RAG with Nuxt and Gemini File Search

Ever wonder how some apps supplement LLMs with there own private data? Heard of RAG but intimidated by it or don’t know where to start?

👉 Gemini File Search fixes that!

It’s a batteries included RAG solution that takes care of chunking, embedding creation and retrieval matching for you. And we’ll show you how to pair it with a Nuxt backend in this course in about an hour!

You’ll build a complete, practical RAG backend with Nuxt and Gemini File Search—then connect it to a working UI so you can see the full flow end to end.

In this course, you’ll learn how to:

• Create and manage Gemini File Search stores
• Upload and index documents
• Ask questions and get ground answers with the fileSearch tool
• Get doc attributions in your answers
• Start with a simple synchronous indexing flow, then evolve to background indexing for lower latency
• Track long-running indexing jobs with storage-backed status and polling endpoints
• Add document-management APIs (list + delete) for real operational control
• Connect the backend to a Nuxt UI that shows indexing progress and grounded responses
• Stream responses from the backend using the AI SDK for a more responsive UX

The course is intentionally hands-on and incremental.

We begin with fundamentals and a clean baseline implementation so each moving part is easy to reason about. Then we progressively harden the architecture—introducing background jobs, status tracking, and endpoint patterns you can reuse in your own applications.

By the end, you won’t just have a working RAG demo—you’ll have a clear mental model and a production-friendly foundation for building document-aware AI features in Nuxt.

If you’re ready to move beyond toy chatbots and build grounded AI workflows that are fast, reliable, and maintainable, this course will get you there.