📚 RAG Learning Assistant

Turn dry textbooks into interactive conversations. Ask questions, get answers with sources, and learn faster.

🎯 Why I Built This

Let's be real - reading through dense textbooks and documentation is hard. It's time-consuming, boring, and for people with ADHD or other neurodivergent conditions, it can feel impossible to stay focused.

I built this tool because learning shouldn't be a battle with text. It should be a conversation.

This is a Retrieval-Augmented Generation (RAG) application that lets you:

• Upload any PDF (textbook, manual, research paper)
• Ask questions in plain English
• Get answers with exact page references so you can verify
• Learn at your own pace, through dialogue instead of monologue

✨ Features

Feature	What It Does
📄 PDF Upload	Upload any document
💬 Natural Language Q&A	Ask questions like you're talking to a tutor
📍 Source Attribution	Every answer shows the page number it came from
🧠 RAG Architecture	Answers are grounded in your document - no hallucinations
🚀 Fast LLM	Powered by Groq's Llama 3.3 (70B) - answers in seconds

🔮 Coming Soon

• 📝 Auto-generated quizzes by chapter
• 🗣️ AI discussion partner that debates the material with you
• 📊 Learning assessment that tracks your progress
• 🎯 Flashcard generation from your uploads

🏗️ How It Works

Pipeline One: Upload & Index (Ingestion)

1. User uploads a PDF file
2. PDF processor extracts text from each page
3. Text is split into chunks (~1000 characters each)
4. Each chunk is converted into a vector (384 numbers)
5. Vectors + original text are stored in Qdrant database

Pipeline Two: Ask & Answer (Query)

1. User asks a question in plain English
2. Question is converted into a vector (same method)
3. System searches for the 3 most similar vectors in the database
4. Those chunks become the "context" for the LLM
5. LLM generates an answer using ONLY the context + question
6. Answer is returned with source references (filename + page number)

Why this matters: The LLM never makes up information. It can only answer from your document. No hallucinations. No "I think so." Just grounded answers you can trust.

🚀 Quick Start

Prerequisites

• Python 3.12+
• Groq API key (free at console.groq.com)

Installation

# Clone the repo
git clone https://github.com/yourusername/rag-learning-assistant.git
cd rag-learning-assistant/backend

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Add your API key
echo "GROQ_API_KEY=your_key_here" > .env

## 🚀 Run the Backend

```bash
uvicorn app.main:app --reload

Your API will be live at http://localhost:8000

💻 Run the Frontend

cd frontend
npm run dev

📤 Upload Your PDF

Use the upload button to select a PDF file (textbook, manual, research paper, etc.)

💬 Ask a Question

Type your question in plain English. For example:

• "What are the main topics in chapter 3?"
• "Explain how this system works"
• "Summarize the key takeaways"

The AI will respond with an answer and show you exactly which page it came from.