Built as a project for an AI Engineering course, this site employees Retrieval Augmented Generation to help Open AI craft a movie recommendation a movie based on user input.
Embeddings for a small sampling of movie summaries were stored in a Supabase vector store.
User input is collected and used to query the embeddings. The closest match is then sent to OpenAI to craft a movie recommendation based on the match. A query to a public api retrieves the image for the movie that is recommended.
This project was built to match a provided figma design.