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Commits
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5a06af5a57 |
v0.42.32.0 fix(sync): coerce non-string frontmatter titles + bounded auto-skip failure ledger (#1939) (#1956)
* fix(import): coerce non-string frontmatter title/slug/type (#1939) YAML `title: 2024-06-01` parses to a Date and `title: 1458` to a number; the old `(frontmatter.X as string)` cast was a compile-time lie, so downstream `.toLowerCase()` threw and (via the importer failure gate) could wedge sync indefinitely. parseMarkdown now coerces via coerceFrontmatterString (Date -> UTC ISO date, deterministic), and the pure assessContentSanity self-protects against a non-string title. * feat(sync): bounded auto-skip failure ledger; poison file can't wedge indexing (#1939) New src/core/sync-failure-ledger.ts owns the failure store + a crash-safe, multi-source, concurrent bounded auto-skip valve. A file that fails N consecutive syncs (GBRAIN_SYNC_AUTOSKIP_AFTER, default 3) auto-skips so it can't freeze all indexing forever, while fresh failures still fail-closed and a `<head>` history-rewrite sentinel hard-blocks even with --skip-failed. - (source_id, path) keying — failures never merge across sources - success clears a path so attempts are truly consecutive - advance-before-ack ordering (a crash can't mark a file skipped while wedged) - shared applySyncFailureGate used by BOTH the incremental and full-sync gates - legacy-row normalization + duplicate collapse on load - cross-process lock + atomic temp-rename, age-based stale-lock break sync.ts re-exports the ledger for existing callers; import.ts records source-scoped and defers the bookmark to the gate under managedBookmark. * fix(doctor): sync_failures severity via one shared decision on both surfaces (#1939) Local buildChecks and remote doctorReportRemote now both route through decideSyncFailureSeverity, so a stuck bookmark escalates WARN -> FAIL consistently (oldest-open age > fail cadence, or large unresolved count), auto-skipped pages stay visible (WARN, not hidden), and the acknowledged/acknowledged_at field-split that caused drift is gone. The remote surface stays subprocess-free (file read + Date.parse only). * chore(test): add trailing newline to e5-lease-cap-ab baseline fixture * fix(sync): address adversarial review findings on the failure ledger (#1939) - #1: a parse-failed file that is later deleted/renamed-away no longer leaves a permanent open ledger row. Removed paths (filtered.deleted, renamed-from, and the "gone from disk" forward-delete skip branch) are treated as resolved so the ledger self-heals instead of aging doctor to a stuck FAIL. - #3: decideSyncFailureSeverity escalates to FAIL on OPEN (blocking) failures only — auto_skipped rows already advanced the bookmark, so they stay WARN-visible regardless of count, matching the state-machine contract. * chore: bump version and changelog (v0.42.30.0) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: document sync-failure ledger + auto-skip valve for v0.42.30.0 KEY_FILES.md: new src/core/sync-failure-ledger.ts entry (bounded auto-skip state machine, decideGateAction/decideSyncFailureSeverity/applySyncFailureGate, GBRAIN_SYNC_AUTOSKIP_AFTER); update sync.ts (failure store moved to ledger, re-exported), doctor.ts (sync_failures severity via shared rule on both surfaces), markdown.ts (coerceFrontmatterString), import.ts (managedBookmark). live-sync.md: poison-file auto-skip tricky-spot. Regenerated llms-full.txt. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * chore: re-bump to v0.42.31.0 (queue collision on 0.42.30.0) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * chore: re-bump to v0.42.32.0 (queue collision) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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8b3c24c891 |
v0.20.0 feat: extract BrainBench to sibling gbrain-evals repo (#195)
* fix(link-extraction): v0.10.5 drive works_at + advises accuracy on rich prose
Extends inferLinkType patterns to cover rich-prose phrasings that miss with
v0.10.4 regexes. Targets the residuals called out in TODOS.md: works_at at
58% type accuracy, advises at 41%.
WORKS_AT_RE additions:
- Rank-prefixed: "senior engineer at", "staff engineer at", "principal/lead"
- Discipline-prefixed: "backend/frontend/full-stack/ML/data/security engineer at"
- Possessive time: "his/her/their/my time at"
- Leadership beyond "leads engineering": "heads up X at", "manages engineering at",
"runs product at", "leads the [team] at"
- Role nouns: "role at", "position at", "tenure as", "stint as"
- Promotion patterns: "promoted to staff/senior/principal at"
ADVISES_RE additions:
- Advisory capacity: "in an advisory capacity", "advisory engagement/partnership/contract"
- "as an advisor": "joined as an advisor", "serves as technical advisor"
- Prefixed advisor nouns: "strategic/technical/security/product/industry advisor to|at"
- Consulting: "consults for", "consulting role at|with"
New EMPLOYEE_ROLE_RE page-level prior: fires when the page describes the subject
as an employee (senior/staff/principal engineer, director, VP, CTO/CEO/CFO) at
some company. Biases outbound company refs toward works_at when per-edge context
is possessive or narrative without an explicit work verb. Scoped to person -> company
links only. Precedence: investor > advisor > employee (investors often hold board
seats which would otherwise mis-classify as advise/works_at).
