Multimodal Retrieval Augmented Generation RAG with Vector Databases

This talk explores how multi-modality enhances neural network capabilities through contrastive learning and specialized architectures to create unified vector spaces for images and text. Learn about building image-text search and Composite Image Retrieval (CIR) using multimodal embeddings with the Milvus open source vector database, and discover how these techniques unlock new possibilities in Retrieval-Augmented Generation (RAG) systems. The 49-minute presentation from Open Data Science provides practical insights into implementing multimodal RAG solutions that can transform how AI systems process and generate content across different data types.