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Deployment Guide

Pre-requisites

To deploy this solution accelerator, ensure you have access to an Azure subscription with the necessary permissions to create resource groups, resources, and assign roles at the resource group level*. Follow the steps in Azure Account Set Up

Check the Azure Products by Region page and select a region where the following services are available:

Here are some example regions where the services are available: East US, East US2, Australia East, UK South, France Central.

Important: Check Azure OpenAI Quota Availability

⚠️ To ensure sufficient quota is available in your subscription, please follow quota check instructions guide before you deploy the solution.

[Optional] Quota Recommendations

By default, the Gpt-4o-mini model capacity in deployment is set to 30k tokens, so we recommend updating the following:

For Global Standard | GPT-4o-mini - increase the capacity to at least 150k tokens post-deployment for optimal performance.

Depending on your subscription quota and capacity, you can adjust quota settings to better meet your specific needs. You can also adjust the deployment parameters for additional optimization. ­

Deployment Options & Steps

Pick from the options below to see step-by-step instructions for GitHub Codespaces, VS Code Dev Containers, and Local Environments.

Open in Codespaces Open in Dev Containers
Deploy in GitHub Codespaces

GitHub Codespaces

You can run this solution using GitHub Codespaces. The button will open a web-based VS Code instance in your browser:

  1. Open the solution accelerator (this may take several minutes):

    Open in Codespaces

  2. Accept the default values on the create Codespaces page.

  3. Open a terminal window if it is not already open.

  4. Continue with the deploying steps.

Deploy in VS Code

VS Code Dev Containers

You can run this solution in VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:

  1. Start Docker Desktop (install it if not already installed).

  2. Open the project:

    Open in Dev Containers

  3. In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.

  4. Continue with the deploying steps.

Deploy in your local Environment

Local Environment

If you're not using one of the above options for opening the project, then you'll need to:

  1. Make sure the following tools are installed:

  2. Clone the repository or download the project code via command-line:

    azd init -t microsoft/build-your-own-copilot-solution-accelerator/

  3. Open the project folder in your terminal or editor.

  4. Continue with the deploying steps.


Consider the following settings during your deployment to modify specific settings:

Configurable Deployment Settings

When you start the deployment, most parameters will have default values, but you can update the below settings by following the steps here:

Setting Description Default value
Environment Name A 3-20 character alphanumeric value used to generate a unique ID to prefix the resources. azdtemp
Cosmos Location A less busy region for CosmosDB, useful in case of availability constraints. eastus2
Deployment Type Select from a drop-down list (Standard, GlobalStandard). GlobalStandard
GPT Model Azure OpenAI GPT model to deploy. gpt-4o-mini
GPT Model Deployment Capacity Configure capacity for GPT models. Choose based on Azure OpenAI quota. 30
Embedding Model OpenAI embedding model used for vector similarity. text-embedding-ada-002
Embedding Model Capacity Set the capacity for embedding models. Choose based on usage and quota. 80
Image Tag The version of the Docker image to use (e.g., latest, dev, hotfix). latest
Azure OpenAI API Version Set the API version for OpenAI model deployments. 2025-04-01-preview
AZURE_LOCATION Sets the Azure region for resource deployment. japaneast
Existing Log Analytics Workspace To reuse an existing Log Analytics Workspace ID instead of creating a new one. (empty)
Existing AI Foundry Project Resource ID To reuse an existing AI Foundry Project Resource ID instead of creating a new one. (empty)
[Optional] Quota Recommendations

By default, the GPT model capacity in deployment is set to 30k tokens.

We recommend increasing the capacity to 100k tokens, if available, for optimal performance.

To adjust quota settings, follow these steps.

⚠️ Warning: Insufficient quota can cause deployment errors. Please ensure you have the recommended capacity or request additional capacity before deploying this solution.

Reusing an Existing Log Analytics Workspace

Guide to get your Existing Workspace ID

Deploying with AZD

Once you've opened the project in Codespaces, Dev Containers, or locally, you can deploy it to Azure by following these steps:

  1. Login to Azure:

    azd auth login

    To authenticate with Azure Developer CLI (azd), use the following command with your Tenant ID:

    azd auth login --tenant-id <tenant-id>

    Note: To retrieve the Tenant ID required for local deployment, you can go to Tenant Properties in Azure Portal from the resource list. Alternatively, follow these steps:

    1. Open the Azure Portal.
    2. Navigate to Azure Active Directory from the left-hand menu.
    3. Under the Overview section, locate the Tenant ID field. Copy the value displayed.

  2. Provision and deploy all the resources:

    azd up

  3. Provide an azd environment name (e.g., "byocaapp").

  4. Select a subscription from your Azure account and choose a location that has quota for all the resources.

    • 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 is complete, please follow the Import Sample Data instructions under Post Deployment Steps to load the sample data correctly.

  6. Open the Azure Portal, go to the deployed resource group, find the App Service and get the app URL from Default domain.

  7. Test the app locally with the sample question with any selected client: Show latest asset value by asset type?. For more sample questions you can test in the application, see Sample Questions.

  8. You can now delete the resources by running azd down, if you are done trying out the application.

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:

  1. Set the environment variable USE_LOCAL_BUILD to True:

    • Linux/macOS:

      export USE_LOCAL_BUILD=True

    • Windows (PowerShell):

      $env:USE_LOCAL_BUILD = $true

  2. Run the az login command

    az login

  3. Run the azd up command again to rebuild and push the updated container:

    azd up

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

  1. Import Sample Data -Run bash command printed in the terminal. The bash command will look like the following:

    bash ./infra/scripts/process_sample_data.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:

    bash ./infra/scripts/process_sample_data.sh <resourceGroupName> <cosmosDbAccountName> <storageAccount> <storageContainerName> <keyvaultName> <sqlServerName> <sqlDatabaseName> <webAppUserManagedIdentityClientId> <webAppUserManagedIdentityDisplayName> <aiFoundryResourceName> <aiSearchResourceName>

  2. Add Authentication Provider

    • Follow steps in App Authentication to configure authentication in app service. Note that Authentication changes can take up to 10 minutes.

  3. Deleting Resources After a Failed Deployment

    • Follow steps in Delete Resource Group if your deployment fails and/or you need to clean up the resources.