Integrations
HTTP Request
Switch
Sticky Note
WhatsApp Business Cloud
Manual Trigger
AI Agent
Embeddings OpenAI
OpenAI Chat Model
Window Buffer Memory (easiest)
Recursive Character Text Splitter
In-Memory Vector Store
Extract from File
Default Data Loader
WhatsApp Trigger
Vector Store Tool

This rantir template builds a simple WhatsApp chatbot acting as a Sales Agent. The agent is backed by a product catalog vector store to better answer user questions.

This template is intended to help introduce rantir users who are interested in building with WhatsApp.

How it works

     
  • This template is divided into two parts: creating the product catalog vector store and building the WhatsApp AI chatbot.
  •  
  • A product brochure is imported via an HTTP request node, and its text contents are extracted.
  •  
  • The text contents are then uploaded to the in-memory vector store to build a knowledge base for the chatbot.
  •  
  • A WhatsApp trigger is used to capture messages from customers, with non-text messages being filtered out.
  •  
  • The customer's message is sent to the AI Agent, which queries the product catalog using the vector store tool.
  •  
  • The Agent's response is sent back to the user via the WhatsApp node.

How to use

Once you've set up and configured your WhatsApp account and credentials:

     
  • First, populate the vector store by clicking the "Test Workflow" button.
  •  
  • Next, activate the workflow to enable the WhatsApp chatbot.
  •  
  • Message your designated WhatsApp number, and you should receive a message from the AI sales agent.
  •  
  • Tweak the data source and behavior as required.

Requirements

     
  • WhatsApp Business Account
  •  
  • OpenAI for LLM

Customizing this workflow

     
  • Upgrade the vector store to Qdrant for persistence and production use cases.
  •  
  • Handle different WhatsApp message types for a richer and more engaging experience for customers.

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