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2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Garry TanandClaude Fable 5
|
7b8676be3e | ||
|
SinabinaandClaude Fable 5
|
b8376f7327 |
@@ -206,7 +206,11 @@ jobs:
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needs: cache-check
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if: needs.cache-check.outputs.hit != 'true'
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runs-on: ubuntu-latest
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timeout-minutes: 15
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# 20 (was 15): shard 4 runs ~14.5 min on master (dream.test.ts ~29s/test
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# dominates it) and hits the 15-min ceiling on slower runners, cancelling
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# mid-run with 0 test failures. Rebalancing via
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# scripts/mine-shard-weights.ts is the real fix; this stops the bleeding.
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timeout-minutes: 20
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strategy:
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fail-fast: false
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matrix:
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@@ -820,10 +820,6 @@ export async function doctorReportRemote(engine: BrainEngine): Promise<DoctorRep
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// v0.42.x (#1794, 4A): pool-budget nudge when GBRAIN_MAX_CONNECTIONS is set.
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checks.push(await checkPoolBudget(engine));
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// #2552: warn when an explicit embed-concurrency override fans out against
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// a local single-slot embedding endpoint (silent backfill starvation).
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checks.push(await checkEmbedConcurrency());
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// v0.42.7 (#1696): link-extraction lag. Strictly SQL (single indexed COUNT),
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// safe on the thin-client/remote path — remote operators on checkout-less
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// Postgres brains are exactly who can't otherwise see the extraction backlog.
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@@ -3819,61 +3815,6 @@ export function computePoolBudgetCheck(
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};
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}
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/**
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* #2552: warn when an explicit GBRAIN_EMBED_CONCURRENCY override fans out
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* against a local single-slot embedding endpoint (Ollama / llama-server /
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* localhost base URL). Requests serialize on the one loaded model, so N
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* parallel pages multiply latency xN and can exceed the fetch timeout with
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* no surfaced error — the backfill silently starves. (When the env var is
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* unset, embed auto-caps at LOCAL_EMBED_CONCURRENCY_CAP and this check
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* reports ok.) Pure; exported for tests.
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*/
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export function computeEmbedConcurrencyCheck(
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isLocalEndpoint: boolean,
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envValue: string | undefined,
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localCap: number,
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): Check {
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const name = 'embed_concurrency';
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if (!isLocalEndpoint) {
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return { name, status: 'ok', message: 'Embedding endpoint is not a local inference server — cloud concurrency defaults apply.' };
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}
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const parsed = envValue ? parseInt(envValue, 10) : NaN;
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if (envValue && Number.isFinite(parsed) && parsed > localCap) {
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return {
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name,
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status: 'warn',
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message:
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`GBRAIN_EMBED_CONCURRENCY=${parsed} against a local embedding endpoint. ` +
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`Local inference servers serialize requests, so ${parsed} parallel pages multiply ` +
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`latency x${parsed} and can exceed the fetch timeout — the embed backfill stalls ` +
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`with no error. Unset GBRAIN_EMBED_CONCURRENCY (auto-caps at ${localCap}) or set it <= ${localCap}.`,
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};
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}
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return {
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name,
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status: 'ok',
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message: `Local embedding endpoint detected; embed concurrency capped at ${envValue ? parsed : localCap}.`,
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};
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}
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/** Thin gateway/env wrapper over `computeEmbedConcurrencyCheck`. */
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export async function checkEmbedConcurrency(): Promise<Check> {
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try {
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const { isLocalEmbeddingEndpoint, LOCAL_EMBED_CONCURRENCY_CAP } = await import('../core/ai/gateway.ts');
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return computeEmbedConcurrencyCheck(
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isLocalEmbeddingEndpoint(),
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process.env.GBRAIN_EMBED_CONCURRENCY,
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LOCAL_EMBED_CONCURRENCY_CAP,
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);
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} catch (err) {
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return {
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name: 'embed_concurrency',
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status: 'ok',
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message: `Skipped (${err instanceof Error ? err.message : String(err)})`,
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};
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}
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}
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/** Thin env/engine wrapper over `computePoolBudgetCheck`. */
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export async function checkPoolBudget(_engine: BrainEngine): Promise<Check> {
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try {
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+8
-30
@@ -1,6 +1,5 @@
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import type { BrainEngine } from '../core/engine.ts';
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import { embedBatch, currentEmbeddingSignature } from '../core/embedding.ts';
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import { isLocalEmbeddingEndpoint, LOCAL_EMBED_CONCURRENCY_CAP } from '../core/ai/gateway.ts';
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import type { ChunkInput } from '../core/types.ts';
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import { chunkText } from '../core/chunkers/recursive.ts';
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import { createProgress, type ProgressReporter } from '../core/progress.ts';
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@@ -177,31 +176,6 @@ export class EmbeddingDimMismatchError extends Error {
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}
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}
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/**
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* #2552: resolve the bulk-embed worker count. Env override or the
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* cloud-tuned default of 20 — but when the operator did NOT set
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* GBRAIN_EMBED_CONCURRENCY and the embedding endpoint is a local inference
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* server (Ollama / llama-server / localhost base URL), cap at
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* LOCAL_EMBED_CONCURRENCY_CAP: 20 parallel pages against a single-slot
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* server serialize on the one loaded model, multiply latency x20 past the
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* fetch timeout, and starve the backfill with no surfaced error. An
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* explicit env value always wins (`gbrain doctor` warns instead).
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* Pacing only ever LOWERS concurrency (Codex P2).
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*/
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export function resolveEmbedConcurrency(paceMaxConcurrency?: number): number {
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const envSet = !!process.env.GBRAIN_EMBED_CONCURRENCY;
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const base = parseInt(process.env.GBRAIN_EMBED_CONCURRENCY || '20', 10);
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let resolved = base;
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if (!envSet && isLocalEmbeddingEndpoint() && base > LOCAL_EMBED_CONCURRENCY_CAP) {
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resolved = LOCAL_EMBED_CONCURRENCY_CAP;
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serr(
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`[embed] local embedding endpoint detected — capping concurrency at ` +
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`${LOCAL_EMBED_CONCURRENCY_CAP} (set GBRAIN_EMBED_CONCURRENCY to override)`,
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);
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}
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return paceMaxConcurrency ? Math.min(resolved, paceMaxConcurrency) : resolved;
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}
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/**
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* Pre-flight check: read the actual schema column dim and compare to the
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* gateway's resolved dim. Throws `EmbeddingDimMismatchError` on mismatch
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@@ -703,8 +677,10 @@ async function embedAll(
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// Paced runs lower this to the resolved cap (the real lever vs pooler-slot
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// starvation); unpaced keeps the env/default 20. Codex P2: only ever LOWER —
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// never raise above an operator's existing env cap.
