Build Multimodal RAG Systems with Weaviate & GPT-4V

AI Impact Summary

This document introduces Multimodal Retrieval-Augmented Generation (MM-RAG) systems, extending RAG to incorporate diverse data types like images, audio, and video. The core technique leverages contrastive learning to create unified embedding spaces across modalities, enabling any-to-any search and retrieval using vector databases like Weaviate. This approach allows for building visual question answering systems and integrating multimodal data into LLM-powered applications, addressing the limitations of LLMs trained on primarily textual data.