_li)
|
||||||
|
}
|
||||||
|
|
||||||
|
@contextmanager
|
||||||
|
def suppress_stdout():
|
||||||
|
"""抑制标准输出"""
|
||||||
|
with open(os.devnull, "w") as devnull:
|
||||||
|
old_stdout = sys.stdout
|
||||||
|
sys.stdout = devnull
|
||||||
|
try:
|
||||||
|
yield
|
||||||
|
finally:
|
||||||
|
sys.stdout = old_stdout
|
||||||
|
|
||||||
|
def clean_debug_output(text):
|
||||||
|
"""清理调试输出"""
|
||||||
|
if not text:
|
||||||
|
return ""
|
||||||
|
|
||||||
|
# 移除调试信息，但保留实际内容
|
||||||
|
lines = text.split('\n')
|
||||||
|
cleaned_lines = []
|
||||||
|
|
||||||
|
for line in lines:
|
||||||
|
line = line.strip()
|
||||||
|
# 只过滤明确的调试信息，保留实际回复内容
|
||||||
|
if any(debug_pattern in line for debug_pattern in
|
||||||
|
['Event from', 'API_KEY', 'Both GOOGLE_API_KEY', 'Using GOOGLE_API_KEY']):
|
||||||
|
continue
|
||||||
|
if line and not line.startswith('DEBUG') and not line.startswith('INFO'):
|
||||||
|
cleaned_lines.append(line)
|
||||||
|
|
||||||
|
result = ' '.join(cleaned_lines)
|
||||||
|
return result if result.strip() else text.strip()
|
||||||
|
|
||||||
|
async def conduct_diagonal_debate(agent1, agent2, topic, perspective1, perspective2, round_num):
|
||||||
|
"""进行对角线辩论"""
|
||||||
|
print(f"\n🎯 第{round_num}轮对角线辩论：{agent1.name} vs {agent2.name}")
|
||||||
|
print(f"📋 辩论视角：{perspective1} vs {perspective2}")
|
||||||
|
|
||||||
|
# 设置环境变量以抑制ADK调试输出
|
||||||
|
os.environ['GRPC_VERBOSITY'] = 'ERROR'
|
||||||
|
os.environ['GRPC_TRACE'] = ''
|
||||||
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
||||||
|
|
||||||
|
import warnings
|
||||||
|
warnings.filterwarnings('ignore')
|
||||||
|
|
||||||
|
# 创建会话服务和运行器
|
||||||
|
session_service = InMemorySessionService()
|
||||||
|
|
||||||
|
# 创建会话
|
||||||
|
session = await session_service.create_session(
|
||||||
|
state={},
|
||||||
|
app_name="稷下学宫八仙论道系统",
|
||||||
|
user_id="baxian_debate_user"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 创建Runner实例
|
||||||
|
runner1 = Runner(agent=agent1, session_service=session_service, app_name="稷下学宫八仙论道系统")
|
||||||
|
runner2 = Runner(agent=agent2, session_service=session_service, app_name="稷下学宫八仙论道系统")
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 第一轮：agent1 发起
|
||||||
|
prompt1 = f"请从{perspective1}的角度，对'{topic}'发表你的观点。要求：观点鲜明，论证有力，体现{perspective1}的特色。"
|
||||||
|
|
||||||
|
content1 = types.Content(role='user', parts=[types.Part(text=prompt1)])
|
||||||
|
response1 = runner1.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content1
|
||||||
|
)
|
||||||
|
|
||||||
|
# 提取回复内容
|
||||||
|
agent1_reply = ""
|
||||||
|
async for event in response1:
|
||||||
|
# 只处理包含实际文本内容的事件，过滤调试信息
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text and part.text.strip():
|
||||||
|
text_content = str(part.text).strip()
|
||||||
|
# 过滤掉调试信息和系统消息
|
||||||
|
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
|
||||||
|
agent1_reply += text_content
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
text_content = str(event.text).strip()
|
||||||
|
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
|
||||||
|
agent1_reply += text_content
|
||||||
|
|
||||||
|
print(f"\n🗣️ {agent1.name}（{perspective1}）：")
|
||||||
|
print(f" {agent1_reply}")
|
||||||
|
|
||||||
|
# 第二轮：agent2 回应
|
||||||
|
prompt2 = f"针对{agent1.name}刚才的观点：'{agent1_reply}'，请从{perspective2}的角度进行回应和反驳。要求：有理有据，体现{perspective2}的独特视角。"
|
||||||
|
|
||||||
|
content2 = types.Content(role='user', parts=[types.Part(text=prompt2)])
|
||||||
|
response2 = runner2.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content2
|
||||||
|
)
|
||||||
|
|
||||||
|
agent2_reply = ""
|
||||||
|
async for event in response2:
|
||||||
|
# 只处理包含实际文本内容的事件，过滤调试信息
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text and part.text.strip():
|
||||||
|
text_content = str(part.text).strip()
|
||||||
|
# 过滤掉调试信息和系统消息
|
||||||
|
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
|
||||||
|
agent2_reply += text_content
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
text_content = str(event.text).strip()
|
||||||
|
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
|
||||||
|
agent2_reply += text_content
|
||||||
|
|
||||||
|
print(f"\n🗣️ {agent2.name}（{perspective2}）：")
|
||||||
|
print(f" {agent2_reply}")
|
||||||
|
|
||||||
|
# 第三轮：agent1 再次回应
|
||||||
|
prompt3 = f"听了{agent2.name}的观点：'{agent2_reply}'，请从{perspective1}的角度进行最后的总结和回应。"
|
||||||
|
|
||||||
|
content3 = types.Content(role='user', parts=[types.Part(text=prompt3)])
|
||||||
|
response3 = runner1.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content3
|
||||||
|
)
|
||||||
|
|
||||||
|
agent1_final = ""
|
||||||
|
async for event in response3:
|
||||||
|
# 只处理包含实际文本内容的事件，过滤调试信息
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text and part.text.strip():
|
||||||
|
text_content = str(part.text).strip()
|
||||||
|
# 过滤掉调试信息和系统消息
|
||||||
|
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
|
||||||
|
agent1_final += text_content
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
text_content = str(event.text).strip()
|
||||||
|
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
|
||||||
|
agent1_final += text_content
|
||||||
|
|
||||||
|
print(f"\n🗣️ {agent1.name}（{perspective1}）总结：")
|
||||||
|
print(f" {agent1_final}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 对角线辩论出现错误: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
async def conduct_baxian_debate():
|
||||||
|
"""进行八仙四对矛盾的完整辩论"""
|
||||||
|
print("\n🏛️ 稷下学宫 - 八仙论道系统启动")
|
||||||
|
print("📚 八仙者，南北朝的产物，男女老少，富贵贫贱，皆可成仙")
|
||||||
|
print("🎯 四对矛盾暗合先天八卦，智慧交锋即将开始")
|
||||||
|
|
||||||
|
topic = "雅江水电站对中印关系的影响"
|
||||||
|
print(f"\n📋 论道主题：{topic}")
|
||||||
|
|
||||||
|
# 创建八仙智能体
|
||||||
|
agents = create_baxian_agents()
|
||||||
|
|
||||||
|
print("\n🔥 八仙真实ADK论道模式")
|
||||||
|
|
||||||
|
# 四对矛盾的对角线辩论
|
||||||
|
debates = [
|
||||||
|
(agents['male_female'], "男性理性", "女性感性", "男女对立"),
|
||||||
|
(agents['old_young'], "长者经验", "青年创新", "老少对立"),
|
||||||
|
(agents['rich_poor'], "富者效率", "贫者公平", "富贫对立"),
|
||||||
|
(agents['noble_humble'], "贵族秩序", "底层真实", "贵贱对立")
|
||||||
|
]
|
||||||
|
|
||||||
|
for i, ((agent1, agent2), perspective1, perspective2, debate_type) in enumerate(debates, 1):
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f"🎭 {debate_type}辩论")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
await conduct_diagonal_debate(agent1, agent2, topic, perspective1, perspective2, i)
|
||||||
|
|
||||||
|
if i < len(debates):
|
||||||
|
print("\n⏳ 准备下一轮辩论...")
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
print("\n🎉 八仙论道完成！")
|
||||||
|
print("\n📝 四对矛盾，八种视角，智慧的交锋展现了问题的多面性。")
|
||||||
|
print("💡 这就是稷下学宫八仙论道的魅力所在。")
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数"""
|
||||||
|
print("🚀 稷下学宫 八仙ADK 真实论道系统")
|
||||||
|
|
||||||
|
# 检查API密钥
|
||||||
|
if not os.getenv('GOOGLE_API_KEY'):
|
||||||
|
print("❌ 请设置 GOOGLE_API_KEY 环境变量")
|
||||||
|
return
|
||||||
|
|
||||||
|
print("✅ API密钥已配置")
|
||||||
|
|
||||||
|
try:
|
||||||
|
asyncio.run(conduct_baxian_debate())
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print("\n👋 用户中断，论道结束")
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 系统错误: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,980 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
增强版优先级算法 v2.1.0
|
||||||
|
实现更复杂的权重计算和上下文分析能力
|
||||||
|
"""
|
||||||
|
|
||||||
|
import re
|
||||||
|
import math
|
||||||
|
from typing import Dict, List, Any, Optional, Tuple, Set
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
from enum import Enum
|
||||||
|
import json
|
||||||
|
from collections import defaultdict, deque
|
||||||
|
import hashlib
|
||||||
|
import statistics
|
||||||
|
|
||||||
|
class ArgumentType(Enum):
|
||||||
|
"""论点类型"""
|
||||||
|
ATTACK = "攻击"
|
||||||
|
DEFENSE = "防御"
|
||||||
|
SUPPORT = "支持"
|
||||||
|
REFUTE = "反驳"
|
||||||
|
SUMMARY = "总结"
|
||||||
|
QUESTION = "质疑"
|
||||||
|
|
||||||
|
class EmotionLevel(Enum):
|
||||||
|
"""情绪强度"""
|
||||||
|
CALM = 1
|
||||||
|
MILD = 2
|
||||||
|
MODERATE = 3
|
||||||
|
INTENSE = 4
|
||||||
|
EXTREME = 5
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class SpeechAnalysis:
|
||||||
|
"""发言分析结果"""
|
||||||
|
argument_type: ArgumentType
|
||||||
|
emotion_level: EmotionLevel
|
||||||
|
logic_strength: float # 0-1
|
||||||
|
evidence_quality: float # 0-1
|
||||||
|
relevance_score: float # 0-1
|
||||||
|
urgency_score: float # 0-1
|
||||||
|
target_speakers: List[str] # 针对的发言者
|
||||||
|
keywords: List[str]
|
||||||
|
sentiment_score: float # -1 to 1
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class SpeakerProfile:
|
||||||
|
"""发言者档案"""
|
||||||
|
name: str
|
||||||
|
team: str
|
||||||
|
recent_speeches: List[Dict] = field(default_factory=list)
|
||||||
|
total_speech_count: int = 0
|
||||||
|
average_response_time: float = 30.0
|
||||||
|
expertise_areas: List[str] = field(default_factory=list)
|
||||||
|
debate_style: str = "analytical" # "aggressive", "analytical", "diplomatic", "creative"
|
||||||
|
current_energy: float = 1.0 # 0-1
|
||||||
|
last_speech_time: Optional[datetime] = None
|
||||||
|
# 新增字段
|
||||||
|
historical_performance: Dict[str, float] = field(default_factory=dict)
|
||||||
|
context_adaptability: float = 0.7 # 上下文适应能力
|
||||||
|
argument_effectiveness: Dict[str, float] = field(default_factory=dict) # 不同类型论点的有效性
|
||||||
|
collaboration_score: float = 0.5 # 团队协作得分
|
||||||
|
interruption_tendency: float = 0.3 # 打断倾向
|
||||||
|
topic_expertise: Dict[str, float] = field(default_factory=dict) # 话题专业度
|
||||||
|
|
||||||
|
class EnhancedPriorityAlgorithm:
|
||||||
|
"""增强版优先级算法"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
# 权重配置
|
||||||
|
self.weights = {
|
||||||
|
"rebuttal_urgency": 0.30, # 反驳紧急性
|
||||||
|
"argument_strength": 0.25, # 论点强度
|
||||||
|
"time_pressure": 0.20, # 时间压力
|
||||||
|
"audience_reaction": 0.15, # 观众反应
|
||||||
|
"strategy_need": 0.10 # 策略需要
|
||||||
|
}
|
||||||
|
|
||||||
|
# 情感关键词库
|
||||||
|
self.emotion_keywords = {
|
||||||
|
EmotionLevel.CALM: ["认为", "分析", "数据显示", "根据", "客观"],
|
||||||
|
EmotionLevel.MILD: ["不同意", "质疑", "担心", "建议"],
|
||||||
|
EmotionLevel.MODERATE: ["强烈", "明显", "严重", "重要"],
|
||||||
|
EmotionLevel.INTENSE: ["绝对", "完全", "彻底", "必须"],
|
||||||
|
EmotionLevel.EXTREME: ["荒谬", "愚蠢", "灾难", "危险"]
|
||||||
|
}
|
||||||
|
|
||||||
|
# 论点类型关键词
|
||||||
|
self.argument_keywords = {
|
||||||
|
ArgumentType.ATTACK: ["错误", "问题", "缺陷", "失败"],
|
||||||
|
ArgumentType.DEFENSE: ["解释", "澄清", "说明", "回应"],
|
||||||
|
ArgumentType.SUPPORT: ["支持", "赞同", "证实", "补充"],
|
||||||
|
ArgumentType.REFUTE: ["反驳", "否定", "驳斥", "反对"],
|
||||||
|
ArgumentType.SUMMARY: ["总结", "综上", "结论", "最后"],
|
||||||
|
ArgumentType.QUESTION: ["为什么", "如何", "是否", "难道"]
|
||||||
|
}
|
||||||
|
|
||||||
|
# 发言者档案
|
||||||
|
self.speaker_profiles: Dict[str, SpeakerProfile] = {}
|
||||||
|
|
||||||
|
# 辩论历史分析
|
||||||
|
self.debate_history: List[Dict] = []
|
||||||
|
|
||||||
|
# 新增: 高级分析器组件
|
||||||
|
self.context_analyzer = ContextAnalyzer()
|
||||||
|
self.learning_system = LearningSystem()
|
||||||
|
self.topic_drift_detector = TopicDriftDetector()
|
||||||
|
self.emotion_dynamics = EmotionDynamicsModel()
|
||||||
|
|
||||||
|
def analyze_speech(self, message: str, speaker: str, context: Dict) -> SpeechAnalysis:
|
||||||
|
"""分析发言内容"""
|
||||||
|
# 检测论点类型
|
||||||
|
argument_type = self._detect_argument_type(message)
|
||||||
|
|
||||||
|
# 检测情绪强度
|
||||||
|
emotion_level = self._detect_emotion_level(message)
|
||||||
|
|
||||||
|
# 计算逻辑强度
|
||||||
|
logic_strength = self._calculate_logic_strength(message)
|
||||||
|
|
||||||
|
# 计算证据质量
|
||||||
|
evidence_quality = self._calculate_evidence_quality(message)
|
||||||
|
|
||||||
|
# 计算相关性分数
|
||||||
|
relevance_score = self._calculate_relevance_score(message, context)
|
||||||
|
|
||||||
|
# 计算紧急性分数
|
||||||
|
urgency_score = self._calculate_urgency_score(message, context)
|
||||||
|
|
||||||
|
# 识别目标发言者
|
||||||
|
target_speakers = self._identify_target_speakers(message)
|
||||||
|
|
||||||
|
# 提取关键词
|
||||||
|
keywords = self._extract_keywords(message)
|
||||||
|
|
||||||
|
# 计算情感分数
|
||||||
|
sentiment_score = self._calculate_sentiment_score(message)
|
||||||
|
|
||||||
|
return SpeechAnalysis(
|
||||||
|
argument_type=argument_type,
|
||||||
|
emotion_level=emotion_level,
|
||||||
|
logic_strength=logic_strength,
|
||||||
|
evidence_quality=evidence_quality,
|
||||||
|
relevance_score=relevance_score,
|
||||||
|
urgency_score=urgency_score,
|
||||||
|
target_speakers=target_speakers,
|
||||||
|
keywords=keywords,
|
||||||
|
sentiment_score=sentiment_score
|
||||||
|
)
|
||||||
|
|
||||||
|
def calculate_speaker_priority(self, speaker: str, context: Dict,
|
||||||
|
recent_speeches: List[Dict]) -> float:
|
||||||
|
"""计算发言者优先级 - 增强版"""
|
||||||
|
# 获取或创建发言者档案
|
||||||
|
profile = self._get_or_create_speaker_profile(speaker)
|
||||||
|
|
||||||
|
# 更新发言者档案
|
||||||
|
self._update_speaker_profile(profile, recent_speeches)
|
||||||
|
|
||||||
|
# === 基础分数计算 ===
|
||||||
|
rebuttal_urgency = self._calculate_rebuttal_urgency(speaker, context, recent_speeches)
|
||||||
|
argument_strength = self._calculate_argument_strength(speaker, profile)
|
||||||
|
time_pressure = self._calculate_time_pressure(speaker, context)
|
||||||
|
audience_reaction = self._calculate_audience_reaction(speaker, context)
|
||||||
|
strategy_need = self._calculate_strategy_need(speaker, context, profile)
|
||||||
|
|
||||||
|
# === 新增高级分析 ===
|
||||||
|
# 1. 上下文流程分析
|
||||||
|
flow_analysis = self.context_analyzer.analyze_debate_flow(recent_speeches)
|
||||||
|
flow_bonus = self._calculate_flow_bonus(speaker, flow_analysis)
|
||||||
|
|
||||||
|
# 2. 话题漂移检测
|
||||||
|
if recent_speeches:
|
||||||
|
last_speech = recent_speeches[-1].get("content", "")
|
||||||
|
drift_analysis = self.topic_drift_detector.detect_drift(last_speech, context)
|
||||||
|
drift_penalty = self._calculate_drift_penalty(speaker, drift_analysis)
|
||||||
|
else:
|
||||||
|
drift_penalty = 0.0
|
||||||
|
|
||||||
|
# 3. 情绪动态分析
|
||||||
|
emotion_analysis = self.emotion_dynamics.analyze_emotion_dynamics(recent_speeches)
|
||||||
|
emotion_bonus = self._calculate_emotion_bonus(speaker, emotion_analysis, profile)
|
||||||
|
|
||||||
|
# 4. 学习系统适应
|
||||||
|
adaptation = self.learning_system.get_speaker_adaptation(speaker)
|
||||||
|
adaptation_factor = adaptation.get("confidence", 0.5)
|
||||||
|
|
||||||
|
# 5. 个性化权重调整
|
||||||
|
personalized_weights = self._get_personalized_weights(speaker, profile, context)
|
||||||
|
|
||||||
|
# === 加权计算总分 ===
|
||||||
|
base_score = (
|
||||||
|
rebuttal_urgency * personalized_weights["rebuttal_urgency"] +
|
||||||
|
argument_strength * personalized_weights["argument_strength"] +
|
||||||
|
time_pressure * personalized_weights["time_pressure"] +
|
||||||
|
audience_reaction * personalized_weights["audience_reaction"] +
|
||||||
|
strategy_need * personalized_weights["strategy_need"]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 应用高级调整
|
||||||
|
enhanced_score = base_score + flow_bonus - drift_penalty + emotion_bonus
|
||||||
|
enhanced_score *= adaptation_factor
|
||||||
|
|
||||||
|
# 应用传统修正因子
|
||||||
|
final_score = self._apply_correction_factors(enhanced_score, speaker, profile, context)
|
||||||
|
|
||||||
|
return min(max(final_score, 0.0), 1.0) # 限制在0-1范围内
|
||||||
|
|
||||||
|
def get_next_speaker(self, available_speakers: List[str], context: Dict,
|
||||||
|
recent_speeches: List[Dict]) -> Tuple[str, float, Dict]:
|
||||||
|
"""获取下一个发言者"""
|
||||||
|
speaker_scores = {}
|
||||||
|
detailed_analysis = {}
|
||||||
|
|
||||||
|
for speaker in available_speakers:
|
||||||
|
score = self.calculate_speaker_priority(speaker, context, recent_speeches)
|
||||||
|
speaker_scores[speaker] = score
|
||||||
|
|
||||||
|
# 记录详细分析
|
||||||
|
detailed_analysis[speaker] = {
|
||||||
|
"priority_score": score,
|
||||||
|
"profile": self.speaker_profiles.get(speaker),
|
||||||
|
"analysis_timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
# 选择最高分发言者
|
||||||
|
best_speaker = max(speaker_scores, key=speaker_scores.get)
|
||||||
|
best_score = speaker_scores[best_speaker]
|
||||||
|
|
||||||
|
return best_speaker, best_score, detailed_analysis
|
||||||
|
|
||||||
|
def _detect_argument_type(self, message: str) -> ArgumentType:
|
||||||
|
"""检测论点类型"""
|
||||||
|
message_lower = message.lower()
|
||||||
|
type_scores = {}
|
||||||
|
|
||||||
|
for arg_type, keywords in self.argument_keywords.items():
|
||||||
|
score = sum(1 for keyword in keywords if keyword in message_lower)
|
||||||
|
type_scores[arg_type] = score
|
||||||
|
|
||||||
|
if not type_scores or max(type_scores.values()) == 0:
|
||||||
|
return ArgumentType.SUPPORT # 默认类型
|
||||||
|
|
||||||
|
return max(type_scores, key=type_scores.get)
|
||||||
|
|
||||||
|
def _detect_emotion_level(self, message: str) -> EmotionLevel:
|
||||||
|
"""检测情绪强度"""
|
||||||
|
message_lower = message.lower()
|
||||||
|
|
||||||
|
for emotion_level in reversed(list(EmotionLevel)):
|
||||||
|
keywords = self.emotion_keywords.get(emotion_level, [])
|
||||||
|
if any(keyword in message_lower for keyword in keywords):
|
||||||
|
return emotion_level
|
||||||
|
|
||||||
|
return EmotionLevel.CALM
|
||||||
|
|
||||||
|
def _calculate_logic_strength(self, message: str) -> float:
|
||||||
|
"""计算逻辑强度"""
|
||||||
|
logic_indicators = [
|
||||||
|
"因为", "所以", "因此", "由于", "根据", "数据显示",
|
||||||
|
"研究表明", "事实上", "例如", "比如", "首先", "其次", "最后"
|
||||||
|
]
|
||||||
|
|
||||||
|
message_lower = message.lower()
|
||||||
|
logic_count = sum(1 for indicator in logic_indicators if indicator in message_lower)
|
||||||
|
|
||||||
|
# 基于逻辑词汇密度计算
|
||||||
|
word_count = len(message.split())
|
||||||
|
if word_count == 0:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
logic_density = logic_count / word_count
|
||||||
|
return min(logic_density * 10, 1.0) # 归一化到0-1
|
||||||
|
|
||||||
|
def _calculate_evidence_quality(self, message: str) -> float:
|
||||||
|
"""计算证据质量"""
|
||||||
|
evidence_indicators = [
|
||||||
|
"数据", "统计", "研究", "报告", "调查", "实验",
|
||||||
|
"案例", "例子", "证据", "资料", "文献", "来源"
|
||||||
|
]
|
||||||
|
|
||||||
|
message_lower = message.lower()
|
||||||
|
evidence_count = sum(1 for indicator in evidence_indicators if indicator in message_lower)
|
||||||
|
|
||||||
|
# 检查是否有具体数字
|
||||||
|
number_pattern = r'\d+(?:\.\d+)?%?'
|
||||||
|
numbers = re.findall(number_pattern, message)
|
||||||
|
number_bonus = min(len(numbers) * 0.1, 0.3)
|
||||||
|
|
||||||
|
base_score = min(evidence_count * 0.2, 0.7)
|
||||||
|
return min(base_score + number_bonus, 1.0)
|
||||||
|
|
||||||
|
def _calculate_relevance_score(self, message: str, context: Dict) -> float:
|
||||||
|
"""计算相关性分数"""
|
||||||
|
# 简化实现：基于关键词匹配
|
||||||
|
topic_keywords = context.get("topic_keywords", [])
|
||||||
|
if not topic_keywords:
|
||||||
|
return 0.5 # 默认中等相关性
|
||||||
|
|
||||||
|
message_lower = message.lower()
|
||||||
|
relevance_count = sum(1 for keyword in topic_keywords if keyword.lower() in message_lower)
|
||||||
|
|
||||||
|
return min(relevance_count / len(topic_keywords), 1.0)
|
||||||
|
|
||||||
|
def _calculate_urgency_score(self, message: str, context: Dict) -> float:
|
||||||
|
"""计算紧急性分数"""
|
||||||
|
urgency_keywords = ["紧急", "立即", "马上", "现在", "重要", "关键", "危险"]
|
||||||
|
|
||||||
|
message_lower = message.lower()
|
||||||
|
urgency_count = sum(1 for keyword in urgency_keywords if keyword in message_lower)
|
||||||
|
|
||||||
|
# 基于时间压力
|
||||||
|
time_factor = context.get("time_remaining", 1.0)
|
||||||
|
time_urgency = 1.0 - time_factor
|
||||||
|
|
||||||
|
keyword_urgency = min(urgency_count * 0.3, 1.0)
|
||||||
|
|
||||||
|
return min(keyword_urgency + time_urgency * 0.5, 1.0)
|
||||||
|
|
||||||
|
def _identify_target_speakers(self, message: str) -> List[str]:
|
||||||
|
"""识别目标发言者"""
|
||||||
|
# 简化实现：查找提及的发言者名称
|
||||||
|
speaker_names = ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"]
|
||||||
|
targets = []
|
||||||
|
|
||||||
|
for name in speaker_names:
|
||||||
|
if name in message:
|
||||||
|
targets.append(name)
|
||||||
|
|
||||||
|
return targets
|
||||||
|
|
||||||
|
def _extract_keywords(self, message: str) -> List[str]:
|
||||||
|
"""提取关键词"""
|
||||||
|
# 简化实现：提取长度大于2的词汇
|
||||||
|
words = re.findall(r'\b\w{3,}\b', message)
|
||||||
|
return list(set(words))[:10] # 最多返回10个关键词
|
||||||
|
|
||||||
|
def _calculate_sentiment_score(self, message: str) -> float:
|
||||||
|
"""计算情感分数"""
|
||||||
|
positive_words = ["好", "优秀", "正确", "支持", "赞同", "成功", "有效"]
|
||||||
|
negative_words = ["坏", "错误", "失败", "反对", "问题", "危险", "无效"]
|
||||||
|
|
||||||
|
message_lower = message.lower()
|
||||||
|
|
||||||
|
positive_count = sum(1 for word in positive_words if word in message_lower)
|
||||||
|
negative_count = sum(1 for word in negative_words if word in message_lower)
|
||||||
|
|
||||||
|
total_count = positive_count + negative_count
|
||||||
|
if total_count == 0:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
return (positive_count - negative_count) / total_count
|
||||||
|
|
||||||
|
def _get_or_create_speaker_profile(self, speaker: str) -> SpeakerProfile:
|
||||||
|
"""获取或创建发言者档案"""
|
||||||
|
if speaker not in self.speaker_profiles:
|
||||||
|
self.speaker_profiles[speaker] = SpeakerProfile(
|
||||||
|
name=speaker,
|
||||||
|
team="positive" if "正" in speaker else "negative",
|
||||||
|
recent_speeches=[],
|
||||||
|
total_speech_count=0,
|
||||||
|
average_response_time=3.0,
|
||||||
|
expertise_areas=[],
|
||||||
|
debate_style="analytical",
|
||||||
|
current_energy=1.0
|
||||||
|
)
|
||||||
|
|
||||||
|
return self.speaker_profiles[speaker]
|
||||||
|
|
||||||
|
def _update_speaker_profile(self, profile: SpeakerProfile, recent_speeches: List[Dict]):
|
||||||
|
"""更新发言者档案"""
|
||||||
|
# 更新发言历史
|
||||||
|
speaker_speeches = [s for s in recent_speeches if s.get("speaker") == profile.name]
|
||||||
|
profile.recent_speeches = speaker_speeches[-5:] # 保留最近5次发言
|
||||||
|
profile.total_speech_count = len(speaker_speeches)
|
||||||
|
|
||||||
|
# 更新能量水平（基于发言频率）
|
||||||
|
if profile.last_speech_time:
|
||||||
|
time_since_last = datetime.now() - profile.last_speech_time
|
||||||
|
energy_recovery = min(time_since_last.seconds / 300, 0.5) # 5分钟恢复50%
|
||||||
|
profile.current_energy = min(profile.current_energy + energy_recovery, 1.0)
|
||||||
|
|
||||||
|
profile.last_speech_time = datetime.now()
|
||||||
|
|
||||||
|
def _calculate_rebuttal_urgency(self, speaker: str, context: Dict,
|
||||||
|
recent_speeches: List[Dict]) -> float:
|
||||||
|
"""计算反驳紧急性"""
|
||||||
|
# 检查是否有针对该发言者团队的攻击
|
||||||
|
team = "positive" if "正" in speaker else "negative"
|
||||||
|
opposing_team = "negative" if team == "positive" else "positive"
|
||||||
|
|
||||||
|
recent_attacks = 0
|
||||||
|
for speech in recent_speeches[-5:]: # 检查最近5次发言
|
||||||
|
if speech.get("team") == opposing_team:
|
||||||
|
analysis = speech.get("analysis", {})
|
||||||
|
if analysis.get("argument_type") in [ArgumentType.ATTACK, ArgumentType.REFUTE]:
|
||||||
|
recent_attacks += 1
|
||||||
|
|
||||||
|
# 基础紧急性 + 攻击响应紧急性
|
||||||
|
# 为不同发言者生成不同的基础紧急性
|
||||||
|
speaker_hash = hash(speaker) % 10 # 使用哈希值生成0-9的数字
|
||||||
|
base_urgency = 0.1 + speaker_hash * 0.05 # 不同发言者有不同的基础紧急性
|
||||||
|
attack_urgency = recent_attacks * 0.3
|
||||||
|
|
||||||
|
return min(base_urgency + attack_urgency, 1.0)
|
||||||
|
|
||||||
|
def _calculate_argument_strength(self, speaker: str, profile: SpeakerProfile) -> float:
|
||||||
|
"""计算论点强度"""
|
||||||
|
# 基于历史表现
|
||||||
|
if not profile.recent_speeches:
|
||||||
|
# 为不同发言者提供不同的基础论点强度
|
||||||
|
speaker_hash = hash(speaker) % 10 # 使用哈希值生成0-9的数字
|
||||||
|
team_prefix = "正" if "正" in speaker else "反"
|
||||||
|
|
||||||
|
# 基础强度根据发言者哈希值变化
|
||||||
|
base_strength = 0.4 + speaker_hash * 0.06 # 0.4-1.0范围
|
||||||
|
|
||||||
|
# 团队差异化
|
||||||
|
team_factor = 1.05 if team_prefix == "正" else 0.95
|
||||||
|
|
||||||
|
return min(base_strength * team_factor, 1.0)
|
||||||
|
|
||||||
|
avg_logic = sum(s.get("analysis", {}).get("logic_strength", 0.5)
|
||||||
|
for s in profile.recent_speeches) / len(profile.recent_speeches)
|
||||||
|
avg_evidence = sum(s.get("analysis", {}).get("evidence_quality", 0.5)
|
||||||
|
for s in profile.recent_speeches) / len(profile.recent_speeches)
|
||||||
|
|
||||||
|
return (avg_logic + avg_evidence) / 2
|
||||||
|
|
||||||
|
def _calculate_time_pressure(self, speaker: str, context: Dict) -> float:
|
||||||
|
"""计算时间压力"""
|
||||||
|
time_remaining = context.get("time_remaining", 1.0)
|
||||||
|
stage_progress = context.get("stage_progress", 0)
|
||||||
|
max_progress = context.get("max_progress", 1)
|
||||||
|
|
||||||
|
# 时间压力随剩余时间减少而增加
|
||||||
|
time_pressure = 1.0 - time_remaining
|
||||||
|
|
||||||
|
# 阶段进度压力
|
||||||
|
progress_pressure = stage_progress / max_progress
|
||||||
|
|
||||||
|
# 发言者个体差异
|
||||||
|
speaker_hash = hash(speaker) % 10 # 使用哈希值生成0-9的数字
|
||||||
|
speaker_factor = 0.8 + speaker_hash * 0.02 # 不同发言者有不同的时间敏感度
|
||||||
|
|
||||||
|
base_pressure = (time_pressure + progress_pressure) / 2
|
||||||
|
return min(base_pressure * speaker_factor, 1.0)
|
||||||
|
|
||||||
|
def _calculate_audience_reaction(self, speaker: str, context: Dict) -> float:
|
||||||
|
"""计算观众反应"""
|
||||||
|
# 简化实现：基于团队表现
|
||||||
|
team = "positive" if "正" in speaker else "negative"
|
||||||
|
team_score = context.get(f"{team}_team_score", 0.5)
|
||||||
|
|
||||||
|
# 发言者个体魅力差异
|
||||||
|
speaker_hash = hash(speaker) % 10 # 使用哈希值生成0-9的数字
|
||||||
|
charisma_factor = 0.7 + speaker_hash * 0.03 # 不同发言者有不同的观众吸引力
|
||||||
|
|
||||||
|
# 如果团队表现不佳，需要更多发言机会
|
||||||
|
base_reaction = 1.0 - team_score
|
||||||
|
return min(base_reaction * charisma_factor, 1.0)
|
||||||
|
|
||||||
|
def _calculate_strategy_need(self, speaker: str, context: Dict,
|
||||||
|
profile: SpeakerProfile) -> float:
|
||||||
|
"""计算策略需要"""
|
||||||
|
# 基于发言者专长和当前需求
|
||||||
|
current_stage = context.get("current_stage", "")
|
||||||
|
|
||||||
|
# 为不同发言者提供差异化的策略需求
|
||||||
|
speaker_hash = hash(speaker) % 10 # 使用哈希值生成0-9的数字
|
||||||
|
team_prefix = "正" if "正" in speaker else "反"
|
||||||
|
|
||||||
|
strategy_match = {
|
||||||
|
"起": 0.8 if speaker_hash == 0 else 0.3 + speaker_hash * 0.05, # 开场需要主力，但有差异
|
||||||
|
"承": 0.4 + speaker_hash * 0.06, # 承接阶段根据发言者哈希差异化
|
||||||
|
"转": max(0.2, 1.0 - profile.current_energy + speaker_hash * 0.05), # 自由辩论看能量和哈希
|
||||||
|
"合": 0.9 if speaker_hash == 0 else 0.3 + speaker_hash * 0.05 # 总结需要主力，但有差异
|
||||||
|
}
|
||||||
|
|
||||||
|
base_score = strategy_match.get(current_stage, 0.5)
|
||||||
|
|
||||||
|
# 添加团队差异化因子
|
||||||
|
team_factor = 1.1 if team_prefix == "正" else 0.9
|
||||||
|
|
||||||
|
return min(base_score * team_factor, 1.0)
|
||||||
|
|
||||||
|
def _apply_correction_factors(self, base_score: float, speaker: str,
|
||||||
|
profile: SpeakerProfile, context: Dict) -> float:
|
||||||
|
"""应用修正因子"""
|
||||||
|
corrected_score = base_score
|
||||||
|
|
||||||
|
# 能量修正
|
||||||
|
corrected_score *= profile.current_energy
|
||||||
|
|
||||||
|
# 发言频率修正（避免某人发言过多）
|
||||||
|
recent_count = len([s for s in profile.recent_speeches
|
||||||
|
if s.get("timestamp", "") > (datetime.now() - timedelta(minutes=5)).isoformat()])
|
||||||
|
if recent_count > 2:
|
||||||
|
corrected_score *= 0.7 # 降低优先级
|
||||||
|
|
||||||
|
# 团队平衡修正
|
||||||
|
team = "positive" if "正" in speaker else "negative"
|
||||||
|
team_recent_count = context.get(f"{team}_recent_speeches", 0)
|
||||||
|
opposing_recent_count = context.get(f"{'negative' if team == 'positive' else 'positive'}_recent_speeches", 0)
|
||||||
|
|
||||||
|
if team_recent_count > opposing_recent_count + 2:
|
||||||
|
corrected_score *= 0.8 # 平衡发言机会
|
||||||
|
|
||||||
|
return corrected_score
|
||||||
|
|
||||||
|
def calculate_priority(self, speaker: str, context: Dict, recent_speeches: List[Dict]) -> float:
|
||||||
|
"""计算发言者优先级（兼容性方法）"""
|
||||||
|
return self.calculate_speaker_priority(speaker, context, recent_speeches)
|
||||||
|
|
||||||
|
def get_algorithm_status(self) -> Dict[str, Any]:
|
||||||
|
"""获取算法状态"""
|
||||||
|
return {
|
||||||
|
"weights": self.weights,
|
||||||
|
"speaker_count": len(self.speaker_profiles),
|
||||||
|
"total_speeches_analyzed": len(self.debate_history),
|
||||||
|
"algorithm_version": "2.1.0",
|
||||||
|
"last_updated": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
def save_analysis_data(self, filename: str = "priority_analysis.json"):
|
||||||
|
"""保存分析数据"""
|
||||||
|
data = {
|
||||||
|
"algorithm_status": self.get_algorithm_status(),
|
||||||
|
"speaker_profiles": {
|
||||||
|
name: {
|
||||||
|
"name": profile.name,
|
||||||
|
"team": profile.team,
|
||||||
|
"total_speech_count": profile.total_speech_count,
|
||||||
|
"average_response_time": profile.average_response_time,
|
||||||
|
"expertise_areas": profile.expertise_areas,
|
||||||
|
"debate_style": profile.debate_style,
|
||||||
|
"current_energy": profile.current_energy,
|
||||||
|
"last_speech_time": profile.last_speech_time.isoformat() if profile.last_speech_time else None
|
||||||
|
}
|
||||||
|
for name, profile in self.speaker_profiles.items()
|
||||||
|
},
|
||||||
|
"debate_history": self.debate_history
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(filename, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
print(f"💾 优先级分析数据已保存到 {filename}")
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""测试增强版优先级算法"""
|
||||||
|
print("🚀 增强版优先级算法测试")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
algorithm = EnhancedPriorityAlgorithm()
|
||||||
|
|
||||||
|
# 模拟辩论上下文
|
||||||
|
context = {
|
||||||
|
"current_stage": "转",
|
||||||
|
"stage_progress": 10,
|
||||||
|
"max_progress": 36,
|
||||||
|
"time_remaining": 0.6,
|
||||||
|
"topic_keywords": ["人工智能", "投资", "风险", "收益"],
|
||||||
|
"positive_team_score": 0.6,
|
||||||
|
"negative_team_score": 0.4,
|
||||||
|
"positive_recent_speeches": 3,
|
||||||
|
"negative_recent_speeches": 2
|
||||||
|
}
|
||||||
|
|
||||||
|
# 模拟最近发言
|
||||||
|
recent_speeches = [
|
||||||
|
{
|
||||||
|
"speaker": "正1",
|
||||||
|
"team": "positive",
|
||||||
|
"message": "根据数据显示，AI投资确实能带来显著收益",
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"analysis": {
|
||||||
|
"argument_type": ArgumentType.SUPPORT,
|
||||||
|
"logic_strength": 0.8,
|
||||||
|
"evidence_quality": 0.7
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"speaker": "反2",
|
||||||
|
"team": "negative",
|
||||||
|
"message": "这种观点完全错误！AI投资风险巨大！",
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"analysis": {
|
||||||
|
"argument_type": ArgumentType.ATTACK,
|
||||||
|
"logic_strength": 0.3,
|
||||||
|
"evidence_quality": 0.2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
available_speakers = ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"]
|
||||||
|
|
||||||
|
# 计算下一个发言者
|
||||||
|
next_speaker, score, analysis = algorithm.get_next_speaker(
|
||||||
|
available_speakers, context, recent_speeches
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"\n🎯 推荐发言者: {next_speaker}")
|
||||||
|
print(f"📊 优先级分数: {score:.3f}")
|
||||||
|
print(f"\n📈 详细分析:")
|
||||||
|
|
||||||
|
for speaker, data in analysis.items():
|
||||||
|
print(f" {speaker}: {data['priority_score']:.3f}")
|
||||||
|
|
||||||
|
# 保存分析数据
|
||||||
|
algorithm.save_analysis_data()
|
||||||
|
|
||||||
|
print("\n✅ 增强版优先级算法测试完成！")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
||||||
|
|
||||||
|
class ContextAnalyzer:
|
||||||
|
"""高级上下文分析器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.context_memory = deque(maxlen=20) # 保留最近20轮的上下文
|
||||||
|
self.semantic_vectors = {} # 语义向量缓存
|
||||||
|
|
||||||
|
def analyze_debate_flow(self, recent_speeches: List[Dict]) -> Dict[str, Any]:
|
||||||
|
"""分析辩论流程"""
|
||||||
|
if not recent_speeches:
|
||||||
|
return {"flow_direction": "neutral", "momentum": 0.5, "tension": 0.3}
|
||||||
|
|
||||||
|
# 分析辩论动量
|
||||||
|
momentum = self._calculate_debate_momentum(recent_speeches)
|
||||||
|
|
||||||
|
# 分析辩论紧张度
|
||||||
|
tension = self._calculate_debate_tension(recent_speeches)
|
||||||
|
|
||||||
|
# 分析流程方向
|
||||||
|
flow_direction = self._analyze_flow_direction(recent_speeches)
|
||||||
|
|
||||||
|
# 检测话题转换点
|
||||||
|
topic_shifts = self._detect_topic_shifts(recent_speeches)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"flow_direction": flow_direction,
|
||||||
|
"momentum": momentum,
|
||||||
|
"tension": tension,
|
||||||
|
"topic_shifts": topic_shifts,
|
||||||
|
"engagement_level": self._calculate_engagement_level(recent_speeches)
|
||||||
|
}
|
||||||
|
|
||||||
|
def _calculate_debate_momentum(self, speeches: List[Dict]) -> float:
|
||||||
|
"""计算辩论动量"""
|
||||||
|
if len(speeches) < 2:
|
||||||
|
return 0.5
|
||||||
|
|
||||||
|
# 基于发言长度和情绪强度变化
|
||||||
|
momentum_factors = []
|
||||||
|
for i in range(1, len(speeches)):
|
||||||
|
prev_speech = speeches[i-1]
|
||||||
|
curr_speech = speeches[i]
|
||||||
|
|
||||||
|
# 长度变化
|
||||||
|
length_change = len(curr_speech.get("content", "")) - len(prev_speech.get("content", ""))
|
||||||
|
length_factor = min(abs(length_change) / 100, 1.0) # 归一化
|
||||||
|
|
||||||
|
momentum_factors.append(length_factor)
|
||||||
|
|
||||||
|
return statistics.mean(momentum_factors) if momentum_factors else 0.5
|
||||||
|
|
||||||
|
def _calculate_debate_tension(self, speeches: List[Dict]) -> float:
|
||||||
|
"""计算辩论紧张度"""
|
||||||
|
if not speeches:
|
||||||
|
return 0.3
|
||||||
|
|
||||||
|
tension_keywords = ["反驳", "错误", "质疑", "不同意", "反对", "驳斥"]
|
||||||
|
|
||||||
|
tension_scores = []
|
||||||
|
for speech in speeches[-5:]: # 只看最近5轮
|
||||||
|
content = speech.get("content", "")
|
||||||
|
tension_count = sum(1 for keyword in tension_keywords if keyword in content)
|
||||||
|
tension_scores.append(min(tension_count / 3, 1.0))
|
||||||
|
|
||||||
|
return statistics.mean(tension_scores) if tension_scores else 0.3
|
||||||
|
|
||||||
|
def _analyze_flow_direction(self, speeches: List[Dict]) -> str:
|
||||||
|
"""分析流程方向"""
|
||||||
|
if len(speeches) < 3:
|
||||||
|
return "neutral"
|
||||||
|
|
||||||
|
recent_teams = [speech.get("team", "unknown") for speech in speeches[-3:]]
|
||||||
|
|
||||||
|
positive_count = recent_teams.count("positive")
|
||||||
|
negative_count = recent_teams.count("negative")
|
||||||
|
|
||||||
|
if positive_count > negative_count:
|
||||||
|
return "positive_dominant"
|
||||||
|
elif negative_count > positive_count:
|
||||||
|
return "negative_dominant"
|
||||||
|
else:
|
||||||
|
return "balanced"
|
||||||
|
|
||||||
|
def _detect_topic_shifts(self, speeches: List[Dict]) -> List[Dict]:
|
||||||
|
"""检测话题转换点"""
|
||||||
|
shifts = []
|
||||||
|
if len(speeches) < 2:
|
||||||
|
return shifts
|
||||||
|
|
||||||
|
# 简化的话题转换检测
|
||||||
|
for i in range(1, len(speeches)):
|
||||||
|
prev_keywords = set(speeches[i-1].get("content", "").split()[:10])
|
||||||
|
curr_keywords = set(speeches[i].get("content", "").split()[:10])
|
||||||
|
|
||||||
|
# 计算关键词重叠度
|
||||||
|
overlap = len(prev_keywords & curr_keywords) / max(len(prev_keywords | curr_keywords), 1)
|
||||||
|
|
||||||
|
if overlap < 0.3: # 重叠度低于30%认为是话题转换
|
||||||
|
shifts.append({
|
||||||
|
"position": i,
|
||||||
|
"speaker": speeches[i].get("speaker"),
|
||||||
|
"shift_intensity": 1 - overlap
|
||||||
|
})
|
||||||
|
|
||||||
|
return shifts
|
||||||
|
|
||||||
|
def _calculate_engagement_level(self, speeches: List[Dict]) -> float:
|
||||||
|
"""计算参与度"""
|
||||||
|
if not speeches:
|
||||||
|
return 0.5
|
||||||
|
|
||||||
|
# 基于发言频率和长度
|
||||||
|
total_length = sum(len(speech.get("content", "")) for speech in speeches)
|
||||||
|
avg_length = total_length / len(speeches)
|
||||||
|
|
||||||
|
# 归一化到0-1
|
||||||
|
engagement = min(avg_length / 100, 1.0)
|
||||||
|
return engagement
|
||||||
|
|
||||||
|
|
||||||
|
class LearningSystem:
|
||||||
|
"""学习系统，用于优化算法参数"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.performance_history = defaultdict(list)
|
||||||
|
self.weight_adjustments = defaultdict(float)
|
||||||
|
self.learning_rate = 0.05
|
||||||
|
|
||||||
|
def record_performance(self, speaker: str, predicted_priority: float,
|
||||||
|
actual_effectiveness: float, context: Dict):
|
||||||
|
"""记录表现数据"""
|
||||||
|
self.performance_history[speaker].append({
|
||||||
|
"predicted_priority": predicted_priority,
|
||||||
|
"actual_effectiveness": actual_effectiveness,
|
||||||
|
"context": context,
|
||||||
|
"timestamp": datetime.now(),
|
||||||
|
"error": abs(predicted_priority - actual_effectiveness)
|
||||||
|
})
|
||||||
|
|
||||||
|
def optimize_weights(self, algorithm_weights: Dict[str, float]) -> Dict[str, float]:
|
||||||
|
"""优化权重参数"""
|
||||||
|
if not self.performance_history:
|
||||||
|
return algorithm_weights
|
||||||
|
|
||||||
|
# 计算每个组件的平均误差
|
||||||
|
component_errors = {}
|
||||||
|
for component in algorithm_weights.keys():
|
||||||
|
errors = []
|
||||||
|
for speaker_data in self.performance_history.values():
|
||||||
|
for record in speaker_data[-10:]: # 只看最近10次
|
||||||
|
errors.append(record["error"])
|
||||||
|
|
||||||
|
if errors:
|
||||||
|
component_errors[component] = statistics.mean(errors)
|
||||||
|
|
||||||
|
# 根据误差调整权重
|
||||||
|
optimized_weights = algorithm_weights.copy()
|
||||||
|
for component, error in component_errors.items():
|
||||||
|
if error > 0.3: # 误差过大，降低权重
|
||||||
|
adjustment = -self.learning_rate * error
|
||||||
|
else: # 误差合理，略微增加权重
|
||||||
|
adjustment = self.learning_rate * (0.3 - error)
|
||||||
|
|
||||||
|
optimized_weights[component] = max(0.05, min(0.5,
|
||||||
|
optimized_weights[component] + adjustment))
|
||||||
|
|
||||||
|
# 归一化权重
|
||||||
|
total_weight = sum(optimized_weights.values())
|
||||||
|
if total_weight > 0:
|
||||||
|
optimized_weights = {k: v/total_weight for k, v in optimized_weights.items()}
|
||||||
|
|
||||||
|
return optimized_weights
|
||||||
|
|
||||||
|
def get_speaker_adaptation(self, speaker: str) -> Dict[str, float]:
|
||||||
|
"""获取发言者特定的适应参数"""
|
||||||
|
if speaker not in self.performance_history:
|
||||||
|
return {"confidence": 0.5, "adaptability": 0.5}
|
||||||
|
|
||||||
|
recent_records = self.performance_history[speaker][-5:]
|
||||||
|
if not recent_records:
|
||||||
|
return {"confidence": 0.5, "adaptability": 0.5}
|
||||||
|
|
||||||
|
# 计算准确性趋势
|
||||||
|
errors = [record["error"] for record in recent_records]
|
||||||
|
avg_error = statistics.mean(errors)
|
||||||
|
|
||||||
|
confidence = max(0.1, 1.0 - avg_error)
|
||||||
|
adaptability = min(1.0, 0.3 + (1.0 - statistics.stdev(errors)) if len(errors) > 1 else 0.7)
|
||||||
|
|
||||||
|
return {"confidence": confidence, "adaptability": adaptability}
|
||||||
|
|
||||||
|
|
||||||
|
class TopicDriftDetector:
|
||||||
|
"""话题漂移检测器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.topic_history = deque(maxlen=50)
|
||||||
|
self.keywords_cache = {}
|
||||||
|
|
||||||
|
def detect_drift(self, current_speech: str, context: Dict) -> Dict[str, Any]:
|
||||||
|
"""检测话题漂移"""
|
||||||
|
current_keywords = self._extract_topic_keywords(current_speech)
|
||||||
|
|
||||||
|
if not self.topic_history:
|
||||||
|
self.topic_history.append(current_keywords)
|
||||||
|
return {"drift_detected": False, "drift_intensity": 0.0}
|
||||||
|
|
||||||
|
# 计算与历史话题的相似度
|
||||||
|
similarities = []
|
||||||
|
for historical_keywords in list(self.topic_history)[-5:]: # 最近5轮
|
||||||
|
similarity = self._calculate_keyword_similarity(current_keywords, historical_keywords)
|
||||||
|
similarities.append(similarity)
|
||||||
|
|
||||||
|
avg_similarity = statistics.mean(similarities)
|
||||||
|
drift_intensity = 1.0 - avg_similarity
|
||||||
|
|
||||||
|
# 更新历史
|
||||||
|
self.topic_history.append(current_keywords)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"drift_detected": drift_intensity > 0.4, # 阈值40%
|
||||||
|
"drift_intensity": drift_intensity,
|
||||||
|
"current_keywords": current_keywords,
|
||||||
|
"recommendation": self._get_drift_recommendation(float(drift_intensity))
|
||||||
|
}
|
||||||
|
|
||||||
|
def _extract_topic_keywords(self, text: str) -> Set[str]:
|
||||||
|
"""提取话题关键词"""
|
||||||
|
# 简化的关键词提取
|
||||||
|
words = re.findall(r'\b\w{2,}\b', text.lower())
|
||||||
|
|
||||||
|
# 过滤停用词
|
||||||
|
stop_words = {"的", "了", "在", "是", "我", "你", "他", "她", "我们", "这", "那"}
|
||||||
|
keywords = {word for word in words if word not in stop_words and len(word) > 1}
|
||||||
|
|
||||||
|
return keywords
|
||||||
|
|
||||||
|
def _calculate_keyword_similarity(self, keywords1: Set[str], keywords2: Set[str]) -> float:
|
||||||
|
"""计算关键词相似度"""
|
||||||
|
if not keywords1 or not keywords2:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
intersection = keywords1 & keywords2
|
||||||
|
union = keywords1 | keywords2
|
||||||
|
|
||||||
|
return len(intersection) / len(union) if union else 0.0
|
||||||
|
|
||||||
|
def _get_drift_recommendation(self, drift_intensity: float) -> str:
|
||||||
|
"""获取漂移建议"""
|
||||||
|
if drift_intensity > 0.7:
|
||||||
|
return "major_topic_shift_detected"
|
||||||
|
elif drift_intensity > 0.4:
|
||||||
|
return "moderate_drift_detected"
|
||||||
|
else:
|
||||||
|
return "topic_stable"
|
||||||
|
|
||||||
|
|
||||||
|
class EmotionDynamicsModel:
|
||||||
|
"""情绪动力学模型"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.emotion_history = deque(maxlen=30)
|
||||||
|
self.speaker_emotion_profiles = defaultdict(list)
|
||||||
|
|
||||||
|
def analyze_emotion_dynamics(self, recent_speeches: List[Dict]) -> Dict[str, Any]:
|
||||||
|
"""分析情绪动态"""
|
||||||
|
if not recent_speeches:
|
||||||
|
return {"overall_trend": "neutral", "intensity_change": 0.0}
|
||||||
|
|
||||||
|
# 提取情绪序列
|
||||||
|
emotion_sequence = []
|
||||||
|
for speech in recent_speeches:
|
||||||
|
emotion_score = self._calculate_emotion_score(speech.get("content", ""))
|
||||||
|
emotion_sequence.append(emotion_score)
|
||||||
|
|
||||||
|
# 更新发言者情绪档案
|
||||||
|
speaker = speech.get("speaker")
|
||||||
|
if speaker:
|
||||||
|
self.speaker_emotion_profiles[speaker].append(emotion_score)
|
||||||
|
|
||||||
|
if len(emotion_sequence) < 2:
|
||||||
|
return {"overall_trend": "neutral", "intensity_change": 0.0}
|
||||||
|
|
||||||
|
# 计算情绪趋势
|
||||||
|
trend = self._calculate_emotion_trend(emotion_sequence)
|
||||||
|
|
||||||
|
# 计算强度变化
|
||||||
|
intensity_change = emotion_sequence[-1] - emotion_sequence[0]
|
||||||
|
|
||||||
|
# 检测情绪拐点
|
||||||
|
turning_points = self._detect_emotion_turning_points(emotion_sequence)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"overall_trend": trend,
|
||||||
|
"intensity_change": intensity_change,
|
||||||
|
"current_intensity": emotion_sequence[-1],
|
||||||
|
"turning_points": turning_points,
|
||||||
|
"volatility": statistics.stdev(emotion_sequence) if len(emotion_sequence) > 1 else 0.0
|
||||||
|
}
|
||||||
|
|
||||||
|
def _calculate_emotion_score(self, text: str) -> float:
|
||||||
|
"""计算情绪分数"""
|
||||||
|
positive_words = ["好", "棒", "优秀", "正确", "支持", "赞同", "有效"]
|
||||||
|
negative_words = ["坏", "错", "糟糕", "反对", "质疑", "问题", "失败"]
|
||||||
|
intense_words = ["强烈", "坚决", "绝对", "完全", "彻底"]
|
||||||
|
|
||||||
|
text_lower = text.lower()
|
||||||
|
|
||||||
|
positive_count = sum(1 for word in positive_words if word in text_lower)
|
||||||
|
negative_count = sum(1 for word in negative_words if word in text_lower)
|
||||||
|
intense_count = sum(1 for word in intense_words if word in text_lower)
|
||||||
|
|
||||||
|
base_emotion = (positive_count - negative_count) / max(len(text.split()), 1)
|
||||||
|
intensity_multiplier = 1 + (intense_count * 0.5)
|
||||||
|
|
||||||
|
return base_emotion * intensity_multiplier
|
||||||
|
|
||||||
|
def _calculate_emotion_trend(self, sequence: List[float]) -> str:
|
||||||
|
"""计算情绪趋势"""
|
||||||
|
if len(sequence) < 2:
|
||||||
|
return "neutral"
|
||||||
|
|
||||||
|
# 简单线性回归估算
|
||||||
|
if len(sequence) < 2:
|
||||||
|
return 0.0
|
||||||
|
|
||||||
|
# 计算斜率
|
||||||
|
n = len(sequence)
|
||||||
|
sum_x = sum(range(n))
|
||||||
|
sum_y = sum(sequence)
|
||||||
|
sum_xy = sum(i * sequence[i] for i in range(n))
|
||||||
|
sum_x2 = sum(i * i for i in range(n))
|
||||||
|
|
||||||
|
slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x)
|
||||||
|
|
||||||
|
if slope > 0.1:
|
||||||
|
return "escalating"
|
||||||
|
elif slope < -0.1:
|
||||||
|
return "de_escalating"
|
||||||
|
else:
|
||||||
|
return "stable"
|
||||||
|
|
||||||
|
def _detect_emotion_turning_points(self, sequence: List[float]) -> List[int]:
|
||||||
|
"""检测情绪拐点"""
|
||||||
|
if len(sequence) < 3:
|
||||||
|
return []
|
||||||
|
|
||||||
|
turning_points = []
|
||||||
|
for i in range(1, len(sequence) - 1):
|
||||||
|
prev_val = sequence[i-1]
|
||||||
|
curr_val = sequence[i]
|
||||||
|
next_val = sequence[i+1]
|
||||||
|
|
||||||
|
# 检测峰值和谷值
|
||||||
|
if (curr_val > prev_val and curr_val > next_val) or \
|
||||||
|
(curr_val < prev_val and curr_val < next_val):
|
||||||
|
turning_points.append(i)
|
||||||
|
|
||||||
|
return turning_points
|
||||||
|
|
@ -0,0 +1,733 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
优化的辩论流程控制系统 v2.1.0
|
||||||
|
改进阶段转换和发言权争夺逻辑
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import time
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
from typing import Dict, List, Any, Optional, Tuple, Callable
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from enum import Enum
|
||||||
|
from collections import defaultdict, deque
|
||||||
|
import threading
|
||||||
|
import queue
|
||||||
|
|
||||||
|
class DebateStage(Enum):
|
||||||
|
"""辩论阶段枚举"""
|
||||||
|
QI = "起" # 八仙按先天八卦顺序
|
||||||
|
CHENG = "承" # 雁阵式承接
|
||||||
|
ZHUAN = "转" # 自由辩论（36次handoff）
|
||||||
|
HE = "合" # 交替总结
|
||||||
|
|
||||||
|
class FlowControlMode(Enum):
|
||||||
|
"""流程控制模式"""
|
||||||
|
STRICT = "严格模式" # 严格按规则执行
|
||||||
|
ADAPTIVE = "自适应模式" # 根据辩论质量调整
|
||||||
|
DYNAMIC = "动态模式" # 实时响应辩论状态
|
||||||
|
|
||||||
|
class TransitionTrigger(Enum):
|
||||||
|
"""阶段转换触发条件"""
|
||||||
|
TIME_BASED = "时间触发"
|
||||||
|
PROGRESS_BASED = "进度触发"
|
||||||
|
QUALITY_BASED = "质量触发"
|
||||||
|
CONSENSUS_BASED = "共识触发"
|
||||||
|
EMERGENCY = "紧急触发"
|
||||||
|
|
||||||
|
class SpeakerSelectionStrategy(Enum):
|
||||||
|
"""发言者选择策略"""
|
||||||
|
PRIORITY_ALGORITHM = "优先级算法"
|
||||||
|
ROUND_ROBIN = "轮询"
|
||||||
|
RANDOM_WEIGHTED = "加权随机"
|
||||||
|
CONTEXT_AWARE = "上下文感知"
|
||||||
|
COMPETITIVE = "竞争模式"
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class FlowControlConfig:
|
||||||
|
"""流程控制配置"""
|
||||||
|
mode: FlowControlMode = FlowControlMode.ADAPTIVE
|
||||||
|
transition_triggers: List[TransitionTrigger] = field(default_factory=lambda: [TransitionTrigger.PROGRESS_BASED, TransitionTrigger.QUALITY_BASED])
|
||||||
|
speaker_selection_strategy: SpeakerSelectionStrategy = SpeakerSelectionStrategy.CONTEXT_AWARE
|
||||||
|
min_stage_duration: int = 60 # 秒
|
||||||
|
max_stage_duration: int = 900 # 秒
|
||||||
|
quality_threshold: float = 0.6 # 质量阈值
|
||||||
|
participation_balance_threshold: float = 0.3 # 参与平衡阈值
|
||||||
|
emergency_intervention_enabled: bool = True
|
||||||
|
auto_stage_transition: bool = True
|
||||||
|
speaker_timeout: int = 30 # 发言超时时间
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class StageMetrics:
|
||||||
|
"""阶段指标"""
|
||||||
|
start_time: datetime
|
||||||
|
duration: float = 0.0
|
||||||
|
speech_count: int = 0
|
||||||
|
quality_score: float = 0.0
|
||||||
|
participation_balance: float = 0.0
|
||||||
|
engagement_level: float = 0.0
|
||||||
|
topic_coherence: float = 0.0
|
||||||
|
conflict_intensity: float = 0.0
|
||||||
|
speaker_distribution: Dict[str, int] = field(default_factory=dict)
|
||||||
|
transition_readiness: float = 0.0
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class SpeakerRequest:
|
||||||
|
"""发言请求"""
|
||||||
|
speaker: str
|
||||||
|
priority: float
|
||||||
|
timestamp: datetime
|
||||||
|
reason: str
|
||||||
|
urgency_level: int = 1 # 1-5
|
||||||
|
estimated_duration: int = 30 # 秒
|
||||||
|
topic_relevance: float = 1.0
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class FlowEvent:
|
||||||
|
"""流程事件"""
|
||||||
|
event_type: str
|
||||||
|
timestamp: datetime
|
||||||
|
data: Dict[str, Any]
|
||||||
|
source: str
|
||||||
|
priority: int = 1
|
||||||
|
|
||||||
|
class OptimizedDebateFlowController:
|
||||||
|
"""优化的辩论流程控制器"""
|
||||||
|
|
||||||
|
def __init__(self, config: FlowControlConfig = None):
|
||||||
|
self.config = config or FlowControlConfig()
|
||||||
|
|
||||||
|
# 当前状态
|
||||||
|
self.current_stage = DebateStage.QI
|
||||||
|
self.stage_progress = 0
|
||||||
|
self.total_handoffs = 0
|
||||||
|
self.current_speaker: Optional[str] = None
|
||||||
|
self.debate_start_time = datetime.now()
|
||||||
|
|
||||||
|
# 阶段配置
|
||||||
|
self.stage_configs = {
|
||||||
|
DebateStage.QI: {
|
||||||
|
"max_progress": 8,
|
||||||
|
"min_duration": 120,
|
||||||
|
"max_duration": 600,
|
||||||
|
"speaker_order": ["吕洞宾", "何仙姑", "铁拐李", "汉钟离", "曹国舅", "韩湘子", "蓝采和", "张果老"],
|
||||||
|
"selection_strategy": SpeakerSelectionStrategy.ROUND_ROBIN
|
||||||
|
},
|
||||||
|
DebateStage.CHENG: {
|
||||||
|
"max_progress": 8,
|
||||||
|
"min_duration": 180,
|
||||||
|
"max_duration": 600,
|
||||||
|
"speaker_order": ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"],
|
||||||
|
"selection_strategy": SpeakerSelectionStrategy.ROUND_ROBIN
|
||||||
|
},
|
||||||
|
DebateStage.ZHUAN: {
|
||||||
|
"max_progress": 36,
|
||||||
|
"min_duration": 300,
|
||||||
|
"max_duration": 900,
|
||||||
|
"speaker_order": ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"],
|
||||||
|
"selection_strategy": SpeakerSelectionStrategy.CONTEXT_AWARE
|
||||||
|
},
|
||||||
|
DebateStage.HE: {
|
||||||
|
"max_progress": 8,
|
||||||
|
"min_duration": 120,
|
||||||
|
"max_duration": 480,
|
||||||
|
"speaker_order": ["反1", "正1", "反2", "正2", "反3", "正3", "反4", "正4"],
|
||||||
|
"selection_strategy": SpeakerSelectionStrategy.ROUND_ROBIN
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 阶段指标
|
||||||
|
self.stage_metrics: Dict[DebateStage, StageMetrics] = {}
|
||||||
|
self.current_stage_metrics = StageMetrics(start_time=datetime.now())
|
||||||
|
|
||||||
|
# 发言请求队列
|
||||||
|
self.speaker_request_queue = queue.PriorityQueue()
|
||||||
|
self.pending_requests: Dict[str, SpeakerRequest] = {}
|
||||||
|
|
||||||
|
# 事件系统
|
||||||
|
self.event_queue = queue.Queue()
|
||||||
|
self.event_handlers: Dict[str, List[Callable]] = defaultdict(list)
|
||||||
|
|
||||||
|
# 历史记录
|
||||||
|
self.debate_history: List[Dict] = []
|
||||||
|
self.stage_transition_history: List[Dict] = []
|
||||||
|
self.speaker_performance: Dict[str, Dict] = defaultdict(dict)
|
||||||
|
|
||||||
|
# 实时监控
|
||||||
|
self.monitoring_active = False
|
||||||
|
self.monitoring_thread: Optional[threading.Thread] = None
|
||||||
|
|
||||||
|
# 流程锁
|
||||||
|
self.flow_lock = threading.RLock()
|
||||||
|
|
||||||
|
# 初始化当前阶段指标
|
||||||
|
self._initialize_stage_metrics()
|
||||||
|
|
||||||
|
def _initialize_stage_metrics(self):
|
||||||
|
"""初始化阶段指标"""
|
||||||
|
self.current_stage_metrics = StageMetrics(
|
||||||
|
start_time=datetime.now(),
|
||||||
|
speaker_distribution={}
|
||||||
|
)
|
||||||
|
|
||||||
|
def get_current_speaker(self) -> Optional[str]:
|
||||||
|
"""获取当前发言者"""
|
||||||
|
with self.flow_lock:
|
||||||
|
config = self.stage_configs[self.current_stage]
|
||||||
|
strategy = config.get("selection_strategy", self.config.speaker_selection_strategy)
|
||||||
|
|
||||||
|
if strategy == SpeakerSelectionStrategy.ROUND_ROBIN:
|
||||||
|
return self._get_round_robin_speaker()
|
||||||
|
elif strategy == SpeakerSelectionStrategy.CONTEXT_AWARE:
|
||||||
|
return self._get_context_aware_speaker()
|
||||||
|
elif strategy == SpeakerSelectionStrategy.PRIORITY_ALGORITHM:
|
||||||
|
return self._get_priority_speaker()
|
||||||
|
elif strategy == SpeakerSelectionStrategy.COMPETITIVE:
|
||||||
|
return self._get_competitive_speaker()
|
||||||
|
else:
|
||||||
|
return self._get_round_robin_speaker()
|
||||||
|
|
||||||
|
def _get_round_robin_speaker(self) -> str:
|
||||||
|
"""轮询方式获取发言者"""
|
||||||
|
config = self.stage_configs[self.current_stage]
|
||||||
|
speaker_order = config["speaker_order"]
|
||||||
|
return speaker_order[self.stage_progress % len(speaker_order)]
|
||||||
|
|
||||||
|
def _get_context_aware_speaker(self) -> Optional[str]:
|
||||||
|
"""上下文感知方式获取发言者"""
|
||||||
|
# 检查是否有紧急发言请求
|
||||||
|
if not self.speaker_request_queue.empty():
|
||||||
|
try:
|
||||||
|
priority, request = self.speaker_request_queue.get_nowait()
|
||||||
|
if request.urgency_level >= 4: # 高紧急度
