fix: Restore all files lost during destructive rebase
A previous `git pull --rebase origin main` dropped 177 local commits,
losing 3400+ files across admin-v2, backend, studio-v2, website,
klausur-service, and many other services. The partial restore attempt
(660295e2) only recovered some files.
This commit restores all missing files from pre-rebase ref 98933f5e
while preserving post-rebase additions (night-scheduler, night-mode UI,
NightModeWidget dashboard integration).
Restored features include:
- AI Module Sidebar (FAB), OCR Labeling, OCR Compare
- GPU Dashboard, RAG Pipeline, Magic Help
- Klausur-Korrektur (8 files), Abitur-Archiv (5+ files)
- Companion, Zeugnisse-Crawler, Screen Flow
- Full backend, studio-v2, website, klausur-service
- All compliance SDKs, agent-core, voice-service
- CI/CD configs, documentation, scripts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
584
backend/llm_gateway/routes/comparison.py
Normal file
584
backend/llm_gateway/routes/comparison.py
Normal file
@@ -0,0 +1,584 @@
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"""
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LLM Comparison Route - Vergleicht Antworten verschiedener LLM Backends.
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Dieses Modul ermoeglicht:
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- Parallele Anfragen an OpenAI, Claude, Self-hosted+Tavily, Self-hosted+EduSearch
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- Speichern von Vergleichsergebnissen fuer QA
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- Parameter-Tuning fuer Self-hosted Modelle
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"""
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import asyncio
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import logging
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import time
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import uuid
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from datetime import datetime, timezone
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from typing import Optional
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from pydantic import BaseModel, Field
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from fastapi import APIRouter, HTTPException, Depends
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from ..models.chat import ChatMessage
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from ..middleware.auth import verify_api_key
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/comparison", tags=["LLM Comparison"])
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class ComparisonRequest(BaseModel):
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"""Request fuer LLM-Vergleich."""
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prompt: str = Field(..., description="User prompt (z.B. Lehrer-Frage)")
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system_prompt: Optional[str] = Field(None, description="Optionaler System Prompt")
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enable_openai: bool = Field(True, description="OpenAI/ChatGPT aktivieren")
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enable_claude: bool = Field(True, description="Claude aktivieren")
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enable_selfhosted_tavily: bool = Field(True, description="Self-hosted + Tavily aktivieren")
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enable_selfhosted_edusearch: bool = Field(True, description="Self-hosted + EduSearch aktivieren")
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# Parameter fuer Self-hosted Modelle
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selfhosted_model: str = Field("llama3.2:3b", description="Self-hosted Modell")
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temperature: float = Field(0.7, ge=0.0, le=2.0, description="Temperature")
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top_p: float = Field(0.9, ge=0.0, le=1.0, description="Top-p Sampling")
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max_tokens: int = Field(2048, ge=1, le=8192, description="Max Tokens")
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# Search Parameter
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search_results_count: int = Field(5, ge=1, le=20, description="Anzahl Suchergebnisse")
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edu_search_filters: Optional[dict] = Field(None, description="Filter fuer EduSearch")
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class LLMResponse(BaseModel):
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"""Antwort eines einzelnen LLM."""
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provider: str
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model: str
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response: str
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latency_ms: int
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tokens_used: Optional[int] = None
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search_results: Optional[list] = None
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error: Optional[str] = None
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timestamp: datetime = Field(default_factory=datetime.utcnow)
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class ComparisonResponse(BaseModel):
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"""Gesamt-Antwort des Vergleichs."""
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comparison_id: str
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prompt: str
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system_prompt: Optional[str]
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responses: list[LLMResponse]
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created_at: datetime = Field(default_factory=datetime.utcnow)
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class SavedComparison(BaseModel):
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"""Gespeicherter Vergleich fuer QA."""
