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Update recall logic and remove unused state-recall

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bielie
2026-02-09 10:09:16 +08:00
parent 24f97f605e
commit 5b9da7aaf4
5 changed files with 827 additions and 695 deletions
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189
modules/story-summary/vector/pipeline/state-recall.js
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@@ -1,189 +0,0 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - State Recall (L0)
// L0 语义锚点召回 + floor bonus + 虚拟 chunk 转换
// ═══════════════════════════════════════════════════════════════════════════
import { getContext } from '../../../../../../../extensions.js';
import { getAllStateVectors, getStateAtoms } from '../storage/state-store.js';
import { getMeta } from '../storage/chunk-store.js';
import { getEngineFingerprint } from '../utils/embedder.js';
import { xbLog } from '../../../../core/debug-core.js';
const MODULE_ID = 'state-recall';
const CONFIG = {
MAX_RESULTS: 20,
MIN_SIMILARITY: 0.55,
};
// ═══════════════════════════════════════════════════════════════════════════
// 工具函数
// ═══════════════════════════════════════════════════════════════════════════
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;
}
// ═══════════════════════════════════════════════════════════════════════════
// L0 向量检索
// ═══════════════════════════════════════════════════════════════════════════
/**
* 检索与 query 相似的 StateAtoms
* @returns {Array<{atom, similarity}>}
*/
export async function searchStateAtoms(queryVector, vectorConfig) {
const { chatId } = getContext();
if (!chatId || !queryVector?.length) return [];
// 检查 fingerprint
const meta = await getMeta(chatId);
const fp = getEngineFingerprint(vectorConfig);
if (meta.fingerprint && meta.fingerprint !== fp) {
xbLog.warn(MODULE_ID, 'fingerprint 不匹配，跳过 L0 召回');
return [];
}
// 获取向量
const stateVectors = await getAllStateVectors(chatId);
if (!stateVectors.length) return [];
// 获取 atoms（用于关联 semantic 等字段）
const atoms = getStateAtoms();
const atomMap = new Map(atoms.map(a => [a.atomId, a]));
// 计算相似度
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.MIN_SIMILARITY)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, CONFIG.MAX_RESULTS);
return scored;
}
// ═══════════════════════════════════════════════════════════════════════════
// Floor Bonus 构建
// ═══════════════════════════════════════════════════════════════════════════
/**
* 构建 L0 相关楼层的加权映射
* @returns {Map<number, number>}
*/
export function buildL0FloorBonus(l0Results, bonusFactor = 0.10) {
const floorBonus = new Map();
for (const r of l0Results || []) {
// 每个楼层只加一次，取最高相似度对应的 bonus
// 简化处理：统一加 bonusFactor，不区分相似度高低
if (!floorBonus.has(r.floor)) {
floorBonus.set(r.floor, bonusFactor);
}
}
return floorBonus;
}
// ═══════════════════════════════════════════════════════════════════════════
// 虚拟 Chunk 转换
// ═══════════════════════════════════════════════════════════════════════════
/**
* 将 L0 结果转换为虚拟 chunk 格式
* 用于和 L1 chunks 统一处理
*/
export function stateToVirtualChunks(l0Results) {
return (l0Results || []).map(r => ({
chunkId: `state-${r.atomId}`,
floor: r.floor,
chunkIdx: -1, // 负值，排序时排在 L1 前面
speaker: '📌', // 固定标记
isUser: false,
text: r.atom.semantic,
textHash: null,
similarity: r.similarity,
isL0: true, // 标记字段
// 保留原始 atom 信息
_atom: r.atom,
}));
}
// ═══════════════════════════════════════════════════════════════════════════
// 每楼层稀疏去重
// ═══════════════════════════════════════════════════════════════════════════
/**
* 合并 L0 和 L1 chunks
* @param {Array} l0Chunks - L0 虚拟 chunks（带 similarity）
* @param {Array} l1Chunks - L1 真实 chunks（无 similarity）
* @param {number} limit - 每楼层上限
* @returns {Array} 合并后的 chunks
*/
export function mergeAndSparsify(l0Chunks, l1Chunks, limit = 2) {
// 构建 L0 楼层 → 最高 similarity 映射
const floorSimilarity = new Map();
for (const c of (l0Chunks || [])) {
const existing = floorSimilarity.get(c.floor) || 0;
if ((c.similarity || 0) > existing) {
floorSimilarity.set(c.floor, c.similarity || 0);
}
}
// L1 继承所属楼层的 L0 similarity
const l1WithScore = (l1Chunks || []).map(c => ({
...c,
similarity: floorSimilarity.get(c.floor) || 0.5,
}));
// 合并并按相似度排序
const all = [...(l0Chunks || []), ...l1WithScore]
.sort((a, b) => {
// 相似度优先
const simDiff = (b.similarity || 0) - (a.similarity || 0);
if (Math.abs(simDiff) > 0.01) return simDiff;
// 同楼层：L0 优先于 L1
if (a.floor === b.floor) {
if (a.isL0 && !b.isL0) return -1;
if (!a.isL0 && b.isL0) return 1;
}
// 按楼层升序
return a.floor - b.floor;
});
// 每楼层稀疏去重
const byFloor = new Map();
for (const c of all) {
const arr = byFloor.get(c.floor) || [];
if (arr.length < limit) {
arr.push(c);
byFloor.set(c.floor, arr);
}
}
// 扁平化并保持排序
return Array.from(byFloor.values())
.flat()
.sort((a, b) => (b.similarity || 0) - (a.similarity || 0));
}

347
modules/story-summary/vector/retrieval/metrics.js
View File

@@ -1,59 +1,60 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Metrics Collector
// 召回质量指标收集与格式化
// Story Summary - Metrics Collector (v2 - 统一命名)
//
// 命名规范：
// - 存储层用 L0/L1/L2/L3（StateAtom/Chunk/Event/Fact）
// - 指标层用语义名称：anchor/evidence/event/constraint/arc
// ═══════════════════════════════════════════════════════════════════════════
/**
* 创建空的指标对象
* @returns {object} 指标对象
*/
export function createMetrics() {
return {
// L0 Query Understanding
l0: {
// Anchor (L0 StateAtoms) - 语义锚点
anchor: {
needRecall: false,
intent: '',
focusEntities: [],
queries: [],
implicitTopics: [],
queryExpansionTime: 0,
atomsMatched: 0,
matched: 0,
floorsHit: 0,
topAtoms: [],
topHits: [],
},
// L1 Constraints (Facts)
l1: {
factsTotal: 0,
factsInjected: 0,
factsFiltered: 0,
// Constraint (L3 Facts) - 世界约束
constraint: {
total: 0,
filtered: 0,
injected: 0,
