1231 lines
50 KiB
JavaScript
1231 lines
50 KiB
JavaScript
// ═══════════════════════════════════════════════════════════════════════════
|
||
// Story Summary - Recall Engine (v8 - Weighted Query Vectors + Floor Aggregation)
|
||
//
|
||
// 命名规范：
|
||
// - 存储层用 L0/L1/L2/L3（StateAtom/Chunk/Event/Fact）
|
||
// - 召回层用语义名称：anchor/evidence/event/constraint
|
||
//
|
||
// v7 → v8 变更：
|
||
// - Query 取 3 条消息（对齐 L0 对结构），加权向量合成替代文本拼接
|
||
// - R1 权重 [0.15, 0.30, 0.55]（焦点 > 近上下文 > 远上下文）
|
||
// - R2 复用 R1 向量 + embed hints 1 条，权重 [0.10, 0.20, 0.45, 0.25]
|
||
// - Dense floor 聚合：max → maxSim×0.6 + meanSim×0.4
|
||
// - Lexical floor 聚合：max → maxScore × (1 + 0.3×log₂(hitCount))
|
||
//
|
||
// 架构：
|
||
// 阶段 1: Query Build（确定性，无 LLM）
|
||
// 阶段 2: Round 1 Dense Retrieval（batch embed 3 段 → 加权平均）
|
||
// 阶段 3: Query Refinement（用已命中记忆产出 hints 段）
|
||
// 阶段 4: Round 2 Dense Retrieval（复用 R1 vec + embed hints → 加权平均）
|
||
// 阶段 5: Lexical Retrieval
|
||
// 阶段 6: Floor W-RRF Fusion + Rerank + L1 配对
|
||
// 阶段 7: L1 配对组装（L0 → top-1 AI L1 + top-1 USER L1）
|
||
// 阶段 8: Causation Trace
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
import { getAllEventVectors, getChunksByFloors, getMeta, getChunkVectorsByIds } from '../storage/chunk-store.js';
|
||
import { getAllStateVectors, getStateAtoms } from '../storage/state-store.js';
|
||
import { getEngineFingerprint, embed } from '../utils/embedder.js';
|
||
import { xbLog } from '../../../../core/debug-core.js';
|
||
import { getContext } from '../../../../../../../extensions.js';
|
||
import {
|
||
buildQueryBundle,
|
||
refineQueryBundle,
|
||
computeLengthFactor,
|
||
FOCUS_BASE_WEIGHT_R2,
|
||
CONTEXT_BASE_WEIGHTS_R2,
|
||
FOCUS_MIN_NORMALIZED_WEIGHT,
|
||
} from './query-builder.js';
|
||
import { getLexicalIndex, searchLexicalIndex } from './lexical-index.js';
|
||
import { rerankChunks } from '../llm/reranker.js';
|
||
import { createMetrics, calcSimilarityStats } from './metrics.js';
|
||
|
||
const MODULE_ID = 'recall';
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// 配置
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
const CONFIG = {
|
||
// 窗口：取 3 条消息（对齐 L0 USER+AI 对结构）
|
||
LAST_MESSAGES_K: 3,
|
||
LAST_MESSAGES_K_WITH_PENDING: 2, // pending 存在时只取 2 条上下文，避免形成 4 段
|
||
|
||
// Anchor (L0 StateAtoms)
|
||
ANCHOR_MIN_SIMILARITY: 0.58,
|
||
|
||
// Event (L2 Events)
|
||
EVENT_CANDIDATE_MAX: 100,
|
||
EVENT_SELECT_MAX: 50,
|
||
EVENT_MIN_SIMILARITY: 0.55,
|
||
EVENT_MMR_LAMBDA: 0.72,
|
||
|
||
// W-RRF 融合（L0-only）
|
||
RRF_K: 60,
|
||
RRF_W_DENSE: 1.0,
|
||
RRF_W_LEX: 0.9,
|
||
FUSION_CAP: 60,
|
||
|
||
// Dense floor 聚合权重
|
||
DENSE_AGG_W_MAX: 0.6,
|
||
DENSE_AGG_W_MEAN: 0.4,
|
||
|
||
// Lexical floor 聚合密度加成
|
||
LEX_DENSITY_BONUS: 0.3,
|
||
|
||
// Rerank（floor-level）
|
||
RERANK_TOP_N: 20,
|
||
RERANK_MIN_SCORE: 0.15,
|
||
|
||
// 因果链
|
||
CAUSAL_CHAIN_MAX_DEPTH: 10,
|
||
CAUSAL_INJECT_MAX: 30,
|
||
};
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// 工具函数
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/**
|
||
* 计算余弦相似度
|
||
*/
|
||
function cosineSimilarity(a, b) {
|
||
if (!a?.length || !b?.length || a.length !== b.length) return 0;
|
||
let dot = 0, nA = 0, nB = 0;
|
||
for (let i = 0; i < a.length; i++) {
|
||
dot += a[i] * b[i];
|
||
nA += a[i] * a[i];
|
||
nB += b[i] * b[i];
|
||
}
|
||
return nA && nB ? dot / (Math.sqrt(nA) * Math.sqrt(nB)) : 0;
|
||
}
|
||
|
||
/**
|
||
* 标准化字符串
|
||
*/
|
||
function normalize(s) {
|
||
return String(s || '')
|
||
.normalize('NFKC')
|
||
.replace(/[\u200B-\u200D\uFEFF]/g, '')
|
||
.trim()
|
||
.toLowerCase();
|
||
}
|
||
|
||
/**
|
||
* 获取最近消息
|
||
*/
|
||
function getLastMessages(chat, count = 3, excludeLastAi = false) {
|
||
if (!chat?.length) return [];
|
||
let messages = [...chat];
|
||
if (excludeLastAi && messages.length > 0 && !messages[messages.length - 1]?.is_user) {
|
||
messages = messages.slice(0, -1);
|
||
}
|
||
return messages.slice(-count);
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// 加权向量工具
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
/**
|
||
* 多向量加权平均
|
||
*
|
||
* @param {number[][]} vectors - 向量数组
|
||
* @param {number[]} weights - 归一化后的权重（sum = 1）
