v0.28.5 fix(wave): PGLite upgrade wedge + embedding dim corruption + bun-link foot-gun (#697)
* fix(engines): pre-add v0.20 + v0.26.3 forward-reference columns in bootstrap The forward-reference bootstrap (PostgresEngine + PGLiteEngine applyForwardReferenceBootstrap) covered v0.18 + v0.19 + v0.26.5 columns but missed two later groups. Brains upgrading from v0.14-era to current master crash before the migration ladder runs: 1. v0.20 Cathedral II — content_chunks.search_vector, parent_symbol_path, doc_comment, symbol_name_qualified. `CREATE INDEX idx_chunks_search_vector` and `CREATE INDEX idx_chunks_symbol_qualified` in schema.sql/PGLITE_SCHEMA_SQL crash with "column search_vector does not exist" / "column symbol_name_qualified does not exist". 2. v0.26.3 — mcp_request_log.agent_name, params, error_message. `CREATE INDEX idx_mcp_log_agent_time ON mcp_request_log(agent_name,...)` crashes with "column agent_name does not exist". Reproduces deterministically on a v0.13/v0.14 brain upgraded straight to current master. The user hits the wall before any of v15-v36 can run. Both engines now probe for these columns and pre-add them via `ALTER TABLE ADD COLUMN IF NOT EXISTS` before SCHEMA_SQL runs. Migrations v26, v27, v33 still run later via runMigrations and remain idempotent (they handle backfill on top of the bootstrap-added columns). Test coverage extended in test/schema-bootstrap-coverage.test.ts: REQUIRED_BOOTSTRAP_COVERAGE now lists 6 new forward references; the strip-and-rebuild block drops the corresponding indexes/triggers so the test exercises a brain that pre-dates v0.20 + v0.26.3 migrations. Repro: brain on schema v13/v14 + run `gbrain init --migrate-only` against current master → fails. With this patch → succeeds; ladder runs to v36. * fix(engines): pre-add v0.27 subagent_messages.provider_id in bootstrap PR #682 covered v0.20 (chunks) + v0.26.3 (mcp_request_log) but missed v0.27's subagent_messages.provider_id. The composite index `idx_subagent_messages_provider ON subagent_messages (job_id, provider_id)` in PGLITE_SCHEMA_SQL crashes on brains pinned at v0.18-v0.26 because provider_id is the SECOND column in the composite — array-extraction patterns that scan only first-column references miss it entirely. This is the wedge surfaced by issue #670 (v0.22.0 → v0.27.0 init --migrate-only crashes with "column 'provider_id' does not exist") and contributing to #661/#657. Both engines now probe for subagent_messages.provider_id and pre-add the column via ALTER TABLE ADD COLUMN IF NOT EXISTS before SCHEMA_SQL runs. Migration v36 (subagent_provider_neutral_persistence_v0_27) still runs later via runMigrations and remains idempotent. Note on the test side: REQUIRED_BOOTSTRAP_COVERAGE is hand-maintained and just gained a v0.27 entry. v0.28.5's Step 3 replaces this array with a SQL parser that auto-derives coverage from PGLITE_SCHEMA_SQL, including composite-index columns. This commit is the targeted follow-up to PR #682's cherry-pick; A2's parser closes the class permanently. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(cli): conditional schema-init on connect (closes #651) Adds `hasPendingMigrations(engine)` next to `runMigrations` in migrate.ts: single getConfig('version') probe, returns true when current < LATEST_VERSION, defensively returns true on getConfig failure (treats wedged-config as pending). `connectEngine` in cli.ts now wraps `engine.initSchema()` in a probe gate: short-lived CLI calls (gbrain stats, query, doctor, etc.) on already-migrated brains skip the bootstrap-probe + SCHEMA_SQL replay + ledger-check entirely. Wedged brains still auto-heal — the probe says "yes pending" and initSchema runs as before. Building on oyi77's investigation in PR #652. Same correctness as #652's unconditional initSchema-on-every-connect, but no perf regression on the hot path. Failure non-fatal: if probe or init throws, log a hint and let subsequent operations surface the real error in context. Test coverage in test/migrate.test.ts: 3 cases covering fully-migrated (false), version-rewound (true), and missing-version-config (defensive true). Pairs with v0.28.5's X1 (post-upgrade auto-apply) — the upgrade path runs initSchema explicitly while every other code path that goes through connectEngine gets the cheap probe. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(upgrade): post-upgrade auto-applies pending schema migrations (X1) Prior behavior: `gbrain upgrade` → `gbrain post-upgrade` → `apply-migrations` only WARNs at apply-migrations.ts:296-302 when schema version is behind LATEST_VERSION, telling the user to run `gbrain init --migrate-only`. 11 wedge incidents over 2 years have proven users don't read that WARN — they file an issue instead. This commit makes `runPostUpgrade` explicitly call `engine.initSchema()` after the orchestrator migration pass, mirroring `init --migrate-only`'s flow. Side-effect: `gbrain upgrade` now walks away with a healthy brain in the cluster A wedge case (#670, #661, #657, #651, #625, #615, #609). Defensive: wrapped in try/catch so a connection or DDL failure falls back to the existing user-facing WARN. The hint to run `gbrain init --migrate-only` is preserved as the manual escape hatch. Pairs with v0.28.5's A1 (hasPendingMigrations probe in connectEngine): the upgrade path runs initSchema explicitly here, while every other code path that goes through connectEngine gets the cheap probe. Codex outside-voice review caught this gap during plan review: "the plan still does not prove `upgrade` will actually run schema migrations." This is the load-bearing fix that makes v0.28.5's headline outcome ("run upgrade, brain works") literally true for cluster A. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test(bootstrap): auto-derive coverage from PGLITE_SCHEMA_SQL (A2) Replaces the hand-maintained REQUIRED_BOOTSTRAP_COVERAGE assertion with a SQL-parser-backed structural check. The new test: 1. parseIndexColumnReferences(PGLITE_SCHEMA_SQL) extracts every column referenced by every CREATE INDEX — including composite-index second and third columns. Codex outside-voice review caught that earlier first-col-only patterns missed v0.27's `idx_subagent_messages_provider ON subagent_messages (job_id, provider_id)`, which is exactly how the v0.28.5 wedge happened. 2. parseBaseTableColumns(PGLITE_SCHEMA_SQL) extracts every column declared in CREATE TABLE bodies (including via ALTER TABLE ADD COLUMN inside the schema blob). 3. parseAlterAddColumns(pglite-engine.ts source) extracts every column that applyForwardReferenceBootstrap adds. 4. Static contract: every (table, column) pair from step 1 must appear in either step 2 or step 3. Otherwise the test fails loud, names every uncovered pair, and points at the bootstrap function for the fix. Self-updating: any future CREATE INDEX added to PGLITE_SCHEMA_SQL on a column that bootstrap doesn't yet provide fails this test at PR time. No human required to remember to update an array. Closes the 11-incident wedge class identified in CLAUDE.md (#239, #243, #266, #357, #366, #374, #375, #378, #395, #396). Helper parsers also have their own unit tests covering composite-index second columns, function-wrapped columns (lower(col)), HNSW operator-class suffixes (vector_cosine_ops), and ALTER TABLE column extraction. Existing REQUIRED_BOOTSTRAP_COVERAGE-based tests preserved as a coarse-grained lower bound; the new parser-based test is the load-bearing structural gate going forward. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: support Voyage 2048d schema setup * fix: harden Voyage schema templating * feat: Voyage 4 embedding support + doctor eval - Add voyage-4-large/4/4-lite/4-nano + domain models to Voyage recipe - Fix AI SDK compatibility: strip encoding_format (Voyage rejects 'float'), patch response to add prompt_tokens from total_tokens - Add embedding_provider doctor check: live smoke test verifying model, API key, dimensions, and DB column alignment - Add embedding provider eval qrels for post-migration quality testing Closes: Voyage AI integration for gbrain embedding pipeline * fix: adaptive embed batch sizing for Voyage token limits Voyage's tokenizer is 3-4x denser than OpenAI tiktoken, causing batches of 50+ texts to exceed the 120K token-per-batch limit even when DB token counts (from tiktoken) suggest they'd fit. Changes: - Add max_batch_tokens to EmbeddingTouchpoint type (provider-declared limit) - Set Voyage recipe to 120K token limit - Gateway embed() now auto-splits batches using conservative char-to-token estimate (1:1 ratio, 80% budget utilization) - On token-limit errors, embedSubBatch recursively halves and retries (down to single-text batches before giving up) - Reduce embedding.ts BATCH_SIZE from 100 to 50 as a secondary guard - Add tests for batch splitting logic and error pattern matching Fixes infinite retry loops where the same oversized batch would fail repeatedly because WHERE embedding IS NULL re-fetches identical rows. * fix(init): error on existing-brain dim mismatch + embedding-migration recipe Adds A4 hard-error path: when `gbrain init --embedding-dimensions N` is run against an existing brain whose `content_chunks.embedding` column is a different `vector(M)`, init exits 1 with an inline four-step ALTER recipe and a pointer to docs/embedding-migrations.md. This kills the silent-corruption pattern surfaced by issue #673: the v0.27 schema seeded `('embedding_dimensions', '1536')` regardless of the flag, so users got a config saying 768 but a column at 1536 — first sync write blew up with "expected 1536, got 768." A4's contract: 1. Connect to engine BEFORE saveConfig so we can read the live column type 2. If column exists AND dim != requested, exit 1 (loud failure) 3. If column doesn't exist (fresh init) OR dim matches, proceed normally Recipe in docs/embedding-migrations.md (and inlined in init's error output) covers all four destructive steps codex's plan-review caught: 1. DROP INDEX IF EXISTS idx_chunks_embedding (HNSW won't survive ALTER) 2. ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(N) 3. UPDATE content_chunks SET embedding = NULL, embedded_at = NULL 4. CREATE INDEX HNSW *only if N <= 2000* (pgvector cap) Step 4 is conditional: dims > 2000 (e.g. Voyage 4 Large 2048d) cannot be HNSW-indexed in pgvector; the recipe explicitly says "Skip reindex" in that case so the user doesn't paste a CREATE INDEX that crashes. Helper `readContentChunksEmbeddingDim` and message builder `embeddingMismatchMessage` live in src/core/embedding-dim-check.ts so doctor 8b (next commit) can reuse the same source of truth. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(gateway): correct dim-mismatch error to point at manual ALTER recipe (#672) Previous error message recommended running `gbrain migrate --embedding-model … --embedding-dimensions …`, but `gbrain migrate` only handles engine migration (postgres ↔ pglite), not embedding reconfiguration. Following that hint produced a different error and confused users further. New message: - Names the actual options: change models OR migrate the existing brain - Inlines a one-line quick recipe (DROP INDEX → ALTER → UPDATE NULL → config set → embed --stale) - Points at docs/embedding-migrations.md (added in commit 306fc0e1) for the full four-step recipe with HNSW conditional handling Closes #672. Note: #671 (config show hides embedding_model / dimensions) appears to be already fixed on master — `Object.entries(loadConfig())` in config.ts:24 correctly enumerates all keys including embedding_*. Will close #671 with that note when shipping v0.28.5. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(types): doctor 8b uses portable executeRaw + Voyage fetch-shim cast #665's doctor 8b dim-probe used `engine.sql\`...\`` directly (Postgres template literal) which doesn't typecheck against the BrainEngine interface (only PostgresEngine has the .sql getter; PGLite does not). Refactored to use `readContentChunksEmbeddingDim` from src/core/embedding-dim-check.ts — same helper init's A4 hard-error path uses, runs portably on both engines. #680's Voyage fetch-shim passes a custom fetch handler to `createOpenAICompatible` for the encoding_format + prompt_tokens normalization. The SDK accepts the field at runtime but the typed parameter on the pinned version doesn't expose it. Cast to the parameter type so the shim ships without a type error. Both fixes are mechanical cleanup of cherry-picked PRs that didn't typecheck against current master's stricter shape. No behavior change. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(cli): mark cli.ts executable so bun-linked installs work `package.json` declares `"bin": { "gbrain": "src/cli.ts" }`, and bun's linker creates `~/.bun/bin/gbrain` as a symlink to the file. The shebang `#!/usr/bin/env bun` works only when the target file is executable — otherwise bun runs it as a script (because it sees the script via the shebang interpreter), but executing the symlinked target itself fails: $ ls -la ~/.bun/bin/gbrain lrwxrwxrwx ... -> ../install/global/node_modules/gbrain/src/cli.ts $ ~/.bun/bin/gbrain --version /opt/homebrew/bin/bash: line 1: /Users/brandon/.bun/bin/gbrain: Permission denied This bites the postinstall hook that calls `gbrain apply-migrations` (masked by the `||` fallback) and any subprocess that invokes the binary by absolute path (e.g., subagent_messages migration v0.16's `execSync('gbrain init --migrate-only', ...)`). Setting the mode in-tree to 755 fixes both. No content change. * test(ci): guard against src/cli.ts mode-bit regression (cluster C) Cluster C cherry-pick (#683) restored the executable bit on src/cli.ts. This commit adds scripts/check-cli-executable.sh that asserts the git index mode is 100755 and wires it into `bun run verify` (and check:all). Why a CI guard: bun-link installs symlink to src/cli.ts directly. If the mode bit ever regresses to 100644, the very first `gbrain --version` fails with `permission denied` — the exact symptom that motivated #683. This guard runs in <100ms, fast enough for the inner verify loop. Failure mode: clear instructions on what command to run to fix (`chmod +x src/cli.ts && git add --chmod=+x src/cli.ts`) plus a pointer back to issue #683 so future maintainers know why the guard exists. Note: darwin and linux only. Windows preserves the git-stored mode regardless of filesystem chmod, so the index-mode check works the same on every platform CI uses. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(upgrade): detect bun-link, warn on npm squatter (#656, #658) Rewrites detectInstallMethod() in src/commands/upgrade.ts:247 with three layered signals per v0.28.5 plan cluster D + codex finding C1: 1. bun-link signal (closes #656): when argv[1] is a symlink, walk up from realpath(argv[1]) up to 6 levels looking for a .git/config whose contents include `garrytan/gbrain` (case-insensitive substring). Returns 'bun-link'. Best-effort: forks, tarballs, and detached source trees fall through to the existing chain. 2. canonical bun authenticity check (closes #658 detection half): when the install lives in node_modules, read package.json and verify repository.url contains `garrytan/gbrain` OR src/cli.ts coexists (squatter ships compiled binary, not source). On 'suspect' verdict, print printSquatterRecovery() — names both git-clone AND release-binary recovery paths so users without a local clone can still recover. 3. Source-marker fallback inside (2). Codex flagged this is spoofable by a determined squatter; accepted — best-effort warning, not assertion. The structural fix is publishing under @garrytan/gbrain (tracked v0.29 follow-up). The squatter's `name: gbrain` field doesn't disambiguate (codex caught this in plan review of my original heuristic). repository.url is the field a careless squatter is least likely to set correctly; src/cli.ts presence is the secondary signal. bun-link installs return 'bun-link' from the switch in runUpgrade, which prints the source-clone upgrade path (`git pull && bun install && bun link`) instead of trying `bun update gbrain` which doesn't apply. README updated with the corresponding "DO NOT use `bun add -g gbrain`" callout naming both #658 and the v0.29 scoped-name plan. Tests in test/upgrade.test.ts cover return-type extension, bun-link signal shape, classifyBunInstall's two-signal check, and the recovery message contents. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * v0.28.5 release: PGLite upgrade wedge + embedding dim corruption + bun-link foot-gun Fix wave bundling 9 community PRs to unwedge users stuck since v0.27. Cluster A — PGLite upgrade wedge (#670, #661, #657, #651, #625, #615, #609): - Bootstrap now covers v0.20+v0.26.3+v0.27 forward references (both engines) - hasPendingMigrations() probe gates initSchema() in connectEngine - Post-upgrade auto-applies pending schema migrations (X1) - SQL-parser-backed bootstrap coverage replaces hand-maintained array (A2) Cluster B — Embedding dim corruption (#673, #672, #666, #640): - Schema templating cascade fixed end-to-end (#641 from @100yenadmin) - gbrain doctor 8b live embedding-provider probe (#665) - Voyage adaptive batch sizing for 120K-token cap (#680) - gbrain init A4 hard-error on existing-brain dim mismatch - docs/embedding-migrations.md with conditional-HNSW four-step recipe - #672 misleading migrate-suggestion error replaced with inline recipe Cluster C — CLI exec bit (#683, dupe of #655): - src/cli.ts mode 100644 → 100755 (#683 from @brandonlipman) - scripts/check-cli-executable.sh CI guard against future regression Cluster D — bun add -g foot-gun (#656, #658): - 3-signal detectInstallMethod rewrite (bun-link, repo.url, source-marker) - Loud-red recovery message names source-clone AND release-binary paths - README "DO NOT use bun add -g gbrain" callout Contributors: @brandonlipman (#682, #683), @mdcruz88 (#668), @ChenyqThu (#627), @alan-mathison-enigma (#610), @oyi77 (#652 building block), @abkrim (#655), @100yenadmin (#641). VERSION 0.27.0 → 0.28.5 package.json 0.27.0 → 0.28.5 schema-embedded.ts regenerated via bun run build:schema llms-full.txt regenerated via bun run build:llms Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test(e2e): v0.28.5 fix-wave end-to-end coverage PGLite-only E2E covering the three regression scenarios v0.28.5 was shipped to fix: 1. cluster A — pre-v0.20 brain (missing v0.20 + v0.26.3 + v0.27 columns) re-runs initSchema cleanly. Strips the column set v0.28.5's bootstrap claims to restore (search_vector, parent_symbol_path, doc_comment, symbol_name_qualified, agent_name, params, error_message, provider_id), resets the version row to 13, then re-runs initSchema. Asserts every column comes back AND version reaches LATEST_VERSION. Closes the gap that pre-v0.28.5 produced 11 wedge incidents. 2. cluster B — fresh init at non-default dims templates the column correctly (768d AND 2048d cases). The 2048d case explicitly verifies idx_chunks_embedding is NOT created (codex finding #8 — pgvector's HNSW cap is 2000). 3. A4 — existing-brain dim mismatch helper produces a recipe that inlines all four steps (DROP INDEX, ALTER TYPE, NULL, conditional reindex). Validates the conditional CREATE INDEX HNSW for dims <= 2000 AND its omission for dims > 2000. The recipe a user copy-pastes won't crash them on Voyage 4 Large. Plus a hasPendingMigrations() lifecycle test covering the four states (fresh / migrated / rewound / re-applied) — pairs with the unit test in test/migrate.test.ts but exercises the engine end-to-end. PGLite-only because none of these cases need real Postgres. Postgres-side bootstrap is covered by test/e2e/postgres-bootstrap.test.ts. Run: bun test test/e2e/v0_28_5-fix-wave.test.ts (no DATABASE_URL needed). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * test: refactor embedding-dim-check.test.ts to canonical PGLite pattern Test-isolation lint (R3+R4) requires PGLiteEngine in beforeAll() context with afterAll() disconnect. Refactored to single-engine-per-file pattern; the fresh-brain test uses a one-off engine inside its own try/finally so the file-level engine stays at LATEST schema for the migrated-brain test. No behavior change to the assertions. `bun run verify` now passes clean (privacy + jsonb + progress + test-isolation + wasm + admin-build + cli-exec + typecheck). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(doctor): make 8b embedding-provider probe non-fatal (CI green) CI Tier 1 was failing on `gbrain doctor exits 0 on healthy DB` because the v0.28.5 doctor 8b check (cherry-picked from #665) pushed `status: 'fail'` in two non-fatal scenarios: 1. No API key configured (`isAvailable('embedding')` returns false) 2. Probe throws (network blip, transient 5xx, DNS, rate limit) Both are noise in CI and on offline workstations — the brain is healthy, the provider just isn't reachable from this environment. The v0.28.5 plan P1 decision called for non-fatal-on-offline behavior: > Doctor 8b probes live every run (taken as-is). Non-fatal on network > failure (warns rather than errors); silently skipped when no API key > configured. This commit aligns the implementation with that decision: - !available → status 'ok' with "Skipped (no provider credentials)" message so the run is visible in --json output without failing exit code - catch block → status 'warn' (was 'fail') so probe failures surface informationally without crashing CI / autopilot's periodic doctor runs The mismatch slipped past plan-time review because #665 was cherry-picked before P1 was finalized; the type-fix pass in 4c26e484 only adjusted the DB-column probe shape, not the API-availability gate. CI Tier 1 (Mechanical) — `test/e2e/mechanical.test.ts:1220` — "gbrain doctor exits 0 on healthy DB" now passes against a fresh Postgres without `OPENAI_API_KEY` / `VOYAGE_API_KEY` set. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Brandon Lipman <brandon@offdeck.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-authored-by: Eva <eva@100yen.org> Co-authored-by: garrytan-agents <garrytan-agents@users.noreply.github.com>
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Garry Tan
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CHANGELOG.md
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All notable changes to GBrain will be documented in this file.
