jtricerolph
8d688b459d
FastAPI backend (Python 3.11, MSSQL ODBC for SambaPOS, Azure DI OCR),
kitchen_db on central PG. React/TS/Vite frontend with navy sidebar layout.
Backend: auth.py (APP_SLUG=kitchen, SimpleNamespace — archive routes use
.kitchen_id/.is_admin without modification), main.py (51 migrations, scheduler,
internal router for KDS bookings feed), api/internal.py, full archive API
(31 routers: invoices, recipes, menus, sambapos, resos, newbook, disputes,
purchase_orders, etc.), models, migrations, OCR pipeline.
kitchen_id pinned to 1 (B1 — single hotel).
Frontend: AuthGate (app=kitchen, token shim for archive compat — B5b pending),
Layout (navy sidebar, 6 sections, Lucide icons, teal --app-primary),
App.tsx (Outlet pattern, UploadApp outside Layout), index.css (full :root block).
strict: false — archive components have type issues; build clean.
Note: 45 archive components call fetch('/api/...') without /kitchen/ prefix
(B5b). Runtime 404s; deferred until after initial testing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1187 lines
43 KiB
Python
1187 lines
43 KiB
Python
"""
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Xero ↔ Flash Purchases Reconciliation
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Accepts a Xero "Account Transactions" XLSX export, parses it,
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matches rows against Flash invoices for the same period, and
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returns a four-section reconciliation report.
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Matching is rule-based first (exact → near-match), with an
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optional LLM pass for remaining unmatched items.
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"""
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from datetime import date, timedelta
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from decimal import Decimal, InvalidOperation
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from typing import Optional
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import io
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import re
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import logging
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import json
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from fastapi import APIRouter, Depends, HTTPException, UploadFile, File
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from sqlalchemy.orm import selectinload
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from pydantic import BaseModel
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from database import get_db
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from models.user import User
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from models.invoice import Invoice, InvoiceStatus
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from models.supplier import Supplier
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from auth import get_current_user, require_cap
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logger = logging.getLogger(__name__)
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router = APIRouter()
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# ============ Pydantic Response Models ============
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class ReconcileMatchedItem(BaseModel):
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date: str
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supplier: str
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ref: str
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amount: str # formatted £
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flash_id: int | None = None
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match_source: str = "rule" # "rule" or "ai"
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class ReconcileDiscrepancyItem(BaseModel):
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date: str
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supplier: str
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ref: str
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flash_amount: str
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xero_amount: str
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difference: str
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differs: list[str] # which fields differ: "amount", "date", "ref"
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flash_id: int | None = None
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flash_date: str | None = None
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xero_date: str | None = None
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flash_ref: str | None = None
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xero_ref: str | None = None
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flash_supplier: str | None = None
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xero_description: str | None = None
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match_source: str = "rule" # "rule" or "ai"
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amount_insight: str | None = None # explanation for amount discrepancy
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class ReconcileUnmatchedFlash(BaseModel):
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date: str
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supplier: str
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ref: str
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net_stock: str
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flash_id: int
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class ReconcileUnmatchedXero(BaseModel):
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date: str
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description: str
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ref: str
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net: str
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is_expected_external: bool = False
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class ReconcileResponse(BaseModel):
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period_start: str
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period_end: str
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flash_total: str
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xero_total: str
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difference: str
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matched_count: int
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discrepancy_count: int
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flash_only_count: int
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xero_only_count: int
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non_stock_excluded_count: int
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non_stock_excluded_total: str
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matched: list[ReconcileMatchedItem]
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discrepancies: list[ReconcileDiscrepancyItem]
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flash_only: list[ReconcileUnmatchedFlash]
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xero_only: list[ReconcileUnmatchedXero]
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llm_matches_attempted: bool = False
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# ============ Supplier alias map for Xero description → Flash supplier ============
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# Xero descriptions often look like "Supplier Name - food" or "Supplier - food crn"
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# This maps known Xero description variants to the Flash supplier name
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SUPPLIER_ALIASES = {
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"j hall & son (bakers) ltd": "Halls",
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"j hall & son bakers ltd": "Halls",
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"halls": "Halls",
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"r & d walker ltd": "R&D Walker",
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"r&d walker ltd": "R&D Walker",
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"r&d walker": "R&D Walker",
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"lambournes of stow-on-the-wold": "Lambournes",
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"lambournes": "Lambournes",
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"bramleys": "Bramleys",
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"cotswold coffee": "Cotswold Coffee",
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"brakes": "Brakes",
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"direct seafoods": "Direct Seafoods",
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}
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# Xero-only entries that are expected (not from Flash) — collapsible sub-section
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EXPECTED_EXTERNAL_PATTERNS = [
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"tesco",
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"revenue jv",
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"journal",
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"internal",
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"petty",
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]
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# ============ Helpers ============
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def normalise_ref(ref: str | None) -> str:
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"""Normalise a reference for comparison: uppercase, strip whitespace/leading #, collapse spaces."""
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if not ref:
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return ""
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r = ref.strip().upper()
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r = r.lstrip("#")
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r = re.sub(r"\s+", " ", r).strip()
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return r
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def extract_supplier_from_xero_desc(description: str) -> str:
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"""
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Extract supplier name from Xero description.
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Xero format: "Supplier Name - food", "Supplier - food crn", etc.
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"""
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desc = description.strip()
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# Strip trailing " - food", " - food crn", " - beverage", etc.
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desc = re.sub(r"\s*-\s*(food|beverage|cleaning|sundry)(\s+crn)?\s*$", "", desc, flags=re.IGNORECASE)
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return desc.strip()
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def normalise_supplier(name: str) -> str:
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"""Normalise supplier name to lowercase for comparison."""
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return name.strip().lower()
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def resolve_supplier(xero_desc: str) -> str:
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"""Resolve a Xero description to a canonical supplier name."""
