Metadata-Version: 2.1
Name: azure-ai-language-conversations
Version: 1.0.0
Summary: Microsoft Azure Conversational Language Understanding Client Library for Python
Home-page: https://github.com/Azure/azure-sdk-for-python
Author: Microsoft Corporation
Author-email: azpysdkhelp@microsoft.com
License: MIT License
Project-URL: Bug Reports, https://github.com/Azure/azure-sdk-for-python/issues
Project-URL: Source, https://github.com/Azure/azure-sdk-python
Description: [![Build Status](https://dev.azure.com/azure-sdk/public/_apis/build/status/azure-sdk-for-python.client?branchName=main)](https://dev.azure.com/azure-sdk/public/_build/latest?definitionId=46?branchName=main)
        
        # Azure Conversational Language Understanding client library for Python
        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
        
        
        [Source code][conversationallanguage_client_src] | [Package (PyPI)][conversationallanguage_pypi_package] | [API reference documentation][api_reference_documentation] | [Samples][conversationallanguage_samples] | [Product documentation][conversationallanguage_docs] | [Analysis REST API][conversationallanguage_restdocs] | [Authoring REST API][conversationallanguage_restdocs_authoring]
        
        ## _Disclaimer_
        
        _Azure SDK Python packages support for Python 2.7 ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_
        
        
        ## Getting started
        
        ### Prerequisites
        
        * Python 3.6 or later is required to use this package.
        * An [Azure subscription][azure_subscription]
        * An existing Azure Language Service Resource
        
        
        ### Install the package
        
        Install the Azure Conversations client library for Python with [pip][pip_link]:
        
        ```bash
        pip install azure-ai-language-conversations
        ```
        
        > Note: This version of the client library defaults to the 2022-05-01 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].
        
        #### Get an API key
        You can get the **endpoint** and an **API key** from the Cognitive Services resource in the [Azure Portal][azure_portal].
        
        Alternatively, use the [Azure CLI][azure_cli] command shown below to get the API key from the Cognitive Service resource.
        
        ```powershell
        az cognitiveservices account keys list --resource-group <resource-group-name> --name <resource-name>
        ```
        
        
        #### Create ConversationAnalysisClient
        Once you've determined your **endpoint** and **API key** you can instantiate a `ConversationAnalysisClient`:
        
        ```python
        from azure.core.credentials import AzureKeyCredential
        from azure.ai.language.conversations import ConversationAnalysisClient
        
        endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
        credential = AzureKeyCredential("<api-key>")
        client = ConversationAnalysisClient(endpoint, credential)
        ```
        
        #### Create ConversationAuthoringClient
        Once you've determined your **endpoint** and **API key** you can instantiate a `ConversationAuthoringClient`:
        
        ```python
        from azure.core.credentials import AzureKeyCredential
        from azure.ai.language.conversations.authoring import ConversationAuthoringClient
        
        endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
        credential = AzureKeyCredential("<api-key>")
        client = ConversationAuthoringClient(endpoint, credential)
        ```
        
        #### Create a client with an Azure Active Directory Credential
        
        To use an [Azure Active Directory (AAD) token credential][cognitive_authentication_aad],
        provide an instance of the desired credential type obtained from the
        [azure-identity][azure_identity_credentials] library.
        Note that regional endpoints do not support AAD authentication. Create a [custom subdomain][custom_subdomain]
        name for your resource in order to use this type of authentication.
        
        Authentication with AAD requires some initial setup:
        
        - [Install azure-identity][install_azure_identity]
        - [Register a new AAD application][register_aad_app]
        - [Grant access][grant_role_access] to the Language service by assigning the "Cognitive Services Language Reader" role to your service principal.
        
        After setup, you can choose which type of [credential][azure_identity_credentials] from azure.identity to use.
        As an example, [DefaultAzureCredential][default_azure_credential]
        can be used to authenticate the client:
        
        Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables:
        `AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, `AZURE_CLIENT_SECRET`
        
        Use the returned token credential to authenticate the client:
        
        ```python
        from azure.ai.textanalytics import ConversationAnalysisClient
        from azure.identity import DefaultAzureCredential
        
        credential = DefaultAzureCredential()
        client = ConversationAnalysisClient(endpoint="https://<my-custom-subdomain>.cognitiveservices.azure.com/", credential=credential)
        ```
        
        ## Key concepts
        
        ### ConversationAnalysisClient
        The [ConversationAnalysisClient][conversationanalysisclient_class] is the primary interface for making predictions using your deployed Conversations models. For asynchronous operations, an async `ConversationAnalysisClient` is in the `azure.ai.language.conversation.aio` namespace.
        
