1095 lines
44 KiB
JavaScript
1095 lines
44 KiB
JavaScript
// ═══════════════════════════════════════════════════════════════════════════
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// Story Summary - Recall Engine (v7 - Two-Stage: L0 Locate → L1 Evidence)
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//
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// 命名规范：
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// - 存储层用 L0/L1/L2/L3（StateAtom/Chunk/Event/Fact）
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// - 召回层用语义名称：anchor/evidence/event/constraint
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//
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// 架构：
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// 阶段 1: Query Build（确定性，无 LLM）
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// 阶段 2: Round 1 Dense Retrieval（L0 + L2）
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// 阶段 3: Query Refinement（用已命中记忆增强）
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// 阶段 4: Round 2 Dense Retrieval（L0 + L2）
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// 阶段 5: Lexical Retrieval + L0 Merge
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// 阶段 6: L0-only W-RRF Fusion + Rerank ‖ 并发 L1 Cosine 预筛选
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// 阶段 7: L1 配对组装（L0 → top-1 AI L1 + top-1 USER L1）
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// 阶段 8: Causation Trace
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// ═══════════════════════════════════════════════════════════════════════════
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import { getAllEventVectors, getChunksByFloors, getMeta, getChunkVectorsByIds } from '../storage/chunk-store.js';
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import { getAllStateVectors, getStateAtoms } from '../storage/state-store.js';
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import { getEngineFingerprint, embed } from '../utils/embedder.js';
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import { xbLog } from '../../../../core/debug-core.js';
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import { getContext } from '../../../../../../../extensions.js';
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import { buildQueryBundle, refineQueryBundle } from './query-builder.js';
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import { getLexicalIndex, searchLexicalIndex } from './lexical-index.js';
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import { rerankChunks } from '../llm/reranker.js';
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import { createMetrics, calcSimilarityStats } from './metrics.js';
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const MODULE_ID = 'recall';
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// ═══════════════════════════════════════════════════════════════════════════
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// 配置
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// ═══════════════════════════════════════════════════════════════════════════
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const CONFIG = {
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// 窗口
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LAST_MESSAGES_K: 2,
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// Anchor (L0 StateAtoms)
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ANCHOR_MIN_SIMILARITY: 0.58,
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// Event (L2 Events)
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EVENT_CANDIDATE_MAX: 100,
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EVENT_SELECT_MAX: 50,
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EVENT_MIN_SIMILARITY: 0.55,
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EVENT_MMR_LAMBDA: 0.72,
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// W-RRF 融合（L0-only）
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RRF_K: 60,
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RRF_W_DENSE: 1.0,
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RRF_W_LEX: 0.9,
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FUSION_CAP: 100,
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// Rerank（L0-only）
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RERANK_TOP_N: 50,
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RERANK_MIN_SCORE: 0.15,
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// 因果链
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CAUSAL_CHAIN_MAX_DEPTH: 10,
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CAUSAL_INJECT_MAX: 30,
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};
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// ═══════════════════════════════════════════════════════════════════════════
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// 工具函数
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* 计算余弦相似度
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* @param {number[]} a
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* @param {number[]} b
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* @returns {number}
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*/
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function cosineSimilarity(a, b) {
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if (!a?.length || !b?.length || a.length !== b.length) return 0;
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let dot = 0, nA = 0, nB = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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nA += a[i] * a[i];
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nB += b[i] * b[i];
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}
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return nA && nB ? dot / (Math.sqrt(nA) * Math.sqrt(nB)) : 0;
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}
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/**
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* 标准化字符串
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* @param {string} s
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* @returns {string}
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*/
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function normalize(s) {
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return String(s || '')
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.normalize('NFKC')
