feat: implement Phase 1 data pipeline and migration

- 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>

app/workers/classify_worker.py

@@ -0,0 +1,76 @@
+"""AI 분류 워커 — Qwen3.5로 도메인/태그/요약 생성"""
+
+from datetime import datetime, timezone
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ai.client import AIClient, parse_json_response
+from core.utils import setup_logger
+from models.document import Document
+
+logger = setup_logger("classify_worker")
+
+# 분류용 텍스트 최대 길이 (Qwen3.5 컨텍스트 관리)
+MAX_CLASSIFY_TEXT = 8000
+
+# 유효한 도메인 목록
+VALID_DOMAINS = {
+    "Knowledge/Philosophy",
+    "Knowledge/Language",
+    "Knowledge/Engineering",
+    "Knowledge/Industrial_Safety",
+    "Knowledge/Programming",
+    "Knowledge/General",
+    "Reference",
+}
+
+
+async def process(document_id: int, session: AsyncSession) -> None:
+    """문서 AI 분류 + 요약"""
+    doc = await session.get(Document, document_id)
+    if not doc:
+        raise ValueError(f"문서 ID {document_id}를 찾을 수 없음")
+
+    if not doc.extracted_text:
+        raise ValueError(f"문서 ID {document_id}: extracted_text가 비어있음")
+
+    client = AIClient()
+    try:
+        # ─── 분류 ───
+        truncated = doc.extracted_text[:MAX_CLASSIFY_TEXT]
+        raw_response = await client.classify(truncated)
+        parsed = parse_json_response(raw_response)
+
+        if not parsed:
+            raise ValueError(f"AI 응답에서 JSON 추출 실패: {raw_response[:200]}")
+
+        # 유효성 검증 + DB 업데이트
+        domain = parsed.get("domain", "")
+        if domain not in VALID_DOMAINS:
+            logger.warning(f"[분류] document_id={document_id}: 알 수 없는 도메인 '{domain}', Knowledge/General로 대체")
+            domain = "Knowledge/General"
+
+        doc.ai_domain = domain
+        doc.ai_sub_group = parsed.get("sub_group", "")
+        doc.ai_tags = parsed.get("tags", [])
+
+        if parsed.get("sourceChannel") and not doc.source_channel:
+            doc.source_channel = parsed["sourceChannel"]
+        if parsed.get("dataOrigin") and not doc.data_origin:
+            doc.data_origin = parsed["dataOrigin"]
+
+        # ─── 요약 ───
+        summary = await client.summarize(doc.extracted_text[:15000])
+        doc.ai_summary = summary
+
+        # ─── 메타데이터 ───
+        doc.ai_model_version = "qwen3.5-35b-a3b"
+        doc.ai_processed_at = datetime.now(timezone.utc)
+
+        logger.info(
+            f"[분류] document_id={document_id}: "
+            f"domain={domain}, tags={doc.ai_tags}, summary={len(summary)}자"
+        )
+
+    finally:
+        await client.close()