feat(03-03): wire get_current_user into /api/topics/*; add load_topics_for_user; POST /api/admin/topics

- api/topics.py: add get_current_user dep to all 5 handlers (list, create, update, delete, suggest)
- list_topics: uses load_topics_for_user (system topics + user's own) with user-scoped doc counts
- create_topic: passes user_id=current_user.id (never creates system topics via regular endpoint)
- update_topic/delete_topic: ownership assertion — system topics and other users' topics return 404
- api/admin.py: add SystemTopicCreate model + POST /api/admin/topics (user_id=NULL, admin-only)
- services/storage.py: add or_ import; load_topics_for_user (D-17); create_topic gains user_id param with namespace-scoped dedup; topic_doc_counts gains optional user_id for user-scoped counts; add load_topics_for_user to __all__
- services/classifier.py: replace load_topics with load_topics_for_user(doc.user_id); pass user_id=doc.user_id to create_topic for AI-suggested topics (D-11)
- Tests: update all topic tests to pass auth headers; implement test_topic_namespace, test_admin_create_system_topic, test_regular_user_cannot_create_system_topic, test_topics_require_auth

backend/services/classifier.py

@@ -5,11 +5,18 @@ Loads settings, selects AI provider, classifies document, auto-creates suggested
 Updated in Plan 05: classify_document and suggest_topics_for_document now accept
 an AsyncSession as their first argument so they can be called from the Celery task
 wrapper and from API route handlers that already hold a session.
+
+Updated in Plan 03-03: classify_document uses load_topics_for_user (D-17) to scope
+topic lookup to the document owner's namespace, and creates AI-suggested topics in
+the user's namespace via create_topic(user_id=doc.user_id) (D-11).
 """
 from __future__ import annotations
 
+import uuid as _uuid
+
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from db.models import Document
 from services import storage
 from ai import get_provider
 
@@ -24,7 +31,7 @@ async def classify_document(
     """
     Classify a document by its ID. Returns the list of assigned topic names.
     If topic_names is provided, restrict classification to those topics.
-    Auto-creates any newly suggested topics.
+    Auto-creates any newly suggested topics in the document owner's namespace (D-11).
     """
     meta = await storage.get_metadata(session, doc_id)
     if meta is None:
@@ -34,9 +41,22 @@ async def classify_document(
     system_prompt = settings.get("system_prompt", "")
     provider = get_provider(settings)
 
-    # Use all known topics if not specified
+    # Load the Document ORM object to get the owner's user_id (D-11, D-17)
+    try:
+        uid = _uuid.UUID(doc_id)
+    except ValueError:
+        uid = None
+
+    doc = await session.get(Document, uid) if uid is not None else None
+    doc_user_id = doc.user_id if doc is not None else None
+
+    # Use namespace-scoped topic list if not specified (D-17)
     if topic_names is None:
-        all_topics = await storage.load_topics(session)
+        if doc_user_id is not None:
+            all_topics = await storage.load_topics_for_user(session, user_id=doc_user_id)
+        else:
+            # Fallback for documents without a user (legacy / test data)
+            all_topics = await storage.load_topics(session)
         topic_names = [t["name"] for t in all_topics]
 
     text = meta.get("extracted_text", "")
@@ -45,11 +65,11 @@ async def classify_document(
     # Collect all topic names to persist (assigned + suggested)
     all_new_names = set(result.suggested_new_topics) | set(result.topics)
 
-    # Auto-create any topic not already in the registry
+    # Auto-create any topic not already in the registry — in the user's namespace (D-11)
     existing_names = {t.lower() for t in topic_names}
     for name in all_new_names:
         if name.strip() and name.lower() not in existing_names:
-            await storage.create_topic(session, name.strip())
+            await storage.create_topic(session, name.strip(), user_id=doc_user_id)
 
     # Final list: everything the AI assigned or suggested
     final_topics = [t for t in list(set(result.topics + result.suggested_new_topics)) if t.strip()]