RAG Chatbot for Procurement: Supabase + TogetherAI + Openrouter
Ingests Google Docs into Supabase embeddings via TogetherAI, then powers a RAG chatbot for accurate procurement queries using chat triggers and similarity search.
This workflow implements a Retrieval-Augmented Generation (RAG) chatbot tailored for commerce and procurement. The first part (run once) fetches content from Google Docs, splits it into 1000-char chunks using a code node, generates embeddings with TogetherAI's API, and stores them in a Supabase 'embed' table for persistent vector storage.
The second part activates on chat messages: it embeds the user query with TogetherAI, performs a vector similarity search via Supabase RPC to retrieve top 5 r
Platform
n8n
Category
Commerce
Price
$24.99
Creator
Fred Garcia
RAG
Chatbot
Supabase
TogetherAI
Openrouter
Embeddings
Google Docs
Vector Search
AI Automation
Procurement
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.