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// #2552: local endpoints auto-cap — see resolveEmbedConcurrency.
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const CONCURRENCY = resolveEmbedConcurrency(staleOpts?.paceMaxConcurrency);
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const BASE_CONCURRENCY = parseInt(process.env.GBRAIN_EMBED_CONCURRENCY || '20', 10);
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const CONCURRENCY = staleOpts?.paceMaxConcurrency
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? Math.min(BASE_CONCURRENCY, staleOpts.paceMaxConcurrency)
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: BASE_CONCURRENCY;
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||||
|
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async function embedOnePage(page: typeof pages[number]) {
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// #1737: bail before doing any work for this page if the run was aborted.
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@@ -879,8 +855,10 @@ async function embedAllStale(
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// Paced runs lower concurrency to the resolved cap (E-1: worker count IS the
|
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// lever on this single pool, no separate permit). Codex P2: pacing only ever
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||||
// LOWERS concurrency — never raise above an operator's existing env cap.
|
||||
// #2552: local endpoints auto-cap — see resolveEmbedConcurrency.
|
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const CONCURRENCY = resolveEmbedConcurrency(staleOpts?.paceMaxConcurrency);
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const BASE_CONCURRENCY = parseInt(process.env.GBRAIN_EMBED_CONCURRENCY || '20', 10);
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const CONCURRENCY = staleOpts?.paceMaxConcurrency
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? Math.min(BASE_CONCURRENCY, staleOpts.paceMaxConcurrency)
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: BASE_CONCURRENCY;
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const pacer = staleOpts?.pacer ?? createNoopPacer();
|
||||
|
||||
// D3 + D3a + D8: wall-clock budget. 30 min default; env override.
|
||||
|
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@@ -683,33 +683,6 @@ export function getEmbeddingDimensions(): number {
|
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return requireConfig().embedding_dimensions ?? DEFAULT_EMBEDDING_DIMENSIONS;
|
||||
}
|
||||
|
||||
/**
|
||||
* #2552: cap for parallel bulk-embed workers against a local inference
|
||||
* server. A single-slot Ollama/llama-server serializes requests, so the
|
||||
* cloud-tuned 20-worker fan-out multiplies latency x20 and blows past the
|
||||
* fetch timeout with no surfaced error (the backfill silently starves).
|
||||
*/
|
||||
export const LOCAL_EMBED_CONCURRENCY_CAP = 2;
|
||||
|
||||
/**
|
||||
* #2552: true when the configured embedding model routes to a local
|
||||
* inference server — the `ollama` / `llama-server` recipes, or any recipe
|
||||
* whose base URL was explicitly pointed at localhost. Bulk callers use this
|
||||
* to pick CPU-safe concurrency defaults; `gbrain doctor` uses it to warn
|
||||
* about an explicit cloud-sized override. Fail-open: unconfigured or
|
||||
* unresolvable gateway → false (cloud behavior, the historical default).
|
||||
*/
|
||||
export function isLocalEmbeddingEndpoint(): boolean {
|
||||
try {
|
||||
const { recipe } = resolveRecipe(getEmbeddingModel());
|
||||
if (recipe.id === 'ollama' || recipe.id === 'llama-server') return true;
|
||||
const base = requireConfig().base_urls?.[recipe.id] ?? '';
|
||||
return /\/\/(localhost|127\.0\.0\.1|\[::1\])(:|\/|$)/i.test(base);
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.28.11: returns the configured multimodal embedding model when set,
|
||||
* or undefined if the brain falls back to `embedding_model` for multimodal
|
||||
|
||||
@@ -29,17 +29,9 @@ export const ollama: Recipe = {
|
||||
trust_custom_dims: true, // #2271: local models carry varied native dims
|
||||
cost_per_1m_tokens_usd: 0,
|
||||
price_last_verified: '2026-04-20',
|
||||
// #2552: Ollama's true batch capacity depends on the locally loaded
|
||||
// model + OLLAMA_NUM_PARALLEL, but the previous `no_batch_cap: true`
|
||||
// meant a whole page went out in ONE request — on a CPU-only box that
|
||||
// multiplies latency past the fetch timeout and the backfill starves
|
||||
// with no surfaced error. Ollama doesn't return a recognizable
|
||||
// token-limit error either, so the recursive-halving safety net never
|
||||
// fires; a conservative static pre-split cap is the only guard.
|
||||
// 4096 tokens x 2 chars/token ~= 8K chars per request (code-dense
|
||||
// pages run ~2 chars/token, not the tiktoken-ish 4).
|
||||
max_batch_tokens: 4096,
|
||||
chars_per_token: 2,
|
||||
// Ollama's batch capacity depends on the locally loaded model + the
|
||||
// OLLAMA_NUM_PARALLEL config; no static cap to declare. v0.32 (#779).
|
||||
no_batch_cap: true,
|
||||
},
|
||||
},
|
||||
setup_hint: 'Install Ollama from https://ollama.ai, then `ollama pull nomic-embed-text` and `ollama serve`.',
|
||||
|
||||
@@ -141,7 +141,6 @@ export const OPS_CHECK_NAMES: ReadonlySet<string> = new Set([
|
||||
'pgbouncer_prepare',
|
||||
'pgvector',
|
||||
'pool_budget',
|
||||
'embed_concurrency',
|
||||
'progressive_batch_audit_health',
|
||||
'queue_health',
|
||||
'reranker_health',
|
||||
|
||||
@@ -56,7 +56,7 @@ import { GBrainError, PAGE_SORT_SQL, ENRICH_ORDER_SQL } from './types.ts';
|
||||
import { finalizeLastSeen } from './chronicle/last-seen.ts';
|
||||
import { computeAnomaliesFromBuckets } from './cycle/anomaly.ts';
|
||||
import { resolveBoostMap, resolveHardExcludes } from './search/source-boost.ts';
|
||||
import { buildSourceFactorCase, buildHardExcludeClause, buildVisibilityClause, buildRecencyComponentSql, buildBestPerPagePoolCte, buildOrFallbackWebsearchQuery, buildWebsearchQueryExpr } from './search/sql-ranking.ts';
|
||||
import { buildSourceFactorCase, buildHardExcludeClause, buildVisibilityClause, buildRecencyComponentSql, buildBestPerPagePoolCte, buildOrFallbackWebsearchQuery } from './search/sql-ranking.ts';
|
||||
import {
|
||||
normalizeEngineColumn,
|
||||
buildVectorCastFragment,
|
||||
@@ -1591,8 +1591,6 @@ export class PGLiteEngine implements BrainEngine {
|
||||
}
|
||||
|
||||
// v0.20.0 Cathedral II Layer 10 C1/C2: language + symbol-kind filters.
