This project is a conversational AI system using Retrieval-Augmented Generation (RAG) to recommend Honeywell vacuum cleaners based on user preferences. The AI engages users in dialogue about their cleaning needs and home environment. Leveraging RAG, it combines large language models with a specialized Honeywell vacuum cleaner database, enabling real-time, accurate, and up-to-date recommendations.
The system processes user input using advanced NLP techniques, matching preferences with retrieved product data. This RAG integration allows for more nuanced, context-aware responses, explaining how specific vacuum features meet the user's requirements.
The AI then recommends the most suitable vacuum cleaner, providing a concise feature summary and purchase link. By utilizing RAG, the system adapts to new product releases without manual updates, ensuring current recommendations.
This project showcases the integration of RAG, NLP, and e-commerce to create a personalized, intelligent shopping experience.