|
||
* @returns {number[]|null}
|
||
*/
|
||
function weightedAverageVectors(vectors, weights) {
|
||
if (!vectors?.length || !weights?.length || vectors.length !== weights.length) return null;
|
||
|
||
const dims = vectors[0].length;
|
||
const result = new Array(dims).fill(0);
|
||
|
||
for (let i = 0; i < vectors.length; i++) {
|
||
const w = weights[i];
|
||
const v = vectors[i];
|
||
if (!v?.length) continue;
|
||
for (let d = 0; d < dims; d++) {
|
||
result[d] += w * v[d];
|
||
}
|
||
}
|
||
|
||
return result;
|
||
}
|
||
|
||
/**
|
||
* 对归一化权重做“目标位最小占比”硬保底
|
||
*
|
||
* @param {number[]} weights - 已归一化权重（sum≈1）
|
||
* @param {number} targetIdx - 目标位置（focus 段索引）
|
||
* @param {number} minWeight - 最小占比（0~1）
|
||
* @returns {number[]} 调整后的归一化权重
|
||
*/
|
||
function clampMinNormalizedWeight(weights, targetIdx, minWeight) {
|
||
if (!weights?.length) return [];
|
||
if (targetIdx < 0 || targetIdx >= weights.length) return weights;
|
||
|
||
const current = weights[targetIdx];
|
||
if (current >= minWeight) return weights;
|
||
|
||
const otherSum = 1 - current;
|
||
if (otherSum <= 0) {
|
||
const out = new Array(weights.length).fill(0);
|
||
out[targetIdx] = 1;
|
||
return out;
|
||
}
|
||
|
||
const remain = 1 - minWeight;
|
||
const scale = remain / otherSum;
|
||
|
||
const out = weights.map((w, i) => (i === targetIdx ? minWeight : w * scale));
|
||
// 数值稳定性：消除浮点误差
|
||
const drift = 1 - out.reduce((a, b) => a + b, 0);
|
||
out[targetIdx] += drift;
|
||
return out;
|
||
}
|
||
|
||
/**
|
||
* 计算 R1 段权重（baseWeight × lengthFactor，归一化）
|
||
*
|
||
* @param {object[]} segments - QuerySegment[]
|
||
* @returns {number[]} 归一化后的权重
|
||
*/
|
||
function computeSegmentWeights(segments) {
|
||
if (!segments?.length) return [];
|
||
|
||
const adjusted = segments.map(s => s.baseWeight * computeLengthFactor(s.charCount));
|
||
const sum = adjusted.reduce((a, b) => a + b, 0);
|
||
const normalized = sum <= 0
|
||
? segments.map(() => 1 / segments.length)
|
||
: adjusted.map(w => w / sum);
|
||
|
||
// focus 段始终在末尾
|
||
const focusIdx = segments.length - 1;
|
||
return clampMinNormalizedWeight(normalized, focusIdx, FOCUS_MIN_NORMALIZED_WEIGHT);
|
||
}
|
||
|
||
/**
|
||
* 计算 R2 权重（R1 段用 R2 基础权重 + hints 段，归一化）
|
||
*
|
||
* @param {object[]} segments - QuerySegment[]（与 R1 相同的段）
|
||
* @param {object|null} hintsSegment - { text, baseWeight, charCount }
|
||
* @returns {number[]} 归一化后的权重（长度 = segments.length + (hints ? 1 : 0)）
|
||
*/
|
||
function computeR2Weights(segments, hintsSegment) {
|
||
if (!segments?.length) return [];
|
||
|
||
// 为 R1 段分配 R2 基础权重（尾部对齐）
|
||
const contextCount = segments.length - 1;
|
||
const r2Base = [];
|
||
for (let i = 0; i < contextCount; i++) {
|
||
const weightIdx = Math.max(0, CONTEXT_BASE_WEIGHTS_R2.length - contextCount + i);
|
||
r2Base.push(CONTEXT_BASE_WEIGHTS_R2[weightIdx] || CONTEXT_BASE_WEIGHTS_R2[0]);
|
||
}
|
||
r2Base.push(FOCUS_BASE_WEIGHT_R2);
|
||
|
||
// 应用 lengthFactor
|
||
const adjusted = r2Base.map((w, i) => w * computeLengthFactor(segments[i].charCount));
|
||
|
||
// 追加 hints
|
||
if (hintsSegment) {
|
||
adjusted.push(hintsSegment.baseWeight * computeLengthFactor(hintsSegment.charCount));
|
||
}
|
||
|
||
// 归一化
|
||
const sum = adjusted.reduce((a, b) => a + b, 0);
|
||
const normalized = sum <= 0
|
||
? adjusted.map(() => 1 / adjusted.length)
|
||
: adjusted.map(w => w / sum);
|
||
|
||
// R2 中 focus 位置固定为“segments 最后一个”
|
||
const focusIdx = segments.length - 1;
|
||
return clampMinNormalizedWeight(normalized, focusIdx, FOCUS_MIN_NORMALIZED_WEIGHT);
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// MMR 选择算法
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
function mmrSelect(candidates, k, lambda, getVector, getScore) {
|
||
const selected = [];
|
||
const ids = new Set();
|
||
|
||
while (selected.length < k && candidates.length) {
|
||
let best = null;
|
||
let bestScore = -Infinity;
|
||
|
||
for (const c of candidates) {
|
||
if (ids.has(c._id)) continue;
|
||
|
||
const rel = getScore(c);
|
||
let div = 0;
|
||
|
||
if (selected.length) {
|
||
const vC = getVector(c);
|
||
if (vC?.length) {
|
