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af7e5379c2 |
v0.35.6.0 feat(search): floor-ratio gate for metadata boost stages (closes #1091) (#1129)
* v0.35.6.0 feat(search): floor-ratio gate for metadata boost stages Opt-in score-based gate on the three metadata-axis boost stages (backlink, salience, recency) inside `runPostFusionStages`. When `SearchOpts.floorRatio` or `search.floor_ratio` config is set, each stage skips results whose post-cosine-rescore score is below `floorRatio * topScore`. Default undefined preserves prior behavior bit-for-bit. Prevents weak-overlap candidates from accumulating metadata boosts and leapfrogging the legitimate primary hit on dense-embedder corpora. Built on the contributor PR from @jayzalowitz (PR #1091, SkyTwin twin-memory layer). Refactored on top: threshold is computed ONCE at runPostFusionStages entry instead of per-stage (single-baseline semantic, order-independent); knobsHash bumped 2->3 so a no-floor cache write can't be served to a floor-enabled lookup; NaN scores skip the boost instead of bypassing the gate; SearchOpts/config/MODE_BUNDLES integration replaces the PR's PostFusionOpts-only surface; no env var (resolveSearchMode is pure by design). Three correctness issues codex outside-voice review caught and this landed with fixed: - Cache contamination via knobsHash() (same bug class as v0.32.3 CDX-4 hotfix for the other search-lite knobs) - NaN scores would have bypassed the gate (NaN < threshold is false in JS); realistic on Voyage flexible-dim / zembed-1 Matryoshka dim drift - Negative top scores would have broken the "single result trivially eligible" claim; gate now disables on no-positive-signal inputs Scope: gates metadata stages only. Exact-match boost (applyExactMatchBoost) runs independently as a lexical-relevance signal by design. Cross-source floor stays global (per-source deferred to v0.36 if federated-read users hit the suppression). Default-on for any mode bundle deferred until gbrain-side ablation against longmemeval / whoknows / suspected-contradictions / BrainBench-Real (TODOS.md). Plan + 9-decision review trail (D1-D9): ~/.claude/plans/swift-sniffing-nygaard.md. Empirical motivation, failure-mode framing, dense-embedder targeting, and the 0.85 starting value all from @jayzalowitz's labeled-retrieval ablation. Integration shape is gbrain-side. Test surface: 30+ new cases (computeFloorThreshold edge cases including T1a NaN / T1b negative top, three boost-function gate parity tests including T6 IRON-RULE applyRecencyBoost regression, runPostFusionStages single-baseline composition pin, KNOBS_HASH_VERSION bump from 2 to 3, floor-ratio-changes-hash cache-contamination prevention, loadOverridesFromConfig coverage for search.floor_ratio config key). bun run verify clean; full unit suite 6753 pass / 0 fail. Co-Authored-By: Jay Zalowitz <jayzalowitz@gmail.com> Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: rewrite v0.35.6.0 CHANGELOG ELI10-lead-first; codify the rule in CLAUDE.md CHANGELOG entry for v0.35.6.0 was readable only by someone who already understood gbrain's internals (RRF, knobsHash, MODE_BUNDLES, runPostFusionStages, Matryoshka, CDX-4). Rewrote it so the first ~150 words explain what shipped in everyday English, with a concrete worked example, before any file paths or function names appear. Itemized changes section keeps the technical precision for engineers who need it. Then codified the rule in CLAUDE.md so future release entries land the same way. The "Release-summary template" section now has an iron rule: "lead ELI10, get precise after." No file paths or internal constants in the first 150 words; user-visible behavior change first; everyday-language column headers in any tables. Technical precision is required (the entry is still the technical record) but lives BELOW the plain-English lead, never before it. Smell test: if a reader who has never opened gbrain can walk away from the first 150 words knowing what shipped and whether they care, the entry passes. bun run build:llms regenerated to pick up the CLAUDE.md change (CI guard test/build-llms.test.ts pins committed bundles against fresh generator output). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Jay Zalowitz <jayzalowitz@gmail.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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81b3f7afac |
feat: knowledge graph layer — auto-link, typed relationships, graph-query (v0.10.3) (#188)
* feat(schema): graph layer migrations v5/v6/v7 + GraphPath/health types
Schema foundation for v0.10.3 knowledge graph layer:
- v5: links UNIQUE constraint widened to (from, to, link_type) so the same
person can both works_at AND advises the same company as separate rows.
Idempotent for fresh + upgrade (drops both old constraint names first).
