Vector search is a method that utilizes numerical representations called vector embeddings to search for data based on similarity rather than exact keyword matches, offering a solution for instances where traditional keyword-based searches fall short. Memgraph has integrated vector search with its graph database capabilities, leveraging the synergy between graph databases' relational insights and vector search's semantic matching to create a robust search platform. This integration simplifies architecture by combining systems and enhances applications like recommendation engines, fraud detection, and knowledge graphs. Memgraph's implementation features native vector search capabilities powered by the USearch library, focusing on achieving high-speed performance and accuracy for similarity searches. It employs a READ_UNCOMMITTED isolation level for vector indices to optimize performance, allowing immediate visibility of changes to vector indices while maintaining ACID properties for the database's other operations. This approach is particularly beneficial for applications requiring high-frequency similarity searches and real-time updates. Memgraph's vector search capabilities can be utilized for tasks such as movie similarity analysis, as demonstrated with the Wikipedia MoviePlots dataset, and are supported by resources and tutorials provided in the Memgraph documentation and Memgraph Academy.