677 lines
29 KiB
JavaScript
677 lines
29 KiB
JavaScript
// ═══════════════════════════════════════════════════════════════════════════
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// Story Summary - Metrics Collector (v6 - Dense-Gated Lexical)
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//
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// v5 → v6 变更：
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// - lexical: 新增 eventFilteredByDense / floorFilteredByDense
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// - event: entityFilter bypass 阈值改为 CONFIG 驱动（0.80）
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// - 其余结构不变
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//
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// v4 → v5 变更：
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// - query: 新增 segmentWeights / r2Weights（加权向量诊断）
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// - fusion: 新增 denseAggMethod / lexDensityBonus（聚合策略可观测）
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// - quality: 新增 rerankRetentionRate（粗排-精排一致性）
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// - 移除 timing 中从未写入的死字段（queryBuild/queryRefine/lexicalSearch/fusion）
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// - 移除从未写入的 arc 区块
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* 创建空的指标对象
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* @returns {object}
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*/
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export function createMetrics() {
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return {
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// Query Build - 查询构建
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query: {
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buildTime: 0,
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refineTime: 0,
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lengths: {
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v0Chars: 0,
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v1Chars: null, // null = 无 hints
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rerankChars: 0,
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},
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segmentWeights: [], // R1 归一化后权重 [context..., focus]
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r2Weights: null, // R2 归一化后权重 [context..., focus, hints]（null = 无 hints）
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},
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// Anchor (L0 StateAtoms) - 语义锚点
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anchor: {
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needRecall: false,
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focusEntities: [],
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matched: 0,
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floorsHit: 0,
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topHits: [],
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},
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// Lexical (MiniSearch) - 词法检索
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lexical: {
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terms: [],
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atomHits: 0,
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chunkHits: 0,
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eventHits: 0,
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searchTime: 0,
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indexReadyTime: 0,
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eventFilteredByDense: 0,
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floorFilteredByDense: 0,
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},
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// Fusion (W-RRF, floor-level) - 多路融合
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fusion: {
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denseFloors: 0,
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lexFloors: 0,
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totalUnique: 0,
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afterCap: 0,
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time: 0,
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denseAggMethod: '', // 聚合方法描述（如 "max×0.6+mean×0.4"）
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lexDensityBonus: 0, // 密度加成系数
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},
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// Constraint (L3 Facts) - 世界约束
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constraint: {
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total: 0,
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filtered: 0,
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injected: 0,
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tokens: 0,
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samples: [],
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},
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// Event (L2 Events) - 事件摘要
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event: {
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inStore: 0,
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considered: 0,
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selected: 0,
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byRecallType: { direct: 0, related: 0, causal: 0, lexical: 0, l0Linked: 0 },
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similarityDistribution: { min: 0, max: 0, mean: 0, median: 0 },
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entityFilter: null,
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causalChainDepth: 0,
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causalCount: 0,
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entitiesUsed: 0,
