We present the VideoEase interactive video retrieval system, which we used to participate in VBS2025. This is the first time that VideoEase has taken part in the VBS challenge. VideoEase is built on the Milvus vector database, which supports vector searches on massive datasets with high-dimensional vectors. The CLIP, BLIP2, and Open-CLIP models play a crucial role in encoding keyframe images and queries into embeddings. A new user interface with simple yet effective components is also introduced. We experiment with VBS24 queries to evaluate the performance of the VideoEase system. Our results show that answer rank is improved by merging outputs from several models with appropriate weights.