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feat(summary): update prompt display, metrics lexical gate, and edge sanitization

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bielie
2026-02-11 22:01:02 +08:00
parent ca117b334f
commit 9f279d902f
4 changed files with 275 additions and 306 deletions
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18
modules/story-summary/vector/retrieval/metrics.js
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@@ -1,5 +1,10 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Metrics Collector (v5 - Weighted Query + Floor Aggregation)
// Story Summary - Metrics Collector (v6 - Dense-Gated Lexical)
//
// v5 → v6 变更：
// - lexical: 新增 eventFilteredByDense / floorFilteredByDense
// - event: entityFilter bypass 阈值改为 CONFIG 驱动（0.80）
// - 其余结构不变
//
// v4 → v5 变更：
// - query: 新增 segmentWeights / r2Weights（加权向量诊断）
@@ -44,6 +49,8 @@ export function createMetrics() {
chunkHits: 0,
eventHits: 0,
searchTime: 0,
eventFilteredByDense: 0,
floorFilteredByDense: 0,
},
// Fusion (W-RRF, floor-level) - 多路融合
@@ -229,7 +236,14 @@ export function formatMetricsLog(metrics) {
lines.push(`├─ atom_hits: ${m.lexical.atomHits}`);
lines.push(`├─ chunk_hits: ${m.lexical.chunkHits}`);
lines.push(`├─ event_hits: ${m.lexical.eventHits}`);
lines.push(`└─ search_time: ${m.lexical.searchTime}ms`);
lines.push(`├─ search_time: ${m.lexical.searchTime}ms`);
if (m.lexical.eventFilteredByDense > 0) {
lines.push(`├─ event_filtered_by_dense: ${m.lexical.eventFilteredByDense}`);
}
if (m.lexical.floorFilteredByDense > 0) {
lines.push(`├─ floor_filtered_by_dense: ${m.lexical.floorFilteredByDense}`);
}
lines.push(`└─ dense_gate_threshold: 0.50`);
lines.push('');
// Fusion (W-RRF, floor-level)

170
modules/story-summary/vector/retrieval/recall.js
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@@ -1,23 +1,23 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Recall Engine (v8 - Weighted Query Vectors + Floor Aggregation)
// Story Summary - Recall Engine (v9 - Dense-Gated Lexical + Entity Bypass Tuning)
//
// 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))
// v8 → v9 变更：
// - recallEvents() 返回 { events, vectorMap }，暴露 event 向量映射
// - Lexical Event 合并前验 dense similarity ≥ 0.50（CONFIG.LEXICAL_EVENT_DENSE_MIN）
// - Lexical Floor 进入融合前验 dense similarity ≥ 0.50（CONFIG.LEXICAL_FLOOR_DENSE_MIN）
// - Entity Bypass 阈值 0.85 → 0.80（CONFIG.EVENT_ENTITY_BYPASS_SIM）
// - metrics 新增 lexical.eventFilteredByDense / lexical.floorFilteredByDense
//
// 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 5: Lexical Retrieval + Dense-Gated Event Merge
// 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
@@ -47,9 +47,9 @@ const MODULE_ID = 'recall';
// ═══════════════════════════════════════════════════════════════════════════
const CONFIG = {
// 窗口：取 3 条消息（对齐 L0 USER+AI 对结构）
// 窗口：取 3 条消息（对齐 L0 对结构），pending 存在时取 2 条上下文
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
LAST_MESSAGES_K_WITH_PENDING: 2,
// Anchor (L0 StateAtoms)
ANCHOR_MIN_SIMILARITY: 0.58,
@@ -59,6 +59,11 @@ const CONFIG = {
EVENT_SELECT_MAX: 50,
EVENT_MIN_SIMILARITY: 0.55,
EVENT_MMR_LAMBDA: 0.72,
EVENT_ENTITY_BYPASS_SIM: 0.80,
// Lexical Dense 门槛
LEXICAL_EVENT_DENSE_MIN: 0.50,
LEXICAL_FLOOR_DENSE_MIN: 0.50,
// W-RRF 融合（L0-only）
RRF_K: 60,
@@ -86,9 +91,6 @@ const CONFIG = {
// 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;
@@ -100,9 +102,6 @@ function cosineSimilarity(a, b) {
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')
@@ -111,9 +110,6 @@ function normalize(s) {
.toLowerCase();
}
/**
* 获取最近消息
*/
function getLastMessages(chat, count = 3, excludeLastAi = false) {
if (!chat?.length) return [];
let messages = [...chat];
@@ -127,13 +123,6 @@ function getLastMessages(chat, count = 3, excludeLastAi = false) {
// 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;
@@ -152,14 +141,6 @@ function weightedAverageVectors(vectors, weights) {
