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

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Created by: Manav Desai || manavdesai17

Manav Desai

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Last update 3 days ago

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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.

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.

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.

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.

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)

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.