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Build private and secure enterprise generative AI applications with Amazon Q Business using IAM Federation

AWS Machine Learning Blog

If you want to use Amazon Q Business to build enterprise generative AI applications, and have yet to adopt organization-wide use of AWS IAM Identity Center , you can use Amazon Q Business IAM Federation to directly manage user access to Amazon Q Business applications from your enterprise identity provider (IdP), such as Okta or Ping Identity.

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Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model

AWS Machine Learning Blog

Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification, and create a cost-effective classification layer capable of handling diverse, unstructured enterprise documents. Categorizing documents is an important first step in IDP systems.

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Discover insights from Amazon S3 with Amazon Q S3 connector 

AWS Machine Learning Blog

For example, a user might ask about the main points discussed in a blog post on cloud security, the installation steps outlined in a user guide, findings from a case study on hybrid cloud usage, market trends noted in an analyst report, or key takeaways from a whitepaper on data encryption.

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Build a receipt and invoice processing pipeline with Amazon Textract

AWS Machine Learning Blog

You can visualize the indexed metadata using OpenSearch Dashboards. This post uses the Amazon Textract IDP CDK constructs (AWS CDK components to define infrastructure for intelligent document processing (IDP) workflows), which allows you to build use case-specific, customizable IDP workflows.

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Intelligent document processing with Amazon Textract, Amazon Bedrock, and LangChain

AWS Machine Learning Blog

Document processing has witnessed significant advancements with the advent of Intelligent Document Processing (IDP). With IDP, businesses can transform unstructured data from various document types into structured, actionable insights, dramatically enhancing efficiency and reducing manual efforts.

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Building AI chatbots using Amazon Lex and Amazon Kendra for filtering query results based on user context

AWS Machine Learning Blog

In this blog post, we focus on retrieving custom search results that apply to a specific user or user group. Solution overview To solve this problem, you can identify one or more unique metadata information that is associated with the documents being indexed and searched. Let’s now explore how to build this solution in more detail.

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Find answers accurately and quickly using Amazon Q Business with the SharePoint Online connector

AWS Machine Learning Blog

A document is a collection of information that consists of a title, the content (or the body), metadata (data about the document), and access control list (ACL) information to make sure answers are provided from documents that the user has access to. In this case, the documents in SharePoint Online can have the federated group information.

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