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* feat(search): autocut — score-discontinuity result-sizing on the rerank separatrix Cut the ranked set at the cross-encoder rerank-score cliff instead of a fixed top-K. Default-ON in reranked modes (balanced/tokenmax), no-op without a reranker. New pure src/core/search/autocut.ts; mode.ts knobs + reranker_top_n_in = searchLimit (no unscored tail); query op autocut param; --explain + glossary. * test(search): autocut pure-fn, agent-surface, behavioral + precision/recall eval gate Adds autocut.test.ts, query-op-autocut.test.ts, autocut-integration.serial.test.ts (IRON-RULE behavioral via rerankerFn seam), autocut-eval.test.ts (in-repo precision-lift-without-recall-regression gate). Updates existing knobsHash/bundle pins to v=7 + reranker_top_n_in. * chore: version + changelog + docs for autocut (v0.41.34.0) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(search): autocut preserves alias-hop exact matches + cache-HIT meta (codex P1/P2) P1: applyAliasHop injects the canonical page after reranking (no rerank_score); autocut would drop it when cutting on the scored set. applyAutocut gains an optional preserve predicate; hybrid passes r => r.alias_hit === true. P2: cache-HIT cachedMeta now carries autocut/adaptive_return/mode/embedding_column. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * chore: bump version to v0.42.3.0 (autocut wave) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: PR titles lead with the version (IRON RULE in CLAUDE.md) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
177 lines
8.1 KiB
Markdown
177 lines
8.1 KiB
Markdown
# Evaluation Metric Glossary
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**Auto-generated from `src/core/eval/metric-glossary.ts`. Do not edit by hand.** Run `bun run scripts/generate-metric-glossary.ts` to regenerate.
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Every metric `gbrain eval *` and `gbrain search stats` reports has a plain-English explanation here. Industry terms are preserved verbatim so users searching the literature find what we report.
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## Retrieval Metrics
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### Precision at k (P@k)
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**Key:** `precision@k`
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**Plain English:** Of the top k results the engine returned, what fraction were actually relevant? High precision means few junk results in the top of the list.
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**Range:** 0..1, higher is better. P@10 = 0.7 means 7 of the top 10 results were on-topic.
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### Recall at k (R@k)
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**Key:** `recall@k`
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**Plain English:** Of all the relevant results that exist in the brain, what fraction did the engine find in its top k? High recall means few missed answers.
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**Range:** 0..1, higher is better. R@10 = 0.81 means out of every 100 questions, the right answer was in the top 10 for 81 of them.
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### Mean Reciprocal Rank (MRR)
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**Key:** `mrr`
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**Plain English:** On average, how far down the list is the FIRST relevant result? An MRR of 1.0 means the first hit is always right; an MRR of 0.5 means it's typically at rank 2.
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**Range:** 0..1, higher is better. Computed as the average of 1/rank-of-first-relevant-result across all test queries.
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### Normalized Discounted Cumulative Gain at k (nDCG@k)
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**Key:** `ndcg@k`
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**Plain English:** Like precision@k, but the engine gets MORE credit for putting good results near the top than near rank k. A perfect ordering scores 1.0; a totally random ordering scores near 0.
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**Range:** 0..1, higher is better. nDCG@10 above 0.65 is the common "ship it" threshold for hybrid retrieval on technical corpora.
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## Retrieval-Quality / Evidence Metrics (NamedThingBench)
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### Hit rate at 1 (Hit@1)
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**Key:** `hit@1`
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**Plain English:** Fraction of queries where the right page is the very first result. NamedThingBench hard-gates title-substring Hit@1 >= 0.95 and alias Hit@1 >= 0.98 — a query that is a page's name or title phrase should land it at rank 1, not "somewhere in the top 10".
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**Range:** 0..1, higher is better.
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### Hit rate at 3 (Hit@3)
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**Key:** `hit@3`
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**Plain English:** Fraction of queries where the right page is in the top 3 results. NamedThingBench requires the multi-chunk-dilution family to hit 1.0 — a page with one strong chunk among many weak ones must never be buried.
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**Range:** 0..1, higher is better.
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### Average rank-1 match score
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**Key:** `avg_rank1_score`
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**Plain English:** The mean base (pre-boost) retrieval score of the TOP result across recent searches, from `gbrain search stats`. It is NOT a labeled accuracy number — it is a drift signal: if this trends DOWN over time, retrieval quality is regressing (the early warning that would have caught the duplicate-page incident before a human did).
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**Range:** 0..1. Watch the trend, not the absolute value; pair with the <0.6 / 0.6-0.85 / >=0.85 bucket counts for shape.
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### Create-safety hint (evidence contract)
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**Key:** `create_safety`
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**Plain English:** A result's answer to "is this page already in the brain — safe to NOT write a new one?" Derived from the strongest evidence, NOT a raw score: exists (alias_hit / exact_title_match / high_vector_match — do not duplicate), probable (solid keyword match — prefer updating), unknown (weak match — look closer). An agent keys its don't-duplicate decision off this, which is what prevents the incident's duplicate-stub class.
