Supporting effective access to digital lifelogs is a challenging research task because of both the volume and variety of multimodal lifelog data, as well as the many and diverse types of information need that should be supported. In this paper, we introduce a new version of LifeSeeker called LifeSeeker 6.0, for the 2024 edition of the ACM Lifelog Search Challenge. Our enhancements include the improvements to the user interface and the backend reconstruction by combining the E-LifeSeeker structure with using contrastive learning between texts. These adjustments are aimed at accelerating the correlation between the huge image collection and the text input, thereby enhancing the retrieval accuracy and efficiency.