tokens: 0,
samples: [],
},
// L2 Narrative Retrieval
l2: {
eventsInStore: 0,
eventsConsidered: 0,
eventsSelected: 0,
byRecallType: { direct: 0, causal: 0, context: 0 },
// Event (L2 Events) - 事件摘要
event: {
inStore: 0,
considered: 0,
selected: 0,
byRecallType: { direct: 0, related: 0, causal: 0 },
similarityDistribution: { min: 0, max: 0, mean: 0, median: 0 },
entityFilterStats: null,
entityFilter: null,
causalChainDepth: 0,
causalEventsCount: 0,
entitiesLoaded: 0,
causalCount: 0,
entitiesUsed: 0,
entityNames: [],
retrievalTime: 0,
},
// L3 Evidence Assembly
l3: {
floorsFromL0: 0,
l1Total: 0,
l1AfterCoarse: 0,
chunksInRange: 0,
chunksInRangeByType: { l0Virtual: 0, l1Real: 0 },
chunksSelected: 0,
chunksSelectedByType: { l0Virtual: 0, l1Real: 0 },
// Evidence (L1 Chunks) - 原文证据
evidence: {
floorsFromAnchors: 0,
chunkTotal: 0,
chunkAfterCoarse: 0,
merged: 0,
mergedByType: { anchorVirtual: 0, chunkReal: 0 },
selected: 0,
selectedByType: { anchorVirtual: 0, chunkReal: 0 },
contextPairsAdded: 0,
tokens: 0,
assemblyTime: 0,
@@ -61,16 +62,22 @@ export function createMetrics() {
beforeRerank: 0,
afterRerank: 0,
rerankTime: 0,
rerankScoreDistribution: null,
rerankScores: null,
},
// L4 Formatting
l4: {
// Arc - 人物弧光
arc: {
injected: 0,
tokens: 0,
},
// Formatting - 格式化
formatting: {
sectionsIncluded: [],
formattingTime: 0,
time: 0,
},
// Budget Summary
// Budget Summary - 预算
budget: {
total: 0,
limit: 0,
@@ -78,26 +85,26 @@ export function createMetrics() {
breakdown: {
constraints: 0,
events: 0,
chunks: 0,
recentOrphans: 0,
distantEvidence: 0,
recentEvidence: 0,
arcs: 0,
},
},
// Total Timing
// Timing - 计时
timing: {
queryExpansion: 0,
l0Search: 0,
l1Constraints: 0,
l2Retrieval: 0,
l3Retrieval: 0,
l3Rerank: 0,
l3Assembly: 0,
l4Formatting: 0,
anchorSearch: 0,
constraintFilter: 0,
eventRetrieval: 0,
evidenceRetrieval: 0,
evidenceRerank: 0,
evidenceAssembly: 0,
formatting: 0,
total: 0,
},
// Quality Indicators
// Quality Indicators - 质量指标
quality: {
constraintCoverage: 100,
eventPrecisionProxy: 0,
@@ -109,6 +116,8 @@ export function createMetrics() {
/**
* 计算相似度分布统计
* @param {number[]} similarities - 相似度数组
* @returns {{min: number, max: number, mean: number, median: number}}
*/
export function calcSimilarityStats(similarities) {
if (!similarities?.length) {
@@ -128,6 +137,8 @@ export function calcSimilarityStats(similarities) {
/**
* 格式化指标为可读日志
* @param {object} metrics - 指标对象
* @returns {string} 格式化后的日志
*/
export function formatMetricsLog(metrics) {
const m = metrics;
@@ -139,51 +150,50 @@ export function formatMetricsLog(metrics) {
lines.push('════════════════════════════════════════');
lines.push('');
// L0 Query Understanding
lines.push('[L0] Query Understanding');
lines.push(`├─ need_recall: ${m.l0.needRecall}`);
if (m.l0.needRecall) {
lines.push(`├─ intent: ${m.l0.intent || 'mixed'}`);
lines.push(`├─ focus_entities: [${(m.l0.focusEntities || []).join(', ')}]`);
lines.push(`├─ queries: [${(m.l0.queries || []).slice(0, 3).join(', ')}]`);
lines.push(`├─ query_expansion_time: ${m.l0.queryExpansionTime}ms`);
lines.push(`├─ atoms_matched: ${m.l0.atomsMatched || 0}`);
lines.push(`└─ floors_hit: ${m.l0.floorsHit || 0}`);
// Anchor (L0 StateAtoms)
lines.push('[Anchor] L0 StateAtoms - 语义锚点');
lines.push(`├─ need_recall: ${m.anchor.needRecall}`);
if (m.anchor.needRecall) {
lines.push(`├─ focus_entities: [${(m.anchor.focusEntities || []).join(', ')}]`);
lines.push(`├─ queries: [${(m.anchor.queries || []).slice(0, 3).join(', ')}]`);
lines.push(`├─ query_expansion_time: ${m.anchor.queryExpansionTime}ms`);
lines.push(`├─ matched: ${m.anchor.matched || 0}`);
lines.push(`└─ floors_hit: ${m.anchor.floorsHit || 0}`);
}
lines.push('');
// L1 Constraints
lines.push('[L1] Constraints (Facts)');
lines.push(`├─ facts_total: ${m.l1.factsTotal}`);
lines.push(`├─ facts_filtered: ${m.l1.factsFiltered || 0}`);
lines.push(`├─ facts_injected: ${m.l1.factsInjected}`);
lines.push(`├─ tokens: ${m.l1.tokens}`);
if (m.l1.samples && m.l1.samples.length > 0) {
lines.push(`└─ samples: "${m.l1.samples.slice(0, 2).join('", "')}"`);
// Constraint (L3 Facts)
lines.push('[Constraint] L3 Facts - 世界约束');
lines.push(`├─ total: ${m.constraint.total}`);
lines.push(`├─ filtered: ${m.constraint.filtered || 0}`);
lines.push(`├─ injected: ${m.constraint.injected}`);
lines.push(`├─ tokens: ${m.constraint.tokens}`);
if (m.constraint.samples && m.constraint.samples.length > 0) {
lines.push(`└─ samples: "${m.constraint.samples.slice(0, 2).join('", "')}"`);
}
lines.push('');
// L2 Narrative Retrieval
lines.push('[L2] Narrative Retrieval');
lines.push(`├─ events_in_store: ${m.l2.eventsInStore}`);
lines.push(`├─ events_considered: ${m.l2.eventsConsidered}`);
// Event (L2 Events)
lines.push('[Event] L2 Events - 事件摘要');
lines.push(`├─ in_store: ${m.event.inStore}`);
lines.push(`├─ considered: ${m.event.considered}`);
if (m.l2.entityFilterStats) {
const ef = m.l2.entityFilterStats;
if (m.event.entityFilter) {
const ef = m.event.entityFilter;
lines.push(`├─ entity_filter:`);
lines.push(`│ ├─ focus_entities: [${(ef.focusEntities || []).join(', ')}]`);
lines.push(`│ ├─ before_filter: ${ef.before}`);
lines.push(`│ ├─ after_filter: ${ef.after}`);
lines.push(`│ └─ filtered_out: ${ef.filtered}`);
lines.push(`│ ├─ before: ${ef.before}`);
lines.push(`│ ├─ after: ${ef.after}`);
lines.push(`│ └─ filtered: ${ef.filtered}`);
}
lines.push(`├─ events_selected: ${m.l2.eventsSelected}`);
lines.push(`├─ selected: ${m.event.selected}`);
lines.push(`├─ by_recall_type:`);
lines.push(`│ ├─ direct: ${m.l2.byRecallType.direct}`);
lines.push(`│ ├─ causal: ${m.l2.byRecallType.causal}`);
lines.push(`│ └─ context: ${m.l2.byRecallType.context}`);
lines.push(`│ ├─ direct: ${m.event.byRecallType.direct}`);
lines.push(`│ ├─ related: ${m.event.byRecallType.related}`);
lines.push(`│ └─ causal: ${m.event.byRecallType.causal}`);
const sim = m.l2.similarityDistribution;
const sim = m.event.similarityDistribution;
if (sim && sim.max > 0) {
lines.push(`├─ similarity_distribution:`);
lines.push(`│ ├─ min: ${sim.min}`);
@@ -192,93 +202,100 @@ export function formatMetricsLog(metrics) {
lines.push(`│ └─ median: ${sim.median}`);
}
lines.push(`├─ causal_chain: depth=${m.l2.causalChainDepth}, events=${m.l2.causalEventsCount}`);
lines.push(`├─ entities_loaded: ${m.l2.entitiesLoaded} [${(m.l2.entityNames || []).join(', ')}]`);
lines.push(`└─ retrieval_time: ${m.l2.retrievalTime}ms`);
lines.push(`├─ causal_chain: depth=${m.event.causalChainDepth}, count=${m.event.causalCount}`);
lines.push(`└─ entities_used: ${m.event.entitiesUsed} [${(m.event.entityNames || []).join(', ')}]`);
lines.push('');
// L3 Evidence Assembly
lines.push('[L3] Evidence Assembly');
lines.push(`├─ floors_from_l0: ${m.l3.floorsFromL0}`);
// Evidence (L1 Chunks)
lines.push('[Evidence] L1 Chunks - 原文证据');
lines.push(`├─ floors_from_anchors: ${m.evidence.floorsFromAnchors}`);
// L1 粗筛信息
if (m.l3.l1Total > 0) {
lines.push(`├─ l1_coarse_filter:`);
lines.push(`│ ├─ total: ${m.l3.l1Total}`);
lines.push(`│ ├─ after: ${m.l3.l1AfterCoarse}`);
lines.push(`│ └─ filtered: ${m.l3.l1Total - m.l3.l1AfterCoarse}`);
// 粗筛信息
if (m.evidence.chunkTotal > 0) {
lines.push(`├─ coarse_filter:`);
lines.push(`│ ├─ total: ${m.evidence.chunkTotal}`);
lines.push(`│ ├─ after: ${m.evidence.chunkAfterCoarse}`);
lines.push(`│ └─ filtered: ${m.evidence.chunkTotal - m.evidence.chunkAfterCoarse}`);
}
lines.push(`├─ chunks_merged: ${m.l3.chunksInRange}`);
if (m.l3.chunksInRangeByType) {
const cir = m.l3.chunksInRangeByType;
lines.push(`│ ├─ l0_virtual: ${cir.l0Virtual || 0}`);
lines.push(`│ └─ l1_real: ${cir.l1Real || 0}`);
lines.push(`├─ merged: ${m.evidence.merged}`);
if (m.evidence.mergedByType) {
const mt = m.evidence.mergedByType;
lines.push(`│ ├─ anchor_virtual: ${mt.anchorVirtual || 0}`);
lines.push(`│ └─ chunk_real: ${mt.chunkReal || 0}`);
}
// Rerank 信息
if (m.l3.rerankApplied) {
if (m.evidence.rerankApplied) {
lines.push(`├─ rerank_applied: true`);
lines.push(`│ ├─ before: ${m.l3.beforeRerank}`);
lines.push(`│ ├─ after: ${m.l3.afterRerank}`);
lines.push(`│ └─ time: ${m.l3.rerankTime}ms`);
if (m.l3.rerankScoreDistribution) {
const rd = m.l3.rerankScoreDistribution;
lines.push(`├─ rerank_scores: min=${rd.min}, max=${rd.max}, mean=${rd.mean}`);
lines.push(`│ ├─ before: ${m.evidence.beforeRerank}`);
lines.push(`│ ├─ after: ${m.evidence.afterRerank}`);
lines.push(`│ └─ time: ${m.evidence.rerankTime}ms`);
if (m.evidence.rerankScores) {
const rs = m.evidence.rerankScores;
lines.push(`├─ rerank_scores: min=${rs.min}, max=${rs.max}, mean=${rs.mean}`);
}
} else {
lines.push(`├─ rerank_applied: false`);
}
lines.push(`├─ chunks_selected: ${m.l3.chunksSelected}`);
if (m.l3.chunksSelectedByType) {
const cs = m.l3.chunksSelectedByType;
lines.push(`│ ├─ l0_virtual: ${cs.l0Virtual || 0}`);
lines.push(`│ └─ l1_real: ${cs.l1Real || 0}`);
lines.push(`├─ selected: ${m.evidence.selected}`);
if (m.evidence.selectedByType) {
const st = m.evidence.selectedByType;
lines.push(`│ ├─ anchor_virtual: ${st.anchorVirtual || 0}`);
lines.push(`│ └─ chunk_real: ${st.chunkReal || 0}`);
}
lines.push(`├─ context_pairs_added: ${m.l3.contextPairsAdded}`);
lines.push(`├─ tokens: ${m.l3.tokens}`);
lines.push(`└─ assembly_time: ${m.l3.assemblyTime}ms`);
lines.push(`├─ context_pairs_added: ${m.evidence.contextPairsAdded}`);
lines.push(`├─ tokens: ${m.evidence.tokens}`);
lines.push(`└─ assembly_time: ${m.evidence.assemblyTime}ms`);
lines.push('');
// L4 Formatting
lines.push('[L4] Prompt Formatting');
lines.push(`├─ sections: [${(m.l4.sectionsIncluded || []).join(', ')}]`);
lines.push(`└─ formatting_time: ${m.l4.formattingTime}ms`);
// Arc
if (m.arc.injected > 0) {
lines.push('[Arc] 人物弧光');
lines.push(`├─ injected: ${m.arc.injected}`);
lines.push(`└─ tokens: ${m.arc.tokens}`);
lines.push('');
}
// Formatting
lines.push('[Formatting] 格式化');
lines.push(`├─ sections: [${(m.formatting.sectionsIncluded || []).join(', ')}]`);
lines.push(`└─ time: ${m.formatting.time}ms`);
lines.push('');
// Budget Summary
lines.push('[Budget Summary]');
lines.push('[Budget] 预算');
lines.push(`├─ total_tokens: ${m.budget.total}`);
lines.push(`├─ budget_limit: ${m.budget.limit}`);
lines.push(`├─ limit: ${m.budget.limit}`);
lines.push(`├─ utilization: ${m.budget.utilization}%`);
lines.push(`└─ breakdown:`);
const bd = m.budget.breakdown || {};
lines.push(` ├─ constraints (L1): ${bd.constraints || 0}`);
lines.push(` ├─ events (L2): ${bd.events || 0}`);
lines.push(` ├─ chunks (L3): ${bd.chunks || 0}`);
lines.push(` ├─ recent_orphans: ${bd.recentOrphans || 0}`);
lines.push(` ├─ constraints: ${bd.constraints || 0}`);
lines.push(` ├─ events: ${bd.events || 0}`);
lines.push(` ├─ distant_evidence: ${bd.distantEvidence || 0}`);
lines.push(` ├─ recent_evidence: ${bd.recentEvidence || 0}`);
lines.push(` └─ arcs: ${bd.arcs || 0}`);
lines.push('');
// Timing
lines.push('[Timing]');
lines.push('[Timing] 计时');
lines.push(`├─ query_expansion: ${m.timing.queryExpansion}ms`);
lines.push(`├─ l0_search: ${m.timing.l0Search}ms`);
lines.push(`├─ l1_constraints: ${m.timing.l1Constraints}ms`);
lines.push(`├─ l2_retrieval: ${m.timing.l2Retrieval}ms`);
lines.push(`├─ l3_retrieval: ${m.timing.l3Retrieval}ms`);
if (m.timing.l3Rerank > 0) {
lines.push(`├─ l3_rerank: ${m.timing.l3Rerank}ms`);
lines.push(`├─ anchor_search: ${m.timing.anchorSearch}ms`);
lines.push(`├─ constraint_filter: ${m.timing.constraintFilter}ms`);
lines.push(`├─ event_retrieval: ${m.timing.eventRetrieval}ms`);
lines.push(`├─ evidence_retrieval: ${m.timing.evidenceRetrieval}ms`);
if (m.timing.evidenceRerank > 0) {
lines.push(`├─ evidence_rerank: ${m.timing.evidenceRerank}ms`);
}
lines.push(`├─ l3_assembly: ${m.timing.l3Assembly}ms`);
lines.push(`├─ l4_formatting: ${m.timing.l4Formatting}ms`);
lines.push(`├─ evidence_assembly: ${m.timing.evidenceAssembly}ms`);
lines.push(`├─ formatting: ${m.timing.formatting}ms`);
lines.push(`└─ total: ${m.timing.total}ms`);
lines.push('');
// Quality Indicators
lines.push('[Quality Indicators]');
lines.push('[Quality] 质量指标');
lines.push(`├─ constraint_coverage: ${m.quality.constraintCoverage}%`);
lines.push(`├─ event_precision_proxy: ${m.quality.eventPrecisionProxy}`);
lines.push(`├─ evidence_density: ${m.quality.evidenceDensity}%`);
@@ -302,25 +319,27 @@ export function formatMetricsLog(metrics) {
/**
* 检测潜在问题
* @param {object} metrics - 指标对象
* @returns {string[]} 问题列表
*/
export function detectIssues(metrics) {
const issues = [];
const m = metrics;
// 召回比例问题
if (m.l2.eventsConsidered > 0) {
const selectRatio = m.l2.eventsSelected / m.l2.eventsConsidered;
// 事件召回比例问题
if (m.event.considered > 0) {
const selectRatio = m.event.selected / m.event.considered;
if (selectRatio < 0.1) {
issues.push(`Event selection ratio too low (${(selectRatio * 100).toFixed(1)}%) - threshold may be too high`);
}
if (selectRatio > 0.6 && m.l2.eventsConsidered > 10) {
if (selectRatio > 0.6 && m.event.considered > 10) {
issues.push(`Event selection ratio high (${(selectRatio * 100).toFixed(1)}%) - may include noise`);
}
}
// 实体过滤问题
if (m.l2.entityFilterStats) {
const ef = m.l2.entityFilterStats;
if (m.event.entityFilter) {
const ef = m.event.entityFilter;
if (ef.filtered === 0 && ef.before > 10) {
issues.push(`No events filtered by entity - focus entities may be too broad or missing`);
}
@@ -330,58 +349,58 @@ export function detectIssues(metrics) {
}
// 相似度问题
if (m.l2.similarityDistribution && m.l2.similarityDistribution.min > 0 && m.l2.similarityDistribution.min < 0.5) {
issues.push(`Low similarity events included (min=${m.l2.similarityDistribution.min})`);
if (m.event.similarityDistribution && m.event.similarityDistribution.min > 0 && m.event.similarityDistribution.min < 0.5) {
issues.push(`Low similarity events included (min=${m.event.similarityDistribution.min})`);
}
// 因果链问题
if (m.l2.eventsSelected > 0 && m.l2.causalEventsCount === 0 && m.l2.byRecallType.direct === 0) {
if (m.event.selected > 0 && m.event.causalCount === 0 && m.event.byRecallType.direct === 0) {
issues.push('No direct or causal events - query expansion may be inaccurate');
}
// L0 atoms 问题
if ((m.l0.atomsMatched || 0) === 0) {
issues.push('L0 atoms not matched - may need to generate anchors');
// 锚点匹配问题
if ((m.anchor.matched || 0) === 0) {
issues.push('No anchors matched - may need to generate anchors');
}
// L1 粗筛问题
if (m.l3.l1Total > 0 && m.l3.l1AfterCoarse > 0) {
const coarseFilterRatio = 1 - (m.l3.l1AfterCoarse / m.l3.l1Total);
// 证据粗筛问题
if (m.evidence.chunkTotal > 0 && m.evidence.chunkAfterCoarse > 0) {
const coarseFilterRatio = 1 - (m.evidence.chunkAfterCoarse / m.evidence.chunkTotal);
if (coarseFilterRatio > 0.9) {
issues.push(`Very high L1 coarse filter ratio (${(coarseFilterRatio * 100).toFixed(0)}%) - query may be too specific`);
issues.push(`Very high evidence coarse filter ratio (${(coarseFilterRatio * 100).toFixed(0)}%) - query may be too specific`);
}
}
// Rerank 相关问题
if (m.l3.rerankApplied) {
if (m.l3.beforeRerank > 0 && m.l3.afterRerank > 0) {
const filterRatio = 1 - (m.l3.afterRerank / m.l3.beforeRerank);
if (m.evidence.rerankApplied) {
if (m.evidence.beforeRerank > 0 && m.evidence.afterRerank > 0) {
const filterRatio = 1 - (m.evidence.afterRerank / m.evidence.beforeRerank);
if (filterRatio > 0.7) {
issues.push(`High rerank filter ratio (${(filterRatio * 100).toFixed(0)}%) - many irrelevant chunks removed`);
}
}
if (m.l3.rerankScoreDistribution) {
const rd = m.l3.rerankScoreDistribution;
if (rd.max < 0.5) {
issues.push(`Low rerank scores (max=${rd.max}) - query may be poorly matched`);
if (m.evidence.rerankScores) {
const rs = m.evidence.rerankScores;
if (rs.max < 0.5) {
issues.push(`Low rerank scores (max=${rs.max}) - query may be poorly matched`);
}
if (rd.mean < 0.3) {
issues.push(`Very low average rerank score (mean=${rd.mean}) - context may be weak`);
if (rs.mean < 0.3) {
issues.push(`Very low average rerank score (mean=${rs.mean}) - context may be weak`);
}
}
if (m.l3.rerankTime > 2000) {
issues.push(`Slow rerank (${m.l3.rerankTime}ms) - may affect response time`);
if (m.evidence.rerankTime > 2000) {
issues.push(`Slow rerank (${m.evidence.rerankTime}ms) - may affect response time`);
}
}
// 证据密度问题
if (m.l3.chunksSelected > 0 && m.l3.chunksSelectedByType) {
const l1Real = m.l3.chunksSelectedByType.l1Real || 0;
const density = l1Real / m.l3.chunksSelected;
if (density < 0.3 && m.l3.chunksSelected > 10) {
issues.push(`Low L1 chunk ratio in selected (${(density * 100).toFixed(0)}%) - may lack concrete evidence`);
if (m.evidence.selected > 0 && m.evidence.selectedByType) {
const chunkReal = m.evidence.selectedByType.chunkReal || 0;
const density = chunkReal / m.evidence.selected;
if (density < 0.3 && m.evidence.selected > 10) {
issues.push(`Low real chunk ratio in selected (${(density * 100).toFixed(0)}%) - may lack concrete evidence`);
}
}

389
modules/story-summary/vector/retrieval/recall.js
View File

@@ -1,5 +1,9 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Recall Engine (v4 - L0 无上限 + L1 粗筛)
// Story Summary - Recall Engine (v5 - 统一命名)
//
// 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
// ═══════════════════════════════════════════════════════════════════════════
