CONTRIBUTING.md

Contributing to LexRAG

Thank you for your interest in LexRAG! We welcome contributions from the legal-tech and AI community.

Code of Conduct

All contributors must abide by our Code of Conduct.

Getting Started

1. Fork the repository
2. Clone your fork: git clone https://github.com/eulogik/LexRAG.git
3. Create a virtual environment: python3 -m venv venv && source venv/bin/activate
4. Install dependencies: pip install -r requirements.txt
5. Install dev dependencies: pip install pytest pytest-cov
6. Run tests: pytest

Development Workflow

• Create a feature branch: git checkout -b feat/my-feature
• Write tests for your changes
• Ensure all tests pass: pytest
• Run linting: ruff check . (if available)
• Commit with conventional commits: feat:, fix:, docs:, refactor:, etc.
• Push and open a Pull Request

Pull Request Guidelines

• Link to any relevant issues
• Include test coverage for new functionality
• Update documentation (README, inline docs) as needed
• Ensure the PR passes CI checks

Project Structure

LexRAG/
├── api/          # FastAPI server, RAG engine, memory, utils
├── embeddings/   # Vector embedding models (fastembed + Qdrant)
├── scrapers/     # Legal document scrapers (UAE, India)
├── scripts/      # Ingestion, updates, batch processing
├── ui/           # Terminal-style SPA frontend
├── tests/        # Test suite
└── data/         # SQLite DB, raw/processed documents

Adding a New Scraper

1. Create scrapers/<jurisdiction>_scraper.py
2. Implement functions that call scripts.ingest.ingest_text()
3. Add the scraper to scripts/daily_update.py
4. Write tests for the scraper

Adding a New LLM Provider

1. Add a streaming function in api/rag_engine.py (e.g., stream_anthropic())
2. Register in stream_provider() dispatcher
3. Add the provider to MODEL_CATALOG in api/main.py
4. Add the provider label in ui/app.js
5. Document the required environment variable

Questions?

Open an issue at https://github.com/eulogik/LexRAG/issues Or reach out to engineering@eulogik.com