{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/fa5b4236b9c940f38aa14a618543c071\" frameborder=\"0\" width=\"1440\" height=\"1080\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1080,"width":1440,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1080,"thumbnail_width":1440,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/fa5b4236b9c940f38aa14a618543c071-506da8a57ff4432a.gif","duration":90.4,"title":"RAG Music Recommender Demo and Tests 🎵","description":"Hi, my name is Hunter, and this Loom is my rag-powered music recommender final project. It extends my module 3 CSV scoring algorithm by letting you type how you feel in natural language, then it retrieves relevant musical context from the knowledge base and generates personalized recommendations. In my demo, I tried late night and got recommendations like Frank Ocean and Daniel Caesar, then intense workout and got a new set with workout focused picks. I also ran seven end to end tests covering retrieval input validation and logging, and all seven passed. No action was requested from viewers."}