import { getAllEventVectors, getChunksByFloors, getMeta, getChunkVectorsByIds } from '../storage/chunk-store.js';
@@ -22,17 +26,17 @@ const CONFIG = {
// Query Expansion
QUERY_EXPANSION_TIMEOUT: 6000,
// L0 配置 - 去掉硬上限，提高阈值
L0_MIN_SIMILARITY: 0.58,
// Anchor (L0 StateAtoms) 配置
ANCHOR_MIN_SIMILARITY: 0.58,
// L1 粗筛配置
L1_MAX_CANDIDATES: 100,
// Evidence (L1 Chunks) 粗筛配置
EVIDENCE_COARSE_MAX: 100,
// L2 配置
L2_CANDIDATE_MAX: 100,
L2_SELECT_MAX: 50,
L2_MIN_SIMILARITY: 0.55,
L2_MMR_LAMBDA: 0.72,
// Event (L2 Events) 配置
EVENT_CANDIDATE_MAX: 100,
EVENT_SELECT_MAX: 50,
EVENT_MIN_SIMILARITY: 0.55,
EVENT_MMR_LAMBDA: 0.72,
// Rerank 配置
RERANK_THRESHOLD: 80,
@@ -48,6 +52,12 @@ const CONFIG = {
// 工具函数
// ═══════════════════════════════════════════════════════════════════════════
/**
* 计算余弦相似度
* @param {number[]} a - 向量A
* @param {number[]} b - 向量B
* @returns {number} 相似度 [0, 1]
*/
function cosineSimilarity(a, b) {
if (!a?.length || !b?.length || a.length !== b.length) return 0;
let dot = 0, nA = 0, nB = 0;
@@ -59,6 +69,11 @@ function cosineSimilarity(a, b) {
return nA && nB ? dot / (Math.sqrt(nA) * Math.sqrt(nB)) : 0;
}
/**
* 标准化字符串（用于实体匹配）
* @param {string} s - 输入字符串
* @returns {string} 标准化后的字符串
*/
function normalize(s) {
return String(s || '')
.normalize('NFKC')
@@ -67,10 +82,21 @@ function normalize(s) {
.toLowerCase();
}
/**
* 清理文本用于召回
* @param {string} text - 原始文本
* @returns {string} 清理后的文本
*/
function cleanForRecall(text) {
return filterText(text).replace(/\[tts:[^\]]*\]/gi, '').trim();
}
/**
* 从 focus entities 中移除用户名
* @param {string[]} focusEntities - 焦点实体列表
* @param {string} userName - 用户名
* @returns {string[]} 过滤后的实体列表
*/
function removeUserNameFromFocus(focusEntities, userName) {
const u = normalize(userName);
if (!u) return Array.isArray(focusEntities) ? focusEntities : [];
@@ -81,6 +107,13 @@ function removeUserNameFromFocus(focusEntities, userName) {
.filter(e => normalize(e) !== u);
}
/**
* 构建 rerank 查询文本
* @param {object} expansion - query expansion 结果
* @param {object[]} lastMessages - 最近消息
* @param {string} pendingUserMessage - 待发送的用户消息
* @returns {string} 查询文本
*/
function buildRerankQuery(expansion, lastMessages, pendingUserMessage) {
const parts = [];
@@ -109,9 +142,18 @@ function buildRerankQuery(expansion, lastMessages, pendingUserMessage) {
}
// ═══════════════════════════════════════════════════════════════════════════
// MMR 选择
// MMR 选择算法
// ═══════════════════════════════════════════════════════════════════════════
/**
* Maximal Marginal Relevance 选择
* @param {object[]} candidates - 候选项
* @param {number} k - 选择数量
* @param {number} lambda - 相关性/多样性权衡参数
* @param {Function} getVector - 获取向量的函数
* @param {Function} getScore - 获取分数的函数
* @returns {object[]} 选中的候选项
*/
function mmrSelect(candidates, k, lambda, getVector, getScore) {
const selected = [];
const ids = new Set();
@@ -152,31 +194,38 @@ function mmrSelect(candidates, k, lambda, getVector, getScore) {
}
// ═══════════════════════════════════════════════════════════════════════════
// L0 检索：无上限，阈值过滤
// [Anchors] L0 StateAtoms 检索
// ═══════════════════════════════════════════════════════════════════════════
async function searchL0(queryVector, vectorConfig, metrics) {
/**
* 检索语义锚点（L0 StateAtoms）
* @param {number[]} queryVector - 查询向量
* @param {object} vectorConfig - 向量配置
* @param {object} metrics - 指标对象
* @returns {Promise<{hits: object[], floors: Set<number>}>}
*/
async function recallAnchors(queryVector, vectorConfig, metrics) {
const { chatId } = getContext();
if (!chatId || !queryVector?.length) {
return { atoms: [], floors: new Set() };
return { hits: [], floors: new Set() };
}
const meta = await getMeta(chatId);
const fp = getEngineFingerprint(vectorConfig);
if (meta.fingerprint && meta.fingerprint !== fp) {
xbLog.warn(MODULE_ID, 'L0 fingerprint 不匹配');
return { atoms: [], floors: new Set() };
xbLog.warn(MODULE_ID, 'Anchor fingerprint 不匹配');
return { hits: [], floors: new Set() };
}
const stateVectors = await getAllStateVectors(chatId);
if (!stateVectors.length) {
return { atoms: [], floors: new Set() };
return { hits: [], floors: new Set() };
}
const atomsList = getStateAtoms();
const atomMap = new Map(atomsList.map(a => [a.atomId, a]));
// ★ 只按阈值过滤，不设硬上限
// 按阈值过滤，不设硬上限
const scored = stateVectors
.map(sv => {
const atom = atomMap.get(sv.atomId);
@@ -190,69 +239,79 @@ async function searchL0(queryVector, vectorConfig, metrics) {
};
})
.filter(Boolean)
.filter(s => s.similarity >= CONFIG.L0_MIN_SIMILARITY)
.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.l0.atomsMatched = scored.length;
metrics.l0.floorsHit = floors.size;
metrics.l0.topAtoms = scored.slice(0, 5).map(s => ({
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 { atoms: scored, floors };
return { hits: scored, floors };
}
// ═══════════════════════════════════════════════════════════════════════════
// 统计 chunks 类型构成
// [Evidence] L1 Chunks 拉取 + 粗筛 + Rerank
// ═══════════════════════════════════════════════════════════════════════════
function countChunksByType(chunks) {
let l0Virtual = 0;
let l1Real = 0;
/**
* 统计 evidence 类型构成
* @param {object[]} chunks - chunk 列表
* @returns {{anchorVirtual: number, chunkReal: number}}
*/
function countEvidenceByType(chunks) {
let anchorVirtual = 0;
let chunkReal = 0;
for (const c of chunks || []) {
if (c.isL0) {
l0Virtual++;
if (c.isAnchorVirtual) {
anchorVirtual++;
} else {
l1Real++;
chunkReal++;
}
}
return { l0Virtual, l1Real };
return { anchorVirtual, chunkReal };
}
// ═══════════════════════════════════════════════════════════════════════════
// L3 拉取 + L1 粗筛 + Rerank
// ═══════════════════════════════════════════════════════════════════════════
async function getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, queryText, metrics) {
/**
* 根据锚点命中楼层拉取证据（L1 Chunks）
