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. --- 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.