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entityNames: [],
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},
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// Evidence (Two-Stage: Floor rerank → L1 pull) - 原文证据
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evidence: {
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// Stage 1: Floor
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floorCandidates: 0,
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floorsSelected: 0,
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l0Collected: 0,
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rerankApplied: false,
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rerankFailed: false,
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beforeRerank: 0,
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afterRerank: 0,
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rerankTime: 0,
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rerankScores: null,
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rerankDocAvgLength: 0,
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// Stage 2: L1
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l1Pulled: 0,
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l1Attached: 0,
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l1CosineTime: 0,
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// 装配
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contextPairsAdded: 0,
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tokens: 0,
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assemblyTime: 0,
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},
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// Diffusion (PPR Spreading Activation) - 图扩散
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diffusion: {
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seedCount: 0,
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graphNodes: 0,
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graphEdges: 0,
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candidatePairs: 0,
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pairsFromWhat: 0,
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pairsFromHow: 0,
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edgeDensity: 0,
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reweightWhoUsed: 0,
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reweightWhereUsed: 0,
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iterations: 0,
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convergenceError: 0,
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pprActivated: 0,
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cosineGatePassed: 0,
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cosineGateFiltered: 0,
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cosineGateNoVector: 0,
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postGatePassRate: 0,
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finalCount: 0,
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scoreDistribution: { min: 0, max: 0, mean: 0 },
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byChannel: { what: 0, where: 0, how: 0, who: 0 },
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time: 0,
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},
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// Formatting - 格式化
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formatting: {
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sectionsIncluded: [],
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time: 0,
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},
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// Budget Summary - 预算
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budget: {
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total: 0,
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limit: 0,
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utilization: 0,
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breakdown: {
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constraints: 0,
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events: 0,
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distantEvidence: 0,
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recentEvidence: 0,
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arcs: 0,
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},
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},
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// Timing - 计时（仅包含实际写入的字段）
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timing: {
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anchorSearch: 0,
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constraintFilter: 0,
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eventRetrieval: 0,
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evidenceRetrieval: 0,
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evidenceRerank: 0,
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evidenceAssembly: 0,
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diffusion: 0,
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formatting: 0,
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total: 0,
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},
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// Quality Indicators - 质量指标
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quality: {
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constraintCoverage: 100,
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eventPrecisionProxy: 0,
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l1AttachRate: 0,
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rerankRetentionRate: 0,
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diffusionEffectiveRate: 0,
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potentialIssues: [],
