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* regenerate CLU with 2023-04-01 * update _patch.py * rerecord and remove test/samples for features no longer supported in GA * regen on latest commit, heath's feedback on README.md * clean up docs and changelog
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sdk/cognitivelanguage/azure-ai-language-conversations/CHANGELOG.md
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| Original file line number | Diff line number | Diff line change |
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@@ -5,8 +5,6 @@ Conversational Language Understanding - aka **CLU** for short - is a cloud-based | |
| - 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 Summarization: Used to analyze conversations in the form of issues/resolution, chapter title, and narrative summarizations | ||
| - Conversational PII: Used to extract and redact personally-identifiable information (PII) | ||
| - Conversational Sentiment Analysis: Used to analyze the sentiment of conversations | ||
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| [Source code][conversationallanguage_client_src] | ||
| | [Package (PyPI)][conversationallanguage_pypi_package] | ||
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@@ -31,10 +29,10 @@ Conversational Language Understanding - aka **CLU** for short - is a cloud-based | |
| Install the Azure Conversations client library for Python with [pip][pip_link]: | ||
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| ```bash | ||
| pip install azure-ai-language-conversations --pre | ||
| pip install azure-ai-language-conversations | ||
| ``` | ||
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| > Note: This version of the client library defaults to the 2022-10-01-preview version of the service | ||
| > Note: This version of the client library defaults to the 2023-04-01 version of the service | ||
| ### Authenticate the client | ||
| In order to interact with the CLU service, you'll need to create an instance of the [ConversationAnalysisClient][conversationanalysisclient_class] class, or [ConversationAuthoringClient][conversationauthoringclient_class] class. You will need an **endpoint**, and an **API key** to instantiate a client object. For more information regarding authenticating with Cognitive Services, see [Authenticate requests to Azure Cognitive Services][cognitive_auth]. | ||
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@@ -337,178 +335,6 @@ with client: | |
| print(f"{summary['aspect']}: {summary['text']}") | ||
| ``` | ||
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| ### Conversational PII | ||
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| You can use this sample if you need to extract and redact pii info from/in conversations | ||
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| ```python | ||
| # import libraries | ||
| import os | ||
| from azure.core.credentials import AzureKeyCredential | ||
| from azure.ai.language.conversations import ConversationAnalysisClient | ||
| # get secrets | ||
| endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"] | ||
| key = os.environ["AZURE_CONVERSATIONS_KEY"] | ||
| # analyze query | ||
| client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key)) | ||
| with client: | ||
| poller = client.begin_conversation_analysis( | ||
| task={ | ||
| "displayName": "Analyze PII in conversation", | ||
| "analysisInput": { | ||
| "conversations": [ | ||
| { | ||
| "conversationItems": [ | ||
| { | ||
| "id": "1", | ||
| "participantId": "0", | ||
| "modality": "transcript", | ||
| "text": "It is john doe.", | ||
| "lexical": "It is john doe", | ||
| "itn": "It is john doe", | ||
| "maskedItn": "It is john doe" | ||
| }, | ||
| { | ||
| "id": "2", | ||
| "participantId": "1", | ||
| "modality": "transcript", | ||
| "text": "Yes, 633-27-8199 is my phone", | ||
| "lexical": "yes six three three two seven eight one nine nine is my phone", | ||
| "itn": "yes 633278199 is my phone", | ||
| "maskedItn": "yes 633278199 is my phone", | ||
| }, | ||
| { | ||
| "id": "3", | ||
| "participantId": "1", | ||
| "modality": "transcript", | ||
| "text": "[email protected] is my email", | ||
| "lexical": "j dot doe at yahoo dot com is my email", | ||
| "maskedItn": "[email protected] is my email", | ||
| "itn": "[email protected] is my email", | ||
