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Amazon
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Combine keyword and semantic search for text and images using Amazon Bedrock and Amazon OpenSearch Service
- Customers today expect efficient product search experiences, impacting business metrics like conversion rates and loyalty.
- Semantic search enhances relevancy by creating vector embeddings for queries, accepting text, image, and more.
- Keyword search remains essential for precise retrieval of product data based on user queries.
- Hybrid search combines keyword and semantic search for more accurate results, improving quality significantly.
- OpenSearch Service, recommended for Amazon Bedrock, provides a managed search infrastructure.
- Multimodal embedding models like Amazon Titan Multimodal Embeddings G1 enable hybrid search functionality.
- Data ingestion workflow involves generating vector embeddings for text, images, and metadata using Amazon Bedrock.
- Query workflow utilizes OpenSearch search pipelines to convert query input to embeddings and deliver search results.
- The process involves creating connectors, pipelines, indices, ingesting data, and testing search functionalities.
- Completing steps like creating IAM roles, registering models, and using search pipelines are crucial for deployment.
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