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## [0.28.5] - 2026-05-06
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## **`gbrain upgrade` is finally self-healing. Wedged brains, blocked binaries, and the `bun add -g` foot-gun all fixed in one wave.**
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## **Nine community PRs landed together. Real users who could not upgrade since v0.27 land — and we close the structural gap that produced the wedge in the first place.**
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This is a fix wave. v0.27 shipped pluggable embedding providers and immediately
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exposed three independent failure modes. The ones in this release are not
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features. They are unstucks.
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If you are wedged on a v0.18-to-v0.27 upgrade, hit a `permission denied` on a
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freshly bun-linked install, accidentally typed `bun add -g gbrain` and got a
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mystery binary, or saw `--embedding-dimensions 768` create a 1536-d column
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anyway, this release is for you. Run `gbrain upgrade` and you walk away with
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a healthy brain on the other side.
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### The numbers that matter
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Source: production deployment plus the `test/schema-bootstrap-coverage.test.ts`
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and `test/embedding-dim-check.test.ts` regression suites added in this wave.
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| Metric | Before v0.28.5 | After v0.28.5 |
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|-----------------------------------------|--------------------|--------------------|
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| `gbrain upgrade` auto-applies migrations| no (WARN only) | **yes** |
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| `connectEngine` perf cost on hot path | full schema replay | single ledger row |
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| Bootstrap coverage drift detection | hand-maintained | **auto-derived** |
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| `--embedding-dimensions 768` honored | no (silent 1536) | **yes** |
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| Existing brain dim mismatch | silent corruption | **hard error + recipe** |
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| `bun-link` install detected | "unknown" | **'bun-link'** |
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| Squatter `gbrain@1.3.x` warned about | no | **yes** |
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| `src/cli.ts` git-tracked as executable | 100644 (broken) | **100755 + CI guard** |
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The single most load-bearing change is the post-upgrade auto-apply of pending
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schema migrations (X1). Until v0.28.5, `gbrain upgrade` warned about pending
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migrations and asked the user to run `gbrain init --migrate-only` themselves.
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Eleven wedge incidents over two years prove that does not happen. The hook in
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`runPostUpgrade()` now closes the loop in one process.
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### What this means for you
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If you have not been able to upgrade since v0.27 dropped: the canonical path is
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back. `gbrain upgrade` walks away with a working brain. If you were stuck on
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a `bun add -g gbrain` install: the warning fires on every upgrade and tells you
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exactly how to recover. If you were silently embedding at the wrong dimension:
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init refuses, doctor flags it, and `docs/embedding-migrations.md` has the full
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ALTER recipe. The structural gap that produced the v0.27 wedge in the first
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place — a hand-maintained array that humans were supposed to remember to update
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— is now an auto-derived parser that fails the build the next time someone
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forgets.
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Run `gbrain upgrade` and run `gbrain doctor`. If anything looks off, file an
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issue with the doctor output and we will look.
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### Itemized changes
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#### Cluster A — PGLite upgrade wedge (closes #670, #661, #657, #651, #625, #615, #609)
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- **`applyForwardReferenceBootstrap` now covers v0.20 + v0.26.3 + v0.27 columns** in both PGLite and Postgres engines. Builds on contributor PRs from @brandonlipman (#682), @mdcruz88 (#668), @ChenyqThu (#627), and @alan-mathison-enigma (#610). Adds explicit probes for `content_chunks.search_vector / parent_symbol_path / doc_comment / symbol_name_qualified` (v0.20 Cathedral II), `mcp_request_log.agent_name / params / error_message` (v0.26.3), and `subagent_messages.provider_id` (v0.27). Older brains pinned at any version v0.13+ now upgrade cleanly to current master.
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- **`gbrain upgrade` auto-applies pending schema migrations.** `runPostUpgrade()` now calls `engine.initSchema()` after the orchestrator migration pass. Wrapped in try/catch so connection failures fall back to the existing WARN message. The headline outcome ("run upgrade, brain works") is now literally true for cluster A. Codex outside-voice review forced this pivot during plan-stage review.
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- **`hasPendingMigrations()` probe in `connectEngine`.** Replaces the unconditional `engine.initSchema()` from PR #652 (oyi77's investigation) with a conditional probe: a single `getConfig('version')` SELECT, defensive `true` on probe failure. Already-migrated brains skip the bootstrap-probe + SCHEMA_SQL replay + ledger-check on every short-lived `gbrain stats` / `query` / `doctor` call.
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- **`REQUIRED_BOOTSTRAP_COVERAGE` array replaced with a SQL-parser-backed contract.** A new helper extracts every `(table, column)` pair referenced by a `CREATE INDEX` in `PGLITE_SCHEMA_SQL` — including composite-index second and third columns — and asserts each one is either declared in CREATE TABLE or added by `applyForwardReferenceBootstrap`. Codex caught the original codex regression: the v0.27 wedge was a composite-index second column (`provider_id` in `(job_id, provider_id)`) that any first-col-only extractor would miss. Future column-with-index drift now fails the build at PR time, no human required.
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|
||||
#### Cluster B — Embedding dimensions actually take effect (closes #673, #672, #666, #640)
|
||||
|
||||
- **Schema templating cascade fixed.** Cherry-picks #641 from @100yenadmin (Eva). `getPostgresSchema(dims, model)` is the runtime substitution wrapper used by every `SCHEMA_SQL` consumer; `applyChunkEmbeddingIndexPolicy()` skips HNSW when dims > 2000 (Voyage 4 Large fits, just falls back to exact scans). `--embedding-dimensions 768` now templates `vector(768)` end-to-end.
|
||||
- **`gbrain doctor` 8b live embedding-provider probe.** Cherry-picks #665. Checks dim parity across config, live provider response, and the `content_chunks.embedding` column type. Surfaces silent corruption loud at doctor-run time. Refactored to use the shared `readContentChunksEmbeddingDim` helper so it works on both PGLite and Postgres.
|
||||
- **Adaptive embed batch sizing for Voyage's 120K-token limit.** Cherry-picks #680. Voyage's tokenizer runs 3-4× denser than tiktoken, so previous batch sizing routinely tripped the 120K cap and stalled secondary backfill. Adaptive splitting recovers automatically; previously 26% of corpus could go un-embedded on Voyage 4 Large.
|
||||
- **`gbrain init` hard-errors on existing-brain dim mismatch (A4).** New behavior: when a fresh init flag conflicts with the existing column type, init exits 1 with an inline four-step ALTER recipe. The recipe explicitly drops the HNSW index, alters the column, wipes stale embeddings, and conditionally reindexes only when dims ≤ 2000 (codex finding #8 — recipes that miss the conditional reindex create a new wedge for Voyage 4 Large users). Loud failure beats silent corruption.
|
||||
- **`docs/embedding-migrations.md`.** Full guide for switching embedding models or dimensions on an existing brain. Linked from doctor 8b and init's error message.
|
||||
- **Misleading dim-mismatch error fixed (#672).** Previous error suggested `gbrain migrate --embedding-model … --embedding-dimensions …`, but `gbrain migrate` only handles engine migration. New error inlines the actual recipe and points at the docs.
|
||||
|
||||
#### Cluster C — CLI executable bit (closes #683 / dupes #655)
|
||||
|
||||
- **`src/cli.ts` shipped with mode `100644`.** Bun-link installs symlink directly to it, so `gbrain --version` failed with `permission denied` on the very first invocation. Cherry-picks #683 from @brandonlipman; #655 from @abkrim is a byte-identical duplicate, closed with credit.
|
||||
- **CI guard against future regression.** New `scripts/check-cli-executable.sh` asserts `git ls-files --stage src/cli.ts` returns `100755`. Wired into `bun run verify`. Failure prints the exact recovery command (`chmod +x src/cli.ts && git add --chmod=+x src/cli.ts`).
|
||||
|
||||
#### Cluster D — `bun add -g gbrain` foot-gun (closes #656, #658)
|
||||
|
||||
- **`detectInstallMethod()` rewritten with three layered signals.** bun-link installs now classify as `'bun-link'` (closes #656) when argv[1] resolves through a symlink into a checkout whose `.git/config` contains `garrytan/gbrain`. Canonical bun installs verify against `package.json.repository.url` plus a `src/cli.ts` source-marker fallback (the npm squatter ships compiled binary, not source). On 'suspect', `gbrain upgrade` prints a loud-red recovery message naming both the source-clone path AND the release-binary path so users without a local clone can recover.
|
||||
- **README updated.** New "DO NOT use `bun add -g gbrain`" callout names the npm-name collision (`gbrain@1.3.x`) and the v0.29 plan to publish under `@garrytan/gbrain` (the only structural fix).
|
||||
|
||||
### For contributors
|
||||
|
||||
- New `test/schema-bootstrap-coverage.test.ts` parser helpers (`parseBaseTableColumns`, `parseIndexColumnReferences`, `parseAlterAddColumns`) replace the hand-maintained `REQUIRED_BOOTSTRAP_COVERAGE` array. Add a new column-with-index to `PGLITE_SCHEMA_SQL`, forget to extend bootstrap, and the test fails loud at PR time.
|
||||
- New `src/core/embedding-dim-check.ts` exports `readContentChunksEmbeddingDim` (engine-portable column-dim probe) and `embeddingMismatchMessage` (the four-step recipe). Used by both init and doctor 8b — single source of truth for the recipe.
|
||||
- 5 community-author commits preserve attribution (`Author:` line on each cherry-pick) so contributor work is visible in `git log`.
|
||||
|
||||
## To take advantage of v0.28.5
|
||||
|
||||
`gbrain upgrade` should do this automatically. If it does not, or if `gbrain doctor` warns about a partial migration:
|
||||
|
||||
1. **Run the orchestrator manually:**
|
||||
```bash
|
||||
gbrain apply-migrations --yes
|
||||
gbrain init --migrate-only
|
||||
```
|
||||
2. **Verify the outcome:**
|
||||
```bash
|
||||
gbrain doctor
|
||||
gbrain stats
|
||||
```
|
||||
3. **If anything fails or the numbers look wrong,** please file an issue:
|
||||
https://github.com/garrytan/gbrain/issues with:
|
||||
- output of `gbrain doctor`
|
||||
- output of `gbrain --version`
|
||||
- which step broke
|
||||
|
||||
## [0.28.4] - 2026-05-06
|
||||
|
||||
## **`gbrain eval cross-modal` — three frontier models score your skill output BEFORE tests cement it.**
|
||||
|
||||
@@ -51,6 +51,14 @@ postinstall hook on global installs, so schema migrations never run and the CLI
|
||||
aborts with `Aborted()` the first time it opens PGLite. Use `git clone + bun install
|
||||
&& bun link` as shown above. See [#218](https://github.com/garrytan/gbrain/issues/218).
|
||||
|
||||
**Do NOT use `bun add -g gbrain` or `npm install -g gbrain`.** The npm registry
|
||||
has an unrelated package squatting that name (`gbrain@1.3.x`) — you'd silently
|
||||
install the wrong binary and overwrite the canonical one. v0.28.5+ detects this
|
||||
and prints a recovery message on `gbrain upgrade`, but the `git clone + bun link`
|
||||
path above is the only reliable install method until we publish under
|
||||
`@garrytan/gbrain` (tracked v0.29 follow-up). See
|
||||
[#658](https://github.com/garrytan/gbrain/issues/658).
|
||||
|
||||
```
|
||||
3 results (hybrid search, 0.12s):
|
||||
|
||||
|
||||
105
docs/embedding-migrations.md
Normal file
105
docs/embedding-migrations.md
Normal file
@@ -0,0 +1,105 @@
|
||||
# Switching embedding models or dimensions on an existing brain
|
||||
|
||||
GBrain stores embeddings in a fixed-dimension `vector(N)` column on
|
||||
`content_chunks`. If you switch to a model with a different dimension
|
||||
(e.g. `text-embedding-3-large` 1536 → `voyage-multilingual-large-2` 2048,
|
||||
or back to a smaller model like `nomic-embed-text` 768), the on-disk
|
||||
column type doesn't change automatically.
|
||||
|
||||
`gbrain init` and `gbrain doctor` both detect and refuse to silently
|
||||
proceed in this case. This doc is the recipe they point at.
|
||||
|
||||
## Why we don't do this automatically
|
||||
|
||||
Switching dimensions requires:
|
||||
|
||||
1. Dropping the HNSW vector index (pgvector won't survive an `ALTER COLUMN TYPE`).
|
||||
2. Altering the column type.
|
||||
3. Wiping every existing embedding (the old vectors are unusable in the new space).
|
||||
4. Re-embedding the entire corpus (can take hours on a 50K-page brain and costs $1-100 in API calls depending on model).
|
||||
5. Conditionally recreating the index (HNSW supports up to 2000 dimensions per pgvector; above that you must use exact scans).
|
||||
|
||||
That's not an upgrade-time auto-run. It's a deliberate, expensive
|
||||
operation. Run it when you've decided you actually want the new model.
|
||||
|
||||
## Recipe — manual `psql` against your brain
|
||||
|
||||
Replace `<NEW_DIMS>` with your target dimension count.
|
||||
|
||||
```sql
|
||||
BEGIN;
|
||||
|
||||
-- 1. Drop the HNSW index. It can't survive the column type change.
|
||||
DROP INDEX IF EXISTS idx_chunks_embedding;
|
||||
|
||||
-- 2. Alter the column type. (You can DROP COLUMN + ADD COLUMN instead
|
||||
-- if the existing data is already gone — same end state.)
|
||||
ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(<NEW_DIMS>);
|
||||
|
||||
-- 3. Clear stale embeddings so they don't survive into the new space.
|
||||
-- Either truncate (faster, drops all chunks) or null out (preserves
|
||||
-- chunk text so re-embed regenerates without re-chunking):
|
||||
UPDATE content_chunks SET embedding = NULL, embedded_at = NULL;
|
||||
|
||||
-- 4. Recreate the HNSW index ONLY IF dims <= 2000. Above that, leave it
|
||||
-- indexless and rely on exact scans (gbrain searchVector handles this
|
||||
-- automatically — search just gets slower, not broken).
|
||||
-- For dims <= 2000 (e.g. 1024, 1536, 768):
|
||||
CREATE INDEX IF NOT EXISTS idx_chunks_embedding
|
||||
ON content_chunks USING hnsw (embedding vector_cosine_ops);
|
||||
-- For dims > 2000 (e.g. 2048 Voyage 4 Large): skip step 4.
|
||||
|
||||
COMMIT;
|
||||
```
|
||||
|
||||
Then update gbrain's config so it knows the new dim:
|
||||
|
||||
```bash
|
||||
gbrain config set embedding_model <model>
|
||||
gbrain config set embedding_dimensions <NEW_DIMS>
|
||||
```
|
||||
|
||||
And re-embed the corpus:
|
||||
|
||||
```bash
|
||||
gbrain embed --stale
|
||||
```
|
||||
|
||||
## PGLite (local brain)
|
||||
|
||||
Same recipe, but you connect to the embedded database differently:
|
||||
|
||||
```bash
|
||||
gbrain config get database_url # confirm engine: pglite
|
||||
# Open a psql-equivalent — for PGLite, the easiest path is to write a small
|
||||
# script that imports PGLiteEngine and runs the SQL via engine.executeRaw.
|
||||
# Or migrate to Postgres temporarily (gbrain migrate --to supabase) if you
|
||||
# want a real psql connection.
|
||||
```
|
||||
|
||||
For most PGLite users the simpler path is to **wipe and re-init** if your
|
||||
corpus is small enough that re-syncing is faster than hand-crafting the
|
||||
migration:
|
||||
|
||||
```bash
|
||||
mv ~/.gbrain/brain.pglite ~/.gbrain/brain.pglite.bak
|
||||
gbrain init --pglite --embedding-dimensions <NEW_DIMS>
|
||||
gbrain sync # re-imports your brain repo from disk
|
||||
```
|
||||
|
||||
## Verify
|
||||
|
||||
After the recipe lands, `gbrain doctor --fast` should report green and
|
||||
`gbrain doctor` (full) should say check 8b passes:
|
||||
|
||||
```
|
||||
✓ embedding_provider dim parity: config 768 / column vector(768) / live probe 768
|
||||
```
|
||||
|
||||
If it doesn't, file an issue with the doctor output and the SQL you ran.
|
||||
|
||||
## v0.29+ plans
|
||||
|
||||
`gbrain migrate-embedding-dim --to <N>` is a tracked TODO. It will run
|
||||
the recipe above with progress reporting + an explicit confirmation
|
||||
gate. Until that lands, this manual recipe is the canonical path.
|
||||
40
evals/embedding-provider-eval.json
Normal file
40
evals/embedding-provider-eval.json
Normal file
@@ -0,0 +1,40 @@
|
||||
{
|
||||
"version": 1,
|
||||
"description": "Embedding provider smoke test — verifies semantic search returns expected results for known brain content. Run after any embedding model change or migration.",
|
||||
"queries": [
|
||||
{
|
||||
"id": "yc-labs-strategy",
|
||||
"query": "YC Labs strategy and product team",
|
||||
"relevant": [
|
||||
"originals/yc-labs-internal-team",
|
||||
"originals/harj-yc-labs-strategy-2026-05"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "garry-tan-person",
|
||||
"query": "Who is Garry Tan",
|
||||
"relevant": [
|
||||
"people/garry-tan"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "gstack-project",
|
||||
"query": "GStack open source AI coding framework",
|
||||
"relevant": [
|
||||
"projects/gstack/gstackbrain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "yc-carry-compensation",
|
||||
"query": "GP carry and compensation structure at YC",
|
||||
"relevant": [
|
||||
"originals/harj-yc-labs-strategy-2026-05"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": "meeting-search",
|
||||
"query": "recent office hours meeting notes",
|
||||
"relevant": []
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1664,6 +1664,14 @@ postinstall hook on global installs, so schema migrations never run and the CLI
|
||||
aborts with `Aborted()` the first time it opens PGLite. Use `git clone + bun install
|
||||
&& bun link` as shown above. See [#218](https://github.com/garrytan/gbrain/issues/218).
|
||||
|
||||
**Do NOT use `bun add -g gbrain` or `npm install -g gbrain`.** The npm registry
|
||||
has an unrelated package squatting that name (`gbrain@1.3.x`) — you'd silently
|
||||
install the wrong binary and overwrite the canonical one. v0.28.5+ detects this
|
||||
and prints a recovery message on `gbrain upgrade`, but the `git clone + bun link`
|
||||
path above is the only reliable install method until we publish under
|
||||
`@garrytan/gbrain` (tracked v0.29 follow-up). See
|
||||
[#658](https://github.com/garrytan/gbrain/issues/658).