ADVISOR_ROLE_RE broadened from "full-time/professional/advises multiple" to catch
any page that self-identifies the subject as an advisor ("is an advisor",
"serves as advisor", possessive "her advisory work/role/engagement").
Tests: 65 pass (16 new v0.10.5 coverage tests + 4 regression guards against
v0.10.4 tightenings). Templated benchmark still 88.9% type_accuracy (10/10 on
works_at and advises). Rich-prose measurement requires the multi-axis report
upgrade (next commit) to validate retroactively.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(eval): type-accuracy runner on rich-prose corpus + wire into all.ts
New Category 2 in BrainBench: per-link-type accuracy measured directly on the
240-page rich-prose world-v1 corpus. Distinct from Cat 1's retrieval metrics,
this measures whether inferLinkType() correctly classifies extracted edges
when the prose varies (the 58% works_at and 41% advises residuals that v0.10.5
regexes targeted).
How it works:
1. Loads all pages from eval/data/world-v1/
2. Derives GOLD expected edges from each page's _facts metadata
(founders → founded, investors → invested_in, advisors → advises,
employees → works_at, attendees → attended, primary_affiliation +
role drives person-page outbound type)
3. Runs extractPageLinks() on each page → INFERRED edges
4. Per (from, to) pair, compares inferred type vs gold type
5. Emits per-link-type table: correct / mistyped / missed / spurious +
type accuracy + recall + precision + strict F1 (triple match)
6. Full confusion matrix rows=gold, cols=inferred
v0.10.5 validation on 240-page corpus (up from pre-v0.10.5 baselines):
- works_at: 58% → 100.0% (+42 pts) — 10/10 correct, 0 mistyped
- advises: 41% → 88.2% (+47 pts) — 15/17 correct
- attended: — → 100.0% 131/134 recall
- founded: 100% → 100.0% 40/40
- invested_in: 89% → 92.0% 69/75
- Overall: 88.5% → 95.7% type accuracy (conditional on edge found)
Strict F1 overall: 53.7%. Lower because the _facts-based gold set only
captures core relationships; rich prose extracts many peripheral mentions
(190 spurious "mentions" edges) that aren't bugs but are correctly-typed
prose references without a _facts counterpart. Spurious counts are signal
for future type-precision tuning, not failure.
Wired into eval/runner/all.ts as Cat 2 so every full benchmark run includes
the rich-prose type accuracy table alongside retrieval metrics.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(eval): Phase 2 adapter interface + EXT-1 ripgrep+BM25 baseline
Phase 2 credibility unlock: BrainBench now compares gbrain to external
baselines on the same corpus and queries. Transforms the benchmark from
internal ablation ("gbrain-graph beats gbrain-grep") to category comparison
("gbrain-graph beats classic BM25 by 32 pts P@5"). This is the #1 fix
from the 4-review arc — addresses Codex's core critique that v1's
before/after was self-referential.
Added:
eval/runner/types.ts — Adapter interface (v1.1 spec)
eval/runner/adapters/ripgrep-bm25.ts — EXT-1 classic IR baseline
eval/runner/adapters/ripgrep-bm25.test.ts — 11 unit tests, all pass
eval/runner/multi-adapter.ts — side-by-side scorer
Adapter interface (eng pass 2 spec):
- Thin 3-method Strategy: init(rawPages, config), query(q, state), snapshot(state)
- BrainState is opaque to runner (never inspected)
- Raw pages passed in-memory; gold/ never crosses adapter boundary
(structural ingestion-boundary enforcement)
- PoisonDisposition enum reserved for future poison-resistance scoring
EXT-1 ripgrep+BM25:
- Classic Lucene-variant IDF + k1/b tuned at standard 1.5/0.75
- Title tokens double-weighted for entity-page slug-match bias
- Stopword filter, alphanumeric tokenization, stable lexicographic tie-break
- Pure in-memory inverted index — no external deps, ~100 LOC core
First side-by-side results on 240-page rich-prose corpus, 145 relational queries:
| Adapter | P@5 | R@5 | Correct top-5 |
|---------------|--------|--------|---------------|
| gbrain-after | 49.1% | 97.9% | 248/261 |
| ripgrep-bm25 | 17.1% | 62.4% | 124/261 |
| Delta | +32.0 | +35.5 | +124 |
gbrain-after is the hybrid graph+grep config from PR #188. Ripgrep+BM25 is
a genuinely strong classic-IR baseline (BM25 is what Lucene/Elasticsearch
ship). gbrain's ~+32-point lead on relational queries reflects real work
by the knowledge graph layer: typed links + traversePaths surface the
correct answers in top-K that BM25 only pulls in via partial-text overlap.
