bfdaf63ba9
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>
507 lines
18 KiB
Python
507 lines
18 KiB
Python
"""
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RAG & Training Test API - Test Runner fuer Retrieval Augmented Generation
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Endpoint: /api/admin/rag-tests
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"""
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from fastapi import APIRouter
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from pydantic import BaseModel
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from typing import List, Optional, Literal
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import httpx
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import asyncio
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import time
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import os
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router = APIRouter(prefix="/api/admin/rag-tests", tags=["RAG Tests"])
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# ==============================================
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# Models
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# ==============================================
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class TestResult(BaseModel):
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name: str
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description: str
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expected: str
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actual: str
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status: Literal["passed", "failed", "pending", "skipped"]
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duration_ms: float
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error_message: Optional[str] = None
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class TestCategoryResult(BaseModel):
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category: str
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display_name: str
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description: str
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tests: List[TestResult]
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passed: int
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failed: int
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total: int
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class FullTestResults(BaseModel):
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categories: List[TestCategoryResult]
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total_passed: int
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total_failed: int
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total_tests: int
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duration_ms: float
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# ==============================================
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# Configuration
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# ==============================================
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BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:8000")
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VECTOR_DB_URL = os.getenv("VECTOR_DB_URL", "http://localhost:6333") # Qdrant
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EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
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# ==============================================
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# Test Implementations
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# ==============================================
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async def test_vector_db_health() -> TestResult:
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"""Test Vector Database Connection (Qdrant)"""
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start = time.time()
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.get(f"{VECTOR_DB_URL}/health")
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duration = (time.time() - start) * 1000
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if response.status_code == 200:
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return TestResult(
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name="Vector Datenbank (Qdrant)",
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description="Prueft ob die Vector-DB fuer Embeddings erreichbar ist",
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expected="Qdrant erreichbar",
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actual="Qdrant aktiv und gesund",
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status="passed",
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duration_ms=duration
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)
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else:
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return TestResult(
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name="Vector Datenbank (Qdrant)",
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description="Prueft ob die Vector-DB fuer Embeddings erreichbar ist",
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expected="Qdrant erreichbar",
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actual=f"HTTP {response.status_code}",
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status="failed",
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duration_ms=duration,
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error_message="Qdrant nicht erreichbar"
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)
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except Exception as e:
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return TestResult(
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name="Vector Datenbank (Qdrant)",
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description="Prueft ob die Vector-DB fuer Embeddings erreichbar ist",
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expected="Qdrant erreichbar",
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actual="Nicht verfuegbar",
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status="skipped",
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duration_ms=(time.time() - start) * 1000,
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error_message=str(e)
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)
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async def test_vector_collections() -> TestResult:
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"""Test Vector Collections"""
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start = time.time()
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.get(f"{VECTOR_DB_URL}/collections")
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duration = (time.time() - start) * 1000
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if response.status_code == 200:
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data = response.json()
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collections = data.get("result", {}).get("collections", [])
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names = [c.get("name", "?") for c in collections]
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return TestResult(
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name="Vector Collections",
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description="Prueft ob RAG-Collections (Dokumente, Embeddings) existieren",
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expected="Collections verfuegbar",
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actual=f"{len(collections)} Collections: {', '.join(names[:3])}",
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status="passed" if collections else "skipped",
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duration_ms=duration,
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error_message=None if collections else "Keine Collections gefunden"
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)
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else:
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return TestResult(
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name="Vector Collections",
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description="Prueft ob RAG-Collections (Dokumente, Embeddings) existieren",
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expected="Collections verfuegbar",
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actual=f"HTTP {response.status_code}",
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status="failed",
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duration_ms=duration,
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error_message="Collections nicht abrufbar"
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)
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except Exception as e:
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return TestResult(
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name="Vector Collections",
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description="Prueft ob RAG-Collections (Dokumente, Embeddings) existieren",
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expected="Collections verfuegbar",
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actual="Nicht verfuegbar",
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status="skipped",
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duration_ms=(time.time() - start) * 1000,
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error_message=str(e)
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)
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async def test_embedding_api() -> TestResult:
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"""Test Embedding Generation API"""
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start = time.time()
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openai_key = os.getenv("OPENAI_API_KEY", "")
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if not openai_key:
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return TestResult(
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name="Embedding API (OpenAI)",
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description="Prueft ob Embeddings generiert werden koennen",
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expected="Embedding-Modell verfuegbar",
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actual="OPENAI_API_KEY nicht gesetzt",
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status="skipped",
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duration_ms=(time.time() - start) * 1000,
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error_message="API Key fehlt"
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)
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try:
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async with httpx.AsyncClient(timeout=15.0) as client:
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response = await client.post(
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"https://api.openai.com/v1/embeddings",
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headers={"Authorization": f"Bearer {openai_key}"},
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json={
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"model": EMBEDDING_MODEL,
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"input": "Test embedding"
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}
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)
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duration = (time.time() - start) * 1000
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if response.status_code == 200:
