Hey HN,<p>About a month ago, I was lucky enough to participate in the Falcon LLM Hackathon, a crazy 5-day sprint to build something innovative with a cutting-edge language model. My team and I had one goal in mind: to make it easy for anyone to build conversational AI apps using their own data.<p>After countless late nights, a lot of debugging, and a few coffee-fueled “aha” moments, we built the first version of RAGGENIE. To our surprise, we won first prize! That win gave us the confidence to take RAGGENIE to the next level – an open-source low-code platform for building retrieval-augmented generation (RAG) applications.<p>Why RAGGENIE?<p>During the hackathon, we noticed how hard it was to integrate large language models with custom data sources, like databases or documents. Most existing tools required too much manual setup or technical expertise, making it hard for smaller teams to experiment with RAG tech. So we set out to simplify that process.<p>RAGGENIE enables teams to quickly spin up custom Copilets (our take on bots) that can chat with your data – without the heavy lifting. It’s perfect for small businesses or teams who want to get into GenAI without dealing with complex pipelines.<p>The Open-Source Decision<p>We’ve always believed in the power of open-source software. After the hackathon, we decided that RAGGENIE should be available to everyone, especially smaller teams who need flexibility and transparency. By making it open-source, we’re hoping to build a community of developers and businesses who can push this technology forward.<p>What You Can Do with RAGGENIE:<p>• Create low-code conversational AI apps that work with your own data (databases, documents, etc.)
• Easily deploy retrieval-augmented generation tools without heavy technical setup
• Customize it to fit your specific use case, whether it’s internal tools or customer-facing bots<p>The Road Ahead<p>We’re just getting started, but I’d love to hear from you! If you’re interested in the tech, have feedback, or want to contribute, check out our repo: github.com/sirocco-ventures/raggenie.<p>I’m also happy to chat about our experience at the hackathon, the challenges we faced, or how you can use RAGGENIE for your own projects. Feel free to ask anything!