README.md

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

# 🎓 RAG Pedagogical Demo

An interactive educational application to learn about Retrieval Augmented Generation (RAG) systems.

## What is RAG?

Retrieval Augmented Generation (RAG) combines information retrieval with language generation to create more accurate and grounded AI responses. Instead of relying solely on a language model's training data, RAG systems:

1. **Retrieve** relevant information from a document corpus
2. **Augment** the query with this retrieved context
3. **Generate** an answer based on both the query and the retrieved information

## Features

- 📚 **Upload your own PDFs** or use the default corpus
- 🔧 **Configure retrieval parameters**: embedding models, chunk size, top-k, similarity threshold
- 🤖 **Configure generation parameters**: LLM selection, temperature, max tokens
- 📊 **Visualize the process**: see retrieved chunks, similarity scores, and prompts
- 🌍 **Bilingual interface**: English and French

## How to Use

1. **Corpus Tab**: Upload a PDF or use the default corpus about RAG
2. **Retrieval Tab**: Choose embedding model and retrieval parameters
3. **Generation Tab**: Select language model and generation settings
4. **Query Tab**: Ask questions and see how RAG works!

## Educational Value

This demo helps you understand:
- How documents are processed and chunked
- How semantic search retrieves relevant information
- How context is provided to language models
- How different parameters affect the results

Perfect for students, educators, and anyone curious about modern AI systems!

## Technology

- **Framework**: Gradio
- **Embeddings**: Sentence Transformers
- **Vector Store**: FAISS
- **LLMs**: HuggingFace Inference API
- **Infrastructure**: HuggingFace ZeroGPU

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*Note: This application runs on ZeroGPU. Initial requests may take longer as models are loaded.*