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Azure Online Experimentation client library for Python - version 1.0.0b1

This package contains Azure Online Experimentation client library for interacting with Microsoft.OnlineExperimentation/workspaces resources.

Getting started

Install the package

python -m pip install azure-onlineexperimentation

Prequisites

Create and authenticate the client

The Azure Online Experimentation client library initialization requires two parameters:

To construct a synchronous client:

import os
from azure.identity import DefaultAzureCredential
from azure.onlineexperimentation import OnlineExperimentationClient

# Create a client with your Online Experimentation workspace endpoint and credentials
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
print(f"Client initialized with endpoint: {endpoint}")

To construct an asynchronous client, instead import OnlineExperimentationClient from azure.onlineexperimentation.aio and DefaultAzureCredential from azure.identity.aio namespaces:

import os
from azure.identity.aio import DefaultAzureCredential
from azure.onlineexperimentation.aio import OnlineExperimentationClient

# Create a client with your Online Experimentation workspace endpoint and credentials
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
print(f"Client initialized with endpoint: {endpoint}")

Key concepts

Online Experimentation Workspace

[Microsoft.OnlineExperimentation/workspaces][az_exp_workspace] Azure resources work in conjunction with Azure App Configuration and Azure Monitor. The Online Experimentation workspace handles management of metrics definitions and their continuous computation to monitor and evaluate experiment results.

Experiment Metrics

Metrics are used to measure the impact of your online experiments. See the samples for how to create and manage various types of experiment metrics.

Troubleshooting

Errors can occur during initial requests and will provide information about how to resolve the error.

Examples

This examples goes theough the experiment metric management lifecycle, to run the example:

  • Set AZURE_ONLINEEXPERIMENTATION_ENDPOINT environment variable to the endpoint property value (URL) from a [Microsoft.OnlineExperimentation/workspaces][az_exp_workspace] resource.
  • Enable DefaultAzureCredential by running az login or Connect-AzAccount, see documentation for details and troubleshooting.
import os
import random
import json
from azure.identity import DefaultAzureCredential
from azure.onlineexperimentation import OnlineExperimentationClient
from azure.onlineexperimentation.models import (
    ExperimentMetric,
    LifecycleStage,
    DesiredDirection,
    UserRateMetricDefinition,
    ObservedEvent,
)
from azure.core.exceptions import HttpResponseError

# [Step 1] Initialize the SDK client
# The endpoint URL from the Microsoft.OnlineExperimentation/workspaces resource
endpoint = os.environ.get("AZURE_ONLINEEXPERIMENTATION_ENDPOINT", "<endpoint-not-set>")
credential = DefaultAzureCredential()

print(f"AZURE_ONLINEEXPERIMENTATION_ENDPOINT is {endpoint}")

client = OnlineExperimentationClient(endpoint=endpoint, credential=credential)

# [Step 2] Define the experiment metric
example_metric = ExperimentMetric(
    lifecycle=LifecycleStage.ACTIVE,
    display_name="% users with LLM interaction who made a high-value purchase",
    description="Percentage of users who received a response from the LLM and then made a purchase of $100 or more",
    categories=["Business"],
    desired_direction=DesiredDirection.INCREASE,
    definition=UserRateMetricDefinition(
        start_event=ObservedEvent(event_name="ResponseReceived"),
        end_event=ObservedEvent(event_name="Purchase", filter="Revenue > 100"),
    ),
)

# [Optional][Step 2a] Validate the metric - checks for input errors without persisting anything
print("Checking if the experiment metric definition is valid...")
print(json.dumps(example_metric.as_dict(), indent=2))

try:
    validation_result = client.validate_metric(example_metric)

    print(f"Experiment metric definition valid: {validation_result.is_valid}.")
    for detail in validation_result.diagnostics or []:
        # Inspect details of why the metric definition was rejected as Invalid
        print(f"- {detail.code}: {detail.message}")

    # [Step 3] Create the experiment metric
    example_metric_id = f"sample_metric_id_{random.randint(10000, 20000)}"

    print(f"Creating the experiment metric {example_metric_id}...")
    # Using upsert to create the metric with If-None-Match header
    create_response = client.create_or_update_metric(
        experiment_metric_id=example_metric_id,
        resource=example_metric,
        match_condition=None,  # This ensures If-None-Match: * header is sent
        etag=None,
    )

    print(f"Experiment metric {create_response.id} created, etag: {create_response.etag}.")

    # [Step 4] Deactivate the experiment metric and update the description
    updated_metric = {
        "lifecycle": LifecycleStage.INACTIVE,  # pauses computation of this metric
        "description": "No longer need to compute this.",
    }

    update_response = client.create_or_update_metric(
        experiment_metric_id=example_metric_id,
        resource=updated_metric,
        etag=create_response.etag,  # Ensures If-Match header is sent
        match_condition=None,  # Not specifying match_condition as we're using etag
    )

    print(f"Updated metric: {update_response.id}, etag: {update_response.etag}.")

    # [Step 5] Delete the experiment metric
    client.delete_metric(
        experiment_metric_id=example_metric_id, etag=update_response.etag  # Ensures If-Match header is sent
    )

    print(f"Deleted metric: {example_metric_id}.")

except HttpResponseError as error:
    print(f"The operation failed with error: {error}")

Next steps

Have a look at the samples folder, containing fully runnable Python code for synchronous and asynchronous clients.

Contributing

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