[ What can you do ] 1. Load your documents (PDFs, CSV, PPTs, Word Docs, etc) and make them searchable instantly. All your data stays private and encrypted.

2. Choose latest open source LLM (Llama 4, Deepseek, GPT-oss, etc) to interact with your docs

3. Access your hosted RAG via API - build your own custom front end or integrate with your existing product / service.

[ Why we built it ] 1. I love Notebook LM, but it has limitations: - you stuck with using Gemini models - no API - I wanted to integrate my notebook with my other apps but couldn't

2. Prior to this my teammate Brian was Head of AI at a public software company, teams across the organization constantly asked him to build various RAG apps for internal use (searching marketing docs, pulling financial information, etc)

But these projects weren’t a company priority, required lots of resources, therefore moved slowly through approvals... and were scrapped at the end.

This is why we decided to build RAG-in-a-Box to make it easy for anyone build their own RAG app.

[ How does it work ] 1. Create a Data Collection (“folder”) this will become your mini RAG application

2. Dump your data

3. Click Launch - your RAG is ready to Go

4. Use Joinable dashboard to start interacting with your RAG app or

5. Connect to your RAG App via API - add data, delete docs, generate markdown responses, pick LLM models, etc all available through API

Optional - embed your RAG App into your existing application or build custom front end or even a mobile app

Here are some quick tutorials to get started: https://joinable.gitbook.io/joinable-api-docs

We’ve just launched and would love you to try it and hear your feedback!