This presentation explores using Azure AI for generating and leveraging image embeddings to enhance multimodal retrieval-augmented generation (RAG) systems with GPT-4 Vision. It compares three primary solutions: Image Embeddings through Azure Machine Learning, Image Embeddings via the Azure AI Model Inference API, and the latest Computer Vision multimodal embeddings (v4.0). Each service's unique capabilities are highlighted, with Azure Machine Learning offering flexible custom embedding pipelines, and the Azure AI Model Inference API providing pre-trained embeddings for rapid deployment. The new multimodal embeddings in Computer Vision v4.0 integrate image and text data for better cross-modal applications. Additionally, the integration of Contrastive Language-Image Pre-training (CLIP) embeddings within Azure ecosystems is examined, showing their effectiveness in improving the accuracy and relevance of multimodal RAG systems. By aligning images and text within a shared vector space, CLIP embeddings enhance GPT-4 Vision models' performance in image understanding and generation tasks.
The comparative analysis presented in this paper serves as a guide for organizations aiming to harness the full potential of Azure AI for image-centric AI workflows, fostering innovation in areas such as content recommendation, visual search, and interactive AI systems.