        ### ConversationAuthoringClient
        You can use the [ConversationAuthoringClient][conversationauthoringclient_class] to interface with the [Azure Language Portal][azure_language_portal] to carry out authoring operations on your language resource/project. For example, you can use it to create a project, populate with training data, train, test, and deploy. For asynchronous operations, an async `ConversationAuthoringClient` is in the `azure.ai.language.conversation.authoring.aio` namespace.
        
        ## Examples
        The `azure-ai-language-conversation` client library provides both synchronous and asynchronous APIs.
        
        The following examples show common scenarios using the `client` [created above](#create-conversationanalysisclient).
        
        ### Analyze Text with a Conversation App
        If you would like to extract custom intents and entities from a user utterance, you can call the `client.analyze_conversation()` 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
        
        # get secrets
        clu_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
        clu_key = os.environ["AZURE_CONVERSATIONS_KEY"]
        project_name = os.environ["AZURE_CONVERSATIONS_PROJECT_NAME"]
        deployment_name = os.environ["AZURE_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={
                    "kind": "Conversation",
                    "analysisInput": {
                        "conversationItem": {
                            "participantId": "1",
                            "id": "1",
                            "modality": "text",
                            "language": "en",
                            "text": query
                        },
                        "isLoggingEnabled": False
                    },
                    "parameters": {
                        "projectName": project_name,
                        "deploymentName": deployment_name,
                        "verbose": True
                    }
                }
            )
        
        # view result
        print("query: {}".format(result["result"]["query"]))
        print("project kind: {}\n".format(result["result"]["prediction"]["projectKind"]))
        
        print("top intent: {}".format(result["result"]["prediction"]["topIntent"]))
        print("category: {}".format(result["result"]["prediction"]["intents"][0]["category"]))
        print("confidence score: {}\n".format(result["result"]["prediction"]["intents"][0]["confidenceScore"]))
        
        print("entities:")
        for entity in result["result"]["prediction"]["entities"]:
            print("\ncategory: {}".format(entity["category"]))
            print("text: {}".format(entity["text"]))
            print("confidence score: {}".format(entity["confidenceScore"]))
            if "resolutions" in entity:
                print("resolutions")
                for resolution in entity["resolutions"]:
                    print("kind: {}".format(resolution["resolutionKind"]))
                    print("value: {}".format(resolution["value"]))
            if "extraInformation" in entity:
                print("extra info")
                for data in entity["extraInformation"]:
                    print("kind: {}".format(data["extraInformationKind"]))
                    if data["extraInformationKind"] == "ListKey":
                        print("key: {}".format(data["key"]))
                    if data["extraInformationKind"] == "EntitySubtype":
                        print("value: {}".format(data["value"]))
        ```
        
        ### Analyze Text with an Orchestration App
        
        If you would like to pass the user utterance to your orchestrator (worflow) app, you can call the `client.analyze_conversation()` 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
        
        # get secrets
        clu_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
        clu_key = os.environ["AZURE_CONVERSATIONS_KEY"]
        project_name = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT_NAME"]
        deployment_name = os.environ["AZURE_CONVERSATIONS_WORKFLOW_DEPLOYMENT_NAME"]
        
        # analyze query
        client = ConversationAnalysisClient(clu_endpoint, AzureKeyCredential(clu_key))
        with client:
            query = "Reserve a table for 2 at the Italian restaurant"
            result = client.analyze_conversation(
                task={
                    "kind": "Conversation",
                    "analysisInput": {
                        "conversationItem": {
                            "participantId": "1",
                            "id": "1",
                            "modality": "text",
                            "language": "en",
                            "text": query
                        },
                        "isLoggingEnabled": False
                    },
                    "parameters": {
                        "projectName": project_name,
                        "deploymentName": deployment_name,
                        "verbose": True
                    }
                }
            )
        
        # view result
        print("query: {}".format(result["result"]["query"]))
        print("project kind: {}\n".format(result["result"]["prediction"]["projectKind"]))
        
        # top intent
        top_intent = result["result"]["prediction"]["topIntent"]
        print("top intent: {}".format(top_intent))
        top_intent_object = result["result"]["prediction"]["intents"][top_intent]
        print("confidence score: {}".format(top_intent_object["confidenceScore"]))
        print("project kind: {}".format(top_intent_object["targetProjectKind"]))
        
        if top_intent_object["targetProjectKind"] == "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))
        ```
        
        ### Import a Conversation Project
        This sample shows a common scenario for the authoring part of the SDK
        
        ```python
        import os
        from azure.core.credentials import AzureKeyCredential
        from azure.ai.language.conversations.authoring import ConversationAuthoringClient
        
        clu_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
        clu_key = os.environ["AZURE_CONVERSATIONS_KEY"]
        
        project_name = "test_project"
        
        exported_project_assets = {
            "projectKind": "Conversation",
            "intents": [{"category": "Read"}, {"category": "Delete"}],
            "entities": [{"category": "Sender"}],
            "utterances": [
                {
                    "text": "Open Blake's email",
                    "dataset": "Train",
                    "intent": "Read",
                    "entities": [{"category": "Sender", "offset": 5, "length": 5}],
                },
                {
                    "text": "Delete last email",
                    "language": "en-gb",
                    "dataset": "Test",
                    "intent": "Delete",
                    "entities": [],
                },
            ],
        }
        
        client = ConversationAuthoringClient(
            clu_endpoint, AzureKeyCredential(clu_key)
        )
        poller = client.begin_import_project(
            project_name=project_name,
            project={
                "assets": exported_project_assets,
                "metadata": {
                    "projectKind": "Conversation",
                    "settings": {"confidenceThreshold": 0.7},
                    "projectName": "EmailApp",
                    "multilingual": True,
                    "description": "Trying out CLU",
                    "language": "en-us",
                },
                "projectFileVersion": "2022-05-01",
            },
        )
        response = poller.result()
        print(response)
        
        ```
        
        
        ## Optional Configuration
        
        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.
        
        ## Troubleshooting
        
        ### General
        
        The Conversations client will raise exceptions defined in [Azure Core][azure_core_exceptions].
        
        ### Logging
        
        This library uses the standard
        [logging][python_logging] library for logging.
        Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO
        level.
        
        Detailed DEBUG level logging, including request/response bodies and unredacted
        headers, can be enabled on a client with the `logging_enable` argument.
        
        See full SDK logging documentation with examples [here][sdk_logging_docs].
        
        ```python
        import sys
        import logging
        from azure.core.credentials import AzureKeyCredential
        from azure.ai.language.conversations import ConversationAnalysisClient
        
        # Create a logger for the 'azure' SDK
        logger = logging.getLogger('azure')
        logger.setLevel(logging.DEBUG)
        
        # Configure a console output
        handler = logging.StreamHandler(stream=sys.stdout)
        logger.addHandler(handler)
        
        endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
        credential = AzureKeyCredential("<my-api-key>")
        
        # This client will log detailed information about its HTTP sessions, at DEBUG level
        client = ConversationAnalysisClient(endpoint, credential, logging_enable=True)
        result = client.analyze_conversation(...)
        ```
        
        Similarly, `logging_enable` can enable detailed logging for a single operation, even when it isn't enabled for the client:
        
        ```python
        result = client.analyze_conversation(..., logging_enable=True)
        ```
        
        ## Next steps
        
        ### More sample code
        
        See the [Sample README][conversationallanguage_samples] for several code snippets illustrating common patterns used in the CLU Python API.
        
        ## Contributing
        
        See the [CONTRIBUTING.md][contributing] for details on building, testing, and contributing to this library.
        
        This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit [cla.microsoft.com][cla].
        
        When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
        
        This project has adopted the [Microsoft Open Source Code of Conduct][code_of_conduct]. For more information see the [Code of Conduct FAQ][coc_faq] or contact [opencode@microsoft.com][coc_contact] with any additional questions or comments.
        
