Document Q&A with RAG

A document question-answering application that uses Retrieval-Augmented Generation (RAG) to provide accurate answers based on uploaded documents.

Features

• 📄 Upload PDF and TXT documents
• 💬 Ask questions about uploaded documents
• 🔍 Get AI-powered answers with relevant source passages
• 🎨 Modern and responsive UI with animations
• 🗄️ Vector similarity search for accurate retrieval
• 🚀 Real-time chat interface with source highlighting

Tech Stack

• Frontend: React, TypeScript, Tailwind CSS, Framer Motion
• Backend: Django, REST Framework
• Database: PostgreSQL with pgvector extension
• AI: OpenAI API (GPT-3.5/4 and text-embedding-ada-002)
• Containerization: Docker & Docker Compose

Prerequisites

• Docker and Docker Compose
• OpenAI API Key

Setup

1. Clone the repository:

git clone [email protected]:jorgearuv/rag-qa.git
cd rag-qa

2. Copy the example environment file:

cp .env.example .env

3. Edit the .env file and add your OpenAI API key

4. Start the application:

docker compose up --build

5. Access the application:

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

Development

Project Structure

.
├── backend/                 # Django backend
│   ├── api/                # Django application
│   │   ├── migrations/     # Database migrations
│   │   ├── __init__.py
│   │   ├── admin.py       # Admin interface
│   │   ├── apps.py        # App configuration
│   │   ├── models.py      # Database models
│   │   ├── serializers.py # API serializers
│   │   ├── tests.py       # Unit tests
│   │   ├── utils.py       # Helper functions
│   │   └── views.py       # API endpoints
│   ├── ragqa/             # Project settings
│   │   ├── __init__.py
│   │   ├── settings.py    # Django settings
│   │   ├── urls.py        # URL routing
│   │   └── wsgi.py        # WSGI config
│   ├── Dockerfile         # Backend container config
│   ├── manage.py          # Django CLI
│   ├── requirements.txt   # Python dependencies
│   └── start.sh          # Container startup script
├── frontend/              # React frontend
│   ├── src/              # Source code
│   │   ├── components/   # React components
│   │   │   ├── ChatInterface.tsx
│   │   │   └── DocumentList.tsx
│   │   ├── constants/    # Configuration
│   │   │   └── config.ts
│   │   ├── lib/         # Utilities
│   │   │   └── utils.ts
│   │   ├── App.tsx      # Main app component
│   │   ├── index.css    # Global styles
│   │   ├── main.tsx     # Entry point
│   │   └── vite-env.d.ts # TypeScript declarations
│   ├── Dockerfile        # Frontend container config
│   ├── index.html        # HTML template
│   ├── package.json      # Node.js dependencies
│   ├── postcss.config.js # PostCSS config
│   ├── tailwind.config.js # Tailwind CSS config
│   ├── tsconfig.json     # TypeScript config
│   ├── tsconfig.app.json # App TS config
│   └── tsconfig.node.json # Node TS config
├── .env.example          # Environment variables template
├── .gitignore           # Git ignore rules
├── docker-compose.yml    # Container orchestration
└── README.md            # Project documentation

• Backend code is in the backend/ directory

  • Django REST API endpoints in api/views.py
  • Database models in api/models.py
  • Document processing in api/utils.py

• Database

  • PostgreSQL with pgvector extension for similarity search
  • Automatic initialization and migrations on startup
  • Data persisted in Docker volume postgres_data

API Documentation

API documentation is available via Django REST Swagger at http://localhost:8000/api/docs/

License

MIT