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Howie/samples 4 (#47240)
* samples * routine samples * Remove sample_skill_in_toolbox.py as part of cleanup * update samples * recording * Update assets.json Tag and enable additional sample tests in test_samples.py * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * upgrade version --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
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‎sdk/ai/azure-ai-projects/CHANGELOG.md‎

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# Release History
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## 2.3.0 (Unreleased)
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### Sample updates
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* Added Routines samples `sample_routines_crud.py` demonstrating CRUD operations and `sample_routines_with_timer_trigger.py` demonstrating triggering a routine by a timer.
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## 2.2.0 (2026-05-29)
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### Features Added

‎sdk/ai/azure-ai-projects/assets.json‎

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"AssetsRepo": "Azure/azure-sdk-assets",
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"AssetsRepoPrefixPath": "python",
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"TagPrefix": "python/ai/azure-ai-projects",
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"Tag": "python/ai/azure-ai-projects_449a9f8e06"
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"Tag": "python/ai/azure-ai-projects_11a3786ca2"
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}

‎sdk/ai/azure-ai-projects/azure/ai/projects/_version.py‎

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# Changes may cause incorrect behavior and will be lost if the code is regenerated.
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# --------------------------------------------------------------------------
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VERSION = "2.2.0"
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VERSION = "2.3.0"

‎sdk/ai/azure-ai-projects/samples/evaluations/sample_agent_trace_evaluation_smart_filter.py‎

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"start_time": start_time,
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"end_time": end_time,
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"max_traces": args.max_traces,
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"filter_strategy": "smart_filtering"
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"filter_strategy": "smart_filtering",
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}
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if args.agent_id:
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print(f"\n✗ Evaluation run failed: {run.error}")
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client.evals.delete(eval_id=eval_object.id)
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print("Evaluation deleted")
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print("Evaluation deleted")

‎sdk/ai/azure-ai-projects/samples/evaluations/sample_scheduled_agent_traces_evaluation_smart_filter.py‎

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Schedule,
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RecurrenceTrigger,
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DailyRecurrenceSchedule,
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EvaluationScheduleTask
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EvaluationScheduleTask,
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)
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import time
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print("An unexpected error occurred. Please check the error details above.")
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raise
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def schedule_trace_evaluation():
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endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
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model_deployment_name = os.environ["FOUNDRY_MODEL_NAME"]
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"start_time": start_time,
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"end_time": end_time,
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"max_traces": args.max_traces,
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"filter_strategy": "smart_filtering"
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"filter_strategy": "smart_filtering",
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}
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if args.agent_id:
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eval_run_object = {
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"eval_id": eval_object.id,
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"name": "trace_eval_with_smart_filter",
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"data_source": data_source
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"data_source": data_source,
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}
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print("Eval Run:")
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client.evals.delete(eval_id=eval_object.id)
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print("Evaluation deleted")
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if __name__ == "__main__":
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main()

‎sdk/ai/azure-ai-projects/samples/hosted_agents/sample_routines_crud.py‎

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# pylint: disable=line-too-long,useless-suppression
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to perform CRUD operations on Routines
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using the synchronous AIProjectClient.
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It creates a routine bound to an existing hosted agent, retrieves it,
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toggles its `enabled` state via `disable` / `enable`, lists routines,
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and finally deletes it. A `CustomRoutineTrigger` is used to keep the
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sample self-contained (no GitHub or schedule resources required).
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Routines are currently a preview feature. In the Python SDK, you access
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these operations via `project_client.beta.routines`.
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USAGE:
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python sample_routines_crud.py
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Before running the sample:
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pip install "azure-ai-projects>=2.2.0" python-dotenv
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Set these environment variables with your own values:
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1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Microsoft Foundry portal.
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2) FOUNDRY_HOSTED_AGENT_NAME - The name of an existing Hosted Agent to invoke
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when the routine fires.
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"""
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import os
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from dotenv import load_dotenv
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from azure.core.exceptions import ResourceNotFoundError
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from azure.identity import DefaultAzureCredential
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from azure.ai.projects import AIProjectClient
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from azure.ai.projects.models import (
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CustomRoutineTrigger,
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InvokeAgentResponsesApiRoutineAction,
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Routine,
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RoutineTrigger,
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)
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load_dotenv()
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endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
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agent_name = os.environ["FOUNDRY_HOSTED_AGENT_NAME"]
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def print_routine_state(routine: Routine) -> None:
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print(f" - routine `{routine.name}` enabled={routine.enabled} description={routine.description!r}")
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with (
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DefaultAzureCredential() as credential,
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AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
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):
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routine_name = "sample-routine"
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try:
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project_client.beta.routines.delete(routine_name)
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print(f"Routine `{routine_name}` deleted")
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except ResourceNotFoundError:
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pass
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triggers: dict[str, RoutineTrigger] = {
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"manual": CustomRoutineTrigger(
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provider="sample-provider",
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event_name="sample-event",
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parameters={"source": "sample_routines_crud"},
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),
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}
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action = InvokeAgentResponsesApiRoutineAction(agent_name=agent_name)
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created = project_client.beta.routines.create_or_update(
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routine_name,
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description="Routine created by the azure-ai-projects sample.",
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enabled=True,
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triggers=triggers,
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action=action,
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)
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print(f"Created routine: {created.name} enabled={created.enabled}")
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disabled = project_client.beta.routines.disable(routine_name)
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print(f"Disabled routine: {disabled.name} enabled={disabled.enabled}")
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fetched = project_client.beta.routines.get(routine_name)
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print("Retrieved routine after disable:")
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print_routine_state(fetched)
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enabled = project_client.beta.routines.enable(routine_name)
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print(f"Enabled routine: {enabled.name} enabled={enabled.enabled}")
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fetched = project_client.beta.routines.get(routine_name)
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print("Retrieved routine after enable:")
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print_routine_state(fetched)
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routines = list(project_client.beta.routines.list())
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print(f"Found {len(routines)} routine(s):")
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for item in routines:
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print(f" - {item.name} enabled={item.enabled}")
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project_client.beta.routines.delete(routine_name)
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print("Routine deleted")

