📊Tracking: Amazon Kendra

Add your +1 👍 to help us prioritize high-level constructs for this service
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### Overview:

Amazon Kendra is an enterprise search service that enables your users to intuitively search unstructured data using natural language. It returns specific answers to questions, giving users an experience that's close to interacting with a human expert. It is highly available and scalable, tightly integrated with other AWS services, and offers enterprise-grade security.

Amazon Kendra users can ask the following types of questions, or queries:

Factoid questions — Simple who, what, when, or where questions, such as Who is Amazon’s CEO? or What is the height of the Space Needle?. Factoid questions have fact-based answers that can be returned in the form of a single word or phrase. The precise answer, however, must be explicitly stated in the ingested text content.

Descriptive questions — Questions whose answer could be a sentence, passage, or an entire document. For example, How do I connect my Echo Plus to my network? or How do I get tax benefits for lower income families?.

Keyword searches— For questions where the intent and scope isn't clear, for example, vacation policy or health benefits, Amazon Kendra uses its deep learning models to return relevant documents.

[AWS Docs](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html)

### Maturity: CloudFormation Resources Only

See the [AWS Construct Library Module Lifecycle doc](https://github.com/aws/aws-cdk-rfcs/blob/master/text/0107-construct-library-module-lifecycle.md) for more information about maturity levels.

### Implementation:

See the [CDK API Reference](https://docs.aws.amazon.com/cdk/api/latest/docs/aws-kendra-readme.html) for more implementation details.

### Issue list:

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This is a 📊Tracking Issue

Start with the linked AWS Kendra documentation and CDK API Reference to understand the service and the intended construct library scope. The issue names no source files or tests; completion would require defining the high-level constructs and implementation checklist before development can begin.

Written by the indexing model from the issue text.