A practical guide to Amazon Nova Multimodal Embeddings

This article provides a practical guide to using Amazon Nova Multimodal Embeddings for semantic search, RAG, and recommendation systems across diverse data types.

• Supports text, images, documents, video, and audio within unified semantic space
• Offers retrieval system mode with purpose-specific parameters (GENERIC_INDEX, TEXT_RETRIEVAL, IMAGE_RETRIEVAL, DOCUMENT_RETRIEVAL, VIDEO_RETRIEVAL, AUDIO_RETRIEVAL)
• Includes ML task mode for CLASSIFICATION and CLUSTERING downstream tasks
• Configurable embedding dimensions (1024 or 3072) and detail levels for workload optimization
• Use cases: product retrieval/classification, intelligent document retrieval, video search, audio fingerprinting
• Multimodal search architecture: embed content, store in vector database, query with similarity matching
• Can integrate as Model Context Protocol tool for agentic RAG systems

Amazon Nova Multimodal Embeddings enables building effective cross-modal retrieval systems and semantic search applications without re-embedding when migrating models.