gbrain/docs/eval-bench.md

Running real-world eval benchmarks against your gbrain changes

Audience: gbrain maintainers and contributors. If you're touching retrieval (search, ranking, embeddings, intent classification, query expansion, source boost, hybrid fusion), this is the doc.

For the NDJSON wire format consumed by gbrain-evals, see eval-capture.md. This doc is the human dev loop that lives on top of that format.

The 4-command loop

# ① Capture: already happening on every MCP / CLI / subagent query, capture is on by default.
#   Inspect what's been collected:
gbrain doctor                                     # surfaces capture failures
psql $DATABASE_URL -c 'SELECT count(*) FROM eval_candidates'

# ② Snapshot: freeze a baseline before your code change.
gbrain eval export --since 7d > baseline.ndjson

# ③ Code change: do whatever you want — tune RRF_K, swap embed model, edit
#    hybrid.ts, add a new boost source, change the intent classifier.

# ④ Replay: re-run every captured query against the current build.
gbrain eval replay --against baseline.ndjson

Output:

Replaying 247 captured queries…
  ...25/247
  ...50/247
  ...
Replayed 247 of 247 captured queries (0 skipped, 0 errored)
Mean Jaccard@k:    0.927
Top-1 stability:   91.5%
Mean latency Δ:    +14ms (current vs captured)

Top 5 regression(s):
  jaccard=0.20  captured=12  current=3   "find every reference to widget-co"
  jaccard=0.43  captured=14  current=8   "show me everything tagged for review"
  jaccard=0.50  captured=8   current=4   "what did alice say about the spec"
  ...

Three numbers tell you whether the change is safe to land:

Metric	What it means	Healthy range
Mean Jaccard@k	Average overlap between captured retrieved slugs and current run's slugs. 1.0 = identical sets.	≥0.85 for "neutral" changes. <0.7 means major retrieval shift.
Top-1 stability	Fraction of queries whose #1 result didn't change.	≥85% for tuning passes. <70% means top-of-funnel broke.
Mean latency Δ	Current minus captured. Positive = slower now.	Within ±50ms of captured. >2× anywhere = regression alarm.

What it actually does

gbrain eval replay reads your NDJSON snapshot and, for each row:

1. Re-executes the same op (searchKeyword for tool_name='search', hybridSearch for tool_name='query') with the captured detail and expand_enabled values threaded back in.
2. Captures the current retrieved_slugs (deduped, in result order).
3. Computes set-Jaccard between captured and current slug sets.
4. Records top-1 match (was the #1 result the same slug?).
5. Records latency delta vs captured latency_ms.

It does NOT compute MRR or nDCG — those need ground-truth relevance labels, not a baseline comparison. For metric-against-truth eval, use gbrain eval --qrels <path> (the legacy IR-eval path, still supported). The replay tool answers a different question: "did my code change move retrieval, and which queries did it move most?"

Best-effort by design

Replay is not pure. Three things can drift between capture and replay:

1. Brain state — your brain probably has more pages now than when the snapshot was taken. Unless you explicitly seed a fixed corpus, mean Jaccard will drop simply because new pages are eligible.
2. Embedding source — if you changed OPENAI_API_KEY between capture and replay (or the embedding model rotated), vector-path results drift even with identical code.
3. Capture cap — captured retrieved_slugs is a deduped set; it doesn't preserve internal ranking metadata. Two tools can return the same slug set with different scores — Jaccard will say 1.0, but a downstream consumer that orders by score may behave differently.

The metrics are regression alarms on real queries, not a hash check. Pair them with manual inspection of the top regressions.

Cost

Every query row in the snapshot embeds the query string via OpenAI to run the vector half of hybridSearch. Cost is identical to a normal gbrain query invocation — text-embedding-3-large at OpenAI list price, batched inside a single replay row.

If you're iterating locally and don't want to pay per change, use --limit 50 to cap rows replayed. The 50 most recent rows are usually enough to catch direction; expand for the final pre-merge run.

# Iteration mode — 50 most recent queries
gbrain eval replay --against baseline.ndjson --limit 50

# Pre-merge — full snapshot
gbrain eval replay --against baseline.ndjson --top-regressions 20

CI integration

gbrain eval replay --against baseline.ndjson --json > replay.json
jq -e '.summary.mean_jaccard >= 0.85' replay.json || exit 1
jq -e '.summary.top1_stability_rate >= 0.85' replay.json || exit 1

Stable JSON shape (schema_version: 1):

{
  "schema_version": 1,
  "summary": {
    "rows_total": 247,
    "rows_replayed": 247,
    "rows_skipped": 0,
    "rows_errored": 0,
    "mean_jaccard": 0.927,
    "top1_stability_rate": 0.915,
    "mean_latency_delta_ms": 14,
    "rows_over_2x_latency": 0
  }
}

--verbose adds a results: [...] array with one entry per replayed row (useful for piping into jq or a notebook for deeper analysis).

When to run this

Before merging anything that touches:

• src/core/search/hybrid.ts (RRF, fusion, dedup, two-pass retrieval)
• src/core/search/source-boost.ts / sql-ranking.ts (per-source ranking)
• src/core/search/intent.ts (auto-detail classification)
• src/core/search/expansion.ts (Haiku query expansion)
• src/core/search/dedup.ts (cross-page result collapse)
• src/core/embedding.ts or any embedding model swap
• src/core/operations.ts query or search op handlers (capture surface)
• src/core/postgres-engine.ts / pglite-engine.ts searchKeyword / searchVector SQL

Skip for: schema-only migrations, doc changes, tests-only PRs, CLI ergonomics that don't touch retrieval.

Building your own corpus

If you don't have captured traffic yet (fresh install, can't dogfood for a week before merging), you can hand-author an NDJSON file:

{"schema_version":1,"id":1,"tool_name":"query","query":"who is alice","retrieved_slugs":["people/alice","people/alice-bio"],"expand_enabled":false,"detail":null,"latency_ms":0,"remote":false}
{"schema_version":1,"id":2,"tool_name":"search","query":"acme deal","retrieved_slugs":["deals/acme-seed","companies/acme"],"latency_ms":0,"remote":false}

Then run gbrain eval replay --against handcrafted.ndjson to confirm the authoritative slugs come back. This is the seam between the BrainBench-Real pipeline (replay against live captures) and the BrainBench fixed-fixture pipeline (gbrain eval --qrels with the sibling gbrain-evals corpus).

Off-switch

If you don't want capture at all (privacy concern, dev environment), edit ~/.gbrain/config.json:

{"eval": {"capture": false}}

Capture stops; existing eval_candidates rows stay until you gbrain eval prune --older-than 0d (or just drop the table).

Failure modes

What you see	What it means
Mean Jaccard@k: 0.4, top regressions all in one source dir	Source boost or hard-exclude regression on that prefix
Top-1 stability: 30%, mean Jaccard still high	RRF tuning shifted the rank order without changing the set — re-tune rrfK
Mean latency Δ: +500ms, jaccard high	Vector path got slower; check embedding API or HNSW probes
rows_errored > 0	One or more queries threw. Inspect first 3 in human output, or --json to see all error_message fields
Many skipped: empty query	Capture ran on rows where someone passed empty query — check why those were captured