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.replace(/[\u200B-\u200D\uFEFF]/g, '')
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.trim()
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.toLowerCase();
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}
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/**
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* 获取最近消息
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* @param {object[]} chat
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* @param {number} count
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* @param {boolean} excludeLastAi
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* @returns {object[]}
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*/
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function getLastMessages(chat, count = 2, excludeLastAi = false) {
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if (!chat?.length) return [];
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let messages = [...chat];
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if (excludeLastAi && messages.length > 0 && !messages[messages.length - 1]?.is_user) {
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messages = messages.slice(0, -1);
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}
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return messages.slice(-count);
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// MMR 选择算法
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* Maximal Marginal Relevance 选择
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* @param {object[]} candidates
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* @param {number} k
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* @param {number} lambda
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* @param {Function} getVector
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* @param {Function} getScore
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* @returns {object[]}
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*/
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function mmrSelect(candidates, k, lambda, getVector, getScore) {
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const selected = [];
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const ids = new Set();
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while (selected.length < k && candidates.length) {
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let best = null;
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let bestScore = -Infinity;
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for (const c of candidates) {
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if (ids.has(c._id)) continue;
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const rel = getScore(c);
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let div = 0;
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if (selected.length) {
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const vC = getVector(c);
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if (vC?.length) {
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for (const s of selected) {
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const sim = cosineSimilarity(vC, getVector(s));
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if (sim > div) div = sim;
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}
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}
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}
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const score = lambda * rel - (1 - lambda) * div;
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if (score > bestScore) {
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bestScore = score;
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best = c;
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}
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}
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if (!best) break;
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selected.push(best);
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ids.add(best._id);
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}
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return selected;
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// [Anchors] L0 StateAtoms 检索
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* 检索语义锚点
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* @param {number[]} queryVector
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* @param {object} vectorConfig
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* @param {object|null} metrics
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* @returns {Promise<{hits: object[], floors: Set<number>}>}
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*/
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async function recallAnchors(queryVector, vectorConfig, metrics) {
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const { chatId } = getContext();
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if (!chatId || !queryVector?.length) {
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return { hits: [], floors: new Set() };
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}
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const meta = await getMeta(chatId);
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const fp = getEngineFingerprint(vectorConfig);
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if (meta.fingerprint && meta.fingerprint !== fp) {
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xbLog.warn(MODULE_ID, 'Anchor fingerprint 不匹配');
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return { hits: [], floors: new Set() };
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}
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const stateVectors = await getAllStateVectors(chatId);
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if (!stateVectors.length) {
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return { hits: [], floors: new Set() };
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}
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const atomsList = getStateAtoms();
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const atomMap = new Map(atomsList.map(a => [a.atomId, a]));
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const scored = stateVectors
