Azure Conversational Language Understanding client library for Python — Azure SDK for Python 2.0.0 documentation

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

Getting started¶

Prerequisites¶

Install the package¶

Install the Azure Conversations client library for Python with pip:

pipinstallazure-ai-language-conversations

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 class, or 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.

Get an API key¶

You can get the endpoint and an API key from the Cognitive Services resource in the Azure Portal.

Alternatively, use the Azure CLI command shown below to get the API key from the Cognitive Service resource.

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:

from azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations import ConversationAnalysisClientendpoint = ""credential = AzureKeyCredential("<api-key>")client = ConversationAnalysisClient(endpoint, credential)

Create ConversationAuthoringClient¶

Once you’ve determined your endpoint and API key you can instantiate a ConversationAuthoringClient:

from azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations.authoring import ConversationAuthoringClientendpoint = ""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, provide an instance of the desired credential type obtained from theazure-identity library. Note that regional endpoints do not support AAD authentication. Create a custom subdomainname for your resource in order to use this type of authentication.

Authentication with AAD requires some initial setup:

After setup, you can choose which type of credential from azure.identity to use. As an example, DefaultAzureCredentialcan 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:

from azure.ai.language.conversations import ConversationAnalysisClientfrom azure.identity import DefaultAzureCredentialcredential = DefaultAzureCredential()client = ConversationAnalysisClient(endpoint="", credential=credential)

Key concepts¶

ConversationAnalysisClient¶

The 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 to interface with the 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.

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:

# import librariesimport osfrom azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations import ConversationAnalysisClient# get secretsclu_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 queyclient = 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 resultprint("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:

# import librariesimport osfrom azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations import ConversationAnalysisClient# get secretsclu_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 queryclient = 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 resultprint("query: {}".format(result["result"]["query"]))print("project kind: {}\n".format(result["result"]["prediction"]["projectKind"]))# top intenttop_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:

# import librariesimport osfrom azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations import ConversationAnalysisClient# get secretsendpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]key = os.environ["AZURE_CONVERSATIONS_KEY"]# analyze queryclient = 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']}")

Import a Conversation Project¶

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

import osfrom azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations.authoring import ConversationAuthoringClientclu_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 describes available configurations for retries, logging, transport protocols, and more.

Troubleshooting¶

General¶

The Conversations client will raise exceptions defined in Azure Core.

Logging¶

This library uses the standardlogging 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.

import sysimport loggingfrom azure.core.credentials import AzureKeyCredentialfrom azure.ai.language.conversations import ConversationAnalysisClient# Create a logger for the 'azure' SDKlogger = logging.getLogger('azure')logger.setLevel(logging.DEBUG)# Configure a console outputhandler = logging.StreamHandler(stream=sys.stdout)logger.addHandler(handler)endpoint = ""credential = AzureKeyCredential("<my-api-key>")# This client will log detailed information about its HTTP sessions, at DEBUG levelclient = 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:

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

Next steps¶

More sample code¶

See the Sample README for several code snippets illustrating common patterns used in the CLU Python API.

Contributing¶

See the 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.

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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Indices and tables¶

Developer Documentation