Files
pn-new-crm/src/lib/services/expense-dedup.service.ts
Matt Ciaccio e77d55ac50 feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.

Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
  entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
  timestamps, jsonb metadata). Partial-unique fingerprint index keeps
  one open row per (port, rule, entity); separate indexes power
  severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
  for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
  confidence; partial index on (port, vendor, amount, date) where
  duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
  action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
  model GENERATED ALWAYS in TS yet, so the migration appends manual
  ALTER + the GIN index).

Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
  auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
  PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
  no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
  using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
  stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
  cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
  pagination on (createdAt, id). PR10 wires the admin UI.

tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00

72 lines
2.3 KiB
TypeScript

/**
* Expense duplicate detection — heuristic match on
* (port + vendor + amount + date ± 3d). PR1 ships the function shape;
* PR8 wires the BullMQ trigger and the merge service.
*/
import { and, between, eq, ne, sql } from 'drizzle-orm';
import { db } from '@/lib/db';
import { expenses } from '@/lib/db/schema/financial';
const DEDUP_WINDOW_DAYS = 3;
export interface DedupCandidate {
/** Existing expense that the new one likely duplicates. */
candidateId: string;
/** 0..1 confidence; 1.0 = exact vendor + amount + same day. */
confidence: number;
}
export async function scanForDuplicates(expenseId: string): Promise<DedupCandidate[]> {
const target = await db.query.expenses.findFirst({ where: eq(expenses.id, expenseId) });
if (!target) return [];
const { portId, establishmentName, amount, expenseDate } = target;
if (!establishmentName || !amount || !expenseDate) return [];
const lo = new Date(expenseDate);
lo.setDate(lo.getDate() - DEDUP_WINDOW_DAYS);
const hi = new Date(expenseDate);
hi.setDate(hi.getDate() + DEDUP_WINDOW_DAYS);
const matches = await db.query.expenses.findMany({
where: and(
eq(expenses.portId, portId),
sql`lower(${expenses.establishmentName}) = lower(${establishmentName})`,
eq(expenses.amount, amount),
between(expenses.expenseDate, lo, hi),
ne(expenses.id, expenseId),
),
limit: 5,
});
return matches.map((m) => ({
candidateId: m.id,
confidence: dayDiff(m.expenseDate, expenseDate) === 0 ? 1.0 : 0.85,
}));
}
function dayDiff(a: Date, b: Date): number {
const ms = Math.abs(a.getTime() - b.getTime());
return Math.round(ms / 86_400_000);
}
/** Mark an expense as a duplicate of the candidate with the highest score. */
export async function markBestDuplicate(expenseId: string): Promise<string | null> {
const candidates = await scanForDuplicates(expenseId);
if (candidates.length === 0) {
await db
.update(expenses)
.set({ dedupScannedAt: sql`now()` })
.where(eq(expenses.id, expenseId));
return null;
}
const best = candidates.reduce((a, b) => (a.confidence >= b.confidence ? a : b));
await db
.update(expenses)
.set({ duplicateOf: best.candidateId, dedupScannedAt: sql`now()` })
.where(eq(expenses.id, expenseId));
return best.candidateId;
}