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breakpilot-compliance/ai-compliance-sdk/internal/iace/pattern_engine.go
Benjamin Admin 5adb1c5f16
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feat(iace): integrate Rule Library as 58 extended hazard patterns (HP045-HP102)
Parsed 171 explicit rules from 4 Rule Library Word documents (R051-R1550),
deduplicated into 58 unique (component, energy_source) patterns, and mapped
to existing IACE IDs (component tags, M-IDs, E-IDs).

Changes:
- hazard_patterns_extended.go: 58 new patterns derived from Rule Library
- pattern_engine.go: combines builtin (44) + extended (58) = 102 total patterns
- iace_handler.go: ListHazardPatterns returns all 102 patterns
- iace.md: updated documentation for 102 patterns
- scripts/generate-rule-patterns.py: mapping + Go code generator
- scripts/parsed-rule-library.json: extracted rule data

Tests: 132 passing (9 new extended pattern tests)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-16 11:24:07 +01:00

244 lines
7.1 KiB
Go

package iace
import "sort"
// MatchInput defines the inputs for the pattern-matching engine.
type MatchInput struct {
ComponentLibraryIDs []string `json:"component_library_ids"`
EnergySourceIDs []string `json:"energy_source_ids"`
LifecyclePhases []string `json:"lifecycle_phases"`
CustomTags []string `json:"custom_tags"`
}
// MatchOutput contains the results of pattern matching.
type MatchOutput struct {
MatchedPatterns []PatternMatch `json:"matched_patterns"`
SuggestedHazards []HazardSuggestion `json:"suggested_hazards"`
SuggestedMeasures []MeasureSuggestion `json:"suggested_measures"`
SuggestedEvidence []EvidenceSuggestion `json:"suggested_evidence"`
ResolvedTags []string `json:"resolved_tags"`
}
// PatternMatch records which pattern fired and why.
type PatternMatch struct {
PatternID string `json:"pattern_id"`
PatternName string `json:"pattern_name"`
Priority int `json:"priority"`
MatchedTags []string `json:"matched_tags"`
}
// HazardSuggestion is a suggested hazard from pattern matching.
type HazardSuggestion struct {
Category string `json:"category"`
SourcePatterns []string `json:"source_patterns"`
Confidence float64 `json:"confidence"`
}
// MeasureSuggestion is a suggested protective measure from pattern matching.
type MeasureSuggestion struct {
MeasureID string `json:"measure_id"`
SourcePatterns []string `json:"source_patterns"`
}
// EvidenceSuggestion is a suggested evidence type from pattern matching.
type EvidenceSuggestion struct {
EvidenceID string `json:"evidence_id"`
SourcePatterns []string `json:"source_patterns"`
}
// PatternEngine evaluates hazard patterns against resolved tags.
type PatternEngine struct {
resolver *TagResolver
patterns []HazardPattern
}
// NewPatternEngine creates a PatternEngine with built-in + extended patterns and resolver.
func NewPatternEngine() *PatternEngine {
// Combine built-in (HP001-HP044) and extended (HP045+) patterns
patterns := GetBuiltinHazardPatterns()
patterns = append(patterns, GetExtendedHazardPatterns()...)
return &PatternEngine{
resolver: NewTagResolver(),
patterns: patterns,
}
}
// Match executes the pattern-matching algorithm:
// 1. Resolve component + energy IDs → tags
// 2. Merge with custom tags
// 3. Sort patterns by priority (descending)
// 4. For each pattern: check required_tags (AND) and excluded_tags (NOT)
// 5. Collect hazards, measures, evidence from fired patterns (deduplicated)
// 6. Calculate confidence scores
func (e *PatternEngine) Match(input MatchInput) *MatchOutput {
// Step 1+2: resolve all tags
allTags := e.resolver.ResolveTags(input.ComponentLibraryIDs, input.EnergySourceIDs, input.CustomTags)
if len(allTags) == 0 {
return &MatchOutput{ResolvedTags: []string{}}
}
tagSet := toSet(allTags)
// Step 3: sort patterns by priority descending
patterns := make([]HazardPattern, len(e.patterns))
copy(patterns, e.patterns)
sort.Slice(patterns, func(i, j int) bool {
return patterns[i].Priority > patterns[j].Priority
})
// Step 4+5: evaluate each pattern
var matchedPatterns []PatternMatch
hazardCatSources := make(map[string][]string) // category → pattern IDs
measureSources := make(map[string][]string) // measure ID → pattern IDs
evidenceSources := make(map[string][]string) // evidence ID → pattern IDs
for _, p := range patterns {
if !patternMatches(p, tagSet, input.LifecyclePhases) {
continue
}
// Collect the tags that contributed to this match
var matchedTags []string
for _, t := range p.RequiredComponentTags {
if tagSet[t] {
matchedTags = append(matchedTags, t)
}
}
for _, t := range p.RequiredEnergyTags {
if tagSet[t] {
matchedTags = append(matchedTags, t)
}
}
matchedPatterns = append(matchedPatterns, PatternMatch{
PatternID: p.ID,
PatternName: p.NameDE,
Priority: p.Priority,
MatchedTags: matchedTags,
})
for _, cat := range p.GeneratedHazardCats {
hazardCatSources[cat] = appendUnique(hazardCatSources[cat], p.ID)
}
for _, mid := range p.SuggestedMeasureIDs {
measureSources[mid] = appendUnique(measureSources[mid], p.ID)
}
for _, eid := range p.SuggestedEvidenceIDs {
evidenceSources[eid] = appendUnique(evidenceSources[eid], p.ID)
}
}
// Step 6: build output with confidence scores
totalPatterns := len(patterns)
firedCount := len(matchedPatterns)
var suggestedHazards []HazardSuggestion
for cat, sources := range hazardCatSources {
confidence := float64(len(sources)) / float64(maxInt(firedCount, 1))
if confidence > 1.0 {
confidence = 1.0
}
suggestedHazards = append(suggestedHazards, HazardSuggestion{
Category: cat,
SourcePatterns: sources,
Confidence: confidence,
})
}
// Sort by confidence descending
sort.Slice(suggestedHazards, func(i, j int) bool {
return suggestedHazards[i].Confidence > suggestedHazards[j].Confidence
})
var suggestedMeasures []MeasureSuggestion
for mid, sources := range measureSources {
suggestedMeasures = append(suggestedMeasures, MeasureSuggestion{
MeasureID: mid,
SourcePatterns: sources,
})
}
sort.Slice(suggestedMeasures, func(i, j int) bool {
return suggestedMeasures[i].MeasureID < suggestedMeasures[j].MeasureID
})
var suggestedEvidence []EvidenceSuggestion
for eid, sources := range evidenceSources {
suggestedEvidence = append(suggestedEvidence, EvidenceSuggestion{
EvidenceID: eid,
SourcePatterns: sources,
})
}
sort.Slice(suggestedEvidence, func(i, j int) bool {
return suggestedEvidence[i].EvidenceID < suggestedEvidence[j].EvidenceID
})
_ = totalPatterns // used conceptually for confidence normalization
return &MatchOutput{
MatchedPatterns: matchedPatterns,
SuggestedHazards: suggestedHazards,
SuggestedMeasures: suggestedMeasures,
SuggestedEvidence: suggestedEvidence,
ResolvedTags: allTags,
}
}
// patternMatches checks if a pattern fires given the resolved tag set and lifecycle phases.
func patternMatches(p HazardPattern, tagSet map[string]bool, lifecyclePhases []string) bool {
// All required component tags must be present (AND)
for _, t := range p.RequiredComponentTags {
if !tagSet[t] {
return false
}
}
// All required energy tags must be present (AND)
for _, t := range p.RequiredEnergyTags {
if !tagSet[t] {
return false
}
}
// None of the excluded tags may be present (NOT)
for _, t := range p.ExcludedComponentTags {
if tagSet[t] {
return false
}
}
// If pattern requires specific lifecycle phases, at least one must match
if len(p.RequiredLifecycles) > 0 && len(lifecyclePhases) > 0 {
found := false
phaseSet := toSet(lifecyclePhases)
for _, lp := range p.RequiredLifecycles {
if phaseSet[lp] {
found = true
break
}
}
if !found {
return false
}
}
return true
}
// appendUnique appends val to slice if not already present.
func appendUnique(slice []string, val string) []string {
for _, s := range slice {
if s == val {
return slice
}
}
return append(slice, val)
}
// maxInt returns the larger of a and b.
func maxInt(a, b int) int {
if a > b {
return a
}
return b
}