Files
accounted/app/api/transactions/uncategorized/route.ts
T
MattssonandClaude Opus 4.7 1af977950b Ai/full autonomous flow (#359)
* Refactor bookkeeping error handling and introduce new error classes

- Introduced new error classes for better error categorization:
  - JournalEntryNotBalancedError
  - FiscalPeriodNotFoundError
  - EntryDateOutsideFiscalPeriodError
  - JournalEntryNotFoundError
  - CannotReverseNonPostedError
  - CannotCorrectNonPostedError
  - EntryAlreadyReversedError
  - CurrencyRevaluationAlreadyExistsError
  - InvalidMappingResultError
  - BookkeepingDatabaseError

- Updated existing functions in engine.ts and transaction-entries.ts to throw specific errors instead of generic ones.
- Enhanced error response handling in get-error-message.ts to provide localized messages for new error types.
- Added unit tests for new error classes and error handling functions to ensure correctness and coverage.

* feat(ai): implement AI proposal application and persistence

- Add apply.ts to handle the application of AI proposals, including match and booking steps.
- Introduce persist.ts for inserting and managing AI requests and proposals, ensuring unique constraints.
- Create re-validate.ts for validating proposals before acceptance, checking for stale conditions.
- Define database migrations for ai_requests and ai_proposals tables, including constraints and indexes.
- Enhance journal_entries with AI provenance tracking, linking entries to AI proposals.
- Update categorization_templates to distinguish AI-corrected templates.
- Add company settings for toggling AI flow and managing backfill processes.
- Extend processing_history to include AI-related events for better tracking.

* feat: add uncategorized transactions API and UI for transaction selection

- Implemented a new API endpoint for fetching uncategorized transactions with pagination and filtering options.
- Created ChangeTransactionDialog component for selecting alternative transactions based on AI proposals.
- Developed ReceiptDetailDialog to display detailed information about receipts, including upload functionality.
- Added TransactionDetailDialog for viewing transaction details with links to the transaction list.
- Introduced receipt quality assessment logic to evaluate extracted receipt data.
- Implemented feature flagging for the AI bookkeeping agent to control availability in different environments.

* feat: add manual receipt extraction dialog and integrate AWS Textract for expense analysis

- Added ManualExtractDialog component for user input when AI fails to extract receipt data.
- Implemented ReceiptsList component to manage and display uploaded receipts, including upload and rescan functionalities.
- Introduced Textract integration for analyzing expenses, extracting fields like total, vendor, and date.
- Updated package.json to include @aws-sdk/client-textract dependency.

* fix(ai): handle livsmedel VAT transition (12% → 6%) in booking prompt and re-validate guard

Add date-aware guidance to BOOKING_SYSTEM_PROMPT for the temporary livsmedel
VAT cut (Prop. 2025/26:55, 2026-04-01 to 2027-12-31), with restaurang/servering
carve-out at 12%. Add a re-validate safety net that rejects clearly-stale rate
labels for grocery-chain merchants relative to the entry date.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-27 10:32:15 +02:00

94 lines
3.6 KiB
TypeScript

import { NextResponse } from 'next/server'
import { createClient } from '@/lib/supabase/server'
import { ensureInitialized } from '@/lib/init'
import { requireCompanyId } from '@/lib/company/context'
import { gateAgentInbox } from '@/lib/ai/feature-flag'
ensureInitialized()
/**
* GET /api/transactions/uncategorized
*
* Paginated list of uncategorized expense transactions for a picker UI
* (e.g. agent-inkorg's "Byt transaktion" flow). Returns expenses only —
* amount < 0 — since match proposals always pair receipts to outgoing
* payments. Includes basic range filters so callers can narrow to matches
* within ±window of a target amount/date.
*
* Query params:
* search Free-text against description/merchant_name (ILIKE)
* amount_center Target amount (signed). Must be accompanied by amount_window.
* amount_window Half-window in SEK — e.g. 50 means amount_center ± 50.
* date_center Target ISO date. Must be accompanied by date_window.
* date_window Half-window in days — e.g. 30 means ±30 days.
* limit Max rows (1-50, default 20).
* offset Row offset for pagination (default 0).
*/
export async function GET(request: Request) {
const gate = gateAgentInbox()
if (gate) return gate
const supabase = await createClient()
const { data: { user } } = await supabase.auth.getUser()
if (!user) return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })
const companyId = await requireCompanyId(supabase, user.id)
const url = new URL(request.url)
const search = url.searchParams.get('search')?.trim() ?? ''
const amountCenterRaw = url.searchParams.get('amount_center')
const amountWindowRaw = url.searchParams.get('amount_window')
const dateCenterRaw = url.searchParams.get('date_center')
const dateWindowRaw = url.searchParams.get('date_window')
const limit = Math.min(Math.max(1, Number(url.searchParams.get('limit')) || 20), 50)
const offset = Math.max(0, Number(url.searchParams.get('offset')) || 0)
let query = supabase
.from('transactions')
.select('id, date, description, amount, currency, merchant_name, category, is_business', { count: 'exact' })
.eq('company_id', companyId)
.is('journal_entry_id', null)
.lt('amount', 0)
.order('date', { ascending: false })
.range(offset, offset + limit - 1)
if (search.length > 0) {
const escaped = search.replace(/[%_]/g, '\\$&')
query = query.or(`description.ilike.%${escaped}%,merchant_name.ilike.%${escaped}%`)
}
if (amountCenterRaw && amountWindowRaw) {
const center = Number(amountCenterRaw)
const window = Math.abs(Number(amountWindowRaw))
if (Number.isFinite(center) && Number.isFinite(window) && window > 0) {
query = query.gte('amount', center - window).lte('amount', center + window)
}
}
if (dateCenterRaw && dateWindowRaw) {
const windowDays = Math.abs(Number(dateWindowRaw))
if (Number.isFinite(windowDays) && windowDays > 0) {
const center = new Date(dateCenterRaw)
if (!Number.isNaN(center.getTime())) {
const msPerDay = 86_400_000
const from = new Date(center.getTime() - windowDays * msPerDay)
const to = new Date(center.getTime() + windowDays * msPerDay)
query = query.gte('date', from.toISOString().slice(0, 10))
query = query.lte('date', to.toISOString().slice(0, 10))
}
}
}
const { data, error, count } = await query
if (error) return NextResponse.json({ error: error.message }, { status: 500 })
return NextResponse.json({
data: {
transactions: data ?? [],
count: count ?? 0,
limit,
offset,
},
})
}