video1.0<iframe src="https://www.loom.com/embed/f77a98e1a02847c595c47ed1c9ac7e16" frameborder="0" width="1440" height="1080" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>10801440Loomhttps://www.loom.com10801440https://cdn.loom.com/sessions/thumbnails/f77a98e1a02847c595c47ed1c9ac7e16-5fa075a1211fc822.gif462.119This is my CodePath 1.10 final project, an upgraded music recommender that uses a pipeline with vector databases and the Gemini LLM for semantic RAG recommendations and explanations. My main path uses four vector databases plus Gemini, but I added two guardrails in case the API key expires or the profile fails, and I fall back to a semantic-only search, and if the whole pipeline fails I revert to the deterministic recommender from my earlier project. In the demo, I ran three queries like chill acoustic study, aggressive rock, and electronic music and got top recommendations with explanations. I also let users type their own natural query, like somber songs, and it returns a tailored set. No action was specifically requested from viewers.