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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
* 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

`Source code <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations>`_ | `Package (PyPI) <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>`_ | `Samples <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/README.md>`_ | `Product documentation <https://docs.microsoft.com/azure/cognitive-services/language-service/conversational-language-understanding/overview>`_ | `Analysis REST API documentation <https://learn.microsoft.com/rest/api/language/2022-10-01-preview/conversation-analysis-runtime>`_ | `Authoring REST API documentation <https://learn.microsoft.com/rest/api/language/2022-10-01-preview/conversational-analysis-authoring>`_

Getting started
---------------

Prerequisites
^^^^^^^^^^^^^


* Python 3.7 or later is required to use this package.
* An `Azure subscription <https://azure.microsoft.com/free/>`_
* An existing Azure Language Service Resource

Install the package
^^^^^^^^^^^^^^^^^^^

Install the Azure Conversations client library for Python with `pip <https://pypi.org/project/pip/>`_\ :

.. code-block:: bash

   pip install azure-ai-language-conversations --pre

..

   Note: This version of the client library defaults to the 2022-10-01-preview 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 <https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html#azure.ai.language.conversations.ConversationAnalysisClient>`_ class, or `ConversationAuthoringClient <https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html#azure.ai.language.conversations.ConversationAuthoringClient>`_ 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 <https://docs.microsoft.com/azure/cognitive-services/authentication/>`_.

Get an API key
~~~~~~~~~~~~~~

You can get the **endpoint** and an **API key** from the Cognitive Services resource in the `Azure Portal <https://portal.azure.com/>`_.

Alternatively, use the `Azure CLI <https://docs.microsoft.com/cli/azure/>`_ command shown below to get the API key from the Cognitive Service resource.

.. code-block:: 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``\ :

.. code-block:: 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``\ :

.. code-block:: 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 <https://docs.microsoft.com/azure/cognitive-services/authentication#authenticate-with-azure-active-directory>`_\ ,
provide an instance of the desired credential type obtained from the
`azure-identity <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials>`_ library.
Note that regional endpoints do not support AAD authentication. Create a `custom subdomain <https://docs.microsoft.com/azure/cognitive-services/authentication#create-a-resource-with-a-custom-subdomain>`_
name for your resource in order to use this type of authentication.

Authentication with AAD requires some initial setup:


* `Install azure-identity <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#install-the-package>`_
* `Register a new AAD application <https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal>`_
* `Grant access <https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal>`_ 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 <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials>`_ from azure.identity to use.
As an example, `DefaultAzureCredential <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential>`_
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:

.. code-block:: python

   from azure.ai.language.conversations 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 <https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html#azure.ai.language.conversations.ConversationAnalysisClient>`_ 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 <https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-conversations/latest/azure.ai.language.conversations.html#azure.ai.language.conversations.ConversationAuthoringClient>`_ to interface with the `Azure Language Portal <https://language.cognitive.azure.com/home>`_ 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:

.. code-block:: 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:

.. code-block:: 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))

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:

.. code-block:: 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 conversations from xxx",
               "analysisInput": {
                   "conversations": [
                       {
                           "conversationItems": [
                               {
                                   "text": "Hello, how can I help you?",
                                   "modality": "text",
                                   "id": "1",
                                   "participantId": "Agent"
                               },
                               {
                                   "text": "How to upgrade Office? I am getting error messages the whole day.",
                                   "modality": "text",
                                   "id": "2",
                                   "participantId": "Customer"
                               },
                               {
                                   "text": "Press the upgrade button please. Then sign in and follow the instructions.",
                                   "modality": "text",
                                   "id": "3",
                                   "participantId": "Agent"
                               }
                           ],
                           "modality": "text",
                           "id": "conversation1",
                           "language": "en"
                       },
                   ]
               },
               "tasks": [
                   {
                       "taskName": "Issue task",
                       "kind": "ConversationalSummarizationTask",
                       "parameters": {
                           "summaryAspects": ["issue"]
                       }
                   },
                   {
                       "taskName": "Resolution task",
                       "kind": "ConversationalSummarizationTask",
                       "parameters": {
                           "summaryAspects": ["resolution"]
                       }
                   },
               ]
           }
       )

       # view result
       result = poller.result()
       task_results = result["tasks"]["items"]
       for task in task_results:
           print(f"\n{task['taskName']} status: {task['status']}")
           task_result = task["results"]
           if task_result["errors"]:
               print("... errors occurred ...")
               for error in task_result["errors"]:
                   print(error)
           else:
               conversation_result = task_result["conversations"][0]
               if conversation_result["warnings"]:
                   print("... view warnings ...")
                   for warning in conversation_result["warnings"]:
                       print(warning)
               else:
                   summaries = conversation_result["summaries"]
                   print("... view task result ...")
                   for summary in summaries:
                       print(f"{summary['aspect']}: {summary['text']}")

Conversational PII
^^^^^^^^^^^^^^^^^^

You can use this sample if you need to extract and redact pii info from/in conversations

.. code-block:: 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": "j.doe@yahoo.com is my email",
                                   "lexical": "j dot doe at yahoo dot com is my email",
                                   "maskedItn": "j.doe@yahoo.com is my email",
                                   "itn": "j.doe@yahoo.com 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"]))

Conversational Sentiment Analysis
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Analyze sentiment in conversations.

.. code-block:: 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": "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"
                 }
               }
             ]
           }
       )

       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']}")

Import a Conversation Project
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This sample shows a common scenario for the authoring part of the SDK

.. code-block:: 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 <https://azuresdkdocs.blob.core.windows.net/$web/python/azure-core/latest/azure.core.html>`_ describes available configurations for retries, logging, transport protocols, and more.

Troubleshooting
---------------

General
^^^^^^^

The Conversations client will raise exceptions defined in `Azure Core <https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md>`_.

Logging
^^^^^^^

This library uses the standard
`logging <https://docs.python.org/3/library/logging.html>`_ 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 <https://docs.microsoft.com/azure/developer/python/azure-sdk-logging>`_.

.. code-block:: 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:

.. code-block:: python

   result = client.analyze_conversation(..., logging_enable=True)

Next steps
----------

More sample code
^^^^^^^^^^^^^^^^

See the `Sample README <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples/README.md>`_ for several code snippets illustrating common patterns used in the CLU Python API.

Contributing
------------

See the `CONTRIBUTING.md <https://github.com/Azure/azure-sdk-for-python/blob/main/CONTRIBUTING.md>`_ 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 <https://cla.microsoft.com>`_.

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 <https://opensource.microsoft.com/codeofconduct/>`_. For more information see the `Code of Conduct FAQ <https://opensource.microsoft.com/codeofconduct/faq/>`_ or contact `opencode@microsoft.com <mailto:opencode@microsoft.com>`_ with any additional questions or comments.

:raw-html-m2r:`<!-- LINKS -->`


.. image:: https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python%2Fsdk%2Ftemplate%2Fazure-template%2FREADME.png
   :target: https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python%2Fsdk%2Ftemplate%2Fazure-template%2FREADME.png
   :alt: Impressions



Indices and tables
------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

.. toctree::
  :maxdepth: 5
  :glob:
  :caption: Developer Documentation

  azure.ai.language.conversations.rst