Build Multimodal Video Search at Scale with AWS Nova Models

Revolutionize Your Video Search with AI

Tired of sifting through endless video archives with outdated keyword searches? AWS is changing the game with a powerful new approach to video content discovery. A recent post details how to build a scalable multimodal video search system using Amazon Nova models and Amazon OpenSearch Service, allowing you to find exactly what you're looking for with natural language queries.

This innovative solution moves beyond simple tags, enabling semantic search that truly understands the richness of your video content. Whether you're a media company, an educational institution, or any organization managing large video libraries, this could be a game-changer for content retrieval and analysis.

Unpacking the Power and Performance

The system showcased by AWS is engineered for serious scale. It successfully processed an astonishing 792,270 videos, totaling 8,480 hours (or 30.5 million seconds), from the Multimedia Commons and MEVA datasets in just 41 hours. This incredible feat was achieved using an ingestion pipeline powered by four Amazon EC2 c7i.48xlarge instances, running 600 parallel workers, capable of processing 19,400 videos per hour.

At the core of this system are the Amazon Nova models. Amazon Nova Multimodal Embeddings segments videos into optimal 15-second chunks and generates highly efficient 1024-dimensional audio-visual embeddings. This smart choice also offers a significant 3x cost savings for storage compared to 3072-dimensional embeddings, with minimal impact on accuracy. Additionally, Amazon Nova Pro enhances search capabilities by adding 10-15 descriptive tags per video from a predefined taxonomy. For those just starting, Amazon Nova 2 Lite is recommended for new deployments due to its improved accuracy and lower tagging cost.

Flexible Search Modes and Cost-Effectiveness

This advanced system supports three powerful search modes: text-to-video, video-to-video, and a highly accurate hybrid search. The hybrid option intelligently combines vector similarity (with a 70% weight) and traditional keyword matching (30% weight) for optimal results. Embeddings are stored in an OpenSearch k-NN index, while metadata tags reside in a separate text index, facilitating comprehensive search queries.

Considering the scale, the cost efficiency is impressive. The first-year total cost for this solution is estimated at $27,328 with OpenSearch on-demand, or a even lower $23,632 with OpenSearch Service Reserved Instances. The one-time ingestion cost was calculated at $18,088, which includes Amazon EC2 compute ($421), Amazon Bedrock Nova Multimodal Embeddings ($17,096 at $0.00056/second batch pricing), and Nova Pro tagging ($571). Annual Amazon OpenSearch Service costs are then $9,240 (on-demand) or $5,544 (Reserved).

Getting Started with Your AI Video Search

To implement this powerful solution, you'll need an AWS account with Amazon Bedrock access in us-east-1, Python 3.9 or later, the AWS CLI configured, an Amazon OpenSearch Service domain (r6g.large or larger is recommended), and an Amazon S3 bucket for video storage. The detailed guide walks you through setting up IAM roles, configuring OpenSearch indexes, and running the ingestion pipeline.

This robust framework provides a clear path for organizations to implement state-of-the-art multimodal video search, unlocking new possibilities for content discovery and utilization. To dive deeper into the architecture and step-by-step implementation, explore the full post on Multimodal Embeddings at Scale AI Data Lake.

Read more: Multimodal Embeddings at Scale AI Data Lake and start building your intelligent video search today!