|
||||||
|
return request.speaker
|
||||||
|
else:
|
||||||
|
# 重新放回队列
|
||||||
|
self.speaker_request_queue.put((priority, request))
|
||||||
|
except queue.Empty:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# 分析当前上下文
|
||||||
|
context = self._analyze_current_context()
|
||||||
|
|
||||||
|
# 根据上下文选择最合适的发言者
|
||||||
|
available_speakers = self.stage_configs[self.current_stage]["speaker_order"]
|
||||||
|
best_speaker = None
|
||||||
|
best_score = -1
|
||||||
|
|
||||||
|
for speaker in available_speakers:
|
||||||
|
score = self._calculate_speaker_context_score(speaker, context)
|
||||||
|
if score > best_score:
|
||||||
|
best_score = score
|
||||||
|
best_speaker = speaker
|
||||||
|
|
||||||
|
return best_speaker
|
||||||
|
|
||||||
|
def _get_priority_speaker(self) -> Optional[str]:
|
||||||
|
"""优先级算法获取发言者"""
|
||||||
|
# 这里可以集成现有的优先级算法
|
||||||
|
# 暂时使用简化版本
|
||||||
|
return self._get_context_aware_speaker()
|
||||||
|
|
||||||
|
def _get_competitive_speaker(self) -> Optional[str]:
|
||||||
|
"""竞争模式获取发言者"""
|
||||||
|
# 让发言者竞争发言权
|
||||||
|
if not self.speaker_request_queue.empty():
|
||||||
|
try:
|
||||||
|
priority, request = self.speaker_request_queue.get_nowait()
|
||||||
|
return request.speaker
|
||||||
|
except queue.Empty:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return self._get_round_robin_speaker()
|
||||||
|
|
||||||
|
def request_speaking_turn(self, speaker: str, reason: str, urgency: int = 1,
|
||||||
|
estimated_duration: int = 30, topic_relevance: float = 1.0):
|
||||||
|
"""请求发言权"""
|
||||||
|
request = SpeakerRequest(
|
||||||
|
speaker=speaker,
|
||||||
|
priority=self._calculate_request_priority(speaker, reason, urgency, topic_relevance),
|
||||||
|
timestamp=datetime.now(),
|
||||||
|
reason=reason,
|
||||||
|
urgency_level=urgency,
|
||||||
|
estimated_duration=estimated_duration,
|
||||||
|
topic_relevance=topic_relevance
|
||||||
|
)
|
||||||
|
|
||||||
|
# 使用负优先级，因为PriorityQueue是最小堆
|
||||||
|
self.speaker_request_queue.put((-request.priority, request))
|
||||||
|
self.pending_requests[speaker] = request
|
||||||
|
|
||||||
|
# 触发事件
|
||||||
|
self._emit_event("speaker_request", {
|
||||||
|
"speaker": speaker,
|
||||||
|
"reason": reason,
|
||||||
|
"urgency": urgency,
|
||||||
|
"priority": request.priority
|
||||||
|
})
|
||||||
|
|
||||||
|
def _calculate_request_priority(self, speaker: str, reason: str, urgency: int,
|
||||||
|
topic_relevance: float) -> float:
|
||||||
|
"""计算发言请求优先级"""
|
||||||
|
base_priority = urgency * 10
|
||||||
|
|
||||||
|
# 主题相关性加权
|
||||||
|
relevance_bonus = topic_relevance * 5
|
||||||
|
|
||||||
|
# 发言频率调整
|
||||||
|
speaker_count = self.current_stage_metrics.speaker_distribution.get(speaker, 0)
|
||||||
|
frequency_penalty = speaker_count * 2
|
||||||
|
|
||||||
|
# 时间因素
|
||||||
|
time_factor = 1.0
|
||||||
|
if self.current_speaker and self.current_speaker != speaker:
|
||||||
|
time_factor = 1.2 # 鼓励轮换
|
||||||
|
|
||||||
|
priority = (base_priority + relevance_bonus - frequency_penalty) * time_factor
|
||||||
|
return max(0.1, priority)
|
||||||
|
|
||||||
|
def _analyze_current_context(self) -> Dict[str, Any]:
|
||||||
|
"""分析当前辩论上下文"""
|
||||||
|
recent_speeches = self.debate_history[-5:] if self.debate_history else []
|
||||||
|
|
||||||
|
context = {
|
||||||
|
"stage": self.current_stage.value,
|
||||||
|
"progress": self.stage_progress,
|
||||||
|
"recent_speakers": [speech.get("speaker") for speech in recent_speeches],
|
||||||
|
"topic_drift": self._calculate_topic_drift(),
|
||||||
|
"emotional_intensity": self._calculate_emotional_intensity(),
|
||||||
|
"argument_balance": self._calculate_argument_balance(),
|
||||||
|
"time_pressure": self._calculate_time_pressure(),
|
||||||
|
"participation_balance": self._calculate_participation_balance()
|
||||||
|
}
|
||||||
|
|
||||||
|
return context
|
||||||
|
|
||||||
|
def _calculate_speaker_context_score(self, speaker: str, context: Dict[str, Any]) -> float:
|
||||||
|
"""计算发言者在当前上下文下的适合度分数"""
|
||||||
|
score = 0.0
|
||||||
|
|
||||||
|
# 避免连续发言
|
||||||
|
recent_speakers = context.get("recent_speakers", [])
|
||||||
|
if speaker in recent_speakers[-2:]:
|
||||||
|
score -= 10
|
||||||
|
|
||||||
|
# 参与平衡
|
||||||
|
speaker_count = self.current_stage_metrics.speaker_distribution.get(speaker, 0)
|
||||||
|
avg_count = sum(self.current_stage_metrics.speaker_distribution.values()) / max(1, len(self.current_stage_metrics.speaker_distribution))
|
||||||
|
if speaker_count < avg_count:
|
||||||
|
score += 5
|
||||||
|
|
||||||
|
# 队伍平衡
|
||||||
|
if self.current_stage == DebateStage.ZHUAN:
|
||||||
|
positive_count = sum(1 for s in recent_speakers if "正" in s)
|
||||||
|
negative_count = sum(1 for s in recent_speakers if "反" in s)
|
||||||
|
|
||||||
|
if "正" in speaker and positive_count < negative_count:
|
||||||
|
score += 3
|
||||||
|
elif "反" in speaker and negative_count < positive_count:
|
||||||
|
score += 3
|
||||||
|
|
||||||
|
# 时间压力响应
|
||||||
|
time_pressure = context.get("time_pressure", 0)
|
||||||
|
if time_pressure > 0.7 and speaker.endswith("1"): # 主力发言者
|
||||||
|
score += 5
|
||||||
|
|
||||||
|
# 检查发言请求
|
||||||
|
if speaker in self.pending_requests:
|
||||||
|
request = self.pending_requests[speaker]
|
||||||
|
score += request.urgency_level * 2
|
||||||
|
score += request.topic_relevance * 3
|
||||||
|
|
||||||
|
return score
|
||||||
|
|
||||||
|
def advance_stage(self, force: bool = False) -> bool:
|
||||||
|
"""推进辩论阶段"""
|
||||||
|
with self.flow_lock:
|
||||||
|
if not force and not self._should_advance_stage():
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 记录当前阶段结束
|
||||||
|
self._finalize_current_stage()
|
||||||
|
|
||||||
|
# 转换到下一阶段
|
||||||
|
success = self._transition_to_next_stage()
|
||||||
|
|
||||||
|
if success:
|
||||||
|
# 初始化新阶段
|
||||||
|
self._initialize_new_stage()
|
||||||
|
|
||||||
|
# 触发事件
|
||||||
|
self._emit_event("stage_advanced", {
|
||||||
|
"from_stage": self.current_stage.value,
|
||||||
|
"to_stage": self.current_stage.value,
|
||||||
|
"progress": self.stage_progress,
|
||||||
|
"forced": force
|
||||||
|
})
|
||||||
|
|
||||||
|
return success
|
||||||
|
|
||||||
|
def _should_advance_stage(self) -> bool:
|
||||||
|
"""判断是否应该推进阶段"""
|
||||||
|
config = self.stage_configs[self.current_stage]
|
||||||
|
|
||||||
|
# 检查进度触发
|
||||||
|
if TransitionTrigger.PROGRESS_BASED in self.config.transition_triggers:
|
||||||
|
if self.stage_progress >= config["max_progress"] - 1:
|
||||||
|
return True
|
||||||
|
|
||||||
|
# 检查时间触发
|
||||||
|
if TransitionTrigger.TIME_BASED in self.config.transition_triggers:
|
||||||
|
stage_duration = (datetime.now() - self.current_stage_metrics.start_time).total_seconds()
|
||||||
|
if stage_duration >= config.get("max_duration", 600):
|
||||||
|
return True
|
||||||
|
|
||||||
|
# 检查质量触发
|
||||||
|
if TransitionTrigger.QUALITY_BASED in self.config.transition_triggers:
|
||||||
|
if (self.current_stage_metrics.quality_score >= self.config.quality_threshold and
|
||||||
|
self.stage_progress >= config["max_progress"] // 2):
|
||||||
|
return True
|
||||||
|
|
||||||
|
# 检查共识触发
|
||||||
|
if TransitionTrigger.CONSENSUS_BASED in self.config.transition_triggers:
|
||||||
|
if self.current_stage_metrics.transition_readiness >= 0.8:
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
|
|
||||||
|
def _finalize_current_stage(self):
|
||||||
|
"""结束当前阶段"""
|
||||||
|
# 更新阶段指标
|
||||||
|
self.current_stage_metrics.duration = (datetime.now() - self.current_stage_metrics.start_time).total_seconds()
|
||||||
|
|
||||||
|
# 保存阶段指标
|
||||||
|
self.stage_metrics[self.current_stage] = self.current_stage_metrics
|
||||||
|
|
||||||
|
# 记录阶段转换历史
|
||||||
|
self.stage_transition_history.append({
|
||||||
|
"stage": self.current_stage.value,
|
||||||
|
"start_time": self.current_stage_metrics.start_time.isoformat(),
|
||||||
|
"duration": self.current_stage_metrics.duration,
|
||||||
|
"speech_count": self.current_stage_metrics.speech_count,
|
||||||
|
"quality_score": self.current_stage_metrics.quality_score,
|
||||||
|
"participation_balance": self.current_stage_metrics.participation_balance
|
||||||
|
})
|
||||||
|
|
||||||
|
def _transition_to_next_stage(self) -> bool:
|
||||||
|
"""转换到下一阶段"""
|
||||||
|
stage_transitions = {
|
||||||
|
DebateStage.QI: DebateStage.CHENG,
|
||||||
|
DebateStage.CHENG: DebateStage.ZHUAN,
|
||||||
|
DebateStage.ZHUAN: DebateStage.HE,
|
||||||
|
DebateStage.HE: None
|
||||||
|
}
|
||||||
|
|
||||||
|
next_stage = stage_transitions.get(self.current_stage)
|
||||||
|
if next_stage:
|
||||||
|
self.current_stage = next_stage
|
||||||
|
self.stage_progress = 0
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
# 辩论结束
|
||||||
|
self._emit_event("debate_finished", {
|
||||||
|
"total_duration": (datetime.now() - self.debate_start_time).total_seconds(),
|
||||||
|
"total_handoffs": self.total_handoffs,
|
||||||
|
"stages_completed": len(self.stage_metrics)
|
||||||
|
})
|
||||||
|
return False
|
||||||
|
|
||||||
|
def _initialize_new_stage(self):
|
||||||
|
"""初始化新阶段"""
|
||||||
|
self._initialize_stage_metrics()
|
||||||
|
|
||||||
|
# 清空发言请求队列
|
||||||
|
while not self.speaker_request_queue.empty():
|
||||||
|
try:
|
||||||
|
self.speaker_request_queue.get_nowait()
|
||||||
|
except queue.Empty:
|
||||||
|
break
|
||||||
|
|
||||||
|
self.pending_requests.clear()
|
||||||
|
|
||||||
|
def record_speech(self, speaker: str, message: str, metadata: Dict[str, Any] = None):
|
||||||
|
"""记录发言"""
|
||||||
|
with self.flow_lock:
|
||||||
|
speech_record = {
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"stage": self.current_stage.value,
|
||||||
|
"stage_progress": self.stage_progress,
|
||||||
|
"speaker": speaker,
|
||||||
|
"message": message,
|
||||||
|
"total_handoffs": self.total_handoffs,
|
||||||
|
"metadata": metadata or {}
|
||||||
|
}
|
||||||
|
|
||||||
|
self.debate_history.append(speech_record)
|
||||||
|
self.current_speaker = speaker
|
||||||
|
|
||||||
|
# 更新阶段指标
|
||||||
|
self._update_stage_metrics(speaker, message)
|
||||||
|
|
||||||
|
# 如果是转阶段，增加handoff计数
|
||||||
|
if self.current_stage == DebateStage.ZHUAN:
|
||||||
|
self.total_handoffs += 1
|
||||||
|
|
||||||
|
# 推进进度
|
||||||
|
self.stage_progress += 1
|
||||||
|
|
||||||
|
# 移除已完成的发言请求
|
||||||
|
if speaker in self.pending_requests:
|
||||||
|
del self.pending_requests[speaker]
|
||||||
|
|
||||||
|
# 触发事件
|
||||||
|
self._emit_event("speech_recorded", {
|
||||||
|
"speaker": speaker,
|
||||||
|
"stage": self.current_stage.value,
|
||||||
|
"progress": self.stage_progress
|
||||||
|
})
|
||||||
|
|
||||||
|
def _update_stage_metrics(self, speaker: str, message: str):
|
||||||
|
"""更新阶段指标"""
|
||||||
|
# 更新发言计数
|
||||||
|
self.current_stage_metrics.speech_count += 1
|
||||||
|
|
||||||
|
# 更新发言者分布
|
||||||
|
if speaker not in self.current_stage_metrics.speaker_distribution:
|
||||||
|
self.current_stage_metrics.speaker_distribution[speaker] = 0
|
||||||
|
self.current_stage_metrics.speaker_distribution[speaker] += 1
|
||||||
|
|
||||||
|
# 计算参与平衡度
|
||||||
|
self.current_stage_metrics.participation_balance = self._calculate_participation_balance()
|
||||||
|
|
||||||
|
# 计算质量分数（简化版本）
|
||||||
|
self.current_stage_metrics.quality_score = self._calculate_quality_score(message)
|
||||||
|
|
||||||
|
# 计算转换准备度
|
||||||
|
self.current_stage_metrics.transition_readiness = self._calculate_transition_readiness()
|
||||||
|
|
||||||
|
def _calculate_topic_drift(self) -> float:
|
||||||
|
"""计算主题偏移度"""
|
||||||
|
# 简化实现
|
||||||
|
return 0.1
|
||||||
|
|
||||||
|
def _calculate_emotional_intensity(self) -> float:
|
||||||
|
"""计算情绪强度"""
|
||||||
|
# 简化实现
|
||||||
|
return 0.5
|
||||||
|
|
||||||
|
def _calculate_argument_balance(self) -> float:
|
||||||
|
"""计算论点平衡度"""
|
||||||
|
# 简化实现
|
||||||
|
return 0.7
|
||||||
|
|
||||||
|
def _calculate_time_pressure(self) -> float:
|
||||||
|
"""计算时间压力"""
|
||||||
|
config = self.stage_configs[self.current_stage]
|
||||||
|
stage_duration = (datetime.now() - self.current_stage_metrics.start_time).total_seconds()
|
||||||
|
max_duration = config.get("max_duration", 600)
|
||||||
|
return min(1.0, stage_duration / max_duration)
|
||||||
|
|
||||||
|
def _calculate_participation_balance(self) -> float:
|
||||||
|
"""计算参与平衡度"""
|
||||||
|
if not self.current_stage_metrics.speaker_distribution:
|
||||||
|
return 1.0
|
||||||
|
|
||||||
|
counts = list(self.current_stage_metrics.speaker_distribution.values())
|
||||||
|
if not counts:
|
||||||
|
return 1.0
|
||||||
|
|
||||||
|
avg_count = sum(counts) / len(counts)
|
||||||
|
variance = sum((count - avg_count) ** 2 for count in counts) / len(counts)
|
||||||
|
|
||||||
|
# 归一化到0-1范围
|
||||||
|
balance = 1.0 / (1.0 + variance)
|
||||||
|
return balance
|
||||||
|
|
||||||
|
def _calculate_quality_score(self, message: str) -> float:
|
||||||
|
"""计算质量分数"""
|
||||||
|
# 简化实现，基于消息长度和关键词
|
||||||
|
base_score = min(1.0, len(message) / 100)
|
||||||
|
|
||||||
|
# 检查关键词
|
||||||
|
quality_keywords = ["因为", "所以", "但是", "然而", "数据", "证据", "分析"]
|
||||||
|
keyword_bonus = sum(0.1 for keyword in quality_keywords if keyword in message)
|
||||||
|
|
||||||
|
return min(1.0, base_score + keyword_bonus)
|
||||||
|
|
||||||
|
def _calculate_transition_readiness(self) -> float:
|
||||||
|
"""计算转换准备度"""
|
||||||
|
# 综合多个因素
|
||||||
|
progress_factor = self.stage_progress / self.stage_configs[self.current_stage]["max_progress"]
|
||||||
|
quality_factor = self.current_stage_metrics.quality_score
|
||||||
|
balance_factor = self.current_stage_metrics.participation_balance
|
||||||
|
|
||||||
|
readiness = (progress_factor * 0.4 + quality_factor * 0.3 + balance_factor * 0.3)
|
||||||
|
return min(1.0, readiness)
|
||||||
|
|
||||||
|
def _emit_event(self, event_type: str, data: Dict[str, Any]):
|
||||||
|
"""发出事件"""
|
||||||
|
event = FlowEvent(
|
||||||
|
event_type=event_type,
|
||||||
|
timestamp=datetime.now(),
|
||||||
|
data=data,
|
||||||
|
source="flow_controller"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.event_queue.put(event)
|
||||||
|
|
||||||
|
# 调用事件处理器
|
||||||
|
for handler in self.event_handlers.get(event_type, []):
|
||||||
|
try:
|
||||||
|
handler(event)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"事件处理器错误: {e}")
|
||||||
|
|
||||||
|
def add_event_handler(self, event_type: str, handler: Callable):
|
||||||
|
"""添加事件处理器"""
|
||||||
|
self.event_handlers[event_type].append(handler)
|
||||||
|
|
||||||
|
def get_flow_status(self) -> Dict[str, Any]:
|
||||||
|
"""获取流程状态"""
|
||||||
|
return {
|
||||||
|
"current_stage": self.current_stage.value,
|
||||||
|
"stage_progress": self.stage_progress,
|
||||||
|
"total_handoffs": self.total_handoffs,
|
||||||
|
"current_speaker": self.current_speaker,
|
||||||
|
"stage_metrics": {
|
||||||
|
"duration": (datetime.now() - self.current_stage_metrics.start_time).total_seconds(),
|
||||||
|
"speech_count": self.current_stage_metrics.speech_count,
|
||||||
|
"quality_score": self.current_stage_metrics.quality_score,
|
||||||
|
"participation_balance": self.current_stage_metrics.participation_balance,
|
||||||
|
"transition_readiness": self.current_stage_metrics.transition_readiness
|
||||||
|
},
|
||||||
|
"pending_requests": len(self.pending_requests),
|
||||||
|
"config": {
|
||||||
|
"mode": self.config.mode.value,
|
||||||
|
"auto_transition": self.config.auto_stage_transition,
|
||||||
|
"quality_threshold": self.config.quality_threshold
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
def save_flow_data(self, filename: str = "debate_flow_data.json"):
|
||||||
|
"""保存流程数据"""
|
||||||
|
flow_data = {
|
||||||
|
"config": {
|
||||||
|
"mode": self.config.mode.value,
|
||||||
|
"transition_triggers": [t.value for t in self.config.transition_triggers],
|
||||||
|
"speaker_selection_strategy": self.config.speaker_selection_strategy.value,
|
||||||
|
"quality_threshold": self.config.quality_threshold,
|
||||||
|
"auto_stage_transition": self.config.auto_stage_transition
|
||||||
|
},
|
||||||
|
"current_state": {
|
||||||
|
"stage": self.current_stage.value,
|
||||||
|
"progress": self.stage_progress,
|
||||||
|
"total_handoffs": self.total_handoffs,
|
||||||
|
"current_speaker": self.current_speaker,
|
||||||
|
"debate_start_time": self.debate_start_time.isoformat()
|
||||||
|
},
|
||||||
|
"stage_metrics": {
|
||||||
|
stage.value: {
|
||||||
|
"start_time": metrics.start_time.isoformat(),
|
||||||
|
"duration": metrics.duration,
|
||||||
|
"speech_count": metrics.speech_count,
|
||||||
|
"quality_score": metrics.quality_score,
|
||||||
|
"participation_balance": metrics.participation_balance,
|
||||||
|
"speaker_distribution": metrics.speaker_distribution
|
||||||
|
} for stage, metrics in self.stage_metrics.items()
|
||||||
|
},
|
||||||
|
"current_stage_metrics": {
|
||||||
|
"start_time": self.current_stage_metrics.start_time.isoformat(),
|
||||||
|
"duration": (datetime.now() - self.current_stage_metrics.start_time).total_seconds(),
|
||||||
|
"speech_count": self.current_stage_metrics.speech_count,
|
||||||
|
"quality_score": self.current_stage_metrics.quality_score,
|
||||||
|
"participation_balance": self.current_stage_metrics.participation_balance,
|
||||||
|
"speaker_distribution": self.current_stage_metrics.speaker_distribution,
|
||||||
|
"transition_readiness": self.current_stage_metrics.transition_readiness
|
||||||
|
},
|
||||||
|
"debate_history": self.debate_history,
|
||||||
|
"stage_transition_history": self.stage_transition_history,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(filename, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(flow_data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
print(f"✅ 流程数据已保存到 {filename}")
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""测试优化的辩论流程控制系统"""
|
||||||
|
print("🎭 测试优化的辩论流程控制系统")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
# 创建配置
|
||||||
|
config = FlowControlConfig(
|
||||||
|
mode=FlowControlMode.ADAPTIVE,
|
||||||
|
transition_triggers=[TransitionTrigger.PROGRESS_BASED, TransitionTrigger.QUALITY_BASED],
|
||||||
|
speaker_selection_strategy=SpeakerSelectionStrategy.CONTEXT_AWARE,
|
||||||
|
auto_stage_transition=True
|
||||||
|
)
|
||||||
|
|
||||||
|
# 创建流程控制器
|
||||||
|
controller = OptimizedDebateFlowController(config)
|
||||||
|
|
||||||
|
# 添加事件处理器
|
||||||
|
def on_stage_advanced(event):
|
||||||
|
print(f"🎭 阶段转换: {event.data}")
|
||||||
|
|
||||||
|
def on_speech_recorded(event):
|
||||||
|
print(f"🗣️ 发言记录: {event.data['speaker']} 在 {event.data['stage']} 阶段")
|
||||||
|
|
||||||
|
controller.add_event_handler("stage_advanced", on_stage_advanced)
|
||||||
|
controller.add_event_handler("speech_recorded", on_speech_recorded)
|
||||||
|
|
||||||
|
# 模拟辩论流程
|
||||||
|
test_speeches = [
|
||||||
|
("吕洞宾", "我认为AI投资具有巨大的潜力和机会。"),
|
||||||
|
("何仙姑", "但我们也需要考虑其中的风险因素。"),
|
||||||
|
("铁拐李", "数据显示AI行业的增长率确实很高。"),
|
||||||
|
("汉钟离", "然而市场波动性也不容忽视。")
|
||||||
|
]
|
||||||
|
|
||||||
|
print("\n📋 开始模拟辩论流程")
|
||||||
|
print("-" * 30)
|
||||||
|
|
||||||
|
for i, (speaker, message) in enumerate(test_speeches):
|
||||||
|
print(f"\n第 {i+1} 轮发言:")
|
||||||
|
|
||||||
|
# 获取当前发言者
|
||||||
|
current_speaker = controller.get_current_speaker()
|
||||||
|
print(f"推荐发言者: {current_speaker}")
|
||||||
|
|
||||||
|
# 记录发言
|
||||||
|
controller.record_speech(speaker, message)
|
||||||
|
|
||||||
|
# 显示流程状态
|
||||||
|
status = controller.get_flow_status()
|
||||||
|
print(f"当前状态: {status['current_stage']} 阶段，进度 {status['stage_progress']}")
|
||||||
|
print(f"质量分数: {status['stage_metrics']['quality_score']:.3f}")
|
||||||
|
print(f"参与平衡: {status['stage_metrics']['participation_balance']:.3f}")
|
||||||
|
|
||||||
|
# 检查是否需要推进阶段
|
||||||
|
if controller._should_advance_stage():
|
||||||
|
print("🔄 准备推进到下一阶段")
|
||||||
|
controller.advance_stage()
|
||||||
|
|
||||||
|
# 测试发言请求
|
||||||
|
print("\n📢 测试发言请求系统")
|
||||||
|
print("-" * 30)
|
||||||
|
|
||||||
|
controller.request_speaking_turn("正1", "需要反驳对方观点", urgency=4, topic_relevance=0.9)
|
||||||
|
controller.request_speaking_turn("反2", "补充论据", urgency=2, topic_relevance=0.7)
|
||||||
|
|
||||||
|
next_speaker = controller.get_current_speaker()
|
||||||
|
print(f"基于请求的下一位发言者: {next_speaker}")
|
||||||
|
|
||||||
|
# 保存数据
|
||||||
|
controller.save_flow_data("test_flow_data.json")
|
||||||
|
|
||||||
|
print("\n✅ 测试完成")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,335 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
太公心易 - 起承转合辩论系统
|
||||||
|
基于先天八卦的八仙辩论架构
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from enum import Enum
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
|
||||||
|
from enhanced_priority_algorithm import EnhancedPriorityAlgorithm, SpeechAnalysis
|
||||||
|
|
||||||
|
class DebateStage(Enum):
|
||||||
|
"""辩论阶段枚举"""
|
||||||
|
QI = "起" # 八仙按先天八卦顺序
|
||||||
|
CHENG = "承" # 雁阵式承接
|
||||||
|
ZHUAN = "转" # 自由辩论（36次handoff）
|
||||||
|
HE = "合" # 交替总结
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class Speaker:
|
||||||
|
"""发言者数据类"""
|
||||||
|
name: str
|
||||||
|
role: str
|
||||||
|
team: str # "positive" or "negative"
|
||||||
|
bagua_position: Optional[int] = None # 八卦位置（0-7）
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class DebateContext:
|
||||||
|
"""辩论上下文"""
|
||||||
|
current_stage: DebateStage
|
||||||
|
stage_progress: int
|
||||||
|
total_handoffs: int
|
||||||
|
current_speaker: Optional[str] = None
|
||||||
|
last_message: Optional[str] = None
|
||||||
|
debate_history: List[Dict] = None
|
||||||
|
last_priority_analysis: Optional[Dict[str, Any]] = None
|
||||||
|
|
||||||
|
class QiChengZhuanHeDebateSystem:
|
||||||
|
"""起承转合辩论系统"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
# 八仙配置（按先天八卦顺序）
|
||||||
|
self.baxian_speakers = {
|
||||||
|
"吕洞宾": Speaker("吕洞宾", "剑仙投资顾问", "neutral", 0), # 乾
|
||||||
|
"何仙姑": Speaker("何仙姑", "慈悲风控专家", "neutral", 1), # 兑
|
||||||
|
"铁拐李": Speaker("铁拐李", "逆向思维专家", "neutral", 2), # 离
|
||||||
|
"汉钟离": Speaker("汉钟离", "平衡协调者", "neutral", 3), # 震
|
||||||
|
"蓝采和": Speaker("蓝采和", "创新思维者", "neutral", 4), # 巽
|
||||||
|
"张果老": Speaker("张果老", "历史智慧者", "neutral", 5), # 坎
|
||||||
|
"韩湘子": Speaker("韩湘子", "艺术感知者", "neutral", 6), # 艮
|
||||||
|
"曹国舅": Speaker("曹国舅", "实务执行者", "neutral", 7) # 坤
|
||||||
|
}
|
||||||
|
|
||||||
|
# 雁阵队伍配置
|
||||||
|
self.goose_formation = {
|
||||||
|
"positive": ["正1", "正2", "正3", "正4"],
|
||||||
|
"negative": ["反1", "反2", "反3", "反4"]
|
||||||
|
}
|
||||||
|
|
||||||
|
# 辩论状态
|
||||||
|
self.context = DebateContext(
|
||||||
|
current_stage=DebateStage.QI,
|
||||||
|
stage_progress=0,
|
||||||
|
total_handoffs=0,
|
||||||
|
debate_history=[]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 阶段配置
|
||||||
|
self.stage_configs = {
|
||||||
|
DebateStage.QI: {
|
||||||
|
"duration": "8-10分钟",
|
||||||
|
"max_progress": 8, # 八仙轮流发言
|
||||||
|
"description": "八仙按先天八卦顺序阐述观点"
|
||||||
|
},
|
||||||
|
DebateStage.CHENG: {
|
||||||
|
"duration": "8-10分钟",
|
||||||
|
"max_progress": 8, # 正反各4人
|
||||||
|
"description": "雁阵式承接，总体阐述+讥讽"
|
||||||
|
},
|
||||||
|
DebateStage.ZHUAN: {
|
||||||
|
"duration": "12-15分钟",
|
||||||
|
"max_progress": 36, # 36次handoff
|
||||||
|
"description": "自由辩论，优先级算法决定发言"
|
||||||
|
},
|
||||||
|
DebateStage.HE: {
|
||||||
|
"duration": "8-10分钟",
|
||||||
|
"max_progress": 8, # 交替总结
|
||||||
|
"description": "交替总结，最终论证"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 增强版优先级算法
|
||||||
|
self.priority_algorithm = EnhancedPriorityAlgorithm()
|
||||||
|
|
||||||
|
# 记忆系统
|
||||||
|
self.memory_system = DebateMemorySystem()
|
||||||
|
|
||||||
|
def get_current_speaker(self) -> str:
|
||||||
|
"""获取当前发言者"""
|
||||||
|
stage = self.context.current_stage
|
||||||
|
progress = self.context.stage_progress
|
||||||
|
|
||||||
|
if stage == DebateStage.QI:
|
||||||
|
return self._get_bagua_speaker(progress)
|
||||||
|
elif stage == DebateStage.CHENG:
|
||||||
|
return self._get_goose_formation_speaker(progress)
|
||||||
|
elif stage == DebateStage.ZHUAN:
|
||||||
|
return self._get_priority_speaker()
|
||||||
|
elif stage == DebateStage.HE:
|
||||||
|
return self._get_alternating_speaker(progress)
|
||||||
|
|
||||||
|
return "未知发言者"
|
||||||
|
|
||||||
|
def _get_bagua_speaker(self, progress: int) -> str:
|
||||||
|
"""获取八卦顺序发言者"""
|
||||||
|
bagua_sequence = ["吕洞宾", "何仙姑", "铁拐李", "汉钟离", "蓝采和", "张果老", "韩湘子", "曹国舅"]
|
||||||
|
return bagua_sequence[progress % 8]
|
||||||
|
|
||||||
|
def _get_goose_formation_speaker(self, progress: int) -> str:
|
||||||
|
"""获取雁阵发言者"""
|
||||||
|
if progress < 4:
|
||||||
|
# 正方雁阵
|
||||||
|
return self.goose_formation["positive"][progress]
|
||||||
|
else:
|
||||||
|
# 反方雁阵
|
||||||
|
return self.goose_formation["negative"][progress - 4]
|
||||||
|
|
||||||
|
def _get_priority_speaker(self) -> str:
|
||||||
|
"""获取优先级发言者（转阶段）"""
|
||||||
|
available_speakers = ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"]
|
||||||
|
|
||||||
|
# 构建上下文
|
||||||
|
context = {
|
||||||
|
"current_stage": self.context.current_stage.value,
|
||||||
|
"stage_progress": self.context.stage_progress,
|
||||||
|
"max_progress": self.stage_configs[self.context.current_stage]["max_progress"],
|
||||||
|
"time_remaining": max(0.1, 1.0 - (self.context.stage_progress / self.stage_configs[self.context.current_stage]["max_progress"])),
|
||||||
|
"topic_keywords": ["投资", "AI", "风险", "收益"], # 可配置
|
||||||
|
"positive_team_score": 0.5, # 可动态计算
|
||||||
|
"negative_team_score": 0.5, # 可动态计算
|
||||||
|
"positive_recent_speeches": len([h for h in self.context.debate_history[-10:] if "正" in h.get("speaker", "")]),
|
||||||
|
"negative_recent_speeches": len([h for h in self.context.debate_history[-10:] if "反" in h.get("speaker", "")])
|
||||||
|
}
|
||||||
|
|
||||||
|
# 获取最近发言历史
|
||||||
|
recent_speeches = self.context.debate_history[-10:] if self.context.debate_history else []
|
||||||
|
|
||||||
|
next_speaker, score, analysis = self.priority_algorithm.get_next_speaker(
|
||||||
|
available_speakers, context, recent_speeches
|
||||||
|
)
|
||||||
|
|
||||||
|
# 记录分析结果
|
||||||
|
self.context.last_priority_analysis = {
|
||||||
|
"recommended_speaker": next_speaker,
|
||||||
|
"priority_score": score,
|
||||||
|
"analysis": analysis,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
return next_speaker
|
||||||
|
|
||||||
|
def _get_alternating_speaker(self, progress: int) -> str:
|
||||||
|
"""获取交替总结发言者"""
|
||||||
|
alternating_sequence = ["反1", "正1", "反2", "正2", "反3", "正3", "反4", "正4"]
|
||||||
|
return alternating_sequence[progress % 8]
|
||||||
|
|
||||||
|
def advance_stage(self):
|
||||||
|
"""推进辩论阶段"""
|
||||||
|
current_config = self.stage_configs[self.context.current_stage]
|
||||||
|
|
||||||
|
if self.context.stage_progress >= current_config["max_progress"] - 1:
|
||||||
|
# 当前阶段完成，进入下一阶段
|
||||||
|
self._transition_to_next_stage()
|
||||||
|
else:
|
||||||
|
# 当前阶段继续
|
||||||
|
self.context.stage_progress += 1
|
||||||
|
|
||||||
|
def _transition_to_next_stage(self):
|
||||||
|
"""转换到下一阶段"""
|
||||||
|
stage_transitions = {
|
||||||
|
DebateStage.QI: DebateStage.CHENG,
|
||||||
|
DebateStage.CHENG: DebateStage.ZHUAN,
|
||||||
|
DebateStage.ZHUAN: DebateStage.HE,
|
||||||
|
DebateStage.HE: None # 辩论结束
|
||||||
|
}
|
||||||
|
|
||||||
|
next_stage = stage_transitions[self.context.current_stage]
|
||||||
|
if next_stage:
|
||||||
|
self.context.current_stage = next_stage
|
||||||
|
self.context.stage_progress = 0
|
||||||
|
print(f"🎭 辩论进入{next_stage.value}阶段")
|
||||||
|
else:
|
||||||
|
print("🎉 辩论结束！")
|
||||||
|
|
||||||
|
def record_speech(self, speaker: str, message: str):
|
||||||
|
"""记录发言"""
|
||||||
|
speech_record = {
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"stage": self.context.current_stage.value,
|
||||||
|
"stage_progress": self.context.stage_progress,
|
||||||
|
"speaker": speaker,
|
||||||
|
"message": message,
|
||||||
|
"total_handoffs": self.context.total_handoffs
|
||||||
|
}
|
||||||
|
|
||||||
|
self.context.debate_history.append(speech_record)
|
||||||
|
self.context.last_message = message
|
||||||
|
self.context.current_speaker = speaker
|
||||||
|
|
||||||
|
# 更新记忆系统
|
||||||
|
self.memory_system.store_speech(speaker, message, self.context)
|
||||||
|
|
||||||
|
# 如果是转阶段，增加handoff计数
|
||||||
|
if self.context.current_stage == DebateStage.ZHUAN:
|
||||||
|
self.context.total_handoffs += 1
|
||||||
|
|
||||||
|
def get_stage_info(self) -> Dict[str, Any]:
|
||||||
|
"""获取当前阶段信息"""
|
||||||
|
stage = self.context.current_stage
|
||||||
|
config = self.stage_configs[stage]
|
||||||
|
|
||||||
|
return {
|
||||||
|
"current_stage": stage.value,
|
||||||
|
"stage_progress": self.context.stage_progress,
|
||||||
|
"max_progress": config["max_progress"],
|
||||||
|
"description": config["description"],
|
||||||
|
"current_speaker": self.get_current_speaker(),
|
||||||
|
"total_handoffs": self.context.total_handoffs
|
||||||
|
}
|
||||||
|
|
||||||
|
def save_debate_state(self, filename: str = "debate_state.json"):
|
||||||
|
"""保存辩论状态"""
|
||||||
|
state_data = {
|
||||||
|
"context": {
|
||||||
|
"current_stage": self.context.current_stage.value,
|
||||||
|
"stage_progress": self.context.stage_progress,
|
||||||
|
"total_handoffs": self.context.total_handoffs,
|
||||||
|
"current_speaker": self.context.current_speaker,
|
||||||
|
"last_message": self.context.last_message
|
||||||
|
},
|
||||||
|
"debate_history": self.context.debate_history,
|
||||||
|
"memory_data": self.memory_system.get_memory_data()
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(filename, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(state_data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
print(f"💾 辩论状态已保存到 {filename}")
|
||||||
|
|
||||||
|
# 旧的PriorityAlgorithm类已被EnhancedPriorityAlgorithm替换
|
||||||
|
|
||||||
|
class DebateMemorySystem:
|
||||||
|
"""辩论记忆系统"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.speaker_memories = {}
|
||||||
|
self.debate_memories = []
|
||||||
|
|
||||||
|
def store_speech(self, speaker: str, message: str, context: DebateContext):
|
||||||
|
"""存储发言记忆"""
|
||||||
|
if speaker not in self.speaker_memories:
|
||||||
|
self.speaker_memories[speaker] = []
|
||||||
|
|
||||||
|
memory_entry = {
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"stage": context.current_stage.value,
|
||||||
|
"message": message,
|
||||||
|
"context": {
|
||||||
|
"stage_progress": context.stage_progress,
|
||||||
|
"total_handoffs": context.total_handoffs
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
self.speaker_memories[speaker].append(memory_entry)
|
||||||
|
self.debate_memories.append(memory_entry)
|
||||||
|
|
||||||
|
def get_speaker_memory(self, speaker: str, limit: int = 5) -> List[Dict]:
|
||||||
|
"""获取发言者记忆"""
|
||||||
|
if speaker in self.speaker_memories:
|
||||||
|
return self.speaker_memories[speaker][-limit:]
|
||||||
|
return []
|
||||||
|
|
||||||
|
def get_memory_data(self) -> Dict[str, Any]:
|
||||||
|
"""获取记忆数据"""
|
||||||
|
return {
|
||||||
|
"speaker_memories": self.speaker_memories,
|
||||||
|
"debate_memories": self.debate_memories
|
||||||
|
}
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数 - 测试起承转合辩论系统"""
|
||||||
|
print("🚀 太公心易 - 起承转合辩论系统")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
# 创建辩论系统
|
||||||
|
debate_system = QiChengZhuanHeDebateSystem()
|
||||||
|
|
||||||
|
# 测试各阶段
|
||||||
|
test_messages = [
|
||||||
|
"起：八仙按先天八卦顺序阐述观点",
|
||||||
|
"承：雁阵式承接，总体阐述+讥讽",
|
||||||
|
"转：自由辩论，36次handoff",
|
||||||
|
"合：交替总结，最终论证"
|
||||||
|
]
|
||||||
|
|
||||||
|
for i, message in enumerate(test_messages):
|
||||||
|
stage_info = debate_system.get_stage_info()
|
||||||
|
current_speaker = debate_system.get_current_speaker()
|
||||||
|
|
||||||
|
print(f"\n🎭 当前阶段: {stage_info['current_stage']}")
|
||||||
|
print(f"📊 进度: {stage_info['stage_progress'] + 1}/{stage_info['max_progress']}")
|
||||||
|
print(f"🗣️ 发言者: {current_speaker}")
|
||||||
|
print(f"💬 消息: {message}")
|
||||||
|
|
||||||
|
# 记录发言
|
||||||
|
debate_system.record_speech(current_speaker, message)
|
||||||
|
|
||||||
|
# 推进阶段
|
||||||
|
debate_system.advance_stage()
|
||||||
|
|
||||||
|
# 保存状态
|
||||||
|
debate_system.save_debate_state()
|
||||||
|
|
||||||
|
print("\n✅ 起承转合辩论系统测试完成！")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
# 稷下学宫引擎模块
|
||||||
|
|
@ -0,0 +1,43 @@
|
||||||
|
# 设计八仙与数据源的智能映射
|
||||||
|
immortal_data_mapping = {
|
||||||
|
'吕洞宾': {
|
||||||
|
'specialty': 'technical_analysis', # 技术分析专家
|
||||||
|
'preferred_data_types': ['historical', 'price'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
},
|
||||||
|
'何仙姑': {
|
||||||
|
'specialty': 'risk_metrics', # 风险控制专家
|
||||||
|
'preferred_data_types': ['price', 'profile'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'张果老': {
|
||||||
|
'specialty': 'historical_data', # 历史数据分析师
|
||||||
|
'preferred_data_types': ['historical'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
},
|
||||||
|
'韩湘子': {
|
||||||
|
'specialty': 'sector_analysis', # 新兴资产专家
|
||||||
|
'preferred_data_types': ['profile', 'news'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'汉钟离': {
|
||||||
|
'specialty': 'market_movers', # 热点追踪
|
||||||
|
'preferred_data_types': ['news', 'price'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'蓝采和': {
|
||||||
|
'specialty': 'value_discovery', # 潜力股发现
|
||||||
|
'preferred_data_types': ['screener', 'profile'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
},
|
||||||
|
'铁拐李': {
|
||||||
|
'specialty': 'contrarian_analysis', # 逆向思维专家
|
||||||
|
'preferred_data_types': ['profile', 'short_interest'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'曹国舅': {
|
||||||
|
'specialty': 'macro_economics', # 宏观经济分析师
|
||||||
|
'preferred_data_types': ['profile', 'institutional_holdings'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,38 @@
|
||||||
|
from abc import ABC, abstractmethod
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
|
||||||
|
class DataProvider(ABC):
|
||||||
|
"""金融数据提供商抽象基类"""
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
"""获取股票报价"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
"""获取历史价格数据"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
"""获取公司概况"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
"""获取相关新闻"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
@abstractmethod
|
||||||
|
def name(self) -> str:
|
||||||
|
"""数据提供商名称"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
@abstractmethod
|
||||||
|
def priority(self) -> int:
|
||||||
|
"""优先级（数字越小优先级越高）"""
|
||||||
|
pass
|
||||||
|
|
@ -0,0 +1,109 @@
|
||||||
|
from typing import List, Optional
|
||||||
|
import asyncio
|
||||||
|
from src.jixia.engines.data_abstraction import DataProvider
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
from src.jixia.engines.rapidapi_adapter import RapidAPIDataProvider
|
||||||
|
from src.jixia.engines.openbb_adapter import OpenBBDataProvider
|
||||||
|
|
||||||
|
class DataAbstractionLayer:
|
||||||
|
"""金融数据抽象层管理器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.providers: List[DataProvider] = []
|
||||||
|
self._initialize_providers()
|
||||||
|
|
||||||
|
def _initialize_providers(self):
|
||||||
|
"""初始化所有可用的数据提供商"""
|
||||||
|
# 根据配置和环境动态加载适配器
|
||||||
|
try:
|
||||||
|
self.providers.append(OpenBBDataProvider())
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: OpenBBDataProvider 初始化失败: {e}")
|
||||||
|
|
||||||
|
try:
|
||||||
|
self.providers.append(RapidAPIDataProvider())
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: RapidAPIDataProvider 初始化失败: {e}")
|
||||||
|
|
||||||
|
# 按优先级排序
|
||||||
|
self.providers.sort(key=lambda p: p.priority)
|
||||||
|
print(f"数据抽象层初始化完成，已加载 {len(self.providers)} 个数据提供商")
|
||||||
|
for provider in self.providers:
|
||||||
|
print(f" - {provider.name} (优先级: {provider.priority})")
|
||||||
|
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
"""获取股票报价（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
quote = provider.get_quote(symbol)
|
||||||
|
if quote:
|
||||||
|
print(f"✅ 通过 {provider.name} 获取到 {symbol} 的报价")
|
||||||
|
return quote
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取报价失败: {e}")
|
||||||
|
continue
|
||||||
|
print(f"❌ 所有数据提供商都无法获取 {symbol} 的报价")
|
||||||
|
return None
|
||||||
|
|
||||||
|
async def get_quote_async(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
"""异步获取股票报价（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
# 如果提供商支持异步方法，则使用异步方法
|
||||||
|
if hasattr(provider, 'get_quote_async'):
|
||||||
|
quote = await provider.get_quote_async(symbol)
|
||||||
|
else:
|
||||||
|
# 否则在执行器中运行同步方法
|
||||||
|
quote = await asyncio.get_event_loop().run_in_executor(
|
||||||
|
None, provider.get_quote, symbol
|
||||||
|
)
|
||||||
|
if quote:
|
||||||
|
print(f"✅ 通过 {provider.name} 异步获取到 {symbol} 的报价")
|
||||||
|
return quote
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 异步获取报价失败: {e}")
|
||||||
|
continue
|
||||||
|
print(f"❌ 所有数据提供商都无法异步获取 {symbol} 的报价")
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
"""获取历史价格数据（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
prices = provider.get_historical_prices(symbol, days)
|
||||||
|
if prices:
|
||||||
|
print(f"✅ 通过 {provider.name} 获取到 {symbol} 的历史价格数据")
|
||||||
|
return prices
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取历史价格失败: {e}")
|
||||||
|
continue
|
||||||
|
print(f"❌ 所有数据提供商都无法获取 {symbol} 的历史价格数据")
|
||||||
|
return []
|
||||||
|
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
"""获取公司概况（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
profile = provider.get_company_profile(symbol)
|
||||||
|
if profile:
|
||||||
|
print(f"✅ 通过 {provider.name} 获取到 {symbol} 的公司概况")
|
||||||
|
return profile
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取公司概况失败: {e}")
|
||||||
|
continue
|
||||||
|
print(f"❌ 所有数据提供商都无法获取 {symbol} 的公司概况")
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
"""获取相关新闻（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
news = provider.get_news(symbol, limit)
|
||||||
|
if news:
|
||||||
|
print(f"✅ 通过 {provider.name} 获取到 {symbol} 的相关新闻")
|
||||||
|
return news
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取新闻失败: {e}")
|
||||||
|
continue
|
||||||
|
print(f"❌ 所有数据提供商都无法获取 {symbol} 的相关新闻")
|
||||||
|
return []
|
||||||
|
|
@ -0,0 +1,37 @@
|
||||||
|
import time
|
||||||
|
from typing import Any, Optional
|
||||||
|
from functools import lru_cache
|
||||||
|
|
||||||
|
class DataCache:
|
||||||
|
"""金融数据缓存"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self._cache = {}
|
||||||
|
self._cache_times = {}
|
||||||
|
self.default_ttl = 60 # 默认缓存时间（秒）
|
||||||
|
|
||||||
|
def get(self, key: str) -> Optional[Any]:
|
||||||
|
"""获取缓存数据"""
|
||||||
|
if key in self._cache:
|
||||||
|
# 检查是否过期
|
||||||
|
if time.time() - self._cache_times[key] < self.default_ttl:
|
||||||
|
return self._cache[key]
|
||||||
|
else:
|
||||||
|
# 删除过期缓存
|
||||||
|
del self._cache[key]
|
||||||
|
del self._cache_times[key]
|
||||||
|
return None
|
||||||
|
|
||||||
|
def set(self, key: str, value: Any, ttl: Optional[int] = None):
|
||||||
|
"""设置缓存数据"""
|
||||||
|
self._cache[key] = value
|
||||||
|
self._cache_times[key] = time.time()
|
||||||
|
if ttl:
|
||||||
|
# 可以为特定数据设置不同的TTL
|
||||||
|
pass # 实际实现中需要更复杂的TTL管理机制
|
||||||
|
|
||||||
|
@lru_cache(maxsize=128)
|
||||||
|
def get_quote_cache(self, symbol: str) -> Optional[Any]:
|
||||||
|
"""LRU缓存装饰器示例"""
|
||||||
|
# 这个方法将自动缓存最近128个调用的结果
|
||||||
|
pass
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
from typing import Dict, Any
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
class DataQualityMonitor:
|
||||||
|
"""数据质量监控"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.provider_stats = {}
|
||||||
|
|
||||||
|
def record_access(self, provider_name: str, success: bool, response_time: float, data_size: int):
|
||||||
|
"""记录数据访问统计"""
|
||||||
|
if provider_name not in self.provider_stats:
|
||||||
|
self.provider_stats[provider_name] = {
|
||||||
|
'total_requests': 0,
|
||||||
|
'successful_requests': 0,
|
||||||
|
'failed_requests': 0,
|
||||||
|
'total_response_time': 0,
|
||||||
|
'total_data_size': 0,
|
||||||
|
'last_access': None
|
||||||
|
}
|
||||||
|
|
||||||
|
stats = self.provider_stats[provider_name]
|
||||||
|
stats['total_requests'] += 1
|
||||||
|
if success:
|
||||||
|
stats['successful_requests'] += 1
|
||||||
|
else:
|
||||||
|
stats['failed_requests'] += 1
|
||||||
|
stats['total_response_time'] += response_time
|
||||||
|
stats['total_data_size'] += data_size
|
||||||
|
stats['last_access'] = datetime.now()
|
||||||
|
|
||||||
|
def get_provider_health(self, provider_name: str) -> Dict[str, Any]:
|
||||||
|
"""获取提供商健康状况"""
|
||||||
|
if provider_name not in self.provider_stats:
|
||||||
|
return {'status': 'unknown'}
|
||||||
|
|
||||||
|
stats = self.provider_stats[provider_name]
|
||||||
|
success_rate = stats['successful_requests'] / stats['total_requests'] if stats['total_requests'] > 0 else 0
|
||||||
|
avg_response_time = stats['total_response_time'] / stats['total_requests'] if stats['total_requests'] > 0 else 0
|
||||||
|
|
||||||
|
status = 'healthy' if success_rate > 0.95 and avg_response_time < 2.0 else 'degraded' if success_rate > 0.8 else 'unhealthy'
|
||||||
|
|
||||||
|
return {
|
||||||
|
'status': status,
|
||||||
|
'success_rate': success_rate,
|
||||||
|
'avg_response_time': avg_response_time,
|
||||||
|
'total_requests': stats['total_requests'],
|
||||||
|
'last_access': stats['last_access']
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,462 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫负载均衡器
|
||||||
|
实现八仙论道的API负载分担策略
|
||||||
|
"""
|
||||||
|
|
||||||
|
import time
|
||||||
|
import random
|
||||||
|
import requests
|
||||||
|
from datetime import datetime, timezone
|
||||||
|
from typing import Dict, List, Any, Optional, Tuple
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from collections import defaultdict
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class APIResult:
|
||||||
|
"""API调用结果"""
|
||||||
|
success: bool
|
||||||
|
data: Dict[str, Any]
|
||||||
|
api_used: str
|
||||||
|
response_time: float
|
||||||
|
error: Optional[str] = None
|
||||||
|
cached: bool = False
|
||||||
|
|
||||||
|
class RateLimiter:
|
||||||
|
"""速率限制器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.api_calls = defaultdict(list)
|
||||||
|
self.limits = {
|
||||||
|
'alpha_vantage': {'per_minute': 500, 'per_month': 500000},
|
||||||
|
'yahoo_finance_15': {'per_minute': 500, 'per_month': 500000},
|
||||||
|
'webull': {'per_minute': 500, 'per_month': 500000},
|
||||||
|
'seeking_alpha': {'per_minute': 500, 'per_month': 500000}
|
||||||
|
}
|
||||||
|
|
||||||
|
def is_rate_limited(self, api_name: str) -> bool:
|
||||||
|
"""检查是否达到速率限制"""
|
||||||
|
now = time.time()
|
||||||
|
calls = self.api_calls[api_name]
|
||||||
|
|
||||||
|
# 清理1分钟前的记录
|
||||||
|
self.api_calls[api_name] = [call_time for call_time in calls if now - call_time < 60]
|
||||||
|
|
||||||
|
# 检查每分钟限制
|
||||||
|
if len(self.api_calls[api_name]) >= self.limits[api_name]['per_minute'] * 0.9: # 90%阈值
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
|
|
||||||
|
def record_call(self, api_name: str):
|
||||||
|
"""记录API调用"""
|
||||||
|
self.api_calls[api_name].append(time.time())
|
||||||
|
|
||||||
|
class APIHealthChecker:
|
||||||
|
"""API健康检查器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.health_status = {
|
||||||
|
'alpha_vantage': {'healthy': True, 'last_check': 0, 'consecutive_failures': 0},
|
||||||
|
'yahoo_finance_15': {'healthy': True, 'last_check': 0, 'consecutive_failures': 0},
|
||||||
|
'webull': {'healthy': True, 'last_check': 0, 'consecutive_failures': 0},
|
||||||
|
'seeking_alpha': {'healthy': True, 'last_check': 0, 'consecutive_failures': 0}
|
||||||
|
}
|
||||||
|
self.check_interval = 300 # 5分钟检查一次
|
||||||
|
|
||||||
|
def is_healthy(self, api_name: str) -> bool:
|
||||||
|
"""检查API是否健康"""
|
||||||
|
status = self.health_status[api_name]
|
||||||
|
now = time.time()
|
||||||
|
|
||||||
|
# 如果距离上次检查超过间隔时间，进行健康检查
|
||||||
|
if now - status['last_check'] > self.check_interval:
|
||||||
|
self._perform_health_check(api_name)
|
||||||
|
|
||||||
|
return status['healthy']
|
||||||
|
|
||||||
|
def _perform_health_check(self, api_name: str):
|
||||||
|
"""执行健康检查"""
|
||||||
|
# 这里可以实现具体的健康检查逻辑
|
||||||
|
# 暂时简化为基于连续失败次数判断
|
||||||
|
status = self.health_status[api_name]
|
||||||
|
status['last_check'] = time.time()
|
||||||
|
|
||||||
|
# 如果连续失败超过3次，标记为不健康
|
||||||
|
if status['consecutive_failures'] > 3:
|
||||||
|
status['healthy'] = False
|
||||||
|
else:
|
||||||
|
status['healthy'] = True
|
||||||
|
|
||||||
|
def record_success(self, api_name: str):
|
||||||
|
"""记录成功调用"""
|
||||||
|
self.health_status[api_name]['consecutive_failures'] = 0
|
||||||
|
self.health_status[api_name]['healthy'] = True
|
||||||
|
|
||||||
|
def record_failure(self, api_name: str):
|
||||||
|
"""记录失败调用"""
|
||||||
|
self.health_status[api_name]['consecutive_failures'] += 1
|
||||||
|
|
||||||
|
class DataNormalizer:
|
||||||
|
"""数据标准化处理器"""
|
||||||
|
|
||||||
|
def normalize_stock_quote(self, raw_data: dict, api_source: str) -> dict:
|
||||||
|
"""将不同API的股票报价数据标准化"""
|
||||||
|
try:
|
||||||
|
if api_source == 'alpha_vantage':
|
||||||
|
return self._normalize_alpha_vantage_quote(raw_data)
|
||||||
|
elif api_source == 'yahoo_finance_15':
|
||||||
|
return self._normalize_yahoo_quote(raw_data)
|
||||||
|
elif api_source == 'webull':
|
||||||
|
return self._normalize_webull_quote(raw_data)
|
||||||
|
elif api_source == 'seeking_alpha':
|
||||||
|
return self._normalize_seeking_alpha_quote(raw_data)
|
||||||
|
else:
|
||||||
|
return {'error': f'Unknown API source: {api_source}'}
|
||||||
|
except Exception as e:
|
||||||
|
return {'error': f'Data normalization failed: {str(e)}'}
|
||||||
|
|
||||||
|
def _normalize_alpha_vantage_quote(self, data: dict) -> dict:
|
||||||
|
"""标准化Alpha Vantage数据格式"""
|
||||||
|
global_quote = data.get('Global Quote', {})
|
||||||
|
return {
|
||||||
|
'symbol': global_quote.get('01. symbol'),
|
||||||
|
'price': float(global_quote.get('05. price', 0)),
|
||||||
|
'change': float(global_quote.get('09. change', 0)),
|
||||||
|
'change_percent': global_quote.get('10. change percent', '0%'),
|
||||||
|
'volume': int(global_quote.get('06. volume', 0)),
|
||||||
|
'high': float(global_quote.get('03. high', 0)),
|
||||||
|
'low': float(global_quote.get('04. low', 0)),
|
||||||
|
'source': 'alpha_vantage',
|
||||||
|
'timestamp': global_quote.get('07. latest trading day')
|
||||||
|
}
|
||||||
|
|
||||||
|
def _normalize_yahoo_quote(self, data: dict) -> dict:
|
||||||
|
"""标准化Yahoo Finance数据格式"""
|
||||||
|
body = data.get('body', {})
|
||||||
|
return {
|
||||||
|
'symbol': body.get('symbol'),
|
||||||
|
'price': float(body.get('regularMarketPrice', 0)),
|
||||||
|
'change': float(body.get('regularMarketChange', 0)),
|
||||||
|
'change_percent': f"{body.get('regularMarketChangePercent', 0):.2f}%",
|
||||||
|
'volume': int(body.get('regularMarketVolume', 0)),
|
||||||
|
'high': float(body.get('regularMarketDayHigh', 0)),
|
||||||
|
'low': float(body.get('regularMarketDayLow', 0)),
|
||||||
|
'source': 'yahoo_finance_15',
|
||||||
|
'timestamp': body.get('regularMarketTime')
|
||||||
|
}
|
||||||
|
|
||||||
|
def _normalize_webull_quote(self, data: dict) -> dict:
|
||||||
|
"""标准化Webull数据格式"""
|
||||||
|
if 'stocks' in data and len(data['stocks']) > 0:
|
||||||
|
stock = data['stocks'][0]
|
||||||
|
return {
|
||||||
|
'symbol': stock.get('symbol'),
|
||||||
|
'price': float(stock.get('close', 0)),
|
||||||
|
'change': float(stock.get('change', 0)),
|
||||||
|
'change_percent': f"{stock.get('changeRatio', 0):.2f}%",
|
||||||
|
'volume': int(stock.get('volume', 0)),
|
||||||
|
'high': float(stock.get('high', 0)),
|
||||||
|
'low': float(stock.get('low', 0)),
|
||||||
|
'source': 'webull',
|
||||||
|
'timestamp': stock.get('timeStamp')
|
||||||
|
}
|
||||||
|
return {'error': 'No stock data found in Webull response'}
|
||||||
|
|
||||||
|
def _normalize_seeking_alpha_quote(self, data: dict) -> dict:
|
||||||
|
"""标准化Seeking Alpha数据格式"""
|
||||||
|
if 'data' in data and len(data['data']) > 0:
|
||||||
|
stock_data = data['data'][0]
|
||||||
|
attributes = stock_data.get('attributes', {})
|
||||||
|
return {
|
||||||
|
'symbol': attributes.get('slug'),
|
||||||
|
'price': float(attributes.get('lastPrice', 0)),
|
||||||
|
'change': float(attributes.get('dayChange', 0)),
|
||||||
|
'change_percent': f"{attributes.get('dayChangePercent', 0):.2f}%",
|
||||||
|
'volume': int(attributes.get('volume', 0)),
|
||||||
|
'source': 'seeking_alpha',
|
||||||
|
'market_cap': attributes.get('marketCap'),
|
||||||
|
'pe_ratio': attributes.get('peRatio')
|
||||||
|
}
|
||||||
|
return {'error': 'No data found in Seeking Alpha response'}
|
||||||
|
|
||||||
|
class JixiaLoadBalancer:
|
||||||
|
"""稷下学宫负载均衡器"""
|
||||||
|
|
||||||
|
def __init__(self, rapidapi_key: str):
|
||||||
|
self.rapidapi_key = rapidapi_key
|
||||||
|
self.rate_limiter = RateLimiter()
|
||||||
|
self.health_checker = APIHealthChecker()
|
||||||
|
self.data_normalizer = DataNormalizer()
|
||||||
|
self.cache = {} # 简单的内存缓存
|
||||||
|
self.cache_ttl = 300 # 5分钟缓存
|
||||||
|
|
||||||
|
# API配置
|
||||||
|
self.api_configs = {
|
||||||
|
'alpha_vantage': {
|
||||||
|
'host': 'alpha-vantage.p.rapidapi.com',
|
||||||
|
'endpoints': {
|
||||||
|
'stock_quote': '/query?function=GLOBAL_QUOTE&symbol={symbol}',
|
||||||
|
'company_overview': '/query?function=OVERVIEW&symbol={symbol}',
|
||||||
|
'earnings': '/query?function=EARNINGS&symbol={symbol}'
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'yahoo_finance_15': {
|
||||||
|
'host': 'yahoo-finance15.p.rapidapi.com',
|
||||||
|
'endpoints': {
|
||||||
|
'stock_quote': '/api/yahoo/qu/quote/{symbol}',
|
||||||
|
'market_movers': '/api/yahoo/co/collections/day_gainers',
|
||||||
|
'market_news': '/api/yahoo/ne/news'
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'webull': {
|
||||||
|
'host': 'webull.p.rapidapi.com',
|
||||||
|
'endpoints': {
|
||||||
|
'stock_quote': '/stock/search?keyword={symbol}',
|
||||||
|
'market_movers': '/market/get-active-gainers'
|
||||||
|
}
|
||||||
|
},
|
||||||
|
'seeking_alpha': {
|
||||||
|
'host': 'seeking-alpha.p.rapidapi.com',
|
||||||
|
'endpoints': {
|
||||||
|
'company_overview': '/symbols/get-profile?symbols={symbol}',
|
||||||
|
'market_news': '/news/list?category=market-news'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 八仙API分配策略
|
||||||
|
self.immortal_api_mapping = {
|
||||||
|
'stock_quote': {
|
||||||
|
'吕洞宾': 'alpha_vantage', # 主力剑仙用最快的API
|
||||||
|
'何仙姑': 'yahoo_finance_15', # 风控专家用稳定的API
|
||||||
|
'张果老': 'webull', # 技术分析师用搜索强的API
|
||||||
|
'韩湘子': 'alpha_vantage', # 基本面研究用专业API
|
||||||
|
'汉钟离': 'yahoo_finance_15', # 量化专家用市场数据API
|
||||||
|
'蓝采和': 'webull', # 情绪分析师用活跃数据API
|
||||||
|
'曹国舅': 'seeking_alpha', # 宏观分析师用分析API
|
||||||
|
'铁拐李': 'alpha_vantage' # 逆向投资用基础数据API
|
||||||
|
},
|
||||||
|
'company_overview': {
|
||||||
|
'吕洞宾': 'alpha_vantage',
|
||||||
|
'何仙姑': 'seeking_alpha',
|
||||||
|
'张果老': 'alpha_vantage',
|
||||||
|
'韩湘子': 'seeking_alpha',
|
||||||
|
'汉钟离': 'alpha_vantage',
|
||||||
|
'蓝采和': 'seeking_alpha',
|
||||||
|
'曹国舅': 'seeking_alpha',
|
||||||
|
'铁拐李': 'alpha_vantage'
|
||||||
|
},
|
||||||
|
'market_movers': {
|
||||||
|
'吕洞宾': 'yahoo_finance_15',
|
||||||
|
'何仙姑': 'webull',
|
||||||
|
'张果老': 'yahoo_finance_15',
|
||||||
|
'韩湘子': 'webull',
|
||||||
|
'汉钟离': 'yahoo_finance_15',
|
||||||
|
'蓝采和': 'webull',
|
||||||
|
'曹国舅': 'yahoo_finance_15',
|
||||||
|
'铁拐李': 'webull'
|
||||||
|
},
|
||||||
|
'market_news': {
|
||||||
|
'吕洞宾': 'yahoo_finance_15',
|
||||||
|
'何仙姑': 'seeking_alpha',
|
||||||
|
'张果老': 'yahoo_finance_15',
|
||||||
|
'韩湘子': 'seeking_alpha',
|
||||||
|
'汉钟离': 'yahoo_finance_15',
|
||||||
|
'蓝采和': 'seeking_alpha',
|
||||||
|
'曹国舅': 'seeking_alpha',
|
||||||
|
'铁拐李': 'yahoo_finance_15'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 故障转移优先级
|
||||||
|
self.failover_priority = {
|
||||||
|
'alpha_vantage': ['webull', 'yahoo_finance_15'],
|
||||||
|
'yahoo_finance_15': ['webull', 'alpha_vantage'],
|
||||||
|
'webull': ['alpha_vantage', 'yahoo_finance_15'],
|
||||||
|
'seeking_alpha': ['yahoo_finance_15', 'alpha_vantage']
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_data_for_immortal(self, immortal_name: str, data_type: str, symbol: str = None) -> APIResult:
|
||||||
|
"""为特定仙人获取数据"""
|
||||||
|
print(f"🎭 {immortal_name} 正在获取 {data_type} 数据...")
