chore: 清理重构后的旧架构残留文件
移除已废弃的 agent 定义、命令配置和脚本文件，这些文件在之前的多源数据入库架构重构后已不再使用。 Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Claude
@@ -1,181 +0,0 @@
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const http = require('http');
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const markdownContent = `# AutoGen
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## 🎯 项目简介
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**AutoGen** 是一个由微软开发的**多智能体 AI 应用程序框架**，可以创建能够自主工作或与人类协作的智能体。
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### ✨ 核心特性
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- **核心 API**：实现消息传递、事件驱动智能体以及本地和分布式运行时
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- **AgentChat API**：提供更简单但更具主见的 API，用于快速原型设计
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- **扩展 API**：支持 LLM 客户端的特定实现（如 OpenAI、Azure OpenAI）
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- **AutoGen Studio**：用于构建多智能体应用程序的无代码 GUI
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- **AutoGen Bench**：用于评估智能体性能的基准测试套件
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## 📦 安装方式
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\`\`\`bash
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# 使用 pip 安装
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pip install -U "autogen-agentchat" "autogen-ext[openai]"
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# 安装 AutoGen Studio
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pip install -U "autogenstudio"
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\`\`\`
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> 💡 **提示**：AutoGen 需要 **Python 3.10 或更高版本**
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## 🚀 快速开始
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### Hello World 示例
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\`\`\`python
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import asyncio
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from autogen_agentchat.agents import AssistantAgent
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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async def main() -> None:
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model_client = OpenAIChatCompletionClient(model="gpt-4o")
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agent = AssistantAgent("assistant", model_client=model_client)
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print(await agent.run(task="Say 'Hello World!'"))
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await model_client.close()
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asyncio.run(main())
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\`\`\`
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## 📊 功能对比
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| 特性 | AutoGen | LangChain | CrewAI |
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|------|---------|-----------|--------|
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| 多智能体协作 | ✅ | ✅ | ✅ |
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| 无代码 GUI | ✅ | ❌ | ❌ |
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| 分布式运行时 | ✅ | ❌ | ❌ |
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| .NET 支持 | ✅ | ❌ | ❌ |
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| 基准测试套件 | ✅ | ❌ | ❌ |
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## 🔧 高级用法
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### 多智能体编排
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使用 \`AgentTool\` 创建基本的多智能体编排设置：
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\`\`\`python
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import asyncio
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.tools import AgentTool
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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async def main() -> None:
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model_client = OpenAIChatCompletionClient(model="gpt-4o")
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# 创建数学专家智能体
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math_agent = AssistantAgent(
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"math_expert",
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model_client=model_client,
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system_message="You are a math expert.",
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description="A math expert assistant.",
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)
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# 创建化学专家智能体
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chemistry_agent = AssistantAgent(
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"chemistry_expert",
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model_client=model_client,
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system_message="You are a chemistry expert.",
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description="A chemistry expert assistant.",
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)
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print("智能体创建成功！")
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\`\`\`
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## 📚 任务清单
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- [x] 安装 AutoGen
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- [ ] 创建第一个智能体
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- [ ] 配置 OpenAI API
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- [ ] 运行多智能体对话
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- [ ] 部署到生产环境
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## 🎓 学习资源
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1. [官方文档](https://microsoft.github.io/autogen/)
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2. [GitHub 仓库](https://github.com/microsoft/autogen)
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3. [API 参考](https://microsoft.github.io/autogen/docs/reference)
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4. [示例代码](https://github.com/microsoft/autogen/tree/main/samples)
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## 💬 常见问题
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### Q: AutoGen 是免费的吗？
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**A**: 是的！AutoGen 使用 MIT 许可证，完全开源免费。
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### Q: 支持哪些 LLM 提供商？
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**A**: AutoGen 支持 OpenAI、Azure OpenAI，以及通过扩展 API 支持其他提供商。
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---
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## 📄 许可证
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MIT License - 详见 [LICENSE](https://github.com/microsoft/autogen/blob/main/LICENSE) 文件
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**Made with ❤️ by Microsoft**
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`;
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const data = JSON.stringify({
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apiKey: 'sk_live_agent_park_webhook_key_2025',
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projects: [{
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name: 'AutoGen',
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nameEn: 'AutoGen',
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description: 'Microsoft 开发的多智能体 AI 应用程序框架，支持自主或与人类协作的智能体',
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descriptionEn: 'A programming framework for creating multi-agent AI applications that can act autonomously or work alongside humans',
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content: markdownContent,