- v6: timeline_entries gets UNIQUE index on (page_id, date, summary) for
ON CONFLICT DO NOTHING idempotency at DB level.
- v7: drops trg_timeline_search_vector trigger. Structured timeline entries
are now graph data, not search text. Markdown timeline still feeds search
via the pages trigger. Side benefit: extraction pagination is no longer
self-invalidating (trigger used to bump pages.updated_at on every insert).
Types: new GraphPath (edge-based traversal result), PageFilters.updated_after,
BrainHealth gets link_coverage / timeline_coverage / most_connected. Postgres
schema regenerated via build:schema.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(graph): auto-link on put_page + extract --source db + security hardening
Core graph layer wired into the operation surface:
- New src/core/link-extraction.ts: extractEntityRefs (canonical extractor used
by both backlinks.ts and the new graph code), extractPageLinks (combines
markdown refs + bare-slug scan + frontmatter source, dedups within-page),
inferLinkType (deterministic regex heuristics for attended/works_at/
invested_in/founded/advises/source/mentions), parseTimelineEntries (parses
multiple date format variants from page content), isAutoLinkEnabled
(engine config flag, defaults true, accepts false/0/no/off case-insensitive).
- put_page operation auto-link post-hook: extracts entity refs from freshly
written content, reconciles links table (adds new, removes stale). Returns
auto_links: { created, removed, errors } in response so MCP callers see
outcomes. Runs in a transaction so concurrent put_page on same slug can't
race the reconciliation. Default on; opt out with auto_link=false config.
- traverse_graph operation extended with link_type and direction params.
Returns GraphPath[] (edges) when filters set, GraphNode[] (nodes) for
backwards compat. Depth hard-capped at TRAVERSE_DEPTH_CAP=10 for remote
callers; without this, depth=1e6 from MCP burns memory on the recursive CTE.
- gbrain extract <links|timeline|all> --source db: walks pages from the
engine instead of from disk. Works for live brains with no local checkout
(MCP-driven Wintermute / OpenClaw). Filesystem mode (--source fs) is
unchanged. New --type and --since filters with date validation upfront
(invalid --since used to silently no-op the filter and reprocess everything).
- Security: auto-link skipped for ctx.remote=true (MCP). Bare-slug regex
matches `people/X` anywhere in page text including code fences and quoted
strings. Without this gate an untrusted MCP caller could plant arbitrary
outbound links by writing pages with intentional slug references; combined
with the new backlink boost, attacker-placed targets would surface higher
in search.
- Postgres orphan_pages aligned to PGLite definition (no inbound AND no
outbound). Comment used to claim alignment but code disagreed; engines
drifted silently when users migrated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(cli): graph-query command + skill updates + v0.10.3 migration file
Agent-facing surface for the graph layer:
- New `gbrain graph-query <slug>` command with --type, --depth, --direction
in|out|both. Maps to traverse_graph operation with the new filters. Renders
the result as an indented edge tree.
- skills/migrations/v0.10.3.md: agent runs this post-upgrade to discover the
graph layer. Tells the agent to run `gbrain extract links --source db`,
then timeline, verify with stats, try graph-query, and lists the inferred
link types so they can be used in subsequent traversals.
- skills/brain-ops/SKILL.md Phase 2.5: documents that put_page now auto-links.
No more manual add_link calls in the Iron Law back-linking path.
- skills/maintain/SKILL.md: graph population phase. Shows the right command
to backfill links + timeline from existing pages.
- cli.ts: register graph-query in CLI_ONLY + handleCliOnly switch. Update help
text to describe `gbrain extract --source fs|db` and the new graph-query.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(graph): unit + e2e + 80-page A/B/C benchmark for graph layer
Coverage for the v0.10.3 graph layer (260+ new test assertions):
- test/link-extraction.test.ts (46 tests): extractEntityRefs both formats,
extractPageLinks dedup + frontmatter source, inferLinkType heuristics
(meeting/CEO/invested/founded/advises/default), parseTimelineEntries
multiple date formats + invalid date rejection, isAutoLinkEnabled
case-insensitive truthy/falsy parsing.
- test/extract-db.test.ts (12 tests): `gbrain extract <links|timeline|all>
--source db` happy paths, --type filter, --dry-run JSON output,
idempotency via DB constraint, type inference from CEO context.
- test/graph-query.test.ts (5 tests): direction in/out/both, type filter,
non-existent slug, indented tree output.
