Semantic search using OpenAI, pg_embedding and Neon

Learn how to build a semantic search app using OpenAI, Neon, and pg_embedding. The app transforms user queries into vector embeddings to perform vector similarity searches, retrieving the most relevant results based on meaning instead of keyword matches. The methodology includes generating embeddings, storing them in a Postgres database using pg_embedding, and retrieving similar items through vector similarity search. Step-by-step instructions and code are included for building the app, from gathering data to deploying the frontend and API.