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[CLU March Preview] Tests (Azure#23524)
* add swagger.md * generate client * update swagger.md * regenerate client * samples - add conversation project sample * tmp commit * update conversations samples in both conversations and orchestration projects * update samples * update sync samples * update samples - file names and secrets * update samples readme * add async samples * update async samples * update samples * add some tests * add qna test * add async tests * update tests * update tests * fix sync tests * fixed async tests * add recorded tests * update changelog * rename body to task * update samples * update conversation samples * fix rebase * re-record tests * update samples to use input models * update and re-record tests * update swagger reference * rename confidence score * update samples * updating samples for extra info * update readme
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‎sdk/cognitivelanguage/azure-ai-language-conversations/CHANGELOG.md

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# Release History
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## 1.0.0b3 (2022-03-01)
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### Features Added
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* Entity resolutions
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* Extra features
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### Breaking Changes
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Input and output structures are different from previous release.
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## 1.0.0b2 (Unreleased)
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### Features Added

‎sdk/cognitivelanguage/azure-ai-language-conversations/README.md

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@@ -67,7 +67,7 @@ The `azure-ai-language-conversation` client library provides both synchronous an
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The following examples show common scenarios using the `client` [created above](#create-conversationanalysisclient).
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### Analyze a conversation with a Conversation App
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### Analyze Text with a Conversation App
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If you would like to extract custom intents and entities from a user utterance, you can call the `client.analyze_conversations()` method with your conversation's project name as follows:
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```python
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from azure.core.credentials import AzureKeyCredential
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from azure.ai.language.conversations import ConversationAnalysisClient
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from azure.ai.language.conversations.models import ConversationAnalysisOptions
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from azure.ai.language.conversations.models import (
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CustomConversationalTask,
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ConversationAnalysisOptions,
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CustomConversationTaskParameters,
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TextConversationItem
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)
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# get secrets
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conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
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conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
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conv_project = os.environ["AZURE_CONVERSATIONS_PROJECT"]
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# prepare data
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query = "One california maki please."
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input = ConversationAnalysisOptions(
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query=query
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)
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clu_endpoint = os.environ["AZURE_CLU_ENDPOINT"]
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clu_key = os.environ["AZURE_CLU_KEY"]
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project_name = os.environ["AZURE_CLU_CONVERSATIONS_PROJECT_NAME"]
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deployment_name = os.environ["AZURE_CLU_CONVERSATIONS_DEPLOYMENT_NAME"]
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# analyze quey
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client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
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client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
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with client:
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result = client.analyze_conversations(
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input,
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project_name=conv_project,
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deployment_name='production'
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)
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query = "Send an email to Carol about the tomorrow's demo"
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result = client.analyze_conversation(
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task=CustomConversationalTask(
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analysis_input=ConversationAnalysisOptions(
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conversation_item=TextConversationItem(
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id=1,
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participant_id=1,
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text=query
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)
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),
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parameters=CustomConversationTaskParameters(
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project_name=project_name,
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deployment_name=deployment_name
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)
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)
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)
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# view result
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print("query: {}".format(result.query))
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print("project kind: {}\n".format(result.prediction.project_kind))
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print("view top intent:")
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print("\ttop intent: {}".format(result.prediction.top_intent))
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print("\tcategory: {}".format(result.prediction.intents[0].category))
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print("\tconfidence score: {}\n".format(result.prediction.intents[0].confidence_score))
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print("view entities:")
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for entity in result.prediction.entities:
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print("\tcategory: {}".format(entity.category))
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print("\ttext: {}".format(entity.text))
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print("\tconfidence score: {}".format(entity.confidence_score))
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print("query: {}".format(result.results.query))
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print("project kind: {}\n".format(result.results.prediction.project_kind))
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print("top intent: {}".format(result.results.prediction.top_intent))
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print("category: {}".format(result.results.prediction.intents[0].category))
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print("confidence score: {}\n".format(result.results.prediction.intents[0].confidence))
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print("entities:")
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for entity in result.results.prediction.entities:
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print("\ncategory: {}".format(entity.category))
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print("text: {}".format(entity.text))
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print("confidence score: {}".format(entity.confidence))
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if entity.resolutions:
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print("resolutions")
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for resolution in entity.resolutions:
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print("kind: {}".format(resolution.resolution_kind))
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print("value: {}".format(resolution.additional_properties["value"]))
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if entity.extra_information:
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print("extra info")
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for data in entity.extra_information:
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print("kind: {}".format(data.extra_information_kind))
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if data.extra_information_kind == "ListKey":
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print("key: {}".format(data.key))
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if data.extra_information_kind == "EntitySubtype":
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print("value: {}".format(data.value))
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```
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### Analyze conversation with a Orchestration App
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### Analyze Text with an Orchestration App
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If you would like to pass the user utterance to your orchestrator (worflow) app, you can call the `client.analyze_conversations()` method with your orchestration's project name. The orchestrator project simply orchestrates the submitted user utterance between your language apps (Luis, Conversation, and Question Answering) to get the best response according to the user intent. See the next example:
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from azure.core.credentials import AzureKeyCredential
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from azure.ai.language.conversations import ConversationAnalysisClient
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from azure.ai.language.conversations.models import ConversationAnalysisOptions
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# get secrets
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conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
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conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
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orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
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# prepare data
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query = "How do you make sushi rice?",
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input = ConversationAnalysisOptions(
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query=query
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from azure.ai.language.conversations.models import (
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CustomConversationalTask,
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ConversationAnalysisOptions,
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CustomConversationTaskParameters,
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TextConversationItem
138155
)
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# analyze query
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client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
142-
with client:
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result = client.analyze_conversations(
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input,
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project_name=orchestration_project,
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deployment_name='production',
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)
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# view result
150-
print("query: {}".format(result.query))
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print("project kind: {}\n".format(result.prediction.project_kind))
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print("view top intent:")
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print("\ttop intent: {}".format(result.prediction.top_intent))
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print("\tcategory: {}".format(result.prediction.intents[0].category))
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print("\tconfidence score: {}\n".format(result.prediction.intents[0].confidence_score))
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print("view Question Answering result:")
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print("\tresult: {}\n".format(result.prediction.intents[0].result))
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```
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### Analyze conversation with a Orchestration (Direct) App
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If you would like to use an orchestrator (orchestration) app, and you want to call a specific one of your language apps directly, you can call the `client.analyze_conversations()` method with your orchestration's project name and the diirect target name which corresponds to your one of you language apps as follows:
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```python
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# import libraries
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import os
169-
from azure.core.credentials import AzureKeyCredential
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from azure.ai.language.conversations import ConversationAnalysisClient
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from azure.ai.language.conversations.models import ConversationAnalysisOptions
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# get secrets
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conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
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conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
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orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
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# prepare data
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query = "How do you make sushi rice?",
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target_intent = "SushiMaking"
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input = ConversationAnalysisOptions(
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query=query,
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direct_target=target_intent,
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parameters={
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"SushiMaking": QuestionAnsweringParameters(
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calling_options={
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"question": query,
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"top": 1,
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"confidenceScoreThreshold": 0.1
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}
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)
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}
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)
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clu_endpoint = os.environ["AZURE_CLU_ENDPOINT"]
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clu_key = os.environ["AZURE_CLU_KEY"]
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project_name = os.environ["AZURE_CLU_ORCHESTRATION_PROJECT_NAME"]
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deployment_name = os.environ["AZURE_CLU_ORCHESTRATION_DEPLOYMENT_NAME"]
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# analyze query
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client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
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client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
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with client:
199-
result = client.analyze_conversations(
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input,
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project_name=orchestration_project,
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deployment_name='production',
203-
)
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query = "How are you?"
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result = client.analyze_conversation(
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task=CustomConversationalTask(
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analysis_input=ConversationAnalysisOptions(
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conversation_item=TextConversationItem(
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id=1,
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participant_id=1,
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text=query
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)
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),
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parameters=CustomConversationTaskParameters(
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project_name=project_name,
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deployment_name=deployment_name
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)
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)
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)
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205183
# view result
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print("query: {}".format(result.query))
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print("project kind: {}\n".format(result.prediction.project_kind))
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print("view top intent:")
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print("\ttop intent: {}".format(result.prediction.top_intent))
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print("\tcategory: {}".format(result.prediction.intents[0].category))
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print("\tconfidence score: {}\n".format(result.prediction.intents[0].confidence_score))
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214-
print("view Question Answering result:")
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print("\tresult: {}\n".format(result.prediction.intents[0].result))
184+
print("query: {}".format(result.results.query))
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print("project kind: {}\n".format(result.results.prediction.project_kind))
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# top intent
188+
top_intent = result.results.prediction.top_intent
189+
print("top intent: {}".format(top_intent))
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top_intent_object = result.results.prediction.intents[top_intent]
191+
print("confidence score: {}".format(top_intent_object.confidence))
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print("project kind: {}".format(top_intent_object.target_kind))
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if top_intent_object.target_kind == "question_answering":
195+
print("\nview qna result:")
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qna_result = top_intent_object.result
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for answer in qna_result.answers:
198+
print("\nanswer: {}".format(answer.answer))
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print("answer: {}".format(answer.confidence))
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```
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219202
## Optional Configuration
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221204
Optional keyword arguments can be passed in at the client and per-operation level. The azure-core [reference documentation][azure_core_ref_docs] describes available configurations for retries, logging, transport protocols, and more.

