Lars
500de132b9
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>
482 lines
16 KiB
Python
482 lines
16 KiB
Python
"""
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AI Prompts Management Endpoints for Mitai Jinkendo
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Handles prompt template configuration (admin-editable).
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"""
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import os
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import json
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import uuid
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import httpx
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from typing import Optional
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from fastapi import APIRouter, Depends, HTTPException
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from db import get_db, get_cursor, r2d
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from auth import require_auth, require_admin
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from models import PromptCreate, PromptUpdate, PromptGenerateRequest
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from placeholder_resolver import (
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resolve_placeholders,
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get_unknown_placeholders,
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get_placeholder_example_values,
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get_available_placeholders
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)
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# Environment variables
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OPENROUTER_KEY = os.getenv("OPENROUTER_API_KEY")
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OPENROUTER_MODEL = os.getenv("OPENROUTER_MODEL", "anthropic/claude-sonnet-4")
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router = APIRouter(prefix="/api/prompts", tags=["prompts"])
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@router.get("")
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def list_prompts(session: dict=Depends(require_auth)):
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"""
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List AI prompts.
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- Admins: see ALL prompts (including pipeline and inactive)
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- Users: see only active single-analysis prompts
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"""
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with get_db() as conn:
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cur = get_cursor(conn)
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is_admin = session.get('role') == 'admin'
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if is_admin:
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# Admin sees everything
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cur.execute("SELECT * FROM ai_prompts ORDER BY sort_order, slug")
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else:
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# Users see only active, non-pipeline prompts
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cur.execute("SELECT * FROM ai_prompts WHERE active=true AND slug NOT LIKE 'pipeline_%' ORDER BY sort_order")
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return [r2d(r) for r in cur.fetchall()]
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@router.post("")
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def create_prompt(p: PromptCreate, session: dict=Depends(require_admin)):
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"""Create new AI prompt (admin only)."""
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with get_db() as conn:
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cur = get_cursor(conn)
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# Check if slug already exists
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cur.execute("SELECT id FROM ai_prompts WHERE slug=%s", (p.slug,))
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if cur.fetchone():
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raise HTTPException(status_code=400, detail=f"Prompt with slug '{p.slug}' already exists")
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prompt_id = str(uuid.uuid4())
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cur.execute(
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"""INSERT INTO ai_prompts (id, name, slug, description, template, category, active, sort_order, created, updated)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)""",
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(prompt_id, p.name, p.slug, p.description, p.template, p.category, p.active, p.sort_order)
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)
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return {"id": prompt_id, "slug": p.slug}
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@router.put("/{prompt_id}")
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def update_prompt(prompt_id: str, p: PromptUpdate, session: dict=Depends(require_admin)):
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"""Update AI prompt template (admin only)."""
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with get_db() as conn:
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cur = get_cursor(conn)
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# Build dynamic UPDATE query
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updates = []
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values = []
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if p.name is not None:
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updates.append('name=%s')
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values.append(p.name)
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if p.description is not None:
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updates.append('description=%s')
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values.append(p.description)
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if p.template is not None:
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updates.append('template=%s')
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values.append(p.template)
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if p.category is not None:
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updates.append('category=%s')
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values.append(p.category)
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if p.active is not None:
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updates.append('active=%s')
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values.append(p.active)
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if p.sort_order is not None:
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updates.append('sort_order=%s')
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values.append(p.sort_order)
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if not updates:
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return {"ok": True}
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cur.execute(
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f"UPDATE ai_prompts SET {', '.join(updates)}, updated=CURRENT_TIMESTAMP WHERE id=%s",
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values + [prompt_id]
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)
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return {"ok": True}
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@router.delete("/{prompt_id}")
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def delete_prompt(prompt_id: str, session: dict=Depends(require_admin)):
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"""Delete AI prompt (admin only)."""
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute("DELETE FROM ai_prompts WHERE id=%s", (prompt_id,))
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if cur.rowcount == 0:
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raise HTTPException(status_code=404, detail="Prompt not found")
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return {"ok": True}
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@router.post("/{prompt_id}/duplicate")
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def duplicate_prompt(prompt_id: str, session: dict=Depends(require_admin)):
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"""Duplicate an existing prompt (admin only)."""
