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Author SHA1 Message Date
Garry TanandClaude Fable 5 c80b8b6757 fix(search): honor sources.config.federated in unqualified local CLI search/query (#2561)
A source registered with `gbrain sources add --federated` was invisible to
an unqualified `gbrain search`/`gbrain query`: the local CLI always emitted
a scalar {sourceId} scope, and nothing on the read path ever consulted
sources.config.federated — contradicting docs/guides/multi-source-brains.md
('Source participates in unqualified gbrain search results').

Fix, at the trusted-local boundary only:
- src/cli.ts makeContext resolves the source WITH its tier and, when the
  tier is non-explicit (local_path / brain_default / sole_non_default /
  seed_default), computes ctx.localFederatedSourceIds = [resolved source,
  ...other config.federated=true sources] (archived excluded).
- New federatedSearchScope (operations.ts) delegates to
  resolveRequestedScope, then widens an unqualified trusted-local scalar
  scope to that set. Used by the search + query handlers only.
- Expansion NEVER applies when ctx.remote !== false (fail-closed source
  isolation), when a per-call source_id/__all__ is passed, when an OAuth
  grant (allowedSources) is present, or when --source/GBRAIN_SOURCE/dotfile
  named the source explicitly.

Deliberately NOT inside sourceScopeOpts: code-intel ops reject multi-source
scopes (resolveCodeIntelScope) and non-search reads keep their scalar
behavior. Cache contamination is already handled — cacheScopeKey folds
sourceIds sets into the query-cache key.

Fixes #2561

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-21 14:43:36 -07:00
11 changed files with 284 additions and 430 deletions
+11 -2
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@@ -808,12 +808,20 @@ async function makeContext(engine: BrainEngine, params: Record<string, unknown>)
// 'default'. Wrapped in try/catch so a doctor / single-source brain that
// never set up sources still returns 'default' silently.
let sourceId: string | undefined;
// #2561: when the source resolved via a NON-explicit tier (path-match /
// brain default / sole-non-default / seed default), unqualified search-shaped
// reads span every `config.federated = true` source. Computed here (the
// trusted local boundary) and consumed by federatedSearchScope in
// operations.ts, which additionally gates on ctx.remote === false.
let localFederated: string[] | undefined;
try {
const { resolveSourceId } = await import('./core/source-resolver.ts');
const { resolveSourceWithTier, localFederatedSourceIds } = await import('./core/source-resolver.ts');
// params.source is set when a CLI flag was parsed for the op (rare; most
// CLI ops don't take --source). Falls through to env/dotfile/path-match.
const explicit = (params.source as string | undefined) ?? null;
sourceId = await resolveSourceId(engine, explicit);
const resolved = await resolveSourceWithTier(engine, explicit);
sourceId = resolved.source_id;
localFederated = await localFederatedSourceIds(engine, resolved.source_id, resolved.tier);
} catch {
// Source resolution failed (e.g. sources table doesn't exist on a fresh
// pre-init brain). Leave sourceId unset; engine read methods fall through
@@ -834,6 +842,7 @@ async function makeContext(engine: BrainEngine, params: Record<string, unknown>)
// table). Matches dispatch.ts's auto-fill so the contract holds across
// every transport.
sourceId: sourceId ?? 'default',
...(localFederated ? { localFederatedSourceIds: localFederated } : {}),
};
}
+3 -39
View File
@@ -18,9 +18,8 @@
* at runtime.
*/
import { chunkText as recursiveChunk, capByEstimatedTokens, DEFAULT_MAX_EST_TOKENS } from './recursive.ts';
import { chunkText as recursiveChunk } from './recursive.ts';
import { buildQualifiedName } from './qualified-names.ts';
import { estimateEmbeddingTokens } from '../cjk.ts';
// Embed the tree-sitter runtime + per-language grammars as files.
// `with { type: 'file' }` returns a path (string) at runtime. Bun bundles
@@ -112,15 +111,7 @@ import G_ZIG from '../../assets/wasm/grammars/tree-sitter-zig.wasm' with { type:
// chunks get the new columns populated. Without this, the v28 backfill
// gives every existing chunk a search_vector but subsequent Layer 5 AST
// work would silently no-op.
//
// v5: estimated-token hard cap on AST-path chunks (capCodeChunks). A node
// splitLargeNode can't subdivide (giant single-statement function, huge
// literal) previously shipped WHOLE regardless of size and could overflow
// strict per-request embedding-token limits (local llama-server crashes
// past ~2,050 tokens, measured). Mirrors the markdown
// chunker's v4 cap; fallback-path chunks are already capped inside
// recursiveChunk.
export const CHUNKER_VERSION = 5;
export const CHUNKER_VERSION = 4;
// Lazy-loaded tree-sitter module (v0.22.x API: Parser is default export)
let Parser: typeof import('web-tree-sitter') | null = null;
@@ -717,7 +708,7 @@ export async function chunkCodeTextFull(
if (chunks.length === 0) {
return { chunks: fallbackChunks(source, filePath, language, opts), edges: rawEdges };
}
return { chunks: capCodeChunks(mergeSmallSiblings(chunks, chunkTarget)), edges: rawEdges };
return { chunks: mergeSmallSiblings(chunks, chunkTarget), edges: rawEdges };
} catch {
return { chunks: fallbackChunks(source, filePath, language, opts), edges: [] };
} finally {
@@ -800,33 +791,6 @@ function mergeSmallSiblings(chunks: CodeChunk[], chunkTarget: number): CodeChunk
return merged;
}
/**
* v5 final safety pass for AST-path chunks: split any chunk whose
* ESTIMATED embedding tokens (conservative per-char-class heuristic,
* cjk.ts) exceed DEFAULT_MAX_EST_TOKENS. Reaches chunks the AST logic
* can't subdivide — splitLargeNode returns [] for nodes with < 2 body
* children (giant single-statement functions, huge literals), which
* previously shipped whole at any size.
