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A feature of Amazon Aurora that enables making calls to ML models like Amazon Bedrock or Amazon SageMaker through SQL functions, allowing direct generation of embeddings within the database and abstracting the vectorization process.
An on-demand, auto-scaling configuration for Amazon Aurora DB instances that automatically adjusts compute and memory capacity based on load, integrated with Knowledge Bases for Amazon Bedrock to simplify vectorization and database capacity management.
A cloud data warehouse that can be leveraged to store vector embeddings as List data types, enabling semantic search capabilities through SQL-based similarity functions within an existing data pipeline.
A cloud data platform that offers capabilities for storing and querying various data types, including vector embeddings, often used in conjunction with its data warehousing features.