‎sdk/cognitivelanguage/azure-ai-language-conversations/azure/ai/language/conversations/__init__.py

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__version__ = VERSION
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__all__ = ['ConversationAnalysisClient']
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try:
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from ._patch import patch_sdk # type: ignore
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patch_sdk()
18-
except ImportError:
19-
pass
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# `._patch.py` is used for handwritten extensions to the generated code
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# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
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from ._patch import patch_sdk
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patch_sdk()

‎sdk/cognitivelanguage/azure-ai-language-conversations/azure/ai/language/conversations/_configuration.py

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66
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
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# --------------------------------------------------------------------------
88

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from typing import TYPE_CHECKING
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from typing import Any
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from azure.core.configuration import Configuration
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from azure.core.credentials import AzureKeyCredential
1213
from azure.core.pipeline import policies
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from ._version import VERSION
1516

16-
if TYPE_CHECKING:
17-
# pylint: disable=unused-import,ungrouped-imports
18-
from typing import Any
1917

20-
from azure.core.credentials import AzureKeyCredential
21-
22-
23-
class ConversationAnalysisClientConfiguration(Configuration):
18+
class ConversationAnalysisClientConfiguration(Configuration): # pylint: disable=too-many-instance-attributes
2419
"""Configuration for ConversationAnalysisClient.
2520
2621
Note that all parameters used to create this instance are saved as instance
2722
attributes.
2823
29-
:param endpoint: Supported Cognitive Services endpoint (e.g., https://:code:`<resource-name>`.api.cognitiveservices.azure.com).
24+
:param endpoint: Supported Cognitive Services endpoint (e.g.,
25+
https://:code:`<resource-name>`.api.cognitiveservices.azure.com).
3026
:type endpoint: str
3127
:param credential: Credential needed for the client to connect to Azure.
3228
:type credential: ~azure.core.credentials.AzureKeyCredential
29+
:keyword api_version: Api Version. Default value is "2022-03-01-preview". Note that overriding
30+
this default value may result in unsupported behavior.
31+
:paramtype api_version: str
3332
"""
3433

3534
def __init__(
3635
self,
37-
endpoint, # type: str
38-
credential, # type: AzureKeyCredential
39-
**kwargs # type: Any
40-
):
41-
# type: (...) -> None
36+
endpoint: str,
37+
credential: AzureKeyCredential,
38+
**kwargs: Any
39+
) -> None:
40+
super(ConversationAnalysisClientConfiguration, self).__init__(**kwargs)
41+
api_version = kwargs.pop('api_version', "2022-03-01-preview") # type: str
42+
4243
if endpoint is None:
4344
raise ValueError("Parameter 'endpoint' must not be None.")
4445
if credential is None:
4546
raise ValueError("Parameter 'credential' must not be None.")
46-
super(ConversationAnalysisClientConfiguration, self).__init__(**kwargs)
4747

4848
self.endpoint = endpoint
4949
self.credential = credential
50-
self.api_version = "2021-11-01-preview"
50+
self.api_version = api_version
5151
kwargs.setdefault('sdk_moniker', 'ai-language-conversations/{}'.format(VERSION))
5252
self._configure(**kwargs)
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