|
||||
|
||||
```
|
||||
3 results (hybrid search, 0.12s):
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "gbrain",
|
||||
"version": "0.28.4",
|
||||
"version": "0.28.5",
|
||||
"description": "Postgres-native personal knowledge brain with hybrid RAG search",
|
||||
"type": "module",
|
||||
"main": "src/core/index.ts",
|
||||
@@ -36,8 +36,9 @@
|
||||
"build:pglite-snapshot": "bun run scripts/build-pglite-snapshot.ts",
|
||||
"test": "bash scripts/run-unit-parallel.sh",
|
||||
"test:full": "bun run verify && bash scripts/run-unit-parallel.sh && bun run test:slow && ([ -n \"$DATABASE_URL\" ] && bash scripts/run-e2e.sh || echo '[test:full] skipped E2E (no DATABASE_URL); run docker-compose -f docker-compose.ci.yml up + bun run test:e2e to include' 1>&2)",
|
||||
"verify": "bun run check:privacy && bun run check:jsonb && bun run check:progress && bun run check:test-isolation && bun run check:wasm && bun run check:admin-build && bun run typecheck",
|
||||
"check:all": "scripts/check-privacy.sh && scripts/check-jsonb-pattern.sh && scripts/check-progress-to-stdout.sh && scripts/check-no-legacy-getconnection.sh && scripts/check-test-isolation.sh && scripts/check-trailing-newline.sh && scripts/check-wasm-embedded.sh && scripts/check-exports-count.sh && scripts/check-admin-build.sh",
|
||||
"verify": "bun run check:privacy && bun run check:jsonb && bun run check:progress && bun run check:test-isolation && bun run check:wasm && bun run check:admin-build && bun run check:cli-exec && bun run typecheck",
|
||||
"check:cli-exec": "scripts/check-cli-executable.sh",
|
||||
"check:all": "scripts/check-privacy.sh && scripts/check-jsonb-pattern.sh && scripts/check-progress-to-stdout.sh && scripts/check-no-legacy-getconnection.sh && scripts/check-test-isolation.sh && scripts/check-trailing-newline.sh && scripts/check-wasm-embedded.sh && scripts/check-exports-count.sh && scripts/check-admin-build.sh && scripts/check-cli-executable.sh",
|
||||
"check:wasm": "scripts/check-wasm-embedded.sh",
|
||||
"check:newlines": "scripts/check-trailing-newline.sh",
|
||||
"test:e2e": "bash scripts/run-e2e.sh",
|
||||
|
||||
23
scripts/check-cli-executable.sh
Executable file
23
scripts/check-cli-executable.sh
Executable file
@@ -0,0 +1,23 @@
|
||||
#!/bin/bash
|
||||
# CI guard: src/cli.ts must be tracked by git in executable mode (100755).
|
||||
#
|
||||
# Why: bun-link installs symlink to src/cli.ts directly. If the mode bit
|
||||
# regresses to 100644, the very first `gbrain --version` invocation fails
|
||||
# with `permission denied`. v0.28.5 (cluster C, #683) fixed the original
|
||||
# regression; this guard prevents future drift.
|
||||
#
|
||||
# Wired into `bun run verify`. Fast, no external deps.
|
||||
set -e
|
||||
|
||||
MODE=$(git ls-files --stage src/cli.ts | awk '{print $1}')
|
||||
if [ "$MODE" != "100755" ]; then
|
||||
echo "FAIL: src/cli.ts is tracked at mode $MODE; expected 100755 (executable)."
|
||||
echo ""
|
||||
echo "Fix: chmod +x src/cli.ts && git add --chmod=+x src/cli.ts"
|
||||
echo ""
|
||||
echo "Background: bun-link installs symlink to this file directly. Mode 100644"
|
||||
echo "produces 'permission denied' on first invocation (issue #683)."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "OK: src/cli.ts is git-tracked as executable (100755)"
|
||||
19
src/cli.ts
Normal file → Executable file
19
src/cli.ts
Normal file → Executable file
@@ -659,6 +659,25 @@ async function connectEngine(): Promise<BrainEngine> {
|
||||
process.env.GBRAIN_NO_RETRY_CONNECT === '1';
|
||||
const { connectWithRetry } = await import('./core/db.ts');
|
||||
await connectWithRetry(engine, toEngineConfig(config), { noRetry });
|
||||
|
||||
// Auto-apply pending schema migrations on connect (#651). Cheap probe
|
||||
// first so already-migrated brains don't pay the bootstrap-probe +
|
||||
// SCHEMA_SQL replay + ledger-check cost on every short-lived CLI call.
|
||||
// This is the conditional version of #652 (oyi77's investigation):
|
||||
// same correctness, no perf regression on the hot path.
|
||||
try {
|
||||
const { hasPendingMigrations } = await import('./core/migrate.ts');
|
||||
if (await hasPendingMigrations(engine)) {
|
||||
await engine.initSchema();
|
||||
}
|
||||
} catch (err) {
|
||||
// Non-fatal: if probe or initSchema fails, surface a hint and continue
|
||||
// with the connected engine. Subsequent operations will surface the
|
||||
// real schema error in context.
|
||||
console.warn(` Schema probe/migrate failed: ${(err as Error).message}`);
|
||||
console.warn(' Try: gbrain init --migrate-only');
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
|
||||
@@ -572,6 +572,80 @@ export async function runDoctor(engine: BrainEngine | null, args: string[], dbSo
|
||||
checks.push({ name: 'embeddings', status: 'warn', message: 'Could not check embedding health' });
|
||||
}
|
||||
|
||||
// 8b. Embedding provider eval — live smoke test of the configured provider.
|
||||
// Verifies: correct model, API key works, dimensions match config, DB column matches.
|
||||
progress.heartbeat('embedding_provider');
|
||||
try {
|
||||
const {
|
||||
getEmbeddingModel,
|
||||
getEmbeddingDimensions,
|
||||
embedOne,
|
||||
isAvailable,
|
||||
} = await import('../core/ai/gateway.ts');
|
||||
|
||||
const configuredModel = getEmbeddingModel();
|
||||
const configuredDims = getEmbeddingDimensions();
|
||||
const available = isAvailable('embedding');
|
||||
|
||||
if (!available) {
|
||||
// Per v0.28.5 plan P1: silently skipped when no API key is configured.
|
||||
// Doctor must stay green on CI / local-only / offline environments where
|
||||
// a full provider probe isn't possible. The skipped status is still
|
||||
// visible in --json output so operators can see it ran.
|
||||
checks.push({
|
||||
name: 'embedding_provider',
|
||||
status: 'ok',
|
||||
message: `Skipped (no provider credentials). Model: ${configuredModel}.`,
|
||||
});
|
||||
} else {
|
||||
// Live embed test
|
||||
const start = Date.now();
|
||||
const vec = await embedOne('gbrain doctor embedding smoke test');
|
||||
const ms = Date.now() - start;
|
||||
const actualDims = vec.length;
|
||||
|
||||
const issues: string[] = [];
|
||||
|
||||
// Check dimensions match config
|
||||
if (actualDims !== configuredDims) {
|
||||
issues.push(`Dimension mismatch: provider returned ${actualDims} but config expects ${configuredDims}`);
|
||||
}
|
||||
|
||||
// Check DB column dimensions match (engine-portable; works on both
|
||||
// Postgres and PGLite via the shared dim-check helper added in v0.28.5).
|
||||
try {
|
||||
const { readContentChunksEmbeddingDim } = await import('../core/embedding-dim-check.ts');
|
||||
const colDim = await readContentChunksEmbeddingDim(engine);
|
||||
if (colDim.exists && colDim.dims !== null && colDim.dims !== actualDims) {
|
||||
issues.push(`DB dimension mismatch: column is vector(${colDim.dims}) but provider returns ${actualDims}-dim. See docs/embedding-migrations.md for the manual ALTER recipe.`);
|
||||
}
|
||||
} catch { /* column or table missing — fresh brain, fine */ }
|
||||
|
||||
if (issues.length > 0) {
|
||||
checks.push({
|
||||
name: 'embedding_provider',
|
||||
status: 'warn',
|
||||
message: `${configuredModel} responds (${ms}ms, ${actualDims} dims) but: ${issues.join('; ')}`,
|
||||
});
|
||||
} else {
|
||||
checks.push({
|
||||
name: 'embedding_provider',
|
||||
status: 'ok',
|
||||
message: `${configuredModel} ✓ ${ms}ms, ${actualDims} dims, DB aligned`,
|
||||
});
|
||||
}
|
||||
}
|
||||
} catch (e: any) {
|
||||
// Per v0.28.5 plan P1: non-fatal on network failure. The probe surfaces
|
||||
// the issue but doesn't fail doctor — common cases (rate limit, transient
|
||||
// 5xx, DNS blip, expired key) shouldn't take down a CI run.
|
||||
checks.push({
|
||||
name: 'embedding_provider',
|
||||
status: 'warn',
|
||||
message: `Embedding provider probe failed: ${e.message?.slice(0, 200) ?? e}`,
|
||||
});
|
||||
}
|
||||
|
||||
// 9. Graph health (link + timeline coverage on entity pages).
|
||||
// dead_links removed in v0.10.1: ON DELETE CASCADE on link FKs makes it always 0.
|
||||
progress.heartbeat('graph_coverage');
|
||||
|
||||
@@ -195,6 +195,32 @@ async function initPGLite(opts: {
|
||||
const engine = await createEngine({ engine: 'pglite' });
|
||||
try {
|
||||
await engine.connect({ database_path: dbPath, engine: 'pglite' });
|
||||
|
||||
// v0.28.5 (A4): refuse to silently re-template an existing brain with a
|
||||
// mismatched embedding dimension. Loud failure beats the v0.27 silent-
|
||||
// corruption pattern that surfaced as #673.
|
||||
if (opts.aiOpts?.embedding_dimensions) {
|
||||
const { readContentChunksEmbeddingDim, embeddingMismatchMessage } = await import('../core/embedding-dim-check.ts');
|
||||
const existing = await readContentChunksEmbeddingDim(engine);
|
||||
if (existing.exists && existing.dims !== null && existing.dims !== opts.aiOpts.embedding_dimensions) {
|
||||
console.error('\n' + embeddingMismatchMessage({
|
||||
currentDims: existing.dims,
|
||||
requestedDims: opts.aiOpts.embedding_dimensions,
|
||||
requestedModel: opts.aiOpts.embedding_model,
|
||||
source: 'init',
|
||||
}) + '\n');
|
||||
if (opts.jsonOutput) {
|
||||
console.log(JSON.stringify({
|
||||
status: 'error',
|
||||
reason: 'embedding_dim_mismatch',
|
||||
current_dims: existing.dims,
|
||||
requested_dims: opts.aiOpts.embedding_dimensions,
|
||||
}));
|
||||
}
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
await engine.initSchema();
|
||||
|
||||
const config: GBrainConfig = {
|
||||
@@ -304,6 +330,30 @@ async function initPostgres(opts: {
|
||||
// Non-fatal
|
||||
}
|
||||
|
||||
// v0.28.5 (A4): refuse to silently re-template an existing brain with a
|
||||
// mismatched embedding dimension (mirror of the PGLite path above).
|
||||
if (opts.aiOpts?.embedding_dimensions) {
|
||||
const { readContentChunksEmbeddingDim, embeddingMismatchMessage } = await import('../core/embedding-dim-check.ts');
|
||||
const existing = await readContentChunksEmbeddingDim(engine);
|
||||
if (existing.exists && existing.dims !== null && existing.dims !== opts.aiOpts.embedding_dimensions) {
|
||||
console.error('\n' + embeddingMismatchMessage({
|
||||
currentDims: existing.dims,
|
||||
requestedDims: opts.aiOpts.embedding_dimensions,
|
||||
requestedModel: opts.aiOpts.embedding_model,
|
||||
source: 'init',
|
||||
}) + '\n');
|
||||
if (opts.jsonOutput) {
|
||||
console.log(JSON.stringify({
|
||||
status: 'error',
|
||||
reason: 'embedding_dim_mismatch',
|
||||
current_dims: existing.dims,
|
||||
requested_dims: opts.aiOpts.embedding_dimensions,
|
||||
}));
|
||||
}
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
console.log('Running schema migration...');
|
||||
await engine.initSchema();
|
||||
|
||||
|
||||
@@ -1,8 +1,10 @@
|
||||
import { execSync } from 'child_process';
|
||||
import { existsSync, readFileSync, writeFileSync, mkdirSync, appendFileSync } from 'fs';
|
||||
import { join } from 'path';
|
||||
import { existsSync, readFileSync, writeFileSync, mkdirSync, appendFileSync, realpathSync, lstatSync } from 'fs';
|
||||
import { join, dirname } from 'path';
|
||||
import { VERSION } from '../version.ts';
|
||||
|
||||
const GBRAIN_GITHUB_REPO = 'garrytan/gbrain';
|
||||
|
||||
export async function runUpgrade(args: string[]) {
|
||||
if (args.includes('--help') || args.includes('-h')) {
|
||||
console.log('Usage: gbrain upgrade\n\nSelf-update the CLI.\n\nDetects install method (bun, binary, clawhub) and runs the appropriate update.\nAfter upgrading, shows what\'s new and offers to set up new features.');
|
||||
@@ -17,6 +19,18 @@ export async function runUpgrade(args: string[]) {
|
||||
|
||||
let upgraded = false;
|
||||
switch (method) {
|
||||
case 'bun-link':
|
||||
// v0.28.5: bun-link installs are source clones. Pull + bun install
|
||||
// is the upgrade path; npm/bun's update mechanism doesn't apply.
|
||||
console.log('Upgrading via bun-link source clone...');
|
||||
console.log(' cd into your gbrain checkout, then run:');
|
||||
console.log(' git pull');
|
||||
console.log(' bun install');
|
||||
console.log(' bun link');
|
||||
console.log('');
|
||||
console.log(' (auto-detect can\'t do this for you because it doesn\'t know which checkout to update.)');
|
||||
break;
|
||||
|
||||
case 'bun':
|
||||
console.log('Upgrading via bun...');
|
||||
try {
|
||||
@@ -218,6 +232,37 @@ export async function runPostUpgrade(args: string[] = []): Promise<void> {
|
||||
console.error('Run `gbrain apply-migrations --yes` manually to retry.');
|
||||
}
|
||||
|
||||
// v0.28.5 (X1): explicitly apply pending schema migrations.
|
||||
// apply-migrations runs orchestrator migrations and only WARNs about
|
||||
// schema-version drift (apply-migrations.ts:296-302). Without this hook,
|
||||
// `gbrain upgrade` leaves wedged brains wedged — the user has to read
|
||||
// the WARN and run `gbrain init --migrate-only` themselves. We've shipped
|
||||
// 11 wedge incidents asking users to read warnings; close the loop here.
|
||||
// A1's hasPendingMigrations probe in connectEngine is belt-and-suspenders
|
||||
// for any path that bypasses upgrade (autopilot, direct CLI on stale brain).
|
||||
try {
|
||||
const { loadConfig: lcSchema, toEngineConfig: toCfgSchema } = await import('../core/config.ts');
|
||||
const { createEngine } = await import('../core/engine-factory.ts');
|
||||
const cfgSchema = lcSchema();
|
||||
if (cfgSchema) {
|
||||
const engine = await createEngine(toCfgSchema(cfgSchema));
|
||||
try {
|
||||
await engine.connect(toCfgSchema(cfgSchema));
|
||||
await engine.initSchema();
|
||||
console.log(' Schema up to date.');
|
||||
} finally {
|
||||
try { await engine.disconnect(); } catch { /* best-effort */ }
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
// Non-fatal: connection or DDL failure here falls back to the existing
|
||||
// user-facing WARN. apply-migrations.ts:296-302 already surfaces the
|
||||
// hint to run `gbrain init --migrate-only`.
|
||||
const msg = e instanceof Error ? e.message : String(e);
|
||||
console.warn(`\nSchema auto-apply skipped: ${msg}`);
|
||||
console.warn('Run `gbrain init --migrate-only` manually if your brain is wedged.');
|
||||
}
|
||||
|
||||
// v0.25.1: agent-readable advisory listing recommended skills the
|
||||
// workspace hasn't installed yet. No-op when everything is installed.
|
||||
try {
|
||||
@@ -244,11 +289,25 @@ function isNewerThan(version: string, baseline: string): boolean {
|
||||
return false;
|
||||
}
|
||||
|
||||
export function detectInstallMethod(): 'bun' | 'binary' | 'clawhub' | 'unknown' {
|
||||
export function detectInstallMethod(): 'bun' | 'bun-link' | 'binary' | 'clawhub' | 'unknown' {
|
||||
const execPath = process.execPath || '';
|
||||
|
||||
// Check if running from node_modules (bun/npm install)
|
||||
// v0.28.5 cluster D: bun-link signal first.
|
||||
// bun link puts a symlink at ~/.bun/bin/gbrain → either the source's bin
|
||||
// entry (compiled CLI) OR src/cli.ts directly. Either way, realpath
|
||||
// resolves into a directory we can walk up from to find a .git/config
|
||||
// pointing at our repo.
|
||||
const bunLinkResult = detectBunLink();
|
||||
if (bunLinkResult === 'bun-link') return 'bun-link';
|
||||
|
||||
// Check if running from node_modules (bun/npm install). Could be canonical
|
||||
// (we publish under garrytan/gbrain) OR the squatter (npm `gbrain@1.3.x`).
|
||||
// Sub-classify and warn loudly on suspect installs (#658).
|
||||
if (execPath.includes('node_modules') || process.argv[1]?.includes('node_modules')) {
|
||||
const verdict = classifyBunInstall();
|
||||
if (verdict === 'suspect') {
|
||||
printSquatterRecovery();
|
||||
}
|
||||
return 'bun';
|
||||
}
|
||||
|
||||
@@ -267,3 +326,131 @@ export function detectInstallMethod(): 'bun' | 'binary' | 'clawhub' | 'unknown'
|
||||
|
||||
return 'unknown';
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.28.5 cluster D, signal 1 — bun-link detection (closes #656).
|
||||
*
|
||||
* argv[1] is what `bun /path/to/cli.ts` was invoked with. When `bun link`
|
||||
* is in play, that path is typically a symlink (~/.bun/bin/gbrain) to
|
||||
* either the source repo's compiled binary or src/cli.ts directly.
|
||||
* Walk up from the realpath looking for a `.git/config` whose remote
|
||||
* url contains `garrytan/gbrain` (case-insensitive substring).
|
||||
*
|
||||
* Returns 'bun-link' when we're confident; null otherwise (caller falls
|
||||
* through to the existing detection chain). Best-effort: forks, tarball
|
||||
* installs, detached source trees, and `.git`-less installs all fall
|
||||
* through, which is acceptable per codex's plan-review feedback.
|
||||
*/
|
||||
function detectBunLink(): 'bun-link' | null {
|
||||
try {
|
||||
const argv1 = process.argv[1];
|
||||
if (!argv1) return null;
|
||||
|
||||
// Symlink check first: `bun link` always creates one.
|
||||
let isSymlink = false;
|
||||
try {
|
||||
isSymlink = lstatSync(argv1).isSymbolicLink();
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
if (!isSymlink) return null;
|
||||
|
||||
const resolved = realpathSync(argv1);
|
||||
let dir = dirname(resolved);
|
||||
// Walk up at most 6 levels looking for .git/config.
|
||||
for (let i = 0; i < 6; i++) {
|
||||
const gitConfigPath = join(dir, '.git', 'config');
|
||||
if (existsSync(gitConfigPath)) {
|
||||
try {
|
||||
const cfg = readFileSync(gitConfigPath, 'utf-8');
|
||||
// Loose substring match: covers https://, git@, ssh://, fork URLs
|
||||
// that mention upstream in [remote "upstream"], and case variants.
|
||||
if (cfg.toLowerCase().includes(GBRAIN_GITHUB_REPO.toLowerCase())) {
|
||||
return 'bun-link';
|
||||
}
|
||||
} catch { /* unreadable config — not our case */ }
|
||||
return null; // found .git/config but no match → not our repo
|
||||
}
|
||||
const parent = dirname(dir);
|
||||
if (parent === dir) break; // reached filesystem root
|
||||
dir = parent;
|
||||
}
|
||||
return null;
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.28.5 cluster D, signal 2 — bun install authenticity check (closes #658).
|
||||
*
|
||||
* When `bun add -g gbrain` (or `npm install -g gbrain`) installs from
|
||||
* npm, the package is the squatter — an unrelated `gbrain@1.3.x` that
|
||||
* silently overwrites our binary. This function reads the install
|
||||
* directory's package.json and checks two non-spoofable signals:
|
||||
* - `repository.url` contains `garrytan/gbrain` (case-insensitive)
|
||||
* - the install dir contains a `src/cli.ts` file (squatter ships
|
||||
* compiled binary, not source)
|
||||
*
|
||||
* If neither matches, returns 'suspect' and the caller surfaces a loud
|
||||
* recovery message. Codex's plan-review noted these signals are spoofable
|
||||
* by a determined squatter — accepted; this is best-effort warning, not
|
||||
* an assertion. The right structural fix is publishing under a scoped
|
||||
* name like `@garrytan/gbrain` (tracked v0.29 follow-up).
|
||||
*/
|
||||
function classifyBunInstall(): 'canonical' | 'suspect' {
|
||||
try {
|
||||
const argv1 = process.argv[1];
|
||||
if (!argv1) return 'suspect';
|
||||
|
||||
// Walk up from argv1 looking for the package.json that owns this install.
|
||||
let dir = dirname(realpathSync(argv1));
|
||||
for (let i = 0; i < 6; i++) {
|
||||
const pkgPath = join(dir, 'package.json');
|
||||
if (existsSync(pkgPath)) {
|
||||
try {
|
||||
const pkg = JSON.parse(readFileSync(pkgPath, 'utf-8'));
|
||||
const repoUrl = (typeof pkg.repository === 'string'
|
||||
? pkg.repository
|
||||
: pkg.repository?.url) ?? '';
|
||||
if (repoUrl.toLowerCase().includes(GBRAIN_GITHUB_REPO.toLowerCase())) {
|
||||
return 'canonical';
|
||||
}
|
||||
// Source-marker fallback: our published-as-source install always
|
||||
// ships src/cli.ts next to package.json. The squatter ships dist/.
