LittleWhiteBox/modules/story-summary/vector/retrieval/metrics.js

// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Metrics Collector (v4 - Two-Stage: L0 Locate → L1 Evidence)
//
// 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
//
// 架构变更（v3 → v4）：
// - evidence 区块反映 L0-only 融合 + L1 按楼层拉取的两阶段架构
// - 删除 mergedByType / selectedByType（不再有混合池）
// - 新增 l0Candidates / l0Selected / l1Pulled / l1Attached / l1CosineTime
// - fusion 区块明确标注 L0-only（删除 anchorCount）
// - quality.chunkRealRatio → quality.l1AttachRate
// ═══════════════════════════════════════════════════════════════════════════

/**
 * 创建空的指标对象
 * @returns {object}
 */
export function createMetrics() {
    return {
        // Query Build - 查询构建
        query: {
            buildTime: 0,
            refineTime: 0,
            lengths: {
                v0Chars: 0,
                v1Chars: null,     // null = NA
                rerankChars: 0,
            },
        },

        // Anchor (L0 StateAtoms) - 语义锚点
        anchor: {
            needRecall: false,
            focusEntities: [],
            matched: 0,
            floorsHit: 0,
            topHits: [],
        },

        // Lexical (MiniSearch) - 词法检索
        lexical: {
            terms: [],
            atomHits: 0,
            chunkHits: 0,
            eventHits: 0,
            searchTime: 0,
        },

        // Fusion (W-RRF, L0-only) - 多路融合
        fusion: {
            denseCount: 0,
            lexCount: 0,
            totalUnique: 0,
            afterCap: 0,
            time: 0,
        },

        // Constraint (L3 Facts) - 世界约束
        constraint: {
            total: 0,
            filtered: 0,
            injected: 0,
            tokens: 0,
            samples: [],
        },

        // Event (L2 Events) - 事件摘要
        event: {
            inStore: 0,
            considered: 0,
            selected: 0,
            byRecallType: { direct: 0, related: 0, causal: 0, lexical: 0 },
            similarityDistribution: { min: 0, max: 0, mean: 0, median: 0 },
            entityFilter: null,
            causalChainDepth: 0,
            causalCount: 0,
            entitiesUsed: 0,
            entityNames: [],
        },

        // Evidence (Two-Stage: L0 rerank → L1 pull) - 原文证据
        evidence: {
            // Stage 1: L0
            l0Candidates: 0,       // W-RRF 融合后的 L0 候选数
            l0Selected: 0,         // rerank 后选中的 L0 数
            rerankApplied: false,
            beforeRerank: 0,
            afterRerank: 0,
            rerankTime: 0,
            rerankScores: null,

            // Stage 2: L1
            l1Pulled: 0,           // 从 DB 拉取的 L1 chunk 总数
            l1Attached: 0,         // 实际挂载的 L1 数（top-1 × 楼层 × 2侧）
            l1CosineTime: 0,       // L1 cosine 打分耗时

            // 装配
            contextPairsAdded: 0,  // 保留兼容（= l1Attached 中 USER 侧数量）
            tokens: 0,
            assemblyTime: 0,
        },

        // Arc - 人物弧光
        arc: {
            injected: 0,
            tokens: 0,
        },

        // Formatting - 格式化
        formatting: {
            sectionsIncluded: [],
            time: 0,
        },

        // Budget Summary - 预算
        budget: {
            total: 0,
            limit: 0,
            utilization: 0,
            breakdown: {
                constraints: 0,
                events: 0,
                distantEvidence: 0,
                recentEvidence: 0,
                arcs: 0,
            },
        },

        // Timing - 计时
        timing: {
            queryBuild: 0,
            queryRefine: 0,
            anchorSearch: 0,
            lexicalSearch: 0,
            fusion: 0,
            constraintFilter: 0,
            eventRetrieval: 0,
            evidenceRetrieval: 0,
            evidenceRerank: 0,
            evidenceAssembly: 0,
            formatting: 0,
            total: 0,
        },