*
* Split pieces inherit the source chunk's metadata verbatim; start/end
* lines become approximate for pieces after the first. Acceptable —
* these chunks exist for embedding + retrieval, and the alternative was
* an embedding request the server rejects (or worse, crashes on).
*/
function capCodeChunks(chunks: CodeChunk[]): CodeChunk[] {
if (chunks.every((c) => estimateEmbeddingTokens(c.text) <= DEFAULT_MAX_EST_TOKENS)) {
return chunks;
}
const out: CodeChunk[] = [];
for (const c of chunks) {
const pieces = capByEstimatedTokens(c.text, DEFAULT_MAX_EST_TOKENS);
for (const piece of pieces) {
out.push({ ...c, text: piece, index: out.length, metadata: { ...c.metadata } });
}
}
return out;
}
function buildMergedChunk(group: CodeChunk[], index: number): CodeChunk {
const first = group[0]!;
const last = group[group.length - 1]!;
+6 -117
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@@ -17,13 +17,7 @@
* Lossless invariant: non-overlapping portions reassemble to original.
*/
import {
countCJKAwareWords,
CJK_SENTENCE_DELIMITERS,
CJK_CLAUSE_DELIMITERS,
charEmbedTokenWeight,
estimateEmbeddingTokens,
} from '../cjk.ts';
import { countCJKAwareWords, CJK_SENTENCE_DELIMITERS, CJK_CLAUSE_DELIMITERS } from '../cjk.ts';
/**
* Markdown chunker version. Folded into the per-page chunker_version column
@@ -39,20 +33,8 @@ import {
* re-embed (not re-chunk) so existing pages pick up the wrapper on the
* post-upgrade reembed sweep. See
* `src/core/contextual-retrieval-service.ts`.
*
* v4: estimated-token hard cap + whitespace-word undercount fix. The word
* pipeline counted a 150-char URL as ONE whitespace word, so URL/phone/
* email-dense docs (CJK density < 0.30 → whitespace fallback) produced
* 3-4K-char chunks that overflow strict per-request embedding-token
* limits (measured: local llama-server crashes past ~2,050 tokens; URL
* soup tokenizes at ~1.6 chars/token). Two changes:
* 1. countWords() floors the count at ceil(nonWhitespaceChars/6) so a
* URL counts roughly per-character, not as one word.
* 2. capByEstimatedTokens() final pass guarantees every chunk fits
* `maxTokens` (default 1500) under a conservative per-char-class
* token estimate, regardless of how word counting misjudged it.
*/
export const MARKDOWN_CHUNKER_VERSION = 4;
export const MARKDOWN_CHUNKER_VERSION = 3;
const DELIMITERS: string[][] = [
['\n\n'], // L0: paragraphs
@@ -66,20 +48,8 @@ export interface ChunkOptions {
chunkSize?: number; // target words per chunk (default 300)
chunkOverlap?: number; // overlap words (default 50)
maxChars?: number; // hard cap on any chunk's char length (default 6000)
/**
* v4: hard cap on any chunk's ESTIMATED embedding tokens (default 1500).
* Estimate = conservative per-char-class weights (see cjk.ts
* estimateEmbeddingTokens) — deliberately high, so the real tokenizer
* count stays below this value. Default leaves headroom for the
* contextual-retrieval wrapper (≤ ~630 chars) under a ~2,050-token
* per-request embedding server limit.
*/
maxTokens?: number;
}
/** v4 default for ChunkOptions.maxTokens — see the field doc above. */
export const DEFAULT_MAX_EST_TOKENS = 1500;
export interface TextChunk {
text: string;
index: number;
@@ -103,7 +73,6 @@ export function chunkText(text: string, opts?: ChunkOptions): TextChunk[] {
const chunkSize = opts?.chunkSize || 300;
const chunkOverlap = opts?.chunkOverlap || 50;
const maxChars = opts?.maxChars || 6000;
const maxTokens = opts?.maxTokens || DEFAULT_MAX_EST_TOKENS;
if (!text || text.trim().length === 0) return [];
@@ -120,9 +89,8 @@ export function chunkText(text: string, opts?: ChunkOptions): TextChunk[] {
const wordCount = countWords(stripped);
if (wordCount <= chunkSize) {
// Single-chunk path: still apply the maxChars + maxTokens caps.
const capped = capByChars(stripped.trim(), maxChars)
.flatMap((t) => capByEstimatedTokens(t, maxTokens));
// Single-chunk path: still apply the maxChars cap.
const capped = capByChars(stripped.trim(), maxChars);
return capped.map((t, i) => ({ text: t, index: i }));
}
@@ -133,14 +101,9 @@ export function chunkText(text: string, opts?: ChunkOptions): TextChunk[] {
// v0.32.7: hard char cap. Catches pathological CJK + whitespace-less text
// that the word-level pipeline can't bound (a single Chinese paragraph can
// exceed 8192 OpenAI embedding tokens at any word count).