* @param {Set<number>} anchorFloors - 锚点命中的楼层
* @param {object[]} anchorHits - 锚点命中结果
* @param {number[]} queryVector - 查询向量
* @param {string} queryText - rerank 查询文本
* @param {object} metrics - 指标对象
* @returns {Promise<object[]>} 证据 chunks
*/
async function pullEvidenceByFloors(anchorFloors, anchorHits, queryVector, queryText, metrics) {
const { chatId } = getContext();
if (!chatId || !l0Floors.size) {
if (!chatId || !anchorFloors.size) {
return [];
}
const floorArray = Array.from(l0Floors);
const floorArray = Array.from(anchorFloors);
// 1. 构建 L0 虚拟 chunks
const l0VirtualChunks = (l0Atoms || []).map(a => ({
chunkId: `state-${a.atomId}`,
// 1. 构建锚点虚拟 chunks（来自 L0 StateAtoms）
const anchorVirtualChunks = (anchorHits || []).map(a => ({
chunkId: `anchor-${a.atomId}`,
floor: a.floor,
chunkIdx: -1,
speaker: '📌',
isUser: false,
text: a.atom?.semantic || '',
similarity: a.similarity,
isL0: true,
isAnchorVirtual: true,
_atom: a.atom,
}));
// 2. 拉取 L1 chunks
// 2. 拉取真实 chunks（来自 L1）
let dbChunks = [];
try {
dbChunks = await getChunksByFloors(chatId, floorArray);
@@ -260,8 +319,8 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, queryText,
xbLog.warn(MODULE_ID, '从 DB 拉取 chunks 失败', e);
}
// 3. ★ L1 向量粗筛
let l1Filtered = [];
// 3. L1 向量粗筛
let coarseFiltered = [];
if (dbChunks.length > 0 && queryVector?.length) {
const chunkIds = dbChunks.map(c => c.chunkId);
let chunkVectors = [];
@@ -270,54 +329,51 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, queryText,
} catch (e) {
xbLog.warn(MODULE_ID, 'L1 向量获取失败', e);
}
const vectorMap = new Map(chunkVectors.map(v => [v.chunkId, v.vector]));
l1Filtered = dbChunks
coarseFiltered = dbChunks
.map(c => {
const vec = vectorMap.get(c.chunkId);
if (!vec?.length) return null;
return {
...c,
isL0: false,
isAnchorVirtual: false,
similarity: cosineSimilarity(queryVector, vec),
};
})
.filter(Boolean)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, CONFIG.L1_MAX_CANDIDATES);
.slice(0, CONFIG.EVIDENCE_COARSE_MAX);
}
// 4. 合并
const allChunks = [...l0VirtualChunks, ...l1Filtered];
const allEvidence = [...anchorVirtualChunks, ...coarseFiltered];
// ★ 更新 metrics
// 更新 metrics
if (metrics) {
metrics.l3.floorsFromL0 = floorArray.length;
metrics.l3.l1Total = dbChunks.length;
metrics.l3.l1AfterCoarse = l1Filtered.length;
metrics.l3.chunksInRange = l0VirtualChunks.length + l1Filtered.length;
metrics.l3.chunksInRangeByType = {
l0Virtual: l0VirtualChunks.length,
l1Real: l1Filtered.length,
};
metrics.evidence.floorsFromAnchors = floorArray.length;
metrics.evidence.chunkTotal = dbChunks.length;
metrics.evidence.chunkAfterCoarse = coarseFiltered.length;
metrics.evidence.merged = allEvidence.length;
metrics.evidence.mergedByType = countEvidenceByType(allEvidence);
}
// 5. 是否需要 Rerank
if (allChunks.length <= CONFIG.RERANK_THRESHOLD) {
if (allEvidence.length <= CONFIG.RERANK_THRESHOLD) {
if (metrics) {
metrics.l3.rerankApplied = false;
metrics.l3.chunksSelected = allChunks.length;
metrics.l3.chunksSelectedByType = countChunksByType(allChunks);
metrics.evidence.rerankApplied = false;
metrics.evidence.selected = allEvidence.length;
metrics.evidence.selectedByType = countEvidenceByType(allEvidence);
}
return allChunks;
return allEvidence;
}
// 6. Rerank 精排
const T_Rerank_Start = performance.now();
const reranked = await rerankChunks(queryText, allChunks, {
const reranked = await rerankChunks(queryText, allEvidence, {
topN: CONFIG.RERANK_TOP_N,
minScore: CONFIG.RERANK_MIN_SCORE,
});
@@ -325,18 +381,18 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, queryText,
const rerankTime = Math.round(performance.now() - T_Rerank_Start);
if (metrics) {
metrics.l3.rerankApplied = true;
metrics.l3.beforeRerank = allChunks.length;
metrics.l3.afterRerank = reranked.length;
metrics.l3.chunksSelected = reranked.length;
metrics.l3.chunksSelectedByType = countChunksByType(reranked);
metrics.l3.rerankTime = rerankTime;
metrics.timing.l3Rerank = rerankTime;
metrics.evidence.rerankApplied = true;
metrics.evidence.beforeRerank = allEvidence.length;
metrics.evidence.afterRerank = reranked.length;
metrics.evidence.selected = reranked.length;
metrics.evidence.selectedByType = countEvidenceByType(reranked);
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.l3.rerankScoreDistribution = {
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)),
@@ -344,16 +400,25 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, queryText,
}
}
xbLog.info(MODULE_ID, `L3: ${dbChunks.length} L1 → ${l1Filtered.length} 粗筛 → ${reranked.length} Rerank (${rerankTime}ms)`);
xbLog.info(MODULE_ID, `Evidence: ${dbChunks.length} L1 → ${coarseFiltered.length} coarse → ${reranked.length} rerank (${rerankTime}ms)`);
return reranked;
}
// ═══════════════════════════════════════════════════════════════════════════
// L2 检索（保持不变）
// [Events] L2 Events 检索
// ═══════════════════════════════════════════════════════════════════════════
async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntities, metrics) {
/**
* 检索事件（L2 Events）
* @param {number[]} queryVector - 查询向量
* @param {object[]} allEvents - 所有事件
* @param {object} vectorConfig - 向量配置
* @param {string[]} focusEntities - 焦点实体
* @param {object} metrics - 指标对象
* @returns {Promise<object[]>} 事件命中结果
*/
async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, metrics) {
const { chatId } = getContext();
if (!chatId || !queryVector?.length || !allEvents?.length) {
return [];
@@ -362,7 +427,7 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
const meta = await getMeta(chatId);
const fp = getEngineFingerprint(vectorConfig);
if (meta.fingerprint && meta.fingerprint !== fp) {
xbLog.warn(MODULE_ID, 'L2 fingerprint 不匹配');
xbLog.warn(MODULE_ID, 'Event fingerprint 不匹配');
return [];
}
