13 Commits

Author	SHA1	Message	Date
Lars	baeddd7c13	feat: Enhance placeholder system with AI context support ... - Introduced `build_ai_placeholder_caption` function in `placeholder_registry.py` to generate AI context captions based on placeholder metadata. - Updated `resolve_placeholders` in `placeholder_resolver.py` to support modifiers for AI context, allowing for enhanced descriptions when placeholders are resolved. - Modified `get_placeholder_catalog` to include AI captions in the output, improving the metadata available for placeholders. - Adjusted `export_placeholder_values` to include AI captions in the exported data, enhancing the information provided to users. These changes improve the flexibility and functionality of the placeholder system, enabling richer context generation for dynamic content.	2026-04-11 21:36:29 +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	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	6e651b5bb5	fix: include stage outputs in debug info for value table ... - stage_debug now includes 'output' dict with all stage outputs - Fixes empty values for stage_X_outputkey in expert mode - Stage outputs are the actual AI responses passed to next stage	2026-03-26 14:33:00 +01:00
Lars	4a2bebe249	fix: value table metadata + |d modifier + cursor insertion (Issues #47, #48) ... BUG: Wertetabelle wurde nicht angezeigt FIX: enable_debug=true wenn save=true (für metadata collection) - metadata wird nur gespeichert wenn debug aktiv - jetzt: debug or save → metadata immer verfügbar BUG: {{placeholder|d}} Modifier funktionierte nicht ROOT CAUSE: catalog wurde bei Exception nicht zu variables hinzugefügt FIX: - variables['_catalog'] = catalog (auch wenn None) - Warning-Log wenn catalog nicht geladen werden kann - Debug warning wenn |d ohne catalog verwendet BUG: Platzhalter in Pipeline-Stages am Ende statt an Cursor FIX: - stageTemplateRefs Map für alle Stage-Textareas - onClick + onKeyUp tracking für Cursor-Position - Insert at cursor: template.slice(0, pos) + placeholder + template.slice(pos) - Focus + Cursor restore nach Insert TECHNICAL: - prompt_executor.py: Besseres Exception Handling für catalog - UnifiedPromptModal.jsx: Refs für alle Template-Felder - prompts.py: enable_debug=debug or save version: 9.6.1 (bugfix) module: prompts 2.1.1	2026-03-26 12:04:20 +01:00
Lars	c0a50dedcd	feat: value table + {{placeholder|d}} modifier (Issue #47) ... FEATURE #47: Wertetabelle nach KI-Analysen - Migration 021: metadata JSONB column in ai_insights - Backend sammelt resolved placeholders mit descriptions beim Speichern - Frontend: Collapsible value table in InsightCard - Zeigt: Platzhalter | Wert | Beschreibung - Sortiert tabellarisch - Funktioniert für base + pipeline prompts FEATURE #48: {{placeholder|d}} Modifier - Syntax: {{weight_aktuell|d}} → "85.2 kg (Aktuelles Gewicht in kg)" - resolve_placeholders() erkennt |d modifier - Hängt description aus catalog an Wert - Fein-granulare Kontrolle pro Platzhalter (nicht global) - Optional: nur wo sinnvoll einsetzen TECHNICAL: - prompt_executor.py: catalog parameter durchgereicht - execute_prompt_with_data() lädt catalog via get_placeholder_catalog() - Catalog als _catalog in variables übergeben, in execute_prompt() extrahiert - Base + Pipeline Prompts unterstützen |d modifier EXAMPLE: Template: "Gewicht: {{weight_aktuell|d}}, Alter: {{age}}" Output: "Gewicht: 85.2 kg (Aktuelles Gewicht in kg), Alter: 55" version: 9.6.0 (feature) module: prompts 2.1.0, insights 1.4.0	2026-03-26 11:52:26 +01:00
Lars	ba92d66880	fix: remove {{ }} from placeholder keys before resolution ... Placeholder resolver returns keys with {{ }} wrappers, but resolve_placeholders expects clean keys. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-26 08:17:22 +01:00
Lars	afc70b5a95	fix: integrate placeholder resolver + JSON unwrapping (Issue #28) ... - Backend: integrate get_placeholder_example_values in execute_prompt_with_data - Backend: now provides BOTH raw data AND processed placeholders - Backend: unwrap Markdown-wrapped JSON (```json ... ```) - Fixes old-style prompts that expect name, weight_trend, caliper_summary Resolves unresolved placeholders issue. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-26 08:14:41 +01:00
Lars	84dad07e15	fix: show debug info on errors + prompt export function ... - Frontend: debug viewer now shows even when test fails - Frontend: export button to download complete prompt config as JSON - Backend: attach debug info to JSON validation errors - Backend: include raw output and length in error details Users can now debug failed prompts and export configs for analysis. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-26 08:07:34 +01:00
Lars	7f2ba4fbad	feat: debug system for prompt execution (Issue #28) ... - Backend: debug mode in prompt_executor with placeholder tracking - Backend: show resolved/unresolved placeholders, final prompts, AI responses - Frontend: test button in UnifiedPromptModal for saved prompts - Frontend: debug output viewer with JSON preview - Frontend: wider placeholder example fields in PlaceholderPicker Resolves pipeline execution debugging issues. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-26 08:01:33 +01:00
Lars	7be7266477	feat: unified prompt executor - Phase 2 complete (Issue #28) ... Backend: - prompt_executor.py: Universal executor for base + pipeline prompts - Dynamic placeholder resolution - JSON output validation - Multi-stage parallel execution (sequential impl) - Reference and inline prompt support - Data loading per module (körper, ernährung, training, schlaf, vitalwerte) Endpoints: - POST /api/prompts/execute - Execute unified prompts - POST /api/prompts/unified - Create unified prompts - PUT /api/prompts/unified/{id} - Update unified prompts Frontend: - api.js: executeUnifiedPrompt, createUnifiedPrompt, updateUnifiedPrompt Next: Phase 3 - Frontend UI consolidation	2026-03-25 14:52:24 +01:00