procurement-ingest/lambdas/po/email_processor/handler.py
Adam Moussa ca4f43a2cc
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feat: decompose email-processor handlers into flat siblings + lazy boto3 clients (refactor phase 5) (#113)
Both email-processor God-handlers split along the seams that already
work in the flat-sibling pattern established by lambdas/shared/, so
bare-name imports keep working under the existing bundling glob.

PO (5-way split): handler.py keeps only the event loop, fail-closed
auth, and email_type routing. extraction.py holds extract_with_claude
and _EMAIL_TAG_RE, importing EXTRACTION_PROMPT from prompts.py and
parse_raw_email from shared/email_parsing.py rather than recreating a
PO-local copy. enrichment.py is a pure code move of enrich_parsed and
pad_zip (PO-only; WO has no enrichment stage) with zero behavior
change. telemetry.py holds the EMF ParseMethod emit wrappers.
persistence.py holds _write_fields/_merge_update/save_*, collapsing
the byte-identical save_new_po/save_revision bodies into one
_save_merge helper that both now call through, preserving the sticky
Cancelled ConditionExpression guard for both callers; save_cancellation
stays separate.

WO (5 concerns, no enrichment stage): the handler loop keeps
validate_ai_fallback and the re.fullmatch(r"[0-9]+", work_order_id)
key guard ahead of both save_work_order and save_event, since the
guard protects the DynamoDB partition key and the '#'-delimited
comment_id range-key segment. _header_date_iso and comment_id
determinism stay colocated with persistence.py's save_event for the
retry-idempotent event_id key.

EXTRACTION_PROMPT (PO) moves to prompts.py with cross-reference
headers to derived_fields.py's authoritative trade/site/fiscal rule
tables; handler.py re-exports it (from prompts import
EXTRACTION_PROMPT) since four tests dereference handler.EXTRACTION_
PROMPT directly. WO's prompt moves the same way.

I/O modules (extraction.py's bedrock client, persistence.py's
dynamodb resource, handler.py's s3 client) get lazy cached boto3
accessors; pure modules (enrichment.py, prompts.py, telemetry.py)
import no boto3. Test monkeypatch surfaces move to the module that
now owns the client (e.g. persistence.dynamodb) everywhere tests
patch it, and the moto-before-handler-import ordering in
_po_parser_support.py is preserved so the moto-backed suites don't
hit real AWS.

Behavior-preservation pins, verified with tests: PO still emits
ParseMethod=ai_fallback before the Bedrock call, with
ai_fallback_rejected as the additive second datapoint on rejection.
WO still emits after its gate with mutually-exclusive ai_fallback /
ai_fallback_rejected. Shadow DerivedFieldAgreement telemetry stays
ai_fallback-only. derived_fields.py is untouched (diff against
feature/phase-3-shared-extraction is empty). handler(event, context)
signatures and the save_* public contract are unchanged on both
pipelines; goldens unchanged.

PO_EXPECTED_TOP_LEVEL_MODULES and its WO equivalent in
tests/test_bundle_consistency.py are updated for the new sibling
modules so the AST bundle-consistency test still fails on an
unshipped or uncommented-out sibling.
2026-07-20 15:34:53 -04:00

147 lines
6.1 KiB
Python

"""
PO email processor Lambda.
Triggered by S3 events when SES delivers a Coupa PO email.
Parses the raw email, tries the deterministic template parser first, falls
back to Claude on Bedrock for structured extraction on a miss/invalid result,
then writes the result to the purchase-orders DynamoDB table.
The concerns are split across flat sibling modules (all bundled into the same
Lambda asset, so bare-name imports resolve):
* extraction.py -- extract_with_claude + the Bedrock client
* enrichment.py -- enrich_parsed + pad_zip + derived-field classification
* telemetry.py -- the EMF ParseMethod / derived-agreement emit wrappers
* persistence.py -- the purchase-orders merge-writes (save_*)
* prompts.py -- EXTRACTION_PROMPT (re-exported below for tests)
This module keeps the S3 event loop, fail-closed sender auth, and email_type
routing.
"""
import logging
import boto3
from email_parsing import parse_raw_email
from enrichment import enrich_parsed
from extraction import extract_with_claude
from persistence import save_cancellation, save_new_po, save_revision
# EXTRACTION_PROMPT is re-exported so handler.EXTRACTION_PROMPT still resolves
# for the tests that dereference it as a module attribute.
from prompts import EXTRACTION_PROMPT # noqa: F401
from ses_auth import authenticate_inbound_email
from telemetry import _emit_parse_method_metric
from template_parser import try_deterministic_parse, validate_ai_fallback
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Lazily-built, cached S3 client. Public name ``s3`` is preserved so the
# monkeypatch attribute is unchanged; building at first CALL (not import) keeps
# the moto-before-handler invariant and honors any patched fake.
s3 = None
def _get_s3():
global s3
if s3 is None:
s3 = boto3.client("s3")
return s3
def handler(event, context):
"""Lambda entry point. Triggered by S3 ObjectCreated events."""
# Direct-invoke healthcheck (post-deploy smoke). This MUST be the very first
# thing handler() does -- before any S3 fetch, before ses_auth, before the
# Records loop -- so it (a) creates no accept path for mail (real mail is an
# S3 ObjectCreated event whose top-level keys AWS controls; email content
# can never set a top-level "healthcheck" key), and (b) emits no EMF metric
# and no log line that could match the sender_auth_rejected metric-filter
# pattern, so two deploys in ~30 min never page that alarm.
if isinstance(event, dict) and event.get("healthcheck") is True:
return {"healthcheck": "ok"}
for record in event.get("Records", []):
bucket = record["s3"]["bucket"]["name"]
key = record["s3"]["object"]["key"]
s3_key = f"s3://{bucket}/{key}"
logger.info(f"Processing email: {s3_key}")
response = _get_s3().get_object(Bucket=bucket, Key=key)
raw_email = response["Body"].read()
# Fail-closed sender authentication (INFRA-107): only mail with an
# SES-stamped dkim=pass verdict for an allowlisted domain may create
# or update purchase orders. Rejected mail is logged and skipped
# without erroring the invocation (no retries / DLQ spam).
if not authenticate_inbound_email(raw_email, s3_key):
continue
email_data = parse_raw_email(raw_email)
logger.info(f"Subject: {email_data['subject']}")
# Deterministic template parse first; fall back to the Bedrock AI
# extractor only on a miss or an invalid (fail-closed) result. The
# metric is emitted before the Bedrock call, so a Bedrock-side error
# still records the ai_fallback outcome (the invocation then errors
# into the errors alarm / DLQ as before).
parsed, parse_method, template_id, parse_reason = try_deterministic_parse(
email_data
)
_emit_parse_method_metric(
parse_method,
template_id,
parse_reason,
parsed.get("po_number") if parsed else None,
)
if parsed is None:
parsed = extract_with_claude(email_data)
# Fail-closed gate on the raw model output (#104 parity): runs
# BEFORE enrich_parsed and BEFORE any dispatch/save. INTENTIONAL
# DOUBLE-COUNT: ParseMethod=ai_fallback was already emitted above,
# BEFORE the Bedrock call (deliberate -- a Bedrock-side error must
# still record the outcome), so a rejected email produces BOTH an
# ai_fallback and an ai_fallback_rejected datapoint. The po_stack
# fallback-rate alarm therefore EXCLUDES the rejected series from
# its rate math (fb already counts these emails once); see
# cdk/po_stack.py and README.
ok, val_reason, normalized = validate_ai_fallback(parsed)
if not ok:
logger.warning(
f"AI-fallback output rejected by validation gate "
f"({val_reason}); skipping: {key}"
)
_emit_parse_method_metric(
"ai_fallback_rejected",
template_id,
val_reason,
str(parsed.get("po_number"))[:64]
if isinstance(parsed, dict) and parsed.get("po_number")
else None,
)
# Skip, never raise: attacker-controlled input must not churn
# the retry/DLQ path.
continue
parsed = normalized
logger.info(
f"Parsed ({parse_method}/{template_id}/{parse_reason}): "
f"type={parsed.get('email_type')}, po={parsed.get('po_number')}"
)
if not parsed.get("po_number"):
logger.warning(f"No PO number found in email, skipping: {key}")
continue
parsed = enrich_parsed(
parsed, s3_key, email_data["subject"], parse_method=parse_method
)
email_type = parsed.get("email_type")
if email_type == "cancellation":
save_cancellation(parsed)
elif email_type == "revision":
save_revision(parsed)
else:
save_new_po(parsed)
return {"statusCode": 200, "body": "OK"}