        // Quality Indicators - 质量指标
        quality: {
            constraintCoverage: 100,
            eventPrecisionProxy: 0,
            l1AttachRate: 0,       // 有 L1 挂载的 L0 占比
            potentialIssues: [],
        },
    };
}

/**
 * 计算相似度分布统计
 * @param {number[]} similarities
 * @returns {{min: number, max: number, mean: number, median: number}}
 */
export function calcSimilarityStats(similarities) {
    if (!similarities?.length) {
        return { min: 0, max: 0, mean: 0, median: 0 };
    }

    const sorted = [...similarities].sort((a, b) => a - b);
    const sum = sorted.reduce((a, b) => a + b, 0);

    return {
        min: Number(sorted[0].toFixed(3)),
        max: Number(sorted[sorted.length - 1].toFixed(3)),
        mean: Number((sum / sorted.length).toFixed(3)),
        median: Number(sorted[Math.floor(sorted.length / 2)].toFixed(3)),
    };
}

/**
 * 格式化指标为可读日志
 * @param {object} metrics
 * @returns {string}
 */
export function formatMetricsLog(metrics) {
    const m = metrics;
    const lines = [];

    lines.push('');
    lines.push('════════════════════════════════════════');
    lines.push('        Recall Metrics Report (v4)      ');
    lines.push('════════════════════════════════════════');
    lines.push('');

    // Query Length
    lines.push('[Query Length] 查询长度');
    lines.push(`├─ query_v0_chars: ${m.query?.lengths?.v0Chars ?? 0}`);
    lines.push(`├─ query_v1_chars: ${m.query?.lengths?.v1Chars == null ? 'NA' : m.query.lengths.v1Chars}`);
    lines.push(`└─ rerank_query_chars: ${m.query?.lengths?.rerankChars ?? 0}`);
    lines.push('');

    // Query Build
    lines.push('[Query] 查询构建');
    lines.push(`├─ build_time: ${m.query.buildTime}ms`);
    lines.push(`└─ refine_time: ${m.query.refineTime}ms`);
    lines.push('');

    // 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(`├─ matched: ${m.anchor.matched || 0}`);
        lines.push(`└─ floors_hit: ${m.anchor.floorsHit || 0}`);
    }
    lines.push('');

    // Lexical (MiniSearch)
    lines.push('[Lexical] MiniSearch - 词法检索');
    lines.push(`├─ terms: [${(m.lexical.terms || []).slice(0, 8).join(', ')}]`);
    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('');

    // Fusion (W-RRF, L0-only)
    lines.push('[Fusion] W-RRF (L0-only) - 多路融合');
    lines.push(`├─ dense_count: ${m.fusion.denseCount}`);
    lines.push(`├─ lex_count: ${m.fusion.lexCount}`);
    lines.push(`├─ total_unique: ${m.fusion.totalUnique}`);
    lines.push(`├─ after_cap: ${m.fusion.afterCap}`);
    lines.push(`└─ time: ${m.fusion.time}ms`);
    lines.push('');

    // 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('');

    // 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.event.entityFilter) {
        const ef = m.event.entityFilter;
        lines.push(`├─ entity_filter:`);
        lines.push(`│   ├─ focus_entities: [${(ef.focusEntities || []).join(', ')}]`);
        lines.push(`│   ├─ before: ${ef.before}`);
        lines.push(`│   ├─ after: ${ef.after}`);
        lines.push(`│   └─ filtered: ${ef.filtered}`);
    }

    lines.push(`├─ selected: ${m.event.selected}`);
    lines.push(`├─ by_recall_type:`);
    lines.push(`│   ├─ direct: ${m.event.byRecallType.direct}`);
    lines.push(`│   ├─ related: ${m.event.byRecallType.related}`);
    lines.push(`│   ├─ causal: ${m.event.byRecallType.causal}`);
    lines.push(`│   └─ lexical: ${m.event.byRecallType.lexical}`);

    const sim = m.event.similarityDistribution;
    if (sim && sim.max > 0) {
        lines.push(`├─ similarity_distribution:`);
        lines.push(`│   ├─ min: ${sim.min}`);
        lines.push(`│   ├─ max: ${sim.max}`);
        lines.push(`│   ├─ mean: ${sim.mean}`);
        lines.push(`│   └─ median: ${sim.median}`);
    }

