Building RAG agents usually means wrestling with vector databases and expensive embeddings. 🤯 Google just changed the game. We're revealing how to use Gemini's new File Search API to build a powerful RAG system in minutes for pennies.We’ll talk about:A step-by-step guide to building a serverless RAG agent in n8n using Google's new File Search API.The Cost Breakdown: How Gemini's pricing ($0.15 per 1M tokens) makes it 10x cheaper than traditional Pinecone/OpenAI setups.The simple 4-step workflow: Create Store → Upload File → Import to Store → Query Agent.A real-world accuracy test: How the agent scored 4.5/5 when quizzed on 200 pages of diverse documents (Golf Rules, Nvidia Financials, Apple 10-K).The honest trade-offs: navigating privacy concerns (Google storage) and why it struggles with "holistic" summary questions.Keywords: Gemini File Search, RAG, n8n, Vector Database, AI Agents, Google AI, No-Code AI, Low-Cost AI, API Integration, Document ProcessingLinks:Newsletter: Sign up for our FREE daily newsletter.Our Community: Get 3-level AI tutorials across industries.Join AI Fire Academy: 500+ advanced AI workflows ($14,500+ Value)Our Socials:Facebook Group: Join 271K+ AI buildersX (Twitter): Follow us for daily AI dropsYouTube: Watch AI walkthroughs & tutorials

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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