A newly proposed framework named Retrieval-Augmented Generation (RAG) aims to improve depression detection by integrating multiple modalities, including text, audio, and video signals. This approach addresses key challenges such as high computational costs and the limitations of static knowledge, which have traditionally hindered effective emotional understanding. By combining these diverse data sources, RAG enhances sentiment analysis capabilities, thereby supporting more accurate detection of depressive states. The framework’s goal is to advance emotional comprehension in mental health applications, leveraging retrieval-augmented techniques to overcome existing barriers. Recent connected studies reinforce the potential of RAG to improve depression detection outcomes and emotional understanding, highlighting its innovative contribution to multimodal analysis in this domain. This development reflects ongoing efforts to refine AI-driven mental health tools through sophisticated data integration and retrieval methods.