AI-Powered Shopping Assistant with RAG & WooCommerce

This advanced n8n workflow creates an intelligent shopping assistant integrating OpenAI’s language models, Retrieval-Augmented Generation (RAG), and WooCommerce. Its purpose is to interpret user chat messages, determine if they are product-related, extract relevant search parameters, and return personalized product suggestions. The workflow begins with listening for chat inputs via a webhook trigger, then utilizes natural language processing (NLP) to understand user intent. If the user seeks a product, the system extracts keywords, price ranges, SKUs, and categories using an information extractor node. These details are then employed to filter WooCommerce product listings, retrieving in-stock items matching the user’s preferences. For general inquiries, the system employs RAG to fetch store information or other knowledge base content from Google Drive. Additionally, the workflow incorporates a vector database (Qdrant) for document embedding and retrieval, enabling efficient document-based responses. The setup allows seamless interaction between the chat interface, AI models, product catalog, and knowledge base, making it highly suitable for eCommerce stores aiming to deliver instant, intelligent shopping assistance and customer support.