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README-Deploy-Luis-Bot.md

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Deploy LUIS Bot

Assuming we have an idea of what intents (and potentially entities) we'd like to detect, we can look into training a LUIS model using the portal.

We can utilize this Getting Starting guide for creating a LUIS model in the portal.

We'll want to make sure that we can deploy the LUIS model with the corresponding Core bot for testing.

Helpful Links

  1. Getting Started With LUIS
  2. Tutorial for LUIS with C# Bot Framework
  3. LUIS Best Practices Guidance
  4. Intents
  5. Utterances
  6. Entities
  7. LUIS Development Lifecycle
  8. Active Learning
  9. Review Endpoint Utterances
  10. LUIS Docker Containers
  11. Bot Framework Emulator
  12. LUIS App Iteratiion
  13. Creating a new version for each cycle
  14. Importing and Exporting the LUIS Model
  15. Using Publish Slots
  16. Bot Framework CLI
  17. LUIS API
  18. Azure CLI
  19. Deploy Bot
  20. Zip a File
  21. Sample LUIS ARM Template

Reminder on the Sample

For example, suppose we want to detect some intents like location, get invoice, talk to an agent, or get pay policy.

Design Intents

We'll use the sample LUIS model that contains these examples. For more information on LUIS, it may be helpful to start with Training a LUiS Model in the Portal.

Deploying LUIS and a Bot

Read this first!

Take a look at this Deploy your Bot doc, as it contains plenty of guidance which we'll pull into a script. Of course, this can be further enhanced through CI/CD pipelines and tooling, but this is a sample for how we can deploy LUIS and a bot.

For this scenario, we'll be use scripting and ARM templates.

We're going to assume that we have:

  1. Bot Framework CLI
  2. Azure CLI
  3. LUIS API

For the LUIS API access, this will depend on provisioning the LUIS application.

From the project, make sure we can access the following from an administrative PowerShell console:

  1. Sample LUIS Model
  2. Deployment script
  3. Template ARM File
  4. Parameters ARM File

The template is based off of the Sample LUIS ARM Template.

Deploy components

We'll want to be in the repo root directory. And we'll also want to fill in some details on the script:

$botName = "mybot"
$location = "westus"
$rgName = "$botName-rg"
$luisAccountName = "luis-$botName"
$tempParams = "parameters.temp.json"
$parametersPath = ".\Scenarios\Scenario-LUIS-CSharp-Office-Bot\Sample-Templates\parameters.json"
$templatePath = ".\Scenarios\Scenario-LUIS-CSharp-Office-Bot\Sample-Templates\template.json"
$codeDir = ".\Scenarios\Scenario-LUIS-CSharp-Office-Bot"
$projFileName = "CoreBot.csproj"

These should line up relative to the repo root directory. If not, make sure that the script has the updated paths included.

Further, there's some API testing we can use too. Make sure these settings reflect the appropriate location:

$luisRestAPIUrl = "https://westus.api.cognitive.microsoft.com/luis/api/v2.0"
$modelFileName = ".\Scenarios\Scenario-LUIS-CSharp-Office-Bot\Sample-Models\luis-office-bot_vLOB-Intents-Entities-0.1.2.json"
$testQuery = "Can you share with me my last pay slip?"

Notice that there's a test query, which we can use for testing against the LUIS model.

Make sure we login to Azure and have the appropriate subscription set.

az login
az account show

Create a resource group for the deployment:

az group create -n $rgName -l $location

Create an Azure AD App registration. See the Bot Deployment Notes for further details. We'll also reset the credential password.

$appRegistrationJson = $(az ad app create --display-name $botName --key-type Password --available-to-other-tenants "true")
$appId = $($appRegistrationJson | ConvertFrom-Json).appId
$credObject = $(az ad app credential reset --id $appId) | ConvertFrom-Json

We're also going to deploy based on updated parameters, see the deployment script for more details. We're replacing some parameters in the file and pass that along to the following command:

az group deployment create --name mydeploy --resource-group $rgName --parameters $tempParams --template-file $templatePath

Deploy LUIS Application

We can use the following to import the LUIS application. Again, please refer to the deployment script to get more details on populating these values.

bf luis:application:import --endpoint $location --subscriptionKey $LuisAPIKey --name $luisAccountName --in $modelFileName

We can also train and check on the status of the LUIS model.

bf luis:train:run --endpoint $location --subscriptionKey $LuisAPIKey --appId $luisAppId --versionId $luisModelObject.versionId
bf luis:train:show --endpoint $location --subscriptionKey $LuisAPIKey --appId $luisAppId --versionId $luisModelObject.versionId

In the context of the script, we try something simple to check on the status (e.g. polling the status).