@@ -395,18 +460,19 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
});
if (metrics) {
metrics.l2.eventsInStore = allEvents.length;
metrics.event.inStore = allEvents.length;
}
let candidates = scored
.filter(s => s.similarity >= CONFIG.L2_MIN_SIMILARITY)
.filter(s => s.similarity >= CONFIG.EVENT_MIN_SIMILARITY)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, CONFIG.L2_CANDIDATE_MAX);
.slice(0, CONFIG.EVENT_CANDIDATE_MAX);
if (metrics) {
metrics.l2.eventsConsidered = candidates.length;
metrics.event.considered = candidates.length;
}
// 实体过滤
if (focusSet.size > 0) {
const beforeFilter = candidates.length;
@@ -416,7 +482,7 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
});
if (metrics) {
metrics.l2.entityFilterStats = {
metrics.event.entityFilter = {
focusEntities: focusEntities || [],
before: beforeFilter,
after: candidates.length,
@@ -425,21 +491,22 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
}
}
// MMR 选择
const selected = mmrSelect(
candidates,
CONFIG.L2_SELECT_MAX,
CONFIG.L2_MMR_LAMBDA,
CONFIG.EVENT_SELECT_MAX,
CONFIG.EVENT_MMR_LAMBDA,
c => c.vector,
c => c.similarity
);
let directCount = 0;
let contextCount = 0;
let relatedCount = 0;
const results = selected.map(s => {
const recallType = s._hasEntityMatch ? 'DIRECT' : 'SIMILAR';
const recallType = s._hasEntityMatch ? 'DIRECT' : 'RELATED';
if (recallType === 'DIRECT') directCount++;
else contextCount++;
else relatedCount++;
return {
event: s.event,
@@ -450,18 +517,23 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
});
if (metrics) {
metrics.l2.eventsSelected = results.length;
metrics.l2.byRecallType = { direct: directCount, context: contextCount, causal: 0 };
metrics.l2.similarityDistribution = calcSimilarityStats(results.map(r => r.similarity));
metrics.event.selected = results.length;
metrics.event.byRecallType = { direct: directCount, related: relatedCount, causal: 0 };
metrics.event.similarityDistribution = calcSimilarityStats(results.map(r => r.similarity));
}
return results;
}
// ═══════════════════════════════════════════════════════════════════════════
// 因果链追溯（保持不变）
// [Causation] 因果链追溯
// ═══════════════════════════════════════════════════════════════════════════
/**
* 构建事件索引
* @param {object[]} allEvents - 所有事件
* @returns {Map<string, object>} 事件索引
*/
function buildEventIndex(allEvents) {
const map = new Map();
for (const e of allEvents || []) {
@@ -470,7 +542,14 @@ function buildEventIndex(allEvents) {
return map;
}
function traceCausalAncestors(recalledEvents, eventIndex, maxDepth = CONFIG.CAUSAL_CHAIN_MAX_DEPTH) {
/**
* 追溯因果链
* @param {object[]} eventHits - 事件命中结果
* @param {Map<string, object>} eventIndex - 事件索引
* @param {number} maxDepth - 最大深度
* @returns {{results: object[], maxDepth: number}}
*/
function traceCausation(eventHits, eventIndex, maxDepth = CONFIG.CAUSAL_CHAIN_MAX_DEPTH) {
const out = new Map();
const idRe = /^evt-\d+$/;
let maxActualDepth = 0;
@@ -497,7 +576,7 @@ function traceCausalAncestors(recalledEvents, eventIndex, maxDepth = CONFIG.CAUS
}
}
for (const r of recalledEvents || []) {
for (const r of eventHits || []) {
const rid = r?.event?.id;
if (!rid) continue;
for (const cid of (r.event?.causedBy || [])) {
@@ -520,6 +599,13 @@ function traceCausalAncestors(recalledEvents, eventIndex, maxDepth = CONFIG.CAUS
// 辅助函数
// ═══════════════════════════════════════════════════════════════════════════
/**
* 获取最近消息
* @param {object[]} chat - 聊天记录
* @param {number} count - 消息数量
* @param {boolean} excludeLastAi - 是否排除最后的 AI 消息
* @returns {object[]} 最近消息
*/
function getLastMessages(chat, count = 4, excludeLastAi = false) {
if (!chat?.length) return [];
@@ -532,6 +618,13 @@ function getLastMessages(chat, count = 4, excludeLastAi = false) {
return messages.slice(-count);
}
/**
* 构建查询文本
* @param {object[]} chat - 聊天记录
* @param {number} count - 消息数量
* @param {boolean} excludeLastAi - 是否排除最后的 AI 消息
* @returns {string} 查询文本
*/
export function buildQueryText(chat, count = 2, excludeLastAi = false) {
if (!chat?.length) return '';
@@ -551,6 +644,14 @@ export function buildQueryText(chat, count = 2, excludeLastAi = false) {
// 主函数
// ═══════════════════════════════════════════════════════════════════════════
/**
* 执行记忆召回
* @param {string} queryText - 查询文本
* @param {object[]} allEvents - 所有事件（L2）
* @param {object} vectorConfig - 向量配置
* @param {object} options - 选项
* @returns {Promise<object>} 召回结果
*/
export async function recallMemory(queryText, allEvents, vectorConfig, options = {}) {
const T0 = performance.now();
const { chat, name1 } = getContext();
@@ -559,8 +660,16 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
const metrics = createMetrics();
if (!allEvents?.length) {
metrics.l0.needRecall = false;
return { events: [], chunks: [], causalEvents: [], focusEntities: [], elapsed: 0, logText: 'No events.', metrics };
metrics.anchor.needRecall = false;
return {
events: [],
evidenceChunks: [],
causalChain: [],
focusEntities: [],
elapsed: 0,
logText: 'No events.',
metrics,
};
}
// ═══════════════════════════════════════════════════════════════════════
@@ -587,11 +696,11 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
const focusEntities = removeUserNameFromFocus(expansion.focus, name1);
metrics.l0.needRecall = true;
metrics.l0.focusEntities = focusEntities;
metrics.l0.queries = expansion.queries || [];
metrics.l0.queryExpansionTime = Math.round(performance.now() - T_QE_Start);
metrics.timing.queryExpansion = metrics.l0.queryExpansionTime;
metrics.anchor.needRecall = true;
metrics.anchor.focusEntities = focusEntities;
metrics.anchor.queries = expansion.queries || [];
metrics.anchor.queryExpansionTime = Math.round(performance.now() - T_QE_Start);
metrics.timing.queryExpansion = metrics.anchor.queryExpansionTime;
