MattssonandClaude Opus 4.7
1af977950b
* 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>
54 lines
1.7 KiB
TypeScript
54 lines
1.7 KiB
TypeScript
import { NextResponse } from 'next/server'
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import { createClient } from '@/lib/supabase/server'
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import { ensureInitialized } from '@/lib/init'
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import { validateQuery } from '@/lib/api/validate'
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import { ListProposalsQuerySchema } from '@/lib/api/schemas'
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import { requireCompanyId } from '@/lib/company/context'
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import { gateAgentInbox } from '@/lib/ai/feature-flag'
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ensureInitialized()
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/**
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* GET /api/ai/proposals
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*
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* List AI proposals for the active company, newest first.
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* Query params:
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* status?: pending | accepted | rejected | skipped | invalidated
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* step_type?: match | booking
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* limit?: default 20, max 100
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* offset?: default 0
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*
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* Returns { data: AIProposal[], count: number }.
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*/
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export async function GET(request: Request) {
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const gate = gateAgentInbox()
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if (gate) return gate
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const supabase = await createClient()
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const { data: { user } } = await supabase.auth.getUser()
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if (!user) return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })
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const companyId = await requireCompanyId(supabase, user.id)
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const qs = validateQuery(request, ListProposalsQuerySchema)
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if (!qs.success) return qs.response
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const { status, step_type, limit, offset } = qs.data
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let query = supabase
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.from('ai_proposals')
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.select('*', { count: 'exact' })
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.eq('company_id', companyId)
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.order('created_at', { ascending: false })
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.range(offset, offset + limit - 1)
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if (status) query = query.eq('status', status)
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if (step_type) query = query.eq('step_type', step_type)
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const { data, error, count } = await query
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if (error) {
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return NextResponse.json({ error: error.message }, { status: 500 })
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}
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return NextResponse.json({ data, count: count ?? data?.length ?? 0 })
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}
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