Files
cayce-ops/cayceops/components/maild/classify.py
T
cayce 903c591a06 audit-2 (critical 4): approval-veto classifier — any negation vetoes approval
Replaces per-pattern negation lists: if ANY negation token (not, no, never,
cannot, cannot, will not, has not, is not, no longer, unable, decline,
regret, unfortunately) appears anywhere in the mail, an approval
classification downgrades to needs_review. Approval requires a mail with
zero negations. The LLM prompt braces stay escaped (round-1 fix retained).

tests: the six mandated sentences all classify non-approval.
2026-10-20 02:20:26 +00:00

109 lines
4.0 KiB
Python

"""maild classification: rules + sentence-level negation, LLM optional.
Approval rule (audit round 2, point 2): a mail may only classify `approval`
when NO sentence contains a negation. Any negation token anywhere in the mail
(conservative: even outside the approval sentence) downgrades an approval to
`needs_review`. Rejections/questions/other are unaffected.
The LLM never decides alone: below-threshold confidence → `needs_review`.
With the `null` provider (default) only deterministic rules run.
"""
from __future__ import annotations
import json
import re
from cayceops.config import Config
from cayceops.models import MailClassification
_APPROVAL_WORDS = re.compile(r"\b(approv|accept|welcome)", re.I)
# any of these tokens anywhere in the mail vetoes an `approval` result
_NEGATIONS = re.compile(
r"\b(not|no|never|cannot|can'?t|won'?t|hasn'?t|isn'?t|"
r"no\s+longer|unable|decline\w*|regret\w*|unfortunately)\b",
re.I,
)
_RULES: list[tuple[MailClassification, re.Pattern[str], float]] = [
# (class, pattern, confidence when matched)
(
MailClassification.REJECTION,
# noqa: E501 (kept on one line: the pattern IS the reviewed artifact)
re.compile(r"\b(declin|reject|not able to|unable to|we regret|won.t be able)\w*", re.I), # noqa: E501
0.9,
),
(
MailClassification.APPROVAL,
re.compile(r"\b(approv|accept|welcome to the|you.re (in|approved)|live|activated|glad to)\w*", re.I), # noqa: E501
0.85,
),
(
MailClassification.QUESTION,
re.compile(r"\b(could you|can you|please (provide|clarify|confirm)|what (is|are)|how (do|does|can)|need more (info|details))\b", re.I), # noqa: E501
0.7,
),
]
_LLM_PROMPT = """Classify this merchant email into exactly one of:
approval, rejection, question, other.
Reply with strict JSON: {{"classification": "...", "confidence": 0.0-1.0}}
Subject and body:
{body}"""
def _rules(text: str) -> tuple[MailClassification, float]:
for kind, pattern, confidence in _RULES:
if pattern.search(text):
return kind, confidence
return MailClassification.OTHER, 0.3
def _has_negation(text: str) -> bool:
return bool(_NEGATIONS.search(text))
def _llm_classify(text: str, cfg: Config) -> tuple[MailClassification, float] | None:
"""Ask the configured LLM. Returns None on any failure (rules take over)."""
from cayceops.llm import get_provider
try:
provider = get_provider(cfg)
raw = provider.complete(
_LLM_PROMPT.format(body=text[:4000]),
system="You classify merchant emails. Reply with JSON only.",
)
data = json.loads(re.search(r"\{.*\}", raw, re.S).group(0))
kind = MailClassification(data["classification"].lower())
confidence = float(data["confidence"])
except Exception: # noqa: BLE001 — LLM is best-effort by design
return None
if kind not in MailClassification or kind is MailClassification.NEEDS_REVIEW:
return None
return kind, min(max(confidence, 0.0), 1.0)
def classify(text: str, cfg: Config) -> tuple[MailClassification, float]:
"""Classify merchant text. Degrades to rules when no LLM is configured."""
if cfg.llm_provider != "null":
llm_result = _llm_classify(text, cfg)
if llm_result is not None:
kind, confidence = llm_result
else: # LLM failed — fall through to rules
kind, confidence = _rules(text)
else:
kind, confidence = _rules(text)
# APPROVAL VETO (audit round 2): any negation anywhere in the mail
# downgrades an approval to needs_review. Approval requires a mail with
# zero negations — "cannot currently accept", "never accept",
# "hasn't been approved", "no longer accepting" all land here.
if kind is MailClassification.APPROVAL and _has_negation(text):
return MailClassification.NEEDS_REVIEW, min(confidence, 0.6)
if confidence < cfg.llm_review_threshold:
return MailClassification.NEEDS_REVIEW, confidence
return kind, confidence