May 8
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Google just dropped Gemini File Search 2.0 with native multimodal retrieval powered by Embedding 2. I built an interactive demo, ran it on the original "Attention Is All You Need" paper, and asked the question — did this just kill Multimodal RAG? In this video I walk through: → A live demo of the new File Search tool — 4 API calls, end to end → What Multimodal RAG actually is, and why it used to be a 6-month engineering project → How a File Search Store collapses the entire RAG infrastructure stack into one managed resource → Why Gemini Embedding 2 is the real unlock — text and images in the same vector space → Honest take: where this is genuinely a sledgehammer to the old way, and where it's still rough If you've spent the last year stitching together parsers, chunkers, embedding pipelines, vector databases, and citation logic — this video is for you. 🔗 Links Gemini API File Search docs: https://ai.google.dev/gemini-api/docs/file-search The official announcement: https://blog.google/technology/developers/gemini-api-file-search-multimodal-rag/ Multimodal RAG developer guide: https://dev.to/googleai/multimodal-rag-with-the-gemini-api-file-search-tool-a-developer-guide-5878 Try the AI Studio sample app: https://ai.studio/apps/acb0ca81-7130-43ae-a31f-bedd96d28294 📌 Disclaimer All opinions are my own and do not belong to my employer. 🔔 If you're new here — I make videos on enterprise AI, agent engineering, and the Google AI stack. Hit subscribe for more honest, hands-on takes on what's actually shipping vs what's hype. #GeminiAPI #MultimodalRAG #FileSearch #GoogleAI #RAG #GenAI #AIEngineering #LLM #Embeddings