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Agent、Task、RAG 配置\n├── data/                # 示例/待入库代码数据\n├── historyCode/         # 历史代码示例\n├── model/               # LLM 和 Embedding 工厂\n├── rag/                 # 向量库与检索逻辑\n├── tools/               # CrewAI 自定义工具\n├── utils/               # 会话存储等工具函数\n├── crew.py              # CrewAI Agent、Task、Crew 编排\n├── streamlit_app.py     # Streamlit 前端应用\n├── main.py              # FastAPI 服务入口\n├── apiTest.py           # API 调用测试脚本\n└── requirements.txt\n```\n\n## Setup\n\n建议使用 Python 3.10+。\n\n```bash\npip install -r requirements.txt\n```\n\n复制环境变量模板：\n\n```bash\ncp .env.example .env\n```\n\n在 `.env` 中配置：\n\n```env\nDEEPSEEK_API_KEY=your_deepseek_api_key\nDASHSCOPE_API_KEY=your_dashscope_api_key\n```\n\n项目默认使用 DeepSeek 作为聊天和 CrewAI Agent 模型；OpenAI 相关配置已留空，仅作为兼容选项保留。\n\n## Run Streamlit App\n\n```bash\nstreamlit run streamlit_app.py\n```\n\n打开浏览器中的 Streamlit 地址后，可以直接输入普通聊天内容，也可以输入代码任务，例如：\n\n```text\n帮我写一个批量重命名文件的 Python 脚本\n```\n\n当系统识别为代码任务时，会启动三阶段多 Agent 协作：\n\n1. 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