||
for (const s of selected) {
|
||
const sim = cosineSimilarity(vC, getVector(s));
|
||
if (sim > div) div = sim;
|
||
}
|
||
}
|
||
}
|
||
|
||
const score = lambda * rel - (1 - lambda) * div;
|
||
if (score > bestScore) {
|
||
bestScore = score;
|
||
best = c;
|
||
}
|
||
}
|
||
|
||
if (!best) break;
|
||
selected.push(best);
|
||
ids.add(best._id);
|
||
}
|
||
|
||
return selected;
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [Anchors] L0 StateAtoms 检索
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
async function recallAnchors(queryVector, vectorConfig, metrics) {
|
||
const { chatId } = getContext();
|
||
if (!chatId || !queryVector?.length) {
|
||
return { hits: [], floors: new Set() };
|
||
}
|
||
|
||
const meta = await getMeta(chatId);
|
||
const fp = getEngineFingerprint(vectorConfig);
|
||
if (meta.fingerprint && meta.fingerprint !== fp) {
|
||
xbLog.warn(MODULE_ID, 'Anchor fingerprint 不匹配');
|
||
return { hits: [], floors: new Set() };
|
||
}
|
||
|
||
const stateVectors = await getAllStateVectors(chatId);
|
||
if (!stateVectors.length) {
|
||
return { hits: [], floors: new Set() };
|
||
}
|
||
|
||
const atomsList = getStateAtoms();
|
||
const atomMap = new Map(atomsList.map(a => [a.atomId, a]));
|
||
|
||
const scored = stateVectors
|
||
.map(sv => {
|
||
const atom = atomMap.get(sv.atomId);
|
||
if (!atom) return null;
|
||
return {
|
||
atomId: sv.atomId,
|
||
floor: sv.floor,
|
||
similarity: cosineSimilarity(queryVector, sv.vector),
|
||
atom,
|
||
};
|
||
})
|
||
.filter(Boolean)
|
||
.filter(s => s.similarity >= CONFIG.ANCHOR_MIN_SIMILARITY)
|
||
.sort((a, b) => b.similarity - a.similarity);
|
||
|
||
const floors = new Set(scored.map(s => s.floor));
|
||
|
||
if (metrics) {
|
||
metrics.anchor.matched = scored.length;
|
||
metrics.anchor.floorsHit = floors.size;
|
||
metrics.anchor.topHits = scored.slice(0, 5).map(s => ({
|
||
floor: s.floor,
|
||
semantic: s.atom?.semantic?.slice(0, 50),
|
||
similarity: Math.round(s.similarity * 1000) / 1000,
|
||
}));
|
||
}
|
||
|
||
return { hits: scored, floors };
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [Events] L2 Events 检索
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, metrics) {
|
||
const { chatId } = getContext();
|
||
if (!chatId || !queryVector?.length || !allEvents?.length) {
|
||
return [];
|
||
}
|
||
|
||
const meta = await getMeta(chatId);
|
||
const fp = getEngineFingerprint(vectorConfig);
|
||
if (meta.fingerprint && meta.fingerprint !== fp) {
|
||
xbLog.warn(MODULE_ID, 'Event fingerprint 不匹配');
|
||
return [];
|
||
}
|
||
|
||
const eventVectors = await getAllEventVectors(chatId);
|
||
const vectorMap = new Map(eventVectors.map(v => [v.eventId, v.vector]));
|
||
|
||
if (!vectorMap.size) {
|
||
return [];
|
||
}
|
||
|
||
const focusSet = new Set((focusEntities || []).map(normalize));
|
||
|
||
const scored = allEvents.map(event => {
|
||
const v = vectorMap.get(event.id);
|
||
const baseSim = v ? cosineSimilarity(queryVector, v) : 0;
|
||
|
||
const participants = (event.participants || []).map(p => normalize(p));
|
||
const hasEntityMatch = participants.some(p => focusSet.has(p));
|
||
|
||
return {
|
||
_id: event.id,
|
||
event,
|
||
similarity: baseSim,
|
||
_hasEntityMatch: hasEntityMatch,
|
||
vector: v,
|
||
};
|
||
});
|
||
|
||
if (metrics) {
|
||
metrics.event.inStore = allEvents.length;
|
||
}
|
||
|
||
let candidates = scored
|
||
.filter(s => s.similarity >= CONFIG.EVENT_MIN_SIMILARITY)
|
||
.sort((a, b) => b.similarity - a.similarity)
|
||
.slice(0, CONFIG.EVENT_CANDIDATE_MAX);
|
||
|
||
if (metrics) {
|
||
metrics.event.considered = candidates.length;
|
||
}
|
||
|
||
// 实体过滤
|
||
if (focusSet.size > 0) {
|
||
const beforeFilter = candidates.length;
|
||
|
||
candidates = candidates.filter(c => {
|
||
if (c.similarity >= 0.85) return true;
|
||
return c._hasEntityMatch;
|
||
});
|
||
|
||
if (metrics) {
|
||
metrics.event.entityFilter = {
|
||
focusEntities: focusEntities || [],
|
||
before: beforeFilter,
|
||
after: candidates.length,
|
||
filtered: beforeFilter - candidates.length,
|
||
};
|
||
}
|
||
}
|
||
|
||
// MMR 选择
|
||
const selected = mmrSelect(
