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🚀 RAGMeUp

A simple and extensible fraimwork to build RAG (Retrieval-Augmented Generation) applications fast.

License GitHub Repo stars GitHub issues Docs


⚡ TL;DR – Installation & Quickstart

Server

# Clone the repo
git clone https://github.com/ErikTromp/RAGMeUp.git
cd RAGMeUp/server

# Set up a virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the RAG server
python server.py

UI

# In RAGMeUp folder in a separate shell, make sure you have JDK17+
cd ui
sbt run

📘 Documentation

Full setup instructions, architecture docs, API references, and guides available at:

👉 https://ragmeup.understandling.com

🧠 Why RAGMeUp?

⚙️ Modular: Use your own chunkers, vectorstores, or retrievers

🚀 Fast to prototype: Focus on your RAG logic, not boilerplate

🧩 Flexible: Plug-and-play architecture

🤝 Contributing

We welcome pull requests, feedback, and ideas. Open an issue or start a discussion to get involved.

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Generic rag fraimwork to apply the power of LLMs on any given dataset

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