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‎azure.yaml‎

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Write-Host "Web app URL: "
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Write-Host "$env:WEB_APP_URL" -ForegroundColor Cyan
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Write-Host "`nRun the following command in your Bash terminal. It will grant the necessary permissions between resources and your user account, and also process and load the sample data into the application."
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Write-Host "bash ./infra/scripts/process_sample_data.sh $env:AZURE_RESOURCE_GROUP" -ForegroundColor Cyan
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Write-Host "bash ./infra/scripts/process_sample_data.sh" -ForegroundColor Cyan
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shell: pwsh
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continueOnError: false
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interactive: true
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echo $WEB_APP_URL
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echo ""
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echo "Run the following command in your Bash terminal. It will grant the necessary permissions between resources and your user account, and also process and load the sample data into the application."
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echo "bash ./infra/scripts/process_sample_data.sh $env:AZURE_RESOURCE_GROUP"
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echo "bash ./infra/scripts/process_sample_data.sh"
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shell: sh
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continueOnError: false
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interactive: true

‎docs/AVMPostDeploymentGuide.md‎

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# AVM Post Deployment Guide
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> **📋 Note**: This guide is specifically for post-deployment steps after using the AVM template. For complete deployment from scratch, see the main [Deployment Guide](./DeploymentGuide.md).
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---
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This document provides guidance on post-deployment steps after deploying the Build Your Own Copilot Accelerator from the [AVM (Azure Verified Modules) repository](https://github.com/Azure/bicep-registry-modules/tree/main/avm/ptn/sa/build-your-own-copilot).
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## Post Deployment Steps
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1. Clone the Repository
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First, clone this repository to access the post-deployment scripts:
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```bash
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git clone https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator.git
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```
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```bash
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cd Build-your-own-copilot-Solution-Accelerator
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```
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2. Import Sample Data -Run bash command printed in the terminal. The bash command will look like the following:
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### 1. Clone the Repository
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First, clone this repository to access the post-deployment scripts:
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```bash
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git clone https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator.git
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cd Build-your-own-copilot-Solution-Accelerator
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```
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### 2. Import Sample Data
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**Choose the appropriate command based on your deployment method:**
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**If you deployed using custom templates, ARM/Bicep deployments, or `az deployment group` commands:**
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```bash
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bash ./infra/scripts/process_sample_data.sh <your-resource-group-name>
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```
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> **Note**: Replace `<your-resource-group-name>` with the actual name of the resource group containing your deployed Azure resources.
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> **💡 Tip**: If the deployment metadata does not exist in Azure or has been deleted, the script will prompt you to manually enter the required configuration values.
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**If you deployed using `azd up` command:**
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```bash
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bash ./infra/scripts/process_sample_data.sh
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```
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> **Note**: The script will automatically take required values from your `azd` environment.
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> **💡 Tip**: Since this guide is for AVM deployments, you'll most likely use the first command with your resource group name.
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### 3. Configure Authentication
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```bash
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bash ./infra/scripts/process_sample_data.sh <resourceGroupName>
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```
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If the deployment does not exist or has been deleted – The script will prompt you to manually enter the required values
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Follow the steps in [App Authentication](./AppAuthentication.md) to configure authentication in App Service.
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3. Add Authentication Provider
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> **Note**: Authentication changes can take up to 10 minutes to propagate.
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Follow steps in [App Authentication](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/docs/AppAuthentication.md) to configure authentication in app service.
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>Note that Authentication changes can take up to 10 minutes.
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### 4. Troubleshooting: Cleaning Up After a Failed Deployment
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4. Deleting Resources After a Failed Deployment
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If your deployment fails and you need to clean up resources, follow the steps in [Delete Resource Group](./DeleteResourceGroup.md).
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Follow steps in [Delete Resource Group](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/docs/DeleteResourceGroup.md) if your deployment fails and/or you need to clean up the resources.
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---
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By following these steps, you’ll ensure a smooth transition from deployment to hands-on usage.
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By following these steps, you'll ensure a smooth transition from deployment to hands-on usage.