|
||
candidates,
|
||
CONFIG.EVENT_SELECT_MAX,
|
||
CONFIG.EVENT_MMR_LAMBDA,
|
||
c => c.vector,
|
||
c => c.similarity
|
||
);
|
||
|
||
let directCount = 0;
|
||
let relatedCount = 0;
|
||
|
||
const results = selected.map(s => {
|
||
const recallType = s._hasEntityMatch ? 'DIRECT' : 'RELATED';
|
||
if (recallType === 'DIRECT') directCount++;
|
||
else relatedCount++;
|
||
|
||
return {
|
||
event: s.event,
|
||
similarity: s.similarity,
|
||
_recallType: recallType,
|
||
};
|
||
});
|
||
|
||
if (metrics) {
|
||
metrics.event.selected = results.length;
|
||
metrics.event.byRecallType = { direct: directCount, related: relatedCount, causal: 0, lexical: 0 };
|
||
metrics.event.similarityDistribution = calcSimilarityStats(results.map(r => r.similarity));
|
||
}
|
||
|
||
return results;
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [Causation] 因果链追溯
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
function buildEventIndex(allEvents) {
|
||
const map = new Map();
|
||
for (const e of allEvents || []) {
|
||
if (e?.id) map.set(e.id, e);
|
||
}
|
||
return map;
|
||
}
|
||
|
||
function traceCausation(eventHits, eventIndex, maxDepth = CONFIG.CAUSAL_CHAIN_MAX_DEPTH) {
|
||
const out = new Map();
|
||
const idRe = /^evt-\d+$/;
|
||
let maxActualDepth = 0;
|
||
|
||
function visit(parentId, depth, chainFrom) {
|
||
if (depth > maxDepth) return;
|
||
if (!idRe.test(parentId)) return;
|
||
|
||
const ev = eventIndex.get(parentId);
|
||
if (!ev) return;
|
||
|
||
if (depth > maxActualDepth) maxActualDepth = depth;
|
||
|
||
const existed = out.get(parentId);
|
||
if (!existed) {
|
||
out.set(parentId, { event: ev, depth, chainFrom: [chainFrom] });
|
||
} else {
|
||
if (depth < existed.depth) existed.depth = depth;
|
||
if (!existed.chainFrom.includes(chainFrom)) existed.chainFrom.push(chainFrom);
|
||
}
|
||
|
||
for (const next of (ev.causedBy || [])) {
|
||
visit(String(next || '').trim(), depth + 1, chainFrom);
|
||
}
|
||
}
|
||
|
||
for (const r of eventHits || []) {
|
||
const rid = r?.event?.id;
|
||
if (!rid) continue;
|
||
for (const cid of (r.event?.causedBy || [])) {
|
||
visit(String(cid || '').trim(), 1, rid);
|
||
}
|
||
}
|
||
|
||
const results = Array.from(out.values())
|
||
.sort((a, b) => {
|
||
const refDiff = b.chainFrom.length - a.chainFrom.length;
|
||
if (refDiff !== 0) return refDiff;
|
||
return a.depth - b.depth;
|
||
})
|
||
.slice(0, CONFIG.CAUSAL_INJECT_MAX);
|
||
|
||
return { results, maxDepth: maxActualDepth };
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [W-RRF] 加权倒数排名融合（floor 粒度）
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
function fuseByFloor(denseRank, lexRank, cap = CONFIG.FUSION_CAP) {
|
||
const k = CONFIG.RRF_K;
|
||
const wD = CONFIG.RRF_W_DENSE;
|
||
const wL = CONFIG.RRF_W_LEX;
|
||
|
||
const buildRankMap = (ranked) => {
|
||
const map = new Map();
|
||
for (let i = 0; i < ranked.length; i++) {
|
||
const id = ranked[i].id;
|
||
if (!map.has(id)) map.set(id, i);
|
||
}
|
||
return map;
|
||
};
|
||
|
||
const denseMap = buildRankMap(denseRank || []);
|
||
const lexMap = buildRankMap(lexRank || []);
|
||
|
||
const allIds = new Set([...denseMap.keys(), ...lexMap.keys()]);
|
||
const totalUnique = allIds.size;
|
||
|
||
const scored = [];
|
||
for (const id of allIds) {
|
||
let score = 0;
|
||
if (denseMap.has(id)) score += wD / (k + denseMap.get(id));
|
||
if (lexMap.has(id)) score += wL / (k + lexMap.get(id));
|
||
scored.push({ id, fusionScore: score });
|
||
}
|
||
|
||
scored.sort((a, b) => b.fusionScore - a.fusionScore);
|
||
return { top: scored.slice(0, cap), totalUnique };
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [Stage 6] Floor 融合 + Rerank + L1 配对
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexicalResult, metrics) {
|
||
const { chatId, chat, name1, name2 } = getContext();
|
||
if (!chatId) return { l0Selected: [], l1ByFloor: new Map() };
|
||
|
||
const T_Start = performance.now();
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6a. Dense floor rank（加权聚合：maxSim×0.6 + meanSim×0.4）
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const denseFloorAgg = new Map();
|
||
for (const a of (anchorHits || [])) {
|
||
const cur = denseFloorAgg.get(a.floor);
|
||
if (!cur) {
|
||
denseFloorAgg.set(a.floor, { maxSim: a.similarity, hitCount: 1, sumSim: a.similarity });
|
||
} else {
|
||
cur.maxSim = Math.max(cur.maxSim, a.similarity);
|
||
cur.hitCount++;
|
||
cur.sumSim += a.similarity;
|
||
}
|
||
}
|
||
|
||
const denseFloorRank = [...denseFloorAgg.entries()]
|
||
.map(([floor, info]) => ({
|
||
id: floor,
|
||
score: info.maxSim * CONFIG.DENSE_AGG_W_MAX
|
||
+ (info.sumSim / info.hitCount) * CONFIG.DENSE_AGG_W_MEAN,
|
||
}))
|
||
.sort((a, b) => b.score - a.score);
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6b. Lexical floor rank（密度加成：maxScore × (1 + 0.3×log₂(hitCount))）
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const atomFloorSet = new Set(getStateAtoms().map(a => a.floor));
|
||
|
||
const lexFloorAgg = new Map();
|
||
for (const { chunkId, score } of (lexicalResult?.chunkScores || [])) {
|
||
const match = chunkId?.match(/^c-(\d+)-/);
|
||
if (!match) continue;
|
||
let floor = parseInt(match[1], 10);
|
||
|
||
// USER floor → AI floor 映射
|
||
if (chat?.[floor]?.is_user) {
|
||
const aiFloor = floor + 1;
|
||
if (aiFloor < chat.length && !chat[aiFloor]?.is_user) {
|
||
floor = aiFloor;
|
||
} else {
|
||
continue;
|
||
}
|
||
}
|
||
|
||
// 预过滤：必须有 L0 atoms
|
||
if (!atomFloorSet.has(floor)) continue;
|
||
|
||
const cur = lexFloorAgg.get(floor);
|
||
if (!cur) {
|
||
lexFloorAgg.set(floor, { maxScore: score, hitCount: 1 });
|
||
} else {
|
||
cur.maxScore = Math.max(cur.maxScore, score);
|
||
cur.hitCount++;
|
||
}
|
||
}
|
||
|
||
const lexFloorRank = [...lexFloorAgg.entries()]
|
||
.map(([floor, info]) => ({
|
||
id: floor,
|
||
score: info.maxScore * (1 + CONFIG.LEX_DENSITY_BONUS * Math.log2(Math.max(1, info.hitCount))),
|
||
}))
|
||
.sort((a, b) => b.score - a.score);
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6c. Floor W-RRF 融合
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const T_Fusion_Start = performance.now();
|
||
const { top: fusedFloors, totalUnique } = fuseByFloor(denseFloorRank, lexFloorRank, CONFIG.FUSION_CAP);
|
||
const fusionTime = Math.round(performance.now() - T_Fusion_Start);
|
||
|
||
if (metrics) {
|
||
metrics.fusion.denseFloors = denseFloorRank.length;
|
||
metrics.fusion.lexFloors = lexFloorRank.length;
|
||
metrics.fusion.totalUnique = totalUnique;
|
||
metrics.fusion.afterCap = fusedFloors.length;
|
||
metrics.fusion.time = fusionTime;
|
||
metrics.fusion.denseAggMethod = `max×${CONFIG.DENSE_AGG_W_MAX}+mean×${CONFIG.DENSE_AGG_W_MEAN}`;
|
||
metrics.fusion.lexDensityBonus = CONFIG.LEX_DENSITY_BONUS;
|
||
metrics.evidence.floorCandidates = fusedFloors.length;
|
||
}
|
||
|
||
if (fusedFloors.length === 0) {
|
||
if (metrics) {
|
||
metrics.evidence.floorsSelected = 0;
|
||
metrics.evidence.l0Collected = 0;
|
||
metrics.evidence.l1Pulled = 0;
|
||
metrics.evidence.l1Attached = 0;
|
||
metrics.evidence.l1CosineTime = 0;
|
||
metrics.evidence.rerankApplied = false;
|
||
}
|
||
return { l0Selected: [], l1ByFloor: new Map() };
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6d. 拉取 L1 chunks + cosine 打分
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const floorsToFetch = new Set();
|
||
for (const f of fusedFloors) {
|
||
floorsToFetch.add(f.id);
|
||
const userFloor = f.id - 1;
|
||
if (userFloor >= 0 && chat?.[userFloor]?.is_user) {
|
||
floorsToFetch.add(userFloor);
|
||
}
|
||
}
|
||
|
||
const l1ScoredByFloor = await pullAndScoreL1(chatId, [...floorsToFetch], queryVector, chat);
|
||
|
||
if (metrics) {
|
||
let totalPulled = 0;
|
||
for (const [key, chunks] of l1ScoredByFloor) {
|
||
if (key === '_cosineTime') continue;
|
||
totalPulled += chunks.length;
|
||
}
|
||
metrics.evidence.l1Pulled = totalPulled;
|
||
metrics.evidence.l1CosineTime = l1ScoredByFloor._cosineTime || 0;
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6e. 构建 rerank documents（每个 floor: USER chunks + AI chunks）
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const rerankCandidates = [];
|
||
for (const f of fusedFloors) {
|
||
const aiFloor = f.id;
|
||
const userFloor = aiFloor - 1;
|
||
|
||
const aiChunks = l1ScoredByFloor.get(aiFloor) || [];
|
||
const userChunks = (userFloor >= 0 && chat?.[userFloor]?.is_user)
|
||
? (l1ScoredByFloor.get(userFloor) || [])
|
||
: [];
|
||
|
||
const parts = [];
|
||
const userName = chat?.[userFloor]?.name || name1 || '用户';
|
||
const aiName = chat?.[aiFloor]?.name || name2 || '角色';
|
||
|
||
if (userChunks.length > 0) {
|
||
parts.push(`${userName}：${userChunks.map(c => c.text).join(' ')}`);
|
||
}
|
||
if (aiChunks.length > 0) {
|
||
parts.push(`${aiName}：${aiChunks.map(c => c.text).join(' ')}`);
|
||
}
|
||
|
||
const text = parts.join('\n');
|
||
if (!text.trim()) continue;
|
||
|
||
rerankCandidates.push({
|
||
floor: aiFloor,
|
||
text,
|
||
fusionScore: f.fusionScore,
|
||
});
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6f. Rerank
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const T_Rerank_Start = performance.now();
|
||
|
||
const reranked = await rerankChunks(rerankQuery, rerankCandidates, {
|
||
topN: CONFIG.RERANK_TOP_N,
|
||
minScore: CONFIG.RERANK_MIN_SCORE,
|
||
});
|
||
|
||
const rerankTime = Math.round(performance.now() - T_Rerank_Start);
|
||
|
||
if (metrics) {
|
||
metrics.evidence.rerankApplied = true;
|
||
metrics.evidence.beforeRerank = rerankCandidates.length;
|
||
metrics.evidence.afterRerank = reranked.length;
|
||
metrics.evidence.rerankFailed = reranked.some(c => c._rerankFailed);
|
||
metrics.evidence.rerankTime = rerankTime;
|
||
metrics.timing.evidenceRerank = rerankTime;
|
||
|
||
const scores = reranked.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)),
|
||
};
|
||
}
|
||
|
||
if (rerankCandidates.length > 0) {
|
||
const totalLen = rerankCandidates.reduce((s, c) => s + (c.text?.length || 0), 0);
|
||
metrics.evidence.rerankDocAvgLength = Math.round(totalLen / rerankCandidates.length);
|
||
}
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6g. 收集 L0 atoms + L1 top-1 配对
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
const atomsList = getStateAtoms();
|
||
const atomsByFloor = new Map();
|
||
for (const atom of atomsList) {
|
||
if (typeof atom.floor !== 'number' || atom.floor < 0) continue;
|
||
if (!atomsByFloor.has(atom.floor)) atomsByFloor.set(atom.floor, []);
|
||
atomsByFloor.get(atom.floor).push(atom);
|
||
}
|
||
|
||
// 重建 denseFloorMap 以获取每层 max cosine（用于 L0 similarity 标注）
|
||
const denseFloorMaxMap = new Map();
|
||
for (const a of (anchorHits || [])) {
|
||
const cur = denseFloorMaxMap.get(a.floor) || 0;
|
||
if (a.similarity > cur) denseFloorMaxMap.set(a.floor, a.similarity);
|
||
}
|
||
|
||
const l0Selected = [];
|
||
const l1ByFloor = new Map();
|
||
let contextPairsAdded = 0;
|
||
|
||
for (const item of reranked) {
|
||
const floor = item.floor;
|
||
const rerankScore = item._rerankScore || 0;
|
||
const denseSim = denseFloorMaxMap.get(floor) || 0;
|
||
|
||
// 收集该 floor 所有 L0 atoms
|
||
const floorAtoms = atomsByFloor.get(floor) || [];
|
||
for (const atom of floorAtoms) {
|
||
l0Selected.push({
|
||
id: `anchor-${atom.atomId}`,
|
||
atomId: atom.atomId,
|
||
floor: atom.floor,
|
||
similarity: denseSim,
|
||
rerankScore,
|
||
atom,
|
||
text: atom.semantic || '',
|
||
});
|
||
}
|
||
|
||
// L1 top-1 配对（cosine 最高）
|
||
const aiChunks = l1ScoredByFloor.get(floor) || [];
|
||
const userFloor = floor - 1;
|
||
const userChunks = (userFloor >= 0 && chat?.[userFloor]?.is_user)
|
||
? (l1ScoredByFloor.get(userFloor) || [])
|
||
: [];
|
||
|
||
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;
|
||
|
||
if (userTop1) contextPairsAdded++;
|
||
l1ByFloor.set(floor, { aiTop1, userTop1 });
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 6h. Metrics
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (metrics) {
|
||
metrics.evidence.floorsSelected = reranked.length;
|
||
metrics.evidence.l0Collected = l0Selected.length;
|
||
|
||
let totalAttached = 0;
|
||
for (const [, pair] of l1ByFloor) {
|
||
if (pair.aiTop1) totalAttached++;
|
||
if (pair.userTop1) totalAttached++;
|
||
}
|
||
metrics.evidence.l1Attached = totalAttached;
|
||
metrics.evidence.contextPairsAdded = contextPairsAdded;
|
||
}
|
||
|
||
const totalTime = Math.round(performance.now() - T_Start);
