AI Application chassis - best AI app practices optimized for Azure

ToC: USER STORY | GETTING STARTED | HOW IT WORKS

User story

Azure AI App Kickstarter overview

L100 level aplication stub for an AI copilot/agent.

Getting Started

Codespaces and DevContainers

This respository has been configured to support GitHub Codespace and DevContainers.

Warning

Do NOT git clone the application under Windows and then open a DevContainer. This would create issues with file end of lines. For DevContainer click on the button above and let Visual Studio Code download the repository for you. Alternatively you can also git clone under Windows Subsystem for Linux (WSL) and ask Visual Studio Code to Re-Open in Container.

Local

It is possible to work with a fully local setup.

• Azure CLI: az
• Azure Developer CLI: azd
• Python: python
• UV: uv
• Optionally Docker: docker

Tip

Az AI Tip: Document here how to quickly deploy the solution. Try to reduce this to azd up by automating as much as possible. Have a look at main.bicep and scripts for examples of how to do that

Quick deploy

Deployment pre-requisites

Codespaces and DevContainer come with all deployment and development pre-requisites already installed.

On Windows you can install the pre-requisites by executing the following commands in a PowerShell terminal:

	winget install Python.Python.3.13
	winget install Microsoft.PowerShell
	winget install Microsoft.AzureCLI
	winget install Microsoft.Azd
	winget install Microsoft.Git

Ubuntu/WSL: TBD

MacOSX: TBD

Deploy with authentication disabled

To deploy Azure AI App Kickstarter just run:

azd up

Deploy with authentication enabled

AZD can automatically configures authentication to secure frontend and/or backend. To do so execute the following command before azd up:

azd env set WITH_AUTHENTICATION true

If you already executed azd up just set the variable and run provisioning again:

azd env set WITH_AUTHENTICATION true
azd provision

[!WARNING] The account executing azd needs to be able to create Application Registrations in your Azure Entra ID tenant.

How it works

• TODO: How to run backend locally
• TODO: How to run frontend locally

User Manual

• TODO : Observability

Tip

Az AI Tip: Document how the solution is used and operated here. Optionally, if the section is too long, create a USER_MANUAL.md file and link to it from here.

External Model

If you have an external Azure OpenAI model already provisioned, you can reference it by setting environment variable prior callin azd up

export AOAI_ENDPOINT="https://<endpoint>.openai.azure.com"
export AOAI_DEPLOYMENT_NAME="gpt-4o-2024-11-20"
export AOAI_API_VERSION="2024-12-01-preview"
export aoaikeysecret="key"

[WARNING!] The aoaikeysecret is not set in azd .azure/./.env file automatically. In order to use it when running the model locally, either set it as env variable or add it to azd .env file.

Architecture

architecture-beta
    group solution(cloud)[Solution]

    service frontend(server)[Frontend] in solution
    service backend(server)[Backend] in solution

    frontend:R --> L:backend

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Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct.

Resources:

• Microsoft Open Source Code of Conduct
• Microsoft Code of Conduct FAQ
• Contact [email protected] with questions or concerns

For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Responsible AI Guidelines

This project follows the below responsible AI guidelines and best practices, please review them before using this project:

• Microsoft Responsible AI Guidelines
• Responsible AI practices for Azure OpenAI models
• Safety evaluations transparency notes

Acknowledgements

• Kudos to Pamela Fox and James Casey for Azure-Samples/openai-chat-app-entra-auth-builtin from which we borrowed most of authentication & authorization setup.
• Special thank you to Michael Hofer for extensive testing and solving o1 compatibility

Authors

• Dominique Broeglin
• Evgeny Minkevich