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comparison_id: str
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prompt: str
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system_prompt: Optional[str]
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responses: list[LLMResponse]
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notes: Optional[str] = None
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rating: Optional[dict] = None # {"openai": 4, "claude": 5, ...}
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created_at: datetime
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created_by: Optional[str] = None
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# In-Memory Storage (in Production: Database)
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_comparisons_store: dict[str, SavedComparison] = {}
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_system_prompts_store: dict[str, dict] = {
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"default": {
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"id": "default",
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"name": "Standard Lehrer-Assistent",
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"prompt": """Du bist ein hilfreicher Assistent fuer Lehrkraefte in Deutschland.
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Deine Aufgaben:
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- Hilfe bei der Unterrichtsplanung
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- Erklaerung von Fachinhalten
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- Erstellung von Arbeitsblaettern und Pruefungen
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- Beratung zu paedagogischen Methoden
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Antworte immer auf Deutsch und beachte den deutschen Lehrplankontext.""",
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"created_at": datetime.now(timezone.utc).isoformat(),
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},
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"curriculum": {
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"id": "curriculum",
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"name": "Lehrplan-Experte",
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"prompt": """Du bist ein Experte fuer deutsche Lehrplaene und Bildungsstandards.
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Du kennst:
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- Lehrplaene aller 16 Bundeslaender
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- KMK Bildungsstandards
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- Kompetenzorientierung im deutschen Bildungssystem
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Beziehe dich immer auf konkrete Lehrplanvorgaben wenn moeglich.""",
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"created_at": datetime.now(timezone.utc).isoformat(),
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},
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"worksheet": {
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"id": "worksheet",
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"name": "Arbeitsblatt-Generator",
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"prompt": """Du bist ein spezialisierter Assistent fuer die Erstellung von Arbeitsblaettern.
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Erstelle didaktisch sinnvolle Aufgaben mit:
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- Klaren Arbeitsanweisungen
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- Differenzierungsmoeglichkeiten
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- Loesungshinweisen
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Format: Markdown mit klarer Struktur.""",
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"created_at": datetime.now(timezone.utc).isoformat(),
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},
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}
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async def _call_openai(prompt: str, system_prompt: Optional[str]) -> LLMResponse:
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"""Ruft OpenAI ChatGPT auf."""
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import os
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import httpx
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start_time = time.time()
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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return LLMResponse(
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provider="openai",
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model="gpt-4o-mini",
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response="",
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latency_ms=0,
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error="OPENAI_API_KEY nicht konfiguriert"
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)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.post(
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"https://api.openai.com/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": "gpt-4o-mini",
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"messages": messages,
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"temperature": 0.7,
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"max_tokens": 2048,
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},
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)
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response.raise_for_status()
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data = response.json()
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latency_ms = int((time.time() - start_time) * 1000)
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content = data["choices"][0]["message"]["content"]
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tokens = data.get("usage", {}).get("total_tokens")
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return LLMResponse(
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provider="openai",
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model="gpt-4o-mini",
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response=content,
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latency_ms=latency_ms,
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tokens_used=tokens,
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)
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except Exception as e:
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return LLMResponse(
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provider="openai",
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model="gpt-4o-mini",
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response="",
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latency_ms=int((time.time() - start_time) * 1000),
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error=str(e),
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)
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async def _call_claude(prompt: str, system_prompt: Optional[str]) -> LLMResponse:
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"""Ruft Anthropic Claude auf."""
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import os
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start_time = time.time()
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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return LLMResponse(
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provider="claude",
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model="claude-3-5-sonnet-20241022",
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response="",
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latency_ms=0,
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error="ANTHROPIC_API_KEY nicht konfiguriert"
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)
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try:
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import anthropic
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client = anthropic.AsyncAnthropic(api_key=api_key)
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response = await client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=2048,
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system=system_prompt or "",
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messages=[{"role": "user", "content": prompt}],
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)
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latency_ms = int((time.time() - start_time) * 1000)
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content = response.content[0].text if response.content else ""
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tokens = response.usage.input_tokens + response.usage.output_tokens
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return LLMResponse(
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provider="claude",
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model="claude-3-5-sonnet-20241022",
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response=content,
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latency_ms=latency_ms,
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tokens_used=tokens,
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)
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except Exception as e:
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return LLMResponse(
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provider="claude",
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model="claude-3-5-sonnet-20241022",
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response="",
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latency_ms=int((time.time() - start_time) * 1000),
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error=str(e),
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)
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async def _search_tavily(query: str, count: int = 5) -> list[dict]:
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"""Sucht mit Tavily API."""