Next in Phase 2: EXT-2 vector-only RAG + EXT-3 hybrid-without-graph
adapters. Both plug into the same Adapter interface.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(eval): Phase 2 EXT-2 vector-only RAG adapter
Second external baseline for BrainBench. Pure cosine-similarity ranking
using the SAME text-embedding-3-large model gbrain uses internally —
apples-to-apples on the embedding layer so any gbrain lead reflects the
graph + hybrid fusion, not a better embedder.
Files:
eval/runner/adapters/vector-only.ts ~130 LOC
eval/runner/adapters/vector-only.test.ts 6 unit tests (cosine math)
Design:
- One vector per page (title + compiled_truth + timeline, capped 8K chars).
- No chunking (intentional; chunked vector RAG would be EXT-2b later).
- No keyword fallback (that's EXT-3 hybrid-without-graph).
- Embeddings in batches of 50 via existing src/core/embedding.ts (retry+backoff).
- Cost on 240 pages: ~$0.02/run.
Three-adapter side-by-side on 240-page rich-prose corpus, 145 relational queries:
| Adapter | P@5 | R@5 | Correct top-5 |
|---------------|--------|--------|---------------|
| gbrain-after | 49.1% | 97.9% | 248/261 |
| ripgrep-bm25 | 17.1% | 62.4% | 124/261 |
| vector-only | 10.8% | 40.7% | 78/261 |
Interesting finding: vector-only scores WORSE than BM25 on relational queries
like "Who invested in X?" — exact entity match matters more than semantic
similarity for these templates. BM25 nails the entity-name term; vector-only
returns topically-similar-but-not-mentioning pages. This is the known failure
mode of pure-vector RAG on precise relational/identity queries. Real-world
vector RAG systems always add keyword fallback; EXT-3 (hybrid-without-graph)
will be that fairer comparator.
gbrain's lead widens in vector-only comparison: +38.4 pts P@5, +57.2 pts R@5.
The graph layer is doing the heavy lifting for relational traversal; pure
vector RAG can't express "traverse 'attended' edges from this meeting page."
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(eval): Phase 2 EXT-3 hybrid-without-graph adapter — graph isolated
Third and closest-to-gbrain external baseline. Runs gbrain's full hybrid
search (vector + keyword + RRF fusion + dedup) WITHOUT the knowledge-graph
layer. Same engine, same embedder, same chunking, same hybrid fusion —
only traversePaths + typed-link extraction turned off.
This is the decisive comparator for "does the knowledge graph do useful
work?" Same everything-else, only graph differs. Any lead gbrain-after has
over EXT-3 is 100% attributable to the graph layer.
Files:
eval/runner/adapters/hybrid-nograph.ts — ~110 LOC
Implementation:
- New PGLiteEngine per run; auto_link set to 'false' (belt).
- importFromContent() used instead of bare putPage() so chunks +
embeddings get populated (hybridSearch needs them).
- NO runExtract() call — typed links/timeline stay empty (suspenders).
- hybridSearch(engine, q.text) answers every query. Aggregate chunks
to page-level by best chunk score.
FOUR-adapter side-by-side on 240-page rich-prose corpus, 145 relational queries:
| Adapter | P@5 | R@5 | Correct/Gold |
|-----------------|--------|--------|--------------|
| gbrain-after | 49.1% | 97.9% | 248/261 |
| hybrid-nograph | 17.8% | 65.1% | 129/261 |
| ripgrep-bm25 | 17.1% | 62.4% | 124/261 |
| vector-only | 10.8% | 40.7% | 78/261 |
The headline delta nobody can hand-wave away:
gbrain-after → hybrid-nograph = +31.4 P@5, +32.9 R@5
hybrid-nograph → ripgrep-bm25 = +0.7 P@5, +2.7 R@5
Hybrid search (vector+keyword+RRF) over pure BM25 gains ~1 point. The
knowledge graph layer over hybrid gains ~31 points. The graph is doing
the work; adding it to a retrieval stack is what actually moves the needle
on relational queries. The vector/keyword/BM25 debate is a footnote.