// v4: estimated-token cap on top — the char cap alone passes token-dense
// content (URL soup at ~1.6 chars/token) that overflows strict embedding
// server limits.
const capped: string[] = [];
for (const chunk of withOverlap) {
for (const piece of capByChars(chunk.trim(), maxChars)) {
capped.push(...capByEstimatedTokens(piece, maxTokens));
}
capped.push(...capByChars(chunk.trim(), maxChars));
}
return capped.map((t, i) => ({ text: t, index: i }));
}
@@ -169,68 +132,6 @@ function capByChars(text: string, maxChars: number): string[] {
return out;
}
/**
* How far back (in chars) the token cap looks for a friendly cut point
* before falling back to a hard cut. 300 covers typical rollup/list line
* lengths so forced splits land at line starts, not mid-URL.
*/
const TOKEN_CAP_CUT_LOOKBACK = 300;
/**
* v4: hard-cap a chunk's ESTIMATED embedding tokens. Final safety pass —
* runs after capByChars on every chunk, so no upstream miscounting
* (whitespace-word fallback, overlap inflation, char-cap survivors) can
* emit a chunk past `maxTokens`.
*
* Cut placement prefers, within the last TOKEN_CAP_CUT_LOOKBACK chars of
* the window: a newline, then any whitespace, then a hard cut. This keeps
* forced splits off mid-line/mid-URL positions for list-shaped content
* and inside code fences. No overlap is added (pieces stay lossless
* modulo the trims the char cap already applies).
*
* @internal exported for the code chunker (code.ts) and tests.
*/
export function capByEstimatedTokens(text: string, maxTokens: number): string[] {
if (text.length === 0) return [];
if (estimateEmbeddingTokens(text) <= maxTokens) return [text];
const out: string[] = [];
let start = 0;
while (start < text.length) {
// Greedily extend the window until the next char would break the cap.
// Always take at least one char so the loop makes forward progress.
let est = 0;
let end = start;
while (end < text.length) {
const w = charEmbedTokenWeight(text.charCodeAt(end));
if (est + w > maxTokens && end > start) break;
est += w;
end++;
}
if (end < text.length) {
const windowStart = Math.max(start + 1, end - TOKEN_CAP_CUT_LOOKBACK);
let cut = text.lastIndexOf('\n', end - 1);
if (cut < windowStart) {
cut = -1;
for (let i = end - 1; i >= windowStart; i--) {
const code = text.charCodeAt(i);
if (code === 0x20 || (code >= 0x09 && code <= 0x0d)) {
cut = i;
break;
}
}
}
if (cut >= windowStart) end = cut + 1;
}
const slice = text.slice(start, end).trim();
if (slice.length > 0) out.push(slice);
start = end;
}
return out;
}
function recursiveSplit(text: string, level: number, target: number): string[] {
if (level >= DELIMITERS.length) {
// Level 4: split on whitespace
@@ -416,19 +317,7 @@ function extractTrailingContext(text: string, targetWords: number): string {
* Delegated to src/core/cjk.ts so the slugify whitelist, expansion
* detection, and PGLite keyword fallback all agree on what "CJK enough"
* means.
*
* v4: floored at ceil(nonWhitespaceChars/6). The whitespace fallback
* counts a 150-char URL as ONE word, so URL/phone/email-dense docs
* (whose ASCII mass pushes CJK density below the 0.30 threshold) were
* sized at a fraction of their real bulk and merged into 3-4K-char
* chunks. The floor makes long whitespace-less runs count roughly
* per-character while leaving normal Latin prose untouched (average
* English word ≈ 5 chars < 6, so the whitespace count still wins).
* Kept local to the chunker — search/expansion.ts keeps the original
* countCJKAwareWords semantics for its query-length check.
*/
function countWords(text: string): number {
const cjkAware = countCJKAwareWords(text);
const nonWhitespace = text.replace(/\s/g, '').length;
return Math.max(cjkAware, Math.ceil(nonWhitespace / 6));
return countCJKAwareWords(text);
}
-61
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@@ -65,64 +65,3 @@ export function countCJKAwareWords(s: string): number {
export function escapeLikePattern(s: string): string {
return s.replace(/\\/g, '\\\\').replace(/%/g, '\\%').replace(/_/g, '\\_');
}
/**
* Conservative per-char-class embedding-token weights (markdown chunker v4).
