Gemini RAG Pipeline for Documentation Expert Chatbot

This workflow creates a Retrieval-Augmented Generation (RAG) pipeline to transform an AI into a specialized expert on any documentation set, using the n8n documentation as an example. It consists of two core phases: a one-time indexing process that scrapes all relevant pages, splits them into chunks, generates embeddings with an AI model, and stores them in n8n's Simple Vector Store; and a real-time chat interface where user questions trigger retrieval of the most relevant chunks, which are then fed to Gemini with instructions to answer solely based on that context.

The benefits are significant: it ensures responses are factual, hallucination-free, and hyper-relevant, far surpassing general AI models for domain-specific knowledge. It's ideal for enterprises needing internal knowledge bases, saving hours of manual research and reducing errors in support or training scenarios.

Use cases include product documentation Q&A, employee onboarding chatbots, customer support agents grounded in FAQs, or compliance querying for regulated industries. Setup is quick (~2 minutes plus 15-20 for indexing), leveraging n8n's built-in nodes—no external services beyond Gemini API required. Note: the vector store is in-memory and resets on n8n restart, so re-index as needed.

This template serves as a hands-on tutorial, enabling rapid deployment of expert AI assistants scalable to any document corpus with minimal customization.