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**Range:** enum: exists | probable | unknown
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## Set-Similarity / Stability Metrics
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### Jaccard similarity at k (set Jaccard @k)
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**Key:** `jaccard@k`
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**Plain English:** How much do two result lists overlap? Compare the top k slugs from the captured baseline against the current run; Jaccard@10 = 1.0 means perfect agreement, 0.0 means zero overlap.
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**Range:** 0..1, higher = more stable. Below 0.5 on a stable corpus means retrieval changed significantly.
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### Top-1 stability rate
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**Key:** `top1_stability`
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**Plain English:** Fraction of queries where the #1 result is the same between two runs. The most aggressive stability check — small ranking shifts that don't change the top answer don't hurt it.
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**Range:** 0..1, higher = more stable. Above 0.85 typically means safe-to-merge for retrieval changes.
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## Statistical-Significance Metrics
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### p-value (paired bootstrap)
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**Key:** `p_value`
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**Plain English:** How likely the observed difference between two modes is just noise. Lower = stronger evidence the difference is real. We compute paired bootstrap with 10,000 resamples and Bonferroni correction across the 12 comparisons (3 modes × 4 metrics).
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**Range:** 0..1, lower = stronger signal. Below 0.05 is the common "statistically significant" threshold; below 0.01 is strong evidence.
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### 95% Confidence Interval (CI)
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**Key:** `confidence_interval`
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**Plain English:** The range we're 95% sure the true value falls inside, given the sample we measured. Narrower CI = more reliable estimate. Computed via bootstrap resampling.
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**Range:** Two-tuple [low, high]. If 0 is inside the CI for a Δ, the difference isn't statistically significant.
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## Operational / Cost Metrics
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### Cache hit rate
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**Key:** `cache_hit_rate`
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**Plain English:** Fraction of searches that reused a recent cached answer instead of running fresh. Higher hit rate = lower latency + lower LLM spend, but stale results may slip through if the threshold is too loose.
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**Range:** 0..1, higher generally better. 0.7-0.9 is the sweet spot for a busy brain; above 0.9 may indicate the similarity threshold is too loose.
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### Average results returned
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**Key:** `avg_results`
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**Plain English:** Mean number of search-result rows the engine returned per call. Should be near the active mode's searchLimit unless the brain is small or the budget is dropping results.
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**Range:** 0..searchLimit. Far below searchLimit suggests budget pressure or sparse retrieval.
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### Average tokens delivered
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**Key:** `avg_tokens`
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**Plain English:** Estimated tokens (chars / 4) in the chunk text returned per search call. The direct measure of how much context an agent loop is paying for each search.
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**Range:** 0..tokenBudget. Approximates OpenAI tiktoken count for English; off by ~5-10% for Anthropic and worse for non-English.
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### Cost per query (USD)
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**Key:** `cost_per_query_usd`
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**Plain English:** Sum of LLM + embedding API charges for one search call. Includes Haiku expansion call (tokenmax mode only) + embedding cost + downstream answer-model cost if measured.
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**Range:** 0..unbounded. Conservative mode is typically <\$0.001 per call; tokenmax with answer-gen can exceed \$0.01.
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### p99 latency (ms)
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**Key:** `p99_latency_ms`
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**Plain English:** 99th percentile wall-clock time per search call. The latency that 1% of users see — long-tail experience, not the average.
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**Range:** 0..unbounded. Warm-cache hits should be <50ms; tokenmax with expansion can exceed 200ms due to the Haiku call.
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## Result-Sizing Metrics
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### Autocut signal
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**Key:** `autocut.signal`
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**Plain English:** Which signal autocut used to size the result set. 'rerank' means it found a real score cliff in the cross-encoder rerank scores and cut there; 'none' means no trustworthy cliff (no reranker, <2 scored results, or the gap was too small) so it returned the full list.
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**Range:** 'rerank' | 'none'. 'none' is not a failure — it means autocut declined to cut because the signal didn't justify it.
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### Autocut gap ratio
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**Key:** `autocut.gap_ratio`
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**Plain English:** The size of the largest score drop autocut found, as a fraction of the top result's score. A gap of 0.40 means the score fell by 40% of the top score at the steepest point. Autocut cuts there only when this clears the sensitivity threshold (autocut_jump, default 0.20).
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**Range:** 0..1, higher = a sharper cliff (more confident cut). Below the autocut_jump threshold → no cut.
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---
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## Coverage
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Every metric printed by any `gbrain eval *` or `gbrain search stats` command resolves through `getMetricGloss()` in `src/core/eval/metric-glossary.ts`. Adding a new metric to the glossary REQUIRES updating this doc; the CI guard catches drift.
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