In the generative AI era, the integration of multimodal AI innovations into digital applications has become essential, especially for enterprise decision-making systems dependent on high-quality, fresh data. Apache Fluss (incubating) serves as a streaming storage solution for real-time analytics, enhancing lakehouse architectures by providing a seamless integration between stream and lake storage. This setup allows for real-time data processing and historical data management, with Fluss operating as the real-time layer and the lakehouse serving as the historical layer. Complementing Fluss, Lance emerges as an AI lakehouse platform optimized for machine learning and multimodal applications, enabling efficient handling of diverse data types and high-performance queries. The combination of Fluss and Lance facilitates real-time multimodal AI analytics by supporting frameworks like Retrieval-Augmented Generation (RAG), which enhances large language models with up-to-date information. The blog details the setup and integration process of these systems, demonstrating how to stream and process image data into a Pandas DataFrame for machine learning workflows, thus offering a robust solution for leveraging both real-time and historical data in AI applications.