7be7266477
feat: unified prompt executor - Phase 2 complete (Issue #28 )
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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
6627b5eee7
feat: Pipeline-System - Backend Infrastructure (Issue #28 , Phase 1)
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Implementiert konfigurierbare mehrstufige Analysen. Admins können
mehrere Pipeline-Konfigurationen erstellen mit unterschiedlichen
Modulen, Zeiträumen und Prompts.
**Backend:**
- Migration 019: pipeline_configs Tabelle + ai_prompts erweitert
- Pipeline-Config Models: PipelineConfigCreate, PipelineConfigUpdate
- Pipeline-Executor: refactored für config-basierte Ausführung
- CRUD-Endpoints: /api/prompts/pipeline-configs (list, create, update, delete, set-default)
- Reset-to-Default: /api/prompts/{id}/reset-to-default für System-Prompts
**Features:**
- 3 Seed-Configs: "Alltags-Check" (default), "Schlaf & Erholung", "Wettkampf-Analyse"
- Dynamische Platzhalter: {{stage1_<slug>}} für alle Stage-1-Ergebnisse
- Backward-compatible: /api/insights/pipeline ohne config_id nutzt default
**Dateien:**
- backend/migrations/019_pipeline_system.sql
- backend/models.py (PipelineConfigCreate, PipelineConfigUpdate)
- backend/routers/insights.py (analyze_pipeline refactored)
- backend/routers/prompts.py (Pipeline-Config CRUD + Reset-to-Default)
**Nächste Schritte:**
- Frontend: Pipeline-Config Dialog + Admin-UI
- Design: Mobile-Responsive + Icons
Issue #28 Progress: Backend 3/3 ✅ | Frontend 0/3 🔲 | Design 0/3 🔲
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-25 09:42:28 +01:00
5e7ef718e0
fix: placeholder picker improvements + insight display names (Issue #28 )
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Backend:
- get_placeholder_catalog(): grouped placeholders with descriptions
- Returns {category: [{key, description, example}]} format
- Categories: Profil, Körper, Ernährung, Training, Schlaf, Vitalwerte, Zeitraum
Frontend - Placeholder Picker:
- Grouped by category with visual separation
- Search/filter across keys and descriptions
- Hover effects for better UX
- Insert at cursor position (not at end)
- Shows: key + description + example value
- 'Keine Platzhalter gefunden' message when filtered
Frontend - Insight Display Names:
- InsightCard receives prompts array
- Finds matching prompt by scope/slug
- Shows prompt.display_name instead of hardcoded SLUG_LABELS
- History tab also shows display_name in group headers
- Fallback chain: display_name → SLUG_LABELS → scope
User-facing improvements:
✓ Platzhalter zeigen echte Daten statt Zahlen
✓ Durchsuchbar + filterbar
✓ Einfügen an Cursor-Position
✓ Insights zeigen custom Namen (z.B. '🍽️ Meine Ernährung')
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-25 06:44:22 +01:00
0c4264de44
feat: display_name + placeholder picker for prompts (Issue #28 )
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Migration 018:
- Add display_name column to ai_prompts
- Migrate existing prompts from hardcoded SLUG_LABELS
- Fallback: name if display_name is NULL
Backend:
- PromptCreate/Update models with display_name field
- create/update/duplicate endpoints handle display_name
- Fallback: use name if display_name not provided
Frontend:
- PromptEditModal: display_name input field
- Placeholder picker: button + dropdown with all placeholders
- Shows example values, inserts {{placeholder}} on click
- Analysis.jsx: use display_name instead of SLUG_LABELS
User-facing changes:
- Prompts now show custom display names (e.g. '🍽️ Ernährung')
- Admin can edit display names instead of hardcoded labels
- Template editor has 'Platzhalter einfügen' button
- No more hardcoded SLUG_LABELS in frontend
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-25 06:31:25 +01:00
500de132b9
feat: AI-Prompts flexibilisierung - Backend & Admin UI (Issue #28 , Part 1)
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Backend complete:
- Migration 017: Add category column to ai_prompts
- placeholder_resolver.py: 20+ placeholders with resolver functions
- Extended routers/prompts.py with CRUD endpoints:
* POST /api/prompts (create)
* PUT /api/prompts/:id (update)
* DELETE /api/prompts/:id (delete)
* POST /api/prompts/:id/duplicate
* PUT /api/prompts/reorder
* POST /api/prompts/preview
* GET /api/prompts/placeholders
* POST /api/prompts/generate (KI-assisted generation)
* POST /api/prompts/:id/optimize (KI analysis)
- Extended models.py with PromptCreate, PromptUpdate, PromptGenerateRequest
Frontend:
- AdminPromptsPage.jsx: Full CRUD UI with category filter, reordering
Meta-Features:
- KI generates prompts from goal description + example data
- KI analyzes and optimizes existing prompts
Next: PromptEditModal, PromptGenerator, api.js integration
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-24 15:32:25 +01:00
b4a1856f79
refactor: modular backend architecture with 14 router modules
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Phase 2 Complete - Backend Refactoring:
- Extracted all endpoints to dedicated router modules
- main.py: 1878 → 75 lines (-96% reduction)
- Created modular structure for maintainability
Router Structure (60 endpoints total):
├── auth.py - 7 endpoints (login, logout, password reset)
├── profiles.py - 7 endpoints (CRUD + current user)
├── weight.py - 5 endpoints (tracking + stats)
├── circumference.py - 4 endpoints (body measurements)
├── caliper.py - 4 endpoints (skinfold tracking)
├── activity.py - 6 endpoints (workouts + Apple Health import)
├── nutrition.py - 4 endpoints (diet + FDDB import)
├── photos.py - 3 endpoints (progress photos)
├── insights.py - 8 endpoints (AI analysis + pipeline)
├── prompts.py - 2 endpoints (AI prompt management)
├── admin.py - 7 endpoints (user management)
├── stats.py - 1 endpoint (dashboard stats)
├── exportdata.py - 3 endpoints (CSV/JSON/ZIP export)
└── importdata.py - 1 endpoint (ZIP import)
Core modules maintained:
- db.py: PostgreSQL connection + helpers
- auth.py: Auth functions (hash, verify, sessions)
- models.py: 11 Pydantic models
Benefits:
- Self-contained modules with clear responsibilities
- Easier to navigate and modify specific features
- Improved code organization and readability
- 100% functional compatibility maintained
- All syntax checks passed
Updated CLAUDE.md with new architecture documentation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-19 11:15:35 +01:00