- test/pglite-engine.test.ts (+26 tests): getAllSlugs, listPages
updated_after filter, multi-type links via v5 migration, removeLink with
and without linkType, addTimelineEntry skipExistenceCheck flag,
getBacklinkCounts for hybrid search boost, traversePaths in/out/both with
cycle prevention via visited array, getHealth graph metrics
(link_coverage / timeline_coverage / most_connected).
- test/e2e/graph-quality.test.ts (6 tests): full pipeline against PGLite
in-memory. Auto-link via put_page operation handler. Reconciliation
removes stale links on edit. auto_link=false config skip.
- test/benchmark-graph-quality.ts: A/B/C comparison on 80 fictional pages,
35 queries across 7 categories. Hard thresholds: link_recall > 90%,
link_precision > 95%, timeline_recall > 85%, type_accuracy > 80%,
relational_recall > 80%. Currently passing all 9.
Built test-first: benchmark caught WORKS_AT_RE matching "founder" inside
slug names (frank-founder), "worked at" past-tense missing from regex,
PGLite Date object vs ISO string comparison bug. All fixed before merge.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore: bump version and changelog (v0.10.3)
CHANGELOG: knowledge graph layer headline. Auto-link on every page write.
Typed relationships (works_at, attended, invested_in, founded, advises).
gbrain extract --source db. graph-query CLI. Backlink boost in hybrid search.
Schema migrations v5/v6/v7 applied automatically.
Security hardening caught during /ship adversarial review: traverse_graph
depth capped at 10 from MCP, auto-link skipped for ctx.remote=true, runAutoLink
reconciliation in transaction, --since validates dates upfront.
TODOS.md: 2 P2 follow-ups (auto-link redundant SQL on skipped writes;
extract --source db not gated on auto_link config).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: sync CLAUDE.md with v0.10.3 graph layer
Updated key files list (extract.ts now describes --source fs|db, added
graph-query.ts and link-extraction.ts), test inventory (extract-db,
link-extraction, graph-query unit tests; e2e/graph-quality), and
test count (51 unit + 7 e2e, 1151 + 105 assertions).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(v0.10.3): wire graph layer into install flow + README + benchmark
Existing brains upgrading to v0.10.3 had no clear path to backfill the new
links/timeline tables. New installs had no instruction to run extract --source db
after import. This wires the knowledge graph into every install touchpoint so the
v0.10.3 features actually reach the user.
- README: headline now sells self-wiring graph + 94% benchmark numbers; new
Knowledge Graph section between Knowledge Model and Search; LINKS+GRAPH command
block expanded; Benchmarks docs group added
- INSTALL_FOR_AGENTS.md: new Step 4.5 (graph backfill) + Upgrade section now runs
gbrain init + post-upgrade and points to migrations/v<N>.md
- skills/setup/SKILL.md Phase C: new step 5 for graph backfill (idempotent,
skip-if-empty); existing file migration becomes step 6
- src/commands/init.ts: post-init hint detects existing brain (page_count > 0)
and prints extract commands for both PGLite and Postgres engines
- docs/GBRAIN_VERIFY.md: new Check #7 (knowledge graph wired) with backfill
fallback + graph-query smoke test
- docs/benchmarks/2026-04-18-graph-quality.md: checked-in benchmark report
matching the existing search-quality format (94% recall, 100% precision,
100% relational recall, idempotent both ways)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(claude): require PR descriptions to cover the whole branch
Adds a rule to CLAUDE.md so future PR bodies always cover the full diff
against the base branch, not just the most recent commit. Includes the
git log + gh pr view incantation to check what's actually in a PR.
This is a reaction to PR #189 being created with a body that described
only the last commit instead of the 7 commits it actually contained.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(upgrade): post-upgrade prints full body + --execute mode + downstream skill upgrade doc
PR #188 review caught two install-flow gaps that this commit closes:
1. `gbrain post-upgrade` only printed the migration headline + description
from YAML frontmatter, never the markdown body that contains the
step-by-step backfill instructions. Agents saw "Knowledge graph layer —
your brain now wires itself" and had no idea to run `gbrain extract
links --source db`. Now prints the full body after the headline.
2. New `--execute` flag reads a structured `auto_execute:` list from
migration frontmatter and runs the safe commands sequentially. Without
`--yes` it prints the plan only (preview mode). With `--yes` it actually
runs them. Stops on first failure with a clear error.
3. Downstream agents (Wintermute etc.) keep local skill forks that gbrain
can't push updates to. New `docs/UPGRADING_DOWNSTREAM_AGENTS.md` lists
the exact diffs each release needs applied to those forks. v0.10.3
diffs for brain-ops, meeting-ingestion, signal-detector, enrich.
