How Hornet Uses Metadata Indexing to Help Users Find the Perfect Match

Hornet, a leading social network and dating app for the global queer community, has integrated semantic vector search into its application to improve user profile matching. The company uses Apache Cassandra® and DataStax Astra DB databases in conjunction with OpenAI's vectorization technology. Storage Attached Indexes (SAIs) enable filtering on queries without specifying partitioning columns, making it easier to combine vector search with other criteria such as geographic distance or profile attributes. The Data API provides a simpler experience for developers and supports JSON document formats, including vector data types for semantic search combined with filtering. This integration allows Hornet to refine user matches based on distance from the user's address and other structured attributes, improving the overall user experience.