azure-ai-projects-emit-from-typespec

name

azure-ai-projects-emit-from-typespec

description

Emit the azure-ai-projects Python SDK from TypeSpec, apply post-emitter fixes, and create a Pull Request. WHEN: \"emit SDK from TypeSpec\", \"generate azure-ai-projects SDK\", \"update azure-ai-projects from TypeSpec\", \"emit from TypeSpec\", \"regenerate azure-ai-projects\". DO NOT USE FOR: other Azure SDK packages, manual code edits without TypeSpec. INVOKES: PostEmitter.ps1 script, git commands, gh CLI for PR creation. Use when this capability is needed.

Emit azure-ai-projects Python SDK from TypeSpec

This skill guides Copilot through emitting the azure-ai-projects Python SDK from TypeSpec, applying post-emitter fixes, updating the changelog, installing package from sources and creating a Pull Request.

Working directory: sdk/ai/azure-ai-projects

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Step 1: Gather information from the user

Ask the user the following questions one at a time, waiting for each answer before proceeding.

1a. Topic branch name

Ask the user to choose one of the following two options for the target topic branch:

1. Create a new topic branch (with default branch name) – Create a new topic branch for the emitted changes. If selected, this default branch name will be used "/", where github-userid is the user's personal GitHub ID (not the Microsoft Enterprise Managed User (EMU) account!) and DD-MM-HHMM is the current date-time using date, month, hour and minute. For example, if the GitHub ID is "dargilco" and the current date and time is May 1st, 2026 at 8:13am, the default branch name would be dargilco/emit-from-typespec-01-05-0813. This should be the default option, and the default branch name should be displayed. If you press enter without typing anything, this option will be selected.

2. Create a new topic branch (branch name given by user) - Ask the user for the branch name. Mention that a common format is "/". If the user enters a branch name feature/azure-ai-projects/2.3.0 then stop and report that they cannot emit directly to the current feature branch.

3. Emit to current branch – Emit directly to the current branch without creating a new topic branch. This is not common, but may be necessary if the user is re-running this workflow because of a previous failure, where the topic branch was already created. If the current branch is named feature/azure-ai-projects/2.3.0 then stop and report that they cannot emit directly to the current feature branch.

1b. TypeSpec source

Ask the user to choose one of the following three options for the TypeSpec source:

1. Latest commit on feature/foundry-release – Automatically find the latest commit to the feature/foundry-release branch in Azure/azure-rest-api-specs that touched files under specification/ai-foundry/data-plane/Foundry, and use that commit hash. This should be the default option. If you press enter without typing anything, this option will be selected.

2. Local TypeSpec folder – Emit from a local clone of the azure-rest-api-specs repository. If selected, ask for the full folder path to the TypeSpec project. This is the folder ending with \specification\ai-foundry\data-plane\Foundry\src\sdk-python-js-azure-ai-projects. If it does not end with that string, stop and report the error to the user. Do not continue.

3. TypeSpec commit hash – Emit from a specific commit in the azure-rest-api-specs repository. If selected, ask for the full commit SHA (40 characters).

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Step 2: Record the current branch

Before creating the topic branch, record the name of the current Git branch. This is the branch that the topic branch will be created from, and the branch the PR will target.

git branch --show-current

Save this as BASE_BRANCH.

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Step 3: Create the topic branch

Create the topic branch off the current branch and switch to it:

git fetch
git switch -c <topic-branch> origin/<BASE_BRANCH>

Replace <topic-branch> with the name provided by the user in Step 1a.

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Step 4: Emit SDK from TypeSpec

If you are emitting from latest commit or a given commit number, edit file tsp-location.yaml to update the full hash commit number, then in the folder sdk/ai/azure-ai-projects run the command: tsp-client update --debug

If you are emitting from a local TypeSpec folder, do not edit the file tsp-location.yaml. Run the command: tsp-client update --debug --local-spec-repo <local-folder-path>, where <local-folder-path> is the full path to the local TypeSpec folder ending with specification\ai-foundry\data-plane\Foundry\src\sdk-python-js-azure-ai-projects.

Note:

• You are only allowed to use the tsp-client update command. Do not use any of the other tsp-client commands.
• If the generation fails, stop and report the error to the user. Do not continue.

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Step 5: Revert changes to files pyproject.toml and MANIFEST.in

After the emit, there will be changes to pyproject.toml and MANIFEST.in that are not needed. Revert any changes to these files by running:

git restore pyproject.toml MANIFEST.in

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Step 6: Commit and push

Stage all changes (excluding file names that start with .env), commit, and push the topic branch:

git add -A -- ':!.env*'
git commit -m "Part 1: Emit SDK from TypeSpec"

Co-authored-by: Copilot <[e-mail]>"
git push -u origin <topic-branch>

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Step 7: Run post-emitter fixes

After a successful emit, run the PowerShell script named PostEmitter.ps1 located in the sdk/ai/azure-ai-projects folder.

