b4613e26f3
backend-lehrer (10 files): - game/database.py (785 → 5), correction_api.py (683 → 4) - classroom_engine/antizipation.py (676 → 5) - llm_gateway schools/edu_search already done in prior batch klausur-service (12 files): - orientation_crop_api.py (694 → 5), pdf_export.py (677 → 4) - zeugnis_crawler.py (676 → 5), grid_editor_api.py (671 → 5) - eh_templates.py (658 → 5), mail/api.py (651 → 5) - qdrant_service.py (638 → 5), training_api.py (625 → 4) website (6 pages): - middleware (696 → 8), mail (733 → 6), consent (628 → 8) - compliance/risks (622 → 5), export (502 → 5), brandbook (629 → 7) studio-v2 (3 components): - B2BMigrationWizard (848 → 3), CleanupPanel (765 → 2) - dashboard-experimental (739 → 2) admin-lehrer (4 files): - uebersetzungen (769 → 4), manager (670 → 2) - ChunkBrowserQA (675 → 6), dsfa/page (674 → 5) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
"""
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Qdrant Vector Database Service — NiBiS RAG Search for Klausurkorrektur.
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"""
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from typing import List, Dict, Optional
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from qdrant_client.models import Filter, FieldCondition, MatchValue
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from qdrant_core import get_qdrant_client
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async def search_nibis_eh(
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query_embedding: List[float],
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year: Optional[int] = None,
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subject: Optional[str] = None,
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niveau: Optional[str] = None,
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limit: int = 5
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) -> List[Dict]:
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"""
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Search in NiBiS Erwartungshorizonte (public, pre-indexed data).
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Unlike search_eh(), this searches in the public NiBiS collection
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and returns plaintext (not encrypted).
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Args:
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query_embedding: Query vector
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year: Optional year filter (2016, 2017, 2024, 2025)
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subject: Optional subject filter
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niveau: Optional niveau filter (eA, gA)
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limit: Max results
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Returns:
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List of matching chunks with metadata
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"""
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client = get_qdrant_client()
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collection = "bp_nibis_eh"
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# Build filter
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must_conditions = []
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if year:
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must_conditions.append(
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FieldCondition(key="year", match=MatchValue(value=year))
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)
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if subject:
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must_conditions.append(
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FieldCondition(key="subject", match=MatchValue(value=subject))
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)
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if niveau:
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must_conditions.append(
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FieldCondition(key="niveau", match=MatchValue(value=niveau))
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)
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query_filter = Filter(must=must_conditions) if must_conditions else None
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try:
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results = client.search(
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collection_name=collection,
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query_vector=query_embedding,
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query_filter=query_filter,
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limit=limit
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)
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return [
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{
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"id": str(r.id),
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"score": r.score,
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"text": r.payload.get("text", ""),
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"year": r.payload.get("year"),
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"subject": r.payload.get("subject"),
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"niveau": r.payload.get("niveau"),
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"task_number": r.payload.get("task_number"),
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"doc_type": r.payload.get("doc_type"),
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"variant": r.payload.get("variant"),
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}
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for r in results
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]
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except Exception as e:
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print(f"NiBiS search error: {e}")
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return []
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