test(suite): add smoke test for docs/templates/suite_template.py #40
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Mock a simple agent and verify the FAQAdapter template pattern records a response and passes a metric
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WalkthroughAdds a new pytest smoke test that mocks an agent and an adapter, runs one DatasetCase through a Scenario via Runner, and asserts a single successful run for case id "faq_1" with answer_correctness == 1.0. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes
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tests/test_suite_template_smoke.py (1)
34-63: Smoke test covers the core contract; consider a couple of small, optional assertionsThe test nicely:
- Builds a single
DatasetCase, runs it throughScenario/Runner, and asserts exactly one run was recorded forfaq_1.- Verifies the run is successful and that
answer_correctnessis1.0, which directly guards the FAQAdapter + metric wiring.- Also implicitly smoke-tests the import path for
AnswerCorrectnessMetric.Optional hardening (no need to block on this):
- If the run object exposes the adapter output (e.g.,
recorded_run.output), you could assert it equals the canned answer for a slightly stronger check.- If trace data is exposed on the run, adding a quick assertion that the expected trace events are present (agent response + knowledge base/tool event) would lock in the trace shape the template advertises.
As-is, the test already meets the stated smoke-test objective.
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tests/test_suite_template_smoke.py (2)
5-11: MockAgent stub is clean and sufficient for the smoke testMinimal
connect()andanswer()behavior keeps the fixture easy to reason about and matches the FAQ-style use case. No changes needed here.
16-33: MockAdapter mirrors the adapter pattern well and captures useful tracesThe adapter:
- Uses a flexible
case.query/case.inputfallback, which is handy for keeping the test aligned if the template changes.- Records both the agent response and a tool/knowledge-base event into
trace.- Computes
successbased on the presence and exact match ofexpected_output, which is appropriate for this smoke test.No functional issues; this is a solid minimal adapter implementation for exercising the template.
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Fix the failing workflow, then I will merge
…test Address ruff lint failures: remove unused imports and place module imports at top of file
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tests/test_suite_template_smoke.py (1)
17-18: Optional: Explicitreturn Noneis redundant.Python functions return
Noneimplicitly, so the explicitreturn Nonestatements are unnecessary. However, they do make the intent clear and don't harm functionality.Apply this diff if you prefer more concise code:
- def prepare(self) -> None: # pragma: no cover - trivial - return None + def prepare(self) -> None: # pragma: no cover - trivial + pass- def cleanup(self) -> None: # pragma: no cover - trivial - return None + def cleanup(self) -> None: # pragma: no cover - trivial + passAlso applies to: 28-29
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tests/test_suite_template_smoke.py (6)
1-3: LGTM! Imports are appropriate for the smoke test.The imports cover all necessary components for building and running the test scenario.
5-10: LGTM! MockAgent is appropriately simple for a smoke test.The mock agent correctly implements the expected interface pattern with
.connect()and.answer()methods.
20-26: LGTM! The execute logic is well-implemented.The method correctly:
- Connects to the agent and retrieves the answer
- Records trace events for observability
- Handles edge cases (None expected_output, whitespace differences)
- Returns a properly structured AdapterOutcome
The defensive
hasattrcheck on line 22 is sensible if the template supports bothqueryandinputattributes across different dataset schemas.
31-44: LGTM! Test setup is clean and well-structured.The test case construction is appropriate with matching expected output, and the scenario components are properly initialized following the agentunit pattern.
46-58: LGTM! Test execution and assertions are thorough.The test properly:
- Runs the scenario through the Runner
- Validates the result structure
- Locates the specific test case by ID
- Asserts the run succeeded
59-60: The test assertion is correct and will pass.The
answer_correctnessmetric is automatically computed by theRunnerwhen no explicit metrics configuration is provided. WhenRunnerConfigis created without ametricsparameter (as in this test),resolve_metrics(None)returns all metrics fromDEFAULT_METRICS, which includesAnswerCorrectnessMetric().In
AnswerCorrectnessMetric.evaluate(), the metric compares the expected output with the actual output. Since bothcase.expected_outputand theresp_textfromMockAdapterare identical strings ("This is a canned FAQ answer."), the exact match condition triggers and returns1.0. The metric is populated inrecorded_run.metricsby theRunner._run_scenario()method during execution, so the assertion will pass.Likely an incorrect or invalid review comment.
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Mock a simple agent and verify the FAQAdapter template pattern records a response and passes a metric
Description
Adds a minimal smoke test for the sample FAQAdapter pattern in suite_template.py. The test mocks an agent with .connect() and .answer(), inlines a small dataset via DatasetSource.from_list, implements a MockAdapter (test-only) that conforms to BaseAdapter and returns an AdapterOutcome, and asserts that the scenario run is recorded and the answer_correctness metric is 1.0 for an exact match.
Fixes #7
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