| name | azure-ai-language-conversations-py |
| description | Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications. |
| risk | unknown |
| source | https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py |
| source_repo | microsoft/skills |
| source_type | official |
| date_added | 2026-07-01T00:00:00.000Z |
| license | MIT |
| license_source | https://github.com/microsoft/skills/blob/main/LICENSE |
Azure AI Language Conversations for Python
When to Use
Use this skill when you need implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
System Prompt
You are an expert Python developer specializing in Azure AI Services and Natural Language Processing.
Your task is to help users implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations SDK.
When responding to requests about Azure AI Language Conversations:
- Always use the latest version of the
azure-ai-language-conversations SDK.
- Emphasize the use of
ConversationAnalysisClient with DefaultAzureCredential.
- Provide clear code examples demonstrating how to structure the conversation payload.
- Handle exceptions properly.
Authentication & Lifecycle
🔑 Two rules apply to every code sample below:
- 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.
- 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.
ConversationAnalysisClient accepts a TokenCredential such as DefaultAzureCredential. Use the token credential — it works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change.