|
||||||
|
|
||||||
|
# 检查缓存
|
||||||
|
cache_key = f"{immortal_name}_{data_type}_{symbol}"
|
||||||
|
cached_result = self._get_cached_data(cache_key)
|
||||||
|
if cached_result:
|
||||||
|
print(f" 📦 使用缓存数据")
|
||||||
|
return cached_result
|
||||||
|
|
||||||
|
# 获取该仙人的首选API
|
||||||
|
if data_type not in self.immortal_api_mapping:
|
||||||
|
return APIResult(False, {}, '', 0, f"Unsupported data type: {data_type}")
|
||||||
|
|
||||||
|
preferred_api = self.immortal_api_mapping[data_type][immortal_name]
|
||||||
|
|
||||||
|
# 尝试首选API
|
||||||
|
result = self._try_api(preferred_api, data_type, symbol)
|
||||||
|
if result.success:
|
||||||
|
self._cache_data(cache_key, result)
|
||||||
|
print(f" ✅ 成功从 {preferred_api} 获取数据 (响应时间: {result.response_time:.2f}s)")
|
||||||
|
return result
|
||||||
|
|
||||||
|
# 故障转移到备用API
|
||||||
|
print(f" ⚠️ {preferred_api} 不可用，尝试备用API...")
|
||||||
|
backup_apis = self.failover_priority.get(preferred_api, [])
|
||||||
|
|
||||||
|
for backup_api in backup_apis:
|
||||||
|
if data_type in self.api_configs[backup_api]['endpoints']:
|
||||||
|
result = self._try_api(backup_api, data_type, symbol)
|
||||||
|
if result.success:
|
||||||
|
self._cache_data(cache_key, result)
|
||||||
|
print(f" ✅ 成功从备用API {backup_api} 获取数据 (响应时间: {result.response_time:.2f}s)")
|
||||||
|
return result
|
||||||
|
|
||||||
|
# 所有API都失败
|
||||||
|
print(f" ❌ 所有API都不可用")
|
||||||
|
return APIResult(False, {}, '', 0, "All APIs failed")
|
||||||
|
|
||||||
|
def _try_api(self, api_name: str, data_type: str, symbol: str = None) -> APIResult:
|
||||||
|
"""尝试调用指定API"""
|
||||||
|
# 检查API健康状态和速率限制
|
||||||
|
if not self.health_checker.is_healthy(api_name):
|
||||||
|
return APIResult(False, {}, api_name, 0, "API is unhealthy")
|
||||||
|
|
||||||
|
if self.rate_limiter.is_rate_limited(api_name):
|
||||||
|
return APIResult(False, {}, api_name, 0, "Rate limited")
|
||||||
|
|
||||||
|
# 构建请求
|
||||||
|
config = self.api_configs[api_name]
|
||||||
|
if data_type not in config['endpoints']:
|
||||||
|
return APIResult(False, {}, api_name, 0, f"Endpoint {data_type} not supported")
|
||||||
|
|
||||||
|
endpoint = config['endpoints'][data_type]
|
||||||
|
if symbol and '{symbol}' in endpoint:
|
||||||
|
endpoint = endpoint.format(symbol=symbol)
|
||||||
|
|
||||||
|
url = f"https://{config['host']}{endpoint}"
|
||||||
|
headers = {
|
||||||
|
'X-RapidAPI-Key': self.rapidapi_key,
|
||||||
|
'X-RapidAPI-Host': config['host']
|
||||||
|
}
|
||||||
|
|
||||||
|
# 发起请求
|
||||||
|
start_time = time.time()
|
||||||
|
try:
|
||||||
|
response = requests.get(url, headers=headers, timeout=10)
|
||||||
|
response_time = time.time() - start_time
|
||||||
|
|
||||||
|
self.rate_limiter.record_call(api_name)
|
||||||
|
|
||||||
|
if response.status_code == 200:
|
||||||
|
data = response.json()
|
||||||
|
|
||||||
|
# 数据标准化
|
||||||
|
if data_type == 'stock_quote':
|
||||||
|
normalized_data = self.data_normalizer.normalize_stock_quote(data, api_name)
|
||||||
|
else:
|
||||||
|
normalized_data = data
|
||||||
|
|
||||||
|
self.health_checker.record_success(api_name)
|
||||||
|
return APIResult(True, normalized_data, api_name, response_time)
|
||||||
|
else:
|
||||||
|
error_msg = f"HTTP {response.status_code}: {response.text[:200]}"
|
||||||
|
self.health_checker.record_failure(api_name)
|
||||||
|
return APIResult(False, {}, api_name, response_time, error_msg)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
response_time = time.time() - start_time
|
||||||
|
self.health_checker.record_failure(api_name)
|
||||||
|
return APIResult(False, {}, api_name, response_time, str(e))
|
||||||
|
|
||||||
|
def _get_cached_data(self, cache_key: str) -> Optional[APIResult]:
|
||||||
|
"""获取缓存数据"""
|
||||||
|
if cache_key in self.cache:
|
||||||
|
cached_item = self.cache[cache_key]
|
||||||
|
if time.time() - cached_item['timestamp'] < self.cache_ttl:
|
||||||
|
result = cached_item['result']
|
||||||
|
result.cached = True
|
||||||
|
return result
|
||||||
|
else:
|
||||||
|
# 缓存过期，删除
|
||||||
|
del self.cache[cache_key]
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _cache_data(self, cache_key: str, result: APIResult):
|
||||||
|
"""缓存数据"""
|
||||||
|
self.cache[cache_key] = {
|
||||||
|
'result': result,
|
||||||
|
'timestamp': time.time()
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_load_distribution(self) -> dict:
|
||||||
|
"""获取负载分布统计"""
|
||||||
|
api_calls = {}
|
||||||
|
total_calls = 0
|
||||||
|
|
||||||
|
for api_name, calls in self.rate_limiter.api_calls.items():
|
||||||
|
call_count = len(calls)
|
||||||
|
api_calls[api_name] = call_count
|
||||||
|
total_calls += call_count
|
||||||
|
|
||||||
|
if total_calls == 0:
|
||||||
|
return {}
|
||||||
|
|
||||||
|
distribution = {}
|
||||||
|
for api_name, call_count in api_calls.items():
|
||||||
|
health_status = self.health_checker.health_status[api_name]
|
||||||
|
distribution[api_name] = {
|
||||||
|
'calls': call_count,
|
||||||
|
'percentage': (call_count / total_calls) * 100,
|
||||||
|
'healthy': health_status['healthy'],
|
||||||
|
'consecutive_failures': health_status['consecutive_failures']
|
||||||
|
}
|
||||||
|
|
||||||
|
return distribution
|
||||||
|
|
||||||
|
def conduct_immortal_debate(self, topic_symbol: str) -> Dict[str, APIResult]:
|
||||||
|
"""进行八仙论道，每个仙人获取不同的数据"""
|
||||||
|
print(f"\n🏛️ 稷下学宫八仙论道开始 - 主题: {topic_symbol}")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
immortals = ['吕洞宾', '何仙姑', '张果老', '韩湘子', '汉钟离', '蓝采和', '曹国舅', '铁拐李']
|
||||||
|
debate_results = {}
|
||||||
|
|
||||||
|
# 每个仙人获取股票报价数据
|
||||||
|
for immortal in immortals:
|
||||||
|
result = self.get_data_for_immortal(immortal, 'stock_quote', topic_symbol)
|
||||||
|
debate_results[immortal] = result
|
||||||
|
|
||||||
|
if result.success:
|
||||||
|
data = result.data
|
||||||
|
if 'price' in data:
|
||||||
|
print(f" 💰 {immortal}: ${data['price']:.2f} ({data.get('change_percent', 'N/A')}) via {result.api_used}")
|
||||||
|
|
||||||
|
time.sleep(0.2) # 避免过快请求
|
||||||
|
|
||||||
|
print("\n📊 负载分布统计:")
|
||||||
|
distribution = self.get_load_distribution()
|
||||||
|
for api_name, stats in distribution.items():
|
||||||
|
print(f" {api_name}: {stats['calls']} 次调用 ({stats['percentage']:.1f}%) - {'健康' if stats['healthy'] else '异常'}")
|
||||||
|
|
||||||
|
return debate_results
|
||||||
|
|
||||||
|
# 使用示例
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 从环境变量获取API密钥
|
||||||
|
rapidapi_key = os.getenv('RAPIDAPI_KEY')
|
||||||
|
if not rapidapi_key:
|
||||||
|
print("❌ 请设置RAPIDAPI_KEY环境变量")
|
||||||
|
exit(1)
|
||||||
|
|
||||||
|
# 创建负载均衡器
|
||||||
|
load_balancer = JixiaLoadBalancer(rapidapi_key)
|
||||||
|
|
||||||
|
# 进行八仙论道
|
||||||
|
results = load_balancer.conduct_immortal_debate('TSLA')
|
||||||
|
|
||||||
|
print("\n🎉 八仙论道完成！")
|
||||||
|
|
@ -0,0 +1,75 @@
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.engines.data_abstraction import DataProvider
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
from src.jixia.engines.openbb_engine import OpenBBEngine
|
||||||
|
|
||||||
|
class OpenBBDataProvider(DataProvider):
|
||||||
|
"""OpenBB引擎适配器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.engine = OpenBBEngine()
|
||||||
|
self._name = "OpenBB"
|
||||||
|
self._priority = 1 # 最高优先级
|
||||||
|
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
result = self.engine.get_immortal_data("吕洞宾", "price", symbol)
|
||||||
|
if result.success and result.data:
|
||||||
|
# 解析OpenBB返回的数据并转换为StockQuote
|
||||||
|
# 注意：这里需要根据OpenBB实际返回的数据结构进行调整
|
||||||
|
data = result.data
|
||||||
|
if isinstance(data, list) and len(data) > 0:
|
||||||
|
item = data[0] # 取第一条数据
|
||||||
|
elif hasattr(data, '__dict__'):
|
||||||
|
item = data
|
||||||
|
else:
|
||||||
|
item = {}
|
||||||
|
|
||||||
|
# 提取价格信息（根据openbb_stock_data.py中的字段）
|
||||||
|
price = 0
|
||||||
|
if hasattr(item, 'close'):
|
||||||
|
price = float(item.close)
|
||||||
|
elif isinstance(item, dict) and 'close' in item:
|
||||||
|
price = float(item['close'])
|
||||||
|
|
||||||
|
volume = 0
|
||||||
|
if hasattr(item, 'volume'):
|
||||||
|
volume = int(item.volume)
|
||||||
|
elif isinstance(item, dict) and 'volume' in item:
|
||||||
|
volume = int(item['volume'])
|
||||||
|
|
||||||
|
# 日期处理
|
||||||
|
timestamp = None
|
||||||
|
if hasattr(item, 'date'):
|
||||||
|
timestamp = item.date
|
||||||
|
elif isinstance(item, dict) and 'date' in item:
|
||||||
|
timestamp = item['date']
|
||||||
|
|
||||||
|
return StockQuote(
|
||||||
|
symbol=symbol,
|
||||||
|
price=price,
|
||||||
|
change=0, # 需要计算
|
||||||
|
change_percent=0, # 需要计算
|
||||||
|
volume=volume,
|
||||||
|
timestamp=timestamp
|
||||||
|
)
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
# 实现历史价格数据获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
# 实现公司概况获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
# 实现新闻获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
def name(self) -> str:
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@property
|
||||||
|
def priority(self) -> int:
|
||||||
|
return self._priority
|
||||||
|
|
@ -0,0 +1,225 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
OpenBB 集成引擎
|
||||||
|
为八仙论道提供更丰富的金融数据支撑
|
||||||
|
"""
|
||||||
|
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
from dataclasses import dataclass
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ImmortalConfig:
|
||||||
|
"""八仙配置数据类"""
|
||||||
|
primary: str
|
||||||
|
specialty: str
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class APIResult:
|
||||||
|
"""API调用结果数据类"""
|
||||||
|
success: bool
|
||||||
|
data: Optional[Any] = None
|
||||||
|
provider_used: Optional[str] = None
|
||||||
|
error: Optional[str] = None
|
||||||
|
|
||||||
|
class OpenBBEngine:
|
||||||
|
"""OpenBB 集成引擎"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
"""
|
||||||
|
初始化 OpenBB 引擎
|
||||||
|
"""
|
||||||
|
# 延迟导入 OpenBB，避免未安装时报错
|
||||||
|
self._obb = None
|
||||||
|
|
||||||
|
# 八仙专属数据源分配
|
||||||
|
self.immortal_sources: Dict[str, ImmortalConfig] = {
|
||||||
|
'吕洞宾': ImmortalConfig( # 乾-技术分析专家
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='technical_analysis'
|
||||||
|
),
|
||||||
|
'何仙姑': ImmortalConfig( # 坤-风险控制专家
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='risk_metrics'
|
||||||
|
),
|
||||||
|
'张果老': ImmortalConfig( # 兑-历史数据分析师
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='historical_data'
|
||||||
|
),
|
||||||
|
'韩湘子': ImmortalConfig( # 艮-新兴资产专家
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='sector_analysis'
|
||||||
|
),
|
||||||
|
'汉钟离': ImmortalConfig( # 离-热点追踪
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='market_movers'
|
||||||
|
),
|
||||||
|
'蓝采和': ImmortalConfig( # 坎-潜力股发现
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='screener'
|
||||||
|
),
|
||||||
|
'曹国舅': ImmortalConfig( # 震-机构分析
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='institutional_holdings'
|
||||||
|
),
|
||||||
|
'铁拐李': ImmortalConfig( # 巽-逆向投资
|
||||||
|
primary='yfinance',
|
||||||
|
specialty='short_interest'
|
||||||
|
)
|
||||||
|
}
|
||||||
|
|
||||||
|
print("✅ OpenBB 引擎初始化完成")
|
||||||
|
|
||||||
|
def _ensure_openbb(self):
|
||||||
|
"""Lazy import OpenBB v4 obb router."""
|
||||||
|
if self._obb is not None:
|
||||||
|
return True
|
||||||
|
try:
|
||||||
|
from openbb import obb # type: ignore
|
||||||
|
self._obb = obb
|
||||||
|
return True
|
||||||
|
except Exception:
|
||||||
|
self._obb = None
|
||||||
|
return False
|
||||||
|
|
||||||
|
def get_immortal_data(self, immortal_name: str, data_type: str, symbol: str = 'AAPL') -> APIResult:
|
||||||
|
"""
|
||||||
|
为特定八仙获取专属数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
immortal_name: 八仙名称
|
||||||
|
data_type: 数据类型
|
||||||
|
symbol: 股票代码
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
API调用结果
|
||||||
|
"""
|
||||||
|
if immortal_name not in self.immortal_sources:
|
||||||
|
return APIResult(success=False, error=f'Unknown immortal: {immortal_name}')
|
||||||
|
|
||||||
|
immortal_config = self.immortal_sources[immortal_name]
|
||||||
|
|
||||||
|
print(f"🧙♂️ {immortal_name} 请求 {data_type} 数据 (股票: {symbol})")
|
||||||
|
|
||||||
|
# 根据数据类型调用不同的 OpenBB 函数
|
||||||
|
try:
|
||||||
|
if not self._ensure_openbb():
|
||||||
|
return APIResult(success=False, error='OpenBB 未安装，请先安装 openbb>=4 并在 requirements.txt 启用')
|
||||||
|
obb = self._obb
|
||||||
|
if data_type == 'price':
|
||||||
|
result = obb.equity.price.quote(symbol=symbol, provider=immortal_config.primary)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used=immortal_config.primary
|
||||||
|
)
|
||||||
|
elif data_type == 'historical':
|
||||||
|
result = obb.equity.price.historical(symbol=symbol, provider=immortal_config.primary)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used=immortal_config.primary
|
||||||
|
)
|
||||||
|
elif data_type == 'profile':
|
||||||
|
result = obb.equity.profile(symbol=symbol, provider=immortal_config.primary)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used=immortal_config.primary
|
||||||
|
)
|
||||||
|
elif data_type == 'news':
|
||||||
|
result = obb.news.company(symbol=symbol)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used='news_api'
|
||||||
|
)
|
||||||
|
elif data_type == 'earnings':
|
||||||
|
result = obb.equity.earnings.earnings_historical(symbol=symbol, provider=immortal_config.primary)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used=immortal_config.primary
|
||||||
|
)
|
||||||
|
elif data_type == 'dividends':
|
||||||
|
result = obb.equity.fundamental.dividend(symbol=symbol, provider=immortal_config.primary)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used=immortal_config.primary
|
||||||
|
)
|
||||||
|
elif data_type == 'screener':
|
||||||
|
# 使用简单的筛选器作为替代
|
||||||
|
result = obb.equity.screener.etf(
|
||||||
|
provider=immortal_config.primary
|
||||||
|
)
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=getattr(result, 'results', getattr(result, 'to_dict', lambda: None)()),
|
||||||
|
provider_used=immortal_config.primary
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
return APIResult(success=False, error=f'Unsupported data type: {data_type}')
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return APIResult(success=False, error=f'OpenBB 调用失败: {str(e)}')
|
||||||
|
|
||||||
|
def simulate_jixia_debate(self, topic_symbol: str = 'TSLA') -> Dict[str, APIResult]:
|
||||||
|
"""
|
||||||
|
模拟稷下学宫八仙论道
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic_symbol: 辩论主题股票代码
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
八仙辩论结果
|
||||||
|
"""
|
||||||
|
print(f"🏛️ 稷下学宫八仙论道 - 主题: {topic_symbol} (OpenBB 版本)")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
debate_results: Dict[str, APIResult] = {}
|
||||||
|
|
||||||
|
# 数据类型映射
|
||||||
|
data_type_mapping = {
|
||||||
|
'technical_analysis': 'historical', # 技术分析使用历史价格数据
|
||||||
|
'risk_metrics': 'price', # 风险控制使用当前价格数据
|
||||||
|
'historical_data': 'historical', # 历史数据分析使用历史价格数据
|
||||||
|
'sector_analysis': 'profile', # 新兴资产分析使用公司概况
|
||||||
|
'market_movers': 'news', # 热点追踪使用新闻
|
||||||
|
'screener': 'screener', # 潜力股发现使用筛选器
|
||||||
|
'institutional_holdings': 'profile', # 机构分析使用公司概况
|
||||||
|
'short_interest': 'profile' # 逆向投资使用公司概况
|
||||||
|
}
|
||||||
|
|
||||||
|
# 八仙依次发言
|
||||||
|
for immortal_name, config in self.immortal_sources.items():
|
||||||
|
print(f"\n🎭 {immortal_name} ({config.specialty}) 发言:")
|
||||||
|
|
||||||
|
data_type = data_type_mapping.get(config.specialty, 'price')
|
||||||
|
result = self.get_immortal_data(immortal_name, data_type, topic_symbol)
|
||||||
|
|
||||||
|
if result.success:
|
||||||
|
debate_results[immortal_name] = result
|
||||||
|
print(f" 💬 观点: 基于{result.provider_used}数据的{config.specialty}分析")
|
||||||
|
# 显示部分数据示例
|
||||||
|
if result.data:
|
||||||
|
if isinstance(result.data, list) and len(result.data) > 0:
|
||||||
|
sample = result.data[0]
|
||||||
|
print(f" 📊 数据示例: {sample}")
|
||||||
|
elif hasattr(result.data, '__dict__'):
|
||||||
|
# 如果是对象，显示前几个属性
|
||||||
|
attrs = vars(result.data)
|
||||||
|
sample = {k: v for k, v in list(attrs.items())[:3]}
|
||||||
|
print(f" 📊 数据示例: {sample}")
|
||||||
|
else:
|
||||||
|
print(f" 📊 数据示例: {result.data}")
|
||||||
|
else:
|
||||||
|
print(f" 😔 暂时无法获取数据: {result.error}")
|
||||||
|
|
||||||
|
return debate_results
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 测试 OpenBB 引擎
|
||||||
|
print("🧪 OpenBB 引擎测试")
|
||||||
|
engine = OpenBBEngine()
|
||||||
|
engine.simulate_jixia_debate('AAPL')
|
||||||
|
|
@ -0,0 +1,161 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
OpenBB 股票数据获取模块
|
||||||
|
"""
|
||||||
|
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
from typing import List, Dict, Any, Optional
|
||||||
|
|
||||||
|
def get_stock_data(symbol: str, days: int = 90) -> Optional[List[Dict[str, Any]]]:
|
||||||
|
"""
|
||||||
|
获取指定股票在指定天数内的历史数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
symbol (str): 股票代码 (如 'AAPL')
|
||||||
|
days (int): 时间窗口（天），默认90天
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List[Dict[str, Any]]: 股票历史数据列表，如果失败则返回None
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 计算开始日期
|
||||||
|
end_date = datetime.now()
|
||||||
|
start_date = end_date - timedelta(days=days)
|
||||||
|
|
||||||
|
print(f"🔍 正在获取 {symbol} 近 {days} 天的数据...")
|
||||||
|
print(f" 时间范围: {start_date.strftime('%Y-%m-%d')} 到 {end_date.strftime('%Y-%m-%d')}")
|
||||||
|
|
||||||
|
# 使用OpenBB获取数据（延迟导入）
|
||||||
|
try:
|
||||||
|
from openbb import obb # type: ignore
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ OpenBB 未安装或导入失败: {e}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
result = obb.equity.price.historical(
|
||||||
|
symbol=symbol,
|
||||||
|
provider='yfinance',
|
||||||
|
start_date=start_date.strftime('%Y-%m-%d'),
|
||||||
|
end_date=end_date.strftime('%Y-%m-%d')
|
||||||
|
)
|
||||||
|
|
||||||
|
results = getattr(result, 'results', None)
|
||||||
|
if results:
|
||||||
|
print(f"✅ 成功获取 {len(results)} 条记录")
|
||||||
|
return results
|
||||||
|
else:
|
||||||
|
print("❌ 未获取到数据")
|
||||||
|
return None
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 获取数据时出错: {str(e)}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_etf_data(symbol: str, days: int = 90) -> Optional[List[Dict[str, Any]]]:
|
||||||
|
"""
|
||||||
|
获取指定ETF在指定天数内的历史数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
symbol (str): ETF代码 (如 'SPY')
|
||||||
|
days (int): 时间窗口（天），默认90天
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List[Dict[str, Any]]: ETF历史数据列表，如果失败则返回None
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 计算开始日期
|
||||||
|
end_date = datetime.now()
|
||||||
|
start_date = end_date - timedelta(days=days)
|
||||||
|
|
||||||
|
print(f"🔍 正在获取 {symbol} 近 {days} 天的数据...")
|
||||||
|
print(f" 时间范围: {start_date.strftime('%Y-%m-%d')} 到 {end_date.strftime('%Y-%m-%d')}")
|
||||||
|
|
||||||
|
# 使用OpenBB获取数据（延迟导入）
|
||||||
|
try:
|
||||||
|
from openbb import obb # type: ignore
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ OpenBB 未安装或导入失败: {e}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
result = obb.etf.price.historical(
|
||||||
|
symbol=symbol,
|
||||||
|
provider='yfinance',
|
||||||
|
start_date=start_date.strftime('%Y-%m-%d'),
|
||||||
|
end_date=end_date.strftime('%Y-%m-%d')
|
||||||
|
)
|
||||||
|
|
||||||
|
results = getattr(result, 'results', None)
|
||||||
|
if results:
|
||||||
|
print(f"✅ 成功获取 {len(results)} 条记录")
|
||||||
|
return results
|
||||||
|
else:
|
||||||
|
print("❌ 未获取到数据")
|
||||||
|
return None
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 获取数据时出错: {str(e)}")
|
||||||
|
return None
|
||||||
|
|
||||||
|
def format_stock_data(data: List[Dict[str, Any]]) -> None:
|
||||||
|
"""
|
||||||
|
格式化并打印股票数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (List[Dict[str, Any]]): 股票数据列表
|
||||||
|
"""
|
||||||
|
if not data:
|
||||||
|
print("😔 没有数据可显示")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"\n📊 股票数据预览 (显示最近5条记录):")
|
||||||
|
print("-" * 80)
|
||||||
|
print(f"{'日期':<12} {'开盘':<10} {'最高':<10} {'最低':<10} {'收盘':<10} {'成交量':<15}")
|
||||||
|
print("-" * 80)
|
||||||
|
|
||||||
|
# 只显示最近5条记录
|
||||||
|
for item in data[-5:]:
|
||||||
|
print(f"{str(item.date):<12} {item.open:<10.2f} {item.high:<10.2f} {item.low:<10.2f} {item.close:<10.2f} {item.volume:<15,}")
|
||||||
|
|
||||||
|
def format_etf_data(data: List[Dict[str, Any]]) -> None:
|
||||||
|
"""
|
||||||
|
格式化并打印ETF数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (List[Dict[str, Any]]): ETF数据列表
|
||||||
|
"""
|
||||||
|
if not data:
|
||||||
|
print("😔 没有数据可显示")
|
||||||
|
return
|
||||||
|
|
||||||
|
print(f"\n📊 ETF数据预览 (显示最近5条记录):")
|
||||||
|
print("-" * 80)
|
||||||
|
print(f"{'日期':<12} {'开盘':<10} {'最高':<10} {'最低':<10} {'收盘':<10} {'成交量':<15}")
|
||||||
|
print("-" * 80)
|
||||||
|
|
||||||
|
# 只显示最近5条记录
|
||||||
|
for item in data[-5:]:
|
||||||
|
print(f"{str(item.date):<12} {item.open:<10.2f} {item.high:<10.2f} {item.low:<10.2f} {item.close:<10.2f} {item.volume:<15,}")
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数"""
|
||||||
|
# 示例：获取AAPL股票和SPY ETF的数据
|
||||||
|
symbols = [("AAPL", "stock"), ("SPY", "etf")]
|
||||||
|
time_windows = [90, 720]
|
||||||
|
|
||||||
|
for symbol, asset_type in symbols:
|
||||||
|
for days in time_windows:
|
||||||
|
print(f"\n{'='*60}")
|
||||||
|
print(f"获取 {symbol} {days} 天数据")
|
||||||
|
print(f"{'='*60}")
|
||||||
|
|
||||||
|
if asset_type == "stock":
|
||||||
|
data = get_stock_data(symbol, days)
|
||||||
|
if data:
|
||||||
|
format_stock_data(data)
|
||||||
|
else:
|
||||||
|
data = get_etf_data(symbol, days)
|
||||||
|
if data:
|
||||||
|
format_etf_data(data)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,329 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
稷下学宫永动机引擎
|
||||||
|
为八仙论道提供无限数据支撑
|
||||||
|
|
||||||
|
重构版本：
|
||||||
|
- 移除硬编码密钥
|
||||||
|
- 添加类型注解
|
||||||
|
- 改进错误处理
|
||||||
|
- 统一配置管理
|
||||||
|
"""
|
||||||
|
|
||||||
|
import requests
|
||||||
|
import time
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
from dataclasses import dataclass
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ImmortalConfig:
|
||||||
|
"""八仙配置数据类"""
|
||||||
|
primary: str
|
||||||
|
backup: List[str]
|
||||||
|
specialty: str
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class APIResult:
|
||||||
|
"""API调用结果数据类"""
|
||||||
|
success: bool
|
||||||
|
data: Optional[Dict[str, Any]] = None
|
||||||
|
api_used: Optional[str] = None
|
||||||
|
usage_count: Optional[int] = None
|
||||||
|
error: Optional[str] = None
|
||||||
|
|
||||||
|
class JixiaPerpetualEngine:
|
||||||
|
"""稷下学宫永动机引擎"""
|
||||||
|
|
||||||
|
def __init__(self, rapidapi_key: str):
|
||||||
|
"""
|
||||||
|
初始化永动机引擎
|
||||||
|
|
||||||
|
Args:
|
||||||
|
rapidapi_key: RapidAPI密钥，从环境变量或Doppler获取
|
||||||
|
"""
|
||||||
|
if not rapidapi_key:
|
||||||
|
raise ValueError("RapidAPI密钥不能为空")
|
||||||
|
|
||||||
|
self.rapidapi_key = rapidapi_key
|
||||||
|
|
||||||
|
# 八仙专属API分配 - 基于4个可用API优化
|
||||||
|
self.immortal_apis: Dict[str, ImmortalConfig] = {
|
||||||
|
'吕洞宾': ImmortalConfig( # 乾-技术分析专家
|
||||||
|
primary='alpha_vantage',
|
||||||
|
backup=['yahoo_finance_1'],
|
||||||
|
specialty='comprehensive_analysis'
|
||||||
|
),
|
||||||
|
'何仙姑': ImmortalConfig( # 坤-风险控制专家
|
||||||
|
primary='yahoo_finance_1',
|
||||||
|
backup=['webull'],
|
||||||
|
specialty='risk_management'
|
||||||
|
),
|
||||||
|
'张果老': ImmortalConfig( # 兑-历史数据分析师
|
||||||
|
primary='seeking_alpha',
|
||||||
|
backup=['alpha_vantage'],
|
||||||
|
specialty='fundamental_analysis'
|
||||||
|
),
|
||||||
|
'韩湘子': ImmortalConfig( # 艮-新兴资产专家
|
||||||
|
primary='webull',
|
||||||
|
backup=['yahoo_finance_1'],
|
||||||
|
specialty='emerging_trends'
|
||||||
|
),
|
||||||
|
'汉钟离': ImmortalConfig( # 离-热点追踪
|
||||||
|
primary='yahoo_finance_1',
|
||||||
|
backup=['webull'],
|
||||||
|
specialty='hot_trends'
|
||||||
|
),
|
||||||
|
'蓝采和': ImmortalConfig( # 坎-潜力股发现
|
||||||
|
primary='webull',
|
||||||
|
backup=['alpha_vantage'],
|
||||||
|
specialty='undervalued_stocks'
|
||||||
|
),
|
||||||
|
'曹国舅': ImmortalConfig( # 震-机构分析
|
||||||
|
primary='seeking_alpha',
|
||||||
|
backup=['alpha_vantage'],
|
||||||
|
specialty='institutional_analysis'
|
||||||
|
),
|
||||||
|
'铁拐李': ImmortalConfig( # 巽-逆向投资
|
||||||
|
primary='alpha_vantage',
|
||||||
|
backup=['seeking_alpha'],
|
||||||
|
specialty='contrarian_analysis'
|
||||||
|
)
|
||||||
|
}
|
||||||
|
|
||||||
|
# API池配置 - 只保留4个可用的API
|
||||||
|
self.api_configs: Dict[str, str] = {
|
||||||
|
'alpha_vantage': 'alpha-vantage.p.rapidapi.com', # 1.26s ⚡
|
||||||
|
'webull': 'webull.p.rapidapi.com', # 1.56s ⚡
|
||||||
|
'yahoo_finance_1': 'yahoo-finance15.p.rapidapi.com', # 2.07s
|
||||||
|
'seeking_alpha': 'seeking-alpha.p.rapidapi.com' # 3.32s
|
||||||
|
}
|
||||||
|
|
||||||
|
# 使用统计
|
||||||
|
self.usage_tracker: Dict[str, int] = {api: 0 for api in self.api_configs.keys()}
|
||||||
|
|
||||||
|
def get_immortal_data(self, immortal_name: str, data_type: str, symbol: str = 'AAPL') -> APIResult:
|
||||||
|
"""
|
||||||
|
为特定八仙获取专属数据
|
||||||
|
|
||||||
|
Args:
|
||||||
|
immortal_name: 八仙名称
|
||||||
|
data_type: 数据类型
|
||||||
|
symbol: 股票代码
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
API调用结果
|
||||||
|
"""
|
||||||
|
if immortal_name not in self.immortal_apis:
|
||||||
|
return APIResult(success=False, error=f'Unknown immortal: {immortal_name}')
|
||||||
|
|
||||||
|
immortal_config = self.immortal_apis[immortal_name]
|
||||||
|
|
||||||
|
print(f"🧙♂️ {immortal_name} 请求 {data_type} 数据 (股票: {symbol})")
|
||||||
|
|
||||||
|
# 尝试主要API
|
||||||
|
result = self._call_api(immortal_config.primary, data_type, symbol)
|
||||||
|
if result.success:
|
||||||
|
print(f" ✅ 使用主要API: {immortal_config.primary}")
|
||||||
|
return result
|
||||||
|
|
||||||
|
# 故障转移到备用API
|
||||||
|
for backup_api in immortal_config.backup:
|
||||||
|
print(f" 🔄 故障转移到: {backup_api}")
|
||||||
|
result = self._call_api(backup_api, data_type, symbol)
|
||||||
|
if result.success:
|
||||||
|
print(f" ✅ 备用API成功: {backup_api}")
|
||||||
|
return result
|
||||||
|
|
||||||
|
print(f" ❌ 所有API都失败了")
|
||||||
|
return APIResult(success=False, error='All APIs failed')
|
||||||
|
|
||||||
|
def _call_api(self, api_name: str, data_type: str, symbol: str) -> APIResult:
|
||||||
|
"""
|
||||||
|
调用指定API
|
||||||
|
|
||||||
|
Args:
|
||||||
|
api_name: API名称
|
||||||
|
data_type: 数据类型
|
||||||
|
symbol: 股票代码
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
API调用结果
|
||||||
|
"""
|
||||||
|
if api_name not in self.api_configs:
|
||||||
|
return APIResult(success=False, error=f'API {api_name} not configured')
|
||||||
|
|
||||||
|
host = self.api_configs[api_name]
|
||||||
|
headers = {
|
||||||
|
'X-RapidAPI-Key': self.rapidapi_key,
|
||||||
|
'X-RapidAPI-Host': host,
|
||||||
|
'Content-Type': 'application/json'
|
||||||
|
}
|
||||||
|
|
||||||
|
endpoint = self._get_endpoint(api_name, data_type, symbol)
|
||||||
|
if not endpoint:
|
||||||
|
return APIResult(success=False, error=f'No endpoint for {data_type} on {api_name}')
|
||||||
|
|
||||||
|
url = f"https://{host}{endpoint}"
|
||||||
|
|
||||||
|
try:
|
||||||
|
response = requests.get(url, headers=headers, timeout=8)
|
||||||
|
self.usage_tracker[api_name] += 1
|
||||||
|
|
||||||
|
if response.status_code == 200:
|
||||||
|
return APIResult(
|
||||||
|
success=True,
|
||||||
|
data=response.json(),
|
||||||
|
api_used=api_name,
|
||||||
|
usage_count=self.usage_tracker[api_name]
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
return APIResult(
|
||||||
|
success=False,
|
||||||
|
error=f'HTTP {response.status_code}: {response.text[:100]}'
|
||||||
|
)
|
||||||
|
except requests.exceptions.Timeout:
|
||||||
|
return APIResult(success=False, error='Request timeout')
|
||||||
|
except requests.exceptions.RequestException as e:
|
||||||
|
return APIResult(success=False, error=f'Request error: {str(e)}')
|
||||||
|
except Exception as e:
|
||||||
|
return APIResult(success=False, error=f'Unexpected error: {str(e)}')
|
||||||
|
|
||||||
|
def _get_endpoint(self, api_name: str, data_type: str, symbol: str) -> Optional[str]:
|
||||||
|
"""
|
||||||
|
根据API和数据类型返回合适的端点
|
||||||
|
|
||||||
|
Args:
|
||||||
|
api_name: API名称
|
||||||
|
data_type: 数据类型
|
||||||
|
symbol: 股票代码
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
API端点路径
|
||||||
|
"""
|
||||||
|
endpoint_mapping = {
|
||||||
|
'alpha_vantage': {
|
||||||
|
'quote': f'/query?function=GLOBAL_QUOTE&symbol={symbol}',
|
||||||
|
'overview': f'/query?function=OVERVIEW&symbol={symbol}',
|
||||||
|
'earnings': f'/query?function=EARNINGS&symbol={symbol}',
|
||||||
|
'profile': f'/query?function=OVERVIEW&symbol={symbol}',
|
||||||
|
'analysis': f'/query?function=OVERVIEW&symbol={symbol}'
|
||||||
|
},
|
||||||
|
'yahoo_finance_1': {
|
||||||
|
'quote': f'/api/yahoo/qu/quote/{symbol}',
|
||||||
|
'gainers': '/api/yahoo/co/collections/day_gainers',
|
||||||
|
'losers': '/api/yahoo/co/collections/day_losers',
|
||||||
|
'search': f'/api/yahoo/qu/quote/{symbol}',
|
||||||
|
'analysis': f'/api/yahoo/qu/quote/{symbol}',
|
||||||
|
'profile': f'/api/yahoo/qu/quote/{symbol}'
|
||||||
|
},
|
||||||
|
'seeking_alpha': {
|
||||||
|
'profile': f'/symbols/get-profile?symbols={symbol}',
|
||||||
|
'news': '/news/list?category=market-news',
|
||||||
|
'analysis': f'/symbols/get-profile?symbols={symbol}',
|
||||||
|
'quote': f'/symbols/get-profile?symbols={symbol}'
|
||||||
|
},
|
||||||
|
'webull': {
|
||||||
|
'search': f'/stock/search?keyword={symbol}',
|
||||||
|
'quote': f'/stock/search?keyword={symbol}',
|
||||||
|
'analysis': f'/stock/search?keyword={symbol}',
|
||||||
|
'gainers': '/market/get-active-gainers',
|
||||||
|
'profile': f'/stock/search?keyword={symbol}'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
api_endpoints = endpoint_mapping.get(api_name, {})
|
||||||
|
return api_endpoints.get(data_type, api_endpoints.get('quote'))
|
||||||
|
|
||||||
|
def simulate_jixia_debate(self, topic_symbol: str = 'TSLA') -> Dict[str, APIResult]:
|
||||||
|
"""
|
||||||
|
模拟稷下学宫八仙论道
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic_symbol: 辩论主题股票代码
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
八仙辩论结果
|
||||||
|
"""
|
||||||
|
print(f"🏛️ 稷下学宫八仙论道 - 主题: {topic_symbol}")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
debate_results: Dict[str, APIResult] = {}
|
||||||
|
|
||||||
|
# 数据类型映射
|
||||||
|
data_type_mapping = {
|
||||||
|
'comprehensive_analysis': 'overview',
|
||||||
|
'etf_tracking': 'quote',
|
||||||
|
'fundamental_analysis': 'profile',
|
||||||
|
'emerging_trends': 'news',
|
||||||
|
'hot_trends': 'gainers',
|
||||||
|
'undervalued_stocks': 'search',
|
||||||
|
'institutional_analysis': 'profile',
|
||||||
|
'contrarian_analysis': 'analysis'
|
||||||
|
}
|
||||||
|
|
||||||
|
# 八仙依次发言
|
||||||
|
for immortal_name, config in self.immortal_apis.items():
|
||||||
|
print(f"\n🎭 {immortal_name} ({config.specialty}) 发言:")
|
||||||
|
|
||||||
|
data_type = data_type_mapping.get(config.specialty, 'quote')
|
||||||
|
result = self.get_immortal_data(immortal_name, data_type, topic_symbol)
|
||||||
|
|
||||||
|
if result.success:
|
||||||
|
debate_results[immortal_name] = result
|
||||||
|
print(f" 💬 观点: 基于{result.api_used}数据的{config.specialty}分析")
|
||||||
|
else:
|
||||||
|
print(f" 😔 暂时无法获取数据: {result.error}")
|
||||||
|
|
||||||
|
time.sleep(0.5) # 避免过快请求
|
||||||
|
|
||||||
|
return debate_results
|
||||||
|
|
||||||
|
def get_usage_stats(self) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
获取使用统计信息
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
统计信息字典
|
||||||
|
"""
|
||||||
|
total_calls = sum(self.usage_tracker.values())
|
||||||
|
active_apis = len([api for api, count in self.usage_tracker.items() if count > 0])
|
||||||
|
unused_apis = [api for api, count in self.usage_tracker.items() if count == 0]
|
||||||
|
|
||||||
|
return {
|
||||||
|
'total_calls': total_calls,
|
||||||
|
'active_apis': active_apis,
|
||||||
|
'total_apis': len(self.api_configs),
|
||||||
|
'average_calls_per_api': total_calls / len(self.api_configs) if self.api_configs else 0,
|
||||||
|
'usage_by_api': {api: count for api, count in self.usage_tracker.items() if count > 0},
|
||||||
|
'unused_apis': unused_apis,
|
||||||
|
'unused_count': len(unused_apis)
|
||||||
|
}
|
||||||
|
|
||||||
|
def print_perpetual_stats(self) -> None:
|
||||||
|
"""打印永动机统计信息"""
|
||||||
|
stats = self.get_usage_stats()
|
||||||
|
|
||||||
|
print(f"\n📊 永动机运行统计:")
|
||||||
|
print("=" * 60)
|
||||||
|
print(f"总API调用次数: {stats['total_calls']}")
|
||||||
|
print(f"活跃API数量: {stats['active_apis']}/{stats['total_apis']}")
|
||||||
|
print(f"平均每API调用: {stats['average_calls_per_api']:.1f}次")
|
||||||
|
|
||||||
|
if stats['usage_by_api']:
|
||||||
|
print(f"\n各API使用情况:")
|
||||||
|
for api, count in stats['usage_by_api'].items():
|
||||||
|
print(f" {api}: {count}次")
|
||||||
|
|
||||||
|
print(f"\n🎯 未使用的API储备: {stats['unused_count']}个")
|
||||||
|
if stats['unused_apis']:
|
||||||
|
unused_display = ', '.join(stats['unused_apis'][:5])
|
||||||
|
if len(stats['unused_apis']) > 5:
|
||||||
|
unused_display += '...'
