def test_conversation_app(self, conv_account, conv_key, conv_project):
# prepare data
query = "One california maki please."
input = ConversationAnalysisOptions(
query=query,
)
# analyze quey
client = ConversationAnalysisClient(conv_account, AzureKeyCredential(conv_key))
with client:
result = client.analyze_conversations(
input,
project_name=conv_project,
deployment_name='production'
)
# assert
assert isinstance(result, AnalyzeConversationResult)
assert result.query == query
assert isinstance(result.prediction, ConversationPrediction)
assert result.prediction.project_kind == 'conversation'
assert result.prediction.top_intent == 'Order'
assert len(result.prediction.entities) > 0
assert len(result.prediction.intents) > 0
assert result.prediction.intents[0].category == 'Order'
assert result.prediction.intents[0].confidence_score > 0
assert result.prediction.entities[0].category == 'OrderItem'
assert result.prediction.entities[0].text == 'california maki'
assert result.prediction.entities[0].confidence_score > 0
def test_orchestration_app_qna_response(self, endpoint, key,
orch_project_name,
orch_deployment_name):
# analyze query
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
query = "How are you?"
result = client.analyze_conversation(task=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1, participant_id=1, text=query)),
parameters=CustomConversationTaskParameters(
project_name=orch_project_name,
deployment_name=orch_deployment_name)))
# assert - main object
top_project = 'ChitChat-QnA'
assert not result is None
assert isinstance(result, CustomConversationalTaskResult)
assert result.results.query == query
# assert - prediction type
assert isinstance(result.results.prediction,
OrchestratorPrediction)
assert result.results.prediction.project_kind == "workflow"
# assert - top matching project
assert result.results.prediction.top_intent == top_project
top_intent_object = result.results.prediction.intents[top_project]
assert isinstance(top_intent_object,
QuestionAnsweringTargetIntentResult)
assert top_intent_object.target_kind == "question_answering"
# assert intent and entities
qna_result = top_intent_object.result
answer = qna_result.answers[0].answer
assert not answer is None
async def test_conversation_app(self, endpoint, key, conv_project_name,
conv_deployment_name):
# analyze query
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
async with client:
query = "Send an email to Carol about the tomorrow's demo"
result = await client.analyze_conversation(
task=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1, participant_id=1, text=query)),
parameters=CustomConversationTaskParameters(
project_name=conv_project_name,
deployment_name=conv_deployment_name)))
# assert - main object
assert not result is None
assert isinstance(result, CustomConversationalTaskResult)
# assert - prediction type
assert result.results.query == query
assert isinstance(result.results.prediction,
ConversationPrediction)
assert result.results.prediction.project_kind == 'conversation'
# assert - top intent
assert result.results.prediction.top_intent == 'Read'
assert len(result.results.prediction.intents) > 0
assert result.results.prediction.intents[0].category == 'Read'
assert result.results.prediction.intents[0].confidence > 0
# assert - entities
assert len(result.results.prediction.entities) > 0
assert result.results.prediction.entities[0].category == 'Contact'
assert result.results.prediction.entities[0].text == 'Carol'
assert result.results.prediction.entities[0].confidence > 0
async def sample_analyze_orchestration_app_luis_response_async():
# [START analyze_orchestration_app_luis_response_async]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations.aio import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
CustomConversationalTask,
ConversationAnalysisOptions,
CustomConversationTaskParameters,
TextConversationItem
)
# get secrets
clu_endpoint = os.environ["AZURE_CLU_ENDPOINT"]
clu_key = os.environ["AZURE_CLU_KEY"]
project_name = os.environ["AZURE_CLU_ORCHESTRATION_PROJECT_NAME"]
deployment_name = os.environ["AZURE_CLU_ORCHESTRATION_DEPLOYMENT_NAME"]
# analyze query
client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
async with client:
query = "Reserve a table for 2 at the Italian restaurant"
result = await client.analyze_conversation(
task=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1,
participant_id=1,
text=query
)
),
parameters=CustomConversationTaskParameters(
project_name=project_name,
deployment_name=deployment_name
)
)
)
# view result
print("query: {}".format(result.results.query))
print("project kind: {}\n".format(result.results.prediction.project_kind))
# top intent
top_intent = result.results.prediction.top_intent
print("top intent: {}".format(top_intent))
top_intent_object = result.results.prediction.intents[top_intent]
print("confidence score: {}".format(top_intent_object.confidence))
print("project kind: {}".format(top_intent_object.target_kind))
if top_intent_object.target_kind == "luis":
print("\nluis response:")
luis_response = top_intent_object.result["prediction"]
print("top intent: {}".format(luis_response["topIntent"]))
print("\nentities:")
for entity in luis_response["entities"]:
print("\n{}".format(entity))
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
from azure.ai.language.conversations.models import (
CustomConversationalTask, ConversationAnalysisOptions,
CustomConversationTaskParameters, TextConversationItem)
# 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=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1, participant_id=1, text=query)),
parameters=CustomConversationTaskParameters(
project_name=project_name, deployment_name=deployment_name)))
# 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))
async def sample_analyze_orchestration_app_qna_response_async():
# [START analyze_orchestration_app_qna_response_async]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations.aio import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
CustomConversationalTask,
ConversationAnalysisOptions,
CustomConversationTaskParameters,
TextConversationItem
)
# get secrets
clu_endpoint = os.environ["AZURE_CLU_ENDPOINT"]
clu_key = os.environ["AZURE_CLU_KEY"]
project_name = os.environ["AZURE_CLU_ORCHESTRATION_PROJECT_NAME"]
deployment_name = os.environ["AZURE_CLU_ORCHESTRATION_DEPLOYMENT_NAME"]
# analyze query
client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
async with client:
query = "How are you?"
result = await client.analyze_conversation(
task=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1,
participant_id=1,
text=query
)
),
parameters=CustomConversationTaskParameters(
project_name=project_name,
deployment_name=deployment_name
)
)
)
# view result
print("query: {}".format(result.results.query))
print("project kind: {}\n".format(result.results.prediction.project_kind))
# top intent
top_intent = result.results.prediction.top_intent
print("top intent: {}".format(top_intent))
top_intent_object = result.results.prediction.intents[top_intent]
print("confidence score: {}".format(top_intent_object.confidence))
print("project kind: {}".format(top_intent_object.target_kind))
if top_intent_object.target_kind == "question_answering":
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.confidence))
async def sample_analyze_orchestration_app_with_params_async():
# [START analyze_orchestration_app_with_params]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations.aio import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
ConversationAnalysisOptions,
QuestionAnsweringParameters,
ConversationParameters,
)
# get secrets
conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT"]
# prepare data
query = "How do you make sushi rice?",
input = ConversationAnalysisOptions(
query=query,
parameters={
"SushiMaking":
QuestionAnsweringParameters(calling_options={
"question": query,
"top": 1,
"confidenceScoreThreshold": 0.1
}),
"SushiOrder":
ConversationParameters(calling_options={"verbose": True})
})
# analyze query
client = ConversationAnalysisClient(conv_endpoint,
AzureKeyCredential(conv_key))
async with client:
result = await client.analyze_conversations(
input,
project_name=orchestration_project,
deployment_name='production',
)
# view result
print("query: {}".format(result.query))
print("project kind: {}\n".format(result.prediction.project_kind))
print("view top intent:")
top_intent = result.prediction.top_intent
print("\ttop intent: {}".format(top_intent))
top_intent_object = result.prediction.intents[top_intent]
print("\tconfidence score: {}\n".format(
top_intent_object.confidence_score))
print("view Question Answering result:")
print("\tresult: {}\n".format(top_intent_object.result))
def test_orchestration_app_conv_response(self, endpoint, key, orch_project_name, orch_deployment_name):
# analyze query
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
with client:
query = "Send an email to Carol about the tomorrow's demo"
result = client.analyze_conversation(
task=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1,
participant_id=1,
text=query
)
),
parameters=CustomConversationTaskParameters(
project_name=orch_project_name,
deployment_name=orch_deployment_name
)
)
)
# assert - main object
top_project = "EmailIntent"
assert not result is None
assert isinstance(result, CustomConversationalTaskResult)
assert result.results.query == query
# assert - prediction type
assert isinstance(result.results.prediction, OrchestratorPrediction)
assert result.results.prediction.project_kind == "workflow"
# assert - top matching project
assert result.results.prediction.top_intent == top_project
top_intent_object = result.results.prediction.intents[top_project]
assert isinstance(top_intent_object, ConversationTargetIntentResult)
assert top_intent_object.target_kind == "conversation"
# assert intent and entities
conversation_result = top_intent_object.result.prediction
assert conversation_result.top_intent == 'SendEmail'
assert len(conversation_result.intents) > 0
assert conversation_result.intents[0].category == 'SendEmail'
assert conversation_result.intents[0].confidence > 0
# assert - entities
assert len(conversation_result.entities) > 0
assert conversation_result.entities[0].category == 'ContactName'
assert conversation_result.entities[0].text == 'Carol'
assert conversation_result.entities[0].confidence > 0
async def sample_analyze_orchestration_app_qna_response_async():
# [START analyze_orchestration_app_qna_response_async]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations.aio import ConversationAnalysisClient
from azure.ai.language.conversations.models import ConversationAnalysisOptions
# get secrets
conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT"]
# prepare data
query = "How do you make sushi rice?",
input = ConversationAnalysisOptions(query=query)
# analyze query
client = ConversationAnalysisClient(conv_endpoint,
AzureKeyCredential(conv_key))
async with client:
result = await client.analyze_conversations(
input,
project_name=orchestration_project,
deployment_name='production',
)
# view result
print("query: {}".format(result.query))
print("project kind: {}\n".format(result.prediction.project_kind))
print("view top intent:")
top_intent = result.prediction.top_intent
print("\ttop intent: {}".format(top_intent))
top_intent_object = result.prediction.intents[top_intent]
print("\tconfidence score: {}\n".format(
top_intent_object.confidence_score))
print("view qna result:")