    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('');

    // Evidence (Two-Stage)
    lines.push('[Evidence] Two-Stage: L0 Locate → L1 Pull');
    lines.push(`├─ Stage 1 (L0):`);
    lines.push(`│   ├─ candidates (post-fusion): ${m.evidence.l0Candidates}`);

    if (m.evidence.rerankApplied) {
        lines.push(`│   ├─ rerank_applied: true`);
        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(`│   └─ selected: ${m.evidence.l0Selected}`);
    lines.push(`├─ Stage 2 (L1):`);
    lines.push(`│   ├─ pulled: ${m.evidence.l1Pulled}`);
    lines.push(`│   ├─ attached: ${m.evidence.l1Attached}`);
    lines.push(`│   └─ cosine_time: ${m.evidence.l1CosineTime}ms`);
    lines.push(`├─ tokens: ${m.evidence.tokens}`);
    lines.push(`└─ assembly_time: ${m.evidence.assemblyTime}ms`);
    lines.push('');

    // 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] 预算');
    lines.push(`├─ total_tokens: ${m.budget.total}`);
    lines.push(`├─ limit: ${m.budget.limit}`);
    lines.push(`├─ utilization: ${m.budget.utilization}%`);
    lines.push(`└─ breakdown:`);
    const bd = m.budget.breakdown || {};
    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] 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`);
    lines.push(`├─ lexical_search: ${m.lexical.searchTime}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`);
    if (m.timing.evidenceRerank > 0) {
        lines.push(`├─ evidence_rerank: ${m.timing.evidenceRerank}ms`);
    }
    lines.push(`├─ l1_cosine: ${m.evidence.l1CosineTime}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}%`);

    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');
    }

    // ─────────────────────────────────────────────────────────────────
    // 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.atomHits === 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（L0-only）
    // ─────────────────────────────────────────────────────────────────

    if (m.fusion.lexCount === 0 && m.fusion.denseCount > 0) {
        issues.push('No lexical L0 candidates in fusion - hybrid retrieval not contributing');
    }

    if (m.fusion.afterCap === 0) {
        issues.push('Fusion produced zero L0 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');
    }

    // ─────────────────────────────────────────────────────────────────
    // L0 Rerank 问题
    // ─────────────────────────────────────────────────────────────────

    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 L0 rerank filter ratio (${(filterRatio * 100).toFixed(0)}%) - many irrelevant L0 in fusion output`);
            }
        }

        if (m.evidence.rerankScores) {
            const rs = m.evidence.rerankScores;
            if (rs.max < 0.5) {
                issues.push(`Low L0 rerank scores (max=${rs.max}) - query may be poorly matched`);
            }
            if (rs.mean < 0.3) {
                issues.push(`Very low average L0 rerank score (mean=${rs.mean}) - context may be weak`);
            }
        }

        if (m.evidence.rerankTime > 2000) {
            issues.push(`Slow L0 rerank (${m.evidence.rerankTime}ms) - may affect response time`);
        }
    }

    // ─────────────────────────────────────────────────────────────────
    // L1 挂载问题
    // ─────────────────────────────────────────────────────────────────

    if (m.evidence.l0Selected > 0 && m.evidence.l1Pulled === 0) {
        issues.push('Zero L1 chunks pulled - L1 vectors may not exist or DB read failed');
    }

    if (m.evidence.l0Selected > 0 && m.evidence.l1Attached === 0 && m.evidence.l1Pulled > 0) {
        issues.push('L1 chunks pulled but none attached - cosine scores may be too low or floor mismatch');
    }

    const l1AttachRate = m.quality.l1AttachRate || 0;
    if (m.evidence.l0Selected > 5 && l1AttachRate < 20) {
        issues.push(`Low L1 attach rate (${l1AttachRate}%) - many L0 lack concrete dialogue evidence`);
    }

    // ─────────────────────────────────────────────────────────────────
    // 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`);
    }

    return issues;
}