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},
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};
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}
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/**
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* 计算相似度分布统计
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* @param {number[]} similarities
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* @returns {{min: number, max: number, mean: number, median: number}}
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*/
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export function calcSimilarityStats(similarities) {
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if (!similarities?.length) {
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return { min: 0, max: 0, mean: 0, median: 0 };
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}
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const sorted = [...similarities].sort((a, b) => a - b);
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const sum = sorted.reduce((a, b) => a + b, 0);
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return {
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min: Number(sorted[0].toFixed(3)),
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max: Number(sorted[sorted.length - 1].toFixed(3)),
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mean: Number((sum / sorted.length).toFixed(3)),
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median: Number(sorted[Math.floor(sorted.length / 2)].toFixed(3)),
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};
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}
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/**
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* 格式化权重数组为紧凑字符串
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* @param {number[]|null} weights
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* @returns {string}
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*/
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function fmtWeights(weights) {
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if (!weights?.length) return 'N/A';
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return '[' + weights.map(w => (typeof w === 'number' ? w.toFixed(3) : String(w))).join(', ') + ']';
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}
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/**
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* 格式化指标为可读日志
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* @param {object} metrics
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* @returns {string}
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*/
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export function formatMetricsLog(metrics) {
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const m = metrics;
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const lines = [];
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lines.push('');
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lines.push('════════════════════════════════════════');
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lines.push(' Recall Metrics Report (v5) ');
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lines.push('════════════════════════════════════════');
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lines.push('');
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// Query Length
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lines.push('[Query Length] 查询长度');
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lines.push(`├─ query_v0_chars: ${m.query?.lengths?.v0Chars ?? 0}`);
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lines.push(`├─ query_v1_chars: ${m.query?.lengths?.v1Chars == null ? 'N/A' : m.query.lengths.v1Chars}`);
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lines.push(`└─ rerank_query_chars: ${m.query?.lengths?.rerankChars ?? 0}`);
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lines.push('');
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// Query Build
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lines.push('[Query] 查询构建');
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lines.push(`├─ build_time: ${m.query.buildTime}ms`);
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lines.push(`├─ refine_time: ${m.query.refineTime}ms`);
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lines.push(`├─ r1_weights: ${fmtWeights(m.query.segmentWeights)}`);
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if (m.query.r2Weights) {
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lines.push(`└─ r2_weights: ${fmtWeights(m.query.r2Weights)}`);
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} else {
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lines.push(`└─ r2_weights: N/A (no hints)`);
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}
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lines.push('');
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// Anchor (L0 StateAtoms)
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lines.push('[Anchor] L0 StateAtoms - 语义锚点');
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lines.push(`├─ need_recall: ${m.anchor.needRecall}`);
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if (m.anchor.needRecall) {
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lines.push(`├─ focus_entities: [${(m.anchor.focusEntities || []).join(', ')}]`);
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lines.push(`├─ matched: ${m.anchor.matched || 0}`);
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lines.push(`└─ floors_hit: ${m.anchor.floorsHit || 0}`);
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}
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lines.push('');
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// Lexical (MiniSearch)
