Retrieving information on news is a complex task that demands considerable effort. The AI Challenge stands as one of the pioneering competitions in the nation, initiating exploration in this domain. This study presents the SnapSeek system, a notable entry in this year’s competition. Utilizing advanced tools such as Milvus and Elasticsearch, SnapSeek facilitates searches across various data types, particularly emphasizing vector embeddings and metadata in textual or encoded forms. Building upon the initial SnapSeek version introduced at LSC 2024, this iteration incorporates significant enhancements, including dataset expansion, metadata enrichment, brush mode feature, temporal search capabilities, human feedback, and contextual news extraction. These advancements collectively enhance the efficiency of information retrieval. Furthermore, SnapSeek offers a minimalist and user-friendly interface, rendering it an effective and appropriate system for retrieving news information.