jantoniofc/skillsbank/azure-ai-language-conversations-py

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
key = os.environ["AZURE_CONVERSATIONS_KEY"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT"]
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with 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']}")
## When to Use
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.
Covers: Azure AI Language Conversations for Python, System Prompt, Basic Conversation Analysis.