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import os
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import httpx
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api_key = os.getenv("TAVILY_API_KEY")
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if not api_key:
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return []
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(
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"https://api.tavily.com/search",
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json={
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"api_key": api_key,
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"query": query,
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"max_results": count,
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"include_domains": [
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"kmk.org", "bildungsserver.de", "bpb.de",
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"bayern.de", "nrw.de", "berlin.de",
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],
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},
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)
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response.raise_for_status()
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data = response.json()
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return data.get("results", [])
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except Exception as e:
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logger.error(f"Tavily search error: {e}")
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return []
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async def _search_edusearch(query: str, count: int = 5, filters: Optional[dict] = None) -> list[dict]:
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"""Sucht mit EduSearch API."""
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import os
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import httpx
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edu_search_url = os.getenv("EDU_SEARCH_URL", "http://edu-search-service:8084")
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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payload = {
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"q": query,
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"limit": count,
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"mode": "keyword",
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}
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if filters:
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payload["filters"] = filters
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response = await client.post(
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f"{edu_search_url}/v1/search",
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json=payload,
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)
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response.raise_for_status()
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data = response.json()
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# Formatiere Ergebnisse
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results = []
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for r in data.get("results", []):
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results.append({
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"title": r.get("title", ""),
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"url": r.get("url", ""),
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"content": r.get("snippet", ""),
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"score": r.get("scores", {}).get("final", 0),
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})
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return results
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except Exception as e:
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logger.error(f"EduSearch error: {e}")
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return []
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async def _call_selfhosted_with_search(
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prompt: str,
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system_prompt: Optional[str],
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search_provider: str,
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search_results: list[dict],
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model: str,
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temperature: float,
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top_p: float,
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max_tokens: int,
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) -> LLMResponse:
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"""Ruft Self-hosted LLM mit Suchergebnissen auf."""
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import os
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import httpx
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start_time = time.time()
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ollama_url = os.getenv("OLLAMA_URL", "http://localhost:11434")
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# Baue Kontext aus Suchergebnissen
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context_parts = []
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for i, result in enumerate(search_results, 1):
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context_parts.append(f"[{i}] {result.get('title', 'Untitled')}")
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context_parts.append(f" URL: {result.get('url', '')}")
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context_parts.append(f" {result.get('content', '')[:500]}")
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context_parts.append("")
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search_context = "\n".join(context_parts)
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|
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# Erweitere System Prompt mit Suchergebnissen
|
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augmented_system = f"""{system_prompt or ''}
|
||||
|
||||
Du hast Zugriff auf folgende Suchergebnisse aus {"Tavily" if search_provider == "tavily" else "EduSearch (deutsche Bildungsquellen)"}:
|
||||
|
||||
{search_context}
|
||||
|
||||
Nutze diese Quellen um deine Antwort zu unterstuetzen. Zitiere relevante Quellen mit [Nummer]."""
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": augmented_system},
|
||||
{"role": "user", "content": prompt},
|
||||
]
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
response = await client.post(
|
||||
f"{ollama_url}/api/chat",
|
||||
json={
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": temperature,
|
||||
"top_p": top_p,
|
||||
"num_predict": max_tokens,
|
||||
},
|
||||
},
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
latency_ms = int((time.time() - start_time) * 1000)
|
||||
content = data.get("message", {}).get("content", "")
|
||||
tokens = data.get("prompt_eval_count", 0) + data.get("eval_count", 0)
|
||||
|
||||
return LLMResponse(
|
||||
provider=f"selfhosted_{search_provider}",
|
||||
model=model,
|
||||
response=content,
|
||||
latency_ms=latency_ms,
|
||||
tokens_used=tokens,
|
||||
search_results=search_results,
|
||||
)
|
||||
except Exception as e:
|
||||
return LLMResponse(
|
||||
provider=f"selfhosted_{search_provider}",
|
||||
model=model,
|
||||
response="",
|
||||
latency_ms=int((time.time() - start_time) * 1000),
|
||||
error=str(e),
|
||||
search_results=search_results,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/run", response_model=ComparisonResponse)
|
||||
async def run_comparison(
|
||||
request: ComparisonRequest,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""
|
||||
Fuehrt LLM-Vergleich durch.