|
||||
if (existsSync(join(dir, 'src', 'cli.ts'))) {
|
||||
return 'canonical';
|
||||
}
|
||||
return 'suspect';
|
||||
} catch {
|
||||
return 'suspect';
|
||||
}
|
||||
}
|
||||
const parent = dirname(dir);
|
||||
if (parent === dir) break;
|
||||
dir = parent;
|
||||
}
|
||||
return 'suspect';
|
||||
} catch {
|
||||
return 'suspect';
|
||||
}
|
||||
}
|
||||
|
||||
function printSquatterRecovery(): void {
|
||||
console.warn('');
|
||||
console.warn(' WARNING: gbrain install does not appear to be from garrytan/gbrain.');
|
||||
console.warn(' This is likely the npm-name collision tracked in issue #658:');
|
||||
console.warn(' https://www.npmjs.com/package/gbrain (an unrelated package).');
|
||||
console.warn('');
|
||||
console.warn(' Recovery options:');
|
||||
console.warn(' 1. Install from source:');
|
||||
console.warn(' bun remove -g gbrain');
|
||||
console.warn(' git clone https://github.com/garrytan/gbrain.git');
|
||||
console.warn(' cd gbrain && bun install && bun link');
|
||||
console.warn('');
|
||||
console.warn(' 2. Download a release binary:');
|
||||
console.warn(' https://github.com/garrytan/gbrain/releases');
|
||||
console.warn('');
|
||||
console.warn(' See docs/INSTALL_FOR_AGENTS.md for the canonical install paths.');
|
||||
console.warn('');
|
||||
}
|
||||
|
||||
@@ -11,12 +11,23 @@
|
||||
|
||||
import type { Implementation } from './types.ts';
|
||||
|
||||
const VOYAGE_OUTPUT_DIMENSION_MODELS = new Set([
|
||||
'voyage-4-large',
|
||||
'voyage-4',
|
||||
'voyage-4-lite',
|
||||
'voyage-3-large',
|
||||
'voyage-3.5',
|
||||
'voyage-3.5-lite',
|
||||
'voyage-code-3',
|
||||
]);
|
||||
|
||||
/**
|
||||
* Build the providerOptions blob for embedMany() that pins output dimensions.
|
||||
*
|
||||
* Matryoshka providers (OpenAI text-embedding-3, Gemini embedding-001) can be
|
||||
* asked to return reduced-dim vectors. openai-compatible + anthropic do not
|
||||
* take a dimension parameter.
|
||||
* asked to return reduced-dim vectors. Anthropic does not take a dimension
|
||||
* parameter. Most openai-compatible providers do not either, but Voyage's
|
||||
* OpenAI-compatible embeddings endpoint accepts `output_dimension`.
|
||||
*/
|
||||
export function dimsProviderOptions(
|
||||
implementation: Implementation,
|
||||
@@ -41,8 +52,12 @@ export function dimsProviderOptions(
|
||||
// Anthropic has no embedding model.
|
||||
return undefined;
|
||||
case 'openai-compatible':
|
||||
// Ollama, LM Studio, vLLM, Voyage-compat, LiteLLM: no standard dim param.
|
||||
// Users pick a model that natively outputs the dims they want.
|
||||
// Most openai-compatible providers (Ollama, LM Studio, vLLM, LiteLLM)
|
||||
// do not expose a standard dimensions knob. Voyage's compat endpoint is
|
||||
// the exception: it accepts output_dimension and defaults to 1024 dims.
|
||||
if (VOYAGE_OUTPUT_DIMENSION_MODELS.has(modelId)) {
|
||||
return { openaiCompatible: { output_dimension: dims } };
|
||||
}
|
||||
return undefined;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -193,11 +193,51 @@ function instantiateEmbedding(recipe: Recipe, modelId: string, cfg: AIGatewayCon
|
||||
recipe.setup_hint,
|
||||
);
|
||||
}
|
||||
// Voyage AI compatibility shim:
|
||||
// 1. Rejects `encoding_format: "float"` (only accepts "base64" or omit).
|
||||
// The AI SDK hardcodes encoding_format: "float" for openai-compatible.
|
||||
// 2. Returns `usage.total_tokens` instead of `usage.prompt_tokens`.
|
||||
// The AI SDK Zod schema requires `prompt_tokens` when usage is present.
|
||||
const voyageFetch = recipe.id === 'voyage'
|
||||
? async (url: string | URL | Request, init?: RequestInit) => {
|
||||
if (init?.body && typeof init.body === 'string') {
|
||||
try {
|
||||
const parsed = JSON.parse(init.body);
|
||||
delete parsed.encoding_format;
|
||||
init = { ...init, body: JSON.stringify(parsed) };
|
||||
} catch { /* not JSON, pass through */ }
|
||||
}
|
||||
const resp = await globalThis.fetch(url, init);
|
||||
// Patch response to add prompt_tokens from total_tokens
|
||||
const text = await resp.text();
|
||||
try {
|
||||
const json = JSON.parse(text);
|
||||
if (json.usage && json.usage.total_tokens != null && json.usage.prompt_tokens == null) {
|
||||
json.usage.prompt_tokens = json.usage.total_tokens;
|
||||
}
|
||||
return new Response(JSON.stringify(json), {
|
||||
status: resp.status,
|
||||
statusText: resp.statusText,
|
||||
headers: resp.headers,
|
||||
});
|
||||
} catch {
|
||||
return new Response(text, {
|
||||
status: resp.status,
|
||||
statusText: resp.statusText,
|
||||
headers: resp.headers,
|
||||
});
|
||||
}
|
||||
}
|
||||
: undefined;
|
||||
// SDK accepts a `fetch` override at runtime but the typed settings don't
|
||||
// expose it on this version pin; cast so v0.28.5's #680 fetch-shim
|
||||
// (Voyage encoding_format + usage normalization) typechecks.
|
||||
const client = createOpenAICompatible({
|
||||
name: recipe.id,
|
||||
baseURL: baseUrl,
|
||||
apiKey: apiKey ?? 'unauthenticated',
|
||||
});
|
||||
...(voyageFetch ? { fetch: voyageFetch } : {}),
|
||||
} as Parameters<typeof createOpenAICompatible>[0]);
|
||||
return client.textEmbeddingModel(modelId);
|
||||
}
|
||||
default:
|
||||
@@ -205,33 +245,128 @@ function instantiateEmbedding(recipe: Recipe, modelId: string, cfg: AIGatewayCon
|
||||
}
|
||||
}
|
||||
|
||||
/** Embed many texts. Truncates to 8000 chars. Throws AIConfigError or AITransientError. */
|
||||
/**
|
||||
* Conservative chars-to-tokens ratio for batch budget estimation.
|
||||
* Voyage's tokenizer runs ~3-4× denser than OpenAI tiktoken on mixed
|
||||
* content (code, markdown, conversation). Using 1 char ≈ 1 token is
|
||||
* intentionally pessimistic to avoid hitting provider batch limits.
|
||||
*/
|
||||
const CHARS_PER_TOKEN_ESTIMATE = 1;
|
||||
|
||||
/** Minimum sub-batch size before we give up splitting and just throw. */
|
||||
const MIN_SUB_BATCH = 1;
|
||||
|
||||
/** Embed many texts. Truncates to 8000 chars. Auto-splits for providers with batch limits. */
|
||||
export async function embed(texts: string[]): Promise<Float32Array[]> {
|
||||
if (!texts || texts.length === 0) return [];
|
||||
|
||||
const cfg = requireConfig();
|
||||
const { model, recipe, modelId } = await resolveEmbeddingProvider(getEmbeddingModel());
|
||||
const truncated = texts.map(t => (t ?? '').slice(0, MAX_CHARS));
|
||||
const providerOpts = dimsProviderOptions(recipe.implementation, modelId, cfg.embedding_dimensions ?? DEFAULT_EMBEDDING_DIMENSIONS);
|
||||
const expected = cfg.embedding_dimensions ?? DEFAULT_EMBEDDING_DIMENSIONS;
|
||||
|
||||
// Determine batch token budget from recipe (if provider declares one).
|
||||
const maxBatchTokens = recipe.touchpoints?.embedding?.max_batch_tokens;
|
||||
|
||||
// Split into sub-batches if the provider has a token budget.
|
||||
const batches = maxBatchTokens
|
||||
? splitByTokenBudget(truncated, maxBatchTokens)
|
||||
: [truncated];
|
||||
|
||||
const allEmbeddings: Float32Array[] = [];
|
||||
|
||||
for (const batch of batches) {
|
||||
const result = await embedSubBatch(batch, model, providerOpts, expected, recipe, modelId);
|
||||
allEmbeddings.push(...result);
|
||||
}
|
||||
|
||||
return allEmbeddings;
|
||||
}
|
||||
|
||||
/**
|
||||
* Split texts into sub-batches that stay under the provider's token budget.
|
||||
* Uses a conservative chars-to-tokens estimate (1:1) since different
|
||||
* providers' tokenizers vary widely.
|
||||
*/
|
||||
function splitByTokenBudget(texts: string[], maxTokens: number): string[][] {
|
||||
// Use 80% of declared limit as safety margin for tokenizer variance.
|
||||
const budget = Math.floor(maxTokens * 0.8);
|
||||
const batches: string[][] = [];
|
||||
let current: string[] = [];
|
||||
let currentTokens = 0;
|
||||
|
||||
for (const text of texts) {
|
||||
const estTokens = Math.ceil(text.length / CHARS_PER_TOKEN_ESTIMATE);
|
||||
if (current.length > 0 && currentTokens + estTokens > budget) {
|
||||
batches.push(current);
|
||||
current = [];
|
||||
currentTokens = 0;
|
||||
}
|
||||
current.push(text);
|
||||
currentTokens += estTokens;
|
||||
}
|
||||
if (current.length > 0) batches.push(current);
|
||||
|
||||
return batches;
|
||||
}
|
||||
|
||||
/** Returns true if the error looks like a provider batch-token-limit error. */
|
||||
function isTokenLimitError(err: unknown): boolean {
|
||||
const msg = err instanceof Error ? err.message : String(err);
|
||||
return (
|
||||
/max.*allowed.*tokens.*batch/i.test(msg) ||
|
||||
/batch.*too.*many.*tokens/i.test(msg) ||
|
||||
/token.*limit.*exceeded/i.test(msg)
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Embed a single sub-batch with automatic halving on token-limit errors.
|
||||
* If the batch is already at MIN_SUB_BATCH and still fails, throws.
|
||||
*/
|
||||
async function embedSubBatch(
|
||||
texts: string[],
|
||||
model: any,
|
||||
providerOpts: any,
|
||||
expectedDims: number,
|
||||
recipe: Recipe,
|
||||
modelId: string,
|
||||
): Promise<Float32Array[]> {
|
||||
try {
|
||||
const result = await embedMany({
|
||||
model,
|
||||
values: truncated,
|
||||
providerOptions: dimsProviderOptions(recipe.implementation, modelId, cfg.embedding_dimensions ?? DEFAULT_EMBEDDING_DIMENSIONS),
|
||||
values: texts,
|
||||
providerOptions: providerOpts,
|
||||
});
|
||||
|
||||
// Verify dims match expectation; mismatch = likely misconfigured provider options.
|
||||
const expected = cfg.embedding_dimensions ?? DEFAULT_EMBEDDING_DIMENSIONS;
|
||||
const first = result.embeddings?.[0];
|
||||
if (first && Array.isArray(first) && first.length !== expected) {
|
||||
if (first && Array.isArray(first) && first.length !== expectedDims) {
|
||||
// v0.28.5 (#672): the previous fix-hint pointed at `gbrain migrate
|
||||
// --embedding-model … --embedding-dimensions …` but `gbrain migrate`
|
||||
// only handles engine migration, not embedding reconfiguration. The
|
||||
// canonical fix is the manual ALTER recipe in
|
||||
// docs/embedding-migrations.md, surfaced inline so the user doesn't
|
||||
// need to context-switch.
|
||||
throw new AIConfigError(
|
||||
`Embedding dim mismatch: model ${modelId} returned ${first.length} but schema expects ${expected}.`,
|
||||
`Run \`gbrain migrate --embedding-model ${getEmbeddingModel()} --embedding-dimensions ${first.length}\` or change models.`,
|
||||
`Embedding dim mismatch: model ${modelId} returned ${first.length} but schema expects ${expectedDims}.`,
|
||||
`Either change models to one that returns ${expectedDims}-d embeddings, ` +
|
||||
`or migrate the existing brain to ${first.length}-d (destructive — see docs/embedding-migrations.md). ` +
|
||||
`Quick recipe: DROP INDEX idx_chunks_embedding; ALTER COLUMN embedding TYPE vector(${first.length}); ` +
|
||||
`UPDATE content_chunks SET embedding = NULL; gbrain config set embedding_dimensions ${first.length}; ` +
|
||||
`gbrain embed --stale.`,
|
||||
);
|
||||
}
|
||||
|
||||
return result.embeddings.map((e: number[]) => new Float32Array(e));
|
||||
} catch (err) {
|
||||
// On token-limit error, try splitting the batch in half and retrying.
|
||||
if (isTokenLimitError(err) && texts.length > MIN_SUB_BATCH) {
|
||||
const mid = Math.ceil(texts.length / 2);
|
||||
const left = await embedSubBatch(texts.slice(0, mid), model, providerOpts, expectedDims, recipe, modelId);
|
||||
const right = await embedSubBatch(texts.slice(mid), model, providerOpts, expectedDims, recipe, modelId);
|
||||
return [...left, ...right];
|
||||
}
|
||||
throw normalizeAIError(err, `embed(${recipe.id}:${modelId})`);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3,6 +3,10 @@ import type { Recipe } from '../types.ts';
|
||||
/**
|
||||
* Voyage AI exposes an OpenAI-compatible /embeddings endpoint.
|
||||
* Base URL: https://api.voyageai.com/v1
|
||||
*
|
||||
* Voyage 4 family (Jan 2026): shared embedding space across all v4 variants,
|
||||
* flexible dims (256/512/1024/2048), 32K context, MoE architecture (large).
|
||||
* You can index with voyage-4-large and query with voyage-4-lite — no reindex.
|
||||
*/
|
||||
export const voyage: Recipe = {
|
||||
id: 'voyage',
|
||||
@@ -16,10 +20,17 @@ export const voyage: Recipe = {
|
||||
},
|
||||
touchpoints: {
|
||||
embedding: {
|
||||
models: ['voyage-3-large', 'voyage-3', 'voyage-3-lite'],
|
||||
models: [
|
||||
'voyage-4-large', 'voyage-4', 'voyage-4-lite', 'voyage-4-nano',
|
||||
'voyage-3.5', 'voyage-3-large', 'voyage-3', 'voyage-3-lite',
|
||||
'voyage-code-3', 'voyage-finance-2', 'voyage-law-2',
|
||||
],
|
||||
default_dims: 1024,
|
||||
cost_per_1m_tokens_usd: 0.18,
|
||||
price_last_verified: '2026-04-20',
|
||||
price_last_verified: '2026-05-06',
|
||||
// Voyage enforces 120K tokens per batch. Use conservative limit
|
||||
// because Voyage's tokenizer runs 3-4× denser than tiktoken.
|
||||
max_batch_tokens: 120_000,
|
||||
},
|
||||
},
|
||||
setup_hint: 'Get an API key at https://dash.voyageai.com/api-keys, then `export VOYAGE_API_KEY=...`',
|
||||
|
||||
@@ -29,6 +29,14 @@ export interface EmbeddingTouchpoint {
|
||||
dims_options?: number[]; // for Matryoshka-aware providers
|
||||
cost_per_1m_tokens_usd?: number;
|
||||
price_last_verified?: string; // ISO date
|
||||
/**
|
||||
* Maximum tokens per batch for this provider's embedding endpoint.
|
||||
* When set, the gateway auto-splits large batches to stay under this
|
||||
* limit. Uses a conservative chars-to-tokens ratio (1 char ≈ 1 token)
|
||||
* since tokenizer density varies widely across providers (e.g. Voyage
|
||||
* tokenizes ~3-4× denser than OpenAI tiktoken on mixed content).
|
||||
*/
|
||||
max_batch_tokens?: number;
|
||||
}
|
||||
|
||||
export interface ExpansionTouchpoint {
|
||||
|
||||
120
src/core/embedding-dim-check.ts
Normal file
120
src/core/embedding-dim-check.ts
Normal file
@@ -0,0 +1,120 @@
|
||||
/**
|
||||
* Detect existing-brain embedding-dimension mismatch (v0.28.5 — A4).
|
||||
*
|
||||
* `gbrain init --embedding-dimensions N` on an existing brain whose
|
||||
* `content_chunks.embedding` column is a different `vector(M)` would
|
||||
* silently create a config/column drift: the config gets templated to N
|
||||
* but the column stays at M. The first sync write blows up with
|
||||
* "expected M, got N" — the silent-corruption pattern v0.28.5 is shipped
|
||||
* to kill.
|
||||
*
|
||||
* Loud-failure path: `gbrain init` AND `gbrain doctor` both consult this
|
||||
* helper. On mismatch they emit the same inline ALTER recipe (see
|
||||
* `embeddingMismatchMessage`) plus a pointer to `docs/embedding-migrations.md`.
|
||||
*/
|
||||
|
||||
import type { BrainEngine } from './engine.ts';
|
||||
import { PGVECTOR_HNSW_VECTOR_MAX_DIMS } from './vector-index.ts';
|
||||
|
||||
export interface ColumnDimResult {
|
||||
/** Whether the `content_chunks.embedding` column exists. False on a fresh brain. */
|
||||
exists: boolean;
|
||||
/** Parsed `vector(N)` dimension if known. null when the column doesn't exist or the type isn't vector. */
|
||||
dims: number | null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Read the actual dimension of `content_chunks.embedding` from the engine.
|
||||
*
|
||||
* Uses information_schema + a vector-specific catalog query. Returns
|
||||
* { exists: false, dims: null } on a fresh brain that doesn't have the
|
||||
* column yet. Returns { exists: true, dims: null } on a brain whose
|
||||
* column type isn't `vector` (shouldn't happen but defensive).
|
||||
*/
|
||||
export async function readContentChunksEmbeddingDim(engine: BrainEngine): Promise<ColumnDimResult> {
|
||||
// Probe column existence first to avoid noisy errors on fresh brains.
|
||||
const existsRows = await engine.executeRaw<{ exists: boolean }>(
|
||||
`SELECT EXISTS (
|
||||
SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = 'public'
|
||||
AND table_name = 'content_chunks'
|
||||
AND column_name = 'embedding'
|
||||
) AS exists`,
|
||||
);
|
||||
const exists = !!existsRows?.[0]?.exists;
|
||||
if (!exists) return { exists: false, dims: null };
|
||||
|
||||
// pgvector stores dim in pg_type.typmod when atttypmod is set; format_type
|
||||
// returns the human-readable `vector(N)`. We parse N out of that.
|
||||
const formatRows = await engine.executeRaw<{ formatted: string | null }>(
|
||||
`SELECT format_type(a.atttypid, a.atttypmod) AS formatted
|
||||
FROM pg_attribute a
|
||||
JOIN pg_class c ON c.oid = a.attrelid
|
||||
JOIN pg_namespace n ON n.oid = c.relnamespace
|
||||
WHERE n.nspname = 'public'
|
||||
AND c.relname = 'content_chunks'
|
||||
AND a.attname = 'embedding'
|
||||
AND NOT a.attisdropped`,
|
||||
);
|
||||
const formatted = formatRows?.[0]?.formatted ?? null;
|
||||
if (!formatted) return { exists: true, dims: null };
|
||||
|
||||
const m = formatted.match(/vector\((\d+)\)/i);
|
||||
return { exists: true, dims: m ? parseInt(m[1], 10) : null };
|
||||
}
|
||||
|
||||
/**
|
||||
* Build the human-readable ALTER recipe printed inline to stderr (or
|
||||
* delivered via `gbrain doctor` output) when an existing brain's column
|
||||
* dim doesn't match the requested dim.
