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AutoRAGsystem 是一个面向开发者的本地项目分析、迭代决策与自动化打包工具。它以 RAG（Retrieval-Augmented Generation）与结构化项目分析为核心，帮助用户扫描项目、理解仓库结构、生成迭代计划，并辅助完成优化、补丁整理与交付打包。
这个项目更适合作为 AI 辅助开发工作流中的项目分析与执行中枢，而不是普通聊天机器人或单一 RAG Demo。
AutoRAGsystem 主要解决三个问题：
- 项目看不清：代码、文档、脚本、配置文件分散，人工理解成本高。
- 迭代没结构：知道要优化，但缺少清晰的扫描、分析、计划、执行流程。
- 交付难整理：修改完成后，缺少统一的打包、说明、总结与验收输出。
AutoRAGsystem 将这些流程压缩成一套更适合本地执行和 AI 协作的自动化工具链。
- 仓库结构扫描：读取项目目录、代码文件、文档与配置，形成结构化项目视图。
- RAG / 上下文增强分析：通过检索与结构化上下文，为 AI 判断提供项目级信息基础。
- 迭代计划生成：根据项目现状生成优化方向、补丁计划与执行步骤。
- 自动化打包辅助：帮助整理发布包、交付说明、运行入口与项目文档。
- 桌面快捷方式与本地运行支持：适合个人开发者在本地环境快速启动。
- 实验模块扩展：包含部分 Aether Track Engine / 结构化判断实验能力，可作为扩展方向使用。
AutoRAGsystem 适合以下使用场景：
| 场景 | 用途 |
|---|---|
| 个人项目整理 | 快速扫描项目结构，找出缺口与优化方向 |
| AI 编程辅助 | 为 Codex、Cursor、Claude、ChatGPT 等工具提供项目级上下文 |
| 开源仓库维护 | 补齐 README、运行说明、结构说明、发布说明 |
| 版本迭代规划 | 把零散修改压缩成可执行的计划 |
| 项目交付打包 | 生成更清晰的交付包、说明文档与验收总结 |
| 本地自动化工作流 | 作为轻量级项目分析和执行工具链使用 |
git clone https://github.com/xingxuling/AutoRAGsystem.git
cd AutoRAGsystempip install -r requirements.txt如果项目中存在多个启动脚本，请优先查看根目录下的快速使用指南或安装说明文件。
根据你的环境选择合适方式：
python main.py或使用项目提供的脚本入口，例如：
./start.shWindows 用户可查看 .bat 或 .ps1 启动脚本。
注意：不同版本的入口文件可能略有差异。如果你的本地文件结构与示例不同，请以仓库实际文件和快速使用指南为准。
扫描项目 → 读取上下文 → 生成分析 → 制定计划 → 执行优化 → 打包交付 → 输出总结
典型流程：
- 将目标项目放入本地工作目录。
- 使用 AutoRAGsystem 扫描项目文件与结构。
- 生成项目分析报告或优化计划。
- 根据计划进行补丁修改、文件整理或文档补全。
- 输出最终交付包、README、使用说明或总结文档。
仓库中可能包含以下类型文件：
AutoRAGsystem/
├── README.md # 项目说明
├── requirements.txt # Python 依赖
├── main.py / autorag.py # 可能的主入口
├── docs/ # 文档目录
├── scripts/ # 启动或辅助脚本
├── examples/ # 示例文件
├── output/ # 输出目录
└── *.bat / *.ps1 / *.sh # Windows / Shell 快捷启动脚本
由于该项目经历过多轮迭代，部分文档或脚本可能属于历史版本。建议后续将稳定文档放入 docs/，将旧文档移动到 docs/archive/，保持根目录简洁。
当前项目更接近：
可运行的本地自动化工具 + 项目分析实验系统 + AI 协作工作流原型。
它已经具备实用价值，但仍建议继续补强以下部分：
- 统一 CLI 入口。
- 增加更清晰的命令示例。
- 补充真实运行截图。
- 整理根目录历史文档。
- 增加测试与 CI 检查。
- 区分稳定功能与实验功能。
- 补充中英双语 README
- 整理根目录文档
- 建立
docs/与docs/archive/结构 - 明确主启动入口
- 增加 CLI 帮助信息
- 补充示例项目
- 加入基础测试
- 建立 GitHub Actions 检查
- 增加项目索引能力
- 支持更稳定的上下文检索
- 输出结构化分析报告
- 对接更多 AI 编程工具
- 自动生成交付说明
- 自动生成发布摘要
- 支持多平台启动包
- 加强桌面快捷方式体验
欢迎提交 Issue、Pull Request 或建议。你可以参与：
- 修复脚本兼容性问题。
- 补充文档与示例。
- 优化 CLI 入口。
- 增加测试用例。
- 改进项目扫描与 RAG 上下文生成能力。
基本流程：
git checkout -b feature/your-feature
# make changes
git commit -m "feat: add your feature"
git push origin feature/your-feature然后在 GitHub 上创建 Pull Request。
本项目采用 MIT License。详情请查看 LICENSE。
AutoRAGsystem is a local project analysis, iteration planning, and automation packaging toolkit for developers. It is built around RAG-style context retrieval and structured repository analysis, helping users scan projects, understand repository structure, generate iteration plans, and prepare optimized delivery packages.