*
* Why this exists: the chunker's "word" counting drastically UNDER-counts
* whitespace-less ASCII runs (a 150-char URL = 1 whitespace word), so
* word-based size targets can emit chunks that overflow an embedding
* server's per-request token limit. Measured on a local Qwen3-embedding
* llama-server stack:
* - URL/phone/email-dense text tokenizes at ~1.6 chars/token
* - base64-ish / minified blobs approach ~1.3 chars/token (worst case)
* - Korean prose tokenizes NO WORSE than 1 char/token in practice
*
* Weights are deliberately HIGH (tokens are overestimated) so any cap
* based on this estimate is safe against real tokenizers:
* - CJK char → 1.0 token (real CJK prose is cheaper)
* - other non-space → 0.75 token (≈1.33 chars/token, covers base64)
* - whitespace → 0.1 token (mostly folds into neighbor tokens)
*/
export const EMBED_TOKEN_WEIGHT_CJK = 1.0;
export const EMBED_TOKEN_WEIGHT_OTHER = 0.75;
export const EMBED_TOKEN_WEIGHT_WS = 0.1;
/** BMP CJK check by UTF-16 code unit — same ranges as CJK_SLUG_CHARS. */
export function isCJKCodeUnit(code: number): boolean {
return (
(code >= 0x4e00 && code <= 0x9fff) || // Han
(code >= 0x3040 && code <= 0x309f) || // Hiragana
(code >= 0x30a0 && code <= 0x30ff) || // Katakana
(code >= 0xac00 && code <= 0xd7af) // Hangul Syllables
);
}
/**
* Per-code-unit token weight. Unrecognized whitespace (exotic Unicode
* spaces) intentionally falls into OTHER — that only overestimates.
*/
export function charEmbedTokenWeight(code: number): number {
if (isCJKCodeUnit(code)) return EMBED_TOKEN_WEIGHT_CJK;
if (
code === 0x20 || (code >= 0x09 && code <= 0x0d) ||
code === 0xa0 || code === 0x3000
) {
return EMBED_TOKEN_WEIGHT_WS;
}
return EMBED_TOKEN_WEIGHT_OTHER;
}
/**
* Tokenizer-free embedding-token estimate (conservative overestimate).
* See weight docs above. Astral chars count as 2 OTHER code units —
* another overestimate, which is the safe direction.
*/
export function estimateEmbeddingTokens(s: string): number {
if (s.length === 0) return 0;
let est = 0;
for (let i = 0; i < s.length; i++) {
est += charEmbedTokenWeight(s.charCodeAt(i));
}
return Math.ceil(est);
}
+61 -2
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@@ -424,6 +424,23 @@ export interface OperationContext {
* satisfied even on single-source brains.
*/
sourceId: string;
/**
* #2561 — federated read scope for UNQUALIFIED local CLI reads.
*
* Set ONLY by the local CLI's context builder (src/cli.ts makeContext), and
* only when the source resolved via a non-explicit tier (local_path /
* brain_default / sole_non_default / seed_default — NOT --source, NOT
* GBRAIN_SOURCE, NOT a .gbrain-source dotfile). Contains the resolved
* source first, then every other `config.federated = true` source, so an
* unqualified `gbrain search "X"` spans federated sources as
* docs/guides/multi-source-brains.md promises.
*
* Consumed exclusively by `federatedSearchScope` and ONLY when
* `ctx.remote === false` — a remote caller's scope stays governed by
* `ctx.auth.allowedSources` / scalar `ctx.sourceId` (source-isolation
* invariant, fail-closed).
*/
localFederatedSourceIds?: string[];
}
/**
@@ -539,6 +556,45 @@ export function resolveRequestedScope(
return sourceScopeOpts(ctx);
}
/**
* #2561 — source scope for the search-shaped read ops (`search`, `query`).
*
* Delegates to `resolveRequestedScope` (the single trust+grant resolver), then
* widens an UNQUALIFIED trusted-local scalar scope to the CLI-computed
* federated set (`ctx.localFederatedSourceIds`, resolved source first). This is
* what makes `sources add --federated` mean something for local search: a
* federated source participates in unqualified `gbrain search "X"` results.
*
* The expansion NEVER applies when:
* - the caller is not strictly trusted-local (`ctx.remote !== false`) —
* remote scope stays grant-governed (fail-closed source isolation);
* - a per-call `source_id` was passed (explicit wins, including `__all__`);
* - the resolver already produced a federated array (OAuth grant);
* - the CLI resolved the source from an explicit signal (--source / env /
* dotfile) — makeContext leaves `localFederatedSourceIds` unset then.
*
* Deliberately NOT inside `sourceScopeOpts`: code-intel ops collapse a
* multi-element scope to an error (`resolveCodeIntelScope`), and non-search
* reads (get_page, get_links, …) keep their long-standing scalar behavior.
*/
export function federatedSearchScope(
ctx: OperationContext,
sourceIdParam?: string,
): { sourceId?: string; sourceIds?: string[] } {
const scope = resolveRequestedScope(ctx, sourceIdParam);
if (
ctx.remote === false &&
sourceIdParam === undefined &&
scope.sourceId !== undefined &&
scope.sourceIds === undefined &&
ctx.localFederatedSourceIds !== undefined &&
ctx.localFederatedSourceIds.length > 1
) {
return { sourceIds: ctx.localFederatedSourceIds };
}
return scope;
}
/**
* Code-intel adapter for `resolveRequestedScope`. Graph traversal
* (code_callers/code_callees/code_blast/code_flow) is single-source by design —
@@ -1448,7 +1504,8 @@ const search: Operation = {
const queryText = p.query as string;
const limit = (p.limit as number) || 20;
const offset = (p.offset as number) || 0;
const scope = sourceScopeOpts(ctx);
// #2561: unqualified trusted-local search spans federated sources.
