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/qdrant_client_wrapper.py

@@ -235,6 +235,74 @@ class QdrantClientWrapper:
         logger.info("Deleted points from '%s' with filter %s", collection, filter_conditions)
         return True
 
+    # ------------------------------------------------------------------
+    # Tenant document helpers
+    # ------------------------------------------------------------------
+
+    async def get_unique_documents(self, collection: str) -> list[dict]:
+        """Get unique documents from a collection by scrolling and grouping."""
+        try:
+            self.client.get_collection(collection)
+        except Exception:
+            return []
+
+        docs: dict[str, dict] = {}
+        offset = None
+        while True:
+            result = self.client.scroll(
+                collection_name=collection,
+                scroll_filter=None,
+                limit=100,
+                offset=offset,
+                with_payload=True,
+                with_vectors=False,
+            )
+            points, next_offset = result
+            for pt in points:
+                payload = pt.payload or {}
+                doc_id = payload.get("document_id", "")
+                if doc_id and doc_id not in docs:
+                    docs[doc_id] = {
+                        "id": doc_id,
+                        "filename": payload.get("filename", ""),
+                        "file_size": payload.get("file_size", 0),
+                        "status": "indexed",
+                        "chunk_count": 0,
+                        "collection": collection,
+                    }
+                if doc_id:
+                    docs[doc_id]["chunk_count"] += 1
+
+            if next_offset is None:
+                break
+            offset = next_offset
+
+        return list(docs.values())
+
+    async def count_by_filter(
+        self, collection: str, filter_conditions: dict[str, Any]
+    ) -> int:
+        """Count points matching filter."""
+        try:
+            self.client.get_collection(collection)
+        except Exception:
+            return 0
+
+        must_conditions = []
+        for key, value in filter_conditions.items():
+            must_conditions.append(
+                qmodels.FieldCondition(
+                    key=key, match=qmodels.MatchValue(value=value)
+                )
+            )
+
+        result = self.client.count(
+            collection_name=collection,
+            count_filter=qmodels.Filter(must=must_conditions),
+            exact=True,
+        )
+        return result.count
+
     # ------------------------------------------------------------------
     # Info
     # ------------------------------------------------------------------