feat(pipeline): MC Quality Overhaul — 74.5% → 92.8% accuracy, 5.3K → 13.6K MCs

Phase 0: Quality Audit script (Claude Sonnet, 1750 samples)
Phase 1: Object ontology expanded 31 → 74 tokens with descriptions + boundaries
Phase 2: 174K controls re-classified via Haiku (10 batches, $50)
  - Generic tokens removed (documentation, procedure, process)
  - L2 sub-topics added (108K + 64K controls)
  - Bad subtopics fixed (stakeholder_*, escalation fragments)
Phase 3: Re-clustering K=18704 (37K objects → 16.7K groups)
Phase 4: Direct MC generation from canonical tokens (gpre2_direct_mc.py)
Phase 5: Regulation-source split (gpre3, dry-run tested)

New features:
- Tenant-isolated document upload API (rag-service)
- BAuA crawler (Playwright, 131 PDFs downloaded)
- OSHA Technical Manual crawler (23 chapters)
- CE obligation extractor (6141 obligations from Qdrant)

RAG ingestion:
- 126 BAuA PDFs (TRBS/TRGS/ASR): 27,664 chunks
- OSHA Technical Manual: 7,241 chunks
- OSHA 1910 Subpart O (full): 745 chunks
- EuGH C-588/21 P: 216 chunks
- EU 2018/1725: 842 chunks

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

rag-service/api/__init__.py

@@ -3,9 +3,11 @@ from fastapi import APIRouter
 from api.collections import router as collections_router
 from api.documents import router as documents_router
 from api.search import router as search_router
+from api.tenant_documents import router as tenant_documents_router
 
 router = APIRouter()
 
 router.include_router(collections_router, tags=["Collections"])
 router.include_router(documents_router, tags=["Documents"])
+router.include_router(tenant_documents_router, tags=["Tenant Documents"])
 router.include_router(search_router, tags=["Search"])