|
||||
|
||||
Sendet den Prompt parallel an alle aktivierten Provider und
|
||||
sammelt die Antworten.
|
||||
"""
|
||||
comparison_id = f"cmp-{uuid.uuid4().hex[:12]}"
|
||||
tasks = []
|
||||
|
||||
# System Prompt vorbereiten
|
||||
system_prompt = request.system_prompt
|
||||
|
||||
# OpenAI
|
||||
if request.enable_openai:
|
||||
tasks.append(("openai", _call_openai(request.prompt, system_prompt)))
|
||||
|
||||
# Claude
|
||||
if request.enable_claude:
|
||||
tasks.append(("claude", _call_claude(request.prompt, system_prompt)))
|
||||
|
||||
# Self-hosted + Tavily
|
||||
if request.enable_selfhosted_tavily:
|
||||
tavily_results = await _search_tavily(request.prompt, request.search_results_count)
|
||||
tasks.append((
|
||||
"selfhosted_tavily",
|
||||
_call_selfhosted_with_search(
|
||||
request.prompt,
|
||||
system_prompt,
|
||||
"tavily",
|
||||
tavily_results,
|
||||
request.selfhosted_model,
|
||||
request.temperature,
|
||||
request.top_p,
|
||||
request.max_tokens,
|
||||
)
|
||||
))
|
||||
|
||||
# Self-hosted + EduSearch
|
||||
if request.enable_selfhosted_edusearch:
|
||||
edu_results = await _search_edusearch(
|
||||
request.prompt,
|
||||
request.search_results_count,
|
||||
request.edu_search_filters,
|
||||
)
|
||||
tasks.append((
|
||||
"selfhosted_edusearch",
|
||||
_call_selfhosted_with_search(
|
||||
request.prompt,
|
||||
system_prompt,
|
||||
"edusearch",
|
||||
edu_results,
|
||||
request.selfhosted_model,
|
||||
request.temperature,
|
||||
request.top_p,
|
||||
request.max_tokens,
|
||||
)
|
||||
))
|
||||
|
||||
# Parallele Ausfuehrung
|
||||
responses = []
|
||||
if tasks:
|
||||
results = await asyncio.gather(*[t[1] for t in tasks], return_exceptions=True)
|
||||
for (name, _), result in zip(tasks, results):
|
||||
if isinstance(result, Exception):
|
||||
responses.append(LLMResponse(
|
||||
provider=name,
|
||||
model="unknown",
|
||||
response="",
|
||||
latency_ms=0,
|
||||
error=str(result),
|
||||
))
|
||||
else:
|
||||
responses.append(result)
|
||||
|
||||
return ComparisonResponse(
|
||||
comparison_id=comparison_id,
|
||||
prompt=request.prompt,
|
||||
system_prompt=system_prompt,
|
||||
responses=responses,
|
||||
)
|
||||
|
||||
|
||||
@router.post("/save/{comparison_id}")
|
||||
async def save_comparison(
|
||||
comparison_id: str,
|
||||
comparison: ComparisonResponse,
|
||||
notes: Optional[str] = None,
|
||||
rating: Optional[dict] = None,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Speichert einen Vergleich fuer spaetere Analyse."""