‎docs/DeploymentGuide.md‎

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@@ -86,7 +86,7 @@ You can run this solution using [GitHub Codespaces](https://docs.github.com/en/c
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</details>
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<details>
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<summary><b>Deploy in VS Code</b></summary>
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<summary><b>Deploy in VS Code Dev Containers</b></summary>
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### VS Code Dev Containers
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<details>
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<summary><b>Configurable Deployment Settings</b></summary>
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When you start the deployment, most parameters will have **default values**, but you can update the below settings by following the steps [here](CustomizingAzdParameters.md):
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| **Setting** | **Description** | **Default value** |
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| ------------------------------------ | -------------------------------------------------------------------------------------------------- | ------------------------ |
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| **Environment Name** | A **3-20 character alphanumeric value** used to generate a unique ID to prefix the resources. | `azdtemp` |
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| **Cosmos Location** | A **less busy** region for **CosmosDB**, useful in case of availability constraints. | `eastus2` |
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| **Deployment Type** | Select from a drop-down list (`Standard`, `GlobalStandard`). | `GlobalStandard` |
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| **GPT Model** | Azure OpenAI GPT model to deploy. | `gpt-4o-mini` |
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| **GPT Model Deployment Capacity** | Configure capacity for **GPT models**. Choose based on Azure OpenAI quota. | `30` |
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| **Embedding Model** | OpenAI embedding model used for vector similarity. | `text-embedding-ada-002` |
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| **Embedding Model Capacity** | Set the capacity for **embedding models**. Choose based on usage and quota. | `80` |
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| **Image Tag** | The version of the Docker image to use (e.g., `latest_waf`, `dev`, `hotfix`). | `latest_waf` |
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| **Azure OpenAI API Version** | Set the API version for OpenAI model deployments. | `2025-04-01-preview` |
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| **AZURE_LOCATION** | Sets the Azure region for resource deployment. | `<User selects during deployment>` |
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| **Existing Log Analytics Workspace** | To reuse an existing Log Analytics Workspace ID instead of creating a new one. | *(empty)* |
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| **Existing AI Foundry Project Resource ID** | To reuse an existing AI Foundry Project Resource ID instead of creating a new one. | *(empty)* |
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When you start the deployment, most parameters will have **default values**, but you can update the below settings by following the steps [here](CustomizingAzdParameters.md)
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</details>
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## Post Deployment Steps
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1. **Import Sample Data**
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-Run bash command printed in the terminal. The bash command will look like the following:
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```shell
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bash ./infra/scripts/process_sample_data.sh
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```
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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:
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```shell
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bash ./infra/scripts/process_sample_data.sh <resourceGroupName> <cosmosDbAccountName> <storageAccount> <storageContainerName> <keyvaultName> <sqlServerName> <sqlDatabaseName> <webAppUserManagedIdentityClientId> <webAppUserManagedIdentityDisplayName> <aiFoundryResourceName> <aiSearchResourceName>
275-
```
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### 1. Import Sample Data
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**Choose the appropriate command based on your deployment method:**
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**If you deployed using `azd up` command:**
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```bash
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bash ./infra/scripts/process_sample_data.sh
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```
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> **Note**: The script will automatically take required values from your `azd` environment.
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258+
**If you deployed using custom templates, ARM/Bicep deployments, or `az deployment group` commands:**
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```bash
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bash ./infra/scripts/process_sample_data.sh <your-resource-group-name>
261+
```
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> **Note**: Replace `<your-resource-group-name>` with the actual name of the resource group containing your deployed Azure resources.
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264+
> **💡 Tip**: If the deployment metadata does not exist in Azure or has been deleted, the script will prompt you to manually enter the required configuration values.
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266+
> **💡 Tip**: Since this guide is for azd deployment, you'll most likely use the first command without resource group name.
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### 2. Configure Authentication
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Follow the steps in [App Authentication](./AppAuthentication.md) to configure authentication in App Service.
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2. **Add Authentication Provider**
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- Follow steps in [App Authentication](./AppAuthentication.md) to configure authentication in app service. Note that Authentication changes can take up to 10 minutes.
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> **Note**: Authentication changes can take up to 10 minutes to propagate.
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3. **Deleting Resources After a Failed Deployment**
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### 3. Troubleshooting: Cleaning Up After a Failed Deployment
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- Follow steps in [Delete Resource Group](DeleteResourceGroup.md) if your deployment fails and/or you need to clean up the resources.
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If your deployment fails and you need to clean up resources, follow the steps in [Delete Resource Group](./DeleteResourceGroup.md).
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## Environment configuration for local development & debugging
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> 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**.

‎docs/SampleQuestions.md‎

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To help you get started, here are some **Sample Prompts** you can ask after selecting the **Karen Berg** client:
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> _Note: Average response time is 08-30 seconds._ <br>
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1. Click on **Karen Berg** client.
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2. Ask the following questions:
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- What were Karen's concerns during our last meeting?

