summarize doc
Amazon Q is a new generative AI-powered application that helps users get work done. Amazon Q can become your tailored business expert and let you discover content, brainstorm ideas, or create summaries using your company’s data safely and securely. You can use Amazon Q to have conversations, solve problems, generate content, gain insights, and take action by connecting to your company’s information repositories, code, data, and enterprise systems. For more information, see Introducing Amazon Q, a new generative AI-powered assistant (preview).
In this post, we show you how to bring Amazon Q, your business expert, to users in Microsoft Teams. (If you use Slack, refer to Deploy a Slack gateway for Amazon Q, your business expert.)
You’ll be able converse with Amazon Q business expert using Teams direct messages (DMs) to ask questions and get answers based on company data, get help creating new content such as email drafts, summarize attached files, and perform tasks.
You can also invite Amazon Q business expert to participate in your Teams channels. In a channel, users can ask Amazon Q business expert questions in a new message, or tag it in an existing thread at any point, to provide additional data points, resolve a debate, or summarize the conversation and capture the next steps.
Amazon Q business expert is amazingly powerful. Check out the following demo—seeing is believing!
In the demo, our Amazon Q business expert application is populated with some Wikipedia pages. You can populate your Amazon Q business expert application with your own company’s documents and knowledge base articles, so it will be able to answer your specific questions!
Everything you need is provided as open source in our GitHub repo.
In this post, we walk you through the process to deploy Amazon Q business expert in your AWS account and add it to Microsoft Teams. When you’re done, you’ll wonder how you ever managed without it!
The following are some of the things it can do:
/new_conversation
.In the following sections, we show how to deploy the project to your own AWS account and Teams account, and start experimenting!
You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. If you don’t have an AWS account, see How do I create and activate a new Amazon Web Services account?
You also need to have an existing, working Amazon Q business expert application. If you haven’t set one up yet, see Creating an Amazon Q application.
Lastly, you need a Microsoft account and a Microsoft Teams subscription to create and publish the app using the steps outlined in this post. If you don’t have these, see if your company can create sandboxes for you to experiment, or create a new account and trial subscription as needed to complete the steps.
We’ve provided pre-built AWS CloudFormation templates that deploy everything you need in your AWS account.
If you’re a developer and you want to build, deploy, or publish the solution from code, refer to the Developer README.
Complete the following steps to launch the CloudFormation stack:
Region | Launch Stack |
---|---|
N. Virginia (us-east-1 ) | |
Oregon (us-west-2 ) |
AMAZON-Q-TEAMS-GATEWAY
).80xxxxx9-7xx3-4xx0-bxx4-5baxxxxx2af5
). You can copy it from the Amazon Q business expert console.When your CloudFormation stack status is CREATE_COMPLETE, choose the Outputs tab, and keep it open—you’ll need it in later steps.
Complete the following steps to register a new app in the Microsoft Azure portal:
MicrosoftAppId
and MicrosoftAppTenantId
.description of my client secret
.MicrosoftAppPassword
.Complete the following steps to register your app in the Microsoft Bot Framework:
TeamsEventHandlerApiEndpoint
from your stack Outputs tab.MicrosoftAppId
value you noted earlier.MicrosoftAppTenantId
value you noted earlier.Let’s configure your Teams secrets in order to verify the signature of each request and post on behalf of your Amazon Q business expert bot.
In this example, we are not enabling Teams token rotation. You can enable it for a production app by implementing rotation via AWS Secrets Manager. Create an issue (or, better yet, a pull request) in the GitHub repo if you want this feature added to a future version.
Complete the following steps to configure a secret in Secrets Manager:
TeamsSecretConsoleUrl
to be redirected to the Secrets Manager console.MicrosoftAppId
, MicrosoftAppPassword
, and MicrosoftAppTenantId
with the values you noted in the previous steps.Complete the following steps to deploy the app to Teams:
https://www.anycompany.com/
. Use real ones for production.MicrosoftAppId
from earlier.MicrosoftAppId
value from the earlier steps.Complete the following step to add your bot to a team:
Now you can test your bot in Microsoft Teams!
Complete the following steps to start using Amazon Q business expert in Teams:
Hello
.You have now deployed a powerful new AI assistant into your sandbox Teams environment.
Play with it, try all the features discussed in this post, and copy the things you saw in the demo video. Most importantly, you can ask about topics related to the documents that you have ingested into your own Amazon Q business expert application. But don’t stop there. You can find additional ways to make it useful, and when you do, let us know by posting a comment.
Once you are convinced how useful it is, talk to your Teams admins (show them this post) and work with them to deploy it in your company’s Teams organizations. Your fellow employees will thank you!
When you’re finished experimenting with this solution, delete your app in Microsoft Teams, Bot Framework, and Azure portal. Then clean up your AWS resources by opening the AWS CloudFormation console and deleting the AMAZON-Q-TEAMS-GATEWAY
stack that you deployed. This deletes the resources that you created by deploying the solution.
The sample Amazon Q business expert Teams application discussed in this post is provided as open source—you can use it as a starting point for your own solution, and help us make it better by contributing back fixes and features via GitHub pull requests. Explore the code, choose Watch in the GitHub repo to be notified of new releases, and check back for the latest updates. We’d also love to hear your suggestions for improvements and features.
For more information on Amazon Q business expert, refer to the Amazon Q (For Business Use) Developer Guide.
Bob Strahan is a Principal Solutions Architect in the AWS Language AI Services team.
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