README_old.md

🎓 RAG Pedagogical Demo

A pedagogical web application demonstrating Retrieval Augmented Generation (RAG) systems for students and learners.

🌟 Features

• Bilingual Interface (English/French)
• Document Processing: Upload PDF documents or use default corpus
• Configurable Retrieval:
  • Choose embedding models
  • Adjust chunk size and overlap
  • Set top-k and similarity thresholds
• Configurable Generation:
  • Select different LLMs
  • Adjust temperature and max tokens
• Educational Visualization:
  • View retrieved chunks with similarity scores
  • See the exact prompt sent to the LLM
  • Understand each step of the RAG pipeline

🚀 Quick Start

Local Installation

# Clone the repository
git clone <your-repo-url>
cd RAG_pedago

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

HuggingFace Spaces

This application is designed to run on HuggingFace Spaces with ZeroGPU support.

1. Create a new Space on HuggingFace
2. Select "Gradio" as the SDK
3. Enable ZeroGPU in Space settings
4. Upload all files from this repository
5. The app will automatically deploy

📚 Usage

1. Corpus Management

• Upload your own PDF document or use the included default corpus about RAG
• Configure chunk size (100-1000 characters) and overlap (0-200 characters)
• Process the corpus to create embeddings

2. Retrieval Configuration

• Choose an embedding model:
  • all-MiniLM-L6-v2: Fast, lightweight
  • all-mpnet-base-v2: Better quality, slower
  • paraphrase-multilingual-MiniLM-L12-v2: Multilingual support
• Set top-k (1-10): Number of chunks to retrieve
• Set similarity threshold (0.0-1.0): Minimum similarity score

3. Generation Configuration

• Select a language model:
  • zephyr-7b-beta: Fast, good quality
  • Mistral-7B-Instruct-v0.2: High quality
  • Llama-2-7b-chat-hf: Alternative option
• Adjust temperature (0.0-2.0): Controls creativity
• Set max tokens (50-1000): Response length

4. Query & Results

• Enter your question
• Use example questions to get started
• View the generated answer
• Examine retrieved chunks with similarity scores
• Inspect the prompt sent to the LLM

🏗️ Architecture

┌─────────────────┐
│  PDF Document   │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Text Chunking  │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Embeddings    │◄──── Embedding Model
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  FAISS Index    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  User Query     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Retrieval     │──► Top-K Chunks
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Generation    │◄──── Language Model
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│     Answer      │
└─────────────────┘

🛠️ Technical Stack

• Framework: Gradio 4.44.0
• Embeddings: Sentence Transformers
• Vector Store: FAISS
• LLMs: HuggingFace Inference API
• GPU: HuggingFace ZeroGPU
• PDF Processing: PyPDF2

📝 Files Structure

RAG_pedago/
├── app.py                 # Main Gradio interface
├── rag_system.py         # Core RAG logic
├── i18n.py               # Internationalization
├── requirements.txt      # Python dependencies
├── default_corpus.pdf    # Default corpus about RAG
├── default_corpus.txt    # Source text for default corpus
└── README.md            # This file

🎯 Educational Goals

This application helps students understand:

1. Document Processing: How text is split into chunks
2. Embeddings: How text is converted to vectors
3. Similarity Search: How relevant information is retrieved
4. Prompt Engineering: How context is provided to LLMs
5. Generation: How LLMs produce answers based on retrieved context
6. Parameter Impact: How different settings affect results

🔧 Configuration for HuggingFace Spaces

Create a README.md in your Space with this header:

---
title: RAG Pedagogical Demo
emoji: 🎓
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
---

🤝 Contributing

Contributions are welcome! Feel free to:

• Add more embedding models
• Include additional LLMs
• Improve the interface
• Add more visualizations
• Enhance documentation

📄 License

MIT License - Feel free to use this for educational purposes.

🙏 Acknowledgments

• HuggingFace for the Spaces platform and ZeroGPU
• Sentence Transformers for embeddings
• FAISS for efficient similarity search
• Gradio for the interface framework

📧 Contact

For questions or feedback, please open an issue on GitHub.