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;
@@ -178,18 +159,11 @@ function clampMinNormalizedWeight(weights, targetIdx, 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 [];
@@ -199,22 +173,13 @@ function computeSegmentWeights(segments) {
? 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++) {
@@ -223,21 +188,17 @@ function computeR2Weights(segments, hintsSegment) {
}
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);
}
@@ -342,26 +303,27 @@ async function recallAnchors(queryVector, vectorConfig, metrics) {
// ═══════════════════════════════════════════════════════════════════════════
// [Events] L2 Events 检索
// 返回 { events, vectorMap }
// ═══════════════════════════════════════════════════════════════════════════
async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, metrics) {
const { chatId } = getContext();
if (!chatId || !queryVector?.length || !allEvents?.length) {
return [];
return { events: [], vectorMap: new Map() };
}
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 [];
return { events: [], vectorMap: new Map() };
}
const eventVectors = await getAllEventVectors(chatId);
const vectorMap = new Map(eventVectors.map(v => [v.eventId, v.vector]));
if (!vectorMap.size) {
return [];
return { events: [], vectorMap };
}
const focusSet = new Set((focusEntities || []).map(normalize));
@@ -400,7 +362,7 @@ async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities,
const beforeFilter = candidates.length;
candidates = candidates.filter(c => {
if (c.similarity >= 0.85) return true;
if (c.similarity >= CONFIG.EVENT_ENTITY_BYPASS_SIM) return true;
return c._hasEntityMatch;
});
@@ -444,7 +406,7 @@ async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities,
metrics.event.similarityDistribution = calcSimilarityStats(results.map(r => r.similarity));
}
return results;
return { events: results, vectorMap };
}
// ═══════════════════════════════════════════════════════════════════════════
@@ -576,12 +538,14 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
.sort((a, b) => b.score - a.score);
// ─────────────────────────────────────────────────────────────────
// 6b. Lexical floor rank（密度加成：maxScore × (1 + 0.3×log₂(hitCount))）
// 6b. Lexical floor rank（密度加成 + Dense 门槛过滤）
// ─────────────────────────────────────────────────────────────────
const atomFloorSet = new Set(getStateAtoms().map(a => a.floor));
const lexFloorAgg = new Map();
let lexFloorFilteredByDense = 0;
for (const { chunkId, score } of (lexicalResult?.chunkScores || [])) {
const match = chunkId?.match(/^c-(\d+)-/);
if (!match) continue;
@@ -600,6 +564,13 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
// 预过滤：必须有 L0 atoms
if (!atomFloorSet.has(floor)) continue;
// Dense 门槛：lexical floor 必须有最低 dense 相关性
const denseInfo = denseFloorAgg.get(floor);
if (!denseInfo || denseInfo.maxSim < CONFIG.LEXICAL_FLOOR_DENSE_MIN) {
lexFloorFilteredByDense++;
continue;
}
const cur = lexFloorAgg.get(floor);
if (!cur) {
lexFloorAgg.set(floor, { maxScore: score, hitCount: 1 });
@@ -616,6 +587,10 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
}))
.sort((a, b) => b.score - a.score);
if (metrics) {
metrics.lexical.floorFilteredByDense = lexFloorFilteredByDense;
}
// ─────────────────────────────────────────────────────────────────
// 6c. Floor W-RRF 融合
// ─────────────────────────────────────────────────────────────────
@@ -756,7 +731,6 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
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;
@@ -772,7 +746,6 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
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({
@@ -786,7 +759,6 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
});
}
// 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)
@@ -804,10 +776,6 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
l1ByFloor.set(floor, { aiTop1, userTop1 });
}
// ─────────────────────────────────────────────────────────────────
