Power video semantic search with Amazon Nova Multimodal Embeddings

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Advanced, long

Summary

Amazon has developed a video semantic search solution utilizing Nova Multimodal Embeddings on Amazon Bedrock, designed to address the complexities of video content by processing multiple unstructured signals simultaneously. This solution natively handles text, documents, images, video, and audio, mapping them into a shared semantic vector space for enhanced retrieval accuracy and cost efficiency. The architecture features an ingestion pipeline that processes video into searchable embeddings, including shot segmentation, parallel processing for visual, audio, and transcription embeddings, celebrity detection via Amazon Rekognition, and caption/genre generation using Amazon Nova 2 Lite. A search pipeline then intelligently routes user queries, leveraging a hybrid approach that fuses semantic and lexical signals, and employs an intent analysis router with Anthropic Claude Haiku to dynamically weight modalities. This optimized approach significantly outperforms a baseline, achieving 90% Recall@5 and 95% Recall@10, compared to 51% and 64% respectively for the baseline.

Key takeaway

For AI Engineers building video search solutions, adopting a multimodal embedding approach with Amazon Nova Multimodal Embeddings and a hybrid search architecture can drastically improve retrieval accuracy. You should implement semantic segmentation and generate separate embeddings for visual, audio, and transcription signals, augmenting them with structured metadata. Leveraging an intent-aware query router, like one powered by Anthropic Claude Haiku, will ensure optimal weighting of modalities, leading to more relevant and efficient search results.

Key insights

Nova Multimodal Embeddings and hybrid search significantly improve video content retrieval by processing diverse modalities.

Principles

• Semantic segmentation preserves context.
• Separate embeddings offer precise control.
• Metadata enrichment captures factual entities.

Method

The solution uses FFmpeg for scene detection, Nova Multimodal Embeddings for 1024-dimensional vectors, Amazon Transcribe for speech-to-text, Amazon Rekognition for celebrity detection, and Amazon Nova 2 Lite for captions/genre, all indexed in Amazon OpenSearch Service.

In practice

• Use FFmpeg for scene-based video segmentation.
• Generate distinct visual, audio, and transcript embeddings.
• Combine semantic and lexical search for comprehensive results.

Topics

• Amazon Nova Multimodal Embeddings
• Video Semantic Search
• Hybrid Search Architecture
• Intent-Aware Query Routing
• Semantic Segmentation

Code references

• aws-samples/sample-video-semantic-search-multimodal-embeddings

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.