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/embed_worker.py

@@ -0,0 +1,44 @@
+"""벡터 임베딩 워커 — GPU 서버 nomic-embed-text 호출"""
+
+from datetime import datetime, timezone
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ai.client import AIClient
+from core.utils import setup_logger
+from models.document import Document
+
+logger = setup_logger("embed_worker")
+
+# 임베딩용 텍스트 최대 길이 (nomic-embed-text: 8192 토큰)
+MAX_EMBED_TEXT = 6000
+EMBED_MODEL_VERSION = "nomic-embed-text-v1.5"
+
+
+async def process(document_id: int, session: AsyncSession) -> None:
+    """문서 벡터 임베딩 생성"""
+    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")
+
+    # title + 본문 앞부분을 결합하여 임베딩 입력 생성
+    title_part = doc.title or ""
+    text_part = doc.extracted_text[:MAX_EMBED_TEXT]
+    embed_input = f"{title_part}\n\n{text_part}".strip()
+
+    if not embed_input:
+        logger.warning(f"[임베딩] document_id={document_id}: 빈 텍스트, 스킵")
+        return
+
+    client = AIClient()
+    try:
+        vector = await client.embed(embed_input)
+        doc.embedding = vector
+        doc.embed_model_version = EMBED_MODEL_VERSION
+        doc.embedded_at = datetime.now(timezone.utc)
+        logger.info(f"[임베딩] document_id={document_id}: {len(vector)}차원 벡터 생성")
+    finally:
+        await client.close()