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with get_db() as conn:
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cur = get_cursor(conn)
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# Load original prompt
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cur.execute("SELECT * FROM ai_prompts WHERE id=%s", (prompt_id,))
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original = r2d(cur.fetchone())
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if not original:
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raise HTTPException(status_code=404, detail="Prompt not found")
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# Create duplicate with new ID and modified name/slug
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new_id = str(uuid.uuid4())
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new_name = f"{original['name']} (Kopie)"
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new_slug = f"{original['slug']}_copy_{uuid.uuid4().hex[:6]}"
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cur.execute(
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"""INSERT INTO ai_prompts (id, name, slug, description, template, category, active, sort_order, created, updated)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)""",
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(new_id, new_name, new_slug, original['description'], original['template'],
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original.get('category', 'ganzheitlich'), original['active'], original['sort_order'])
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)
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return {"id": new_id, "slug": new_slug, "name": new_name}
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@router.put("/reorder")
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def reorder_prompts(order: list[str], session: dict=Depends(require_admin)):
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"""
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Reorder prompts by providing list of IDs in desired order.
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Args:
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order: List of prompt IDs in new order
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"""
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with get_db() as conn:
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cur = get_cursor(conn)
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for idx, prompt_id in enumerate(order):
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cur.execute(
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"UPDATE ai_prompts SET sort_order=%s WHERE id=%s",
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(idx, prompt_id)
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)
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return {"ok": True}
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@router.post("/preview")
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def preview_prompt(data: dict, session: dict=Depends(require_auth)):
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"""
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Preview a prompt template with real user data (without calling AI).
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Args:
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data: {"template": "Your template with {{placeholders}}"}
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Returns:
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{
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"resolved": "Template with replaced placeholders",
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"unknown_placeholders": ["list", "of", "unknown"]
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}
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"""
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template = data.get('template', '')
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profile_id = session['profile_id']
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resolved = resolve_placeholders(template, profile_id)
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unknown = get_unknown_placeholders(template)
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return {
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"resolved": resolved,
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"unknown_placeholders": unknown
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}
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@router.get("/placeholders")
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def list_placeholders(session: dict=Depends(require_auth)):
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"""
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Get list of available placeholders with example values.
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Returns:
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Dict mapping placeholder to example value using current user's data
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"""
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profile_id = session['profile_id']
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return get_placeholder_example_values(profile_id)
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# ── KI-Assisted Prompt Engineering ───────────────────────────────────────────
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async def call_openrouter(prompt: str, max_tokens: int = 1500) -> str:
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"""Call OpenRouter API to get AI response."""
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if not OPENROUTER_KEY:
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raise HTTPException(status_code=500, detail="OpenRouter API key not configured")
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async with httpx.AsyncClient() as client:
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resp = await client.post(
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"https://openrouter.ai/api/v1/chat/completions",
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headers={"Authorization": f"Bearer {OPENROUTER_KEY}"},
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json={
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"model": OPENROUTER_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": max_tokens
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},
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timeout=60.0
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)
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if resp.status_code != 200:
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raise HTTPException(status_code=resp.status_code, detail=f"OpenRouter API error: {resp.text}")
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return resp.json()['choices'][0]['message']['content'].strip()
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def collect_example_data(profile_id: str, data_categories: list[str]) -> dict:
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"""Collect example data from user's profile for specified categories."""