|
||||
// #2380: slash-bearing queries match both the split-word and literal
|
||||
// slash forms — see buildWebsearchQueryExpr in ./search/sql-ranking.ts.
|
||||
const params: unknown[] = [query, innerLimit, limit, offset];
|
||||
let extraFilter = '';
|
||||
if (opts?.language) {
|
||||
@@ -1632,7 +1630,6 @@ export class PGLiteEngine implements BrainEngine {
|
||||
// FTS config name (e.g. 'english', 'pt_br'). Validated by getFtsLanguage()
|
||||
// — safe to interpolate into raw SQL.
|
||||
const ftsLang = getFtsLanguage();
|
||||
const ftsQueryExpr = buildWebsearchQueryExpr(ftsLang, '$1', query);
|
||||
|
||||
const keywordSql =
|
||||
`WITH ranked AS (
|
||||
@@ -1640,14 +1637,14 @@ export class PGLiteEngine implements BrainEngine {
|
||||
p.slug, p.id as page_id, p.title, p.type, p.source_id,
|
||||
p.effective_date, p.effective_date_source,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
ts_rank(cc.search_vector, ${ftsQueryExpr}) * ${sourceFactorCase} AS score,
|
||||
ts_rank(cc.search_vector, websearch_to_tsquery('${ftsLang}', $1)) * ${sourceFactorCase} AS score,
|
||||
CASE WHEN p.updated_at < (
|
||||
SELECT MAX(te.created_at) FROM timeline_entries te WHERE te.page_id = p.id
|
||||
) THEN true ELSE false END AS stale
|
||||
FROM content_chunks cc
|
||||
JOIN pages p ON p.id = cc.page_id
|
||||
JOIN sources s ON s.id = p.source_id
|
||||
WHERE cc.search_vector @@ ${ftsQueryExpr} ${detailFilter}${extraFilter} ${hardExcludeClause} ${visibilityClause}
|
||||
WHERE cc.search_vector @@ websearch_to_tsquery('${ftsLang}', $1) ${detailFilter}${extraFilter} ${hardExcludeClause} ${visibilityClause}
|
||||
-- v0.27.1: hide image rows from default text-keyword search so
|
||||
-- OCR text doesn't drown text-page hits. Image-similarity queries
|
||||
-- run a separate vector path on embedding_image.
|
||||
@@ -1715,10 +1712,7 @@ export class PGLiteEngine implements BrainEngine {
|
||||
// FTS config name (e.g. 'english', 'pt_br'). Validated by getFtsLanguage()
|
||||
// — safe to interpolate into raw SQL.
|
||||
const ftsLang = getFtsLanguage();
|
||||
const ftsQueryExpr = buildWebsearchQueryExpr(ftsLang, '$1', query);
|
||||
|
||||
// #2380: slash-bearing queries match both the split-word and literal
|
||||
// slash forms — see buildWebsearchQueryExpr in ./search/sql-ranking.ts.
|
||||
const params: unknown[] = [query, limit, offset];
|
||||
let extraFilter = '';
|
||||
if (opts?.type) {
|
||||
@@ -1766,7 +1760,7 @@ export class PGLiteEngine implements BrainEngine {
|
||||
COALESCE(rep.chunk_index, 0) as chunk_index,
|
||||
COALESCE(rep.chunk_text, '') as chunk_text,
|
||||
COALESCE(rep.chunk_source, 'compiled_truth') as chunk_source,
|
||||
ts_rank_cd(p.search_vector, ${ftsQueryExpr}) * ${sourceFactorCase} AS score,
|
||||
ts_rank_cd(p.search_vector, websearch_to_tsquery('${ftsLang}', $1)) * ${sourceFactorCase} AS score,
|
||||
CASE WHEN p.updated_at < (
|
||||
SELECT MAX(te.created_at) FROM timeline_entries te WHERE te.page_id = p.id
|
||||
) THEN true ELSE false END AS stale
|
||||
@@ -1781,7 +1775,7 @@ export class PGLiteEngine implements BrainEngine {
|
||||
ORDER BY (cc.chunk_source = 'compiled_truth') DESC, cc.chunk_index ASC
|
||||
LIMIT 1
|
||||
) rep ON true
|
||||
WHERE p.search_vector @@ ${ftsQueryExpr}
|
||||
WHERE p.search_vector @@ websearch_to_tsquery('${ftsLang}', $1)
|
||||
${extraFilter} ${hardExcludeClause} ${visibilityClause}
|
||||
ORDER BY score DESC, p.id ASC
|
||||
LIMIT $2 OFFSET $3`;
|
||||
@@ -1968,8 +1962,6 @@ export class PGLiteEngine implements BrainEngine {
|
||||
});
|
||||
}
|
||||
|
||||
// #2380: slash-bearing queries match both the split-word and literal
|
||||
// slash forms — see buildWebsearchQueryExpr in ./search/sql-ranking.ts.
|
||||
const params: unknown[] = [query, limit, offset];
|
||||
let extraFilter = '';
|
||||
if (opts?.language) {
|
||||
@@ -2004,21 +1996,20 @@ export class PGLiteEngine implements BrainEngine {
|
||||
// FTS config name (e.g. 'english', 'pt_br'). Validated by getFtsLanguage()
|
||||
// — safe to interpolate into raw SQL.
