Recommending relevant content to users is crucial for keeping them engaged in an app. With the emergence of vector databases and Open AI, performing semantic searches with a single query has become more efficient. This article demonstrates how to create a Flutter movie-viewing app that recommends another movie based on what the user is viewing, using Supabase as the vector database and Open AI API for embedding generation. The app populates its database with movie data and generates embeddings using Open AI, then stores them in Supabase for querying. A database function is created to perform a vector similarity search, allowing the app to recommend similar movies to the selected one. The Flutter interface displays the list of recommended movies, showcasing the functionality of the similarity recommendation system powered by Open AI.