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>
119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
from datetime import datetime, timezone
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from typing import Optional
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from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, UploadFile
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from sqlalchemy.ext.asyncio import AsyncSession
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from deps.db import get_db
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from services import classifier, extractor, storage
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from tasks.document_tasks import extract_and_classify
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router = APIRouter(prefix="/api/documents", tags=["documents"])
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ALLOWED_MIME_TYPES = {
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"application/pdf",
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"application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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"application/msword",
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"text/plain",
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"text/markdown",
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"image/png",
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"image/jpeg",
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"image/jpg",
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"image/tiff",
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"image/webp",
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}
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@router.post("/upload")
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async def upload_document(
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file: UploadFile = File(...),
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auto_classify: bool = Form(True),
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session: AsyncSession = Depends(get_db),
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):
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content = await file.read()
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if len(content) == 0:
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raise HTTPException(400, "Empty file")
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mime = file.content_type or "application/octet-stream"
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saved = await storage.save_upload(session, content, file.filename or "upload", mime)
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# Extract text from the in-memory bytes (avoid a second MinIO round-trip at upload time)
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text = extractor.extract_text_from_bytes(content, mime)
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now = datetime.now(timezone.utc).isoformat()
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meta = {
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"id": saved["id"],
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"original_name": file.filename or "upload",
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"filename": saved["filename"],
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"mime_type": mime,
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"size_bytes": len(content),
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"extracted_text": text,
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# Phase 1 cutover: topics are empty at upload time; the Celery worker
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# fills them in asynchronously after extract_and_classify completes.
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"topics": [],
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"created_at": now,
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"classified_at": None,
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}
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await storage.save_metadata(session, meta)
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if auto_classify:
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# Queue the extract+classify task on the Celery documents queue (STORE-08).
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# The task re-fetches bytes from MinIO, extracts text, and classifies.
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# The upload response returns topics=[] — polling GET /api/documents/{id}
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# will show the populated topics once the worker completes.
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extract_and_classify.delay(str(saved["id"]))
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return meta
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@router.get("")
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async def list_documents(
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topic: Optional[str] = Query(None),
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page: int = Query(1, ge=1),
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per_page: int = Query(20, ge=1, le=100),
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session: AsyncSession = Depends(get_db),
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):
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docs = await storage.list_metadata(session, topic=topic)
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total = len(docs)
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start = (page - 1) * per_page
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return {"items": docs[start : start + per_page], "total": total, "page": page, "per_page": per_page}
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@router.get("/{doc_id}")
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async def get_document(doc_id: str, session: AsyncSession = Depends(get_db)):
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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 HTTPException(404, "Document not found")
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return meta
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@router.delete("/{doc_id}")
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async def delete_document(doc_id: str, session: AsyncSession = Depends(get_db)):
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ok = await storage.delete_document(session, doc_id)
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if not ok:
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raise HTTPException(404, "Document not found")
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return {"success": True}
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@router.post("/{doc_id}/classify")
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async def classify_document(
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doc_id: str,
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body: dict = {},
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session: AsyncSession = Depends(get_db),
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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 HTTPException(404, "Document not found")
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topic_names = body.get("topics") if body else None
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try:
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# The /classify endpoint calls classifier synchronously and returns the
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# topic list immediately — this preserves the historical behavior.
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# The upload-time path uses Celery .delay() instead (Phase 1 cutover).
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topics = await classifier.classify_document(session, doc_id, topic_names)
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except Exception as e:
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raise HTTPException(500, f"Classification failed: {e}")
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return {"topics": topics}
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