|
||||
const ftsLang = getFtsLanguage();
|
||||
const ftsQueryExpr = buildWebsearchQueryExpr(ftsLang, '$1', query);
|
||||
|
||||
const { rows } = await this.db.query(
|
||||
`SELECT
|
||||
p.slug, p.id as page_id, p.title, p.type, p.source_id,
|
||||
p.effective_date, p.effective_date_source,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
ts_rank(cc.search_vector, ${ftsQueryExpr}) * ${sourceFactorCase} AS score,
|
||||
ts_rank(cc.search_vector, websearch_to_tsquery('${ftsLang}', $1)) * ${sourceFactorCase} AS score,
|
||||
CASE WHEN p.updated_at < (
|
||||
SELECT MAX(te.created_at) FROM timeline_entries te WHERE te.page_id = p.id
|
||||
) THEN true ELSE false END AS stale
|
||||
FROM content_chunks cc
|
||||
JOIN pages p ON p.id = cc.page_id
|
||||
JOIN sources s ON s.id = p.source_id
|
||||
WHERE cc.search_vector @@ ${ftsQueryExpr} ${detailFilter}${extraFilter} ${hardExcludeClause} ${visibilityClause}
|
||||
WHERE cc.search_vector @@ websearch_to_tsquery('${ftsLang}', $1) ${detailFilter}${extraFilter} ${hardExcludeClause} ${visibilityClause}
|
||||
ORDER BY score DESC
|
||||
LIMIT $2 OFFSET $3`,
|
||||
params
|
||||
|
||||
@@ -64,7 +64,7 @@ import { ConnectionManager } from './connection-manager.ts';
|
||||
import { logConnectionEvent } from './connection-audit.ts';
|
||||
import { validateSlug, contentHash, rowToPage, rowToStalePage, rowToChunk, rowToSearchResult, parseEmbedding, tryParseEmbedding, takeRowToTake, takeHitRowToHit, isUndefinedTableError, warnOncePerProcess } from './utils.ts';
|
||||
import { resolveBoostMap, resolveHardExcludes } from './search/source-boost.ts';
|
||||
import { buildSourceFactorCase, buildHardExcludeClause, buildVisibilityClause, buildRecencyComponentSql, buildBestPerPagePoolCte, buildOrFallbackWebsearchQuery, buildWebsearchQueryExpr } from './search/sql-ranking.ts';
|
||||
import { buildSourceFactorCase, buildHardExcludeClause, buildVisibilityClause, buildRecencyComponentSql, buildBestPerPagePoolCte, buildOrFallbackWebsearchQuery } from './search/sql-ranking.ts';
|
||||
import { DEFAULT_EMBEDDING_MODEL, DEFAULT_EMBEDDING_DIMENSIONS } from './ai/defaults.ts';
|
||||
import { DELETE_BATCH_SIZE } from './engine-constants.ts';
|
||||
|
||||
@@ -1691,8 +1691,6 @@ export class PostgresEngine implements BrainEngine {
|
||||
const hardExcludePrefixes = resolveHardExcludes(opts?.exclude_slug_prefixes, opts?.include_slug_prefixes);
|
||||
const hardExcludeClause = buildHardExcludeClause('p.slug', hardExcludePrefixes);
|
||||
|
||||
// #2380: slash-bearing queries match both the split-word and literal
|
||||
// slash forms — see buildWebsearchQueryExpr in ./search/sql-ranking.ts.
|
||||
const params: unknown[] = [query];
|
||||
let typeClause = '';
|
||||
if (type) {
|
||||
@@ -1763,7 +1761,6 @@ export class PostgresEngine implements BrainEngine {
|
||||
// FTS config name (e.g. 'english', 'pt_br'). Validated by getFtsLanguage()
|
||||
// — safe to interpolate into raw SQL.
|
||||
const ftsLang = getFtsLanguage();
|
||||
const ftsQueryExpr = buildWebsearchQueryExpr(ftsLang, '$1', query);
|
||||
|
||||
const rawQuery = `
|
||||
WITH ranked_chunks AS (
|
||||
@@ -1771,11 +1768,11 @@ export class PostgresEngine implements BrainEngine {
|
||||
p.slug, p.id as page_id, p.title, p.type, p.source_id,
|
||||
p.effective_date, p.effective_date_source,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
ts_rank(cc.search_vector, ${ftsQueryExpr}) * ${sourceFactorCase} AS score
|
||||
ts_rank(cc.search_vector, websearch_to_tsquery('${ftsLang}', $1)) * ${sourceFactorCase} AS score
|
||||
FROM content_chunks cc
|
||||
JOIN pages p ON p.id = cc.page_id
|
||||
JOIN sources s ON s.id = p.source_id
|
||||
WHERE cc.search_vector @@ ${ftsQueryExpr}
|
||||
WHERE cc.search_vector @@ websearch_to_tsquery('${ftsLang}', $1)
|
||||
${typeClause}
|
||||
${typesClause}
|
||||
${excludeSlugsClause}
|
||||
@@ -1866,10 +1863,7 @@ export class PostgresEngine implements BrainEngine {
|
||||
// FTS config name (e.g. 'english', 'pt_br'). Validated by getFtsLanguage()
|
||||
// — safe to interpolate into raw SQL.
|
||||
const ftsLang = getFtsLanguage();
|
||||
const ftsQueryExpr = buildWebsearchQueryExpr(ftsLang, '$1', query);
|
||||
|
||||
// #2380: slash-bearing queries match both the split-word and literal
|
||||
// slash forms — see buildWebsearchQueryExpr in ./search/sql-ranking.ts.
|
||||
const params: unknown[] = [query];
|
||||
let typeClause = '';
|
||||
if (opts?.type) {
|
||||
@@ -1929,7 +1923,7 @@ export class PostgresEngine implements BrainEngine {
|
||||
COALESCE(rep.chunk_index, 0) as chunk_index,
|
||||
COALESCE(rep.chunk_text, '') as chunk_text,
|
||||
COALESCE(rep.chunk_source, 'compiled_truth') as chunk_source,
|
||||
ts_rank_cd(p.search_vector, ${ftsQueryExpr}) * ${sourceFactorCase} AS score,
|
||||
ts_rank_cd(p.search_vector, websearch_to_tsquery('${ftsLang}', $1)) * ${sourceFactorCase} AS score,
|
||||
false AS stale
|
||||
FROM pages p
|
||||
JOIN sources s ON s.id = p.source_id
|
||||
@@ -1942,7 +1936,7 @@ export class PostgresEngine implements BrainEngine {
|
||||
ORDER BY (cc.chunk_source = 'compiled_truth') DESC, cc.chunk_index ASC
|
||||
LIMIT 1
|
||||
) rep ON true
|
||||
WHERE p.search_vector @@ ${ftsQueryExpr}
|
||||
WHERE p.search_vector @@ websearch_to_tsquery('${ftsLang}', $1)
|
||||
${typeClause}
|
||||
${typesClause}
|
||||
${excludeSlugsClause}
|
||||
@@ -2006,8 +2000,6 @@ export class PostgresEngine implements BrainEngine {
|
||||
const hardExcludePrefixes = resolveHardExcludes(opts?.exclude_slug_prefixes, opts?.include_slug_prefixes);
|
||||
const hardExcludeClause = buildHardExcludeClause('p.slug', hardExcludePrefixes);
|
||||
|
||||
// #2380: slash-bearing queries match both the split-word and literal
|
||||
// slash forms — see buildWebsearchQueryExpr in ./search/sql-ranking.ts.
