feat(memory-v2): close Phase A with auto-review, product events, and H5 recall UI.
Add candidate auto-review pipeline, shadow audit tooling, admin metrics page, and user-visible memory recall hints in chat with phase-a readiness checks. Co-authored-by: Cursor <cursoragent@cursor.com>
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john
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const MS_PER_HOUR = 3600000;
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const MS_PER_DAY = 86400000;
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const FALSE_STORE_PATTERNS = [
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{
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code: 'question_mark',
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pattern: /[？?]\s*$/,
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message: 'Content ends with a question mark',
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},
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{
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code: 'what_is_question',
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pattern: /^(?:什么是|解释一下|介绍一下|请问|能不能|是否可以|怎么|如何|为什么|啥是)/u,
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message: 'Content looks like a question rather than a fact',
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},
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{
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code: 'imperative_only',
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pattern: /^(?:查一下|搜一下|看看|继续|下一步|帮我|请帮我)/u,
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message: 'Content looks like an instruction without durable facts',
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},
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{
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code: 'trivial_greeting',
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pattern: /^(?:你好|您好|谢谢|好的|可以|收到|再见|hi|hello|thanks)[！!。.\s]*$/iu,
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message: 'Content is a trivial greeting or acknowledgement',
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},
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{
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code: 'routing_leak',
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pattern: /(?:\[Memory Context\]|【Memind 任务编排】|agent_orchestration)/u,
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message: 'Content contains internal routing or memory envelope text',
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},
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];
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export function parseSinceArg(value, now = Date.now()) {
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const raw = String(value ?? '7d').trim().toLowerCase();
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const match = raw.match(/^(\d+)(h|d|w)$/);
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if (!match) {
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throw new Error(`Invalid --since value "${value}". Expected formats like 24h, 7d, 2w.`);
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}
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const amount = Number(match[1]);
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if (!Number.isFinite(amount) || amount <= 0) {
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throw new Error(`Invalid --since value "${value}". Amount must be positive.`);
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}
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const unit = match[2];
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const multiplier = unit === 'h' ? MS_PER_HOUR : unit === 'w' ? MS_PER_DAY * 7 : MS_PER_DAY;
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return {
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sinceMs: now - amount * multiplier,
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label: raw,
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};
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}
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export function detectFalseStoreCandidate(content) {
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const text = String(content ?? '').replace(/\s+/g, ' ').trim();
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if (!text) {
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return { suspicious: true, code: 'empty_content', message: 'Content is empty' };
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}
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for (const rule of FALSE_STORE_PATTERNS) {
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if (rule.pattern.test(text)) {
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return { suspicious: true, code: rule.code, message: rule.message };
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}
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}
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return { suspicious: false };
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}
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function normalizeCandidate(row) {
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return {
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id: String(row.id),
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userId: String(row.user_id ?? row.userId),
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sessionId: row.session_id == null ? null : String(row.session_id ?? row.sessionId),
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memoryType: String(row.memory_type ?? row.memoryType ?? ''),
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content: String(row.content ?? ''),
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status: String(row.status ?? ''),
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policyReason: String(row.policy_reason ?? row.policyReason ?? ''),
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confidence: Number(row.confidence ?? 0),
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importance: Number(row.importance ?? 0),
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createdAt: Number(row.created_at ?? row.createdAt ?? 0),
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updatedAt: Number(row.updated_at ?? row.updatedAt ?? 0),
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};
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}
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function normalizeMemoryItem(row) {
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return {
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id: String(row.id),
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userId: String(row.user_id ?? row.userId),
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label: String(row.label ?? ''),
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content: String(row.memory_text ?? row.content ?? ''),
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status: String(row.status ?? ''),
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createdAt: Number(row.created_at ?? row.createdAt ?? 0),
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updatedAt: Number(row.updated_at ?? row.updatedAt ?? 0),
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};
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}
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function countByField(items, field) {
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const counts = new Map();
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for (const item of items) {
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const key = String(item[field] ?? 'unknown');
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counts.set(key, (counts.get(key) ?? 0) + 1);
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}
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return Object.fromEntries([...counts.entries()].sort((a, b) => b[1] - a[1]));
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}
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function topEntries(counts, limit = 10) {
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return Object.entries(counts)
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.sort((a, b) => b[1] - a[1])
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.slice(0, limit)
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.map(([key, count]) => ({ key, count }));
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}
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export function auditMemoryV2Shadow({
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candidates = [],
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memoryItems = [],
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pgvectorMemoryIds = new Set(),
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pgvectorConfigured = false,
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agentMemoryEvents = [],
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sinceMs = 0,
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nowMs = Date.now(),
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falseStoreSampleLimit = 20,
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pgvectorLagSampleLimit = 20,
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} = {}) {
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const normalizedCandidates = candidates.map(normalizeCandidate);
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const normalizedItems = memoryItems.map(normalizeMemoryItem);
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const inWindowCandidates = normalizedCandidates.filter((item) => item.createdAt >= sinceMs);
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const inWindowItems = normalizedItems.filter((item) => item.updatedAt >= sinceMs);
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const candidateStatusCounts = countByField(inWindowCandidates, 'status');
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const policyReasonCounts = countByField(inWindowCandidates, 'policyReason');
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const memoryTypeCounts = countByField(inWindowCandidates, 'memoryType');
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const suspiciousCandidates = inWindowCandidates
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.filter((item) => ['candidate', 'accepted'].includes(item.status))
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.map((item) => {
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const verdict = detectFalseStoreCandidate(item.content);
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if (!verdict.suspicious) return null;
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return {
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id: item.id,
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userId: item.userId,
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status: item.status,
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policyReason: item.policyReason,
