This n8n workflow creates an intelligent WhatsApp chatbot designed for health-related interactions, leveraging language models and image processing. The workflow begins by listening for incoming WhatsApp messages through a trigger node. It then checks if the message contains an image. If an image is received, the workflow downloads the image, generates a contextual response based on the image and caption using a language model, and sends the response back to the user. If the message contains text, it processes the text through an AI language model to generate an appropriate reply and responds via WhatsApp. The workflow integrates Google’s Gemini language model for sophisticated conversational capabilities, making it ideal for health advice, patient engagement, or health troubleshooting support via WhatsApp. Its automated and intelligent design enables real-time, context-aware interactions that can enhance user experience in digital health services.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatGoogleGemini, @n8n/n8n-nodes-langchain.outputParserStructured, httpRequest, stickyNote, switch, whatsApp, whatsAppTrigger