Multimodal Retrieval-Augmented Generation (RAG) with Vector Database
Multi-modality elevates the capabilities of neural network models to a whole new level. By leveraging contrastive learning and specialized model architectures, we can create a unified vector space for images and text, enhancing multimodal representations. This talk will share insights into building image-text search and Composite Image Retrieval (CIR) using multimodal embeddings and the Milvus open source vector database, demonstrating how multi-modality unlocks new use cases in Retrieval-Augmented Generation (RAG).
Join the Webinar
Loading...
Meet the Speaker
Join the session for live Q&A with the speaker
Stefan Webb
Developer Advocate, Zilliz
Stefan Webb is a Developer Advocate at Zilliz, where he advocates for the open-source vector database, Milvus. Prior to this, he spent three years in industry as an Applied ML Researcher at Twitter and Meta, collaborating with product teams to tackle their most complex challenges. Stefan holds a PhD from the University of Oxford and has published papers at prestigious machine learning conferences such as NeurIPS, ICLR, and ICML. He is passionate about generative AI and is eager to leverage his deep technical expertise to contribute to the open-source community.