|
||||
const params: unknown[] = [query];
|
||||
let typeClause = '';
|
||||
if (type) {
|
||||
@@ -2068,19 +2060,18 @@ export class PostgresEngine implements BrainEngine {
|
||||
// FTS config name (e.g. 'english', 'pt_br'). Validated by getFtsLanguage()
|
||||
// — safe to interpolate into raw SQL.
|
||||
const ftsLang = getFtsLanguage();
|
||||
const ftsQueryExpr = buildWebsearchQueryExpr(ftsLang, '$1', query);
|
||||
|
||||
const rawQuery = `
|
||||
SELECT
|
||||
p.slug, p.id as page_id, p.title, p.type, p.source_id,
|
||||
p.effective_date, p.effective_date_source,
|
||||
cc.id as chunk_id, cc.chunk_index, cc.chunk_text, cc.chunk_source,
|
||||
ts_rank(cc.search_vector, ${ftsQueryExpr}) * ${sourceFactorCase} AS score,
|
||||
ts_rank(cc.search_vector, websearch_to_tsquery('${ftsLang}', $1)) * ${sourceFactorCase} AS score,
|
||||
false AS stale
|
||||
FROM content_chunks cc
|
||||
JOIN pages p ON p.id = cc.page_id
|
||||
JOIN sources s ON s.id = p.source_id
|
||||
WHERE cc.search_vector @@ ${ftsQueryExpr}
|
||||
WHERE cc.search_vector @@ websearch_to_tsquery('${ftsLang}', $1)
|
||||
${typeClause}
|
||||
${typesClause}
|
||||
${excludeSlugsClause}
|
||||
|
||||
@@ -251,28 +251,6 @@ export function buildOrFallbackWebsearchQuery(query: string): string | null {
|
||||
return tokens.join(' OR ');
|
||||
}
|
||||
|
||||
/**
|
||||
* #2380: FTS query expression for slash-bearing queries. Postgres' default
|
||||
* text-search parser classifies `foo/bar` as a single `file`-alias lexeme —
|
||||
* on BOTH the query side and the index side. So a raw `foo/bar` query only
|
||||
* matched documents carrying the identical joined lexeme (literal paths),
|
||||
* and a slash-split query only matches documents whose text had the words
|
||||
* separated. Neither form alone covers both document shapes; OR the two
|
||||
* parses so a slash query matches prose ("foo and bar", stemmed, AND
|
||||
* semantics) AND literal slash forms ("src/core/x.ts") alike.
|
||||
*
|
||||
* Slash-free queries return the plain single-parse expression — byte-
|
||||
* identical SQL and identical ts_rank to the historical behavior.
|
||||
*
|
||||
* `ftsLang` is validated by getFtsLanguage() (safe to interpolate);
|
||||
* `param` is a `$N` placeholder, never user text.
|
||||
*/
|
||||
export function buildWebsearchQueryExpr(ftsLang: string, param: string, query: string): string {
|
||||
const plain = `websearch_to_tsquery('${ftsLang}', ${param})`;
|
||||
if (!query.includes('/')) return plain;
|
||||
return `(websearch_to_tsquery('${ftsLang}', translate(${param}, '/', ' ')) || ${plain})`;
|
||||
}
|
||||
|
||||
// ============================================================
|
||||
// v0.29.1 — Recency component SQL builder
|
||||
// ============================================================
|
||||
|
||||
@@ -23,6 +23,15 @@
|
||||
* hold conventions and shared rule files, not skills. Files like
|
||||
* `_brain-filing-rules.md` live at the root and are not considered
|
||||
* skills by either loader.
|
||||
*
|
||||
* ClawHub-installed workspace skills (#1767): a skill dir carrying
|
||||
* `.clawhub/origin.json` is an externally-managed runtime integration
|
||||
* (e.g. an email or catalog skill), not a gbrain-routable skill. The
|
||||
* derive path SKIPS those so `gbrain doctor` resolver_health doesn't
|
||||
* hard-fail on them — UNLESS the skill's SKILL.md frontmatter declares
|
||||
* `triggers:`, which is the explicit opt-in to gbrain routing (and the
|
||||
* same surface that makes it reachable). An explicit manifest.json that
|
||||
* lists a ClawHub skill also keeps strict checking (verbatim path).
|
||||
*/
|
||||
|
||||
import { existsSync, readFileSync, readdirSync, statSync } from 'fs';
|
||||
@@ -60,9 +69,27 @@ function parseSkillName(skillMdPath: string): string | null {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Does the SKILL.md frontmatter declare a `triggers:` key? A ClawHub-
|
||||
* installed skill that ships gbrain `triggers:` has explicitly opted in
|
||||
* to gbrain routing and gets full resolver checks (#1767).
|
||||
*/
|
||||
function declaresTriggers(skillMdPath: string): boolean {
|
||||
try {
|
||||
const content = readFileSync(skillMdPath, 'utf-8');
|
||||
const fmMatch = content.match(/^---\n([\s\S]*?)\n---/);
|
||||
if (!fmMatch) return false;
|
||||
return /^triggers:/m.test(fmMatch[1]);
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Walk skillsDir, return every `<skillsDir>/<dir>/SKILL.md` as a
|
||||
* ManifestEntry. Dotfile and underscore-prefixed dirs are skipped.
|
||||
* ManifestEntry. Dotfile and underscore-prefixed dirs are skipped, as
|
||||
* are ClawHub-installed external skills that haven't opted in to gbrain
|
||||
* routing via `triggers:` frontmatter (#1767).
