💬 Build a product catalog chatbot with Mistral AI, Google Drive & Supabase RAG

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Description

AI Product Catalog Chatbot with Google Drive Ingestion & Supabase RAG

Overview

This workflow builds a dual-system that connects automated document ingestion with a live product catalog chatbot powered by Mistral AI and Supabase.

It includes:

Ingestion Pipeline: Automatically fetches JSON files from Google Drive, processes their content, and stores vector embeddings in Supabase.
Chatbot: An AI agent that queries the Supabase vector store (RAG) to answer user questions about the product catalog.

It uses Mistral AI for chat intelligence and embeddings, and Supabase for vector storage and semantic product search.

Chatbot Flow

Trigger: When chat message received or Webhook (from live website)
Model: Mistral Cloud Chat Model (mistral-medium-latest)
Memory: Simple Memory (Buffer Window) — keeps last 15 messages for conversational context
Vector Search Tool: Supabase Vector Store
Embeddings: Mistral Cloud
Agent: product catalog agent
- Responds to user queries using the products table in Supabase.
- Searches vectors for relevant items and returns structured product details (name, specs, images, and links).
- Maintains chat session history for natural follow-up questions.

Document → Knowledge Base Pipeline

Triggered manually (Execute workflow) to populate or refresh the Supabase vector store.

Steps

Google Drive (List Files)
→ Fetch all files from the configured Google Drive folder.
Loop Over Items
→ For each file:
- Google Drive (Get File)
  → Download the JSON document.
- Extract from File
  → Parse and read raw JSON content.
- Map Data into Fields (Set node)
  → Clean and normalize JSON keys (e.g., page_title, comprehensive_summary, key_topics).
- Convert Data into Chunks (Code node)
  → Merge text fields like summary and markdown.
  → Split content into overlapping 2,000-character chunks.
  → Add metadata such as title, URL, and chunk index.
- Embeddings (Mistral Cloud)
  → Generate vector embeddings for each text chunk.
- Insert into Supabase Vectorstore
  → Save chunks + embeddings into the website_mark table.
- Wait
  → Pause for 30 seconds before the next file to respect rate limits.

Integrations Used

Service	Purpose	Credential
Google Drive	File source for catalog JSON documents	`Google Drive account dbt`
Mistral AI	Chat model & embeddings	`Mistral Cloud account dbt`
Supabase	Vector storage & RAG search	`Supabase DB account dbt`
Webhook / Chat	User-facing interface for chatbot	`Website or Webhook`

Sample JSON Data Format (for Ingestion)

The ingestion pipeline expects structured JSON product files, which can include different categories such as Apparel or Tools.

Apparel Example (T-Shirts)

[
  {
    "Name": "Classic Crewneck T-Shirt",
    "Item Number": "A-TSH-NVY-M",
    "Image URL": "https://www.example.com/images/tshirt-navy.jpg",
    "Image Markdown": "![Classic Crewneck T-Shirt](https://www.example.com/images/tshirt-navy.jpg)",
    "Size Chart URL": "https://www.example.com/charts/tshirt-sizing",
    "Materials": "100% Pima Cotton",
    "Color": "Navy Blue",
    "Size": "M",
    "Fit": "Regular Fit",
    "Collection": "Core Essentials"
  }
]

Tools Example (Drill Bits)

[
  {
    "Name": "Titanium Drill Bit, 1/4\"",
    "Item Number": "T-DB-TIN-250",
    "Image URL": "https://www.example.com/images/drill-bit-1-4.jpg",
    "Image Markdown": "![Titanium Drill Bit, 1/4\"](https://www.example.com/images/drill-bit-1-4.jpg)",
    "Spec Sheet URL": "https://www.example.com/specs/T-DB-TIN-250",
    "Materials": "HSS with Titanium Coating",
    "Type": "Twist Drill Bit",
    "Size (in)": "1/4",
    "Shank Type": "Hex",
    "Application": "Metal, Wood, Plastic"
  }
]

Agent System Prompt Summary

> “You are an AI product catalog assistant.
Use only the Supabase vector database as your knowledge base.
Provide accurate, structured responses with clear formatting — including product names, attributes, and URLs.
If data is unavailable, reply politely: ‘I couldn’t find that product in the catalog.’”

Key Features

Automated JSON ingestion from Google Drive → Supabase
Intelligent text chunking and metadata mapping
Dual-workflow architecture (Ingestion + Chatbot)
Live conversational product search via RAG
Supports both embedded chat and webhook channels

Summary

> A powerful end-to-end workflow that transforms your product data into a searchable, AI-ready knowledge base, enabling real-time product Q&A through a Mistral-powered chatbot.

Perfect for eCommerce teams, distributors, or B2B companies managing large product catalogs.

Need Help or More Workflows?

Want to customize this workflow for your business or integrate it with your tools?
Our team at Digital Biz Tech can tailor it precisely to your use case — from automation pipelines to AI-powered product discovery.

💡 We can help you set it up for free — from connecting credentials to deploying it live.

Contact: rajeet.nair@digitalbiz.tech
Website: https://www.digitalbiz.tech
LinkedIn: https://www.linkedin.com/company/digital-biz-tech/
You can also DM us on LinkedIn for any help.

🔗 Nodes Used

Webhook, Google Drive, AI Agent, Simple Memory, Character Text Splitter, Supabase Vector Store

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup