275 lines
12 KiB
Python
275 lines
12 KiB
Python
# Copyright (c) 2026 Patrick Motsch
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# All rights reserved.
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"""Synthetic Trustee benchmark fixture for the Feature Data Sub-Agent eval.
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Builds an in-memory snapshot of one fictional Swiss KMU mandate
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("Demo AG") with:
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* 3 fiscal years (2023, 2024, 2025) of `TrusteeDataAccountBalance` rows
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-- both annual totals (periodMonth=0) and monthly snapshots.
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* 8 representative accounts spanning all major chart-of-accounts blocks
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(cash, banks, receivables, payables, revenue, materials, personnel,
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operating expenses).
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* Per-month `TrusteeDataJournalEntry` + multiple `TrusteeDataJournalLine`
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rows so debit/credit/COUNT aggregations have meaningful answers.
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The data is deterministic (no RNG) so a question's gold-standard answer
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is stable across runs.
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This module deliberately stays decoupled from the production DB pipeline
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-- the harness uses :class:`FakeFeatureDataProvider` (see
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``gateway/tests/eval/fakeFeatureDataProvider.py``) to serve queries
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against this in-memory snapshot, mirroring the public methods of
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``FeatureDataProvider`` (browseTable / queryTable / aggregateTable).
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Dict, List
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_MANDATE_ID = "m-demo-ag"
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_FEATURE_INSTANCE_ID = "fi-demo-ag-trustee"
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# ---------------------------------------------------------------------------
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# Account master data
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# ---------------------------------------------------------------------------
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_ACCOUNT_MASTER: List[Dict[str, Any]] = [
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{"accountNumber": "1000", "label": "Hauptkasse", "accountType": "asset", "currency": "CHF"},
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{"accountNumber": "1020", "label": "ZKB Geschaeftskonto", "accountType": "asset", "currency": "CHF"},
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{"accountNumber": "1021", "label": "PostFinance", "accountType": "asset", "currency": "CHF"},
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{"accountNumber": "1100", "label": "Forderungen aus Lieferungen und Leistungen", "accountType": "asset", "currency": "CHF"},
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{"accountNumber": "2000", "label": "Verbindlichkeiten aus Lieferungen", "accountType": "liability", "currency": "CHF"},
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{"accountNumber": "3000", "label": "Ertrag aus Beratung", "accountType": "revenue", "currency": "CHF"},
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{"accountNumber": "5400", "label": "Materialaufwand", "accountType": "expense", "currency": "CHF"},
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{"accountNumber": "6000", "label": "Mietaufwand", "accountType": "expense", "currency": "CHF"},
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]
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# Annual closing balances per (year, accountNumber) -- the canonical reference.
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# Asset/expense balances are positive, liability/revenue balances are stored
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# as positive numbers (sign by accountType, like most accounting systems).
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_ANNUAL_CLOSING: Dict[int, Dict[str, float]] = {
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2023: {
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"1000": 4_800.00,
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"1020": 132_500.00,
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"1021": 22_400.00,
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"1100": 58_200.00,
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"2000": 41_300.00,
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"3000": 410_000.00,
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"5400": 92_000.00,
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"6000": 36_000.00,
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},
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2024: {
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"1000": 5_200.00,
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"1020": 148_900.00,
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"1021": 26_750.00,
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"1100": 61_400.00,
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"2000": 44_100.00,
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"3000": 462_500.00,
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"5400": 104_300.00,
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"6000": 39_000.00,
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},
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2025: {
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"1000": 5_900.00,
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"1020": 152_400.00,
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"1021": 28_100.00,
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"1100": 66_800.00,
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"2000": 47_900.00,
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"3000": 488_700.00,
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"5400": 112_100.00,
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"6000": 42_000.00,
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},
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}
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def _openingFromPriorYear(year: int, accountNumber: str) -> float:
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"""Opening balance of year N = closing balance of year N-1 (0 if N-1 is unknown)."""
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prior = year - 1
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return float(_ANNUAL_CLOSING.get(prior, {}).get(accountNumber, 0.0))
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def _monthlyProgression(opening: float, closing: float, month: int) -> float:
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"""Linear interpolation between opening and closing for monthly snapshots.