const scope = federatedSearchScope(ctx);
// T4/D5 — per-call mode honored ONLY for trusted/local callers so a remote
// OAuth client can't escalate to the costly tokenmax bundle. Local + unknown
@@ -1610,7 +1667,9 @@ const query: Operation = {
// is spread into BOTH the image-similarity searchVector path and the text
// hybridSearch path below, so both honor the same grant.
const sourceIdParam = typeof p.source_id === 'string' ? p.source_id : undefined;
const querySourceScope = resolveRequestedScope(ctx, sourceIdParam);
// #2561: unqualified trusted-local query spans federated sources (per-call
// source_id / remote grants still resolve through resolveRequestedScope).
const querySourceScope = federatedSearchScope(ctx, sourceIdParam);
// v0.27.1: image-similarity branch. Bypasses hybridSearch (which is
// text-only); embeds the image via embedMultimodal and runs a direct
+39
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@@ -353,6 +353,45 @@ export async function resolveSourceWithTier(
return { source_id: 'default', tier: 'seed_default' };
}
/**
* #2561 — compute the federated read scope for an UNQUALIFIED local CLI call.
*
* `sources add --federated` promises that a `config.federated = true` source
* "participates in unqualified `gbrain search` results"
* (docs/guides/multi-source-brains.md). This helper turns that promise into a
* scope: given the resolved source and WHICH tier resolved it, return
* `[resolvedSource, ...other federated source ids]` — or `undefined` when the
* expansion must not apply:
*
* - explicit tiers (`flag` / `env` / `dotfile`): the user named a source;
* scalar scope stands (that IS the qualified case);
* - no other federated source exists: keep the scalar fast path unchanged.
*
* Archived sources are excluded (same rationale as pickSoleNonDefaultSource);
* the archived column is v34+, so fall back to the un-archived query on older
* brains. Callers put the result on `OperationContext.localFederatedSourceIds`
* — consumed only by `federatedSearchScope` and only when `remote === false`.
*/
export async function localFederatedSourceIds(
engine: BrainEngine,
sourceId: string,
tier: SourceTier,
): Promise<string[] | undefined> {
if (tier === 'flag' || tier === 'env' || tier === 'dotfile') return undefined;
let rows: Array<{ id: string }>;
try {
rows = await engine.executeRaw<{ id: string }>(
`SELECT id FROM sources WHERE config->>'federated' = 'true' AND archived = false ORDER BY id`,
);
} catch {
rows = await engine.executeRaw<{ id: string }>(
`SELECT id FROM sources WHERE config->>'federated' = 'true' ORDER BY id`,
);
}
const ids = [sourceId, ...rows.map((r) => r.id).filter((id) => id !== sourceId)];
return ids.length > 1 ? ids : undefined;
}
/** Exposed for tests. */
export const __testing = {
readDotfileWalk,
+3 -6
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@@ -15,22 +15,19 @@ import { describe, test, expect } from 'bun:test';
import { CHUNKER_VERSION } from '../src/core/chunkers/code.ts';
describe('Layer 12 — CHUNKER_VERSION constant', () => {
test('bumped to 5 for the estimated-token hard cap', () => {
test('bumped to 4 for Cathedral II', () => {
// v3: v0.19.0 Chonkie parity (tokenizer + small-sibling merge).
// v4: v0.20.0 Cathedral II (qualified names + parent scope + doc_comment
// + fence extraction + chunk-grain FTS). Folded into content_hash
// so any bump forces clean re-chunks on next sync.
// v5: estimated-token hard cap on AST-path chunks (capCodeChunks) so
// un-subdividable giant nodes can't overflow strict embedding
// server token limits.
expect(CHUNKER_VERSION).toBe(5);
expect(CHUNKER_VERSION).toBe(4);
});
test('is stable across imports (not recomputed at call time)', async () => {
const a = (await import('../src/core/chunkers/code.ts')).CHUNKER_VERSION;
const b = (await import('../src/core/chunkers/code.ts')).CHUNKER_VERSION;
expect(a).toBe(b);
expect(a).toBe(5);
expect(a).toBe(4);
});
});
+2 -2
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@@ -10,8 +10,8 @@ import { describe, test, expect } from 'bun:test';
import { chunkCodeText, detectCodeLanguage, CHUNKER_VERSION } from '../../src/core/chunkers/code.ts';
describe('CHUNKER_VERSION', () => {
test('v5: estimated-token hard cap on AST-path chunks', () => {
expect(CHUNKER_VERSION).toBe(5);
test('v0.20.0 Cathedral II Layer 12 bumped to 4', () => {
expect(CHUNKER_VERSION).toBe(4);
});
});
+5 -6
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@@ -135,14 +135,13 @@ describe('Recursive Text Chunker', () => {
});
describe('CJK chunking (v0.32.7)', () => {
test('MARKDOWN_CHUNKER_VERSION is 4', async () => {
test('MARKDOWN_CHUNKER_VERSION is 3', async () => {
// v0.40.3.0: bumped 2→3 to signal the post-upgrade reembed sweep that
// contextual retrieval wrapping is now applied at embed time.
// v4: estimated-token hard cap + whitespace-word undercount floor
// (URL-dense docs produced chunks past strict embedding server token
// limits). Boundary change → forces re-chunk for chunker_version < 4.