Timing: hybrid-nograph init is ~2 min (embeds 240 pages once); query loop
is fast. gbrain-after is ~1.5s total because traversePaths doesn't need
embeddings. Runs at ~$0.02 Opus-equivalent in embedding cost.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(eval): Phase 2 query validator + Tier 5 Fuzzy + Tier 5.5 synthetic + N=5 tolerance bands
Closes multiple Phase 2 items in one commit since they form a cohesive
package: query schema enforcement + new query tiers + per-query-set
statistical rigor.
Added:
eval/runner/queries/validator.ts — hand-rolled Query schema validator
eval/runner/queries/validator.test.ts — 24 unit tests, all pass
eval/runner/queries/tier5-fuzzy.ts — 30 hand-authored Tier 5 Fuzzy/Vibe queries
eval/runner/queries/tier5_5-synthetic.ts — 50 SYNTHETIC-labeled outsider-style queries (author: "synthetic-outsider-v1")
eval/runner/queries/index.ts — aggregator + validateAll()
Modified:
eval/runner/multi-adapter.ts — N=5 runs per adapter (BRAINBENCH_N override), page-order shuffle, mean±stddev reporting
Query validator (hand-rolled, no zod dep to match gbrain codebase style):
- Temporal verb regex enforces as_of_date (per eng pass 2 spec):
/\\b(is|was|were|current|now|at the time|during|as of|when did)\\b/i
- Validates tier enum, expected_output_type enum, gold shape per type
- gold.relevant must be non-empty slug[] for cited-source-pages queries
- abstention requires gold.expected_abstention === true
- externally-authored tier requires author field
- batch validation catches duplicate IDs
Tier 5 Fuzzy/Vibe (30 queries, hand-authored):
- Vague recall: "Someone who was a senior engineer at a biotech company..."
- Trait-based: "The engineer who pushed back on microservices"
- Cultural/epithet: "Who is known as a 'systems builder' in security?"
- Abstention bait: "Which Layer 1 project did the crypto guy leave?" (prose
mentions but never names; good systems abstain)
- Addresses Codex's circularity critique — vague queries where graph-heavy
systems shouldn't inherently win.
Tier 5.5 Synthetic Outsider (50 queries, AI-authored placeholder):
- Clearly labeled author: "synthetic-outsider-v1"
- Phrasing variety not in the 4 template families:
* fragment style ("crypto founder Goldman Sachs background")
* polite/natural ("Can you pull up what we have on...")
* comparison ("What is the difference between X and Y?")
* follow-up ("And who else advises Orbit Labs?")
* typos/misspellings ("adam lopez bioinformatcis")
* similarity ("Find me someone like Alice Davis...")
* imperative ("Pull up Alice Davis")
- Real Tier 5.5 from outside researchers supersedes synthetic via
PRs to eval/external-authors/ (docs ship in follow-up commit).
N=5 tolerance bands:
- Default N=5, override via BRAINBENCH_N env var (e.g. BRAINBENCH_N=1 for dev loops)
- Per-run seeded Fisher-Yates shuffle of page ingest order (LCG seed = run_idx+1)
- Surfaces order-dependent adapter bugs (tie-break-by-first-seen etc.)
- Reports mean ± sample-stddev per metric
- "stddev = 0" is honest signal that the adapter is deterministic, not a bug.
LLM-judge metrics (future) will naturally produce non-zero stddev.
Validation: all 80 Tier 5 + 5.5 queries pass validateAll(). 24 validator
unit tests pass.
Next commit: world.html contributor explorer (Phase 3).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(eval): Phase 3 world.html explorer + eval:* CLI surface
Contributor DX magical moment. Static HTML explorer renders the full
canonical world (240 entities) as an explorable tree, opens in any browser,
zero install. Every string HTML-entity-encoded (XSS-safe — direct vuln
class per eng pass 2, confidence 9/10).
Added:
eval/generators/world-html.ts — renderer (~240 LOC; single-file
HTML with inline CSS + minimal JS)
eval/generators/world-html.test.ts — 16 tests (XSS + rendering correctness)
eval/cli/world-view.ts — render + open in default browser
eval/cli/query-validate.ts — CLI wrapper for queries/validator
eval/cli/query-new.ts — scaffold a query template
Modified:
package.json — 7 new eval:* scripts
.gitignore — ignore generated world.html
package.json scripts shipped:
bun run test:eval all eval unit tests (57 pass)
bun run eval:run full 4-adapter N=5 side-by-side
bun run eval:run:dev N=1 fast dev iteration
bun run eval:world:view render world.html + open in browser
bun run eval:world:render render only (CI-friendly, --no-open)
bun run eval:query:validate validate built-in T5+T5.5 (or a file path)
bun run eval:query:new scaffold a new Query JSON template
bun run eval:type-accuracy per-link-type accuracy report
XSS safety:
escapeHtml() encodes the 5 critical chars (& < > " '). Tested directly
with representative Opus-generated attacks:
<img src=x onerror=alert('xss')> → <img src=x onerror=alert('xss')>
<script>fetch('/steal')</script> → <script>fetch('/steal')</script>
Ledger metadata (generated_at, model) also escaped — covers the less
obvious attack surface where Opus could emit tag-like content into the
metadata file.