|
||||
*
|
||||
* Steps cover the four-step contract from `docs/embedding-migrations.md`:
|
||||
* 1. DROP INDEX (HNSW can't survive ALTER COLUMN TYPE)
|
||||
* 2. ALTER COLUMN TYPE
|
||||
* 3. Wipe stale embeddings
|
||||
* 4. Conditional reindex (HNSW only when dims <= 2000)
|
||||
*/
|
||||
export function embeddingMismatchMessage(opts: {
|
||||
currentDims: number;
|
||||
requestedDims: number;
|
||||
requestedModel?: string;
|
||||
source?: 'init' | 'doctor';
|
||||
}): string {
|
||||
const { currentDims, requestedDims, requestedModel, source } = opts;
|
||||
const supportsHnsw = requestedDims <= PGVECTOR_HNSW_VECTOR_MAX_DIMS;
|
||||
const reindexLine = supportsHnsw
|
||||
? `CREATE INDEX IF NOT EXISTS idx_chunks_embedding\n ON content_chunks USING hnsw (embedding vector_cosine_ops);`
|
||||
: `-- Skip reindex. dims=${requestedDims} exceeds pgvector's HNSW cap of ${PGVECTOR_HNSW_VECTOR_MAX_DIMS};\n-- searchVector falls back to exact scan.`;
|
||||
|
||||
const header = source === 'doctor'
|
||||
? `Embedding dimension mismatch detected.`
|
||||
: `Refusing to silently re-template existing brain.`;
|
||||
|
||||
const lines = [
|
||||
header,
|
||||
``,
|
||||
` Existing column: vector(${currentDims})`,
|
||||
` Requested: vector(${requestedDims})${requestedModel ? ` (${requestedModel})` : ''}`,
|
||||
``,
|
||||
`Switching dims is destructive: it drops every embedding in your brain and`,
|
||||
`requires a full re-embed (potentially hours and $1-100 in API calls).`,
|
||||
``,
|
||||
`If you actually want to switch, run this manually against your brain's DB:`,
|
||||
``,
|
||||
` BEGIN;`,
|
||||
` DROP INDEX IF EXISTS idx_chunks_embedding;`,
|
||||
` ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(${requestedDims});`,
|
||||
` UPDATE content_chunks SET embedding = NULL, embedded_at = NULL;`,
|
||||
` ${reindexLine.split('\n').join('\n ')}`,
|
||||
` COMMIT;`,
|
||||
``,
|
||||
`Then re-embed:`,
|
||||
` gbrain config set embedding_dimensions ${requestedDims}`,
|
||||
requestedModel ? ` gbrain config set embedding_model ${requestedModel}` : '',
|
||||
` gbrain embed --stale`,
|
||||
``,
|
||||
`Full guide: docs/embedding-migrations.md`,
|
||||
].filter(Boolean);
|
||||
|
||||
return lines.join('\n');
|
||||
}
|
||||
@@ -26,11 +26,12 @@ export interface EmbedBatchOptions {
|
||||
}
|
||||
|
||||
/**
|
||||
* Embed a batch of texts via the gateway. Sub-batches of 100 so upstream
|
||||
* Embed a batch of texts via the gateway. Sub-batches of 50 so upstream
|
||||
* progress callbacks fire incrementally on large imports. The gateway
|
||||
* handles truncation, retries, and provider dispatch.
|
||||
* handles truncation, retries, provider dispatch, and adaptive batch
|
||||
* splitting for providers with token-per-batch limits (e.g. Voyage).
|
||||
*/
|
||||
const BATCH_SIZE = 100;
|
||||
const BATCH_SIZE = 50;
|
||||
export async function embedBatch(
|
||||
texts: string[],
|
||||
options: EmbedBatchOptions = {},
|
||||
|
||||
@@ -1649,6 +1649,32 @@ async function runMigrationSQL(
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Cheap probe: does this engine have schema migrations pending?
|
||||
*
|
||||
* Reads the `version` config row in a single round-trip (no schema replay,
|
||||
* no migration apply). Used by `connectEngine` to gate `initSchema()` so
|
||||
* short-lived CLI invocations on already-migrated brains don't pay the
|
||||
* full bootstrap-probe + SCHEMA_SQL replay + ledger-check cost on every
|
||||
* `gbrain stats` / `gbrain query` / `gbrain doctor`.
|
||||
*
|
||||
* Defensive: treats a getConfig failure (config table missing, query error)
|
||||
* as "yes pending" so the caller falls through to the full initSchema path.
|
||||
* Worst case on a wedged brain is one extra schema replay — same as before.
|
||||
*
|
||||
* Closes #651 in cooperation with the post-upgrade auto-apply hook (X1)
|
||||
* without the perf cost #652 would have introduced on every CLI call.
|
||||
*/
|
||||
export async function hasPendingMigrations(engine: BrainEngine): Promise<boolean> {
|
||||
try {
|
||||
const currentStr = await engine.getConfig('version');
|
||||
const current = parseInt(currentStr || '1', 10);
|
||||
return current < LATEST_VERSION;
|
||||
} catch {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
export async function runMigrations(engine: BrainEngine): Promise<{ applied: number; current: number }> {
|
||||
const currentStr = await engine.getConfig('version');
|
||||
const current = parseInt(currentStr || '1', 10);
|
||||
|
||||
@@ -217,7 +217,14 @@ export class PGLiteEngine implements BrainEngine {
|
||||
* - `links.origin_page_id` column (indexed by `idx_links_origin`) — v0.13
|
||||
* - `content_chunks.symbol_name` column (indexed by `idx_chunks_symbol_name`) — v0.19
|
||||
* - `content_chunks.language` column (indexed by `idx_chunks_language`) — v0.19
|
||||
* - `content_chunks.search_vector` + `parent_symbol_path` + `doc_comment`
|
||||
* + `symbol_name_qualified` columns (indexed by `idx_chunks_search_vector`
|
||||
* and `idx_chunks_symbol_qualified`) — v0.20 Cathedral II
|
||||
* - `pages.deleted_at` column (indexed by `pages_deleted_at_purge_idx`) — v0.26.5
|
||||
* - `mcp_request_log.agent_name` + `params` + `error_message` columns
|
||||
* (indexed by `idx_mcp_log_agent_time`) — v0.26.3
|
||||
* - `subagent_messages.provider_id` column (indexed by
|
||||
* `idx_subagent_messages_provider`) — v0.27
|
||||
*
|
||||
* **Maintenance contract:** when a future migration adds a column-with-index
|
||||
* or new-table-with-FK referenced by PGLITE_SCHEMA_SQL, extend this method
|
||||
@@ -245,7 +252,17 @@ export class PGLiteEngine implements BrainEngine {
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema='public' AND table_name='content_chunks' AND column_name='symbol_name') AS symbol_name_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema='public' AND table_name='content_chunks' AND column_name='language') AS language_exists
|
||||
WHERE table_schema='public' AND table_name='content_chunks' AND column_name='language') AS language_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema='public' AND table_name='content_chunks' AND column_name='search_vector') AS search_vector_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.tables
|
||||
WHERE table_schema='public' AND table_name='mcp_request_log') AS mcp_log_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema='public' AND table_name='mcp_request_log' AND column_name='agent_name') AS agent_name_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.tables
|
||||
WHERE table_schema='public' AND table_name='subagent_messages') AS subagent_messages_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema='public' AND table_name='subagent_messages' AND column_name='provider_id') AS subagent_provider_id_exists
|
||||
`);
|
||||
const probe = rows[0] as {
|
||||
pages_exists: boolean;
|
||||
@@ -257,17 +274,27 @@ export class PGLiteEngine implements BrainEngine {
|
||||
chunks_exists: boolean;
|
||||
symbol_name_exists: boolean;
|
||||
language_exists: boolean;
|
||||
search_vector_exists: boolean;
|
||||
mcp_log_exists: boolean;
|
||||
agent_name_exists: boolean;
|
||||
subagent_messages_exists: boolean;
|
||||
subagent_provider_id_exists: boolean;
|
||||
};
|
||||
|
||||
const needsPagesBootstrap = probe.pages_exists && !probe.source_id_exists;
|
||||
const needsLinksBootstrap = probe.links_exists
|
||||
&& (!probe.link_source_exists || !probe.origin_page_id_exists);
|
||||
const needsChunksBootstrap = probe.chunks_exists
|
||||
&& (!probe.symbol_name_exists || !probe.language_exists);
|
||||
&& (!probe.symbol_name_exists || !probe.language_exists || !probe.search_vector_exists);
|
||||
const needsPagesDeletedAt = probe.pages_exists && !probe.deleted_at_exists;
|
||||
// v0.26.3 (v33): idx_mcp_log_agent_time in PGLITE_SCHEMA_SQL needs agent_name col.
|
||||
const needsMcpLogBootstrap = probe.mcp_log_exists && !probe.agent_name_exists;
|
||||
// v0.27 (v36): idx_subagent_messages_provider in PGLITE_SCHEMA_SQL needs
|
||||
// provider_id (the SECOND column in the composite index `(job_id, provider_id)`).
|
||||
const needsSubagentProviderId = probe.subagent_messages_exists && !probe.subagent_provider_id_exists;
|
||||
|
||||
// Fresh installs (no tables yet) and modern brains both no-op.
|
||||
if (!needsPagesBootstrap && !needsLinksBootstrap && !needsChunksBootstrap && !needsPagesDeletedAt) return;
|
||||
if (!needsPagesBootstrap && !needsLinksBootstrap && !needsChunksBootstrap && !needsPagesDeletedAt && !needsMcpLogBootstrap && !needsSubagentProviderId) return;
|
||||
|
||||
console.log(' Pre-v0.21 brain detected, applying forward-reference bootstrap');
|
||||
|
||||
@@ -305,14 +332,19 @@ export class PGLiteEngine implements BrainEngine {
|
||||
}
|
||||
|
||||
if (needsChunksBootstrap) {
|
||||
// v26 (content_chunks_code_metadata) adds the full code-chunk metadata
|
||||
// surface (language, symbol_name, symbol_type, start_line, end_line).
|
||||
// The bootstrap only adds the two columns the schema blob's partial
|
||||
// indexes reference (idx_chunks_symbol_name, idx_chunks_language).
|
||||
// v26 runs later via runMigrations and adds the rest idempotently.
|
||||
// v26 (content_chunks_code_metadata) adds symbol_name + language; v27
|
||||
// (Cathedral II) adds parent_symbol_path + doc_comment +
|
||||
// symbol_name_qualified + search_vector. PGLITE_SCHEMA_SQL has indexes
|
||||
// (idx_chunks_search_vector, idx_chunks_symbol_qualified) that need the
|
||||
// v27 columns to exist before they run. v26 + v27 run later via
|
||||
// runMigrations and are idempotent.
|
||||
await this.db.exec(`
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS language TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS symbol_name TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS parent_symbol_path TEXT[];
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS doc_comment TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS symbol_name_qualified TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS search_vector TSVECTOR;
|
||||
`);
|
||||
}
|
||||
|
||||
@@ -325,6 +357,31 @@ export class PGLiteEngine implements BrainEngine {
|
||||
ALTER TABLE pages ADD COLUMN IF NOT EXISTS deleted_at TIMESTAMPTZ;
|
||||
`);
|
||||
}
|
||||
|
||||
if (needsMcpLogBootstrap) {
|
||||
// v33 (admin_dashboard_columns_v0_26_3) adds agent_name + params +
|
||||
// error_message to mcp_request_log. PGLITE_SCHEMA_SQL's
|
||||
// `CREATE INDEX idx_mcp_log_agent_time ON mcp_request_log(agent_name,...)`
|
||||
// crashes without agent_name. v33 runs later via runMigrations and is
|
||||
// idempotent (and also handles backfill).
|
||||
await this.db.exec(`
|
||||
ALTER TABLE mcp_request_log ADD COLUMN IF NOT EXISTS agent_name TEXT;
|
||||
ALTER TABLE mcp_request_log ADD COLUMN IF NOT EXISTS params JSONB;
|
||||
ALTER TABLE mcp_request_log ADD COLUMN IF NOT EXISTS error_message TEXT;
|
||||
`);
|
||||
}
|
||||
|
||||
if (needsSubagentProviderId) {
|
||||
// v36 (subagent_provider_neutral_persistence_v0_27) adds provider_id +
|
||||
// schema_version on subagent_messages and subagent_tool_executions.
|
||||
// PGLITE_SCHEMA_SQL's `CREATE INDEX idx_subagent_messages_provider ON
|
||||
// subagent_messages (job_id, provider_id)` crashes without provider_id
|
||||
// (composite-index second column). v36 runs later via runMigrations and
|
||||
// is idempotent.
|
||||
await this.db.exec(`
|
||||
ALTER TABLE subagent_messages ADD COLUMN IF NOT EXISTS provider_id TEXT;
|
||||
`);
|
||||
}
|
||||
}
|
||||
|
||||
async withReservedConnection<T>(fn: (conn: ReservedConnection) => Promise<T>): Promise<T> {
|
||||
|
||||
@@ -16,6 +16,8 @@
|
||||
* test/edge-bundle.test.ts has a drift detection test.
|
||||
*/
|
||||
|
||||
import { applyChunkEmbeddingIndexPolicy } from './vector-index.ts';
|
||||
|
||||
const PGLITE_SCHEMA_SQL_TEMPLATE = `
|
||||
-- GBrain PGLite schema (local embedded Postgres)
|
||||
|
||||
@@ -212,8 +214,8 @@ CREATE TABLE IF NOT EXISTS config (
|
||||
INSERT INTO config (key, value) VALUES
|
||||
('version', '1'),
|
||||
('engine', 'pglite'),
|
||||
('embedding_model', 'text-embedding-3-large'),
|
||||
('embedding_dimensions', '1536'),
|
||||
('embedding_model', '__EMBEDDING_MODEL__'),
|
||||
('embedding_dimensions', '__EMBEDDING_DIMS__'),
|
||||
('chunk_strategy', 'semantic')
|
||||
ON CONFLICT (key) DO NOTHING;
|
||||
|
||||
@@ -532,9 +534,14 @@ DROP FUNCTION IF EXISTS update_page_search_vector_from_timeline();
|
||||
* Defaults preserve v0.13 behavior (1536d + text-embedding-3-large).
|
||||
*/
|
||||
export function getPGLiteSchema(dims: number = 1536, model: string = 'text-embedding-3-large'): string {
|
||||
return PGLITE_SCHEMA_SQL_TEMPLATE
|
||||
.replace(/__EMBEDDING_DIMS__/g, String(dims))
|
||||
.replace(/__EMBEDDING_MODEL__/g, model);
|
||||
const parsedDims = Number(dims);
|
||||
if (!Number.isInteger(parsedDims) || parsedDims <= 0) {
|
||||
throw new Error(`Invalid embedding dimensions: ${dims}`);
|
||||
}
|
||||
const sanitizedModel = String(model).replace(/'/g, "''");
|
||||
return applyChunkEmbeddingIndexPolicy(PGLITE_SCHEMA_SQL_TEMPLATE, parsedDims)
|
||||
.replace(/__EMBEDDING_DIMS__/g, String(parsedDims))
|
||||
.replace(/__EMBEDDING_MODEL__/g, sanitizedModel);
|
||||
}
|
||||
|
||||
/** Back-compat: pre-computed default-1536 schema for existing callers. */
|
||||
|
||||
@@ -4,6 +4,7 @@ import { MAX_SEARCH_LIMIT, clampSearchLimit } from './engine.ts';
|
||||
import { runMigrations } from './migrate.ts';
|
||||
import { SCHEMA_SQL } from './schema-embedded.ts';
|
||||
import { verifySchema } from './schema-verify.ts';
|
||||
import { applyChunkEmbeddingIndexPolicy } from './vector-index.ts';
|
||||
import type {
|
||||
Page, PageInput, PageFilters, PageType,
|
||||
Chunk, ChunkInput, StaleChunkRow,
|
||||
@@ -24,6 +25,22 @@ import { validateSlug, contentHash, rowToPage, rowToChunk, rowToSearchResult, pa
|
||||
import { resolveBoostMap, resolveHardExcludes } from './search/source-boost.ts';
|
||||
import { buildSourceFactorCase, buildHardExcludeClause, buildVisibilityClause } from './search/sql-ranking.ts';
|
||||
|
||||
function escapeSqlStringLiteral(value: string): string {
|
||||
return value.replace(/'/g, "''");
|
||||
}
|
||||
|
||||
export function getPostgresSchema(dims: number = 1536, model: string = 'text-embedding-3-large'): string {
|
||||
const parsedDims = Number(dims);
|
||||
if (!Number.isInteger(parsedDims) || parsedDims <= 0) {
|
||||
throw new Error(`Invalid embedding dimensions: ${dims}`);
|
||||
}
|
||||
const sanitizedModel = escapeSqlStringLiteral(String(model));
|
||||
return applyChunkEmbeddingIndexPolicy(SCHEMA_SQL, parsedDims)
|
||||
.replace(/vector\(1536\)/g, `vector(${parsedDims})`)
|
||||
.replace(/'text-embedding-3-large'/g, `'${sanitizedModel}'`)
|
||||
.replace(/\('embedding_dimensions', '1536'\)/g, `('embedding_dimensions', '${parsedDims}')`);
|
||||
}
|
||||
|
||||
// CONNECTION_ERROR_PATTERNS / isConnectionError were used by the per-call
|
||||
// executeRaw retry that #406 originally shipped. Eng-review D3 dropped that
|
||||
// retry as unsound (regex idempotence-boundary doesn't hold for writable
|
||||
@@ -128,9 +145,7 @@ export class PostgresEngine implements BrainEngine {
|
||||
model = gw.getEmbeddingModel().split(':').slice(1).join(':') || model;
|
||||
} catch { /* gateway not yet configured — use defaults */ }
|
||||
|
||||
const sql = SCHEMA_SQL
|
||||
.replace(/vector\(1536\)/g, `vector(${dims})`)
|
||||
.replace(/'text-embedding-3-large'/g, `'${model}'`);
|
||||
const sql = getPostgresSchema(dims, model);
|
||||
|
||||
// Advisory lock prevents concurrent initSchema() calls from deadlocking
|
||||
// on DDL statements (DROP TRIGGER + CREATE TRIGGER acquire AccessExclusiveLock).
|
||||
@@ -179,7 +194,14 @@ export class PostgresEngine implements BrainEngine {
|
||||
* - `links.origin_page_id` column (indexed by `idx_links_origin`) — v0.13
|
||||
* - `content_chunks.symbol_name` column (indexed by `idx_chunks_symbol_name`) — v0.19
|
||||
* - `content_chunks.language` column (indexed by `idx_chunks_language`) — v0.19
|
||||
* - `content_chunks.search_vector` + `parent_symbol_path` + `doc_comment`
|
||||
* + `symbol_name_qualified` columns (indexed by `idx_chunks_search_vector`
|
||||
* and `idx_chunks_symbol_qualified`) — v0.20 Cathedral II
|
||||
* - `pages.deleted_at` column (indexed by `pages_deleted_at_purge_idx`) — v0.26.5
|
||||
* - `mcp_request_log.agent_name` + `params` + `error_message` columns
|
||||
* (indexed by `idx_mcp_log_agent_time`) — v0.26.3
|
||||
* - `subagent_messages.provider_id` column (indexed by
|
||||
* `idx_subagent_messages_provider`) — v0.27
|
||||
*
|
||||
* Keep this in sync with the PGLite version; covered by
|
||||
* `test/schema-bootstrap-coverage.test.ts` (PGLite side) and
|
||||
@@ -201,6 +223,11 @@ export class PostgresEngine implements BrainEngine {
|
||||
chunks_exists: boolean;
|
||||
symbol_name_exists: boolean;
|
||||
language_exists: boolean;
|
||||
search_vector_exists: boolean;
|
||||
mcp_log_exists: boolean;
|
||||
agent_name_exists: boolean;
|
||||
subagent_messages_exists: boolean;
|
||||
subagent_provider_id_exists: boolean;
|
||||
}[]>`
|
||||
SELECT
|
||||
EXISTS (SELECT 1 FROM information_schema.tables
|
||||
@@ -220,7 +247,17 @@ export class PostgresEngine implements BrainEngine {
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = current_schema() AND table_name = 'content_chunks' AND column_name = 'symbol_name') AS symbol_name_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = current_schema() AND table_name = 'content_chunks' AND column_name = 'language') AS language_exists
|
||||
WHERE table_schema = current_schema() AND table_name = 'content_chunks' AND column_name = 'language') AS language_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = current_schema() AND table_name = 'content_chunks' AND column_name = 'search_vector') AS search_vector_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.tables
|
||||
WHERE table_schema = current_schema() AND table_name = 'mcp_request_log') AS mcp_log_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = current_schema() AND table_name = 'mcp_request_log' AND column_name = 'agent_name') AS agent_name_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.tables
|
||||
WHERE table_schema = current_schema() AND table_name = 'subagent_messages') AS subagent_messages_exists,
|
||||
EXISTS (SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = current_schema() AND table_name = 'subagent_messages' AND column_name = 'provider_id') AS subagent_provider_id_exists
|
||||
`;
|
||||
const probe = probeRows[0]!;
|
||||
|
||||
@@ -228,12 +265,17 @@ export class PostgresEngine implements BrainEngine {
|
||||
const needsLinksBootstrap = probe.links_exists
|
||||
&& (!probe.link_source_exists || !probe.origin_page_id_exists);
|
||||
const needsChunksBootstrap = probe.chunks_exists
|
||||
&& (!probe.symbol_name_exists || !probe.language_exists);
|
||||
&& (!probe.symbol_name_exists || !probe.language_exists || !probe.search_vector_exists);
|
||||
// v0.26.5: pages_deleted_at_purge_idx in SCHEMA_SQL crashes if the column
|
||||
// doesn't exist yet. Migration v34 also adds it, but bootstrap runs first.
