hyungi_document_server/app/services/digest/pipeline.py

"""Phase 4 digest pipeline orchestration.

Step:
    1. AIClient 생성
    2. 7일 window 로 documents 로드 (loader)
    3. country 별 cluster_country (clustering)
    4. cluster 별 select_for_llm (selection)
    5. cluster 별 summarize_cluster_with_fallback (summarizer, LLM)
    6. DELETE+INSERT 단일 트랜잭션 (idempotent)
    7. start/end 로그 + generation_ms + fallback 비율 health metric
"""

import asyncio
import hashlib
import time
from datetime import datetime, timedelta, timezone
from zoneinfo import ZoneInfo

from sqlalchemy import delete

from ai.client import AIClient
from core.database import async_session
from core.utils import setup_logger
from models.digest import DigestTopic, GlobalDigest

from .clustering import LAMBDA, cluster_country
from .loader import load_news_window
from .selection import select_for_llm
from .summarizer import summarize_cluster_with_fallback

logger = setup_logger("digest_pipeline")

WINDOW_DAYS = 7
KST = ZoneInfo("Asia/Seoul")


def _kst_today() -> datetime:
    return datetime.now(KST).date()


def _summary_hash(text: str) -> str:
    return hashlib.sha256((text or "").encode("utf-8")).hexdigest()[:16]


def _build_topic_row(
    country: str,
    rank: int,
    cluster: dict,
    selected: list[dict],
    llm_result: dict,
    primary_model: str,
) -> DigestTopic:
    """LLM 결과 + cluster 메타 → DigestTopic ORM 인스턴스.

    article_ids 는 코드가 cluster.members 에서 직접 주입 (LLM 생성 금지 → id 위조 불가).
    """
    article_ids = [int(m["id"]) for m in cluster["members"]]
    centroid_sample = {
        "selected_doc_ids": [int(m["id"]) for m in selected],
        "summary_hashes": [_summary_hash(m.get("ai_summary") or "") for m in selected],
    }
    return DigestTopic(
        country=country,
        topic_rank=rank,
        topic_label=llm_result["topic_label"],
        summary=llm_result["summary"],
        article_ids=article_ids,
        article_count=len(article_ids),
        importance_score=float(cluster["importance_score"]),
        raw_weight_sum=float(cluster["raw_weight_sum"]),
        centroid_sample=centroid_sample,
        llm_model=primary_model,
        llm_fallback_used=bool(llm_result["llm_fallback_used"]),
    )


async def run_digest_pipeline() -> dict:
    """전체 파이프라인 실행. worker entry 에서 호출.

    Returns:
        실행 통계 dict {llm_calls, fallback_used, total_topics, generation_ms}
    """
    start = time.time()

    window_end = datetime.now(timezone.utc)
    window_start = window_end - timedelta(days=WINDOW_DAYS)
    digest_date = _kst_today()

    logger.info(
        f"[global_digest] start window={window_start.date()} ~ {window_end.date()} "
        f"digest_date={digest_date} decay_lambda={LAMBDA:.4f}"
    )

    docs_by_country = await load_news_window(window_start, window_end)
    if not docs_by_country:
        logger.warning("[global_digest] 7일 window에 뉴스 0건 — digest 생성 스킵")
        return {
            "llm_calls": 0,
            "fallback_used": 0,
            "total_topics": 0,
            "generation_ms": int((time.time() - start) * 1000),
        }

    client = AIClient()
    primary_model = client.ai.primary.model

    all_topic_rows: list[DigestTopic] = []
    stats = {"llm_calls": 0, "fallback_used": 0}

    try:
        # 2026-06-15: cluster 호출을 gather 로 동시 실행. 실제 동시성은 전역 MLX gate
        # (config.mlx_gate_concurrency, BACKGROUND 우선순위) 가 제한한다. rank/순서 보존.
        jobs = []
        for country, docs in docs_by_country.items():
            clusters = cluster_country(country, docs)
            if not clusters:
                continue  # sparse country 자동 제외
            for rank, cluster in enumerate(clusters, start=1):
                selected = select_for_llm(cluster)
                jobs.append((country, rank, cluster, selected))

        async def _run_one(cluster, selected):
            return await summarize_cluster_with_fallback(client, cluster, selected)

        results = await asyncio.gather(*[_run_one(c, s) for (_, _, c, s) in jobs])

        for (country, rank, cluster, selected), llm_result in zip(jobs, results):
            stats["llm_calls"] += 1
            if llm_result["llm_fallback_used"]:
                stats["fallback_used"] += 1
            all_topic_rows.append(
                _build_topic_row(country, rank, cluster, selected, llm_result, primary_model)
            )
    finally:
        await client.close()

    generation_ms = int((time.time() - start) * 1000)
    total_articles = sum(len(d) for d in docs_by_country.values())
    countries_with_topics = len({r.country for r in all_topic_rows})

    if stats["fallback_used"] == 0:
        status = "success"
    elif stats["llm_calls"] and stats["fallback_used"] / stats["llm_calls"] > 0.5:
        status = "failed"
    else:
        status = "partial"

    async with async_session() as session:
        # idempotent: 같은 날짜 row 가 있으면 CASCADE 로 topics 까지 삭제
        await session.execute(
            delete(GlobalDigest).where(GlobalDigest.digest_date == digest_date)
        )
        new_digest = GlobalDigest(
            digest_date=digest_date,
            window_start=window_start,
            window_end=window_end,
            decay_lambda=LAMBDA,
            total_articles=total_articles,
            total_countries=countries_with_topics,
            total_topics=len(all_topic_rows),
            generation_ms=generation_ms,
            llm_calls=stats["llm_calls"],
            llm_failures=stats["fallback_used"],
            status=status,
        )
        new_digest.topics = all_topic_rows
        session.add(new_digest)
        await session.commit()

    fallback_pct = (
        (stats["fallback_used"] / stats["llm_calls"] * 100.0)
        if stats["llm_calls"] else 0.0
    )
    logger.info(
        f"[global_digest] done countries={countries_with_topics} "
        f"topics={len(all_topic_rows)} llm_calls={stats['llm_calls']} "
        f"fallback={stats['fallback_used']}/{stats['llm_calls']} ({fallback_pct:.2f}%) "
        f"status={status} elapsed={generation_ms / 1000:.1f}s"
    )

    return {
        "llm_calls": stats["llm_calls"],
        "fallback_used": stats["fallback_used"],
        "total_topics": len(all_topic_rows),
        "generation_ms": generation_ms,
        "status": status,
    }