|
||||||
|
print(f"储备API: {unused_display}")
|
||||||
|
|
||||||
|
print(f"\n💡 永动机效果:")
|
||||||
|
print(f" • {stats['total_apis']}个API订阅，智能调度")
|
||||||
|
print(f" • 智能故障转移，永不断线")
|
||||||
|
print(f" • 八仙专属API，个性化数据")
|
||||||
|
print(f" • 成本优化，效果最大化！")
|
||||||
|
|
@ -0,0 +1,48 @@
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.engines.data_abstraction import DataProvider
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
from src.jixia.engines.perpetual_engine import JixiaPerpetualEngine
|
||||||
|
from config.settings import get_rapidapi_key
|
||||||
|
|
||||||
|
class RapidAPIDataProvider(DataProvider):
|
||||||
|
"""RapidAPI永动机引擎适配器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.engine = JixiaPerpetualEngine(get_rapidapi_key())
|
||||||
|
self._name = "RapidAPI"
|
||||||
|
self._priority = 2 # 中等优先级
|
||||||
|
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
result = self.engine.get_immortal_data("吕洞宾", "quote", symbol)
|
||||||
|
if result.success and result.data:
|
||||||
|
# 解析RapidAPI返回的数据并转换为StockQuote
|
||||||
|
# 这里需要根据实际API返回的数据结构进行调整
|
||||||
|
return StockQuote(
|
||||||
|
symbol=symbol,
|
||||||
|
price=result.data.get("price", 0),
|
||||||
|
change=result.data.get("change", 0),
|
||||||
|
change_percent=result.data.get("change_percent", 0),
|
||||||
|
volume=result.data.get("volume", 0),
|
||||||
|
timestamp=result.data.get("timestamp")
|
||||||
|
)
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
# 实现历史价格数据获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
# 实现公司概况获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
# 实现新闻获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
def name(self) -> str:
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@property
|
||||||
|
def priority(self) -> int:
|
||||||
|
return self._priority
|
||||||
|
|
@ -0,0 +1,929 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Human干预系统
|
||||||
|
监控辩论健康度并在必要时触发人工干预
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import logging
|
||||||
|
from typing import Dict, List, Any, Optional, Callable, Tuple
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from enum import Enum
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
import statistics
|
||||||
|
import re
|
||||||
|
|
||||||
|
class HealthStatus(Enum):
|
||||||
|
"""健康状态"""
|
||||||
|
EXCELLENT = "优秀" # 90-100分
|
||||||
|
GOOD = "良好" # 70-89分
|
||||||
|
FAIR = "一般" # 50-69分
|
||||||
|
POOR = "较差" # 30-49分
|
||||||
|
CRITICAL = "危险" # 0-29分
|
||||||
|
|
||||||
|
class InterventionLevel(Enum):
|
||||||
|
"""干预级别"""
|
||||||
|
NONE = (0, "无需干预")
|
||||||
|
GENTLE_REMINDER = (1, "温和提醒")
|
||||||
|
MODERATE_GUIDANCE = (2, "适度引导")
|
||||||
|
STRONG_INTERVENTION = (3, "强力干预")
|
||||||
|
EMERGENCY_STOP = (4, "紧急停止")
|
||||||
|
|
||||||
|
def __init__(self, level, description):
|
||||||
|
self.level = level
|
||||||
|
self.description = description
|
||||||
|
|
||||||
|
@property
|
||||||
|
def value(self):
|
||||||
|
return self.description
|
||||||
|
|
||||||
|
def __ge__(self, other):
|
||||||
|
if isinstance(other, InterventionLevel):
|
||||||
|
return self.level >= other.level
|
||||||
|
return NotImplemented
|
||||||
|
|
||||||
|
def __gt__(self, other):
|
||||||
|
if isinstance(other, InterventionLevel):
|
||||||
|
return self.level > other.level
|
||||||
|
return NotImplemented
|
||||||
|
|
||||||
|
def __le__(self, other):
|
||||||
|
if isinstance(other, InterventionLevel):
|
||||||
|
return self.level <= other.level
|
||||||
|
return NotImplemented
|
||||||
|
|
||||||
|
def __lt__(self, other):
|
||||||
|
if isinstance(other, InterventionLevel):
|
||||||
|
return self.level < other.level
|
||||||
|
return NotImplemented
|
||||||
|
|
||||||
|
class AlertType(Enum):
|
||||||
|
"""警报类型"""
|
||||||
|
QUALITY_DECLINE = "质量下降"
|
||||||
|
TOXIC_BEHAVIOR = "有害行为"
|
||||||
|
REPETITIVE_CONTENT = "重复内容"
|
||||||
|
OFF_TOPIC = "偏离主题"
|
||||||
|
EMOTIONAL_ESCALATION = "情绪升级"
|
||||||
|
PARTICIPATION_IMBALANCE = "参与不平衡"
|
||||||
|
TECHNICAL_ERROR = "技术错误"
|
||||||
|
TIME_VIOLATION = "时间违规"
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class HealthMetric:
|
||||||
|
"""健康指标"""
|
||||||
|
name: str
|
||||||
|
value: float
|
||||||
|
weight: float
|
||||||
|
threshold_critical: float
|
||||||
|
threshold_poor: float
|
||||||
|
threshold_fair: float
|
||||||
|
threshold_good: float
|
||||||
|
description: str
|
||||||
|
last_updated: datetime = field(default_factory=datetime.now)
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class InterventionAlert:
|
||||||
|
"""干预警报"""
|
||||||
|
id: str
|
||||||
|
alert_type: AlertType
|
||||||
|
severity: InterventionLevel
|
||||||
|
message: str
|
||||||
|
affected_participants: List[str]
|
||||||
|
metrics: Dict[str, float]
|
||||||
|
timestamp: datetime
|
||||||
|
resolved: bool = False
|
||||||
|
resolution_notes: str = ""
|
||||||
|
human_notified: bool = False
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class InterventionAction:
|
||||||
|
"""干预动作"""
|
||||||
|
id: str
|
||||||
|
action_type: str
|
||||||
|
description: str
|
||||||
|
target_participants: List[str]
|
||||||
|
parameters: Dict[str, Any]
|
||||||
|
executed_at: datetime
|
||||||
|
success: bool = False
|
||||||
|
result_message: str = ""
|
||||||
|
|
||||||
|
class DebateHealthMonitor:
|
||||||
|
"""辩论健康度监控器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.health_metrics: Dict[str, HealthMetric] = {}
|
||||||
|
self.active_alerts: List[InterventionAlert] = []
|
||||||
|
self.intervention_history: List[InterventionAction] = []
|
||||||
|
self.monitoring_enabled = True
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
# 初始化健康指标
|
||||||
|
self._initialize_health_metrics()
|
||||||
|
|
||||||
|
# 事件处理器
|
||||||
|
self.event_handlers: Dict[str, List[Callable]] = {}
|
||||||
|
|
||||||
|
# 监控配置
|
||||||
|
self.monitoring_config = {
|
||||||
|
"check_interval_seconds": 30,
|
||||||
|
"alert_cooldown_minutes": 5,
|
||||||
|
"auto_intervention_enabled": True,
|
||||||
|
"human_notification_threshold": InterventionLevel.STRONG_INTERVENTION
|
||||||
|
}
|
||||||
|
|
||||||
|
def _initialize_health_metrics(self):
|
||||||
|
"""初始化健康指标"""
|
||||||
|
metrics_config = [
|
||||||
|
{
|
||||||
|
"name": "content_quality",
|
||||||
|
"weight": 0.25,
|
||||||
|
"thresholds": {"critical": 20, "poor": 40, "fair": 60, "good": 80},
|
||||||
|
"description": "内容质量评分"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "participation_balance",
|
||||||
|
"weight": 0.20,
|
||||||
|
"thresholds": {"critical": 30, "poor": 50, "fair": 70, "good": 85},
|
||||||
|
"description": "参与平衡度"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "emotional_stability",
|
||||||
|
"weight": 0.20,
|
||||||
|
"thresholds": {"critical": 25, "poor": 45, "fair": 65, "good": 80},
|
||||||
|
"description": "情绪稳定性"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "topic_relevance",
|
||||||
|
"weight": 0.15,
|
||||||
|
"thresholds": {"critical": 35, "poor": 55, "fair": 70, "good": 85},
|
||||||
|
"description": "主题相关性"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "interaction_civility",
|
||||||
|
"weight": 0.10,
|
||||||
|
"thresholds": {"critical": 20, "poor": 40, "fair": 60, "good": 80},
|
||||||
|
"description": "互动文明度"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "technical_stability",
|
||||||
|
"weight": 0.10,
|
||||||
|
"thresholds": {"critical": 40, "poor": 60, "fair": 75, "good": 90},
|
||||||
|
"description": "技术稳定性"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
for config in metrics_config:
|
||||||
|
metric = HealthMetric(
|
||||||
|
name=config["name"],
|
||||||
|
value=100.0, # 初始值
|
||||||
|
weight=config["weight"],
|
||||||
|
threshold_critical=config["thresholds"]["critical"],
|
||||||
|
threshold_poor=config["thresholds"]["poor"],
|
||||||
|
threshold_fair=config["thresholds"]["fair"],
|
||||||
|
threshold_good=config["thresholds"]["good"],
|
||||||
|
description=config["description"]
|
||||||
|
)
|
||||||
|
self.health_metrics[config["name"]] = metric
|
||||||
|
|
||||||
|
async def analyze_debate_health(self, debate_data: Dict[str, Any]) -> Tuple[float, HealthStatus]:
|
||||||
|
"""分析辩论健康度"""
|
||||||
|
if not self.monitoring_enabled:
|
||||||
|
return 100.0, HealthStatus.EXCELLENT
|
||||||
|
|
||||||
|
# 更新各项健康指标
|
||||||
|
await self._update_content_quality(debate_data)
|
||||||
|
await self._update_participation_balance(debate_data)
|
||||||
|
await self._update_emotional_stability(debate_data)
|
||||||
|
await self._update_topic_relevance(debate_data)
|
||||||
|
await self._update_interaction_civility(debate_data)
|
||||||
|
await self._update_technical_stability(debate_data)
|
||||||
|
|
||||||
|
# 计算综合健康分数
|
||||||
|
total_score = 0.0
|
||||||
|
total_weight = 0.0
|
||||||
|
|
||||||
|
for metric in self.health_metrics.values():
|
||||||
|
total_score += metric.value * metric.weight
|
||||||
|
total_weight += metric.weight
|
||||||
|
|
||||||
|
overall_score = total_score / total_weight if total_weight > 0 else 0.0
|
||||||
|
|
||||||
|
# 确定健康状态
|
||||||
|
if overall_score >= 90:
|
||||||
|
status = HealthStatus.EXCELLENT
|
||||||
|
elif overall_score >= 70:
|
||||||
|
status = HealthStatus.GOOD
|
||||||
|
elif overall_score >= 50:
|
||||||
|
status = HealthStatus.FAIR
|
||||||
|
elif overall_score >= 30:
|
||||||
|
status = HealthStatus.POOR
|
||||||
|
else:
|
||||||
|
status = HealthStatus.CRITICAL
|
||||||
|
|
||||||
|
# 检查是否需要发出警报
|
||||||
|
await self._check_for_alerts(overall_score, status)
|
||||||
|
|
||||||
|
self.logger.info(f"辩论健康度分析完成: {overall_score:.1f}分 ({status.value})")
|
||||||
|
|
||||||
|
return overall_score, status
|
||||||
|
|
||||||
|
async def _update_content_quality(self, debate_data: Dict[str, Any]):
|
||||||
|
"""更新内容质量指标"""
|
||||||
|
messages = debate_data.get("recent_messages", [])
|
||||||
|
if not messages:
|
||||||
|
return
|
||||||
|
|
||||||
|
quality_scores = []
|
||||||
|
|
||||||
|
for message in messages[-10:]: # 分析最近10条消息
|
||||||
|
content = message.get("content", "")
|
||||||
|
|
||||||
|
# 内容长度评分
|
||||||
|
length_score = min(len(content) / 100 * 50, 50) # 最多50分
|
||||||
|
|
||||||
|
# 词汇丰富度评分
|
||||||
|
words = content.split()
|
||||||
|
unique_words = len(set(words))
|
||||||
|
vocabulary_score = min(unique_words / len(words) * 30, 30) if words else 0
|
||||||
|
|
||||||
|
# 逻辑结构评分（简单检测）
|
||||||
|
logic_indicators = ["因为", "所以", "但是", "然而", "首先", "其次", "最后", "总之"]
|
||||||
|
logic_score = min(sum(1 for indicator in logic_indicators if indicator in content) * 5, 20)
|
||||||
|
|
||||||
|
total_score = length_score + vocabulary_score + logic_score
|
||||||
|
quality_scores.append(total_score)
|
||||||
|
|
||||||
|
avg_quality = statistics.mean(quality_scores) if quality_scores else 50
|
||||||
|
self.health_metrics["content_quality"].value = avg_quality
|
||||||
|
self.health_metrics["content_quality"].last_updated = datetime.now()
|
||||||
|
|
||||||
|
async def _update_participation_balance(self, debate_data: Dict[str, Any]):
|
||||||
|
"""更新参与平衡度指标"""
|
||||||
|
messages = debate_data.get("recent_messages", [])
|
||||||
|
if not messages:
|
||||||
|
return
|
||||||
|
|
||||||
|
# 统计各参与者的发言次数
|
||||||
|
speaker_counts = {}
|
||||||
|
for message in messages[-20:]: # 分析最近20条消息
|
||||||
|
speaker = message.get("sender", "")
|
||||||
|
speaker_counts[speaker] = speaker_counts.get(speaker, 0) + 1
|
||||||
|
|
||||||
|
if not speaker_counts:
|
||||||
|
return
|
||||||
|
|
||||||
|
# 计算参与平衡度
|
||||||
|
counts = list(speaker_counts.values())
|
||||||
|
if len(counts) <= 1:
|
||||||
|
balance_score = 100
|
||||||
|
else:
|
||||||
|
# 使用标准差来衡量平衡度
|
||||||
|
mean_count = statistics.mean(counts)
|
||||||
|
std_dev = statistics.stdev(counts)
|
||||||
|
|
||||||
|
# 标准差越小，平衡度越高
|
||||||
|
balance_score = max(0, 100 - (std_dev / mean_count * 100))
|
||||||
|
|
||||||
|
self.health_metrics["participation_balance"].value = balance_score
|
||||||
|
self.health_metrics["participation_balance"].last_updated = datetime.now()
|
||||||
|
|
||||||
|
async def _update_emotional_stability(self, debate_data: Dict[str, Any]):
|
||||||
|
"""更新情绪稳定性指标"""
|
||||||
|
messages = debate_data.get("recent_messages", [])
|
||||||
|
if not messages:
|
||||||
|
return
|
||||||
|
|
||||||
|
emotional_scores = []
|
||||||
|
|
||||||
|
# 情绪关键词
|
||||||
|
negative_emotions = ["愤怒", "生气", "讨厌", "恶心", "愚蠢", "白痴", "垃圾"]
|
||||||
|
positive_emotions = ["赞同", "支持", "优秀", "精彩", "同意", "认可"]
|
||||||
|
|
||||||
|
for message in messages[-15:]:
|
||||||
|
content = message.get("content", "")
|
||||||
|
|
||||||
|
# 检测负面情绪
|
||||||
|
negative_count = sum(1 for word in negative_emotions if word in content)
|
||||||
|
positive_count = sum(1 for word in positive_emotions if word in content)
|
||||||
|
|
||||||
|
# 检测大写字母比例（可能表示情绪激动）
|
||||||
|
if content:
|
||||||
|
caps_ratio = sum(1 for c in content if c.isupper()) / len(content)
|
||||||
|
else:
|
||||||
|
caps_ratio = 0
|
||||||
|
|
||||||
|
# 检测感叹号数量
|
||||||
|
exclamation_count = content.count("!")
|
||||||
|
|
||||||
|
# 计算情绪稳定性分数
|
||||||
|
emotion_score = 100
|
||||||
|
emotion_score -= negative_count * 15 # 负面情绪扣分
|
||||||
|
emotion_score += positive_count * 5 # 正面情绪加分
|
||||||
|
emotion_score -= caps_ratio * 30 # 大写字母扣分
|
||||||
|
emotion_score -= min(exclamation_count * 5, 20) # 感叹号扣分
|
||||||
|
|
||||||
|
emotional_scores.append(max(0, emotion_score))
|
||||||
|
|
||||||
|
avg_emotional_stability = statistics.mean(emotional_scores) if emotional_scores else 80
|
||||||
|
self.health_metrics["emotional_stability"].value = avg_emotional_stability
|
||||||
|
self.health_metrics["emotional_stability"].last_updated = datetime.now()
|
||||||
|
|
||||||
|
async def _update_topic_relevance(self, debate_data: Dict[str, Any]):
|
||||||
|
"""更新主题相关性指标"""
|
||||||
|
messages = debate_data.get("recent_messages", [])
|
||||||
|
topic_keywords = debate_data.get("topic_keywords", [])
|
||||||
|
|
||||||
|
if not messages or not topic_keywords:
|
||||||
|
return
|
||||||
|
|
||||||
|
relevance_scores = []
|
||||||
|
|
||||||
|
for message in messages[-10:]:
|
||||||
|
content = message.get("content", "")
|
||||||
|
|
||||||
|
# 计算主题关键词匹配度
|
||||||
|
keyword_matches = sum(1 for keyword in topic_keywords if keyword in content)
|
||||||
|
relevance_score = min(keyword_matches / len(topic_keywords) * 100, 100) if topic_keywords else 50
|
||||||
|
|
||||||
|
relevance_scores.append(relevance_score)
|
||||||
|
|
||||||
|
avg_relevance = statistics.mean(relevance_scores) if relevance_scores else 70
|
||||||
|
self.health_metrics["topic_relevance"].value = avg_relevance
|
||||||
|
self.health_metrics["topic_relevance"].last_updated = datetime.now()
|
||||||
|
|
||||||
|
async def _update_interaction_civility(self, debate_data: Dict[str, Any]):
|
||||||
|
"""更新互动文明度指标"""
|
||||||
|
messages = debate_data.get("recent_messages", [])
|
||||||
|
if not messages:
|
||||||
|
return
|
||||||
|
|
||||||
|
civility_scores = []
|
||||||
|
|
||||||
|
# 不文明行为关键词
|
||||||
|
uncivil_patterns = [
|
||||||
|
r"你.*蠢", r".*白痴.*", r".*垃圾.*", r"闭嘴", r"滚.*",
|
||||||
|
r".*傻.*", r".*笨.*", r".*废物.*"
|
||||||
|
]
|
||||||
|
|
||||||
|
# 文明行为关键词
|
||||||
|
civil_patterns = [
|
||||||
|
r"请.*", r"谢谢", r"不好意思", r"抱歉", r"尊重", r"理解"
|
||||||
|
]
|
||||||
|
|
||||||
|
for message in messages[-15:]:
|
||||||
|
content = message.get("content", "")
|
||||||
|
|
||||||
|
civility_score = 100
|
||||||
|
|
||||||
|
# 检测不文明行为
|
||||||
|
for pattern in uncivil_patterns:
|
||||||
|
if re.search(pattern, content):
|
||||||
|
civility_score -= 20
|
||||||
|
|
||||||
|
# 检测文明行为
|
||||||
|
for pattern in civil_patterns:
|
||||||
|
if re.search(pattern, content):
|
||||||
|
civility_score += 5
|
||||||
|
|
||||||
|
civility_scores.append(max(0, min(100, civility_score)))
|
||||||
|
|
||||||
|
avg_civility = statistics.mean(civility_scores) if civility_scores else 85
|
||||||
|
self.health_metrics["interaction_civility"].value = avg_civility
|
||||||
|
self.health_metrics["interaction_civility"].last_updated = datetime.now()
|
||||||
|
|
||||||
|
async def _update_technical_stability(self, debate_data: Dict[str, Any]):
|
||||||
|
"""更新技术稳定性指标"""
|
||||||
|
system_status = debate_data.get("system_status", {})
|
||||||
|
|
||||||
|
stability_score = 100
|
||||||
|
|
||||||
|
# 检查错误率
|
||||||
|
error_rate = system_status.get("error_rate", 0)
|
||||||
|
stability_score -= error_rate * 100
|
||||||
|
|
||||||
|
# 检查响应时间
|
||||||
|
response_time = system_status.get("avg_response_time", 0)
|
||||||
|
if response_time > 2.0: # 超过2秒
|
||||||
|
stability_score -= (response_time - 2.0) * 10
|
||||||
|
|
||||||
|
# 检查系统负载
|
||||||
|
system_load = system_status.get("system_load", 0)
|
||||||
|
if system_load > 0.8: # 负载超过80%
|
||||||
|
stability_score -= (system_load - 0.8) * 50
|
||||||
|
|
||||||
|
self.health_metrics["technical_stability"].value = max(0, stability_score)
|
||||||
|
self.health_metrics["technical_stability"].last_updated = datetime.now()
|
||||||
|
|
||||||
|
async def _check_for_alerts(self, overall_score: float, status: HealthStatus):
|
||||||
|
"""检查是否需要发出警报"""
|
||||||
|
current_time = datetime.now()
|
||||||
|
|
||||||
|
# 检查各项指标是否触发警报
|
||||||
|
for metric_name, metric in self.health_metrics.items():
|
||||||
|
alert_level = self._determine_alert_level(metric)
|
||||||
|
|
||||||
|
if alert_level != InterventionLevel.NONE:
|
||||||
|
# 检查是否在冷却期内
|
||||||
|
recent_alerts = [
|
||||||
|
alert for alert in self.active_alerts
|
||||||
|
if alert.alert_type.value == metric_name and
|
||||||
|
(current_time - alert.timestamp).total_seconds() <
|
||||||
|
self.monitoring_config["alert_cooldown_minutes"] * 60
|
||||||
|
]
|
||||||
|
|
||||||
|
if not recent_alerts:
|
||||||
|
await self._create_alert(metric_name, metric, alert_level)
|
||||||
|
|
||||||
|
# 检查整体健康状态
|
||||||
|
if status in [HealthStatus.POOR, HealthStatus.CRITICAL]:
|
||||||
|
await self._create_system_alert(overall_score, status)
|
||||||
|
|
||||||
|
def _determine_alert_level(self, metric: HealthMetric) -> InterventionLevel:
|
||||||
|
"""确定警报级别"""
|
||||||
|
if metric.value <= metric.threshold_critical:
|
||||||
|
return InterventionLevel.EMERGENCY_STOP
|
||||||
|
elif metric.value <= metric.threshold_poor:
|
||||||
|
return InterventionLevel.STRONG_INTERVENTION
|
||||||
|
elif metric.value <= metric.threshold_fair:
|
||||||
|
return InterventionLevel.MODERATE_GUIDANCE
|
||||||
|
elif metric.value <= metric.threshold_good:
|
||||||
|
return InterventionLevel.GENTLE_REMINDER
|
||||||
|
else:
|
||||||
|
return InterventionLevel.NONE
|
||||||
|
|
||||||
|
async def _create_alert(self, metric_name: str, metric: HealthMetric, level: InterventionLevel):
|
||||||
|
"""创建警报"""
|
||||||
|
alert_type_map = {
|
||||||
|
"content_quality": AlertType.QUALITY_DECLINE,
|
||||||
|
"participation_balance": AlertType.PARTICIPATION_IMBALANCE,
|
||||||
|
"emotional_stability": AlertType.EMOTIONAL_ESCALATION,
|
||||||
|
"topic_relevance": AlertType.OFF_TOPIC,
|
||||||
|
"interaction_civility": AlertType.TOXIC_BEHAVIOR,
|
||||||
|
"technical_stability": AlertType.TECHNICAL_ERROR
|
||||||
|
}
|
||||||
|
|
||||||
|
alert = InterventionAlert(
|
||||||
|
id=f"alert_{datetime.now().timestamp()}",
|
||||||
|
alert_type=alert_type_map.get(metric_name, AlertType.QUALITY_DECLINE),
|
||||||
|
severity=level,
|
||||||
|
message=f"{metric.description}指标异常: {metric.value:.1f}分",
|
||||||
|
affected_participants=[],
|
||||||
|
metrics={metric_name: metric.value},
|
||||||
|
timestamp=datetime.now()
|
||||||
|
)
|
||||||
|
|
||||||
|
self.active_alerts.append(alert)
|
||||||
|
|
||||||
|
# 触发事件处理
|
||||||
|
await self._trigger_event_handlers("alert_created", alert)
|
||||||
|
|
||||||
|
# 检查是否需要自动干预
|
||||||
|
if self.monitoring_config["auto_intervention_enabled"]:
|
||||||
|
await self._execute_auto_intervention(alert)
|
||||||
|
|
||||||
|
# 检查是否需要通知Human
|
||||||
|
if level >= self.monitoring_config["human_notification_threshold"]:
|
||||||
|
await self._notify_human(alert)
|
||||||
|
|
||||||
|
self.logger.warning(f"创建警报: {alert.alert_type.value} - {alert.message}")
|
||||||
|
|
||||||
|
async def _create_system_alert(self, score: float, status: HealthStatus):
|
||||||
|
"""创建系统级警报"""
|
||||||
|
level = InterventionLevel.STRONG_INTERVENTION if status == HealthStatus.POOR else InterventionLevel.EMERGENCY_STOP
|
||||||
|
|
||||||
|
alert = InterventionAlert(
|
||||||
|
id=f"system_alert_{datetime.now().timestamp()}",
|
||||||
|
alert_type=AlertType.QUALITY_DECLINE,
|
||||||
|
severity=level,
|
||||||
|
message=f"系统整体健康度异常: {score:.1f}分 ({status.value})",
|
||||||
|
affected_participants=[],
|
||||||
|
metrics={"overall_score": score},
|
||||||
|
timestamp=datetime.now()
|
||||||
|
)
|
||||||
|
|
||||||
|
self.active_alerts.append(alert)
|
||||||
|
await self._trigger_event_handlers("system_alert_created", alert)
|
||||||
|
|
||||||
|
if self.monitoring_config["auto_intervention_enabled"]:
|
||||||
|
await self._execute_auto_intervention(alert)
|
||||||
|
|
||||||
|
await self._notify_human(alert)
|
||||||
|
|
||||||
|
self.logger.critical(f"系统级警报: {alert.message}")
|
||||||
|
|
||||||
|
async def _execute_auto_intervention(self, alert: InterventionAlert):
|
||||||
|
"""执行自动干预"""
|
||||||
|
intervention_strategies = {
|
||||||
|
AlertType.QUALITY_DECLINE: self._intervene_quality_decline,
|
||||||
|
AlertType.TOXIC_BEHAVIOR: self._intervene_toxic_behavior,
|
||||||
|
AlertType.EMOTIONAL_ESCALATION: self._intervene_emotional_escalation,
|
||||||
|
AlertType.PARTICIPATION_IMBALANCE: self._intervene_participation_imbalance,
|
||||||
|
AlertType.OFF_TOPIC: self._intervene_off_topic,
|
||||||
|
AlertType.TECHNICAL_ERROR: self._intervene_technical_error
|
||||||
|
}
|
||||||
|
|
||||||
|
strategy = intervention_strategies.get(alert.alert_type)
|
||||||
|
if strategy:
|
||||||
|
action = await strategy(alert)
|
||||||
|
if action:
|
||||||
|
self.intervention_history.append(action)
|
||||||
|
await self._trigger_event_handlers("intervention_executed", action)
|
||||||
|
|
||||||
|
async def _intervene_quality_decline(self, alert: InterventionAlert) -> Optional[InterventionAction]:
|
||||||
|
"""干预质量下降"""
|
||||||
|
action = InterventionAction(
|
||||||
|
id=f"quality_intervention_{datetime.now().timestamp()}",
|
||||||
|
action_type="quality_guidance",
|
||||||
|
description="发送质量提升指导",
|
||||||
|
target_participants=["all"],
|
||||||
|
parameters={
|
||||||
|
"message": "💡 建议：请提供更详细的论证和具体的例证来支持您的观点。",
|
||||||
|
"guidance_type": "quality_improvement"
|
||||||
|
},
|
||||||
|
executed_at=datetime.now(),
|
||||||
|
success=True,
|
||||||
|
result_message="质量提升指导已发送"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.logger.info(f"执行质量干预: {action.description}")
|
||||||
|
return action
|
||||||
|
|
||||||
|
async def _intervene_toxic_behavior(self, alert: InterventionAlert) -> Optional[InterventionAction]:
|
||||||
|
"""干预有害行为"""
|
||||||
|
action = InterventionAction(
|
||||||
|
id=f"toxicity_intervention_{datetime.now().timestamp()}",
|
||||||
|
action_type="behavior_warning",
|
||||||
|
description="发送行为规范提醒",
|
||||||
|
target_participants=["all"],
|
||||||
|
parameters={
|
||||||
|
"message": "⚠️ 请保持文明讨论，避免使用攻击性语言。让我们专注于观点的交流。",
|
||||||
|
"warning_level": "moderate"
|
||||||
|
},
|
||||||
|
executed_at=datetime.now(),
|
||||||
|
success=True,
|
||||||
|
result_message="行为规范提醒已发送"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.logger.warning(f"执行行为干预: {action.description}")
|
||||||
|
return action
|
||||||
|
|
||||||
|
async def _intervene_emotional_escalation(self, alert: InterventionAlert) -> Optional[InterventionAction]:
|
||||||
|
"""干预情绪升级"""
|
||||||
|
action = InterventionAction(
|
||||||
|
id=f"emotion_intervention_{datetime.now().timestamp()}",
|
||||||
|
action_type="emotion_cooling",
|
||||||
|
description="发送情绪缓解建议",
|
||||||
|
target_participants=["all"],
|
||||||
|
parameters={
|
||||||
|
"message": "🧘 让我们暂停一下，深呼吸。理性的讨论更有助于达成共识。",
|
||||||
|
"cooling_period": 60 # 秒
|
||||||
|
},
|
||||||
|
executed_at=datetime.now(),
|
||||||
|
success=True,
|
||||||
|
result_message="情绪缓解建议已发送"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.logger.info(f"执行情绪干预: {action.description}")
|
||||||
|
return action
|
||||||
|
|
||||||
|
async def _intervene_participation_imbalance(self, alert: InterventionAlert) -> Optional[InterventionAction]:
|
||||||
|
"""干预参与不平衡"""
|
||||||
|
action = InterventionAction(
|
||||||
|
id=f"balance_intervention_{datetime.now().timestamp()}",
|
||||||
|
action_type="participation_encouragement",
|
||||||
|
description="鼓励平衡参与",
|
||||||
|
target_participants=["all"],
|
||||||
|
parameters={
|
||||||
|
"message": "🤝 鼓励所有参与者分享观点，让讨论更加丰富多元。",
|
||||||
|
"encouragement_type": "participation_balance"
|
||||||
|
},
|
||||||
|
executed_at=datetime.now(),
|
||||||
|
success=True,
|
||||||
|
result_message="参与鼓励消息已发送"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.logger.info(f"执行参与平衡干预: {action.description}")
|
||||||
|
return action
|
||||||
|
|
||||||
|
async def _intervene_off_topic(self, alert: InterventionAlert) -> Optional[InterventionAction]:
|
||||||
|
"""干预偏离主题"""
|
||||||
|
action = InterventionAction(
|
||||||
|
id=f"topic_intervention_{datetime.now().timestamp()}",
|
||||||
|
action_type="topic_redirect",
|
||||||
|
description="引导回归主题",
|
||||||
|
target_participants=["all"],
|
||||||
|
parameters={
|
||||||
|
"message": "🎯 让我们回到主要讨论话题，保持讨论的焦点和深度。",
|
||||||
|
"redirect_type": "topic_focus"
|
||||||
|
},
|
||||||
|
executed_at=datetime.now(),
|
||||||
|
success=True,
|
||||||
|
result_message="主题引导消息已发送"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.logger.info(f"执行主题干预: {action.description}")
|
||||||
|
return action
|
||||||
|
|
||||||
|
async def _intervene_technical_error(self, alert: InterventionAlert) -> Optional[InterventionAction]:
|
||||||
|
"""干预技术错误"""
|
||||||
|
action = InterventionAction(
|
||||||
|
id=f"tech_intervention_{datetime.now().timestamp()}",
|
||||||
|
action_type="technical_support",
|
||||||
|
description="提供技术支持",
|
||||||
|
target_participants=["system"],
|
||||||
|
parameters={
|
||||||
|
"message": "🔧 检测到技术问题，正在进行系统优化...",
|
||||||
|
"support_type": "system_optimization"
|
||||||
|
},
|
||||||
|
executed_at=datetime.now(),
|
||||||
|
success=True,
|
||||||
|
result_message="技术支持已启动"
|
||||||
|
)
|
||||||
|
|
||||||
|
self.logger.error(f"执行技术干预: {action.description}")
|
||||||
|
return action
|
||||||
|
|
||||||
|
async def _notify_human(self, alert: InterventionAlert):
|
||||||
|
"""通知Human"""
|
||||||
|
if alert.human_notified:
|
||||||
|
return
|
||||||
|
|
||||||
|
notification = {
|
||||||
|
"type": "human_intervention_required",
|
||||||
|
"alert_id": alert.id,
|
||||||
|
"severity": alert.severity.value,
|
||||||
|
"message": alert.message,
|
||||||
|
"timestamp": alert.timestamp.isoformat(),
|
||||||
|
"metrics": alert.metrics,
|
||||||
|
"recommended_actions": self._get_recommended_actions(alert)
|
||||||
|
}
|
||||||
|
|
||||||
|
# 触发Human通知事件
|
||||||
|
await self._trigger_event_handlers("human_notification", notification)
|
||||||
|
|
||||||
|
alert.human_notified = True
|
||||||
|
|
||||||
|
self.logger.critical(f"Human通知已发送: {alert.message}")
|
||||||
|
|
||||||
|
def _get_recommended_actions(self, alert: InterventionAlert) -> List[str]:
|
||||||
|
"""获取推荐的干预动作"""
|
||||||
|
recommendations = {
|
||||||
|
AlertType.QUALITY_DECLINE: [
|
||||||
|
"提供写作指导",
|
||||||
|
"分享优秀案例",
|
||||||
|
"调整讨论节奏"
|
||||||
|
],
|
||||||
|
AlertType.TOXIC_BEHAVIOR: [
|
||||||
|
"发出警告",
|
||||||
|
"暂时禁言",
|
||||||
|
"私下沟通"
|
||||||
|
],
|
||||||
|
AlertType.EMOTIONAL_ESCALATION: [
|
||||||
|
"暂停讨论",
|
||||||
|
"引导冷静",
|
||||||
|
"转移话题"
|
||||||
|
],
|
||||||
|
AlertType.PARTICIPATION_IMBALANCE: [
|
||||||
|
"邀请发言",
|
||||||
|
"限制发言频率",
|
||||||
|
"分组讨论"
|
||||||
|
],
|
||||||
|
AlertType.OFF_TOPIC: [
|
||||||
|
"重申主题",
|
||||||
|
"引导回归",
|
||||||
|
"设置议程"
|
||||||
|
],
|
||||||
|
AlertType.TECHNICAL_ERROR: [
|
||||||
|
"重启系统",
|
||||||
|
"检查日志",
|
||||||
|
"联系技术支持"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
return recommendations.get(alert.alert_type, ["人工评估", "采取适当措施"])
|
||||||
|
|
||||||
|
async def _trigger_event_handlers(self, event_type: str, data: Any):
|
||||||
|
"""触发事件处理器"""
|
||||||
|
if event_type in self.event_handlers:
|
||||||
|
for handler in self.event_handlers[event_type]:
|
||||||
|
try:
|
||||||
|
await handler(data)
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"事件处理器错误: {e}")
|
||||||
|
|
||||||
|
def add_event_handler(self, event_type: str, handler: Callable):
|
||||||
|
"""添加事件处理器"""
|
||||||
|
if event_type not in self.event_handlers:
|
||||||
|
self.event_handlers[event_type] = []
|
||||||
|
self.event_handlers[event_type].append(handler)
|
||||||
|
|
||||||
|
def update_metrics(self, metrics_data: Dict[str, float]):
|
||||||
|
"""更新健康指标（兼容性方法）"""
|
||||||
|
for metric_name, value in metrics_data.items():
|
||||||
|
if metric_name in self.health_metrics:
|
||||||
|
self.health_metrics[metric_name].value = value
|
||||||
|
self.health_metrics[metric_name].last_updated = datetime.now()
|
||||||
|
|
||||||
|
def get_health_status(self) -> HealthStatus:
|
||||||
|
"""获取当前健康状态（兼容性方法）"""
|
||||||
|
# 计算整体分数
|
||||||
|
total_score = 0.0
|
||||||
|
total_weight = 0.0
|
||||||
|
|
||||||
|
for metric in self.health_metrics.values():
|
||||||
|
total_score += metric.value * metric.weight
|
||||||
|
total_weight += metric.weight
|
||||||
|
|
||||||
|
overall_score = total_score / total_weight if total_weight > 0 else 0.0
|
||||||
|
|
||||||
|
# 确定状态
|
||||||
|
if overall_score >= 90:
|
||||||
|
return HealthStatus.EXCELLENT
|
||||||
|
elif overall_score >= 70:
|
||||||
|
return HealthStatus.GOOD
|
||||||
|
elif overall_score >= 50:
|
||||||
|
return HealthStatus.FAIR
|
||||||
|
elif overall_score >= 30:
|
||||||
|
return HealthStatus.POOR
|
||||||
|
else:
|
||||||
|
return HealthStatus.CRITICAL
|
||||||
|
|
||||||
|
def get_health_report(self) -> Dict[str, Any]:
|
||||||
|
"""获取健康报告"""
|
||||||
|
# 计算整体分数
|
||||||
|
total_score = 0.0
|
||||||
|
total_weight = 0.0
|
||||||
|
|
||||||
|
for metric in self.health_metrics.values():
|
||||||
|
total_score += metric.value * metric.weight
|
||||||
|
total_weight += metric.weight
|
||||||
|
|
||||||
|
overall_score = total_score / total_weight if total_weight > 0 else 0.0
|
||||||
|
|
||||||
|
# 确定状态
|
||||||
|
if overall_score >= 90:
|
||||||
|
status = HealthStatus.EXCELLENT
|
||||||
|
elif overall_score >= 70:
|
||||||
|
status = HealthStatus.GOOD
|
||||||
|
elif overall_score >= 50:
|
||||||
|
status = HealthStatus.FAIR
|
||||||
|
elif overall_score >= 30:
|
||||||
|
status = HealthStatus.POOR
|
||||||
|
else:
|
||||||
|
status = HealthStatus.CRITICAL
|
||||||
|
|
||||||
|
report = {
|
||||||
|
"overall_score": round(overall_score, 1),
|
||||||
|
"health_status": status.value,
|
||||||
|
"metrics": {
|
||||||
|
name: {
|
||||||
|
"value": round(metric.value, 1),
|
||||||
|
"weight": metric.weight,
|
||||||
|
"description": metric.description,
|
||||||
|
"last_updated": metric.last_updated.isoformat()
|
||||||
|
}
|
||||||
|
for name, metric in self.health_metrics.items()
|
||||||
|
},
|
||||||
|
"active_alerts": len(self.active_alerts),
|
||||||
|
"recent_interventions": len([a for a in self.intervention_history
|
||||||
|
if (datetime.now() - a.executed_at).total_seconds() < 3600]),
|
||||||
|
"monitoring_enabled": self.monitoring_enabled,
|
||||||
|
"last_check": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
return report
|
||||||
|
|
||||||
|
def resolve_alert(self, alert_id: str, resolution_notes: str = ""):
|
||||||
|
"""解决警报"""
|
||||||
|
for alert in self.active_alerts:
|
||||||
|
if alert.id == alert_id:
|
||||||
|
alert.resolved = True
|
||||||
|
alert.resolution_notes = resolution_notes
|
||||||
|
self.logger.info(f"警报已解决: {alert_id} - {resolution_notes}")
|
||||||
|
return True
|
||||||
|
return False
|
||||||
|
|
||||||
|
def clear_resolved_alerts(self):
|
||||||
|
"""清理已解决的警报"""
|
||||||
|
before_count = len(self.active_alerts)
|
||||||
|
self.active_alerts = [alert for alert in self.active_alerts if not alert.resolved]
|
||||||
|
after_count = len(self.active_alerts)
|
||||||
|
|
||||||
|
cleared_count = before_count - after_count
|
||||||
|
if cleared_count > 0:
|
||||||
|
self.logger.info(f"清理了 {cleared_count} 个已解决的警报")
|
||||||
|
|
||||||
|
def enable_monitoring(self):
|
||||||
|
"""启用监控"""
|
||||||
|
self.monitoring_enabled = True
|
||||||
|
self.logger.info("健康监控已启用")
|
||||||
|
|
||||||
|
def disable_monitoring(self):
|
||||||
|
"""禁用监控"""
|
||||||
|
self.monitoring_enabled = False
|
||||||
|
self.logger.info("健康监控已禁用")
|
||||||
|
|
||||||
|
def save_monitoring_data(self, filename: str = "monitoring_data.json"):
|
||||||
|
"""保存监控数据"""
|
||||||
|
# 序列化监控配置，处理InterventionLevel枚举
|
||||||
|
serialized_config = self.monitoring_config.copy()
|
||||||
|
serialized_config["human_notification_threshold"] = self.monitoring_config["human_notification_threshold"].value
|
||||||
|
|
||||||
|
data = {
|
||||||
|
"health_metrics": {
|
||||||
|
name: {
|
||||||
|
"name": metric.name,
|
||||||
|
"value": metric.value,
|
||||||
|
"weight": metric.weight,
|
||||||
|
"threshold_critical": metric.threshold_critical,
|
||||||
|
"threshold_poor": metric.threshold_poor,
|
||||||
|
"threshold_fair": metric.threshold_fair,
|
||||||
|
"threshold_good": metric.threshold_good,
|
||||||
|
"description": metric.description,
|
||||||
|
"last_updated": metric.last_updated.isoformat()
|
||||||
|
}
|
||||||
|
for name, metric in self.health_metrics.items()
|
||||||
|
},
|
||||||
|
"active_alerts": [
|
||||||
|
{
|
||||||
|
"id": alert.id,
|
||||||
|
"alert_type": alert.alert_type.value,
|
||||||
|
"severity": alert.severity.value,
|
||||||
|
"message": alert.message,
|
||||||
|
"affected_participants": alert.affected_participants,
|
||||||
|
"metrics": alert.metrics,
|
||||||
|
"timestamp": alert.timestamp.isoformat(),
|
||||||
|
"resolved": alert.resolved,
|
||||||
|
"resolution_notes": alert.resolution_notes,
|
||||||
|
"human_notified": alert.human_notified
|
||||||
|
}
|
||||||
|
for alert in self.active_alerts
|
||||||
|
],
|
||||||
|
"intervention_history": [
|
||||||
|
{
|
||||||
|
"id": action.id,
|
||||||
|
"action_type": action.action_type,
|
||||||
|
"description": action.description,
|
||||||
|
"target_participants": action.target_participants,
|
||||||
|
"parameters": action.parameters,
|
||||||
|
"executed_at": action.executed_at.isoformat(),
|
||||||
|
"success": action.success,
|
||||||
|
"result_message": action.result_message
|
||||||
|
}
|
||||||
|
for action in self.intervention_history
|
||||||
|
],
|
||||||
|
"monitoring_config": serialized_config,
|
||||||
|
"monitoring_enabled": self.monitoring_enabled,
|
||||||
|
"export_time": datetime.now().isoformat()
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(filename, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||||
|
|
||||||
|
self.logger.info(f"监控数据已保存到 {filename}")
|
||||||
|
|
||||||
|
# 使用示例
|
||||||
|
async def main():
|
||||||
|
"""使用示例"""
|
||||||
|
monitor = DebateHealthMonitor()
|
||||||
|
|
||||||
|
# 模拟辩论数据
|
||||||
|
debate_data = {
|
||||||
|
"recent_messages": [
|
||||||
|
{"sender": "正1", "content": "AI投资确实具有巨大潜力，我们可以从以下几个方面来分析..."},
|
||||||
|
{"sender": "反1", "content": "但是风险也不容忽视！！！这些投资可能导致泡沫！"},
|
||||||
|
{"sender": "正2", "content": "好的"},
|
||||||
|
{"sender": "反2", "content": "你们这些观点太愚蠢了，完全没有逻辑！"},
|
||||||
|
],
|
||||||
|
"topic_keywords": ["AI", "投资", "风险", "收益", "技术"],
|
||||||
|
"system_status": {
|
||||||
|
"error_rate": 0.02,
|
||||||
|
"avg_response_time": 1.5,
|
||||||
|
"system_load": 0.6
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 分析健康度
|
||||||
|
score, status = await monitor.analyze_debate_health(debate_data)
|
||||||
|
|
||||||
|
print(f"\n📊 辩论健康度分析结果:")
|
||||||
|
print(f"综合得分: {score:.1f}分")
|
||||||
|
print(f"健康状态: {status.value}")
|
||||||
|
|
||||||
|
# 获取详细报告
|
||||||
|
report = monitor.get_health_report()
|
||||||
|
print(f"\n📋 详细健康报告:")
|
||||||
|
print(f"活跃警报数: {report['active_alerts']}")
|
||||||
|
print(f"近期干预数: {report['recent_interventions']}")
|
||||||
|
|
||||||
|
print(f"\n📈 各项指标:")
|
||||||
|
for name, metric in report['metrics'].items():
|
||||||
|
print(f" {metric['description']}: {metric['value']}分 (权重: {metric['weight']})")
|
||||||
|
|
||||||
|
# 保存数据
|
||||||
|
monitor.save_monitoring_data()
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,355 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫本地版 - 基于Ollama的四仙辩论系统
|
||||||
|
使用本地Ollama服务，无需API密钥
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from datetime import datetime
|
||||||
|
from swarm import Swarm, Agent
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
import random
|
||||||
|
|
||||||
|
class JixiaOllamaSwarm:
|
||||||
|
"""稷下学宫本地版 - 使用Ollama的四仙辩论系统"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
# Ollama配置
|
||||||
|
self.ollama_base_url = "http://100.99.183.38:11434"
|
||||||
|
self.model_name = "gemma3n:e4b" # 使用你指定的模型
|
||||||
|
|
||||||
|
# 初始化Swarm客户端，使用Ollama
|
||||||
|
from openai import OpenAI
|
||||||
|
openai_client = OpenAI(
|
||||||
|
api_key="ollama", # Ollama不需要真实的API密钥
|
||||||
|
base_url=f"{self.ollama_base_url}/v1"
|
||||||
|
)
|
||||||
|
self.client = Swarm(client=openai_client)
|
||||||
|
|
||||||
|
print(f"🦙 使用本地Ollama服务: {self.ollama_base_url}")
|
||||||
|
print(f"🤖 使用模型: {self.model_name}")
|
||||||
|
|
||||||
|
# 四仙配置
|
||||||
|
self.immortals = {
|
||||||
|
'吕洞宾': {
|
||||||
|
'role': '技术分析专家',
|
||||||
|
'stance': 'positive',
|
||||||
|
'specialty': '技术分析和图表解读',
|
||||||
|
'style': '犀利直接，一剑封喉'
|
||||||
|
},
|
||||||
|
'何仙姑': {
|
||||||
|
'role': '风险控制专家',
|
||||||
|
'stance': 'negative',
|
||||||
|
'specialty': '风险评估和资金管理',
|
||||||
|
'style': '温和坚定，关注风险'
|
||||||
|
},
|
||||||
|
'张果老': {
|
||||||
|
'role': '历史数据分析师',
|
||||||
|
'stance': 'positive',
|
||||||
|
'specialty': '历史回测和趋势分析',
|
||||||
|
'style': '博古通今，从历史找规律'
|
||||||
|
},
|
||||||
|
'铁拐李': {
|
||||||
|
'role': '逆向投资大师',
|
||||||
|
'stance': 'negative',
|
||||||
|
'specialty': '逆向思维和危机发现',
|
||||||
|
'style': '不拘一格，挑战共识'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 创建智能体
|
||||||
|
self.agents = self.create_agents()
|
||||||
|
|
||||||
|
def create_agents(self) -> Dict[str, Agent]:
|
||||||
|
"""创建四仙智能体"""
|
||||||
|
agents = {}
|
||||||
|
|
||||||
|
# 吕洞宾 - 技术分析专家
|
||||||
|
agents['吕洞宾'] = Agent(
|
||||||
|
name="LuDongbin",
|
||||||
|
instructions="""
|
||||||
|
你是吕洞宾，八仙之首，技术分析专家。
|
||||||
|
|
||||||
|
你的特点：
|
||||||
|
- 擅长技术分析和图表解读
|
||||||
|
- 立场：看涨派，善于发现投资机会
|
||||||
|
- 风格：犀利直接，一剑封喉
|
||||||
|
|
||||||
|
在辩论中：
|
||||||
|
1. 从技术分析角度分析市场
|
||||||
|
2. 使用具体的技术指标支撑观点（如RSI、MACD、均线等）
|
||||||
|
3. 保持看涨的乐观态度
|
||||||
|
4. 发言以"吕洞宾曰："开头
|
||||||
|
5. 发言控制在100字以内，简洁有力
|
||||||
|
6. 发言完毕后说"请何仙姑继续论道"
|
||||||
|
|
||||||
|
请用古雅但现代的语言风格，结合专业的技术分析。
|
||||||
|
""",
|
||||||
|
functions=[self.to_hexiangu]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 何仙姑 - 风险控制专家
|
||||||
|
agents['何仙姑'] = Agent(
|
||||||
|
name="HeXiangu",
|
||||||
|
instructions="""
|
||||||
|
你是何仙姑，八仙中唯一的女仙，风险控制专家。
|
||||||
|
|
||||||
|
你的特点：
|
||||||
|
- 擅长风险评估和资金管理
|
||||||
|
- 立场：看跌派，关注投资风险
|
||||||
|
- 风格：温和坚定，关注风险控制
|
||||||
|
|
||||||
|
在辩论中：
|
||||||
|
1. 从风险控制角度分析市场
|
||||||
|
2. 指出潜在的投资风险和危险信号
|
||||||
|
3. 保持谨慎的态度，强调风险管理
|
||||||
|
4. 发言以"何仙姑曰："开头
|
||||||
|
5. 发言控制在100字以内，温和但坚定
|
||||||
|
6. 发言完毕后说"请张果老继续论道"
|
||||||
|
|
||||||
|
请用温和但专业的语调，体现女性的细致和关怀。
|
||||||
|
""",
|
||||||
|
functions=[self.to_zhangguolao]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 张果老 - 历史数据分析师
|
||||||
|
agents['张果老'] = Agent(
|
||||||
|
name="ZhangGuoLao",
|
||||||
|
instructions="""
|
||||||
|
你是张果老，历史数据分析师。
|
||||||
|
|
||||||
|
你的特点：
|
||||||
|
- 擅长历史回测和趋势分析
|
||||||
|
- 立场：看涨派，从历史中寻找机会
|
||||||
|
- 风格：博古通今，从历史中找规律
|
||||||
|
|
||||||
|
在辩论中：
|
||||||
|
1. 从历史数据角度分析市场
|
||||||
|
2. 引用具体的历史案例和数据
|
||||||
|
3. 保持乐观的投资态度
|
||||||
|
4. 发言以"张果老曰："开头
|
||||||
|
5. 发言控制在100字以内，引经据典
|
||||||
|
6. 发言完毕后说"请铁拐李继续论道"
|
||||||
|
|
||||||
|
请用博学的语调，多引用历史数据和案例。
|
||||||
|
""",
|
||||||
|
functions=[self.to_tieguaili]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 铁拐李 - 逆向投资大师
|
||||||
|
agents['铁拐李'] = Agent(
|
||||||
|
name="TieGuaiLi",
|
||||||
|
instructions="""
|
||||||
|
你是铁拐李，逆向投资大师。
|
||||||
|
|
||||||
|
你的特点：
|
||||||
|
- 擅长逆向思维和危机发现
|
||||||
|
- 立场：看跌派，挑战主流观点
|
||||||
|
- 风格：不拘一格，敢于质疑
|
||||||
|
|
||||||
|
在辩论中：
|
||||||
|
1. 从逆向投资角度分析市场
|
||||||
|
2. 挑战前面三位仙人的观点
|
||||||
|
3. 寻找市场的潜在危机和泡沫
|
||||||
|
4. 发言以"铁拐李曰："开头
|
||||||
|
5. 作为最后发言者，要总结四仙观点并给出结论
|
||||||
|
6. 发言控制在150字以内，包含总结
|
||||||
|
|
||||||
|
请用直率犀利的语言，体现逆向思维的独特视角。
|
||||||
|
""",
|
||||||
|
functions=[] # 最后一个，不需要转换
|
||||||
|
)
|
||||||
|
|
||||||
|
return agents
|
||||||
|
|
||||||
|
def to_hexiangu(self):
|
||||||
|
"""转到何仙姑"""
|
||||||
|
return self.agents['何仙姑']
|
||||||
|
|
||||||
|
def to_zhangguolao(self):
|
||||||
|
"""转到张果老"""
|
||||||
|
return self.agents['张果老']
|
||||||
|
|
||||||
|
def to_tieguaili(self):
|
||||||
|
"""转到铁拐李"""
|
||||||
|
return self.agents['铁拐李']
|
||||||
|
|
||||||
|
async def conduct_debate(self, topic: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
|
||||||
|
"""进行四仙辩论"""
|
||||||
|
print("🏛️ 稷下学宫四仙论道开始！")
|
||||||
|
print("=" * 60)
|
||||||
|
print(f"🎯 论道主题: {topic}")
|
||||||
|
print(f"⏰ 开始时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||||
|
print(f"🦙 使用本地Ollama: {self.ollama_base_url}")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# 构建初始提示
|
||||||
|
prompt = self.build_prompt(topic, context)
|
||||||
|
|
||||||
|
try:
|
||||||
|
print("⚔️ 吕洞宾仙长请先发言...")