This script applies azure-ai-projects specific corrections to the emitted code (restores pyproject.toml, fixes enum names, patches Sphinx doc-string issues, and runs black formatting).

If the script fails, stop and report the error to the user. Do not continue. Do not attempt to analyze the script failures and fix them with Copilot. The script should be fixed by the engineering team if it is not working.

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Step 8: Commit and push

Stage all changes (excluding file names that start with .env), commit, and push the topic branch:

git add -A -- ':!.env*'
git commit -m "Part 2: Apply post-emitter-fixes.cmd"

Co-authored-by: Copilot <[e-mail]>"
git push -u origin <topic-branch>

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Step 9: Fix patched code related to preview feature headers

The emitted code may have introduced another beta sub-client (a new property on class BetaOperations). It may have also added another enum value to the existing internal class _FoundryFeaturesOptInKeys. This means that the client library needs to set a new HTTP request header when making REST API calls to the service, to opt-in to the new service features which are still in preview. If that's the case, do the following:

• Update the dictionary _BETA_OPERATION_FEATURE_HEADERS defined in azure\ai\projects\models\_patch.py, to include a new key-value pair to map the new beta sub-client name to the proper value from _FoundryFeaturesOptInKeys. If no new beta sub-client was introduced, but a new enum value was added to _FoundryFeaturesOptInKeys, you will need to update one of the existing key-value pairs in _BETA_OPERATION_FEATURE_HEADERS to a comma-separated join of multiple values from _FoundryFeaturesOptInKeys.

• Do a similar change to the dictionary EXPECTED_FOUNDRY_FEATURES defined in the test file tests\foundry_features_header\foundry_features_header_test_base.py: add a new key-value pair if a new beta sub-client was introduced, or update an existing key-value pair to include the new enum value if no new beta sub-client was introduced.

• Finally, look at the two files azure\ai\projects\operations\_patch.py and azure\ai\projects\aio\operations\_patch.py. They define the public BetaOperations classes for the sync and async clients. To support a new sub-client, you will need to add a new property to this class with the proper doc string. You will need to update the import statement at the top of the file to import the new sub-client class. And you will need to update __all__ statement at the bottom of the file to include the new sub-client class name. Follow the examples you see there for BetaDatasetsOperations or BetaSkillsOperations.

If a new enum value was added to _AgentDefinitionOptInKeys, please print a note on screen that mentions which value was added, and tell the user that a review is needed to make sure this new value is properly used. But otherwise continue on.

Important: Under the azure\ai\projects folder, you are only allowed to edit Python source files that start with "_patch". If you see that changes are needed in other files, stop and report this to the user instead of making the changes yourself.

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Step 10: Update samples and tests

If there were any breaking changes in existing APIs, like class or method renames:

• update the patched code accordingly in the client library to reflect those changes. Changes should be made to Python source file names that start with "_patch", under the azure\ai\projects folder.
• update the samples accordingly to reflect those changes. Changes should be made under sdk/ai/azure-ai-projects/samples folder.
• update the tests accordingly to reflect those changes. Changes should be made under sdk/ai/azure-ai-projects/tests folder.

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Step 11: Install package from sources

In the folder sdk\ai\azure-ai-projects, run pip install -e . to install the package from sources. If there are any errors, stop and report the error to the user. Do not continue.

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Step 12: Run apiview-stub-generator to update api.md and api.metadata.yml files

In the folder sdk\ai\azure-ai-projects, run the following command:

azpysdk apistub .

This will update the api.md and api.metadata.yml files under in local folder.

If it fails, stop and do not continue. If succeeded, do the following cleanup and continue to the next step:

rmdir /s /q build

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Step 13: Commit and push

Stage all changes (excluding file names that start with .env), commit, and push the topic branch:

git add -A -- ':!.env*'
git commit -m "Part 3: Additional edits"

Co-authored-by: Copilot <[e-mail]>"
git push -u origin <topic-branch>

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Step 14: Create a Pull Request

Create a draft PR from the topic branch to the base branch (recorded in Step 2):

gh pr create --draft --base <BASE_BRANCH> --head <topic-branch> --assignee @me --title "<PR title>" --body "<PR body>"

• Title: Use a descriptive title such as [azure-ai-projects] Emit SDK from TypeSpec (<short description>).
• Body: Include which TypeSpec source was used and a summary of the changelog entry.

You must show the user the resulting PR URL on screen when done, before you continue to the next step.

Open a new tab in the default browser and navigate to the PR URL.

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Step 15: Optionally run tests locally

Prompt the user with this message: "Tests will run as part of the Pull Request. However, you can optionally run tests locally in a Python virtual environment, right now. It will take a few minutes. Do you want to run tests locally? (yes/no)"

If the user answers "yes", run all tests from recordings. Follow these guidelines:

• Run tests in a local Python virtual environment. Create this virtual environment if it does not already exists:

  python -m venv .venv
  

  and activate it:

  .venv\Scripts\activate
  

• Show test progress on screen, as tests are run.

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Source: Azure/azure-sdk-for-python — distributed by TomeVault.