Bot Framework v4 core bot sample.
This bot has been created using Bot Framework, it shows how to:
- Use LUIS to implement core AI capabilities
- Implement a multi-turn conversation using Dialogs
- Handle user interruptions for such things as
Help
orCancel
- Prompt for and validate requests for information from the user
- Use Application Insights to monitor your bot
This sample is a Spring Boot app and uses the Azure CLI and azure-webapp Maven plugin to deploy to Azure.
This bot uses LUIS, an AI based cognitive service, to implement language understanding and Application Insights, an extensible Application Performance Management (APM) service for web developers on multiple platforms.
LUIS language model setup, training, and application configuration steps can be found here.
If you wish to create a LUIS application via the CLI, these steps can be found in the README-LUIS.md.
Application Insights resource creation steps can be found here.
- From the root of this project folder:
- Build the sample using
mvn package
- Run it by using
java -jar .\target\bot-corebot-app-insights-sample.jar
- Build the sample using
Bot Framework Emulator is a desktop application that allows bot developers to test and debug their bots on localhost or running remotely through a tunnel.
- Install the latest Bot Framework Emulator from here
- Launch Bot Framework Emulator
- File -> Open Bot
- Enter a Bot URL of
http://localhost:3978/api/messages
As described on Deploy your bot, you will perform the first 4 steps to setup the Azure app, then deploy the code using the azure-webapp Maven plugin.
From a command (or PowerShell) prompt in the root of the bot folder, execute:
az login
az account set --subscription "<azure-subscription>"
If you aren't sure which subscription to use for deploying the bot, you can view the list of subscriptions for your account by using az account list
command.
az ad app create --display-name "<botname>" --password "<appsecret>" --available-to-other-tenants
Replace <botname>
and <appsecret>
with your own values.
<botname>
is the unique name of your bot.
<appsecret>
is a minimum 16 character password for your bot.
Record the appid
from the returned JSON
Replace the values for <appid>
, <appsecret>
, <botname>
, and <groupname>
in the following commands:
az deployment sub create --name "corebotAppInsightsDeploy" --location "westus" --template-file ".\deploymentTemplates\template-with-new-rg.json" --parameters appId="<appid>" appSecret="<appsecret>" botId="<botname>" botSku=S1 newAppServicePlanName="corebotAppInsightsPlan" newWebAppName="corebotAppInsights" groupLocation="westus" newAppServicePlanLocation="westus"
az deployment group create --resource-group "<groupname>" --template-file ".\deploymentTemplates\template-with-preexisting-rg.json" --parameters appId="<appid>" appSecret="<appsecret>" botId="<botname>" newWebAppName="corebotAppInsights" newAppServicePlanName="corebotAppInsightsPlan" appServicePlanLocation="westus" --name "corebotAppInsights"
In src/main/resources/application.properties update
MicrosoftAppPassword
with the botsecret valueMicrosoftAppId
with the appid from the first step
- Execute
mvn clean package
- Execute
mvn azure-webapp:deploy -Dgroupname="<groupname>" -Dbotname="<bot-app-service-name>"
If the deployment is successful, you will be able to test it via "Test in Web Chat" from the Azure Portal using the "Bot Channel Registration" for the bot.
After the bot is deployed, you only need to execute #6 if you make changes to the bot.
- Spring Boot
- Maven Plugin for Azure App Service
- Bot Basics
- Dialogs
- Gathering Input Using Prompts
- Activity processing
- Azure for Java cloud developers
- Azure Bot Service Introduction
- Azure Bot Service Documentation
- Azure CLI
- Azure Portal
- Language Understanding using LUIS
- Channels and Bot Connector Service
- Spring Boot
- Application insights Overview
- Getting Started with Application Insights
- Filtering and preprocessing telemetry in the Application Insights SDK