WhatsApp RAG Chatbot with Supabase, Gemini 2.5 Flash, and OpenAI Embeddings

WhatsApp RAG Chatbot with Supabase, Gemini 2.5 Flash, and OpenAI Embeddings

This n8n template demonstrates how to build a WhatsApp-based AI chatbot that answers user questions using document retrieval (RAG) powered by Supabase for storage, OpenAI embeddings for semantic search, and Gemini 2.5 Flash LLM for generating high-quality responses.

Use cases are many: Turn your WhatsApp into a knowledge assistant for FAQs, customer support, or internal company documents — all without coding.

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Good to know

• The workflow uses OpenAI embeddings for both document embeddings and query embeddings, ensuring accurate semantic search.
• Gemini 2.5 Flash LLM is used to generate user-friendly answers from the retrieved context.
• Messages are processed in real-time and sent back directly to WhatsApp.
• Workflow is modular — you can split document ingestion and query handling for large-scale setups.
• Supabase and WhatsApp API credentials must be configured before running.

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How it works

1. Trigger: A new WhatsApp message triggers the workflow via webhook.
2. Message Check: Determines if the message is a query or a document upload.
3. Document Handling:
   • Fetch file URL from WhatsApp.
   • Convert binary to text.
   • Generate embeddings with OpenAI and store them in Supabase.
4. Query Handling:
   • Generate query embeddings with OpenAI.
   • Retrieve relevant context from Supabase.
   • Pass context to Gemini 2.5 Flash LLM to compose a response.
5. Response: Send the answer back to the user on WhatsApp.

Optional: Add Gmail node to forward chat logs or daily summaries.

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How to use

• Configure WhatsApp Business API webhook for incoming messages.
• Add your Supabase and OpenAI credentials in n8n’s credentials manager.
• Upload documents via WhatsApp to populate the Supabase vector store.
• Ask queries — the bot retrieves context and answers using Gemini 2.5 Flash.

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Requirements

• WhatsApp Business API (or Twilio WhatsApp Sandbox)
• Supabase account (vector storage for embeddings)
• OpenAI API key (for generating embeddings)
• Gemini API access (for LLM responses)

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Customising this workflow

• Swap WhatsApp with Telegram, Slack, or email for different chat channels.
• Extend ingestion to other sources like Google Drive or Notion.
• Adjust the number of retrieved documents or prompt style in Gemini for tone control.
• Add a Gmail output node to send logs or alerts automatically.