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Not realistic in detail but deterministic and monotonic per account, so
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questions about "Stand per Ende März" produce stable answers.
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"""
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if month <= 0:
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return float(closing)
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frac = month / 12.0
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return round(float(opening) + (float(closing) - float(opening)) * frac, 2)
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# ---------------------------------------------------------------------------
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# Journal entries / lines -- minimal but realistic
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# ---------------------------------------------------------------------------
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_JOURNAL_ENTRIES_2025: List[Dict[str, Any]] = [
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{"month": 3, "day": 15, "reference": "RG-2025-0042", "description": "Beratung Kunde ACME AG", "amount": 18_500.00, "debit": "1100", "credit": "3000"},
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{"month": 3, "day": 22, "reference": "EK-2025-0017", "description": "Materialeinkauf Buehler AG", "amount": 9_200.00, "debit": "5400", "credit": "2000"},
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{"month": 3, "day": 28, "reference": "MIETE-2025-03", "description": "Mietzins Buero Maerz", "amount": 3_000.00, "debit": "6000", "credit": "1020"},
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{"month": 4, "day": 5, "reference": "RG-2025-0051", "description": "Beratung Kunde Bell AG", "amount": 24_300.00, "debit": "1100", "credit": "3000"},
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{"month": 4, "day": 18, "reference": "EK-2025-0024", "description": "Materialeinkauf Industriebedarf", "amount": 7_800.00, "debit": "5400", "credit": "2000"},
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{"month": 6, "day": 12, "reference": "RG-2025-0079", "description": "Beratung Kunde Bell AG", "amount": 32_100.00, "debit": "1100", "credit": "3000"},
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{"month": 6, "day": 30, "reference": "MIETE-2025-Q2", "description": "Mietzins Buero Q2-Abrechnung", "amount": 3_500.00, "debit": "6000", "credit": "1020"},
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{"month": 9, "day": 4, "reference": "RG-2025-0114", "description": "Beratung Kunde Migros", "amount": 41_500.00, "debit": "1100", "credit": "3000"},
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{"month": 9, "day": 25, "reference": "EK-2025-0061", "description": "Materialeinkauf Buehler AG", "amount": 12_400.00, "debit": "5400", "credit": "2000"},
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{"month": 11, "day": 14, "reference": "RG-2025-0188", "description": "Beratung Kunde ACME AG", "amount": 28_700.00, "debit": "1100", "credit": "3000"},
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]
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# ---------------------------------------------------------------------------
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# Snapshot containers
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# ---------------------------------------------------------------------------
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@dataclass
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class BenchmarkFixture:
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"""In-memory rows that mimic feature DB tables.
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Each ``rowsByTable[tableName]`` is a list of column dicts compatible
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with the Pydantic feature data models (TrusteeDataAccountBalance, etc.).
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"""
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mandateId: str
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featureInstanceId: str
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rowsByTable: Dict[str, List[Dict[str, Any]]] = field(default_factory=dict)
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selectedTables: List[Dict[str, Any]] = field(default_factory=list)
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def _buildSelectedTables() -> List[Dict[str, Any]]:
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"""Return the DATA_OBJECT-shaped descriptors the sub-agent expects.
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Mirrors what the catalog would return for the trustee feature; the
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real `getDataObjects("trustee")` call would yield the same shape but
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we hard-code the three tables we actually populate.
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"""
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return [
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{
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"objectKey": "data.feature.trustee.TrusteeDataAccount",
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"label": {"de": "Kontenplan", "en": "Chart of accounts"},
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"meta": {
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"table": "TrusteeDataAccount",
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"fields": ["id", "accountNumber", "label", "accountType", "currency", "isActive"],
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},
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},
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{
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"objectKey": "data.feature.trustee.TrusteeDataAccountBalance",
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"label": {"de": "Kontosalden", "en": "Account balances"},
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"meta": {
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"table": "TrusteeDataAccountBalance",
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"fields": [
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"id", "accountNumber", "periodYear", "periodMonth",
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"openingBalance", "debitTotal", "creditTotal",
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"closingBalance", "currency",
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],
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},
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},
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{
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"objectKey": "data.feature.trustee.TrusteeDataJournalLine",
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"label": {"de": "Buchungszeilen", "en": "Journal lines"},
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"meta": {
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"table": "TrusteeDataJournalLine",
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"fields": [
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"id", "journalEntryId", "accountNumber",
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"debitAmount", "creditAmount", "currency", "description",
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],
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},
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},
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]
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def buildTrusteeBenchmarkFixture() -> BenchmarkFixture:
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"""Materialize the full in-memory benchmark snapshot.
