Answer backlog questions from Slack with Supabase, Gemini, and OpenRouter RAG
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 12 nodes and runs when someone submits an n8n form (“Index Product Backlog”) (plus 1 other trigger). Along the way it connects Supabase Vector Store, Embeddings Google Gemini and OpenRouter Chat Model.
How it runs
Trigger: When someone submits an n8n form (“Index Product Backlog”) (plus 1 other trigger).
- Extract from File: extracts data from a file (pdf)
- Respond to Webhook: sends the response back to the webhook caller
- RAG Agent (AI Agent): runs an AI agent, with model: OpenRouter Chat Model; 1 tool (search_backlog)
- Supabase Vector Store: stores documents in the Supabase vector store, with embeddings: Embeddings Google Gemini; document loader: Default Data Loader
- Post Answer to Slack (HTTP Request): calls an HTTP API (POST)
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 (10)
- n8n Form Trigger
n8n-nodes-base.formTrigger - Extract from File
n8n-nodes-base.extractFromFile - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Webhook
n8n-nodes-base.webhook - Respond to Webhook
n8n-nodes-base.respondToWebhook - OpenRouter Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenRouter - AI Agent
@n8n/n8n-nodes-langchain.agent - HTTP Request
n8n-nodes-base.httpRequest
From the author
Quick Overview This workflow indexes a product backlog PDF into a Supabase vector store and lets users query it from Slack using a slash command, with Google Gemini embeddings for retrieval and an OpenRouter chat model to generate answers. How it works 1.…