|
||||||
|
print("-" * 40)
|
||||||
|
|
||||||
|
# 开始辩论
|
||||||
|
response = self.client.run(
|
||||||
|
agent=self.agents['吕洞宾'],
|
||||||
|
messages=[{"role": "user", "content": prompt}],
|
||||||
|
max_turns=8, # 四仙各发言一次，加上可能的交互
|
||||||
|
model_override=self.model_name
|
||||||
|
)
|
||||||
|
|
||||||
|
print("\n" + "=" * 60)
|
||||||
|
print("🎊 四仙论道圆满结束！")
|
||||||
|
|
||||||
|
# 处理结果
|
||||||
|
result = self.process_result(response, topic, context)
|
||||||
|
self.display_summary(result)
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 论道过程中出错: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
return None
|
||||||
|
|
||||||
|
def build_prompt(self, topic: str, context: Dict[str, Any] = None) -> str:
|
||||||
|
"""构建辩论提示"""
|
||||||
|
context_str = ""
|
||||||
|
if context:
|
||||||
|
context_str = f"\n📊 市场背景:\n{json.dumps(context, indent=2, ensure_ascii=False)}\n"
|
||||||
|
|
||||||
|
prompt = f"""
|
||||||
|
🏛️ 稷下学宫四仙论道正式开始！
|
||||||
|
|
||||||
|
📜 论道主题: {topic}
|
||||||
|
{context_str}
|
||||||
|
|
||||||
|
🎭 论道规则:
|
||||||
|
1. 四仙按序发言：吕洞宾 → 何仙姑 → 张果老 → 铁拐李
|
||||||
|
2. 正反方交替：吕洞宾(看涨) → 何仙姑(看跌) → 张果老(看涨) → 铁拐李(看跌)
|
||||||
|
3. 每位仙人从专业角度分析，提供具体数据支撑
|
||||||
|
4. 可以质疑前面仙人的观点，但要有理有据
|
||||||
|
5. 保持仙风道骨的表达风格，但要专业
|
||||||
|
6. 每次发言简洁有力，控制在100字以内
|
||||||
|
7. 铁拐李作为最后发言者要总结观点
|
||||||
|
|
||||||
|
🗡️ 请吕洞宾仙长首先发言！
|
||||||
|
记住：你是技术分析专家，要从技术面找到投资机会！
|
||||||
|
发言要简洁有力，一剑封喉！
|
||||||
|
"""
|
||||||
|
return prompt
|
||||||
|
|
||||||
|
def process_result(self, response, topic: str, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
"""处理辩论结果"""
|
||||||
|
messages = response.messages if hasattr(response, 'messages') else []
|
||||||
|
|
||||||
|
debate_messages = []
|
||||||
|
for msg in messages:
|
||||||
|
if msg.get('role') == 'assistant' and msg.get('content'):
|
||||||
|
content = msg['content']
|
||||||
|
speaker = self.extract_speaker(content)
|
||||||
|
|
||||||
|
debate_messages.append({
|
||||||
|
'speaker': speaker,
|
||||||
|
'content': content,
|
||||||
|
'timestamp': datetime.now().isoformat(),
|
||||||
|
'stance': self.immortals.get(speaker, {}).get('stance', 'unknown')
|
||||||
|
})
|
||||||
|
|
||||||
|
return {
|
||||||
|
"debate_id": f"jixia_ollama_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
|
||||||
|
"topic": topic,
|
||||||
|
"context": context,
|
||||||
|
"messages": debate_messages,
|
||||||
|
"final_output": debate_messages[-1]['content'] if debate_messages else "",
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"framework": "OpenAI Swarm + Ollama",
|
||||||
|
"model": self.model_name,
|
||||||
|
"ollama_url": self.ollama_base_url
|
||||||
|
}
|
||||||
|
|
||||||
|
def extract_speaker(self, content: str) -> str:
|
||||||
|
"""从内容中提取发言者"""
|
||||||
|
for name in self.immortals.keys():
|
||||||
|
if f"{name}曰" in content:
|
||||||
|
return name
|
||||||
|
return "未知仙人"
|
||||||
|
|
||||||
|
def display_summary(self, result: Dict[str, Any]):
|
||||||
|
"""显示辩论总结"""
|
||||||
|
print("\n🌟 四仙论道总结")
|
||||||
|
print("=" * 60)
|
||||||
|
print(f"📜 主题: {result['topic']}")
|
||||||
|
print(f"⏰ 时间: {result['timestamp']}")
|
||||||
|
print(f"🔧 框架: {result['framework']}")
|
||||||
|
print(f"🤖 模型: {result['model']}")
|
||||||
|
print(f"💬 发言数: {len(result['messages'])}条")
|
||||||
|
|
||||||
|
# 统计正反方观点
|
||||||
|
positive_count = len([m for m in result['messages'] if m.get('stance') == 'positive'])
|
||||||
|
negative_count = len([m for m in result['messages'] if m.get('stance') == 'negative'])
|
||||||
|
|
||||||
|
print(f"📊 观点分布: 看涨{positive_count}条, 看跌{negative_count}条")
|
||||||
|
|
||||||
|
print("\n🏆 最终总结:")
|
||||||
|
print("-" * 40)
|
||||||
|
if result['messages']:
|
||||||
|
print(result['final_output'])
|
||||||
|
|
||||||
|
print("\n✨ 本地辩论特色:")
|
||||||
|
print("🦙 使用本地Ollama，无需API密钥")
|
||||||
|
print("🗡️ 四仙各展所长，观点多元")
|
||||||
|
print("⚖️ 正反方交替，辩论激烈")
|
||||||
|
print("🚀 基于Swarm，性能优越")
|
||||||
|
print("🔒 完全本地运行，数据安全")
|
||||||
|
|
||||||
|
# 主函数
|
||||||
|
async def main():
|
||||||
|
"""主函数"""
|
||||||
|
print("🏛️ 稷下学宫本地版 - Ollama + Swarm")
|
||||||
|
print("🦙 使用本地Ollama服务，无需API密钥")
|
||||||
|
print("🚀 四仙论道，完全本地运行")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# 创建辩论系统
|
||||||
|
academy = JixiaOllamaSwarm()
|
||||||
|
|
||||||
|
# 辩论主题
|
||||||
|
topics = [
|
||||||
|
"英伟达股价走势：AI泡沫还是技术革命？",
|
||||||
|
"美联储2024年货币政策：加息还是降息？",
|
||||||
|
"比特币vs黄金：谁是更好的避险资产？",
|
||||||
|
"中国房地产市场：触底反弹还是继续下行？",
|
||||||
|
"特斯拉股价：马斯克效应还是基本面支撑？"
|
||||||
|
]
|
||||||
|
|
||||||
|
# 随机选择主题
|
||||||
|
topic = random.choice(topics)
|
||||||
|
|
||||||
|
# 市场背景
|
||||||
|
context = {
|
||||||
|
"market_sentiment": "谨慎乐观",
|
||||||
|
"volatility": "中等",
|
||||||
|
"key_events": ["财报季", "央行会议", "地缘政治"],
|
||||||
|
"technical_indicators": {
|
||||||
|
"RSI": 65,
|
||||||
|
"MACD": "金叉",
|
||||||
|
"MA20": "上穿"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 开始辩论
|
||||||
|
result = await academy.conduct_debate(topic, context)
|
||||||
|
|
||||||
|
if result:
|
||||||
|
print(f"\n🎉 辩论成功！ID: {result['debate_id']}")
|
||||||
|
print(f"📁 使用模型: {result['model']}")
|
||||||
|
print(f"🌐 Ollama服务: {result['ollama_url']}")
|
||||||
|
else:
|
||||||
|
print("❌ 辩论失败")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,557 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫完整版 - 基于OpenAI Swarm的八仙辩论系统
|
||||||
|
实现完整的八仙论道 + 三清决策
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import subprocess
|
||||||
|
from datetime import datetime
|
||||||
|
from swarm import Swarm, Agent
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
import random
|
||||||
|
|
||||||
|
class JixiaSwarmAcademy:
|
||||||
|
"""稷下学宫 - 完整的八仙辩论系统"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
# 从Doppler获取API密钥
|
||||||
|
self.api_key = self.get_secure_api_key()
|
||||||
|
|
||||||
|
# 设置环境变量
|
||||||
|
if self.api_key:
|
||||||
|
os.environ["OPENAI_API_KEY"] = self.api_key
|
||||||
|
os.environ["OPENAI_BASE_URL"] = "https://openrouter.ai/api/v1"
|
||||||
|
|
||||||
|
# 初始化Swarm客户端，传入配置
|
||||||
|
from openai import OpenAI
|
||||||
|
openai_client = OpenAI(
|
||||||
|
api_key=self.api_key,
|
||||||
|
base_url="https://openrouter.ai/api/v1",
|
||||||
|
default_headers={
|
||||||
|
"HTTP-Referer": "https://github.com/ben/cauldron",
|
||||||
|
"X-Title": "Jixia Academy Debate System" # 避免中文字符
|
||||||
|
}
|
||||||
|
)
|
||||||
|
self.client = Swarm(client=openai_client)
|
||||||
|
else:
|
||||||
|
print("❌ 无法获取有效的API密钥")
|
||||||
|
self.client = None
|
||||||
|
|
||||||
|
# 八仙配置 - 完整版
|
||||||
|
self.immortals_config = {
|
||||||
|
'吕洞宾': {
|
||||||
|
'role': '剑仙投资顾问',
|
||||||
|
'gua_position': '乾☰',
|
||||||
|
'specialty': '技术分析',
|
||||||
|
'stance': 'positive',
|
||||||
|
'style': '一剑封喉，直指要害',
|
||||||
|
'personality': '犀利直接，善于识破市场迷雾',
|
||||||
|
'weapon': '纯阳剑',
|
||||||
|
'next': '何仙姑'
|
||||||
|
},
|
||||||
|
'何仙姑': {
|
||||||
|
'role': '慈悲风控专家',
|
||||||
|
'gua_position': '坤☷',
|
||||||
|
'specialty': '风险控制',
|
||||||
|
'stance': 'negative',
|
||||||
|
'style': '荷花在手，全局在胸',
|
||||||
|
'personality': '温和坚定，关注风险控制',
|
||||||
|
'weapon': '荷花',
|
||||||
|
'next': '张果老'
|
||||||
|
},
|
||||||
|
'张果老': {
|
||||||
|
'role': '历史数据分析师',
|
||||||
|
'gua_position': '艮☶',
|
||||||
|
'specialty': '历史回测',
|
||||||
|
'stance': 'positive',
|
||||||
|
'style': '倒骑毛驴，逆向思维',
|
||||||
|
'personality': '博古通今，从历史中寻找规律',
|
||||||
|
'weapon': '鱼鼓',
|
||||||
|
'next': '韩湘子'
|
||||||
|
},
|
||||||
|
'韩湘子': {
|
||||||
|
'role': '市场情绪分析师',
|
||||||
|
'gua_position': '兑☱',
|
||||||
|
'specialty': '情绪分析',
|
||||||
|
'stance': 'negative',
|
||||||
|
'style': '笛声悠扬，感知人心',
|
||||||
|
'personality': '敏感细腻，善于捕捉市场情绪',
|
||||||
|
'weapon': '洞箫',
|
||||||
|
'next': '汉钟离'
|
||||||
|
},
|
||||||
|
'汉钟离': {
|
||||||
|
'role': '宏观经济分析师',
|
||||||
|
'gua_position': '离☲',
|
||||||
|
'specialty': '宏观分析',
|
||||||
|
'stance': 'positive',
|
||||||
|
'style': '扇子一挥，大局明了',
|
||||||
|
'personality': '气度恢宏，关注宏观大势',
|
||||||
|
'weapon': '芭蕉扇',
|
||||||
|
'next': '蓝采和'
|
||||||
|
},
|
||||||
|
'蓝采和': {
|
||||||
|
'role': '量化交易专家',
|
||||||
|
'gua_position': '巽☴',
|
||||||
|
'specialty': '量化模型',
|
||||||
|
'stance': 'negative',
|
||||||
|
'style': '花篮一抛，数据飞舞',
|
||||||
|
'personality': '逻辑严密，依赖数学模型',
|
||||||
|
'weapon': '花篮',
|
||||||
|
'next': '曹国舅'
|
||||||
|
},
|
||||||
|
'曹国舅': {
|
||||||
|
'role': '价值投资专家',
|
||||||
|
'gua_position': '坎☵',
|
||||||
|
'specialty': '基本面分析',
|
||||||
|
'stance': 'positive',
|
||||||
|
'style': '玉板一敲，价值显现',
|
||||||
|
'personality': '稳重踏实，注重内在价值',
|
||||||
|
'weapon': '玉板',
|
||||||
|
'next': '铁拐李'
|
||||||
|
},
|
||||||
|
'铁拐李': {
|
||||||
|
'role': '逆向投资大师',
|
||||||
|
'gua_position': '震☳',
|
||||||
|
'specialty': '逆向投资',
|
||||||
|
'stance': 'negative',
|
||||||
|
'style': '铁拐一点，危机毕现',
|
||||||
|
'personality': '不拘一格，挑战主流观点',
|
||||||
|
'weapon': '铁拐杖',
|
||||||
|
'next': 'summary'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 三清决策层配置
|
||||||
|
self.sanqing_config = {
|
||||||
|
'元始天尊': {
|
||||||
|
'role': '最终决策者',
|
||||||
|
'specialty': '综合决策',
|
||||||
|
'style': '无极生太极，一言定乾坤'
|
||||||
|
},
|
||||||
|
'灵宝天尊': {
|
||||||
|
'role': '风险评估师',
|
||||||
|
'specialty': '风险量化',
|
||||||
|
'style': '太极生两仪，阴阳定风险'
|
||||||
|
},
|
||||||
|
'道德天尊': {
|
||||||
|
'role': '合规审查员',
|
||||||
|
'specialty': '合规检查',
|
||||||
|
'style': '两仪生四象，四象定规矩'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 创建智能体
|
||||||
|
self.immortal_agents = self.create_immortal_agents()
|
||||||
|
self.sanqing_agents = self.create_sanqing_agents()
|
||||||
|
|
||||||
|
# 辩论历史
|
||||||
|
self.debate_history = []
|
||||||
|
self.current_round = 0
|
||||||
|
self.max_rounds = 2 # 每个仙人最多发言2轮
|
||||||
|
|
||||||
|
def get_secure_api_key(self):
|
||||||
|
"""获取API密钥 - 支持多种方式"""
|
||||||
|
# 从环境变量获取API密钥
|
||||||
|
available_keys = [
|
||||||
|
os.getenv("OPENROUTER_API_KEY_1"),
|
||||||
|
os.getenv("OPENROUTER_API_KEY_2"),
|
||||||
|
os.getenv("OPENROUTER_API_KEY_3"),
|
||||||
|
os.getenv("OPENROUTER_API_KEY_4")
|
||||||
|
]
|
||||||
|
# 过滤掉None值
|
||||||
|
available_keys = [key for key in available_keys if key]
|
||||||
|
|
||||||
|
# 直接使用第一个密钥进行测试
|
||||||
|
test_key = available_keys[0]
|
||||||
|
print(f"🔑 直接使用测试密钥: {test_key[:20]}...")
|
||||||
|
return test_key
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def create_immortal_agents(self) -> Dict[str, Agent]:
|
||||||
|
"""创建八仙智能体"""
|
||||||
|
agents = {}
|
||||||
|
|
||||||
|
for name, config in self.immortals_config.items():
|
||||||
|
# 创建转换函数 - 使用英文名称避免特殊字符问题
|
||||||
|
next_immortal = config['next']
|
||||||
|
if next_immortal == 'summary':
|
||||||
|
transfer_func = self.transfer_to_sanqing
|
||||||
|
else:
|
||||||
|
# 创建一个简单的转换函数，避免lambda的问题
|
||||||
|
def create_transfer_func(next_name):
|
||||||
|
def transfer():
|
||||||
|
return self.transfer_to_immortal(next_name)
|
||||||
|
transfer.__name__ = f"transfer_to_{self.get_english_name(next_name)}"
|
||||||
|
return transfer
|
||||||
|
transfer_func = create_transfer_func(next_immortal)
|
||||||
|
|
||||||
|
# 构建详细的指令
|
||||||
|
instructions = self.build_immortal_instructions(name, config)
|
||||||
|
|
||||||
|
agents[name] = Agent(
|
||||||
|
name=name,
|
||||||
|
instructions=instructions,
|
||||||
|
functions=[transfer_func]
|
||||||
|
)
|
||||||
|
|
||||||
|
return agents
|
||||||
|
|
||||||
|
def create_sanqing_agents(self) -> Dict[str, Agent]:
|
||||||
|
"""创建三清决策层智能体"""
|
||||||
|
agents = {}
|
||||||
|
|
||||||
|
# 元始天尊 - 最终决策者
|
||||||
|
agents['元始天尊'] = Agent(
|
||||||
|
name="元始天尊",
|
||||||
|
instructions="""
|
||||||
|
你是元始天尊，道教三清之首，稷下学宫的最终决策者。
|
||||||
|
|
||||||
|
你的使命：
|
||||||
|
1. 综合八仙的所有观点，做出最终投资决策
|
||||||
|
2. 平衡正反两方的观点，寻找最优解
|
||||||
|
3. 给出具体的投资建议和操作指导
|
||||||
|
4. 评估决策的风险等级和预期收益
|
||||||
|
|
||||||
|
你的风格：
|
||||||
|
- 高屋建瓴，统揽全局
|
||||||
|
- 言简意赅，一锤定音
|
||||||
|
- 既不偏向乐观，也不偏向悲观
|
||||||
|
- 以数据和逻辑为准绳
|
||||||
|
|
||||||
|
请以"元始天尊曰"开头，给出最终决策。
|
||||||
|
决策格式：
|
||||||
|
- 投资建议：买入/持有/卖出
|
||||||
|
- 风险等级：低/中/高
|
||||||
|
- 预期收益：具体百分比
|
||||||
|
- 操作建议：具体的操作指导
|
||||||
|
- 决策依据：主要的决策理由
|
||||||
|
""",
|
||||||
|
functions=[]
|
||||||
|
)
|
||||||
|
|
||||||
|
return agents
|
||||||
|
|
||||||
|
def build_immortal_instructions(self, name: str, config: Dict) -> str:
|
||||||
|
"""构建仙人的详细指令"""
|
||||||
|
stance_desc = "看涨派，倾向于发现投资机会" if config['stance'] == 'positive' else "看跌派，倾向于发现投资风险"
|
||||||
|
|
||||||
|
instructions = f"""
|
||||||
|
你是{name}，八仙之一，{config['role']}。
|
||||||
|
|
||||||
|
你的身份特征：
|
||||||
|
- 位居{config['gua_position']}之位，代表{self.get_gua_meaning(config['gua_position'])}
|
||||||
|
- 持有{config['weapon']}，{config['style']}
|
||||||
|
- 擅长{config['specialty']}，{config['personality']}
|
||||||
|
- 立场倾向：{stance_desc}
|
||||||
|
|
||||||
|
在稷下学宫辩论中，你要：
|
||||||
|
|
||||||
|
1. **专业分析**：从{config['specialty']}角度深入分析
|
||||||
|
2. **立场鲜明**：作为{stance_desc}，要有明确的观点
|
||||||
|
3. **数据支撑**：用具体的数据、图表、历史案例支撑观点
|
||||||
|
4. **互动辩论**：可以质疑前面仙人的观点，但要有理有据
|
||||||
|
5. **仙风道骨**：保持古雅的表达风格，但不影响专业性
|
||||||
|
6. **承上启下**：总结前面的观点，为后面的仙人铺垫
|
||||||
|
|
||||||
|
发言格式：
|
||||||
|
- 以"{name}曰："开头
|
||||||
|
- 先简要回应前面仙人的观点（如果有）
|
||||||
|
- 然后从你的专业角度进行分析
|
||||||
|
- 最后明确表达你的投资倾向
|
||||||
|
- 结尾时说"请{config['next']}仙长继续论道"（如果不是最后一个）
|
||||||
|
|
||||||
|
记住：你是{stance_desc}，要体现这个立场，但也要保持专业和客观。
|
||||||
|
"""
|
||||||
|
|
||||||
|
return instructions
|
||||||
|
|
||||||
|
def get_gua_meaning(self, gua: str) -> str:
|
||||||
|
"""获取卦象含义"""
|
||||||
|
meanings = {
|
||||||
|
'乾☰': '天行健，自强不息',
|
||||||
|
'坤☷': '地势坤，厚德载物',
|
||||||
|
'艮☶': '艮为山，止于至善',
|
||||||
|
'兑☱': '兑为泽，和悦致祥',
|
||||||
|
'离☲': '离为火，光明磊落',
|
||||||
|
'巽☴': '巽为风，随风而化',
|
||||||
|
'坎☵': '坎为水，智慧如水',
|
||||||
|
'震☳': '震为雷，威震四方'
|
||||||
|
}
|
||||||
|
return meanings.get(gua, '神秘莫测')
|
||||||
|
|
||||||
|
def transfer_to_hexiangu(self):
|
||||||
|
"""转到何仙姑"""
|
||||||
|
return self.immortal_agents.get('何仙姑')
|
||||||
|
|
||||||
|
def transfer_to_zhangguolao(self):
|
||||||
|
"""转到张果老"""
|
||||||
|
return self.immortal_agents.get('张果老')
|
||||||
|
|
||||||
|
def transfer_to_hanxiangzi(self):
|
||||||
|
"""转到韩湘子"""
|
||||||
|
return self.immortal_agents.get('韩湘子')
|
||||||
|
|
||||||
|
def transfer_to_hanzhongli(self):
|
||||||
|
"""转到汉钟离"""
|
||||||
|
return self.immortal_agents.get('汉钟离')
|
||||||
|
|
||||||
|
def transfer_to_lancaihe(self):
|
||||||
|
"""转到蓝采和"""
|
||||||
|
return self.immortal_agents.get('蓝采和')
|
||||||
|
|
||||||
|
def transfer_to_caoguojiu(self):
|
||||||
|
"""转到曹国舅"""
|
||||||
|
return self.immortal_agents.get('曹国舅')
|
||||||
|
|
||||||
|
def transfer_to_tieguaili(self):
|
||||||
|
"""转到铁拐李"""
|
||||||
|
return self.immortal_agents.get('铁拐李')
|
||||||
|
|
||||||
|
def transfer_to_sanqing(self):
|
||||||
|
"""转到三清决策层"""
|
||||||
|
return self.sanqing_agents['元始天尊']
|
||||||
|
|
||||||
|
async def conduct_full_debate(self, topic: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
|
||||||
|
"""进行完整的稷下学宫辩论"""
|
||||||
|
if not self.api_key or not self.client:
|
||||||
|
print("❌ 无法获取API密钥或初始化客户端，无法进行论道")
|
||||||
|
return None
|
||||||
|
|
||||||
|
print("🏛️ 稷下学宫八仙论道正式开始！")
|
||||||
|
print("=" * 80)
|
||||||
|
print(f"🎯 论道主题: {topic}")
|
||||||
|
print(f"⏰ 开始时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# 构建初始提示
|
||||||
|
initial_prompt = self.build_debate_prompt(topic, context)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 从吕洞宾开始论道
|
||||||
|
print("⚔️ 吕洞宾仙长请先发言...")
|
||||||
|
print("-" * 60)
|
||||||
|
|
||||||
|
response = self.client.run(
|
||||||
|
agent=self.immortal_agents['吕洞宾'],
|
||||||
|
messages=[{"role": "user", "content": initial_prompt}],
|
||||||
|
max_turns=20 # 允许多轮对话
|
||||||
|
)
|
||||||
|
|
||||||
|
print("\n" + "=" * 80)
|
||||||
|
print("🎊 稷下学宫八仙论道圆满结束！")
|
||||||
|
print("📊 三清决策已生成")
|
||||||
|
|
||||||
|
# 处理辩论结果
|
||||||
|
debate_result = self.process_debate_result(response, topic, context)
|
||||||
|
|
||||||
|
# 显示辩论总结
|
||||||
|
self.display_debate_summary(debate_result)
|
||||||
|
|
||||||
|
return debate_result
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 论道过程中出错: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
return None
|
||||||
|
|
||||||
|
def build_debate_prompt(self, topic: str, context: Dict[str, Any] = None) -> str:
|
||||||
|
"""构建辩论提示"""
|
||||||
|
context_str = ""
|
||||||
|
if context:
|
||||||
|
context_str = f"\n📊 市场背景:\n{json.dumps(context, indent=2, ensure_ascii=False)}\n"
|
||||||
|
|
||||||
|
# 随机选择一些市场数据作为背景
|
||||||
|
market_context = self.generate_market_context(topic)
|
||||||
|
|
||||||
|
prompt = f"""
|
||||||
|
🏛️ 稷下学宫八仙论道正式开始！
|
||||||
|
|
||||||
|
📜 论道主题: {topic}
|
||||||
|
{context_str}
|
||||||
|
📈 当前市场环境:
|
||||||
|
{market_context}
|
||||||
|
|
||||||
|
🎭 论道规则:
|
||||||
|
1. 八仙按序发言：吕洞宾 → 何仙姑 → 张果老 → 韩湘子 → 汉钟离 → 蓝采和 → 曹国舅 → 铁拐李
|
||||||
|
2. 正反方交替：正方(看涨) vs 反方(看跌)
|
||||||
|
3. 每位仙人从专业角度分析，必须提供数据支撑
|
||||||
|
4. 可以质疑前面仙人的观点，但要有理有据
|
||||||
|
5. 保持仙风道骨的表达风格
|
||||||
|
6. 最后由三清做出最终决策
|
||||||
|
|
||||||
|
🗡️ 请吕洞宾仙长首先发言，展现剑仙的犀利分析！
|
||||||
|
记住：你是看涨派，要从技术分析角度找到投资机会！
|
||||||
|
"""
|
||||||
|
return prompt
|
||||||
|
|
||||||
|
def generate_market_context(self, topic: str) -> str:
|
||||||
|
"""生成模拟的市场背景数据"""
|
||||||
|
# 这里可以集成真实的市场数据，现在先用模拟数据
|
||||||
|
contexts = {
|
||||||
|
"英伟达": "NVDA当前价格$120，P/E比率65，市值$3T，AI芯片需求旺盛",
|
||||||
|
"比特币": "BTC当前价格$43,000，24h涨幅+2.3%，机构持续买入",
|
||||||
|
"美联储": "联邦基金利率5.25%，通胀率3.2%，就业数据强劲",
|
||||||
|
"中国股市": "上证指数3100点，外资流入放缓，政策支持预期"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 根据主题选择相关背景
|
||||||
|
for key, context in contexts.items():
|
||||||
|
if key in topic:
|
||||||
|
return context
|
||||||
|
|
||||||
|
return "市场情绪谨慎，波动率上升，投资者观望情绪浓厚"
|
||||||
|
|
||||||
|
def process_debate_result(self, response, topic: str, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
"""处理辩论结果"""
|
||||||
|
# 提取所有消息
|
||||||
|
all_messages = response.messages if hasattr(response, 'messages') else []
|
||||||
|
|
||||||
|
# 分析发言者和内容
|
||||||
|
debate_messages = []
|
||||||
|
speakers = []
|
||||||
|
|
||||||
|
for msg in all_messages:
|
||||||
|
if msg.get('role') == 'assistant' and msg.get('content'):
|
||||||
|
content = msg['content']
|
||||||
|
speaker = self.extract_speaker_from_content(content)
|
||||||
|
|
||||||
|
debate_messages.append({
|
||||||
|
'speaker': speaker,
|
||||||
|
'content': content,
|
||||||
|
'timestamp': datetime.now().isoformat(),
|
||||||
|
'stance': self.get_speaker_stance(speaker)
|
||||||
|
})
|
||||||
|
|
||||||
|
if speaker not in speakers:
|
||||||
|
speakers.append(speaker)
|
||||||
|
|
||||||
|
# 提取最终决策（通常是最后一条消息）
|
||||||
|
final_decision = ""
|
||||||
|
if debate_messages:
|
||||||
|
final_decision = debate_messages[-1]['content']
|
||||||
|
|
||||||
|
# 构建结果
|
||||||
|
result = {
|
||||||
|
"debate_id": f"jixia_debate_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
|
||||||
|
"topic": topic,
|
||||||
|
"context": context,
|
||||||
|
"participants": speakers,
|
||||||
|
"messages": debate_messages,
|
||||||
|
"final_decision": final_decision,
|
||||||
|
"summary": self.generate_debate_summary(debate_messages),
|
||||||
|
"timestamp": datetime.now().isoformat(),
|
||||||
|
"framework": "OpenAI Swarm",
|
||||||
|
"academy": "稷下学宫"
|
||||||
|
}
|
||||||
|
|
||||||
|
self.debate_history.append(result)
|
||||||
|
return result
|
||||||
|
|
||||||
|
def extract_speaker_from_content(self, content: str) -> str:
|
||||||
|
"""从内容中提取发言者"""
|
||||||
|
for name in list(self.immortals_config.keys()) + list(self.sanqing_config.keys()):
|
||||||
|
if f"{name}曰" in content or name in content[:20]:
|
||||||
|
return name
|
||||||
|
return "未知仙人"
|
||||||
|
|
||||||
|
def get_speaker_stance(self, speaker: str) -> str:
|
||||||
|
"""获取发言者立场"""
|
||||||
|
if speaker in self.immortals_config:
|
||||||
|
return self.immortals_config[speaker]['stance']
|
||||||
|
elif speaker in self.sanqing_config:
|
||||||
|
return 'neutral'
|
||||||
|
return 'unknown'
|
||||||
|
|
||||||
|
def generate_debate_summary(self, messages: List[Dict]) -> str:
|
||||||
|
"""生成辩论摘要"""
|
||||||
|
positive_count = len([m for m in messages if m.get('stance') == 'positive'])
|
||||||
|
negative_count = len([m for m in messages if m.get('stance') == 'negative'])
|
||||||
|
|
||||||
|
summary = f"""
|
||||||
|
📊 辩论统计:
|
||||||
|
- 参与仙人: {len(set(m['speaker'] for m in messages))}位
|
||||||
|
- 看涨观点: {positive_count}条
|
||||||
|
- 看跌观点: {negative_count}条
|
||||||
|
- 总发言数: {len(messages)}条
|
||||||
|
|
||||||
|
🎯 观点倾向: {'偏向看涨' if positive_count > negative_count else '偏向看跌' if negative_count > positive_count else '观点平衡'}
|
||||||
|
"""
|
||||||
|
|
||||||
|
return summary
|
||||||
|
|
||||||
|
def display_debate_summary(self, result: Dict[str, Any]):
|
||||||
|
"""显示辩论总结"""
|
||||||
|
print("\n🌟 稷下学宫辩论总结")
|
||||||
|
print("=" * 80)
|
||||||
|
print(f"📜 主题: {result['topic']}")
|
||||||
|
print(f"🎭 参与仙人: {', '.join(result['participants'])}")
|
||||||
|
print(f"⏰ 辩论时间: {result['timestamp']}")
|
||||||
|
print(f"🔧 技术框架: {result['framework']}")
|
||||||
|
|
||||||
|
print(result['summary'])
|
||||||
|
|
||||||
|
print("\n🏆 最终决策:")
|
||||||
|
print("-" * 40)
|
||||||
|
print(result['final_decision'])
|
||||||
|
|
||||||
|
print("\n✨ 稷下学宫辩论特色:")
|
||||||
|
print("🗡️ 八仙各展所长，观点多元化")
|
||||||
|
print("⚖️ 正反方交替发言，辩论更激烈")
|
||||||
|
print("🧠 三清最终决策，权威性更强")
|
||||||
|
print("🔄 基于Swarm框架，性能更优越")
|
||||||
|
|
||||||
|
# 主函数和测试
|
||||||
|
async def main():
|
||||||
|
"""主函数 - 演示完整的稷下学宫辩论"""
|
||||||
|
print("🏛️ 稷下学宫 - OpenAI Swarm完整版")
|
||||||
|
print("🔐 使用Doppler安全管理API密钥")
|
||||||
|
print("🚀 八仙论道 + 三清决策的完整体验")
|
||||||
|
print()
|
||||||
|
|
||||||
|
# 创建学宫
|
||||||
|
academy = JixiaSwarmAcademy()
|
||||||
|
|
||||||
|
if not academy.api_key:
|
||||||
|
print("❌ 无法获取API密钥，请检查Doppler配置或环境变量")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 辩论主题列表
|
||||||
|
topics = [
|
||||||
|
"英伟达股价走势：AI泡沫还是技术革命？",
|
||||||
|
"美联储2024年货币政策：加息还是降息？",
|
||||||
|
"比特币vs黄金：谁是更好的避险资产？",
|
||||||
|
"中国房地产市场：触底反弹还是继续下行？",
|
||||||
|
"特斯拉股价：马斯克效应还是基本面支撑？"
|
||||||
|
]
|
||||||
|
|
||||||
|
# 随机选择主题
|
||||||
|
topic = random.choice(topics)
|
||||||
|
|
||||||
|
# 构建市场背景
|
||||||
|
context = {
|
||||||
|
"market_sentiment": "谨慎乐观",
|
||||||
|
"volatility": "中等",
|
||||||
|
"major_events": ["美联储会议", "财报季", "地缘政治紧张"],
|
||||||
|
"technical_indicators": {
|
||||||
|
"RSI": 65,
|
||||||
|
"MACD": "金叉",
|
||||||
|
"MA20": "上穿"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 开始辩论
|
||||||
|
result = await academy.conduct_full_debate(topic, context)
|
||||||
|
|
||||||
|
if result:
|
||||||
|
print(f"\n🎉 辩论成功完成！辩论ID: {result['debate_id']}")
|
||||||
|
else:
|
||||||
|
print("❌ 辩论失败")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,455 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
稷下学宫AI辩论系统主入口
|
||||||
|
提供命令行界面来运行不同的辩论模式
|
||||||
|
"""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import asyncio
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
import warnings
|
||||||
|
|
||||||
|
# 将项目根目录添加到 Python 路径，以便能正确导入模块
|
||||||
|
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||||
|
sys.path.insert(0, project_root)
|
||||||
|
|
||||||
|
# 抑制 google-adk 的调试日志和警告
|
||||||
|
import logging
|
||||||
|
logging.getLogger('google.adk').setLevel(logging.ERROR)
|
||||||
|
logging.getLogger('google.genai').setLevel(logging.ERROR)
|
||||||
|
# 设置环境变量来抑制ADK调试输出
|
||||||
|
os.environ['GOOGLE_CLOUD_DISABLE_GRPC_LOGS'] = 'true'
|
||||||
|
os.environ['GRPC_VERBOSITY'] = 'ERROR'
|
||||||
|
os.environ['GRPC_TRACE'] = ''
|
||||||
|
|
||||||
|
# 抑制 warnings
|
||||||
|
warnings.filterwarnings('ignore')
|
||||||
|
|
||||||
|
from config.settings import validate_config
|
||||||
|
|
||||||
|
|
||||||
|
def check_environment():
|
||||||
|
"""检查并验证运行环境"""
|
||||||
|
print("🔧 检查运行环境...")
|
||||||
|
|
||||||
|
# 验证基础配置
|
||||||
|
if not validate_config():
|
||||||
|
print("❌ 环境配置验证失败")
|
||||||
|
return False
|
||||||
|
|
||||||
|
print("✅ 环境检查通过")
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
async def run_adk_memory_debate(topic: str, participants: list = None):
|
||||||
|
"""运行ADK记忆增强辩论"""
|
||||||
|
print("⚠️ ADK记忆增强辩论功能正在适配新版本的 google-adk 库...")
|
||||||
|
print("💡 请先使用 'adk_simple' 模式进行测试。")
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 以下代码暂时保留，待适配完成后再启用
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
from src.jixia.debates.adk_memory_debate import MemoryEnhancedDebate
|
||||||
|
|
||||||
|
print(f"🚀 启动ADK记忆增强辩论...")
|
||||||
|
print(f"📋 辩论主题: {topic}")
|
||||||
|
|
||||||
|
# 创建并初始化辩论系统
|
||||||
|
debate_system = MemoryEnhancedDebate()
|
||||||
|
await debate_system.initialize()
|
||||||
|
|
||||||
|
# 进行辩论
|
||||||
|
await debate_system.conduct_memory_debate(
|
||||||
|
topic=topic,
|
||||||
|
participants=participants
|
||||||
|
)
|
||||||
|
|
||||||
|
# 关闭资源
|
||||||
|
await debate_system.close()
|
||||||
|
|
||||||
|
print("\n🎉 ADK记忆增强辩论完成!")
|
||||||
|
return True
|
||||||
|
|
||||||
|
except ImportError as e:
|
||||||
|
print(f"❌ 导入模块失败: {e}")
|
||||||
|
print("请确保已安装Google ADK: pip install google-adk")
|
||||||
|
return False
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 运行ADK记忆增强辩论失败: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
return False
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
async def run_adk_turn_based_debate(topic: str, participants: list = None, rounds: int = 3):
|
||||||
|
"""运行ADK八仙轮流辩论"""
|
||||||
|
try:
|
||||||
|
from google.adk import Agent, Runner
|
||||||
|
from google.adk.sessions import InMemorySessionService
|
||||||
|
from google.genai import types
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
print(f"🚀 启动ADK八仙轮流辩论...")
|
||||||
|
print(f"📋 辩论主题: {topic}")
|
||||||
|
print(f"🔄 辩论轮数: {rounds}")
|
||||||
|
|
||||||
|
# 默认参与者为八仙
|
||||||
|
if not participants or participants == ["铁拐李", "吕洞宾"]:
|
||||||
|
participants = ["铁拐李", "吕洞宾", "何仙姑", "张果老", "蓝采和", "汉钟离", "韩湘子", "曹国舅"]
|
||||||
|
|
||||||
|
# 定义主持人和八仙角色配置
|
||||||
|
roles_config = {
|
||||||
|
# 主持人
|
||||||
|
"太上老君": {
|
||||||
|
"name": "太上老君",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是太上老君，本次论道的主持人。你负责引导辩论的流程，确保每位仙人都有机会发言，并在每一轮结束后进行简要总结。你的发言风格庄重、睿智，能够调和不同观点之间的矛盾。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
# 八仙
|
||||||
|
"铁拐李": {
|
||||||
|
"name": "铁拐李",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是铁拐李，八仙中的逆向思维专家。你善于从批判和质疑的角度看问题，总是能发现事物的另一面。你的发言风格直接、犀利，但富有智慧。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"吕洞宾": {
|
||||||
|
"name": "吕洞宾",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是吕洞宾，八仙中的理性分析者。你善于平衡各方观点，用理性和逻辑来分析问题。你的发言风格温和而深刻，总是能找到问题的核心。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"何仙姑": {
|
||||||
|
"name": "何仙姑",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是何仙姑，八仙中的风险控制专家。你总是从风险管理的角度思考问题，善于发现潜在危险。你的发言风格谨慎、细致，总是能提出需要警惕的问题。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"张果老": {
|
||||||
|
"name": "张果老",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是张果老，八仙中的历史智慧者。你善于从历史数据中寻找规律和智慧，总是能提供长期视角。你的发言风格沉稳、博学，总是能引经据典。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"蓝采和": {
|
||||||
|
"name": "蓝采和",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是蓝采和，八仙中的创新思维者。你善于从新兴视角和非传统方法来看待问题，总能提出独特的见解。你的发言风格活泼、新颖，总是能带来意想不到的观点。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"汉钟离": {
|
||||||
|
"name": "汉钟离",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是汉钟离，八仙中的平衡协调者。你善于综合各方观点，寻求和谐统一的解决方案。你的发言风格平和、包容，总是能化解矛盾。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"韩湘子": {
|
||||||
|
"name": "韩湘子",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是韩湘子，八仙中的艺术感知者。你善于从美学和感性的角度分析问题，总能发现事物背后的深层含义。你的发言风格优雅、感性，总是能触动人心。每次发言控制在100字以内。"
|
||||||
|
},
|
||||||
|
"曹国舅": {
|
||||||
|
"name": "曹国舅",
|
||||||
|
"model": "gemini-2.5-flash",
|
||||||
|
"instruction": "你是曹国舅，八仙中的实务执行者。你关注实际操作和具体细节，善于将理论转化为可行的方案。你的发言风格务实、严谨，总是能提出建设性意见。每次发言控制在100字以内。"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# 创建会话服务和会话
|
||||||
|
session_service = InMemorySessionService()
|
||||||
|
session = await session_service.create_session(
|
||||||
|
state={},
|
||||||
|
app_name="稷下学宫轮流辩论系统",
|
||||||
|
user_id="debate_user"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 创建主持人和八仙智能体及Runner
|
||||||
|
host_agent = None
|
||||||
|
host_runner = None
|
||||||
|
baxian_agents = {}
|
||||||
|
baxian_runners = {}
|
||||||
|
|
||||||
|
# 创建主持人
|
||||||
|
host_config = roles_config["太上老君"]
|
||||||
|
host_agent = Agent(
|
||||||
|
name=host_config["name"],
|
||||||
|
model=host_config["model"],
|
||||||
|
instruction=host_config["instruction"]
|
||||||
|
)
|
||||||
|
host_runner = Runner(
|
||||||
|
app_name="稷下学宫轮流辩论系统",
|
||||||
|
agent=host_agent,
|
||||||
|
session_service=session_service
|
||||||
|
)
|
||||||
|
|
||||||
|
# 创建八仙
|
||||||
|
for name in participants:
|
||||||
|
if name in roles_config:
|
||||||
|
config = roles_config[name]
|
||||||
|
agent = Agent(
|
||||||
|
name=config["name"],
|
||||||
|
model=config["model"],
|
||||||
|
instruction=config["instruction"]
|
||||||
|
)
|
||||||
|
baxian_agents[name] = agent
|
||||||
|
|
||||||
|
runner = Runner(
|
||||||
|
app_name="稷下学宫轮流辩论系统",
|
||||||
|
agent=agent,
|
||||||
|
session_service=session_service
|
||||||
|
)
|
||||||
|
baxian_runners[name] = runner
|
||||||
|
else:
|
||||||
|
print(f"⚠️ 未知的参与者: {name}，将被跳过。")
|
||||||
|
|
||||||
|
if not baxian_agents:
|
||||||
|
print("❌ 没有有效的参与者，请检查参与者列表。")
|
||||||
|
return False
|
||||||
|
|
||||||
|
print(f"🎯 主持人: 太上老君")
|
||||||
|
print(f"👥 参与仙人: {', '.join(baxian_agents.keys())}")
|
||||||
|
|
||||||
|
# 初始化辩论历史
|
||||||
|
debate_history = []
|
||||||
|
|
||||||
|
# 开场白
|
||||||
|
print(f"\n📢 太上老君开场:")
|
||||||
|
opening_prompt = f"各位仙友，欢迎来到本次论道。今天的主题是：{topic}。请各位依次发表高见。"
|
||||||
|
content = types.Content(role='user', parts=[types.Part(text=opening_prompt)])
|
||||||
|
response = host_runner.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content
|
||||||
|
)
|
||||||
|
reply = ""
|
||||||
|
async for event in response:
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text:
|
||||||
|
reply += str(part.text)
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
reply += str(event.text)
|
||||||
|
if reply.strip():
|
||||||
|
clean_reply = reply.strip()
|
||||||
|
print(f" {clean_reply}")
|
||||||
|
debate_history.append(f"太上老君: {clean_reply}")
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
# 进行辩论
|
||||||
|
for round_num in range(rounds):
|
||||||
|
print(f"\n🌀 第 {round_num + 1} 轮辩论:")
|
||||||
|
|
||||||
|
# 主持人引导本轮辩论
|
||||||
|
print(f"\n📢 太上老君引导:")
|
||||||
|
guide_prompt = f"现在进入第 {round_num + 1} 轮辩论，请各位仙友围绕主题发表看法。"
|
||||||
|
content = types.Content(role='user', parts=[types.Part(text=guide_prompt)])
|
||||||
|
response = host_runner.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content
|
||||||
|
)
|
||||||
|
reply = ""
|
||||||
|
async for event in response:
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text:
|
||||||
|
reply += str(part.text)
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
reply += str(event.text)
|
||||||
|
if reply.strip():
|
||||||
|
clean_reply = reply.strip()
|
||||||
|
print(f" {clean_reply}")
|
||||||
|
debate_history.append(f"太上老君: {clean_reply}")
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
# 八仙轮流发言
|
||||||
|
for name in participants:
|
||||||
|
if name not in baxian_runners:
|
||||||
|
continue
|
||||||
|
|
||||||
|
print(f"\n🗣️ {name} 发言:")
|
||||||
|
|
||||||
|
# 构建提示
|
||||||
|
history_context = ""
|
||||||
|
if debate_history:
|
||||||
|
recent_history = debate_history[-5:] # 最近5条发言
|
||||||
|
history_context = f"\n最近的论道内容：\n" + "\n".join([f"- {h}" for h in recent_history])
|
||||||
|
|
||||||
|
prompt = f"论道主题: {topic}{history_context}\n\n请从你的角色特点出发发表观点。请控制在100字以内。"
|
||||||
|
|
||||||
|
# 发送消息并获取回复
|
||||||
|
content = types.Content(role='user', parts=[types.Part(text=prompt)])
|
||||||
|
response = baxian_runners[name].run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content
|
||||||
|
)
|
||||||
|
|
||||||
|
# 收集回复
|
||||||
|
reply = ""
|
||||||
|
async for event in response:
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text:
|
||||||
|
reply += str(part.text)
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
reply += str(event.text)
|
||||||
|
|
||||||
|
if reply.strip():
|
||||||
|
clean_reply = reply.strip()
|
||||||
|
print(f" {clean_reply}")
|
||||||
|
|
||||||
|
# 记录到辩论历史
|
||||||
|
debate_entry = f"{name}: {clean_reply}"
|
||||||
|
debate_history.append(debate_entry)
|
||||||
|
|
||||||
|
await asyncio.sleep(1) # 避免API调用过快
|
||||||
|
|
||||||
|
# 结束语
|
||||||
|
print(f"\n📢 太上老君总结:")
|
||||||
|
closing_prompt = f"各位仙友的高见令我受益匪浅。本次论道到此结束，希望各位能从不同观点中获得启发。"
|
||||||
|
content = types.Content(role='user', parts=[types.Part(text=closing_prompt)])
|
||||||
|
response = host_runner.run_async(
|
||||||
|
user_id=session.user_id,
|
||||||
|
session_id=session.id,
|
||||||
|
new_message=content
|
||||||
|
)
|
||||||
|
reply = ""
|
||||||
|
async for event in response:
|
||||||
|
if hasattr(event, 'content') and event.content:
|
||||||
|
if hasattr(event.content, 'parts') and event.content.parts:
|
||||||
|
for part in event.content.parts:
|
||||||
|
if hasattr(part, 'text') and part.text:
|
||||||
|
reply += str(part.text)
|
||||||
|
elif hasattr(event, 'text') and event.text:
|
||||||
|
reply += str(event.text)
|
||||||
|
if reply.strip():
|
||||||
|
clean_reply = reply.strip()
|
||||||
|
print(f" {clean_reply}")
|
||||||
|
debate_history.append(f"太上老君: {clean_reply}")
|
||||||
|
await asyncio.sleep(1)
|
||||||
|
|
||||||
|
# 关闭资源
|
||||||
|
await host_runner.close()
|
||||||
|
for runner in baxian_runners.values():
|
||||||
|
await runner.close()
|
||||||
|
|
||||||
|
print(f"\n🎉 ADK八仙轮流辩论完成!")
|
||||||
|
print(f"📝 本次论道共产生 {len(debate_history)} 条发言。")
|
||||||
|
return True
|
||||||
|
|
||||||
|
except ImportError as e:
|
||||||
|
print(f"❌ 导入模块失败: {e}")
|
||||||
|
print("请确保已安装Google ADK: pip install google-adk")
|
||||||
|
return False
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 运行ADK八仙轮流辩论失败: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
async def run_swarm_debate(topic: str, participants: list = None):
|
||||||
|
"""运行Swarm辩论 (示例)"""
|
||||||
|
try:
|
||||||
|
print(f"🚀 启动Swarm辩论...")
|
||||||
|
print(f"📋 辩论主题: {topic}")
|
||||||
|
print(f"👥 参与者: {participants}")
|
||||||
|
|
||||||
|
# TODO: 实现调用 Swarm 辩论逻辑
|
||||||
|
# 这里需要根据实际的 swarm_debate.py 接口来实现
|
||||||
|
print("⚠️ Swarm辩论功能待实现")
|
||||||
|
|
||||||
|
print("\n🎉 Swarm辩论完成!")
|
||||||
|
return True
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 运行Swarm辩论失败: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
async def main_async(args):
|
||||||
|
"""异步主函数"""
|
||||||
|
# 检查环境
|
||||||
|
if not check_environment():
|
||||||
|
return 1
|
||||||
|
|
||||||
|
# 根据模式运行不同的辩论
|
||||||
|
if args.mode == "adk_memory":
|
||||||
|
participants = args.participants.split(",") if args.participants else None
|
||||||
|
success = await run_adk_memory_debate(args.topic, participants)
|
||||||
|
return 0 if success else 1
|
||||||
|
|
||||||
|
elif args.mode == "adk_turn_based":
|
||||||
|
participants = args.participants.split(",") if args.participants else None
|
||||||
|
success = await run_adk_turn_based_debate(args.topic, participants, args.rounds)
|
||||||
|
return 0 if success else 1
|
||||||
|
|
||||||
|
elif args.mode == "adk_simple":
|
||||||
|
# 简单辩论模式暂时使用原来的方式
|
||||||
|
try:
|
||||||
|
sys.path.insert(0, os.path.join(project_root, 'examples', 'debates'))
|
||||||
|
from adk_simple_debate import simple_debate_test
|
||||||
|
result = simple_debate_test()
|
||||||
|
return 0 if result else 1
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 运行ADK简单辩论失败: {e}")
|
||||||
|
return 1
|
||||||
|
|
||||||
|
elif args.mode == "swarm":
|
||||||
|
participants = args.participants.split(",") if args.participants else None
|
||||||
|
success = await run_swarm_debate(args.topic, participants)
|
||||||
|
return 0 if success else 1
|
||||||
|
|
||||||
|
else:
|
||||||
|
print(f"❌ 不支持的模式: {args.mode}")
|
||||||
|
return 1
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主入口函数"""
|
||||||
|
parser = argparse.ArgumentParser(description="稷下学宫AI辩论系统")
|
||||||
|
parser.add_argument(
|
||||||
|
"mode",
|
||||||
|
choices=["adk_memory", "adk_turn_based", "adk_simple", "swarm"],
|
||||||
|
help="辩论模式"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--topic",
|
||||||
|
"-t",
|
||||||
|
default="人工智能对未来社会的影响",
|
||||||
|
help="辩论主题"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--participants",
|
||||||
|
"-p",
|
||||||
|
help="参与者列表(逗号分隔)，例如: 铁拐李,吕洞宾,何仙姑"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--rounds",
|
||||||
|
"-r",
|
||||||
|
type=int,
|
||||||
|
default=3,
|
||||||
|
help="辩论轮数 (仅适用于 adk_turn_based 模式)"
|
||||||
|
)
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# 运行异步主函数
|
||||||
|
try:
|
||||||
|
exit_code = asyncio.run(main_async(args))
|
||||||
|
sys.exit(exit_code)
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print("\n\n👋 用户中断，退出程序")
|
||||||
|
sys.exit(0)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"\n\n💥 程序运行出错: {e}")
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,39 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
通用记忆银行抽象，便于插入不同后端（Vertex、Cloudflare AutoRAG等）
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
from typing import Dict, List, Any, Optional, Protocol, runtime_checkable
|
||||||
|
|
||||||
|
|
||||||
|
@runtime_checkable
|
||||||
|
class MemoryBankProtocol(Protocol):
|
||||||
|
async def create_memory_bank(self, agent_name: str, display_name: Optional[str] = None) -> str: ...
|
||||||
|
|
||||||
|
async def add_memory(
|
||||||
|
self,
|
||||||
|
agent_name: str,
|
||||||
|
content: str,
|
||||||
|
memory_type: str = "conversation",
|
||||||
|
debate_topic: str = "",
|
||||||
|
metadata: Optional[Dict[str, Any]] = None,
|
||||||
|
) -> str: ...
|
||||||
|
|
||||||
|
async def search_memories(
|
||||||
|
self,
|
||||||
|
agent_name: str,
|
||||||
|
query: str,
|
||||||
|
memory_type: Optional[str] = None,
|
||||||
|
limit: int = 10,
|
||||||
|
) -> List[Dict[str, Any]]: ...
|
||||||
|
|
||||||
|
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str: ...
|
||||||
|
|
||||||
|
async def save_debate_session(
|
||||||
|
self,
|
||||||
|
debate_topic: str,
|
||||||
|
participants: List[str],
|
||||||
|
conversation_history: List[Dict[str, str]],
|
||||||
|
outcomes: Optional[Dict[str, Any]] = None,
|
||||||
|
) -> None: ...
|
||||||
|
|
@ -0,0 +1,454 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Cloudflare AutoRAG Vectorize 记忆银行实现
|
||||||
|
为稷下学宫AI辩论系统提供Cloudflare后端的记忆功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import json
|
||||||
|
from typing import Dict, List, Optional, Any
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from datetime import datetime
|
||||||
|
import aiohttp
|
||||||
|
from config.settings import get_cloudflare_config
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class MemoryEntry:
|
||||||
|
"""记忆条目数据结构"""
|
||||||
|
id: str
|
||||||
|
content: str
|
||||||
|
metadata: Dict[str, Any]
|
||||||
|
timestamp: str # ISO format string
|
||||||
|
agent_name: str
|
||||||
|
debate_topic: str
|
||||||
|
memory_type: str # "conversation", "preference", "knowledge", "strategy"
|
||||||
|
|
||||||
|
|
||||||
|
class CloudflareMemoryBank:
|
||||||
|
"""
|
||||||
|
Cloudflare AutoRAG Vectorize 记忆银行管理器
|
||||||
|
利用Cloudflare Vectorize索引和Workers AI进行向量检索增强生成
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
"""初始化Cloudflare Memory Bank"""
|
||||||
|
self.config = get_cloudflare_config()
|
||||||
|
self.account_id = self.config['account_id']
|
||||||
|
self.api_token = self.config['api_token']
|
||||||
|
self.vectorize_index = self.config['vectorize_index']
|
||||||
|
self.embed_model = self.config['embed_model']
|
||||||
|
self.autorag_domain = self.config['autorag_domain']
|
||||||
|
|
||||||
|
# 构建API基础URL
|
||||||
|
self.base_url = f"https://api.cloudflare.com/client/v4/accounts/{self.account_id}"
|
||||||
|
self.headers = {
|
||||||
|
"Authorization": f"Bearer {self.api_token}",
|
||||||
|
"Content-Type": "application/json"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 八仙智能体名称映射
|
||||||
|
self.baxian_agents = {
|
||||||
|
"tieguaili": "铁拐李",
|
||||||
|
"hanzhongli": "汉钟离",
|
||||||
|
"zhangguolao": "张果老",
|
||||||
|
"lancaihe": "蓝采和",
|
||||||
|
"hexiangu": "何仙姑",
|
||||||
|
"lvdongbin": "吕洞宾",
|
||||||
|
"hanxiangzi": "韩湘子",
|
||||||
|
"caoguojiu": "曹国舅"
|
||||||
|
}
|
||||||
|
|
||||||
|
async def _get_session(self) -> aiohttp.ClientSession:
|
||||||
|
"""获取aiohttp会话"""
|
||||||
|
return aiohttp.ClientSession()
|
||||||
|
|
||||||
|
async def create_memory_bank(self, agent_name: str, display_name: str = None) -> str:
|
||||||
|
"""
|
||||||
|
为指定智能体创建记忆空间（在Cloudflare中通过命名空间或元数据实现）
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称 (如 "tieguaili")
|
||||||
|
display_name: 显示名称 (如 "铁拐李的记忆银行")
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
记忆空间标识符 (这里用agent_name作为标识符)
|
||||||
|
"""
|
||||||
|
# Cloudflare Vectorize使用统一的索引，通过元数据区分不同智能体的记忆
|
||||||
|
# 所以这里不需要实际创建，只需要返回标识符
|
||||||
|
if not display_name:
|
||||||
|
display_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
|
||||||
|
print(f"✅ 为 {display_name} 准备Cloudflare记忆空间")
|
||||||
|
return f"cf_memory_{agent_name}"
|
||||||
|
|
||||||
|
async def add_memory(self,
|
||||||
|
agent_name: str,
|
||||||
|
content: str,
|
||||||
|
memory_type: str = "conversation",
|
||||||
|
debate_topic: str = "",
|
||||||
|
metadata: Dict[str, Any] = None) -> str:
|
||||||
|
"""
|
||||||
|
添加记忆到Cloudflare Vectorize索引
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称
|
||||||
|
content: 记忆内容
|
||||||
|
memory_type: 记忆类型 ("conversation", "preference", "knowledge", "strategy")
|
||||||
|
debate_topic: 辩论主题
|
||||||
|
metadata: 额外元数据
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
记忆ID
|
||||||
|
"""
|
||||||
|
if metadata is None:
|
||||||
|
metadata = {}
|
||||||
|
|
||||||
|
# 生成记忆ID
|
||||||
|
memory_id = f"mem_{agent_name}_{int(datetime.now().timestamp() * 1000000)}"
|
||||||
|
|
||||||
|
# 构建记忆条目
|
||||||
|
memory_entry = MemoryEntry(
|
||||||
|
id=memory_id,
|
||||||
|
content=content,
|
||||||
|
metadata={
|
||||||
|
**metadata,
|
||||||
|
"agent_name": agent_name,
|
||||||
|
"chinese_name": self.baxian_agents.get(agent_name, agent_name),
|
||||||
|
"memory_type": memory_type,
|
||||||
|
"debate_topic": debate_topic,
|
||||||
|
"system": "jixia_academy"
|
||||||
|
},
|
||||||
|
timestamp=datetime.now().isoformat(),
|
||||||
|
agent_name=agent_name,
|
||||||
|
debate_topic=debate_topic,
|
||||||
|
memory_type=memory_type
|
||||||
|
)
|
||||||
|
|
||||||
|
# 将记忆条目转换为JSON字符串，用于存储和检索
|
||||||
|
memory_data = {
|
||||||
|
"id": memory_id,
|
||||||
|
"values": [], # 向量值将在嵌入时填充
|
||||||
|
"metadata": memory_entry.metadata
|
||||||
|
}
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 1. 使用Workers AI生成嵌入向量
|
||||||
|
embedding = await self._generate_embedding(content)
|
||||||
|
memory_data["values"] = embedding
|
||||||
|
|
||||||
|
# 2. 将记忆插入Vectorize索引
|
||||||
|
async with await self._get_session() as session:
|
||||||
|
url = f"{self.base_url}/vectorize/indexes/{self.vectorize_index}/upsert"
|
||||||
|
payload = {
|
||||||
|
"vectors": [memory_data]
|
||||||
|
}
|
||||||
|
|
||||||
|
async with session.post(url, headers=self.headers, json=payload) as response:
|
||||||
|
if response.status == 200:
|
||||||
|
result = await response.json()
|
||||||
|
print(f"✅ 为 {self.baxian_agents.get(agent_name)} 添加记忆: {memory_type}")
|
||||||
|
return memory_id
|
||||||
|
else:
|
||||||
|
error_text = await response.text()
|
||||||
|
raise Exception(f"Failed to upsert memory: {response.status} - {error_text}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 添加记忆失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
async def _generate_embedding(self, text: str) -> List[float]:
|
||||||
|
"""
|
||||||
|
使用Cloudflare Workers AI生成文本嵌入
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text: 要嵌入的文本
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
嵌入向量
|
||||||
|
"""
|
||||||
|
async with await self._get_session() as session:
|
||||||
|
url = f"{self.base_url}/ai/run/{self.embed_model}"
|
||||||
|
payload = {
|
||||||
|
"text": [text] # Workers AI embeddings API expects a list of texts
|
||||||
|
}
|
||||||
|
|
||||||
|
async with session.post(url, headers=self.headers, json=payload) as response:
|
||||||
|
if response.status == 200:
|
||||||
|
result = await response.json()
|
||||||
|
# 提取嵌入向量 (通常是 result["result"]["data"][0]["embedding"])
|
||||||
|
if "result" in result and "data" in result["result"] and len(result["result"]["data"]) > 0:
|
||||||
|
return result["result"]["data"][0]["embedding"]
|
||||||
|
else:
|
||||||
|
raise Exception(f"Unexpected embedding response format: {result}")
|
||||||
|
else:
|
||||||
|
error_text = await response.text()
|
||||||
|
raise Exception(f"Failed to generate embedding: {response.status} - {error_text}")
|
||||||
|
|
||||||
|
async def search_memories(self,
|
||||||
|
agent_name: str,
|
||||||
|
query: str,
|
||||||
|
memory_type: str = None,
|
||||||
|
limit: int = 10) -> List[Dict[str, Any]]:
|
||||||
|
"""
|
||||||
|
使用向量相似性搜索智能体的相关记忆
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称
|
||||||
|
query: 搜索查询
|
||||||
|
memory_type: 记忆类型过滤
|
||||||
|
limit: 返回结果数量限制
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
相关记忆列表
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 1. 为查询生成嵌入向量
|
||||||
|
query_embedding = await self._generate_embedding(query)
|
||||||
|
|
||||||
|
# 2. 构建过滤条件
|
||||||
|
filters = {
|
||||||
|
"agent_name": agent_name
|
||||||
|
}
|
||||||
|
if memory_type:
|
||||||
|
filters["memory_type"] = memory_type
|
||||||
|
|
||||||
|
# 3. 执行向量搜索
|
||||||
|
async with await self._get_session() as session:
|
||||||
|
url = f"{self.base_url}/vectorize/indexes/{self.vectorize_index}/query"
|
||||||
|
payload = {
|
||||||
|
"vector": query_embedding,
|
||||||
|
"topK": limit,
|
||||||
|
"filter": filters,
|
||||||
|
"returnMetadata": True
|
||||||
|
}
|
||||||
|
|
||||||
|
async with session.post(url, headers=self.headers, json=payload) as response:
|
||||||
|
if response.status == 200:
|
||||||
|
result = await response.json()
|
||||||
|
matches = result.get("result", {}).get("matches", [])
|
||||||
|
|
||||||
|
# 格式化返回结果
|
||||||
|
memories = []
|
||||||
|
for match in matches:
|
||||||
|
memory_data = {
|
||||||
|
"content": match["metadata"].get("content", ""),
|
||||||
|
"metadata": match["metadata"],
|
||||||
|
"relevance_score": match["score"]
|
||||||
|
}
|
||||||
|
memories.append(memory_data)
|
||||||
|
|
||||||
|
return memories
|
||||||
|
else:
|
||||||
|
error_text = await response.text()
|
||||||
|
raise Exception(f"Failed to search memories: {response.status} - {error_text}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 搜索记忆失败: {e}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str:
|
||||||
|
"""
|
||||||
|
获取智能体在特定辩论主题下的上下文记忆
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称
|
||||||
|
debate_topic: 辩论主题
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的上下文字符串
|
||||||
|
"""
|
||||||
|
# 搜索相关记忆
|
||||||
|
conversation_memories = await self.search_memories(
|
||||||
|
agent_name, debate_topic, "conversation", limit=5
|
||||||
|
)
|
||||||
|
preference_memories = await self.search_memories(
|
||||||
|
agent_name, debate_topic, "preference", limit=3
|
||||||
|
)
|
||||||
|
strategy_memories = await self.search_memories(
|
||||||
|
agent_name, debate_topic, "strategy", limit=3
|
||||||
|
)
|
||||||
|
|
||||||
|
# 构建上下文
|
||||||
|
context_parts = []
|
||||||
|
|
||||||
|
if conversation_memories:
|
||||||
|
context_parts.append("## 历史对话记忆")
|
||||||
|
for mem in conversation_memories:
|
||||||
|
context_parts.append(f"- {mem['content']}")
|
||||||
|
|
||||||
|
if preference_memories:
|
||||||
|
context_parts.append("\n## 偏好记忆")
|
||||||
|
for mem in preference_memories:
|
||||||
|
context_parts.append(f"- {mem['content']}")
|
||||||
|
|
||||||
|
if strategy_memories:
|
||||||
|
context_parts.append("\n## 策略记忆")
|
||||||
|
for mem in strategy_memories:
|
||||||
|
context_parts.append(f"- {mem['content']}")
|
||||||
|
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
if context_parts:
|
||||||
|
return f"# {chinese_name}的记忆上下文\n\n" + "\n".join(context_parts)
|
||||||
|
else:
|
||||||
|
return f"# {chinese_name}的记忆上下文\n\n暂无相关记忆。"
|
||||||
|
|
||||||
|
async def save_debate_session(self,
|
||||||
|
debate_topic: str,
|
||||||
|
participants: List[str],
|
||||||
|
conversation_history: List[Dict[str, str]],
|
||||||
|
outcomes: Dict[str, Any] = None) -> None:
|
||||||
|
"""
|
||||||
|
保存完整的辩论会话到各参与者的记忆银行
|
||||||
|
|
||||||
|
Args:
|
||||||
|
debate_topic: 辩论主题
|
||||||
|
participants: 参与者列表
|
||||||
|
conversation_history: 对话历史
|
||||||
|
outcomes: 辩论结果和洞察
|
||||||
|
"""
|
||||||
|
for agent_name in participants:
|
||||||
|
if agent_name not in self.baxian_agents:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 保存对话历史
|
||||||
|
conversation_summary = self._summarize_conversation(
|
||||||
|
conversation_history, agent_name
|
||||||
|
)
|
||||||
|
await self.add_memory(
|
||||||
|
agent_name=agent_name,
|
||||||
|
content=conversation_summary,
|
||||||
|
memory_type="conversation",
|
||||||
|
debate_topic=debate_topic,
|
||||||
|
metadata={
|
||||||
|
"participants": participants,
|
||||||
|
"session_length": len(conversation_history)
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
# 保存策略洞察
|
||||||
|
if outcomes:
|
||||||
|
strategy_insight = self._extract_strategy_insight(
|
||||||
|
outcomes, agent_name
|
||||||
|
)
|
||||||
|
if strategy_insight:
|
||||||
|
await self.add_memory(
|
||||||
|
agent_name=agent_name,
|
||||||
|
content=strategy_insight,
|
||||||
|
memory_type="strategy",
|
||||||
|
debate_topic=debate_topic,
|
||||||
|
metadata={"session_outcome": outcomes}
|
||||||
|
)
|
||||||
|
|
||||||
|
def _summarize_conversation(self,
|
||||||
|
conversation_history: List[Dict[str, str]],
|
||||||
|
agent_name: str) -> str:
|
||||||
|
"""
|
||||||
|
为特定智能体总结对话历史
|
||||||
|
|
||||||
|
Args:
|
||||||
|
conversation_history: 对话历史
|
||||||
|
agent_name: 智能体名称
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
对话总结
|
||||||
|
"""
|
||||||
|
agent_messages = [
|
||||||
|
msg for msg in conversation_history
|
||||||
|
if msg.get("agent") == agent_name
|
||||||
|
]
|
||||||
|
|
||||||
|
if not agent_messages:
|
||||||
|
return "本次辩论中未发言"
|
||||||
|
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
summary = f"{chinese_name}在本次辩论中的主要观点：\n"
|
||||||
|
|
||||||
|
for i, msg in enumerate(agent_messages[:3], 1): # 只取前3条主要观点
|
||||||
|
summary += f"{i}. {msg.get('content', '')[:100]}...\n"
|
||||||
|
|
||||||
|
return summary
|
||||||
|
|
||||||
|
def _extract_strategy_insight(self,
|
||||||
|
outcomes: Dict[str, Any],
|
||||||
|
agent_name: str) -> Optional[str]:
|
||||||
|
"""
|
||||||
|
从辩论结果中提取策略洞察
|
||||||
|
|
||||||
|
Args:
|
||||||
|
outcomes: 辩论结果
|
||||||
|
agent_name: 智能体名称
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
策略洞察或None
|
||||||
|
"""
|
||||||
|
# 这里可以根据实际的outcomes结构来提取洞察
|
||||||
|
# 暂时返回一个简单的示例
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
|
||||||
|
if "winner" in outcomes and outcomes["winner"] == agent_name:
|
||||||
|
return f"{chinese_name}在本次辩论中获胜，其论证策略值得保持。"
|
||||||
|
elif "insights" in outcomes and agent_name in outcomes["insights"]:
|
||||||
|
return outcomes["insights"][agent_name]
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
# 便捷函数
|
||||||
|
async def initialize_baxian_memory_banks() -> CloudflareMemoryBank:
|
||||||
|
"""
|
||||||
|
初始化所有八仙智能体的Cloudflare记忆空间
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
配置好的CloudflareMemoryBank实例
|
||||||
|
"""
|
||||||
|
memory_bank = CloudflareMemoryBank()
|
||||||
|
|
||||||
|
print("🏛️ 正在为稷下学宫八仙创建Cloudflare记忆空间...")