‎infra/main.bicep‎

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@description('Optional. Version of the GPT model to deploy.')
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param embeddingModelVersion string = '2'
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@description('Optional. API version for the Azure OpenAI service.')
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param azureOpenaiAPIVersion string = '2025-04-01-preview'
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@description('Optional. API version for the Azure AI Services.')
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param azureAIServicesAPIVersion string = '2025-04-01-preview'
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@minValue(10)
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@description('Optional. Capacity of the GPT deployment:')
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// ========== Private DNS Zones ========== //
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var privateDnsZones = [
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'privatelink.cognitiveservices.azure.com'
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'privatelink.openai.azure.com'
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'privatelink.services.ai.azure.com'
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'privatelink.azurewebsites.net'
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'privatelink.blob.${environment().suffixes.storage}'
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// DNS Zone Index Constants
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var dnsZoneIndex = {
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cognitiveServices: 0
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openAI: 1
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aiServices: 2
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appService: 3
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storageBlob: 4
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storageQueue: 5
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storageFile: 6
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cosmosDB: 7
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keyVault: 8
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sqlServer: 9
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searchService: 10
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aiServices: 1
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appService: 2
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storageBlob: 3
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storageQueue: 4
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storageFile: 5
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cosmosDB: 6
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keyVault: 7
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sqlServer: 8
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searchService: 9
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}
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// List of DNS zone indices that correspond to AI-related services.
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var aiRelatedDnsZoneIndices = [
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dnsZoneIndex.cognitiveServices
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dnsZoneIndex.openAI
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dnsZoneIndex.aiServices
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]
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}
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{
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name: 'AZURE-OPENAI-PREVIEW-API-VERSION'
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value: azureOpenaiAPIVersion
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value: azureAIServicesAPIVersion
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}
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{
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name: 'AZURE-OPENAI-ENDPOINT'
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value: aiFoundryAiServices.outputs.endpoints['OpenAI Language Model Instance API']
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value: aiFoundryAiServices.outputs.endpoint
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}
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{
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name: 'AZURE-OPENAI-EMBEDDING-MODEL'
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name: 'AZURE-SEARCH-ENDPOINT'
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value: 'https://${aiSearchName}.search.windows.net'
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}
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{
632+
name: 'AZURE-AI-AGENT-ENDPOINT'
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value: aiFoundryAiServices.outputs.aiProjectInfo.apiEndpoint
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}
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]
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enableTelemetry: enableTelemetry
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}
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name: 'ai-services-dns-zone-cognitiveservices'
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privateDnsZoneResourceId: avmPrivateDnsZones[dnsZoneIndex.cognitiveServices]!.outputs.resourceId
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}
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{
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name: 'ai-services-dns-zone-openai'
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privateDnsZoneResourceId: avmPrivateDnsZones[dnsZoneIndex.openAI]!.outputs.resourceId
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}
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{
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name: 'ai-services-dns-zone-aiservices'
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privateDnsZoneResourceId: avmPrivateDnsZones[dnsZoneIndex.aiServices]!.outputs.resourceId
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diagnosticSettings: enableMonitoring ? [{ workspaceResourceId: logAnalyticsWorkspaceResourceId }] : null
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// WAF aligned configuration for Scalability
10681065
skuName: enableScalability || enableRedundancy ? 'P1v3' : 'B3'
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skuCapacity: enableScalability ? 3 : 1
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// skuCapacity: enableScalability ? 3 : 1
1067+
skuCapacity: 1 // skuCapacity set to 1 (not 3) due to multiple agents created per type during WAF deployment
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// WAF aligned configuration for Redundancy
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zoneRedundant: enableRedundancy ? true : false
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}
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11101108
AZURE_SEARCH_URL_COLUMN: azureSearchUrlColumn
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AZURE_OPENAI_RESOURCE: aiFoundryAiServices.outputs.name
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AZURE_OPENAI_MODEL: gptModelName
1113-
AZURE_OPENAI_ENDPOINT: aiFoundryAiServices.outputs.endpoints['OpenAI Language Model Instance API']
1111+
AZURE_OPENAI_ENDPOINT: aiFoundryAiServices.outputs.endpoint
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AZURE_OPENAI_TEMPERATURE: azureOpenAITemperature
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AZURE_OPENAI_TOP_P: azureOpenAITopP
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AZURE_OPENAI_MAX_TOKENS: azureOpenAIMaxTokens
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AZURE_OPENAI_STOP_SEQUENCE: azureOpenAIStopSequence
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AZURE_OPENAI_SYSTEM_MESSAGE: azureOpenAISystemMessage
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AZURE_OPENAI_PREVIEW_API_VERSION: azureOpenaiAPIVersion
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AZURE_OPENAI_PREVIEW_API_VERSION: azureAIServicesAPIVersion
11201118
AZURE_OPENAI_STREAM: azureOpenAIStream
11211119
AZURE_SEARCH_QUERY_TYPE: azureSearchQueryType
11221120
AZURE_SEARCH_VECTOR_COLUMNS: azureSearchVectorFields
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AZURE_SEARCH_PERMITTED_GROUPS_COLUMN: azureSearchPermittedGroupsField
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AZURE_SEARCH_STRICTNESS: azureSearchStrictness
11251123
AZURE_OPENAI_EMBEDDING_NAME: embeddingModel
1126-
AZURE_OPENAI_EMBEDDING_ENDPOINT : aiFoundryAiServices.outputs.endpoints['OpenAI Language Model Instance API']
1124+
AZURE_OPENAI_EMBEDDING_ENDPOINT : aiFoundryAiServices.outputs.endpoint
11271125
SQLDB_SERVER: sqlServerFqdn
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SQLDB_DATABASE: sqlDbName
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USE_INTERNAL_STREAM: useInternalStream
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11391137
USE_AI_PROJECT_CLIENT: useAIProjectClientFlag
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AZURE_AI_AGENT_ENDPOINT: aiFoundryAiServices.outputs.aiProjectInfo.apiEndpoint
11411139
AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME: gptModelName
1142-
AZURE_AI_AGENT_API_VERSION: azureOpenaiAPIVersion
1140+
AZURE_AI_AGENT_API_VERSION: azureAIServicesAPIVersion
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AZURE_SEARCH_CONNECTION_NAME: aiSearchName
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AZURE_CLIENT_ID: userAssignedIdentity.outputs.clientId
11451143
}
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13441342
? applicationInsights!.outputs.connectionString
13451343
: ''
13461344