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All rows include ``mandateId`` and ``featureInstanceId`` columns so the
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fake provider can scope them the same way the real one does.
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"""
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accountRows: List[Dict[str, Any]] = []
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for i, acc in enumerate(_ACCOUNT_MASTER):
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accountRows.append({
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"id": f"acc-{i:03d}",
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"accountNumber": acc["accountNumber"],
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"label": acc["label"],
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"accountType": acc["accountType"],
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"currency": acc["currency"],
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"isActive": True,
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"mandateId": _MANDATE_ID,
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"featureInstanceId": _FEATURE_INSTANCE_ID,
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})
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balanceRows: List[Dict[str, Any]] = []
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rowIdx = 0
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for year, closings in _ANNUAL_CLOSING.items():
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for accountNumber, closing in closings.items():
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opening = _openingFromPriorYear(year, accountNumber)
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balanceRows.append({
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"id": f"bal-{rowIdx:04d}",
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"accountNumber": accountNumber,
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"periodYear": year,
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"periodMonth": 0,
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"openingBalance": opening,
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"debitTotal": round(max(closing - opening, 0.0) * 1.2, 2),
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"creditTotal": round(max(closing - opening, 0.0) * 0.2, 2),
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"closingBalance": float(closing),
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"currency": "CHF",
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"mandateId": _MANDATE_ID,
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"featureInstanceId": _FEATURE_INSTANCE_ID,
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})
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rowIdx += 1
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for month in range(1, 13):
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monthly = _monthlyProgression(opening, closing, month)
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balanceRows.append({
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"id": f"bal-{rowIdx:04d}",
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"accountNumber": accountNumber,
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"periodYear": year,
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"periodMonth": month,
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"openingBalance": opening,
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"debitTotal": round((monthly - opening) * 1.2, 2) if monthly > opening else 0.0,
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"creditTotal": round((monthly - opening) * 0.2, 2) if monthly > opening else 0.0,
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"closingBalance": monthly,
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"currency": "CHF",
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"mandateId": _MANDATE_ID,
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"featureInstanceId": _FEATURE_INSTANCE_ID,
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})
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rowIdx += 1
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lineRows: List[Dict[str, Any]] = []
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for j, entry in enumerate(_JOURNAL_ENTRIES_2025):
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entryId = f"je-2025-{j:03d}"
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lineRows.append({
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"id": f"jl-{j*2:04d}",
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"journalEntryId": entryId,
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"accountNumber": entry["debit"],
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"debitAmount": float(entry["amount"]),
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"creditAmount": 0.0,
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"currency": "CHF",
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"description": entry["description"],
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"mandateId": _MANDATE_ID,
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"featureInstanceId": _FEATURE_INSTANCE_ID,
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})
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lineRows.append({
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"id": f"jl-{j*2+1:04d}",
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"journalEntryId": entryId,
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"accountNumber": entry["credit"],
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"debitAmount": 0.0,
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"creditAmount": float(entry["amount"]),
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"currency": "CHF",
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"description": entry["description"],
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"mandateId": _MANDATE_ID,
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"featureInstanceId": _FEATURE_INSTANCE_ID,
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})
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fixture = BenchmarkFixture(
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mandateId=_MANDATE_ID,
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featureInstanceId=_FEATURE_INSTANCE_ID,
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rowsByTable={
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"TrusteeDataAccount": accountRows,
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"TrusteeDataAccountBalance": balanceRows,
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"TrusteeDataJournalLine": lineRows,
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},
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selectedTables=_buildSelectedTables(),
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)
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return fixture
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