Building a Movie Similarity Search Engine with Vector Search in Memgraph

In a tutorial by David Ivekovic, Memgraph's vector search capabilities are demonstrated through the creation of a movie similarity search engine using the Wikipedia Movie Plots dataset. The process involves running Memgraph with vector search enabled, loading and preprocessing the dataset to focus on Christopher Nolan's films, and generating 384-dimensional vector embeddings using the SentenceTransformer Python library. These embeddings are then stored in Memgraph, allowing users to query the database for similar movies based on plot descriptions. The tutorial provides examples of finding movies like "Inception" and "Memento" and suggests expanding the dataset and experimenting with various similarity metrics. This approach highlights the potential of vector search and graph databases for semantic searches and applications in movie recommendations and content discovery.