I Built a RAG Pipeline in n8n That Answers Questions Over 3,000 Pages in Under 5 Seconds

Three weeks ago I needed a way to query a large document corpus without sending everything to an LLM every time. The answer was a RAG (Retrieval-Augmented Generation) pipeline — but I wanted to build it inside n8n, not a Python script that I'd have to maintain separately. Here's the architecture I landed on, and why each decision was made. The Problem I had 3,000+ pages of documentation spread across Google Drive. I needed Claude to answer questions about it accurately — not halluci...