The Azure AI folder hierarchy (afh) is designed to orient you within your current working context, and help you work with your code, data and shared files most efficiently. This afh directory houses your Azure AI projects, and each specific project's folder has a code, data and shared folder.
Files, folders, and repos you store in your project folder persist across compute instance restarts but are lost if the machine is deleted.
- Use
codefor working with git repositories and private code files - Use
datafor storing private data files
The code folder is where we recommend you clone your git repositories, or otherwise bring in or create your code files. This is a storage location directly on your compute instance, and will be performant for large repositories. We recommend you use the data folder to store and reference local or private data in a consistent way.
- Use
sharedfor working with a project's shared files and assets - Use
shared\Users\{user-name}\promptflowto reference and edit prompt flows
The shared folder is where you will find the project's shared files and assets, including prompt flows that are being developed in the Azure AI Studio. To find specific files or flows, navigate to the relevant user folder. Flows will be in the promptflow folder.
For those that launched VS Code from the Azure AI Studio, you are remotely connected to a custom container running on your compute instance. The file explorer is loaded in a dedicated folder for the project you were working on in the Azure AI Studio. This development environment comes pre-configured with the Azure AI SDK, the Azure AI CLI, and the Prompt flow SDK and CLI.
To set up these capabilities in your local environment instead, or to learn more, follow steps here: Azure AI SDK.
To get started with the AI SDK, we recommend the aistudio-copilot-sample repo as a comprehensive starter repository that includes a few different copilot implementations. For the full list of samples, check out the Azure AI Samples repository.
- Open a terminal
- Clone a sample repo into your project's
codefolder. You may be prompted to authenticate to Github
cd code
git clone https://github.com/azure/aistudio-copilot-sample- If you have existing notebooks or code files, you can import
import azure.ai.generativeand use intellisense to browse capabilities included in that package
If you prefer to work interactively, the AI CLI has everything you need to build generative AI solutions.
- Open a terminal to get started
ai helpwill guide you through CLI capabilitiesai initwill configure your resources in your development environment
You can use the Azure AI SDK and AI CLI to create, reference and work with the flows.
Prompt flows already created in the Azure AI Studio can be found at shared\Users\{user-name}\promptflow. You can also create new flows in your code or shared folder using the CLIs and SDKs.
- To reference an existing flow using the Azure AI CLI, use
ai flow invoke - To create a new flow using the Azure AI CLI, use
ai flow new
For prompt flow specific capabilities that are not present in the AI SDK and CLI, you can work directly with the Prompt flow CLI or SDK, or the Prompt flow VS Code extension (all pre-installed in this environment). See prompt flow capabilities for more details.
For client app developers seeking cross-language compatibility and seamless integration of OpenAI capabilities, explore the Azure AI Hub at https://aka.ms/azai. Discover app templates and SDK samples in your preferred programming language.