국가법령 XML은 <편>/<장> 태그가 아닌 <조문단위 조문키="xxxx000">에
"제X장 ..." 형태로 장 구분자가 포함됨. 이를 파싱하여 분할.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- 모니터링 법령 12개 → 26개 (산업안전/건설/위험물/소방/전기/가스/근로/환경)
- lawSearch.do로 검색, lawService.do로 본문 조회
- 대형 법령 편/장 단위 분할 저장 (fallback: 편→장→전체)
- 저장 경로: PKM/Inbox/ (AI 자동 분류 연계)
- 변경 감지 시 user_note에 이력 자동 기록
- CalDAV + SMTP 알림
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- config.yaml: 6개 domain × 3단계 taxonomy + 13개 document_types 정의
- classify.txt: 영문 프롬프트, taxonomy 경로 기반 분류 + 분류 규칙 주입
- classify_worker: taxonomy 검증, confidence 기반 분류, document_type 저장
- migration 008: document_type, importance, ai_confidence 컬럼
- API: DocumentResponse에 document_type, importance, ai_confidence 추가
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- original_path/format/hash + conversion_status 필드 추가 (migration 007)
- extract_worker: 텍스트 추출 후 xlsx→ods, docx→odt 등 ODF 변환
- 변환본은 .derived/{doc_id}.ods 에 저장
- 원본 메타 보존 (original_path/format/hash)
- file_watcher: .derived/ .preview/ 디렉토리 제외
- DocumentViewer: ODF 포맷이면 편집 버튼 자동 표시
- edit_url 있으면 "편집", 없으면 "Synology Drive에서 열기"
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- queue.py: process_stage enum에 'preview' 추가
- classify_worker: ai_summary에 strip_thinking() 적용
- CLAUDE.md: 현재 아키텍처 전면 반영 (파이프라인, UI, 인프라, 코딩규칙)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- queue_consumer: extract 완료 시 classify + preview 동시 등록
- classify_worker: _move_to_knowledge() 제거, 파일 원본 위치 유지
- DocumentCard: 좌측 domain별 색상 바 (4px) 추가
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- xlsx, docx, pptx, odt, ods, odp, odoc, osheet 지원
- LibreOffice --convert-to txt로 텍스트 추출 (60s timeout)
- 추가 의존성 없음 (Docker에 이미 설치된 LibreOffice 사용)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Markdown split editor: textarea + marked preview, Ctrl+S 저장
- PUT /api/documents/{id}/content: 원본 파일 저장 + extracted_text 갱신
- GET /api/documents/{id}/preview: PDF 미리보기 캐시 서빙
- preview_worker: LibreOffice headless → PDF 변환 (timeout 60s, retry 1회)
- queue_consumer: preview stage 추가 (embed 후 자동 트리거)
- DocumentViewer: 포맷별 분기 (markdown/pdf/preview-pdf/image/text/cad)
- 오피스/CAD 문서: 새 탭 편집 버튼
- Dockerfile: LibreOffice headless 설치
- migration 005: preview_status, preview_hash, preview_at 컬럼
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Shared session between queue consumer and workers caused
MissingGreenlet errors in APScheduler context. Each worker
call now gets its own session with explicit commit/rollback.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
HIGH:
- Lock setup TOTP/NAS endpoints behind _require_setup() guard
(prevented unauthenticated admin 2FA takeover after setup)
- Sanitize upload filename with Path().name + resolve() validation
(prevented path traversal writing outside Inbox)
MEDIUM:
- Add score > 0.01 filter to hybrid search via subquery
(prevented returning irrelevant documents with zero score)
- Implement Inbox → Knowledge file move after classification
(classify_worker now moves files based on ai_domain)
- Add Anthropic Messages API support in _request()
(premium/Claude path now sends correct format and parses
content[0].text instead of choices[0].message.content)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Implement kordoc /parse endpoint (HWP/HWPX/PDF via kordoc lib,
text files direct read, images flagged for OCR)
- Add queue consumer with APScheduler (1min interval, stage chaining
extract→classify→embed, stale item recovery, retry logic)
- Add extract worker (kordoc HTTP call + direct text read)
- Add classify worker (Qwen3.5 AI classification with think-tag
stripping and robust JSON extraction from AI responses)
- Add embed worker (GPU server nomic-embed-text, graceful failure)
- Add DEVONthink migration script with folder mapping for 16 DBs,
dry-run mode, batch commits, and idempotent file_path UNIQUE
- Enhance ai/client.py with strip_thinking() and parse_json_response()
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
