fix(gooddata-eval): three false-negative sources in eval scoring - #1788
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Included review availability: 3 reviews are currently available. Your included PR review attempts over the past 7 days set your current allowance at 5 reviews per hour. 📝 WalkthroughWalkthroughThe change adds multipart chat-result capture and rendering, centralizes MAQL normalization, updates evaluation flows to use rendered answers, and adds anomaly-detection granularity validation. ChangesEvaluation flow updates
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🟡 Moderate · up to ANOMALY alert evaluations without fixture granularity may report success even when the alert uses an interval other than the DAY interval requested during the conversation. Clarify and enforce the intended default before merging. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.py`:
- Line 462: Extend strict alert evaluation to compare the expected granularity
stored by the granularity field with the granularity passed to
create_metric_alert for ANOMALY alerts. Add the resulting granularity evaluation
field to strict_pass and the best-run score, and add a regression test
confirming a mismatched interval fails.
In `@packages/gooddata-eval/src/gooddata_eval/core/evaluators/_maql.py`:
- Line 74: Update the normalization flow around the re.sub call in the MAQL
evaluator so whitespace is collapsed only outside protected quoted or literal
regions, preserving internal whitespace exactly. Keep protected-region
separation and reconstruction intact, and add a regression test proving literals
such as “A B” and “A B” remain distinct.
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packages/gooddata-eval/src/gooddata_eval/core/agentic/_catalog.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/conversation.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/general_question.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/guardrail.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/kda_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/metric_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/visualization.pypackages/gooddata-eval/src/gooddata_eval/core/chat/render.pypackages/gooddata-eval/src/gooddata_eval/core/chat/sse_client.pypackages/gooddata-eval/src/gooddata_eval/core/evaluators/_maql.pypackages/gooddata-eval/src/gooddata_eval/core/evaluators/_text_utils.pypackages/gooddata-eval/src/gooddata_eval/core/evaluators/metric_skill.pypackages/gooddata-eval/src/gooddata_eval/core/models.pypackages/gooddata-eval/tests/test_agentic_alert_skill.pypackages/gooddata-eval/tests/test_agentic_metric_skill.pypackages/gooddata-eval/tests/test_chat_render.pypackages/gooddata-eval/tests/test_maql_normalize.py
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/evaluators/_maql.py`:
- Around line 66-73: The MAQL normalization flow applies _IFNULL_RE,
_SELECT_WRAP_RE, _INNER_SELECT_RE, and brace-whitespace rewrites before
protected quoted regions are isolated, altering literal contents. Tokenize or
separate protected regions first, then apply these structural rewrites only
outside them, preserving wrapper-like text and brace whitespace inside quoted
literals; add regressions covering both cases.
- Line 67: Update the IFNULL parsing lambda around _IFNULL_RE to extract the
first argument without assuming that IFNULL is immediately followed by “(”, so
expressions with intervening whitespace normalize identically. Prefer using a
regex capture for the first argument, and add a regression test covering
whitespace between IFNULL and the opening parenthesis.
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packages/gooddata-eval/src/gooddata_eval/core/agentic/_catalog.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.pypackages/gooddata-eval/src/gooddata_eval/core/evaluators/_maql.pypackages/gooddata-eval/tests/test_agentic_alert_skill.pypackages/gooddata-eval/tests/test_maql_normalize.py
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## master #1788 +/- ##
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+ Coverage 81.76% 81.86% +0.10%
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Files 275 277 +2
Lines 19903 20016 +113
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+ Hits 16273 16387 +114
+ Misses 3630 3629 -1 ☔ View full report in Codecov by Harness. 🚀 New features to boost your workflow:
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| """Attribute filters applied to the alert condition.""" | ||
| attributes: list | None = None | ||
| """Expected group-by attributes; ``None`` means the fixture states no expectation.""" | ||
| granularity: str | None = None |
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Nit: this granularity can be Enum – we know the values that can enter the granularity.
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Inline comments:
In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.py`:
- Line 364: Update _normalize_expected_output to normalize a missing ANOMALY
granularity to AnomalyDetectionGranularity.DAY, so the normalized expected
result is asserted by _check_granularity consistently with the simulated-user
requirement.
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packages/gooddata-eval/src/gooddata_eval/core/agentic/_catalog.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/alert_skill.pypackages/gooddata-eval/tests/test_agentic_alert_skill.py
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Capture every multipart response part, not just text/visualization/ alertProposal, and render them into what the judge and the simulated user see. Normalize whitespace around MAQL punctuation before comparing, in one shared module the agentic and non-agentic metric comparators both use, and keep quoted literals out of every rewrite. Read the fixture's anomaly granularity, assert it, and stop telling the simulated user to refuse one, which deadlocked ANOMALY alert items. jira: QA-29230 risk: low Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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QA-29230: capture every multipart response part, not just text/visualization/ alertProposal, and render them into what the judge and the simulated user see. QA-29226: normalize whitespace around MAQL punctuation before comparing, in one shared module the agentic and non-agentic metric comparators both use. QA-29225: read the fixture's anomaly granularity and stop telling the simulated user to refuse one, which deadlocked ANOMALY alert items.
Summary by CodeRabbit
New Features
Bug Fixes