azure-ai-contentsafety-py

# 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 CONTENT_SAFETY_KEY=<your-api-key> ``` ## Authentication ### API Key ```python from azure.ai.contentsafety import ContentSafetyClient from azure.core.credentials import AzureKeyCredential import os client = ContentSafetyClient( endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"], credential=AzureKeyCredential(os.environ["CONTENT_SAFETY_KEY"]) ) ``` ### Entra ID ```python from azure.ai.contentsafety import ContentSafetyClient from azure.identity import DefaultAzureCredential client = ContentSafetyClient( endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"], credential=DefaultAzureCredential() ) ``` ## Analyze Text ```python from azure.ai.contentsafety import ContentSafetyClient from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory from azure.core.credentials import AzureKeyCredential client = ContentSafetyClient(endpoint, AzureKeyCredential(key)) 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.core.credentials import AzureKeyCredential import base64 client = ContentSafetyClient(endpoint, AzureKeyCredential(key)) # 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") ) response = client.analyze_image(request) ``` ## Text Blocklist Management ### Create Blocklist ```python from azure.ai.contentsafety import BlocklistClient from azure.ai.contentsafety.models import TextBlocklist from azure.core.credentials import AzureKeyCredential blocklist_client = BlocklistClient(endpoint, AzureKeyCredential(key)) 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 ```python 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 ```python 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): ```python 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. **Use blocklists** for domain-specific terms 2. **Set severity thresholds** appropriate for your use case 3. **Handle multiple categories** — content can be harmful in multiple ways 4. **Use halt_on_blocklist_hit** for immediate rejection 5. **Log analysis results** for audit and improvement 6. **Consider 8-severity mode** for finer-grained control 7. **Pre-moderate AI outputs** before showing to users ## When to Use This skill is applicable to execute the workflow or actions described in the overview. ## Diff History - **v00.33.0**: Ingested from antigravity-awesome-skills community repo --- ## Why This Skill Exists Implement — Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity <!-- SR_40: auto-generated from frontmatter `purpose`/`description` (OPP-Phase3). Expand with domain-specific rationale. --> ## What If Fails - condition: Código não disponível para análise <!-- SR_40: auto-generated from frontmatter `what_if_fails` (OPP-Phase3). -->