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example_data = {}
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with get_db() as conn:
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cur = get_cursor(conn)
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# Profil
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cur.execute("SELECT * FROM profiles WHERE id=%s", (profile_id,))
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profile = r2d(cur.fetchone())
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example_data['profil'] = {
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'name': profile.get('name', 'Nutzer'),
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'age': profile.get('dob', 'unbekannt'),
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'height': profile.get('height', 'unbekannt'),
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'sex': profile.get('sex', 'unbekannt')
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}
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# Körper
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if 'körper' in data_categories:
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cur.execute(
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"SELECT weight, date FROM weight_log WHERE profile_id=%s ORDER BY date DESC LIMIT 3",
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(profile_id,)
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)
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weights = [r2d(r) for r in cur.fetchall()]
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example_data['körper'] = {
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'weight_entries': weights,
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'latest_weight': f"{weights[0]['weight']:.1f} kg" if weights else "nicht verfügbar"
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}
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# Ernährung
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if 'ernährung' in data_categories:
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cur.execute(
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"""SELECT kcal, protein, carb, fat, date FROM nutrition_log
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WHERE profile_id=%s ORDER BY date DESC LIMIT 3""",
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(profile_id,)
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)
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nutrition = [r2d(r) for r in cur.fetchall()]
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example_data['ernährung'] = {
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'recent_entries': nutrition
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}
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# Training
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if 'training' in data_categories:
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cur.execute(
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"""SELECT activity_type, duration_min, kcal_active, date FROM activity_log
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WHERE profile_id=%s ORDER BY date DESC LIMIT 5""",
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(profile_id,)
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)
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activities = [r2d(r) for r in cur.fetchall()]
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example_data['training'] = {
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'recent_activities': activities
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}
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return example_data
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@router.post("/generate")
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async def generate_prompt(req: PromptGenerateRequest, session: dict=Depends(require_admin)):
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"""
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Generate AI prompt using KI based on user's goal description.
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This is a meta-feature: KI helps create better prompts for KI analysis.
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"""
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profile_id = session['profile_id']
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# Collect example data
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example_data = collect_example_data(profile_id, req.data_categories)
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# Get available placeholders for selected categories
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available_placeholders = get_available_placeholders(req.data_categories)
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placeholders_list = []
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for cat, phs in available_placeholders.items():
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placeholders_list.extend(phs)
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# Build meta-prompt for prompt generation
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meta_prompt = f"""Du bist ein Experte für Prompt-Engineering im Bereich Fitness & Gesundheit.
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**Aufgabe:**
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Erstelle einen optimalen KI-Prompt für folgendes Analyseziel:
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"{req.goal}"
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**Verfügbare Datenbereiche:**
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{', '.join(req.data_categories)}
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**Beispieldaten (aktuelle Werte des Nutzers):**
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```json
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{json.dumps(example_data, indent=2, ensure_ascii=False)}
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```
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**Verfügbare Platzhalter:**
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{', '.join(placeholders_list)}
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**Anforderungen an den Prompt:**
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1. Nutze relevante Platzhalter ({{{{platzhalter_name}}}}) - diese werden durch echte Daten ersetzt
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2. Sei spezifisch und klar in den Anweisungen
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3. Fordere strukturierte Antworten (z.B. Abschnitte, Bullet Points)
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4. Gib der KI Kontext über ihre Rolle/Expertise (z.B. "Du bist ein Sportwissenschaftler")
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5. Fordere konkrete, umsetzbare Handlungsempfehlungen
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6. Sprache: Deutsch
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7. Der Prompt sollte 150-300 Wörter lang sein
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{f'**Gewünschtes Antwort-Format:**\\n{req.example_output}' if req.example_output else ''}
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**Generiere jetzt NUR den Prompt-Text (keine Erklärung, keine Metakommentare):**
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"""
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# Call AI to generate prompt
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generated_prompt = await call_openrouter(meta_prompt, max_tokens=1000)
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# Extract placeholders used
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import re
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placeholders_used = list(set(re.findall(r'\{\{(\w+)\}\}', generated_prompt)))
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# Generate title from goal
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title = generate_title_from_goal(req.goal)
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# Infer category
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category = infer_category(req.data_categories)
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return {
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"template": generated_prompt,
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"placeholders_used": placeholders_used,
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"example_data": example_data,
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"suggested_title": title,
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"suggested_category": category
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}
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def generate_title_from_goal(goal: str) -> str:
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"""Generate a title from the goal description."""
