"Developer Spends 3 Hours Setting Up Vector Embeddings, Finds 1 Taylor Swift Song"

A developer created a semantic search pipeline using OpenAI and Supabase, storing vector embeddings in a pgvector-powered table. The pipeline converts text into vectors, searches for similar documents, and generates human-like responses using Chat Completions. The project involved setting up OpenAI and Supabase, creating a custom SQL function, and refactoring code into reusable functions. The result is a conversational search experience that returns relevant answers to user queries, such as "What Taylor Swift song is about Summer?" with a response like "The Taylor Swift song about summer is 'Cruel Summer.'"