def sample_analyze_orchestration_direct_target():
# [START analyze_orchestration_app_qna_response]
# 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_WORKFLOW_PROJECT_NAME"]
deployment_name = os.environ[
"AZURE_CONVERSATIONS_WORKFLOW_DEPLOYMENT_NAME"]
# analyze query
client = ConversationAnalysisClient(clu_endpoint,
AzureKeyCredential(clu_key))
with client:
query = "How are you?"
qna_app = "ChitChat-QnA"
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,
"directTarget": qna_app,
"targetProjectParameters": {
"ChitChat-QnA": {
"targetProjectKind": "QuestionAnswering",
"callingOptions": {
"question": query
}
}
}
}
})
# view result
print("query: {}".format(result["result"]["query"]))
print("project kind: {}\n".format(
result["result"]["prediction"]["projectKind"]))
# top intent
top_intent = result["result"]["prediction"]["topIntent"]
print("top intent: {}".format(top_intent))
top_intent_object = result["result"]["prediction"]["intents"][top_intent]
print("confidence score: {}".format(top_intent_object["confidenceScore"]))
print("project kind: {}".format(top_intent_object["targetProjectKind"]))
if top_intent_object["targetProjectKind"] == "QuestionAnswering":
print("\nview qna result:")
qna_result = top_intent_object["result"]
for answer in qna_result["answers"]:
print("\nanswer: {}".format(answer["answer"]))
print("answer: {}".format(answer["confidenceScore"]))
def sample_analyze_conversation_with_dict_parms():
# [START analyze_conversation_app]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import DateTimeResolution
# get secrets
clu_endpoint = os.environ["AZURE_CLU_ENDPOINT"]
clu_key = os.environ["AZURE_CLU_KEY"]
project_name = os.environ["AZURE_CLU_CONVERSATIONS_PROJECT_NAME"]
deployment_name = os.environ["AZURE_CLU_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": "CustomConversation",
"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("query: {}".format(result.results.query))
print("project kind: {}\n".format(result.results.prediction.project_kind))
print("top intent: {}".format(result.results.prediction.top_intent))
print("category: {}".format(result.results.prediction.intents[0].category))
print("confidence score: {}\n".format(result.results.prediction.intents[0].confidence))
print("entities:")
for entity in result.results.prediction.entities:
print("\ncategory: {}".format(entity.category))
print("text: {}".format(entity.text))
print("confidence score: {}".format(entity.confidence))
if entity.resolutions:
print("resolutions")
for resolution in entity.resolutions:
print("kind: {}".format(resolution.resolution_kind))
print("value: {}".format(resolution.additional_properties["value"]))
if entity.extra_information:
print("extra info")
for data in entity.extra_information:
print("kind: {}".format(data.extra_information_kind))
if data.extra_information_kind == "ListKey":
print("key: {}".format(data.key))
if data.extra_information_kind == "EntitySubtype":
print("value: {}".format(data.value))
def test_conversational_summarization(self, endpoint, key):
# analyze query
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
poller = client.begin_conversation_analysis(
task={
"displayName": "Analyze conversations from xxx",
"analysisInput": {
"conversations": [
{
"conversationItems": [
{
"text": "Hello, how can I help you?",
"modality": "text",
"id": "1",
"participantId": "Agent"
},
{
"text": "How to upgrade Office? I am getting error messages the whole day.",
"modality": "text",
"id": "2",
"participantId": "Customer"
},
{
"text": "Press the upgrade button please. Then sign in and follow the instructions.",
"modality": "text",
"id": "3",
"participantId": "Agent"
}
],
"modality": "text",
"id": "conversation1",
"language": "en"
},
]
},
"tasks": [
{
"taskName": "analyze 1",
"kind": "ConversationalSummarizationTask",
"parameters": {
"summaryAspects": ["Issue, Resolution"]
}
}
]
}
)
# assert - main object
result = poller.result()
assert not result is None
assert result["status"] == "succeeded"
# assert - task result
task_result = result["tasks"]["items"][0]
assert task_result["status"] == "succeeded"
assert task_result["kind"] == "conversationalSummarizationResults"
# assert - conv result
conversation_result = task_result["results"]["conversations"][0]
summaries = conversation_result["summaries"]
assert summaries is not None
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("query: {}".format(result["result"]["query"]))
print("project kind: {}\n".format(result["result"]["prediction"]["projectKind"]))
print("top intent: {}".format(result["result"]["prediction"]["topIntent"]))
print("category: {}".format(result["result"]["prediction"]["intents"][0]["category"]))
print("confidence score: {}\n".format(result["result"]["prediction"]["intents"][0]["confidenceScore"]))
print("entities:")
for entity in result["result"]["prediction"]["entities"]:
print("\ncategory: {}".format(entity["category"]))
print("text: {}".format(entity["text"]))
print("confidence score: {}".format(entity["confidenceScore"]))
if "resolutions" in entity:
print("resolutions")
for resolution in entity["resolutions"]:
print("kind: {}".format(resolution["resolutionKind"]))
print("value: {}".format(resolution["value"]))
if "extraInformation" in entity:
print("extra info")
for data in entity["extraInformation"]:
print("kind: {}".format(data["extraInformationKind"]))
if data["extraInformationKind"] == "ListKey":
print("key: {}".format(data["key"]))
if data["extraInformationKind"] == "EntitySubtype":
print("value: {}".format(data["value"]))