azure-ai-textanalytics-authoring 1.0.0b1

Microsoft Corporation Azure Ai Textanalytics Authoring Client Library for Python

Azure Text Authoring client library for Python

Text Authoring is part of Azure AI Language. It provides APIs and SDKs to create, manage, train, evaluate, and deploy text projects and models (for example, custom single/multi-label classification). With the TextAuthoringClient, you can automate much of what you’d otherwise do in Language Studio, including:

• Project management: Create, update, export, and import projects.
• Training jobs: Start, monitor, and cancel model training jobs.
• Evaluation: Retrieve model evaluation summaries and document-level results.
• Deployments: Create, update, swap, and delete deployments.
• Snapshots: Load a trained model snapshot back into a project.
• Resource assignment: Assign or unassign Azure resources to a deployment.

Source code | Package (PyPI) | API reference | Samples | Product docs | REST API docs

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Getting started

Prerequisites

• Python 3.7 or later
• An Azure subscription
• An Azure AI Language resource

Install the package

Install the Azure Text Authoring client library for Python with pip:

pip install azure-ai-textanalytics-authoring

Note: This version of the client library defaults to the 2025-05-15-preview version of the service.

This table shows the relationship between SDK versions and supported API versions of the service

SDK version	Supported API version of service
1.0.0b1 - Latest preview release	2023-04-01, 2024-11-15-preview, 2025-05-15-preview (default)

Authenticate the client

To interact with the Text Authoring service, you'll need to create an instance of the TextAuthoringClient. 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 API key from your 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 a TextAuthoringClient 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 the azure-identity library.

Note: Regional endpoints do not support AAD authentication.
You must create a custom subdomain for your resource in order to use this type of authentication.

Authentication with AAD requires some initial setup:

• Install azure-identity
• Register a new AAD application
• Grant access to the Language service by assigning the Cognitive Services Language Owner role to your service principal

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

from azure.ai.textanalytics.authoring import TextAuthoringClient
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()
client = TextAuthoringClient(
    endpoint="https://<my-custom-subdomain>.cognitiveservices.azure.com/",
    credential=credential,
)

Create a TextAuthoringClient using AzureKeyCredential

Once you've determined your endpoint and API key, you can instantiate a TextAuthoringClient:

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.authoring import TextAuthoringClient

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

Key concepts

TextAuthoringClient

The TextAuthoringClient is the primary entry point for interacting with the Text Authoring service.
It provides top-level APIs to manage projects (create, delete, list) and retrieve project-scoped clients.

You can call get_project_client(project_name) to obtain a TextAuthoringProjectClient, which exposes operations specific to a given project.

For asynchronous operations, an async version of the client is available in the azure.ai.textanalytics.authoring.aio namespace.

TextAuthoringProjectClient

The TextAuthoringProjectClient is a project-scoped client returned by TextAuthoringClient.get_project_client(project_name).

It organizes project functionality into operation groups:

• deployment → [DeploymentOperations]: manage deployments (create, update, delete, swap).
• exported_model → [ExportedModelOperations]: handle exported model operations.
• project → [ProjectOperations]: manage project-level operations (train, import/export, cancel jobs, assign resources).
• trained_model → [TrainedModelOperations]: interact with trained models (evaluation, snapshots, delete models).

This separation ensures you can focus on project-level actions while still using the main TextAuthoringClient for higher-level management.

Examples

The azure-ai-textanalytics-authoring client library provides both synchronous and asynchronous APIs.

The following examples show common Text Authoring scenarios using the TextAuthoringClient or TextAuthoringProjectClient (created above).

Create a Text Project

import os
from azure.identity import DefaultAzureCredential
from azure.ai.textanalytics.authoring import TextAuthoringClient
from azure.ai.textanalytics.authoring.models import (
    CreateProjectOptions,
    ProjectKind,
)


def sample_text_create_project():
    # settings
    endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
    project_name = os.environ.get("PROJECT_NAME", "<project-name>")
    language_tag = os.environ.get("LANGUAGE_TAG", "<language-tag>")  # e.g., "en"
    storage_container = os.environ.get("STORAGE_INPUT_CONTAINER_NAME", "<storage-container-name>")
    description = os.environ.get("DESCRIPTION", "<description>")

    # create a client with AAD
    credential = DefaultAzureCredential()
    client = TextAuthoringClient(endpoint, credential=credential)

    # build project definition (Custom Multi-Label Classification)
    body = CreateProjectOptions(
        project_kind=ProjectKind.CUSTOM_MULTI_LABEL_CLASSIFICATION,
        storage_input_container_name=storage_container,
        project_name=project_name,
        language=language_tag,
        description=description,
        multilingual=True,
    )

    # create project
    result = client.create_project(project_name=project_name, body=body)

    # print project details (direct attribute access; no getattr)
    print("=== Create Text Authoring Project Result ===")
    print(f"Project Name: {result.project_name}")
    print(f"Language: {result.language}")
    print(f"Kind: {result.project_kind}")
    print(f"Multilingual: {result.multilingual}")
    print(f"Description: {result.description}")
    print(f"Storage Input Container: {result.storage_input_container_name}")

