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name: azure-ai-language-conversations-py\ndescription: 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.\nlicense: MIT\nmetadata:\nauthor: Microsoft\nversion: \"1.0.0\"

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Azure AI Language Conversations for Python

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System Prompt

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You are an expert Python developer specializing in Azure AI Services and Natural Language Processing.\nYour task is to help users implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations SDK.

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When responding to requests about Azure AI Language Conversations:

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  1. Always use the latest version of the azure-ai-language-conversations SDK.
    2. \n
  2. Emphasize the use of ConversationAnalysisClient with DefaultAzureCredential.
    3. \n
  3. Provide clear code examples demonstrating how to structure the conversation payload.
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  4. Handle exceptions properly.
    5. \n
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Authentication & Lifecycle

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\uD83D\uDD11 Two rules apply to every code sample below:

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  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.\n
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    • Local dev: DefaultAzureCredential works as-is.
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    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
      • \n
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    2. \n
  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:\n
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    • Sync: with <Client>(...) as client:
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    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)
      • \n
    \n
    3. \n
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Snippets may abbreviate this setup, but production code should always follow both rules.

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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.

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Legacy: API Key (existing keyed deployments)

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New code should use DefaultAzureCredential. 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.

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import os\nfrom azure.core.credentials import AzureKeyCredential\nfrom azure.ai.language.conversations import ConversationAnalysisClient\n\nendpoint = os.environ[\"AZURE_CONVERSATIONS_ENDPOINT\"]\nkey = os.environ[\"AZURE_CONVERSATIONS_KEY\"]\n\nwith ConversationAnalysisClient(endpoint, AzureKeyCredential(key)) as client:\n    # See \"Basic Conversation Analysis\" below for the analyze_conversation payload\n    ...\n
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Best Practices

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Examples

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Basic Conversation Analysis

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import os\nfrom azure.identity import DefaultAzureCredential\nfrom azure.ai.language.conversations import ConversationAnalysisClient\n\nendpoint = os.environ[\"AZURE_CONVERSATIONS_ENDPOINT\"]\nproject_name = os.environ[\"AZURE_CONVERSATIONS_PROJECT\"]\ndeployment_name = os.environ[\"AZURE_CONVERSATIONS_DEPLOYMENT\"]\n\n# DefaultAzureCredential works locally and in Azure with no code change.\ncredential = DefaultAzureCredential()\n\nwith ConversationAnalysisClient(endpoint, credential) as client:\n    query = \"Send an email to Carol about the tomorrow's meeting\"\n    result = client.analyze_conversation(\n        task={\n            \"kind\": \"Conversation\",\n            \"analysisInput\": {\n                \"conversationItem\": {\n                    \"participantId\": \"1\",\n                    \"id\": \"1\",\n                    \"modality\": \"text\",\n                    \"language\": \"en\",\n                    \"text\": query\n                },\n                \"isLoggingEnabled\": False\n            },\n            \"parameters\": {\n                \"projectName\": project_name,\n                \"deploymentName\": deployment_name,\n                \"verbose\": True\n            }\n        }\n    )\n\n    print(f\"Top intent: {result['result']['prediction']['topIntent']}\")\n
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Reference Files

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FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.
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