        <!-- LINKS -->
        [azure_cli]: https://docs.microsoft.com/cli/azure/
        [azure_portal]: https://portal.azure.com/
        [azure_subscription]: https://azure.microsoft.com/free/
        [cla]: https://cla.microsoft.com
        [coc_contact]: mailto:opencode@microsoft.com
        [coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/
        [code_of_conduct]: https://opensource.microsoft.com/codeofconduct/
        [cognitive_auth]: https://docs.microsoft.com/azure/cognitive-services/authentication/
        [contributing]: https://github.com/Azure/azure-sdk-for-python/blob/main/CONTRIBUTING.md
        [python_logging]: https://docs.python.org/3/library/logging.html
        [sdk_logging_docs]: https://docs.microsoft.com/azure/developer/python/azure-sdk-logging
        [azure_core_ref_docs]: https://azuresdkdocs.blob.core.windows.net/$web/python/azure-core/latest/azure.core.html
        [azure_core_readme]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md
        [pip_link]:https://pypi.org/project/pip/
        [conversationallanguage_client_src]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations
        [conversationallanguage_pypi_package]: https://pypi.org/project/azure-ai-language-conversations/
        [api_reference_documentation]:https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html
        [conversationallanguage_refdocs]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations
        [conversationallanguage_docs]: https://docs.microsoft.com/azure/cognitive-services/language-service/conversational-language-understanding/overview
        [conversationallanguage_samples]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/README.md
        [conversationallanguage_restdocs]: https://docs.microsoft.com/rest/api/language/conversation-analysis-runtime/
        [conversationallanguage_restdocs_authoring]: https://docs.microsoft.com/rest/api/language/conversational-analysis-authoring
        [conversationanalysisclient_class]: https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html#azure.ai.language.conversations.ConversationAnalysisClient
        [conversationauthoringclient_class]: https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html#azure.ai.language.conversations.ConversationAuthoringClient
        [azure_core_exceptions]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md
        [azure_language_portal]: https://language.cognitive.azure.com/home
        [cognitive_authentication_aad]: https://docs.microsoft.com/azure/cognitive-services/authentication#authenticate-with-azure-active-directory
        [azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials
        [custom_subdomain]: https://docs.microsoft.com/azure/cognitive-services/authentication#create-a-resource-with-a-custom-subdomain
        [install_azure_identity]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#install-the-package
        [register_aad_app]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal
        [grant_role_access]: https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal
        [default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential
        
        ![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python%2Fsdk%2Ftemplate%2Fazure-template%2FREADME.png)
        
        
        # Release History
        
        ## 1.1.0b1 (2022-05-26)
        
        ### Features Added
        * Conversation summarization task (Long-running operation)
        * Conversation PII extraction task (Long-running operation)
        
        ### Breaking Changes
        * Client now uses python dictionaries for method parameters and results instead of classes.
        * Many input and result parameter name changes in `analyze_conversation()` method
        
        ## 1.0.0 (2022-06-27)
        
        ### Features Added
        * Added Azure Active Directory (AAD) authentication support
        * Added more resolution types for entities
        * Added support for authoring operations with `ConversationAuthoringClient` under the `azure.ai.language.conversations.authoring` namespace.
        
        ### Breaking Changes
        * Client now uses python dictionaries for method parameters and results instead of classes.
        
        ## 1.0.0b3 (2022-04-19)
        
        ### Features Added
        * Entity resolutions
        * Extra features
        
        ### Breaking Changes
        * The `ConversationAnalysisOptions` model used as input to the `analyze_conversation` operation is now wrapped in a `CustomConversationalTask` which combines the analysis options with the project parameters into a single model.
        * The `query` within the `ConversationAnalysisOptions` is now further qualified as a `TextConversationItem` with additional properties.
        * The output `AnalyzeConversationResult` is now wrapped in a `CustomConversationalTaskResult` according to the input model.
        
        ### Other Changes
        * Python 2.7 is no longer supported. Please use Python version 3.6 or later.
        
        ## 1.0.0b1 (2021-11-03)
        
        ### Features Added
        * Initial release
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
