Power video semantic search with Amazon Nova Multimodal Embeddings

This article demonstrates how to build a video semantic search solution using Amazon Nova Multimodal Embeddings on AWS, enabling intelligent retrieval across visual, audio, and transcript signals.

• Nova Multimodal Embeddings natively processes video, audio, text, and images into shared semantic vector space
• Scene-based segmentation preserves semantic continuity instead of arbitrary fixed-length chunks
• Separate visual, audio, and transcript embeddings provide precise modality-specific search control
• Hybrid search combines semantic embeddings with lexical metadata matching for entity queries
• Intent-aware routing uses Claude Haiku to assign dynamic weights across modalities per query
• S3 Vectors stores embeddings cost-effectively; OpenSearch indexes metadata and vectors
• Hybrid approach achieved 90% Recall@5 versus 51% baseline, 40-point improvement
• Reference implementation available on GitHub for deployment with custom content

The solution demonstrates how multimodal embeddings combined with intelligent routing and hybrid search significantly outperforms text-only approaches for video retrieval at scale.