Crossmodal search with Amazon Nova Multimodal Embeddings

This article demonstrates how Amazon Nova Multimodal Embeddings enables crossmodal search by processing text, images, documents, video, and audio through a single unified model architecture available via Amazon Bedrock.

• Single model processes all modalities into shared vector space for direct similarity calculations
• Solves traditional search limitations: keyword-only, separate text/image systems, incompatible embeddings
• Ecommerce use case: customers search using photos, text descriptions, or both combined
• Matryoshka learning supports flexible dimensions (3072, 1024, 384, 256) for cost/accuracy tradeoffs
• Implementation uses S3 Vectors for storage, Amazon Bedrock APIs for embedding generation
• Cosine similarity ranking provides intuitive matching across all content types
• Code examples show text embedding, image embedding, and combined query processing

Amazon Nova Multimodal Embeddings simplifies building crossmodal search by eliminating separate embedding systems, reducing operational complexity while enabling richer user search experiences.