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Azure AI Gateway

Design and configure Azure API Management as an AI Gateway for LLM traffic routing and rate limiting

microsoftGitHub microsoftOfficialSource repo Original GitHub link Publisher page

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10

Skill

n/a

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39.5 KB

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SKILL.md

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git-repo

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references/sdk/azure-ai-contentsafety-ts.md

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Azure AI Content Safety — TypeScript SDK Quick Reference

Condensed from azure-ai-contentsafety-ts. Full patterns (blocklist CRUD, image moderation, severity thresholds) in the azure-ai-contentsafety-ts plugin skill if installed.

Install

npm install @azure-rest/ai-content-safety @azure/identity @azure/core-auth

Quick Start

import ContentSafetyClient, { isUnexpected } from "@azure-rest/ai-content-safety";
import { AzureKeyCredential } from "@azure/core-auth";
const client = ContentSafetyClient(endpoint, new AzureKeyCredential(key));

Non-Obvious Patterns

REST client — ContentSafetyClient is a function, not a class
Text: client.path("/text:analyze").post({ body: { text, categories: [...] } })
Image: client.path("/image:analyze").post({ body: { image: { content: base64 } } })
Blocklist create: .path("/text/blocklists/{blocklistName}", name).patch({...})
API key import: AzureKeyCredential from @azure/core-auth (not @azure/identity)

Best Practices

Always use isUnexpected() — type guard for error handling
Set appropriate thresholds — different categories may need different severity levels
Use blocklists for domain-specific terms to supplement AI detection
Log moderation decisions — keep audit trail for compliance
Handle edge cases — empty text, very long text, unsupported image formats