This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions.
Think of it like this: instead of a general-knowledge AI, you're building an expert librarian.
The workflow is split into two main parts:
Part 1: Indexing the Knowledge (Building the Library)
This is a one-time process you run manually. The workflow automatically scrapes all the pages of the n8n documentation, breaks them down into small, digestible chunks, and uses an AI model to create a special numerical representation (an "embedding") for each chunk. These embeddings are then stored in your own private knowledge base (a Supabase vector store). This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph.
Part 2: The AI Agent (The Expert Librarian)
This is the chat interface. When you ask a question, the AI agent doesn't guess the answer. Instead, it uses your question to find the most relevant "index cards" (chunks) from the knowledge base it just built. It then feeds these specific, relevant chunks to a powerful language model (like Gemini) with a strict instruction: "Answer the user's question using ONLY this information." This ensures the answers are accurate, factual, and grounded in your provided documents.
Setup time: ~15-20 minutes
This is an advanced workflow that requires setting up a free external database. Follow these steps carefully.
Set up Supabase (Your Knowledge Base):
Workflow Setup
sticky notes in the top-right of the workflow to:
Configure n8n Credentials:
Configure the Workflow Nodes:
Supabase
nodes: Your Supabase Vector Store
, Official n8n Documentation
and Keep Supabase Instance Alive
.Gemini
nodes: Gemini Chunk Embedding
, Gemini Query Embedding
and Gemini 2.5 Flash
.Build the Knowledge Base:
Start Indexing
manual trigger node at the top-left.Chat with Your Expert Agent:
RAG Chatbot
chat trigger node and copy its Public URL.