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 Processing

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