feat(gooddata-eval): extend detail.latency_breakdown to 5 remaining kinds - #1772
feat(gooddata-eval): extend detail.latency_breakdown to 5 remaining kinds#1772Tomkess wants to merge 1 commit into
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Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review. 📝 WalkthroughWalkthroughAgentic evaluation results now retain tool-call and reasoning-step events. Multi-turn runs normalize event timestamps and indexes. Shared trace scoring, run counts, timings, user context, and latency breakdowns now appear in evaluation results and failures. Tests cover telemetry retention and normalized event output. ChangesAgentic evaluation updates
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: ⚪ Minimal · up to The telemetry and event-normalization changes are covered by the updated tests, with no unresolved merge risk identified. Suggested reviewers: Poem
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## master #1772 +/- ##
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+ Coverage 81.58% 81.66% +0.08%
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Files 275 275
Lines 19863 19847 -16
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+ Hits 16205 16209 +4
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In `@packages/gooddata-eval/src/gooddata_eval/core/agentic/general_question.py`:
- Around line 113-114: Propagate telemetry when constructing later results: in
packages/gooddata-eval/src/gooddata_eval/core/agentic/general_question.py lines
113-114, update the later GeneralQuestionResult construction to populate
tool_call_events and reasoning_step_events from the corresponding chat result;
apply the same change to the later SearchResult construction in
packages/gooddata-eval/src/gooddata_eval/core/agentic/search_tool.py lines
104-105. Ensure selecting any K-run preserves its complete latency_breakdown.
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📒 Files selected for processing (8)
packages/gooddata-eval/src/gooddata_eval/core/agentic/general_question.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/kda_skill.pypackages/gooddata-eval/src/gooddata_eval/core/agentic/search_tool.pypackages/gooddata-eval/src/gooddata_eval/core/evaluators/alert_skill.pypackages/gooddata-eval/src/gooddata_eval/core/evaluators/metric_skill.pypackages/gooddata-eval/tests/test_agentic_general_question.pypackages/gooddata-eval/tests/test_agentic_kda_skill.pypackages/gooddata-eval/tests/test_agentic_search_tool.py
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…on every K-run Only the first (conv_id_0) GeneralQuestionResult/SearchResult construction populated the new tool_call_events/reasoning_step_events fields -- the loop handling runs 2..K used a differently-indented literal that an earlier replace_all missed, so those runs kept the dataclass's empty-list default. Selecting one of those later runs as `best` silently produced an empty detail.latency_breakdown despite the run having real tool calls/reasoning. Caught by CodeRabbit on PR #1772, reproduced with a k=2 regression test per file before fixing.
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The mechanical parts look right. Both the first-run block and the retry-loop block in 1. def _accumulate(result) -> None:
def _accumulate(result) -> None:
print(result.anything_at_all.nonexistent)
Annotating it as 2. It's a verbatim copy of Same offset shifting, same index rebasing, same 3. The most intricate new logic has no test. The added kda tests only assert Two smaller notes:
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… it, test it Addresses hkad98's review on #1772: - _accumulate (kda_skill.py) was untyped, so every attribute access inside it (result.tool_call_events, result.turn_wall_clock_sec) went unchecked -- annotating it as ChatResult isn't enough on its own if the underlying logic stays duplicated, so: - Extracted the turn-offset/index-rebasing arithmetic -- previously hand-copied identically across alert_skill.py, metric_skill.py, visualization.py, conversation.py, and now kda_skill.py -- into one shared shift_and_index_events() in models.py. All five call sites now use it instead of maintaining five copies that can silently drift. - Added a real regression test exercising the actual new logic: two KDA iterations, asserting the second iteration's call_ts/result_ts are shifted by the first iteration's turn_wall_clock_sec and its tool/ reasoning indices are rebased past the first iteration's own counts -- not just that latency_breakdown ends up non-empty. - Fixed the general_question/search_tool regression tests to use distinct ChatResult objects per mocked run (side_effect) instead of one shared return_value -- the previous version couldn't have caught a double-shift bug, since both runs pointed at the same already-mutated object.
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@hkad98 all three addressed in b37afd4:
Also fixed the two smaller notes: Left the Full suite green (476 passed), lint/format clean. |
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Re-reviewed b37afd4. All three verified, and the extraction went further than I asked — approving on the code.
1. Typing. _accumulate(result: ChatResult) — the body is now checked rather than silently Unknown.
2. Extraction. shift_and_index_events() in models.py is arithmetically identical to what it replaced, and you retrofitted all five call sites (kda_skill, alert_skill, metric_skill, visualization, conversation) rather than just the new one. I diffed each against its original:
- The offset increments now happen inside the helper, i.e. before the caller's
all_*_events.extend(...), whereas four of the five sites previously incremented after. Nothing between the old and new positions readsturn_offset/*_index_offset, so this is behaviour-preserving at every site — includingvisualization.py, whereturn_offset += ...had been sitting at the very end of the block afterreasoning_steps.extendandresponse_id. - Returning the three offsets as a tuple keeps the helper honest about the caller still owning event accumulation, and the docstring says why. Good call.
Going from one implementation to five callers of one is a strictly better outcome than the "extract for the second copy" I suggested.
3. The test. test_run_agentic_kda_skill_shifts_timestamps_and_indices_across_iterations asserts the real math, not just presence: 10.2/10.3 for the create call and 10.3/10.4 for execute (iteration 1's turn_wall_clock_sec of 10.0 plus each within-turn call_ts), tool index from 0 because iteration 1 had no tool calls, reasoning index from 1 because it had exactly one step. That asymmetry between the two index streams is precisely the off-by-one I was worried about, and this test fails if either offset is dropped or applied twice. The side_effect-with-distinct-objects change in the general_question/search_tool tests closes the double-mutation hole too.
