Build conversational AI with Azure Language Understanding

Guides implementing Conversational Language Understanding (CLU) in Python using the azure-ai-language-conversations SDK's ConversationAnalysisClient, for analyzing conversation intent and entities and integrating language understanding into applications. Its embedded system prompt frames the assistant as an expert in Azure AI Services and NLP, instructed to always use the latest SDK version, emphasize ConversationAnalysisClient with DefaultAzureCredential, provide clear payload-structuring examples, and handle exceptions properly. It enforces two authentication and lifecycle rules on every code sample: prefer DefaultAzureCredential, which works unchanged locally via Azure CLI, VS Code, or Developer CLI and in Azure via managed identity or workload identity, setting AZURE_TOKEN_CREDENTIALS=prod in production to constrain the credential chain, over connection strings or account and API keys, which bypass Entra audit and rotation; and wrap every client in a context manager, with <Client>(...) as client: for sync or async with <Client>(...) as client: plus async with DefaultAzureCredential() as credential: from azure.identity.aio for async, so HTTP transports, sockets, and token caches release deterministically. It documents a legacy AzureKeyCredential path only for existing keyed deployments not yet migrated to Entra ID, such as regulated environments mid-rollout, explicitly stating new code should use DefaultAzureCredential instead. Best practices call for picking sync or async and staying consistent within a module rather than mixing azure.ai.language.conversations and its .aio async counterpart in the same call path, using environment variables for the endpoint, project, and deployment name, and clearly mapping participantId and id in the conversationItem payload. Its worked example constructs a full analyze_conversation call with a Conversation-kind task, a conversationItem payload covering participant, id, modality, language, and text, isLoggingEnabled: False, and parameters naming the project and deployment, then reads result['result']['prediction']['topIntent'] from the response.

Use it when implementing CLU using the azure-ai-language-conversations Python SDK, working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into an application.

Input is a conversational text query plus the Azure Conversations endpoint, project name, and deployment name. Output is Python code that calls analyze_conversation and returns intent and entity predictions, for example:

import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient

endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT"]

credential = DefaultAzureCredential()

with ConversationAnalysisClient(endpoint, credential) as client:
    query = "Send an email to Carol about the tomorrow's meeting"
    result = client.analyze_conversation(
        task={
            "kind": "Conversation",
            "analysisInput": {
                "conversationItem": {
                    "participantId": "1",
                    "id": "1",
                    "modality": "text",
                    "language": "en",
                    "text": query
                },
                "isLoggingEnabled": False
            },
            "parameters": {
                "projectName": project_name,
                "deploymentName": deployment_name,
                "verbose": True
            }
        }
    )

    print(f"Top intent: {result['result']['prediction']['topIntent']}")

Built on the azure-ai-language-conversations Python SDK, both sync and .aio async variants of ConversationAnalysisClient, and Azure Identity's DefaultAzureCredential, with a legacy fallback to azure.core.credentials.AzureKeyCredential for existing keyed deployments.

Python developers building conversational AI or NLP features on Azure who need correct authentication lifecycle handling and payload structure for Conversational Language Understanding.