Vector search is a technique used to retrieve semantically similar items based on vector embedding representations in a multi-dimensional space, and it's gaining popularity in AI applications. Couchbase Lite 3.2 now supports vector search, enabling cloud-to-edge support for these applications. This feature allows for semantic searches on local data to be performed even when the device is offline, alleviating data privacy concerns by restricting searches to authenticated users. Vector search also reduces cost-per-query and provides low-latency searches, making it an attractive option for edge applications. With this feature, developers can build applications that leverage both cloud and edge capabilities, providing a unified experience for their users.