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.map(sv => {
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const atom = atomMap.get(sv.atomId);
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if (!atom) return null;
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return {
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atomId: sv.atomId,
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floor: sv.floor,
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similarity: cosineSimilarity(queryVector, sv.vector),
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atom,
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};
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})
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.filter(Boolean)
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.filter(s => s.similarity >= CONFIG.ANCHOR_MIN_SIMILARITY)
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.sort((a, b) => b.similarity - a.similarity);
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const floors = new Set(scored.map(s => s.floor));
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if (metrics) {
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metrics.anchor.matched = scored.length;
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metrics.anchor.floorsHit = floors.size;
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metrics.anchor.topHits = scored.slice(0, 5).map(s => ({
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floor: s.floor,
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semantic: s.atom?.semantic?.slice(0, 50),
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similarity: Math.round(s.similarity * 1000) / 1000,
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}));
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}
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return { hits: scored, floors };
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// [Events] L2 Events 检索
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* 检索事件
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* @param {number[]} queryVector
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* @param {object[]} allEvents
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* @param {object} vectorConfig
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* @param {string[]} focusEntities
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* @param {object|null} metrics
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* @returns {Promise<object[]>}
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*/
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async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, metrics) {
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const { chatId } = getContext();
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if (!chatId || !queryVector?.length || !allEvents?.length) {
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return [];
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}
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const meta = await getMeta(chatId);
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const fp = getEngineFingerprint(vectorConfig);
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if (meta.fingerprint && meta.fingerprint !== fp) {
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xbLog.warn(MODULE_ID, 'Event fingerprint 不匹配');
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return [];
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}
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const eventVectors = await getAllEventVectors(chatId);
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const vectorMap = new Map(eventVectors.map(v => [v.eventId, v.vector]));
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if (!vectorMap.size) {
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return [];
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}
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const focusSet = new Set((focusEntities || []).map(normalize));
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const scored = allEvents.map(event => {
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const v = vectorMap.get(event.id);
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const baseSim = v ? cosineSimilarity(queryVector, v) : 0;
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const participants = (event.participants || []).map(p => normalize(p));
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const hasEntityMatch = participants.some(p => focusSet.has(p));
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return {
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_id: event.id,
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event,
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similarity: baseSim,
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_hasEntityMatch: hasEntityMatch,
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vector: v,
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};
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});
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if (metrics) {
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metrics.event.inStore = allEvents.length;
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}
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let candidates = scored
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.filter(s => s.similarity >= CONFIG.EVENT_MIN_SIMILARITY)
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.sort((a, b) => b.similarity - a.similarity)
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.slice(0, CONFIG.EVENT_CANDIDATE_MAX);
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if (metrics) {
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metrics.event.considered = candidates.length;
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}
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// 实体过滤
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if (focusSet.size > 0) {
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const beforeFilter = candidates.length;
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candidates = candidates.filter(c => {
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if (c.similarity >= 0.85) return true;
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return c._hasEntityMatch;
