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/azure-aigateway

@d0c21c0 official
by microsoftmicrosoft/github-copilot-for-azure253 stars
206

Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.

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Use this Skill: https://skilld.dev/gh/microsoft/github-copilot-for-azure/azure-aigateway

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referencessdkazure-ai-contentsafety-py.md

≈341 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Azure AI Content Safety — Python SDK Quick Reference

Condensed from azure-ai-contentsafety-py. Full patterns (blocklist management, image analysis, 8-severity mode) in the azure-ai-contentsafety-py plugin skill if installed.

Install

pip install azure-ai-contentsafety

Quick Start

from azure.ai.contentsafety import ContentSafetyClient, BlocklistClient
from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
client = ContentSafetyClient(endpoint=endpoint, credential=credential)

Non-Obvious Patterns

  • Two clients: ContentSafetyClient (analyze) and BlocklistClient (blocklist management)
  • Image from file: base64-encode bytes, pass via ImageData(content=base64_str)
  • 8-severity mode: AnalyzeTextOptions(text=..., output_type=AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS)
  • Blocklist analyze: AnalyzeTextOptions(text=..., blocklist_names=[...], halt_on_blocklist_hit=True)

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

Source: SKILL.md on GitHub

Third-party checks

1 warning16d5 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    This skill provides configuration guidance for Azure API Management as an AI Gateway. It incorporates security best practices such as Managed Identity authentication and content safety policies. All external resources originate from trusted Microsoft sources, and no security risks were identified.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • Runlayer6mo

    7/9 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at d0c21c0. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 20 hours ago.

Activeupdated 5 months ago

Capability

metadata
{
  "author": "Microsoft",
  "version": "0.0.0-placeholder"
}
compatibility
Requires Azure CLI (az) for configuration and testing

Topics

  • MCP
  • azure
  • api-management
  • ai-gateway
  • llm
  • semantic-caching
  • rate-limiting
  • content-safety
  • token-limiting
  • load-balancing

README badge

README badge for microsoft/github-copilot-for-azure/azure-aigateway

What it does

Configures Azure API Management as a gateway to enforce semantic caching, token limits, content safety, and rate limiting across AI models, MCP tools, and agents. Use this skill to add Azure OpenAI or AI Foundry backends, apply LLM governance policies, and test the gateway with curl or Azure CLI.

Generated from the current SKILL.md.

Frequently asked

Does this skill work with models other than Azure OpenAI?
Yes. The skill configures Azure API Management to govern any AI model backend, including AI Foundry models. You add backends via the `az apim backend create` command.
Can I use this skill to rate-limit MCP tools?
Yes. The skill includes the `rate-limit-by-key` policy for protecting MCP tools and APIs from overuse.
What do I need installed to use this skill?
You need the Azure CLI (az) installed and configured. The skill also assumes Azure API Management is already deployed; use the azure-prepare skill to deploy APIM first if needed.
Does this skill provide content safety and jailbreak detection?
Yes. The skill includes the `llm-content-safety` policy for filtering harmful content and detecting jailbreak attempts on AI agents.
Can semantic caching really save that much on API costs?
The skill documents 60-80% cost savings using the `azure-openai-semantic-cache-lookup` and `azure-openai-semantic-cache-store` policies, though actual savings depend on request patterns and cache hit rates.

Generated from the current SKILL.md. These answers refresh after source changes.