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lines.push('[Lexical] MiniSearch - 词法检索');
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lines.push(`├─ terms: [${(m.lexical.terms || []).slice(0, 8).join(', ')}]`);
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lines.push(`├─ atom_hits: ${m.lexical.atomHits}`);
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lines.push(`├─ chunk_hits: ${m.lexical.chunkHits}`);
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lines.push(`├─ event_hits: ${m.lexical.eventHits}`);
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lines.push(`├─ search_time: ${m.lexical.searchTime}ms`);
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if (m.lexical.indexReadyTime > 0) {
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lines.push(`├─ index_ready_time: ${m.lexical.indexReadyTime}ms`);
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}
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if (m.lexical.eventFilteredByDense > 0) {
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lines.push(`├─ event_filtered_by_dense: ${m.lexical.eventFilteredByDense}`);
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}
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if (m.lexical.floorFilteredByDense > 0) {
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lines.push(`├─ floor_filtered_by_dense: ${m.lexical.floorFilteredByDense}`);
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}
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lines.push(`└─ dense_gate_threshold: 0.50`);
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lines.push('');
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// Fusion (W-RRF, floor-level)
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lines.push('[Fusion] W-RRF (floor-level) - 多路融合');
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lines.push(`├─ dense_floors: ${m.fusion.denseFloors}`);
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lines.push(`├─ lex_floors: ${m.fusion.lexFloors}`);
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if (m.fusion.lexDensityBonus > 0) {
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lines.push(`│ └─ density_bonus: ${m.fusion.lexDensityBonus}`);
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}
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lines.push(`├─ total_unique: ${m.fusion.totalUnique}`);
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lines.push(`├─ after_cap: ${m.fusion.afterCap}`);
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lines.push(`└─ time: ${m.fusion.time}ms`);
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lines.push('');
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// Constraint (L3 Facts)
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lines.push('[Constraint] L3 Facts - 世界约束');
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lines.push(`├─ total: ${m.constraint.total}`);
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lines.push(`├─ filtered: ${m.constraint.filtered || 0}`);
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lines.push(`├─ injected: ${m.constraint.injected}`);
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lines.push(`├─ tokens: ${m.constraint.tokens}`);
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if (m.constraint.samples && m.constraint.samples.length > 0) {
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lines.push(`└─ samples: "${m.constraint.samples.slice(0, 2).join('", "')}"`);
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}
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lines.push('');
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// Event (L2 Events)
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lines.push('[Event] L2 Events - 事件摘要');
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lines.push(`├─ in_store: ${m.event.inStore}`);
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lines.push(`├─ considered: ${m.event.considered}`);
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if (m.event.entityFilter) {
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const ef = m.event.entityFilter;
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lines.push(`├─ entity_filter:`);
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lines.push(`│ ├─ focus_entities: [${(ef.focusEntities || []).join(', ')}]`);
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lines.push(`│ ├─ before: ${ef.before}`);
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lines.push(`│ ├─ after: ${ef.after}`);
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lines.push(`│ └─ filtered: ${ef.filtered}`);
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}
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lines.push(`├─ selected: ${m.event.selected}`);
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lines.push(`├─ by_recall_type:`);
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lines.push(`│ ├─ direct: ${m.event.byRecallType.direct}`);
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lines.push(`│ ├─ related: ${m.event.byRecallType.related}`);
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lines.push(`│ ├─ causal: ${m.event.byRecallType.causal}`);
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if (m.event.byRecallType.l0Linked) {
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lines.push(`│ ├─ lexical: ${m.event.byRecallType.lexical}`);
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lines.push(`│ └─ l0_linked: ${m.event.byRecallType.l0Linked}`);
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} else {
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lines.push(`│ └─ lexical: ${m.event.byRecallType.lexical}`);
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}
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const sim = m.event.similarityDistribution;
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if (sim && sim.max > 0) {
