Ragie - Fully managed RAG-as-a-Service for developers

Hi Product Hunt! 😸 We're excited to officially launch Ragie on Product Hunt right on the heels of announcing our $5.5M seed round! So... what is Ragie? ✅ fully managed RAG-as-a-Service for developers 🤖 easy to use APIs and SDKs get you started in minutes 🔁 connectors that keep your application synchronized with data sources like Google Drive, Notion, Confluence and more Ragie enables developers to build AI applications connected to their own data in record time. At our core, we are a data ingest pipeline that extracts, chunks and indexes your data when you send us documents. You can send documents via our API or you can simply connect your application using one of our many connectors. When you are ready to use your chunks for generation, Ragie has a simple retrieval API. Our retrieval API uses re-ranking to make sure that you always get the best chunks and avoid hallucinations in your application. We're also working on hybrid search which should be released next week. We think one of the benefits of using Ragie is the fact that your application automatically improves as Ragie improves. With Ragie, you can focus on building your application instead of a RAG pipeline. I'll be in the comments all day and would love to hear what you think!

Hi Product Hunt! We are super excited to launch Ragie on Product Hunt. Ragie can handle a wide variety of data types during ingest including PDFs and PowerPoints which go through a pipeline of extraction steps including OCR and LLM based ones so we can get the most information out of the data. Each data type - text, json, images, tables go through their own chunking strategy before they are embedded and indexed in a vector db. We build multiple semantic and keyword indexes out of the box which powers our powerful retrieval API which seamlessly picks the best combination of semantic, hierarchical and hybrid search indexes for the most accurate and relevant results. With Ragie, you can rest assured that our combination of extraction, chunking and indexing will always give you the best retrieval results out of the box without you having to worry about how to build and maintain a RAG pipeline. If you have any questions for us, please let us know.

Congrats on the launch @foobarfighter and @mirafiq 🙌 Q: What's been the most challenging part of building Ragie up until this point?

@foobarfighter and team have built the best "RAG-as-a-service" product. One of the best engineering teams we have ever worked with. Maybe more surprising to hear... As a venture fund, we use Ragie internally at Chapter One for custom search engine that connects our Google Suite & other internal data to LLMs. So we are 2x believers... we invested and our developers actually use & love the product!

Congratulations on the launch of Ragie, @foobarfighter! 🎉 It's amazing to see how you've streamlined RAG-as-a-Service for developers. The features you’ve highlighted, like the easy-to-use APIs and instant connectivity to platforms like Google Drive and Notion, are game-changers for anyone looking to integrate AI into their applications quickly. Your emphasis on automated improvements in the application as Ragie evolves is a huge plus for developers focused on innovation without getting bogged down by the underlying complexities. The re-ranking feature for the retrieval API sounds promising; it could significantly enhance the user experience by minimizing hallucinations. I'm excited to see how Ragie will empower developers to create next-gen AI applications. Looking forward to seeing what the community thinks and what’s next on your roadmap. Best of luck with the growth post-launch!

Congrats on the launch @foobarfighter! 🎉 I’m curious about the retrieval API—how does it handle different data formats? Looking forward to the hybrid search feature too!

The way it handles data ingestion and retrieval is impressiv. Can’t wait to see how the hybrid search feature rolls out next week!

Hey there, Ragie looks super cool! Quick question - do I need to be familiar with data pipelines to get started? I’m a product manager and while I love AI, my coding skills are pretty basic. Would love to know if that might be an issue. thanks!

Very interesting, RAG improves the performance of generative AI and this makes it very easy to integrate RAG with LLM models. Does Ragie handle data tables like let's say sales data or financial data which will be stored as csv files or in SQL databases?

Ragie is unbelievable! I’ve started to use it for all my retrieval needs in production. And it has an A+ developer experience. Really, really excited for more people to start building with it - it’s a gem.