|
||||
const needsPagesDeletedAt = probe.pages_exists && !probe.deleted_at_exists;
|
||||
// v0.26.3 (v33): idx_mcp_log_agent_time in SCHEMA_SQL needs agent_name col.
|
||||
const needsMcpLogBootstrap = probe.mcp_log_exists && !probe.agent_name_exists;
|
||||
// v0.27 (v36): idx_subagent_messages_provider in SCHEMA_SQL needs provider_id
|
||||
// (the SECOND column in the composite index `(job_id, provider_id)`).
|
||||
const needsSubagentProviderId = probe.subagent_messages_exists && !probe.subagent_provider_id_exists;
|
||||
|
||||
if (!needsPagesBootstrap && !needsLinksBootstrap && !needsChunksBootstrap && !needsPagesDeletedAt) return;
|
||||
if (!needsPagesBootstrap && !needsLinksBootstrap && !needsChunksBootstrap && !needsPagesDeletedAt && !needsMcpLogBootstrap && !needsSubagentProviderId) return;
|
||||
|
||||
console.log(' Pre-v0.21 brain detected, applying forward-reference bootstrap');
|
||||
|
||||
@@ -271,13 +313,19 @@ export class PostgresEngine implements BrainEngine {
|
||||
}
|
||||
|
||||
if (needsChunksBootstrap) {
|
||||
// v26 (content_chunks_code_metadata) adds the full code-chunk metadata
|
||||
// surface. The bootstrap only adds the two columns the schema blob's
|
||||
// partial indexes reference (idx_chunks_symbol_name, idx_chunks_language).
|
||||
// v26 runs later via runMigrations and adds the rest idempotently.
|
||||
// v26 (content_chunks_code_metadata) adds symbol_name + language; v27
|
||||
// (Cathedral II) adds parent_symbol_path + doc_comment +
|
||||
// symbol_name_qualified + search_vector. The schema blob has indexes
|
||||
// (idx_chunks_search_vector line 141, idx_chunks_symbol_qualified
|
||||
// line 142) that need the v27 columns to exist before they run.
|
||||
// v26 + v27 run later via runMigrations and are idempotent.
|
||||
await conn.unsafe(`
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS language TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS symbol_name TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS parent_symbol_path TEXT[];
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS doc_comment TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS symbol_name_qualified TEXT;
|
||||
ALTER TABLE content_chunks ADD COLUMN IF NOT EXISTS search_vector TSVECTOR;
|
||||
`);
|
||||
}
|
||||
|
||||
@@ -290,6 +338,31 @@ export class PostgresEngine implements BrainEngine {
|
||||
ALTER TABLE pages ADD COLUMN IF NOT EXISTS deleted_at TIMESTAMPTZ;
|
||||
`);
|
||||
}
|
||||
|
||||
if (needsMcpLogBootstrap) {
|
||||
// v33 (admin_dashboard_columns_v0_26_3) adds agent_name + params +
|
||||
// error_message to mcp_request_log. SCHEMA_SQL's
|
||||
// `CREATE INDEX idx_mcp_log_agent_time ON mcp_request_log(agent_name,...)`
|
||||
// crashes without agent_name. v33 runs later via runMigrations and is
|
||||
// idempotent (and also handles backfill).
|
||||
await conn.unsafe(`
|
||||
ALTER TABLE mcp_request_log ADD COLUMN IF NOT EXISTS agent_name TEXT;
|
||||
ALTER TABLE mcp_request_log ADD COLUMN IF NOT EXISTS params JSONB;
|
||||
ALTER TABLE mcp_request_log ADD COLUMN IF NOT EXISTS error_message TEXT;
|
||||
`);
|
||||
}
|
||||
|
||||
if (needsSubagentProviderId) {
|
||||
// v36 (subagent_provider_neutral_persistence_v0_27) adds provider_id +
|
||||
// schema_version on subagent_messages and subagent_tool_executions.
|
||||
// SCHEMA_SQL's `CREATE INDEX idx_subagent_messages_provider ON
|
||||
// subagent_messages (job_id, provider_id)` crashes without provider_id
|
||||
// (composite-index second column). v36 runs later via runMigrations and
|
||||
// is idempotent.
|
||||
await conn.unsafe(`
|
||||
ALTER TABLE subagent_messages ADD COLUMN IF NOT EXISTS provider_id TEXT;
|
||||
`);
|
||||
}
|
||||
}
|
||||
|
||||
async transaction<T>(fn: (engine: BrainEngine) => Promise<T>): Promise<T> {
|
||||
|
||||
16
src/core/vector-index.ts
Normal file
16
src/core/vector-index.ts
Normal file
@@ -0,0 +1,16 @@
|
||||
export const PGVECTOR_HNSW_VECTOR_MAX_DIMS = 2000;
|
||||
|
||||
const CHUNK_EMBEDDING_HNSW_INDEX =
|
||||
'CREATE INDEX IF NOT EXISTS idx_chunks_embedding ON content_chunks USING hnsw (embedding vector_cosine_ops);';
|
||||
|
||||
export function chunkEmbeddingIndexSql(dims: number): string {
|
||||
if (dims <= PGVECTOR_HNSW_VECTOR_MAX_DIMS) return CHUNK_EMBEDDING_HNSW_INDEX;
|
||||
return [
|
||||
'-- idx_chunks_embedding skipped: pgvector HNSW vector indexes support',
|
||||
`-- at most ${PGVECTOR_HNSW_VECTOR_MAX_DIMS} dimensions; exact vector scans remain available.`,
|
||||
].join('\n');
|
||||
}
|
||||
|
||||
export function applyChunkEmbeddingIndexPolicy(sql: string, dims: number): string {
|
||||
return sql.replaceAll(CHUNK_EMBEDDING_HNSW_INDEX, chunkEmbeddingIndexSql(dims));
|
||||
}
|
||||
112
test/ai/adaptive-embed-batch.test.ts
Normal file
112
test/ai/adaptive-embed-batch.test.ts
Normal file
@@ -0,0 +1,112 @@
|
||||
/**
|
||||
* Tests for adaptive embed batch splitting (fix/adaptive-embed-batch-sizing).
|
||||
*
|
||||
* Validates that splitByTokenBudget correctly partitions texts to stay
|
||||
* under provider token limits, and that embedSubBatch retries with
|
||||
* halved batches on token-limit errors.
|
||||
*/
|
||||
|
||||
import { describe, expect, test } from 'bun:test';
|
||||
|
||||
// We test the logic by importing the gateway module and exercising the
|
||||
// exported embed() function through its internal helpers. Since
|
||||
// splitByTokenBudget and isTokenLimitError are module-private, we test
|
||||
// them indirectly through behavior.
|
||||
|
||||
// Direct unit tests for splitByTokenBudget logic (extracted for clarity).
|
||||
describe('splitByTokenBudget logic', () => {
|
||||
// Re-implement the algorithm locally for unit testing since it's private.
|
||||
function splitByTokenBudget(texts: string[], maxTokens: number): string[][] {
|
||||
const budget = Math.floor(maxTokens * 0.8);
|
||||
const batches: string[][] = [];
|
||||
let current: string[] = [];
|
||||
let currentTokens = 0;
|
||||
|
||||
for (const text of texts) {
|
||||
const estTokens = Math.ceil(text.length / 1); // 1 char ≈ 1 token
|
||||
if (current.length > 0 && currentTokens + estTokens > budget) {
|
||||
batches.push(current);
|
||||
current = [];
|
||||
currentTokens = 0;
|
||||
}
|
||||
current.push(text);
|
||||
currentTokens += estTokens;
|
||||
}
|
||||
if (current.length > 0) batches.push(current);
|
||||
return batches;
|
||||
}
|
||||
|
||||
test('single small text stays in one batch', () => {
|
||||
const result = splitByTokenBudget(['hello'], 120_000);
|
||||
expect(result).toHaveLength(1);
|
||||
expect(result[0]).toEqual(['hello']);
|
||||
});
|
||||
|
||||
test('texts fitting within budget stay in one batch', () => {
|
||||
const texts = Array.from({ length: 10 }, () => 'a'.repeat(1000));
|
||||
// 10 * 1000 = 10K chars, well under 120K * 0.8 = 96K budget
|
||||
const result = splitByTokenBudget(texts, 120_000);
|
||||
expect(result).toHaveLength(1);
|
||||
expect(result[0]).toHaveLength(10);
|
||||
});
|
||||
|
||||
test('texts exceeding budget are split into multiple batches', () => {
|
||||
// Each text is 50K chars. Budget = 120K * 0.8 = 96K.
|
||||
// First text fits (50K < 96K). Second would make it 100K > 96K → new batch.
|
||||
const texts = ['a'.repeat(50_000), 'b'.repeat(50_000), 'c'.repeat(50_000)];
|
||||
const result = splitByTokenBudget(texts, 120_000);
|
||||
expect(result).toHaveLength(3);
|
||||
expect(result[0]).toHaveLength(1);
|
||||
expect(result[1]).toHaveLength(1);
|
||||
expect(result[2]).toHaveLength(1);
|
||||
});
|
||||
|
||||
test('packs multiple texts into one batch when under budget', () => {
|
||||
// Each text is 30K chars. Budget = 96K. Two fit (60K < 96K), three don't (90K < 96K still fits!).
|
||||
// Actually 30K * 3 = 90K < 96K, so three fit.
|
||||
const texts = ['a'.repeat(30_000), 'b'.repeat(30_000), 'c'.repeat(30_000), 'd'.repeat(30_000)];
|
||||
const result = splitByTokenBudget(texts, 120_000);
|
||||
// 3 fit in first batch (90K < 96K), 4th starts new batch
|
||||
expect(result).toHaveLength(2);
|
||||
expect(result[0]).toHaveLength(3);
|
||||
expect(result[1]).toHaveLength(1);
|
||||
});
|
||||
|
||||
test('empty input returns empty array', () => {
|
||||
const result = splitByTokenBudget([], 120_000);
|
||||
expect(result).toHaveLength(0);
|
||||
});
|
||||
|
||||
test('single text larger than budget still goes in a batch', () => {
|
||||
// A single huge text can't be split further by this function.
|
||||
const texts = ['a'.repeat(200_000)];
|
||||
const result = splitByTokenBudget(texts, 120_000);
|
||||
expect(result).toHaveLength(1);
|
||||
expect(result[0]).toHaveLength(1);
|
||||
});
|
||||
});
|
||||
|
||||
describe('isTokenLimitError pattern matching', () => {
|
||||
function isTokenLimitError(err: unknown): boolean {
|
||||
const msg = err instanceof Error ? err.message : String(err);
|
||||
return (
|
||||
/max.*allowed.*tokens.*batch/i.test(msg) ||
|
||||
/batch.*too.*many.*tokens/i.test(msg) ||
|
||||
/token.*limit.*exceeded/i.test(msg)
|
||||
);
|
||||
}
|
||||
|
||||
test('matches Voyage error format', () => {
|
||||
const msg = 'Request to model \'voyage-4-large\' failed. The max allowed tokens per submitted batch is 120000.';
|
||||
expect(isTokenLimitError(new Error(msg))).toBe(true);
|
||||
});
|
||||
|
||||
test('does not match unrelated errors', () => {
|
||||
expect(isTokenLimitError(new Error('Connection refused'))).toBe(false);
|
||||
expect(isTokenLimitError(new Error('Invalid API key'))).toBe(false);
|
||||
});
|
||||
|
||||
test('matches token limit exceeded variant', () => {
|
||||
expect(isTokenLimitError(new Error('Token limit exceeded for batch request'))).toBe(true);
|
||||
});
|
||||
});
|
||||
@@ -151,8 +151,18 @@ describe('dims.dimsProviderOptions', () => {
|
||||
expect(opts).toBeUndefined();
|
||||
});
|
||||
|
||||
test('openai-compatible returns undefined (no standard dim param)', () => {
|
||||
test('openai-compatible returns undefined for providers without a dim param', () => {
|
||||
const opts = dimsProviderOptions('openai-compatible', 'nomic-embed-text', 768);
|
||||
expect(opts).toBeUndefined();
|
||||
});
|
||||
|
||||
test('Voyage openai-compatible returns output_dimension', () => {
|
||||
const opts = dimsProviderOptions('openai-compatible', 'voyage-4-large', 2048);
|
||||
expect(opts).toEqual({ openaiCompatible: { output_dimension: 2048 } });
|
||||
});
|
||||
|
||||
test('Voyage model without flexible dimensions returns undefined', () => {
|
||||
const opts = dimsProviderOptions('openai-compatible', 'voyage-3-lite', 1024);
|
||||
expect(opts).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import { describe, test, expect } from 'bun:test';
|
||||
import { getPGLiteSchema, PGLITE_SCHEMA_SQL } from '../../src/core/pglite-schema.ts';
|
||||
import { getPostgresSchema } from '../../src/core/postgres-engine.ts';
|
||||
|
||||
describe('getPGLiteSchema', () => {
|
||||
test('default produces v0.13-compatible schema (1536d + text-embedding-3-large)', () => {
|
||||
@@ -14,6 +15,8 @@ describe('getPGLiteSchema', () => {
|
||||
const sql = getPGLiteSchema(768, 'gemini-embedding-001');
|
||||
expect(sql).toMatch(/vector\(768\)/);
|
||||
expect(sql).toMatch(/'gemini-embedding-001'/);
|
||||
expect(sql).toMatch(/\('embedding_model', 'gemini-embedding-001'\)/);
|
||||
expect(sql).toMatch(/\('embedding_dimensions', '768'\)/);
|
||||
expect(sql).not.toMatch(/vector\(1536\)/);
|
||||
});
|
||||
|
||||
@@ -21,9 +24,37 @@ describe('getPGLiteSchema', () => {
|
||||
const sql = getPGLiteSchema(1024, 'voyage-3-large');
|
||||
expect(sql).toMatch(/vector\(1024\)/);
|
||||
expect(sql).toMatch(/'voyage-3-large'/);
|
||||
expect(sql).toMatch(/\('embedding_model', 'voyage-3-large'\)/);
|
||||
expect(sql).toMatch(/\('embedding_dimensions', '1024'\)/);
|
||||
expect(sql).toContain('idx_chunks_embedding ON content_chunks USING hnsw');
|
||||
});
|
||||
|
||||
test('Voyage 2048d skips unsupported HNSW index but keeps vector column', () => {
|
||||
const sql = getPGLiteSchema(2048, 'voyage-4-large');
|
||||
expect(sql).toMatch(/vector\(2048\)/);
|
||||
expect(sql).toMatch(/'voyage-4-large'/);
|
||||
expect(sql).toMatch(/\('embedding_dimensions', '2048'\)/);
|
||||
expect(sql).not.toContain('idx_chunks_embedding ON content_chunks USING hnsw');
|
||||
expect(sql).toContain('exact vector scans remain available');
|
||||
});
|
||||
|
||||
test('PGLITE_SCHEMA_SQL back-compat constant is the default-dim schema', () => {
|
||||
expect(PGLITE_SCHEMA_SQL).toBe(getPGLiteSchema());
|
||||
});
|
||||
});
|
||||
|
||||
describe('getPostgresSchema', () => {
|
||||
test('Voyage 2048d updates vector column and seeded config but skips HNSW', () => {
|
||||
const sql = getPostgresSchema(2048, 'voyage-4-large');
|
||||
expect(sql).toMatch(/vector\(2048\)/);
|
||||
expect(sql).toMatch(/\('embedding_model', 'voyage-4-large'\)/);
|
||||
expect(sql).toMatch(/\('embedding_dimensions', '2048'\)/);
|
||||
expect(sql).not.toContain('idx_chunks_embedding ON content_chunks USING hnsw');
|
||||
});
|
||||
|
||||
test('escapes configured model before inserting into schema SQL literals', () => {
|
||||
const sql = getPostgresSchema(1024, "voyage-weird'quoted");
|
||||
expect(sql).toContain("'voyage-weird''quoted'");
|
||||
expect(sql).not.toContain("'voyage-weird'quoted'");
|
||||
});
|
||||
});
|
||||
|
||||
274
test/e2e/v0_28_5-fix-wave.test.ts
Normal file
274
test/e2e/v0_28_5-fix-wave.test.ts
Normal file
@@ -0,0 +1,274 @@
|
||||
/**
|
||||
* E2E coverage for the v0.28.5 fix wave.
|
||||
*
|
||||
* Three regression scenarios this wave was shipped to fix:
|
||||
*
|
||||
* 1. PGLite upgrade wedge — a brain pinned at any version v0.13+ that
|
||||
* hits `init --migrate-only` against current master used to crash with
|
||||
* `column "..." does not exist`. The bootstrap's per-engine forward-
|
||||
* reference probe now restores the missing columns before SCHEMA_SQL
|
||||
* replays. Test: rewind a fresh-LATEST PGLite brain to a pre-v0.20
|
||||
* shape (drop v0.20+v0.26.3+v0.27 columns + their indexes), then
|
||||
* re-run initSchema and assert all migrations through LATEST_VERSION
|
||||
* apply with no crash.
|
||||
*
|
||||
* 2. Embedding-dim corruption — `gbrain init --embedding-dimensions 768`
|
||||
* previously created a `vector(1536)` column anyway because the schema
|
||||
* blob hardcoded the dim. After v0.28.5 (cluster B / #641) the dim
|
||||
* flag templates end-to-end. Test: fresh PGLite init configured for
|
||||
* 768-d, query the actual column type, assert it's `vector(768)`.
|
||||
*
|
||||
* 3. Existing-brain dim-mismatch hard error (A4) — re-init'ing an
|
||||
* existing 1536-d brain with a different requested dim should refuse
|
||||
* with the inline ALTER recipe instead of silently corrupting config.
|
||||
* Test: build a brain at 1536, then call the helper init uses to
|
||||
* detect mismatches, assert it surfaces the right (existing, requested)
|
||||
* pair AND that the recipe message inlines all four steps including
|
||||
* the conditional HNSW reindex (codex finding #8).
|
||||
*
|
||||
* PGLite-only — none of these require a real Postgres. The Postgres-side
|
||||
* bootstrap is covered by `test/e2e/postgres-bootstrap.test.ts`; this file
|
||||
* is the PGLite-side equivalent specifically for the wedge classes the
|
||||
* v0.28.5 wave fixed.
|
||||
*
|
||||
* Run: bun test test/e2e/v0_28_5-fix-wave.test.ts
|
||||
*/
|
||||
|
||||
import { describe, test, expect } from 'bun:test';
|
||||
import { PGLiteEngine } from '../../src/core/pglite-engine.ts';
|
||||
import { LATEST_VERSION } from '../../src/core/migrate.ts';
|
||||
import {
|
||||
readContentChunksEmbeddingDim,
|
||||
embeddingMismatchMessage,
|
||||
} from '../../src/core/embedding-dim-check.ts';
|
||||
|
||||
describe('v0.28.5 cluster A — PGLite upgrade wedge regression', () => {
|
||||
test('pre-v0.20 brain (missing v0.20+v0.26.3+v0.27 columns) re-runs initSchema cleanly', async () => {
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
// Build a fresh LATEST brain.
|
||||
await engine.initSchema();
|
||||
const db = (engine as any).db;
|
||||
|
||||
// Rewind to a pre-v0.20 shape: drop the columns the v0.20+v0.26.3+v0.27
|
||||
// bootstrap claims to restore. CASCADE handles dependent indexes and
|
||||
// triggers. Also reset version so runMigrations replays.
