feat(rag): optimize RAG pipeline — JSON-Mode, CoT, Hybrid Search, Re-Ranking, Cross-Reg Dedup, chunk 1024

Phase 1 (LLM Quality):
- Add format=json to all Ollama payloads (obligation_extractor, control_generator, citation_backfill)
- Add Chain-of-Thought analysis steps to Pass 0a/0b system prompts

Phase 2 (Retrieval Quality):
- Hybrid search via Qdrant Query API with RRF fusion + automatic text index (legal_rag.go)
- Fallback to dense-only search if Query API unavailable
- Cross-encoder re-ranking with BGE Reranker v2 (RERANK_ENABLED=false by default)
- CPU-only PyTorch dependency to keep Docker image small

Phase 3 (Data Layer):
- Cross-regulation dedup pass (threshold 0.95) links controls across regulations
- DedupResult.link_type field distinguishes dedup_merge vs cross_regulation
- Chunk size defaults updated 512/50 → 1024/128 for new ingestions only
- Existing collections and controls are NOT affected

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend-compliance/requirements.txt

@@ -22,6 +22,11 @@ python-multipart>=0.0.22
 # AI / Anthropic (compliance AI assistant)
 anthropic==0.75.0
 
+# Re-Ranking (cross-encoder, CPU-only PyTorch to keep image small)
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+sentence-transformers>=3.0.0
+
 # PDF Generation (GDPR export, audit reports)
 weasyprint>=68.0
 reportlab==4.2.5