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extracted = extract_supplier_from_xero_desc(xero_desc)
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norm = normalise_supplier(extracted)
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if norm in SUPPLIER_ALIASES:
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return SUPPLIER_ALIASES[norm]
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return extracted
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def is_expected_external(xero_desc: str) -> bool:
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"""Check if a Xero entry is expected to be outside Flash."""
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desc_lower = xero_desc.lower()
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return any(pat in desc_lower for pat in EXPECTED_EXTERNAL_PATTERNS)
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def levenshtein(s1: str, s2: str) -> int:
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"""Compute Levenshtein distance between two strings."""
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if len(s1) < len(s2):
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return levenshtein(s2, s1)
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if len(s2) == 0:
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return len(s1)
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prev_row = list(range(len(s2) + 1))
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for i, c1 in enumerate(s1):
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curr_row = [i + 1]
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for j, c2 in enumerate(s2):
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insertions = prev_row[j + 1] + 1
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deletions = curr_row[j] + 1
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substitutions = prev_row[j] + (c1 != c2)
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curr_row.append(min(insertions, deletions, substitutions))
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prev_row = curr_row
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return prev_row[-1]
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def alphanumeric_only(s: str) -> str:
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"""Strip non-alphanumeric characters for fuzzy ref comparison."""
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return re.sub(r"[^A-Z0-9]", "", s.upper())
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def amounts_match(a: Decimal, b: Decimal, tolerance: Decimal = Decimal("0.01")) -> bool:
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return abs(a - b) <= tolerance
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def fmt_money(d: Decimal) -> str:
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"""Format a decimal as £X,XXX.XX"""
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return f"£{d:,.2f}"
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def parse_xero_date(val) -> date | None:
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"""Parse a date from an openpyxl cell value."""
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from datetime import datetime
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if isinstance(val, datetime):
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return val.date()
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if isinstance(val, date):
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return val
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if isinstance(val, str):
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val = val.strip()
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for fmt in ("%d %b %Y", "%d/%m/%Y", "%Y-%m-%d", "%d %B %Y"):
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try:
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return datetime.strptime(val, fmt).date()
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except ValueError:
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continue
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return None
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def parse_xero_amount(val) -> Decimal:
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"""Parse a numeric cell to Decimal, returning 0 for None/empty."""
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if val is None:
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return Decimal("0")
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if isinstance(val, (int, float)):
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return Decimal(str(val)).quantize(Decimal("0.01"))
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if isinstance(val, str):
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val = val.strip().replace(",", "").replace("£", "")
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if not val or val == "-":
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return Decimal("0")
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try:
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return Decimal(val).quantize(Decimal("0.01"))
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except InvalidOperation:
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return Decimal("0")
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return Decimal("0")
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# ============ XLSX Parsing ============
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def parse_xero_xlsx(file_bytes: bytes) -> tuple[date, date, list[dict]]:
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"""
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Parse a Xero Account Transactions XLSX export.
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Returns (period_start, period_end, rows) where each row is:
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{
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"date": date,
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"description": str,
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"ref": str,
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"debit": Decimal,
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"credit": Decimal,
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"net": Decimal, # debit - credit
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}
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"""
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from openpyxl import load_workbook
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wb = load_workbook(filename=io.BytesIO(file_bytes), read_only=True, data_only=True)
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ws = wb.active
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# Extract period from header rows (typically row 3)
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period_start = None
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period_end = None
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for row_idx in range(1, 6):
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cell_val = ws.cell(row=row_idx, column=1).value
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if cell_val and isinstance(cell_val, str) and "period" in cell_val.lower():
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# "For the period 1 March 2026 to 31 March 2026"
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match = re.search(
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r"(\d{1,2}\s+\w+\s+\d{4})\s+to\s+(\d{1,2}\s+\w+\s+\d{4})",
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cell_val, re.IGNORECASE
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)
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if match:
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from datetime import datetime
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for fmt in ("%d %B %Y", "%d %b %Y"):
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try:
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period_start = datetime.strptime(match.group(1), fmt).date()
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period_end = datetime.strptime(match.group(2), fmt).date()
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break
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except ValueError:
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continue
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break
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if not period_start or not period_end:
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raise ValueError("Could not extract report period from XLSX header rows. Expected 'For the period DD Month YYYY to DD Month YYYY' in rows 1-5.")
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# Find header row (look for "Date" in column A)
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header_row = None
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for row_idx in range(1, 10):
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cell_val = ws.cell(row=row_idx, column=1).value
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if cell_val and isinstance(cell_val, str) and cell_val.strip().lower() == "date":
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header_row = row_idx
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break
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if not header_row:
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raise ValueError("Could not find column header row (expected 'Date' in column A within first 10 rows).")
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# Map column headers
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col_map = {}
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for col_idx in range(1, 20):
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val = ws.cell(row=header_row, column=col_idx).value
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if val and isinstance(val, str):
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col_map[val.strip().lower()] = col_idx
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required = ["date", "description", "debit", "credit"]
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for req in required:
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if req not in col_map:
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raise ValueError(f"Missing required column '{req}' in header row {header_row}. Found: {list(col_map.keys())}")
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ref_col = col_map.get("reference", col_map.get("ref"))
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source_col = col_map.get("source")
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# Parse data rows
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rows = []
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for row_idx in range(header_row + 1, ws.max_row + 1):
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date_val = ws.cell(row=row_idx, column=col_map["date"]).value
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parsed_date = parse_xero_date(date_val)
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if not parsed_date:
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continue # Skip non-data rows (section headers, totals, blanks)
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description = ws.cell(row=row_idx, column=col_map["description"]).value or ""
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ref = ""
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if ref_col:
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ref = ws.cell(row=row_idx, column=ref_col).value or ""
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ref = str(ref).strip()
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source = ""
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if source_col:
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source = str(ws.cell(row=row_idx, column=source_col).value or "").strip()
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debit = parse_xero_amount(ws.cell(row=row_idx, column=col_map["debit"]).value)
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credit = parse_xero_amount(ws.cell(row=row_idx, column=col_map["credit"]).value)
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net = debit - credit # Credits (credit notes) become negative
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is_credit_note = "credit note" in source.lower() or credit > 0
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rows.append({
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"date": parsed_date,
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"description": str(description).strip(),
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"ref": ref,
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"source": source,
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"debit": debit,
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"credit": credit,
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"net": net,
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"is_credit_note": is_credit_note,
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})
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wb.close()
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return period_start, period_end, rows
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def group_xero_by_ref(rows: list[dict]) -> list[dict]:
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"""
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Group Xero rows by reference (Xero sometimes splits an invoice across
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stock/non-stock lines with the same ref). Sum the nets.
