Accelerate your Amazon Q implementation: starter kits for SMBs

by
Amazon AWS

Whether you’re a small or medium-sized business (SMB) or a managed service provider at the beginning of your cloud journey, you might be wondering how to get started. Questions like “Am I following best practices?”, “Am I optimizing my cloud costs?”, and “How difficult is the learning curve?” are quite common. AWS is here to provide a concept called starter kits.

Starter kits are complete, deployable solutions that address common, repeatable business problems. They deploy the services that make up a solution according to best practices, helping you optimize costs and become familiar with these kinds of architectural patterns without a large investment in training. Most of all, starter kits save you time—time that can be better spent on your business or with your customers.

In this post, we showcase a starter kit for Amazon Q Business. If you have a repository of documents that you need to turn into a knowledge base quickly, or simply want to test out the capabilities of Amazon Q Business without a large investment of time at the console, then this solution is for you.

This deployment guide covers the steps to set up an Amazon Q solution that connects to Amazon Simple Storage Service (Amazon S3) and a web crawler data source, and integrates with AWS IAM Identity Center for authentication. An AWS CloudFormation template automates the deployment of this solution.

Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. It empowers employees to be more creative, data-driven, efficient, prepared, and productive.

Solution overview

The following diagram illustrates the solution architecture.

Solution Architecture

The workflow involves the following steps:

  1. The user authenticates using an AWS Identity and Access Management (IAM) identity user name and password before accessing the Amazon Q web application.
  2. Upon successful authentication, the user can access the Amazon Q web UI and ask a question.
  3. Amazon Q retrieves relevant information from its index, which is populated using data from the connected data sources (Amazon S3 and a web crawler).
  4. Amazon Q then generates a response using its internal large language model (LLM) and presents it to the user through the Amazon Q web UI.
  5. The user can provide feedback on the response through the Amazon Q web UI.

Prerequisites

Before deploying the solution, make sure you have the following in place:

  • AWS account – You will need an active AWS account with the necessary permissions to deploy CloudFormation stacks and create the required resources.
  • Amazon S3 bucket – Make sure you have an existing S3 bucket that will be used as the data source for Amazon Q. To create a S3 bucket, refer to Create your first S3 bucket.
  • AWS IAM Identity Center – Configure AWS IAM Identity Center in your AWS environment. You will need to provide the necessary details, such as the IAM Identity Center instance Amazon Resource Name (ARN), during the deployment process.

Deploy the solution using AWS CloudFormation

Complete the following steps to deploy the CloudFormation template:

  1. Sign in to the AWS Management Console.
  2. Choose one of the following Launch Stack options for your desired AWS Region to open the AWS CloudFormation console and create a new stack. Please note that this stack will default to us-east-1.
    Launch Stack to create solution resources
  3. For Stack name, enter a name for your application (for example, AMAZON-Q-STARTER-KIT).
  4. In the Parameters section, for IAMIdentityCenterARN, enter the ARN of your IAM Identity Center instance.
  5. For QBusinessApplicationName, enter a name for the Amazon Q Business application.
  6. For S3DataSourceBucket, enter the name of the S3 bucket you created earlier.
  7. For WebCrawlerDataSourceUrl, enter the URL of the web crawler data source.
  8. Choose Next.

Parameters section for IAMIdentityCenterARN

  1. On the Configure stack options page, leave everything as default, select I acknowledge that AWS CloudFormation might create IAM resources and and choose Next.

acknowledge AWS CloudFormation

  1. On the Review and create page, choose Submit.
  2. On the Amazon Q Business console, you will see the new application you created.
  3. Choose the new Amazon Q Business application, and in the Data sources section, select the data source s3_datasource and choose Sync now.
  4. Select the data source webpage-datasource and choose Sync now.
  5. To add groups and users to your Amazon Q application, refer to instructions.

Test the solution

To validate the Amazon Q solution is functioning as expected, perform the following tests:

  1. Test data ingestion:
    1. Upload a test file to the S3 bucket.
    2. Verify that the file is successfully ingested and processed by Amazon Q.
    3. Check the Amazon Q web experience UI for the processed data.
  2. Test web crawler functionality:
  3. Verify that the web crawler is able to retrieve and ingest the data from the website.
  4. Make sure the data is displayed correctly in the Amazon Q web experience UI.

Clean up

To clean up, delete the CloudFormation stack and the S3 bucket you created.

Conclusion

The Amazon Q starter kit provides a streamlined solution for SMBs to use the power of generative AI and intelligent question-answering. By automating the deployment and integration with key data sources, this kit eases the complexity of setting up Amazon Q, empowering businesses to quickly unlock insights and improve productivity.

If your SMB has a repository of documents that need to be transformed into a valuable knowledge base, or you simply want to explore the capabilities of Amazon Q, we encourage you to take advantage of this starter kit. Get started today and experience the transformative benefits of enterprise-grade question-answering tailored for your business needs, and let us know what you think in the comments. To explore more generative AI use cases, refer to AI Use Case Explorer.

About the Authors

Nneoma Okoroafor is a Partner Solutions Architect focused on AI/ML and generative AI. Nneoma is passionate about providing guidance to AWS Partners on using the latest technologies and techniques to deliver innovative solutions to customers.

Joshua Amah is a Partner Solutions Architect with Amazon Web Services. He primarily serves consulting partners, providing architectural guidance and recommendations for new and existing workloads. Outside of work, he enjoys playing soccer, golf, and spending time with family and friends.

Jason Brown is a Partner Solutions Architect focused on helping AWS Distribution Partners and their Seller Partners build and grow their AWS practices. Jason is passionate about building solutions for MSPs and VARs in the small business space. Outside the office, Jason is an avid traveler and enjoys offshore fishing.

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