|
||
if (metrics) {
|
||
metrics.timing.evidenceRetrieval = Math.max(0, totalTime - fusionTime - rerankTime);
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Evidence: ${denseFloorRank.length} dense floors + ${lexFloorRank.length} lex floors → fusion=${fusedFloors.length} → rerank=${reranked.length} floors → L0=${l0Selected.length} L1 attached=${metrics?.evidence?.l1Attached || 0} (${totalTime}ms)`
|
||
);
|
||
|
||
return { l0Selected, l1ByFloor };
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// [L1] 拉取 + Cosine 打分
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
async function pullAndScoreL1(chatId, floors, queryVector, chat) {
|
||
const T0 = performance.now();
|
||
|
||
const result = new Map();
|
||
|
||
if (!chatId || !floors?.length || !queryVector?.length) {
|
||
result._cosineTime = 0;
|
||
return result;
|
||
}
|
||
|
||
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;
|
||
}
|
||
|
||
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]));
|
||
|
||
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);
|
||
}
|
||
|
||
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
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
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 lastMessagesCount = pendingUserMessage
|
||
? CONFIG.LAST_MESSAGES_K_WITH_PENDING
|
||
: CONFIG.LAST_MESSAGES_K;
|
||
const lastMessages = getLastMessages(chat, lastMessagesCount, 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 = bundle.querySegments.reduce((sum, s) => sum + s.text.length, 0);
|
||
metrics.query.lengths.v1Chars = null;
|
||
metrics.query.lengths.rerankChars = String(bundle.rerankQuery || '').length;
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Query Build: focus=[${bundle.focusEntities.join(',')}] segments=${bundle.querySegments.length} lexTerms=[${bundle.lexicalTerms.slice(0, 5).join(',')}]`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 2: Round 1 Dense Retrieval（batch embed → 加权平均）
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const segmentTexts = bundle.querySegments.map(s => s.text);
|
||
if (!segmentTexts.length) {
|
||
metrics.timing.total = Math.round(performance.now() - T0);
|
||
return {
|
||
events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [],
|
||
focusEntities: bundle.focusEntities,
|
||
elapsed: metrics.timing.total,
|
||
logText: 'No query segments.',
|
||
metrics,
|
||
};
|
||
}
|
||
|
||
let r1Vectors;
|
||
try {
|
||
r1Vectors = await embed(segmentTexts, vectorConfig, { timeout: 10000 });
|
||
} catch (e1) {
|
||
xbLog.warn(MODULE_ID, 'Round 1 向量化失败，500ms 后重试', e1);
|
||
await new Promise(r => setTimeout(r, 500));
|
||
try {
|
||
r1Vectors = await embed(segmentTexts, vectorConfig, { timeout: 15000 });
|
||
} catch (e2) {
|
||
xbLog.error(MODULE_ID, 'Round 1 向量化重试仍失败', e2);
|
||
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, after retry).',
|
||
metrics,
|
||
};
|
||
}
|
||
}
|
||
|
||
if (!r1Vectors?.length || r1Vectors.some(v => !v?.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 vectors (round 1).',
|
||
metrics,
|
||
};
|
||
}
|
||
|
||
const r1Weights = computeSegmentWeights(bundle.querySegments);
|
||
const queryVector_v0 = weightedAverageVectors(r1Vectors, r1Weights);
|
||
|
||
if (metrics) {
|
||
metrics.query.segmentWeights = r1Weights.map(w => Number(w.toFixed(3)));
|
||
}
|
||
|
||
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: 'Weighted average produced empty vector.',
|
||
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=${eventHits_v0.length} weights=[${r1Weights.map(w => w.toFixed(2)).join(',')}] (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;
|
||
|
||
// 更新 v1 长度指标
|
||
if (metrics.query?.lengths && bundle.hintsSegment) {
|
||
metrics.query.lengths.v1Chars = metrics.query.lengths.v0Chars + bundle.hintsSegment.text.length;
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Refinement: focus=[${bundle.focusEntities.join(',')}] hasHints=${!!bundle.hintsSegment} (${metrics.query.refineTime}ms)`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 4: Round 2 Dense Retrieval（复用 R1 向量 + embed hints）
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