// contextual retrieval wrapping is now applied at embed time. Chunk
// boundaries themselves are unchanged; the bump forces re-embed for
// pages where chunker_version < 3.
const mod = await import('../../src/core/chunkers/recursive.ts');
expect(mod.MARKDOWN_CHUNKER_VERSION).toBe(4);
expect(mod.MARKDOWN_CHUNKER_VERSION).toBe(3);
});
test('long pure-Chinese paragraph splits into multiple chunks', () => {
-195
View File
@@ -1,195 +0,0 @@
/**
* Markdown chunker v4 / code chunker v5 — estimated-token hard cap
* regression tests.
*
* Reproduces a field failure: a local llama-server embedding backend
* (`-ub 2048`) crashes deterministically (trace/BPT trap → EOF at the
* client) when a single chunk exceeds ~2,050 real tokens. Two content
* shapes triggered it:
*
* 1. Korean docs carrying one long source URL per line.
* The URLs' ASCII mass pushes CJK density below 0.30, flipping
* countCJKAwareWords to whitespace counting, where a 150-char URL
* counts as ONE word → chunks ballooned to 3-4K chars ≈ 2,000+
* real tokens (URL soup tokenizes at ~1.6 chars/token).
*
* 2. Large JSON code blocks (~7K chars) that the word pipeline
* undercounts the same way (few whitespace tokens).
*
* The fix: every emitted chunk must satisfy
* estimateEmbeddingTokens(chunk) <= maxTokens (default 1500)
* where the estimate deliberately OVERSTATES real tokenizer counts.
*/
import { describe, test, expect } from 'bun:test';
import { chunkText, capByEstimatedTokens, DEFAULT_MAX_EST_TOKENS } from '../../src/core/chunkers/recursive.ts';
import { chunkCodeText } from '../../src/core/chunkers/code.ts';
import { estimateEmbeddingTokens } from '../../src/core/cjk.ts';
/** Synthesize the failing shape: Korean rollup lines each ending in a long Notion URL. */
function urlDenseKoreanRollup(lines: number): string {
const out: string[] = ['# 링크가 줄마다 붙는 한국어 예시 문서', ''];
for (let i = 0; i < lines; i++) {
const hex32 = (i * 2654435761 >>> 0).toString(16).padStart(8, '0').repeat(4);
out.push(
`- **항목 ${i}**: 이 줄은 청커 동작 검증을 위한 의미 없는 한국어 예시 문장입니다 · 전화 000-0000-${String(1000 + i)} · ` +
`이메일 user${i}@example.com · 링크: https://docs.example.com/pages/${hex32}?v=abcdef0123456789&ref=sample`,
);
}
return out.join('\n');
}
/** Synthesize a large pretty-printed JSON block with CJK values. */
function bigJsonBlock(targetChars: number): string {
const entries: string[] = [];
let i = 0;
let len = 0;
while (len < targetChars) {
const row =
` "item_${i}": { "name": "예시-${i}", "url": "https://example.com/api/v2/items/${i}?token=abc${i}def", "qty": ${i % 100}, "memo": "한국어 값이 섞인 예시 데이터" }`;
entries.push(row);
len += row.length;
i++;
}
return `{\n${entries.join(',\n')}\n}`;
}
describe('v4 estimated-token cap — URL-dense Korean doc (field-failure shape)', () => {
test('every chunk stays under the estimated-token cap', () => {
const md = urlDenseKoreanRollup(60);
const chunks = chunkText(md);
expect(chunks.length).toBeGreaterThan(0);
for (const c of chunks) {
expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS);
}
});
test('no chunk reaches the measured 3K-char danger zone for URL soup', () => {
const md = urlDenseKoreanRollup(60);
const chunks = chunkText(md);
// 1500 est tokens at the OTHER weight (0.75/char) bounds chunks to
// ~2,000 chars for pure ASCII — well under the ~3,300 chars where
// URL-dense content crosses ~2,050 real tokens (1.6 chars/token).
for (const c of chunks) {
expect(c.text.length).toBeLessThanOrEqual(2600);
}
});
test('content is preserved (no lines dropped by the cap)', () => {
const md = urlDenseKoreanRollup(60);
const chunks = chunkText(md);
const joined = chunks.map((c) => c.text).join('\n');
// Spot-check first / middle / last rollup lines survive chunking.
for (const marker of ['항목 0', '항목 30', '항목 59']) {
expect(joined).toContain(marker);
}
});
});
describe('v4 estimated-token cap — large JSON blocks', () => {
test('7K-char pretty JSON through the prose path stays under the cap', () => {
const md = `설정 파일 원문 보존:\n\n\`\`\`\n${bigJsonBlock(7000)}\n\`\`\`\n`;
const chunks = chunkText(md);
expect(chunks.length).toBeGreaterThan(1);
for (const c of chunks) {
expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS);
}
});
test('7K-char minified JSON (single whitespace-less token) stays under the cap', () => {
const minified = bigJsonBlock(7000).replace(/\n\s*/g, '');
const chunks = chunkText(minified);
expect(chunks.length).toBeGreaterThan(1);
for (const c of chunks) {
expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS);
}
});
test('json fence via the code chunker stays under the cap (+header slack)', async () => {
const chunks = await chunkCodeText(bigJsonBlock(7000), 'fence.json');
expect(chunks.length).toBeGreaterThan(0);
for (const c of chunks) {
// buildChunk prepends a short "[JSON] fence.json:…" header AFTER the
// body-level cap; allow ~60 est tokens of header slack. Real-token
// safety margin (2,050 overestimated 1,500) absorbs this easily.
expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(DEFAULT_MAX_EST_TOKENS + 60);
}
});
});
describe('v4 word-count floor — behavior preserved for normal content', () => {
test('Latin prose chunking is unchanged by the floor (avg word < 6 chars)', () => {
const prose = Array.from({ length: 120 }, (_, i) =>
`This is sentence number ${i} and it talks about ordinary things in plain words.`,
).join(' ');
const chunks = chunkText(prose);
// Historical behavior: ~1,560 whitespace words → multiple ~300-word chunks.
expect(chunks.length).toBeGreaterThan(3);
for (const c of chunks) {
const words = c.text.split(/\s+/).length;
expect(words).toBeLessThanOrEqual(300 * 1.5 + 50); // merge cap + overlap
}
});
test('Korean prose (CJK-dense, no URLs) never triggers the token cap', () => {
const prose = Array.from({ length: 80 }, (_, i) =>
`이 문장은 순수 한국어 산문의 청킹 동작을 확인하기 위한 ${i}번째 예시 문장입니다.`,
).join(' ');
const chunks = chunkText(prose);
expect(chunks.length).toBeGreaterThan(1);
for (const c of chunks) {
// CJK-dense chunks are char-counted (≈450 max) — nowhere near 1500.
expect(estimateEmbeddingTokens(c.text)).toBeLessThanOrEqual(700);
}
});
});
describe('capByEstimatedTokens unit behavior', () => {
test('returns input unchanged when under the cap', () => {
expect(capByEstimatedTokens('short text', 1500)).toEqual(['short text']);
expect(capByEstimatedTokens('', 1500)).toEqual([]);
});
test('prefers newline cut points within the lookback window', () => {
const line = 'x'.repeat(100);
const text = Array.from({ length: 40 }, () => line).join('\n');
const pieces = capByEstimatedTokens(text, 1000);
expect(pieces.length).toBeGreaterThan(1);
for (const p of pieces) {
// Every piece should be whole lines (multiples of the 100-char line).
for (const l of p.split('\n')) {
expect(l).toBe(line);
}
}
});
test('makes forward progress on whitespace-less input (hard cut)', () => {
const blob = 'a'.repeat(10_000);
const pieces = capByEstimatedTokens(blob, 1000);
expect(pieces.length).toBeGreaterThan(1);
expect(pieces.join('')).toBe(blob);
for (const p of pieces) {
expect(estimateEmbeddingTokens(p)).toBeLessThanOrEqual(1000);
}
});
});
describe('estimateEmbeddingTokens — weight sanity', () => {
test('overestimates URL-dense ASCII (0.75/char ≥ measured ~0.63/char)', () => {
const url = 'https://docs.example.com/pages/a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4?v=abc&ref=sample';
const est = estimateEmbeddingTokens(url);
expect(est).toBeGreaterThanOrEqual(Math.floor(url.length * 0.7));
});
test('counts CJK at 1 token/char', () => {
expect(estimateEmbeddingTokens('가나다라마')).toBe(5);
});
test('whitespace is nearly free', () => {
expect(estimateEmbeddingTokens(' \n\t ')).toBeLessThanOrEqual(1);
});
test('empty string is 0', () => {
expect(estimateEmbeddingTokens('')).toBe(0);
});
});
+154
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@@ -0,0 +1,154 @@
/**
* #2561 — sources.config.federated participates in UNQUALIFIED local CLI
* search/query.
*
* Pre-fix: the local CLI always emitted a scalar `{sourceId}` scope (required
* field, auto-filled 'default'), so a source registered with
* `gbrain sources add --federated` was invisible to an unqualified
* `gbrain search "X"` — contradicting docs/guides/multi-source-brains.md
* ("Source participates in unqualified `gbrain search` results").
*
* Fix: the CLI context builder computes `ctx.localFederatedSourceIds`
* (resolved source + every other federated source) whenever the source
* resolved via a NON-explicit tier; `federatedSearchScope` widens the scalar
* scope to that set for the `search` / `query` ops — trusted-local only
* (`ctx.remote === false`), never for remote callers, never when a per-call
* `source_id` or an explicit --source/env/dotfile was given.
