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Taxonomy Trigger Engine.
Matches patient utterances against all 65 clinical domain trigger definitions
using phrase matching, regex patterns, partial substring matching, and
negation handling.
Safety-critical: This is the last line of defense for catching clinical
emergencies that the ML model may miss. False negatives here can be
life-threatening.
Design principles:
- Fail open: If in doubt, nominate the domain (better to over-escalate)
- Exhaustive matching: Check ALL domains, not just the first match
- Negation requires explicit evidence: Only suppress if negation pattern
clearly matches
- Pre-compiled regex: All patterns compiled at config load time
- Case-insensitive matching throughout
"""
from __future__ import annotations
import logging
import re
from typing import Any, Dict, List, Optional, Set, Tuple
from decision.engine.config_loader import DecisionConfigLoader
from decision.engine.models import (
ConfidenceTier,
MatchType,
NegationAction,
NegationResult,
TriggerMatch,
)
logger = logging.getLogger("decision.trigger_engine")
class TaxonomyTriggerEngine:
"""
Matches text against taxonomy trigger definitions.
Usage:
engine = TaxonomyTriggerEngine(config)
matches = engine.match_all(text)
# Returns: Dict[str, DomainMatchResult] keyed by domain name
"""
def __init__(self, config: DecisionConfigLoader):
self._config = config
self._domains: Dict[str, Dict[str, Any]] = config.taxonomy_triggers
logger.info(
"TaxonomyTriggerEngine initialized with %d domains", len(self._domains)
)
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def match_all(self, text: str) -> Dict[str, DomainMatchResult]:
"""
Match text against ALL taxonomy domains.
Returns a dict of domain_name -> DomainMatchResult for every domain
that has at least one trigger match (before negation).
Negation is evaluated but does NOT remove the domain from results.
The caller decides what to do based on negation_result.
"""
text_lower = text.lower().strip()
if not text_lower:
return {}
results: Dict[str, DomainMatchResult] = {}
for domain_name, triggers in self._domains.items():
result = self._match_domain(domain_name, triggers, text, text_lower)
if result and result.highest_confidence != ConfidenceTier.NONE:
results[domain_name] = result
return results
def match_domain(self, domain_name: str, text: str) -> Optional[DomainMatchResult]:
"""Match text against a single domain's triggers."""
triggers = self._domains.get(domain_name)
if not triggers:
return None
text_lower = text.lower().strip()
return self._match_domain(domain_name, triggers, text, text_lower)
def check_negation(
self, domain_name: str, text: str
) -> NegationResult:
"""Check if text matches negation patterns for a domain."""
triggers = self._domains.get(domain_name, {})
text_lower = text.lower().strip()
return self._evaluate_negation(domain_name, triggers, text_lower)
# ------------------------------------------------------------------
# Internal matching
# ------------------------------------------------------------------
def _match_domain(
self,
domain_name: str,
triggers: Dict[str, Any],
text: str,
text_lower: str,
) -> Optional[DomainMatchResult]:
"""Match text against a single domain's trigger definition."""
lexical = triggers.get("lexical_signals", {})
priority = triggers.get("priority", 0)
all_matches: List[TriggerMatch] = []
# Check each confidence tier (high → medium → low)
for tier_name, tier_enum in [
("high", ConfidenceTier.HIGH),
("medium", ConfidenceTier.MEDIUM),
("low", ConfidenceTier.LOW),
]:
tier = lexical.get(tier_name, {})
tier_matches = self._match_tier(
domain_name, tier, tier_enum, text, text_lower, priority
)
all_matches.extend(tier_matches)
if not all_matches:
return None
# Determine highest confidence from matches
highest = ConfidenceTier.NONE
for m in all_matches:
if m.confidence_tier > highest:
highest = m.confidence_tier
# Evaluate negation
negation = self._evaluate_negation(domain_name, triggers, text_lower)
# Apply negation to adjust confidence
effective_confidence = highest
if negation.is_negated:
if negation.action == NegationAction.SUPPRESS:
effective_confidence = ConfidenceTier.NONE
elif negation.action == NegationAction.DOWNGRADE_CONFIDENCE:
effective_confidence = self._downgrade_tier(highest)
return DomainMatchResult(
domain=domain_name,
priority=priority,
matches=tuple(all_matches),
highest_confidence=highest,
effective_confidence=effective_confidence,
negation_result=negation,
)
def _match_tier(
self,
domain_name: str,
tier: Dict[str, Any],
tier_enum: ConfidenceTier,
text: str,
text_lower: str,
priority: int,
) -> List[TriggerMatch]:
"""Match text against a single confidence tier."""
matches: List[TriggerMatch] = []
# 1. Phrase matching (exact substring, case-insensitive)
for phrase in tier.get("phrases", []):
phrase_lower = phrase.lower()
idx = text_lower.find(phrase_lower)
if idx >= 0:
matched_span = text[idx : idx + len(phrase)]
matches.append(
TriggerMatch(
domain=domain_name,
confidence_tier=tier_enum,
match_type=MatchType.PHRASE,
matched_text=phrase,
matched_span=matched_span,
priority=priority,
)
)
# 2. Regex matching (pre-compiled)
for compiled_re in tier.get("_compiled_regex", []):
m = compiled_re.search(text)
if m:
matches.append(
TriggerMatch(
domain=domain_name,
confidence_tier=tier_enum,
match_type=MatchType.REGEX,
matched_text=compiled_re.pattern,
matched_span=m.group(0),
priority=priority,
)
)
# 3. Partial matching (substring, case-insensitive)
for partial in tier.get("partials", []):
partial_lower = partial.lower()
idx = text_lower.find(partial_lower)
if idx >= 0:
matched_span = text[idx : idx + len(partial)]
matches.append(
TriggerMatch(
domain=domain_name,
confidence_tier=tier_enum,
match_type=MatchType.PARTIAL,
matched_text=partial,
matched_span=matched_span,
priority=priority,
)
)
return matches
def _evaluate_negation(
self,
domain_name: str,
triggers: Dict[str, Any],
text_lower: str,
) -> NegationResult:
"""
Evaluate negation patterns for a domain.
SAFETY DESIGN: Negation requires an EXPLICIT match against a known
negation pattern. We do NOT use generic "no/not" detection because
that risks suppressing true emergencies.
"""
negation_config = triggers.get("negation_handling", {})
compiled_patterns: List[str] = negation_config.get("_compiled_patterns", [])
action_str = negation_config.get("action", "downgrade_confidence")
try:
action = NegationAction(action_str)
except ValueError:
action = NegationAction.DOWNGRADE_CONFIDENCE
for pattern in compiled_patterns:
if pattern in text_lower:
return NegationResult(
is_negated=True,
action=action,
matched_pattern=pattern,
)
return NegationResult(
is_negated=False,
action=action,
)
@staticmethod
def _downgrade_tier(tier: ConfidenceTier) -> ConfidenceTier:
"""Downgrade confidence by one level."""
if tier == ConfidenceTier.HIGH:
return ConfidenceTier.MEDIUM
elif tier == ConfidenceTier.MEDIUM:
return ConfidenceTier.LOW
elif tier == ConfidenceTier.LOW:
return ConfidenceTier.NONE
return ConfidenceTier.NONE
# ---------------------------------------------------------------------------
# Domain Match Result
# ---------------------------------------------------------------------------
class DomainMatchResult:
"""
Result of matching a single domain against patient text.
Contains all trigger matches, the highest raw confidence,
effective confidence (after negation), and negation details.
"""
__slots__ = (
"domain",
"priority",
"matches",
"highest_confidence",
"effective_confidence",
"negation_result",
)
def __init__(
self,
domain: str,
priority: int,
matches: Tuple[TriggerMatch, ...],
highest_confidence: ConfidenceTier,
effective_confidence: ConfidenceTier,
negation_result: NegationResult,
):
self.domain = domain
self.priority = priority
self.matches = matches
self.highest_confidence = highest_confidence
self.effective_confidence = effective_confidence
self.negation_result = negation_result
@property
def is_negated(self) -> bool:
return self.negation_result.is_negated
@property
def is_suppressed(self) -> bool:
return self.effective_confidence == ConfidenceTier.NONE
@property
def match_count(self) -> int:
return len(self.matches)
def __repr__(self) -> str:
neg = " [NEGATED]" if self.is_negated else ""
return (
f"DomainMatchResult({self.domain}, "
f"confidence={self.effective_confidence.value}, "
f"priority={self.priority}, "
f"matches={self.match_count}{neg})"
)
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