re-gen clu LLC client (#21417)

* regenrate client

* update model names

* tmp commit

* update model names

* fixing tests

* fix samples

* update readme

* fix remaining samples

* fix env key error

* add recorded tests

* update samples

* add additional samples

* async samples

* disable some samples

* update samples readme

* revert setup.py

* fix broken link

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

@@ -2,9 +2,9 @@
 
 # Azure Conversational Language Understanding client library for Python
 Conversational Language Understanding, aka **CLU** for short, is a cloud-based conversational AI service which is mainly used in bots to extract useful information from user utterance (natural language processing).
-The CLU **analyze api** encompasses two projects; deepstack, and workflow projects.
-You can use the "deepstack" project if you want to extract intents (intention behind a user utterance] and custom entities.
-You can also use the "workflow" project which orchestrates multiple language apps to get the best response (language apps like Question Answering, Luis, and Deepstack).
+The CLU **analyze api** encompasses two projects; conversation, and orchestration projects.
+You can use the "conversation" project if you want to extract intents (intention behind a user utterance) and custom entities.
+You can also use the "orchestration" project which orchestrates multiple language apps to get the best response (language apps like Question Answering, Luis, and Conversation).
 
 [Source code][conversationallanguage_client_src] | [Package (PyPI)][conversationallanguage_pypi_package] | [API reference documentation][conversationallanguage_refdocs] | [Product documentation][conversationallanguage_docs] | [Samples][conversationallanguage_samples]
 
@@ -67,16 +67,16 @@ The `azure-ai-language-conversation` client library provides both synchronous an
 
 The following examples show common scenarios using the `client` [created above](#create-conversationanalysisclient).
 
-### Analyze a conversation with a Deepstack App
-If you would like to extract custom intents and entities from a user utterance, you can call the `client.analyze_conversations()` method with your deepstack's project name as follows:
+### Analyze a conversation with a Conversation App
+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:
 
 ```python
 # import libraries
 import os
 from azure.core.credentials import AzureKeyCredential
 
 from azure.ai.language.conversations import ConversationAnalysisClient
-from azure.ai.language.conversations.models import AnalyzeConversationOptions
+from azure.ai.language.conversations.models import ConversationAnalysisOptions
 
 # get secrets
 conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
@@ -85,7 +85,7 @@ conv_project = os.environ["AZURE_CONVERSATIONS_PROJECT"]
 
 # prepare data
 query = "One california maki please."
-input = AnalyzeConversationOptions(
+input = ConversationAnalysisOptions(
     query=query
 )
 
@@ -103,7 +103,7 @@ print("query: {}".format(result.query))
 print("project kind: {}\n".format(result.prediction.project_kind))
 
 print("view top intent:")
-print("top intent: {}".format(result.prediction.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))
 
@@ -114,26 +114,26 @@ for entity in result.prediction.entities:
     print("\tconfidence score: {}".format(entity.confidence_score))
 ```
 
-### Analyze conversation with a Workflow App
+### Analyze conversation with a Orchestration App
 
-If you would like to pass the user utterance to your orchestrator (worflow) app, you can call the `client.analyze_conversations()` method with your workflow's project name. The orchestrator project simply orchestrates the submitted user utterance between your language apps (Luis, Deepstack, and Question Answering) to get the best response according to the user intent. See the next example:
+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:
 
 ```python
 # import libraries
 import os
 from azure.core.credentials import AzureKeyCredential
 
 from azure.ai.language.conversations import ConversationAnalysisClient
-from azure.ai.language.conversations.models import AnalyzeConversationOptions
+from azure.ai.language.conversations.models import ConversationAnalysisOptions
 
 # get secrets
 conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
 conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
-workflow_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
+orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
 
 # prepare data
 query = "How do you make sushi rice?",
-input = AnalyzeConversationOptions(
+input = ConversationAnalysisOptions(
     query=query
 )
 
@@ -142,7 +142,7 @@ client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
 with client:
     result = client.analyze_conversations(
         input,
-        project_name=workflow_project,
+        project_name=orchestration_project,
         deployment_name='production',
     )
 
@@ -151,35 +151,35 @@ print("query: {}".format(result.query))
 print("project kind: {}\n".format(result.prediction.project_kind))
 
 print("view top intent:")
-print("top intent: {}".format(result.prediction.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 Question Answering result:")
 print("\tresult: {}\n".format(result.prediction.intents[0].result))
 ```
 
-### Analyze conversation with a Workflow (Direct) App
+### Analyze conversation with a Orchestration (Direct) App
 