| } | ||
| ], | ||
| "modality": "transcript", | ||
| "id": "1", | ||
| "language": "en" | ||
| } | ||
| ] | ||
| }, | ||
| "tasks": [ | ||
| { | ||
| "kind": "ConversationalPIITask", | ||
| "parameters": { | ||
| "redactionSource": "lexical", | ||
| "piiCategories": [ | ||
| "all" | ||
| ] | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ) | ||
| # view result | ||
| result = poller.result() | ||
| task_result = result["tasks"]["items"][0] | ||
| print("... view task status ...") | ||
| print("status: {}".format(task_result["status"])) | ||
| conv_pii_result = task_result["results"] | ||
| if conv_pii_result["errors"]: | ||
| print("... errors occurred ...") | ||
| for error in conv_pii_result["errors"]: | ||
| print(error) | ||
| else: | ||
| conversation_result = conv_pii_result["conversations"][0] | ||
| if conversation_result["warnings"]: | ||
| print("... view warnings ...") | ||
| for warning in conversation_result["warnings"]: | ||
| print(warning) | ||
| else: | ||
| print("... view task result ...") | ||
| for conversation in conversation_result["conversationItems"]: | ||
| print("conversation id: {}".format(conversation["id"])) | ||
| print("... entities ...") | ||
| for entity in conversation["entities"]: | ||
| print("text: {}".format(entity["text"])) | ||
| print("category: {}".format(entity["category"])) | ||
| print("confidence: {}".format(entity["confidenceScore"])) | ||
| print("offset: {}".format(entity["offset"])) | ||
| print("length: {}".format(entity["length"])) | ||
| ``` | ||
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| ### Conversational Sentiment Analysis | ||
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| Analyze sentiment in conversations. | ||
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| ```python | ||
| # import libraries | ||
| import os | ||
| from azure.core.credentials import AzureKeyCredential | ||
| from azure.ai.language.conversations import ConversationAnalysisClient | ||
| # get secrets | ||
| endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"] | ||
| key = os.environ["AZURE_CONVERSATIONS_KEY"] | ||
| # analyze query | ||
| client = ConversationAnalysisClient(endpoint, AzureKeyCredential(key)) | ||
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| with client: | ||
| poller = client.begin_conversation_analysis( | ||
| task={ | ||
| "displayName": "Sentiment Analysis from a call center conversation", | ||
| "analysisInput": { | ||
| "conversations": [ | ||
| { | ||
| "id": "1", | ||
| "language": "en", | ||
| "modality": "transcript", | ||
| "conversationItems": [ | ||
| { | ||
| "participantId": "1", | ||
| "id": "1", | ||
| "text": "I like the service. I do not like the food", | ||
| "lexical": "i like the service i do not like the food", | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| "tasks": [ | ||
| { | ||
| "taskName": "Conversation Sentiment Analysis", | ||
| "kind": "ConversationalSentimentTask", | ||
| "parameters": { | ||
| "modelVersion": "latest", | ||
| "predictionSource": "text" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| ) | ||
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| result = poller.result() | ||
| task_result = result["tasks"]["items"][0] | ||
| print("... view task status ...") | ||
| print(f"status: {task_result['status']}") | ||
| conv_sentiment_result = task_result["results"] | ||
| if conv_sentiment_result["errors"]: | ||
| print("... errors occurred ...") | ||
| for error in conv_sentiment_result["errors"]: | ||
| print(error) | ||
| else: | ||
| conversation_result = conv_sentiment_result["conversations"][0] | ||
| if conversation_result["warnings"]: | ||
| print("... view warnings ...") | ||
| for warning in conversation_result["warnings"]: | ||
| print(warning) | ||
| else: | ||
| print("... view task result ...") | ||
| for conversation in conversation_result["conversationItems"]: | ||
| print(f"Participant ID: {conversation['participantId']}") | ||
| print(f"Sentiment: {conversation['sentiment']}") | ||
| print(f"confidenceScores: {conversation['confidenceScores']}") | ||
| ``` | ||
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| ### Import a Conversation Project | ||
| This sample shows a common scenario for the authoring part of the SDK | ||
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