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contentEn: markdownContent, // 使用相同内容用于测试
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status: 'ACTIVE',
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source: 'GitHub',
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tags: [
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{ name: '多智能体', nameEn: 'Multi-Agent' },
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{ name: '框架', nameEn: 'Framework' },
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{ name: '微软', nameEn: 'Microsoft' },
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{ name: 'Python', nameEn: 'Python' },
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{ name: 'AI', nameEn: 'AI' },
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{ name: 'LLM', nameEn: 'LLM' }
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],
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links: [
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{ type: 'GITHUB', url: 'https://github.com/microsoft/autogen', title: 'GitHub 仓库' },
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{ type: 'WEBSITE', url: 'https://microsoft.github.io/autogen/', title: '官方文档' },
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{ type: 'WEBSITE', url: 'https://pypi.org/project/autogen-agentchat/', title: 'PyPI 包' }
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]
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}]
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});
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const options = {
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hostname: '127.0.0.1',
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port: 3001,
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path: '/api/webhook/projects',
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Content-Length': Buffer.byteLength(data)
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}
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};
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const req = http.request(options, (res) => {
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let responseData = '';
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res.on('data', (chunk) => {
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responseData += chunk;
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});
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res.on('end', () => {
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console.log('Status:', res.statusCode);
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console.log('Response:', responseData);
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});
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});
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req.on('error', (error) => {
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console.error('Error:', error.message);
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});
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req.write(data);
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req.end();
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@@ -1,59 +0,0 @@
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const http = require('http');
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const data = JSON.stringify({
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apiKey: 'sk_live_agent_park_webhook_key_2025',
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projects: [{
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name: 'AutoGen',
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nameEn: 'AutoGen',
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description: 'Microsoft 开发的多智能体 AI 应用程序框架，支持自主或与人类协作的智能体',
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descriptionEn: 'A programming framework for creating multi-agent AI applications that can act autonomously or work alongside humans',
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content: 'AutoGen 是一个由微软开发的创建多智能体 AI 应用程序的框架。主要特性：\n\n1. 核心 API：实现消息传递、事件驱动智能体以及本地和分布式运行时\n2. AgentChat API：提供更简单但更具主见的 API，用于快速原型设计\n3. 扩展 API：支持 LLM 客户端的特定实现（如 OpenAI、Azure OpenAI）\n4. AutoGen Studio：用于构建多智能体应用程序的无代码 GUI\n5. AutoGen Bench：用于评估智能体性能的基准测试套件\n\n支持 Python 3.10+ 和 .NET，使用 MIT 许可证。',
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contentEn: 'AutoGen is a framework for creating multi-agent AI applications. Key features: Core API, AgentChat API, Extensions API, AutoGen Studio, AutoGen Bench. Supports Python 3.10+ and .NET. MIT licensed.',
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status: 'ACTIVE',
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source: 'GitHub',
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tags: [
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{ name: '多智能体', nameEn: 'Multi-Agent' },
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{ name: '框架', nameEn: 'Framework' },
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{ name: '微软', nameEn: 'Microsoft' },
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{ name: 'Python', nameEn: 'Python' },
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{ name: 'AI', nameEn: 'AI' },
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{ name: 'LLM', nameEn: 'LLM' }
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],
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links: [
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{ type: 'GITHUB', url: 'https://github.com/microsoft/autogen', title: 'GitHub 仓库' },
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{ type: 'WEBSITE', url: 'https://microsoft.github.io/autogen/', title: '官方文档' },
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{ type: 'WEBSITE', url: 'https://pypi.org/project/autogen-agentchat/', title: 'PyPI 包' }
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]
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}]
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});
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const options = {
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hostname: '127.0.0.1',
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port: 3001,
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path: '/api/webhook/projects',
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Content-Length': Buffer.byteLength(data)
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}
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};
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const req = http.request(options, (res) => {
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let responseData = '';
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res.on('data', (chunk) => {
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responseData += chunk;
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});
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res.on('end', () => {
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console.log('Status:', res.statusCode);
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console.log('Response:', responseData);
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});
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});
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req.on('error', (error) => {
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console.error('Error:', error.message);
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});
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req.write(data);
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req.end();
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@@ -1,113 +0,0 @@
