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š Get access to our Gemini File Search n8n workflows + advanced RAG blueprints in our community https://www.theaiautomators.com/?utm_source=youtube&utm_medium=video&utm_campaign=tutorial&utm_content=gemini-file-search Lots of people are calling Gemini File Search a "game-changer" that will "kill RAG." But after two days of production testing and n8n integration, I've uncovered 5 key issues that nobody's talking about. In this deep-dive, I'll show you exactly what works, what doesn't, and where Gemini File Search actually fits in the RAG landscape. šÆ What You'll Learn: ā How Gemini File Search actually works (ingestion, chunking, embeddings, retrieval) ā The 5 critical limitations hitting production RAG systems ā Why you still need data pipelines (duplicate handling, record management) ā Metadata extraction challenges and workarounds ā Real pricing comparison: Gemini vs OpenAI file search ā Three different n8n integration approaches with pros/cons ā Vendor lock-in considerations and data privacy implications ā Complete production ingestion + inference workflows š Useful Links: Context Expansion & Document Hierarchy: https://www.youtube.com/watch?v=y72TrpffdSk Gemini File Search: https://blog.google/technology/developers/file-search-gemini-api/ Gemini File Search Docs: https://ai.google.dev/gemini-api/docs/file-search ā±ļø Timestamps: 00:00 - What is Gemini File Search? 03:04 - #1 You Still Need Data Pipelines 10:07 - #2 Mid-Range Black Box RAG 11:25 - #3 No Markdown & Basic Chunking 13:38 - #4 Metadata Challenges 19:09 - #5 Vendor Lock-In & Data Privacy 20:02 - The Verdict š¬ Questions or Comments? Are you considering Gemini File Search for your RAG systems? What's your biggest concern about managed RAG solutions? Drop your thoughts below!