world.html structure:
- Left rail: entities grouped by type with counts (companies, people,
meetings, concepts), alphabetical within type
- Right pane: per-entity cards with title + slug + compiled_truth +
timeline + canonical _facts as collapsed JSON
- URL fragment deep-links (#people/alice-chen)
- Sticky rail on desktop; responsive stack on mobile
- Vanilla JS for active-link highlighting on scroll (no framework)
Generated file: ~1MB for 240 entities (full prose). Gitignored; rebuild
with `bun run eval:world:view`. Regeneration is ~50ms.
Contributor TTHW (Tier 5.5 query authoring):
1. bun run eval:world:view # see entities
2. bun run eval:query:new --tier externally-authored --author "@me"
3. edit template with real slug + query text
4. bun run eval:query:validate path/to/file.json
5. submit PR
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs(eval): Phase 3 contributor docs + CI workflow for eval/ tests
Ships the contributor-onboarding surface promised in the plan. With this
commit, external researchers have a self-serve path from clone to PR in
under 5 minutes.
Added:
eval/README.md — 5-minute quickstart,
directory map, methodology
one-pager, adapter scorecard
eval/CONTRIBUTING.md — three contributor paths:
1. Write Tier 5.5 queries
2. Submit an external adapter
3. Reproduce a scorecard
eval/RUNBOOK.md — operational troubleshooting:
generation failures, runner
failures, query validation,
world.html rendering, CI
eval/CREDITS.md — contributor attribution
(synthetic-outsider-v1 labeled
as placeholder; real submissions
land here)
.github/PULL_REQUEST_TEMPLATE/tier5-queries.md — structured PR template
for Tier 5.5 submissions
.github/workflows/eval-tests.yml — CI: validates queries,
runs all eval unit tests,
renders world.html on every PR
touching eval/** or
src/core/link-extraction.ts
CI scope (intentionally narrow):
- Triggers on paths: eval/**, src/core/link-extraction.ts, src/core/search/**
- Runs: bun run eval:query:validate (80 queries), test:eval (57 tests),
eval:world:render (smoke-test the HTML renderer)
- Pinned actions by commit SHA (matches existing .github/workflows/test.yml)
- Zero API calls — all Opus/OpenAI paths stubbed or skipped in unit tests
- Fast: ~30s total wall clock
Contributor TTHW (clone → first merged PR):
- Path 1 (Tier 5.5 queries): ~5 min
- Path 2 (external adapter): ~30 min for a simple adapter
- Path 3 (reproduce scorecard): ~15 min wall clock (N=5 run)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(eval): teardown PGLite engines so bun run eval:run exits 0
The multi-adapter runner left PGLite engines alive after each run.
GbrainAfterAdapter and HybridNoGraphAdapter both instantiate a
PGLiteEngine in init() but never disconnect it; Bun's shutdown path
exits with code 99 when embedded-Postgres workers outlive main().
Added optional `teardown?(state)` to the Adapter interface, implemented
it on both engine-backed adapters, and call it from scoreOneRun after
the N=5 loop. ripgrep-bm25 and vector-only hold no DB resources and
don't need a teardown.
Verified: gbrain-after, hybrid-nograph, ripgrep-bm25, vector-only all
exit 0 at N=1. Full test:eval passes (57 tests). No metric change.