This project is best understood as a project-aware automation hub for AI-assisted development workflows, not as a generic chatbot or a simple RAG demo.
AutoRAGsystem focuses on three core problems:
- Repository complexity: source code, documentation, scripts, and configuration files are often scattered and hard to understand quickly.
- Unstructured iteration: developers may know that a project needs improvement but lack a clear scan-analysis-plan-execute workflow.
- Messy delivery: after modifications, projects often lack clean packaging, documentation, release notes, and acceptance summaries.
AutoRAGsystem compresses these steps into a lightweight local automation workflow suitable for both human developers and AI coding agents.
- Repository structure scanning: reads project directories, code files, documents, and configurations to build a structured project view.
- RAG / context-enhanced analysis: provides project-level context for AI-assisted reasoning.
- Iteration plan generation: helps generate optimization directions, patch plans, and execution steps.
- Packaging support: assists with release packages, delivery notes, documentation, and project summaries.
- Local desktop workflow support: suitable for personal developers who need fast local execution.
- Experimental extensions: includes some Aether Track Engine / structured reasoning experiments as optional extensions.
| Use Case | Purpose |
|---|---|
| Personal project cleanup | Scan project structure and identify missing pieces |
| AI coding assistance | Provide repository-level context for Codex, Cursor, Claude, ChatGPT, and similar tools |
| Open-source maintenance | Improve README files, usage guides, project structure, and release notes |
| Iteration planning | Convert scattered ideas into executable plans |
| Delivery packaging | Prepare cleaner packages, summaries, and acceptance documents |
| Local automation workflows | Act as a lightweight project analysis and execution toolkit |
git clone https://github.com/xingxuling/AutoRAGsystem.git
cd AutoRAGsystempip install -r requirements.txtIf the repository contains multiple startup scripts, check the quick start or installation guide in the root directory first.
Depending on your local version, try:
python main.pyor use one of the provided startup scripts:
./start.shWindows users may check the .bat or .ps1 scripts.
Note: entry points may vary across versions. Please follow the actual files and quick start guide in the repository.
Scan project → Retrieve context → Generate analysis → Create plan → Apply improvements → Package delivery → Output summary
Typical workflow:
- Place the target project in your local workspace.
- Use AutoRAGsystem to scan the files and structure.
- Generate an analysis report or optimization plan.
- Apply patches, reorganize files, or improve documentation.
- Output the final delivery package, README, usage guide, or project summary.
The repository may contain the following types of files:
AutoRAGsystem/
├── README.md # Project description
├── requirements.txt # Python dependencies
├── main.py / autorag.py # Possible main entry points
├── docs/ # Documentation
├── scripts/ # Startup or helper scripts
├── examples/ # Example files
├── output/ # Output directory
└── *.bat / *.ps1 / *.sh # Windows / Shell startup scripts
Since this project has gone through multiple iterations, some files may belong to historical versions. A recommended cleanup step is to move stable documentation into docs/ and older files into docs/archive/.
The current project can be described as:
A runnable local automation toolkit + project analysis experiment + AI collaboration workflow prototype.
It is already useful, but the following areas should be improved next:
- Unify the CLI entry point.
- Add clearer command examples.
- Add real screenshots.
- Clean up historical documents in the root directory.
- Add tests and CI checks.
- Separate stable features from experimental modules.
- Add bilingual README
- Organize root documentation
- Create
docs/anddocs/archive/ - Clarify the main startup entry
- Add CLI help messages
- Add example projects
- Add basic tests
- Add GitHub Actions checks
- Add project indexing
- Improve context retrieval
- Output structured analysis reports
- Integrate with more AI coding tools
- Auto-generate delivery notes
- Auto-generate release summaries
- Support multi-platform startup packages
- Improve desktop shortcut experience
Issues, pull requests, and suggestions are welcome.
You can help with:
- Fixing script compatibility issues.
- Improving documentation and examples.
- Optimizing the CLI entry point.
- Adding test cases.
- Improving project scanning and RAG context generation.
Basic workflow:
git checkout -b feature/your-feature
# make changes
git commit -m "feat: add your feature"
git push origin feature/your-featureThen open a Pull Request on GitHub.
This project is licensed under the MIT License. See LICENSE for details.