azure-appconfiguration-py

tarafından microsoft

Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings. Triggers: "azure-appconfiguration", "AzureAppConfigurationClient", "feature flags", "configuration", "key-value settings".

npx skills add https://github.com/microsoft/skills --skill azure-appconfiguration-py

Azure App Configuration SDK for Python

Centralized configuration management with feature flags and dynamic settings.

Installation

pip install azure-appconfiguration

Environment Variables

AZURE_APPCONFIGURATION_ENDPOINT=https://<name>.azconfig.io  # Required for Entra ID auth
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

  1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
    • Local dev: DefaultAzureCredential works as-is.
    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
    • Sync: with <Client>(...) as client:
    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

import os
from azure.appconfiguration import AzureAppConfigurationClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

with AzureAppConfigurationClient(
    base_url=os.environ["AZURE_APPCONFIGURATION_ENDPOINT"],
    credential=credential
) as client:
    # Use client here (see following sections for operations)
    ...

Configuration Settings

Get Setting

setting = client.get_configuration_setting(key="app:settings:message")
print(f"{setting.key} = {setting.value}")

Get with Label

# Labels allow environment-specific values
setting = client.get_configuration_setting(
    key="app:settings:message",
    label="production"
)

Set Setting

from azure.appconfiguration import ConfigurationSetting

setting = ConfigurationSetting(
    key="app:settings:message",
    value="Hello, World!",
    label="development",
    content_type="text/plain",
    tags={"environment": "dev"}
)

client.set_configuration_setting(setting)

Delete Setting

client.delete_configuration_setting(
    key="app:settings:message",
    label="development"
)

List Settings

All Settings

settings = client.list_configuration_settings()
for setting in settings:
    print(f"{setting.key} [{setting.label}] = {setting.value}")

Filter by Key Prefix

settings = client.list_configuration_settings(
    key_filter="app:settings:*"
)

Filter by Label

settings = client.list_configuration_settings(
    label_filter="production"
)

Feature Flags

Set Feature Flag

from azure.appconfiguration import ConfigurationSetting
import json

feature_flag = ConfigurationSetting(
    key=".appconfig.featureflag/beta-feature",
    value=json.dumps({
        "id": "beta-feature",
        "enabled": True,
        "conditions": {
            "client_filters": []
        }
    }),
    content_type="application/vnd.microsoft.appconfig.ff+json;charset=utf-8"
)

client.set_configuration_setting(feature_flag)

Get Feature Flag

setting = client.get_configuration_setting(
    key=".appconfig.featureflag/beta-feature"
)
flag_data = json.loads(setting.value)
print(f"Feature enabled: {flag_data['enabled']}")

List Feature Flags

flags = client.list_configuration_settings(
    key_filter=".appconfig.featureflag/*"
)
for flag in flags:
    data = json.loads(flag.value)
    print(f"{data['id']}: {'enabled' if data['enabled'] else 'disabled'}")

Read-Only Settings

# Make setting read-only
client.set_read_only(
    configuration_setting=setting,
    read_only=True
)

# Remove read-only
client.set_read_only(
    configuration_setting=setting,
    read_only=False
)

Snapshots

Create Snapshot

from azure.appconfiguration import ConfigurationSnapshot, ConfigurationSettingFilter

snapshot = ConfigurationSnapshot(
    name="v1-snapshot",
    filters=[
        ConfigurationSettingFilter(key="app:*", label="production")
    ]
)

created = client.begin_create_snapshot(
    name="v1-snapshot",
    snapshot=snapshot
).result()

List Snapshot Settings

settings = client.list_configuration_settings(
    snapshot_name="v1-snapshot"
)

Async Client

from azure.appconfiguration.aio import AzureAppConfigurationClient
from azure.identity.aio import DefaultAzureCredential

async def main():
    async with DefaultAzureCredential() as credential:
        async with AzureAppConfigurationClient(
            base_url=endpoint,
            credential=credential
        ) as client:
            setting = await client.get_configuration_setting(key="app:message")
            print(setting.value)

Client Operations

OperationDescription
get_configuration_settingGet single setting
set_configuration_settingCreate or update setting
delete_configuration_settingDelete setting
list_configuration_settingsList with filters
set_read_onlyLock/unlock setting
begin_create_snapshotCreate point-in-time snapshot
list_snapshotsList all snapshots

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
  2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
  3. Use labels for environment separation (dev, staging, prod)
  4. Use key prefixes for logical grouping (app:database:, app:cache:)
  5. Make production settings read-only to prevent accidental changes
  6. Create snapshots before deployments for rollback capability
  7. Use Entra ID instead of connection strings in production
  8. Refresh settings periodically in long-running applications
  9. Use feature flags for gradual rollouts and A/B testing

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

microsoft tarafından daha fazla skill

oss-growth
microsoft
OSS büyüme korsanı kişiliği
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK'sini (agent-framework-azure-ai) kullanarak Azure AI Foundry aracıları oluşturun. AzureAIAgentsProvider ile kalıcı aracılar oluştururken, barındırılan araçları (kod yorumlayıcı, dosya arama, web araması) kullanırken, MCP sunucularını entegre ederken, konuşma iş parçacıklarını yönetirken veya akış yanıtları uygularken kullanın. Fonksiyon araçlarını, yapılandırılmış çıktıları ve çok araçlı aracıları kapsar.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Azure Application Insights ile web uygulamalarını enstrümante etme rehberi. Telemetri desenleri, SDK kurulumu ve yapılandırma referansları sağlar. NE ZAMAN: uygulama nasıl enstrümante edilir, App Insights SDK, telemetri desenleri, App Insights nedir, Application Insights rehberliği, enstrümantasyon örnekleri, APM en iyi uygulamaları.
devops
applicationinsights-web-ts
microsoft
Tarayıcı/web uygulamalarını Application Insights JavaScript SDK'sı (@microsoft/applicationinsights-web) ile izleyin. Gerçek Kullanıcı İzleme (RUM) için kullanın — sayfa görünümleri, tıklamalar, AJAX/fetch bağımlılıkları, özel durumlar, özel olaylar ve arka uç OpenTelemetry izleriyle ilişkilendirilen tarayıcı tarafı GenAI aracı izleri. SDK Loader Script ve npm kurulumunu, çerçeve uzantılarını (React, React Native, Angular), Tıklama Analitiğini, telemetri başlatıcılarını ve tarayıcıdan yayılan aracı/araç/model yayılımları için OTel GenAI anlamsal kurallarını kapsar.
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Java ile anomali tespiti uygulamaları oluşturun. Tek değişkenli/çok değişkenli anomali tespiti, zaman serisi analizi veya yapay zeka destekli izleme uygularken kullanın.
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDK'sini kullanarak Konuşma Dili Anlama (CLU) uygulayın. ConversationAnalysisClient ile konuşma niyetini ve varlıklarını analiz etmek, NLP özellikleri oluşturmak veya dil anlamayı uygulamalara entegre etmek için kullanın.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Makine öğrenimi çalışma alanları, işler, modeller, veri kümeleri, bilgi işlem ve iş akışları için kullanın. Tetikleyiciler: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development