Show HN: I built a RAG APIs that works like Stripe Checkout

I'm building LiquidIndex, an RAG API service that makes it easy to add personalized context to AI apps. Instead of building a whole system to upload, process, and search through a user’s files or notes, you just call a few APIs. It handles the hard parts - like connecting to Notion or Google Drive, breaking the data into usable pieces, and making it searchable. Then, when you want to answer a question using that data, you just query it, and it gives you the most relevant results - optionally with an LLM-generated answer. No pipelines, no infrastructure, no headaches. Here are the core APIs: 1. Create a customer (This is a space to put data) 2. Create an upload session (This is where users upload their data) 3. Query Current connectors: File Upload, Google Drive, Notion, Dropbox Supported File Types: PDF, text files, Markdown, CSVs, and XLSX (these include google docs and sheets) Website: https://liquidindex.dev/ https://liquidindex.dev/ Check out the playground to get a feel of how it works!