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});
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if (metrics) {
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metrics.event.entityFilter = {
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focusEntities: focusEntities || [],
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before: beforeFilter,
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after: candidates.length,
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filtered: beforeFilter - candidates.length,
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};
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}
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}
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// MMR 选择
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const selected = mmrSelect(
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candidates,
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CONFIG.EVENT_SELECT_MAX,
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CONFIG.EVENT_MMR_LAMBDA,
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c => c.vector,
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c => c.similarity
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);
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let directCount = 0;
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let relatedCount = 0;
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const results = selected.map(s => {
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const recallType = s._hasEntityMatch ? 'DIRECT' : 'RELATED';
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if (recallType === 'DIRECT') directCount++;
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else relatedCount++;
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return {
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event: s.event,
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similarity: s.similarity,
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_recallType: recallType,
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};
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});
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if (metrics) {
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metrics.event.selected = results.length;
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metrics.event.byRecallType = { direct: directCount, related: relatedCount, causal: 0, lexical: 0 };
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metrics.event.similarityDistribution = calcSimilarityStats(results.map(r => r.similarity));
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}
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return results;
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// [Causation] 因果链追溯
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* 构建事件索引
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* @param {object[]} allEvents
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* @returns {Map<string, object>}
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*/
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function buildEventIndex(allEvents) {
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const map = new Map();
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for (const e of allEvents || []) {
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if (e?.id) map.set(e.id, e);
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}
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return map;
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}
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/**
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* 追溯因果链
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* @param {object[]} eventHits
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* @param {Map<string, object>} eventIndex
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* @param {number} maxDepth
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* @returns {{results: object[], maxDepth: number}}
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*/
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function traceCausation(eventHits, eventIndex, maxDepth = CONFIG.CAUSAL_CHAIN_MAX_DEPTH) {
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const out = new Map();
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const idRe = /^evt-\d+$/;
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let maxActualDepth = 0;
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function visit(parentId, depth, chainFrom) {
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if (depth > maxDepth) return;
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if (!idRe.test(parentId)) return;
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const ev = eventIndex.get(parentId);
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if (!ev) return;
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if (depth > maxActualDepth) maxActualDepth = depth;
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const existed = out.get(parentId);
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if (!existed) {
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out.set(parentId, { event: ev, depth, chainFrom: [chainFrom] });
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} else {
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if (depth < existed.depth) existed.depth = depth;
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if (!existed.chainFrom.includes(chainFrom)) existed.chainFrom.push(chainFrom);
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}
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for (const next of (ev.causedBy || [])) {
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visit(String(next || '').trim(), depth + 1, chainFrom);
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}
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}
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for (const r of eventHits || []) {
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const rid = r?.event?.id;
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if (!rid) continue;
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for (const cid of (r.event?.causedBy || [])) {
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visit(String(cid || '').trim(), 1, rid);