let queryVector_v1;
|
||
|
||
if (bundle.hintsSegment) {
|
||
try {
|
||
const [hintsVec] = await embed([bundle.hintsSegment.text], vectorConfig, { timeout: 10000 });
|
||
|
||
if (hintsVec?.length) {
|
||
const r2Weights = computeR2Weights(bundle.querySegments, bundle.hintsSegment);
|
||
queryVector_v1 = weightedAverageVectors([...r1Vectors, hintsVec], r2Weights);
|
||
|
||
if (metrics) {
|
||
metrics.query.r2Weights = r2Weights.map(w => Number(w.toFixed(3)));
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Round 2 weights: [${r2Weights.map(w => w.toFixed(2)).join(',')}]`
|
||
);
|
||
} else {
|
||
queryVector_v1 = queryVector_v0;
|
||
}
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, 'Round 2 hints 向量化失败，降级使用 Round 1 向量', e);
|
||
queryVector_v1 = queryVector_v0;
|
||
}
|
||
} else {
|
||
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=${eventHits.length}`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 5: Lexical Retrieval
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const T_Lex_Start = performance.now();
|
||
|
||
let lexicalResult = {
|
||
atomIds: [], atomFloors: new Set(),
|
||
chunkIds: [], chunkFloors: new Set(),
|
||
eventIds: [], chunkScores: [], searchTime: 0,
|
||
};
|
||
|
||
try {
|
||
const index = await getLexicalIndex();
|
||
if (index) {
|
||
lexicalResult = searchLexicalIndex(index, bundle.lexicalTerms);
|
||
}
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, 'Lexical 检索失败', e);
|
||
}
|
||
|
||
const lexTime = Math.round(performance.now() - T_Lex_Start);
|
||
|
||
if (metrics) {
|
||
metrics.lexical.atomHits = lexicalResult.atomIds.length;
|
||
metrics.lexical.chunkHits = lexicalResult.chunkIds.length;
|
||
metrics.lexical.eventHits = lexicalResult.eventIds.length;
|
||
metrics.lexical.searchTime = lexTime;
|
||
metrics.lexical.terms = bundle.lexicalTerms.slice(0, 10);
|
||
}
|
||
|
||
// 合并 L2 events（lexical 命中但 dense 未命中的 events）
|
||
const existingEventIds = new Set(eventHits.map(e => e.event?.id).filter(Boolean));
|
||
const eventIndex = buildEventIndex(allEvents);
|
||
let lexicalEventCount = 0;
|
||
|
||
for (const eid of lexicalResult.eventIds) {
|
||
if (!existingEventIds.has(eid)) {
|
||
const ev = eventIndex.get(eid);
|
||
if (ev) {
|
||
eventHits.push({
|
||
event: ev,
|
||
similarity: 0,
|
||
_recallType: 'LEXICAL',
|
||
});
|
||
existingEventIds.add(eid);
|
||
lexicalEventCount++;
|
||
}
|
||
}
|
||
}
|
||
|
||
if (metrics && lexicalEventCount > 0) {
|
||
metrics.event.byRecallType.lexical = lexicalEventCount;
|
||
metrics.event.selected += lexicalEventCount;
|
||
}
|
||
|
||
xbLog.info(MODULE_ID,
|
||
`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedEvents=+${lexicalEventCount} (${lexTime}ms)`
|
||
);
|
||
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
// 阶段 6: Floor 粒度融合 + Rerank + L1 配对
|
||
// ═══════════════════════════════════════════════════════════════════
|
||
|
||
const { l0Selected, l1ByFloor } = await locateAndPullEvidence(
|
||
anchorHits,
|
||
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 v8]', 'color: #7c3aed; font-weight: bold');
|
||
console.log(`Total: ${metrics.timing.total}ms`);
|
||
console.log(`Query Build: ${metrics.query.buildTime}ms | Refine: ${metrics.query.refineTime}ms`);
|
||
console.log(`R1 weights: [${r1Weights.map(w => w.toFixed(2)).join(', ')}]`);
|
||
console.log(`Focus: [${bundle.focusEntities.join(', ')}]`);
|
||
console.log(`Round 2 Anchors: ${anchorHits.length} hits → ${anchorFloors_dense.size} floors`);
|
||
console.log(`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length}`);
|
||
console.log(`Fusion (floor, weighted): dense=${metrics.fusion.denseFloors} lex=${metrics.fusion.lexFloors} → cap=${metrics.fusion.afterCap} (${metrics.fusion.time}ms)`);
|
||
console.log(`Floor Rerank: ${metrics.evidence.beforeRerank || 0} → ${metrics.evidence.floorsSelected || 0} floors → L0=${metrics.evidence.l0Collected || 0} (${metrics.evidence.rerankTime || 0}ms)`);
|
||
console.log(`L1: ${metrics.evidence.l1Pulled || 0} pulled → ${metrics.evidence.l1Attached || 0} attached (${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,
|
||
};
|
||
}
|