Vector functions in SingleStoreDB enable the solution of AI problems such as face matching, product photo matching, object recognition, text similarity matching and sentiment analysis. The dot_product function is used for cosine similarity to find a matching image from among 16 million records in just 5 milliseconds. Other vector functions supported include euclidean distance calculation, transforming JSON arrays to binary vectors, vector math and manipulation. SingleStoreDB's parallelization and SIMD-based vector processing capabilities allow developers to focus on their application logic while leveraging the database's performance for fast query execution. The use of a database for vector similarity testing offers several benefits, including the ability to express filters in SQL and apply them before vector matching operations, making it easier to implement complex queries. An example demonstrates how to load 16 million records into a table, query the table using dot_product, and find the top five closest matches in just 0.005 seconds. The process of generating vectors for images using facenet involves downloading the necessary software, generating vectors from the dataset, and converting them to a format that can be stored in a SingleStoreDB database. The article also provides instructions on how to create a table, load data into it, and query the table using dot_product to find close matches. Additionally, a shortcut is provided for querying pre-made vectors, allowing developers to quickly explore face matching capabilities without having to generate their own vectors.