pre-release patches

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

@@ -3,7 +3,7 @@
 ## 1.1.0b1 (Unreleased)
 
 ### Features Added
-* Conversation issue summarization task (Long-running operation)
+* Conversation summarization task (Long-running operation)
 * Conversation PII extraction task (Long-running operation)
 
 ### Breaking Changes

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

@@ -4,7 +4,7 @@
 Conversational Language Understanding - aka **CLU** for short - is a cloud-based conversational AI service which provides many language understanding capabilities like:
 - Conversation App: It's used in extracting intents and entities in conversations
 - Workflow app: Acts like an orchestrator to select the best candidate to analyze conversations to get best response from apps like Qna, Luis, and Conversation App
-- Conversational Issue Summarization: Used to summarize conversations in the form of issues, and final resolutions
+- Conversational Summarization: Used to summarize conversations in the form of issues, and final resolutions
 - Conversational PII: Used to extract and redact personally-identifiable info (PII) 
 
 [Source code][conversationallanguage_client_src] | [Package (PyPI)][conversationallanguage_pypi_package] | [API reference documentation][api_reference_documentation] | [Product documentation][conversationallanguage_docs] | [Samples][conversationallanguage_samples]
@@ -200,7 +200,7 @@ if top_intent_object["targetProjectKind"] == "Luis":
 
 ```
 
-### Conversational Issue Summarization
+### Conversational Summarization
 
 You can use this sample if you need to summarize a conversation in the form of an issue, and final resolution. For example, a dialog from tech support:
 
@@ -267,13 +267,13 @@ with client:
     task_result = result["tasks"]["items"][0]
     print("... view task status ...")
     print("status: {}".format(task_result["status"]))
-    issue_resolution_result = task_result["results"]
-    if issue_resolution_result["errors"]:
+    resolution_result = task_result["results"]
+    if resolution_result["errors"]:
         print("... errors occured ...")
-        for error in issue_resolution_result["errors"]:
+        for error in resolution_result["errors"]:
             print(error)
     else:
-        conversation_result = issue_resolution_result["conversations"][0]
+        conversation_result = resolution_result["conversations"][0]
         if conversation_result["warnings"]:
             print("... view warnings ...")
             for warning in conversation_result["warnings"]:

sdk/cognitivelanguage/azure-ai-language-conversations/azure/ai/language/conversations/_operations/_patch.py

@@ -6,9 +6,94 @@
 
 Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize
 """
-from typing import List
+from typing import List, MutableMapping
+from async_timeout import Any
+JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object
 
-__all__: List[str] = []  # Add all objects you want publicly available to users at this package level
+from azure.core.tracing.decorator import distributed_trace
+
+from ._operations import ConversationAnalysisClientOperationsMixin as ConversationAnalysisClientOperationsMixinGenerated
+
+class ConversationAnalysisClientOperationsMixin(ConversationAnalysisClientOperationsMixinGenerated):
+        @distributed_trace
+        def analyze_conversation(
+            self,
+            task: JSON,
+            **kwargs: Any
+        ) -> JSON:
+            """Analyzes the input conversation utterance.
+
+            :param task: A single conversational task to execute.
+            :type task: JSON
+            :return: JSON object
+            :rtype: JSON
+            :raises: ~azure.core.exceptions.HttpResponseError
+
+            Example:
+                .. code-block:: python
+
+                    kind = 'Conversation'
+
+                    # JSON input template you can fill out and use as your body input.
+                    task = {
+                        "kind": "Conversation",
+                        "analysisInput": {
+                            "conversationItem": {
+                                "id": "",
+                                "participantId": "",
+                                "modality": "text",
+                                "language": "",
+                                "text": ""
+                            }
+                        },
+                        "parameters": {
+                            "projectName": "",
+                            "deploymentName": "",
+                        }
+                    }
+
+                    # response body for status code(s): 200
+                    response.json() == {
+                        "kind": "ConversationResult",
+                        "result": {
+                        "query": "",
+                            "prediction": {
+                                "topIntent": "",
+                                "projectKind": "Conversation",
+                                "intents": [
+                                    {
+                                        "category": "",
+                                        "confidenceScore": 1
+                                    },
+                                    {
+                                        "category": "",
+                                        "confidenceScore": 0
+                                    },
+                                    {
+                                        "category": "",
+                                        "confidenceScore": 0
+                                    }
+                                ],
+                                "entities": [
+                                    {
+                                        "category": "",
+                                        "text": "",
+                                        "offset": 29,
+                                        "length": 12,
+                                        "confidenceScore": 1
+                                    }
+                                ]
+                            }
+                        }
+                    }
+            """
+            task["parameters"]["stringIndexType"] = "UnicodeCodePoint"
+            return super().analyze_conversation(task, **kwargs)
+
+
+__all__: List[str] = [
+    "ConversationAnalysisClientOperationsMixin"
+]  # Add all objects you want publicly available to users at this package level
 
 def patch_sdk():
     """Do not remove from this file.

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

@@ -6,9 +6,94 @@
 
 Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize
 """
-from typing import List
+from typing import List, MutableMapping
+from async_timeout import Any
+JSON = MutableMapping[str, Any] # pylint: disable=unsubscriptable-object
 
-__all__: List[str] = []  # Add all objects you want publicly available to users at this package level
+from azure.core.tracing.decorator import distributed_trace
+
+from ._operations import ConversationAnalysisClientOperationsMixin as ConversationAnalysisClientOperationsMixinGenerated
+
+class ConversationAnalysisClientOperationsMixin(ConversationAnalysisClientOperationsMixinGenerated):
+        @distributed_trace
+        async def analyze_conversation(
+            self,
+            task: JSON,
+            **kwargs: Any
+        ) -> JSON:
+            """Analyzes the input conversation utterance.
+
+            :param task: A single conversational task to execute.
+            :type task: JSON
+            :return: JSON object
+            :rtype: JSON
+            :raises: ~azure.core.exceptions.HttpResponseError
+
+            Example:
+                .. code-block:: python
+
+                    kind = 'Conversation'
+
+                    # JSON input template you can fill out and use as your body input.
+                    task = {
+                        "kind": "Conversation",
+                        "analysisInput": {
+                            "conversationItem": {
+                                "id": "",
+                                "participantId": "",
+                                "modality": "text",
+                                "language": "",
+                                "text": ""
+                            }
+                        },
+                        "parameters": {
+                            "projectName": "",
+                            "deploymentName": "",
+                        }
+                    }
+
+                    # response body for status code(s): 200
+                    response.json() == {
+                        "kind": "ConversationResult",
+                        "result": {
+                        "query": "",
+                            "prediction": {
+                                "topIntent": "",
+                                "projectKind": "Conversation",
+                                "intents": [
+                                    {
+                                        "category": "",
+                                        "confidenceScore": 1
+                                    },
+                                    {
+                                        "category": "",
+                                        "confidenceScore": 0
+                                    },
+                                    {
+                                        "category": "",
+                                        "confidenceScore": 0
+                                    }
+                                ],
+                                "entities": [
+                                    {
+                                        "category": "",
+                                        "text": "",
+                                        "offset": 29,
+                                        "length": 12,
+                                        "confidenceScore": 1
+                                    }
+                                ]
+                            }
+                        }
+                    }
+            """
+            task["parameters"]["stringIndexType"] = "UnicodeCodePoint"
+            return await super().analyze_conversation(task, **kwargs)
+
+
+__all__: List[str] = [
+    "ConversationAnalysisClientOperationsMixin"
+]  # Add all objects you want publicly available to users at this package level
 
 def patch_sdk():
     """Do not remove from this file.

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

@@ -5,7 +5,7 @@ languages:
 products:
   - azure
   - azure-cognitive-services
-  - azure-ai-language-understanding
+  - azure-language-service
 urlFragment: conversationslanguageunderstanding-samples
 ---
 
@@ -25,7 +25,7 @@ These sample programs show common scenarios for the Conversational Language Unde
 | [sample_analyze_orchestration_app_conv_response.py][sample_analyze_orchestration_app_conv_response] and [sample_analyze_orchestration_app_conv_response_async.py][sample_analyze_orchestration_app_conv_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a conversation project. |
 | [sample_analyze_orchestration_app_luis_response.py][sample_analyze_orchestration_app_luis_response] and [sample_analyze_orchestration_app_luis_response_async.py][sample_analyze_orchestration_app_luis_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a Luis project. |
 | [sample_analyze_orchestration_app_qna_response.py][sample_analyze_orchestration_app_qna_response] and [sample_analyze_orchestration_app_qna_response_async.py][sample_analyze_orchestration_app_qna_response_async]| Analyze user utterance using an orchestration project, which selects the best candidate from one of your different apps to analyze user query (ex: Qna, Conversation, and Luis). In this case, it uses a Qna project. |
-| [sample_conv_issue_summarization.py][sample_conv_issue_summarization] and [sample_conv_issue_summarization_async.py][sample_conv_issue_summarization_async]| Summarize conversation in the form of issues and resolutions (ex: tech support conversation) |
+| [sample_conv_summarization.py][sample_conv_summarization] and [sample_conv_summarization_async.py][sample_conv_summarization_async]| Summarize conversation in the form of issues and resolutions (ex: tech support conversation) |
