Explore how to build a Retrieval Augmented Generation (RAG) system that combines both image and text data. We will use MongoDB as a scalable database to store and retrieve multimodal information, leveraging its capabilities for handling large datasets. The session will involve integrating text and image embeddings, using RAG to generate responses based on retrieved content, and optimizing performance for real-time applications. Attendees will gain insights into using MongoDB as a backend for multimodal retrieval systems.