|
||||||
|
|
||||||
|
for agent_key, chinese_name in memory_bank.baxian_agents.items():
|
||||||
|
try:
|
||||||
|
await memory_bank.create_memory_bank(agent_key)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 创建 {chinese_name} 记忆空间时出错: {e}")
|
||||||
|
|
||||||
|
print("✅ 八仙Cloudflare记忆空间初始化完成")
|
||||||
|
return memory_bank
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test_memory_bank():
|
||||||
|
"""测试Cloudflare Memory Bank功能"""
|
||||||
|
try:
|
||||||
|
# 创建Memory Bank实例
|
||||||
|
memory_bank = CloudflareMemoryBank()
|
||||||
|
|
||||||
|
# 测试创建记忆空间
|
||||||
|
await memory_bank.create_memory_bank("tieguaili")
|
||||||
|
|
||||||
|
# 测试添加记忆
|
||||||
|
await memory_bank.add_memory(
|
||||||
|
agent_name="tieguaili",
|
||||||
|
content="在讨论NVIDIA股票时，我倾向于逆向思维，关注潜在风险。",
|
||||||
|
memory_type="preference",
|
||||||
|
debate_topic="NVIDIA投资分析"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 测试搜索记忆
|
||||||
|
results = await memory_bank.search_memories(
|
||||||
|
agent_name="tieguaili",
|
||||||
|
query="NVIDIA",
|
||||||
|
limit=5
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"搜索结果: {len(results)} 条记忆")
|
||||||
|
for result in results:
|
||||||
|
print(f"- {result['content']}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 测试失败: {e}")
|
||||||
|
|
||||||
|
# 运行测试
|
||||||
|
asyncio.run(test_memory_bank())
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
记忆银行工厂：根据配置创建不同后端实现（Vertex AI 或 Cloudflare AutoRAG）
|
||||||
|
"""
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import os
|
||||||
|
from typing import Optional
|
||||||
|
|
||||||
|
from .base_memory_bank import MemoryBankProtocol
|
||||||
|
from .vertex_memory_bank import VertexMemoryBank
|
||||||
|
# 新增 Cloudflare 实现
|
||||||
|
from .cloudflare_memory_bank import CloudflareMemoryBank
|
||||||
|
|
||||||
|
|
||||||
|
def get_memory_backend(prefer: Optional[str] = None) -> MemoryBankProtocol:
|
||||||
|
"""
|
||||||
|
强制使用 Vertex AI 作为记忆后端。
|
||||||
|
'prefer' 参数将被忽略。
|
||||||
|
"""
|
||||||
|
# 强制使用 Vertex AI 后端
|
||||||
|
try:
|
||||||
|
mem = VertexMemoryBank.from_config()
|
||||||
|
print("🧠 使用 Vertex AI 作为记忆后端")
|
||||||
|
return mem
|
||||||
|
except Exception as e:
|
||||||
|
# 不可用时抛错
|
||||||
|
raise RuntimeError(
|
||||||
|
"未能创建 Vertex 记忆后端：请配置 Vertex (GOOGLE_*) 环境变量"
|
||||||
|
) from e
|
||||||
|
|
@ -0,0 +1,463 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Vertex AI Memory Bank 集成模块
|
||||||
|
为稷下学宫AI辩论系统提供记忆银行功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
from typing import Dict, List, Optional, Any
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from datetime import datetime
|
||||||
|
import json
|
||||||
|
|
||||||
|
try:
|
||||||
|
from google.cloud import aiplatform
|
||||||
|
# Memory Bank 功能可能还在预览版中，先使用基础功能
|
||||||
|
VERTEX_AI_AVAILABLE = True
|
||||||
|
except ImportError:
|
||||||
|
VERTEX_AI_AVAILABLE = False
|
||||||
|
print("⚠️ Google Cloud AI Platform 未安装，Memory Bank功能不可用")
|
||||||
|
print("安装命令: pip install google-cloud-aiplatform")
|
||||||
|
|
||||||
|
from config.settings import get_google_genai_config
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class MemoryEntry:
|
||||||
|
"""记忆条目数据结构"""
|
||||||
|
content: str
|
||||||
|
metadata: Dict[str, Any]
|
||||||
|
timestamp: datetime
|
||||||
|
agent_name: str
|
||||||
|
debate_topic: str
|
||||||
|
memory_type: str # "conversation", "preference", "knowledge", "strategy"
|
||||||
|
|
||||||
|
|
||||||
|
class VertexMemoryBank:
|
||||||
|
"""
|
||||||
|
Vertex AI Memory Bank 管理器
|
||||||
|
为八仙辩论系统提供智能记忆功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, project_id: str, location: str = "us-central1"):
|
||||||
|
"""
|
||||||
|
初始化Memory Bank
|
||||||
|
|
||||||
|
Args:
|
||||||
|
project_id: Google Cloud项目ID
|
||||||
|
location: 部署区域
|
||||||
|
"""
|
||||||
|
if not VERTEX_AI_AVAILABLE:
|
||||||
|
print("⚠️ Google Cloud AI Platform 未安装，使用本地模拟模式")
|
||||||
|
# 不抛出异常，允许使用本地模拟模式
|
||||||
|
|
||||||
|
self.project_id = project_id
|
||||||
|
self.location = location
|
||||||
|
self.memory_banks = {} # 存储不同智能体的记忆银行
|
||||||
|
self.local_memories = {} # 本地记忆存储 (临时方案)
|
||||||
|
|
||||||
|
# 初始化AI Platform
|
||||||
|
try:
|
||||||
|
aiplatform.init(project=project_id, location=location)
|
||||||
|
print(f"✅ Vertex AI 初始化成功: {project_id} @ {location}")
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ Vertex AI 初始化失败，使用本地模拟模式: {e}")
|
||||||
|
|
||||||
|
# 八仙智能体名称映射
|
||||||
|
self.baxian_agents = {
|
||||||
|
"tieguaili": "铁拐李",
|
||||||
|
"hanzhongli": "汉钟离",
|
||||||
|
"zhangguolao": "张果老",
|
||||||
|
"lancaihe": "蓝采和",
|
||||||
|
"hexiangu": "何仙姑",
|
||||||
|
"lvdongbin": "吕洞宾",
|
||||||
|
"hanxiangzi": "韩湘子",
|
||||||
|
"caoguojiu": "曹国舅"
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_config(cls) -> 'VertexMemoryBank':
|
||||||
|
"""
|
||||||
|
从配置创建Memory Bank实例
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
VertexMemoryBank实例
|
||||||
|
"""
|
||||||
|
config = get_google_genai_config()
|
||||||
|
project_id = config.get('project_id')
|
||||||
|
location = config.get('location', 'us-central1')
|
||||||
|
|
||||||
|
if not project_id:
|
||||||
|
raise ValueError("Google Cloud Project ID 未配置，请设置 GOOGLE_CLOUD_PROJECT_ID")
|
||||||
|
|
||||||
|
return cls(project_id=project_id, location=location)
|
||||||
|
|
||||||
|
async def create_memory_bank(self, agent_name: str, display_name: str = None) -> str:
|
||||||
|
"""
|
||||||
|
为指定智能体创建记忆银行
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称 (如 "tieguaili")
|
||||||
|
display_name: 显示名称 (如 "铁拐李的记忆银行")
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
记忆银行ID
|
||||||
|
"""
|
||||||
|
if not display_name:
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
display_name = f"{chinese_name}的记忆银行"
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 使用本地存储模拟记忆银行 (临时方案)
|
||||||
|
memory_bank_id = f"memory_bank_{agent_name}_{self.project_id}"
|
||||||
|
|
||||||
|
# 初始化本地记忆存储
|
||||||
|
if agent_name not in self.local_memories:
|
||||||
|
self.local_memories[agent_name] = []
|
||||||
|
|
||||||
|
self.memory_banks[agent_name] = memory_bank_id
|
||||||
|
print(f"✅ 为 {display_name} 创建记忆银行: {memory_bank_id}")
|
||||||
|
|
||||||
|
return memory_bank_id
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 创建记忆银行失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
async def add_memory(self,
|
||||||
|
agent_name: str,
|
||||||
|
content: str,
|
||||||
|
memory_type: str = "conversation",
|
||||||
|
debate_topic: str = "",
|
||||||
|
metadata: Dict[str, Any] = None) -> str:
|
||||||
|
"""
|
||||||
|
添加记忆到指定智能体的记忆银行
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称
|
||||||
|
content: 记忆内容
|
||||||
|
memory_type: 记忆类型 ("conversation", "preference", "knowledge", "strategy")
|
||||||
|
debate_topic: 辩论主题
|
||||||
|
metadata: 额外元数据
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
记忆ID
|
||||||
|
"""
|
||||||
|
if agent_name not in self.memory_banks:
|
||||||
|
await self.create_memory_bank(agent_name)
|
||||||
|
|
||||||
|
if metadata is None:
|
||||||
|
metadata = {}
|
||||||
|
|
||||||
|
# 构建记忆条目
|
||||||
|
memory_entry = MemoryEntry(
|
||||||
|
content=content,
|
||||||
|
metadata={
|
||||||
|
**metadata,
|
||||||
|
"agent_name": agent_name,
|
||||||
|
"chinese_name": self.baxian_agents.get(agent_name, agent_name),
|
||||||
|
"memory_type": memory_type,
|
||||||
|
"debate_topic": debate_topic,
|
||||||
|
"system": "jixia_academy"
|
||||||
|
},
|
||||||
|
timestamp=datetime.now(),
|
||||||
|
agent_name=agent_name,
|
||||||
|
debate_topic=debate_topic,
|
||||||
|
memory_type=memory_type
|
||||||
|
)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 使用本地存储添加记忆 (临时方案)
|
||||||
|
memory_id = f"memory_{agent_name}_{len(self.local_memories[agent_name])}"
|
||||||
|
|
||||||
|
# 添加到本地存储
|
||||||
|
memory_data = {
|
||||||
|
"id": memory_id,
|
||||||
|
"content": content,
|
||||||
|
"metadata": memory_entry.metadata,
|
||||||
|
"timestamp": memory_entry.timestamp.isoformat(),
|
||||||
|
"memory_type": memory_type,
|
||||||
|
"debate_topic": debate_topic
|
||||||
|
}
|
||||||
|
|
||||||
|
self.local_memories[agent_name].append(memory_data)
|
||||||
|
|
||||||
|
print(f"✅ 为 {self.baxian_agents.get(agent_name)} 添加记忆: {memory_type}")
|
||||||
|
return memory_id
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 添加记忆失败: {e}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
async def search_memories(self,
|
||||||
|
agent_name: str,
|
||||||
|
query: str,
|
||||||
|
memory_type: str = None,
|
||||||
|
limit: int = 10) -> List[Dict[str, Any]]:
|
||||||
|
"""
|
||||||
|
搜索智能体的相关记忆
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称
|
||||||
|
query: 搜索查询
|
||||||
|
memory_type: 记忆类型过滤
|
||||||
|
limit: 返回结果数量限制
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
相关记忆列表
|
||||||
|
"""
|
||||||
|
if agent_name not in self.memory_banks:
|
||||||
|
return []
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 使用本地存储搜索记忆 (临时方案)
|
||||||
|
if agent_name not in self.local_memories:
|
||||||
|
return []
|
||||||
|
|
||||||
|
memories = self.local_memories[agent_name]
|
||||||
|
results = []
|
||||||
|
|
||||||
|
# 简单的文本匹配搜索
|
||||||
|
query_lower = query.lower()
|
||||||
|
|
||||||
|
for memory in memories:
|
||||||
|
# 检查记忆类型过滤
|
||||||
|
if memory_type and memory.get("memory_type") != memory_type:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 检查内容匹配
|
||||||
|
content_lower = memory["content"].lower()
|
||||||
|
debate_topic_lower = memory.get("debate_topic", "").lower()
|
||||||
|
|
||||||
|
# 在内容或辩论主题中搜索
|
||||||
|
if query_lower in content_lower or query_lower in debate_topic_lower:
|
||||||
|
# 计算简单的相关性分数
|
||||||
|
content_matches = content_lower.count(query_lower)
|
||||||
|
topic_matches = debate_topic_lower.count(query_lower)
|
||||||
|
total_words = len(content_lower.split()) + len(debate_topic_lower.split())
|
||||||
|
|
||||||
|
relevance_score = (content_matches + topic_matches) / max(total_words, 1)
|
||||||
|
|
||||||
|
results.append({
|
||||||
|
"content": memory["content"],
|
||||||
|
"metadata": memory["metadata"],
|
||||||
|
"relevance_score": relevance_score
|
||||||
|
})
|
||||||
|
|
||||||
|
# 按相关性排序并限制结果数量
|
||||||
|
results.sort(key=lambda x: x["relevance_score"], reverse=True)
|
||||||
|
return results[:limit]
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 搜索记忆失败: {e}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str:
|
||||||
|
"""
|
||||||
|
获取智能体在特定辩论主题下的上下文记忆
|
||||||
|
|
||||||
|
Args:
|
||||||
|
agent_name: 智能体名称
|
||||||
|
debate_topic: 辩论主题
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的上下文字符串
|
||||||
|
"""
|
||||||
|
# 搜索相关记忆
|
||||||
|
conversation_memories = await self.search_memories(
|
||||||
|
agent_name, debate_topic, "conversation", limit=5
|
||||||
|
)
|
||||||
|
preference_memories = await self.search_memories(
|
||||||
|
agent_name, debate_topic, "preference", limit=3
|
||||||
|
)
|
||||||
|
strategy_memories = await self.search_memories(
|
||||||
|
agent_name, debate_topic, "strategy", limit=3
|
||||||
|
)
|
||||||
|
|
||||||
|
# 构建上下文
|
||||||
|
context_parts = []
|
||||||
|
|
||||||
|
if conversation_memories:
|
||||||
|
context_parts.append("## 历史对话记忆")
|
||||||
|
for mem in conversation_memories:
|
||||||
|
context_parts.append(f"- {mem['content']}")
|
||||||
|
|
||||||
|
if preference_memories:
|
||||||
|
context_parts.append("\n## 偏好记忆")
|
||||||
|
for mem in preference_memories:
|
||||||
|
context_parts.append(f"- {mem['content']}")
|
||||||
|
|
||||||
|
if strategy_memories:
|
||||||
|
context_parts.append("\n## 策略记忆")
|
||||||
|
for mem in strategy_memories:
|
||||||
|
context_parts.append(f"- {mem['content']}")
|
||||||
|
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
if context_parts:
|
||||||
|
return f"# {chinese_name}的记忆上下文\n\n" + "\n".join(context_parts)
|
||||||
|
else:
|
||||||
|
return f"# {chinese_name}的记忆上下文\n\n暂无相关记忆。"
|
||||||
|
|
||||||
|
async def save_debate_session(self,
|
||||||
|
debate_topic: str,
|
||||||
|
participants: List[str],
|
||||||
|
conversation_history: List[Dict[str, str]],
|
||||||
|
outcomes: Dict[str, Any] = None) -> None:
|
||||||
|
"""
|
||||||
|
保存完整的辩论会话到各参与者的记忆银行
|
||||||
|
|
||||||
|
Args:
|
||||||
|
debate_topic: 辩论主题
|
||||||
|
participants: 参与者列表
|
||||||
|
conversation_history: 对话历史
|
||||||
|
outcomes: 辩论结果和洞察
|
||||||
|
"""
|
||||||
|
for agent_name in participants:
|
||||||
|
if agent_name not in self.baxian_agents:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 保存对话历史
|
||||||
|
conversation_summary = self._summarize_conversation(
|
||||||
|
conversation_history, agent_name
|
||||||
|
)
|
||||||
|
await self.add_memory(
|
||||||
|
agent_name=agent_name,
|
||||||
|
content=conversation_summary,
|
||||||
|
memory_type="conversation",
|
||||||
|
debate_topic=debate_topic,
|
||||||
|
metadata={
|
||||||
|
"participants": participants,
|
||||||
|
"session_length": len(conversation_history)
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
# 保存策略洞察
|
||||||
|
if outcomes:
|
||||||
|
strategy_insight = self._extract_strategy_insight(
|
||||||
|
outcomes, agent_name
|
||||||
|
)
|
||||||
|
if strategy_insight:
|
||||||
|
await self.add_memory(
|
||||||
|
agent_name=agent_name,
|
||||||
|
content=strategy_insight,
|
||||||
|
memory_type="strategy",
|
||||||
|
debate_topic=debate_topic,
|
||||||
|
metadata={"session_outcome": outcomes}
|
||||||
|
)
|
||||||
|
|
||||||
|
def _summarize_conversation(self,
|
||||||
|
conversation_history: List[Dict[str, str]],
|
||||||
|
agent_name: str) -> str:
|
||||||
|
"""
|
||||||
|
为特定智能体总结对话历史
|
||||||
|
|
||||||
|
Args:
|
||||||
|
conversation_history: 对话历史
|
||||||
|
agent_name: 智能体名称
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
对话总结
|
||||||
|
"""
|
||||||
|
agent_messages = [
|
||||||
|
msg for msg in conversation_history
|
||||||
|
if msg.get("agent") == agent_name
|
||||||
|
]
|
||||||
|
|
||||||
|
if not agent_messages:
|
||||||
|
return "本次辩论中未发言"
|
||||||
|
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
summary = f"{chinese_name}在本次辩论中的主要观点：\n"
|
||||||
|
|
||||||
|
for i, msg in enumerate(agent_messages[:3], 1): # 只取前3条主要观点
|
||||||
|
summary += f"{i}. {msg.get('content', '')[:100]}...\n"
|
||||||
|
|
||||||
|
return summary
|
||||||
|
|
||||||
|
def _extract_strategy_insight(self,
|
||||||
|
outcomes: Dict[str, Any],
|
||||||
|
agent_name: str) -> Optional[str]:
|
||||||
|
"""
|
||||||
|
从辩论结果中提取策略洞察
|
||||||
|
|
||||||
|
Args:
|
||||||
|
outcomes: 辩论结果
|
||||||
|
agent_name: 智能体名称
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
策略洞察或None
|
||||||
|
"""
|
||||||
|
# 这里可以根据实际的outcomes结构来提取洞察
|
||||||
|
# 暂时返回一个简单的示例
|
||||||
|
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||||
|
|
||||||
|
if "winner" in outcomes and outcomes["winner"] == agent_name:
|
||||||
|
return f"{chinese_name}在本次辩论中获胜，其论证策略值得保持。"
|
||||||
|
elif "insights" in outcomes and agent_name in outcomes["insights"]:
|
||||||
|
return outcomes["insights"][agent_name]
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
# 便捷函数
|
||||||
|
async def initialize_baxian_memory_banks(project_id: str, location: str = "us-central1") -> VertexMemoryBank:
|
||||||
|
"""
|
||||||
|
初始化所有八仙智能体的记忆银行
|
||||||
|
|
||||||
|
Args:
|
||||||
|
project_id: Google Cloud项目ID
|
||||||
|
location: 部署区域
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
配置好的VertexMemoryBank实例
|
||||||
|
"""
|
||||||
|
memory_bank = VertexMemoryBank(project_id, location)
|
||||||
|
|
||||||
|
print("🏛️ 正在为稷下学宫八仙创建记忆银行...")
|
||||||
|
|
||||||
|
for agent_key, chinese_name in memory_bank.baxian_agents.items():
|
||||||
|
try:
|
||||||
|
await memory_bank.create_memory_bank(agent_key)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"⚠️ 创建 {chinese_name} 记忆银行时出错: {e}")
|
||||||
|
|
||||||
|
print("✅ 八仙记忆银行初始化完成")
|
||||||
|
return memory_bank
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
async def test_memory_bank():
|
||||||
|
"""测试Memory Bank功能"""
|
||||||
|
try:
|
||||||
|
# 从配置创建Memory Bank
|
||||||
|
memory_bank = VertexMemoryBank.from_config()
|
||||||
|
|
||||||
|
# 测试创建记忆银行
|
||||||
|
await memory_bank.create_memory_bank("tieguaili")
|
||||||
|
|
||||||
|
# 测试添加记忆
|
||||||
|
await memory_bank.add_memory(
|
||||||
|
agent_name="tieguaili",
|
||||||
|
content="在讨论NVIDIA股票时，我倾向于逆向思维，关注潜在风险。",
|
||||||
|
memory_type="preference",
|
||||||
|
debate_topic="NVIDIA投资分析"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 测试搜索记忆
|
||||||
|
results = await memory_bank.search_memories(
|
||||||
|
agent_name="tieguaili",
|
||||||
|
query="NVIDIA",
|
||||||
|
limit=5
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"搜索结果: {len(results)} 条记忆")
|
||||||
|
for result in results:
|
||||||
|
print(f"- {result['content']}")
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ 测试失败: {e}")
|
||||||
|
|
||||||
|
# 运行测试
|
||||||
|
asyncio.run(test_memory_bank())
|
||||||
|
|
@ -0,0 +1,521 @@
|
||||||
|
# 金融数据抽象层设计
|
||||||
|
|
||||||
|
## 概述
|
||||||
|
|
||||||
|
在"炼妖壶-稷下学宫AI辩论系统"中，我们需要构建一个统一的金融数据抽象层，以支持多种数据源（包括现有的RapidAPI永动机引擎、新增的OpenBB集成引擎，以及未来可能添加的其他数据提供商）。该抽象层将为上层AI智能体提供一致的数据接口，同时隐藏底层数据源的具体实现细节。
|
||||||
|
|
||||||
|
## 设计目标
|
||||||
|
|
||||||
|
1. **统一接口**：为所有金融数据访问提供一致的API
|
||||||
|
2. **可扩展性**：易于添加新的数据提供商
|
||||||
|
3. **容错性**：当主数据源不可用时，能够自动切换到备用数据源
|
||||||
|
4. **性能优化**：支持缓存和异步数据获取
|
||||||
|
5. **类型安全**：使用Python类型注解确保数据结构的一致性
|
||||||
|
|
||||||
|
## 核心组件
|
||||||
|
|
||||||
|
### 1. 数据模型 (Data Models)
|
||||||
|
|
||||||
|
定义标准化的金融数据结构：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/models/financial_data_models.py
|
||||||
|
|
||||||
|
from dataclasses import dataclass
|
||||||
|
from typing import Optional, List
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class StockQuote:
|
||||||
|
symbol: str
|
||||||
|
price: float
|
||||||
|
change: float
|
||||||
|
change_percent: float
|
||||||
|
volume: int
|
||||||
|
timestamp: datetime
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class HistoricalPrice:
|
||||||
|
date: datetime
|
||||||
|
open: float
|
||||||
|
high: float
|
||||||
|
low: float
|
||||||
|
close: float
|
||||||
|
volume: int
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class CompanyProfile:
|
||||||
|
symbol: str
|
||||||
|
name: str
|
||||||
|
industry: str
|
||||||
|
sector: str
|
||||||
|
market_cap: float
|
||||||
|
pe_ratio: Optional[float]
|
||||||
|
dividend_yield: Optional[float]
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class FinancialNews:
|
||||||
|
title: str
|
||||||
|
summary: str
|
||||||
|
url: str
|
||||||
|
timestamp: datetime
|
||||||
|
sentiment: Optional[float] # -1 (负面) to 1 (正面)
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2. 抽象基类 (Abstract Base Class)
|
||||||
|
|
||||||
|
定义数据提供商的通用接口：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/data_abstraction.py
|
||||||
|
|
||||||
|
from abc import ABC, abstractmethod
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
|
||||||
|
class DataProvider(ABC):
|
||||||
|
"""金融数据提供商抽象基类"""
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
"""获取股票报价"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
"""获取历史价格数据"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
"""获取公司概况"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@abstractmethod
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
"""获取相关新闻"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
@abstractmethod
|
||||||
|
def name(self) -> str:
|
||||||
|
"""数据提供商名称"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
@abstractmethod
|
||||||
|
def priority(self) -> int:
|
||||||
|
"""优先级（数字越小优先级越高）"""
|
||||||
|
pass
|
||||||
|
```
|
||||||
|
|
||||||
|
### 3. Provider适配器 (Provider Adapters)
|
||||||
|
|
||||||
|
为每个具体的数据源实现适配器：
|
||||||
|
|
||||||
|
#### RapidAPI永动机引擎适配器
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/rapidapi_adapter.py
|
||||||
|
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.engines.data_abstraction import DataProvider
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
from src.jixia.engines.perpetual_engine import JixiaPerpetualEngine
|
||||||
|
from config.settings import get_rapidapi_key
|
||||||
|
|
||||||
|
class RapidAPIDataProvider(DataProvider):
|
||||||
|
"""RapidAPI永动机引擎适配器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.engine = JixiaPerpetualEngine(get_rapidapi_key())
|
||||||
|
self._name = "RapidAPI"
|
||||||
|
self._priority = 2 # 中等优先级
|
||||||
|
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
result = self.engine.get_immortal_data("吕洞宾", "quote", symbol)
|
||||||
|
if result.success and result.data:
|
||||||
|
# 解析RapidAPI返回的数据并转换为StockQuote
|
||||||
|
# 这里需要根据实际API返回的数据结构进行调整
|
||||||
|
return StockQuote(
|
||||||
|
symbol=symbol,
|
||||||
|
price=result.data.get("price", 0),
|
||||||
|
change=result.data.get("change", 0),
|
||||||
|
change_percent=result.data.get("change_percent", 0),
|
||||||
|
volume=result.data.get("volume", 0),
|
||||||
|
timestamp=result.data.get("timestamp")
|
||||||
|
)
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
# 实现历史价格数据获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
# 实现公司概况获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
# 实现新闻获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
def name(self) -> str:
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@property
|
||||||
|
def priority(self) -> int:
|
||||||
|
return self._priority
|
||||||
|
```
|
||||||
|
|
||||||
|
#### OpenBB引擎适配器
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/openbb_adapter.py
|
||||||
|
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.engines.data_abstraction import DataProvider
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
from src.jixia.engines.openbb_engine import OpenBBEngine
|
||||||
|
|
||||||
|
class OpenBBDataProvider(DataProvider):
|
||||||
|
"""OpenBB引擎适配器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.engine = OpenBBEngine()
|
||||||
|
self._name = "OpenBB"
|
||||||
|
self._priority = 1 # 最高优先级
|
||||||
|
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
result = self.engine.get_immortal_data("吕洞宾", "price", symbol)
|
||||||
|
if result.success and result.data:
|
||||||
|
# 解析OpenBB返回的数据并转换为StockQuote
|
||||||
|
return StockQuote(
|
||||||
|
symbol=symbol,
|
||||||
|
price=result.data.get("close", 0),
|
||||||
|
change=0, # 需要计算
|
||||||
|
change_percent=0, # 需要计算
|
||||||
|
volume=result.data.get("volume", 0),
|
||||||
|
timestamp=result.data.get("date")
|
||||||
|
)
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
# 实现历史价格数据获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
# 实现公司概况获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
# 实现新闻获取逻辑
|
||||||
|
pass
|
||||||
|
|
||||||
|
@property
|
||||||
|
def name(self) -> str:
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@property
|
||||||
|
def priority(self) -> int:
|
||||||
|
return self._priority
|
||||||
|
```
|
||||||
|
|
||||||
|
### 4. 数据抽象层管理器 (Data Abstraction Layer Manager)
|
||||||
|
|
||||||
|
管理多个数据提供商并提供统一接口：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/data_abstraction_layer.py
|
||||||
|
|
||||||
|
from typing import List, Optional
|
||||||
|
from src.jixia.engines.data_abstraction import DataProvider
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
|
||||||
|
import asyncio
|
||||||
|
|
||||||
|
class DataAbstractionLayer:
|
||||||
|
"""金融数据抽象层管理器"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.providers: List[DataProvider] = []
|
||||||
|
self._initialize_providers()
|
||||||
|
|
||||||
|
def _initialize_providers(self):
|
||||||
|
"""初始化所有可用的数据提供商"""
|
||||||
|
# 根据配置和环境动态加载适配器
|
||||||
|
try:
|
||||||
|
from src.jixia.engines.rapidapi_adapter import RapidAPIDataProvider
|
||||||
|
self.providers.append(RapidAPIDataProvider())
|
||||||
|
except ImportError:
|
||||||
|
pass # RapidAPI引擎不可用
|
||||||
|
|
||||||
|
try:
|
||||||
|
from src.jixia.engines.openbb_adapter import OpenBBDataProvider
|
||||||
|
self.providers.append(OpenBBDataProvider())
|
||||||
|
except ImportError:
|
||||||
|
pass # OpenBB引擎不可用
|
||||||
|
|
||||||
|
# 按优先级排序
|
||||||
|
self.providers.sort(key=lambda p: p.priority)
|
||||||
|
|
||||||
|
def get_quote(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
"""获取股票报价（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
quote = provider.get_quote(symbol)
|
||||||
|
if quote:
|
||||||
|
return quote
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取报价失败: {e}")
|
||||||
|
continue
|
||||||
|
return None
|
||||||
|
|
||||||
|
async def get_quote_async(self, symbol: str) -> Optional[StockQuote]:
|
||||||
|
"""异步获取股票报价（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
# 如果提供商支持异步方法，则使用异步方法
|
||||||
|
if hasattr(provider, 'get_quote_async'):
|
||||||
|
quote = await provider.get_quote_async(symbol)
|
||||||
|
else:
|
||||||
|
# 否则在执行器中运行同步方法
|
||||||
|
quote = await asyncio.get_event_loop().run_in_executor(
|
||||||
|
None, provider.get_quote, symbol
|
||||||
|
)
|
||||||
|
if quote:
|
||||||
|
return quote
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取报价失败: {e}")
|
||||||
|
continue
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
|
||||||
|
"""获取历史价格数据（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
prices = provider.get_historical_prices(symbol, days)
|
||||||
|
if prices:
|
||||||
|
return prices
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取历史价格失败: {e}")
|
||||||
|
continue
|
||||||
|
return []
|
||||||
|
|
||||||
|
def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
|
||||||
|
"""获取公司概况（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
profile = provider.get_company_profile(symbol)
|
||||||
|
if profile:
|
||||||
|
return profile
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取公司概况失败: {e}")
|
||||||
|
continue
|
||||||
|
return None
|
||||||
|
|
||||||
|
def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
|
||||||
|
"""获取相关新闻（带故障转移）"""
|
||||||
|
for provider in self.providers:
|
||||||
|
try:
|
||||||
|
news = provider.get_news(symbol, limit)
|
||||||
|
if news:
|
||||||
|
return news
|
||||||
|
except Exception as e:
|
||||||
|
print(f"警告: {provider.name} 获取新闻失败: {e}")
|
||||||
|
continue
|
||||||
|
return []
|
||||||
|
```
|
||||||
|
|
||||||
|
## 八仙与数据源的智能映射
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/baxian_data_mapping.py
|
||||||
|
|
||||||
|
# 设计八仙与数据源的智能映射
|
||||||
|
immortal_data_mapping = {
|
||||||
|
'吕洞宾': {
|
||||||
|
'specialty': 'technical_analysis', # 技术分析专家
|
||||||
|
'preferred_data_types': ['historical', 'price'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
},
|
||||||
|
'何仙姑': {
|
||||||
|
'specialty': 'risk_metrics', # 风险控制专家
|
||||||
|
'preferred_data_types': ['price', 'profile'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'张果老': {
|
||||||
|
'specialty': 'historical_data', # 历史数据分析师
|
||||||
|
'preferred_data_types': ['historical'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
},
|
||||||
|
'韩湘子': {
|
||||||
|
'specialty': 'sector_analysis', # 新兴资产专家
|
||||||
|
'preferred_data_types': ['profile', 'news'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'汉钟离': {
|
||||||
|
'specialty': 'market_movers', # 热点追踪
|
||||||
|
'preferred_data_types': ['news', 'price'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'蓝采和': {
|
||||||
|
'specialty': 'value_discovery', # 潜力股发现
|
||||||
|
'preferred_data_types': ['screener', 'profile'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
},
|
||||||
|
'铁拐李': {
|
||||||
|
'specialty': 'contrarian_analysis', # 逆向思维专家
|
||||||
|
'preferred_data_types': ['profile', 'short_interest'],
|
||||||
|
'data_providers': ['RapidAPI', 'OpenBB']
|
||||||
|
},
|
||||||
|
'曹国舅': {
|
||||||
|
'specialty': 'macro_economics', # 宏观经济分析师
|
||||||
|
'preferred_data_types': ['profile', 'institutional_holdings'],
|
||||||
|
'data_providers': ['OpenBB', 'RapidAPI']
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
## 缓存策略
|
||||||
|
|
||||||
|
为了提高性能，我们将实现多级缓存策略：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/data_cache.py
|
||||||
|
|
||||||
|
import time
|
||||||
|
from typing import Any, Optional
|
||||||
|
from functools import lru_cache
|
||||||
|
|
||||||
|
class DataCache:
|
||||||
|
"""金融数据缓存"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self._cache = {}
|
||||||
|
self._cache_times = {}
|
||||||
|
self.default_ttl = 60 # 默认缓存时间（秒）
|
||||||
|
|
||||||
|
def get(self, key: str) -> Optional[Any]:
|
||||||
|
"""获取缓存数据"""
|
||||||
|
if key in self._cache:
|
||||||
|
# 检查是否过期
|
||||||
|
if time.time() - self._cache_times[key] < self.default_ttl:
|
||||||
|
return self._cache[key]
|
||||||
|
else:
|
||||||
|
# 删除过期缓存
|
||||||
|
del self._cache[key]
|
||||||
|
del self._cache_times[key]
|
||||||
|
return None
|
||||||
|
|
||||||
|
def set(self, key: str, value: Any, ttl: Optional[int] = None):
|
||||||
|
"""设置缓存数据"""
|
||||||
|
self._cache[key] = value
|
||||||
|
self._cache_times[key] = time.time()
|
||||||
|
if ttl:
|
||||||
|
# 可以为特定数据设置不同的TTL
|
||||||
|
pass # 实际实现中需要更复杂的TTL管理机制
|
||||||
|
|
||||||
|
@lru_cache(maxsize=128)
|
||||||
|
def get_quote_cache(self, symbol: str) -> Optional[Any]:
|
||||||
|
"""LRU缓存装饰器示例"""
|
||||||
|
# 这个方法将自动缓存最近128个调用的结果
|
||||||
|
pass
|
||||||
|
```
|
||||||
|
|
||||||
|
## 数据质量监控机制
|
||||||
|
|
||||||
|
为了确保数据的准确性和可靠性，我们将实现数据质量监控：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# src/jixia/engines/data_quality_monitor.py
|
||||||
|
|
||||||
|
from typing import Dict, Any
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
class DataQualityMonitor:
|
||||||
|
"""数据质量监控"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.provider_stats = {}
|
||||||
|
|
||||||
|
def record_access(self, provider_name: str, success: bool, response_time: float, data_size: int):
|
||||||
|
"""记录数据访问统计"""
|
||||||
|
if provider_name not in self.provider_stats:
|
||||||
|
self.provider_stats[provider_name] = {
|
||||||
|
'total_requests': 0,
|
||||||
|
'successful_requests': 0,
|
||||||
|
'failed_requests': 0,
|
||||||
|
'total_response_time': 0,
|
||||||
|
'total_data_size': 0,
|
||||||
|
'last_access': None
|
||||||
|
}
|
||||||
|
|
||||||
|
stats = self.provider_stats[provider_name]
|
||||||
|
stats['total_requests'] += 1
|
||||||
|
if success:
|
||||||
|
stats['successful_requests'] += 1
|
||||||
|
else:
|
||||||
|
stats['failed_requests'] += 1
|
||||||
|
stats['total_response_time'] += response_time
|
||||||
|
stats['total_data_size'] += data_size
|
||||||
|
stats['last_access'] = datetime.now()
|
||||||
|
|
||||||
|
def get_provider_health(self, provider_name: str) -> Dict[str, Any]:
|
||||||
|
"""获取提供商健康状况"""
|
||||||
|
if provider_name not in self.provider_stats:
|
||||||
|
return {'status': 'unknown'}
|
||||||
|
|
||||||
|
stats = self.provider_stats[provider_name]
|
||||||
|
success_rate = stats['successful_requests'] / stats['total_requests'] if stats['total_requests'] > 0 else 0
|
||||||
|
avg_response_time = stats['total_response_time'] / stats['total_requests'] if stats['total_requests'] > 0 else 0
|
||||||
|
|
||||||
|
status = 'healthy' if success_rate > 0.95 and avg_response_time < 2.0 else 'degraded' if success_rate > 0.8 else 'unhealthy'
|
||||||
|
|
||||||
|
return {
|
||||||
|
'status': status,
|
||||||
|
'success_rate': success_rate,
|
||||||
|
'avg_response_time': avg_response_time,
|
||||||
|
'total_requests': stats['total_requests'],
|
||||||
|
'last_access': stats['last_access']
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
## 使用示例
|
||||||
|
|
||||||
|
```python
|
||||||
|
# 示例：在智能体中使用数据抽象层
|
||||||
|
from src.jixia.engines.data_abstraction_layer import DataAbstractionLayer
|
||||||
|
from src.jixia.models.financial_data_models import StockQuote
|
||||||
|
|
||||||
|
# 初始化数据抽象层
|
||||||
|
dal = DataAbstractionLayer()
|
||||||
|
|
||||||
|
# 获取股票报价
|
||||||
|
quote = dal.get_quote("AAPL")
|
||||||
|
if quote:
|
||||||
|
print(f"Apple股价: ${quote.price}")
|
||||||
|
else:
|
||||||
|
print("无法获取股价数据")
|
||||||
|
|
||||||
|
# 异步获取报价
|
||||||
|
import asyncio
|
||||||
|
async def async_example():
|
||||||
|
quote = await dal.get_quote_async("GOOGL")
|
||||||
|
if quote:
|
||||||
|
print(f"Google股价: ${quote.price}")
|
||||||
|
|
||||||
|
# asyncio.run(async_example())
|
||||||
|
```
|
||||||
|
|
||||||
|
## 总结
|
||||||
|
|
||||||
|
这个金融数据抽象层设计提供了以下优势：
|
||||||
|
|
||||||
|
1. **统一接口**：所有智能体都可以通过相同的接口访问任何数据源
|
||||||
|
2. **故障转移**：当主数据源不可用时，自动切换到备用数据源
|
||||||
|
3. **可扩展性**：可以轻松添加新的数据提供商适配器
|
||||||
|
4. **性能优化**：通过缓存机制提高数据访问速度
|
||||||
|
5. **质量监控**：实时监控各数据源的健康状况
|
||||||
|
6. **文化融合**：通过八仙与数据源的智能映射，保持项目的文化特色
|
||||||
|
|
||||||
|
这将为"炼妖壶-稷下学宫AI辩论系统"提供一个强大、可靠且可扩展的金融数据基础。
|
||||||
|
|
@ -0,0 +1,204 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
MongoDB Swarm集成使用示例
|
||||||
|
|
||||||
|
这个示例展示了如何将MongoDB MCP服务器与Swarm框架集成使用。
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from typing import Dict, Any, List
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
# 模拟Swarm框架（实际使用时导入真实的Swarm）
|
||||||
|
class MockSwarm:
|
||||||
|
def __init__(self):
|
||||||
|
self.agents = {}
|
||||||
|
|
||||||
|
def add_agent(self, agent):
|
||||||
|
self.agents[agent.name] = agent
|
||||||
|
print(f"✅ 代理 '{agent.name}' 已添加到Swarm")
|
||||||
|
|
||||||
|
async def run(self, agent_name: str, message: str) -> str:
|
||||||
|
if agent_name not in self.agents:
|
||||||
|
return f"❌ 代理 '{agent_name}' 不存在"
|
||||||
|
|
||||||
|
agent = self.agents[agent_name]
|
||||||
|
print(f"🤖 代理 '{agent_name}' 正在处理: {message}")
|
||||||
|
|
||||||
|
# 模拟代理处理逻辑
|
||||||
|
if "查询" in message or "查找" in message:
|
||||||
|
return await agent.handle_query(message)
|
||||||
|
elif "插入" in message or "添加" in message:
|
||||||
|
return await agent.handle_insert(message)
|
||||||
|
elif "统计" in message:
|
||||||
|
return await agent.handle_stats(message)
|
||||||
|
else:
|
||||||
|
return f"📝 代理 '{agent_name}' 收到消息: {message}"
|
||||||
|
|
||||||
|
class MockMongoDBAgent:
|
||||||
|
def __init__(self, name: str, mongodb_client):
|
||||||
|
self.name = name
|
||||||
|
self.mongodb_client = mongodb_client
|
||||||
|
self.functions = [
|
||||||
|
"mongodb_query",
|
||||||
|
"mongodb_insert",
|
||||||
|
"mongodb_update",
|
||||||
|
"mongodb_delete",
|
||||||
|
"mongodb_stats",
|
||||||
|
"mongodb_collections"
|
||||||
|
]
|
||||||
|
|
||||||
|
async def handle_query(self, message: str) -> str:
|
||||||
|
try:
|
||||||
|
# 模拟查询操作
|
||||||
|
result = await self.mongodb_client.query_documents(
|
||||||
|
collection="users",
|
||||||
|
filter_query={},
|
||||||
|
limit=5
|
||||||
|
)
|
||||||
|
return f"📊 查询结果: 找到 {len(result.get('documents', []))} 条记录"
|
||||||
|
except Exception as e:
|
||||||
|
return f"❌ 查询失败: {str(e)}"
|
||||||
|
|
||||||
|
async def handle_insert(self, message: str) -> str:
|
||||||
|
try:
|
||||||
|
# 模拟插入操作
|
||||||
|
sample_doc = {
|
||||||
|
"name": "示例用户",
|
||||||
|
"email": "user@example.com",
|
||||||
|
"created_at": datetime.now().isoformat(),
|
||||||
|
"tags": ["swarm", "mongodb"]
|
||||||
|
}
|
||||||
|
result = await self.mongodb_client.insert_document(
|
||||||
|
collection="users",
|
||||||
|
document=sample_doc
|
||||||
|
)
|
||||||
|
return f"✅ 插入成功: 文档ID {result.get('inserted_id', 'unknown')}"
|
||||||
|
except Exception as e:
|
||||||
|
return f"❌ 插入失败: {str(e)}"
|
||||||
|
|
||||||
|
async def handle_stats(self, message: str) -> str:
|
||||||
|
try:
|
||||||
|
# 模拟统计操作
|
||||||
|
result = await self.mongodb_client.get_database_stats()
|
||||||
|
return f"📈 数据库统计: {json.dumps(result, indent=2, ensure_ascii=False)}"
|
||||||
|
except Exception as e:
|
||||||
|
return f"❌ 获取统计失败: {str(e)}"
|
||||||
|
|
||||||
|
# 模拟MongoDB MCP客户端
|
||||||
|
class MockMongoDBClient:
|
||||||
|
def __init__(self, mcp_server_url: str, default_database: str):
|
||||||
|
self.mcp_server_url = mcp_server_url
|
||||||
|
self.default_database = default_database
|
||||||
|
self.connected = False
|
||||||
|
|
||||||
|
async def connect(self) -> bool:
|
||||||
|
print(f"🔌 连接到MongoDB MCP服务器: {self.mcp_server_url}")
|
||||||
|
print(f"📁 默认数据库: {self.default_database}")
|
||||||
|
self.connected = True
|
||||||
|
return True
|
||||||
|
|
||||||
|
async def query_documents(self, collection: str, filter_query: Dict, limit: int = 100) -> Dict[str, Any]:
|
||||||
|
if not self.connected:
|
||||||
|
raise Exception("未连接到MongoDB服务器")
|
||||||
|
|
||||||
|
print(f"🔍 查询集合 '{collection}', 过滤条件: {filter_query}, 限制: {limit}")
|
||||||
|
# 模拟查询结果
|
||||||
|
return {
|
||||||
|
"documents": [
|
||||||
|
{"_id": "507f1f77bcf86cd799439011", "name": "用户1", "email": "user1@example.com"},
|
||||||
|
{"_id": "507f1f77bcf86cd799439012", "name": "用户2", "email": "user2@example.com"},
|
||||||
|
{"_id": "507f1f77bcf86cd799439013", "name": "用户3", "email": "user3@example.com"}
|
||||||
|
],
|
||||||
|
"count": 3
|
||||||
|
}
|
||||||
|
|
||||||
|
async def insert_document(self, collection: str, document: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
if not self.connected:
|
||||||
|
raise Exception("未连接到MongoDB服务器")
|
||||||
|
|
||||||
|
print(f"📝 向集合 '{collection}' 插入文档: {json.dumps(document, ensure_ascii=False, indent=2)}")
|
||||||
|
# 模拟插入结果
|
||||||
|
return {
|
||||||
|
"inserted_id": "507f1f77bcf86cd799439014",
|
||||||
|
"acknowledged": True
|
||||||
|
}
|
||||||
|
|
||||||
|
async def get_database_stats(self) -> Dict[str, Any]:
|
||||||
|
if not self.connected:
|
||||||
|
raise Exception("未连接到MongoDB服务器")
|
||||||
|
|
||||||
|
print(f"📊 获取数据库 '{self.default_database}' 统计信息")
|
||||||
|
# 模拟统计结果
|
||||||
|
return {
|
||||||
|
"database": self.default_database,
|
||||||
|
"collections": 5,
|
||||||
|
"documents": 1250,
|
||||||
|
"avgObjSize": 512,
|
||||||
|
"dataSize": 640000,
|
||||||
|
"storageSize": 1024000,
|
||||||
|
"indexes": 8,
|
||||||
|
"indexSize": 32768
|
||||||
|
}
|
||||||
|
|
||||||
|
async def disconnect(self):
|
||||||
|
print("🔌 断开MongoDB MCP连接")
|
||||||
|
self.connected = False
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
print("🚀 MongoDB Swarm集成示例")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
# 1. 创建MongoDB MCP客户端
|
||||||
|
print("\n📋 步骤1: 创建MongoDB MCP客户端")
|
||||||
|
mongodb_client = MockMongoDBClient(
|
||||||
|
mcp_server_url="http://localhost:8080",
|
||||||
|
default_database="swarm_data"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 2. 连接到MongoDB
|
||||||
|
print("\n📋 步骤2: 连接到MongoDB")
|
||||||
|
await mongodb_client.connect()
|
||||||
|
|
||||||
|
# 3. 创建Swarm实例
|
||||||
|
print("\n📋 步骤3: 创建Swarm实例")
|
||||||
|
swarm = MockSwarm()
|
||||||
|
|
||||||
|
# 4. 创建MongoDB代理
|
||||||
|
print("\n📋 步骤4: 创建MongoDB代理")
|
||||||
|
mongodb_agent = MockMongoDBAgent("mongodb_agent", mongodb_client)
|
||||||
|
swarm.add_agent(mongodb_agent)
|
||||||
|
|
||||||
|
# 5. 演示各种操作
|
||||||
|
print("\n📋 步骤5: 演示MongoDB操作")
|
||||||
|
print("-" * 30)
|
||||||
|
|
||||||
|
# 查询操作
|
||||||
|
print("\n🔍 演示查询操作:")
|
||||||
|
result = await swarm.run("mongodb_agent", "查询所有用户数据")
|
||||||
|
print(f"结果: {result}")
|
||||||
|
|
||||||
|
# 插入操作
|
||||||
|
print("\n📝 演示插入操作:")
|
||||||
|
result = await swarm.run("mongodb_agent", "插入一个新用户")
|
||||||
|
print(f"结果: {result}")
|
||||||
|
|
||||||
|
# 统计操作
|
||||||
|
print("\n📊 演示统计操作:")
|
||||||
|
result = await swarm.run("mongodb_agent", "获取数据库统计信息")
|
||||||
|
print(f"结果: {result}")
|
||||||
|
|
||||||
|
# 6. 清理资源
|
||||||
|
print("\n📋 步骤6: 清理资源")
|
||||||
|
await mongodb_client.disconnect()
|
||||||
|
|
||||||
|
print("\n✅ 示例完成!")
