{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/cf1d8eae22e943d883aae4ed3f05db10\" frameborder=\"0\" width=\"1600\" height=\"1200\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1200,"width":1600,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1200,"thumbnail_width":1600,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/cf1d8eae22e943d883aae4ed3f05db10-e247fcf934cb9eb1.gif","duration":300.988,"title":"Song Recommendation System with RAG Pipeline 🚀","description":"I built a song recommendation system using a rack style flow and a RAG engine. My project connects to a Supabase database, and the main logic is in a Python recommended file. I use mood and genre as features, and the dataset is 18 songs stored with an embedding vector database built from a CSV. The output is generated by Gemini, and I included a system diagram plus unit and integration tests with pytest, including edge cases like missing context. I asked you to run the pytest command if you want to verify it, though it may be slow at first."}