1347-
@description('The API version used for the Azure AI Agent service.')
1348-
output AZURE_AI_AGENT_API_VERSION string = azureOpenaiAPIVersion
1345+
@description('The API version used for the Azure AI Agent service.')
1346+
output AZURE_AI_AGENT_API_VERSION string = azureAIServicesAPIVersion
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13501348
@description('The endpoint URL of the Azure AI Agent project.')
13511349
output AZURE_AI_AGENT_ENDPOINT string = aiFoundryAiServices.outputs.aiProjectInfo.apiEndpoint
@@ -1372,13 +1370,13 @@ output AZURE_COSMOSDB_DATABASE string = cosmosDbDatabaseName
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output AZURE_COSMOSDB_ENABLE_FEEDBACK string = azureCosmosDbEnableFeedback
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@description('The endpoint URL for the Azure OpenAI Embedding model.')
1375-
output AZURE_OPENAI_EMBEDDING_ENDPOINT string = aiFoundryAiServices.outputs.endpoints['OpenAI Language Model Instance API']
1373+
output AZURE_OPENAI_EMBEDDING_ENDPOINT string = aiFoundryAiServices.outputs.endpoint
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13771375
@description('The name of the Azure OpenAI Embedding model.')
13781376
output AZURE_OPENAI_EMBEDDING_NAME string = embeddingModel
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13801378
@description('The endpoint URL for the Azure OpenAI service.')
1381-
output AZURE_OPENAI_ENDPOINT string = aiFoundryAiServices.outputs.endpoints['OpenAI Language Model Instance API']
1379+
output AZURE_OPENAI_ENDPOINT string = aiFoundryAiServices.outputs.endpoint
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13831381
@description('The maximum number of tokens for Azure OpenAI responses.')
13841382
output AZURE_OPENAI_MAX_TOKENS string = azureOpenAIMaxTokens
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13871385
output AZURE_OPENAI_MODEL string = gptModelName
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13891387
@description('The preview API version for Azure OpenAI.')
1390-
output AZURE_OPENAI_PREVIEW_API_VERSION string = azureOpenaiAPIVersion
1388+
output AZURE_OPENAI_PREVIEW_API_VERSION string = azureAIServicesAPIVersion
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13921390
@description('The Azure OpenAI resource name.')
13931391
output AZURE_OPENAI_RESOURCE string = aiFoundryAiServices.outputs.name

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