Amazon Web Services has unveiled a new capability for video search that interprets user intent rather than relying on exact matches. The feature, available through Amazon Bedrock, uses Nova Multimodal Embeddings to analyze video content across multiple signal types simultaneously. This means users can search for videos using natural language queries that capture the meaning behind their requests, not just specific keywords.
The system is designed to retrieve relevant video results even when the search terms do not appear directly in the content. For example, a query about "how to fix a leaking faucet" would return videos demonstrating that task, even if the phrase itself is not spoken or written in the footage. This approach aims to make video search more intuitive and accessible for users who may not know the exact terms to use.
A reference implementation is provided for developers to deploy and test with their own video libraries. The code is available on AWS documentation pages and can be customized for different use cases. AWS emphasizes that the tool works across all types of video signals, including visuals, audio, and text overlays.
The announcement follows Amazon's push to expand its AI-powered services on Bedrock. Nova Multimodal Embeddings is part of a suite of models designed to handle multiple data types in a single system. This integration allows for more sophisticated search capabilities without requiring users to switch between different search methods.
Developers interested in testing the feature can set up the reference implementation in their own AWS environments. The documentation includes step-by-step instructions for deployment and configuration.
Source: aws.amazon.com