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

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.

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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.

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Document → Knowledge Base Pipeline Triggered manually (Execute workflow) to populate or refresh the Supabase vector store.