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# ArabicRAG: Arabic Retrieval-Augmented Generation
### Project Structure
# arabic_legal_search/
# ├── config.py
# ├── document_processor.py
# ├── embedding_generator.py
# ├── search_engine.py
# ├── response_generator.py
# ├── utils.py
# ├── main.py
# └── requirements.txt
## Overview
ArabicRAG is an open-source project designed to leverage the power of retrieval-augmented generation for processing and understanding Arabic legal documents. The system integrates advanced NLP techniques to retrieve relevant documents and generate context-aware responses.
## Features
- **Document Processing**: Load and preprocess Arabic text documents efficiently.
- **Embedding Generation**: Utilize multilingual models to generate embeddings for Arabic text.
- **Efficient Search**: Leverage FAISS for fast and efficient similarity search in large document corpora.
- **Response Generation**: Use state-of-the-art transformer models to generate responses based on retrieved context.
## Installation
To set up your environment and run ArabicRAG, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/maljefairi/arabicRAG
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
## Usage
After installation, you can run the main script to start processing documents:
```bash
python main.py
```
## Contributing
Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
- **Dr. Mohammed Al-Jefairi** - maljefairi@sidramail.com
- **GitHub**: [maljefairi](https://github.com/maljefairi/arabicRAG)
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