‎sdk/ai/azure-ai-projects/samples/hosted_agents/sample_routines_with_timer_trigger.py‎

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# pylint: disable=line-too-long,useless-suppression
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# ------------------------------------
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# ------------------------------------
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"""
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DESCRIPTION:
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This sample demonstrates how to create a Routine that fires automatically
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from a one-shot timer trigger, then record the resulting runs by polling
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`list_runs(...)` using the synchronous AIProjectClient.
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The routine is bound to an existing hosted agent and scheduled to fire a
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short time in the future. The sample then polls the run history until a
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terminal phase is reached (or a deadline elapses), printing each observed
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transition. The routine is deleted at the end of the sample.
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Routines are currently a preview feature. In the Python SDK, you access
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these operations via `project_client.beta.routines`.
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USAGE:
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python sample_routines_with_timer_trigger.py
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Before running the sample:
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pip install "azure-ai-projects>=2.2.0" python-dotenv
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Set these environment variables with your own values:
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1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
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page of your Microsoft Foundry portal.
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2) FOUNDRY_HOSTED_AGENT_NAME - The name of an existing Hosted Agent to invoke
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when the routine timer fires.
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"""
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import datetime
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import json
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import os
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import time
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from dotenv import load_dotenv
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from azure.core.exceptions import ResourceNotFoundError
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from azure.core.settings import settings
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settings.tracing_implementation = "opentelemetry"
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from opentelemetry import trace
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter
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from azure.monitor.opentelemetry import configure_azure_monitor
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from azure.ai.projects.telemetry import AIProjectInstrumentor
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from azure.identity import DefaultAzureCredential
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from azure.ai.projects import AIProjectClient
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from azure.ai.projects.models import (
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InvokeAgentResponsesApiRoutineAction,
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RoutineRun,
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RoutineRunPhase,
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TimerRoutineTrigger,
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)
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load_dotenv()
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# Console exporter: spans printed to stdout as they finish.
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tracer_provider = TracerProvider()
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tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
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trace.set_tracer_provider(tracer_provider)
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tracer = trace.get_tracer(__name__)
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AIProjectInstrumentor().instrument()
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endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
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agent_name = os.environ["FOUNDRY_HOSTED_AGENT_NAME"]
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with (
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DefaultAzureCredential() as credential,
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AIProjectClient(endpoint=endpoint, credential=credential, allow_preview=True) as project_client,
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):
79+
# Azure Monitor exporter: same spans also sent to the Application Insights
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# resource attached to the Foundry project, viewable in the "Tracing" tab
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# on ai.azure.com.
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configure_azure_monitor(connection_string=project_client.telemetry.get_application_insights_connection_string())
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routine_name = "sample-routine-timer"
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try:
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project_client.beta.routines.delete(routine_name)
88+
print(f"Routine `{routine_name}` deleted")
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except ResourceNotFoundError:
90+
pass
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fire_at = datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(seconds=20)
93+
created = project_client.beta.routines.create_or_update(
94+
routine_name,
95+
description="Routine used by the timer-trigger sample.",
96+
enabled=True,
97+
triggers={"once": TimerRoutineTrigger(at=fire_at)},
98+
action=InvokeAgentResponsesApiRoutineAction(agent_name=agent_name),
99+
)
100+
print(f"Created routine: {created.name} enabled={created.enabled} fire_at={fire_at.isoformat()}")
101+
102+
terminal_phases = {RoutineRunPhase.COMPLETED, RoutineRunPhase.FAILED}
103+
seen_phases: dict[str, RoutineRunPhase] = {}
104+
final_run: RoutineRun | None = None
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106+
deadline = time.monotonic() + 180
107+
while time.monotonic() < deadline:
108+
runs = list(project_client.beta.routines.list_runs(routine_name, limit=20, order="desc"))
109+
for run in runs:
110+
if run.id is None:
111+
continue
112+
if seen_phases.get(run.id) == run.phase:
113+
continue
114+
seen_phases[run.id] = run.phase # type: ignore[assignment]
115+
print(
116+
f" - run_id={run.id} phase={run.phase} status={run.status} "
117+
f"trigger_type={run.trigger_type} triggered_at={run.triggered_at} ended_at={run.ended_at}"
118+
)
119+
if str(run.status).lower() == "finished":
120+
final_run = run
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122+
if final_run is not None:
123+
break
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time.sleep(5)
125+
126+
if final_run:
127+
print("Final run:")
128+
print(json.dumps(final_run.as_dict(), indent=2, default=str))
129+
# Note: retrieving the response body produced by a routine-dispatched
130+
# run via `openai_client.responses.retrieve(final_run.response_id)` is
131+
# not yet supported by the service for this scenario.
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else:
133+
print("Timer did not produce a terminal run within the deadline.")
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135+
project_client.beta.routines.delete(routine_name)
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print("Routine deleted")

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