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#!/usr/bin/env python
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
Create different types of experiment metrics
"""
import os
from azure.identity import DefaultAzureCredential
from azure.onlineexperimentation import OnlineExperimentationClient
from azure.onlineexperimentation.models import (
ExperimentMetric,
LifecycleStage,
DesiredDirection,
EventCountMetricDefinition,
UserCountMetricDefinition,
EventRateMetricDefinition,
UserRateMetricDefinition,
SumMetricDefinition,
AverageMetricDefinition,
PercentileMetricDefinition,
ObservedEvent,
AggregatedValue,
)
from azure.core.exceptions import HttpResponseError
def create_event_count_metric():
# Create a client with your Azure Online Experimentation workspace endpoint and credentials
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the Event Count metric - counts all occurrences of a specific event type
prompt_sent_metric = ExperimentMetric(
lifecycle=LifecycleStage.ACTIVE,
display_name="Total number of prompts sent",
description="Counts the total number of prompts sent by users to the chatbot",
categories=["Usage"],
desired_direction=DesiredDirection.INCREASE,
definition=EventCountMetricDefinition(event=ObservedEvent(event_name="PromptSent")),
)
try:
# Create the metric with ID "prompt_sent_count"
response = client.create_or_update_metric("prompt_sent_count", prompt_sent_metric)
print(f"Created metric: {response.id}")
print(f"Display name: {response.display_name}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
def create_user_count_metric():
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the User Count metric with a filter - counts unique users who performed a specific action
users_prompt_sent_metric = ExperimentMetric(
lifecycle=LifecycleStage.ACTIVE,
display_name="Users with at least one prompt sent on checkout page",
description="Counts unique users who sent at least one prompt while on the checkout page",
categories=["Usage"],
desired_direction=DesiredDirection.INCREASE,
definition=UserCountMetricDefinition(
event=ObservedEvent(event_name="PromptSent", filter="Page == 'checkout.html'")
),
)
try:
# Create the metric with ID "users_prompt_sent"
response = client.create_or_update_metric("users_prompt_sent", users_prompt_sent_metric)
print(f"Created metric: {response.id}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
def create_event_rate_metric():
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the Event Rate metric - measures a percentage of events meeting a condition
relevance_metric = ExperimentMetric(
lifecycle=LifecycleStage.ACTIVE,
display_name="% evaluated conversations with good relevance",
description="Percentage of evaluated conversations where the LLM response has good relevance (score >= 4)",
categories=["Quality"],
desired_direction=DesiredDirection.INCREASE,
definition=EventRateMetricDefinition(
event=ObservedEvent(event_name="EvaluateLLM"), rate_condition="Relevance > 4"
),
)
try:
# Create the metric
response = client.create_or_update_metric("pct_relevance_good", relevance_metric)
print(f"Created metric: {response.id}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
def create_user_rate_metric():
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the User Rate metric - measures percentage of users who performed action B after action A
conversion_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"),
),
)
try:
# Create the metric
response = client.create_or_update_metric("pct_chat_to_high_value_purchase_conversion", conversion_metric)
print(f"Created metric: {response.id}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
def create_sum_metric():
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the Sum metric - sums a numeric value across all events of a type
revenue_metric = ExperimentMetric(
lifecycle=LifecycleStage.ACTIVE,
display_name="Total revenue",
description="Sum of revenue from all purchase transactions",
categories=["Business"],
desired_direction=DesiredDirection.INCREASE,
definition=SumMetricDefinition(
value=AggregatedValue(event_name="Purchase", event_property="Revenue", filter="Revenue > 0")
),
)
try:
# Create the metric
response = client.create_or_update_metric("total_revenue", revenue_metric)
print(f"Created metric: {response.id}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
def create_average_metric():
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the Average metric - calculates the mean of a numeric value across events
avg_revenue_metric = ExperimentMetric(
lifecycle=LifecycleStage.ACTIVE,
display_name="Average revenue per purchase",
description="The average revenue per purchase transaction in USD",
categories=["Business"],
desired_direction=DesiredDirection.INCREASE,
definition=AverageMetricDefinition(value=AggregatedValue(event_name="Purchase", event_property="Revenue")),
)
try:
# Create the metric
response = client.create_or_update_metric("avg_revenue_per_purchase", avg_revenue_metric)
print(f"Created metric: {response.id}")
print(f"Display name: {response.display_name}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
def create_percentile_metric():
endpoint = os.environ["AZURE_ONLINEEXPERIMENTATION_ENDPOINT"]
client = OnlineExperimentationClient(endpoint, DefaultAzureCredential())
# Define the Percentile metric - calculates a specific percentile of a numeric value
p95_response_time_metric = ExperimentMetric(
lifecycle=LifecycleStage.ACTIVE,
display_name="P95 LLM response time [seconds]",
description="The 95th percentile of response time in seconds for LLM responses",
categories=["Performance"],
desired_direction=DesiredDirection.DECREASE,
definition=PercentileMetricDefinition(
value=AggregatedValue(event_name="ResponseReceived", event_property="ResponseTimeSeconds"), percentile=95
),
)
try:
# Create the metric
response = client.create_or_update_metric("p95_response_time_seconds", p95_response_time_metric)
print(f"Created metric: {response.id}")
except HttpResponseError as error:
print(f"Failed to create metric: {error}")
if __name__ == "__main__":
create_event_count_metric()
create_user_count_metric()
create_event_rate_metric()
create_user_rate_metric()
create_sum_metric()
create_average_metric()
create_percentile_metric()
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