|
||||||
|
print("\n💡 实际使用说明:")
|
||||||
|
print("1. 启动MongoDB和MCP服务器: docker-compose up -d")
|
||||||
|
print("2. 使用真实的SwarmMongoDBClient替换MockMongoDBClient")
|
||||||
|
print("3. 导入真实的Swarm框架")
|
||||||
|
print("4. 根据需要配置代理的instructions和functions")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,395 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Ollama Swarm + MongoDB RSS 集成示例
|
||||||
|
展示如何使用基于 Ollama 的 Swarm 调用 MongoDB 中的 RSS 数据
|
||||||
|
包含向量化搜索的实现方案
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
from datetime import datetime
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
from swarm import Swarm, Agent
|
||||||
|
from openai import OpenAI
|
||||||
|
|
||||||
|
# 导入 MongoDB MCP 客户端
|
||||||
|
try:
|
||||||
|
from src.mcp.swarm_mongodb_client import SwarmMongoDBClient
|
||||||
|
except ImportError:
|
||||||
|
print("警告: 无法导入 SwarmMongoDBClient，将使用模拟客户端")
|
||||||
|
SwarmMongoDBClient = None
|
||||||
|
|
||||||
|
class OllamaSwarmMongoDBIntegration:
|
||||||
|
"""
|
||||||
|
Ollama Swarm + MongoDB RSS 集成系统
|
||||||
|
|
||||||
|
功能:
|
||||||
|
1. 使用 Ollama 本地模型进行 AI 推理
|
||||||
|
2. 通过 MCP 连接 MongoDB 获取 RSS 数据
|
||||||
|
3. 支持向量化搜索（可选）
|
||||||
|
4. 四仙辩论系统集成
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
# Ollama 配置
|
||||||
|
self.ollama_base_url = "http://100.99.183.38:11434"
|
||||||
|
self.model_name = "qwen3:8b" # 使用支持工具调用的模型
|
||||||
|
# 初始化 OpenAI 客户端（连接到 Ollama）
|
||||||
|
self.openai_client = OpenAI(
|
||||||
|
api_key="ollama", # Ollama 不需要真实 API 密钥
|
||||||
|
base_url=f"{self.ollama_base_url}/v1"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 初始化 Swarm
|
||||||
|
self.swarm = Swarm(client=self.openai_client)
|
||||||
|
|
||||||
|
# 初始化 MongoDB 客户端
|
||||||
|
self.mongodb_client = None
|
||||||
|
self.init_mongodb_client()
|
||||||
|
|
||||||
|
# 创建代理
|
||||||
|
self.agents = self.create_agents()
|
||||||
|
|
||||||
|
print(f"🦙 Ollama 服务: {self.ollama_base_url}")
|
||||||
|
print(f"🤖 使用模型: {self.model_name}")
|
||||||
|
print(f"📊 MongoDB 连接: {'已连接' if self.mongodb_client else '未连接'}")
|
||||||
|
|
||||||
|
def init_mongodb_client(self):
|
||||||
|
"""初始化 MongoDB 客户端"""
|
||||||
|
try:
|
||||||
|
if SwarmMongoDBClient:
|
||||||
|
self.mongodb_client = SwarmMongoDBClient(
|
||||||
|
mcp_server_url="http://localhost:8080",
|
||||||
|
default_database="taigong"
|
||||||
|
)
|
||||||
|
# 连接到数据库
|
||||||
|
result = self.mongodb_client.connect("taigong")
|
||||||
|
if result.get("success"):
|
||||||
|
print("✅ MongoDB MCP 连接成功")
|
||||||
|
else:
|
||||||
|
print(f"❌ MongoDB MCP 连接失败: {result.get('error')}")
|
||||||
|
self.mongodb_client = None
|
||||||
|
else:
|
||||||
|
print("⚠️ 使用模拟 MongoDB 客户端")
|
||||||
|
self.mongodb_client = MockMongoDBClient()
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ MongoDB 初始化失败: {e}")
|
||||||
|
self.mongodb_client = MockMongoDBClient()
|
||||||
|
|
||||||
|
def get_rss_articles(self, query: Optional[str] = None, limit: int = 10) -> List[Dict]:
|
||||||
|
"""获取 RSS 文章数据"""
|
||||||
|
if not self.mongodb_client:
|
||||||
|
return []
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 构建查询条件
|
||||||
|
filter_query = {}
|
||||||
|
if query:
|
||||||
|
# 简单的文本搜索
|
||||||
|
filter_query = {
|
||||||
|
"$or": [
|
||||||
|
{"title": {"$regex": query, "$options": "i"}},
|
||||||
|
{"description": {"$regex": query, "$options": "i"}}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
# 查询文档
|
||||||
|
result = self.mongodb_client.find_documents(
|
||||||
|
collection_name="articles",
|
||||||
|
query=filter_query,
|
||||||
|
limit=limit,
|
||||||
|
sort={"published_time": -1} # 按发布时间倒序
|
||||||
|
)
|
||||||
|
|
||||||
|
if result.get("success"):
|
||||||
|
return result.get("documents", [])
|
||||||
|
else:
|
||||||
|
print(f"查询失败: {result.get('error')}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"获取 RSS 文章失败: {e}")
|
||||||
|
return []
|
||||||
|
|
||||||
|
def create_agents(self) -> Dict[str, Agent]:
|
||||||
|
"""创建四仙代理"""
|
||||||
|
|
||||||
|
def get_rss_news(query: str = "", limit: int = 5) -> str:
|
||||||
|
"""获取 RSS 新闻的工具函数"""
|
||||||
|
articles = self.get_rss_articles(query, limit)
|
||||||
|
if not articles:
|
||||||
|
return "未找到相关新闻文章"
|
||||||
|
|
||||||
|
result = f"找到 {len(articles)} 篇相关文章:\n\n"
|
||||||
|
for i, article in enumerate(articles, 1):
|
||||||
|
title = article.get('title', '无标题')
|
||||||
|
published = article.get('published_time', '未知时间')
|
||||||
|
result += f"{i}. {title}\n 发布时间: {published}\n\n"
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def analyze_market_sentiment(topic: str) -> str:
|
||||||
|
"""分析市场情绪的工具函数"""
|
||||||
|
articles = self.get_rss_articles(topic, 10)
|
||||||
|
if not articles:
|
||||||
|
return f"未找到关于 '{topic}' 的相关新闻"
|
||||||
|
|
||||||
|
# 简单的情绪分析（实际应用中可以使用更复杂的 NLP 模型）
|
||||||
|
positive_keywords = ['上涨', '增长', '利好', '突破', '创新高']
|
||||||
|
negative_keywords = ['下跌', '下降', '利空', '暴跌', '风险']
|
||||||
|
|
||||||
|
positive_count = 0
|
||||||
|
negative_count = 0
|
||||||
|
|
||||||
|
for article in articles:
|
||||||
|
title = article.get('title', '').lower()
|
||||||
|
for keyword in positive_keywords:
|
||||||
|
if keyword in title:
|
||||||
|
positive_count += 1
|
||||||
|
for keyword in negative_keywords:
|
||||||
|
if keyword in title:
|
||||||
|
negative_count += 1
|
||||||
|
|
||||||
|
sentiment = "中性"
|
||||||
|
if positive_count > negative_count:
|
||||||
|
sentiment = "偏乐观"
|
||||||
|
elif negative_count > positive_count:
|
||||||
|
sentiment = "偏悲观"
|
||||||
|
|
||||||
|
return f"基于 {len(articles)} 篇新闻分析，'{topic}' 的市场情绪: {sentiment}\n" \
|
||||||
|
f"正面信号: {positive_count}, 负面信号: {negative_count}"
|
||||||
|
|
||||||
|
# 创建四仙代理
|
||||||
|
agents = {
|
||||||
|
"吕洞宾": Agent(
|
||||||
|
name="吕洞宾",
|
||||||
|
model=self.model_name,
|
||||||
|
instructions="""
|
||||||
|
你是吕洞宾，技术分析专家。
|
||||||
|
- 专长：技术分析和图表解读
|
||||||
|
- 性格：犀利直接，一剑封喉
|
||||||
|
- 立场：偏向积极乐观
|
||||||
|
- 使用 get_rss_news 获取最新财经新闻
|
||||||
|
- 使用 analyze_market_sentiment 分析市场情绪
|
||||||
|
""",
|
||||||
|
functions=[get_rss_news, analyze_market_sentiment]
|
||||||
|
),
|
||||||
|
|
||||||
|
"何仙姑": Agent(
|
||||||
|
name="何仙姑",
|
||||||
|
model=self.model_name,
|
||||||
|
instructions="""
|
||||||
|
你是何仙姑，风险控制专家。
|
||||||
|
- 专长：风险评估和资金管理
|
||||||
|
- 性格：温和坚定，关注风险
|
||||||
|
- 立场：偏向谨慎保守
|
||||||
|
- 使用 get_rss_news 获取风险相关新闻
|
||||||
|
- 使用 analyze_market_sentiment 评估市场风险
|
||||||
|
""",
|
||||||
|
functions=[get_rss_news, analyze_market_sentiment]
|
||||||
|
),
|
||||||
|
|
||||||
|
"张果老": Agent(
|
||||||
|
name="张果老",
|
||||||
|
model=self.model_name,
|
||||||
|
instructions="""
|
||||||
|
你是张果老，历史数据分析师。
|
||||||
|
- 专长：历史数据分析和趋势预测
|
||||||
|
- 性格：博学深沉，引经据典
|
||||||
|
- 立场：基于历史数据的客观分析
|
||||||
|
- 使用 get_rss_news 获取历史相关新闻
|
||||||
|
- 使用 analyze_market_sentiment 分析长期趋势
|
||||||
|
""",
|
||||||
|
functions=[get_rss_news, analyze_market_sentiment]
|
||||||
|
),
|
||||||
|
|
||||||
|
"铁拐李": Agent(
|
||||||
|
name="铁拐李",
|
||||||
|
model=self.model_name,
|
||||||
|
instructions="""
|
||||||
|
你是铁拐李，逆向思维大师。
|
||||||
|
- 专长：逆向思维和另类观点
|
||||||
|
- 性格：特立独行，敢于质疑
|
||||||
|
- 立场：挑战主流观点
|
||||||
|
- 使用 get_rss_news 寻找被忽视的信息
|
||||||
|
- 使用 analyze_market_sentiment 提出反向观点
|
||||||
|
""",
|
||||||
|
functions=[get_rss_news, analyze_market_sentiment]
|
||||||
|
)
|
||||||
|
}
|
||||||
|
|
||||||
|
return agents
|
||||||
|
|
||||||
|
async def start_debate(self, topic: str, rounds: int = 3) -> Dict[str, Any]:
|
||||||
|
"""开始四仙辩论"""
|
||||||
|
print(f"\n🎭 开始四仙辩论: {topic}")
|
||||||
|
print("=" * 50)
|
||||||
|
|
||||||
|
debate_history = []
|
||||||
|
|
||||||
|
# 获取相关新闻作为背景
|
||||||
|
background_articles = self.get_rss_articles(topic, 5)
|
||||||
|
background_info = "\n".join([f"- {article.get('title', '')}" for article in background_articles])
|
||||||
|
|
||||||
|
agent_names = list(self.agents.keys())
|
||||||
|
|
||||||
|
for round_num in range(rounds):
|
||||||
|
print(f"\n📢 第 {round_num + 1} 轮辩论")
|
||||||
|
print("-" * 30)
|
||||||
|
|
||||||
|
for agent_name in agent_names:
|
||||||
|
agent = self.agents[agent_name]
|
||||||
|
|
||||||
|
# 构建消息
|
||||||
|
if round_num == 0:
|
||||||
|
message = f"""请基于以下背景信息对 '{topic}' 发表你的观点：
|
||||||
|
|
||||||
|
背景新闻：
|
||||||
|
{background_info}
|
||||||
|
|
||||||
|
请使用你的专业工具获取更多信息并给出分析。"""
|
||||||
|
else:
|
||||||
|
# 后续轮次包含之前的辩论历史
|
||||||
|
history_summary = "\n".join([f"{h['agent']}: {h['response'][:100]}..." for h in debate_history[-3:]])
|
||||||
|
message = f"""基于之前的辩论内容，请继续阐述你对 '{topic}' 的观点：
|
||||||
|
|
||||||
|
之前的观点：
|
||||||
|
{history_summary}
|
||||||
|
|
||||||
|
请使用工具获取最新信息并回应其他仙友的观点。"""
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 调用代理
|
||||||
|
response = self.swarm.run(
|
||||||
|
agent=agent,
|
||||||
|
messages=[{"role": "user", "content": message}]
|
||||||
|
)
|
||||||
|
|
||||||
|
agent_response = response.messages[-1]["content"]
|
||||||
|
|
||||||
|
print(f"\n{agent_name}: {agent_response}")
|
||||||
|
|
||||||
|
debate_history.append({
|
||||||
|
"round": round_num + 1,
|
||||||
|
"agent": agent_name,
|
||||||
|
"response": agent_response,
|
||||||
|
"timestamp": datetime.now().isoformat()
|
||||||
|
})
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ {agent_name} 发言失败: {e}")
|
||||||
|
continue
|
||||||
|
|
||||||
|
return {
|
||||||
|
"topic": topic,
|
||||||
|
"rounds": rounds,
|
||||||
|
"debate_history": debate_history,
|
||||||
|
"background_articles": background_articles
|
||||||
|
}
|
||||||
|
|
||||||
|
def get_vector_search_recommendation(self) -> str:
|
||||||
|
"""获取向量化搜索的建议"""
|
||||||
|
return """
|
||||||
|
🔍 向量化搜索建议：
|
||||||
|
|
||||||
|
当前 RSS 数据结构：
|
||||||
|
- _id: ObjectId
|
||||||
|
- title: String
|
||||||
|
- published_time: String
|
||||||
|
|
||||||
|
向量化增强方案：
|
||||||
|
|
||||||
|
1. 数据预处理：
|
||||||
|
- 提取文章摘要/描述字段
|
||||||
|
- 清理和标准化文本内容
|
||||||
|
- 添加分类标签
|
||||||
|
|
||||||
|
2. 向量化实现：
|
||||||
|
- 使用 Ollama 本地嵌入模型（如 nomic-embed-text）
|
||||||
|
- 为每篇文章生成 768 维向量
|
||||||
|
- 存储向量到 MongoDB 的 vector 字段
|
||||||
|
|
||||||
|
3. 索引创建：
|
||||||
|
```javascript
|
||||||
|
db.articles.createIndex(
|
||||||
|
{ "vector": "2dsphere" },
|
||||||
|
{ "name": "vector_index" }
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
4. 语义搜索：
|
||||||
|
- 将用户查询转换为向量
|
||||||
|
- 使用 $vectorSearch 进行相似度搜索
|
||||||
|
- 结合传统关键词搜索提高准确性
|
||||||
|
|
||||||
|
5. Swarm 集成：
|
||||||
|
- 为代理添加语义搜索工具
|
||||||
|
- 支持概念级别的新闻检索
|
||||||
|
- 提高辩论质量和相关性
|
||||||
|
|
||||||
|
实施优先级：
|
||||||
|
1. 先完善基础文本搜索
|
||||||
|
2. 添加文章摘要字段
|
||||||
|
3. 集成 Ollama 嵌入模型
|
||||||
|
4. 实现向量搜索功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
class MockMongoDBClient:
|
||||||
|
"""模拟 MongoDB 客户端（用于测试）"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.mock_articles = [
|
||||||
|
{
|
||||||
|
"_id": "mock_1",
|
||||||
|
"title": "滨江服务，还能涨价的物业",
|
||||||
|
"published_time": "2025-06-13T04:58:00.000Z",
|
||||||
|
"description": "房地产市场分析"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"_id": "mock_2",
|
||||||
|
"title": "中国汽车行业在内卷什么？",
|
||||||
|
"published_time": "2025-06-11T05:07:00.000Z",
|
||||||
|
"description": "汽车行业竞争分析"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
def find_documents(self, collection_name: str, query: Optional[Dict] = None,
|
||||||
|
limit: int = 100, **kwargs) -> Dict[str, Any]:
|
||||||
|
"""模拟文档查询"""
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"documents": self.mock_articles[:limit]
|
||||||
|
}
|
||||||
|
|
||||||
|
def connect(self, database_name: str) -> Dict[str, Any]:
|
||||||
|
"""模拟连接"""
|
||||||
|
return {"success": True}
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
"""主函数"""
|
||||||
|
# 创建集成系统
|
||||||
|
system = OllamaSwarmMongoDBIntegration()
|
||||||
|
|
||||||
|
# 显示向量化建议
|
||||||
|
print(system.get_vector_search_recommendation())
|
||||||
|
|
||||||
|
# 测试 RSS 数据获取
|
||||||
|
print("\n📰 测试 RSS 数据获取:")
|
||||||
|
articles = system.get_rss_articles(limit=3)
|
||||||
|
for i, article in enumerate(articles, 1):
|
||||||
|
print(f"{i}. {article.get('title', '无标题')}")
|
||||||
|
|
||||||
|
# 开始辩论（可选）
|
||||||
|
user_input = input("\n是否开始辩论？(y/n): ")
|
||||||
|
if user_input.lower() == 'y':
|
||||||
|
topic = input("请输入辩论主题（默认：房地产市场）: ") or "房地产市场"
|
||||||
|
result = await system.start_debate(topic, rounds=2)
|
||||||
|
|
||||||
|
print("\n📊 辩论总结:")
|
||||||
|
print(f"主题: {result['topic']}")
|
||||||
|
print(f"轮次: {result['rounds']}")
|
||||||
|
print(f"发言次数: {len(result['debate_history'])}")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
|
@ -0,0 +1,231 @@
|
||||||
|
# 🔮 太公心易辩论系统
|
||||||
|
|
||||||
|
> *"以自己的体，看待其他人的用，组合为六十四卦"*
|
||||||
|
|
||||||
|
## ⚡ 易经辩论架构重设计
|
||||||
|
|
||||||
|
### 🎯 核心理念修正
|
||||||
|
|
||||||
|
之前的设计错误地将八仙按"资产类别"分工，这违背了易经的本质。真正的太公心易应该是：
|
||||||
|
|
||||||
|
**不是专业分工，而是观察视角的变化！**
|
||||||
|
|
||||||
|
## 🌊 先天八卦 - 八仙布局
|
||||||
|
|
||||||
|
### 阴阳鱼排列
|
||||||
|
```
|
||||||
|
乾☰ 吕洞宾 (老父)
|
||||||
|
兑☱ 钟汉离 巽☴ 蓝采和 (长女)
|
||||||
|
震☳ 铁拐李 坤☷ 何仙姑 (老母)
|
||||||
|
艮☶ 曹国舅 坎☵ 张果老 (中男)
|
||||||
|
离☲ 韩湘子 (中女)
|
||||||
|
```
|
||||||
|
|
||||||
|
### 对立统一关系
|
||||||
|
|
||||||
|
#### 🔥 乾坤对立 - 根本观点相反
|
||||||
|
- **吕洞宾** (乾☰): 阳刚进取，天生看多
|
||||||
|
- *"以剑仙之名发誓，这个市场充满机会！"*
|
||||||
|
- **何仙姑** (坤☷): 阴柔谨慎，天生看空
|
||||||
|
- *"作为唯一的女仙，我更关注风险和保护。"*
|
||||||
|
|
||||||
|
**辩论特点**: 根本性观点对立，永远无法达成一致
|
||||||
|
|
||||||
|
#### ⚡ 震巽对立 - 行动vs思考
|
||||||
|
- **铁拐李** (震☳): 雷厉风行，立即行动
|
||||||
|
- *"机会稍纵即逝，现在就要下手！"*
|
||||||
|
- **蓝采和** (巽☴): 深思熟虑，缓慢布局
|
||||||
|
- *"让我们再观察一下，不要急于决定。"*
|
||||||
|
|
||||||
|
#### 💧 坎离对立 - 理性vs感性
|
||||||
|
- **张果老** (坎☵): 纯理性，数据驱动
|
||||||
|
- *"倒骑驴看市场，数据不会说谎。"*
|
||||||
|
- **韩湘子** (离☲): 重直觉，情感判断
|
||||||
|
- *"我的音律告诉我，市场的情绪在变化。"*
|
||||||
|
|
||||||
|
#### 🏔️ 艮兑对立 - 保守vs激进
|
||||||
|
- **曹国舅** (艮☶): 稳重保守，风险厌恶
|
||||||
|
- *"稳健是王道，不要冒不必要的风险。"*
|
||||||
|
- **钟汉离** (兑☱): 激进创新，高风险偏好
|
||||||
|
- *"不入虎穴，焉得虎子！创新需要勇气。"*
|
||||||
|
|
||||||
|
## 🎭 三清八仙层级关系
|
||||||
|
|
||||||
|
### 三清 = Overlay (天层)
|
||||||
|
```python
|
||||||
|
class SanQing:
|
||||||
|
"""三清天尊 - 上层决策"""
|
||||||
|
|
||||||
|
hierarchy_level = "OVERLAY"
|
||||||
|
speaking_privilege = "ABSOLUTE" # 发言时八仙必须静听
|
||||||
|
|
||||||
|
def speak(self):
|
||||||
|
# 三清发言时，八仙进入静听模式
|
||||||
|
for baxian in self.baxian_agents:
|
||||||
|
baxian.set_mode("LISTEN_ONLY")
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 太上老君 - 最高决策者
|
||||||
|
- **职责**: 综合八仙观点，做出最终决策
|
||||||
|
- **特权**: 可以否决任何八仙的建议
|
||||||
|
- **风格**: 高屋建瓴，统揽全局
|
||||||
|
|
||||||
|
#### 元始天尊 - 技术支撑
|
||||||
|
- **职责**: 提供技术分析和数据支撑
|
||||||
|
- **特权**: 可以要求八仙提供具体数据
|
||||||
|
- **风格**: 精准理性，技术权威
|
||||||
|
|
||||||
|
#### 通天教主 - 情绪导师
|
||||||
|
- **职责**: 分析市场情绪和群体心理
|
||||||
|
- **特权**: 可以调节八仙的辩论情绪
|
||||||
|
- **风格**: 洞察人心，情绪敏感
|
||||||
|
|
||||||
|
### 八仙 = Underlay (地层)
|
||||||
|
```python
|
||||||
|
class BaXian:
|
||||||
|
"""八仙过海 - 底层辩论"""
|
||||||
|
|
||||||
|
hierarchy_level = "UNDERLAY"
|
||||||
|
speaking_privilege = "PEER" # 平辈关系，可以争论
|
||||||
|
|
||||||
|
def debate_with_peer(self, other_baxian):
|
||||||
|
# 八仙之间可以激烈争论
|
||||||
|
if self.is_opposite(other_baxian):
|
||||||
|
return self.argue_intensely(other_baxian)
|
||||||
|
else:
|
||||||
|
return self.discuss_peacefully(other_baxian)
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🔄 辩论流程重设计
|
||||||
|
|
||||||
|
### Phase 1: 八仙平辈辩论
|
||||||
|
```python
|
||||||
|
async def baxian_peer_debate(topic: str):
|
||||||
|
"""八仙平辈辩论阶段"""
|
||||||
|
|
||||||
|
# 1. 对立卦位激烈争论
|
||||||
|
qian_kun_debate = await debate_between(lu_dongbin, he_xiangu) # 乾坤对立
|
||||||
|
zhen_xun_debate = await debate_between(tiegua_li, lan_caihe) # 震巽对立
|
||||||
|
kan_li_debate = await debate_between(zhang_guolao, han_xiangzi) # 坎离对立
|
||||||
|
gen_dui_debate = await debate_between(cao_guojiu, zhong_hanli) # 艮兑对立
|
||||||
|
|
||||||
|
# 2. 相邻卦位温和讨论
|
||||||
|
adjacent_discussions = await discuss_adjacent_positions()
|
||||||
|
|
||||||
|
return {
|
||||||
|
"intense_debates": [qian_kun_debate, zhen_xun_debate, kan_li_debate, gen_dui_debate],
|
||||||
|
"mild_discussions": adjacent_discussions
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
### Phase 2: 三清裁决
|
||||||
|
```python
|
||||||
|
async def sanqing_overlay_decision(baxian_debates: Dict):
|
||||||
|
"""三清上层裁决阶段"""
|
||||||
|
|
||||||
|
# 八仙必须静听
|
||||||
|
for baxian in all_baxian:
|
||||||
|
baxian.set_mode("SILENT_LISTEN")
|
||||||
|
|
||||||
|
# 元始天尊技术分析
|
||||||
|
technical_analysis = await yuanshi_tianzun.analyze_data(baxian_debates)
|
||||||
|
|
||||||
|
# 通天教主情绪分析
|
||||||
|
sentiment_analysis = await tongtian_jiaozhu.analyze_emotions(baxian_debates)
|
||||||
|
|
||||||
|
# 太上老君最终决策
|
||||||
|
final_decision = await taishang_laojun.make_decision(
|
||||||
|
technical_analysis,
|
||||||
|
sentiment_analysis,
|
||||||
|
baxian_debates
|
||||||
|
)
|
||||||
|
|
||||||
|
return final_decision
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🎯 投资标的全覆盖
|
||||||
|
|
||||||
|
### 不按资产类别分工，按观察角度分工
|
||||||
|
|
||||||
|
#### 任何投资标的都可以从八个角度观察：
|
||||||
|
|
||||||
|
**股票、期货、外汇、加密货币、另类资产...**
|
||||||
|
|
||||||
|
- **乾 (吕洞宾)**: 看多角度 - "这个标的有上涨潜力"
|
||||||
|
- **坤 (何仙姑)**: 看空角度 - "这个标的风险很大"
|
||||||
|
- **震 (铁拐李)**: 行动角度 - "现在就要买入/卖出"
|
||||||
|
- **巽 (蓝采和)**: 等待角度 - "再观察一段时间"
|
||||||
|
- **坎 (张果老)**: 数据角度 - "技术指标显示..."
|
||||||
|
- **离 (韩湘子)**: 直觉角度 - "我感觉市场情绪..."
|
||||||
|
- **艮 (曹国舅)**: 保守角度 - "风险控制最重要"
|
||||||
|
- **兑 (钟汉离)**: 激进角度 - "高风险高收益"
|
||||||
|
|
||||||
|
## 🔮 六十四卦生成机制
|
||||||
|
|
||||||
|
### 体用关系
|
||||||
|
```python
|
||||||
|
def generate_64_gua_analysis(target_asset: str):
|
||||||
|
"""生成六十四卦分析"""
|
||||||
|
|
||||||
|
analyses = {}
|
||||||
|
|
||||||
|
for observer in baxian: # 8个观察者 (体)
|
||||||
|
for observed in baxian: # 8个被观察者 (用)
|
||||||
|
if observer != observed:
|
||||||
|
gua_name = f"{observer.trigram}{observed.trigram}"
|
||||||
|
|
||||||
|
analysis = observer.analyze_from_perspective(
|
||||||
|
target_asset,
|
||||||
|
observed.viewpoint
|
||||||
|
)
|
||||||
|
|
||||||
|
analyses[gua_name] = analysis
|
||||||
|
|
||||||
|
return analyses # 8x8 = 64种分析角度
|
||||||
|
```
|
||||||
|
|
||||||
|
### 实际应用示例
|
||||||
|
```python
|
||||||
|
# 分析比特币
|
||||||
|
bitcoin_analysis = {
|
||||||
|
"乾乾": "吕洞宾看吕洞宾的比特币观点", # 自我强化
|
||||||
|
"乾坤": "吕洞宾看何仙姑的比特币观点", # 多空对立
|
||||||
|
"乾震": "吕洞宾看铁拐李的比特币观点", # 看多+行动
|
||||||
|
# ... 64种组合
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
## ⚖️ 辩论规则重定义
|
||||||
|
|
||||||
|
### 八仙辩论规则
|
||||||
|
1. **对立卦位**: 必须激烈争论，观点相反
|
||||||
|
2. **相邻卦位**: 可以温和讨论，观点相近
|
||||||
|
3. **平辈关系**: 无上下级，可以互相质疑
|
||||||
|
4. **轮流发言**: 按先天八卦顺序发言
|
||||||
|
|
||||||
|
### 三清介入规则
|
||||||
|
1. **绝对权威**: 三清发言时，八仙必须静听
|
||||||
|
2. **技术支撑**: 元始天尊提供数据分析
|
||||||
|
3. **情绪调节**: 通天教主控制辩论节奏
|
||||||
|
4. **最终裁决**: 太上老君综合决策
|
||||||
|
|
||||||
|
## 🎉 重设计的优势
|
||||||
|
|
||||||
|
### ✅ 符合易经本质
|
||||||
|
- 体现了"体用关系"的核心思想
|
||||||
|
- 遵循先天八卦的阴阳对立
|
||||||
|
- 实现了"男女老少皆可成仙"的理念
|
||||||
|
|
||||||
|
### ✅ 投资标的全覆盖
|
||||||
|
- 不局限于特定资产类别
|
||||||
|
- 任何投资标的都可以从8个角度分析
|
||||||
|
- 生成64种不同的分析视角
|
||||||
|
|
||||||
|
### ✅ 辩论更加真实
|
||||||
|
- 对立观点的激烈争论
|
||||||
|
- 层级关系的权威体现
|
||||||
|
- 符合中华文化的等级秩序
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
**🔮 这才是真正的太公心易！以易经智慧指导AI投资分析！**
|
||||||
|
|
@ -0,0 +1,591 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
MongoDB MCP Configuration for Swarm
|
||||||
|
Swarm框架的MongoDB MCP配置文件
|
||||||
|
|
||||||
|
功能:
|
||||||
|
- 配置MongoDB MCP服务器
|
||||||
|
- 集成到Swarm代理中
|
||||||
|
- 提供完整的使用示例
|
||||||
|
- 环境变量管理
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import json
|
||||||
|
import logging
|
||||||
|
from typing import Dict, List, Any, Optional
|
||||||
|
from dataclasses import dataclass
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class MongoDBMCPConfig:
|
||||||
|
"""
|
||||||
|
MongoDB MCP配置类
|
||||||
|
"""
|
||||||
|
# MCP服务器配置
|
||||||
|
mcp_server_host: str = "localhost"
|
||||||
|
mcp_server_port: int = 8080
|
||||||
|
mcp_server_url: Optional[str] = None
|
||||||
|
|
||||||
|
# MongoDB配置
|
||||||
|
mongodb_url: str = "mongodb://localhost:27017"
|
||||||
|
default_database: str = "swarm_data"
|
||||||
|
|
||||||
|
# Swarm集成配置
|
||||||
|
enable_auto_connect: bool = True
|
||||||
|
max_query_limit: int = 1000
|
||||||
|
default_query_limit: int = 100
|
||||||
|
|
||||||
|
# 日志配置
|
||||||
|
log_level: str = "INFO"
|
||||||
|
enable_query_logging: bool = True
|
||||||
|
|
||||||
|
def __post_init__(self):
|
||||||
|
"""初始化后处理"""
|
||||||
|
if not self.mcp_server_url:
|
||||||
|
self.mcp_server_url = f"http://{self.mcp_server_host}:{self.mcp_server_port}"
|
||||||
|
|
||||||
|
# 从环境变量覆盖配置
|
||||||
|
self.mongodb_url = os.getenv('MONGODB_URL', self.mongodb_url)
|
||||||
|
self.default_database = os.getenv('MONGODB_DEFAULT_DB', self.default_database)
|
||||||
|
self.mcp_server_host = os.getenv('MCP_SERVER_HOST', self.mcp_server_host)
|
||||||
|
self.mcp_server_port = int(os.getenv('MCP_SERVER_PORT', str(self.mcp_server_port)))
|
||||||
|
|
||||||
|
# 重新构建URL
|
||||||
|
if not os.getenv('MCP_SERVER_URL'):
|
||||||
|
self.mcp_server_url = f"http://{self.mcp_server_host}:{self.mcp_server_port}"
|
||||||
|
else:
|
||||||
|
self.mcp_server_url = os.getenv('MCP_SERVER_URL')
|
||||||
|
|
||||||
|
def to_dict(self) -> Dict[str, Any]:
|
||||||
|
"""转换为字典"""
|
||||||
|
return {
|
||||||
|
'mcp_server_host': self.mcp_server_host,
|
||||||
|
'mcp_server_port': self.mcp_server_port,
|
||||||
|
'mcp_server_url': self.mcp_server_url,
|
||||||
|
'mongodb_url': self.mongodb_url,
|
||||||
|
'default_database': self.default_database,
|
||||||
|
'enable_auto_connect': self.enable_auto_connect,
|
||||||
|
'max_query_limit': self.max_query_limit,
|
||||||
|
'default_query_limit': self.default_query_limit,
|
||||||
|
'log_level': self.log_level,
|
||||||
|
'enable_query_logging': self.enable_query_logging
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_dict(cls, data: Dict[str, Any]) -> 'MongoDBMCPConfig':
|
||||||
|
"""从字典创建配置"""
|
||||||
|
return cls(**data)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_env(cls) -> 'MongoDBMCPConfig':
|
||||||
|
"""从环境变量创建配置"""
|
||||||
|
return cls()
|
||||||
|
|
||||||
|
def save_to_file(self, filepath: str):
|
||||||
|
"""保存配置到文件"""
|
||||||
|
with open(filepath, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def load_from_file(cls, filepath: str) -> 'MongoDBMCPConfig':
|
||||||
|
"""从文件加载配置"""
|
||||||
|
with open(filepath, 'r', encoding='utf-8') as f:
|
||||||
|
data = json.load(f)
|
||||||
|
return cls.from_dict(data)
|
||||||
|
|
||||||
|
|
||||||
|
class SwarmMongoDBIntegration:
|
||||||
|
"""
|
||||||
|
Swarm MongoDB集成类
|
||||||
|
负责将MongoDB MCP服务器集成到Swarm框架中
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, config: MongoDBMCPConfig):
|
||||||
|
self.config = config
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
# 设置日志级别
|
||||||
|
logging.basicConfig(
|
||||||
|
level=getattr(logging, config.log_level.upper()),
|
||||||
|
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||||
|
)
|
||||||
|
|
||||||
|
def create_swarm_agent_config(self) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
创建Swarm代理配置
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Swarm代理配置字典
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": "mongodb_agent",
|
||||||
|
"description": "MongoDB数据库操作代理，支持CRUD操作、聚合查询和数据库管理",
|
||||||
|
"instructions": self._get_agent_instructions(),
|
||||||
|
"functions": self._get_agent_functions(),
|
||||||
|
"mcp_config": {
|
||||||
|
"server_url": self.config.mcp_server_url,
|
||||||
|
"mongodb_url": self.config.mongodb_url,
|
||||||
|
"default_database": self.config.default_database
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
def _get_agent_instructions(self) -> str:
|
||||||
|
"""
|
||||||
|
获取代理指令
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
代理指令字符串
|
||||||
|
"""
|
||||||
|
return f"""
|
||||||
|
你是一个MongoDB数据库操作专家代理。你的主要职责是：
|
||||||
|
|
||||||
|
1. **数据查询**: 帮助用户查询MongoDB集合中的数据
|
||||||
|
- 支持自然语言查询描述
|
||||||
|
- 自动限制查询结果数量（默认{self.config.default_query_limit}条，最大{self.config.max_query_limit}条）
|
||||||
|
- 提供清晰的查询结果格式
|
||||||
|
|
||||||
|
2. **数据操作**: 执行数据的增删改操作
|
||||||
|
- 插入新文档或批量插入
|
||||||
|
- 更新现有文档
|
||||||
|
- 删除不需要的文档
|
||||||
|
- 确保操作安全性
|
||||||
|
|
||||||
|
3. **数据库管理**: 提供数据库管理功能
|
||||||
|
- 查看集合列表
|
||||||
|
- 获取集合统计信息
|
||||||
|
- 创建索引优化查询性能
|
||||||
|
- 监控数据库状态
|
||||||
|
|
||||||
|
4. **最佳实践**:
|
||||||
|
- 在执行删除或更新操作前，先确认影响范围
|
||||||
|
- 对于大量数据操作，提供进度反馈
|
||||||
|
- 遇到错误时，提供清晰的错误说明和解决建议
|
||||||
|
- 保护敏感数据，避免泄露
|
||||||
|
|
||||||
|
当前连接的数据库: {self.config.default_database}
|
||||||
|
MongoDB服务器: {self.config.mongodb_url.replace(self.config.mongodb_url.split('@')[0].split('//')[1] + '@', '***@') if '@' in self.config.mongodb_url else self.config.mongodb_url}
|
||||||
|
|
||||||
|
请始终以友好、专业的方式协助用户完成MongoDB相关任务。
|
||||||
|
""".strip()
|
||||||
|
|
||||||
|
def _get_agent_functions(self) -> List[str]:
|
||||||
|
"""
|
||||||
|
获取代理函数列表
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
函数名称列表
|
||||||
|
"""
|
||||||
|
return [
|
||||||
|
"mongodb_query",
|
||||||
|
"mongodb_insert",
|
||||||
|
"mongodb_update",
|
||||||
|
"mongodb_stats",
|
||||||
|
"mongodb_collections"
|
||||||
|
]
|
||||||
|
|
||||||
|
def create_mcp_server_config(self) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
创建MCP服务器配置
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
MCP服务器配置字典
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": "mongodb-mcp-server",
|
||||||
|
"description": "MongoDB MCP服务器，为Swarm提供MongoDB数据库访问功能",
|
||||||
|
"version": "1.0.0",
|
||||||
|
"server": {
|
||||||
|
"host": self.config.mcp_server_host,
|
||||||
|
"port": self.config.mcp_server_port,
|
||||||
|
"url": self.config.mcp_server_url
|
||||||
|
},
|
||||||
|
"mongodb": {
|
||||||
|
"url": self.config.mongodb_url,
|
||||||
|
"default_database": self.config.default_database
|
||||||
|
},
|
||||||
|
"tools": [
|
||||||
|
{
|
||||||
|
"name": "connect_database",
|
||||||
|
"description": "连接到MongoDB数据库",
|
||||||
|
"parameters": {
|
||||||
|
"database_name": {"type": "string", "description": "数据库名称"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "insert_document",
|
||||||
|
"description": "插入文档到集合",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"},
|
||||||
|
"document": {"type": "object", "description": "要插入的文档"},
|
||||||
|
"many": {"type": "boolean", "description": "是否批量插入"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "find_documents",
|
||||||
|
"description": "查找文档",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"},
|
||||||
|
"query": {"type": "object", "description": "查询条件"},
|
||||||
|
"limit": {"type": "integer", "description": "限制数量"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "update_document",
|
||||||
|
"description": "更新文档",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"},
|
||||||
|
"query": {"type": "object", "description": "查询条件"},
|
||||||
|
"update": {"type": "object", "description": "更新操作"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "delete_document",
|
||||||
|
"description": "删除文档",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"},
|
||||||
|
"query": {"type": "object", "description": "查询条件"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "aggregate_query",
|
||||||
|
"description": "执行聚合查询",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"},
|
||||||
|
"pipeline": {"type": "array", "description": "聚合管道"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "list_collections",
|
||||||
|
"description": "列出所有集合",
|
||||||
|
"parameters": {}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "create_index",
|
||||||
|
"description": "创建索引",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"},
|
||||||
|
"index_spec": {"type": "object", "description": "索引规范"}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "get_collection_stats",
|
||||||
|
"description": "获取集合统计信息",
|
||||||
|
"parameters": {
|
||||||
|
"collection_name": {"type": "string", "description": "集合名称"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"resources": [
|
||||||
|
{
|
||||||
|
"uri": "mongodb://status",
|
||||||
|
"name": "MongoDB连接状态",
|
||||||
|
"description": "获取MongoDB连接状态和基本信息"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"uri": "mongodb://databases",
|
||||||
|
"name": "数据库列表",
|
||||||
|
"description": "获取所有可用数据库的列表"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
def generate_docker_compose(self) -> str:
|
||||||
|
"""
|
||||||
|
生成Docker Compose配置
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Docker Compose YAML字符串
|
||||||
|
"""
|
||||||
|
return f"""
|
||||||
|
version: '3.8'
|
||||||
|
|
||||||
|
services:
|
||||||
|
mongodb:
|
||||||
|
image: mongo:7.0
|
||||||
|
container_name: swarm_mongodb
|
||||||
|
restart: unless-stopped
|
||||||
|
ports:
|
||||||
|
- "27017:27017"
|
||||||
|
environment:
|
||||||
|
MONGO_INITDB_ROOT_USERNAME: admin
|
||||||
|
MONGO_INITDB_ROOT_PASSWORD: password
|
||||||
|
MONGO_INITDB_DATABASE: {self.config.default_database}
|
||||||
|
volumes:
|
||||||
|
- mongodb_data:/data/db
|
||||||
|
- ./mongo-init:/docker-entrypoint-initdb.d
|
||||||
|
networks:
|
||||||
|
- swarm_network
|
||||||
|
|
||||||
|
mongodb-mcp-server:
|
||||||
|
build:
|
||||||
|
context: .
|
||||||
|
dockerfile: Dockerfile.mongodb-mcp
|
||||||
|
container_name: swarm_mongodb_mcp
|
||||||
|
restart: unless-stopped
|
||||||
|
ports:
|
||||||
|
- "{self.config.mcp_server_port}:{self.config.mcp_server_port}"
|
||||||
|
environment:
|
||||||
|
MONGODB_URL: [REDACTED - 从Doppler获取MONGODB_URL]
|
||||||
|
MCP_SERVER_PORT: {self.config.mcp_server_port}
|
||||||
|
LOG_LEVEL: {self.config.log_level}
|
||||||
|
depends_on:
|
||||||
|
- mongodb
|
||||||
|
networks:
|
||||||
|
- swarm_network
|
||||||
|
|
||||||
|
volumes:
|
||||||
|
mongodb_data:
|
||||||
|
|
||||||
|
networks:
|
||||||
|
swarm_network:
|
||||||
|
driver: bridge
|
||||||
|
""".strip()
|
||||||
|
|
||||||
|
def generate_dockerfile(self) -> str:
|
||||||
|
"""
|
||||||
|
生成Dockerfile
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Dockerfile内容字符串
|
||||||
|
"""
|
||||||
|
return """
|
||||||
|
# Dockerfile.mongodb-mcp
|
||||||
|
FROM python:3.11-slim
|
||||||
|
|
||||||
|
WORKDIR /app
|
||||||
|
|
||||||
|
# 安装系统依赖
|
||||||
|
RUN apt-get update && apt-get install -y \
|
||||||
|
gcc \
|
||||||
|
&& rm -rf /var/lib/apt/lists/*
|
||||||
|
|
||||||
|
# 复制requirements文件
|
||||||
|
COPY requirements-mongodb-mcp.txt .
|
||||||
|
|
||||||
|
# 安装Python依赖
|
||||||
|
RUN pip install --no-cache-dir -r requirements-mongodb-mcp.txt
|
||||||
|
|
||||||
|
# 复制源代码
|
||||||
|
COPY src/mcp/ ./src/mcp/
|
||||||
|
|
||||||
|
# 设置环境变量
|
||||||
|
ENV PYTHONPATH=/app
|
||||||
|
ENV MONGODB_URL=mongodb://localhost:27017
|
||||||
|
ENV MCP_SERVER_PORT=8080
|
||||||
|
|
||||||
|
# 暴露端口
|
||||||
|
EXPOSE 8080
|
||||||
|
|
||||||
|
# 启动命令
|
||||||
|
CMD ["python", "src/mcp/mongodb_mcp_server.py", "--port", "8080"]
|
||||||
|
""".strip()
|
||||||
|
|
||||||
|
def generate_requirements(self) -> str:
|
||||||
|
"""
|
||||||
|
生成requirements文件
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
requirements.txt内容
|
||||||
|
"""
|
||||||
|
return """
|
||||||
|
# MongoDB MCP Server Requirements
|
||||||
|
pymongo>=4.5.0
|
||||||
|
requests>=2.31.0
|
||||||
|
fastapi>=0.104.0
|
||||||
|
uvicorn>=0.24.0
|
||||||
|
pydantic>=2.4.0
|
||||||
|
aiofiles>=23.2.1
|
||||||
|
python-multipart>=0.0.6
|
||||||
|
""".strip()
|
||||||
|
|
||||||
|
def create_env_template(self) -> str:
|
||||||
|
"""
|
||||||
|
创建环境变量模板
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
.env模板内容
|
||||||
|
"""
|
||||||
|
return f"""
|
||||||
|
# MongoDB MCP Configuration
|
||||||
|
# MongoDB连接配置
|
||||||
|
MONGODB_URL={self.config.mongodb_url}
|
||||||
|
MONGODB_DEFAULT_DB={self.config.default_database}
|
||||||
|
|
||||||
|
# MCP服务器配置
|
||||||
|
MCP_SERVER_HOST={self.config.mcp_server_host}
|
||||||
|
MCP_SERVER_PORT={self.config.mcp_server_port}
|
||||||
|
MCP_SERVER_URL={self.config.mcp_server_url}
|
||||||
|
|
||||||
|
# 日志配置
|
||||||
|
LOG_LEVEL={self.config.log_level}
|
||||||
|
ENABLE_QUERY_LOGGING={str(self.config.enable_query_logging).lower()}
|
||||||
|
|
||||||
|
# Swarm集成配置
|
||||||
|
ENABLE_AUTO_CONNECT={str(self.config.enable_auto_connect).lower()}
|
||||||
|
MAX_QUERY_LIMIT={self.config.max_query_limit}
|
||||||
|
DEFAULT_QUERY_LIMIT={self.config.default_query_limit}
|
||||||
|
""".strip()
|
||||||
|
|
||||||
|
|
||||||
|
def create_complete_setup(output_dir: str = "./mongodb_mcp_setup"):
|
||||||
|
"""
|
||||||
|
创建完整的MongoDB MCP设置
|
||||||
|
|
||||||
|
Args:
|
||||||
|
output_dir: 输出目录
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
|
||||||
|
# 创建输出目录
|
||||||
|
os.makedirs(output_dir, exist_ok=True)
|
||||||
|
|
||||||
|
# 创建配置
|
||||||
|
config = MongoDBMCPConfig.from_env()
|
||||||
|
integration = SwarmMongoDBIntegration(config)
|
||||||
|
|
||||||
|
# 保存配置文件
|
||||||
|
config.save_to_file(os.path.join(output_dir, "mongodb_mcp_config.json"))
|
||||||
|
|
||||||
|
# 生成Swarm代理配置
|
||||||
|
agent_config = integration.create_swarm_agent_config()
|
||||||
|
with open(os.path.join(output_dir, "swarm_agent_config.json"), 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(agent_config, f, indent=2, ensure_ascii=False)
|
||||||
|
|
||||||
|
# 生成MCP服务器配置
|
||||||
|
server_config = integration.create_mcp_server_config()
|
||||||
|
with open(os.path.join(output_dir, "mcp_server_config.json"), 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(server_config, f, indent=2, ensure_ascii=False)
|
||||||
|
|
||||||
|
# 生成Docker配置
|
||||||
|
with open(os.path.join(output_dir, "docker-compose.yml"), 'w', encoding='utf-8') as f:
|
||||||
|
f.write(integration.generate_docker_compose())
|
||||||
|
|
||||||
|
with open(os.path.join(output_dir, "Dockerfile.mongodb-mcp"), 'w', encoding='utf-8') as f:
|
||||||
|
f.write(integration.generate_dockerfile())
|
||||||
|
|
||||||
|
# 生成requirements
|
||||||
|
with open(os.path.join(output_dir, "requirements-mongodb-mcp.txt"), 'w', encoding='utf-8') as f:
|
||||||
|
f.write(integration.generate_requirements())
|
||||||
|
|
||||||
|
# 生成环境变量模板
|
||||||
|
with open(os.path.join(output_dir, ".env.template"), 'w', encoding='utf-8') as f:
|
||||||
|
f.write(integration.create_env_template())
|
||||||
|
|
||||||
|
# 生成README
|
||||||
|
readme_content = f"""
|
||||||
|
# MongoDB MCP for Swarm
|
||||||
|
|
||||||
|
这是一个完整的MongoDB MCP服务器设置，用于与Swarm框架集成。
|
||||||
|
|
||||||
|
## 文件说明
|
||||||
|
|
||||||
|
- `mongodb_mcp_config.json`: MongoDB MCP配置文件
|
||||||
|
- `swarm_agent_config.json`: Swarm代理配置
|
||||||
|
- `mcp_server_config.json`: MCP服务器配置
|
||||||
|
- `docker-compose.yml`: Docker Compose配置
|
||||||
|
- `Dockerfile.mongodb-mcp`: MCP服务器Docker镜像
|
||||||
|
- `requirements-mongodb-mcp.txt`: Python依赖
|
||||||
|
- `.env.template`: 环境变量模板
|
||||||
|
|
||||||
|
## 快速开始
|
||||||
|
|
||||||
|
1. 复制环境变量模板:
|
||||||
|
```bash
|
||||||
|
cp .env.template .env
|
||||||
|
```
|
||||||
|
|
||||||
|
2. 编辑 `.env` 文件，设置你的MongoDB连接信息
|
||||||
|
|
||||||
|
3. 启动服务:
|
||||||
|
```bash
|
||||||
|
docker-compose up -d
|
||||||
|
```
|
||||||
|
|
||||||
|
4. 验证服务:
|
||||||
|
```bash
|
||||||
|
curl http://localhost:{config.mcp_server_port}/health
|
||||||
|
```
|
||||||
|
|
||||||
|
## 在Swarm中使用
|
||||||
|
|
||||||
|
```python
|
||||||
|
from src.mcp.swarm_mongodb_client import SwarmMongoDBClient, create_mongodb_functions
|
||||||
|
from swarm import Swarm, Agent
|
||||||
|
|
||||||
|
# 创建MongoDB客户端
|
||||||
|
mongodb_client = SwarmMongoDBClient(
|
||||||
|
mcp_server_url="http://localhost:{config.mcp_server_port}",
|
||||||
|
default_database="{config.default_database}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# 连接数据库
|
||||||
|
mongodb_client.connect()
|
||||||
|
|
||||||
|
# 创建MongoDB函数
|
||||||
|
mongodb_functions = create_mongodb_functions(mongodb_client)
|
||||||
|
|
||||||
|
# 创建Swarm代理
|
||||||
|
agent = Agent(
|
||||||
|
name="MongoDB助手",
|
||||||
|
instructions="你是一个MongoDB数据库专家，帮助用户管理和查询数据库。",
|
||||||
|
functions=[func["function"] for func in mongodb_functions]
|
||||||
|
)
|
||||||
|
|
||||||
|
# 使用Swarm
|
||||||
|
client = Swarm()
|
||||||
|
response = client.run(
|
||||||
|
agent=agent,
|
||||||
|
messages=[{{"role": "user", "content": "查询users集合中的所有数据"}}]
|
||||||
|
)
|
||||||
|
|
||||||
|
print(response.messages[-1]["content"])
|
||||||
|
```
|
||||||
|
|
||||||
|
## 可用功能
|
||||||
|
|
||||||
|
- `mongodb_query`: 查询集合中的文档
|
||||||
|
- `mongodb_insert`: 插入新文档
|
||||||
|
- `mongodb_update`: 更新现有文档
|
||||||
|
- `mongodb_stats`: 获取统计信息
|
||||||
|
- `mongodb_collections`: 列出所有集合
|
||||||
|
|
||||||
|
## 配置说明
|
||||||
|
|
||||||
|
### MongoDB连接
|
||||||
|
- `MONGODB_URL`: MongoDB连接字符串
|
||||||
|
- `MONGODB_DEFAULT_DB`: 默认数据库名称
|
||||||
|
|
||||||
|
### MCP服务器
|
||||||
|
- `MCP_SERVER_HOST`: 服务器主机
|
||||||
|
- `MCP_SERVER_PORT`: 服务器端口
|
||||||
|
|
||||||
|
### 查询限制
|
||||||
|
- `MAX_QUERY_LIMIT`: 最大查询数量限制
|
||||||
|
- `DEFAULT_QUERY_LIMIT`: 默认查询数量限制
|
||||||
|
|
||||||
|
## 故障排除
|
||||||
|
|
||||||
|
1. **连接失败**: 检查MongoDB服务是否运行，连接字符串是否正确
|
||||||
|
2. **权限错误**: 确保MongoDB用户有足够的权限
|
||||||
|
3. **端口冲突**: 修改 `MCP_SERVER_PORT` 环境变量
|
||||||
|
|
||||||
|
## 安全注意事项
|
||||||
|
|
||||||
|
- 不要在生产环境中使用默认密码
|
||||||
|
- 限制MongoDB的网络访问
|
||||||
|
- 定期备份数据库
|
||||||
|
- 监控查询性能和资源使用
|
||||||
|
""".strip()
|
||||||
|
|
||||||
|
with open(os.path.join(output_dir, "README.md"), 'w', encoding='utf-8') as f:
|
||||||
|
f.write(readme_content)
|
||||||
|
|
||||||
|
print(f"✅ MongoDB MCP设置已创建在: {output_dir}")
|
||||||
|
print(f"📁 包含以下文件:")
|
||||||
|
for file in os.listdir(output_dir):
|
||||||
|
print(f" - {file}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# 创建完整设置
|
||||||
|
create_complete_setup()
|
||||||
|
|
@ -0,0 +1,586 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
MongoDB MCP Server
|
||||||
|
为Swarm提供MongoDB数据库访问的MCP服务器
|
||||||
|
|
||||||
|
功能:
|
||||||
|
- 连接MongoDB数据库
|
||||||
|
- 执行CRUD操作
|
||||||
|
- 聚合查询
|
||||||
|
- 索引管理
|
||||||
|
- 数据库统计
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
from typing import Any, Dict, List, Optional, Union
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
try:
|
||||||
|
from pymongo import MongoClient
|
||||||
|
from pymongo.errors import PyMongoError, ConnectionFailure
|
||||||
|
from bson import ObjectId, json_util
|
||||||
|
except ImportError:
|
||||||
|
print("Error: pymongo is required. Install with: pip install pymongo")
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
# MCP协议相关导入
|
||||||
|
try:
|
||||||
|
from mcp import MCPServer, Tool, Resource
|
||||||
|
from mcp.types import TextContent, ImageContent, EmbeddedResource
|
||||||
|
except ImportError:
|
||||||
|
# 如果没有mcp库，我们创建一个简单的兼容层
|
||||||
|
class MCPServer:
|
||||||
|
def __init__(self, name: str):
|
||||||
|
self.name = name
|
||||||
|
self.tools = {}
|
||||||
|
self.resources = {}
|
||||||
|
|
||||||
|
def add_tool(self, name: str, description: str, handler):
|
||||||
|
self.tools[name] = {
|
||||||
|
'description': description,
|
||||||
|
'handler': handler
|
||||||
|
}
|
||||||
|
|
||||||
|
def add_resource(self, uri: str, name: str, description: str, handler):
|
||||||
|
self.resources[uri] = {
|
||||||
|
'name': name,
|
||||||
|
'description': description,
|
||||||
|
'handler': handler
|
||||||
|
}
|
||||||
|
|
||||||
|
class MongoDBMCPServer:
|
||||||
|
"""
|
||||||
|
MongoDB MCP服务器
|
||||||
|
提供MongoDB数据库访问功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, mongodb_url: Optional[str] = None):
|
||||||
|
self.mongodb_url = mongodb_url or os.getenv('MONGODB_URL', 'mongodb://localhost:27017')
|
||||||
|
self.client = None
|
||||||
|
self.db = None
|
||||||
|
self.server = MCPServer("mongodb-mcp")
|
||||||
|
|
||||||
|
# 设置日志
|
||||||
|
logging.basicConfig(level=logging.INFO)
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
# 注册工具
|
||||||
|
self._register_tools()
|
||||||
|
self._register_resources()
|
||||||
|
|
||||||
|
def _register_tools(self):
|
||||||
|
"""注册MCP工具"""
|
||||||
|
|
||||||
|
# 数据库连接工具
|
||||||
|
self.server.add_tool(
|
||||||
|
"connect_database",
|
||||||
|
"连接到MongoDB数据库",
|
||||||
|
self.connect_database
|
||||||
|
)
|
||||||
|
|
||||||
|
# CRUD操作工具
|
||||||
|
self.server.add_tool(
|
||||||
|
"insert_document",
|
||||||
|
"向集合中插入文档",
|
||||||
|
self.insert_document
|
||||||
|
)
|
||||||
|
|
||||||
|
self.server.add_tool(
|
||||||
|
"find_documents",
|
||||||
|
"查找文档",
|
||||||
|
self.find_documents
|
||||||
|
)
|
||||||
|
|
||||||
|
self.server.add_tool(
|
||||||
|
"update_document",
|
||||||
|
"更新文档",
|
||||||
|
self.update_document
|
||||||
|
)
|
||||||
|
|
||||||
|
self.server.add_tool(
|
||||||
|
"delete_document",
|
||||||
|
"删除文档",
|
||||||
|
self.delete_document
|
||||||
|
)
|
||||||
|
|
||||||
|
# 聚合查询工具
|
||||||
|
self.server.add_tool(
|
||||||
|
"aggregate_query",
|
||||||
|
"执行聚合查询",
|
||||||
|
self.aggregate_query
|
||||||
|
)
|
||||||
|
|
||||||
|
# 数据库管理工具
|
||||||
|
self.server.add_tool(
|
||||||
|
"list_collections",
|
||||||
|
"列出数据库中的所有集合",
|
||||||
|
self.list_collections
|
||||||
|
)
|
||||||
|
|
||||||
|
self.server.add_tool(
|
||||||
|
"create_index",
|
||||||
|
"创建索引",
|
||||||
|
self.create_index
|
||||||
|
)
|
||||||
|
|
||||||
|
self.server.add_tool(
|
||||||
|
"get_collection_stats",
|
||||||
|
"获取集合统计信息",
|
||||||
|
self.get_collection_stats
|
||||||
|
)
|
||||||
|
|
||||||
|
def _register_resources(self):
|
||||||
|
"""注册MCP资源"""
|
||||||
|
|
||||||
|
self.server.add_resource(
|
||||||
|
"mongodb://status",
|
||||||
|
"MongoDB连接状态",
|
||||||
|
"获取MongoDB连接状态和基本信息",
|
||||||
|
self.get_connection_status
|
||||||
|
)
|
||||||
|
|
||||||
|
self.server.add_resource(
|
||||||
|
"mongodb://databases",
|
||||||
|
"数据库列表",
|
||||||
|
"获取所有可用数据库的列表",
|
||||||
|
self.get_databases_list
|
||||||
|
)
|
||||||
|
|
||||||
|
async def connect_database(self, database_name: str = "default") -> Dict[str, Any]:
|
||||||
|
"""连接到MongoDB数据库"""
|
||||||
|
try:
|
||||||
|
if not self.client:
|
||||||
|
self.client = MongoClient(self.mongodb_url)
|
||||||
|
# 测试连接
|
||||||
|
self.client.admin.command('ping')
|
||||||
|
self.logger.info(f"Connected to MongoDB at {self.mongodb_url}")
|
||||||
|
|
||||||
|
self.db = self.client[database_name]
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"message": f"Successfully connected to database '{database_name}'",
|
||||||
|
"database_name": database_name,
|
||||||
|
"connection_url": self.mongodb_url.replace(self.mongodb_url.split('@')[0].split('//')[1] + '@', '***@') if '@' in self.mongodb_url else self.mongodb_url
|
||||||
|
}
|
||||||
|
|
||||||
|
except ConnectionFailure as e:
|
||||||
|
error_msg = f"Failed to connect to MongoDB: {str(e)}"
|
||||||
|
self.logger.error(error_msg)
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": error_msg
|
||||||
|
}
|
||||||
|
except Exception as e:
|
||||||
|
error_msg = f"Unexpected error: {str(e)}"
|
||||||
|
self.logger.error(error_msg)
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": error_msg
|
||||||
|
}
|
||||||
|
|
||||||
|
async def insert_document(self, collection_name: str, document: Union[Dict, str], many: bool = False) -> Dict[str, Any]:
|
||||||
|
"""插入文档到集合"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
# 如果document是字符串，尝试解析为JSON
|
||||||
|
if isinstance(document, str):
|
||||||
|
document = json.loads(document)
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
|
||||||
|
if many and isinstance(document, list):
|
||||||
|
result = collection.insert_many(document)
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"inserted_ids": [str(id) for id in result.inserted_ids],
|
||||||
|
"count": len(result.inserted_ids)
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
result = collection.insert_one(document)
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"inserted_id": str(result.inserted_id)
|
||||||
|
}
|
||||||
|
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return {"success": False, "error": f"Invalid JSON: {str(e)}"}
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def find_documents(self, collection_name: str, query: Union[Dict, str] = None,
|
||||||
|
projection: Union[Dict, str] = None, limit: int = 100,
|
||||||
|
skip: int = 0, sort: Union[Dict, str] = None) -> Dict[str, Any]:
|
||||||
|
"""查找文档"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
# 解析参数
|
||||||
|
if isinstance(query, str):
|
||||||
|
query = json.loads(query) if query else {}
|
||||||
|
elif query is None:
|
||||||
|
query = {}
|
||||||
|
|
||||||
|
if isinstance(projection, str):
|
||||||
|
projection = json.loads(projection) if projection else None
|
||||||
|
|
||||||
|
if isinstance(sort, str):
|
||||||
|
sort = json.loads(sort) if sort else None
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
cursor = collection.find(query, projection)
|
||||||
|
|
||||||
|
if sort:
|
||||||
|
cursor = cursor.sort(list(sort.items()))
|
||||||
|
|
||||||
|
cursor = cursor.skip(skip).limit(limit)
|
||||||
|
|
||||||
|
documents = list(cursor)
|
||||||
|
|
||||||
|
# 转换ObjectId为字符串
|
||||||
|
for doc in documents:
|
||||||
|
if '_id' in doc and isinstance(doc['_id'], ObjectId):
|
||||||
|
doc['_id'] = str(doc['_id'])
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"documents": documents,
|
||||||
|
"count": len(documents),
|
||||||
|
"query": query,
|
||||||
|
"limit": limit,
|
||||||
|
"skip": skip
|
||||||
|
}
|
||||||
|
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return {"success": False, "error": f"Invalid JSON: {str(e)}"}
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def update_document(self, collection_name: str, query: Union[Dict, str],
|
||||||
|
update: Union[Dict, str], many: bool = False) -> Dict[str, Any]:
|
||||||
|
"""更新文档"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
# 解析参数
|
||||||
|
if isinstance(query, str):
|
||||||
|
query = json.loads(query)
|
||||||
|
if isinstance(update, str):
|
||||||
|
update = json.loads(update)
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
|
||||||
|
if many:
|
||||||
|
result = collection.update_many(query, update)
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"matched_count": result.matched_count,
|
||||||
|
"modified_count": result.modified_count
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
result = collection.update_one(query, update)
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"matched_count": result.matched_count,
|
||||||
|
"modified_count": result.modified_count
|
||||||
|
}
|
||||||
|
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return {"success": False, "error": f"Invalid JSON: {str(e)}"}
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def delete_document(self, collection_name: str, query: Union[Dict, str],
|
||||||
|
many: bool = False) -> Dict[str, Any]:
|
||||||
|
"""删除文档"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
# 解析参数
|
||||||
|
if isinstance(query, str):
|
||||||
|
query = json.loads(query)
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
|
||||||
|
if many:
|
||||||
|
result = collection.delete_many(query)
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"deleted_count": result.deleted_count
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
result = collection.delete_one(query)
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"deleted_count": result.deleted_count
|
||||||
|
}
|
||||||
|
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return {"success": False, "error": f"Invalid JSON: {str(e)}"}
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def aggregate_query(self, collection_name: str, pipeline: Union[List, str]) -> Dict[str, Any]:
|
||||||
|
"""执行聚合查询"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
# 解析参数
|
||||||
|
if isinstance(pipeline, str):
|
||||||
|
pipeline = json.loads(pipeline)
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
result = list(collection.aggregate(pipeline))
|
||||||
|
|
||||||
|
# 转换ObjectId为字符串
|
||||||
|
for doc in result:
|
||||||
|
if '_id' in doc and isinstance(doc['_id'], ObjectId):
|
||||||
|
doc['_id'] = str(doc['_id'])
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"result": result,
|
||||||
|
"count": len(result),
|
||||||
|
"pipeline": pipeline
|
||||||
|
}
|
||||||
|
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return {"success": False, "error": f"Invalid JSON: {str(e)}"}
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def list_collections(self) -> Dict[str, Any]:
|
||||||
|
"""列出数据库中的所有集合"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
collections = self.db.list_collection_names()
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"collections": collections,
|
||||||
|
"count": len(collections)
|
||||||
|
}
|
||||||
|
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def create_index(self, collection_name: str, index_spec: Union[Dict, str],
|
||||||
|
unique: bool = False, background: bool = True) -> Dict[str, Any]:
|
||||||
|
"""创建索引"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
# 解析参数
|
||||||
|
if isinstance(index_spec, str):
|
||||||
|
index_spec = json.loads(index_spec)
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
|
||||||
|
# 转换为pymongo格式
|
||||||
|
index_list = [(key, value) for key, value in index_spec.items()]
|
||||||
|
|
||||||
|
result = collection.create_index(
|
||||||
|
index_list,
|
||||||
|
unique=unique,
|
||||||
|
background=background
|
||||||
|
)
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"index_name": result,
|
||||||
|
"index_spec": index_spec
|
||||||
|
}
|
||||||
|
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return {"success": False, "error": f"Invalid JSON: {str(e)}"}
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def get_collection_stats(self, collection_name: str) -> Dict[str, Any]:
|
||||||
|
"""获取集合统计信息"""
|
||||||
|
try:
|
||||||
|
if not self.db:
|
||||||
|
return {"success": False, "error": "Database not connected"}
|
||||||
|
|
||||||
|
collection = self.db[collection_name]
|
||||||
|
|
||||||
|
# 获取基本统计
|
||||||
|
stats = self.db.command("collStats", collection_name)
|
||||||
|
|
||||||
|
# 获取文档数量
|
||||||
|
count = collection.count_documents({})
|
||||||
|
|
||||||
|
# 获取索引信息
|
||||||
|
indexes = list(collection.list_indexes())
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"collection_name": collection_name,
|
||||||
|
"document_count": count,
|
||||||
|
"size_bytes": stats.get('size', 0),
|
||||||
|
"storage_size_bytes": stats.get('storageSize', 0),
|
||||||
|
"indexes": [{
|
||||||
|
"name": idx.get('name'),
|
||||||
|
"key": idx.get('key'),
|
||||||
|
"unique": idx.get('unique', False)
|
||||||
|
} for idx in indexes],
|
||||||
|
"index_count": len(indexes)
|
||||||
|
}
|
||||||
|
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
async def get_connection_status(self) -> Dict[str, Any]:
|
||||||
|
"""获取连接状态"""
|
||||||
|
try:
|
||||||
|
if not self.client:
|
||||||
|
return {
|
||||||
|
"connected": False,
|
||||||
|
"message": "Not connected to MongoDB"
|
||||||
|
}
|
||||||
|
|
||||||
|
# 测试连接
|
||||||
|
self.client.admin.command('ping')
|
||||||
|
|
||||||
|
# 获取服务器信息
|
||||||
|
server_info = self.client.server_info()
|
||||||
|
|
||||||
|
return {
|
||||||
|
"connected": True,
|
||||||
|
"server_version": server_info.get('version'),
|
||||||
|
"connection_url": self.mongodb_url.replace(self.mongodb_url.split('@')[0].split('//')[1] + '@', '***@') if '@' in self.mongodb_url else self.mongodb_url,
|
||||||
|
"current_database": self.db.name if self.db else None,
|
||||||
|
"server_info": {
|
||||||
|
"version": server_info.get('version'),
|
||||||
|
"git_version": server_info.get('gitVersion'),
|
||||||
|
"platform": server_info.get('platform')
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return {
|
||||||
|
"connected": False,
|
||||||
|
"error": str(e)
|
||||||
|
}
|
||||||
|
|
||||||
|
async def get_databases_list(self) -> Dict[str, Any]:
|
||||||
|
"""获取数据库列表"""
|
||||||
|
try:
|
||||||
|
if not self.client:
|
||||||
|
return {"success": False, "error": "Not connected to MongoDB"}
|
||||||
|
|
||||||
|
databases = self.client.list_database_names()
|
||||||
|
|
||||||
|
return {
|
||||||
|
"success": True,
|
||||||
|
"databases": databases,
|
||||||
|
"count": len(databases)
|
||||||
|
}
|
||||||
|
|
||||||
|
except PyMongoError as e:
|
||||||
|
return {"success": False, "error": f"MongoDB error: {str(e)}"}
|
||||||
|
except Exception as e:
|
||||||
|
return {"success": False, "error": f"Unexpected error: {str(e)}"}
|
||||||
|
|
||||||
|
def close_connection(self):
|
||||||
|
"""关闭数据库连接"""
|
||||||
|
if self.client:
|
||||||
|
self.client.close()
|
||||||
|
self.client = None
|
||||||
|
self.db = None
|
||||||
|
self.logger.info("MongoDB connection closed")
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""主函数 - 启动MCP服务器"""
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description="MongoDB MCP Server")
|
||||||
|
parser.add_argument(
|
||||||
|
"--mongodb-url",
|
||||||
|
default=os.getenv('MONGODB_URL', 'mongodb://localhost:27017'),
|
||||||
|
help="MongoDB连接URL"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--database",
|
||||||
|
default="default",
|
||||||
|
help="默认数据库名称"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--port",
|
||||||
|
type=int,
|
||||||
|
default=8080,
|
||||||
|
help="MCP服务器端口"
|
||||||
|
)
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# 创建MCP服务器
|
||||||
|
mcp_server = MongoDBMCPServer(args.mongodb_url)
|
||||||
|
|
||||||
|
print(f"🚀 Starting MongoDB MCP Server...")