*/
import { describe, test, expect, beforeAll, afterAll } from 'bun:test';
import { PGLiteEngine } from '../src/core/pglite-engine.ts';
import { localFederatedSourceIds } from '../src/core/source-resolver.ts';
import {
federatedSearchScope,
operations,
type OperationContext,
} from '../src/core/operations.ts';
let engine: PGLiteEngine;
const search = operations.find((o) => o.name === 'search')!;
function ctxOf(overrides: Partial<OperationContext> = {}): OperationContext {
return {
engine: engine as any,
config: {} as any,
logger: console as any,
dryRun: false,
remote: false,
sourceId: 'default',
...overrides,
};
}
beforeAll(async () => {
engine = new PGLiteEngine();
await engine.connect({});
await engine.initSchema();
// Seeded 'default' source is federated=true. Add:
// wiki — federated (must join unqualified search)
// private — NOT federated (must stay invisible unless explicitly named)
// oldnews — federated but archived (must stay excluded)
await engine.executeRaw(
`INSERT INTO sources (id, name, local_path, config) VALUES ('wiki', 'wiki', '/tmp/wiki', '{"federated": true}'::jsonb)`,
);
await engine.executeRaw(
`INSERT INTO sources (id, name, local_path, config) VALUES ('private', 'private', '/tmp/private', '{}'::jsonb)`,
);
await engine.executeRaw(
`INSERT INTO sources (id, name, local_path, config, archived) VALUES ('oldnews', 'oldnews', '/tmp/oldnews', '{"federated": true}'::jsonb, true)`,
);
const pages: Array<[slug: string, sourceId: string, where: string]> = [
['notes/home', 'default', 'default'],
['wiki/topic', 'wiki', 'wiki'],
['private/topic', 'private', 'private'],
['old/topic', 'oldnews', 'oldnews'],
];
for (const [slug, sourceId, where] of pages) {
await engine.putPage(slug, {
type: 'note', title: `Topic in ${where}`, compiled_truth: `the zebra telescope in ${where}`, frontmatter: {},
}, { sourceId });
await engine.upsertChunks(slug, [
{ chunk_index: 0, chunk_text: `the zebra telescope in ${where}`, chunk_source: 'compiled_truth' },
], { sourceId });
}
// Keyword-only search path: no embedding provider needed in tests.
await engine.setConfig('search.mcp_keyword_only', 'true');
}, 60_000);
afterAll(async () => {
if (engine) await engine.disconnect();
}, 60_000);
describe('localFederatedSourceIds — CLI-side scope computation', () => {
test('non-explicit tier: resolved source first, then other federated, archived excluded', async () => {
expect(await localFederatedSourceIds(engine, 'default', 'seed_default')).toEqual(['default', 'wiki']);
});
test('non-federated resolved source still joins its own scope', async () => {
expect(await localFederatedSourceIds(engine, 'private', 'brain_default')).toEqual(['private', 'default', 'wiki']);
});
test('explicit tiers (--source / env / dotfile) never expand', async () => {
expect(await localFederatedSourceIds(engine, 'default', 'flag')).toBeUndefined();
expect(await localFederatedSourceIds(engine, 'default', 'env')).toBeUndefined();
expect(await localFederatedSourceIds(engine, 'default', 'dotfile')).toBeUndefined();
});
test('single federated source (the resolved one) keeps the scalar fast path', async () => {
const solo = { executeRaw: async () => [{ id: 'default' }] } as any;
expect(await localFederatedSourceIds(solo, 'default', 'seed_default')).toBeUndefined();
});
});
describe('federatedSearchScope — trust + explicitness matrix', () => {
test('trusted local + unqualified widens to the federated set', () => {
const ctx = ctxOf({ localFederatedSourceIds: ['default', 'wiki'] });
expect(federatedSearchScope(ctx)).toEqual({ sourceIds: ['default', 'wiki'] });
});
test('remote caller NEVER widens (fail-closed), even if the field is set', () => {
const ctx = ctxOf({ remote: true, localFederatedSourceIds: ['default', 'wiki'] });
expect(federatedSearchScope(ctx)).toEqual({ sourceId: 'default' });
});
test('per-call source_id wins over the federated set', () => {
const ctx = ctxOf({ localFederatedSourceIds: ['default', 'wiki'] });
expect(federatedSearchScope(ctx, 'wiki')).toEqual({ sourceId: 'wiki' });
});
test('per-call __all__ keeps the whole-brain semantics for trusted local', () => {
const ctx = ctxOf({ localFederatedSourceIds: ['default', 'wiki'] });
expect(federatedSearchScope(ctx, '__all__')).toEqual({});
});
test('a federated OAuth grant wins over the local set', () => {
const ctx = ctxOf({
localFederatedSourceIds: ['default', 'wiki'],
auth: { allowedSources: ['a', 'b'] } as OperationContext['auth'],
});
expect(federatedSearchScope(ctx)).toEqual({ sourceIds: ['a', 'b'] });
});
test('no local federated set → unchanged scalar scope', () => {
expect(federatedSearchScope(ctxOf())).toEqual({ sourceId: 'default' });
});
});
describe('search op — unqualified local search spans federated sources', () => {
test('federated source results appear; non-federated + archived stay invisible', async () => {
const ctx = ctxOf({
localFederatedSourceIds: await localFederatedSourceIds(engine, 'default', 'seed_default'),
});
const results = (await search.handler(ctx, { query: 'zebra telescope' })) as Array<{ slug: string }>;
const slugs = results.map((r) => r.slug);
expect(slugs).toContain('notes/home');
expect(slugs).toContain('wiki/topic'); // pre-#2561 this was missing
expect(slugs).not.toContain('private/topic');
expect(slugs).not.toContain('old/topic');
});
test('explicit source resolution (no federated set on ctx) stays single-source', async () => {
const results = (await search.handler(ctxOf(), { query: 'zebra telescope' })) as Array<{ slug: string }>;
const slugs = results.map((r) => r.slug);
expect(slugs).toEqual(['notes/home']);
});
});