 | [sample_conv_pii_transcript_input.py][sample_conv_pii_transcript_input] and [sample_conv_pii_transcript_input_async.py][sample_conv_pii_transcript_input_async]| Extract and redact personally-identifiable info from/in conversations |
 
 ## Prerequisites
@@ -79,8 +79,8 @@ what you can do with the Azure Conversational Language Understanding client libr
 [sample_analyze_orchestration_app_qna_response]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_analyze_orchestration_app_qna_response.py
 [sample_analyze_orchestration_app_qna_response_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_analyze_orchestration_app_qna_response_async.py
 
-[sample_conv_issue_summarization]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_issue_summarization.py
-[sample_conv_issue_summarization_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_issue_summarization_async.py
+[sample_conv_summarization]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_summarization.py
+[sample_conv_summarization_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_summarization_async.py
 
 [sample_conv_pii_transcript_input]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_pii_transcript_input.py
 [sample_conv_pii_transcript_input_async]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_pii_transcript_input_async.py

sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_issue_summarization_async.py → sdk/cognitivelanguage/azure-ai-language-conversations/samples/async/sample_conv_summarization_async.py

@@ -5,15 +5,15 @@
 # ------------------------------------
 
 """
-FILE: sample_conv_issue_summarization_async.py
+FILE: sample_conv_summarization_async.py
 
 DESCRIPTION:
     This sample demonstrates how to analyze a conversation for issue resolution.
 
     For more info about how to setup a CLU conversation project, see the README.
 
 USAGE:
-    python sample_conv_issue_summarization_async.py
+    python sample_conv_summarization_async.py
 
     Set the environment variables with your own values before running the sample:
     1) AZURE_CONVERSATIONS_ENDPOINT                       - endpoint for your CLU resource.
@@ -22,7 +22,7 @@
 
 import asyncio
 
-async def sample_conv_issue_summarization_async():
+async def sample_conv_summarization_async():
     # [START analyze_conversation_app]
     # import libraries
     import os
@@ -86,13 +86,13 @@ async def sample_conv_issue_summarization_async():
         task_result = result["tasks"]["items"][0]
         print("... view task status ...")
         print("status: {}".format(task_result["status"]))
-        issue_resolution_result = task_result["results"]
-        if issue_resolution_result["errors"]:
+        resolution_result = task_result["results"]
+        if resolution_result["errors"]:
             print("... errors occured ...")
-            for error in issue_resolution_result["errors"]:
+            for error in resolution_result["errors"]:
                 print(error)
         else:
-            conversation_result = issue_resolution_result["conversations"][0]
+            conversation_result = resolution_result["conversations"][0]
             if conversation_result["warnings"]:
                 print("... view warnings ...")
                 for warning in conversation_result["warnings"]:
@@ -107,7 +107,7 @@ async def sample_conv_issue_summarization_async():
 
 
 async def main():
-    await sample_conv_issue_summarization_async()
+    await sample_conv_summarization_async()
 
 if __name__ == '__main__':
     loop = asyncio.get_event_loop()

sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_issue_summarization.py → sdk/cognitivelanguage/azure-ai-language-conversations/samples/sample_conv_summarization.py

@@ -5,22 +5,22 @@
 # ------------------------------------
 
 """
-FILE: sample_conv_issue_summarization.py
+FILE: sample_conv_summarization.py
 
 DESCRIPTION:
     This sample demonstrates how to analyze a conversation for issue resolution.
 
     For more info about how to setup a CLU conversation project, see the README.
 
 USAGE:
-    python sample_conv_issue_summarization.py
+    python sample_conv_summarization.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.
 """
 
-def sample_conv_issue_summarization():
+def sample_conv_summarization():
     # [START analyze_conversation_app]
     # import libraries
     import os
@@ -84,13 +84,13 @@ def sample_conv_issue_summarization():
         task_result = result["tasks"]["items"][0]
         print("... view task status ...")
         print("status: {}".format(task_result["status"]))
-        issue_resolution_result = task_result["results"]
-        if issue_resolution_result["errors"]:
+        resolution_result = task_result["results"]
+        if resolution_result["errors"]:
             print("... errors occured ...")
-            for error in issue_resolution_result["errors"]:
+            for error in resolution_result["errors"]:
                 print(error)
         else:
-            conversation_result = issue_resolution_result["conversations"][0]
+            conversation_result = resolution_result["conversations"][0]
             if conversation_result["warnings"]:
                 print("... view warnings ...")
                 for warning in conversation_result["warnings"]:
@@ -105,4 +105,4 @@ def sample_conv_issue_summarization():
 
 
 if __name__ == '__main__':
-    sample_conv_issue_summarization()
+    sample_conv_summarization()

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

@@ -20,7 +20,7 @@ autorest
 ### Settings
 
 ```yaml
-input-file: https://raw.githubusercontent.com/Azure/azure-rest-api-specs/d1716d13b0814a9d0785eda9a74529a315212f53/specification/cognitiveservices/data-plane/Language/preview/2022-05-15-preview/analyzeconversations.json
+input-file: https://raw.githubusercontent.com/Azure/azure-rest-api-specs/e7f37e4e43b1d12fd1988fda3ed39624c4b23303/specification/cognitiveservices/data-plane/Language/preview/2022-05-15-preview/analyzeconversations.json
 output-folder: ../azure/ai/language/conversations
 namespace: azure.ai.language.conversations
 package-name: azure-ai-language-conversations