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Rows with empty refs are NOT grouped — each stays separate.
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"""
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grouped = {}
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no_ref = []
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for row in rows:
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norm = normalise_ref(row["ref"])
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if not norm:
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no_ref.append({
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"date": row["date"],
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"description": row["description"],
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"ref": row["ref"],
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"net": row["net"],
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"is_credit_note": row.get("is_credit_note", False),
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"raw_rows": [row],
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})
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else:
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if norm not in grouped:
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grouped[norm] = {
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"date": row["date"],
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"description": row["description"],
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"ref": row["ref"], # keep original formatting from first row
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"net": Decimal("0"),
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"is_credit_note": row.get("is_credit_note", False),
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"raw_rows": [],
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}
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grouped[norm]["net"] += row["net"]
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grouped[norm]["raw_rows"].append(row)
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# If any row is a credit note, mark the group
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if row.get("is_credit_note"):
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grouped[norm]["is_credit_note"] = True
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return list(grouped.values()) + no_ref
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# ============ Flash Invoice Querying ============
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async def get_flash_invoices(
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db: AsyncSession,
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kitchen_id: int,
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period_start: date,
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period_end: date,
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) -> tuple[list[dict], int, Decimal]:
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"""
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Get Flash invoices for the period. Returns:
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- stock_invoices: list of dicts for matching
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- non_stock_count: count of excluded non-stock-only invoices
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- non_stock_total: total of excluded non-stock amounts
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Each stock invoice dict has:
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{
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"id": int,
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"date": date,
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"supplier_name": str,
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"ref": str (invoice_number),
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"net_stock": Decimal,
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"document_type": str,
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}
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"""
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from models.line_item import LineItem
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result = await db.execute(
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select(Invoice)
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.where(
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Invoice.kitchen_id == kitchen_id,
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Invoice.status == InvoiceStatus.CONFIRMED,
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)
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.options(selectinload(Invoice.line_items))
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.order_by(Invoice.invoice_date.desc().nullslast())
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)
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all_invoices = result.scalars().all()
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# Get supplier map
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supplier_result = await db.execute(
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select(Supplier).where(Supplier.kitchen_id == kitchen_id)
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)
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suppliers_map = {s.id: s for s in supplier_result.scalars().all()}
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stock_invoices = []
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non_stock_count = 0
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non_stock_total = Decimal("0")
|
|
|
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for inv in all_invoices:
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inv_date = inv.invoice_date or inv.created_at.date()
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if not (period_start <= inv_date <= period_end):
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continue
|
|
|
|
# Calculate net_stock (stock items only)
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net_stock = Decimal("0")
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net_non_stock = Decimal("0")
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if inv.line_items:
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for item in inv.line_items:
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item_net = item.amount or Decimal("0")
|
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if item.is_non_stock:
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net_non_stock += item_net
|
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else:
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net_stock += item_net
|
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|
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# Credit notes: negate if positive
|
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if inv.document_type == "credit_note":
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if net_stock > 0:
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net_stock = -net_stock
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if net_non_stock > 0:
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net_non_stock = -net_non_stock
|
|
|
|
# Get supplier name
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supplier_name = ""
|
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if inv.supplier_id and inv.supplier_id in suppliers_map:
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supplier_name = suppliers_map[inv.supplier_id].name
|
|
elif inv.vendor_name:
|
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supplier_name = inv.vendor_name
|
|
else:
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supplier_name = "Unknown"
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|
|
|
if net_stock == 0 and net_non_stock != 0:
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|
# Entirely non-stock — exclude from match pool
|
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non_stock_count += 1
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non_stock_total += net_non_stock
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|
continue
|
|
|
|
if net_stock == 0 and net_non_stock == 0:
|
|
# Zero-value invoice — nothing to reconcile
|
|
continue
|
|
|
|
# Build line item summaries for insight generation
|
|
line_summaries = []
|
|
if inv.line_items:
|
|
for item in inv.line_items:
|
|
line_summaries.append({
|
|
"description": item.description or "",
|
|
"amount": str(item.amount or 0),
|
|
"is_non_stock": item.is_non_stock,
|
|
})
|
|
|
|
stock_invoices.append({
|
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"id": inv.id,
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|
"date": inv_date,
|
|
"supplier_name": supplier_name,
|
|
"ref": inv.invoice_number or "",
|
|
"net_stock": net_stock.quantize(Decimal("0.01")),
|
|
"net_non_stock": net_non_stock.quantize(Decimal("0.01")),
|
|
"document_type": inv.document_type or "invoice",
|
|
"line_items": line_summaries,
|
|
})
|
|
|
|
# Track non-stock portion of mixed invoices separately
|
|
if net_non_stock != 0:
|
|
non_stock_count += 1
|
|
non_stock_total += net_non_stock
|
|
|
|
return stock_invoices, non_stock_count, non_stock_total.quantize(Decimal("0.01"))
|
|
|
|
|
|
# ============ Build supplier alias map from DB ============
|
|
|
|
async def build_supplier_alias_map(db: AsyncSession, kitchen_id: int) -> dict[str, str]:
|
|
"""
|
|
Build a normalised-name → canonical-name map from the Supplier table aliases.
|
|
Merges with the hardcoded SUPPLIER_ALIASES.