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code: verdict.code,
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message: verdict.message,
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contentPreview: item.content.slice(0, 120),
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createdAt: item.createdAt,
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};
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})
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.filter(Boolean);
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const activeItems = normalizedItems.filter((item) => item.status === 'active');
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const pgvectorLagUsers = new Map();
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const pgvectorMissingSamples = [];
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if (pgvectorConfigured) {
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for (const item of activeItems) {
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if (pgvectorMemoryIds.has(item.id)) continue;
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pgvectorLagUsers.set(item.userId, (pgvectorLagUsers.get(item.userId) ?? 0) + 1);
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if (pgvectorMissingSamples.length < pgvectorLagSampleLimit) {
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pgvectorMissingSamples.push({
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memoryId: item.id,
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userId: item.userId,
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label: item.label,
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updatedAt: item.updatedAt,
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contentPreview: item.content.slice(0, 120),
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});
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}
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}
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}
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const resolvedEvents = agentMemoryEvents.filter((event) => {
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const createdAt = Number(event.created_at ?? event.createdAt ?? 0);
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return createdAt >= sinceMs;
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});
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const resolvedWithHits = resolvedEvents.filter((event) => {
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const data = event.data_json ?? event.data ?? {};
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const count = Number(data.count ?? data.memoryCount ?? data.memories?.length ?? 0);
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return count > 0;
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});
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const acceptedCount = candidateStatusCounts.accepted ?? 0;
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const candidateCount = candidateStatusCounts.candidate ?? 0;
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const reviewedCount = acceptedCount + (candidateStatusCounts.rejected ?? 0);
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const autoAcceptRate = reviewedCount > 0 ? acceptedCount / reviewedCount : null;
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const falseStoreRate = inWindowCandidates.length > 0
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? suspiciousCandidates.length / inWindowCandidates.length
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: null;
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const resolveHitRate = resolvedEvents.length > 0
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? resolvedWithHits.length / resolvedEvents.length
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: null;
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const thresholds = {
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falseStoreRateMax: 0.05,
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pgvectorLagUsersMax: 0,
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};
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const issues = [];
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if (falseStoreRate != null && falseStoreRate > thresholds.falseStoreRateMax) {
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issues.push({
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code: 'false_store_rate_high',
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message: `False-store sample rate ${(falseStoreRate * 100).toFixed(1)}% exceeds ${thresholds.falseStoreRateMax * 100}%`,
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});
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}
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if (pgvectorConfigured && pgvectorLagUsers.size > thresholds.pgvectorLagUsersMax) {
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issues.push({
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code: 'pgvector_sync_lag',
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message: `${pgvectorLagUsers.size} user(s) have active memories missing from pgvector`,
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});
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}
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return {
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ok: issues.length === 0,
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generatedAt: nowMs,
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window: {
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sinceMs,
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untilMs: nowMs,
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candidateCount: inWindowCandidates.length,
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memoryItemCount: inWindowItems.length,
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agentMemoryResolvedEvents: resolvedEvents.length,
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},
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summary: {
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candidateStatusCounts,
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policyReasonCounts,
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memoryTypeCounts,
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autoAcceptRate,
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falseStoreRate,
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resolveHitRate,
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suspiciousCandidateCount: suspiciousCandidates.length,
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pgvectorLagUserCount: pgvectorConfigured ? pgvectorLagUsers.size : null,
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activeMemoryCount: activeItems.length,
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pgvectorMemoryCount: pgvectorConfigured ? pgvectorMemoryIds.size : null,
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},
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topPolicyReasons: topEntries(policyReasonCounts),
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pgvectorLagUsers: [...pgvectorLagUsers.entries()]
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.sort((a, b) => b[1] - a[1])
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.slice(0, pgvectorLagSampleLimit)
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.map(([userId, missingCount]) => ({ userId, missingCount })),
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sampleFalseStores: suspiciousCandidates.slice(0, falseStoreSampleLimit),
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pgvectorMissingSamples,
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issues,
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};
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}
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export function formatMemoryV2ShadowAuditReport(report) {
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const lines = [
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`memory v2 shadow audit: ${report.ok ? 'ok' : 'issues found'}`,
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`window: ${new Date(report.window.sinceMs).toISOString()} -> ${new Date(report.window.untilMs).toISOString()}`,
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`candidates: ${report.window.candidateCount}`,
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`memory items updated: ${report.window.memoryItemCount}`,
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`agent_memory_resolved events: ${report.window.agentMemoryResolvedEvents}`,
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'',
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'summary:',
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JSON.stringify(report.summary, null, 2),
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];
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if (report.topPolicyReasons.length > 0) {
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lines.push('', 'top policy reasons:');
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for (const item of report.topPolicyReasons) {
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lines.push(`- ${item.key}: ${item.count}`);
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}
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}
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if (report.pgvectorLagUsers.length > 0) {
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lines.push('', 'pgvector lag users:');
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for (const item of report.pgvectorLagUsers) {
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lines.push(`- ${item.userId}: ${item.missingCount} missing`);
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}
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}
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if (report.sampleFalseStores.length > 0) {
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lines.push('', 'sample false stores:');
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for (const item of report.sampleFalseStores) {
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lines.push(`- [${item.code}] ${item.id} (${item.policyReason}): ${item.contentPreview}`);
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}
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}
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if (report.issues.length > 0) {
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lines.push('', 'issues:');
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for (const item of report.issues) {
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lines.push(`- ${item.code}: ${item.message}`);
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}
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}
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return lines.join('\n');
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}
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