An original work introduces GENIUS, a generative AI model that offers a groundbreaking alternative for information retrieval within large multimodal datasets. Diverging from traditional, resource-intensive embedding-based search, GENIUS directly generates unique ID codes from query embeddings for texts, images, or image-text pairs. This innovative method, leveraging residual quantization and generative data augmentation, dramatically improves efficiency and scalability, achieving state-of-the-art performance in generative retrieval and significantly narrowing the gap with existing embedding-based approaches.