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// Simple Hugging Face scraper
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const https = require('https');
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const { HttpsProxyAgent } = require('https-proxy-agent');
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async function scrapeHuggingFace() {
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try {
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console.log('Fetching Hugging Face models page...');
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const proxyUrl = process.env.HTTPS_PROXY || process.env.HTTP_PROXY;
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const agent = proxyUrl ? new HttpsProxyAgent(proxyUrl) : undefined;
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const html = await new Promise((resolve, reject) => {
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const options = {
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headers: {
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
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'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
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'Accept-Language': 'en-US,en;q=0.9',
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}
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};
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if (agent) {
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options.agent = agent;
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}
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const req = https.get('https://huggingface.co/models', options, (res) => {
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let data = '';
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res.on('data', chunk => data += chunk);
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res.on('end', () => {
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if (res.statusCode === 200) {
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resolve(data);
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} else {
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reject(new Error(`HTTP ${res.statusCode}: ${res.statusMessage}`));
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}
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});
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});
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req.on('error', reject);
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req.setTimeout(30000, () => {
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req.destroy();
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reject(new Error('Request timeout'));
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});
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});
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console.log(`Page fetched successfully, size: ${html.length} bytes`);
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// Extract model information
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const models = [];
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const modelLinkRegex = /<a\s+href="\/models\/([^"]+)"[^>]*>/gi;
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const seenModels = new Set();
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let match;
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while ((match = modelLinkRegex.exec(html)) !== null) {
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const modelId = decodeURIComponent(match[1]);
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// Filter: must have org/model format
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if (!modelId.includes('/')) continue;
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if (modelId.includes('/discussions')) continue;
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if (modelId.includes('/blob')) continue;
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if (modelId.includes('/tree')) continue;
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if (modelId.includes('/commit')) continue;
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if (seenModels.has(modelId)) continue;
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seenModels.add(modelId);
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models.push({
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source: 'huggingface',
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name: modelId,
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url: `https://huggingface.co/${modelId}`,
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description: modelId,
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metadata: {
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likes: 0,
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downloads: 0,
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pipeline: ''
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}
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});
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if (models.length >= 100) break;
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}
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console.log(`Extracted ${models.length} unique models`);
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// Take first 25 (they should be roughly ordered by popularity on the page)
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const topModels = models.slice(0, 25);
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const fs = require('fs');
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const outputPath = 'D:\\Code\\AI\\agent-park-v2\\.trending-workspace\\20260106-1226\\scraped-huggingface-projects.json';
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fs.writeFileSync(outputPath, JSON.stringify(topModels, null, 2), 'utf8');
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console.log(`\nData saved to: ${outputPath}`);
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console.log('\nTop 25 Models:');
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topModels.forEach((m, i) => {
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console.log(`${i + 1}. ${m.name}`);
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});
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} catch (error) {
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console.error('Error:', error.message);
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// Output empty result on error
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const fs = require('fs');
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const outputPath = 'D:\\Code\\AI\\agent-park-v2\\.trending-workspace\\20260106-1226\\scraped-huggingface-projects.json';
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const errorResult = {
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error: error.message,
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projects: [],
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timestamp: new Date().toISOString()
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};
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fs.writeFileSync(outputPath, JSON.stringify(errorResult, null, 2), 'utf8');
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console.log(`Error result saved to: ${outputPath}`);
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}
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}
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scrapeHuggingFace();
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