qna_result = result.prediction.intents[top_intent].result
for answer in qna_result.answers:
print("\tanswer: {}\n".format(answer.answer))
def sample_analyze_orchestration_app_luis_response():
# [START analyze_orchestration_app_luis_response]
# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import ConversationAnalysisOptions
# get secrets
conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT"]
# prepare data
query = "book me a flight ticket to Bali",
input = ConversationAnalysisOptions(query=query)
# analyze query
client = ConversationAnalysisClient(conv_endpoint,
AzureKeyCredential(conv_key))
with client:
result = client.analyze_conversations(
input,
project_name=orchestration_project,
deployment_name='production',
)
# view result
print("query: {}".format(result.query))
print("project kind: {}\n".format(result.prediction.project_kind))
print("view top intent:")
top_intent = result.prediction.top_intent
print("\ttop intent: {}".format(top_intent))
top_intent_object = result.prediction.intents[top_intent]
print("\tconfidence score: {}\n".format(
top_intent_object.confidence_score))
print("view luis response:")
luis_response = result.prediction.intents[top_intent].result
print("\tluis response: {}\n".format(luis_response))
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
from azure.ai.language.conversations.models import ConversationAnalysisOptions
# get secrets
conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
conv_project = os.environ["AZURE_CONVERSATIONS_PROJECT"]
# prepare data
query = "One california maki please."
input = ConversationAnalysisOptions(query=query)
# analyze quey
client = ConversationAnalysisClient(conv_endpoint,
AzureKeyCredential(conv_key))
with client:
result = client.analyze_conversations(input,
project_name=conv_project,
deployment_name='production')
# view result
print("query: {}".format(result.query))
print("project kind: {}\n".format(result.prediction.project_kind))
print("view top intent:")
print("\ttop intent: {}".format(result.prediction.top_intent))
print("\tcategory: {}".format(result.prediction.intents[0].category))
print("\tconfidence score: {}\n".format(
result.prediction.intents[0].confidence_score))
print("view entities:")
for entity in result.prediction.entities:
print("\tcategory: {}".format(entity.category))
print("\ttext: {}".format(entity.text))
print("\tconfidence score: {}".format(entity.confidence_score))
def test_orchestration_app_with_parameters(self, conv_account, conv_key, orchestration_project):
# prepare data
query = "How do you make sushi rice?",
input = ConversationAnalysisOptions(
query=query,
parameters={
"SushiMaking": QuestionAnsweringParameters(
calling_options={
"question": query,
"top": 1,
"confidenceScoreThreshold": 0.1
}
),
"SushiOrder": ConversationParameters(
calling_options={
"verbose": True
}
)
}
)
# analyze query
client = ConversationAnalysisClient(conv_account, AzureKeyCredential(conv_key))
with client:
result = client.analyze_conversations(
input,
project_name=orchestration_project,
deployment_name='production',
)
# assert
top_intent = "SushiMaking"
assert isinstance(result, AnalyzeConversationResult)
# assert result.query == query --> weird behavior here!
assert isinstance(result.prediction, OrchestratorPrediction)
assert result.prediction.project_kind == "workflow"
assert result.prediction.top_intent == top_intent
assert isinstance(result.prediction.intents[top_intent], QuestionAnsweringTargetIntentResult)
async def test_orchestration_app_luis_response(self, endpoint, key,
orch_project_name,
orch_deployment_name):
# analyze query
client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key))
async with client:
query = "Reserve a table for 2 at the Italian restaurant"
result = await client.analyze_conversation(
task=CustomConversationalTask(
analysis_input=ConversationAnalysisOptions(
conversation_item=TextConversationItem(
id=1, participant_id=1, text=query)),
parameters=CustomConversationTaskParameters(
project_name=orch_project_name,
deployment_name=orch_deployment_name)))
# assert - main object
top_project = "RestaurantIntent"
assert not result is None
assert isinstance(result, CustomConversationalTaskResult)
assert result.results.query == query
# assert - prediction type
assert isinstance(result.results.prediction,
OrchestratorPrediction)
assert result.results.prediction.project_kind == "workflow"
# assert - top matching project
assert result.results.prediction.top_intent == top_project
top_intent_object = result.results.prediction.intents[top_project]
assert isinstance(top_intent_object, LUISTargetIntentResult)
assert top_intent_object.target_kind == "luis"
# assert intent and entities
top_intent = "RestaurantReservation.Reserve"
luis_result = top_intent_object.result["prediction"]
assert luis_result["topIntent"] == top_intent
assert len(luis_result["intents"]) > 0
assert luis_result["intents"][top_intent]["score"] > 0
# assert - entities
assert len(luis_result["entities"]) > 0