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WhatsApp RAG chatbot with Supabase, Gemini 2.5 Flash, and OpenAI embeddings

n8nn8n11 modulesv1.0
GmailSlackNotionWhatsAppOpenAIGoogle DriveTelegramTwilioGemini

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 — al

At a glance

WhatsApp RAG chatbot with Supabase, Gemini 2.5 Flash, and OpenAI embeddings is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects Gmail, Slack, Notion, WhatsApp. It's free to download. Follow the 5-step import below to go live in minutes.

Platform
n8n
Connects
Gmail, Slack, Notion, WhatsApp, OpenAI, Google Drive
Modules
11
Price
Free
Version
v1.0
WhatsApp RAG chatbot with Supabase, Gemini 2.5 Flash, and OpenAI embeddings workflow diagram

About this workflow

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.

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How to import this n8n workflow

  1. 1

    Download the workflow JSON file after purchase.

  2. 2

    Open n8n → click the menu → Import from File.

  3. 3

    Select the downloaded JSON and import.

  4. 4

    Set up credentials for each node that requires them.

  5. 5

    Click Execute Workflow to test, then activate.

Setup guide

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