-If you would like to use an orchestrator (workflow) app, and you want to call a specific one of your language apps directly, you can call the `client.analyze_conversations()` method with your workflow's project name and the diirect target name which corresponds to your one of you language apps as follows:
+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:
 
 ```python
 # import libraries
 import os
 from azure.core.credentials import AzureKeyCredential
 
 from azure.ai.language.conversations import ConversationAnalysisClient
-from azure.ai.language.conversations.models import AnalyzeConversationOptions
+from azure.ai.language.conversations.models import ConversationAnalysisOptions
 
 # get secrets
 conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
 conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
-workflow_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
+orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT")
 
 # prepare data
 query = "How do you make sushi rice?",
 target_intent = "SushiMaking"
-input = AnalyzeConversationOptions(
+input = ConversationAnalysisOptions(
     query=query,
     direct_target=target_intent,
     parameters={
@@ -198,7 +198,7 @@ client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
 with client:
     result = client.analyze_conversations(
         input,
-        project_name=workflow_project,
+        project_name=orchestration_project,
         deployment_name='production',
     )
 
@@ -207,7 +207,7 @@ print("query: {}".format(result.query))
 print("project kind: {}\n".format(result.prediction.project_kind))
 
 print("view top intent:")
-print("top intent: {}".format(result.prediction.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))
 

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

@@ -47,7 +47,7 @@ def __init__(
 
         self.endpoint = endpoint
         self.credential = credential
-        self.api_version = "2021-07-15-preview"
+        self.api_version = "2021-11-01-preview"
         kwargs.setdefault('sdk_moniker', 'ai-language-conversations/{}'.format(VERSION))
         self._configure(**kwargs)
 

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

@@ -24,7 +24,7 @@
     from azure.core.rest import HttpRequest, HttpResponse
 
 class ConversationAnalysisClient(ConversationAnalysisClientOperationsMixin):
-    """This API accepts a request and mediates among multiple language projects, such as LUIS Generally Available, Question Answering, LUIS Deepstack, and then calls the best candidate service to handle the request. At last, it returns a response with the candidate service's response as a payload.
+    """This API accepts a request and mediates among multiple language projects, such as LUIS Generally Available, Question Answering, Conversation, and then calls the best candidate service to handle the request. At last, it returns a response with the candidate service's response as a payload.
 
  In some cases, this API needs to forward requests and responses between the caller and an upstream service.
 

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

@@ -41,7 +41,7 @@ def __init__(
 
         self.endpoint = endpoint
         self.credential = credential
-        self.api_version = "2021-07-15-preview"
+        self.api_version = "2021-11-01-preview"
         kwargs.setdefault('sdk_moniker', 'ai-language-conversations/{}'.format(VERSION))
         self._configure(**kwargs)
 

sdk/cognitivelanguage/azure-ai-language-conversations/azure/ai/language/conversations/aio/_conversation_analysis_client.py

@@ -19,7 +19,7 @@
 from .operations import ConversationAnalysisClientOperationsMixin
 
 class ConversationAnalysisClient(ConversationAnalysisClientOperationsMixin):
-    """This API accepts a request and mediates among multiple language projects, such as LUIS Generally Available, Question Answering, LUIS Deepstack, and then calls the best candidate service to handle the request. At last, it returns a response with the candidate service's response as a payload.
+    """This API accepts a request and mediates among multiple language projects, such as LUIS Generally Available, Question Answering, Conversation, and then calls the best candidate service to handle the request. At last, it returns a response with the candidate service's response as a payload.
 
  In some cases, this API needs to forward requests and responses between the caller and an upstream service.
 

sdk/cognitivelanguage/azure-ai-language-conversations/azure/ai/language/conversations/aio/operations/_operations.py

