AWS Machine Learning Blog

Category: Amazon Kendra

Reimagine search on GitHub repositories with the power of the Amazon Kendra GitHub connector

Amazon Kendra offers highly accurate semantic and natural language search powered by machine learning (ML). Many organizations use GitHub as a code hosting platform for version control and to redefine collaboration of open-source software projects. A GitHub account repository might include many content types, such as files, issues, issue comments, issue comment attachments, pull requests, […]

Intelligently search your Jira projects with Amazon Kendra Jira cloud connector

July 2023: This post was reviewed for accuracy. Organizations use agile project management platforms such as Atlassian Jira to enable teams to collaborate to plan, track, and ship deliverables. Jira captures organizational knowledge about the workings of the deliverables in the issues and comments logged during project implementation. However, making this knowledge easily and securely […]

Search for knowledge in Quip documents with intelligent search using the Quip connector for Amazon Kendra

Organizations use collaborative document authoring solutions like Salesforce Quip to embed real-time, collaborative documents inside Salesforce records. Quip is Salesforce’s productivity platform that transforms the way enterprises work together, delivering modern collaboration securely and simply across any device. A Quip repository captures invaluable organizational knowledge in the form of collaborative documents and workflows. However, finding […]

Getting started with the Amazon Kendra Box connector

Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. For many organizations, Box Content Cloud is a core part of their content storage and lifecycle management […]

Enable Amazon Kendra search for a scanned or image-based text document

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document formats, […]

Improve search accuracy with Spell Checker in Amazon Kendra

Amazon Kendra is an intelligent search service powered by machine learning. You can receive spelling suggestions for misspelled terms in your queries by utilizing the Amazon Kendra Spell Checker. Spell Checker helps reduce the frequency of queries returning irrelevant results by providing spelling suggestions for unrecognized terms. In this post, we explore how to use […]

Unravel the knowledge in Slack workspaces with intelligent search using the Amazon Kendra Slack connector

Organizations use messaging platforms like Slack to bring the right people together to securely communicate with each other and collaborate to get work done. A Slack workspace captures invaluable organizational knowledge in the form of the information that flows through it as the users collaborate. However, making this knowledge easily and securely available to users […]

Securely search unstructured data on Windows file systems with the Amazon Kendra connector for Amazon FSx for Windows File Server

Critical information can be scattered across multiple data sources in your organization, including sources such as Windows file systems stored on Amazon FSx for Windows File Server. You can now use the Amazon Kendra connector for FSx for Windows File Server to index documents (HTML, PDF, MS Word, MS PowerPoint, and plain text) stored in […]

How InpharmD uses Amazon Kendra and Amazon Lex to drive evidence-based patient care

The intersection of DI and AI: Drug information refers to the discovery, use, and management of healthcare and medical information. Healthcare providers have many challenges associated with drug information discovery, such as intensive time involvement, lack of accessibility, and accuracy of reliable data. The average clinical query requires a literature search that takes an average of 18.5 hours. In addition, drug information often lies in disparate information silos, behind pay walls and design walls, and quickly becomes stale.

Enrich your content and metadata to enhance your search experience with custom document enrichment in Amazon Kendra

Amazon Kendra customers can now enrich document metadata and content during the document ingestion process using custom document enrichment (CDE). Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even […]