docs: update Architecture + SDK Flow with Control Pipeline + Dependency Engine
Architecture (architecture-data.ts): - Replace document-crawler with control-pipeline (Port 8098) - Add 9 DB tables, 5 RAG collections, 10 API endpoints - Add edges: control-pipeline → PostgreSQL, Qdrant, Ollama SDK Flow (steps-betrieb.ts): - Add 4 new steps (seq 5200-5500): - Canonical Control Library (7-stage generation pipeline) - Pass 0a: Obligation Extraction (181k obligations) - Pass 0b: Atomic Composition (MCP-taugliche controls) - Dependency Engine + Evaluation (5 types, auto-generation) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Benjamin Admin
@@ -250,4 +250,95 @@ export const STEPS_BETRIEB: SDKFlowStep[] = [
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url: '/sdk/isms',
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completion: 100,
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},
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// ── Control Pipeline ─────────────────────────────────────────────────────
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{
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id: 'control-library',
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name: 'Canonical Control Library',
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nameShort: 'Control Library',
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package: 'betrieb',
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seq: 5200,
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checkpointId: 'CP-CLIB',
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checkpointType: 'REQUIRED',
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checkpointReviewer: 'NONE',
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description: 'Verwaltung der ~33.000 Rich Controls aus dem RAG-Korpus. 7-Stufen-Pipeline mit Lizenz-Gate.',
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descriptionLong: 'Die Canonical Control Library ist das zentrale Verzeichnis aller aus Regulierungstexten generierten Compliance Controls. Die 7-Stufen-Pipeline verarbeitet ~105.000 RAG-Chunks: (1) RAG Scan, (2) Lizenz-Klassifikation (Rule 1/2/3), (3a) Strukturierung (Rule 1+2) oder (3b) Reformulierung (Rule 3), (4) Harmonisierung (Embedding-Dedup), (5) Anchor Search (Open-Source-Referenzen), (6) Speicherung, (7) Chunk-Tracking. Domains: AUTH, CRYP, NET, DATA, SEC, AI, COMP, GOV, LAB, FIN u.a.',
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legalBasis: 'UrhG §44b (Text & Data Mining), UrhG §23 (Hinreichender Abstand)',
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inputs: ['ragChunks'],
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outputs: ['canonicalControls'],
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prerequisiteSteps: [],
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dbTables: ['canonical_controls', 'canonical_processed_chunks', 'canonical_generation_jobs'],
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dbMode: 'read/write',
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ragCollections: ['bp_compliance_gesetze', 'bp_compliance_datenschutz', 'bp_compliance_ce', 'bp_dsfa_corpus', 'bp_legal_templates'],
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ragPurpose: 'Quelldokumente fuer Control-Generierung (Gesetze, Verordnungen, Standards)',
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isOptional: false,
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url: '/sdk/control-library',
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completion: 100,
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},
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{
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id: 'obligation-extraction',
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name: 'Pass 0a: Obligation Extraction',
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nameShort: 'Pass 0a',
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package: 'betrieb',
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seq: 5300,
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checkpointId: 'CP-P0A',
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checkpointType: 'REQUIRED',
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checkpointReviewer: 'NONE',
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description: 'Extraktion von ~181.000 normativen Pflichten aus Rich Controls via Claude Haiku (Batch API).',
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descriptionLong: 'Pass 0a zerlegt jeden Rich Control in einzelne normative Obligations via Claude Haiku (Anthropic Batch API, 50% Kostenreduktion). Jede Obligation wird klassifiziert: Pflicht/Empfehlung/Kann, Test-Obligation ja/nein, Reporting-Obligation ja/nein. Quality Gate mit 6 Regeln: nur normative Aussagen, ein Hauptverb, Test/Reporting separat, kein Evidence-Level-Split. Ergebnis: ~181.000 validierte Obligations mit action, object, condition, normative_strength.',
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legalBasis: 'Pipeline-intern (Normative Obligation Extraction)',
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inputs: ['canonicalControls'],
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outputs: ['obligationCandidates'],
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prerequisiteSteps: ['control-library'],
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dbTables: ['obligation_candidates'],
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dbMode: 'read/write',
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ragCollections: [],
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isOptional: false,
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url: '/sdk/control-library',
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completion: 90,
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},
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{
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id: 'atomic-composition',
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name: 'Pass 0b: Atomic Composition',
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nameShort: 'Pass 0b',
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package: 'betrieb',
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seq: 5400,
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checkpointId: 'CP-P0B',
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checkpointType: 'REQUIRED',
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checkpointReviewer: 'NONE',
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description: 'Komposition atomarer MCP-tauglicher Controls aus Obligations via Claude Sonnet + Pre-LLM Ontology-Filter.',
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descriptionLong: 'Pass 0b verwandelt jede validierte Obligation in ein eigenstaendiges atomares Control via Claude Sonnet (Anthropic Batch API). Vor dem LLM-Call klassifiziert die Control Ontology (26 Action Types) jede Obligation: atomic (an LLM senden), composite (ueberspringen), evidence (ueberspringen), framework_container (ueberspringen). MCP-taugliche Output-Felder: assertion (pruefbare Aussage), pass_criteria, fail_criteria, check_type (technical_config_check, document_clause_check, code_pattern_check), dependency_hints, lifecycle_phase_order (1-13). Canonical Key Format: action_type:normalized_object:control_phase.',
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legalBasis: 'Pipeline-intern (Atomic Control Composition)',
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inputs: ['obligationCandidates'],
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outputs: ['atomicControls'],
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prerequisiteSteps: ['obligation-extraction'],
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dbTables: ['canonical_controls', 'control_parent_links'],
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dbMode: 'read/write',
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ragCollections: [],
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isOptional: false,
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url: '/sdk/control-library',
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completion: 80,
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},
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{
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id: 'dependency-engine',
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name: 'Dependency Engine + Evaluation',
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nameShort: 'Dependencies',
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package: 'betrieb',
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seq: 5500,
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checkpointId: 'CP-DEP',
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checkpointType: 'REQUIRED',
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checkpointReviewer: 'NONE',
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description: '5 Dependency-Typen, generische Condition Language, automatische Generierung via Ontology + Domain Packs.',
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descriptionLong: 'Die Dependency Engine modelliert logische Abhaengigkeiten zwischen Controls: supersedes (A ersetzt B), prerequisite (A muss vor B), compensating_control (A kompensiert B-Failure), scope_exclusion (A schliesst B aus), conditional_requirement (B nur unter Bedingung). Generische Condition Language (AND/OR/NOT + Feldoperatoren). Priority-basierte Konfliktloesung. Zykluserkennung (DFS). Automatische Generierung via: (1) Ontology (Phase-Sequenz), (2) Pattern-Regeln, (3) Domain Packs (DSGVO, AI Act, CRA, Security, Arbeitsrecht). MCP-Output mit dependency_resolution Trace.',
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legalBasis: 'Pipeline-intern (Control Dependency Resolution)',
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inputs: ['atomicControls'],
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outputs: ['evaluatedControls', 'dependencyGraph'],
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prerequisiteSteps: ['atomic-composition'],
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dbTables: ['control_dependencies', 'control_evaluation_results'],
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dbMode: 'read/write',
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ragCollections: [],
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isOptional: false,
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url: '/sdk/control-library',
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completion: 100,
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},
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]
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