curo1305
5950a3f5c2
- 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
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
"""
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Classification orchestrator.
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Loads settings, selects AI provider, classifies document, auto-creates suggested topics.
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Updated in Plan 05: classify_document and suggest_topics_for_document now accept
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an AsyncSession as their first argument so they can be called from the Celery task
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wrapper and from API route handlers that already hold a session.
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Updated in Plan 03-03: classify_document uses load_topics_for_user (D-17) to scope
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topic lookup to the document owner's namespace, and creates AI-suggested topics in
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the user's namespace via create_topic(user_id=doc.user_id) (D-11).
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"""
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from __future__ import annotations
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import uuid as _uuid
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from sqlalchemy.ext.asyncio import AsyncSession
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from db.models import Document
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from services import storage
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from ai import get_provider
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MAX_AI_CHARS = 8_000
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async def classify_document(
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session: AsyncSession,
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doc_id: str,
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topic_names: list[str] | None = None,
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) -> list[str]:
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"""
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Classify a document by its ID. Returns the list of assigned topic names.
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If topic_names is provided, restrict classification to those topics.
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Auto-creates any newly suggested topics in the document owner's namespace (D-11).
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"""
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meta = await storage.get_metadata(session, doc_id)
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if meta is None:
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raise ValueError(f"Document {doc_id} not found")
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settings = storage.load_settings()
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system_prompt = settings.get("system_prompt", "")
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provider = get_provider(settings)
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# Load the Document ORM object to get the owner's user_id (D-11, D-17)
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try:
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uid = _uuid.UUID(doc_id)
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except ValueError:
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uid = None
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doc = await session.get(Document, uid) if uid is not None else None
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doc_user_id = doc.user_id if doc is not None else None
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# Use namespace-scoped topic list if not specified (D-17)
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if topic_names is None:
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if doc_user_id is not None:
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all_topics = await storage.load_topics_for_user(session, user_id=doc_user_id)
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else:
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# Fallback for documents without a user (legacy / test data)
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all_topics = await storage.load_topics(session)
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topic_names = [t["name"] for t in all_topics]
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text = meta.get("extracted_text", "")
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result = await provider.classify(text[:MAX_AI_CHARS], topic_names, system_prompt)
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# Collect all topic names to persist (assigned + suggested)
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all_new_names = set(result.suggested_new_topics) | set(result.topics)
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# Auto-create any topic not already in the registry — in the user's namespace (D-11)
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existing_names = {t.lower() for t in topic_names}
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for name in all_new_names:
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if name.strip() and name.lower() not in existing_names:
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await storage.create_topic(session, name.strip(), user_id=doc_user_id)
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# Final list: everything the AI assigned or suggested
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final_topics = [t for t in list(set(result.topics + result.suggested_new_topics)) if t.strip()]
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await storage.update_document_topics(session, doc_id, final_topics)
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return final_topics
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async def suggest_topics_for_document(session: AsyncSession, doc_id: str) -> list[str]:
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"""Return AI-suggested topic names without modifying the document."""
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meta = await storage.get_metadata(session, doc_id)
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if meta is None:
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raise ValueError(f"Document {doc_id} not found")
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settings = storage.load_settings()
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system_prompt = settings.get("system_prompt", "")
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provider = get_provider(settings)
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text = meta.get("extracted_text", "")
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return await provider.suggest_topics(text[:MAX_AI_CHARS], system_prompt)
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