# 🎓 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

```bash
# 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:

```yaml
---
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.