🧠 RagApp – End-to-End Retrieval-Augmented Generation (RAG) System

RagApp is a minimal yet extensible full-stack project to build a complete Retrieval-Augmented Generation (RAG) system from scratch — covering every major component from data ingestion to LLM-based response generation and deployment.

This project is designed for hands-on learning, modular experimentation, and evolving into a production-ready architecture using best practices in backend, LLM orchestration, and modern tooling.

🔧 We will incrementally evolve this into a production-ready, full-stack RAG system.

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✅ What to Expect

• 🔎 Clean, modular architecture with retriever-generator separation
• 🚀 FastAPI backend (async-first, OpenAPI support)
• 🧠 Support for OpenAI and local LLMs (via API)
• 📦 Easy setup using uv and Python 3.12+
• 🧪 Focus on fast prototyping, inference efficiency, and open-source extensibility
• 🔁 Full development cycle: dev → test → deploy

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📦 Project Setup

1. Prerequisites

• Python ≥ 3.12
• uv package manager
  (Install with pip install uv if not already installed)

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2. Clone the Repository

git clone https://github.com/silvaxxx1/RagApp.git
cd RagApp

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3. Install Dependencies

uv init
uv add -r requirements.txt

This sets up a virtual environment (.venv) and installs all required packages.

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4. Set Up Environment Variables

Copy the example config:

cp uv.example .env

Edit .env to add your OpenAI key and other configs:

APP_NAME="RagApp"
APP_VERSION="0.1"
OPENAI_API_KEY="your-openai-key-here"

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5. Run the Backend Server

uvicorn app.main:app --reload

Open your browser to: http://localhost:8000/docs ✅ Swagger UI will show all available endpoints.

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📌 Work in Progress

This repository is under active development. Upcoming features include:

• 📄 Document chunking and embedding
• 🔍 Vector DB integration (FAISS/Chroma)
• 🧠 Prompting and LLM generation modules
• ⚙️ RAG pipeline orchestration
• 🖥️ Optional frontend (React/Tailwind or minimal HTML)
• 🚀 Dockerized and cloud deployment setup

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🗺️ Roadmap

• Init project with uv and FastAPI
• Embedding pipeline with OpenAI/Transformers
• Vector search integration
• Generation + post-processing logic
• Full RAG chain API
• CI/CD & Docker deployment

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🤝 Contributing

Feel free to fork, clone, and build along! PRs and ideas are welcome. Let’s create a clean, reusable, and battle-tested open RAG template for all.

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📄 License

This project is licensed under the MIT License. See LICENSE for details.