{"id":25228989,"url":"https://github.com/tinbyte/rag_sql_quiz_generation","last_synced_at":"2026-04-11T06:02:12.451Z","repository":{"id":276851925,"uuid":"930514859","full_name":"TINBYTE/RAG_SQL_QUIZ_GENERATION","owner":"TINBYTE","description":"An AI-powered platform that dynamically generates and grades exam questions using Retrieval-Augmented Generation (RAG). 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By leveraging **Retrieval-Augmented Generation (RAG)**, the system retrieves relevant course content and uses advanced NLP techniques to generate questions dynamically. The project also includes features like automated grading and a user-friendly interface, ensuring a seamless experience for both educators and students.  \n\n---\n\n## **📂 Features**  \n- **Knowledge Base Construction**:  \n  Curated and structured database of course materials, including lecture notes, textbooks, and multimedia.  \n\n- **Document Retrieval \u0026 Preprocessing**:  \n  Efficient retrieval mechanisms and data preprocessing to extract relevant information.  \n\n- **Dynamic Question Generation**:  \n  AI-powered generation of multiple-choice and open-ended questions with customizable difficulty levels.  \n\n- **Automated Answer Verification \u0026 Grading**:  \n  Instant evaluation of responses with intelligent grading algorithms.  \n\n- **Interactive User Interface**:  \n  Built with **Next.js**, the platform offers a sleek, responsive interface for taking exams and receiving feedback.\n\n---\n\n## **🛠️ Tech Stack**  \n### **Frontend**  \n- **[Next.js](https://nextjs.org/)**: For creating a modern, responsive user interface.  \n\n### **Backend**  \n- **Python**: Core language for retrieval, question generation, and grading logic.  \n- **FastAPI/Flask**: For building APIs to connect the front end with backend services.  \n\n### **AI \u0026 NLP Tools**  \n- **Hugging Face Transformers**: For leveraging models like T5 and GPT for question generation.  \n- **Elasticsearch**: For efficient document retrieval.  \n- **Sentence-BERT**: For semantic similarity in answer grading.  \n\n### **Database**  \n- **PostgreSQL**: To store and manage structured data.  \n\n---\n\n## **📚 Project Structure**  \n```\nRAG-System-Exam-Generation/\n├── frontend/            # Next.js frontend code  \n├── backend/             # APIs, NLP pipelines, and grading logic  \n├── data/                # Knowledge base data (lecture notes, textbooks, etc.)  \n├── models/              # Pre-trained and fine-tuned NLP models  \n├── scripts/             # Utility scripts for data preprocessing and testing  \n└── README.md            # Project documentation  \n```\n\n---\n\n## **🌟 How It Works**  \n1. **Knowledge Base Construction**:  \n   Upload structured course materials into the system.  \n\n2. **Document Retrieval**:  \n   The RAG system retrieves relevant content using semantic search.  \n\n3. **Question Generation**:  \n   AI models generate exam questions tailored to the retrieved content.  \n\n4. **Answer Verification \u0026 Grading**:  \n   Student responses are automatically graded based on predefined algorithms.  \n\n5. **User Interaction**:  \n   Students access exams via a user-friendly interface and receive instant feedback.  \n\n---\n\n## **🤝 Team Members**  \n- **Nourdin**: Knowledge Base Construction  \n- **Nordin \u0026 Youssef**: Document Retrieval \u0026 Preprocessing  \n- **Youssef**: NLP for Question Generation  \n- **Abdelfattah Bouhlali**: Answer Verification, Grading, and User Interface  \n\n\n---\n\n## **💻 Installation \u0026 Setup**  \n1. Clone the repository:  \n   ```bash\n   git clone https://github.com/\u003cyour-username\u003e/RAG-System-Exam-Generation.git\n   cd RAG-System-Exam-Generation\n   ```  \n2. Install backend dependencies:  \n   ```bash\n   pip install -r backend/requirements.txt\n   ```  \n3. Run the backend server:  \n   ```bash\n   python backend/app.py\n   ```  \n4. Start the Next.js frontend:  \n   ```bash\n   cd frontend  \n   npm install  \n   npm run dev  \n   ```  \n5. Access the platform at `http://localhost:3000`.\n\n---\n\n## **🎯 Future Plans**  \n- Expand question types (e.g., drag-and-drop, matching).  \n- Add support for adaptive exams based on student performance.  \n- Implement multilingual support for diverse educational contexts.  \n\n---\n\n## **📜 License**  \nThis project is licensed under the MIT License. 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