@@ -26,17 +26,17 @@ class ConversationAnalysisClientOperationsMixin:
     @distributed_trace_async
     async def analyze_conversations(
         self,
-        analyze_conversation_options: "_models.AnalyzeConversationOptions",
+        conversation_analysis_options: "_models.ConversationAnalysisOptions",
         *,
         project_name: str,
         deployment_name: str,
         **kwargs: Any
     ) -> "_models.AnalyzeConversationResult":
         """Analyzes the input conversation utterance.
 
-        :param analyze_conversation_options: Post body of the request.
-        :type analyze_conversation_options:
-         ~azure.ai.language.conversations.models.AnalyzeConversationOptions
+        :param conversation_analysis_options: Post body of the request.
+        :type conversation_analysis_options:
+         ~azure.ai.language.conversations.models.ConversationAnalysisOptions
         :keyword project_name: The name of the project to use.
         :paramtype project_name: str
         :keyword deployment_name: The name of the specific deployment of the project to use.
@@ -53,7 +53,7 @@ async def analyze_conversations(
 
         content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
 
-        json = self._serialize.body(analyze_conversation_options, 'AnalyzeConversationOptions')
+        json = self._serialize.body(conversation_analysis_options, 'ConversationAnalysisOptions')
 
         request = build_analyze_conversations_request(
             content_type=content_type,

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

@@ -7,89 +7,99 @@
 # --------------------------------------------------------------------------
 
 try:
-    from ._models_py3 import AnalyzeConversationOptions
+    from ._models_py3 import AnalysisParameters
     from ._models_py3 import AnalyzeConversationResult
-    from ._models_py3 import AnalyzeParameters
+    from ._models_py3 import AnswerSpan
     from ._models_py3 import BasePrediction
-    from ._models_py3 import DSTargetIntentResult
-    from ._models_py3 import DeepStackEntityResolution
-    from ._models_py3 import DeepstackCallingOptions
-    from ._models_py3 import DeepstackEntity
-    from ._models_py3 import DeepstackIntent
-    from ._models_py3 import DeepstackParameters
-    from ._models_py3 import DeepstackPrediction
-    from ._models_py3 import DeepstackResult
-    from ._models_py3 import DictionaryNormalizedValueResolution
+    from ._models_py3 import ConversationAnalysisOptions
+    from ._models_py3 import ConversationCallingOptions
+    from ._models_py3 import ConversationEntity
+    from ._models_py3 import ConversationIntent
+    from ._models_py3 import ConversationParameters
+    from ._models_py3 import ConversationPrediction
+    from ._models_py3 import ConversationResult
+    from ._models_py3 import ConversationTargetIntentResult
     from ._models_py3 import Error
     from ._models_py3 import ErrorResponse
     from ._models_py3 import InnerErrorModel
+    from ._models_py3 import KnowledgeBaseAnswer
+    from ._models_py3 import KnowledgeBaseAnswerDialog
+    from ._models_py3 import KnowledgeBaseAnswerPrompt
+    from ._models_py3 import KnowledgeBaseAnswers
     from ._models_py3 import LUISCallingOptions
     from ._models_py3 import LUISParameters
     from ._models_py3 import LUISTargetIntentResult
+    from ._models_py3 import NoneLinkedTargetIntentResult
+    from ._models_py3 import OrchestratorPrediction
     from ._models_py3 import QuestionAnsweringParameters
     from ._models_py3 import QuestionAnsweringTargetIntentResult
     from ._models_py3 import TargetIntentResult
-    from ._models_py3 import WorkflowPrediction
 except (SyntaxError, ImportError):
-    from ._models import AnalyzeConversationOptions  # type: ignore
+    from ._models import AnalysisParameters  # type: ignore
     from ._models import AnalyzeConversationResult  # type: ignore
-    from ._models import AnalyzeParameters  # type: ignore
+    from ._models import AnswerSpan  # type: ignore
     from ._models import BasePrediction  # type: ignore
-    from ._models import DSTargetIntentResult  # type: ignore
-    from ._models import DeepStackEntityResolution  # type: ignore
-    from ._models import DeepstackCallingOptions  # type: ignore
-    from ._models import DeepstackEntity  # type: ignore
-    from ._models import DeepstackIntent  # type: ignore
-    from ._models import DeepstackParameters  # type: ignore
-    from ._models import DeepstackPrediction  # type: ignore
-    from ._models import DeepstackResult  # type: ignore
