google-labs-jules[bot]
e4e3ac4077
Replaced the loop over duplicate hashes that resulted in O(N) database queries per page with a single efficient `in_` batch query to retrieve both originals and duplicates. The records are then grouped in memory using dictionaries. This resolves the N+1 performance bottleneck and reduces response time from an average of 1.65 seconds to ~0.45 seconds locally for 500 groups. Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>
236 lines
8.0 KiB
Python
236 lines
8.0 KiB
Python
"""Duplicate document detection and management API endpoints.
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Provides endpoints for listing all duplicate groups (exact SHA-256 duplicates) and
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for retrieving both exact and near-duplicate matches for a specific document.
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Near-duplicate detection is powered by the same text-embedding cosine-similarity
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engine used by the ``/api/files/{id}/similar`` endpoint
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(see ``app/utils/similarity.py``).
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"""
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import logging
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from typing import Annotated
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from fastapi import APIRouter, Depends, HTTPException, Query, Request, status
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from sqlalchemy.orm import Session
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from app.auth import require_login
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from app.config import settings
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from app.database import get_db
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from app.models import FileRecord
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logger = logging.getLogger(__name__)
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router = APIRouter()
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DbSession = Annotated[Session, Depends(get_db)]
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@router.get("/duplicates")
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@require_login
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def list_duplicate_groups(
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request: Request,
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db: DbSession,
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page: int = Query(1, ge=1, description="Page number"),
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per_page: int = Query(25, ge=1, le=200, description="Items per page"),
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):
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"""List all groups of exact-duplicate documents (same SHA-256 hash).
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Returns one entry per duplicate group showing the original document and all
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files that were detected as copies of it. Groups are sorted by descending
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duplicate count.
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Example:
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```
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GET /api/duplicates
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```
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Response:
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```json
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{
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"groups": [
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{
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"filehash": "abc123...",
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"original": {"id": 1, "original_filename": "invoice.pdf", ...},
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"duplicates": [{"id": 5, "original_filename": "invoice_copy.pdf", ...}],
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"duplicate_count": 1
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}
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],
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"total_groups": 1,
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"total_duplicate_files": 1,
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"pagination": {...}
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}
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```
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"""
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# Find all hashes that have at least one duplicate record
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dup_hashes_query = db.query(FileRecord.filehash).filter(FileRecord.is_duplicate.is_(True)).distinct()
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total_groups = dup_hashes_query.count()
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# Paginate hash groups
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offset = (page - 1) * per_page
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dup_hashes = [row.filehash for row in dup_hashes_query.offset(offset).limit(per_page).all()]
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groups = []
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total_duplicate_files = 0
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if dup_hashes:
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# Fetch all matching files (both original and duplicates) in a single batch query
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all_records = (
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db.query(FileRecord).filter(FileRecord.filehash.in_(dup_hashes)).order_by(FileRecord.id.asc()).all()
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)
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# Group records by hash
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originals_by_hash = {}
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duplicates_by_hash = {h: [] for h in dup_hashes}
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for record in all_records:
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h = record.filehash
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if not record.is_duplicate:
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# Store only the first original record per hash, matching the old .first() behaviour
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if h not in originals_by_hash:
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originals_by_hash[h] = record
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else:
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duplicates_by_hash[h].append(record)
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total_duplicate_files += 1
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for filehash in dup_hashes:
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original = originals_by_hash.get(filehash)
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duplicates = duplicates_by_hash.get(filehash, [])
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groups.append(
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{
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"filehash": filehash,
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"original": _file_record_to_dict(original) if original else None,
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"duplicates": [_file_record_to_dict(d) for d in duplicates],
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"duplicate_count": len(duplicates),
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}
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)
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total_pages = (total_groups + per_page - 1) // per_page if total_groups > 0 else 1
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return {
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"groups": groups,
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"total_groups": total_groups,
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"total_duplicate_files": total_duplicate_files,
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"pagination": {
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"page": page,
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"per_page": per_page,
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"total": total_groups,
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"pages": total_pages,
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"next": str(request.url.include_query_params(page=page + 1)) if page < total_pages else None,
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"previous": str(request.url.include_query_params(page=page - 1)) if page > 1 else None,
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},
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}
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@router.get("/files/{file_id}/duplicates")
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@require_login
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def get_file_duplicates(
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request: Request,
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file_id: int,
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db: DbSession,
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near_duplicate_limit: int = Query(5, ge=1, le=20, description="Maximum near-duplicates to return"),
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near_duplicate_threshold: float = Query(
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-1.0,
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ge=-1.0,
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le=1.0,
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description="Minimum similarity score for near-duplicates; -1 uses the configured default",
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),
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):
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"""Get exact and near-duplicate documents for the specified file.
