LexAI
AI-Powered Legal Research Assistant (Proof of Concept)
LexAI is a proof-of-concept AI-powered legal research assistant that helps users access location-specific legal information through a simple web interface. By combining large language models with structured jurisdictional data, LexAI delivers clear, tailored answers to everyday legal questions.
Features
- GPT-4 Integration: Uses OpenAI's GPT-4 to generate concise, relevant legal responses.
- Jurisdiction-Specific Search: Preloaded embeddings for Boulder County and Denver, Colorado.
- Semantic Search Engine: Uses cosine similarity for embedding-based document retrieval.
- Modern Web Interface: Built with Gradio for real-time interaction.
- Modular Design: Clean separation of logic for UI, inference, and API handling.
- Fully Tested: Unit tests cover embedding loading, matching logic, and OpenAI API integration.
- Developer Ready: Comes with dev tools like Ruff, Black, pytest, mypy, and test coverage support.
AI-Powered Legal Research Assistant
LexAI is an AI-powered legal research assistant that helps users access location-specific legal information through a simple web interface. By combining large language models with structured jurisdictional data, LexAI delivers clear, tailored answers to everyday legal questions.
Features
- GPT-4 Integration: Uses OpenAI's GPT-4 to generate concise, relevant legal responses.
- Jurisdiction-Specific Search: Preloaded embeddings for Boulder County and Denver, Colorado.
- Semantic Search Engine: Uses cosine similarity for embedding-based document retrieval.
- Modern Web Interface: Built with Gradio for real-time interaction.
- Modular Design: Clean separation of logic for UI, inference, and API handling.
- Fully Tested: Unit tests cover embedding loading, matching logic, and OpenAI API integration.
- Developer Friendly: Includes dev tools like Ruff, Black, pytest, mypy, and coverage reports.
Getting Started
1. Clone the Repository
git clone https://github.com/alexulanch/lexai.git
cd lexai
2. Install Git LFS (if needed)
This project uses Git LFS to manage large files such as .npz embedding data.
git lfs install
git lfs pull
3. Install Dependencies
pip install -r requirements.txt
# For development tools:
pip install -r dev-requirements.txt
4. Set Up Environment
Copy the template and add your API key:
cp .env.example .env
Then edit .env and set:
OPENAI_API_KEY=your_openai_api_key
Running the App
python -m lexai
Then open http://127.0.0.1:7860 in your browser.
Project Structure
.
├── LICENSE
├── README.md
├── assets/
│ └── screenshot.png
├── dev-requirements.txt
├── .env.example
├── lexai/
│ ├── __main__.py
│ ├── config.py
│ ├── core/
│ │ ├── data_loader.py
│ │ ├── match_engine.py
│ │ └── matcher.py
│ ├── data/
│ │ ├── boulder_embeddings.npz
│ │ └── denver_embeddings.npz
│ ├── services/
│ │ ├── lexai_service.py
│ │ └── openai_client.py
│ └── ui/
│ ├── formatters.py
│ └── gradio_interface.py
├── pyproject.toml
├── pytest.ini
├── requirements.txt
└── tests/
├── test_data_loader.py
├── test_matcher.py
└── test_openai_client.py
Testing
To run tests:
pytest
To check code coverage:
pytest --cov=lexai --cov-report=term
To lint:
ruff check .
To format code:
black .
License
MIT License
Acknowledgements