README.md

metadata

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.