Build a RAG chatbot using Google Gemini and a Supabase vector store
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 16 nodes and runs when a chat message arrives (plus 2 other triggers). Along the way it connects Google Gemini Chat Model, Postgres Chat Memory, Supabase Vector Store and Embeddings Google Gemini.
How it runs
Trigger: When a chat message arrives (plus 2 other triggers).
- Knowledge-Base AI Agent (AI Agent): runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Supabase Retriever); memory: Persistent Chat History
- Parse File Size (Code): runs custom JavaScript code
- Format Error Alert (Edit Fields (Set)): sets 1 field: alertMessage
- File < 10MB? (If): checks a condition and branches into true / false
- Supabase Inserter (Supabase Vector Store): stores documents in the Supabase vector store, with embeddings: Gemini Embeddings; document loader: Document Parser
- Reject Upload (Stop and Error): stops the run with an error
- Success Response (Respond to Webhook): sends the response back to the webhook caller
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (15)
- Error Trigger
n8n-nodes-base.errorTrigger - Edit Fields (Set)
n8n-nodes-base.set - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Postgres Chat Memory
@n8n/n8n-nodes-langchain.memoryPostgresChat - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - n8n Form Trigger
n8n-nodes-base.formTrigger - If
n8n-nodes-base.if - Code
n8n-nodes-base.code - Stop and Error
n8n-nodes-base.stopAndError - Respond to Webhook
n8n-nodes-base.respondToWebhook - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini
From the author
🧠 AI Chatbot with RAG: Google Gemini & Supabase Vector Store 📌 Summary Build a custom, intelligent knowledge base in minutes. This n8n workflow provides a complete Retrieval-Augmented Generation (RAG) system using Google Gemini and Supabase. It features a…