"""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()