Merge branch 'km-agentframework-v2' of https://github.com/microsoft/Conversation-Knowledge-Mining-Solution-Accelerator into km-agentframework-v2

Author: Pavan-Microsoft

azure.yaml

@@ -18,13 +18,19 @@ hooks:
       run: |
         Write-Host "Web app URL: "
         Write-Host "$env:WEB_APP_URL" -ForegroundColor Cyan
+
+        Write-Host "`nRun the following command in the bash terminal to create agents:"
+        Write-Host "bash ./infra/scripts/agent_scripts/run_create_agents_scripts.sh" -ForegroundColor Cyan
       shell: pwsh
       continueOnError: false
       interactive: true
     posix:
       run: |
         echo "Web app URL: "
         echo $WEB_APP_URL
+
+        echo "\nRun the following command in the bash terminal to create agents:"
+        echo "bash ./infra/scripts/agent_scripts/run_create_agents_scripts.sh"
       shell: sh
       continueOnError: false
       interactive: true

documents/DeploymentGuide.md

@@ -238,9 +238,45 @@ Once you've opened the project in [Codespaces](#github-codespaces), [Dev Contain
     -- This deployment will take *7-10 minutes* to provision the resources in your account and set up the solution with sample data.
     - If you encounter an error or timeout during deployment, changing the location may help, as there could be availability constraints for the resources.
 
-5. Once the deployment has completed successfully, open the [Azure Portal](https://portal.azure.com/), go to the deployed resource group, find the App Service, and get the app URL from `Default domain`.
+5. Once the deployment has completed successfully, copy the bash command from terminal: (ex: `bash ./infra/scripts/agent_scripts/run_create_agents_scripts.sh`) for later use.
 
-6. If you are done trying out the application, you can delete the resources by running `azd down`.
+> **Note**: If you are running this deployment in GitHub Codespaces or VS Code Dev Container or Visual Studio Code (WEB) skip to step 7.
+
+6. Create and activate a virtual environment in bash terminal:
+  
+    ```shell
+    python -m venv .venv
+    ```
+
+    ```shell
+    source .venv/Scripts/activate
+    ```
+
+7. Login to Azure:
+
+    ```shell
+    az login
+    ```
+
+    Alternatively, login to Azure using a device code (recommended when using VS Code Web):
+
+    ```shell
+    az login --use-device-code
+    ```
+
+8. Run the bash script from the output of the azd deployment. The script will look like the following:
+    ```Shell
+    bash ./infra/scripts/agent_scripts/run_create_agents_scripts.sh
+    ```
+
+    If you don't have azd env then you need to pass parameters along with the command. Then the command will look like the following:
+    ```Shell
+    bash ./infra/scripts/agent_scripts/run_create_agents_scripts.sh <project-endpoint> <solution-name> <gpt-model-name> <ai-foundry-resource-id> <api-app-name> <azure-ai-search-connection-name> <azure-ai-search-index> <resource-group>
+    ```
+
+9. Once the script has run successfully, open the [Azure Portal](https://portal.azure.com/), go to the deployed resource group, find the App Service, and get the app URL from `Default domain`.
+
+10. If you are done trying out the application, you can delete the resources by running `azd down`.
    > **Note:** If you deployed with `enableRedundancy=true` and Log Analytics workspace replication is enabled, you must first disable replication before running `azd down` else resource group delete will fail. Follow the steps in [Handling Log Analytics Workspace Deletion with Replication Enabled](./LogAnalyticsReplicationDisable.md), wait until replication returns `false`, then run `azd down`.
 
 ### 🛠️ Troubleshooting

documents/LocalDebuggingSetup.md

@@ -70,6 +70,9 @@ If you don't have an existing environment, you must first deploy the Azure resou
 |-------------|-------|------|
 | `SOLUTION_NAME` |  | Prefix used to uniquely identify resources in the deployment |
 | `RESOURCE_GROUP_NAME` |  | Name of the Azure Resource Group |
+| `AGENT_NAME_CONVERSATION` | | Name of the conversation agent |
+| `AGENT_NAME_TITLE` |  | Name of the title agent |
+| `API_APP_NAME` |  | Name of the Azure App Service for the API |
 | `APP_ENV` | `dev` | Set APP_ENV in your .env file to control Azure authentication. Set the environment variable to dev to use Azure CLI credentials, or to prod to use Managed Identity for production. Ensure you're logged in via az login when using dev in local. |
 | `APPINSIGHTS_INSTRUMENTATIONKEY` |  | Instrumentation Key for Azure Application Insights |
 | `APPLICATIONINSIGHTS_CONNECTION_STRING` |  | Connection string for Application Insights |
@@ -80,6 +83,7 @@ If you don't have an existing environment, you must first deploy the Azure resou
 | `AZURE_AI_SEARCH_INDEX` | `call_transcripts_index` | Name of the Azure AI Search index |
 | `AZURE_AI_SEARCH_CONNECTION_NAME` |  | Connection name for Azure AI Search |
 | `AZURE_AI_FOUNDRY_NAME` |  | Name of the Azure AI Foundry resource |
+| `AZURE_AI_FOUNDRY_RESOURCE_ID` | | Resource ID of the Azure AI Foundry resource |
 | `AZURE_AI_SEARCH_NAME` |  | Name of the Azure AI Search service |
 | `AZURE_EXISTING_AI_PROJECT_RESOURCE_ID` |  | Resource ID of existing AI project (if using existing foundry project) |
 | `AZURE_COSMOSDB_ACCOUNT` |  | Name of the Azure Cosmos DB account |

infra/main.bicep

@@ -1544,6 +1544,10 @@ module webSiteBackend 'modules/web-sites.bicep' = {
         name: 'appsettings'
         properties: {
           REACT_APP_LAYOUT_CONFIG: reactAppLayoutConfig
+          AGENT_NAME_CONVERSATION: ''
+          AGENT_NAME_TITLE: ''
+          API_APP_NAME: 'api-${solutionSuffix}'
+          AZURE_AI_FOUNDRY_RESOURCE_ID: !empty(existingAiFoundryAiProjectResourceId) ? existingAiFoundryAiProjectResourceId : aiFoundryAiServices.outputs.resourceId
           AZURE_OPENAI_DEPLOYMENT_MODEL: gptModelName
           AZURE_OPENAI_ENDPOINT: !empty(existingOpenAIEndpoint) ? existingOpenAIEndpoint : 'https://${aiFoundryAiServices.outputs.name}.openai.azure.com/'
           AZURE_OPENAI_API_VERSION: azureOpenAIApiVersion
@@ -1741,3 +1745,15 @@ output API_APP_URL string = 'https://api-${solutionSuffix}.azurewebsites.net'
 
 @description('Contains web application URL.')
 output WEB_APP_URL string = 'https://app-${solutionSuffix}.azurewebsites.net'
+
+@description('Contains API application name.')
+output API_APP_NAME string = 'api-${solutionSuffix}'
+
+@description('Contains AI Foundry resource ID.')
+output AZURE_AI_FOUNDRY_RESOURCE_ID string = !empty(existingAiFoundryAiProjectResourceId) ? existingAiFoundryAiProjectResourceId : aiFoundryAiServices.outputs.resourceId
+
+@description('Contains Conversation Agent name.')
+output AGENT_NAME_CONVERSATION string = ''
+
+@description('Contains Title Agent name.')
+output AGENT_NAME_TITLE string = ''