Import a Project

import os
from azure.identity import DefaultAzureCredential
from azure.core.exceptions import HttpResponseError
from azure.ai.textanalytics.authoring import TextAuthoringClient
from azure.ai.textanalytics.authoring.models import (
    CreateProjectOptions,
    ExportedProject,
    ProjectSettings,
    ExportedCustomSingleLabelClassificationProjectAsset,
    ExportedCustomSingleLabelClassificationDocument,
    ExportedDocumentClass,
    ExportedClass,
    ProjectKind,
    StringIndexType,
)


def sample_import_project():
    # settings
    endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
    project_name = os.environ.get("PROJECT_NAME", "<project-name>")
    language_tag = os.environ.get("LANGUAGE_TAG", "<language-tag>")  # e.g., "en"
    storage_container = os.environ.get("STORAGE_INPUT_CONTAINER_NAME", "<storage-container-name>")
    doc1_path = os.environ.get("DOC1_PATH", "<doc1-path>")  # e.g., "01.txt"
    doc2_path = os.environ.get("DOC2_PATH", "<doc2-path>")  # e.g., "02.txt"

    # create a client with AAD
    credential = DefaultAzureCredential()
    client = TextAuthoringClient(endpoint, credential=credential)

    # project-scoped client
    project_client = client.get_project_client(project_name)

    # ---------- Arrange metadata (project definition) ----------
    project_metadata = CreateProjectOptions(
        project_kind=ProjectKind.CUSTOM_SINGLE_LABEL_CLASSIFICATION,
        storage_input_container_name=storage_container,
        project_name=project_name,
        language=language_tag,
        description="Sample project imported via Python SDK.",
        multilingual=False,
        settings=ProjectSettings(),
    )

    # ---------- Arrange assets (classes + labeled documents) ----------
    project_assets = ExportedCustomSingleLabelClassificationProjectAsset(
        classes=[
            ExportedClass(category="ClassA"),
            ExportedClass(category="ClassB"),
            ExportedClass(category="ClassC"),
        ],
        documents=[
            ExportedCustomSingleLabelClassificationDocument(
                class_property=ExportedDocumentClass(category="ClassA"),
                location=doc1_path,
                language=language_tag,
            ),
            ExportedCustomSingleLabelClassificationDocument(
                class_property=ExportedDocumentClass(category="ClassB"),
                location=doc2_path,
                language=language_tag,
            ),
        ],
    )

    exported_project = ExportedProject(
        project_file_version="2022-05-01",
        string_index_type=StringIndexType.UTF16_CODE_UNIT,
        metadata=project_metadata,
        assets=project_assets,
    )

    # ---------- Import (LRO) with error handling ----------
    poller = project_client.project.begin_import(body=exported_project)
    try:
        poller.result()  # completes with None; raises on failure
        print("Import completed.")
        print(f"done: {poller.done()}")
        print(f"status: {poller.status()}")
    except HttpResponseError as e:
        msg = getattr(getattr(e, "error", None), "message", str(e))
        print(f"Operation failed: {msg}")

Train a Model

import os
from azure.identity import DefaultAzureCredential
from azure.core.exceptions import HttpResponseError
from azure.ai.textanalytics.authoring import TextAuthoringClient
from azure.ai.textanalytics.authoring.models import (
    TrainingJobDetails,
    EvaluationDetails,
    EvaluationKind,
)


def sample_train_project():
    # settings
    endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
    project_name = os.environ.get("PROJECT_NAME", "<project-name>")
    model_label = os.environ.get("MODEL_LABEL", "<model-label>")
    training_config_version = os.environ.get("TRAINING_CONFIG_VERSION", "<training-config-version>")

    # create a client with AAD
    credential = DefaultAzureCredential()
    client = TextAuthoringClient(endpoint, credential=credential)

    # project-scoped client
    project_client = client.get_project_client(project_name)

    # build training job details (80/20 split by percentage)
    training_job_details = TrainingJobDetails(
        model_label=model_label,
        training_config_version=training_config_version,
        evaluation_options=EvaluationDetails(
            kind=EvaluationKind.PERCENTAGE,
            testing_split_percentage=20,
            training_split_percentage=80,
        ),
    )

    # begin training (LRO) and handle success/error
    poller = project_client.project.begin_train(body=training_job_details)
    try:
        poller.result()  # completes with None; raises on failure
        print("Train completed.")
        print(f"done: {poller.done()}")
        print(f"status: {poller.status()}")
    except HttpResponseError as e:
        msg = getattr(getattr(e, "error", None), "message", str(e))
        print(f"Operation failed: {msg}")

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 Text Authoring client will raise exceptions defined in Azure Core.
These exceptions provide consistent error handling across Azure SDK libraries.

Logging

This library uses Python’s built-in logging module for diagnostic logging.

• Basic information about HTTP requests (URLs, headers) is logged at INFO level.
• Detailed request/response information (including bodies and unredacted headers) is logged at DEBUG level.

You can enable logging when constructing the client by passing logging_enable=True.

import sys
import logging
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics.authoring import TextAuthoringClient

# Configure logger
logger = logging.getLogger("azure")
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler(stream=sys.stdout)
logger.addHandler(handler)

endpoint = "https://<endpoint>.cognitiveservices.azure.com/"
credential = DefaultAzureCredential()

# This client will log detailed HTTP information
client = TextAuthoringClient(endpoint, credential, logging_enable=True)

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

result = client.create_project(
    project_name="<project-name>",
    body={...},
    logging_enable=True,
)

Next steps

More sample code

See the Sample README for additional examples that demonstrate common Text Authoring workflows such as:

• Creating and managing projects
• Importing and exporting project assets
• Training models and retrieving evaluation results
• Deploying and swapping models
• Assigning or unassigning resources
• Loading snapshots and managing trained models

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Contributing

See the CONTRIBUTING.md guide 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 repositories 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.

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Release History

1.0.0b1 (2025-09-19)

Features Added

• Initial release