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}
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}
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const results = Array.from(out.values())
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.sort((a, b) => {
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const refDiff = b.chainFrom.length - a.chainFrom.length;
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if (refDiff !== 0) return refDiff;
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return a.depth - b.depth;
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})
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.slice(0, CONFIG.CAUSAL_INJECT_MAX);
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return { results, maxDepth: maxActualDepth };
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// [W-RRF] 加权倒数排名融合（L0-only）
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* @typedef {object} RankedItem
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* @property {string} id - 唯一标识符
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* @property {number} score - 该路的原始分数
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*/
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/**
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* W-RRF 融合两路 L0 候选（dense + lexical）
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*
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* @param {RankedItem[]} denseRank - Dense 路（cosine 降序）
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* @param {RankedItem[]} lexRank - Lexical 路（MiniSearch score 降序）
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* @param {number} cap - 输出上限
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* @returns {{top: {id: string, fusionScore: number}[], totalUnique: number}}
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*/
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function fuseL0Candidates(denseRank, lexRank, cap = CONFIG.FUSION_CAP) {
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const k = CONFIG.RRF_K;
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const wD = CONFIG.RRF_W_DENSE;
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const wL = CONFIG.RRF_W_LEX;
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const buildRankMap = (ranked) => {
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const map = new Map();
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for (let i = 0; i < ranked.length; i++) {
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const id = ranked[i].id;
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if (!map.has(id)) map.set(id, i);
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}
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return map;
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};
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const denseMap = buildRankMap(denseRank || []);
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const lexMap = buildRankMap(lexRank || []);
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const allIds = new Set([
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...denseMap.keys(),
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...lexMap.keys(),
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]);
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const totalUnique = allIds.size;
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const scored = [];
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for (const id of allIds) {
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let score = 0;
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if (denseMap.has(id)) {
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score += wD / (k + denseMap.get(id));
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}
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if (lexMap.has(id)) {
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score += wL / (k + lexMap.get(id));
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}
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scored.push({ id, fusionScore: score });
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}
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scored.sort((a, b) => b.fusionScore - a.fusionScore);
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return {
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top: scored.slice(0, cap),
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totalUnique,
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};
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}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [Stage 6] L0-only 融合 + Rerank ‖ 并发 L1 Cosine 预筛选
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/**
|
||
* L0 融合 + rerank，并发拉取 L1 并 cosine 打分
|
||
*
|
||
* @param {object[]} anchorHits - L0 dense 命中（Round 2）
|
||
* @param {Set<number>} anchorFloors - L0 命中楼层（含 lexical 扩展）
|
||
* @param {number[]} queryVector - 查询向量（v1）
|
||
* @param {string} rerankQuery - rerank 查询文本
|
||
* @param {object} lexicalResult - 词法检索结果
|
||
* @param {object} metrics
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||
* @returns {Promise<{l0Selected: object[], l1ByFloor: Map<number, {aiTop1: object|null, userTop1: object|null}>}>}
|
||
*/
|
||
async function locateAndPullEvidence(anchorHits, anchorFloors, queryVector, rerankQuery, lexicalResult, metrics) {
|
||
const { chatId, chat } = getContext();
|
||
if (!chatId) return { l0Selected: [], l1ByFloor: new Map() };
|
||
|
||
const T_Start = performance.now();
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6a. 构建 L0 候选对象（用于 rerank）
|
||
//
|
||
// 重要：支持 lexical-only 的 L0（atom）进入候选池。
|
||
// 否则 hybrid 会退化为 dense-only：lexical 命中的 atom 若未被 dense 命中会被直接丢弃。
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const l0ObjectMap = new Map();
|
||
for (const a of (anchorHits || [])) {
|
||
const id = `anchor-${a.atomId}`;
|
||
l0ObjectMap.set(id, {
|
||
id,
|
||
atomId: a.atomId,
|
||
floor: a.floor,
|
||
similarity: a.similarity,
|
||
atom: a.atom,
|
||
text: a.atom?.semantic || '',
|
||
});
|
||
}
|
||
|
||
// lexical-only atoms：从全量 StateAtoms 补齐（similarity 记为 0，靠 lex rank 贡献 W-RRF）
|
||
const lexAtomIds = lexicalResult?.atomIds || [];
|
||
if (lexAtomIds.length > 0) {
|
||
const atomsList = getStateAtoms();
|
||
const atomMap = new Map(atomsList.map(a => [a.atomId, a]));
|
||
|
||
for (const atomId of lexAtomIds) {
