curo1305
1882edfff6
- backend/api/auth.py: register, login (TOTP+backup), refresh, logout, me, change-password; per-account Redis rate limit; HIBP check - backend/main.py: Origin validation middleware, CSP headers middleware, CORS locked to settings.cors_origins, Redis lifespan (app.state.redis), admin bootstrap, auth router included, slowapi SlowAPIMiddleware - backend/services/email.py: already created in Plan 01 (verified exists) - Python 3.9 compat: fixed match statement in ai/__init__.py, str|None union syntax in openai_provider.py, api/documents.py, api/topics.py, api/settings.py, services/classifier.py All 17 tests in test_auth_api.py pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
72 lines
2.6 KiB
Python
72 lines
2.6 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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"""
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from __future__ import annotations
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from sqlalchemy.ext.asyncio import AsyncSession
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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.
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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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# Use all known topics if not specified
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if topic_names is None:
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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
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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())
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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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