|
|
"""
|
|
alias_map = dict(SUPPLIER_ALIASES) # start with hardcoded
|
|
|
|
result = await db.execute(
|
|
select(Supplier).where(Supplier.kitchen_id == kitchen_id)
|
|
)
|
|
suppliers = result.scalars().all()
|
|
|
|
for s in suppliers:
|
|
# Map canonical name
|
|
alias_map[normalise_supplier(s.name)] = s.name
|
|
# Map aliases
|
|
if s.aliases:
|
|
for alias in s.aliases:
|
|
alias_map[normalise_supplier(alias)] = s.name
|
|
|
|
return alias_map
|
|
|
|
|
|
def resolve_supplier_with_map(xero_desc: str, alias_map: dict[str, str]) -> str:
|
|
"""Resolve a Xero description to a canonical supplier name using dynamic alias map."""
|
|
extracted = extract_supplier_from_xero_desc(xero_desc)
|
|
norm = normalise_supplier(extracted)
|
|
if norm in alias_map:
|
|
return alias_map[norm]
|
|
# Try partial match — check if any alias key is contained in the extracted name
|
|
for alias_key, canonical in alias_map.items():
|
|
if alias_key in norm or norm in alias_key:
|
|
return canonical
|
|
return extracted
|
|
|
|
|
|
def suppliers_match(flash_supplier: str, xero_supplier: str) -> bool:
|
|
"""Check if a Flash supplier and resolved Xero supplier match."""
|
|
return normalise_supplier(flash_supplier) == normalise_supplier(xero_supplier)
|
|
|
|
|
|
# ============ Matching Engine ============
|
|
|
|
def run_matching(
|
|
flash_items: list[dict],
|
|
xero_items: list[dict],
|
|
alias_map: dict[str, str],
|
|
) -> tuple[list, list, list[dict], list[dict]]:
|
|
"""
|
|
Three-pass matching:
|
|
1. Exact: same normalised ref + same date + amount within £0.01
|
|
2. Near-match: one field differs (flagged as discrepancy)
|
|
3. Leftovers: unmatched on each side
|
|
|
|
Returns (matched, discrepancies, flash_unmatched, xero_unmatched)
|
|
"""
|
|
matched = []
|
|
discrepancies = []
|
|
|
|
flash_used = set()
|
|
xero_used = set()
|
|
|
|
# Resolve Xero suppliers up front
|
|
for xi, xero in enumerate(xero_items):
|
|
xero["_resolved_supplier"] = resolve_supplier_with_map(xero["description"], alias_map)
|
|
xero["_norm_ref"] = normalise_ref(xero["ref"])
|
|
xero["_idx"] = xi
|
|
|
|
for fi, flash in enumerate(flash_items):
|
|
flash["_norm_ref"] = normalise_ref(flash["ref"])
|
|
flash["_idx"] = fi
|
|
|
|
# ---- Pass 1: Exact match ----
|
|
for fi, flash in enumerate(flash_items):
|
|
if fi in flash_used:
|
|
continue
|
|
if not flash["_norm_ref"]:
|
|
continue # Can't exact-match without a ref
|
|
for xi, xero in enumerate(xero_items):
|
|
if xi in xero_used:
|
|
continue
|
|
if flash["_norm_ref"] == xero["_norm_ref"] and \
|
|
flash["date"] == xero["date"] and \
|
|
amounts_match(flash["net_stock"], xero["net"]):
|
|
matched.append({
|
|
"date": flash["date"].isoformat(),
|
|
"supplier": flash["supplier_name"],
|
|
"ref": flash["ref"] or xero["ref"],
|
|
"amount": fmt_money(flash["net_stock"]),
|
|
"flash_id": flash["id"],
|
|
})
|
|
flash_used.add(fi)
|
|
xero_used.add(xi)
|
|
break
|
|
|
|
# ---- Pass 2: Near-match (discrepancies) ----
|
|
for fi, flash in enumerate(flash_items):
|
|
if fi in flash_used:
|
|
continue
|
|
best_match = None
|
|
best_score = 0 # higher = more confident
|
|
|
|
for xi, xero in enumerate(xero_items):
|
|
if xi in xero_used:
|
|
continue
|
|
|
|
differs = []
|
|
score = 0
|
|
|
|
f_ref = flash["_norm_ref"]
|
|
x_ref = xero["_norm_ref"]
|
|
f_supplier = flash["supplier_name"]
|
|
x_supplier = xero["_resolved_supplier"]
|
|
same_supplier = suppliers_match(f_supplier, x_supplier)
|
|
|
|
# 2a: Same ref, same date, amount differs
|
|
if f_ref and x_ref and f_ref == x_ref and flash["date"] == xero["date"]:
|
|
if not amounts_match(flash["net_stock"], xero["net"]):
|
|
differs.append("amount")
|
|
score = 10
|
|
|
|
# 2b: Same ref, amount matches, date differs ≤ 3 days
|
|
elif f_ref and x_ref and f_ref == x_ref and \
|
|
amounts_match(flash["net_stock"], xero["net"]):
|
|
date_diff = abs((flash["date"] - xero["date"]).days)
|
|
if date_diff <= 3:
|
|
differs.append("date")
|
|
score = 9
|
|
|
|
# 2c: Same supplier, amount matches (within £0.01), date within 3 days, refs differ
|
|
# This catches cases where refs are completely different
|
|
# (e.g. Cotswold Coffee 126621 vs 145323, R&D Walker Nº4 vs 106891)
|
|
elif same_supplier and amounts_match(flash["net_stock"], xero["net"], Decimal("0.01")):
|
|
date_diff = abs((flash["date"] - xero["date"]).days)
|
|
if date_diff == 0:
|
|
differs.append("ref")
|
|
score = 8
|
|
elif date_diff <= 3:
|
|
differs.extend(["ref", "date"])
|
|
score = 7
|
|
|
|
# 2d: Same supplier, same ref, both amount and date differ slightly
|
|