Agreed on deferring the turn_offset / simulated-user-generation-time gap — it's pre-existing and now lives in one place, which is a better position to fix it from.
One blocker left, and it isn't yours: the PR is now CONFLICTING. #1771 (judge faults / run counts / trace linking) landed on master and rewrote the same regions:
| File | Lines changed by #1771 |
|---|---|
agentic/general_question.py |
262 |
agentic/kda_skill.py |
187 |
core/models.py |
+38 |
Six files conflict: alert_skill.py, general_question.py, kda_skill.py, metric_skill.py, search_tool.py, models.py.
This is more than marker-resolution — #1771 restructured the K-run loops and run-result plumbing that this PR threads events through, and it added its own block to models.py right where shift_and_index_events goes. Worth rebasing carefully rather than taking either side wholesale, and re-running the new 2-iteration kda test afterwards, since it's the thing that will actually catch a mis-resolved offset. Ping me if the rebase forces changes to the shift logic itself and I'll take another look; if it comes out clean, this is good to merge.
…inds latency_breakdown (#1758) was wired into only the 9 test kinds the downstream consumer had enabled, not the SDK's full kind catalog. This closes the gap for metric_skill, alert_skill, agentic_search, agentic_general_question and agentic_kda_skill -- all of which already receive real tool_call_events/ reasoning_step_events through the same chat pipeline as their wired siblings. dashboard_summary is deliberately excluded: it calls a plain REST /summary endpoint, not the chat/SSE pipeline, so it has no tool-call or reasoning-step events to report at all. Also extracts the per-turn timestamp/index rebasing -- previously hand-copied identically across alert_skill, metric_skill, visualization and conversation, and needed a fifth time for agentic_kda_skill's simulated-user loop -- into one shared shift_and_index_events() in models.py. All five call sites use it, so there is one implementation rather than five that can drift. Tests: a two-iteration kda test asserts the real rebasing math (iteration 2's call_ts shifted by iteration 1's turn_wall_clock_sec, tool index from 0 while reasoning index starts from 1), plus per-run event-propagation guards for search_tool (which still builds its result in two places) and general_question. Rebased onto #1771, which restructured the same K-run loops: general_question now builds all K runs and one shared detail dict in a single place, so the propagation fix collapses to one site there; search_tool still has two.
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@hkad98 thanks for the detailed re-review — and you were right that the rebase was the real work, not marker resolution. Rebased onto Re-applied onto #1771's new shapes rather than taking either side wholesale:
The shift logic itself did not need changing, so per your note I don't think this needs another look on that front — but the canary you named is the evidence: Full suite 702 passed, 0 failed on the rebased tree (up from 476 — #1771 brought a lot with it), |
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Summary
detail.latency_breakdown(Per-tool-call/reasoning-step latency breakdown (detail.latency_breakdown) #1758) was wired into only the 9 test kindsgdc-mic-ai-evaluationcurrently has enabled — not the SDK's full kind catalog. This closes the gap for the 5 kinds that were skipped purely by scope, not by any technical limitation:metric_skill,alert_skill(single-turn evaluators — sameChatResultsignature asvisualization/general_question, which already have it)agentic_search,agentic_general_question(single-message agentic kinds)agentic_kda_skill(multi-turn simulated-user loop — needs per-turn timestamp/index re-offsetting, since its loop can span multiple turns)dashboard_summaryis deliberately excluded: it calls a plain REST/summaryendpoint, not the chat/SSE pipeline (core/summary/http_client.py—ChatResult.model_validate({"textResponse": summary}), nothing else populated). It has no tool-call or reasoning-step events to report at all; wiring it in would only ever produce an empty list.Shared helper (per review)
The per-turn timestamp/index rebasing was hand-copied identically across
alert_skill,metric_skill,visualizationandconversation, andagentic_kda_skillneeded it a fifth time. Extracted into oneshift_and_index_events()inmodels.py; all five call sites now use it, so there's one implementation rather than five that can drift. Verified no inline offset arithmetic remains outside the helper.Rebased onto #1771
#1771 restructured the same K-run loops this PR threads events through, so this was re-applied onto the new shapes rather than resolved as markers:
general_question.py— perf: take Langfuse trace linking off the eval item critical path #1771 consolidated all K runs into one_run_single_general_question()and one shareddetaildict, so the event population and thelatency_breakdownkey each collapse to a single site (was two).search_tool.py— still buildsSearchResultin two places (run 0 and thek>1loop); both populated, and the regression test guards exactly that.kda_skill.py—_run_onceunchanged in shape; accumulation re-applied via the shared helper.models.py— perf: take Langfuse trace linking off the eval item critical path #1771's addition there is adjacent, not overlapping.Test plan
uv run pytest packages/gooddata-eval/tests/— 702 passed, 0 failed (on the rebased tree)test_run_agentic_kda_skill_shifts_timestamps_and_indices_across_iterations— asserts the real rebasing math across two iterations:call_ts/result_tsshifted by iteration 1'sturn_wall_clock_sec(10.2/10.3 and 10.3/10.4), tool index from 0 (iteration 1 had no tool calls) while the reasoning index starts from 1 (it had exactly one step). Fails if either offset is dropped or applied twice — re-run and passing after the rebase.search_toolandgeneral_question, each usingside_effectwith distinctChatResultobjects per run so a double-mutation bug can't pass silently.ruff check/ruff format --check— clean across the package (85 files)Summary by CodeRabbit