Google dropped Gemini File Search a few weeks ago and of course people decided it's the new "RAG killer" 🙄 I don't buy into the "this changes everything" narrative without testing first. So I spent the last few days testing it properly with n8n… and here's what I discovered. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗲𝗺𝗶𝗻𝗶 𝗙𝗶𝗹𝗲 𝗦𝗲𝗮𝗿𝗰𝗵? It's basically RAG-as-a-Service, a way to chat with your documents using AI: → Upload your files → Google handles all the technical stuff → You ask questions in plain English → No databases to set up or manage → Free storage (for now) → Dirt cheap at $0.15 per 1M tokens for processing documents Sounds like the perfect solution, right? 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼𝗯𝗼𝗱𝘆'𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 👇 ✅ The Good: → Insanely fast to get started: No technical setup required. Upload and go → Handles messy documents: Scanned PDFs, images with text, 100+ file formats all work → Ridiculously cheap: Compared to OpenAI's $3/GB storage fees, this is a steal → Metadata filtering works well: You can organize your knowledge base by topic, date, department, etc. ❌ The Reality Check: → You still need a data pipeline: It doesn't handle duplicates or updates. Upload the same file 3 times? You'll get duplicate chunks in responses (I tested this) → Chunking is basic; I found chunks starting/ending mid-sentence. No markdown hierarchy preservation → You lose control over quality: It's a black box. If answers aren't good, you can't tweak how it processes your documents → Your data lives with Google: Privacy-sensitive industry? This might not fly with compliance 𝗦𝗼 𝘄𝗵𝗲𝗻 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝗸𝗲 𝘀𝗲𝗻𝘀𝗲? → Testing out AI chat for your company docs → Budget is tight and you need something working fast → Simple Q&A use cases (HR policies, product docs, internal wikis) → Your team doesn't have developers or IT resources 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮 𝗰𝘂𝘀𝘁𝗼𝗺 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺: → Answers need to be extremely accurate (legal, medical, financial) → Privacy regulations require self-hosted solutions → You need advanced features for better results → You want the flexibility to improve the system over time The bottom line? 👇 Gemini File Search isn't killing custom RAG systems. It's making AI document chat accessible to more people, and that's actually a good thing. But "accessible" doesn't mean "production-ready for everyone" It's perfect for getting started fast, but serious implementations will eventually need more control. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘁𝗲𝘀𝘁 𝗶𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗱𝗮𝗰𝗵𝗲𝘀? I built a complete n8n template that handles all the messy parts: ✅ Data ingestion pipeline with duplicate handling ✅ Version control for document updates ✅ Metadata enrichment for better search results 👉 Drop "Search" in the comments, like this post and I'll send you the full guide + template. 🚀 Have you tested Gemini File Search yet? What's your take? 🤔