WhatsApp RAG chatbot with Supabase, Gemini 2.5 Flash, and OpenAI embeddings
Transform WhatsApp into a powerful AI assistant that provides instant answers based on your private document library using advanced RAG technology. By combining Gemini 2.5 Flash with Supabase vector storage and OpenAI embeddings, this workflow enables sophisticated retrieval-augmented generation for real-time mobile support. It offers a complete no-code solution for building a portable knowledge expert accessible anywhere via chat.
Start BuildingWhat This Recipe Does
Transform your business communication by deploying a highly intelligent WhatsApp chatbot that understands your specific company data. This automation leverages Retrieval-Augmented Generation (RAG) to ensure your AI assistant provides accurate, context-aware responses based on your private knowledge base rather than general information. By integrating WhatsApp with Supabase and Gemini 2.5 Flash, the system instantly retrieves relevant documents and processes them to answer customer inquiries, support requests, or internal team questions in real-time. This solution eliminates the common problem of AI hallucinations by grounding every response in your actual business data. The result is a professional, 24/7 automated communication channel that reduces the workload on human staff while significantly improving response times and accuracy for your clients. Whether you are managing complex product catalogs or extensive service documentation, this automation ensures your customers get the right information exactly when they need it on the world's most popular messaging platform.
What You'll Get
Forms, dashboards, and UI components ready to use
Background automations that run on your schedule
REST APIs for external integrations
Langchain.vectorStoreSupabase, WhatsAppTrigger, Switch, Langchain.documentDefaultDataLoader, Langchain.embeddingsOpenAi configured and ready
How It Works
- 1
Click "Start Building" and connect your accounts
Runwork will guide you through connecting Langchain.vectorStoreSupabase and WhatsAppTrigger
- 2
Describe any customizations you need
The AI will adapt the recipe to your specific requirements
- 3
Preview, test, and deploy
Your app is ready to use in minutes, not weeks
Who Uses This
- Customer support teams can automate responses to complex product questions by linking the bot to their technical documentation and FAQs.
- Sales organizations can provide instant information on inventory, pricing, and service details to leads messaging via WhatsApp.
- Internal HR departments can deploy the bot to answer employee questions about company policies, benefits, and handbooks stored in their database.
Frequently Asked Questions
How does the chatbot know my specific business information?
The system uses a process called RAG, which searches your Supabase database for relevant documents before the AI generates a response, ensuring the answer is based on your data.
Can I control which documents the AI uses to answer questions?
Yes, you can manage the data stored in your Supabase database, allowing you to update or remove information as your business requirements change.
Do I need to be a developer to manage the responses?
Once the initial connection is established, the chatbot operates automatically. You simply focus on keeping your knowledge base updated.
What happens if the chatbot cannot find an answer in my data?
The system is designed to prioritize your data; however, you can configure the logic to hand off complex queries to a human agent if no relevant information is found.
Importing from n8n?
This recipe uses nodes like Langchain.vectorStoreSupabase, WhatsAppTrigger, Switch, Langchain.documentDefaultDataLoader and 6 more. With Runwork, you don't need to learn n8n's workflow syntax—just describe what you want in plain English.
Based on n8n community workflow. View original
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Start with this recipe and customize it to your needs.
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