|
||
const id = `anchor-${atomId}`;
|
||
if (l0ObjectMap.has(id)) continue;
|
||
|
||
const atom = atomMap.get(atomId);
|
||
if (!atom) continue;
|
||
if (typeof atom.floor !== 'number' || atom.floor < 0) continue;
|
||
|
||
l0ObjectMap.set(id, {
|
||
id,
|
||
atomId,
|
||
floor: atom.floor,
|
||
similarity: 0,
|
||
atom,
|
||
text: atom.semantic || '',
|
||
});
|
||
}
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6b. 构建两路排名（L0-only）
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
// Dense 路：anchorHits 按 similarity 排序
|
||
const denseRank = (anchorHits || [])
|
||
.map(a => ({ id: `anchor-${a.atomId}`, score: a.similarity }))
|
||
.sort((a, b) => b.score - a.score);
|
||
|
||
// Lexical 路：从 lexicalResult.atomIds 构建排名（允许 lexical-only）
|
||
// atomIds 已按 MiniSearch score 排序（searchLexicalIndex 返回顺序）；W-RRF 依赖 rank，score 为占位
|
||
const lexRank = (lexAtomIds || [])
|
||
.map(atomId => ({ id: `anchor-${atomId}`, score: 1 }))
|
||
.filter(item => l0ObjectMap.has(item.id));
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6c. W-RRF 融合（L0-only）
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const T_Fusion_Start = performance.now();
|
||
|
||
const { top: fusionResult, totalUnique } = fuseL0Candidates(denseRank, lexRank, CONFIG.FUSION_CAP);
|
||
|
||
const fusionTime = Math.round(performance.now() - T_Fusion_Start);
|
||
|
||
// 构建 rerank 候选列表
|
||
const rerankCandidates = fusionResult
|
||
.map(f => l0ObjectMap.get(f.id))
|
||
.filter(Boolean);
|
||
|
||
if (metrics) {
|
||
metrics.fusion.denseCount = denseRank.length;
|
||
metrics.fusion.lexCount = lexRank.length;
|
||
metrics.fusion.totalUnique = totalUnique;
|
||
metrics.fusion.afterCap = rerankCandidates.length;
|
||
metrics.fusion.time = fusionTime;
|
||
metrics.evidence.l0Candidates = rerankCandidates.length;
|
||
}
|
||
|
||
if (rerankCandidates.length === 0) {
|
||
if (metrics) {
|
||
metrics.evidence.l0Selected = 0;
|
||
metrics.evidence.l1Pulled = 0;
|
||
metrics.evidence.l1Attached = 0;
|
||
metrics.evidence.l1CosineTime = 0;
|
||
metrics.evidence.rerankApplied = false;
|
||
}
|
||
return { l0Selected: [], l1ByFloor: new Map() };
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6d. 收集所有候选 L0 的楼层（用于并发拉取 L1）
|
||
// 包含 AI 楼层本身 + 上方 USER 楼层
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const candidateFloors = new Set();
|
||
for (const c of rerankCandidates) {
|
||
candidateFloors.add(c.floor);
|
||
// 上方 USER 楼层
|
||
const userFloor = c.floor - 1;
|
||
if (userFloor >= 0 && chat?.[userFloor]?.is_user) {
|
||
candidateFloors.add(userFloor);
|
||
}
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6e. 并发：rerank L0 ‖ 拉取 L1 chunks + 向量 + cosine 打分
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const T_Rerank_Start = performance.now();
|
||
|
||
// 并发任务 1：rerank L0
|
||
const rerankPromise = rerankChunks(rerankQuery, rerankCandidates, {
|
||
topN: CONFIG.RERANK_TOP_N,
|
||
minScore: CONFIG.RERANK_MIN_SCORE,
|
||
});
|
||
|
||
// 并发任务 2：拉取 L1 chunks + 向量 → cosine 打分
|
||
const l1Promise = pullAndScoreL1(chatId, Array.from(candidateFloors), queryVector, chat);
|
||
|
||
// 等待两个任务完成
|
||
const [rerankedL0, l1ScoredByFloor] = await Promise.all([rerankPromise, l1Promise]);
|
||
|
||
const rerankTime = Math.round(performance.now() - T_Rerank_Start);
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6f. 记录 rerank metrics
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (metrics) {
|
||
metrics.evidence.rerankApplied = true;
|
||
metrics.evidence.beforeRerank = rerankCandidates.length;
|
||
metrics.evidence.afterRerank = rerankedL0.length;
|
||
metrics.evidence.l0Selected = rerankedL0.length;
|
||
metrics.evidence.rerankTime = rerankTime;
|
||
metrics.timing.evidenceRerank = rerankTime;
|
||
|
||
const scores = rerankedL0.map(c => c._rerankScore || 0).filter(s => s > 0);
|
||
if (scores.length > 0) {
|
||
scores.sort((a, b) => a - b);
|
||
metrics.evidence.rerankScores = {
|
||
min: Number(scores[0].toFixed(3)),
|
||
max: Number(scores[scores.length - 1].toFixed(3)),
|
||
mean: Number((scores.reduce((a, b) => a + b, 0) / scores.length).toFixed(3)),
|
||
};
|
||
}
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6g. 构建最终 l0Selected + l1ByFloor
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const l0Selected = rerankedL0.map(item => ({
|
||
id: item.id,
|
||
atomId: item.atomId,
|
||
floor: item.floor,
|
||
similarity: item.similarity,
|
||
rerankScore: item._rerankScore || 0,
|
||
atom: item.atom,
|
||
text: item.text,
|
||
}));
|
||
|
||
// 为每个选中的 L0 楼层组装 top-1 L1 配对
|
||
const selectedFloors = new Set(l0Selected.map(l => l.floor));
|
||
const l1ByFloor = new Map();
|
||
let contextPairsAdded = 0;
|
||
|
||
for (const floor of selectedFloors) {
|
||
const aiChunks = l1ScoredByFloor.get(floor) || [];
|
||
const userFloor = floor - 1;
|
||
const userChunks = (userFloor >= 0 && chat?.[userFloor]?.is_user)
|
||
? (l1ScoredByFloor.get(userFloor) || [])
|
||
: [];
|
||
|
||
// top-1：取 cosine 最高的
|
||
const aiTop1 = aiChunks.length > 0
|
||
? aiChunks.reduce((best, c) => (c._cosineScore > best._cosineScore ? c : best))
|
||
: null;
|
||
|
||
const userTop1 = userChunks.length > 0
|
||
? userChunks.reduce((best, c) => (c._cosineScore > best._cosineScore ? c : best))
|
||
: null;
|
||
|
||
// context pair = 上方 USER 楼层成功挂载（用于 metrics）
|
||
if (userTop1) contextPairsAdded++;
|
||
|
||
l1ByFloor.set(floor, { aiTop1, userTop1 });
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6h. L1 metrics
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (metrics) {
|
||
let totalPulled = 0;
|
||
let totalAttached = 0;
|
||
|
||
for (const [, scored] of l1ScoredByFloor) {
|
||
totalPulled += scored.length;
|
||
}
|
||
|
||
for (const [, pair] of l1ByFloor) {
|
||
if (pair.aiTop1) totalAttached++;
|
||
if (pair.userTop1) totalAttached++;
|
||
}
|
||
|
||
metrics.evidence.l1Pulled = totalPulled;
|
||
metrics.evidence.l1Attached = totalAttached;
|
||
metrics.evidence.contextPairsAdded = contextPairsAdded;
|
||
metrics.evidence.l1CosineTime = l1ScoredByFloor._cosineTime || 0;
|
||
}
|
||
|
||
const totalTime = Math.round(performance.now() - T_Start);
|
||
if (metrics) {
|
||
metrics.timing.evidenceRetrieval = Math.max(0, totalTime - fusionTime - rerankTime);
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Evidence: ${anchorHits?.length || 0} L0 dense → fusion=${rerankCandidates.length} → rerank=${rerankedL0.length} → L1 attached=${metrics?.evidence?.l1Attached || 0} (${totalTime}ms)`
|
||
);
|
||
