In this paper, we introduce LifeInsight – an interactive lifelog retrieval system developed for the sixth annual Lifelog Search Challenge (LSC’23). LifeInsight incorporates semantic search mechanisms from state-of-the-art lifelog retrieval systems while focusing on providing insights into the lifelogger’s routine using spatial information to support question-answering tasks. The system employs the Bootstrapping Language-Image Pre-training (BLIP) model for zero-shot image-text retrieval, which has been shown to achieve higher recall scores than the CLIP model on the Flickr30K dataset. In addition, the Elastic Search filtering mechanism is utilized to remove irrelevant images. Apart from semantic search mechanisms, the system also supports visual similarity search by comparing the inner product distance between the vectors in the lifelog image corpus and the query image. Furthermore, the system includes an explicit relevance feedback function, AI-based query description rewriting, and visual-example-generating features to re-phrase the query to describe it better and support end-users envisioning the targeted image for retrieval.