|
||||
*/
|
||||
function deriveManifest(skillsDir: string): ManifestEntry[] {
|
||||
const out: ManifestEntry[] = [];
|
||||
@@ -93,6 +120,12 @@ function deriveManifest(skillsDir: string): ManifestEntry[] {
|
||||
const skillMd = join(subdirAbs, 'SKILL.md');
|
||||
if (!existsSync(skillMd)) continue;
|
||||
|
||||
// ClawHub-installed external skill (#1767): skip unless it opts in
|
||||
// to gbrain routing by declaring `triggers:` in its frontmatter.
|
||||
if (existsSync(join(subdirAbs, '.clawhub', 'origin.json')) && !declaresTriggers(skillMd)) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const frontmatterName = parseSkillName(skillMd);
|
||||
const name = frontmatterName && frontmatterName !== '' ? frontmatterName : entry;
|
||||
out.push({ name, path: `${entry}/SKILL.md` });
|
||||
|
||||
@@ -28,8 +28,8 @@ describe('v0.32 #779: no_batch_cap suppresses the missing-max_batch_tokens warni
|
||||
resetGateway();
|
||||
});
|
||||
|
||||
test('LiteLLM and llama-server declare no_batch_cap: true', () => {
|
||||
for (const id of ['litellm', 'llama-server']) {
|
||||
test('Ollama, LiteLLM, llama-server all declare no_batch_cap: true', () => {
|
||||
for (const id of ['ollama', 'litellm', 'llama-server']) {
|
||||
const r = getRecipe(id);
|
||||
expect(r, `${id} not registered`).toBeDefined();
|
||||
expect(
|
||||
@@ -39,18 +39,6 @@ describe('v0.32 #779: no_batch_cap suppresses the missing-max_batch_tokens warni
|
||||
}
|
||||
});
|
||||
|
||||
test('#2552: Ollama declares a conservative static batch cap, not no_batch_cap', () => {
|
||||
// A CPU-only Ollama box wedges when a whole page ships in one request;
|
||||
// Ollama never returns a token-limit error so the recursive-halving
|
||||
// safety net can't fire. The pre-split cap is the only guard.
|
||||
const r = getRecipe('ollama');
|
||||
expect(r).toBeDefined();
|
||||
const e = r!.touchpoints.embedding!;
|
||||
expect(e.no_batch_cap).toBeUndefined();
|
||||
expect(e.max_batch_tokens).toBe(4096);
|
||||
expect(e.chars_per_token).toBe(2);
|
||||
});
|
||||
|
||||
test('configureGateway does NOT warn for ollama/litellm/llama-server', () => {
|
||||
warnSpy.mockClear();
|
||||
resetGateway();
|
||||
|
||||
@@ -382,6 +382,35 @@ describe("DRY detection — checkResolvable", () => {
|
||||
});
|
||||
});
|
||||
|
||||
describe("#1767 — ClawHub workspace skills are not resolver-required", () => {
|
||||
let dir: string;
|
||||
afterEachCleanup(() => dir && rmSync(dir, { recursive: true, force: true }));
|
||||
|
||||
test("ClawHub skill without gbrain metadata produces no unreachable/mece_gap", () => {
|
||||
dir = mkdtempSync(join(tmpdir(), "gbrain-clawhub-"));
|
||||
// Native gbrain skill: routable via frontmatter triggers. No manifest.json
|
||||
// (the OpenClaw derive path from the issue repro).
|
||||
mkdirSync(join(dir, "query"), { recursive: true });
|
||||
writeFileSync(
|
||||
join(dir, "query", "SKILL.md"),
|
||||
`---\nname: query\ndescription: test\ntriggers:\n - "what do we know"\n---\n\n# query\n`
|
||||
);
|
||||
// ClawHub-installed integration: no triggers, no resolver row.
|
||||
mkdirSync(join(dir, "agentmail", ".clawhub"), { recursive: true });
|
||||
writeFileSync(
|
||||
join(dir, "agentmail", ".clawhub", "origin.json"),
|
||||
JSON.stringify({ registry: "https://clawhub.ai", slug: "agentmail" })
|
||||
);
|
||||
writeFileSync(join(dir, "agentmail", "SKILL.md"), `---\nname: agentmail\ndescription: email integration\n---\n\n# agentmail\n`);
|
||||
|
||||
const report = checkResolvable(dir);
|
||||
const agentmailIssues = report.issues.filter(i => i.skill === "agentmail");
|
||||
expect(agentmailIssues).toEqual([]);
|
||||
expect(report.ok).toBe(true);
|
||||
expect(report.summary.total_skills).toBe(1);
|
||||
});
|
||||
});
|
||||
|
||||
describe("v0.22.4 regression — actual repo skills/ has 0 errors", () => {
|
||||
test("repo skills/ pass check-resolvable cleanly (zero errors AND zero warnings)", () => {
|
||||
// The v0.22.4 (Part A) contract was zero warnings AND zero errors.
|
||||
|
||||
@@ -1,118 +0,0 @@
|
||||
/**
|
||||
* #2552: cloud-tuned embedding defaults silently wedge CPU-only local
|
||||
* endpoints (Ollama). Three-part fix under test:
|
||||
*
|
||||
* 1. `isLocalEmbeddingEndpoint()` — gateway helper detecting local
|
||||
* inference servers (ollama / llama-server recipes, localhost base URL).
|
||||
* 2. `resolveEmbedConcurrency()` — embed auto-caps the 20-worker fan-out
|
||||
* at LOCAL_EMBED_CONCURRENCY_CAP for local endpoints unless the
|
||||
* operator set GBRAIN_EMBED_CONCURRENCY explicitly.
|
||||
* 3. `computeEmbedConcurrencyCheck()` — doctor warns when an explicit env
|
||||
* override fans out against a local endpoint.
|
||||
*
|
||||
* Serial: mutates process.env and the module-global gateway config.
