This session explores how Cosmos DB Vector Search, combined with Azure AI-generated image embeddings, enhances multimodal retrieval-augmented generation (RAG) systems using GPT-4 Vision. It compares image embeddings generated via the Azure AI Model Inference API with the latest multimodal embeddings from Computer Vision v4.0.
The presentation highlights how these new multimodal embeddings integrate image and text data to improve cross-modal retrieval. It also examines the role of Contrastive Language-Image Pre-training (CLIP) embeddings within the Azure ecosystem and their impact on accuracy and relevance in multimodal RAG applications.
Through a comparative analysis, we demonstrate how Azure AI and Cosmos DB optimize image-centric AI workflows, unlocking new possibilities for advanced multimodal AI systems.