azure-ai-contentsafety-py

name	azure-ai-contentsafety-py
description	Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".
license	MIT
metadata	{"author":"Microsoft","version":"1.0.0","package":"azure-ai-contentsafety"}

Azure AI Content Safety SDK for Python

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

Installation

pip install azure-ai-contentsafety

Environment Variables

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.

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.

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

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

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

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

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

response = client.analyze_image(request)

Text Blocklist Management

Create Blocklist

from azure.ai.contentsafety import BlocklistClient
from azure.ai.contentsafety.models import TextBlocklist
from azure.identity import DefaultAzureCredential

with BlocklistClient(endpoint, DefaultAzureCredential()) as blocklist_client:
    blocklist = TextBlocklist(
        blocklist_name="my-blocklist",
        description="Custom terms to block"
    )

    result = blocklist_client.create_or_update_text_blocklist(
        blocklist_name="my-blocklist",
        options=blocklist
    )

Add Block Items

from azure.ai.contentsafety.models import AddOrUpdateTextBlocklistItemsOptions, TextBlocklistItem

items = AddOrUpdateTextBlocklistItemsOptions(
    blocklist_items=[
        TextBlocklistItem(text="blocked-term-1"),
        TextBlocklistItem(text="blocked-term-2")
    ]
)

result = blocklist_client.add_or_update_blocklist_items(
    blocklist_name="my-blocklist",
    options=items
)

Analyze with Blocklist

from azure.ai.contentsafety.models import AnalyzeTextOptions

request = AnalyzeTextOptions(
    text="Text containing blocked-term-1",
    blocklist_names=["my-blocklist"],
    halt_on_blocklist_hit=True
)

response = client.analyze_text(request)

if response.blocklists_match:
    for match in response.blocklists_match:
        print(f"Blocked: {match.blocklist_item_text}")

Severity Levels

Text analysis returns 4 severity levels (0, 2, 4, 6) by default. For 8 levels (0-7):

from azure.ai.contentsafety.models import AnalyzeTextOptions, AnalyzeTextOutputType

request = AnalyzeTextOptions(
    text="Your text",
    output_type=AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS
)

Harm Categories

Category	Description
Hate	Attacks based on identity (race, religion, gender, etc.)
Sexual	Sexual content, relationships, anatomy
Violence	Physical harm, weapons, injury
SelfHarm	Self-injury, suicide, eating disorders

Severity Scale

Level	Text Range	Image Range	Meaning
0	Safe	Safe	No harmful content
2	Low	Low	Mild references
4	Medium	Medium	Moderate content
6	High	High	Severe content

Client Types

Client	Purpose
ContentSafetyClient	Analyze text and images
BlocklistClient	Manage custom blocklists

Best Practices

1. Pick sync OR async and stay consistent. Do not mix azure.ai.contentsafety sync clients with azure.ai.contentsafety.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 ContentSafetyClient(...) as client: (sync) or async with ContentSafetyClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
3. Use blocklists for domain-specific terms
4. Set severity thresholds appropriate for your use case
5. Handle multiple categories — content can be harmful in multiple ways
6. Use halt_on_blocklist_hit for immediate rejection
7. Log analysis results for audit and improvement
8. Consider 8-severity mode for finer-grained control
9. Pre-moderate AI outputs before showing to users