Remove Metadata Remove ML Remove White Paper
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Secure your Amazon Kendra indexes with the ACL using a JWT shared secret key

AWS Machine Learning Blog

In the terminal with the AWS Command Line Interface (AWS CLI) or AWS CloudShell , run the following commands to upload the documents and metadata to the data source bucket: aws s3 cp s3://aws-ml-blog/artifacts/building-a-secure-search-application-with-access-controls-kendra/docs.zip. Expand Additional configuration.

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Discover insights from Amazon S3 with Amazon Q S3 connector 

AWS Machine Learning Blog

In a terminal with the AWS Command Line Interface (AWS CLI) or AWS CloudShell , run the following commands to upload the documents to the data source bucket: aws s3 cp s3://aws-ml-blog/artifacts/building-a-secure-search-application-with-access-controls-kendra/docs.zip. In the IAM role section, select Create new service role (Recommended).

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Search for answers accurately using Amazon Kendra S3 Connector with VPC support

AWS Machine Learning Blog

When end-users ask natural language questions, Amazon Kendra uses machine learning (ML) algorithms to understand the context and return the most relevant answers. The Amazon Kendra’s S3 connector supports indexing documents and their associated metadata stored in an S3 bucket. For Description , enter AWS White Paper documentation.

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Unleash the power of generative AI with Amazon Q Business: How CCoEs can scale cloud governance best practices and drive innovation

AWS Machine Learning Blog

You add access control information to a document in an Amazon S3 data source using a metadata file associated with the document. Leveraging his AI/ML domain expertise, Al develops and executes global go-to-market strategies that drive transformative results for AWS customers at scale. About the Authors Steven Craig is a Sr.