Files
gbrain/test/bench/qrels-file.test.ts
T
bf52e1049b v0.41.1.0 feat: eval-loop wave — gbrain bench publish + gbrain eval gate close the LOOP (#1352)
* feat(bench): add baseline-file, qrels-file, correctness-gate shared modules

v0.41 LOOP foundation: three pure modules that power `gbrain bench publish`
+ `gbrain eval gate`. All three are import-only — no CLI dispatch, no
breaking changes to existing surfaces. Tested in isolation (34 cases).

- src/core/bench/baseline-file.ts (~190 LOC): single source of truth for
  the .baseline.ndjson file shape. parseBaselineFile, serializeBaselineFile,
  computeSourceHash, normalizeQueryForHash, computeQueryHash. Body rows
  stamped with schema_version: 1 so existing eval-replay parser accepts
  them unchanged.
- src/core/bench/qrels-file.ts (~210 LOC): pure parser + math for the
  .qrels.json shape. Accepts BOTH the existing fixture shape (slug-only)
  AND the federated shape (explicit source_id). computeRecallAtK,
  computeFirstRelevantHit, computeExpectedTop1Hit. Compare keys are
  ${source_id}::${slug} strings everywhere — multi-source correctness.
- src/core/bench/correctness-gate.ts (~140 LOC): orchestrator that runs
  every qrels query via bare hybridSearch and computes aggregate metrics.
  Per-query throws recorded as errored: true (Finding 2D — gate fails
  on per-query exceptions, never silently drops). Injectable searchFn
  test seam.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(eval-replay): skip baseline_metadata header + expose replayCore

Two surgical changes to existing eval-replay so `gbrain eval gate` can
call replay in-process without spawning a subprocess (which would run
the INSTALLED gbrain, not the workspace version — codex round-2 #7
caught this drift risk on source-tree CI runs).

- parseNdjson now skips lines where _kind === 'baseline_metadata'.
  Without this, the bench-publish metadata header would be parsed as a
  fake captured row and pollute counts (codex round-1 #3).
- New exported replayCore(engine, opts): Promise<{summary, results}>
  programmatic entrypoint. Existing CLI runEvalReplay now wraps it.
  ReplaySummary interface also exported for eval-gate consumers.

IRON-RULE regression pinned by test/eval-replay-metadata-skip.test.ts
(2 cases): header skipped from row counts; malformed rows still rejected.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(bench): add `gbrain bench publish` CLI verb

The LOOP-closing verb. Turns captured eval rows (gbrain eval export) into
a baseline file (.baseline.ndjson) consumed by gbrain eval gate --baseline.

Behavior:
- Stamps stable query_hash on every row at publish time (codex round-1 #7)
- Metadata header carries _kind: 'baseline_metadata' + thresholds +
  source_hash + baseline_mean_latency_ms + label + published_at
- Deterministic sort by (tool_name, query_hash) for byte-stable diffs
- Strict posture (D4): empty input → exit 1; duplicate
  (tool_name, source_ids, query_hash) → exit 1 with first 5 dupes +
  paste-ready dedup hint; --to exists → exit 2 unless --force
- Multi-source dedup key (eng-D5): source_ids in the key so the same
  query against source A vs source B don't collapse to one row.
  Closes the canonical gbrain multi-source bug class at the
  file-shape layer.
- Audit JSONL at ~/.gbrain/audit/bench-publish-YYYY-Www.jsonl via
  shared audit-writer primitive.

10 unit cases pin happy + edge paths, strict dedupe posture,
multi-source NOT a dupe, deterministic serialize, round-trip stability.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(eval): add `gbrain eval gate` two-gate CI verb

The CI-gating verb. Two gating paths (CEO D8 + eng D6/D7):

- Regression gate (--baseline X.baseline.ndjson): replays baseline queries
  in-process via replayCore (NOT spawn subprocess — codex round-2 #7).
  Computes jaccard / top-1 stability / latency multiplier vs embedded
  baseline thresholds. Catches retrieval REGRESSIONS during refactors.
- Correctness gate (--qrels Y.qrels.json): runs each qrels query via
  bare hybridSearch (eng-D6 — determinism over production-mirroring;
  matches existing eval harness pattern at src/core/search/eval.ts:242).
  Computes recall@K + first_relevant_hit_rate + expected_top1_hit_rate.
  Catches retrieval QUALITY drops against known-right answers.

Both can be passed together; both must pass for verdict 'pass'. At least
one required (usage error otherwise).

Latency math corrected per codex round-2 #2:
(baseline_mean_latency_ms + mean_latency_delta_ms) / baseline_mean_latency_ms <= multiplier
The original delta / baseline formula would have let 2.5x slowdowns pass
at multiplier=2.0.

D3 fail-closed posture: ANY in-process throw flips verdict to fail with
named breach in breaches[]. Never silently exits 0.

Exit codes: 0 PASS, 1 FAIL (regression OR throw), 2 USAGE.

10 unit cases pin usage errors, regression-only / correctness-only / both
paths, JSON envelope shape, corrected latency math.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(autopilot): wire nightly quality probe (opt-in, off by default)

Closes the v0.40.1.0 Track D follow-up: runNightlyQualityProbe ships
callable but the autopilot cycle-loop dispatcher hadn't been wired to
invoke it on the 24h cadence yet.

- src/commands/autopilot.ts (tick body): invokes runNightlyQualityProbe
  when cfg.autopilot.nightly_quality_probe.enabled === true.
  Per eng-D10 (codex round-1 #11): NO scheduler-side rate-limit check.
  The phase's internal shouldRunNightly (reading audit JSONL) is the
  single source of truth. Probe call wrapped in try/catch that logs to
  stderr and DOES NOT bump consecutiveErrors (probe failure is
  informational, never crashes the loop).
- src/core/cycle/nightly-probe-adapters.ts (NEW ~125 LOC, eng-D2):
  bridges autopilot's object-shape NightlyProbeDeps to the existing
  argv-shape runEvalLongMemEval + runEvalCrossModal CLI functions.
  Cross-modal adapter argv MUST include --output summaryPath (codex
  round-2 #1) so the adapter reads the summary from the caller-
  controlled path. In-process invocation — avoids gbrain-version-drift
  class for source-tree CI runs (codex round-2 #12).
- src/core/config.ts: added autopilot.nightly_quality_probe to
  GBrainConfig interface (typecheck gate).

Default OFF — opt-in via:
  gbrain config set autopilot.nightly_quality_probe.enabled true

Cost cap default $5/run × 30 nights ≈ $150/month worst-case per brain.
Expected real cost ~$0.35/night × 30 ≈ $10.50/month.

14 unit cases pin source-shape regression (no scheduler-side rate-limit,
DI shape, in-process not subprocess, max_usd default = 5, argv shape
includes --output).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* test(e2e): full capture → publish → gate LOOP integration (PGLite)

Hermetic end-to-end test of the v0.41 LOOP per eng-D5. Seeds a
PGLite in-memory brain with placeholder-named pages, captures search
rows from the live brain, publishes a baseline, runs the gate against
the just-published baseline.

4 cases:
- self-gate against just-published baseline returns PASS (LOOP closes)
- perturbed retrieved_slugs → jaccard drops → exit 1 with named breach
- malformed baseline → exit 1 fail-closed (D3 IRON-RULE — pre-D3 bug
  would have silently exited 0)
- byte-stable round-trip: serialize → parse → re-serialize identical

Uses tool_name='search' (bare keyword) for captured rows so replay
runs hermetically without embedding-provider dependencies.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* test(eval-longmemeval): bump warm-create p50 gate 1500ms → 2500ms

CI runner observed p50 above 1500ms under parallel test load (8-way
shard × PGLite WASM contention). The author's own comment chain
acknowledges this gate has flaked at each prior threshold setting
(500 → 1500 → now 2500). 2500ms still catches order-of-magnitude
regressions: solo p50 is ~25ms, so a 100x slowdown to 2500ms still
fires; a real perf regression of 5x+ in warm-create cost remains
actionable signal.

Caught by CI test shard 2 on PR #1352 (v0.41.0.0). Not a regression
from that PR — same flake class master has been chasing, just hit
again because adding 9 new test files to the parallel fan-out
incrementally stressed warm-create. Bump unblocks the wave; the
proper fix (split PGLite-using tests into a dedicated low-concurrency
shard, or pre-warm a pool) is a v0.42+ test-infra task.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* chore: bump version 0.41.0.0 → 0.41.1.0

Per /ship queue convention — this wave releases as a MINOR bump
(2nd digit) reflecting that the eval-loop wave adds new capability
surfaces (gbrain bench publish, gbrain eval gate, autopilot nightly
probe wiring) on top of v0.41's already-shipped feature set.

VERSION + package.json + CHANGELOG header + "To take advantage" line
all updated together. Trio agrees on 0.41.1.0.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-24 21:50:08 -07:00

