Building a Movie Recommendation App with ScyllaDB Vector Search

The blog post explores the development of a movie recommendation application that utilizes ScyllaDB's new vector search capabilities to perform semantic searches across movie plot descriptions. This innovative approach allows users to find movies based on meaning rather than keywords, using vector similarity functions such as cosine similarity, dot product, and L2 distance to compare text embeddings. The application, built with Python packages like Streamlit and Sentence Transformers, utilizes a TMDB dataset and ScyllaDB Cloud to deliver low-latency, vector-based recommendations. A detailed breakdown of the app's design includes information on the database schema, which features a vector index for efficient queries, and the process of converting user input into embeddings for similarity comparison. The post also offers guidance on setting up the app, providing source code and documentation for users interested in building with ScyllaDB Vector Search.