fix: Remove preprovision hooks from azure.yaml and update main.parameters.json, adjust gptDeploymentCapacity in main.bicep

Author: Vamshi-Microsoft

azure.yaml

@@ -9,19 +9,6 @@ metadata:
   template: [email protected]
 
 hooks:
-  preprovision:
-    windows:
-      run: |
-        # $timestamp = Get-Date -Format "yyyyMMdd-HHmmss"; $logFile = "azd_preprovision_$timestamp.log"; ./infra/scripts/docker-build.ps1 $env:AZURE_SUBSCRIPTION_ID $env:AZURE_ENV_NAME $env:AZURE_LOCATION $env:AZURE_RESOURCE_GROUP $env:USE_LOCAL_BUILD $env:AZURE_ENV_IMAGETAG *>&1 | Tee-Object -FilePath $logFile
-      shell: pwsh
-      continueOnError: false
-      interactive: true
-    posix:
-      run: |
-        # timestamp=$(date +"%Y%m%d-%H%M%S"); logFile="azd_preprovision_$timestamp.log"; sed -i 's/\r$//' ./infra/scripts/docker-build.sh; ./infra/scripts/docker-build.sh "$AZURE_SUBSCRIPTION_ID" "$AZURE_ENV_NAME" "$AZURE_LOCATION" "$AZURE_RESOURCE_GROUP" "$USE_LOCAL_BUILD" "$AZURE_ENV_IMAGETAG" 2>&1 | tee "$logFile"
-      shell: sh
-      continueOnError: false
-      interactive: true
   postprovision:
     windows:
       run: |

documents/CustomizingAzdParameters.md

@@ -22,7 +22,6 @@ By default this template will use the environment name as the prefix to prevent
 | `AZURE_ENV_IMAGETAG`                      | string  | `latest`        | Sets the image tag (`latest`, `dev`, `hotfix`, etc.).   |
 | `AZURE_OPENAI_EMBEDDING_MODEL_CAPACITY`   | integer | `80`                     | Sets the capacity for the embedding model deployment.                      |
 | `AZURE_ENV_LOG_ANALYTICS_WORKSPACE_ID`    | string  | `<Existing Workspace Id>`            | Reuses an existing Log Analytics Workspace instead of creating a new one.  |
-| `USE_LOCAL_BUILD`    | string  | `false`           | Indicates whether to use a local container build for deployment.  |
 | `AZURE_EXISTING_AI_PROJECT_RESOURCE_ID`    | string  | `<Existing AI Project resource Id>`            | Reuses an existing AIFoundry and AIFoundryProject instead of creating a new one.  |
 
 

documents/DeploymentGuide.md

@@ -170,32 +170,25 @@ Once you've opened the project in [Codespaces](#github-codespaces), [Dev Contain
 
 6. If you are done trying out the application, you can delete the resources by running `azd down`.
 
-### Publishing Local Build Container to Azure Container Registry
-
-If you need to rebuild the source code and push the updated container to the deployed Azure Container Registry, follow these steps:
+## Post Deployment Steps
+1. Optional: Publishing Local Build Container to Azure Container Registry 
 
-1. Set the environment variable `USE_LOCAL_BUILD` to `True`:
+   If you need to rebuild the source code and push the updated container to the deployed Azure Container Registry, follow these steps:
 
    - **Linux/macOS**:
      ```bash
-     export USE_LOCAL_BUILD=True
+     cd ./infra/scripts/
+     ./docker-build.sh
      ```
 
    - **Windows (PowerShell)**:
      ```powershell
-     $env:USE_LOCAL_BUILD = $true
+     cd .\infra\scripts\
+     .\docker-build.ps1
      ```
-2. Run the `az login` command
-   ```bash
-   az login
-   ```
 
-3. Run the `azd up` command again to rebuild and push the updated container:
-   ```bash
-   azd up
-   ```
+    This will create a new Azure Container Registry, rebuild the source code, package it into a container, and push it to the Container Registry created.
 
-This will rebuild the source code, package it into a container, and push it to the Azure Container Registry associated with your deployment.
 
 ## Post Deployment Steps
 

infra/main.bicep

@@ -47,7 +47,7 @@ param azureOpenAIApiVersion string = '2025-01-01-preview'
 @description('Capacity of the GPT deployment:')
 // You can increase this, but capacity is limited per model/region, so you will get errors if you go over
 // https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits
-param gptDeploymentCapacity int = 30
+param gptDeploymentCapacity int = 150
 
 @minLength(1)
 @description('Name of the Text Embedding model to deploy:')
@@ -78,13 +78,13 @@ var uniqueId = toLower(uniqueString(subscription().id, environmentName, solution
   azd:{
     type: 'location'
     usageName: [
-      'OpenAI.GlobalStandard.gpt-4o-mini,30'
+      'OpenAI.GlobalStandard.gpt-4o-mini,150'
       'OpenAI.Standard.text-embedding-ada-002,80'
     ]
   }
 })
 @description('Location for AI Foundry deployment. This is the location where the AI Foundry resources will be deployed.')
-param AZURE_AI_SERVICE_LOCATION string
+param aiDeploymentsLocation string
 
 var solutionPrefix = 'km${padLeft(take(uniqueId, 12), 12, '0')}'
 
@@ -121,7 +121,7 @@ module aifoundry 'deploy_ai_foundry.bicep' = {
   name: 'deploy_ai_foundry'
   params: {
     solutionName: solutionPrefix
-    solutionLocation: AZURE_AI_SERVICE_LOCATION
+    solutionLocation: aiDeploymentsLocation
     keyVaultName: kvault.outputs.keyvaultName
     cuLocation: contentUnderstandingLocation
     deploymentType: deploymentType

infra/main.parameters.json

@@ -9,7 +9,7 @@
       "value": "${AZURE_LOCATION}"
     },
     "contentUnderstandingLocation" :{
-        "value": "${AZURE_CONTENT_UNDERSTANDING_LOCATION}"
+      "value": "${AZURE_CONTENT_UNDERSTANDING_LOCATION}"
     },
     "secondaryLocation": {
       "value": "${AZURE_SECONDARY_LOCATION}"
@@ -41,9 +41,6 @@
     "existingLogAnalyticsWorkspaceId": {
       "value": "${AZURE_ENV_LOG_ANALYTICS_WORKSPACE_ID}"
     },
-    "useLocalBuild": {
-      "value": "${USE_LOCAL_BUILD}"
-    },
     "azureExistingAIProjectResourceId": {
       "value": "${AZURE_EXISTING_AI_PROJECT_RESOURCE_ID}"
     }