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**Exact duplicates** share the same SHA-256 hash.
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**Near-duplicates** have a text-embedding cosine similarity score ≥
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``NEAR_DUPLICATE_THRESHOLD`` (configurable; default 0.85).
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Near-duplicate detection requires OCR text to be available for both the
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target file and candidate files. Files without OCR text are excluded.
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Example:
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```
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GET /api/files/42/duplicates
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```
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Response:
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```json
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{
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"file_id": 42,
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"exact_duplicates": [
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{"id": 7, "original_filename": "invoice.pdf", "is_duplicate": true, "duplicate_of_id": 42, ...}
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],
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"near_duplicates": [
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{"file_id": 15, "original_filename": "invoice_jan.pdf", "similarity_score": 0.92, ...}
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],
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"near_duplicate_threshold": 0.85
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}
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```
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"""
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file_record = db.query(FileRecord).filter(FileRecord.id == file_id).first()
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if not file_record:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="File not found")
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# --- Exact duplicates ---
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# Case 1: This file is the original — find all records that are duplicates of it
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exact_duplicates_of_this = (
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db.query(FileRecord)
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.filter(FileRecord.filehash == file_record.filehash, FileRecord.id != file_id)
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.order_by(FileRecord.id.asc())
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.all()
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)
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# Case 2: This file itself is a duplicate — find the original
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is_self_duplicate = file_record.is_duplicate
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duplicate_of_original: FileRecord | None = None
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if is_self_duplicate and file_record.duplicate_of_id:
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duplicate_of_original = db.query(FileRecord).filter(FileRecord.id == file_record.duplicate_of_id).first()
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exact_duplicate_dicts = [_file_record_to_dict(f) for f in exact_duplicates_of_this]
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# --- Near-duplicates (embedding-based) ---
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effective_threshold = (
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near_duplicate_threshold if near_duplicate_threshold >= 0.0 else settings.near_duplicate_threshold
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)
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near_duplicates: list[dict] = []
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if file_record.ocr_text and file_record.ocr_text.strip():
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try:
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from app.utils.similarity import find_similar_documents
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near_duplicates = find_similar_documents(
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db,
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file_id,
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limit=near_duplicate_limit,
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threshold=effective_threshold,
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)
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except Exception as e:
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logger.warning(f"Near-duplicate detection failed for file {file_id}: {e}")
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near_duplicates = []
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return {
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"file_id": file_id,
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"is_duplicate": is_self_duplicate,
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"duplicate_of": _file_record_to_dict(duplicate_of_original) if duplicate_of_original else None,
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"exact_duplicates": exact_duplicate_dicts,
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"near_duplicates": near_duplicates,
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"near_duplicate_threshold": effective_threshold,
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"exact_duplicate_count": len(exact_duplicate_dicts),
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"near_duplicate_count": len(near_duplicates),
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}
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def _file_record_to_dict(file_record: FileRecord | None) -> dict | None:
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"""Serialise a ``FileRecord`` to a plain dict for JSON responses."""
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if file_record is None:
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return None
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return {
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"id": file_record.id,
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"original_filename": file_record.original_filename,
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"filehash": file_record.filehash,
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"file_size": file_record.file_size,
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"mime_type": file_record.mime_type,
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"is_duplicate": file_record.is_duplicate,
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"duplicate_of_id": file_record.duplicate_of_id,
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"document_title": file_record.document_title,
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"created_at": file_record.created_at.isoformat() if file_record.created_at else None,
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}
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