Building an n8n Documentation Chatbot with RAG Workflow

This advanced n8n workflow demonstrates how to create a specialized AI-powered documentation chatbot using a Retrieval-Augmented Generation (RAG) pipeline integrated with n8n nodes and OpenAI models. The process begins with an indexing phase, where the workflow automatically scrapes the official n8n documentation webpage, extracts relevant links, and processes each page into digestible chunks. These chunks are transformed into vectors using OpenAI embeddings and stored temporarily in an in-memory vector store. This indexing is designed to be a one-time setup (or manual re-run) to build a knowledge base. Subsequently, the workflow facilitates a powerful chatbot interface where users can ask n8n-related questions. The chatbot retrieves relevant documentation snippets dynamically from memory, ensuring responses are fact-based, accurate, and derived solely from the official docs. This setup is highly useful for building intelligent, context-aware assistive tools for documentation, onboarding, or technical support scenarios.