elif f_ref and x_ref and f_ref == x_ref and same_supplier:
|
|
date_diff = abs((flash["date"] - xero["date"]).days)
|
|
amt_diff = abs(flash["net_stock"] - xero["net"])
|
|
if date_diff <= 3 and amt_diff <= Decimal("5.00"):
|
|
differs.extend(["amount", "date"])
|
|
score = 5
|
|
|
|
# 2e: Credit note matching — same supplier, same amount,
|
|
# but refs differ completely (Xero uses CN prefix, Flash uses supplier ref)
|
|
elif same_supplier and amounts_match(flash["net_stock"], xero["net"]) and \
|
|
(flash["document_type"] == "credit_note" or xero.get("is_credit_note", False)):
|
|
date_diff = abs((flash["date"] - xero["date"]).days)
|
|
if date_diff <= 5:
|
|
differs.append("ref")
|
|
if date_diff > 0:
|
|
differs.append("date")
|
|
score = 8 # High confidence — credit notes with matching supplier+amount
|
|
|
|
# 2f: Same supplier, date within 3 days, amounts close but not exact
|
|
elif same_supplier:
|
|
date_diff = abs((flash["date"] - xero["date"]).days)
|
|
amt_diff = abs(flash["net_stock"] - xero["net"])
|
|
if date_diff <= 3 and amt_diff <= Decimal("5.00") and amt_diff > Decimal("0.01"):
|
|
differs_list = ["amount"]
|
|
if date_diff > 0:
|
|
differs_list.append("date")
|
|
if f_ref != x_ref:
|
|
differs_list.append("ref")
|
|
differs.extend(differs_list)
|
|
score = 4
|
|
|
|
if score > best_score:
|
|
best_score = score
|
|
best_match = (xi, xero, differs)
|
|
|
|
if best_match:
|
|
xi, xero, differs = best_match
|
|
f_net = flash["net_stock"]
|
|
x_net = xero["net"]
|
|
diff_val = f_net - x_net
|
|
amt_diff_abs = abs(float(diff_val))
|
|
|
|
# Safety check: reject any near-match where amount differs by more than £5
|
|
# AND refs are different — these are almost certainly different invoices
|
|
f_ref_norm = normalise_ref(flash["ref"])
|
|
x_ref_norm = normalise_ref(xero["ref"])
|
|
refs_differ = f_ref_norm != x_ref_norm
|
|
|
|
if refs_differ and amt_diff_abs > 5.0:
|
|
logger.warning(
|
|
f"REJECTED false discrepancy: Flash {flash['supplier_name']} "
|
|
f"{flash['ref']}={f_net} vs Xero {xero['ref']}={x_net} "
|
|
f"diff={amt_diff_abs:.2f} score={best_score}"
|
|
)
|
|
continue # Skip — leave both as unmatched
|
|
|
|
# Add "amount" to differs if amounts don't actually match
|
|
if amt_diff_abs > 0.01 and "amount" not in differs:
|
|
differs.append("amount")
|
|
|
|
logger.info(
|
|
f"Discrepancy: Flash {flash['supplier_name']} {flash['ref']}={f_net} "
|
|
f"vs Xero {xero['ref']}={x_net} differs={differs} score={best_score}"
|
|
)
|
|
|
|
discrepancies.append({
|
|
"date": flash["date"].isoformat(),
|
|
"supplier": flash["supplier_name"],
|
|
"ref": flash["ref"] or xero["ref"],
|
|
"flash_amount": fmt_money(f_net),
|
|
"xero_amount": fmt_money(x_net),
|
|
"difference": fmt_money(diff_val),
|
|
"differs": differs,
|
|
"flash_id": flash["id"],
|
|
"flash_date": flash["date"].isoformat(),
|
|
"xero_date": xero["date"].isoformat(),
|
|
"flash_ref": flash["ref"],
|
|
"xero_ref": xero["ref"],
|
|
"flash_supplier": flash["supplier_name"],
|
|
"xero_description": xero["description"],
|
|
})
|
|
flash_used.add(fi)
|
|
xero_used.add(xi)
|
|
|
|
# ---- Leftovers ----
|
|
flash_unmatched = [f for fi, f in enumerate(flash_items) if fi not in flash_used]
|
|
xero_unmatched = [x for xi, x in enumerate(xero_items) if xi not in xero_used]
|
|
|
|
return matched, discrepancies, flash_unmatched, xero_unmatched
|
|
|
|
|
|
# ============ Amount Discrepancy Insights ============
|
|
|
|
def check_non_stock_explains_diff(flash: dict, diff_val: Decimal) -> str | None:
|
|
"""
|
|
Check if the amount discrepancy is explained by non-stock items.
|
|
If Xero includes the full invoice (stock + non-stock) but Flash only shows stock,
|
|
the difference should equal the non-stock total.
|
|
"""
|
|
net_non_stock = flash.get("net_non_stock", Decimal("0"))
|
|
if net_non_stock == 0:
|
|
return None
|
|
|
|
# diff_val = flash_stock - xero_net (negative when Xero is higher)
|
|
# If Xero has full invoice, diff = -net_non_stock
|
|
if amounts_match(abs(diff_val), abs(net_non_stock), Decimal("0.02")):
|
|
non_stock_items = [
|
|
li for li in flash.get("line_items", []) if li.get("is_non_stock")
|
|
]
|
|
item_names = ", ".join(
|
|
li["description"][:40] for li in non_stock_items if li.get("description")
|
|
)
|
|
return (
|
|
f"Non-stock items account for the difference "
|
|
f"({fmt_money(abs(net_non_stock))}). "
|
|
f"Xero likely includes full invoice total. "
|
|
f"Non-stock: {item_names}" if item_names else
|
|
f"Non-stock items account for the difference "
|
|
f"({fmt_money(abs(net_non_stock))}). "
|
|
f"Xero likely includes full invoice total."
|
|
)
|
|
|
|
# Check if non-stock is a partial explanation (diff is larger but non-stock is a chunk)
|
|
if abs(net_non_stock) > Decimal("1.00") and abs(diff_val) > abs(net_non_stock):
|
|
remainder = abs(diff_val) - abs(net_non_stock)
|
|
return (
|
|
f"Non-stock items total {fmt_money(abs(net_non_stock))} "
|
|
f"but difference is {fmt_money(abs(diff_val))} — "
|
|
f"non-stock explains part, {fmt_money(remainder)} remains unexplained."
|
|
)
|
|
|
|
return None
|
|
|
|
|
|
def check_line_item_combinations(flash: dict, diff_val: Decimal) -> str | None:
|
|
"""
|
|
Check if any single line item or small combination matches the difference.
|
|
This catches cases where a specific item was excluded/included differently.
|
|
"""
|
|
line_items = flash.get("line_items", [])
|
|
if not line_items:
|
|
return None
|
|
|
|
target = abs(diff_val)
|
|
|
|
# Check single items
|
|
for li in line_items:
|
|
amt = abs(Decimal(li["amount"]))
|
|
if amounts_match(amt, target, Decimal("0.02")) and amt > Decimal("0.50"):
|
|
desc = li["description"][:50] if li["description"] else "unnamed item"
|
|
ns = " (non-stock)" if li.get("is_non_stock") else ""
|
|
return f'Single line item matches difference: "{desc}"{ns} = {fmt_money(amt)}'
|
|
|
|
return None
|
|
|
|
|
|
async def generate_llm_insight(
|
|
db: AsyncSession,
|
|
kitchen_id: int,
|
|
flash: dict,
|
|
xero_net: Decimal,
|
|
diff_val: Decimal,
|
|
) -> str | None:
|
|
"""Use LLM to analyse line items and suggest cause of amount variance."""
|
|
from services.llm_service import call_llm
|
|
|
|
line_items = flash.get("line_items", [])
|
|
if not line_items:
|
|
return None
|
|
|
|
# Build concise line item list
|
|
li_summary = []
|
|
for li in line_items:
|
|
li_summary.append({
|
|
"description": li["description"][:60] if li["description"] else "—",
|
|
"amount": li["amount"],
|
|
"non_stock": li["is_non_stock"],
|
|
})
|
|
|
|
system_msg = (
|
|
"You are a kitchen accounts assistant. Flash is a food stock/GP system that "
|
|
"tracks invoices — it separates stock (food) items from non-stock items "
|
|
"(chemicals, packaging, equipment, etc). Xero is the accounting system that "
|
|
"records the full invoice total posted to the food purchases account. "
|
|
"An amount discrepancy means Flash stock total differs from the Xero net. "
|
|
"Common causes: non-stock items not split out in Xero, line items missing, "
|
|
"rounding, or Xero posting error."
|
|
)
|
|
|
|
user_msg = (
|
|
f"Invoice: {flash['supplier_name']} ref {flash['ref']} dated {flash['date']}\n"
|
|
f"Flash stock total: {fmt_money(flash['net_stock'])}\n"
|
|
f"Xero net: {fmt_money(xero_net)}\n"
|
|
f"Difference: {fmt_money(diff_val)} (Flash - Xero)\n\n"
|
|
f"Flash line items:\n{json.dumps(li_summary, indent=2)}\n\n"
|
|
f"Can you identify which line items or combination likely accounts for "
|
|
f"the {fmt_money(abs(diff_val))} difference? "
|
|
f"Reply in ONE short sentence (max 120 chars). "
|
|
f"If unclear, say 'Unable to determine cause'."
|
|
)
|
|
|
|
result = await call_llm(
|
|
db=db,
|
|
kitchen_id=kitchen_id,
|
|
feature="reconciliation_insight",
|
|
messages=[{"role": "user", "content": user_msg}],
|
|
system_message=system_msg,
|
|
)
|
|
|
|
if result["status"] not in ("success", "cached"):
|
|
return None
|
|
|
|
text = result.get("result", "")
|
|
if isinstance(text, dict):
|
|
text = str(text)
|
|
text = text.strip().strip('"').strip("'")
|
|
if text and len(text) < 200:
|
|
return text
|
|
return text[:200] + "..." if text else None
|
|
|
|
|
|
async def generate_amount_insights(
|
|
db: AsyncSession,
|
|
kitchen_id: int,
|
|
discrepancies: list[dict],
|
|
flash_lookup: dict[int, dict],
|
|
) -> list[dict]:
|
|
"""
|
|
For each amount discrepancy, try to explain the variance:
|
|
1. Code check: does non-stock total match the difference?
|
|
2. Code check: does a single line item match the difference?
|
|
3. LLM fallback: send line items for analysis
|
|
"""
|
|
for disc in discrepancies:
|
|
if "amount" not in disc.get("differs", []):
|
|
continue
|
|
|
|
flash_id = disc.get("flash_id")
|
|
if not flash_id or flash_id not in flash_lookup:
|
|
continue
|
|
|
|
flash = flash_lookup[flash_id]
|
|
|
|
# Parse the difference back to Decimal
|
|
diff_str = disc["difference"].replace("£", "").replace(",", "")
|
|
try:
|
|
diff_val = Decimal(diff_str)
|
|
except InvalidOperation:
|
|
continue
|
|
|
|
# 1. Non-stock check
|
|
insight = check_non_stock_explains_diff(flash, diff_val)
|
|
if insight:
|
|
disc["amount_insight"] = insight
|
|
continue
|
|
|
|
# 2. Single line item check
|
|
insight = check_line_item_combinations(flash, diff_val)
|
|
if insight:
|
|
disc["amount_insight"] = insight
|
|
continue
|
|
|
|
# 3. LLM fallback
|
|
xero_str = disc["xero_amount"].replace("£", "").replace(",", "")
|
|
try:
|
|
xero_net = Decimal(xero_str)
|
|
except InvalidOperation:
|
|
continue
|
|
|
|
insight = await generate_llm_insight(db, kitchen_id, flash, xero_net, diff_val)
|
|
if insight:
|
|
disc["amount_insight"] = f"🤖 {insight}"
|
|
|
|
return discrepancies
|
|
|
|
|
|
# ============ LLM Fallback Matching ============
|
|
|
|
async def llm_match_remaining(
|
|
db: AsyncSession,
|
|
kitchen_id: int,
|
|
flash_unmatched: list[dict],
|
|
xero_unmatched: list[dict],
|
|
) -> tuple[list, list, list[dict], list[dict]]:
|
|
"""
|
|
Use LLM to attempt matching remaining unmatched items.
