If you are using Amazon Lex V2, refer to the Amazon Lex V2 guide instead.

 

If you are using Amazon Lex V1, we recommend upgrading your bots to Amazon Lex V2. We are no longer adding new features to V1 and strongly recommend using V2 for all new bots.

Step 1: Create an Amazon Kendra Index

Begin by creating an Amazon Kendra index of documents that answer customer questions. An index provides a search API for client queries. You create the index from source documents. Amazon Kendra returns answers it finds in indexed documents to the bot, which displays them to the agent.

The quality and accuracy of the responses suggested by Amazon Kendra depend on the documents that you index. Documents should include files that are frequently accessed by the agent and must be stored in an S3 bucket. You can index unstructured and semi-structured data in .html, Microsoft Office (.doc, .ppt), PDF, and text formats.

To create an Amazon Kendra index, see Getting started with an S3 bucket (console) in the Amazon Kendra Developer Guide.

To add questions and answers (FAQs) that help answer customer queries, see Adding questions and answers in the Amazon Kendra Developer Guide. For this tutorial, use the ML_FAQ.csv file on GitHub.

Next step

Step 2: Create an Amazon Lex Bot