Amazon Kendra Documentation

Amazon Kendra is an intelligent search service powered by machine learning. Kendra is designed to reimagine enterprise search for your websites and applications so your employees and customers can more easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.

Amazon Kendra uses machine learning to deliver more relevant answers from unstructured data. Search for general keywords like "health benefits" or ask natural language questions like "how long is maternity leave?” and Kendra is designed to use reading comprehension to give specific answers like "14 weeks”. For more general questions like "how do I configure my VPN?" Kendra is designed to give descriptive answers by extracting the most relevant text passage.

Amazon Kendra also supports FAQ matching and extracts answers from curated FAQs using a specialized model that is designed to pinpoint the closest question in the FAQ and return the corresponding answer. 

Amazon Kendra can even find answers in tables embedded in HTML pages. You can ask questions such as “What’s the credit card with lowest annual fees?” where the answer is found in a credit card comparison table on a marketing webpage.

To complement the Intelligent Search features described above, Amazon Kendra uses a deep learning semantic search model for accurate document ranking. Overall, this provides a richer search experience that presents specific answers, as well as related content to explore if you need more information. 

Amazon Kendra uses ML to continuously optimize search results based on end-user search patterns and feedback. For example, when users search for “How do I change my health benefits?" multiple human resources (HR) benefit documents will compete for a top spot. To determine the most relevant document for this question, Amazon Kendra can learn from the user interactions and feedback to promote preferred documents to the top of the list. It applies incremental learning techniques automatically without the need for ML expertise.

Customers can fine-tune search results and boost specific answers and documents in the results based on specific business objectives. For example, relevance tuning lets you boost results based on more authoritative data sources, authors, or document freshness.

To extend Amazon Kendra’s understanding of your specific business vocabulary, you can provide your own custom synonyms. Amazon Kendra uses these to automatically expand queries to include content and answers that match the extended vocabulary. 

Using connectors with Amazon Kendra is quicker and easier—just add data sources to your Amazon Kendra index and select the connector type. Connectors can be scheduled to automatically sync your index with your data source, so you're securely searching through the most up-to-date content. Amazon Kendra offers native connectors for popular data sources such as Amazon Simple Storage Service (S3), Microsoft SharePoint, Salesforce, ServiceNow, Google Drive, Confluence, and many more. If a native connector is not available, Amazon Kendra offers a custom data source connector as well as a host of partner-supported connectors.

Kendra uses deep learning models to better understand natural language queries and document content and structures for a wide range of internal use cases like HR, operations, support, and R&D. Kendra is also optimized to understand complex language from many different professional domains. 

You can now deploy a fully functional and customizable search experience with Amazon Kendra in a few steps, without any coding or ML experience. Experience Builder delivers an intuitive visual workflow to quickly build, customize, and launch your search application securely on the cloud. You can start with the ready-to-use search experience template in the builder, which can be customized by dragging and dropping the components you want, such as filters or sorting. You can invite others to collaborate or test your search application for feedback, and then share the project with all users when you are ready to deploy the experience. Amazon Kendra Experience Builder integrates with AWS IAM Identity Center (successor to AWS Single Sign-On), supporting popular identity providers such as Azure AD and Okta, delivering secure end-user single sign-on authentication while accessing the search experience.

Amazon Kendra Search Analytics Dashboard helps you to better understand quality and usability metrics across your search applications. The dashboard helps administrators and content creators understand how easily the end users are finding relevant search results, the quality of the search results, and gaps in the content. It provides a snapshot of how your users interact with your search application and how effective your search results are. The analytics data can be viewed in a visual dashboard in the console, or you can build your own dashboards by accessing the data through an API. It can empower you to dive deep into search trends and user behavior to identify insights, and also helps to bring clarity to potential areas of improvement. For more information about Amazon Kendra Search Analytics Dashboard, please visit the documentation.

With Amazon Kendra Custom Document Enrichment capabilities, you can build a custom ingestion pipeline that can pre-process documents before they get indexed into Amazon Kendra. For example, while ingesting content from a repository like SharePoint using our connectors, you can enrich documents with additional metadata, convert scanned documents to text, classify documents, extract entities, and further transform the document using custom ETL processes. The enrichment is performed by rules that can be configured in the console or by invoking functions from AWS Lambda. These functions can optionally call other AWS AI Services such as Amazon Comprehend, Amazon Transcribe, or Amazon Textract. For more information about Amazon Kendra Custom Document Enrichment, please visit the documentation.

Amazon Kendra includes the functionality to auto-complete an end user’s search query. Query auto-completion can help guide users towards more precise and commonly asked questions. More precise questions typically help result in more relevant and useful answers. For example, if you start typing "Where is" in the search box, Kendra can suggest options like "Where is the IT desk?", or "Where is the cafeteria?" and other related commonly asked questions, to complete the query.

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.aws.amazon.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.amazon.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.