Building a Personal Fitness Insights Engine with Vector Search
This project showcases a sophisticated approach to making personal fitness data searchable and meaningful using modern AI tools. The system combines Strava activity data, vector embeddings, and an intelligent search interface, creating a personalized fitness knowledge base. The system consists of three main components: Strava data collection, vector storage in Supabase, and semantic activity analysis. The workflow begins with authenticating and fetching detailed activity data from Strava using a Python-based OAuth flow....