azure-ai-contentsafety-py

Files (3)

• SKILL.md
• capabilities.md · references/
• non-hero-scenarios.md · references/

SKILL.md (first lines)

# Azure AI Content Safety SDK for Python

Detect harmful user-generated and AI-generated content in applications.

## Installation

```bash
pip install azure-ai-contentsafety
```

## Environment Variables

```bash
CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
CONTENT_SAFETY_KEY=<your-api-key>  # Only required for the legacy API-key auth path below
```

## 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.

```python
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions

# 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 ContentSafetyClient(
    endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
    credential=credential,
) as client:
    response = client.analyze_text(AnalyzeTextOptions(text="Hello, world!"))
```

### Legacy: API Key (existing keyed deployments)

New code should use `DefaultAzureCredential` above. Use `AzureKeyCredential` only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.

```python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions

with ContentSafetyClient(
    endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["CONTENT_SAFETY_KEY"]),
) as client:
    response = client.analyze_text(AnalyzeTextOptions(text="Hello, world!"))
```

The `BlocklistClient` accepts the same `AzureKeyCredential` if you also need to manage blocklists with a key.

## Analyze Text

```python
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
from azure.identity import DefaultAzureCredential

with ContentSafetyClient(endpoint, DefaultAzureCredential()) as client:
    request = AnalyzeTextOptions(text="Your text content to analyze")
    response = client.analyze_text(request)

    # Check each category
    for category in [TextCategory.HATE, TextCategory.SELF_HARM, 
                     TextCategory.SEXUAL, TextCategory.VIOLENCE]:
        result = next((r for r in response.categories_analysis 
                       if r.category == category), None)
        if result:
            print(f"{category}: severity {result.severity}")
```

## Analyze Image

```python
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
from azure.identity import DefaultAzureCredential
import base64

with ContentSafetyClient(endpoint, DefaultAzureCredential()) as client:
    # From file
    with open("image.jpg", "rb") as f:
        image_data = base64.b64encode(f.read()).decode("utf-8")

    request = AnalyzeImageOptions(
        image=ImageData(content=image_data)
    )

    response = client.analyze_image(request)

    for result in response.categories_analysis:
        print(f"{result.category}: severity {result.severity}")
```

### Image from URL

```python
from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData

request = AnalyzeImageOptions(
    image=ImageData(blob_url="https://example.com/image.jpg")

From microsoft/skills, MIT.

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