azure-ai-language-conversations 1.0.0b3

Microsoft Azure Conversational Language Understanding Client Library for Python

Azure Conversational Language Understanding client library for Python

Conversational Language Understanding, aka CLU for short, is a cloud-based conversational AI service which is mainly used in bots to extract useful information from user utterance (natural language processing). The CLU analyze api encompasses two projects; conversation, and orchestration projects. You can use the "conversation" project if you want to extract intents (intention behind a user utterance) and custom entities. You can also use the "orchestration" project which orchestrates multiple language apps to get the best response (language apps like Question Answering, Luis, and Conversation).

Source code | Package (PyPI) | API reference documentation | Product documentation | Samples

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
• An existing Text Analytics resource

Note: the new unified Cognitive Language Services are not currently available for deployment.

Install the package

Install the Azure Conversations client library for Python with pip:

pip install azure-ai-language-conversations

Authenticate the client

In order to interact with the CLU service, you'll need to create an instance of the ConversationAnalysisClient 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 AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient

endpoint = "https://<my-custom-subdomain>.cognitiveservices.azure.com/"
credential = AzureKeyCredential("<api-key>")
client = ConversationAnalysisClient(endpoint, 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.

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_conversations() method with your conversation's project name as follows:

# import libraries
import os
from azure.core.credentials import AzureKeyCredential

from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
    CustomConversationalTask,
    ConversationAnalysisOptions,
    CustomConversationTaskParameters,
    TextConversationItem
)

# 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=CustomConversationalTask(
                analysis_input=ConversationAnalysisOptions(
                    conversation_item=TextConversationItem(
                        id=1,
                        participant_id=1,
                        text=query
                    )
                ),
                parameters=CustomConversationTaskParameters(
                    project_name=project_name,
                    deployment_name=deployment_name
                )
            )
        )

# view result
print("query: {}".format(result.results.query))
print("project kind: {}\n".format(result.results.prediction.project_kind))

print("top intent: {}".format(result.results.prediction.top_intent))
print("category: {}".format(result.results.prediction.intents[0].category))
print("confidence score: {}\n".format(result.results.prediction.intents[0].confidence))

print("entities:")
for entity in result.results.prediction.entities:
    print("\ncategory: {}".format(entity.category))
    print("text: {}".format(entity.text))
    print("confidence score: {}".format(entity.confidence))
    if entity.resolutions:
        print("resolutions")
        for resolution in entity.resolutions:
            print("kind: {}".format(resolution.resolution_kind))
            print("value: {}".format(resolution.additional_properties["value"]))
    if entity.extra_information:
        print("extra info")
        for data in entity.extra_information:
            print("kind: {}".format(data.extra_information_kind))
            if data.extra_information_kind == "ListKey":
                print("key: {}".format(data.key))
            if data.extra_information_kind == "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_conversations() 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 libraries
import os
from azure.core.credentials import AzureKeyCredential

from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
    CustomConversationalTask,
    ConversationAnalysisOptions,
    CustomConversationTaskParameters,
    TextConversationItem
)

# 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 = "How are you?"
    result = client.analyze_conversation(
            task=CustomConversationalTask(
                analysis_input=ConversationAnalysisOptions(
                    conversation_item=TextConversationItem(
                        id=1,
                        participant_id=1,
                        text=query
                    )
                ),
                parameters=CustomConversationTaskParameters(
                    project_name=project_name,
                    deployment_name=deployment_name
                )
            )
        )

# view result
print("query: {}".format(result.results.query))
print("project kind: {}\n".format(result.results.prediction.project_kind))

# top intent
top_intent = result.results.prediction.top_intent
print("top intent: {}".format(top_intent))
top_intent_object = result.results.prediction.intents[top_intent]
print("confidence score: {}".format(top_intent_object.confidence))
print("project kind: {}".format(top_intent_object.target_kind))

if top_intent_object.target_kind == "question_answering":
    print("\nview qna result:")
    qna_result = top_intent_object.result
    for answer in qna_result.answers:
        print("\nanswer: {}".format(answer.answer))
        print("answer: {}".format(answer.confidence))

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 standard 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.

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_conversations(...)

Similarly, logging_enable can enable detailed logging for a single operation, even when it isn't enabled for the client:

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

Next steps

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 [e-mail] with any additional questions or comments.

Release History

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