* docs(bench): 2026-04-19 multi-adapter scorecard
Reproducibility run of the 4-adapter side-by-side at commit
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c0b621923b |
fix: JSONB double-encode + splitBody wiki + parseEmbedding (v0.12.1) (#196)
* fix: splitBody and inferType for wiki-style markdown content - splitBody now requires explicit timeline sentinel (<!-- timeline -->, --- timeline ---, or --- directly before ## Timeline / ## History). A bare --- in body text is a markdown horizontal rule, not a separator. This fixes the 83% content truncation @knee5 reported on a 1,991-article wiki where 4,856 of 6,680 wikilinks were lost. - serializeMarkdown emits <!-- timeline --> sentinel for round-trip stability. - inferType extended with /writing/, /wiki/analysis/, /wiki/guides/, /wiki/hardware/, /wiki/architecture/, /wiki/concepts/. Path order is most-specific-first so projects/blog/writing/essay.md → writing, not project. - PageType union extended: writing, analysis, guide, hardware, architecture. Updates test/import-file.test.ts to use the new sentinel. Co-Authored-By: @knee5 (PR #187) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: JSONB double-encode bug on Postgres + parseEmbedding NaN scores Two related Postgres-string-typed-data bugs that PGLite hid: 1. JSONB double-encode (postgres-engine.ts:107,668,846 + files.ts:254): ${JSON.stringify(value)}::jsonb in postgres.js v3 stringified again on the wire, storing JSONB columns as quoted string literals. Every frontmatter->>'key' returned NULL on Postgres-backed brains; GIN indexes were inert. Switched to sql.json(value), which is the postgres.js-native JSONB encoder (Parameter with OID 3802). Affected columns: pages.frontmatter, raw_data.data, ingest_log.pages_updated, files.metadata. page_versions.frontmatter is downstream via INSERT...SELECT and propagates the fix. 2. pgvector embeddings returning as strings (utils.ts): getEmbeddingsByChunkIds returned "[0.1,0.2,...]" instead of Float32Array on Supabase, producing [NaN] cosine scores. Adds parseEmbedding() helper handling Float32Array, numeric arrays, and pgvector string format. Throws loud on malformed vectors (per Codex's no-silent-NaN requirement); returns null for non-vector strings (treated as "no embedding here"). rowToChunk delegates to parseEmbedding. E2E regression test at test/e2e/postgres-jsonb.test.ts asserts jsonb_typeof = 'object' AND col->>'k' returns expected scalar across all 5 affected columns — the test that should have caught the original bug. Runs in CI via the existing pgvector service. Co-Authored-By: @knee5 (PR #187 — JSONB triple-fix) Co-Authored-By: @leonardsellem (PR #175 — parseEmbedding) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat: extract wikilink syntax with ancestor-search slug resolution extractMarkdownLinks now handles [[page]] and [[page|Display Text]] alongside standard [text](page.md). For wiki KBs where authors omit leading ../ (thinking in wiki-root-relative terms), resolveSlug walks ancestor directories until it finds a matching slug. Without this, wikilinks under tech/wiki/analysis/ targeting [[../../finance/wiki/concepts/foo]] silently dangled when the correct relative depth was 3 × ../ instead of 2. Co-Authored-By: @knee5 (PR #187) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat: gbrain repair-jsonb + v0.12.1 migration + CI grep guard - New gbrain repair-jsonb command. Detects rows where jsonb_typeof(col) = 'string' and rewrites them via (col #>> '{}')::jsonb across 5 affected columns: pages.frontmatter, raw_data.data, ingest_log.pages_updated, files.metadata, page_versions.frontmatter. Idempotent — re-running is a no-op. PGLite engines short-circuit cleanly (the bug never affected the parameterized encode path PGLite uses). --dry-run shows what would be repaired; --json for scripting. - New v0_12_1.ts migration orchestrator. Phases: schema → repair → verify. Modeled on v0_12_0 pattern, registered in migrations/index.ts. Runs automatically via gbrain upgrade / apply-migrations. - CI grep guard at scripts/check-jsonb-pattern.sh fails the build if anyone reintroduces the ${JSON.stringify(x)}::jsonb interpolation pattern. Wired into bun test via package.json. Best-effort static analysis (multi-line and helper-wrapped variants are caught by the E2E round-trip test instead). - Updates apply-migrations.test.ts expectations to account for the new v0.12.1 entry in the registry. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: bump version and changelog (v0.12.1) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs: update project documentation for v0.12.1 - CLAUDE.md: document repair-jsonb command, v0_12_1 migration, splitBody sentinel contract, inferType wiki subtypes, CI grep guard, new test files (repair-jsonb, migrations-v0_12_1, markdown) - README.md: add gbrain repair-jsonb to ADMIN command reference - INSTALL_FOR_AGENTS.md: fix verification count (6 -> 7), add v0.12.1 upgrade guidance for Postgres brains - docs/GBRAIN_VERIFY.md: add check #8 for JSONB integrity on Postgres-backed brains - docs/UPGRADING_DOWNSTREAM_AGENTS.md: add v0.12.1 section with migration steps, splitBody contract, wiki subtype inference - skills/migrate/SKILL.md: document native wikilink extraction via gbrain extract links (v0.12.1+) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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ecebd5552a |
feat: GBrain v0.2.0 — incremental sync, file storage, install skill (#2)