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goal_lower = goal.lower()
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# Simple keyword matching
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if 'protein' in goal_lower:
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return 'Protein-Analyse'
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elif 'gewicht' in goal_lower or 'abnehmen' in goal_lower:
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return 'Gewichtstrend-Analyse'
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elif 'training' in goal_lower or 'aktivität' in goal_lower:
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return 'Trainingsanalyse'
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elif 'schlaf' in goal_lower:
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return 'Schlaf-Analyse'
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elif 'regeneration' in goal_lower or 'erholung' in goal_lower:
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return 'Regenerations-Analyse'
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elif 'kraft' in goal_lower or 'muskel' in goal_lower:
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return 'Kraftentwicklung'
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elif 'ausdauer' in goal_lower or 'cardio' in goal_lower:
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return 'Ausdauer-Analyse'
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else:
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return 'Neue Analyse'
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def infer_category(data_categories: list[str]) -> str:
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"""Infer prompt category from selected data categories."""
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if len(data_categories) == 1:
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return data_categories[0]
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elif len(data_categories) > 2:
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return 'ganzheitlich'
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else:
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# 2 categories: prefer the first one
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return data_categories[0] if data_categories else 'ganzheitlich'
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@router.post("/{prompt_id}/optimize")
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async def optimize_prompt(prompt_id: str, session: dict=Depends(require_admin)):
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"""
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Analyze and optimize an existing prompt using KI.
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Returns suggestions for improvement with score, strengths, weaknesses,
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and an optimized version of the prompt.
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"""
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute("SELECT * FROM ai_prompts WHERE id=%s", (prompt_id,))
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prompt = r2d(cur.fetchone())
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if not prompt:
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raise HTTPException(status_code=404, detail="Prompt not found")
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# Build meta-prompt for optimization
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meta_prompt = f"""Du bist ein Experte für Prompt-Engineering.
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**Analysiere folgenden KI-Prompt und schlage Verbesserungen vor:**
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```
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{prompt['template']}
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```
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**Analysiere folgende Aspekte:**
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1. **Klarheit & Präzision:** Ist die Anweisung klar und eindeutig?
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2. **Struktur & Lesbarkeit:** Ist der Prompt gut strukturiert?
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3. **Platzhalter-Nutzung:** Werden relevante Platzhalter genutzt? Fehlen wichtige Daten?
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4. **Antwort-Format:** Wird eine strukturierte Ausgabe gefordert?
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5. **Kontext:** Hat die KI genug Rollenkontext (z.B. "Du bist ein Ernährungsexperte")?
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6. **Handlungsempfehlungen:** Werden konkrete, umsetzbare Schritte gefordert?
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**Gib deine Analyse als JSON zurück (NUR das JSON, keine zusätzlichen Kommentare):**
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```json
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{{
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"score": 0-100,
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"strengths": ["Stärke 1", "Stärke 2", "Stärke 3"],
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"weaknesses": ["Schwäche 1", "Schwäche 2"],
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"optimized_prompt": "Vollständig optimierte Version des Prompts",
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"changes_summary": "Kurze Zusammenfassung was verbessert wurde (2-3 Sätze)"
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}}
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```
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**Wichtig:**
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- Die optimierte Version sollte alle Platzhalter beibehalten und ggf. ergänzen
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- Sprache: Deutsch
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- Der optimierte Prompt sollte 150-400 Wörter lang sein
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"""
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# Call AI for optimization
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response = await call_openrouter(meta_prompt, max_tokens=1500)
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# Parse JSON response
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try:
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# Extract JSON from markdown code blocks if present
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if '```json' in response:
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json_start = response.find('```json') + 7
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json_end = response.find('```', json_start)
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json_str = response[json_start:json_end].strip()
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elif '```' in response:
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json_start = response.find('```') + 3
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json_end = response.find('```', json_start)
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json_str = response[json_start:json_end].strip()
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else:
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json_str = response
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analysis = json.loads(json_str)
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except json.JSONDecodeError as e:
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raise HTTPException(
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status_code=500,
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detail=f"Failed to parse AI response as JSON: {str(e)}. Response: {response[:200]}"
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)
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# Ensure required fields
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if not all(k in analysis for k in ['score', 'strengths', 'weaknesses', 'optimized_prompt', 'changes_summary']):
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raise HTTPException(
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status_code=500,
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detail=f"AI response missing required fields. Got: {list(analysis.keys())}"
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)
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return analysis
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