A barebones setup to quick start RAG app development in Python ragtify cuts through the noise, providing a minimal, pre-configured foundation for your next RAG project. By orchestrating Ollama, Qdrant, and a modern React + FastAPI stack, it eliminates boilerplate fatigue so you can focus immediately on building and experimenting with your RAG pipelines.

---

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Docker: Get Docker (Docker Desktop recommended for Mac/Windows)

⚡️ Quick Start

Get up and running in seconds.

1. Launch ragtify

Run the magic script to start all services, set up local domains, and run migrations:

./ragtify up

2. Access the Platform

Once the services are initialized, open your browser and visit:

👉 http://ragtify.local:3001

3. Explore

• Frontend (Chat & Settings): http://ragtify.local:3001
• Backend API: http://api.ragtify.local:8000
• API Documentation: http://api.ragtify.local:8000/docs

---

🛠 Command Line Interface

The included ragtify script is your control center.

Command	Description
./ragtify up	Start all services
./ragtify down	Stop all services safely
./ragtify ui	Render the React UI changes
./ragtify logs	View real-time logs for all containers
./ragtify status	Check the health of your stack
./ragtify migrate	Run database migrations manually
./ragtify build	Rebuild services from scratch

---

🏗 Architecture

ragtify is built with a modular, production-ready stack:

• Frontend: React 18 with Tailwind CSS (Dark Mode enabled! 🌙)
• Backend: FastAPI (Python) for high-performance async APIs
• AI Engine: Ollama (Local LLMs like Llama 3)
• Vector DB: Qdrant for semantic search
• Database: MySQL for structured data

📝 License

MIT