|
|
Returns (new_discrepancies, new_exact, remaining_flash, remaining_xero)
|
|
"""
|
|
from services.llm_service import call_llm
|
|
|
|
if not flash_unmatched or not xero_unmatched:
|
|
return [], [], flash_unmatched, xero_unmatched
|
|
|
|
# Build concise representations
|
|
flash_summary = []
|
|
for i, f in enumerate(flash_unmatched):
|
|
flash_summary.append({
|
|
"idx": i,
|
|
"date": f["date"].isoformat(),
|
|
"supplier": f["supplier_name"],
|
|
"ref": f["ref"],
|
|
"amount": str(f["net_stock"]),
|
|
})
|
|
|
|
xero_summary = []
|
|
for i, x in enumerate(xero_unmatched):
|
|
xero_summary.append({
|
|
"idx": i,
|
|
"date": x["date"].isoformat(),
|
|
"description": x["description"],
|
|
"ref": x["ref"],
|
|
"amount": str(x["net"]),
|
|
})
|
|
|
|
system_msg = """You are a bookkeeping reconciliation assistant. You are given two lists of unmatched invoice entries — one from Flash (the kitchen invoice system) and one from Xero (the accounting system).
|
|
|
|
Your job is to identify probable matches between the two lists. These are entries that likely represent the same real-world invoice but have data discrepancies (different reference numbers, slightly different amounts, date offsets, supplier name variants, etc).
|
|
|
|
For each probable match, explain which fields differ and why you think they are the same invoice.
|
|
|
|
IMPORTANT: Only suggest matches you are reasonably confident about. It is better to leave items unmatched than to create false matches. Consider supplier names, dates, amounts, and reference numbers holistically."""
|
|
|
|
user_msg = f"""Here are the unmatched Flash invoices:
|
|
{json.dumps(flash_summary, indent=2)}
|
|
|
|
Here are the unmatched Xero entries:
|
|
{json.dumps(xero_summary, indent=2)}
|
|
|
|
Return a JSON array of matches. Each match should be:
|
|
{{
|
|
"flash_idx": <index in flash list>,
|
|
"xero_idx": <index in xero list>,
|
|
"confidence": "high" or "medium",
|
|
"differs": ["field1", "field2"],
|
|
"reasoning": "brief explanation"
|
|
}}
|
|
|
|
Only include matches with high or medium confidence. Return an empty array [] if no good matches found."""
|
|
|
|
result = await call_llm(
|
|
db=db,
|
|
kitchen_id=kitchen_id,
|
|
feature="reconciliation_matching",
|
|
messages=[{"role": "user", "content": user_msg}],
|
|
system_message=system_msg,
|
|
)
|
|
|
|
if result["status"] not in ("success", "cached"):
|
|
logger.info(f"LLM reconciliation matching unavailable: {result['status']}")
|
|
return [], [], flash_unmatched, xero_unmatched
|
|
|
|
# Parse LLM response
|
|
new_discrepancies = []
|
|
llm_text = result.get("result", "")
|
|
if isinstance(llm_text, dict):
|
|
llm_text = json.dumps(llm_text)
|
|
if not llm_text:
|
|
return [], [], flash_unmatched, xero_unmatched
|
|
|
|
try:
|
|
# Extract JSON from response (may be wrapped in markdown code block)
|
|
json_match = re.search(r"\[.*\]", str(llm_text), re.DOTALL)
|
|
if not json_match:
|
|
return [], [], flash_unmatched, xero_unmatched
|
|
matches = json.loads(json_match.group())
|
|
except (json.JSONDecodeError, AttributeError):
|
|
logger.warning("Failed to parse LLM reconciliation response")
|
|
return [], [], flash_unmatched, xero_unmatched
|
|
|
|
flash_matched = set()
|
|
xero_matched = set()
|
|
|
|
for m in matches:
|
|
fi = m.get("flash_idx")
|
|
xi = m.get("xero_idx")
|
|
confidence = m.get("confidence", "medium")
|
|
|
|
if fi is None or xi is None:
|
|
continue
|
|
if fi >= len(flash_unmatched) or xi >= len(xero_unmatched):
|
|
continue
|
|
if fi in flash_matched or xi in xero_matched:
|
|
continue
|
|
|
|
flash = flash_unmatched[fi]
|
|
xero = xero_unmatched[xi]
|
|
|
|
# Safety check: reject LLM matches where refs differ AND amount diff > £5
|
|
f_ref_norm = normalise_ref(flash["ref"])
|
|
x_ref_norm = normalise_ref(xero["ref"])
|
|
amt_diff_abs = abs(float(flash["net_stock"] - xero["net"]))
|
|
if f_ref_norm != x_ref_norm and amt_diff_abs > 5.0:
|
|
logger.warning(
|
|
f"REJECTED LLM false match: Flash {flash['supplier_name']} "
|
|
f"{flash['ref']}={flash['net_stock']} vs Xero {xero['ref']}={xero['net']} "
|
|
f"diff={amt_diff_abs:.2f}"
|
|
)
|
|
continue
|
|
|
|
differs = m.get("differs", [])
|
|
if not differs:
|
|
differs = ["unknown"]
|
|
|
|
diff_val = flash["net_stock"] - xero["net"]
|
|
new_discrepancies.append({
|
|
"date": flash["date"].isoformat(),
|
|
"supplier": flash["supplier_name"],
|
|
"ref": flash["ref"] or xero["ref"],
|
|
"flash_amount": fmt_money(flash["net_stock"]),
|
|
"xero_amount": fmt_money(xero["net"]),
|
|
"difference": fmt_money(diff_val),
|
|
"differs": differs,
|
|
"flash_id": flash["id"],
|
|
"flash_date": flash["date"].isoformat(),
|
|
"xero_date": xero["date"].isoformat(),
|
|
"flash_ref": flash["ref"],
|
|
"xero_ref": xero["ref"],
|
|
"match_source": "ai",
|
|
})
|
|
flash_matched.add(fi)
|
|
xero_matched.add(xi)
|
|
|
|
remaining_flash = [f for i, f in enumerate(flash_unmatched) if i not in flash_matched]
|
|
remaining_xero = [x for i, x in enumerate(xero_unmatched) if i not in xero_matched]
|
|
|
|
return new_discrepancies, [], remaining_flash, remaining_xero
|
|
|
|
|
|
# ============ Main Endpoint ============
|
|
|
|
@router.post("/purchases/reconcile", response_model=ReconcileResponse)
|
|
async def reconcile_purchases(
|
|
file: UploadFile = File(...),
|
|
current_user: User = Depends(get_current_user),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
"""
|
|
Upload a Xero Account Transactions XLSX and reconcile against Flash invoices.