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lines.push(`├─ similarity_distribution:`);
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lines.push(`│ ├─ min: ${sim.min}`);
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lines.push(`│ ├─ max: ${sim.max}`);
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lines.push(`│ ├─ mean: ${sim.mean}`);
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lines.push(`│ └─ median: ${sim.median}`);
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}
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lines.push(`├─ causal_chain: depth=${m.event.causalChainDepth}, count=${m.event.causalCount}`);
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lines.push(`└─ entities_used: ${m.event.entitiesUsed} [${(m.event.entityNames || []).join(', ')}]`);
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lines.push('');
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// Evidence (Two-Stage: Floor Rerank → L1 Pull)
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lines.push('[Evidence] Two-Stage: Floor Rerank → L1 Pull');
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lines.push(`├─ Stage 1 (Floor Rerank):`);
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lines.push(`│ ├─ floor_candidates (post-fusion): ${m.evidence.floorCandidates}`);
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if (m.evidence.rerankApplied) {
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lines.push(`│ ├─ rerank_applied: true`);
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if (m.evidence.rerankFailed) {
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lines.push(`│ │ ⚠ rerank_failed: using fusion order`);
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}
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lines.push(`│ │ ├─ before: ${m.evidence.beforeRerank} floors`);
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lines.push(`│ │ ├─ after: ${m.evidence.afterRerank} floors`);
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lines.push(`│ │ └─ time: ${m.evidence.rerankTime}ms`);
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if (m.evidence.rerankScores) {
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const rs = m.evidence.rerankScores;
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lines.push(`│ ├─ rerank_scores: min=${rs.min}, max=${rs.max}, mean=${rs.mean}`);
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}
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if (m.evidence.rerankDocAvgLength > 0) {
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lines.push(`│ ├─ rerank_doc_avg_length: ${m.evidence.rerankDocAvgLength} chars`);
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}
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} else {
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lines.push(`│ ├─ rerank_applied: false`);
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}
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lines.push(`│ ├─ floors_selected: ${m.evidence.floorsSelected}`);
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lines.push(`│ └─ l0_atoms_collected: ${m.evidence.l0Collected}`);
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lines.push(`├─ Stage 2 (L1):`);
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lines.push(`│ ├─ pulled: ${m.evidence.l1Pulled}`);
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lines.push(`│ ├─ attached: ${m.evidence.l1Attached}`);
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lines.push(`│ └─ cosine_time: ${m.evidence.l1CosineTime}ms`);
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lines.push(`├─ tokens: ${m.evidence.tokens}`);
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lines.push(`└─ assembly_time: ${m.evidence.assemblyTime}ms`);
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lines.push('');
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// Diffusion (PPR)
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lines.push('[Diffusion] PPR Spreading Activation');
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lines.push(`├─ seeds: ${m.diffusion.seedCount}`);
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lines.push(`├─ graph: ${m.diffusion.graphNodes} nodes, ${m.diffusion.graphEdges} edges`);
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lines.push(`├─ candidate_pairs: ${m.diffusion.candidatePairs || 0} (what=${m.diffusion.pairsFromWhat || 0}, how=${m.diffusion.pairsFromHow || 0})`);
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lines.push(`├─ edge_density: ${m.diffusion.edgeDensity || 0}%`);
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if (m.diffusion.graphEdges > 0) {
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const ch = m.diffusion.byChannel || {};
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lines.push(`│ ├─ by_channel: what=${ch.what || 0}, how=${ch.how || 0}, who=${ch.who || 0}, where=${ch.where || 0}`);
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lines.push(`│ └─ reweight_used: who=${m.diffusion.reweightWhoUsed || 0}, where=${m.diffusion.reweightWhereUsed || 0}`);
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}
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if (m.diffusion.iterations > 0) {
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lines.push(`├─ ppr: ${m.diffusion.iterations} iterations, ε=${Number(m.diffusion.convergenceError).toExponential(1)}`);
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}
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lines.push(`├─ activated (excl seeds): ${m.diffusion.pprActivated}`);
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if (m.diffusion.pprActivated > 0) {
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lines.push(`├─ cosine_gate: ${m.diffusion.cosineGatePassed} passed, ${m.diffusion.cosineGateFiltered} filtered`);
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const passPrefix = m.diffusion.cosineGateNoVector > 0 ? '│ ├─' : '│ └─';
