Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock

AWS has published a comprehensive technical guide for building an intelligent photo search system that moves beyond basic metadata to understand complex relationships and contexts. The solution integrates Amazon Rekognition for face detection and object labeling, Amazon Neptune as a graph database to map connections between people and objects, and Amazon Bedrock with Anthropic's Claude 3.5 Sonnet for generating contextual image captions. This serverless architecture enables natural language queries like 'Find all photos of grandparents with their grandchildren at birthday parties' by processing uploaded photos through automated workflows triggered from S3 storage.

The complete implementation uses AWS CDK for infrastructure-as-code deployment and includes Amazon API Gateway for REST endpoints, DynamoDB for metadata storage, and Lambda functions for orchestration. The system automatically processes reference photos to build recognition models, stores relationship graphs in Neptune, and creates searchable metadata that understands both visual content and semantic connections. Available on GitHub, this solution demonstrates how organizations can scale photo management for thousands of images across corporate, healthcare, and educational use cases while maintaining serverless cost efficiency and customization capabilities.