g1nation
354 lines
15 KiB
TypeScript
354 lines
15 KiB
TypeScript
/**
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* ============================================================
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* RetrievalOrchestrator — Unified RAG Pipeline
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*
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* Astra의 모든 검색 소스를 통합 관리하는 오케스트레이터입니다.
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*
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* 검색 흐름:
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* ① Query Planning — 의도 분류 + 검색 전략 결정
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* ② Parallel Search — Brain + Memory + Project + Episode 동시 검색
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* ③ Result Fusion — 통합 스코어링 + 중복 제거
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* ④ Context Budget — 토큰 예산 내에서 최종 선택
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* ============================================================
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*/
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import * as fs from 'fs';
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import * as path from 'path';
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import { BrainProfile } from '../config';
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import { findBrainFiles, summarizeText } from '../utils';
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import { isInside } from '../lib/paths';
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import { MemoryManager } from '../memory';
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import { RetrievalChunk, RetrievalResult, ContextBudgetConfig } from './types';
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import { tokenize, expandQuery, scoreTfIdfPreTokenized, extractBestExcerpt } from './scoring';
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import { selectWithinBudget, assembleContext, estimateTokens } from './contextBudget';
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import { getBrainTokenIndex } from './brainIndex';
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export { tokenize, expandQuery, scoreTfIdf, scoreTfIdfPreTokenized, extractBestExcerpt } from './scoring';
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export { selectWithinBudget, assembleContext, estimateTokens } from './contextBudget';
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export { getBrainTokenIndex, clearBrainTokenIndex } from './brainIndex';
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export * from './types';
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interface RetrievalOptions {
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brain: BrainProfile;
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memoryManager: MemoryManager;
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workspacePath?: string;
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chatHistory?: Array<{ role: string; content: string }>;
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contextBudget?: Partial<ContextBudgetConfig>;
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brainFileLimit?: number;
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includeRawConversations?: boolean;
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/**
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* Optional absolute folder paths constraining brain-file search to those
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* subtrees. When provided and non-empty, only brain files inside one of
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* the folders are considered. Empty / undefined preserves whole-brain
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* search (legacy behavior). Folders that escape the brain root are
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* silently dropped by the caller (see `agentKnowledgeMap.resolveScopeForAgent`).
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*/
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scopeFolders?: string[];
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}
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export class RetrievalOrchestrator {
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/**
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* 통합 검색을 수행합니다.
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* 모든 소스에서 검색 → TF-IDF 스코어링 → 중복 제거 → 예산 내 선택
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*/
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public retrieve(query: string, options: RetrievalOptions): RetrievalResult {
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const fusionLog: string[] = [];
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const allChunks: RetrievalChunk[] = [];
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const queryTokens = tokenize(query);
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const expandedTokens = expandQuery(queryTokens);
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fusionLog.push(`Query tokens: [${queryTokens.slice(0, 10).join(', ')}]`);
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fusionLog.push(`Expanded tokens: [${expandedTokens.slice(0, 15).join(', ')}]`);
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// ── ① Brain File Search (TF-IDF enhanced) ──
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const scopeFolders = options.scopeFolders ?? [];
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const brainChunks = this.searchBrainFiles(
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query,
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expandedTokens,
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options.brain,
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options.brainFileLimit || 8,
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options.includeRawConversations || false,
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scopeFolders
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);
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allChunks.push(...brainChunks);
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fusionLog.push(
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scopeFolders.length > 0
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? `Brain search (scoped to ${scopeFolders.length} folder(s)): ${brainChunks.length} chunks`
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: `Brain search: ${brainChunks.length} chunks found`
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);
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// ── ② Memory Layers ──
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const memoryChunks = this.searchMemoryLayers(
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query,
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options.memoryManager,
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options.chatHistory || [],
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options.workspacePath
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);
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allChunks.push(...memoryChunks);
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fusionLog.push(`Memory search: ${memoryChunks.length} chunks found`);
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// ── ③ Result Fusion — normalize scores across sources ──
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this.normalizeScores(allChunks);
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fusionLog.push(`Total chunks before budget: ${allChunks.length}`);
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// ── ④ Context Budget Selection ──
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const { selected, dropped, tokensUsed } = selectWithinBudget(
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allChunks,
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options.contextBudget
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);
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// Pull lesson/playbook/qa-finding chunks out so callers can inject them as a prominent
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// "verify before finalizing" block rather than burying them in the brain-knowledge section.