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lines.push(`${passPrefix} pass_rate: ${m.diffusion.postGatePassRate || 0}%`);
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if (m.diffusion.cosineGateNoVector > 0) {
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lines.push(`│ ├─ no_vector: ${m.diffusion.cosineGateNoVector}`);
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}
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}
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lines.push(`├─ final_injected: ${m.diffusion.finalCount}`);
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if (m.diffusion.finalCount > 0) {
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const ds = m.diffusion.scoreDistribution;
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lines.push(`├─ scores: min=${ds.min}, max=${ds.max}, mean=${ds.mean}`);
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}
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lines.push(`└─ time: ${m.diffusion.time}ms`);
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lines.push('');
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// Formatting
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lines.push('[Formatting] 格式化');
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lines.push(`├─ sections: [${(m.formatting.sectionsIncluded || []).join(', ')}]`);
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lines.push(`└─ time: ${m.formatting.time}ms`);
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lines.push('');
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// Budget Summary
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lines.push('[Budget] 预算');
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lines.push(`├─ total_tokens: ${m.budget.total}`);
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lines.push(`├─ limit: ${m.budget.limit}`);
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lines.push(`├─ utilization: ${m.budget.utilization}%`);
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lines.push(`└─ breakdown:`);
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const bd = m.budget.breakdown || {};
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lines.push(` ├─ constraints: ${bd.constraints || 0}`);
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lines.push(` ├─ events: ${bd.events || 0}`);
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lines.push(` ├─ distant_evidence: ${bd.distantEvidence || 0}`);
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lines.push(` ├─ recent_evidence: ${bd.recentEvidence || 0}`);
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lines.push(` └─ arcs: ${bd.arcs || 0}`);
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lines.push('');
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// Timing
|
||
lines.push('[Timing] 计时');
|
||
lines.push(`├─ query_build: ${m.query.buildTime}ms`);
|
||
lines.push(`├─ query_refine: ${m.query.refineTime}ms`);
|
||
lines.push(`├─ anchor_search: ${m.timing.anchorSearch}ms`);
|
||
const lexicalTotal = (m.lexical.searchTime || 0) + (m.lexical.indexReadyTime || 0);
|
||
lines.push(`├─ lexical_search: ${lexicalTotal}ms (query=${m.lexical.searchTime || 0}ms, index_ready=${m.lexical.indexReadyTime || 0}ms)`);
|
||
lines.push(`├─ fusion: ${m.fusion.time}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`);
|
||
lines.push(`├─ floor_rerank: ${m.timing.evidenceRerank || 0}ms`);
|
||
lines.push(`├─ l1_cosine: ${m.evidence.l1CosineTime}ms`);
|
||
lines.push(`├─ diffusion: ${m.timing.diffusion}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] 质量指标');
|
||
lines.push(`├─ constraint_coverage: ${m.quality.constraintCoverage}%`);
|
||
lines.push(`├─ event_precision_proxy: ${m.quality.eventPrecisionProxy}`);
|
||
lines.push(`├─ l1_attach_rate: ${m.quality.l1AttachRate}%`);
|
||
lines.push(`├─ rerank_retention_rate: ${m.quality.rerankRetentionRate}%`);
|
||
lines.push(`├─ diffusion_effective_rate: ${m.quality.diffusionEffectiveRate}%`);
|
||
|
||
if (m.quality.potentialIssues && m.quality.potentialIssues.length > 0) {
|
||
lines.push(`└─ potential_issues:`);
|
||
m.quality.potentialIssues.forEach((issue, i) => {
|
||
const prefix = i === m.quality.potentialIssues.length - 1 ? ' └─' : ' ├─';
|
||
lines.push(`${prefix} ⚠ ${issue}`);
|
||
});
|
||
} else {
|
||
lines.push(`└─ potential_issues: none`);
|
||
}
|
||
|
||
lines.push('');
|
||
lines.push('════════════════════════════════════════');
|
||
lines.push('');
|
||
|
||
return lines.join('\n');
|
||
}
|
||
|
||
/**
|
||
* 检测潜在问题
|
||
* @param {object} metrics
|
||
* @returns {string[]}
|
||
*/
|
||
export function detectIssues(metrics) {
|
||
const issues = [];
|
||
const m = metrics;
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 查询构建问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if ((m.anchor.focusEntities || []).length === 0) {
|
||
issues.push('No focus entities extracted - entity lexicon may be empty or messages too short');
|
||
}
|
||
|
||
// 权重极端退化检测
|
||
const segWeights = m.query.segmentWeights || [];
|
||
if (segWeights.length > 0) {
|
||
const focusWeight = segWeights[segWeights.length - 1] || 0;
|
||
if (focusWeight < 0.15) {
|
||
issues.push(`Focus segment weight very low (${(focusWeight * 100).toFixed(0)}%) - focus message may be too short`);
|
||
}
|
||
const allLow = segWeights.every(w => w < 0.1);
|
||
if (allLow) {
|
||
issues.push('All segment weights below 10% - all messages may be extremely short');
|
||
}
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 锚点匹配问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if ((m.anchor.matched || 0) === 0 && m.anchor.needRecall) {
|
||
issues.push('No anchors matched - may need to generate anchors');
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 词法检索问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if ((m.lexical.terms || []).length > 0 && m.lexical.chunkHits === 0 && m.lexical.eventHits === 0) {
|
||