Next, we can update the web app settings associated with the bot. In this case, we'll populate LUIS settings.

az webapp config appsettings set -g $rgName -n $botName --settings "LuisAPIKey=$LuisAPIKey"
az webapp config appsettings set -g $rgName -n $botName --settings "LuisAppId=$LuisAppId"
#just need location as hostname.
az webapp config appsettings set -g $rgName -n $botName --settings "LuisAPIHostName=$location"

We can publish the model to staging, and also run a test query against the staging slot for LUIS.

bf luis:application:publish --endpoint $location --subscriptionKey $LuisAPIKey --versionId $luisModelObject.versionId --appId $LuisAppId --staging
bf luis:application:query --endpoint $location --subscriptionKey $LuisAPIKey --appId $LuisAppId --query $testQuery --staging

Assuming we're satisfied with the staging version of the model, let's take some steps towards production slot.

We can set the LUIS model to have a public endpoint. This depends on having access to the endpoint and of course the appropriate API Key. Please also check the LUIS Rest API.

Note, this is for testing purposes. In the case that LUIS doesn't need a public endpoint, please do not enable this, and instead think about service to service calls or VNET integrated calls.

$luisRestAPIUrl = "https://westus.api.cognitive.microsoft.com/luis/api/v2.0"
$appSettingsUrl = "$luisRestAPIUrl/apps/$LuisAppId/settings"
Invoke-WebRequest "$appSettingsUrl" -Headers  @{ "Ocp-Apim-Subscription-Key" = "$LuisAPIKey" } -Body (ConvertTo-Json @{ "public"=$true}) -Method PUT -ContentType 'application/json'

We can publish the model to the production slot and then run a test query against the production slot for LUIS.

bf luis:application:publish --endpoint $location --subscriptionKey $LuisAPIKey --versionId $luisModelObject.versionId --appId $LuisAppId
bf luis:application:query --endpoint $location --subscriptionKey $LuisAPIKey --appId $LuisAppId --query $testQuery

Deploy the bot

In the case of a C# bot, we'll use Azure CLI to prepare the bot environment.

Note, this will create a .deployment file in the bot code folder. In the script we move the existing one, but this is a consideration that the .deployment file will be used as part of deploying the bot.

az bot prepare-deploy --lang Csharp --code-dir $codeDir --proj-file-path $projFileName

Next, we want to zip the bot code directory. When we zip this in the script, the destination folder cannot be a child of the source folder. The contents of this will be zipped up for deployment.

Please refer to this PowerShell sample for Creating a zip archive of a folder.

$workingDirectory = Get-Location | select -ExpandProperty Path
$source = "$workingDirectory\$codeDir"
$destinationFolder = "$workingDirectory\$codeDir-deployment" #cannot be part of the source folder.
$destinationPath = "$destinationFolder\$botName.zip"

mkdir $destinationFolder -Force

If(Test-path $destinationPath) 
{
    Remove-item $destinationPath
}

Add-Type -assembly "system.io.compression.filesystem"
[io.compression.zipfile]::CreateFromDirectory($Source, $destinationPath)

We can now publish the bot .zip file.

az webapp deployment source config-zip -g $rgName --name $botName --src $destinationPath

Test Web Chat Bot

Let's first check that the bot has the appropriate Azure App Settings configured.

Check Azure App Settings

Setting Description
LuisAPIHostName The LUIS API Host name, in the form of Location.api.cognitive.microsoft.com. For example, westus.api.cognitive.microsoft.com would use westus. This also depends on bot implementation.
LuisAPIKey The API key. Note that in the luis portal (https://luis.ai), this key is a
GUID without the dashes.
LuisAppID This is a GUID for the LUIS Application ID.
MicrosoftAppId This is the Azure AD App registration ID GUID.
MicrosoftAppPassword This is the Azure AD App registration password.
WEBSITE_NODE_DEFAULT_VERSION Applicable for Node.js bots.

In the Azure Portal, we can navigate to the Bot Service and check on the 'Test in Web Chat'.

Assuming can load the bot (and the appropriate settings are in place), we should see the bot load for some testing.

Test Web Chat

Clean up

Please remember to clean up the resource group that is holds the components.

az group delete -g $rgName

Also, be sure to clean up the Azure AD App registration.

az ad app delete --id $appId