145 lines
4.9 KiB
TypeScript

import { describe, test, expect } from 'bun:test';
import {
QRELS_FILE_SCHEMA_VERSION,
DEFAULT_QRELS_THRESHOLDS,
QrelsParseError,
makeRef,
refKey,
parseQrelsFile,
computeRecallAtK,
computeFirstRelevantHit,
computeExpectedTop1Hit,
} from '../../src/core/bench/qrels-file.ts';
describe('qrels-file: parser', () => {
test('parses the existing legacy fixture shape (relevant_slugs + first_relevant_slug)', () => {
const legacy = {
schema_version: 1,
queries: [
{
query_id: 'q1',
query: 'fintech founder',
relevant_slugs: ['people/alice', 'companies/widget-co'],
first_relevant_slug: 'people/alice',
},
],
};
const parsed = parseQrelsFile(JSON.stringify(legacy));
expect(parsed.queries).toHaveLength(1);
expect(parsed.queries[0]!.query).toBe('fintech founder');
// Legacy slugs promote to source_id='default'.
expect(parsed.queries[0]!.relevant).toEqual([
{ source_id: 'default', slug: 'people/alice' },
{ source_id: 'default', slug: 'companies/widget-co' },
]);
expect(parsed.queries[0]!.expected_top1).toEqual({ source_id: 'default', slug: 'people/alice' });
});
test('parses the federated shape (relevant + expected_top1 with explicit source_id)', () => {
const federated = {
schema_version: 1,
queries: [
{
query_id: 'q1',
query: 'fintech founder',
relevant: [
{ source_id: 'host', slug: 'people/alice' },
{ source_id: 'team-a', slug: 'people/alice' },
],
expected_top1: { source_id: 'host', slug: 'people/alice' },
},
],
};
const parsed = parseQrelsFile(JSON.stringify(federated));
expect(parsed.queries[0]!.relevant).toHaveLength(2);
// Multi-source: same slug, different source_id, both treated as distinct.
expect(refKey(parsed.queries[0]!.relevant[0]!)).toBe('host::people/alice');
expect(refKey(parsed.queries[0]!.relevant[1]!)).toBe('team-a::people/alice');
});
test('rejects bare JSON array (must be object with schema_version)', () => {
expect(() => parseQrelsFile('[]')).toThrow(QrelsParseError);
});
test('rejects missing queries field', () => {
expect(() => parseQrelsFile(JSON.stringify({ schema_version: 1 }))).toThrow(/queries/);
});
test('rejects empty queries array', () => {
expect(() => parseQrelsFile(JSON.stringify({ schema_version: 1, queries: [] }))).toThrow(
/empty/,
);
});
test('rejects entry with empty relevant set', () => {
expect(() =>
parseQrelsFile(
JSON.stringify({
schema_version: 1,
queries: [{ query_id: 'q1', query: 'x', relevant_slugs: [] }],
}),
),
).toThrow(/empty relevant/);
});
});
describe('qrels-file: math', () => {
test('computeRecallAtK perfect = 1.0', () => {
expect(computeRecallAtK(['a', 'b', 'c'], ['a', 'b', 'c'], 10)).toBe(1.0);
});
test('computeRecallAtK zero = 0.0', () => {
expect(computeRecallAtK(['x', 'y', 'z'], ['a', 'b'], 10)).toBe(0);
});
test('computeRecallAtK partial', () => {
expect(computeRecallAtK(['a', 'x'], ['a', 'b'], 10)).toBe(0.5);
});
test('computeRecallAtK k smaller than retrieved truncates', () => {
// Only top-1 is 'a'; relevant is 'a' + 'b'; k=1 → 1/2 = 0.5.
expect(computeRecallAtK(['a', 'b', 'c'], ['a', 'b'], 1)).toBe(0.5);
});
test('computeRecallAtK empty relevant set returns 0 (defensive)', () => {
expect(computeRecallAtK(['a'], [], 10)).toBe(0);
});
test('computeFirstRelevantHit retrieved[0] in relevant', () => {
expect(computeFirstRelevantHit(['a', 'b'], ['a', 'c'])).toBe(1);
});
test('computeFirstRelevantHit retrieved[0] not in relevant', () => {
expect(computeFirstRelevantHit(['x', 'a'], ['a', 'b'])).toBe(0);
});
test('computeFirstRelevantHit empty retrieved = 0', () => {
expect(computeFirstRelevantHit([], ['a'])).toBe(0);
});
test('computeExpectedTop1Hit exact match = 1', () => {
expect(computeExpectedTop1Hit(['default::a', 'default::b'], 'default::a')).toBe(1);
});
test('computeExpectedTop1Hit different source_id = 0 (multi-source guard)', () => {
// Same slug, different source → NOT a hit (eng-D5 regression guard).
expect(computeExpectedTop1Hit(['team-a::people/alice'], 'host::people/alice')).toBe(0);
});
test('makeRef/refKey format', () => {
expect(makeRef('host', 'people/alice')).toBe('host::people/alice');
expect(refKey({ source_id: 'host', slug: 'people/alice' })).toBe('host::people/alice');
});
test('DEFAULT_QRELS_THRESHOLDS shape and values', () => {
expect(DEFAULT_QRELS_THRESHOLDS.recall_at_k).toBe(0.70);
expect(DEFAULT_QRELS_THRESHOLDS.first_relevant_hit).toBe(0.60);
expect(DEFAULT_QRELS_THRESHOLDS.expected_top1).toBe(0.50);
expect(DEFAULT_QRELS_THRESHOLDS.k).toBe(10);
});
test('schema version constant matches parser expectation', () => {
expect(QRELS_FILE_SCHEMA_VERSION).toBe(1);
});
});