|
||||
await db.exec(`
|
||||
DROP INDEX IF EXISTS idx_chunks_search_vector;
|
||||
DROP INDEX IF EXISTS idx_chunks_symbol_qualified;
|
||||
DROP TRIGGER IF EXISTS chunk_search_vector_trigger ON content_chunks;
|
||||
DROP FUNCTION IF EXISTS update_chunk_search_vector;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS parent_symbol_path CASCADE;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS doc_comment CASCADE;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS symbol_name_qualified CASCADE;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS search_vector CASCADE;
|
||||
|
||||
DROP INDEX IF EXISTS idx_mcp_log_agent_time;
|
||||
ALTER TABLE mcp_request_log DROP COLUMN IF EXISTS agent_name CASCADE;
|
||||
ALTER TABLE mcp_request_log DROP COLUMN IF EXISTS params CASCADE;
|
||||
ALTER TABLE mcp_request_log DROP COLUMN IF EXISTS error_message CASCADE;
|
||||
|
||||
DROP INDEX IF EXISTS idx_subagent_messages_provider;
|
||||
ALTER TABLE subagent_messages DROP COLUMN IF EXISTS provider_id CASCADE;
|
||||
|
||||
UPDATE config SET value = '13' WHERE key = 'version';
|
||||
`);
|
||||
|
||||
// Re-run initSchema — bootstrap must restore the dropped columns
|
||||
// before SCHEMA_SQL replay, and runMigrations must walk through LATEST.
|
||||
// Pre-v0.28.5 this crashed with `column "search_vector" does not exist`
|
||||
// OR `column "agent_name" does not exist` OR `column "provider_id"
|
||||
// does not exist`, depending on which stripped column the schema blob
|
||||
// hit first.
|
||||
await engine.initSchema();
|
||||
|
||||
// Confirm we landed at LATEST.
|
||||
const versionRow = await engine.executeRaw<{ value: string }>(
|
||||
`SELECT value FROM config WHERE key = 'version'`,
|
||||
);
|
||||
expect(versionRow[0]?.value).toBe(String(LATEST_VERSION));
|
||||
|
||||
// Confirm the v0.27 wedge column specifically is back. Codex's plan-
|
||||
// review caught that this is a composite-index second column —
|
||||
// earlier first-col-only extractors missed it.
|
||||
const providerCol = await engine.executeRaw<{ exists: boolean }>(
|
||||
`SELECT EXISTS (
|
||||
SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = 'public'
|
||||
AND table_name = 'subagent_messages'
|
||||
AND column_name = 'provider_id'
|
||||
) AS exists`,
|
||||
);
|
||||
expect(providerCol[0]?.exists).toBe(true);
|
||||
|
||||
// Confirm the v0.20 + v0.26.3 columns are also restored.
|
||||
const searchVectorCol = await engine.executeRaw<{ exists: boolean }>(
|
||||
`SELECT EXISTS (
|
||||
SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = 'public'
|
||||
AND table_name = 'content_chunks'
|
||||
AND column_name = 'search_vector'
|
||||
) AS exists`,
|
||||
);
|
||||
expect(searchVectorCol[0]?.exists).toBe(true);
|
||||
|
||||
const agentNameCol = await engine.executeRaw<{ exists: boolean }>(
|
||||
`SELECT EXISTS (
|
||||
SELECT 1 FROM information_schema.columns
|
||||
WHERE table_schema = 'public'
|
||||
AND table_name = 'mcp_request_log'
|
||||
AND column_name = 'agent_name'
|
||||
) AS exists`,
|
||||
);
|
||||
expect(agentNameCol[0]?.exists).toBe(true);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 60000);
|
||||
|
||||
test('hasPendingMigrations correctly reports state across the upgrade lifecycle', async () => {
|
||||
const { hasPendingMigrations } = await import('../../src/core/migrate.ts');
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
// Fresh brain → no version row yet → defensive true.
|
||||
expect(await hasPendingMigrations(engine)).toBe(true);
|
||||
|
||||
// After initSchema → version === LATEST → false.
|
||||
await engine.initSchema();
|
||||
expect(await hasPendingMigrations(engine)).toBe(false);
|
||||
|
||||
// Rewind version → true again.
|
||||
await engine.setConfig('version', '13');
|
||||
expect(await hasPendingMigrations(engine)).toBe(true);
|
||||
|
||||
// Re-apply → false. Closes the loop.
|
||||
await engine.initSchema();
|
||||
expect(await hasPendingMigrations(engine)).toBe(false);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 60000);
|
||||
});
|
||||
|
||||
describe('v0.28.5 cluster B — embedding dim corruption regression', () => {
|
||||
test('fresh init at non-default dims templates the column correctly', async () => {
|
||||
// The wedge: v0.27 silently created vector(1536) regardless of the
|
||||
// --embedding-dimensions flag. v0.28.5 (#641) plumbs the dim through
|
||||
// `getPGLiteSchema(dims)` so the column is templated correctly.
|
||||
const { configureGateway } = await import('../../src/core/ai/gateway.ts');
|
||||
configureGateway({
|
||||
embedding_model: 'openai:text-embedding-3-small',
|
||||
embedding_dimensions: 768,
|
||||
env: { ...process.env },
|
||||
});
|
||||
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
await engine.initSchema();
|
||||
const dim = await readContentChunksEmbeddingDim(engine);
|
||||
expect(dim.exists).toBe(true);
|
||||
expect(dim.dims).toBe(768);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
// Reset gateway so subsequent tests in the suite see defaults again.
|
||||
configureGateway({ env: { ...process.env } });
|
||||
}
|
||||
}, 60000);
|
||||
|
||||
test('large-dim init (>2000) templates the column without HNSW (Voyage 4 Large case)', async () => {
|
||||
// Codex finding #8: dims > 2000 cannot be HNSW-indexed in pgvector.
|
||||
// The schema templating path must skip the HNSW CREATE INDEX while
|
||||
// still creating the underlying `vector(N)` column.
|
||||
const { configureGateway } = await import('../../src/core/ai/gateway.ts');
|
||||
configureGateway({
|
||||
embedding_model: 'voyage:voyage-4-large',
|
||||
embedding_dimensions: 2048,
|
||||
env: { ...process.env },
|
||||
});
|
||||
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
// initSchema must NOT crash even though the HNSW index would otherwise
|
||||
// refuse a 2048-d vector column.
|
||||
await engine.initSchema();
|
||||
const dim = await readContentChunksEmbeddingDim(engine);
|
||||
expect(dim.exists).toBe(true);
|
||||
expect(dim.dims).toBe(2048);
|
||||
|
||||
// Confirm the HNSW index was correctly skipped.
|
||||
const idx = await engine.executeRaw<{ exists: boolean }>(
|
||||
`SELECT EXISTS (
|
||||
SELECT 1 FROM pg_indexes
|
||||
WHERE schemaname = 'public'
|
||||
AND tablename = 'content_chunks'
|
||||
AND indexname = 'idx_chunks_embedding'
|
||||
) AS exists`,
|
||||
);
|
||||
expect(idx[0]?.exists).toBe(false);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
configureGateway({ env: { ...process.env } });
|
||||
}
|
||||
}, 60000);
|
||||
});
|
||||
|
||||
describe('v0.28.5 A4 — existing-brain dim mismatch loud failure', () => {
|
||||
test('readContentChunksEmbeddingDim correctly identifies a brain at 1536 + mismatch message inlines all four steps', async () => {
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
// Default initSchema with no gateway override → 1536.
|
||||
await engine.initSchema();
|
||||
const existing = await readContentChunksEmbeddingDim(engine);
|
||||
expect(existing.exists).toBe(true);
|
||||
expect(existing.dims).toBe(1536);
|
||||
|
||||
// Simulate the user passing --embedding-dimensions 768 against this
|
||||
// existing 1536 brain. Build the mismatch message that init would
|
||||
// print to stderr before exiting 1.
|
||||
const msg = embeddingMismatchMessage({
|
||||
currentDims: existing.dims!,
|
||||
requestedDims: 768,
|
||||
requestedModel: 'ollama:nomic-embed-text',
|
||||
source: 'init',
|
||||
});
|
||||
|
||||
// Codex finding #8: the recipe MUST inline the four steps including
|
||||
// a conditional reindex. 768 is HNSW-eligible, so the recipe should
|
||||
// include the HNSW CREATE INDEX line.
|
||||
expect(msg).toContain('vector(1536)');
|
||||
expect(msg).toContain('vector(768)');
|
||||
expect(msg).toContain('DROP INDEX IF EXISTS idx_chunks_embedding');
|
||||
expect(msg).toContain('ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(768)');
|
||||
expect(msg).toContain('UPDATE content_chunks SET embedding = NULL');
|
||||
expect(msg).toContain('USING hnsw'); // HNSW reindex line for dims <= 2000
|
||||
expect(msg).toContain('docs/embedding-migrations.md');
|
||||
expect(msg).toContain('gbrain config set embedding_dimensions 768');
|
||||
expect(msg).toContain('gbrain embed --stale');
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 60000);
|
||||
|
||||
test('mismatch message for dims > 2000 explicitly skips the HNSW reindex (codex finding #8)', () => {
|
||||
// The exact case the user pasting a recipe would otherwise crash on:
|
||||
// CREATE INDEX HNSW on a 2048-d vector column is rejected by pgvector.
|
||||
const msg = embeddingMismatchMessage({
|
||||
currentDims: 1536,
|
||||
requestedDims: 2048,
|
||||
requestedModel: 'voyage:voyage-4-large',
|
||||
source: 'doctor',
|
||||
});
|
||||
|
||||
expect(msg).toContain('vector(2048)');
|
||||
expect(msg).toContain('Skip reindex');
|
||||
expect(msg).toContain("exceeds pgvector's HNSW cap");
|
||||
// The HNSW CREATE INDEX line must NOT appear in the 2048-d recipe —
|
||||
// a user pasting it would crash trying to recreate the index.
|
||||
expect(msg).not.toMatch(/CREATE INDEX[^\n]*idx_chunks_embedding[^\n]*USING hnsw/);
|
||||
});
|
||||
});
|
||||
97
test/embedding-dim-check.test.ts
Normal file
97
test/embedding-dim-check.test.ts
Normal file
@@ -0,0 +1,97 @@
|
||||
/**
|
||||
* v0.28.5 (A4) — Existing-brain dimension-mismatch detection unit tests.
|
||||
*
|
||||
* Pairs with `gbrain init` and `gbrain doctor`'s loud-failure paths. Validates
|
||||
* that:
|
||||
* 1. readContentChunksEmbeddingDim correctly reports null on a fresh brain.
|
||||
* 2. After initSchema, it returns the actual templated dim (1536 default).
|
||||
* 3. embeddingMismatchMessage produces a recipe that explicitly drops the
|
||||
* HNSW index, alters the column, wipes embeddings, and conditionally
|
||||
* reindexes — codex's #8 finding from plan review.
|
||||
*/
|
||||
|
||||
import { test, expect, describe, beforeAll, afterAll } from 'bun:test';
|
||||
import { PGLiteEngine } from '../src/core/pglite-engine.ts';
|
||||
import {
|
||||
readContentChunksEmbeddingDim,
|
||||
embeddingMismatchMessage,
|
||||
} from '../src/core/embedding-dim-check.ts';
|
||||
|
||||
// Canonical pattern: single engine per file, init once, disconnect once.
|
||||
// The two tests below diverge in whether they want a migrated brain or a
|
||||
// pre-initSchema brain — handled by inline reset / second-engine instead of
|
||||
// resetting in beforeEach (keeps the migrated state cached for the LATEST case).
|
||||
let engine: PGLiteEngine;
|
||||
|
||||
beforeAll(async () => {
|
||||
engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
await engine.initSchema();
|
||||
});
|
||||
|
||||
afterAll(async () => {
|
||||
await engine.disconnect();
|
||||
});
|
||||
|
||||
describe('readContentChunksEmbeddingDim', () => {
|
||||
test('returns dims from a migrated brain (default 1536)', async () => {
|
||||
const result = await readContentChunksEmbeddingDim(engine);
|
||||
expect(result.exists).toBe(true);
|
||||
expect(result.dims).toBe(1536);
|
||||
}, 30000);
|
||||
|
||||
test('returns { exists: false, dims: null } on a fresh brain (no initSchema)', async () => {
|
||||
// One-off engine for the fresh-brain case. Never call initSchema so
|
||||
// content_chunks doesn't exist yet. Cleaned up at end of test.
|
||||
const fresh = new PGLiteEngine();
|
||||
await fresh.connect({});
|
||||
try {
|
||||
const result = await readContentChunksEmbeddingDim(fresh);
|
||||
expect(result.exists).toBe(false);
|
||||
expect(result.dims).toBeNull();
|
||||
} finally {
|
||||
await fresh.disconnect();
|
||||
}
|
||||
}, 30000);
|
||||
});
|
||||
|
||||
describe('embeddingMismatchMessage', () => {
|
||||
test('inlines all four recipe steps for HNSW-eligible dims', () => {
|
||||
const msg = embeddingMismatchMessage({
|
||||
currentDims: 1536,
|
||||
requestedDims: 768,
|
||||
requestedModel: 'nomic-embed-text',
|
||||
source: 'init',
|
||||
});
|
||||
expect(msg).toContain('vector(1536)');
|
||||
expect(msg).toContain('vector(768)');
|
||||
expect(msg).toContain('DROP INDEX IF EXISTS idx_chunks_embedding');
|
||||
expect(msg).toContain('ALTER TABLE content_chunks ALTER COLUMN embedding TYPE vector(768)');
|
||||
expect(msg).toContain('UPDATE content_chunks SET embedding = NULL');
|
||||
expect(msg).toContain('CREATE INDEX IF NOT EXISTS idx_chunks_embedding');
|
||||
expect(msg).toContain('docs/embedding-migrations.md');
|
||||
});
|
||||
|
||||
test('skips HNSW recreate when requested dims exceed pgvector cap', () => {
|
||||
// Codex finding #8: 2048d (Voyage 4 Large) cannot be HNSW-indexed in pgvector.
|
||||
// The recipe must NOT instruct a CREATE INDEX HNSW for that dim.
|
||||
const msg = embeddingMismatchMessage({
|
||||
currentDims: 1536,
|
||||
requestedDims: 2048,
|
||||
requestedModel: 'voyage-4-large',
|
||||
source: 'init',
|
||||
});
|
||||
expect(msg).toContain('vector(2048)');
|
||||
expect(msg).toContain('Skip reindex');
|
||||
expect(msg).toContain("exceeds pgvector's HNSW cap");
|
||||
// The HNSW CREATE INDEX line must NOT appear in the 2048d recipe.
|
||||
expect(msg).not.toContain('CREATE INDEX IF NOT EXISTS idx_chunks_embedding\n ON content_chunks USING hnsw');
|
||||
});
|
||||
|
||||
test('source: doctor uses a different header than source: init', () => {
|
||||
const initMsg = embeddingMismatchMessage({ currentDims: 1536, requestedDims: 768, source: 'init' });
|
||||
const doctorMsg = embeddingMismatchMessage({ currentDims: 1536, requestedDims: 768, source: 'doctor' });
|
||||
expect(initMsg).toContain('Refusing to silently re-template');
|
||||
expect(doctorMsg).toContain('Embedding dimension mismatch detected');
|
||||
});
|
||||
});
|
||||
@@ -1,5 +1,5 @@
|
||||
import { describe, test, expect, beforeAll, afterAll, spyOn } from 'bun:test';
|
||||
import { LATEST_VERSION, runMigrations, MIGRATIONS, getIdleBlockers } from '../src/core/migrate.ts';
|
||||
import { LATEST_VERSION, runMigrations, MIGRATIONS, getIdleBlockers, hasPendingMigrations } from '../src/core/migrate.ts';
|
||||
import type { IdleBlocker } from '../src/core/migrate.ts';
|
||||
import type { BrainEngine } from '../src/core/engine.ts';
|
||||
import { PGLiteEngine } from '../src/core/pglite-engine.ts';
|
||||
@@ -20,6 +20,49 @@ describe('migrate', () => {
|
||||
// and are covered in the E2E suite (test/e2e/mechanical.test.ts)
|
||||
});
|
||||
|
||||
// v0.28.5 — A1: cheap probe used by `connectEngine` to gate `initSchema()`
|
||||
// so already-migrated brains don't pay the schema-replay cost on every
|
||||
// short-lived CLI invocation. Closes #651 in cooperation with X1's
|
||||
// post-upgrade auto-apply, without #652's perf regression.
|
||||
describe('hasPendingMigrations', () => {
|
||||
test('returns false on a fully-migrated brain (version === LATEST)', async () => {
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
await engine.initSchema(); // applies all migrations through LATEST_VERSION
|
||||
expect(await hasPendingMigrations(engine)).toBe(false);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 30000);
|
||||
|
||||
test('returns true when version config is behind LATEST_VERSION', async () => {
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
await engine.initSchema();
|
||||
// Simulate an older brain by rewinding the version row.
|
||||
await engine.setConfig('version', '1');
|
||||
expect(await hasPendingMigrations(engine)).toBe(true);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 30000);
|
||||
|
||||
test('returns true when version config is missing entirely (defensive default)', async () => {
|
||||
const engine = new PGLiteEngine();
|
||||
await engine.connect({});
|
||||
try {
|
||||
// Don't call initSchema. Probe against an empty PGlite — getConfig should
|
||||
// either return null (treated as version=1) or throw on missing config
|
||||
// table; either way the probe must say "yes pending."
|
||||
expect(await hasPendingMigrations(engine)).toBe(true);
|
||||
} finally {
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 30000);
|
||||
});
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// v0.18.0 — v16 sources_table_additive (Step 1, Lane A)
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
@@ -60,9 +60,28 @@ const REQUIRED_BOOTSTRAP_COVERAGE: ForwardReference[] = [
|
||||
// v0.19+ — forward-referenced by `CREATE INDEX idx_chunks_language
|
||||
// ON content_chunks(language) WHERE language IS NOT NULL`.
|
||||
{ kind: 'column', table: 'content_chunks', column: 'language' },
|
||||
// v0.20+ Cathedral II — forward-referenced by `CREATE INDEX
|
||||
// idx_chunks_search_vector ON content_chunks USING GIN(search_vector)`.
|
||||
{ kind: 'column', table: 'content_chunks', column: 'search_vector' },
|
||||
// v0.20+ Cathedral II — forward-referenced by `CREATE INDEX
|
||||
// idx_chunks_symbol_qualified ON content_chunks(symbol_name_qualified)`.
|
||||
{ kind: 'column', table: 'content_chunks', column: 'symbol_name_qualified' },
|
||||
// v0.20+ Cathedral II — populated by update_chunk_search_vector trigger;
|
||||
// present in PGLITE_SCHEMA_SQL CREATE TABLE definition.
|
||||
{ kind: 'column', table: 'content_chunks', column: 'parent_symbol_path' },
|
||||
{ kind: 'column', table: 'content_chunks', column: 'doc_comment' },
|
||||
// v0.26.5 — forward-referenced by `CREATE INDEX pages_deleted_at_purge_idx
|
||||
// ON pages (deleted_at) WHERE deleted_at IS NOT NULL`.
|
||||
{ kind: 'column', table: 'pages', column: 'deleted_at' },
|
||||
// v0.26.3 (v33) — forward-referenced by `CREATE INDEX idx_mcp_log_agent_time
|
||||
// ON mcp_request_log(agent_name, created_at DESC)`.
|
||||
{ kind: 'column', table: 'mcp_request_log', column: 'agent_name' },
|
||||
// v0.27 (v36) — forward-referenced by `CREATE INDEX
|
||||
// idx_subagent_messages_provider ON subagent_messages (job_id, provider_id)`.
|
||||
// Composite-index second column; the array-based test pattern misses these
|
||||
// by default, which is why this fix wave's Step 3 replaces this with a
|
||||
// SQL parser that extracts every column referenced by any DDL.
|
||||
{ kind: 'column', table: 'subagent_messages', column: 'provider_id' },
|
||||
];
|
||||
|
||||
test('applyForwardReferenceBootstrap covers every forward reference declared in REQUIRED_BOOTSTRAP_COVERAGE', async () => {
|
||||
@@ -90,11 +109,28 @@ test('applyForwardReferenceBootstrap covers every forward reference declared in
|
||||
|
||||
DROP INDEX IF EXISTS idx_chunks_symbol_name;
|
||||
DROP INDEX IF EXISTS idx_chunks_language;
|
||||
DROP INDEX IF EXISTS idx_chunks_search_vector;
|
||||
DROP INDEX IF EXISTS idx_chunks_symbol_qualified;
|
||||
DROP TRIGGER IF EXISTS chunk_search_vector_trigger ON content_chunks;
|
||||
DROP FUNCTION IF EXISTS update_chunk_search_vector;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS symbol_name;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS language;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS parent_symbol_path;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS doc_comment;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS symbol_name_qualified;
|
||||
ALTER TABLE content_chunks DROP COLUMN IF EXISTS search_vector;
|
||||
|
||||
DROP INDEX IF EXISTS pages_deleted_at_purge_idx;
|
||||
ALTER TABLE pages DROP COLUMN IF EXISTS deleted_at;
|
||||
|
||||
DROP INDEX IF EXISTS idx_mcp_log_agent_time;
|
||||
DROP INDEX IF EXISTS idx_mcp_log_time_agent;
|
||||
ALTER TABLE mcp_request_log DROP COLUMN IF EXISTS agent_name;
|
||||
ALTER TABLE mcp_request_log DROP COLUMN IF EXISTS params;
|
||||
ALTER TABLE mcp_request_log DROP COLUMN IF EXISTS error_message;
|
||||
|
||||
DROP INDEX IF EXISTS idx_subagent_messages_provider;
|
||||
ALTER TABLE subagent_messages DROP COLUMN IF EXISTS provider_id;
|
||||
`);
|
||||
|
||||
// Run bootstrap in isolation (NOT initSchema). This is what we're testing.
|
||||
@@ -160,3 +196,296 @@ test('after bootstrap, PGLITE_SCHEMA_SQL replays without crashing on missing for
|
||||
await engine.disconnect();
|
||||
}
|
||||
}, 30000);
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// v0.28.5 — A2 structural prevention: auto-derive coverage from SQL.
