Files
connectai/tests/largeInputMapReduce.test.ts
T
koriweb 76d5fedfb5 v2.2.256: 코어 채팅 큰 입력 청킹·통합 + 실제 컨텍스트 창 정렬 + 모델 핸들 race 수정
큰 입력 시 "Failed to acquire LM Studio model handle … Operation canceled"
로 턴 전체가 죽던 문제를 3계층으로 해결. 일반 채팅(코어 경로)은 그동안
단일 예산 호출이라 약한 모델·큰 입력에서 무너졌다 — 그 갭을 메움.

- 핸들 race 수정: getModelHandle 을 재시도 루프 안으로 이동. 취소/죽은-핸들
  류 에러는 SDK 재생성 후 1회 자동 재시도(실제 사용자 취소는 존중). 라이프
  사이클의 동시 로드가 abort 되며 SDK 가 coalesce 한 JIT 조회까지 죽던 것.
- Phase 1 실제 창 정렬: llm.getContextLength()(캐시)로 실측 창에 예산 클램프.
  설정값보다 작은 창으로 로드된 경우 서버 truncation/빈 답변 차단. 배지에 표시.
- Phase 2 코어 Map-Reduce: 단일 입력이 (유효 창 × ratio) 초과 시 청크→질의
  인지형 추출→통합. 부분/전체 폴백, 무관 시 정직 신호. 동시성 기본 2.
- Phase 3 메타 노출: 진행/결과 배지 표시, [조각 k] 출처 옵트인.

신규 설정 5종. /meet·/review 전용 경로는 불변. 테스트 +25건, 전체 684 통과.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-19 18:05:44 +09:00

160 lines
5.6 KiB
TypeScript

/**
* Phase 2 — large-input map-reduce core.
*
* Pure orchestration with an injected `callLLM`, so no network / SDK is touched.
*/
import {
runMapReduce,
shouldMapReduce,
chunkCharBudget,
inputBudgetTokens,
type MapReduceConfig,
type MapReduceDeps,
} from '../src/agent/handlePrompt/largeInputMapReduce';
import type { ChatMessage } from '../src/agent';
const estimateTokens = (s: string) => Math.ceil((s || '').length / 4);
const cfg: MapReduceConfig = {
enabled: true,
triggerRatio: 0.6,
concurrency: 2,
maxDepth: 3,
showProvenance: false,
};
function isExtract(messages: ChatMessage[]): boolean {
return /추출기/.test(messages[0]?.content ?? '');
}
function chunkLabel(messages: ChatMessage[]): string {
const m = (messages[1]?.content ?? '').match(/자료 조각 (\d+)\/(\d+)/);
return m ? m[1] : '?';
}
// ~12 short markdown sections → forces multiple chunks under a small window.
const bigContent = Array.from({ length: 12 }, (_, i) =>
`## 섹션 ${i + 1}\n안건 ${i + 1}: 결정사항과 수치 ${i * 10}. ` + '내용 '.repeat(40)
).join('\n\n');
describe('shouldMapReduce', () => {
test('triggers only above window * triggerRatio and when enabled', () => {
expect(shouldMapReduce(6200, 10000, cfg)).toBe(true); // > 6000
expect(shouldMapReduce(5000, 10000, cfg)).toBe(false); // < 6000
expect(shouldMapReduce(99999, 10000, { ...cfg, enabled: false })).toBe(false);
expect(shouldMapReduce(100, 0, cfg)).toBe(false); // unknown window
});
});
describe('budget helpers', () => {
test('inputBudgetTokens reserves output + safety', () => {
// 10000 - sys(500) - max(2048, 1000)=2048 - safety(512) = 6940
expect(inputBudgetTokens(10000, 500, 512)).toBe(6940);
});
test('chunkCharBudget is positive and scales with the window', () => {
const small = chunkCharBudget(4000, 200, 512);
const big = chunkCharBudget(16000, 200, 512);
expect(small).toBeGreaterThan(0);
expect(big).toBeGreaterThan(small);
});
});
describe('runMapReduce', () => {
function deps(callLLM: MapReduceDeps['callLLM']): MapReduceDeps {
return { callLLM, estimateTokens };
}
const params = {
intent: '회의록을 안건별로 정리해줘',
largeContent: bigContent,
windowTokens: 4000,
systemTokens: 200,
safetyMargin: 512,
cfg,
};
test('extracts relevant facts per chunk and condenses them', async () => {
const seen: string[] = [];
const r = await runMapReduce(
deps(async (messages) => {
expect(isExtract(messages)).toBe(true);
const k = chunkLabel(messages);
seen.push(k);
return `추출-${k}`;
}),
params,
);
expect(r.allIrrelevant).toBe(false);
expect(r.chunkCount).toBeGreaterThan(1);
expect(r.relevantCount).toBe(r.chunkCount);
expect(r.condensedContext).toContain('추출-1');
// every chunk was visited
expect(seen.length).toBe(r.chunkCount);
});
test('all-irrelevant chunks → allIrrelevant with empty context', async () => {
const r = await runMapReduce(
deps(async () => '(관련 없음)'),
params,
);
expect(r.allIrrelevant).toBe(true);
expect(r.relevantCount).toBe(0);
expect(r.condensedContext).toBe('');
});
test('respects concurrency limit', async () => {
let active = 0;
let peak = 0;
await runMapReduce(
deps(async (messages) => {
active++;
peak = Math.max(peak, active);
await new Promise((res) => setTimeout(res, 5));
active--;
return `x-${chunkLabel(messages)}`;
}),
params,
);
expect(peak).toBeLessThanOrEqual(cfg.concurrency);
});
test('a failing chunk extraction falls back to truncated raw (not a crash)', async () => {
let call = 0;
const r = await runMapReduce(
deps(async (messages) => {
if (isExtract(messages) && ++call === 1) throw new Error('boom');
return `ok-${chunkLabel(messages)}`;
}),
params,
);
expect(r.allIrrelevant).toBe(false);
// The failed chunk still contributed (raw fallback), so relevantCount === chunkCount.
expect(r.relevantCount).toBe(r.chunkCount);
});
test('tags provenance when showProvenance is on', async () => {
const r = await runMapReduce(
deps(async (messages) => `발췌-${chunkLabel(messages)}`),
{ ...params, cfg: { ...cfg, showProvenance: true } },
);
expect(r.condensedContext).toMatch(/\[조각 \d+\]/);
});
test('hierarchical reduce kicks in when extractions overflow the context ceiling', async () => {
// Tiny window so even a few extractions exceed the ceiling → reduce rounds run.
let reduceCalls = 0;
const r = await runMapReduce(
deps(async (messages) => {
if (isExtract(messages)) {
return '관련 사실 '.repeat(60); // big extraction per chunk
}
reduceCalls++;
return '통합본'; // reduce collapses to something small
}),
{ ...params, windowTokens: 2200 },
);
expect(reduceCalls).toBeGreaterThan(0);
expect(r.reduceDepth).toBeGreaterThan(0);
expect(r.allIrrelevant).toBe(false);
});
});