Files
accounted/app/api/ai/learning/remember/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

128 lines
4.0 KiB
TypeScript

import { NextResponse } from 'next/server'
import { createClient } from '@/lib/supabase/server'
import { ensureInitialized } from '@/lib/init'
import { validateBody } from '@/lib/api/validate'
import { RememberLearningSchema } from '@/lib/api/schemas'
import { requireCompanyId } from '@/lib/company/context'
import { requireWritePermission } from '@/lib/auth/require-write'
import { calculateConfidence } from '@/lib/bookkeeping/counterparty-templates'
import { gateAgentInbox } from '@/lib/ai/feature-flag'
import type { AIProposal } from '@/types'
ensureInitialized()
/**
* POST /api/ai/learning/remember
*
* Called from the UI's learning-prompt dialog after a user edited and
* accepted an AI booking proposal. Upserts a categorization_templates row
* with source='ai_corrected' so next time's proposal for the same
* counterparty starts from the user's preference.
*
* This is the ONLY path that creates an ai_corrected template — the
* "silent learning" rule means every template with this source represents
* an explicit user choice.
*/
export async function POST(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 writeCheck = await requireWritePermission(supabase, user.id)
if (!writeCheck.ok) return writeCheck.response
const companyId = await requireCompanyId(supabase, user.id)
const validation = await validateBody(request, RememberLearningSchema)
if (!validation.success) return validation.response
const {
proposal_id,
counterparty_name,
debit_account,
credit_account,
vat_treatment,
category,
} = validation.data
// Verify the proposal is accepted + belongs to this company.
const { data: proposal } = await supabase
.from('ai_proposals')
.select('*')
.eq('id', proposal_id)
.eq('company_id', companyId)
.maybeSingle()
if (!proposal) return NextResponse.json({ error: 'Not found' }, { status: 404 })
const typed = proposal as AIProposal
if (typed.status !== 'accepted') {
return NextResponse.json(
{ error: 'Endast accepterade förslag kan lagras som mall.' },
{ status: 400 }
)
}
if (typed.step_type !== 'booking') {
return NextResponse.json(
{ error: 'Endast bokföringssteget kan lagras som mall.' },
{ status: 400 }
)
}
// Upsert the template. Existing row for the same (user_id, counterparty_name)
// gets its source bumped up and occurrence incremented.
const { data: existing } = await supabase
.from('categorization_templates')
.select('*')
.eq('user_id', user.id)
.eq('counterparty_name', counterparty_name)
.maybeSingle()
const today = new Date().toISOString().slice(0, 10)
if (existing) {
const newOccurrence = existing.occurrence_count + 1
await supabase
.from('categorization_templates')
.update({
debit_account,
credit_account,
vat_treatment,
category,
source: 'ai_corrected',
occurrence_count: newOccurrence,
confidence: calculateConfidence(newOccurrence),
last_seen_date: today,
is_active: true,
})
.eq('id', existing.id)
return NextResponse.json({ data: { template_id: existing.id, updated: true } })
}
const { data: created, error: insertError } = await supabase
.from('categorization_templates')
.insert({
user_id: user.id,
company_id: companyId,
counterparty_name,
counterparty_aliases: [counterparty_name],
debit_account,
credit_account,
vat_treatment,
category,
source: 'ai_corrected',
occurrence_count: 1,
confidence: calculateConfidence(1),
last_seen_date: today,
is_active: true,
})
.select()
.single()
if (insertError) return NextResponse.json({ error: insertError.message }, { status: 500 })
return NextResponse.json({ data: { template_id: created.id, updated: false } })
}