Building Semantic Search with Amazon Nova Embeddings: A Practical Guide

Full-text search breaks when users don't know the exact terms. A user searching "how to handle payment failures" won't find your article titled "Dunning Strategy for Involuntary Churn" even though it's exactly what they need. Semantic search fixes this by matching on meaning, not keywords. I recently built semantic search into a production research tool using Amazon Nova Multimodal Embeddings. Here's the architecture, the implementation, and what I learned. Why Nova Embeddings? Amazon Nova Multimodal Embeddings ( amazon.nova-2-multimodal-embeddings-v1:0 ) generates 384-dimensional vectors from text. What makes it interesting: Low dimensionality : 384 dims vs. 1536 for OpenAI's ada-002. Faster similarity computation, less storage, still accurate enough for most use cases. Multimodal : Same model handles text and images. Useful if you later want to search across content types. AWS native : If you're already on AWS (Bedrock), no additional vendor relationship needed. The Architecture User