Mobile app developers aim to engage users by offering personalized experiences that connect with their tastes, history, and habits, which is enhanced by using semantic and vector search technologies. Unlike traditional keyword searches, vector search interprets the meaning of queries to deliver relevant information, and when combined with Retrieval Augmented Generation (RAG), it makes AI outputs more personal. Couchbase distinguishes itself by providing vector search capabilities from cloud to edge, with Couchbase Mobile allowing for offline, low-latency access through its embedded database, Couchbase Lite. This platform supports vector embedding and search locally on devices, ensuring reliable app performance without internet dependency, and facilitates seamless data synchronization with Couchbase Capella and Couchbase Server. By integrating vector search on-device, developers can achieve enhanced privacy, bandwidth efficiency, and synchronization across app ecosystems, making Couchbase Mobile a promising solution for mobile and IoT applications.