issues.push('Lexical search returned zero hits - terms may not match any indexed content');
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 融合问题（floor-level）
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.fusion.lexFloors === 0 && m.fusion.denseFloors > 0) {
|
||
issues.push('No lexical floors in fusion - hybrid retrieval not contributing');
|
||
}
|
||
|
||
if (m.fusion.afterCap === 0) {
|
||
issues.push('Fusion produced zero floor candidates - all retrieval paths may have failed');
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 事件召回问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.event.considered > 0) {
|
||
const denseSelected =
|
||
(m.event.byRecallType?.direct || 0) +
|
||
(m.event.byRecallType?.related || 0);
|
||
|
||
const denseSelectRatio = denseSelected / m.event.considered;
|
||
|
||
if (denseSelectRatio < 0.1) {
|
||
issues.push(`Dense event selection ratio too low (${(denseSelectRatio * 100).toFixed(1)}%) - threshold may be too high`);
|
||
}
|
||
if (denseSelectRatio > 0.6 && m.event.considered > 10) {
|
||
issues.push(`Dense event selection ratio high (${(denseSelectRatio * 100).toFixed(1)}%) - may include noise`);
|
||
}
|
||
}
|
||
|
||
// 实体过滤问题
|
||
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');
|
||
}
|
||
if (ef.before > 0 && ef.filtered > ef.before * 0.8) {
|
||
issues.push(`Too many events filtered (${ef.filtered}/${ef.before}) - focus may be too narrow`);
|
||
}
|
||
}
|
||
|
||
// 相似度问题
|
||
if (m.event.similarityDistribution && m.event.similarityDistribution.min > 0 && m.event.similarityDistribution.min < 0.5) {
|
||
issues.push(`Low similarity events included (min=${m.event.similarityDistribution.min})`);
|
||
}
|
||
|
||
// 因果链问题
|
||
if (m.event.selected > 0 && m.event.causalCount === 0 && m.event.byRecallType.direct === 0) {
|
||
issues.push('No direct or causal events - query may not align with stored events');
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// Floor Rerank 问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.evidence.rerankFailed) {
|
||
issues.push('Rerank API failed — using fusion rank order as fallback, relevance scores are zero');
|
||
}
|
||
|
||
if (m.evidence.rerankApplied && !m.evidence.rerankFailed) {
|
||
if (m.evidence.rerankScores) {
|
||
const rs = m.evidence.rerankScores;
|
||
if (rs.max < 0.3) {
|
||
issues.push(`Low floor rerank scores (max=${rs.max}) - query-document domain mismatch`);
|
||
}
|
||
if (rs.mean < 0.2) {
|
||
issues.push(`Very low average floor rerank score (mean=${rs.mean}) - context may be weak`);
|
||
}
|
||
}
|
||
|
||
if (m.evidence.rerankTime > 3000) {
|
||
issues.push(`Slow floor rerank (${m.evidence.rerankTime}ms) - may affect response time`);
|
||
}
|
||
|
||
if (m.evidence.rerankDocAvgLength > 3000) {
|
||
issues.push(`Large rerank documents (avg ${m.evidence.rerankDocAvgLength} chars) - may reduce rerank precision`);
|
||
}
|
||
}
|
||
|
||
// Rerank 保留率
|
||
const retentionRate = m.evidence.floorCandidates > 0
|
||
? Math.round(m.evidence.floorsSelected / m.evidence.floorCandidates * 100)
|
||
: 0;
|
||
m.quality.rerankRetentionRate = retentionRate;
|
||
|
||
if (m.evidence.floorCandidates > 0 && retentionRate < 25) {
|
||
issues.push(`Low rerank retention rate (${retentionRate}%) - fusion ranking poorly aligned with reranker`);
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// L1 挂载问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.evidence.floorsSelected > 0 && m.evidence.l1Pulled === 0) {
|
||
issues.push('Zero L1 chunks pulled - L1 vectors may not exist or DB read failed');
|
||
}
|
||
|
||
if (m.evidence.floorsSelected > 0 && m.evidence.l1Attached === 0 && m.evidence.l1Pulled > 0) {
|
||
issues.push('L1 chunks pulled but none attached - cosine scores may be too low');
|
||
}
|
||
|
||
const l1AttachRate = m.quality.l1AttachRate || 0;
|
||
if (m.evidence.floorsSelected > 3 && l1AttachRate < 50) {
|
||
issues.push(`Low L1 attach rate (${l1AttachRate}%) - selected floors lack L1 chunks`);
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 预算问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.budget.utilization > 90) {
|
||
issues.push(`High budget utilization (${m.budget.utilization}%) - may be truncating content`);
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// 性能问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.timing.total > 8000) {
|
||
issues.push(`Slow recall (${m.timing.total}ms) - consider optimization`);
|
||
}
|
||
|
||
if (m.query.buildTime > 100) {
|
||
issues.push(`Slow query build (${m.query.buildTime}ms) - entity lexicon may be too large`);
|
||
}
|
||
|
||
if (m.evidence.l1CosineTime > 1000) {
|
||
issues.push(`Slow L1 cosine scoring (${m.evidence.l1CosineTime}ms) - too many chunks pulled`);
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────
|
||
// Diffusion 问题
|
||
// ─────────────────────────────────────────────────────────────────
|
||
|
||
if (m.diffusion.graphEdges === 0 && m.diffusion.seedCount > 0) {
|
||
issues.push('No diffusion graph edges - atoms may lack edges fields');
|
||
}
|
||
|
||
if (m.diffusion.pprActivated > 0 && m.diffusion.cosineGatePassed === 0) {
|
||
issues.push('All PPR-activated nodes failed cosine gate - graph structure diverged from query semantics');
|
||
}
|
||
|
||
m.quality.diffusionEffectiveRate = m.diffusion.pprActivated > 0
|
||
? Math.round((m.diffusion.finalCount / m.diffusion.pprActivated) * 100)
|
||
: 0;
|
||
|
||
if (m.diffusion.cosineGateNoVector > 5) {
|
||
issues.push(`${m.diffusion.cosineGateNoVector} PPR nodes missing vectors - L0 vectorization may be incomplete`);
|
||
}
|
||
|
||
if (m.diffusion.time > 50) {
|
||
issues.push(`Slow diffusion (${m.diffusion.time}ms) - graph may be too dense`);
|
||
}
|
||
|
||
if (m.diffusion.pprActivated > 0 && (m.diffusion.postGatePassRate < 20 || m.diffusion.postGatePassRate > 60)) {
|
||
issues.push(`Diffusion post-gate pass rate out of target (${m.diffusion.postGatePassRate}%)`);
|
||
}
|
||
|
||
return issues;
|
||
}
|