|
||||
saved = SavedComparison(
|
||||
comparison_id=comparison_id,
|
||||
prompt=comparison.prompt,
|
||||
system_prompt=comparison.system_prompt,
|
||||
responses=comparison.responses,
|
||||
notes=notes,
|
||||
rating=rating,
|
||||
created_at=comparison.created_at,
|
||||
)
|
||||
_comparisons_store[comparison_id] = saved
|
||||
return {"status": "saved", "comparison_id": comparison_id}
|
||||
|
||||
|
||||
@router.get("/history")
|
||||
async def get_comparison_history(
|
||||
limit: int = 50,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Gibt gespeicherte Vergleiche zurueck."""
|
||||
comparisons = list(_comparisons_store.values())
|
||||
comparisons.sort(key=lambda x: x.created_at, reverse=True)
|
||||
return {"comparisons": comparisons[:limit]}
|
||||
|
||||
|
||||
@router.get("/history/{comparison_id}")
|
||||
async def get_comparison(
|
||||
comparison_id: str,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Gibt einen bestimmten Vergleich zurueck."""
|
||||
if comparison_id not in _comparisons_store:
|
||||
raise HTTPException(status_code=404, detail="Vergleich nicht gefunden")
|
||||
return _comparisons_store[comparison_id]
|
||||
|
||||
|
||||
# System Prompt Management
|
||||
|
||||
@router.get("/prompts")
|
||||
async def list_system_prompts(
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Listet alle gespeicherten System Prompts."""
|
||||
return {"prompts": list(_system_prompts_store.values())}
|
||||
|
||||
|
||||
@router.post("/prompts")
|
||||
async def create_system_prompt(
|
||||
name: str,
|
||||
prompt: str,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Erstellt einen neuen System Prompt."""
|
||||
prompt_id = f"sp-{uuid.uuid4().hex[:8]}"
|
||||
_system_prompts_store[prompt_id] = {
|
||||
"id": prompt_id,
|
||||
"name": name,
|
||||
"prompt": prompt,
|
||||
"created_at": datetime.now(timezone.utc).isoformat(),
|
||||
}
|
||||
return {"status": "created", "prompt_id": prompt_id}
|
||||
|
||||
|
||||
@router.put("/prompts/{prompt_id}")
|
||||
async def update_system_prompt(
|
||||
prompt_id: str,
|
||||
name: str,
|
||||
prompt: str,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Aktualisiert einen System Prompt."""
|
||||
if prompt_id not in _system_prompts_store:
|
||||
raise HTTPException(status_code=404, detail="System Prompt nicht gefunden")
|
||||
|
||||
_system_prompts_store[prompt_id].update({
|
||||
"name": name,
|
||||
"prompt": prompt,
|
||||
"updated_at": datetime.now(timezone.utc).isoformat(),
|
||||
})
|
||||
return {"status": "updated", "prompt_id": prompt_id}
|
||||
|
||||
|
||||
@router.delete("/prompts/{prompt_id}")
|
||||
async def delete_system_prompt(
|
||||
prompt_id: str,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Loescht einen System Prompt."""
|
||||
if prompt_id not in _system_prompts_store:
|
||||
raise HTTPException(status_code=404, detail="System Prompt nicht gefunden")
|
||||
if prompt_id in ["default", "curriculum", "worksheet"]:
|
||||
raise HTTPException(status_code=400, detail="Standard-Prompts koennen nicht geloescht werden")
|
||||
|
||||
del _system_prompts_store[prompt_id]
|
||||
return {"status": "deleted", "prompt_id": prompt_id}
|
||||
|
||||
|
||||
@router.get("/prompts/{prompt_id}")
|
||||
async def get_system_prompt(
|
||||
prompt_id: str,
|
||||
_: str = Depends(verify_api_key),
|
||||
):
|
||||
"""Gibt einen System Prompt zurueck."""
|
||||
if prompt_id not in _system_prompts_store:
|
||||
raise HTTPException(status_code=404, detail="System Prompt nicht gefunden")
|
||||
return _system_prompts_store[prompt_id]
|
||||
Reference in New Issue
Block a user