-    from ._models import DictionaryNormalizedValueResolution  # type: ignore
+    from ._models import ConversationAnalysisOptions  # type: ignore
+    from ._models import ConversationCallingOptions  # type: ignore
+    from ._models import ConversationEntity  # type: ignore
+    from ._models import ConversationIntent  # type: ignore
+    from ._models import ConversationParameters  # type: ignore
+    from ._models import ConversationPrediction  # type: ignore
+    from ._models import ConversationResult  # type: ignore
+    from ._models import ConversationTargetIntentResult  # type: ignore
     from ._models import Error  # type: ignore
     from ._models import ErrorResponse  # type: ignore
     from ._models import InnerErrorModel  # type: ignore
+    from ._models import KnowledgeBaseAnswer  # type: ignore
+    from ._models import KnowledgeBaseAnswerDialog  # type: ignore
+    from ._models import KnowledgeBaseAnswerPrompt  # type: ignore
+    from ._models import KnowledgeBaseAnswers  # type: ignore
     from ._models import LUISCallingOptions  # type: ignore
     from ._models import LUISParameters  # type: ignore
     from ._models import LUISTargetIntentResult  # type: ignore
+    from ._models import NoneLinkedTargetIntentResult  # type: ignore
+    from ._models import OrchestratorPrediction  # type: ignore
     from ._models import QuestionAnsweringParameters  # type: ignore
     from ._models import QuestionAnsweringTargetIntentResult  # type: ignore
     from ._models import TargetIntentResult  # type: ignore
-    from ._models import WorkflowPrediction  # type: ignore
 
 from ._conversation_analysis_client_enums import (
     ErrorCode,
     InnerErrorCode,
     ProjectKind,
-    ResolutionKind,
     TargetKind,
 )
 
 __all__ = [
-    'AnalyzeConversationOptions',
+    'AnalysisParameters',
     'AnalyzeConversationResult',
-    'AnalyzeParameters',
+    'AnswerSpan',
     'BasePrediction',
-    'DSTargetIntentResult',
-    'DeepStackEntityResolution',
-    'DeepstackCallingOptions',
-    'DeepstackEntity',
-    'DeepstackIntent',
-    'DeepstackParameters',
-    'DeepstackPrediction',
-    'DeepstackResult',
-    'DictionaryNormalizedValueResolution',
+    'ConversationAnalysisOptions',
+    'ConversationCallingOptions',
+    'ConversationEntity',
+    'ConversationIntent',
+    'ConversationParameters',
+    'ConversationPrediction',
+    'ConversationResult',
+    'ConversationTargetIntentResult',
     'Error',
     'ErrorResponse',
     'InnerErrorModel',
+    'KnowledgeBaseAnswer',
+    'KnowledgeBaseAnswerDialog',
+    'KnowledgeBaseAnswerPrompt',
+    'KnowledgeBaseAnswers',
     'LUISCallingOptions',
     'LUISParameters',
     'LUISTargetIntentResult',
+    'NoneLinkedTargetIntentResult',
+    'OrchestratorPrediction',
     'QuestionAnsweringParameters',
     'QuestionAnsweringTargetIntentResult',
     'TargetIntentResult',
-    'WorkflowPrediction',
     'ErrorCode',
     'InnerErrorCode',
     'ProjectKind',
-    'ResolutionKind',
     'TargetKind',
 ]

sdk/cognitivelanguage/azure-ai-language-conversations/azure/ai/language/conversations/models/_conversation_analysis_client_enums.py

@@ -20,7 +20,13 @@ class ErrorCode(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
     UNAUTHORIZED = "Unauthorized"
     FORBIDDEN = "Forbidden"
     NOT_FOUND = "NotFound"
+    PROJECT_NOT_FOUND = "ProjectNotFound"
+    OPERATION_NOT_FOUND = "OperationNotFound"
+    AZURE_COGNITIVE_SEARCH_NOT_FOUND = "AzureCognitiveSearchNotFound"
+    AZURE_COGNITIVE_SEARCH_INDEX_NOT_FOUND = "AzureCognitiveSearchIndexNotFound"
     TOO_MANY_REQUESTS = "TooManyRequests"
+    AZURE_COGNITIVE_SEARCH_THROTTLING = "AzureCognitiveSearchThrottling"
+    AZURE_COGNITIVE_SEARCH_INDEX_LIMIT_REACHED = "AzureCognitiveSearchIndexLimitReached"
     INTERNAL_SERVER_ERROR = "InternalServerError"
     SERVICE_UNAVAILABLE = "ServiceUnavailable"
 
@@ -42,17 +48,11 @@ class ProjectKind(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
     CONVERSATION = "conversation"
     WORKFLOW = "workflow"
 
-class ResolutionKind(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
-    """The type of an entity resolution.
-    """
-
-    #: Dictionary normalized entities.
-    DICTIONARY_NORMALIZED_VALUE = "DictionaryNormalizedValue"
-
 class TargetKind(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
     """The type of a target service.
     """
 
     LUIS = "luis"
-    LUIS_DEEPSTACK = "luis_deepstack"
+    CONVERSATION = "conversation"
     QUESTION_ANSWERING = "question_answering"
+    NON_LINKED = "non_linked"