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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_analyze_conversation_app.py
DESCRIPTION:
This sample demonstrates how to analyze user query for intents and entities using
a conversation project with a language parameter.
For more info about how to setup a CLU conversation project, see the README.
USAGE:
python sample_analyze_conversation_app.py
Set the environment variables with your own values before running the sample:
1) AZURE_CONVERSATIONS_ENDPOINT - endpoint for your CLU resource.
2) AZURE_CONVERSATIONS_KEY - API key for your CLU resource.
3) AZURE_CONVERSATIONS_PROJECT_NAME - project name for your CLU conversations project.
4) AZURE_CONVERSATIONS_DEPLOYMENT_NAME - deployment name for your CLU conversations project.
"""
def sample_analyze_conversation_app():
# [START analyze_conversation_app]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
# get secrets
clu_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
clu_key = os.environ["AZURE_CONVERSATIONS_KEY"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT_NAME"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT_NAME"]
# analyze quey
client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
with client:
query = "Send an email to Carol about the tomorrow's demo"
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
}
}
)
# view result
print(f"query: {result['result']['query']}")
print(f"project kind: {result['result']['prediction']['projectKind']}\n")
print(f"top intent: {result['result']['prediction']['topIntent']}")
print(f"category: {result['result']['prediction']['intents'][0]['category']}")
print(f"confidence score: {result['result']['prediction']['intents'][0]['confidenceScore']}\n")
print("entities:")
for entity in result['result']['prediction']['entities']:
print(f"\ncategory: {entity['category']}")
print(f"text: {entity['text']}")
print(f"confidence score: {entity['confidenceScore']}")
if "resolutions" in entity:
print("resolutions")
for resolution in entity['resolutions']:
print(f"kind: {resolution['resolutionKind']}")
print(f"value: {resolution['value']}")
if "extraInformation" in entity:
print("extra info")
for data in entity['extraInformation']:
print(f"kind: {data['extraInformationKind']}")
if data['extraInformationKind'] == "ListKey":
print(f"key: {data['key']}")
if data['extraInformationKind'] == "EntitySubtype":
print(f"value: {data['value']}")
# [END analyze_conversation_app]
if __name__ == '__main__':
sample_analyze_conversation_app()
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