README.md

metadata

title: Study RAG Assistant
emoji: 📚
colorFrom: blue
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: false

📚 Study RAG Assistant (Functional RAG)

This Hugging Face Space runs a fully functional Retrieval-Augmented Generation (RAG) system built with:

• Gradio (UI)
• FAISS (vector search)
• Sentence Transformers (embeddings)
• Transformers (LLM generation)
• PDF + Jupyter Notebook parsing

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🚀 What It Does

You can:

• Upload up to 10 .ipynb notebooks
• Upload up to 5 .pdf files
• Index them into vector embeddings
• Chat grounded strictly in your documents
• Generate structured Notes
• Generate tricky Quiz questions (10–50)
• See citations from retrieved chunks

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⚙️ How It Works

1. Files are parsed and chunked
2. Chunks are embedded using all-MiniLM-L6-v2
3. FAISS stores embeddings for fast retrieval
4. Top-k chunks are retrieved per query
5. The LLM generates grounded responses

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📁 Required Files

This Space must contain: