135 lines
6.1 KiB
Go
135 lines
6.1 KiB
Go
package engine
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import (
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"strings"
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"testing"
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"github.com/local/glpi-neural-brain/internal/config"
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"github.com/local/glpi-neural-brain/internal/graph"
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"github.com/local/glpi-neural-brain/internal/model"
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)
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func TestArticleContentToDraftFormatsConceptWithoutInventedSteps(t *testing.T) {
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content := model.KnowledgeArticleContent{
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Title: "Btrfs-Snapshots und ZFS-History in einer Timeline einordnen",
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ProblemDescription: "Bei der forensischen Timeline-Analyse müssen Dateisystemartefakte mit unterschiedlicher Semantik korrekt eingeordnet werden.",
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Scope: "Gilt für die vergleichende Analyse von Btrfs- und ZFS-Artefakten.",
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KeyPoints: []string{
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"Ein Snapshot beschreibt einen referenzierten Dateisystemzustand und nicht automatisch eine vollständige Ereignishistorie.",
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"Zeitstempel müssen mit Artefakttyp, Erzeugungsmechanismus und Datenquelle dokumentiert werden.",
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},
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DecisionCriteria: []string{
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"Für eine Ereignistimeline sind nur Zeitangaben geeignet, deren Herkunft und Semantik nachvollziehbar sind.",
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"Snapshot-Zeitpunkte dürfen nicht ohne zusätzliche Belege als Zeitpunkt jeder enthaltenen Dateiänderung interpretiert werden.",
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},
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}
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draft := articleContentToDraft(content, []string{"S1", "S2"}, "concept")
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if strings.TrimSpace(draft.Answer) == "" {
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t.Fatal("concept article must have a useful answer without artificial solution steps")
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}
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if !strings.Contains(draft.Answer, "## Kernaussagen") || !strings.Contains(draft.Answer, "## Einordnung und Abgrenzung") {
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t.Fatalf("concept sections missing: %s", draft.Answer)
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}
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if strings.Contains(draft.Answer, "1. ") {
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t.Fatalf("concept article contains invented numbered steps: %s", draft.Answer)
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}
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}
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func TestArticleContentToDraftKeepsOperationalStepsNumbered(t *testing.T) {
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content := model.KnowledgeArticleContent{
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Title: "Audit Logging prüfen",
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ProblemDescription: "Audit-Ereignisse fehlen in der zentralen Protokollierung.",
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SolutionSteps: []string{
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"Aktivieren Sie die zentrale Audit-Protokollierung.",
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"Erzeugen Sie ein dokumentiertes Testereignis.",
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},
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}
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draft := articleContentToDraft(content, []string{"S1"}, "troubleshooting")
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if !strings.Contains(draft.Answer, "1. Aktivieren") || !strings.Contains(draft.Answer, "2. Erzeugen") {
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t.Fatalf("operational steps were not numbered: %s", draft.Answer)
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}
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}
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func TestNormalizeArticleContentCleansConceptFields(t *testing.T) {
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content := normalizeArticleContent(model.KnowledgeArticleContent{
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KeyPoints: []string{" 1. Erster Punkt ", "Erster Punkt"},
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DecisionCriteria: []string{" - Kriterium A ", ""},
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})
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if len(content.KeyPoints) != 1 || content.KeyPoints[0] != "Erster Punkt" {
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t.Fatalf("unexpected key points: %#v", content.KeyPoints)
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}
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if len(content.DecisionCriteria) != 1 || content.DecisionCriteria[0] != "Kriterium A" {
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t.Fatalf("unexpected decision criteria: %#v", content.DecisionCriteria)
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}
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}
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func TestArticleQualityContextCarriesArticleType(t *testing.T) {
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e := &Engine{}
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contextValue := e.articleQualityContext(model.KnowledgeArticleDraft{Title: "Vergleich", Text: "Beschreibung", Answer: "Kernaussagen"}, "concept", nil, nil)
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if !strings.Contains(contextValue, "ARTIKELTYP: concept") {
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t.Fatalf("article type missing from quality context: %s", contextValue)
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}
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}
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func TestArticleDraftContextCarriesArticleType(t *testing.T) {
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e := &Engine{Cfg: config.Config{MaxContextChars: 4000}}
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contextValue := e.articleDraftContext(nil, model.ArticlePlanDecision{ArticleType: "decision_guide", Action: "create"}, model.KnowledgeBrief{}, nil)
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if !strings.Contains(contextValue, "ARTIKELTYP: decision_guide") {
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t.Fatalf("article type missing from draft context: %s", contextValue)
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}
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}
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func TestValidateArticleDraftUsesLowerConceptAnswerMinimum(t *testing.T) {
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e := &Engine{Cfg: config.Config{
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ArticleMinTextChars: 100,
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ArticleMinAnswerChars: 420,
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ArticleMinConfidence: .7,
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ArticleMinSources: 1,
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ArticleMinProductionRatio: 1,
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ArticleMaxGenerationDepth: 2,
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}}
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draft := model.KnowledgeArticleDraft{
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Title: "Mobile Authentifizierung einordnen",
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Text: strings.Repeat("Fachlich belegte Einordnung. ", 6),
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Answer: "## Kernaussagen\n- Authentifizierung bestätigt eine Identität anhand belegter Merkmale.\n- Biometrische Merkmale können die lokale Nutzerprüfung unterstützen.\n\n## Einordnung und Abgrenzung\n- Autorisierung entscheidet anschließend über erlaubte Aktionen und Ressourcen.",
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Confidence: .9,
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}
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sources := []articleSource{{Node: model.Node{Kind: "knowledge", Status: "production"}}}
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if err := e.validateArticleDraft(draft, "concept", sources, 1, 1); err != nil {
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t.Fatalf("grounded concept draft should pass type-aware validation: %v", err)
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}
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if err := e.validateArticleDraft(draft, "how_to", sources, 1, 1); err == nil {
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t.Fatal("the same short answer must not pass the operational how-to minimum")
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}
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}
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func TestArticleDraftValidationMetadataIsStructured(t *testing.T) {
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err := newArticleDraftValidationError("answer_too_short", "answer", 311, 420, "too short")
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metadata := articleDraftValidationMetadata(err)
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if metadata["reason"] != "answer_too_short" || metadata["field"] != "answer" || metadata["actual"] != 311 || metadata["required"] != 420 {
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t.Fatalf("unexpected validation metadata: %#v", metadata)
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}
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}
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func TestSelectReviewEvidenceLimitsAndDiversifies(t *testing.T) {
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results := []model.ResearchResult{
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{Title: "A1", URL: "https://a.example/1", Relevance: .9, SourceQualityScore: .9, Fetched: true},
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{Title: "A2", URL: "https://a.example/2", Relevance: .89, SourceQualityScore: .9, Fetched: true},
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{Title: "B", URL: "https://b.example/1", Relevance: .8, SourceQualityScore: .95, Fetched: true},
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{Title: "C", URL: "https://c.example/1", Relevance: .7, SourceQualityScore: .8, Fetched: true},
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}
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selected := selectReviewEvidence(results, 3)
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if len(selected) != 3 {
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t.Fatalf("expected 3 evidence items, got %d", len(selected))
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}
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domains := map[string]bool{}
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for _, result := range selected {
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domains[graph.SourceFromURL(result.URL)] = true
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}
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if len(domains) != 3 {
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t.Fatalf("expected domain diversity, got %+v", selected)
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}
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}
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