The auto-booking cascade end to end (retrieval → selector), minus the write.
- lib/agent/categorize/candidates.ts (Tier 1): assembles the deterministic
candidate slate for a transaction — the learned counterparty template
(strongest, carries its own VAT) plus mapping rules / patterns / per-merchant
history via the same engine gnubok_suggest_categories uses. No model call.
Deduped by account (highest confidence wins), capped; suggestions get the
category's default VAT treatment derived.
- POST /api/agent/categorize: loads the transaction + company VAT context,
runs Tier 1 → Tier 2 selectAccount, returns the proposed account + VAT +
confidence + reasoning + the candidate slate. Never posts anything — the
caller renders an approval card. Gated on configured (any provider incl.
local), same gates as /api/agent/ask.
12 tests: candidate merge/dedupe/VAT-derivation, and the route (401/429/400/
403/404/503 + happy path threading entity type, VAT, underlag, samples).
Co-authored-by: Jakob Wennberg <311770904+jakobwennberg-oss@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
The core of the "optimal" RIP-4 categorizer, built to the researched 2026
architecture (retrieve → SELECT → escalate). Given a transaction, its underlag,
and the deterministic candidate accounts the engine already retrieved, the
model reasons and then CHOOSES from a closed set:
- a retrieved candidate account (the known path), or
- a standard business category → deterministic BAS account (the novel path,
a first-time vendor with no candidate), or
- needs_review (routed to a human, never auto-applied).
Because it picks from a closed enum, the model can't invent an account; the
account + VAT resolution stays deterministic and validated (the model chooses,
code resolves the numbers). It runs on any backend via getAiService()
.generateStructured — Bedrock or a local model.
Founder chose the optimal path (the model selects on every transaction, LLM
calls are fine), so confidence uses self-consistency: N samples (default 3),
majority vote, agreement fraction, combined with the model's stated confidence
and floored by the winning candidate's deterministic confidence — never the
model's verbalized confidence alone (systematically overconfident). reasoning
precedes choice in the schema (reason-before-choice); an unknown/hallucinated
choice degrades to needs_review.
13 unit tests (candidate/category/needs_review resolution, reverse-charge gating,
self-consistency majority + agreement + candidate floor, prompt/schema shape).
Not yet wired: Tier 1 candidate gathering + a route + the ApprovalCard UI.
Co-authored-by: Jakob Wennberg <311770904+jakobwennberg-oss@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
