8 Commits

Author	SHA1	Message	Date
Lars	ab616ba044	feat: Introduce admin reference value types management in API and UI ... - Added new routes and API endpoints for managing reference value types in the admin section. - Updated the frontend to include navigation and components for reference value types management. - Enhanced the backend to support the new reference value types in the data layer and versioning.	2026-04-06 19:51:23 +02:00
Lars	f0e6fd04fb	feat: Add personal reference values management in settings and API ... - Introduced new routes and API endpoints for managing personal reference values. - Updated the SettingsPage to include a section for reference values with navigation to manage them. - Enhanced the backend to support reference values in the data layer and versioning. - Added necessary imports and UI components for a seamless user experience.	2026-04-06 19:45:06 +02:00
Lars	b888f5d3c8	feat: Phase 4 - End Node Template Engine (v0.9n) ... Backend: - workflow_models.py: EndNodeOutputMode enum (AUTO, TEMPLATE) - workflow_executor.py: execute_end_node() with Jinja2 rendering - Template Context: {{node_id.analysis_core}}, {{node_id.decision_signals.key}} - Conditional Rendering: {% if node_id %} for optional paths - AUTO Mode: Backward compatible (concatenates all analyses) - TEMPLATE Mode: Custom Jinja2 templates with placeholders Features: - Access node results: {{node_id.analysis_core}} - Access signals: {{node_id.decision_signals.relevanz}} - Optional paths: {% if node_id %}...{% endif %} - Default values: {{node_id|default("N/A")}} Version: 0.9n Module: workflow 0.6.0 Konzept: konzept_workflow_engine_konsolidated.md (Section 11) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-05 07:07:49 +02:00
Lars	e2a132353d	feat: Phase 4 - Join Nodes and Path Consolidation ... Backend Implementation (v0.9m, workflow 0.5.0): - join_evaluator.py (394 lines): Join-Strategie-Evaluator - evaluate_join_node(): Hauptlogik für Join-Node Execution - Join-Strategien: wait_all, wait_any, best_effort - Skip-Handling: ignore_skipped, use_placeholder, require_minimum - Result Consolidation: merge analysis_cores, combine signals - Partial Execution: korrekte Behandlung von SKIPPED/FAILED Pfaden - workflow_executor.py: execute_join_node() Integration - BFS-Traversierung erweitert für Join-Nodes - NodeExecutionState List → Dict Konvertierung für Signale - Signal-Name-Kollisionen via node_id Präfix gelöst Testing (49 Tests passing): - test_phase4_join_nodes.py: 18 neue Unit Tests - Join-Strategien (wait_all, wait_any, best_effort) - Skip-Handling (ignore, placeholder) - Result Consolidation (merge, combine) - Partial Execution (mixed status paths) - Helper Functions (collect, check, merge, combine) - Backward Compatibility: 31 Phase 2/3 Tests (alle passing) - test_phase2_workflow_executor.py: 1 Test aktualisiert - test_phase3_logic_evaluator.py: 20 Tests unverändert Konzept: konzept_workflow_engine_konsolidated.md (Sektion 8.8) Anforderungsanalyse: phase4_anforderungsanalyse.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-04 12:27:31 +02:00
Lars	2ce0874dcb	feat: Phase 3 - Logic Nodes + Conditional Branching ... Backend: - logic_evaluator.py (NEU, 307 Zeilen): Deterministischer Logic Evaluator - Vergleichsoperatoren: EQ, NEQ, IN, NOT_IN, GT, LT, GTE, LTE, CONTAINS - Logische Operatoren: AND, OR, NOT mit Verschachtelung - Resolve signal references (node_id.question_type) - Error handling für UNCLEAR/INVALID/NOT_DECIDABLE Signale - workflow_executor.py (ERWEITERT): - execute_logic_node(): Bedingungen evaluieren, Pfade aktivieren/deaktivieren - execute_workflow(): BFS-Traversierung mit Edge-Activation statt Sequential - _apply_fallback(): 4 Fallback-Strategien (CONSERVATIVE_SKIP, DEFAULT_PATH, UNCERTAINTY_PATH, DOCUMENT_ONLY) - _has_active_incoming_edge(): Prüft ob Node erreichbar ist - _get_edges_by_label(): Findet then/else/uncertainty Pfade - workflow_models.py (ERWEITERT): - LogicOperator.CONTAINS hinzugefügt - version.py: 0.9k → 0.9l, workflow 0.3.0 → 0.4.0 Tests: - test_phase3_logic_evaluator.py (NEU): 20 Unit Tests (alle passing) - Comparison operators (EQ, NEQ, IN, GT, LT, CONTAINS) - Logical operators (AND, OR, NOT) - Nested expressions - Error handling (missing refs, UNCLEAR/INVALID signals) - test_phase2_workflow_executor.py (AKTUALISIERT): 11 Tests (alle passing) - execute_node() graph parameter hinzugefügt (Phase 3 requirement) - test_execute_node_unknown_type: logic → join (logic jetzt implementiert) - test_phase3_workflow_branching.py (NEU): Integration Tests vorbereitet - Erfordert vollständige DB-Mock-Strategie (wird in E2E-Test nachgeholt) Phase 2 Backward Compatibility: ✅ Alle Phase 2 Tests bestehen weiterhin Konzept: .claude/task/Workflow_engine_prompting_engine/konzept_workflow_engine_konsolidated.md Anforderungsanalyse: .claude/task/Workflow_engine_prompting_engine/phase3_anforderungsanalyse.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-04 08:02:22 +02:00
Lars	1f8791f4dd	feat: Phase 2 - Normalisierung + Workflow Executor ... Backend: - normalization_engine.py (200 Zeilen): Synonym-Mapping, 5 Statuswerte * normalize_decision_signal(): Kaskade (exact → case → synonym → invalid) * apply_synonym_mapping(): DB-basierte Synonyme (case-insensitive) * normalize_all_signals(): Batch-Processing gegen Katalog * load_question_catalog(): Lädt normalization_rules aus DB - workflow_executor.py (440 Zeilen): Sequenzielle Workflow-Ausführung * execute_workflow(): Traversiert DAG in topologischer Reihenfolge * execute_node(): Führt analysis nodes aus (start/end = no-op) * aggregate_results(): Kombiniert analysis_core + normalized_signals * save_execution_state(): Persistiert in workflow_executions - workflow_models.py: Erweitert um Phase 2 Models * SignalStatus Enum (valid, normalized, unclear, invalid, not_decidable) * NormalizedSignal (question_type, raw_value, normalized_value, status) * NodeExecutionState (node_id, status, analysis_core, normalized_signals) * ExecutionResult (execution_id, workflow_id, status, node_states, aggregated_result) - workflow_engine.py: Neue Funktion get_execution_order() * Flattened topological sort für sequenzielle Execution * Phase 7: Wird zu levels (parallele Execution) - prompt_executor.py: execute_workflow_prompt() Implementierung * Ruft workflow_executor.execute_workflow() auf * Konvertiert ExecutionResult zu API-Response - routers/workflows.py (230 Zeilen): Workflow Execution API * POST /api/workflows/{id}/execute (mit enable_debug) * GET /api/workflows/executions/{id} (lädt gespeicherten State) * GET /api/workflows (listet alle aktiven Workflows) * GET /api/workflows/{id} (lädt einzelnen Workflow mit Graph) - main.py: Router-Registrierung (workflows.router) Tests: - test_phase2_normalization.py (17 Tests): Alle Normalisierungs-Szenarien * Exact match, case-insensitive, synonym mapping, invalid, whitespace * Batch-Normalisierung, not_in_catalog, mixed validity - test_phase2_workflow_executor.py (10 Tests): Executor + Aggregation * aggregate_results mit verschiedenen Konstellationen * execute_node für start/end/analysis/unknown * Integration mit question_augmenter + result_container_parser Alle 27 Unit-Tests bestanden. version: 0.9k (backend) module: workflow 0.3.0 Konzept: .claude/task/Workflow_engine_prompting_engine/anforderungsanalyse_umsetzungsplan.md (Phase 2)	2026-04-03 21:20:23 +02:00
Lars	ca562b7130	feat: Phase 1 - Fragenergänzung + Strukturierter Container ... Backend: - question_augmenter.py (290 Zeilen): Hybrid-Modell für Fragenergänzungen * merge_question_augmentations(): Knotengebundene Fragen überschreiben Prompt-Defaults * augment_prompt_with_questions(): Markdown-formatierte Fragenergänzung * parse_question_augmentations_from_jsonb(): JSONB → QuestionAugmentation[] - result_container_parser.py (250 Zeilen): Markdown-Sektionen-Parsing * parse_result_container(): Extrahiert Analysekern, Entscheidungsanteil, Begründungsanker * validate_decision_signal(): Normalisierung gegen answer_spectrum * Fallback-Parsing bei unstrukturierten Antworten - routers/workflow_questions.py (236 Zeilen): CRUD für workflow_question_catalog * GET /api/workflow/questions (mit active_only Filter) * POST/PUT/DELETE (Admin only, Soft Delete) - prompt_executor.py: Integration in execute_base_prompt() * Fragenergänzung vor LLM-Call (wenn node_questions oder catalog vorhanden) * Result-Container-Parsing nach LLM-Response - main.py: Router-Registrierung (workflow_questions) Tests: - test_phase1_question_augmenter.py (8 Tests): Hybrid-Modell, Formatierung, JSONB-Parsing - test_phase1_result_container_parser.py (17 Tests): Sektion-Extraktion, Decision-Parsing, Validierung Alle 25 Unit-Tests bestanden. version: 0.9j (backend) module: workflow 0.2.0 Konzept: .claude/task/Workflow_engine_prompting_engine/konzept_workflow_engine_konsolidated.md (Phase 1)	2026-04-03 18:02:25 +02:00
Lars	b5be6e21a5	feat: Phase 0 - Workflow Engine Foundation ... Backend: - DB-Migration 034: workflow_definitions, workflow_question_catalog, workflow_executions - ai_prompts.question_augmentations JSONB-Spalte (Hybridmodell: Prompt-Defaults) - 6 Grundtypen Fragenergänzungen mit Normalisierungsregeln (Seed-Daten) - Pydantic-Modelle (16 Models, 11 Enums) in workflow_models.py - Workflow-Engine: Graph-Parsing, Topologische Sortierung, DAG-Validierung - Dispatcher-Erweiterung type='workflow' (Stub für Phase 1-3) - Adjacency Lists, Erreichbarkeits-Prüfungen, Zyklen-Erkennung Testing: - 22 Unit-Tests (alle bestanden): Graph-Parsing, Validierung, Topologische Sortierung - Fixtures: simple_valid_graph, parallel_graph, branching_graph Version: - APP_VERSION 0.9i - DB_SCHEMA_VERSION 20260403 - Module: workflow 0.1.0 Anforderungsanalyse: .claude/task/Workflow_engine_prompting_engine/anforderungsanalyse_umsetzungsplan.md Konzept-Basis: .claude/task/Workflow_engine_prompting_engine/konzept_workflow_engine_konsolidated.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-03 16:55:51 +02:00