build-docs

작성자: microsoft

문서 빌드 및 독스트링 검증을 위한 가이드입니다. 문서 빌드, 독스트링 확인, 또는 문서 검증을 요청받았을 때 사용하세요.

npx skills add https://github.com/microsoft/semantic-link-labs --skill build-docs

Building and Validating Documentation

This skill covers documentation building and docstring validation workflows for the Semantic Link Labs project.

When to Use This Skill

Use this skill when you need to:

  • Build documentation locally
  • Validate docstrings are properly formatted
  • Check for documentation warnings or errors
  • Ensure new functions have proper documentation
  • Preview documentation before pushing

Documentation Framework

ComponentDetails
FrameworkSphinx with numpydoc
Themesphinx_rtd_theme
HostingReadTheDocs
Source locationdocs/source/
Build outputdocs/build/html/
Configdocs/source/conf.py

Building Documentation Locally

Prerequisites

Install documentation dependencies:

pip install -r docs/requirements.txt

Build Commands

# Navigate to docs directory
cd docs

# Generate API documentation from source code
sphinx-apidoc -f -o source ../src/sempy_labs/

# Build HTML documentation
make html

# Or on Windows
make.bat html

View Built Documentation

Open docs/build/html/index.html in a browser.


ReadTheDocs Configuration

The project uses .readthedocs.yaml for automated builds:

version: 2
build:
  os: ubuntu-22.04
  tools:
    python: "3.12"
  jobs:
    pre_build:
      - sphinx-apidoc -f -o docs/source src/sempy_labs/
sphinx:
  configuration: docs/source/conf.py
python:
   install:
   - requirements: docs/requirements.txt

Docstring Standards

Docstring Style

This project uses numpydoc style for all docstrings.

Required Sections

  1. Short description — One-line summary of what the function does
  2. Extended description — Detailed explanation (optional but recommended)
  3. Parameters — Document each parameter with type and description
  4. Returns — Document return value(s)

Optional Sections

  • Raises — Document exceptions that may be raised
  • Examples — Provide usage examples
  • Notes — Additional context or implementation details
  • See Also — Related functions

Example Docstring

@log
def list_workspaces(
    capacity: Optional[str | UUID] = None,
    workspace_state: Optional[str] = None,
) -> pd.DataFrame:
    """
    Lists workspaces for the organization.

    This is a wrapper function for the following API: `Workspaces - List Workspaces <https://learn.microsoft.com/rest/api/fabric/admin/workspaces/list-workspaces>`_.

    Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).

    Parameters
    ----------
    capacity : str | uuid.UUID, default=None
        Returns only the workspaces in the specified Capacity.
    workspace_state : str, default=None
        Return only the workspace with the requested state.
        You can find the possible states in `Workspace States <https://learn.microsoft.com/rest/api/fabric/admin/workspaces/list-workspaces?tabs=HTTP#workspacestate>`_.

    Returns
    -------
    pandas.DataFrame
        A pandas dataframe showing a list of workspaces for the organization.
        Columns include: 'Id', 'Name', 'State', 'Type', 'Capacity Id'.

    Raises
    ------
    FabricHTTPException
        If the API request fails.

    Examples
    --------
    >>> import sempy_labs as labs
    >>> df = labs.list_workspaces()
    >>> df = labs.list_workspaces(capacity="My Capacity")
    """
    pass

Parameter Documentation Patterns

Standard Parameter Formats

# Simple parameter
item_type : str
    The type of item to filter by.

# Parameter with default
item_type : str, default=None
    The type of item to filter by. If None, returns all types.

# Union type parameter
workspace : str | uuid.UUID, default=None
    The Fabric workspace name or ID.
    Defaults to None which resolves to the workspace of the attached lakehouse
    or if no lakehouse attached, resolves to the workspace of the notebook.

# Boolean parameter
readonly : bool, default=True
    If True, opens in read-only mode. If False, allows modifications.

# List parameter
columns : List[str], default=None
    A list of column names to include. If None, includes all columns.

API Reference Links

Always include links to API documentation:

"""
This is a wrapper function for the following API: `Items - List Items <https://learn.microsoft.com/rest/api/fabric/core/items/list-items>`_.
"""

Service Principal Note

For functions supporting Service Principal authentication:

"""
Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
"""

Common Documentation Issues

Missing or Incomplete Docstrings

Symptom: Sphinx warning about missing docstring.

Fix: Add complete numpydoc-style docstring with all required sections.

Type Annotation Mismatches

Symptom: Warning about type mismatch between signature and docstring.

Fix: Ensure docstring parameter types match function signature type hints.

