# SPDX-FileCopyrightText: 2022-present deepset GmbH # # SPDX-License-Identifier: Apache-2.0 import datetime import logging from unittest.mock import Mock, patch from haystack import Pipeline, component from haystack.core.serialization import generate_qualified_class_name from haystack.telemetry._telemetry import pipeline_running, tutorial_running from haystack.utils.auth import Secret, TokenSecret @patch("haystack.telemetry._telemetry.telemetry") def test_pipeline_running(telemetry): telemetry.send_event = Mock() @component class Component: def _get_telemetry_data(self): return {"key": "values"} @component.output_types(value=int) def run(self): pass pipe = Pipeline() pipe.add_component("component", Component()) pipeline_running(pipe) expected_type = generate_qualified_class_name(type(pipe)) # First run is always sent telemetry.send_event.assert_called_once_with( "Pipeline run (2.x)", { "pipeline_id": str(id(pipe)), "pipeline_type": expected_type, "runs": 1, "components": {"test.test_telemetry.Component": [{"name": "component", "key": "values"}]}, }, ) # Running again before one minute has passed should not send another event telemetry.send_event.reset_mock() pipeline_running(pipe) telemetry.send_event.assert_not_called() # Set the last telemetry sent time to pretend one minute has passed pipe._last_telemetry_sent = pipe._last_telemetry_sent - datetime.timedelta(minutes=1) telemetry.send_event.reset_mock() pipeline_running(pipe) telemetry.send_event.assert_called_once_with( "Pipeline run (2.x)", { "pipeline_id": str(id(pipe)), "pipeline_type": expected_type, "runs": 3, "components": {"test.test_telemetry.Component": [{"name": "component", "key": "values"}]}, }, ) # More than a day has passed but the seconds component of the timedelta is below the threshold: # the event must still be sent (regression test for using timedelta.seconds instead of total_seconds()) pipe._last_telemetry_sent = datetime.datetime.now() - datetime.timedelta(days=1, seconds=5) telemetry.send_event.reset_mock() pipeline_running(pipe) telemetry.send_event.assert_called_once_with( "Pipeline run (2.x)", { "pipeline_id": str(id(pipe)), "pipeline_type": expected_type, "runs": 4, "components": {"test.test_telemetry.Component": [{"name": "component", "key": "values"}]}, }, ) @patch("haystack.telemetry._telemetry.telemetry") def test_pipeline_running_with_non_serializable_component(telemetry): telemetry.send_event = Mock() @component class Component: def __init__(self, api_key: Secret = TokenSecret("api_key")): self.api_key = api_key def _get_telemetry_data(self): return {"key": "values"} @component.output_types(value=int) def run(self): pass pipe = Pipeline() pipe.add_component("component", Component()) pipeline_running(pipe) telemetry.send_event.assert_called_once_with( "Pipeline run (2.x)", { "pipeline_id": str(id(pipe)), "pipeline_type": "haystack.core.pipeline.pipeline.Pipeline", "runs": 1, "components": {"test.test_telemetry.Component": [{"name": "component", "key": "values"}]}, }, ) def test_pipeline_running_with_non_dict_telemetry_data(caplog): @component class Component: def __init__(self, api_key: Secret = TokenSecret("api_key")): self.api_key = api_key # telemetry data should be a dictionary but is a list def _get_telemetry_data(self): return ["values"] @component.output_types(value=int) def run(self): pass pipe = Pipeline() pipe.add_component("my_component", Component()) with caplog.at_level(logging.DEBUG): pipeline_running(pipe) assert "TypeError: Telemetry data for component my_component must be a dictionary" in caplog.text def test_send_telemetry_preserves_function_metadata(): """ Regression test for https://github.com/deepset-ai/haystack/issues/11568. The ``send_telemetry`` decorator must use ``functools.wraps`` so that decorated functions such as ``pipeline_running`` and ``tutorial_running`` keep their own metadata (``__name__``, ``__doc__`` and ``__annotations__``) instead of exposing the ``send_telemetry_wrapper`` wrapper's. """ # ``__name__`` comes from the wrapped function, not the wrapper. assert pipeline_running.__name__ == "pipeline_running" assert tutorial_running.__name__ == "tutorial_running" # ``__doc__`` is preserved. assert pipeline_running.__doc__ is not None assert "Collects telemetry data for a pipeline run" in pipeline_running.__doc__ # ``__annotations__`` are preserved, e.g. the wrapped functions' parameters. assert "pipeline" in pipeline_running.__annotations__ assert "tutorial_id" in tutorial_running.__annotations__ # ``functools.wraps`` also exposes the undecorated function through ``__wrapped__``. assert pipeline_running.__wrapped__.__name__ == "pipeline_running"