|
||||
*/
|
||||
|
||||
import { afterAll, afterEach, describe, expect, test } from 'bun:test';
|
||||
import {
|
||||
configureGateway,
|
||||
resetGateway,
|
||||
isLocalEmbeddingEndpoint,
|
||||
LOCAL_EMBED_CONCURRENCY_CAP,
|
||||
} from '../src/core/ai/gateway.ts';
|
||||
import { resolveEmbedConcurrency } from '../src/commands/embed.ts';
|
||||
import { computeEmbedConcurrencyCheck } from '../src/commands/doctor.ts';
|
||||
|
||||
const SAVED_ENV = process.env.GBRAIN_EMBED_CONCURRENCY;
|
||||
|
||||
afterEach(() => {
|
||||
resetGateway();
|
||||
if (SAVED_ENV === undefined) delete process.env.GBRAIN_EMBED_CONCURRENCY;
|
||||
else process.env.GBRAIN_EMBED_CONCURRENCY = SAVED_ENV;
|
||||
});
|
||||
|
||||
afterAll(() => {
|
||||
resetGateway();
|
||||
});
|
||||
|
||||
describe('#2552 isLocalEmbeddingEndpoint', () => {
|
||||
test('false when the gateway is not configured (fail-open to cloud behavior)', () => {
|
||||
resetGateway();
|
||||
expect(isLocalEmbeddingEndpoint()).toBe(false);
|
||||
});
|
||||
|
||||
test('true for the ollama recipe', () => {
|
||||
configureGateway({ embedding_model: 'ollama:nomic-embed-text', env: {} });
|
||||
expect(isLocalEmbeddingEndpoint()).toBe(true);
|
||||
});
|
||||
|
||||
test('true for the llama-server recipe', () => {
|
||||
configureGateway({ embedding_model: 'llama-server:my-gguf', env: {} });
|
||||
expect(isLocalEmbeddingEndpoint()).toBe(true);
|
||||
});
|
||||
|
||||
test('false for a cloud recipe', () => {
|
||||
configureGateway({
|
||||
embedding_model: 'openai:text-embedding-3-small',
|
||||
env: { OPENAI_API_KEY: 'fake' },
|
||||
});
|
||||
expect(isLocalEmbeddingEndpoint()).toBe(false);
|
||||
});
|
||||
|
||||
test('true when a cloud recipe base URL is explicitly pointed at localhost', () => {
|
||||
configureGateway({
|
||||
embedding_model: 'openai:text-embedding-3-small',
|
||||
env: { OPENAI_API_KEY: 'fake' },
|
||||
base_urls: { openai: 'http://localhost:8080/v1' },
|
||||
});
|
||||
expect(isLocalEmbeddingEndpoint()).toBe(true);
|
||||
});
|
||||
});
|
||||
|
||||
describe('#2552 resolveEmbedConcurrency', () => {
|
||||
test('caps at LOCAL_EMBED_CONCURRENCY_CAP for a local endpoint when env is unset', () => {
|
||||
delete process.env.GBRAIN_EMBED_CONCURRENCY;
|
||||
configureGateway({ embedding_model: 'ollama:nomic-embed-text', env: {} });
|
||||
expect(resolveEmbedConcurrency()).toBe(LOCAL_EMBED_CONCURRENCY_CAP);
|
||||
});
|
||||
|
||||
test('explicit env override always wins, even against a local endpoint', () => {
|
||||
process.env.GBRAIN_EMBED_CONCURRENCY = '10';
|
||||
configureGateway({ embedding_model: 'ollama:nomic-embed-text', env: {} });
|
||||
expect(resolveEmbedConcurrency()).toBe(10);
|
||||
});
|
||||
|
||||
test('cloud endpoints keep the historical default of 20', () => {
|
||||
delete process.env.GBRAIN_EMBED_CONCURRENCY;
|
||||
configureGateway({ env: { OPENAI_API_KEY: 'fake' } });
|
||||
expect(resolveEmbedConcurrency()).toBe(20);
|
||||
});
|
||||
|
||||
test('pacing only ever lowers concurrency', () => {
|
||||
delete process.env.GBRAIN_EMBED_CONCURRENCY;
|
||||
configureGateway({ embedding_model: 'ollama:nomic-embed-text', env: {} });
|
||||
expect(resolveEmbedConcurrency(1)).toBe(1);
|
||||
expect(resolveEmbedConcurrency(16)).toBe(LOCAL_EMBED_CONCURRENCY_CAP);
|
||||
});
|
||||
});
|
||||
|
||||
describe('#2552 computeEmbedConcurrencyCheck (doctor)', () => {
|
||||
test('ok for non-local endpoints', () => {
|
||||
expect(computeEmbedConcurrencyCheck(false, '20', 2).status).toBe('ok');
|
||||
});
|
||||
|
||||
test('warn when an explicit override exceeds the local cap', () => {
|
||||
const check = computeEmbedConcurrencyCheck(true, '20', 2);
|
||||
expect(check.status).toBe('warn');
|
||||
expect(check.message).toContain('GBRAIN_EMBED_CONCURRENCY=20');
|
||||
});
|
||||
|
||||
test('ok when env is unset against a local endpoint (auto-cap applies)', () => {
|
||||
expect(computeEmbedConcurrencyCheck(true, undefined, 2).status).toBe('ok');
|
||||
});
|
||||
|
||||
test('ok when the override is at or under the cap', () => {
|
||||
expect(computeEmbedConcurrencyCheck(true, '2', 2).status).toBe('ok');
|
||||
expect(computeEmbedConcurrencyCheck(true, '1', 2).status).toBe('ok');
|
||||
});
|
||||
});
|
||||
@@ -216,67 +216,6 @@ describe('PGLiteEngine: Search', () => {
|
||||
expect(results.length).toBe(0);
|
||||
});
|
||||
|
||||
// Regression (#2380): queries containing `/` used to bypass FTS AND
|
||||
// semantics. Postgres' default text-search parser classifies `foo/bar` as
|
||||
// a `file`-alias token mapped to the `simple` dictionary, so it became a
|
||||
// single un-stemmed lexeme `'foo/bar'` that never matches indexed text —
|
||||
// the primary FTS pass returned 0 and the OR fallback took over, matching
|
||||
// pages that contain EITHER term. searchKeyword/searchTitles now normalize
|
||||
// `/` to whitespace before websearch_to_tsquery parses, so the primary
|
||||
// AND pass matches directly.
|
||||
test('searchKeyword: slash query matches with AND semantics, not OR fallback', async () => {
|
||||
// Decoy shares only ONE of the two query terms ('enterprise').
|
||||
await engine.putPage('concepts/enterprise-pricing', {
|
||||
type: 'concept', title: 'Widget Pricing',
|
||||
compiled_truth: 'Enterprise pricing for widgets.',
|
||||
});
|
||||
await engine.upsertChunks('concepts/enterprise-pricing', [
|
||||
{ chunk_index: 0, chunk_text: 'Enterprise pricing for widgets', chunk_source: 'compiled_truth' },
|
||||
]);
|
||||
|
||||
// Both terms co-occur only in the novamind chunk. Pre-fix this returned
|
||||
// BOTH pages (primary pass zero-hit → OR fallback); post-fix the primary
|
||||
// AND pass returns exactly the co-occurrence page.
