azure-ai-textanalytics-py

Topics in the source

• Azure AI Text Analytics SDK for Python
• Installation
• Environment Variables
• Authentication & Lifecycle
• Legacy: API Key (existing keyed deployments)
• Sentiment Analysis
• Entity Recognition
• PII Detection

Client library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more. 🔑 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 AZURETOKENCREDENTIALS=prod (or AZURETOKENCREDENTIALS= ) 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 (...) as client: - Async: async with (...) 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. 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. | Client |

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