Before Google’s launch of Gemini File Search API, building a RAG based application has been a bit of a puzzle for some while hard nut to crack for many others. If you are like me, you know that dealing with RAG has been one of those things that you realize you’ve been doing something the hard way for way too long and yet you couldn’t do anything about it, right?
Last week I discovered Google’s new Gemini File Search API and it was such a relief to learn about it knowing that for months, I’d been watching developers struggle with building RAG (Retrieval-Augmented Generation) systems. The process has been complicated, expensive, and honestly, a bit of a nightmare.
Vector databases to set up. Chunking strategies to figure out. Embedding pipelines to maintain. Infrastructure costs piling up. And that’s before you even get to the actual AI part.
