This comprehensive n8n workflow creates a sophisticated AI-powered WhatsApp chatbot integrated with a Retrieval-Augmented Generation (RAG) system. It facilitates real-time customer interactions by responding to Webhook requests from Meta, processing incoming messages, and delivering intelligent, context-aware replies.
The workflow begins with setting up webhook nodes to handle GET (verification) and POST (message receipt) requests from Meta for WhatsApp communication. When a message is received, the workflow checks if it contains text and, if so, forwards it to an AI agent configured specifically for an electronics store. This AI agent uses a system prompt to provide detailed product info, troubleshooting guidance, and customer support, always referencing a knowledge base.
Simultaneously, the workflow involves managing a vector database (Qdrant) to store and retrieve document embeddings, enabling the system to fetch relevant information efficiently. Google Drive integration is used to access and download documents, which are then processed and embedded using OpenAI’s models. The embeddings are stored in Qdrant, allowing the AI to perform fast retrieval for context-aware conversations.
Additional nodes facilitate message echoing, response handling, and memory management through a window buffer, ensuring the conversation context is preserved.
This setup is ideal for businesses seeking to automate customer support, product inquiries, and troubleshooting through WhatsApp, leveraging AI’s ability to reference a knowledge base dynamically. Practical implementation scenarios include electronics retail, technical support, or any customer service environment that benefits from intelligent, automated messaging.
The workflow also includes detailed sticky notes providing setup instructions for webhook verification, document vectorization, and node configuration, ensuring clarity in deployment.
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