|
||
return { l0Selected, l1ByFloor };
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [L1] 拉取 + Cosine 打分（并发子任务）
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/**
|
||
* 从 IndexedDB 拉取指定楼层的 L1 chunks + 向量，用 queryVector cosine 打分
|
||
*
|
||
* @param {string} chatId
|
||
* @param {number[]} floors - 需要拉取的楼层列表
|
||
* @param {number[]} queryVector - 查询向量（v1）
|
||
* @param {object[]} chat - 聊天消息数组
|
||
* @returns {Promise<Map<number, object[]>>} floor → scored chunks（带 _cosineScore）
|
||
*/
|
||
async function pullAndScoreL1(chatId, floors, queryVector, chat) {
|
||
const T0 = performance.now();
|
||
|
||
/** @type {Map<number, object[]>} */
|
||
const result = new Map();
|
||
|
||
if (!chatId || !floors?.length || !queryVector?.length) {
|
||
result._cosineTime = 0;
|
||
return result;
|
||
}
|
||
|
||
// 拉取 chunks
|
||
let dbChunks = [];
|
||
try {
|
||
dbChunks = await getChunksByFloors(chatId, floors);
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, 'L1 chunks 拉取失败', e);
|
||
result._cosineTime = Math.round(performance.now() - T0);
|
||
return result;
|
||
}
|
||
|
||
if (!dbChunks.length) {
|
||
result._cosineTime = Math.round(performance.now() - T0);
|
||
return result;
|
||
}
|
||
|
||
// 拉取向量
|
||
const chunkIds = dbChunks.map(c => c.chunkId);
|
||
let chunkVectors = [];
|
||
try {
|
||
chunkVectors = await getChunkVectorsByIds(chatId, chunkIds);
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, 'L1 向量拉取失败', e);
|
||
result._cosineTime = Math.round(performance.now() - T0);
|
||
return result;
|
||
}
|
||
|
||
const vectorMap = new Map(chunkVectors.map(v => [v.chunkId, v.vector]));
|
||
|
||
// Cosine 打分 + 按楼层分组
|
||
for (const chunk of dbChunks) {
|
||
const vec = vectorMap.get(chunk.chunkId);
|
||
const cosineScore = vec?.length ? cosineSimilarity(queryVector, vec) : 0;
|
||
|
||
const scored = {
|
||
chunkId: chunk.chunkId,
|
||
floor: chunk.floor,
|
||
chunkIdx: chunk.chunkIdx,
|
||
speaker: chunk.speaker,
|
||
isUser: chunk.isUser,
|
||
text: chunk.text,
|
||
_cosineScore: cosineScore,
|
||
};
|
||
|
||
if (!result.has(chunk.floor)) {
|
||
result.set(chunk.floor, []);
|
||
}
|
||
result.get(chunk.floor).push(scored);
|
||
}
|
||
|
||
// 每楼层按 cosine 降序排序
|
||
for (const [, chunks] of result) {
|
||
chunks.sort((a, b) => b._cosineScore - a._cosineScore);
|
||
}
|
||
|
||
result._cosineTime = Math.round(performance.now() - T0);
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`L1 pull: ${floors.length} floors → ${dbChunks.length} chunks → scored (${result._cosineTime}ms)`
|
||
);
|
||
|
||
return result;
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// 主函数
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/**
|
||
* 执行记忆召回
|
||
*
|
||
* @param {object[]} allEvents - 所有事件（L2）
|
||
* @param {object} vectorConfig - 向量配置
|
||
* @param {object} options
|
||
* @param {boolean} options.excludeLastAi
|
||
* @param {string|null} options.pendingUserMessage
|
||
* @returns {Promise<object>}
|
||
*/
|
||
export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||
const T0 = performance.now();
|
||
const { chat } = getContext();
|
||
const { pendingUserMessage = null, excludeLastAi = false } = options;
|
||
|
||
const metrics = createMetrics();
|
||
|
||
if (!allEvents?.length) {
|
||
metrics.anchor.needRecall = false;
|
||
metrics.timing.total = Math.round(performance.now() - T0);
|
||
return {
|
||
events: [],
|
||
l0Selected: [],
|
||
l1ByFloor: new Map(),
|
||
causalChain: [],
|
||
focusEntities: [],
|
||
elapsed: metrics.timing.total,
|
||
logText: 'No events.',
|
||
metrics,
|
||
};
|
||
}
|
||
|
||
metrics.anchor.needRecall = true;
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 1: Query Build
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const T_Build_Start = performance.now();
|
||
|
||
const lastMessages = getLastMessages(chat, CONFIG.LAST_MESSAGES_K, excludeLastAi);
|
||
|
||
const bundle = buildQueryBundle(lastMessages, pendingUserMessage);
|
||
|
||
metrics.query.buildTime = Math.round(performance.now() - T_Build_Start);
|
||
metrics.anchor.focusEntities = bundle.focusEntities;
|
||
|
||
if (metrics.query?.lengths) {
|
||
metrics.query.lengths.v0Chars = String(bundle.queryText_v0 || '').length;
|
||
metrics.query.lengths.v1Chars = null;
|
||
metrics.query.lengths.rerankChars = String(bundle.rerankQuery || bundle.queryText_v0 || '').length;
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Query Build: focus=[${bundle.focusEntities.join(',')}] lexTerms=[${bundle.lexicalTerms.slice(0, 5).join(',')}]`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 2: Round 1 Dense Retrieval
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
let queryVector_v0;
|
||
try {
|
||
const [vec] = await embed([bundle.queryText_v0], vectorConfig, { timeout: 10000 });
|
||
queryVector_v0 = vec;
|
||
} catch (e) {
|
||
xbLog.error(MODULE_ID, 'Round 1 向量化失败', e);
|
||
metrics.timing.total = Math.round(performance.now() - T0);
|
||
return {
|
||
events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [],
|
||
focusEntities: bundle.focusEntities,
|
||
elapsed: metrics.timing.total,
|
||
logText: 'Embedding failed (round 1).',
|
||
metrics,
|
||
};
|
||
}
|
||
|
||
if (!queryVector_v0?.length) {
|
||
metrics.timing.total = Math.round(performance.now() - T0);
|
||
return {
|
||
events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [],
|
||
focusEntities: bundle.focusEntities,
|
||
elapsed: metrics.timing.total,
|
||
logText: 'Empty query vector (round 1).',
|
||
metrics,
|
||
};
|
||
}
|
||
|
||
const T_R1_Anchor_Start = performance.now();
|
||
const { hits: anchorHits_v0 } = await recallAnchors(queryVector_v0, vectorConfig, null);
|
||
const r1AnchorTime = Math.round(performance.now() - T_R1_Anchor_Start);
|
||
|
||
const T_R1_Event_Start = performance.now();
|
||
const eventHits_v0 = await recallEvents(queryVector_v0, allEvents, vectorConfig, bundle.focusEntities, null);
|
||
const r1EventTime = Math.round(performance.now() - T_R1_Event_Start);
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Round 1: anchors=${anchorHits_v0.length} events=${eventHits_v0.length} (anchor=${r1AnchorTime}ms event=${r1EventTime}ms)`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 3: Query Refinement
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const T_Refine_Start = performance.now();
|
||
|
||
refineQueryBundle(bundle, anchorHits_v0, eventHits_v0);
|
||
|
||
metrics.query.refineTime = Math.round(performance.now() - T_Refine_Start);