|
||||
const results = await engine.searchKeyword('NovaMind/enterprise');
|
||||
expect(results.length).toBe(1);
|
||||
expect(results[0].slug).toBe('companies/novamind');
|
||||
});
|
||||
|
||||
test('searchTitles: slash query matches with AND semantics, not OR fallback', async () => {
|
||||
await engine.putPage('companies/novamind-enterprise', {
|
||||
type: 'company', title: 'NovaMind Enterprise Platform',
|
||||
compiled_truth: 'Placeholder body.',
|
||||
});
|
||||
await engine.putPage('guides/enterprise-sales', {
|
||||
type: 'concept', title: 'Enterprise Sales Guide',
|
||||
compiled_truth: 'Placeholder body.',
|
||||
});
|
||||
|
||||
// Pre-fix: `NovaMind/Enterprise` parsed as one file-alias lexeme → the
|
||||
// primary title pass returned 0 and the OR fallback matched BOTH titles.
|
||||
const results = await engine.searchTitles('NovaMind/Enterprise');
|
||||
expect(results.length).toBe(1);
|
||||
expect(results[0].slug).toBe('companies/novamind-enterprise');
|
||||
});
|
||||
|
||||
test('searchKeyword: slash query still matches the literal slash form (file paths)', async () => {
|
||||
// The INDEX side also emits the joined file-alias lexeme for literal
|
||||
// `foo/bar` text, so a query normalized to split words alone would go
|
||||
// blind to documents containing the literal slash form (paths, URLs).
|
||||
// buildWebsearchQueryExpr ORs both parses; this pins the raw arm.
|
||||
await engine.putPage('runbooks/widget-deploy', {
|
||||
type: 'concept', title: 'Widget Deploy Runbook',
|
||||
compiled_truth: 'Runbook for the acme/widget deployment pipeline.',
|
||||
});
|
||||
await engine.upsertChunks('runbooks/widget-deploy', [
|
||||
{ chunk_index: 0, chunk_text: 'Runbook for the acme/widget deployment pipeline', chunk_source: 'compiled_truth' },
|
||||
]);
|
||||
|
||||
const results = await engine.searchKeyword('acme/widget');
|
||||
expect(results.length).toBe(1);
|
||||
expect(results[0].slug).toBe('runbooks/widget-deploy');
|
||||
});
|
||||
|
||||
test('tsvector trigger populates search_vector on insert', async () => {
|
||||
// Verify the PL/pgSQL trigger fires and content_chunks.search_vector is
|
||||
// populated from chunk_text. v0.20.0 Cathedral II Layer 3 moved FTS from
|
||||
|
||||
@@ -166,6 +166,55 @@ describe('loadOrDeriveManifest', () => {
|
||||
expect(r.skills.map(s => s.name)).toEqual(['apple', 'mango', 'zebra']);
|
||||
});
|
||||
|
||||
// #1767 — ClawHub-installed workspace skills are external integrations,
|
||||
// not gbrain-routable skills. The derive path skips them unless they
|
||||
// opt in via `triggers:` frontmatter.
|
||||
it('skips ClawHub-origin skills without triggers frontmatter (#1767)', () => {
|
||||
const dir = scratch();
|
||||
writeSkill(dir, 'query', 'query');
|
||||
writeSkill(dir, 'agentmail', 'agentmail');
|
||||
mkdirSync(join(dir, 'agentmail', '.clawhub'), { recursive: true });
|
||||
writeFileSync(
|
||||
join(dir, 'agentmail', '.clawhub', 'origin.json'),
|
||||
JSON.stringify({ registry: 'https://clawhub.ai', slug: 'agentmail' })
|
||||
);
|
||||
const r = loadOrDeriveManifest(dir);
|
||||
expect(r.derived).toBe(true);
|
||||
expect(r.skills.map(s => s.name)).toEqual(['query']);
|
||||
});
|
||||
|
||||
it('includes ClawHub-origin skills that opt in via triggers frontmatter (#1767)', () => {
|
||||
const dir = scratch();
|
||||
writeSkill(dir, 'agentmail', 'agentmail');
|
||||
mkdirSync(join(dir, 'agentmail', '.clawhub'), { recursive: true });
|
||||
writeFileSync(
|
||||
join(dir, 'agentmail', '.clawhub', 'origin.json'),
|
||||
JSON.stringify({ registry: 'https://clawhub.ai', slug: 'agentmail' })
|
||||
);
|
||||
writeFileSync(
|
||||
join(dir, 'agentmail', 'SKILL.md'),
|
||||
`---\nname: agentmail\ndescription: test\ntriggers:\n - "send email"\n---\n\n# agentmail\n`
|
||||
);
|
||||
const r = loadOrDeriveManifest(dir);
|
||||
expect(r.derived).toBe(true);
|
||||
expect(r.skills.map(s => s.name)).toEqual(['agentmail']);
|
||||
});
|
||||
|
||||
it('keeps ClawHub-origin skills listed in an explicit manifest.json (#1767)', () => {
|
||||
// Explicit manifest.json is a deliberate declaration — strict checking stays.
|
||||
const dir = scratch();
|
||||
writeSkill(dir, 'agentmail', 'agentmail');
|
||||
mkdirSync(join(dir, 'agentmail', '.clawhub'), { recursive: true });
|
||||
writeFileSync(
|
||||
join(dir, 'agentmail', '.clawhub', 'origin.json'),
|
||||
JSON.stringify({ registry: 'https://clawhub.ai', slug: 'agentmail' })
|
||||
);
|
||||
writeManifest(dir, { skills: [{ name: 'agentmail', path: 'agentmail/SKILL.md' }] });
|
||||
const r = loadOrDeriveManifest(dir);
|
||||
expect(r.derived).toBe(false);
|
||||
expect(r.skills.map(s => s.name)).toEqual(['agentmail']);
|
||||
});
|
||||
|
||||
it('treats dirs without SKILL.md as not-a-skill', () => {
|
||||
const dir = scratch();
|
||||
writeSkill(dir, 'query', 'query');
|
||||
|
||||
Reference in New Issue
Block a user