|
|
"""
|
|
# Validate file type
|
|
if not file.filename or not file.filename.lower().endswith(".xlsx"):
|
|
raise HTTPException(status_code=400, detail="Please upload an XLSX file")
|
|
|
|
# Read file into memory (no persistence)
|
|
file_bytes = await file.read()
|
|
if len(file_bytes) > 10 * 1024 * 1024: # 10MB limit
|
|
raise HTTPException(status_code=400, detail="File too large (max 10MB)")
|
|
|
|
# Parse XLSX
|
|
try:
|
|
period_start, period_end, xero_rows = parse_xero_xlsx(file_bytes)
|
|
except ValueError as e:
|
|
raise HTTPException(status_code=400, detail=str(e))
|
|
except Exception as e:
|
|
logger.error(f"XLSX parsing failed: {e}")
|
|
raise HTTPException(status_code=400, detail=f"Failed to parse XLSX: {str(e)}")
|
|
|
|
# Group Xero rows by reference
|
|
xero_items = group_xero_by_ref(xero_rows)
|
|
|
|
# Get Flash invoices for the period
|
|
flash_items, non_stock_count, non_stock_total = await get_flash_invoices(
|
|
db, current_user.kitchen_id, period_start, period_end
|
|
)
|
|
|
|
# Build supplier alias map from DB
|
|
alias_map = await build_supplier_alias_map(db, current_user.kitchen_id)
|
|
|
|
# Run rule-based matching
|
|
matched, discrepancies, flash_unmatched, xero_unmatched = run_matching(
|
|
flash_items, xero_items, alias_map
|
|
)
|
|
|
|
# LLM fallback for remaining unmatched
|
|
llm_attempted = False
|
|
if flash_unmatched and xero_unmatched:
|
|
llm_disc, llm_exact, flash_unmatched, xero_unmatched = await llm_match_remaining(
|
|
db, current_user.kitchen_id, flash_unmatched, xero_unmatched
|
|
)
|
|
if llm_disc or llm_exact:
|
|
llm_attempted = True
|
|
discrepancies.extend(llm_disc)
|
|
matched.extend(llm_exact)
|
|
|
|
# Generate insights for amount discrepancies
|
|
flash_lookup = {f["id"]: f for f in flash_items}
|
|
discrepancies = await generate_amount_insights(
|
|
db, current_user.kitchen_id, discrepancies, flash_lookup
|
|
)
|
|
|
|
# Calculate totals
|
|
flash_total = sum(f["net_stock"] for f in flash_items)
|
|
xero_total = sum(x["net"] for x in xero_items)
|
|
difference = flash_total - xero_total
|
|
|
|
# Build response
|
|
matched_response = [ReconcileMatchedItem(**m) for m in matched]
|
|
|
|
discrepancy_response = [ReconcileDiscrepancyItem(**d) for d in discrepancies]
|
|
|
|
flash_only_response = [
|
|
ReconcileUnmatchedFlash(
|
|
date=f["date"].isoformat(),
|
|
supplier=f["supplier_name"],
|
|
ref=f["ref"],
|
|
net_stock=fmt_money(f["net_stock"]),
|
|
flash_id=f["id"],
|
|
)
|
|
for f in flash_unmatched
|
|
]
|
|
|
|
xero_only_response = [
|
|
ReconcileUnmatchedXero(
|
|
date=x["date"].isoformat(),
|
|
description=x["description"],
|
|
ref=x["ref"],
|
|
net=fmt_money(x["net"]),
|
|
is_expected_external=is_expected_external(x["description"]),
|
|
)
|
|
for x in xero_unmatched
|
|
]
|
|
|
|
return ReconcileResponse(
|
|
period_start=period_start.isoformat(),
|
|
period_end=period_end.isoformat(),
|
|
flash_total=fmt_money(flash_total),
|
|
xero_total=fmt_money(xero_total),
|
|
difference=fmt_money(difference),
|
|
matched_count=len(matched_response),
|
|
discrepancy_count=len(discrepancy_response),
|
|
flash_only_count=len(flash_only_response),
|
|
xero_only_count=len(xero_only_response),
|
|
non_stock_excluded_count=non_stock_count,
|
|
non_stock_excluded_total=fmt_money(non_stock_total),
|
|
matched=matched_response,
|
|
discrepancies=discrepancy_response,
|
|
flash_only=flash_only_response,
|
|
xero_only=xero_only_response,
|
|
llm_matches_attempted=llm_attempted,
|
|
)
|