// ═══════════════════════════════════════════════════════════════════════
// Step 2: 向量化查询
@@ -604,54 +713,70 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
} catch (e) {
xbLog.error(MODULE_ID, '向量化失败', e);
metrics.timing.total = Math.round(performance.now() - T0);
return { events: [], chunks: [], causalEvents: [], focusEntities, elapsed: metrics.timing.total, logText: 'Embedding failed.', metrics };
return {
events: [],
evidenceChunks: [],
causalChain: [],
focusEntities,
elapsed: metrics.timing.total,
logText: 'Embedding failed.',
metrics,
};
}
if (!queryVector?.length) {
metrics.timing.total = Math.round(performance.now() - T0);
return { events: [], chunks: [], causalEvents: [], focusEntities, elapsed: metrics.timing.total, logText: 'Empty query vector.', metrics };
return {
events: [],
evidenceChunks: [],
causalChain: [],
focusEntities,
elapsed: metrics.timing.total,
logText: 'Empty query vector.',
metrics,
};
}
// ═══════════════════════════════════════════════════════════════════════
// Step 3: L0 检索
// Step 3: Anchor (L0) 检索
// ═══════════════════════════════════════════════════════════════════════
const T_L0_Start = performance.now();
const T_Anchor_Start = performance.now();
const { atoms: l0Atoms, floors: l0Floors } = await searchL0(queryVector, vectorConfig, metrics);
const { hits: anchorHits, floors: anchorFloors } = await recallAnchors(queryVector, vectorConfig, metrics);
metrics.timing.l0Search = Math.round(performance.now() - T_L0_Start);
metrics.timing.anchorSearch = Math.round(performance.now() - T_Anchor_Start);
// ═══════════════════════════════════════════════════════════════════════
// Step 4: L3 拉取 + L1 粗筛 + Rerank
// Step 4: Evidence (L1) 拉取 + 粗筛 + Rerank
// ═══════════════════════════════════════════════════════════════════════
const T_L3_Start = performance.now();
const T_Evidence_Start = performance.now();
const rerankQuery = buildRerankQuery(expansion, lastMessages, pendingUserMessage);
const chunks = await getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, rerankQuery, metrics);
const evidenceChunks = await pullEvidenceByFloors(anchorFloors, anchorHits, queryVector, rerankQuery, metrics);
metrics.timing.l3Retrieval = Math.round(performance.now() - T_L3_Start);
metrics.timing.evidenceRetrieval = Math.round(performance.now() - T_Evidence_Start);
// ═══════════════════════════════════════════════════════════════════════
// Step 5: L2 独立检索
// Step 5: Event (L2) 独立检索
// ═══════════════════════════════════════════════════════════════════════
const T_L2_Start = performance.now();
const T_Event_Start = performance.now();
const eventResults = await searchL2Events(queryVector, allEvents, vectorConfig, focusEntities, metrics);
const eventHits = await recallEvents(queryVector, allEvents, vectorConfig, focusEntities, metrics);
metrics.timing.l2Retrieval = Math.round(performance.now() - T_L2_Start);
metrics.timing.eventRetrieval = Math.round(performance.now() - T_Event_Start);
// ═══════════════════════════════════════════════════════════════════════
// Step 6: 因果链追溯
// ═══════════════════════════════════════════════════════════════════════
const eventIndex = buildEventIndex(allEvents);
const { results: causalMap, maxDepth: causalMaxDepth } = traceCausalAncestors(eventResults, eventIndex);
const { results: causalMap, maxDepth: causalMaxDepth } = traceCausation(eventHits, eventIndex);
const recalledIdSet = new Set(eventResults.map(x => x?.event?.id).filter(Boolean));
const causalEvents = causalMap
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,
@@ -661,11 +786,11 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
chainFrom: x.chainFrom,
}));
if (metrics.l2.byRecallType) {
metrics.l2.byRecallType.causal = causalEvents.length;
if (metrics.event.byRecallType) {
metrics.event.byRecallType.causal = causalChain.length;
}
metrics.l2.causalChainDepth = causalMaxDepth;
metrics.l2.causalEventsCount = causalEvents.length;
metrics.event.causalChainDepth = causalMaxDepth;
metrics.event.causalCount = causalChain.length;
// ═══════════════════════════════════════════════════════════════════════
// 完成
@@ -673,24 +798,24 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
metrics.timing.total = Math.round(performance.now() - T0);
metrics.l2.entityNames = focusEntities;
metrics.l2.entitiesLoaded = focusEntities.length;
metrics.event.entityNames = focusEntities;
metrics.event.entitiesUsed = focusEntities.length;
console.group('%c[Recall v4]', 'color: #7c3aed; font-weight: bold');
console.group('%c[Recall v5]', 'color: #7c3aed; font-weight: bold');
console.log(`Elapsed: ${metrics.timing.total}ms`);
console.log(`Query Expansion: focus=[${expansion.focus.join(', ')}]`);
console.log(`L0: ${l0Atoms.length} atoms → ${l0Floors.size} floors`);
console.log(`L3: ${metrics.l3.l1Total || 0} L1 → ${metrics.l3.l1AfterCoarse || 0} 粗筛 → ${chunks.length} final`);
if (metrics.l3.rerankApplied) {
console.log(`L3 Rerank: ${metrics.l3.beforeRerank} → ${metrics.l3.afterRerank} (${metrics.l3.rerankTime}ms)`);
console.log(`Anchors: ${anchorHits.length} hits → ${anchorFloors.size} floors`);
console.log(`Evidence: ${metrics.evidence.chunkTotal || 0} L1 → ${metrics.evidence.chunkAfterCoarse || 0} coarse → ${evidenceChunks.length} final`);
if (metrics.evidence.rerankApplied) {
console.log(`Evidence Rerank: ${metrics.evidence.beforeRerank} → ${metrics.evidence.afterRerank} (${metrics.evidence.rerankTime}ms)`);
}
console.log(`L2: ${eventResults.length} events, ${causalEvents.length} causal`);
console.log(`Events: ${eventHits.length} hits, ${causalChain.length} causal`);
console.groupEnd();
return {
events: eventResults,
causalEvents,
chunks,
events: eventHits,
causalChain,
evidenceChunks,
expansion,
focusEntities,
elapsed: metrics.timing.total,