* refactor: extract importFile from import.ts + add tag reconciliation Shared single-file import function used by both import and sync. Adds tag reconciliation (removes stale tags on reimport), >1MB file skip, and import->sync checkpoint continuity (writes git HEAD to config table after import so sync picks up seamlessly). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add sync pure functions, updateSlug engine method, and sync tests - buildSyncManifest: parses git diff --name-status -M output - isSyncable: filters to .md pages, excludes hidden/ops/.raw/skip-list - pathToSlug: converts file paths to page slugs with optional prefix - updateSlug: renames page slug in-place (preserves page_id, chunks, embeddings) - rewriteLinks: stub for v0.2 (FKs use page_id, already correct) - 20 new tests, all passing (39 total across 3 files) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add gbrain sync command with CLI, MCP, and watch mode 18-step sync protocol: read config, git pull, ancestry validation, git diff --name-status -M for net changes, isSyncable filter, process deletes/renames/adds/modifies via importFile, batch optimization, sync state checkpoint in Postgres config table. Watch mode with polling and consecutive error counter. MCP sync_brain tool returns structured SyncResult. Stale page deletion for un-syncable files. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add files table, gbrain files commands, and config show redaction - files table: page_slug FK with ON DELETE SET NULL + ON UPDATE CASCADE, storage_path, storage_url, mime_type, content_hash for dedup - gbrain files list/upload/sync/verify commands for Supabase Storage - gbrain config show redacts postgresql:// passwords and secret keys - CLI help updated with FILES section Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add install skill for GBrain onboarding 6-phase install workflow: environment discovery, Supabase setup (magic path via CLI OAuth or fallback 2-copy-paste), init + import, ongoing sync cron, optional file migration with mandatory verification, and agent teaching (AGENTS.md rules). Every error gets what + why + fix. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: update project documentation for v0.2.0 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add v0.2 features to README (sync, files, install skill) README.md: added sync command to IMPORT/EXPORT section, added FILES section with 4 commands, added files table to schema diagram, added install skill to skills table, updated MCP tools count from 20 to 21 (sync_brain added). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: OpenClaw DX improvements (skill count, upgrade docs, config show help) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor: consolidate version to single source of truth Create src/version.ts that reads from package.json via static import (safe for bun compiled binaries). Update mcp/server.ts from hardcoded '0.1.0' to use shared VERSION. Bump skills/manifest.json to 0.2.0. * fix: upgrade detection order, npm→bun naming, clawhub false positives Reorder detection: node_modules first, binary second, clawhub last. Rename 'npm' install method to 'bun'. Use 'clawhub --version' instead of 'which clawhub' to avoid false positives from dangling symlinks. Add 120s timeout to execSync calls to prevent hanging. Add --help flag. * feat: per-command --help, unknown command check before DB connection Add COMMAND_HELP map covering all 28 commands. Check --help before init/upgrade dispatch and before connectEngine() so help works without a database. Use COMMAND_HELP keys as known-command set to catch unknown commands before wasting a DB round-trip. * docs: standardize npm references to bun, add Upgrade section to README Fix init.ts: npx→bunx, npm→bun for supabase CLI guidance. Fix README: npm install→bun add for standalone CLI install. Add ## Upgrade section to README with all three install methods. Update install skill Upgrading section to list bun, ClawHub, and binary. * test: full coverage audit — CLI dispatch, upgrade detection, config, edge cases New test files: - test/cli.test.ts: COMMAND_HELP ↔ switch consistency, version from package.json, per-command --help, unknown command handling, global help - test/upgrade.test.ts: detection order verification, npm→bun naming, clawhub --version (not which), timeout presence - test/config.test.ts: redactUrl for postgresql URLs, edge cases Extended existing tests: - test/sync.test.ts: empty string pathToSlug, uppercase .MD rejection, deeply nested files, multiple renames, unknown status codes - test/markdown.test.ts: multiple --- separators, missing frontmatter, no frontmatter at all, empty string, type inference from paths Tests: 39 → 83 (+44 new). All pass. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * test: 100% coverage — import-file mock engine, files utils, chunker edge cases New test files: - test/import-file.test.ts (9 tests): mock BrainEngine to test importFile without DB — MAX_FILE_SIZE skip, content_hash dedup, tag reconciliation (remove stale + add new), compiled_truth/timeline chunking, noEmbed flag, sequential chunk_index - test/files.test.ts (22 tests): getMimeType for all extensions + uppercase + unknown + no-extension, fileHash consistency + different content + empty, collectFiles pattern (skip .md, skip hidden dirs, recurse, sorted output) Extended: - test/chunkers/recursive.test.ts (+6 tests): single newline splits, word-only text, clause delimiters, lossless preservation, default options, mixed delimiter hierarchy Tests: 83 → 118 (+35 new). All pass. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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b22cbd349a |
feat: GBrain v0.1.0 — Postgres-native personal knowledge brain (#1)
