The Problem
You want AI to answer questions about your documents - company policies, product specs, research papers, whatever. But building a custom AI chatbot seems overwhelming, and enterprise subscriptions cost hundreds per month.
The Solution
Google's Gemini File Search API lets you upload documents and query them with AI. I built this entire demo in two hours with about 50 lines of code. No complex setup. No expensive subscriptions. It just works.
What I Built
A working demo that searches across 12 complete classic novels (~7MB of full text) and answers questions grounded in the source material. The entire backend is about 50 lines of code.
- Upload your documents → Google handles the rest
- Ask questions in plain English → Get answers with sources
- No vector database → No infrastructure to manage
- No AI expertise required → Just basic programming
The Cost
Compare that to enterprise chatbot platforms charging $200-500/month, or spending weeks building a custom RAG pipeline with vector databases and embedding models.
You Don't Need
Vector databases (Pinecone, Weaviate, etc.)RAG frameworks (LangChain, LlamaIndex)Embedding model selection and tuningChunking strategy optimizationExpensive enterprise subscriptions
The Bottom Line
If you want AI-powered search over your documents, this is the easy path. Upload your files to Google's File Search API, write a few lines of code, and you're done. Save the complex setups for when you actually need fine-grained control.