Changes:
- src/commands/upgrade.ts:
- runPostUpgrade(args) accepts flags
- Prints full body via extractBody()
- Parses auto_execute: list via extractAutoExecute() (hand-rolled, no yaml dep)
- --execute previews, --execute --yes runs
- Fix cosmetic bug: `recipe: null` no longer prints "show null" message
- src/cli.ts: pass args to runPostUpgrade
- skills/migrations/v0.10.3.md:
- Add auto_execute: list (gbrain init + extract links/timeline + stats)
- Fix typo: completion record version was 0.10.1, now 0.10.3
- test/upgrade.test.ts: 5 new tests covering body printing, plan preview,
actual execution, no-auto_execute case, and --help output
- docs/UPGRADING_DOWNSTREAM_AGENTS.md: NEW
- CLAUDE.md: key files list updated
Test: 13 upgrade tests pass (was 8, +5 new). Full unit suite: 1078 pass,
zero regressions, 32 expected E2E skips (no DATABASE_URL).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* bench(graph): add Configuration A baseline (no graph) vs C comparison
Previous benchmark showed C numbers only (94.4% link recall, 100% relational
recall, etc.) but never quantified what a pre-v0.10.3 brain actually loses.
Reviewer caught this gap.
Adds measureBaselineRelational() that simulates a no-graph fallback:
- Outgoing queries: regex-extract entity refs from the seed page content
- Incoming queries: grep-style scan of all pages for the seed slug
This is what an agent without the structured links table can do today.
Honest result on the 5 relational queries in the benchmark:
- Recall: 100% A vs 100% C (+0%) — markdown contains the refs either way
- Precision: 58.8% A vs 100.0% C (+70%) — without typed links, you get the
right answers buried in 41% noise
Per-query breakdown shows the divergence is concentrated in INCOMING queries:
"Who works at startup-0?" returns 5 candidates without graph (2 employees +
3 noise pages that mention startup-0) vs exactly 2 with graph. For an LLM
agent, that's ~3x less reading work per relational question.
Also documented what the benchmark deliberately doesn't test (multi-hop,
search ranking with backlink boost, aggregate queries, type-disagreement
queries) so future benchmark work has a roadmap.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* bench(graph): add 4 missing categories — multi-hop, aggregate, type-disagreement, ranking
The previous benchmark commit (
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d547a64600 |
feat: search quality boost — compiled truth ranking + detail parameter (v0.8.1) (#64)
* feat: search quality boost — compiled truth ranking, detail parameter, cosine re-scoring Compiled truth chunks now rank 2x higher in hybrid search via RRF normalization + source boost. New --detail flag (low/medium/high) controls timeline inclusion. Cosine re-scoring blends query-chunk similarity before dedup for query-specific ranking. Also: remove DISTINCT ON from keyword search (dedup handles per-page capping), add chunk_id + chunk_index to SearchResult, add getEmbeddingsByChunkIds to BrainEngine interface. Inspired by Ramp Labs' "Latent Briefing" paper (April 2026). * feat: RRF normalization, source-aware dedup, detail param in operations RRF scores normalized to 0-1 before 2.0x compiled truth boost. Source-aware dedup guarantees compiled truth chunk per page. Detail parameter added to query operation, dedupResults added to bare search operation. Debug logging via GBRAIN_SEARCH_DEBUG=1. * chore: bump version and changelog (v0.8.1) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: CJK word count in query expansion CJK text is not space-delimited. A query like "向量搜索优化" was counted as 1 word and silently skipped expansion. Now counts characters for CJK queries instead of space-separated tokens. Co-Authored-By: YIING99 <yiing99@users.noreply.github.com> * feat: retrieval evaluation harness — P@k, R@k, MRR, nDCG@k + gbrain eval Full IR evaluation framework: precisionAtK, recallAtK, mrr, ndcgAtK metrics with runEval() orchestrator. gbrain eval CLI with single-run table and A/B comparison mode (--config-a / --config-b) for parameter tuning. HybridSearchOpts now accepts rrfK and dedupOpts overrides. Co-Authored-By: 4shut0sh <4shut0sh@users.noreply.github.com> * test: search quality tests — RRF boost, dedup guarantee, cosine similarity, E2E benchmark 42 new tests across 3 files: - test/search.test.ts: RRF normalization, compiled truth 2x boost, dedup key collision prevention, cosine similarity edge cases, CJK word count detection - test/dedup.test.ts: source-aware compiled truth guarantee, layer interactions, custom