Learn RAG - Interactive RAG Learning Platform

An interactive application to learn and practice Retrieval-Augmented Generation (RAG) concepts with hands-on experience.

Features

• 📚 Learn: Visual overview of the RAG pipeline
• 📤 Ingest: Upload documents with configurable chunking strategies
• 💬 Query: Ask questions and see the RAG pipeline in action
• 🗄️ Manage: View and delete documents from the vector database

Tech Stack

Backend

• FastAPI - Modern Python web framework
• LangChain - LLM application framework
• LanceDB - Vector database for embeddings
• OpenAI - Embeddings and language model

Frontend

• React - UI library
• Framer Motion - Animations
• Lucide React - Icons

Quick Start

Prerequisites

• Python 3.10+
• Node.js 18+
• uv (Python package manager)
• OpenAI API key

Backend Setup

cd backend

# Create .env file with your OpenAI API key
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

# Install dependencies with uv
uv sync

# Run the server
```bash
uv run uvicorn app.main:app

Frontend Setup

cd frontend

# Install dependencies
npm install

# Start development server
npm start

The frontend will be available at http://localhost:3000 and will proxy API requests to the backend at http://localhost:8000.

API Endpoints

Documents

• POST /api/documents/upload - Upload and process a document
• GET /api/documents - List all documents
• DELETE /api/documents/{document_id} - Delete a specific document
• DELETE /api/documents - Delete all documents

Query

• POST /api/query - Query the RAG system

Learning

• GET /api/chunking-strategies - Get available chunking strategies
• GET /api/pipeline-overview - Get RAG pipeline overview

Chunking Strategies

1. Fixed Size - Split text into fixed character chunks
2. Sentence-Based - Split at sentence boundaries
3. Paragraph-Based - Split at paragraph boundaries
4. Semantic (Token) - Token-based splitting optimized for LLMs

License

MIT