// 6h. Metrics
// ─────────────────────────────────────────────────────────────────
if (metrics) {
metrics.evidence.floorsSelected = reranked.length;
metrics.evidence.l0Collected = l0Selected.length;
@@ -827,7 +795,7 @@ async function locateAndPullEvidence(anchorHits, queryVector, rerankQuery, lexic
}
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)`
`Evidence: ${denseFloorRank.length} dense floors + ${lexFloorRank.length} lex floors (${lexFloorFilteredByDense} lex filtered by dense) → fusion=${fusedFloors.length} → rerank=${reranked.length} floors → L0=${l0Selected.length} L1 attached=${metrics?.evidence?.l1Attached || 0} (${totalTime}ms)`
);
return { l0Selected, l1ByFloor };
@@ -1031,7 +999,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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 { events: 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,
@@ -1048,7 +1016,6 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
metrics.query.refineTime = Math.round(performance.now() - T_Refine_Start);
// 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;
}
@@ -1094,7 +1061,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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);
let { events: eventHits, vectorMap: eventVectorMap } = await recallEvents(queryVector_v1, allEvents, vectorConfig, bundle.focusEntities, metrics);
metrics.timing.eventRetrieval = Math.round(performance.now() - T_R2_Event_Start);
xbLog.info(MODULE_ID,
@@ -1102,7 +1069,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
);
// ═══════════════════════════════════════════════════════════════════
// 阶段 5: Lexical Retrieval
// 阶段 5: Lexical Retrieval + Dense-Gated Event Merge
// ═══════════════════════════════════════════════════════════════════
const T_Lex_Start = performance.now();
@@ -1133,32 +1100,53 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
}
// 合并 L2 events（lexical 命中但 dense 未命中的 events）
// ★ Dense 门槛：验证 event 向量与 queryVector_v1 的 cosine similarity
const existingEventIds = new Set(eventHits.map(e => e.event?.id).filter(Boolean));
const eventIndex = buildEventIndex(allEvents);
let lexicalEventCount = 0;
let lexicalEventFilteredByDense = 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 (existingEventIds.has(eid)) continue;
const ev = eventIndex.get(eid);
if (!ev) continue;
// Dense gate: 验证 event 向量与 query 的语义相关性
const evVec = eventVectorMap.get(eid);
if (!evVec?.length) {
// 无向量无法验证相关性，丢弃
lexicalEventFilteredByDense++;
continue;
}
const sim = cosineSimilarity(queryVector_v1, evVec);
if (sim < CONFIG.LEXICAL_EVENT_DENSE_MIN) {
lexicalEventFilteredByDense++;
continue;
}
// 通过门槛，使用实际 dense similarity（而非硬编码 0）
eventHits.push({
event: ev,
similarity: sim,
_recallType: 'LEXICAL',
});
existingEventIds.add(eid);
lexicalEventCount++;
}
if (metrics) {
metrics.lexical.eventFilteredByDense = lexicalEventFilteredByDense;
if (lexicalEventCount > 0) {
metrics.event.byRecallType.lexical = lexicalEventCount;
metrics.event.selected += 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} (${lexTime}ms)`
`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedEvents=+${lexicalEventCount} filteredByDense=${lexicalEventFilteredByDense} (${lexTime}ms)`
);
// ═══════════════════════════════════════════════════════════════════
@@ -1204,13 +1192,13 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
metrics.event.entityNames = bundle.focusEntities;
metrics.event.entitiesUsed = bundle.focusEntities.length;
console.group('%c[Recall v8]', 'color: #7c3aed; font-weight: bold');
console.group('%c[Recall v9]', '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=${lexicalResult.eventIds.length}`);
console.log(`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} evtMerged=+${lexicalEventCount} evtFiltered=${lexicalEventFilteredByDense} floorFiltered=${metrics.lexical.floorFilteredByDense || 0}`);
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)`);