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
// The hand-maintained REQUIRED_BOOTSTRAP_COVERAGE array is the contract
|
||||
// that's failed 11 times across 6 schema versions: every release that
|
||||
// added a column-with-index in the schema blob without a corresponding
|
||||
// bootstrap addition has triggered a wedge incident.
|
||||
//
|
||||
// Codex outside-voice review of v0.28.5's plan caught a critical hole in
|
||||
// the array-based approach: composite indexes like
|
||||
// `idx_subagent_messages_provider ON subagent_messages (job_id, provider_id)`
|
||||
// have a SECOND-column forward reference (`provider_id`) that a first-col-
|
||||
// only extractor would miss entirely. v0.27 wedged exactly this way.
|
||||
//
|
||||
// This parser extracts every column referenced by a CREATE INDEX in
|
||||
// PGLITE_SCHEMA_SQL — including composite-index second/third columns —
|
||||
// and asserts each one is either in the baseline CREATE TABLE OR added
|
||||
// by `applyForwardReferenceBootstrap`. Self-updating: any future
|
||||
// CREATE INDEX in the schema blob is structurally covered the moment
|
||||
// it's added, with no human required to remember to update an array.
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Parse `CREATE TABLE [IF NOT EXISTS] <name> (<body>)` blocks.
|
||||
* Returns a map from table name → set of column names declared in the body.
|
||||
*
|
||||
* Body parser is naive but sufficient for `pglite-schema.ts`: splits on
|
||||
* commas at depth 0 (respecting nested parens for things like `vector(N)`,
|
||||
* `numeric(p, s)`, `CHECK (col IN ('a', 'b'))`), skips constraint lines
|
||||
* (CONSTRAINT/PRIMARY/UNIQUE/CHECK/FOREIGN), and grabs the first identifier
|
||||
* of each remaining row as the column name.
|
||||
*/
|
||||
function parseBaseTableColumns(sql: string): Map<string, Set<string>> {
|
||||
const result = new Map<string, Set<string>>();
|
||||
const re = /CREATE\s+TABLE\s+(?:IF\s+NOT\s+EXISTS\s+)?(\w+)\s*\(/gi;
|
||||
let m: RegExpExecArray | null;
|
||||
while ((m = re.exec(sql)) !== null) {
|
||||
const tableName = m[1].toLowerCase();
|
||||
const bodyStart = m.index + m[0].length;
|
||||
let depth = 1;
|
||||
let i = bodyStart;
|
||||
while (i < sql.length && depth > 0) {
|
||||
const ch = sql[i];
|
||||
if (ch === '(') depth++;
|
||||
else if (ch === ')') depth--;
|
||||
i++;
|
||||
}
|
||||
const body = sql.slice(bodyStart, i - 1);
|
||||
|
||||
const columns = new Set<string>();
|
||||
// Split body on commas at depth 0.
|
||||
let parenDepth = 0;
|
||||
let start = 0;
|
||||
const parts: string[] = [];
|
||||
for (let j = 0; j < body.length; j++) {
|
||||
const ch = body[j];
|
||||
if (ch === '(') parenDepth++;
|
||||
else if (ch === ')') parenDepth--;
|
||||
else if (ch === ',' && parenDepth === 0) {
|
||||
parts.push(body.slice(start, j));
|
||||
start = j + 1;
|
||||
}
|
||||
}
|
||||
parts.push(body.slice(start));
|
||||
|
||||
for (const partRaw of parts) {
|
||||
const part = partRaw.trim();
|
||||
if (!part) continue;
|
||||
// Skip constraint lines.
|
||||
if (/^(CONSTRAINT|PRIMARY|UNIQUE|CHECK|FOREIGN|EXCLUDE)\b/i.test(part)) continue;
|
||||
// First whitespace-separated token is the column name.
|
||||
const colMatch = part.match(/^["`]?(\w+)["`]?/);
|
||||
if (colMatch) columns.add(colMatch[1].toLowerCase());
|
||||
}
|
||||
result.set(tableName, columns);
|
||||
}
|
||||
|
||||
// Also walk ALTER TABLE ... ADD COLUMN statements in the schema blob
|
||||
// itself. Several columns (e.g. `pages.search_vector`) are added by an
|
||||
// inline ALTER inside PGLITE_SCHEMA_SQL after the original CREATE TABLE.
|
||||
// The schema-blob replay adds them in order, so they are NOT
|
||||
// forward-references that bootstrap must provide — the schema blob
|
||||
// itself self-heals on already-existing tables.
|
||||
const alterRe = /ALTER\s+TABLE\s+(?:IF\s+EXISTS\s+)?(?:ONLY\s+)?(\w+)\s+ADD\s+COLUMN\s+(?:IF\s+NOT\s+EXISTS\s+)?["`]?(\w+)["`]?/gi;
|
||||
let am: RegExpExecArray | null;
|
||||
while ((am = alterRe.exec(sql)) !== null) {
|
||||
const tableName = am[1].toLowerCase();
|
||||
const colName = am[2].toLowerCase();
|
||||
if (!result.has(tableName)) result.set(tableName, new Set());
|
||||
result.get(tableName)!.add(colName);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse `CREATE [UNIQUE] INDEX [IF NOT EXISTS] <name> ON <table> [USING method] (<cols>)`.
|
||||
* Returns every (table, column) pair referenced — including composite-index
|
||||
* second/third columns. Function-call wrappers like `lower(col)` are unwrapped
|
||||
* to their inner identifier; literal-only expressions like `(slug, NULLS LAST)`
|
||||
* keep the bare column.
|
||||
*
|
||||
* Out of scope: WHERE-clause columns in partial indexes (rare in our schema;
|
||||
* those columns are always also referenced in the index column list itself).
|
||||
* Trigger function bodies are out of scope (they reference NEW.col / OLD.col
|
||||
* which the existing test file's strip-list handles separately).
|
||||
*/
|
||||
function parseIndexColumnReferences(sql: string): Array<{ table: string; column: string }> {
|
||||
const result: Array<{ table: string; column: string }> = [];
|
||||
// Match CREATE INDEX up through the column-list paren group.
|
||||
const re = /CREATE\s+(?:UNIQUE\s+)?INDEX\s+(?:IF\s+NOT\s+EXISTS\s+)?\w+\s+ON\s+(\w+)\s*(?:USING\s+\w+\s*)?\(/gi;
|
||||
let m: RegExpExecArray | null;
|
||||
while ((m = re.exec(sql)) !== null) {
|
||||
const table = m[1].toLowerCase();
|
||||
const argsStart = m.index + m[0].length;
|
||||
let depth = 1;
|
||||
let i = argsStart;
|
||||
while (i < sql.length && depth > 0) {
|
||||
const ch = sql[i];
|
||||
if (ch === '(') depth++;
|
||||
else if (ch === ')') depth--;
|
||||
i++;
|
||||
}
|
||||
const args = sql.slice(argsStart, i - 1);
|
||||
|
||||
// Split args on commas at depth 0.
|
||||
let parenDepth = 0;
|
||||
let start = 0;
|
||||
const parts: string[] = [];
|
||||
for (let j = 0; j < args.length; j++) {
|
||||
const ch = args[j];
|
||||
if (ch === '(') parenDepth++;
|
||||
else if (ch === ')') parenDepth--;
|
||||
else if (ch === ',' && parenDepth === 0) {
|
||||
parts.push(args.slice(start, j));
|
||||
start = j + 1;
|
||||
}
|
||||
}
|
||||
parts.push(args.slice(start));
|
||||
|
||||
for (const partRaw of parts) {
|
||||
// Strip ASC/DESC, NULLS FIRST/LAST modifiers.
|
||||
const partClean = partRaw
|
||||
.replace(/\s+(?:ASC|DESC)\s*$/i, '')
|
||||
.replace(/\s+NULLS\s+(?:FIRST|LAST)\s*$/i, '')
|
||||
.trim();
|
||||
if (!partClean) continue;
|
||||
// Two shapes to extract from:
|
||||
// `col` — plain identifier
|
||||
// `col vector_cosine_ops` — column followed by operator class (HNSW)
|
||||
// `col COLLATE "C"` — column with collation
|
||||
// `lower(col)` — function-wrapped
|
||||
// For shapes 1-3, the column is the LEADING identifier. For shape 4,
|
||||
// the column is the LAST identifier before a close paren.
|
||||
let col: string | null = null;
|
||||
if (partClean.includes('(')) {
|
||||
// Function-wrapped: `lower(col)` → grab the last identifier inside.
|
||||
const fnMatch = partClean.match(/(\w+)\s*\)\s*$/);
|
||||
if (fnMatch) col = fnMatch[1];
|
||||
} else {
|
||||
// Plain or operator-class-suffixed: leading identifier wins.
|
||||
const leadMatch = partClean.match(/^["`]?(\w+)["`]?/);
|
||||
if (leadMatch) col = leadMatch[1];
|
||||
}
|
||||
if (col && !/^(true|false|null|asc|desc)$/i.test(col)) {
|
||||
result.push({ table, column: col.toLowerCase() });
|
||||
}
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
test('parseBaseTableColumns + parseIndexColumnReferences extract structural references', () => {
|
||||
// Sanity checks for the parser helpers themselves. Runs in-process (no DB).
|
||||
const fixture = `
|
||||
CREATE TABLE IF NOT EXISTS pages (
|
||||
id INTEGER PRIMARY KEY,
|
||||
slug TEXT NOT NULL,
|
||||
embedding vector(1536),
|
||||
CONSTRAINT pages_slug_key UNIQUE (slug)
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_pages_slug ON pages (slug);
|
||||
CREATE INDEX idx_pages_lower ON pages (lower(slug));
|
||||
CREATE INDEX idx_pages_composite ON pages (slug, id DESC);
|
||||
CREATE INDEX idx_pages_hnsw ON pages USING hnsw (embedding vector_cosine_ops);
|
||||
`;
|
||||
const baseCols = parseBaseTableColumns(fixture);
|
||||
expect(baseCols.get('pages')).toBeDefined();
|
||||
expect(baseCols.get('pages')!.has('id')).toBe(true);
|
||||
expect(baseCols.get('pages')!.has('slug')).toBe(true);
|
||||
expect(baseCols.get('pages')!.has('embedding')).toBe(true);
|
||||
// Constraint lines must NOT leak as columns.
|
||||
expect(baseCols.get('pages')!.has('constraint')).toBe(false);
|
||||
|
||||
const refs = parseIndexColumnReferences(fixture);
|
||||
// Single-col index.
|
||||
expect(refs).toContainEqual({ table: 'pages', column: 'slug' });
|
||||
// Function-wrapped column.
|
||||
expect(refs.some(r => r.table === 'pages' && r.column === 'slug')).toBe(true);
|
||||
// Composite — BOTH columns must be captured (codex's case).
|
||||
expect(refs).toContainEqual({ table: 'pages', column: 'id' });
|
||||
// USING hnsw with operator class.
|
||||
expect(refs).toContainEqual({ table: 'pages', column: 'embedding' });
|
||||
});
|
||||
|
||||
test('parseIndexColumnReferences catches v0.27 composite second-column case', () => {
|
||||
// The exact codex regression: `idx_subagent_messages_provider ON
|
||||
// subagent_messages (job_id, provider_id)` has provider_id as the SECOND
|
||||
// column. A first-col-only extractor would miss this — v0.27 wedged exactly
|
||||
// because earlier patterns missed it.
|
||||
const fixture = `
|
||||
CREATE INDEX IF NOT EXISTS idx_subagent_messages_provider
|
||||
ON subagent_messages (job_id, provider_id);
|
||||
`;
|
||||
const refs = parseIndexColumnReferences(fixture);
|
||||
expect(refs).toContainEqual({ table: 'subagent_messages', column: 'job_id' });
|
||||
expect(refs).toContainEqual({ table: 'subagent_messages', column: 'provider_id' });
|
||||
});
|
||||
|
||||
/**
|
||||
* Parse `ALTER TABLE [IF EXISTS] [ONLY] <table> ADD COLUMN [IF NOT EXISTS] <col>`
|
||||
* statements out of an arbitrary SQL string. Used to extract the (table, column)
|
||||
* pairs that `applyForwardReferenceBootstrap` adds, so we can verify static
|
||||
* coverage without running a DB.
|
||||
*/
|
||||
function parseAlterAddColumns(sql: string): Array<{ table: string; column: string }> {
|
||||
const result: Array<{ table: string; column: string }> = [];
|
||||
const re = /ALTER\s+TABLE\s+(?:IF\s+EXISTS\s+)?(?:ONLY\s+)?(\w+)\s+ADD\s+COLUMN\s+(?:IF\s+NOT\s+EXISTS\s+)?["`]?(\w+)["`]?/gi;
|
||||
let m: RegExpExecArray | null;
|
||||
while ((m = re.exec(sql)) !== null) {
|
||||
result.push({ table: m[1].toLowerCase(), column: m[2].toLowerCase() });
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
test('every CREATE INDEX column in PGLITE_SCHEMA_SQL is covered by CREATE TABLE or bootstrap (A2 static check)', async () => {
|
||||
// The structural test that closes the 11-incident wedge class. Static
|
||||
// contract: every column referenced by a CREATE INDEX in PGLITE_SCHEMA_SQL
|
||||
// must be either (a) declared in the current CREATE TABLE body, or
|
||||
// (b) added by `applyForwardReferenceBootstrap` in pglite-engine.ts.
|
||||
//
|
||||
// Codex outside-voice review caught the 11th wedge: composite-index second
|
||||
// columns (`provider_id` in `(job_id, provider_id)`) are forward references
|
||||
// that earlier extractors missed. This parser walks the full column list
|
||||
// of every index — composite or not — and asserts each one is covered.
|
||||
//
|
||||
// Self-updating: when a future migration adds a CREATE INDEX in
|
||||
// PGLITE_SCHEMA_SQL on a column that bootstrap doesn't yet provide, this
|
||||
// test fails loud at PR time. No human required to update an array.
|
||||
const { readFileSync } = await import('fs');
|
||||
const { resolve: resolvePath } = await import('path');
|
||||
const { PGLITE_SCHEMA_SQL } = await import('../src/core/pglite-schema.ts');
|
||||
|
||||
const enginePath = resolvePath(process.cwd(), 'src/core/pglite-engine.ts');
|
||||
const engineSrc = readFileSync(enginePath, 'utf-8');
|
||||
|
||||
const tableColumns = parseBaseTableColumns(PGLITE_SCHEMA_SQL);
|
||||
const indexRefs = parseIndexColumnReferences(PGLITE_SCHEMA_SQL);
|
||||
const bootstrapAdds = parseAlterAddColumns(engineSrc);
|
||||
|
||||
// Build the "covered" set: for each (table, column) pair, true iff it's in
|
||||
// the table's CREATE TABLE columns OR added by an ALTER TABLE in the
|
||||
// bootstrap function.
|
||||
const covered = (table: string, column: string): boolean => {
|
||||
const cols = tableColumns.get(table);
|
||||
if (cols && cols.has(column)) return true;
|
||||
return bootstrapAdds.some(a => a.table === table && a.column === column);
|
||||
};
|
||||
|
||||
// Sanity checks: parser caught the codex case AND bootstrap provides it.
|
||||
expect(indexRefs).toContainEqual({ table: 'subagent_messages', column: 'provider_id' });
|
||||
expect(bootstrapAdds).toContainEqual({ table: 'subagent_messages', column: 'provider_id' });
|
||||
expect(covered('subagent_messages', 'provider_id')).toBe(true);
|
||||
|
||||
// The actual contract: every index column reference must be covered.
|
||||
const uncovered: Array<{ table: string; column: string }> = [];
|
||||
for (const ref of indexRefs) {
|
||||
if (!covered(ref.table, ref.column)) {
|
||||
uncovered.push(ref);
|
||||
}
|
||||
}
|
||||
|
||||
if (uncovered.length > 0) {
|
||||
const list = uncovered.map(u => ` ${u.table}.${u.column}`).join('\n');
|
||||
throw new Error(
|
||||
`PGLITE_SCHEMA_SQL has ${uncovered.length} CREATE INDEX column reference(s) ` +
|
||||
`that are neither in the table's CREATE TABLE body nor added by ` +
|
||||
`applyForwardReferenceBootstrap:\n${list}\n\n` +
|
||||
`Fix: extend applyForwardReferenceBootstrap in src/core/pglite-engine.ts ` +
|
||||
`(and the matching Postgres engine) with the missing ALTER TABLE ADD COLUMN.`,
|
||||
);
|
||||
}
|
||||
}, 30000);
|
||||
|
||||
@@ -61,8 +61,8 @@ describe('detectInstallMethod heuristic (source analysis)', () => {
|
||||
expect(timeoutMatches.length).toBeGreaterThanOrEqual(2); // bun + clawhub detection at minimum
|
||||
});
|
||||
|
||||
test('return type is bun | binary | clawhub | unknown', () => {
|
||||
expect(source).toContain("'bun' | 'binary' | 'clawhub' | 'unknown'");
|
||||
test('return type includes bun-link variant (v0.28.5 cluster D)', () => {
|
||||
expect(source).toContain("'bun' | 'bun-link' | 'binary' | 'clawhub' | 'unknown'");
|
||||
});
|
||||
|
||||
test('does not reference npm in case labels or messages', () => {
|
||||
@@ -71,6 +71,32 @@ describe('detectInstallMethod heuristic (source analysis)', () => {
|
||||
expect(source).not.toContain('via npm');
|
||||
expect(source).not.toContain('npm upgrade');
|
||||
});
|
||||
|
||||
// v0.28.5 cluster D: 3-signal layered detection.
|
||||
test('bun-link signal walks .git/config for garrytan/gbrain match', () => {
|
||||
// detectBunLink reads .git/config and matches our repo name as a
|
||||
// case-insensitive substring. Confirm both the function exists and
|
||||
// that it does the loose substring check (not a strict URL parse).
|
||||
expect(source).toContain('function detectBunLink');
|
||||
expect(source).toContain('GBRAIN_GITHUB_REPO');
|
||||
expect(source).toContain('toLowerCase()');
|
||||
});
|
||||
|
||||
test('classifyBunInstall checks repository.url AND src/cli.ts marker', () => {
|
||||
// Codex feedback: repository.url alone is spoofable by future squatter
|
||||
// updates; the source-marker fallback (src/cli.ts presence) is
|
||||
// belt-and-suspenders.
|
||||
expect(source).toContain('function classifyBunInstall');
|
||||
expect(source).toContain('pkg.repository');
|
||||
expect(source).toContain("'src', 'cli.ts'");
|
||||
});
|
||||
|
||||
test('squatter recovery message names both source-clone AND release-binary paths', () => {
|
||||
expect(source).toContain('printSquatterRecovery');
|
||||
expect(source).toContain('git clone');
|
||||
expect(source).toContain('releases');
|
||||
expect(source).toContain('#658');
|
||||
});
|
||||
});
|
||||
|
||||
describe('post-upgrade behavior (post v0.12.0 merge)', () => {
|
||||
|
||||
Reference in New Issue
Block a user