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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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