maxPerPage, empty/single result edge cases - test/e2e/search-quality.test.ts: full pipeline against PGLite with basis vector embeddings — chunk_id/chunk_index fields, detail parameter filtering, getEmbeddingsByChunkIds, keyword multi-chunk, vector ordering Also: export rrfFusion + cosineSimilarity for unit testing, fix PGLite getEmbeddingsByChunkIds to parse string vectors from pgvector. * test: search quality benchmark with A/B comparison (baseline vs PR#64) Benchmark measures P@1, MRR, nDCG@5, and source accuracy across 8 queries against 5 seeded pages. Key finding: boost helps entity lookups but over-corrects temporal queries. Validates the --detail parameter as the right control mechanism. Output at docs/benchmarks/2026-04-13.md. * feat: query intent classifier — auto-selects detail level, 100% source accuracy Zero-latency heuristic classifier detects query intent from text patterns: - "Who is Pedro?" → entity → detail=low (compiled truth only) - "When did we last meet?" → temporal → detail=high (no boost, natural ranking) - "Variant fund announcement" → event → detail=high - General queries → detail=medium (default with boost) The key insight: skip the 2.0x compiled truth boost for detail=high queries. Temporal/event queries want natural ranking where timeline entries can win. Benchmark results (source accuracy = does the top chunk match expected type): - Baseline: 100% (already good, no boost needed) - Boost only: 71.4% (boost over-corrects temporal queries) - Boost + intent classifier: 100% (best of both worlds) 35 unit tests for the classifier. 590 total tests pass. * feat: query intent classifier — auto-selects detail level, 100% source accuracy Heuristic classifier detects query intent from text patterns (zero latency, no LLM call). Maps temporal queries ("when did we last meet") to detail=high, entity queries ("who is X") to detail=low, events to detail=high. Benchmark results (29 pages, 20 queries, graded relevance): - Baseline: P@1=0.947, MRR=0.974, source accuracy=89.5% - Boost only: P@1=0.895, MRR=0.939, source accuracy=63.2% (over-correction) - Boost + intent: P@1=0.947, MRR=0.974, source accuracy=89.5% (fully recovered) The intent classifier eliminates the boost's over-correction on temporal queries while preserving its benefits for entity lookups. 35 unit tests for the classifier. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * test: search quality benchmark with A/B comparison (baseline vs PR#64) Rich benchmark: 29 pages, 58 chunks, 20 queries with graded relevance. Now measures CHUNK-LEVEL quality, not just page-level retrieval. Key findings (C. Boost+Intent vs A. Baseline): - Unique pages in top-10: 7.2 → 8.7 (+21% broader coverage) - Compiled truth ratio: 51.6% → 66.8% (+15pp more signal) - CT-first rate: 100% (compiled truth leads for entity queries) - Timeline accessible: 100% (temporal queries still find dates) - Source accuracy: 89.5% maintained (intent classifier prevents regression) The boost alone (B) causes -26pp source accuracy regression. Intent classifier (C) recovers it fully. * docs: clean benchmark report — ELI10 search quality analysis for PR#64 Replaces two drafts with one clean report. Explains what changed, why it matters, and what the numbers mean. All fictional data, no private info. Key findings: 21% more page coverage per query, 29% more compiled truth in results. Intent classifier prevents boost from burying timeline for temporal queries. Full per-query breakdown with before/after comparison. * chore: remove auto-generated benchmark file (clean version is 2026-04-14-search-quality.md) * docs: update project documentation for search quality boost CLAUDE.md: added search/intent.ts, search/eval.ts, commands/eval.ts to key files. Added 5 new test files (search, dedup, intent, eval, e2e/search-quality). Updated test count from 23+4 to 28+5. Added docs/benchmarks/ to key files. README.md: updated search pipeline diagram with intent classifier, RRF normalization, compiled truth boost, cosine re-scoring, and 5-layer dedup. Added --detail flag explanation and benchmark instructions. CHANGELOG.md: added search quality entries to v0.9.3 (intent classifier, --detail flag, gbrain eval, CJK fix). Credited @4shut0sh and @YIING99. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: headline benchmark gains in changelog * docs: add community attribution rule to CHANGELOG voice section --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: YIING99 <yiing99@users.noreply.github.com> Co-authored-by: 4shut0sh <4shut0sh@users.noreply.github.com> |