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Author	SHA1	Message	Date
Lars	922c846b03	chore: Remove generated test results file	2026-04-05 07:47:00 +02:00
Lars	7deca6c64d	test: Add unit tests for End Node Template Engine ... - test_end_node_template.py: Tests for execute_end_node() - Tests AUTO mode (backward compatible concatenation) - Tests TEMPLATE mode (Jinja2 rendering, conditionals) - Tests error handling (missing template, syntax errors) Note: Tests require Jinja2, run in Docker or CI/CD environment. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-05 07:46:49 +02:00
Lars	fac76c28da	fix: Handle None workflow_id in success path ... Also use 'N/A' placeholder in ExecutionResult when workflow_id is None (when using graph_data directly instead of workflow_definitions). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-05 07:28:30 +02:00
Lars	6016eec250	fix: Add ON CONFLICT to workflow_executions insert ... Prevents duplicate key violation when save_execution_state is called multiple times with the same execution_id (e.g., during error handling). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-05 07:26:10 +02:00
Lars	c95b4e185d	fix: Edge format normalization and nullable workflow_id ... Fixes: 1. Edge Format Mismatch: - graph_data uses React Flow format (source/target) - WorkflowEdge expects backend format (from/to) - Added normalization in parse_workflow_graph() 2. UUID Validation Error: - workflow_id can be None when using graph_data (Phase 5) - save_execution_state now accepts Optional[str] - ExecutionResult uses "N/A" placeholder when None Changes: - workflow_engine.py: normalize edges before Pydantic validation - workflow_executor.py: Optional[str] for workflow_id parameter Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-05 07:22:32 +02:00
Lars	fe28cce921	fix: Workflow executor graph parsing and error handling ... Fixes: - graph_data was incorrectly json.dumps() encoded (should stay as dict) - workflow_id=None in error handler caused ValidationError - parse_workflow_graph expects Dict, not str Changes: - Use graph_dict directly instead of json.dumps(graph_data) - Set workflow_id="" when None in error handler Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-05 07:18:43 +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	cab5758b0d	fix: Save prompt_name in graph_data for readable node display ... Bug: - Analysis nodes showed "Prompt #7edc6d6b-6cd5..." in canvas - After re-selecting prompt, showed readable name "Pipeline: Ernährungs-Analyse" - After loading workflow, showed UUID again Root Cause: - Serializer saved only prompt_id, not prompt_name - Deserializer expected prompt_name but got null - AnalysisNode fallback logic: data.prompt_name || `Prompt #{prompt_id}` - Result: Showed UUID as fallback Fix: - workflowSerializer.js line 25: Added prompt_name to serialization - Now saves both prompt_id AND prompt_name in graph_data - On load: prompt_name is restored → AnalysisNode shows readable name Testing: - Create workflow with analysis node + prompt selection - Save → Canvas should show "Pipeline: Körper-Analyse" (not UUID) - Reload → Canvas should still show readable name (not UUID)	2026-04-04 22:50:40 +02:00
Lars	d9bcaaaac6	fix: Add missing GET /api/prompts/{id} endpoint ... Critical Backend Bug: - Frontend calls api.getPrompt(id) → GET /api/prompts/{uuid} - Backend had NO endpoint for single prompt retrieval by ID - Result: 405 Method Not Allowed Backend Endpoints Before: ✓ GET /api/prompts - List all ✓ POST /api/prompts - Create ✓ PUT /api/prompts/{id} - Update ✗ GET /api/prompts/{id} - MISSING! Backend Endpoints After: ✓ GET /api/prompts - List all ✓ GET /api/prompts/{id} - Get single (NEW) ✓ POST /api/prompts - Create ✓ PUT /api/prompts/{id} - Update Implementation: - Added get_prompt(prompt_id: str) function - Returns single prompt by UUID - 404 if not found - Requires auth (admin or user) This fixes: - Workflow loading after save (loadWorkflow calls getPrompt) - Workflow editing from admin list (Edit button calls getPrompt) - All 405 Method Not Allowed errors Root Cause: Backend was incomplete, missing basic CRUD read-by-id endpoint	2026-04-04 22:43:07 +02:00
Lars	84c1fa3c1d	fix: UUID handling - remove parseInt() for prompt IDs ... Critical Bug Fixes: 1. Prompt IDs are UUIDs (strings), NOT numbers 2. parseInt(UUID) produces wrong results: - parseInt("3b4d7d64-...") = 3 (truncates at first non-digit) - parseInt("aa291dde-...") = NaN 3. This caused: - Prompt selection: saved as NaN instead of UUID - Load workflow: GET /api/prompts/3 instead of /api/prompts/3b4d7d64-... - 405 Method Not Allowed errors Changes: - useEffect: loadWorkflow(id) instead of loadWorkflow(parseInt(id)) - Prompt onChange: prompt_id: promptId (string) instead of parseInt(promptId) - Removed NaN check (unnecessary for UUID strings) Root Cause: Backend uses UUID primary keys, frontend assumed integer IDs Testing: Console logs still active for verification	2026-04-04 22:39:08 +02:00
Lars	2b8bebd381	debug: Add comprehensive console logging for workflow editor debugging ... Debug Logs Added: - useEffect: Log ID validation and loadWorkflow calls - loadWorkflow: Log API response, graph_data, deserialization results - handleNodeUpdate: Log updates and resulting node state - handleSave: Log serialization, API calls, navigation Bug Fixes: - useEffect: Add !isNaN(parseInt(id)) check to prevent /api/prompts/NaN calls - Prompt selection: String conversion for value prop (Number vs String mismatch) - <select value={String(selectedNode.data.prompt_id)}> - <option value={String(prompt.id)}> - onChange: find with String(p.id) === promptId Issues to Debug: - Why does useEffect run with id=undefined after navigate? - Why does loadWorkflow not populate nodes/edges? - Why does prompt selection not persist? Next Step: User tests with Browser Console open, reports logs	2026-04-04 22:30:43 +02:00
Lars	2f70a39052	fix: Phase 5 - Workflow Editor UX Fixes (Round 3) ... Frontend Fixes: - AdminPromptsPage: Edit button navigates to workflow-editor for workflow type prompts - WorkflowEditorPage: Fixed save navigation (alert before navigate) - WorkflowEditorPage: selectedNode derived from selectedNodeId (eliminates stale state) - FallbackConfig: Show node labels instead of IDs in fallback edge dropdown - WorkflowCanvas: Enable edge deletion with deletable: true - WorkflowEditorPage: Hide sidebar when config panel is open Bugs Fixed: - C1: Save error "Method Not Allowed" after success - C2: Edit button in admin doesn't open workflow editor - H1: Prompt selection not displayed when re-editing node - H2: Fallback edge dropdown shows node_1/node_2 instead of names - H3: Cannot delete edges - M1: Sidebar takes space when config panel open Technical Changes: - Replaced useState(selectedNode) with useState(selectedNodeId) + derived selectedNode - Removed sync useEffect (no longer needed with derived state) - Added nodes prop to FallbackConfig for label lookup - Swapped alert/navigate order to prevent navigation errors Testing: Manual testing required (see manual test cases)	2026-04-04 21:16:15 +02:00
Lars	7d22b052dd	fix: Phase 5 - Workflow save + node persistence bugs ... KRITISCHE FIXES: 1. Backend: Workflow-Type Support - models.py: graph_data Feld hinzugefügt - models.py: slug Optional (auto-generiert) - prompts.py: 'workflow' in erlaubten Typen - prompts.py: graph_data in INSERT/UPDATE - prompts.py: Auto-Slug-Generierung aus Name - FIX: "Field required: slug" Error behoben 2. Frontend: Node-Updates Persistence - selectedNode sync mit nodes array (useEffect) - FIX: Änderungen gingen verloren (stale state) - FIX: Prompt-Auswahl nicht sichtbar nach Edit - FIX: Fallback-Strategy nicht gespeichert - FIX: Node-Name Änderungen nicht übernommen BEHOBEN: - ❌ Save fehlgeschlagen → ✅ Workflows speicherbar - ❌ Node-Name ignoriert → ✅ Live-Update - ❌ Prompt verschwindet → ✅ Bleibt sichtbar - ❌ Fallback nicht saved → ✅ Persistiert Tested: Backend API akzeptiert jetzt type='workflow' Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-04 19:17:41 +02:00
Lars	e3ef18674a	fix: Phase 5 - Critical UX bugs in Workflow Editor ... Behebt 5 kritische Bugs die Editor unbenutzbar machten: BUG-01: Config Panel - Close Button hinzugefügt (×) - User war im Config Panel "gefangen" - Jetzt: Click × zum Deselektieren BUG-02: Save UX - Validierungs-Feedback verbessert - Speichern-Button zeigt Lock-Icon (🔒) bei Fehlern - Tooltip erklärt warum Speichern blockiert ist - Error-Message mit Hinweis auf Validierung BUG-03: Analysis Node - Prompt-Auswahl implementiert - Dropdown zum Auswählen von Basis-Prompts - Lädt verfügbare Prompts via API - Zeigt gewählten Prompt-Namen an BUG-04: Label-Input - UX verbessert - Header zeigt "Node-Konfiguration" (nicht Label) - Input hat Placeholder und Hilfetext - "Änderungen automatisch übernommen" Hinweis BUG-05: Admin Page - "Neuer Workflow" Button - Button neben "+ Neuer Prompt" - Navigiert zu /workflow-editor/new - Workflow-Filter im Type-Filter hinzugefügt Tested: Manuell durch User (alle Bugs bestätigt gefixt) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-04 18:59:47 +02:00
Lars	dc59596f01	feat: Phase 5 - Visual Workflow Editor (Option B) ... Backend (Mini-Backend 1-2h): - Migration 016: ai_prompts.graph_data JSONB column - workflow_executor: graph_data parameter support (backward-compatible) - prompt_executor: execute_workflow_prompt uses graph_data Frontend (Main effort 25-35h): - WorkflowCanvas: React Flow wrapper component - 5 Custom Nodes: Start, End, Analysis, Logic, Join - 4 Config Panels: QuestionAugmentation, LogicExpression, Fallback, Join - workflowValidation: Structural + logical validation - workflowSerializer: Canvas ↔ JSONB conversion - WorkflowEditorPage: Main orchestration (420 LOC) - Route: /workflow-editor/:id - CSS: workflowEditor.css (300 LOC) Architecture: - Option B: ai_prompts.type='workflow' (not separate table) - panels/ subdirectory for clean separation - WorkflowCanvas reusable component - User GUI identical (Workflows = Prompts) - Backward-compatible (type='pipeline' unchanged) Version: v0.9m → v0.9n (Phase 5 complete) Module: workflow 0.5.0 → 0.6.0 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-04 17:56:00 +02:00
Lars	a7058c30be	feat: Enhance EmojiIconPicker with search functionality and keyword support	2026-04-04 14:22:44 +02:00
Lars	2101080719	feat: Expand EmojiIconPicker with additional curated emoji groups and enhance functionality for custom groups	2026-04-04 14:17:35 +02:00
Lars	5aae999a65	feat: Add EmojiIconPicker component and integrate it into Admin pages for icon selection	2026-04-04 14:07:54 +02:00
Lars	dc87e7f3b8	cursor_Setup	2026-04-04 14:05:50 +02:00
Lars	c607cd1833	fix: Convert joined signals Dict to List for NodeExecutionState ... NodeExecutionState expects normalized_signals as List[NormalizedSignal], but join_evaluator returns Dict[str, NormalizedSignal]. Fix: Convert dict to list before returning NodeExecutionState. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-04 12:33:58 +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	16dc08cd7d	test: Add regression test for hybrid model - node spectrum overrides catalog	2026-04-03 21:55:19 +02:00
Lars	c588372f3a	fix: Hybrid model - node-specific question spectrums override catalog (Phase 1 requirement)	2026-04-03 21:49:13 +02:00
Lars	585f189b13	fix: Remove extra_vars parameter from resolve_placeholders call - function doesn't support it yet	2026-04-03 21:44:39 +02:00
Lars	acd4830795	fix: Access topological_order directly from engine, not from non-existent validator attribute	2026-04-03 21:38:45 +02:00
Lars	ac2e7cf5bb	fix: Use dict keys for all RealDictCursor row access in Phase 2 code	2026-04-03 21:36:44 +02:00
Lars	0725461056	fix: Use dict keys instead of numeric indices for RealDictCursor rows	2026-04-03 21:34:47 +02:00
Lars	ce4666a535	fix: Import call_openrouter from routers.prompts instead of non-existent openrouter module	2026-04-03 21:33:09 +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
Lars	c04e72a397	fix: Placeholder Catalog nutzt Registry als Single Source of Truth ... Problem: - get_placeholder_catalog() hatte hardcodierte Liste (Körper: 11, Ernährung: 8, Training: 9) - Registry hat vollständige Cluster (Körper: 17, Ernährung: 14, Aktivität: 17) - Export zeigte unvollständige Placeholder-Zählungen Lösung: - get_placeholder_catalog() nutzt jetzt get_registry() als primäre Quelle - Fallback auf Legacy-Liste nur für nicht-registrierte Placeholder - Automatisch aktuell bei neuen Registry-Einträgen Betroffen: - /api/prompts/placeholders/export-values (Settings Export) - /api/prompts/placeholders/export-values-extended (Metadata Export) - /api/prompts/execute (Prompt Test Debug-Export) - /api/prompts/placeholders/catalog (Catalog Endpoint) Erwartete Zahlen nach Deploy: - Körper: 17 (statt 11) - Ernährung: 14 (statt 8) - Aktivität: 17 (statt 9) - Total: ~70-75 Placeholder (48 Registry + Legacy) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-03 08:47:22 +02:00
Lars	10f608438c	Add tests for Activity Cluster registration and smoke tests for login functionality ... - Implemented a new test script `test_activity_registration.py` to verify the registration of Activity placeholders, ensuring all expected placeholders are registered, have complete metadata, and correct evidence distribution. - Created a new smoke test suite `dev-smoke-test.spec.js` to validate the login process, dashboard loading, and navigation to key sections, while checking for critical console errors. - Added a JSON file `test-results.last-run.json` to track the status of the last test run, indicating failures if any tests do not pass.	2026-04-03 08:22:08 +02:00
Lars	485aec40a0	feat: Activity Cluster Placeholder Registry - Complete Implementation (17 Placeholders) ... Implements complete placeholder registry for Activity & Training metrics following Phase 0c Multi-Layer Architecture pattern. SCOPE: 17 Activity Placeholders - Group 1 (3): Legacy Resolver - activity_summary, activity_detail, trainingstyp_verteilung - Group 2 (7): Basic Metrics - volume, frequency, quality, load, monotony, strain, rest compliance - Group 3 (7): Advanced Metrics - 5x ability_balance, vo2max_trend, activity_score IMPLEMENTATION: - File: backend/placeholder_registrations/activity_metrics.py (~1,100 lines) - Pattern: Nutrition Part A (common_metadata + evidence-based tagging) - Evidence: CODE_DERIVED (58%), DRAFT_DERIVED (16%), MIXED (15%), TO_VERIFY (6%), UNRESOLVED (5%) - Formulas: All documented in known_limitations (Load Model, Monotony, Strain, Ability Balance, Activity Score) CRITICAL ISSUES IDENTIFIED (NOT FIXED per NO LOGIC CHANGES): 1. quality_label field mismatch (quality_sessions_pct) - TO_VERIFY 2. RPE moderate quality mapping bug (proxy_internal_load_7d) - CODE_DERIVED 3. JSONB dependencies (6 placeholders) - ability_balance_*, rest_day_compliance 4. vo2max_trend_28d questionable category (Recovery vs. Activity) - TO_VERIFY TESTING: ✓ All 17 placeholders registered successfully ✓ Registry size: 48 (31 pre-existing + 17 new) ✓ Dev backend integration: no errors ✓ Auto-registration on module import: working ARCHITECTURE ALIGNMENT: - Phase 0c Multi-Layer: 14/17 aligned (Group 2 + 3) - Old Resolver Pattern: 3/17 (Group 1 - documented, should be refactored) - Layer separation: data_layer → resolver → export FILES: - NEW: backend/placeholder_registrations/activity_metrics.py - MODIFIED: backend/placeholder_registrations/__init__.py (added import) - MODIFIED: CLAUDE.md (placeholder registry rules) DOCUMENTATION: - Gap Analysis: .claude/task/rework_0b_placeholder/ACTIVITY_CLUSTER_GAP_ANALYSIS.md - Code Inspection: .claude/task/rework_0b_placeholder/ACTIVITY_CLUSTER_CODE_INSPECTION.md - Implementation Report: .claude/task/rework_0b_placeholder/ACTIVITY_CLUSTER_IMPLEMENTATION_REPORT.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-03 08:20:25 +02:00
Lars	57800b686a	fix: Body Cluster - PlaceholderType.TEXT_SUMMARY → INTERPRETED ... - caliper_summary + circ_summary used invalid PlaceholderType.TEXT_SUMMARY - TEXT_SUMMARY is OutputType, not PlaceholderType - Changed to PlaceholderType.INTERPRETED (summaries interpret raw data) Valid PlaceholderType values: ATOMIC, RAW_DATA, INTERPRETED, SCORE, META Valid OutputType values: NUMERIC, STRING, BOOLEAN, JSON, LIST, TEXT_SUMMARY	2026-04-02 19:11:06 +02:00
Lars	fbaaf08e29	feat: Body Cluster - Placeholder Registry Implementation ... Registers 17 body composition and measurement placeholders with complete metadata: Weight & Trends (5): - weight_aktuell: Latest weight snapshot - weight_trend: 28d delta with direction (increasing/decreasing/stable) - weight_7d_median: 7d median for noise reduction - weight_28d_slope: Linear regression slope (kg/day, 28d window) - weight_90d_slope: Linear regression slope (kg/day, 90d window) Body Composition (5): - kf_aktuell: Latest body fat percentage - fm_28d_change: Fat mass delta (28d) - lbm_28d_change: Lean body mass delta (28d) - waist_hip_ratio: Waist-to-hip ratio - recomposition_quadrant: FM/LBM change classification (optimal/cut_with_risk/bulk/unfavorable) Circumference Deltas (5): - waist_28d_delta: Waist circumference change (28d) - arm_28d_delta: Arm circumference change (28d) - chest_28d_delta: Chest circumference change (28d) - hip_28d_delta: Hip circumference change (28d) - thigh_28d_delta: Thigh circumference change (28d) Summaries (2): - caliper_summary: Body fat text summary (BF% + method + date) - circ_summary: Circumference summary (Best-of-Each strategy) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder (374 total fields) - CODE_DERIVED: Technical fields, formulas from code inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - MIXED: Calculation logic, formulas, thresholds - TO_VERIFY: Architecture layer decisions Critical formulas documented in known_limitations: - Linear Regression: slope = Σ((x - x̄)(y - ȳ)) / Σ((x - x̄)²) - FM/LBM Calculation: FM = weight × (BF% / 100), LBM = weight - FM - Circumference Delta Logic: latest IN window vs. oldest BEFORE window (can span >28d) - Recomposition Quadrants: Sign-based (FM sign × LBM sign → quadrant) - Best-of-Each (circ_summary): Each measurement point shows individually latest value (mixed dates) Known limitations captured: - weight_trend: Zeit-Inkonsistenz (canonical requires 28d, code accepts parameter) - Circumference Deltas: Reference logic can extend beyond window if measurements sparse - FM/LBM: Requires same-date weight + body_fat_pct measurements - Recomposition: No tolerance zone for "stable" (small changes trigger quadrant flips) - Summaries: Text format (canonical recommends structured JSON, kept as-is per NO-CHANGE rule) Evidence distribution: - CODE_DERIVED: 62% (metadata from code inspection) - DRAFT_DERIVED: 18% (from canonical requirements) - MIXED: 15% (formulas, calculation logic) - TO_VERIFY: 5% (architecture decisions) - UNRESOLVED: <1% Registry now contains 31 placeholders total (14 Nutrition + 17 Body). Files: - backend/placeholder_registrations/body_metrics.py (NEW, 1307 lines) - backend/placeholder_registrations/__init__.py (UPDATED, +body_metrics import) Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Change Plan: .claude/task/rework_0b_placeholder/BODY_CLUSTER_CHANGE_PLAN.md Code Inspection: .claude/task/rework_0b_placeholder/BODY_CLUSTER_CODE_INSPECTION.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 18:57:15 +02:00
Lars	5bf8895fb3	fix: Nutrition Cluster Abschluss - Metadaten-Konsistenz ... Behebt letzte Inkonsistenzen im Export: 1. protein_g_per_kg: - time_window: 'mixed' → '7d' (dominante Komponente) - Kommentar angepasst: weight ist snapshot, aber protein (7d) ist primär - known_limitations dokumentiert die Inkonsistenz weiterhin 2. protein_adequacy_28d: - unit: 'score' → 'score (0-100)' (Konsistenz mit macro_consistency_score) - Klarere Skalen-Angabe im Export Finaler Export-Status: 14/14 Nutrition Placeholders konsistent - Alle haben korrekte Category (Ernährung) - Alle haben präzise Units - Alle haben eindeutige Time Windows - Alle haben korrekte Output Types Abschlussarbeit für Ernährungs-Cluster. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 13:07:35 +02:00
Lars	ffdf9074c3	fix: Part C OutputType - use STRING instead of TEXT ... Fixed AttributeError: OutputType has no attribute TEXT. Correct enum values are: NUMERIC, STRING, BOOLEAN, JSON, LIST, TEXT_SUMMARY. Affected placeholders: - energy_deficit_surplus: OutputType.STRING - intake_volatility: OutputType.STRING Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:56:13 +02:00
Lars	ffb30eaff5	feat: Placeholder Registry Part C - Nutrition Consistency & Balance ... Registers 5 nutrition-related placeholders with complete metadata: - macro_consistency_score: CV-based Makro-Konsistenz Score (0-100) - energy_balance_7d: Energiebilanz (kcal/day avg, intake - TDEE) - energy_deficit_surplus: Status (deficit/maintenance/surplus) - intake_volatility: Klassifikation (stable/moderate/high) - nutrition_days: Anzahl valider Ernährungstage (30d) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder - CODE_DERIVED: Technical fields, formulas from code inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - MIXED: Calculation logic (TDEE model, thresholds, formulas) - TO_VERIFY: Architecture layer decisions Critical details documented: - macro_consistency_score: CV formula + thresholds explicitly documented - energy_balance_7d: TDEE model (weight_kg × 32.5), unit clarified (kcal/day avg) - energy_deficit_surplus: Status thresholds (<-200, -200 to +200, >+200) - intake_volatility: Category mapping from macro_consistency_score - nutrition_days: Validation criteria (any entry = valid day) Known limitations captured: - TDEE model is simplified (no activity/age/gender adjustment) - Thresholds are somewhat arbitrary (e.g., 200 kcal for deficit/surplus) - High volatility not necessarily bad (context-dependent) Registry now contains 14 placeholders total: - Part A: 4 (kcal_avg, protein_avg, carb_avg, fat_avg) - Part B: 5 (protein targets + adequacy) - Part C: 5 (consistency + balance + meta) Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:55:03 +02:00
Lars	0c19e0c0ed	fix: Part B protein placeholders - aggregate by date ... Fixes calculate_protein_g_per_kg and calculate_protein_days_in_target: **Problem:** Both functions were treating individual nutrition_log entries as days, causing incorrect calculations when multiple entries exist per day (e.g., from CSV imports: 233 entries across 7 days). **Solution:** 1. calculate_protein_g_per_kg: - Added GROUP BY date, SUM(protein_g) to aggregate by day - Now averages daily totals, not individual entries - Correct: 7 days → 7 values, not 233 entries → 233 values 2. calculate_protein_days_in_target: - Added GROUP BY date, SUM(protein_g) to aggregate by day - Calculates target range in absolute grams (not g/kg per entry) - Counts unique DAYS in range, not entries - Correct format: "5/7" (5 of 7 days), not "150/233" (entries) **Impact:** - protein_g_per_kg: was returning "nicht verfügbar" → now returns correct value - protein_days_in_target: was returning "nicht verfügbar" → now returns correct format **Root Cause:** Functions expected 7 unique dates but got 233 entries. With export date 2026-04-02 and last data 2026-03-26, the 7-day window had insufficient unique dates. Issue reported by user: Part B placeholders not showing correct values in extended export (registry metadata was correct, but computed values failed). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:43:33 +02:00
Lars	b00f6ac512	feat: Placeholder Registry Part B - Protein Placeholders ... Registers 5 protein-related placeholders with complete metadata: - protein_ziel_low: Lower protein target (1.6 g/kg × latest weight) - protein_ziel_high: Upper protein target (2.2 g/kg × latest weight) - protein_g_per_kg: Protein intake per kg body weight - protein_days_in_target: Days in protein range (format: 5/7) - protein_adequacy_28d: Protein adequacy score (0-100) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder - CODE_DERIVED: Technical fields from source inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - UNRESOLVED: Fields requiring clarification - TO_VERIFY: Assumptions needing verification Critical issues documented in known_limitations: - protein_g_per_kg: Weight basis inconsistency (protein 7d avg / weight latest) - protein_adequacy_28d: Score logic explicitly documented (1.4-1.6-2.2 thresholds) Registry now contains 9 placeholders total (4 Part A + 5 Part B). Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Change Plan: .claude/task/rework_0b_placeholder/NUTRITION_PART_B_CHANGE_PLAN.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:27:58 +02:00
Lars	81681f0de3	fix: Handle missing TimeWindow enum in export endpoint ... Error: NameError TimeWindow not defined Fix: Graceful degradation if old metadata enums not available Gap report now optional (empty if old system unavailable) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 11:54:02 +02:00
Lars	645967a2ab	feat: Placeholder Registry Framework + Part A Nutrition Metrics ... Part A Implementation (Nutrition Basis Metrics): - Registry-based metadata system (flexible, not hardcoded) - 4 placeholders registered: kcal_avg, protein_avg, carb_avg, fat_avg - Evidence-based tagging (code-derived, draft-derived, unresolved, to_verify) - Single source of truth for all consumers (Prompt, GUI, Export, Validation) Technical: - backend/placeholder_registry.py: Core registry framework - backend/placeholder_registrations/nutrition_part_a.py: Part A registrations - backend/placeholder_registry_export.py: Export integration - backend/routers/prompts.py: /placeholders/export-values-extended integration Metadata completeness: - 22 metadata fields per placeholder - Evidence tracking for all fields - Architecture alignment (Layer 1/2a/2b) NO LOGIC CHANGE: - Data Layer unchanged (nutrition_metrics.py) - Resolver unchanged (placeholder_resolver.py) - Values identical (only metadata/export enhanced) Breaking Change Risk: NONE Deploy Risk: VERY LOW (only export enhancement) Plan: .claude/task/rework_0b_placeholder/NUTRITION_PART_A_CHANGE_PLAN.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 11:46:16 +02:00
Lars	6cdc159a94	fix: add missing Header import in prompts.py ... NameError: name 'Header' is not defined Added Header to fastapi imports for export endpoints auth fix. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 21:25:33 +02:00
Lars	650313347f	feat: Placeholder Metadata V2 - Normative Implementation + ZIP Export Fix ... MAJOR CHANGES: - Enhanced metadata schema with 7 QA fields - Deterministic derivation logic (no guessing) - Conservative inference (prefer unknown over wrong) - Real source tracking (skip safe wrappers) - Legacy mismatch detection - Activity quality filter policies - Completeness scoring (0-100) - Unresolved fields tracking - Fixed ZIP/JSON export auth (query param support) FILES CHANGED: - backend/placeholder_metadata.py (schema extended) - backend/placeholder_metadata_enhanced.py (NEW, 418 lines) - backend/generate_complete_metadata_v2.py (NEW, 334 lines) - backend/tests/test_placeholder_metadata_v2.py (NEW, 302 lines) - backend/routers/prompts.py (V2 integration + auth fix) - docs/PLACEHOLDER_METADATA_VALIDATION.md (NEW, 541 lines) PROBLEMS FIXED: ✓ value_raw extraction (type-aware, JSON parsing) ✓ Units for dimensionless values (scores, correlations) ✓ Safe wrappers as sources (now skipped) ✓ Time window guessing (confidence flags) ✓ Legacy inconsistencies (marked with flag) ✓ Missing quality filters (activity placeholders) ✓ No completeness metric (0-100 score) ✓ Orphaned placeholders (tracked) ✓ Unresolved fields (explicit list) ✓ ZIP/JSON export auth (query token support for downloads) AUTH FIX: - export-catalog-zip now accepts token via query param (?token=xxx) - export-values-extended now accepts token via query param - Allows browser downloads without custom headers Konzept: docs/PLACEHOLDER_METADATA_REQUIREMENTS_V2_NORMATIVE.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 21:23:37 +02:00
Lars	087e8dd885	feat: Add Placeholder Metadata Export to Admin Panel ... Adds download functionality for complete placeholder metadata catalog. Backend: - Fix: None-template handling in placeholder_metadata_extractor.py - Prevents TypeError when template is None in ai_prompts - New endpoint: GET /api/prompts/placeholders/export-catalog-zip - Generates ZIP with 4 files: JSON catalog, Markdown catalog, Gap Report, Export Spec - Admin-only endpoint with on-the-fly generation - Returns streaming ZIP download Frontend: - Admin Panel: New "Placeholder Metadata Export" section - Button: "Complete JSON exportieren" - Downloads extended JSON - Button: "Complete ZIP" - Downloads all 4 catalog files as ZIP - Displays file descriptions - api.js: Added exportPlaceholdersExtendedJson() function Features: - Non-breaking: Existing endpoints unchanged - In-memory ZIP generation (no temp files) - Formatted filenames with date - Admin-only access for ZIP download - JSON download available for all authenticated users Use Cases: - Backup/archiving of placeholder metadata - Offline documentation access - Import into other tools - Compliance reporting Files in ZIP: 1. PLACEHOLDER_CATALOG_EXTENDED.json - Machine-readable metadata 2. PLACEHOLDER_CATALOG_EXTENDED.md - Human-readable catalog 3. PLACEHOLDER_GAP_REPORT.md - Unresolved fields analysis 4. PLACEHOLDER_EXPORT_SPEC.md - API specification Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 20:37:52 +02:00
Lars	b7afa98639	docs: Add placeholder metadata deployment guide ... Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 20:33:46 +02:00
Lars	a04e7cc042	feat: Complete Placeholder Metadata System (Normative Standard v1.0.0) ... Implements comprehensive metadata system for all 116 placeholders according to PLACEHOLDER_METADATA_REQUIREMENTS_V2_NORMATIVE standard. Backend: - placeholder_metadata.py: Complete schema (PlaceholderMetadata, Registry, Validation) - placeholder_metadata_extractor.py: Automatic extraction with heuristics - placeholder_metadata_complete.py: Hand-curated metadata for all 116 placeholders - generate_complete_metadata.py: Metadata generation with manual corrections - generate_placeholder_catalog.py: Documentation generator (4 output files) - routers/prompts.py: New extended export endpoint (non-breaking) - tests/test_placeholder_metadata.py: Comprehensive test suite Documentation: - PLACEHOLDER_GOVERNANCE.md: Mandatory governance guidelines - PLACEHOLDER_METADATA_IMPLEMENTATION_SUMMARY.md: Complete implementation docs Features: - Normative compliant metadata for all 116 placeholders - Non-breaking extended export API endpoint - Automatic + manual metadata curation - Validation framework with error/warning levels - Gap reporting for unresolved fields - Catalog generator (JSON, Markdown, Gap Report, Export Spec) - Test suite (20+ tests) - Governance rules for future placeholders API: - GET /api/prompts/placeholders/export-values-extended (NEW) - GET /api/prompts/placeholders/export-values (unchanged, backward compatible) Architecture: - PlaceholderType enum: atomic, raw_data, interpreted, legacy_unknown - TimeWindow enum: latest, 7d, 14d, 28d, 30d, 90d, custom, mixed, unknown - OutputType enum: string, number, integer, boolean, json, markdown, date, enum - Complete source tracking (resolver, data_layer, tables) - Runtime value resolution - Usage tracking (prompts, pipelines, charts) Statistics: - 6 new Python modules (~2500+ lines) - 1 modified module (extended) - 2 new documentation files - 4 generated documentation files (to be created in Docker) - 20+ test cases - 116 placeholders inventoried Next Steps: 1. Run in Docker: python /app/generate_placeholder_catalog.py 2. Test extended export endpoint 3. Verify all 116 placeholders have complete metadata Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 20:32:37 +02:00
Lars	c21a624a50	fix: E2 protein-adequacy endpoint - undefined variable 'values' -> 'daily_values'	2026-03-29 07:38:04 +02:00