Crossmodal search with Amazon Nova Multimodal Embeddings

Amazon Nova Multimodal Embeddings processes text, documents, images, video, and audio through a single model architecture. Available through Amazon Bedrock, the model converts different input modalities into numerical embeddings within the same vector space, supporting direct similarity calculations regardless of content type. We developed this unified model to reduce the need for separate embedding models, which complicate architectures, are difficult to maintain and operate, and further limit use cases to a one-dimensional approach.

In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications ...