Commit Graph

9 Commits

Author SHA1 Message Date
7f94a41965 feat: batch import/export for prompts (Issue #28 Debug B)
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Dev→Prod Sync in 2 Klicks: Export → Import

Backend:
- GET /api/prompts/export-all → JSON mit allen Prompts
- POST /api/prompts/import?overwrite=true/false → Import + Create/Update
  - Returns: created, updated, skipped counts
  - Validates JSON structure
  - Handles stages JSON conversion

Frontend AdminPromptsPage:
- Button "📦 Alle exportieren" → downloads all-prompts-{date}.json
- Button "📥 Importieren" → file upload dialog
  - User-Prompt: Überschreiben? Ja/Nein
  - Success-Message mit Statistik (created/updated/skipped)

Frontend api.js:
- exportAllPrompts()
- importPrompts(data, overwrite)

Use Cases:
1. Backup: Prompts als JSON sichern
2. Dev→Prod: Auf dev.mitai entwickeln → exportieren → auf mitai.jinkendo importieren
3. Versionierung: Prompts in Git speichern

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-26 09:44:08 +01:00
97e57481f9 fix: Analysis page now uses unified prompt executor (Issue #28)
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BREAKING: Analysis page switched from old /insights/run to new /prompts/execute

Changes:
- Backend: Added save=true parameter to /prompts/execute
  - When enabled, saves final output to ai_insights table
  - Extracts content from pipeline output (last stage)
- Frontend api.js: Added save parameter to executeUnifiedPrompt()
- Frontend Analysis.jsx: Switched from api.runInsight() to api.executeUnifiedPrompt()
  - Transforms new result format to match InsightCard expectations
  - Pipeline outputs properly extracted and displayed

Fixes: PIPELINE_MASTER responses (old template being sent to AI)
The old /insights/run endpoint used raw template field, which for the
legacy "pipeline" prompt was literally "PIPELINE_MASTER". The new
executor properly handles stages and data processing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-26 09:38:58 +01:00
7f2ba4fbad feat: debug system for prompt execution (Issue #28)
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- 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
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)
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