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const lessonChunks = selected.filter((c) => c.metadata.isLesson);
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const selectedChunks = selected.filter((c) => !c.metadata.isLesson);
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fusionLog.push(`Selected: ${selectedChunks.length} (+${lessonChunks.length} lesson), Dropped: ${dropped.length}, Tokens: ${tokensUsed}`);
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return {
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query,
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totalChunks: allChunks.length,
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selectedChunks,
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droppedChunks: dropped,
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lessonChunks,
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totalTokensUsed: tokensUsed,
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contextBudget: options.contextBudget?.totalBudget || 8000,
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fusionLog
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};
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}
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/**
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* 검색 결과를 최종 컨텍스트 문자열로 변환합니다 (레슨 청크는 제외 — 별도 블록으로 주입).
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*/
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public buildContextString(result: RetrievalResult): string {
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return assembleContext(result.selectedChunks);
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}
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// ─── Brain File Search ───
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private searchBrainFiles(
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query: string,
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expandedTokens: string[],
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brain: BrainProfile,
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limit: number,
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includeRaw: boolean,
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scopeFolders: string[] = []
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): RetrievalChunk[] {
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try {
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const scoped = (file: string) => scopeFolders.length === 0
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|| scopeFolders.some((folder) => isInside(folder, file));
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const allFiles = findBrainFiles(brain.localBrainPath)
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.filter(scoped)
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.filter((file) => includeRaw || !this.isRawConversation(path.relative(brain.localBrainPath, file)));
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if (allFiles.length === 0) return [];
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// Tokenized docs from the persistent mtime-keyed index — unchanged files are not re-read
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// or re-tokenized, so per-query work over a large brain drops from O(total content) to O(files) stats.
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const indexed = getBrainTokenIndex(brain.localBrainPath, allFiles);
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if (indexed.length === 0) return [];
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const scored = scoreTfIdfPreTokenized(
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expandedTokens,
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indexed.map((d) => ({
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tokens: d.tokens,
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titleTokens: d.titleTokens,
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lastModified: d.mtimeMs,
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conflictCount: d.conflictCount,
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}))
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);
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// Always consider lesson cards for the top slots even if they didn't crack the raw-score top-`limit`:
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// they're short, high-signal, and we want them surfaced when relevant. We keep the regular top-`limit`
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// and additively pull in up to a few lesson cards (deduped by index).
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const ranked = scored.filter((x) => x.score > 0).sort((a, b) => b.score - a.score);
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const pickedIdx = new Set<number>();
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for (const s of ranked.slice(0, limit)) pickedIdx.add(s.index);
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const LESSON_EXTRA = 3;
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let lessonExtra = 0;
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for (const s of ranked) {
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if (lessonExtra >= LESSON_EXTRA) break;
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if (pickedIdx.has(s.index)) continue;
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if ((indexed[s.index].kind || '') === '') continue;
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pickedIdx.add(s.index);
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lessonExtra++;
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}
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// Preserve rank order for the chosen set.
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const chosen = ranked.filter((s) => pickedIdx.has(s.index));
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const topResults: RetrievalChunk[] = [];
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for (const s of chosen) {
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const doc = indexed[s.index];
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const isLesson = (doc.kind || '') !== '';
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// Only the chosen files are actually read off disk (for excerpt extraction).
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let content = '';
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try { content = fs.readFileSync(doc.filePath, 'utf8'); } catch { /* deleted just now — skip */ continue; }
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// Lesson cards: hand back the whole card (they're meant to be short) so the Prevention Checklist
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// survives; fall back to a generous excerpt for long ones. Regular notes: the usual 400-char excerpt.
