974181674d
## Context
v1 shipped a Claude Haiku-based extractor that validated only 10/71
backfilled rows. Haiku fumbles the arithmetic on pension salary-sacrifice,
conflates RSU vest with regular earnings, and occasionally misreads YTD
vs this-period columns — so 86% of rows land with validated=false and the
downstream dashboards under-report take-home.
Meta UK uses a stable two-variant template (pre/post 2022-01-31 boundary),
so a regex parser is both faster (ms vs. 30-90s + $0.01-0.05/call) and
more accurate. v2 introduces that parser as the primary path, keeps
Claude as the fallback for non-Meta payslips, and surfaces new fields
the dashboard needs to attribute PAYE between cash salary and RSU vests
correctly.
## This change
### Parser (new)
`payslip_ingest/parsers/meta_uk.py` detects the layout variant by header
presence:
- **Variant A** (pre-2022): vertical Description/This Period/This Year.
`AE Pension EE` is a positive deduction against a pre-sacrifice gross —
maps to `pension_employee` for the existing validation formula to hold.
- **Variant B** (post-2022): side-by-side Payments | Deductions | Year to
Date. `AE Pension EE` is NEGATIVE in Payments (salary sacrifice) — maps
to `pension_sacrifice` and is already netted into Total Payment.
`rsu_vest = RSU Tax Offset + RSU Excs Refund` (Meta's template inflates
Taxable Pay without using a matching offset deduction).
Column boundaries come from the header row's anchor positions; each data
row slices into 3 cells and the last numeric token per cell is the amount.
Anchor misses raise ParserError so the caller falls back to Claude rather
than silently returning bad data.
### New fields
Schema + DB + Claude prompt gain:
- `salary`, `bonus`, `pension_sacrifice` — earnings decomposition for the
dashboard's bonus-sacrifice visibility and earnings-breakdown chart
- `taxable_pay`, `ytd_tax_paid`, `ytd_taxable_pay`, `ytd_gross` — powers
the YTD-effective-rate method of attributing cash tax vs RSU tax, which
is the only method that's accurate month-to-month
All new columns default to 0 / null so v1 rows continue to round-trip.
### Orchestration
processor.py tries `parse_meta_uk(pdftotext(pdf))` first. On success the
result goes straight to the DB — zero Claude tokens spent, extraction in
milliseconds. On ParserError it falls through to ClaudeExtractor as before.
ProcessResult gains an `extractor` field ("meta_uk_regex" | "claude") so
backfill logs show the hit rate.
## Tests
- `test_meta_uk_parser.py` — 11 tests covering variant A, variant B
(standard + bonus month + bonus-sacrificed month), malformed inputs,
and end-to-end totals validation for all 4 golden fixtures.
- `test_processor.py` — 2 new tests proving the regex-first short-circuit
and the Claude fallback on non-Meta inputs.
Fixtures under `tests/fixtures/` are hand-crafted `pdftotext -layout`
emulations — real Meta numbers from the plan's sample payslips for
variant B, synthesized realistic variant A and bonus-sacrificed samples.
0001_initial.py reformat is yapf cleanup touched during the session's
format pass; not a behavior change.
## Test Plan
### Automated
```
$ poetry run pytest
============================= test session starts ==============================
collected 53 items
tests/test_extractor.py ..... [ 9%]
tests/test_meta_uk_parser.py ........... [ 30%]
tests/test_paperless.py ...... [ 41%]
tests/test_processor.py .............. [ 67%]
tests/test_schema.py .... [ 75%]
tests/test_tax_year.py ........ [ 90%]
tests/test_webhook.py ..... [100%]
============================== 53 passed in 1.66s ==============================
$ poetry run ruff check .
All checks passed!
$ poetry run mypy .
Success: no issues found in 24 source files
$ poetry run yapf --style pyproject.toml --diff --recursive payslip_ingest tests
(no output — all files are yapf-clean)
```
### Manual Verification
Smoke-test the parser against a real Meta payslip PDF on the deploy host:
```
# After 0003 migration applied to prod DB
$ poetry run python -c "
from payslip_ingest.parsers import parse_meta_uk
import subprocess
text = subprocess.check_output(['pdftotext', '-layout', '/path/to/real.pdf', '-']).decode()
p = parse_meta_uk(text)
print(p.model_dump_json(indent=2))
"
```
Expected: JSON with salary/bonus/rsu_vest/pension_sacrifice populated and
`validate_totals(p)` returning True.
## Reproduce locally
1. `cd payslip-ingest && poetry install`
2. `poetry run pytest tests/test_meta_uk_parser.py -v`
3. Expected: 11 tests pass, each fixture validates totals within 2p.
Closes: code-un1
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
82 lines
4.5 KiB
Python
82 lines
4.5 KiB
Python
import os
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from datetime import date, datetime
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from decimal import Decimal
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from typing import Any
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from sqlalchemy import JSON, TIMESTAMP, Boolean, Date, Integer, Numeric, String, text
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from sqlalchemy.dialects.postgresql import JSONB
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from sqlalchemy.ext.asyncio import AsyncEngine, async_sessionmaker, create_async_engine
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from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
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SCHEMA_NAME = "payslip_ingest"
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class Base(DeclarativeBase):
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pass
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# JSONB on Postgres, plain JSON (as text) on SQLite — tests use SQLite, prod uses Postgres.
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JSON_TYPE = JSONB().with_variant(JSON(), "sqlite")
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class Payslip(Base):
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__tablename__ = "payslip"
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__table_args__ = {"schema": SCHEMA_NAME} # noqa: RUF012
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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paperless_doc_id: Mapped[int] = mapped_column(Integer, unique=True, nullable=False)
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pay_date: Mapped[date] = mapped_column(Date, nullable=False)
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pay_period_start: Mapped[date | None] = mapped_column(Date, nullable=True)
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pay_period_end: Mapped[date | None] = mapped_column(Date, nullable=True)
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employer: Mapped[str | None] = mapped_column(String, nullable=True)
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currency: Mapped[str] = mapped_column(String(3), nullable=False, server_default="GBP")
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gross_pay: Mapped[Decimal] = mapped_column(Numeric(12, 2), nullable=False)
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income_tax: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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national_insurance: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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pension_employee: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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pension_employer: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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student_loan: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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rsu_vest: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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rsu_offset: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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salary: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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bonus: Mapped[Decimal] = mapped_column(Numeric(12, 2), nullable=False, server_default=text("0"))
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pension_sacrifice: Mapped[Decimal] = mapped_column(Numeric(12, 2),
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nullable=False,
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server_default=text("0"))
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taxable_pay: Mapped[Decimal | None] = mapped_column(Numeric(12, 2), nullable=True)
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ytd_tax_paid: Mapped[Decimal | None] = mapped_column(Numeric(12, 2), nullable=True)
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ytd_taxable_pay: Mapped[Decimal | None] = mapped_column(Numeric(12, 2), nullable=True)
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ytd_gross: Mapped[Decimal | None] = mapped_column(Numeric(12, 2), nullable=True)
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other_deductions: Mapped[dict[str, Any] | None] = mapped_column(JSON_TYPE, nullable=True)
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net_pay: Mapped[Decimal] = mapped_column(Numeric(12, 2), nullable=False)
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tax_year: Mapped[str] = mapped_column(String, nullable=False)
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raw_extraction: Mapped[dict[str, Any]] = mapped_column(JSON_TYPE, nullable=False)
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validated: Mapped[bool] = mapped_column(Boolean, nullable=False, server_default=text("true"))
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created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True),
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nullable=False,
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server_default=text("now()"))
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def create_engine_from_env() -> AsyncEngine:
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url = os.environ["DB_CONNECTION_STRING"]
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return create_async_engine(url, pool_pre_ping=True)
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def make_session_factory(engine: AsyncEngine) -> async_sessionmaker[Any]:
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return async_sessionmaker(engine, expire_on_commit=False)
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