|
||
metrics.anchor.focusEntities = bundle.focusEntities;
|
||
|
||
if (metrics.query?.lengths) {
|
||
metrics.query.lengths.v1Chars = bundle.queryText_v1 == null ? null : String(bundle.queryText_v1).length;
|
||
metrics.query.lengths.rerankChars = String(bundle.rerankQuery || bundle.queryText_v1 || bundle.queryText_v0 || '').length;
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Refinement: focus=[${bundle.focusEntities.join(',')}] hasV1=${!!bundle.queryText_v1} (${metrics.query.refineTime}ms)`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 4: Round 2 Dense Retrieval
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const queryTextFinal = bundle.queryText_v1 || bundle.queryText_v0;
|
||
|
||
let queryVector_v1;
|
||
try {
|
||
const [vec] = await embed([queryTextFinal], vectorConfig, { timeout: 10000 });
|
||
queryVector_v1 = vec;
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, 'Round 2 向量化失败，降级使用 Round 1 向量', e);
|
||
queryVector_v1 = queryVector_v0;
|
||
}
|
||
|
||
const T_R2_Anchor_Start = performance.now();
|
||
const { hits: anchorHits, floors: anchorFloors_dense } = await recallAnchors(queryVector_v1, vectorConfig, metrics);
|
||
metrics.timing.anchorSearch = Math.round(performance.now() - T_R2_Anchor_Start);
|
||
|
||
const T_R2_Event_Start = performance.now();
|
||
let eventHits = await recallEvents(queryVector_v1, allEvents, vectorConfig, bundle.focusEntities, metrics);
|
||
metrics.timing.eventRetrieval = Math.round(performance.now() - T_R2_Event_Start);
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Round 2: anchors=${anchorHits.length} floors=${anchorFloors_dense.size} events=${eventHits.length}`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 5: Lexical Retrieval + L0 Merge
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const T_Lex_Start = performance.now();
|
||
|
||
let lexicalResult = {
|
||
atomIds: [], atomFloors: new Set(),
|
||
chunkIds: [], chunkFloors: new Set(),
|
||
eventIds: [], chunkScores: [], searchTime: 0,
|
||
};
|
||
|
||
try {
|
||
const index = await getLexicalIndex();
|
||
if (index) {
|
||
lexicalResult = searchLexicalIndex(index, bundle.lexicalTerms);
|
||
}
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, 'Lexical 检索失败', e);
|
||
}
|
||
|
||
const lexTime = Math.round(performance.now() - T_Lex_Start);
|
||
|
||
if (metrics) {
|
||
metrics.lexical.atomHits = lexicalResult.atomIds.length;
|
||
metrics.lexical.chunkHits = lexicalResult.chunkIds.length;
|
||
metrics.lexical.eventHits = lexicalResult.eventIds.length;
|
||
metrics.lexical.searchTime = lexTime;
|
||
metrics.lexical.terms = bundle.lexicalTerms.slice(0, 10);
|
||
}
|
||
|
||
// 合并 L0 floors（dense + lexical）
|
||
const anchorFloors = new Set(anchorFloors_dense);
|
||
for (const f of lexicalResult.atomFloors) {
|
||
anchorFloors.add(f);
|
||
}
|
||
|
||
// 合并 L2 events（lexical 命中但 dense 未命中的 events）
|
||
const existingEventIds = new Set(eventHits.map(e => e.event?.id).filter(Boolean));
|
||
const eventIndex = buildEventIndex(allEvents);
|
||
let lexicalEventCount = 0;
|
||
|
||
for (const eid of lexicalResult.eventIds) {
|
||
if (!existingEventIds.has(eid)) {
|
||
const ev = eventIndex.get(eid);
|
||
if (ev) {
|
||
eventHits.push({
|
||
event: ev,
|
||
similarity: 0,
|
||
_recallType: 'LEXICAL',
|
||
});
|
||
existingEventIds.add(eid);
|
||
lexicalEventCount++;
|
||
}
|
||
}
|
||
}
|
||
|
||
if (metrics && lexicalEventCount > 0) {
|
||
metrics.event.byRecallType.lexical = lexicalEventCount;
|
||
metrics.event.selected += lexicalEventCount;
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Lexical: atoms=${lexicalResult.atomIds.length} chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedFloors=${anchorFloors.size} mergedEvents=+${lexicalEventCount} (${lexTime}ms)`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 6: L0-only W-RRF Fusion + Rerank ‖ 并发 L1 Cosine
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const { l0Selected, l1ByFloor } = await locateAndPullEvidence(
|
||
anchorHits,
|
||
anchorFloors,
|
||
queryVector_v1,
|
||
bundle.rerankQuery,
|
||
lexicalResult,
|
||
metrics
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 7: Causation Trace
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const { results: causalMap, maxDepth: causalMaxDepth } = traceCausation(eventHits, eventIndex);
|
||
|
||
const recalledIdSet = new Set(eventHits.map(x => x?.event?.id).filter(Boolean));
|
||
const causalChain = causalMap
|
||
.filter(x => x?.event?.id && !recalledIdSet.has(x.event.id))
|
||
.map(x => ({
|
||
event: x.event,
|
||
similarity: 0,
|
||
_recallType: 'CAUSAL',
|
||
_causalDepth: x.depth,
|
||
chainFrom: x.chainFrom,
|
||
}));
|
||
|
||
if (metrics.event.byRecallType) {
|
||
metrics.event.byRecallType.causal = causalChain.length;
|
||
}
|
||
metrics.event.causalChainDepth = causalMaxDepth;
|
||
metrics.event.causalCount = causalChain.length;
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 完成
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
metrics.timing.total = Math.round(performance.now() - T0);
|
||
metrics.event.entityNames = bundle.focusEntities;
|
||
metrics.event.entitiesUsed = bundle.focusEntities.length;
|
||
|
||
console.group('%c[Recall v7]', 'color: #7c3aed; font-weight: bold');
|
||
console.log(`Total: ${metrics.timing.total}ms`);
|
||
console.log(`Query Build: ${metrics.query.buildTime}ms | Refine: ${metrics.query.refineTime}ms`);
|
||
console.log(`Focus: [${bundle.focusEntities.join(', ')}]`);
|
||
console.log(`Round 2 Anchors: ${anchorHits.length} hits → ${anchorFloors.size} floors`);
|
||
console.log(`Lexical: atoms=${lexicalResult.atomIds.length} chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length}`);
|
||
console.log(`Fusion (L0-only): dense=${metrics.fusion.denseCount} lex=${metrics.fusion.lexCount} → cap=${metrics.fusion.afterCap} (${metrics.fusion.time}ms)`);
|
||
console.log(`L0 Rerank: ${metrics.evidence.beforeRerank || 0} → ${metrics.evidence.l0Selected || 0} (${metrics.evidence.rerankTime || 0}ms)`);
|
||
console.log(`L1 Pull: ${metrics.evidence.l1Pulled || 0} chunks → ${metrics.evidence.l1Attached || 0} attached (${metrics.evidence.l1CosineTime || 0}ms)`);
|
||
console.log(`Events: ${eventHits.length} hits, ${causalChain.length} causal`);
|
||
console.groupEnd();
|
||
|
||
return {
|
||
events: eventHits,
|
||
causalChain,
|
||
l0Selected,
|
||
l1ByFloor,
|
||
focusEntities: bundle.focusEntities,
|
||
elapsed: metrics.timing.total,
|
||
metrics,
|
||
};
|
||
}
|