azure-ai-language-conversations

import os from azure.ai.language.conversations import ConversationAnalysisClient from azure.core.credentials import AzureKeyCredential # from azure.identity import DefaultAzureCredential # Uncomment for Azure AD authentication # --- Set these environment variables before running --- # export LANGUAGE_ENDPOINT='https://your-language-resource.cognitiveservices.azure.com/' # export LANGUAGE_KEY='your-azure-language-resource-key' # export CLU_PROJECT_NAME='your-clu-project-name' # export CLU_DEPLOYMENT_NAME='your-clu-deployment-name' # ----------------------------------------------------- language_endpoint = os.environ.get("LANGUAGE_ENDPOINT", "") language_key = os.environ.get("LANGUAGE_KEY", "") clu_project_name = os.environ.get("CLU_PROJECT_NAME", "") clu_deployment_name = os.environ.get("CLU_DEPLOYMENT_NAME", "") if not all([language_endpoint, language_key, clu_project_name, clu_deployment_name]): print("ERROR: Please set the environment variables LANGUAGE_ENDPOINT, LANGUAGE_KEY, CLU_PROJECT_NAME, and CLU_DEPLOYMENT_NAME.") print("Refer to the comments in the quickstart code for examples.") exit(1) # Authenticate with AzureKeyCredential # For production scenarios, consider using DefaultAzureCredential from azure.identity for Azure AD authentication: # client = ConversationAnalysisClient(endpoint=language_endpoint, credential=DefaultAzureCredential()) client = ConversationAnalysisClient( endpoint=language_endpoint, credential=AzureKeyCredential(language_key) ) text = "How do I get to the nearest ATM?" print(f"\nAnalyzing conversation: '{text}' for project '{clu_project_name}' (deployment '{clu_deployment_name}')") try: response = client.analyze_conversation( task={ "kind": "Conversation", "analysisInput": { "conversationItem": { "participantId": "1", "id": "1", "text": text } }, "parameters": { "projectName": clu_project_name, "deploymentName": clu_deployment_name, "verbose": True # Set to False for less detailed output } } ) result = response.as_dict() prediction = result["result"]["prediction"] if prediction["projectKind"] == "Conversation": print(" Top intent:", prediction["topIntent"]) print(" Entities:") for entity in prediction["entities"]: print(f" - {entity['category']}: {entity['text']} (confidence: {entity['confidence']:.2f})") else: # This branch handles orchestrator projects or other unknown kinds print(f" Prediction project kind: {prediction['projectKind']}. Full prediction: {prediction}") except Exception as e: print(f"\nAn error occurred: {e}") print("Please ensure your environment variables are correct and the CLU project is deployed.")