Amazon Bedrock Knowledge Bases now supports natural language querying to retrieve structured data from your data sources. With this launch, Bedrock Knowledge Bases offers an end-to-end managed workflow for customers to build custom generative AI applications that can access and incorporate contextual information from a variety of structured and unstructured data sources. Using advanced natural language processing, Bedrock Knowledge Bases can transform natural language queries into SQL queries, allowing users to retrieve data directly from the source without the need to move or preprocess the data.
Developers often face challenges integrating structured data into generative AI applications. This includes difficulties training large language models (LLMs) to convert natural language queries to SQL queries based on complex database schemas, as well as ensuring appropriate data governance and security controls are in place. Bedrock Knowledge Bases eliminates these hurdles by providing a managed natural language to SQL (NL2SQL) module. A retail analyst can now simply ask “What were my top 5 selling products last month?”, and then Bedrock Knowledge Base automatically translates that query into SQL, execute the query against the database, and return the results – or even provide a summarized narrative response. To generate accurate SQL queries, Bedrock Knowledge Base leverages database schema, previous query history, and other contextual information that are provided about the data sources.
Bedrock Knowledge Bases supports structured data retrieval from Amazon Redshift and Amazon Sagemaker Lakehouse at this time and is available in all commercial regions where Bedrock Knowledge Bases is supported. To learn more, visit here and here. For details on pricing, please refer here.
Source:: Amazon AWS