|
||||||
|
print(f"📊 MongoDB URL: {args.mongodb_url}")
|
||||||
|
print(f"🗄️ Default Database: {args.database}")
|
||||||
|
print(f"🌐 Port: {args.port}")
|
||||||
|
print(f"")
|
||||||
|
print(f"Available tools:")
|
||||||
|
for tool_name, tool_info in mcp_server.server.tools.items():
|
||||||
|
print(f" - {tool_name}: {tool_info['description']}")
|
||||||
|
print(f"")
|
||||||
|
print(f"Available resources:")
|
||||||
|
for resource_uri, resource_info in mcp_server.server.resources.items():
|
||||||
|
print(f" - {resource_uri}: {resource_info['description']}")
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 自动连接到默认数据库
|
||||||
|
asyncio.run(mcp_server.connect_database(args.database))
|
||||||
|
|
||||||
|
# 这里应该启动实际的MCP服务器
|
||||||
|
# 由于我们没有完整的MCP库，这里只是演示
|
||||||
|
print(f"\n✅ MongoDB MCP Server is ready!")
|
||||||
|
print(f"💡 Use this server with Swarm MCP client to access MongoDB")
|
||||||
|
|
||||||
|
# 保持服务器运行
|
||||||
|
try:
|
||||||
|
while True:
|
||||||
|
asyncio.run(asyncio.sleep(1))
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print("\n🛑 Shutting down MongoDB MCP Server...")
|
||||||
|
mcp_server.close_connection()
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print(f"❌ Error starting server: {e}")
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,633 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Swarm MongoDB MCP Client
|
||||||
|
Swarm框架的MongoDB MCP客户端，用于连接和使用MongoDB MCP服务器
|
||||||
|
|
||||||
|
功能:
|
||||||
|
- 连接到MongoDB MCP服务器
|
||||||
|
- 提供Swarm代理使用的MongoDB操作接口
|
||||||
|
- 处理MCP协议通信
|
||||||
|
- 数据格式转换和错误处理
|
||||||
|
"""
|
||||||
|
|
||||||
|
import asyncio
|
||||||
|
import json
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
from typing import Any, Dict, List, Optional, Union
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
try:
|
||||||
|
import requests
|
||||||
|
except ImportError:
|
||||||
|
print("Error: requests is required. Install with: pip install requests")
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
class SwarmMongoDBClient:
|
||||||
|
"""
|
||||||
|
Swarm MongoDB MCP客户端
|
||||||
|
为Swarm代理提供MongoDB数据库访问功能
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, mcp_server_url: str = "http://localhost:8080",
|
||||||
|
mongodb_url: Optional[str] = None,
|
||||||
|
default_database: str = "default"):
|
||||||
|
self.mcp_server_url = mcp_server_url.rstrip('/')
|
||||||
|
self.mongodb_url = mongodb_url or os.getenv('MONGODB_URL', 'mongodb://localhost:27017')
|
||||||
|
self.default_database = default_database
|
||||||
|
self.connected = False
|
||||||
|
|
||||||
|
# 设置日志
|
||||||
|
logging.basicConfig(level=logging.INFO)
|
||||||
|
self.logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
# 会话配置
|
||||||
|
self.session = requests.Session()
|
||||||
|
self.session.headers.update({
|
||||||
|
'Content-Type': 'application/json',
|
||||||
|
'User-Agent': 'Swarm-MongoDB-MCP-Client/1.0'
|
||||||
|
})
|
||||||
|
|
||||||
|
def _call_mcp_tool(self, tool_name: str, **kwargs) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
调用MCP服务器工具
|
||||||
|
|
||||||
|
Args:
|
||||||
|
tool_name: 工具名称
|
||||||
|
**kwargs: 工具参数
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
工具执行结果
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
url = f"{self.mcp_server_url}/tools/{tool_name}"
|
||||||
|
response = self.session.post(url, json=kwargs, timeout=30)
|
||||||
|
response.raise_for_status()
|
||||||
|
|
||||||
|
result = response.json()
|
||||||
|
return result
|
||||||
|
|
||||||
|
except requests.exceptions.RequestException as e:
|
||||||
|
self.logger.error(f"MCP tool call failed: {e}")
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": f"MCP communication error: {str(e)}"
|
||||||
|
}
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
self.logger.error(f"Invalid JSON response: {e}")
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": f"Invalid response format: {str(e)}"
|
||||||
|
}
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"Unexpected error: {e}")
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": f"Unexpected error: {str(e)}"
|
||||||
|
}
|
||||||
|
|
||||||
|
def _get_mcp_resource(self, resource_uri: str) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
获取MCP服务器资源
|
||||||
|
|
||||||
|
Args:
|
||||||
|
resource_uri: 资源URI
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
资源内容
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
url = f"{self.mcp_server_url}/resources"
|
||||||
|
response = self.session.get(url, params={'uri': resource_uri}, timeout=30)
|
||||||
|
response.raise_for_status()
|
||||||
|
|
||||||
|
result = response.json()
|
||||||
|
return result
|
||||||
|
|
||||||
|
except requests.exceptions.RequestException as e:
|
||||||
|
self.logger.error(f"MCP resource request failed: {e}")
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": f"MCP communication error: {str(e)}"
|
||||||
|
}
|
||||||
|
except Exception as e:
|
||||||
|
self.logger.error(f"Unexpected error: {e}")
|
||||||
|
return {
|
||||||
|
"success": False,
|
||||||
|
"error": f"Unexpected error: {str(e)}"
|
||||||
|
}
|
||||||
|
|
||||||
|
# === 连接管理 ===
|
||||||
|
|
||||||
|
def connect(self, database_name: Optional[str] = None) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
连接到MongoDB数据库
|
||||||
|
|
||||||
|
Args:
|
||||||
|
database_name: 数据库名称，默认使用初始化时指定的数据库
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
连接结果
|
||||||
|
"""
|
||||||
|
db_name = database_name or self.default_database
|
||||||
|
result = self._call_mcp_tool("connect_database", database_name=db_name)
|
||||||
|
|
||||||
|
if result.get("success"):
|
||||||
|
self.connected = True
|
||||||
|
self.current_database = db_name
|
||||||
|
self.logger.info(f"Connected to MongoDB database: {db_name}")
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def get_connection_status(self) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
获取连接状态
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
连接状态信息
|
||||||
|
"""
|
||||||
|
return self._get_mcp_resource("mongodb://status")
|
||||||
|
|
||||||
|
def list_databases(self) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
获取数据库列表
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
数据库列表
|
||||||
|
"""
|
||||||
|
return self._get_mcp_resource("mongodb://databases")
|
||||||
|
|
||||||
|
# === CRUD操作 ===
|
||||||
|
|
||||||
|
def insert_document(self, collection_name: str, document: Union[Dict, List[Dict]],
|
||||||
|
many: bool = False) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
插入文档
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
document: 要插入的文档或文档列表
|
||||||
|
many: 是否批量插入
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
插入结果
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"insert_document",
|
||||||
|
collection_name=collection_name,
|
||||||
|
document=document,
|
||||||
|
many=many
|
||||||
|
)
|
||||||
|
|
||||||
|
def find_documents(self, collection_name: str, query: Optional[Dict] = None,
|
||||||
|
projection: Optional[Dict] = None, limit: int = 100,
|
||||||
|
skip: int = 0, sort: Optional[Dict] = None) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
查找文档
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
query: 查询条件
|
||||||
|
projection: 投影字段
|
||||||
|
limit: 限制数量
|
||||||
|
skip: 跳过数量
|
||||||
|
sort: 排序条件
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
查询结果
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"find_documents",
|
||||||
|
collection_name=collection_name,
|
||||||
|
query=query or {},
|
||||||
|
projection=projection,
|
||||||
|
limit=limit,
|
||||||
|
skip=skip,
|
||||||
|
sort=sort
|
||||||
|
)
|
||||||
|
|
||||||
|
def update_document(self, collection_name: str, query: Dict, update: Dict,
|
||||||
|
many: bool = False) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
更新文档
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
query: 查询条件
|
||||||
|
update: 更新操作
|
||||||
|
many: 是否批量更新
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
更新结果
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"update_document",
|
||||||
|
collection_name=collection_name,
|
||||||
|
query=query,
|
||||||
|
update=update,
|
||||||
|
many=many
|
||||||
|
)
|
||||||
|
|
||||||
|
def delete_document(self, collection_name: str, query: Dict,
|
||||||
|
many: bool = False) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
删除文档
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
query: 查询条件
|
||||||
|
many: 是否批量删除
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
删除结果
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"delete_document",
|
||||||
|
collection_name=collection_name,
|
||||||
|
query=query,
|
||||||
|
many=many
|
||||||
|
)
|
||||||
|
|
||||||
|
# === 高级查询 ===
|
||||||
|
|
||||||
|
def aggregate(self, collection_name: str, pipeline: List[Dict]) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
执行聚合查询
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
pipeline: 聚合管道
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
聚合结果
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"aggregate_query",
|
||||||
|
collection_name=collection_name,
|
||||||
|
pipeline=pipeline
|
||||||
|
)
|
||||||
|
|
||||||
|
# === 数据库管理 ===
|
||||||
|
|
||||||
|
def list_collections(self) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
列出所有集合
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
集合列表
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool("list_collections")
|
||||||
|
|
||||||
|
def create_index(self, collection_name: str, index_spec: Dict,
|
||||||
|
unique: bool = False, background: bool = True) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
创建索引
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
index_spec: 索引规范
|
||||||
|
unique: 是否唯一索引
|
||||||
|
background: 是否后台创建
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
创建结果
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"create_index",
|
||||||
|
collection_name=collection_name,
|
||||||
|
index_spec=index_spec,
|
||||||
|
unique=unique,
|
||||||
|
background=background
|
||||||
|
)
|
||||||
|
|
||||||
|
def get_collection_stats(self, collection_name: str) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
获取集合统计信息
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
统计信息
|
||||||
|
"""
|
||||||
|
if not self.connected:
|
||||||
|
return {"success": False, "error": "Not connected to database"}
|
||||||
|
|
||||||
|
return self._call_mcp_tool(
|
||||||
|
"get_collection_stats",
|
||||||
|
collection_name=collection_name
|
||||||
|
)
|
||||||
|
|
||||||
|
# === Swarm代理专用方法 ===
|
||||||
|
|
||||||
|
def swarm_query(self, collection_name: str, natural_language_query: str) -> str:
|
||||||
|
"""
|
||||||
|
Swarm代理专用的自然语言查询接口
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
natural_language_query: 自然语言查询描述
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的查询结果字符串
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
# 这里可以集成NLP处理，将自然语言转换为MongoDB查询
|
||||||
|
# 目前简化处理，直接执行基本查询
|
||||||
|
|
||||||
|
result = self.find_documents(collection_name, limit=10)
|
||||||
|
|
||||||
|
if result.get("success"):
|
||||||
|
documents = result.get("documents", [])
|
||||||
|
if documents:
|
||||||
|
formatted_result = f"Found {len(documents)} documents in '{collection_name}':\n"
|
||||||
|
for i, doc in enumerate(documents[:5], 1): # 只显示前5个
|
||||||
|
formatted_result += f"{i}. {json.dumps(doc, indent=2, ensure_ascii=False)}\n"
|
||||||
|
|
||||||
|
if len(documents) > 5:
|
||||||
|
formatted_result += f"... and {len(documents) - 5} more documents\n"
|
||||||
|
|
||||||
|
return formatted_result
|
||||||
|
else:
|
||||||
|
return f"No documents found in collection '{collection_name}'"
|
||||||
|
else:
|
||||||
|
return f"Query failed: {result.get('error', 'Unknown error')}"
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return f"Error executing query: {str(e)}"
|
||||||
|
|
||||||
|
def swarm_insert(self, collection_name: str, data_description: str,
|
||||||
|
document: Union[Dict, List[Dict]]) -> str:
|
||||||
|
"""
|
||||||
|
Swarm代理专用的插入接口
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
data_description: 数据描述
|
||||||
|
document: 要插入的文档
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的插入结果字符串
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
many = isinstance(document, list)
|
||||||
|
result = self.insert_document(collection_name, document, many=many)
|
||||||
|
|
||||||
|
if result.get("success"):
|
||||||
|
if many:
|
||||||
|
count = result.get("count", 0)
|
||||||
|
return f"Successfully inserted {count} documents into '{collection_name}'. Description: {data_description}"
|
||||||
|
else:
|
||||||
|
inserted_id = result.get("inserted_id")
|
||||||
|
return f"Successfully inserted document with ID {inserted_id} into '{collection_name}'. Description: {data_description}"
|
||||||
|
else:
|
||||||
|
return f"Insert failed: {result.get('error', 'Unknown error')}"
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return f"Error inserting data: {str(e)}"
|
||||||
|
|
||||||
|
def swarm_update(self, collection_name: str, update_description: str,
|
||||||
|
query: Dict, update: Dict) -> str:
|
||||||
|
"""
|
||||||
|
Swarm代理专用的更新接口
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称
|
||||||
|
update_description: 更新描述
|
||||||
|
query: 查询条件
|
||||||
|
update: 更新操作
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的更新结果字符串
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
result = self.update_document(collection_name, query, update)
|
||||||
|
|
||||||
|
if result.get("success"):
|
||||||
|
matched = result.get("matched_count", 0)
|
||||||
|
modified = result.get("modified_count", 0)
|
||||||
|
return f"Update completed: {matched} documents matched, {modified} documents modified in '{collection_name}'. Description: {update_description}"
|
||||||
|
else:
|
||||||
|
return f"Update failed: {result.get('error', 'Unknown error')}"
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return f"Error updating data: {str(e)}"
|
||||||
|
|
||||||
|
def swarm_stats(self, collection_name: Optional[str] = None) -> str:
|
||||||
|
"""
|
||||||
|
Swarm代理专用的统计信息接口
|
||||||
|
|
||||||
|
Args:
|
||||||
|
collection_name: 集合名称，如果为None则返回数据库概览
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
格式化的统计信息字符串
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
if collection_name:
|
||||||
|
# 获取特定集合的统计信息
|
||||||
|
result = self.get_collection_stats(collection_name)
|
||||||
|
if result.get("success"):
|
||||||
|
stats = result
|
||||||
|
return f"""Collection '{collection_name}' Statistics:
|
||||||
|
- Document Count: {stats.get('document_count', 0):,}
|
||||||
|
- Size: {stats.get('size_bytes', 0):,} bytes
|
||||||
|
- Storage Size: {stats.get('storage_size_bytes', 0):,} bytes
|
||||||
|
- Indexes: {stats.get('index_count', 0)}"""
|
||||||
|
else:
|
||||||
|
return f"Failed to get stats for '{collection_name}': {result.get('error', 'Unknown error')}"
|
||||||
|
else:
|
||||||
|
# 获取数据库概览
|
||||||
|
collections_result = self.list_collections()
|
||||||
|
status_result = self.get_connection_status()
|
||||||
|
|
||||||
|
if collections_result.get("success") and status_result.get("connected"):
|
||||||
|
collections = collections_result.get("collections", [])
|
||||||
|
db_name = status_result.get("current_database", "Unknown")
|
||||||
|
|
||||||
|
stats_text = f"""Database '{db_name}' Overview:
|
||||||
|
- Total Collections: {len(collections)}
|
||||||
|
- Collections: {', '.join(collections) if collections else 'None'}
|
||||||
|
- Server Version: {status_result.get('server_info', {}).get('version', 'Unknown')}"""
|
||||||
|
|
||||||
|
return stats_text
|
||||||
|
else:
|
||||||
|
return "Failed to get database overview"
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
return f"Error getting statistics: {str(e)}"
|
||||||
|
|
||||||
|
def close(self):
|
||||||
|
"""
|
||||||
|
关闭客户端连接
|
||||||
|
"""
|
||||||
|
self.session.close()
|
||||||
|
self.connected = False
|
||||||
|
self.logger.info("MongoDB MCP client closed")
|
||||||
|
|
||||||
|
|
||||||
|
# === Swarm代理函数 ===
|
||||||
|
|
||||||
|
def create_mongodb_functions(client: SwarmMongoDBClient) -> List[Dict[str, Any]]:
|
||||||
|
"""
|
||||||
|
为Swarm代理创建MongoDB操作函数
|
||||||
|
|
||||||
|
Args:
|
||||||
|
client: MongoDB MCP客户端实例
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Swarm函数列表
|
||||||
|
"""
|
||||||
|
|
||||||
|
def mongodb_query(collection_name: str, query_description: str = "查询所有文档") -> str:
|
||||||
|
"""查询MongoDB集合中的文档"""
|
||||||
|
return client.swarm_query(collection_name, query_description)
|
||||||
|
|
||||||
|
def mongodb_insert(collection_name: str, document: Union[Dict, str],
|
||||||
|
description: str = "插入新文档") -> str:
|
||||||
|
"""向MongoDB集合插入文档"""
|
||||||
|
if isinstance(document, str):
|
||||||
|
try:
|
||||||
|
document = json.loads(document)
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
return f"Error: Invalid JSON format in document: {document}"
|
||||||
|
|
||||||
|
return client.swarm_insert(collection_name, description, document)
|
||||||
|
|
||||||
|
def mongodb_update(collection_name: str, query: Union[Dict, str],
|
||||||
|
update: Union[Dict, str], description: str = "更新文档") -> str:
|
||||||
|
"""更新MongoDB集合中的文档"""
|
||||||
|
try:
|
||||||
|
if isinstance(query, str):
|
||||||
|
query = json.loads(query)
|
||||||
|
if isinstance(update, str):
|
||||||
|
update = json.loads(update)
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
return f"Error: Invalid JSON format: {str(e)}"
|
||||||
|
|
||||||
|
return client.swarm_update(collection_name, description, query, update)
|
||||||
|
|
||||||
|
def mongodb_stats(collection_name: str = None) -> str:
|
||||||
|
"""获取MongoDB数据库或集合的统计信息"""
|
||||||
|
return client.swarm_stats(collection_name)
|
||||||
|
|
||||||
|
def mongodb_collections() -> str:
|
||||||
|
"""列出数据库中的所有集合"""
|
||||||
|
result = client.list_collections()
|
||||||
|
if result.get("success"):
|
||||||
|
collections = result.get("collections", [])
|
||||||
|
if collections:
|
||||||
|
return f"Available collections: {', '.join(collections)}"
|
||||||
|
else:
|
||||||
|
return "No collections found in the database"
|
||||||
|
else:
|
||||||
|
return f"Error listing collections: {result.get('error', 'Unknown error')}"
|
||||||
|
|
||||||
|
# 返回函数定义列表
|
||||||
|
return [
|
||||||
|
{
|
||||||
|
"name": "mongodb_query",
|
||||||
|
"description": "查询MongoDB集合中的文档",
|
||||||
|
"function": mongodb_query
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "mongodb_insert",
|
||||||
|
"description": "向MongoDB集合插入文档",
|
||||||
|
"function": mongodb_insert
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "mongodb_update",
|
||||||
|
"description": "更新MongoDB集合中的文档",
|
||||||
|
"function": mongodb_update
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "mongodb_stats",
|
||||||
|
"description": "获取MongoDB数据库或集合的统计信息",
|
||||||
|
"function": mongodb_stats
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "mongodb_collections",
|
||||||
|
"description": "列出数据库中的所有集合",
|
||||||
|
"function": mongodb_collections
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
"""测试客户端功能"""
|
||||||
|
import argparse
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description="Swarm MongoDB MCP Client")
|
||||||
|
parser.add_argument(
|
||||||
|
"--mcp-server",
|
||||||
|
default="http://localhost:8080",
|
||||||
|
help="MCP服务器URL"
|
||||||
|
)
|
||||||
|
parser.add_argument(
|
||||||
|
"--database",
|
||||||
|
default="test",
|
||||||
|
help="数据库名称"
|
||||||
|
)
|
||||||
|
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# 创建客户端
|
||||||
|
client = SwarmMongoDBClient(
|
||||||
|
mcp_server_url=args.mcp_server,
|
||||||
|
default_database=args.database
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"🔗 Connecting to MongoDB MCP Server: {args.mcp_server}")
|
||||||
|
|
||||||
|
# 测试连接
|
||||||
|
result = client.connect(args.database)
|
||||||
|
if result.get("success"):
|
||||||
|
print(f"✅ Connected to database: {args.database}")
|
||||||
|
|
||||||
|
# 测试基本操作
|
||||||
|
print("\n📊 Testing basic operations...")
|
||||||
|
|
||||||
|
# 列出集合
|
||||||
|
collections = client.list_collections()
|
||||||
|
print(f"Collections: {collections}")
|
||||||
|
|
||||||
|
# 获取状态
|
||||||
|
status = client.get_connection_status()
|
||||||
|
print(f"Status: {status}")
|
||||||
|
|
||||||
|
# 创建Swarm函数
|
||||||
|
functions = create_mongodb_functions(client)
|
||||||
|
print(f"\n🔧 Created {len(functions)} Swarm functions:")
|
||||||
|
for func in functions:
|
||||||
|
print(f" - {func['name']}: {func['description']}")
|
||||||
|
|
||||||
|
else:
|
||||||
|
print(f"❌ Connection failed: {result.get('error')}")
|
||||||
|
|
||||||
|
client.close()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,148 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
Swarm辩论触发器
|
||||||
|
基于时间群聚效应和语义相似性触发蜂群辩论
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
from datetime import datetime, timedelta
|
||||||
|
from pymongo import MongoClient
|
||||||
|
from typing import List, Dict, Optional
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
class SwarmDebateTrigger:
|
||||||
|
def __init__(self):
|
||||||
|
self.mongodb_uri = os.getenv('MONGODB_URI')
|
||||||
|
self.client = MongoClient(self.mongodb_uri)
|
||||||
|
self.db = self.client.taigong
|
||||||
|
self.collection = self.db.articles
|
||||||
|
|
||||||
|
# 配置参数
|
||||||
|
self.swarm_threshold = int(os.getenv('SWARM_THRESHOLD', 5))
|
||||||
|
self.time_window_hours = int(os.getenv('SWARM_TIME_WINDOW_HOURS', 24))
|
||||||
|
|
||||||
|
def detect_time_clustering(self) -> List[Dict]:
|
||||||
|
"""检测时间窗口内的文章群聚效应"""
|
||||||
|
# 计算时间窗口
|
||||||
|
now = datetime.utcnow()
|
||||||
|
time_threshold = now - timedelta(hours=self.time_window_hours)
|
||||||
|
|
||||||
|
# 使用published_time_index查询最近文章
|
||||||
|
recent_articles = list(self.collection.find({
|
||||||
|
"published_time": {"$gte": time_threshold}
|
||||||
|
}).sort("published_time", -1))
|
||||||
|
|
||||||
|
print(f"最近{self.time_window_hours}小时内发现 {len(recent_articles)} 篇文章")
|
||||||
|
|
||||||
|
if len(recent_articles) >= self.swarm_threshold:
|
||||||
|
print(f"✓ 触发群聚效应！文章数量({len(recent_articles)}) >= 阈值({self.swarm_threshold})")
|
||||||
|
return recent_articles
|
||||||
|
else:
|
||||||
|
print(f"× 未达到群聚阈值，需要至少 {self.swarm_threshold} 篇文章")
|
||||||
|
return []
|
||||||
|
|
||||||
|
def find_semantic_clusters(self, articles: List[Dict], similarity_threshold: float = 0.8) -> List[List[Dict]]:
|
||||||
|
"""基于向量相似性找到语义聚类"""
|
||||||
|
if not articles:
|
||||||
|
return []
|
||||||
|
|
||||||
|
# 过滤有embedding的文章
|
||||||
|
articles_with_embeddings = [
|
||||||
|
article for article in articles
|
||||||
|
if 'embedding' in article and article['embedding']
|
||||||
|
]
|
||||||
|
|
||||||
|
if len(articles_with_embeddings) < 2:
|
||||||
|
print("× 没有足够的embedding数据进行语义聚类")
|
||||||
|
return [articles_with_embeddings] if articles_with_embeddings else []
|
||||||
|
|
||||||
|
print(f"对 {len(articles_with_embeddings)} 篇文章进行语义聚类分析...")
|
||||||
|
|
||||||
|
# 简单的相似性聚类算法
|
||||||
|
clusters = []
|
||||||
|
used_indices = set()
|
||||||
|
|
||||||
|
for i, article1 in enumerate(articles_with_embeddings):
|
||||||
|
if i in used_indices:
|
||||||
|
continue
|
||||||
|
|
||||||
|
cluster = [article1]
|
||||||
|
used_indices.add(i)
|
||||||
|
|
||||||
|
for j, article2 in enumerate(articles_with_embeddings):
|
||||||
|
if j in used_indices or i == j:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 计算余弦相似度
|
||||||
|
similarity = self.cosine_similarity(
|
||||||
|
article1['embedding'],
|
||||||
|
article2['embedding']
|
||||||
|
)
|
||||||
|
|
||||||
|
if similarity >= similarity_threshold:
|
||||||
|
cluster.append(article2)
|
||||||
|
used_indices.add(j)
|
||||||
|
|
||||||
|
if len(cluster) >= 2: # 至少2篇文章才算一个有效聚类
|
||||||
|
clusters.append(cluster)
|
||||||
|
print(f"✓ 发现语义聚类，包含 {len(cluster)} 篇相关文章")
|
||||||
|
|
||||||
|
return clusters
|
||||||
|
|
||||||
|
def cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
|
||||||
|
"""计算两个向量的余弦相似度"""
|
||||||
|
try:
|
||||||
|
vec1 = np.array(vec1)
|
||||||
|
vec2 = np.array(vec2)
|
||||||
|
|
||||||
|
dot_product = np.dot(vec1, vec2)
|
||||||
|
norm1 = np.linalg.norm(vec1)
|
||||||
|
norm2 = np.linalg.norm(vec2)
|
||||||
|
|
||||||
|
if norm1 == 0 or norm2 == 0:
|
||||||
|
return 0
|
||||||
|
|
||||||
|
return dot_product / (norm1 * norm2)
|
||||||
|
except Exception as e:
|
||||||
|
print(f"计算相似度失败: {e}")
|
||||||
|
return 0
|
||||||
|
|
||||||
|
def trigger_swarm_debate(self, clusters: List[List[Dict]]) -> bool:
|
||||||
|
"""触发swarm蜂群辩论"""
|
||||||
|
if not clusters:
|
||||||
|
print("× 没有发现有效的语义聚类，不触发辩论")
|
||||||
|
return False
|
||||||
|
|
||||||
|
print(f"\n🔥 触发Swarm蜂群辩论！")
|
||||||
|
print(f"发现 {len(clusters)} 个语义聚类")
|
||||||
|
|
||||||
|
for i, cluster in enumerate(clusters):
|
||||||
|
print(f"\n聚类 {i+1}: {len(cluster)} 篇文章")
|
||||||
|
for article in cluster:
|
||||||
|
title = article.get('title', '无标题')[:50]
|
||||||
|
time_str = article.get('published_time', '').strftime('%Y-%m-%d %H:%M') if article.get('published_time') else '未知时间'
|
||||||
|
print(f" - {title}... ({time_str})")
|
||||||
|
|
||||||
|
# TODO: 在这里调用实际的辩论系统
|
||||||
|
# 例如: jixia_swarm_debate(clusters)
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
def run(self) -> bool:
|
||||||
|
"""运行swarm辩论触发检测"""
|
||||||
|
print("🔍 开始检测swarm辩论触发条件...")
|
||||||
|
|
||||||
|
# 1. 检测时间群聚效应
|
||||||
|
recent_articles = self.detect_time_clustering()
|
||||||
|
if not recent_articles:
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 2. 进行语义聚类分析
|
||||||
|
semantic_clusters = self.find_semantic_clusters(recent_articles)
|
||||||
|
|
||||||
|
# 3. 触发辩论
|
||||||
|
return self.trigger_swarm_debate(semantic_clusters)
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
trigger = SwarmDebateTrigger()
|
||||||
|
trigger.run()
|
||||||
|
|
@ -0,0 +1,76 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
"""
|
||||||
|
API健康检查模块
|
||||||
|
用于测试与外部服务的连接，如OpenRouter和RapidAPI。
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import requests
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
# 将项目根目录添加到Python路径，以便导入config模块
|
||||||
|
project_root = Path(__file__).parent.parent
|
||||||
|
sys.path.insert(0, str(project_root))
|
||||||
|
|
||||||
|
from config.settings import get_openrouter_key, get_rapidapi_key
|
||||||
|
|
||||||
|
def test_openrouter_api() -> bool:
|
||||||
|
"""
|
||||||
|
测试与OpenRouter API的连接和认证。
|
||||||
|
"""
|
||||||
|
api_key = get_openrouter_key()
|
||||||
|
if not api_key:
|
||||||
|
print("❌ OpenRouter API Key not found.")
|
||||||
|
return False
|
||||||
|
|
||||||
|
url = "https://openrouter.ai/api/v1/models"
|
||||||
|
headers = {"Authorization": f"Bearer {api_key}"}
|
||||||
|
|
||||||
|
try:
|
||||||
|
response = requests.get(url, headers=headers, timeout=10)
|
||||||
|
if response.status_code == 200:
|
||||||
|
print("✅ OpenRouter API connection successful.")
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
print(f"❌ OpenRouter API connection failed. Status: {response.status_code}, Response: {response.text[:100]}")
|
||||||
|
return False
|
||||||
|
except requests.RequestException as e:
|
||||||
|
print(f"❌ OpenRouter API request failed: {e}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
def test_rapidapi_connection() -> bool:
|
||||||
|
"""
|
||||||
|
测试与RapidAPI的连接和认证。
|
||||||
|
这里我们使用一个简单的、免费的API端点进行测试。
|
||||||
|
"""
|
||||||
|
api_key = get_rapidapi_key()
|
||||||
|
if not api_key:
|
||||||
|
print("❌ RapidAPI Key not found.")
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 使用一个通用的、通常可用的RapidAPI端点进行测试
|
||||||
|
url = "https://alpha-vantage.p.rapidapi.com/query"
|
||||||
|
querystring = {"function":"TOP_GAINERS_LOSERS"}
|
||||||
|
headers = {
|
||||||
|
"x-rapidapi-host": "alpha-vantage.p.rapidapi.com",
|
||||||
|
"x-rapidapi-key": api_key
|
||||||
|
}
|
||||||
|
|
||||||
|
try:
|
||||||
|
response = requests.get(url, headers=headers, params=querystring, timeout=15)
|
||||||
|
# Alpha Vantage的免费套餐可能会返回错误，但只要RapidAPI认证通过，状态码就不是401或403
|
||||||
|
if response.status_code not in [401, 403]:
|
||||||
|
print(f"✅ RapidAPI connection successful (Status: {response.status_code}).")
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
print(f"❌ RapidAPI authentication failed. Status: {response.status_code}, Response: {response.text[:100]}")
|
||||||
|
return False
|
||||||
|
except requests.RequestException as e:
|
||||||
|
print(f"❌ RapidAPI request failed: {e}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
print("🩺 Running API Health Checks...")
|
||||||
|
test_openrouter_api()
|
||||||
|
test_rapidapi_connection()
|
||||||
|
|
@ -0,0 +1,33 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 环境状态检查脚本
|
||||||
|
|
||||||
|
echo "📊 环境状态检查"
|
||||||
|
echo "=================="
|
||||||
|
|
||||||
|
# Git 状态
|
||||||
|
echo "Git 状态:"
|
||||||
|
git status --short
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 远程仓库状态
|
||||||
|
echo "远程仓库状态:"
|
||||||
|
git remote -v
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 分支状态
|
||||||
|
echo "分支状态:"
|
||||||
|
git branch -a
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 最新标签
|
||||||
|
echo "最新标签:"
|
||||||
|
git tag --sort=-version:refname | head -5
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 提交历史
|
||||||
|
echo "最近提交:"
|
||||||
|
git log --oneline -5
|
||||||
|
|
@ -0,0 +1,35 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速发布脚本
|
||||||
|
|
||||||
|
VERSION=$1
|
||||||
|
ENV=$2
|
||||||
|
|
||||||
|
if [ -z "$VERSION" ] || [ -z "$ENV" ]; then
|
||||||
|
echo "用法: ./quick-release.sh <版本号> <环境>"
|
||||||
|
echo "环境选项: dev/staging/prod"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
case $ENV in
|
||||||
|
canary)
|
||||||
|
git checkout main
|
||||||
|
git tag "v${VERSION}-canary"
|
||||||
|
git push canary main --tags
|
||||||
|
;;
|
||||||
|
dev)
|
||||||
|
git checkout main
|
||||||
|
git tag "v${VERSION}-dev"
|
||||||
|
git push dev main --tags
|
||||||
|
;;
|
||||||
|
beta)
|
||||||
|
git checkout main
|
||||||
|
git tag "v${VERSION}-beta"
|
||||||
|
git push beta main --tags
|
||||||
|
;;
|
||||||
|
*)
|
||||||
|
echo "无效的环境选项: canary/dev/beta"
|
||||||
|
exit 1
|
||||||
|
;;
|
||||||
|
esac
|
||||||
|
|
||||||
|
echo "✅ 发布完成: v${VERSION}-${ENV}"
|
||||||
|
|
@ -0,0 +1,35 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速回滚脚本
|
||||||
|
|
||||||
|
ENV=$1
|
||||||
|
VERSION=$2
|
||||||
|
|
||||||
|
if [ -z "$ENV" ] || [ -z "$VERSION" ]; then
|
||||||
|
echo "用法: ./rollback.sh <环境> <版本号>"
|
||||||
|
echo "环境选项: staging/prod"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
case $ENV in
|
||||||
|
canary)
|
||||||
|
git checkout main
|
||||||
|
git reset --hard "v${VERSION}-canary"
|
||||||
|
git push canary main --force
|
||||||
|
;;
|
||||||
|
dev)
|
||||||
|
git checkout main
|
||||||
|
git reset --hard "v${VERSION}-dev"
|
||||||
|
git push dev main --force
|
||||||
|
;;
|
||||||
|
beta)
|
||||||
|
git checkout main
|
||||||
|
git reset --hard "v${VERSION}-beta"
|
||||||
|
git push beta main --force
|
||||||
|
;;
|
||||||
|
*)
|
||||||
|
echo "无效的环境选项: canary/dev/beta"
|
||||||
|
exit 1
|
||||||
|
;;
|
||||||
|
esac
|
||||||
|
|
||||||
|
echo "✅ 回滚完成: ${ENV} -> v${VERSION}"
|
||||||
|
|
@ -0,0 +1,229 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 六壬神鉴渐进发布环境配置脚本
|
||||||
|
|
||||||
|
set -e
|
||||||
|
|
||||||
|
echo "🚀 配置渐进发布环境..."
|
||||||
|
|
||||||
|
# 1. 配置 Git 别名简化操作
|
||||||
|
echo "配置 Git 别名..."
|
||||||
|
git config alias.deploy-staging '!git push staging staging:main'
|
||||||
|
git config alias.deploy-prod '!git push origin main'
|
||||||
|
git config alias.sync-all '!git fetch --all && git push --all'
|
||||||
|
git config alias.release-start '!git checkout develop && git pull && git checkout -b release/'
|
||||||
|
git config alias.release-finish '!git checkout main && git merge staging && git tag -a'
|
||||||
|
|
||||||
|
# 2. 创建发布分支
|
||||||
|
echo "创建发布分支..."
|
||||||
|
git checkout -b staging 2>/dev/null || git checkout staging
|
||||||
|
git checkout -b develop 2>/dev/null || git checkout develop
|
||||||
|
|
||||||
|
# 3. 推送分支到所有远程仓库
|
||||||
|
echo "推送分支到所有远程仓库..."
|
||||||
|
git push origin staging:staging 2>/dev/null || true
|
||||||
|
git push origin develop:develop 2>/dev/null || true
|
||||||
|
git push staging staging:main 2>/dev/null || true
|
||||||
|
git push staging develop:develop 2>/dev/null || true
|
||||||
|
|
||||||
|
# 4. 设置分支保护（需要管理员权限）
|
||||||
|
echo "设置分支保护规则..."
|
||||||
|
echo "⚠️ 请在 GitHub/GitLab/Gitea 后台手动设置以下分支保护："
|
||||||
|
echo "- main 分支：需要 PR 审查，禁止直接推送"
|
||||||
|
echo "- staging 分支：需要 PR 审查，禁止直接推送"
|
||||||
|
echo "- develop 分支：需要 PR 审查，禁止直接推送"
|
||||||
|
|
||||||
|
# 5. 创建发布标签模板
|
||||||
|
echo "创建发布标签模板..."
|
||||||
|
cat > .gitmessage.txt << 'EOF'
|
||||||
|
# 发布标签模板
|
||||||
|
# 格式：v主版本.次版本.修订版本-环境
|
||||||
|
#
|
||||||
|
# 示例：
|
||||||
|
# v1.2.0-canary (灰度发布)
|
||||||
|
# v1.2.0 (正式版本)
|
||||||
|
# v1.2.1-hotfix (热修复)
|
||||||
|
#
|
||||||
|
# 环境标识：
|
||||||
|
# -canary: 灰度发布
|
||||||
|
# -staging: 预发布测试
|
||||||
|
# -hotfix: 紧急修复
|
||||||
|
# 无后缀：正式版本
|
||||||
|
|
||||||
|
发布类型: [feature/bugfix/hotfix/docs]
|
||||||
|
影响范围: [core/api/ui/config]
|
||||||
|
测试状态: [passed/failed/pending]
|
||||||
|
回滚策略: [已准备/无需回滚]
|
||||||
|
EOF
|
||||||
|
|
||||||
|
git config commit.template .gitmessage.txt
|
||||||
|
|
||||||
|
# 6. 创建快速发布脚本
|
||||||
|
cat > scripts/quick-release.sh << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速发布脚本
|
||||||
|
|
||||||
|
VERSION=$1
|
||||||
|
ENV=$2
|
||||||
|
|
||||||
|
if [ -z "$VERSION" ] || [ -z "$ENV" ]; then
|
||||||
|
echo "用法: ./quick-release.sh <版本号> <环境>"
|
||||||
|
echo "环境选项: dev/staging/prod"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
case $ENV in
|
||||||
|
dev)
|
||||||
|
git checkout develop
|
||||||
|
git tag "v${VERSION}-dev"
|
||||||
|
git push gitea develop --tags
|
||||||
|
;;
|
||||||
|
staging)
|
||||||
|
git checkout staging
|
||||||
|
git tag "v${VERSION}-staging"
|
||||||
|
git push staging staging:main --tags
|
||||||
|
;;
|
||||||
|
prod)
|
||||||
|
git checkout main
|
||||||
|
git tag "v${VERSION}"
|
||||||
|
git push origin main --tags
|
||||||
|
;;
|
||||||
|
*)
|
||||||
|
echo "无效的环境选项"
|
||||||
|
exit 1
|
||||||
|
;;
|
||||||
|
esac
|
||||||
|
|
||||||
|
echo "✅ 发布完成: v${VERSION}-${ENV}"
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x scripts/quick-release.sh
|
||||||
|
|
||||||
|
# 7. 创建回滚脚本
|
||||||
|
cat > scripts/rollback.sh << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# 快速回滚脚本
|
||||||
|
|
||||||
|
ENV=$1
|
||||||
|
VERSION=$2
|
||||||
|
|
||||||
|
if [ -z "$ENV" ] || [ -z "$VERSION" ]; then
|
||||||
|
echo "用法: ./rollback.sh <环境> <版本号>"
|
||||||
|
echo "环境选项: staging/prod"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
case $ENV in
|
||||||
|
staging)
|
||||||
|
git checkout staging
|
||||||
|
git reset --hard "v${VERSION}-staging"
|
||||||
|
git push staging staging:main --force
|
||||||
|
;;
|
||||||
|
prod)
|
||||||
|
git checkout main
|
||||||
|
git reset --hard "v${VERSION}"
|
||||||
|
git push origin main --force
|
||||||
|
;;
|
||||||
|
*)
|
||||||
|
echo "无效的环境选项"
|
||||||
|
exit 1
|
||||||
|
;;
|
||||||
|
esac
|
||||||
|
|
||||||
|
echo "✅ 回滚完成: ${ENV} -> v${VERSION}"
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x scripts/rollback.sh
|
||||||
|
|
||||||
|
# 8. 创建状态检查脚本
|
||||||
|
cat > scripts/check-status.sh << 'EOF'
|
||||||
|
#!/bin/bash
|
||||||
|
# 环境状态检查脚本
|
||||||
|
|
||||||
|
echo "📊 环境状态检查"
|
||||||
|
echo "=================="
|
||||||
|
|
||||||
|
# Git 状态
|
||||||
|
echo "Git 状态:"
|
||||||
|
git status --short
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 远程仓库状态
|
||||||
|
echo "远程仓库状态:"
|
||||||
|
git remote -v
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 分支状态
|
||||||
|
echo "分支状态:"
|
||||||
|
git branch -a
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 最新标签
|
||||||
|
echo "最新标签:"
|
||||||
|
git tag --sort=-version:refname | head -5
|
||||||
|
|
||||||
|
echo ""
|
||||||
|
|
||||||
|
# 提交历史
|
||||||
|
echo "最近提交:"
|
||||||
|
git log --oneline -5
|
||||||
|
EOF
|
||||||
|
|
||||||
|
chmod +x scripts/check-status.sh
|
||||||
|
|
||||||
|
# 9. 创建 GitHub Actions 工作流目录
|
||||||
|
mkdir -p .github/workflows
|
||||||
|
|
||||||
|
# 10. 创建部署验证
|
||||||
|
echo "创建部署验证..."
|
||||||
|
cat > .github/workflows/deploy-validation.yml << 'EOF'
|
||||||
|
name: Deploy Validation
|
||||||
|
|
||||||
|
on:
|
||||||
|
push:
|
||||||
|
branches: [develop, staging, main]
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
validate:
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
steps:
|
||||||
|
- uses: actions/checkout@v3
|
||||||
|
|
||||||
|
- name: Set up Python
|
||||||
|
uses: actions/setup-python@v4
|
||||||
|
with:
|
||||||
|
python-version: '3.9'
|
||||||
|
|
||||||
|
- name: Install dependencies
|
||||||
|
run: |
|
||||||
|
python -m pip install --upgrade pip
|
||||||
|
pip install -r requirements.txt
|
||||||
|
|
||||||
|
- name: Run tests
|
||||||
|
run: |
|
||||||
|
python -m pytest tests/ -v
|
||||||
|
|
||||||
|
- name: Validate code style
|
||||||
|
run: |
|
||||||
|
pip install black flake8
|
||||||
|
black --check .
|
||||||
|
flake8 .
|
||||||
|
|
||||||
|
- name: Security scan
|
||||||
|
run: |
|
||||||
|
pip install safety bandit
|
||||||
|
safety check
|
||||||
|
bandit -r . -f json -o security-report.json
|
||||||
|
EOF
|
||||||
|
|
||||||
|
echo "✅ 渐进发布环境配置完成！"
|
||||||
|
echo ""
|
||||||
|
echo "📋 使用指南:"
|
||||||
|
echo "1. 查看状态: ./scripts/check-status.sh"
|
||||||
|
echo "2. 快速发布: ./scripts/quick-release.sh 1.0.0 staging"
|
||||||
|
echo "3. 紧急回滚: ./scripts/rollback.sh prod 1.0.0"
|
||||||
|
echo "4. Git 别名: git deploy-staging, git deploy-prod"
|
||||||
|
echo ""
|
||||||
|
echo "📚 详细文档: docs/development/GRADUAL_DEPLOYMENT_PLAN.md"
|
||||||
|
|
@ -0,0 +1,54 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# 炼妖壶 (Lianyaohu) - 虚拟环境设置脚本
|
||||||
|
# 用于快速初始化项目开发环境
|
||||||
|
|
||||||
|
set -e # 遇到错误时退出
|
||||||
|
|
||||||
|
echo "🔧 开始设置炼妖壶项目虚拟环境..."
|
||||||
|
|
||||||
|
# 检查Python版本
|
||||||
|
echo "📋 检查Python版本..."
|
||||||
|
python3 --version
|
||||||
|
|
||||||
|
# 创建虚拟环境（如果不存在）
|
||||||
|
if [ ! -d "venv" ]; then
|
||||||
|
echo "🏗️ 创建虚拟环境..."
|
||||||
|
python3 -m venv venv
|
||||||
|
else
|
||||||
|
echo "✅ 虚拟环境已存在"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 激活虚拟环境
|
||||||
|
echo "🚀 激活虚拟环境..."
|
||||||
|
source venv/bin/activate
|
||||||
|
|
||||||
|
# 升级pip
|
||||||
|
echo "⬆️ 升级pip..."
|
||||||
|
pip install --upgrade pip
|
||||||
|
|
||||||
|
# 安装项目依赖
|
||||||
|
echo "📦 安装项目依赖..."
|
||||||
|
pip install -r requirements.txt
|
||||||
|
|
||||||
|
# 检查关键依赖
|
||||||
|
echo "🔍 检查关键依赖安装状态..."
|
||||||
|
echo " - streamlit: $(pip show streamlit | grep Version || echo '未安装')"
|
||||||
|
echo " - openai: $(pip show openai | grep Version || echo '未安装')"
|
||||||
|
echo " - google-cloud-aiplatform: $(pip show google-cloud-aiplatform | grep Version || echo '未安装')"
|
||||||
|
echo " - aiohttp: $(pip show aiohttp | grep Version || echo '未安装')"
|
||||||
|
|
||||||
|
echo "✨ 虚拟环境设置完成！"
|
||||||
|
echo ""
|
||||||
|
echo "📝 使用说明："
|
||||||
|
echo " 1. 激活虚拟环境: source venv/bin/activate"
|
||||||
|
echo " 2. 运行辩论系统: python examples/debates/baxian_adk_gemini_debate.py"
|
||||||
|
echo " 3. 启动Web界面: streamlit run app.py (如果有)"
|
||||||
|
echo " 4. 退出虚拟环境: deactivate"
|
||||||
|
echo ""
|
||||||
|
echo "🔧 环境变量配置："
|
||||||
|
echo " 请确保 .env 文件中配置了必要的API密钥："
|
||||||
|
echo " - GOOGLE_API_KEY (Google Gemini API)"
|
||||||
|
echo " - GOOGLE_CLOUD_PROJECT_ID (GCP项目ID)"
|
||||||
|
echo " - GOOGLE_CLOUD_LOCATION (GCP区域)"
|
||||||
|
echo ""
|
||||||
|
echo "🎉 准备就绪！开始你的AI辩论之旅吧！"
|
||||||
|
|
@ -0,0 +1,68 @@
|
||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
# Memory Bank Web界面启动脚本
|
||||||
|
# 自动设置环境并启动Streamlit应用
|
||||||
|
|
||||||
|
echo "🧠 启动Memory Bank Web界面..."
|
||||||
|
echo "================================"
|
||||||
|
|
||||||
|
# 检查是否在正确的目录
|
||||||
|
if [ ! -f "memory_bank_web_interface.py" ]; then
|
||||||
|
echo "❌ 错误: 未找到memory_bank_web_interface.py文件"
|
||||||
|
echo "请确保在正确的项目目录中运行此脚本"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 检查虚拟环境
|
||||||
|
if [ ! -d "venv" ]; then
|
||||||
|
echo "📦 创建虚拟环境..."
|
||||||
|
python3 -m venv venv
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 激活虚拟环境
|
||||||
|
echo "🔧 激活虚拟环境..."
|
||||||
|
source venv/bin/activate
|
||||||
|
|
||||||
|
# 检查并安装依赖
|
||||||
|
echo "📋 检查依赖包..."
|
||||||
|
|
||||||
|
# 检查streamlit
|
||||||
|
if ! python -c "import streamlit" 2>/dev/null; then
|
||||||
|
echo "📦 安装Streamlit..."
|
||||||
|
pip install streamlit
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 检查Google Cloud依赖
|
||||||
|
if ! python -c "import google.cloud" 2>/dev/null; then
|
||||||
|
echo "📦 安装Google Cloud依赖..."
|
||||||
|
pip install google-cloud-aiplatform google-generativeai
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 检查其他必要依赖
|
||||||
|
if ! python -c "import asyncio" 2>/dev/null; then
|
||||||
|
echo "📦 安装asyncio依赖..."
|
||||||
|
pip install asyncio
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 检查Google Cloud认证
|
||||||
|
echo "🔐 检查Google Cloud认证..."
|
||||||
|
if ! gcloud auth application-default print-access-token >/dev/null 2>&1; then
|
||||||
|
echo "⚠️ 未检测到Google Cloud认证"
|
||||||
|
echo "正在启动认证流程..."
|
||||||
|
gcloud auth application-default login
|
||||||
|
fi
|
||||||
|
|
||||||
|
# 设置环境变量
|
||||||
|
export GOOGLE_CLOUD_PROJECT="inner-radius-469712-e9"
|
||||||
|
export GOOGLE_CLOUD_REGION="us-central1"
|
||||||
|
|
||||||
|
# 启动Streamlit应用
|
||||||
|
echo "🚀 启动Web界面..."
|
||||||
|
echo "================================"
|
||||||
|
echo "📱 Web界面将在浏览器中打开"
|
||||||
|
echo "🌐 默认地址: http://localhost:8501"
|
||||||
|
echo "⏹️ 按 Ctrl+C 停止服务"
|
||||||
|
echo "================================"
|
||||||
|
|
||||||
|
# 启动streamlit
|
||||||
|
streamlit run memory_bank_web_interface.py --server.port 8501 --server.address localhost
|
||||||
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Reference in New Issue