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data = response.json()
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dims = len(data.get("data", [{}])[0].get("embedding", []))
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return TestResult(
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name="Embedding API (OpenAI)",
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description="Prueft ob Embeddings generiert werden koennen",
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expected="Embedding-Modell verfuegbar",
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actual=f"{EMBEDDING_MODEL}: {dims} Dimensionen",
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status="passed",
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duration_ms=duration
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)
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else:
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return TestResult(
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name="Embedding API (OpenAI)",
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description="Prueft ob Embeddings generiert werden koennen",
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expected="Embedding-Modell verfuegbar",
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actual=f"HTTP {response.status_code}",
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status="failed",
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duration_ms=duration,
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error_message="Embedding-Generierung fehlgeschlagen"
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)
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except Exception as e:
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return TestResult(
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name="Embedding API (OpenAI)",
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description="Prueft ob Embeddings generiert werden koennen",
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expected="Embedding-Modell verfuegbar",
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actual=f"Fehler: {str(e)}",
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status="failed",
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duration_ms=(time.time() - start) * 1000,
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error_message=str(e)
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)
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async def test_document_api() -> TestResult:
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"""Test Document Management API"""
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start = time.time()
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.get(f"{BACKEND_URL}/api/rag/documents")
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duration = (time.time() - start) * 1000
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if response.status_code == 200:
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data = response.json()
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count = len(data) if isinstance(data, list) else data.get("total", 0)
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return TestResult(
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name="Dokument-Verwaltung API",
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description="Prueft ob die RAG-Dokument-Verwaltung verfuegbar ist",
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expected="Dokument-API verfuegbar",
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actual=f"{count} Dokumente indiziert",
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status="passed",
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duration_ms=duration
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)
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elif response.status_code == 404:
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return TestResult(
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name="Dokument-Verwaltung API",
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description="Prueft ob die RAG-Dokument-Verwaltung verfuegbar ist",
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expected="Dokument-API verfuegbar",
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actual="Endpoint nicht implementiert",
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status="skipped",
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duration_ms=duration,
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error_message="RAG API nicht aktiviert"
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)
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else:
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return TestResult(
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name="Dokument-Verwaltung API",
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description="Prueft ob die RAG-Dokument-Verwaltung verfuegbar ist",
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expected="Dokument-API verfuegbar",
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actual=f"HTTP {response.status_code}",
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status="failed",
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duration_ms=duration,
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error_message=f"Unerwarteter Status: {response.status_code}"
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)
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except Exception as e:
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return TestResult(
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name="Dokument-Verwaltung API",
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description="Prueft ob die RAG-Dokument-Verwaltung verfuegbar ist",
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expected="Dokument-API verfuegbar",
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actual=f"Fehler: {str(e)}",
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status="failed",
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duration_ms=(time.time() - start) * 1000,
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error_message=str(e)
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)
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async def test_training_api() -> TestResult:
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"""Test Training Jobs API"""
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start = time.time()
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.get(f"{BACKEND_URL}/api/training/jobs")
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duration = (time.time() - start) * 1000
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if response.status_code == 200:
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data = response.json()
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count = len(data) if isinstance(data, list) else data.get("total", 0)
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return TestResult(
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name="Training Jobs API",
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description="Prueft ob die Modell-Training-Verwaltung verfuegbar ist",
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expected="Training-API verfuegbar",
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actual=f"{count} Training Jobs",
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status="passed",
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duration_ms=duration
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)
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elif response.status_code == 404:
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return TestResult(
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name="Training Jobs API",
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description="Prueft ob die Modell-Training-Verwaltung verfuegbar ist",
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expected="Training-API verfuegbar",
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actual="Endpoint nicht implementiert",
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status="skipped",
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duration_ms=duration,
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error_message="Training API nicht aktiviert"
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)
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else:
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return TestResult(
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name="Training Jobs API",
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description="Prueft ob die Modell-Training-Verwaltung verfuegbar ist",
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expected="Training-API verfuegbar",
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actual=f"HTTP {response.status_code}",
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status="failed",
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duration_ms=duration,
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error_message=f"Unerwarteter Status: {response.status_code}"
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)
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except Exception as e:
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return TestResult(
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name="Training Jobs API",
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description="Prueft ob die Modell-Training-Verwaltung verfuegbar ist",
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expected="Training-API verfuegbar",
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actual=f"Fehler: {str(e)}",
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status="failed",
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duration_ms=(time.time() - start) * 1000,
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error_message=str(e)
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)
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async def test_edu_search_api() -> TestResult:
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"""Test EduSearch RAG API"""
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start = time.time()
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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response = await client.get(f"{BACKEND_URL}/v1/edu-search/seeds")
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duration = (time.time() - start) * 1000
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if response.status_code == 200:
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data = response.json()
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count = len(data) if isinstance(data, list) else data.get("total", 0)
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return TestResult(
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name="EduSearch Seeds API",
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description="Prueft ob die Bildungs-Suchmaschinen-Seeds verfuegbar sind",
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expected="EduSearch API verfuegbar",
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actual=f"{count} Crawler Seeds",
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status="passed",
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duration_ms=duration
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)
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elif response.status_code == 404:
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return TestResult(
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name="EduSearch Seeds API",
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description="Prueft ob die Bildungs-Suchmaschinen-Seeds verfuegbar sind",
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expected="EduSearch API verfuegbar",
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actual="Endpoint nicht implementiert",
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status="skipped",
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duration_ms=duration,
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error_message="EduSearch nicht aktiviert"
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)
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else:
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return TestResult(
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name="EduSearch Seeds API",
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description="Prueft ob die Bildungs-Suchmaschinen-Seeds verfuegbar sind",
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expected="EduSearch API verfuegbar",
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actual=f"HTTP {response.status_code}",
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status="failed",
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duration_ms=duration,
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error_message=f"Unerwarteter Status"
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)
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except Exception as e:
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return TestResult(
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name="EduSearch Seeds API",
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description="Prueft ob die Bildungs-Suchmaschinen-Seeds verfuegbar sind",
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expected="EduSearch API verfuegbar",
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actual=f"Fehler: {str(e)}",
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status="failed",
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duration_ms=(time.time() - start) * 1000,
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error_message=str(e)
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)
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# ==============================================
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# Category Runners
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# ==============================================
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async def run_vector_tests() -> TestCategoryResult:
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"""Run Vector DB tests"""
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tests = await asyncio.gather(
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test_vector_db_health(),
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test_vector_collections(),
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)
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passed = sum(1 for t in tests if t.status == "passed")
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failed = sum(1 for t in tests if t.status == "failed")
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return TestCategoryResult(
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category="vector-db",
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display_name="Vector Datenbank",
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description="Tests fuer Qdrant Vector Store",
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tests=list(tests),
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passed=passed,
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failed=failed,
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total=len(tests)
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)
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async def run_embedding_tests() -> TestCategoryResult:
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"""Run Embedding tests"""
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tests = await asyncio.gather(
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test_embedding_api(),
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)
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passed = sum(1 for t in tests if t.status == "passed")
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failed = sum(1 for t in tests if t.status == "failed")
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return TestCategoryResult(
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category="embeddings",
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display_name="Embeddings",
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description="Tests fuer Embedding-Generierung",
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tests=list(tests),
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passed=passed,
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failed=failed,
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total=len(tests)
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)
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async def run_rag_tests() -> TestCategoryResult:
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"""Run RAG Pipeline tests"""
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tests = await asyncio.gather(
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test_document_api(),
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test_edu_search_api(),
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)
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passed = sum(1 for t in tests if t.status == "passed")
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failed = sum(1 for t in tests if t.status == "failed")
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return TestCategoryResult(
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category="rag-pipeline",
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display_name="RAG Pipeline",
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description="Tests fuer Retrieval Augmented Generation",
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tests=list(tests),
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passed=passed,
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failed=failed,
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total=len(tests)
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)
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async def run_training_tests() -> TestCategoryResult:
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"""Run Training tests"""
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tests = await asyncio.gather(
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test_training_api(),
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)
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passed = sum(1 for t in tests if t.status == "passed")
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failed = sum(1 for t in tests if t.status == "failed")
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return TestCategoryResult(
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category="training",
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display_name="Model Training",
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description="Tests fuer Fine-Tuning und Training Jobs",
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tests=list(tests),
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passed=passed,
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failed=failed,
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total=len(tests)
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)
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# ==============================================
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# API Endpoints
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# ==============================================
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@router.post("/{category}", response_model=TestCategoryResult)
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async def run_category_tests(category: str):
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"""Run tests for a specific category"""
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runners = {
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"vector-db": run_vector_tests,
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"embeddings": run_embedding_tests,
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"rag-pipeline": run_rag_tests,
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"training": run_training_tests,
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}
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if category not in runners:
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return TestCategoryResult(
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category=category,
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display_name=f"Unbekannt: {category}",
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description="Kategorie nicht gefunden",
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tests=[],
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passed=0,
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failed=0,
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total=0
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)
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return await runners[category]()
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@router.post("/run-all", response_model=FullTestResults)
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async def run_all_tests():
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"""Run all RAG tests"""
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start = time.time()
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categories = await asyncio.gather(
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run_vector_tests(),
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run_embedding_tests(),
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run_rag_tests(),
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run_training_tests(),
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)
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total_passed = sum(c.passed for c in categories)
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total_failed = sum(c.failed for c in categories)
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total_tests = sum(c.total for c in categories)
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return FullTestResults(
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categories=list(categories),
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total_passed=total_passed,
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total_failed=total_failed,
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total_tests=total_tests,
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duration_ms=(time.time() - start) * 1000
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)
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@router.get("/categories")
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async def get_categories():
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"""Get available test categories"""
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return {
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"categories": [
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{"id": "vector-db", "name": "Vector DB", "description": "Qdrant Health & Collections"},
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{"id": "embeddings", "name": "Embeddings", "description": "Embedding-Generierung"},
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{"id": "rag-pipeline", "name": "RAG Pipeline", "description": "Dokumente & Suche"},
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{"id": "training", "name": "Training", "description": "Fine-Tuning Jobs"},
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]
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}
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