* chore: add CLAUDE.md with project context and gstack skill routing rules Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: initialize project with Bun + TypeScript package.json with dependencies (postgres, pgvector, openai, anthropic, MCP SDK, gray-matter). TypeScript config targeting ESNext with bundler module resolution. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add foundation layer — engine interface, Postgres engine, schema BrainEngine pluggable interface with full PostgresEngine: CRUD, search (keyword + vector), links, tags, timeline, versions, stats, health, ingest log, config. Trigger-based tsvector spanning pages + timeline_entries. Markdown parser with frontmatter, compiled_truth / timeline splitting, and round-trip serialization. 19 tests passing. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add 3-tier chunking and embedding service Recursive delimiter-aware chunker (5-level hierarchy, 300-word chunks, 50-word overlap). Semantic chunker with Savitzky-Golay boundary detection and recursive fallback. LLM-guided chunker via Claude Haiku with sliding window topic detection. OpenAI embedding service with batch support, exponential backoff, and rate limit handling. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add hybrid search with RRF fusion, expansion, and 4-layer dedup Hybrid search merges vector (pgvector HNSW) + keyword (tsvector) via Reciprocal Rank Fusion. Multi-query expansion via Claude Haiku generates 2 alternative phrasings. 4-layer dedup pipeline: by source, cosine similarity, type diversity (60% cap), per-page cap. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add GBRAIN_V0 spec, pluggable engine architecture, SQLite engine plan GBRAIN_V0.md: full product spec with architecture decisions, CLI commands, schema, search architecture, chunking strategies, first-time experience, and future plans. ENGINES.md: pluggable engine interface, capability matrix, how to add new backends. SQLITE_ENGINE.md: complete SQLite implementation plan with schema, FTS5 setup, vector search options, and contributor guide. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add CLI with all commands Full CLI dispatcher with 25+ commands: init (Supabase wizard), get, put, delete, list, search, query (hybrid RRF), import (bulk with progress bar), export (round-trip), embed, stats, health, tag/untag/tags, link/unlink/ backlinks/graph, timeline/timeline-add, history/revert, config, upgrade, serve, call. Smart slug resolution on reads. Version snapshots on updates. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add MCP stdio server with all brain tools 20 MCP tools mirroring CLI operations: get/put/delete/list pages, search (keyword), query (hybrid RRF + expansion), tags, links with graph traversal, timeline, stats, health, version history, and revert. Auto-chunks and embeds on put_page. CLI and MCP share the same engine. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat: add 6 skill files and ClawHub manifest Fat markdown skills for AI agents: ingest (meetings/docs/articles with timeline merge), query (3-layer search + synthesis + citations), maintain (health checks, stale detection, orphan audit), enrich (external API enrichment), briefing (daily briefing compilation), migrate (universal migration from Obsidian/Notion/Logseq/markdown/CSV/JSON/Roam). ClawHub manifest for skill distribution. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add README, CONTRIBUTING, update CLAUDE.md test references README with quickstart, commands, architecture, library usage, MCP setup, and links to design docs. CONTRIBUTING with setup, project structure, and guides for adding commands and engines. CLAUDE.md updated to reference actual test files instead of planned-but-unwritten import test. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address adversarial review findings — 5 critical/high fixes - revertToVersion: add page_id check to prevent cross-page data corruption - traverseGraph: use UNION instead of UNION ALL for cycle safety - embedAll: preserve all chunks when embedding stale subset only - embedding: throw on retry exhaustion instead of returning zero vectors - putPage: validate slugs to prevent path traversal on export Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: bump version and changelog (v0.1.0) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: expand README with schema, install, search architecture, and motivation Why it exists, how search works (with ASCII diagram), full database schema with all 9 tables and index details, chunking strategies explained, storage estimates, setup wizard walkthrough, knowledge model with example page, library usage with more examples, expanded skills table. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: add MIT license (Copyright 2026 Garry Tan) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add OpenClaw install flow as primary option in README OpenClaw users just say "install gbrain" and the orchestrator handles everything: package install, Supabase setup wizard, skill registration. Shows the conversational interface for querying, ingesting, and briefings. ClawHub and standalone CLI paths follow as alternatives. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: add prerequisites and explicit OpenClaw install instructions Prerequisites table listing Supabase, OpenAI, and Anthropic dependencies with links. Environment variable setup. Explicit step-by-step prompt for OpenClaw users showing exactly what to tell the orchestrator. Note that search degrades gracefully without API keys (keyword-only without OpenAI, no expansion without Anthropic). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: scrub named references, add PG essay demo section to README Replace all Pedro/Brex/Jensen Huang/River AI examples with Paul Graham essay examples using the kindling corpus. Add "Try it" section to README showing the power of hybrid search on PG essays in 90 seconds. Update test fixtures to use concept pages instead of person pages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |