finish removing samples for luis response (#36927)
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Krista Pratico
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@ -23,7 +23,6 @@ These sample programs show common scenarios for the Conversational Language Unde
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|[sample_analyze_conversation_app.py][sample_analyze_conversation_app] and [sample_analyze_conversation_app_async.py][sample_analyze_conversation_app_async] | Analyze intents and entities in your utterance using a conversation project. |
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| [sample_analyze_orchestration_app_conv_response.py][sample_analyze_orchestration_app_conv_response] and [sample_analyze_orchestration_app_conv_response_async.py][sample_analyze_orchestration_app_conv_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a conversation project. |
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| [sample_analyze_orchestration_app_luis_response.py][sample_analyze_orchestration_app_luis_response] and [sample_analyze_orchestration_app_luis_response_async.py][sample_analyze_orchestration_app_luis_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a Luis project. |
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| [sample_analyze_orchestration_app_qna_response.py][sample_analyze_orchestration_app_qna_response] and [sample_analyze_orchestration_app_qna_response_async.py][sample_analyze_orchestration_app_qna_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a Qna project. |
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| [sample_conv_summarization.py][sample_conv_summarization] and [sample_conv_summarization_async.py][sample_conv_summarization_async]| Summarize conversation in the form of issues and resolutions (ex: tech support conversation) |
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| [sample_manage_projects.py][sample_manage_projects] and [sample_manage_projects_async.py][sample_manage_projects_async]| Shows common authoring operations on projects. |
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@ -73,9 +72,6 @@ what you can do with the Azure Conversational Language Understanding client libr
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[sample_analyze_orchestration_app_conv_response]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_orchestration_app_conv_response.py
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[sample_analyze_orchestration_app_conv_response_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_analyze_orchestration_app_conv_response_async.py
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[sample_analyze_orchestration_app_luis_response]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_orchestration_app_luis_response.py
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[sample_analyze_orchestration_app_luis_response_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_analyze_orchestration_app_luis_response_async.py
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[sample_analyze_orchestration_app_qna_response]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_orchestration_app_qna_response.py
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[sample_analyze_orchestration_app_qna_response_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_analyze_orchestration_app_qna_response_async.py
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@ -1,91 +0,0 @@
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# coding=utf-8
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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FILE: sample_analyze_orchestration_app_luis_response_async.py
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DESCRIPTION:
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This sample demonstrates how to analyze user query using an orchestration project.
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In this sample, orchestration project's top intent will map to a LUIS project.
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For more info about how to setup a CLU orchestration project, see the README.
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USAGE:
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python sample_analyze_orchestration_app_luis_response_async.py
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Set the environment variables with your own values before running the sample:
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1) AZURE_CONVERSATIONS_ENDPOINT - endpoint for your CLU resource.
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2) AZURE_CONVERSATIONS_KEY - API key for your CLU resource.
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3) AZURE_CONVERSATIONS_WORKFLOW_PROJECT_NAME - project name for your CLU orchestration project.
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4) AZURE_CONVERSATIONS_WORKFLOW_DEPLOYMENT_NAME - deployment name for your CLU orchestration project.
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"""
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import asyncio
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async def sample_analyze_orchestration_app_luis_response_async():
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# [START analyze_orchestration_app_luis_response]
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# import libraries
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import os
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from azure.core.credentials import AzureKeyCredential
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from azure.ai.language.conversations.aio import ConversationAnalysisClient
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# get secrets
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clu_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
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clu_key = os.environ["AZURE_CONVERSATIONS_KEY"]
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project_name = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT_NAME"]
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deployment_name = os.environ["AZURE_CONVERSATIONS_WORKFLOW_DEPLOYMENT_NAME"]
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# analyze query
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client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
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async with client:
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query = "Reserve a table for 2 at the Italian restaurant"
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result = await client.analyze_conversation(
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task={
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"kind": "Conversation",
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"analysisInput": {
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"conversationItem": {
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"participantId": "1",
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"id": "1",
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"modality": "text",
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"language": "en",
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"text": query
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},
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"isLoggingEnabled": False
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},
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"parameters": {
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"projectName": project_name,
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"deploymentName": deployment_name,
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"verbose": True
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}
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}
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)
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# view result
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print(f"query: {result['result']['query']}")
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print(f"project kind: {result['result']['prediction']['projectKind']}\n")
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# top intent
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top_intent = result['result']['prediction']['topIntent']
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print(f"top intent: {top_intent}")
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top_intent_object = result['result']['prediction']['intents'][top_intent]
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print(f"confidence score: {top_intent_object['confidenceScore']}")
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print(f"project kind: {top_intent_object['targetProjectKind']}")
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if top_intent_object['targetProjectKind'] == "Luis":
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print("\nluis response:")
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luis_response = top_intent_object['result']['prediction']
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print(f"top intent: {luis_response['topIntent']}")
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print("\nentities:")
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for entity in luis_response['entities']:
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print(f"\n{entity}")
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# [END analyze_orchestration_app_luis_response]
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async def main():
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await sample_analyze_orchestration_app_luis_response_async()
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if __name__ == '__main__':
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asyncio.run(main())
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