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const excerpt = isLesson
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? (content.length <= 2500 ? content.trim() : extractBestExcerpt(content, expandedTokens, 1500))
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: extractBestExcerpt(content, expandedTokens, 400);
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const cap = isLesson ? 2500 : 400;
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topResults.push({
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id: `brain-${s.index}`,
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source: 'brain-memory' as const,
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title: doc.relativePath,
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content: summarizeText(excerpt, cap),
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score: s.score,
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tokenEstimate: estimateTokens(excerpt),
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metadata: {
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filePath: doc.filePath,
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category: this.inferCategory(doc.relativePath),
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isProjectEvidence: this.isProjectEvidence(doc.relativePath, content),
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lastUpdated: doc.mtimeMs,
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// Phase 5: Scoring Intelligence Integration
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conflictDetected: s.conflictDetected,
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conflictSeverity: s.conflictSeverity,
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informationDensity: s.informationDensity,
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...(isLesson ? { isLesson: true, lessonKind: doc.kind } : {}),
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},
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});
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}
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return topResults;
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} catch {
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return [];
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}
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}
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// ─── Memory Layer Search ───
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private searchMemoryLayers(
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query: string,
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memoryManager: MemoryManager,
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chatHistory: Array<{ role: string; content: string }>,
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workspacePath?: string
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): RetrievalChunk[] {
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const chunks: RetrievalChunk[] = [];
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// Long-Term Memory
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const ltm = memoryManager.getLongTermMemory();
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const ltmContext = ltm.buildContext(query);
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if (ltmContext) {
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chunks.push({
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id: 'ltm-context',
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source: 'long-term-memory',
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title: ltmContext.label,
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content: ltmContext.content,
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score: ltmContext.relevance,
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tokenEstimate: estimateTokens(ltmContext.content),
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metadata: { category: 'long-term' }
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});
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}
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// Project Memory
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if (workspacePath) {
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const pm = memoryManager.getProjectMemory(workspacePath);
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const pmContext = pm.buildContext(query);
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if (pmContext) {
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chunks.push({
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id: 'pm-context',
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source: 'project-memory',
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title: pmContext.label,
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content: pmContext.content,
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score: pmContext.relevance,
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tokenEstimate: estimateTokens(pmContext.content),
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metadata: { category: 'project', isProjectEvidence: true }
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});
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}
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}
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// Procedural Memory
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const proc = memoryManager.getProceduralMemory();
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const procContext = proc.buildContext(query);
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if (procContext) {
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chunks.push({
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id: 'proc-context',
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source: 'procedural-memory',
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title: procContext.label,
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content: procContext.content,
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score: procContext.relevance,
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tokenEstimate: estimateTokens(procContext.content),
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metadata: { category: 'procedural' }
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});
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}
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// Episodic Memory
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const ep = memoryManager.getEpisodicMemory();
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const epContext = ep.buildContext(query);
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if (epContext) {
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chunks.push({
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id: 'ep-context',
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source: 'episodic-memory',
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title: epContext.label,
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content: epContext.content,
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score: epContext.relevance,
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tokenEstimate: estimateTokens(epContext.content),
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metadata: { category: 'episodic' }
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});
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}
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return chunks;
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}
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// ─── Score Normalization ───
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/**
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* 서로 다른 스코어 스케일을 가진 소스들의 점수를 0~1로 정규화합니다.
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*/
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private normalizeScores(chunks: RetrievalChunk[]): void {
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// Group by source
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const groups = new Map<string, RetrievalChunk[]>();
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for (const chunk of chunks) {
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if (!groups.has(chunk.source)) groups.set(chunk.source, []);
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groups.get(chunk.source)!.push(chunk);
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}
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// Normalize each group independently
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for (const [, group] of groups) {
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const maxScore = Math.max(...group.map((c) => c.score), 0.001);
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for (const chunk of group) {
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chunk.score = chunk.score / maxScore;
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}
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}
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// Source priority boost (some sources are inherently more valuable for RAG)
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const sourceBoost: Record<string, number> = {
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'brain-trace': 1.0,
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'brain-memory': 0.9,
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'project-memory': 0.85,
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'long-term-memory': 0.8,
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'procedural-memory': 0.95, // Procedural is highly specific
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'episodic-memory': 0.7,
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'project-scan': 0.6,
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'recent-knowledge': 0.75
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};
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for (const chunk of chunks) {
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const boost = sourceBoost[chunk.source] || 0.5;
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chunk.score *= boost;
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// Lesson cards are short, high-signal guardrails — nudge relevant ones above ordinary brain notes
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// so they survive the budget. Modest (1.4×) so they don't crowd everything out when many match.
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if (chunk.metadata.isLesson) chunk.score *= 1.4;
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}
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}
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// ─── Helpers ───
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private isRawConversation(relativePath: string): boolean {
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return /(^|[\\/])(00_Raw|raw-data|conversations?|transcripts?)([\\/]|$)/i.test(relativePath);
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}
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private inferCategory(relativePath: string): string {
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const normalized = relativePath.toLowerCase();
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if (/(decisions?|adr|planning)/i.test(normalized)) return 'decision';
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if (/(records|development|bugs)/i.test(normalized)) return 'project-record';
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if (/(architecture|design|pattern)/i.test(normalized)) return 'architecture';
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if (/(knowledge|wiki|topics)/i.test(normalized)) return 'knowledge';
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return 'general';
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}
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private isProjectEvidence(relativePath: string, content: string): boolean {
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const normalized = relativePath.toLowerCase();
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if (/(records|planning|development|bugs|retrospectives|projectchronicle)/i.test(normalized)) return true;
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if (/adr-\d+|(^|[\\/])decisions?([\\/]|$)/i.test(normalized)) return true;
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return false;
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}
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}
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