A practical guide to Amazon Nova Multimodal Embeddings

Embedding models power many modern applications—from semantic search and Retrieval-Augmented Generation (RAG) to recommendation systems and content understanding. However, selecting an embedding model requires careful consideration—after you’ve ingested your data, migrating to a different model means re-embedding your entire corpus, rebuilding vector indexes, and validating search quality from scratch. The right embedding model should deliver strong baseline performance, adapt to your specific use-case, and support the modalities you need now and in the future.

The Amazon Nova Multimodal Embeddings model generates embeddings tailored to your specific use case—from single-modality text or image search to complex multimodal applications spanning documents, videos, and mixed content.

In this post, you will learn how to use Amazon Nova Multimodal Embeddings for your specific use cases:

• Simplify your architecture with cross-modal search and visual document retrieval
• Optimize performance by selecting embedding parameters matched to your workload
• Implement common patterns through solution ...