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Getting Started

Estimated time to read: 3 minutes

Select your workshop experience:

Placeholder

What You'll Learn

In this section, you'll learn how to:

  • Authenticate with Azure
  • Deploy Azure resources for the workshop
  • Configure the workshop environment

Authenticate with Azure

You need to authenticate with Azure so the agent app can access the Azure AI Agents Service and models. Follow these steps:

  1. Ensure the Codespace has been created.
  2. In the Codespace, open a new terminal window by selecting Terminal > New Terminal from the VS Code menu.

  3. Run the following command to authenticate with Azure:

    az login --use-device-code

    Note

    You'll be prompted to open a browser link and log in to your Azure account. Be sure to copy the authentication code first.

    1. A browser window will open automatically, select your account type and click Next.
    2. Sign in with your Azure subscription Username and Password.
    3. Paste the authentication code.
    4. Select OK, then Done.

    Warning

    If you have multiple Azure tenants, then you will need to select the appropriate tenant when authenticating.

    az login --use-device-code --tenant <tenant_id>

  4. Next, select the appropriate subscription from the command line.

  5. Leave the terminal window open for the next steps.

Deploy the Azure Resources

The following resources will be created in the rg-zava-agent-wks-nnnn resource group in your Azure subscription.

  • An Azure AI Foundry hub named fdy-zava-agent-wks-nnnn
  • An Azure AI Foundry project named prj-zava-agent-wks-nnnn
  • Serverless (pay-as-you-go) Models:
    • GPT-4o Mini model deployment named gpt-4o-mini.
    • Text-embedding-3-small model named text-embedding-3-small.
    • See pricing details here.

You will need 120K TPM quota availability for the gpt-4o Global Standard SKU, due to the frequency of calls made by the agent to the model. Review your quota availability in the AI Foundry Management Center.

We have provided a bash script to automate the deployment of the resources required for the workshop. Alternatively, you may deploy resources manually using Azure AI Foundry studio. Select the desired tab.

Automated Deployment

The deploy.sh script deploys resources to the westus region by default. To run the script, open the VS Code terminal and run the following command:

cd infra && ./deploy.sh

Workshop Configuration

The deploy script generates the .env file, which contains the project endpoint, model deployment name, and Bing connection name.

You'll see this file when you open the Python workspace in VS Code. Your .env file will look similar to this but with your project endpoint.

PROJECT_ENDPOINT="<your_project_endpoint>"
GPT_MODEL_DEPLOYMENT_NAME="<your_model_deployment_name>"
EMBEDDING_MODEL_DEPLOYMENT_NAME="<your_embedding_model_deployment_name>"
APPLICATIONINSIGHTS_CONNECTION_STRING="<your_application_insights_connection_string>"
DEV_TUNNEL_URL="<your_dev_tunnel_url>"

The automated deployment script stores project variables securely by using the Secret Manager feature for safe storage of app secrets in development in ASP.NET Core.

You can view the secrets by running the following command after you have opened the C# workspace in VS Code:

dotnet user-secrets list