# Function signature
def my_func(workspace: Optional[str | UUID] = None) -> pd.DataFrame:

# Docstring should match
"""
Parameters
----------
workspace : str | uuid.UUID, default=None
    ...

Returns
-------
pandas.DataFrame
    ...
"""

Indentation Errors

Symptom: Warning about unexpected indentation.

Fix: Use consistent 4-space indentation in docstrings.

Broken Links

Symptom: Warning about broken reference.

Fix: Verify URLs are correct and use proper RST link syntax:

`Link Text <https://example.com>`_

Sphinx Configuration

Key settings in docs/source/conf.py:

# Extensions
extensions = [
    'sphinx.ext.autodoc',
    'sphinx.ext.napoleon',
    'sphinx.ext.intersphinx',
]

# Napoleon settings for numpydoc
napoleon_numpy_docstring = True

# Mock imports for packages not available during build
autodoc_mock_imports = [
    'delta', 'synapse', 'jwt', 'semantic-link-sempy',
    'pyspark', 'anywidget', 'sqlglot'
]

Validating Documentation

Check for Warnings

cd docs
make html 2>&1 | grep -i warning

Clean Build

cd docs
make clean
make html

Verify Specific Module

# Generate docs for specific module
sphinx-apidoc -f -o source ../src/sempy_labs/admin/
make html

Pre-Commit Documentation Check

Before committing changes with new or modified functions:

  1. Verify docstring completeness:

    • Short description present
    • All parameters documented with types
    • Return value documented
    • API reference link included (if applicable)
  2. Build documentation locally:

    cd docs && make html
    
  3. Check for warnings in build output

  4. Preview the generated HTML to ensure proper rendering

microsoft의 다른 스킬

oss-growth
microsoft
OSS 성장 해커 페르소나
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK(agent-framework-azure-ai)를 사용하여 Azure AI Foundry 에이전트를 구축합니다. AzureAIAgentsProvider로 지속적 에이전트를 만들 때, 호스팅 도구(코드 인터프리터, 파일 검색, 웹 검색)를 사용할 때, MCP 서버를 통합할 때, 대화 스레드를 관리할 때, 또는 스트리밍 응답을 구현할 때 사용합니다. 함수 도구, 구조화된 출력, 다중 도구 에이전트를 다룹니다.
development
airunway-aks-setup
microsoft
AKS에서 AI Runway 설정 — 빈 클러스터에서 실행 중인 모델까지. 클러스터 검증, 컨트롤러 설치, GPU 평가, 공급자 설정, 첫 배포를 다룹니다. 시기: "AI Runway 설정", "AKS 클러스터 온보딩", "AI Runway 설치", "airunway 설정", "AKS에 모델 배포", "AKS에서 GPU 추론", "AKS에서 KAITO 설정", "AKS에서 LLM 실행", "AKS에서 vLLM", "AKS에서 모델 서빙 설정", "AI Runway 컨트롤러".
devops
appinsights-instrumentation
microsoft
Azure Application Insights로 웹앱을 계측하기 위한 지침입니다. 원격 분석 패턴, SDK 설정, 구성 참조를 제공합니다. WHEN: 앱 계측 방법, App Insights SDK, 원격 분석 패턴, App Insights란 무엇인가, Application Insights 지침, 계측 예시, APM 모범 사례.
devops
applicationinsights-web-ts
microsoft
브라우저/웹 앱을 Application Insights JavaScript SDK(@microsoft/applicationinsights-web)로 계측합니다. Real User Monitoring(RUM) — 페이지 뷰, 클릭, AJAX/fetch 종속성, 예외, 사용자 지정 이벤트, 백엔드 OpenTelemetry 트레이스와 상관관계가 있는 브라우저 측 GenAI 에이전트 트레이스에 사용합니다. SDK Loader Script 및 npm 설정, 프레임워크 확장(React, React Native, Angular), Click Analytics, 텔레메트리 이니셜라이저, 브라우저에서 생성된 에이전트/도구/모델 스팬에 대한 OTel GenAI 의미론적 규칙을 다룹니다.
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Java로 이상 탐지 애플리케이션을 구축하세요. 단변량/다변량 이상 탐지, 시계열 분석 또는 AI 기반 모니터링을 구현할 때 사용하세요.
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDK를 사용하여 대화형 언어 이해(CLU)를 구현합니다. ConversationAnalysisClient로 대화 의도와 엔터티를 분석하거나, NLP 기능을 구축하거나, 애플리케이션에 언어 이해를 통합할 때 사용합니다.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. ML 작업 영역, 작업, 모델, 데이터 세트, 컴퓨팅 및 파이프라인에 사용합니다. 트리거: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development