Multimodal RAG: Expanding Beyond Text for Smarter AI

Retrieval Augmented Generation (RAG) has evolved from a text-based technique to Multimodal RAG, which integrates different data types such as images and videos to provide more reliable knowledge to AI models. The Milvus vector database enables the storage and search of diverse data types, while NVIDIA GPUs accelerate these complex operations. Key components of a multimodal RAG pipeline include Vision Language Models (VLMs), vector databases like Milvus, text embedding models, large language models (LLMs), and orchestration frameworks. Multimodal RAG systems offer multi-format processing, image analysis via VLMs, and efficient indexing and retrieval capabilities.