- Judge baseline excerpt and library chapter separately; build_memoir_compare_summary for gate, nine-dim and leaf deltas. - Memoir SSE chapter payload: baseline_judge, compare_summary, baseline_judge_error. - MemoirJudgeOutput: loose score coercion and post-validate clamp; memoir judge prompt caps from settings. - app-eval-web: two-column MemoirScoreCard layout, MemoirCompareSummary, chapter blocks and CSS. - Add memoir_compare_summary, log_events, celery_log_context, memoir_pipeline_progress; tests and migration 0014. - Misc: memory/evidence and enrichment paths, task/orchestrator updates, internal-eval docs, env examples.
153 lines
5.5 KiB
Python
153 lines
5.5 KiB
Python
"""Celery:story 变更后重组关联章节的 canonical_markdown(物化视图)。"""
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import time
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from datetime import datetime, timezone
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from celery import shared_task
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from app.core.chapter_pipeline_lock import (
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acquire_chapter_pipeline_lock,
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release_chapter_pipeline_lock,
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)
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from app.core.config import settings
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from app.core.db import get_sync_db
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from app.core.logging import get_logger
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from app.core.memoir_pipeline_progress import merge_fanout_item
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from app.core.memoir_pipeline_trace import new_memoir_correlation_id
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from app.core.memory_compaction_schedule import schedule_memory_compaction_run
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from app.features.memoir import repo as memoir_repo
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from app.features.memoir.models import Chapter
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logger = get_logger(__name__)
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@shared_task(bind=True, max_retries=8, default_retry_delay=30)
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def recompose_chapter(
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self, chapter_id: str, memoir_correlation_id: str | None = None
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) -> dict:
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"""
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按章节物化 canonical_markdown:仅当 markdown_compose_dirty 为 True 时执行;
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与 pipeline 共用章节级 Redis 锁,拿不到锁则跳过(依赖后续触发重试)。
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"""
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lock_ttl = int(settings.chapter_pipeline_lock_ttl_seconds)
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tid = str(self.request.id)
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t0 = time.perf_counter()
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merge_fanout_item(
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memoir_correlation_id,
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list_name="recompose_chapters",
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id_field="chapter_id",
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item_id=chapter_id,
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task_id=tid,
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status="running",
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)
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user_id: str | None = None
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composed = False
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with get_sync_db() as session:
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chapter = session.get(Chapter, chapter_id)
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if not chapter:
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ms = (time.perf_counter() - t0) * 1000
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logger.info(
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"event=recompose_chapter status=not_found chapter_id={} duration_ms={:.1f} "
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"msg=章节重组跳过(章节不存在)",
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chapter_id,
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ms,
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)
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merge_fanout_item(
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memoir_correlation_id,
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list_name="recompose_chapters",
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id_field="chapter_id",
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item_id=chapter_id,
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task_id=tid,
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status="not_found",
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)
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return {"status": "not_found"}
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if chapter.markdown_compose_dirty is not True:
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ms = (time.perf_counter() - t0) * 1000
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logger.info(
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"event=recompose_chapter status=skip_not_dirty chapter_id={} duration_ms={:.1f} "
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"msg=章节重组跳过(无需重组)",
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chapter_id,
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ms,
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)
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merge_fanout_item(
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memoir_correlation_id,
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list_name="recompose_chapters",
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id_field="chapter_id",
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item_id=chapter_id,
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task_id=tid,
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status="skip_not_dirty",
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)
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return {"status": "skip_not_dirty"}
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uid = str(chapter.user_id)
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stage = str(chapter.category)
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lock_handle = acquire_chapter_pipeline_lock(uid, stage, ttl_seconds=lock_ttl)
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if lock_handle is None:
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ms = (time.perf_counter() - t0) * 1000
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logger.info(
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"event=recompose_chapter status=lock_busy_retry "
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"chapter_id={} user_id={} stage={} retry_on_lock={} duration_ms={:.1f} "
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"msg=章节重组等待锁或重试",
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chapter_id,
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uid,
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stage,
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settings.memoir_recompose_retry_on_lock_contention,
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ms,
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)
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if settings.memoir_recompose_retry_on_lock_contention:
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countdown = max(15, min(120, lock_ttl // 4))
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raise self.retry(countdown=countdown)
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merge_fanout_item(
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memoir_correlation_id,
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list_name="recompose_chapters",
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id_field="chapter_id",
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item_id=chapter_id,
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task_id=tid,
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status="skip_lock_contention",
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)
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return {"status": "skip_lock_contention"}
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try:
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composed = memoir_repo.compose_chapter_from_story_links_sync(
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session, chapter_id
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)
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session.commit()
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user_id = uid
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except Exception as exc:
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session.rollback()
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logger.warning(
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"recompose_chapter failed chapter_id={} err={}", chapter_id, exc
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)
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raise self.retry(exc=exc) from exc
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finally:
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release_chapter_pipeline_lock(lock_handle)
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if user_id:
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schedule_memory_compaction_run(
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user_id,
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{
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"trigger_source": "chapter_recompose",
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"trigger_time": datetime.now(timezone.utc).isoformat(),
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"pipeline_run_id": str(self.request.id),
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"memoir_correlation_id": new_memoir_correlation_id(),
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"recomposed_chapter_ids": [chapter_id],
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},
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)
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ms = (time.perf_counter() - t0) * 1000
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st = "composed" if composed else "empty"
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logger.info(
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"event=recompose_chapter status={} chapter_id={} user_id={} duration_ms={:.1f} "
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"msg=章节物化重组完成",
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st,
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chapter_id,
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user_id or "-",
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ms,
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)
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merge_fanout_item(
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memoir_correlation_id,
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list_name="recompose_chapters",
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id_field="chapter_id",
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item_id=chapter_id,
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task_id=tid,
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status="composed" if composed else "empty",
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)
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return {"status": "composed" if composed else "empty", "chapter_id": chapter_id}
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