ac49bc7f23
- 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.
240 lines
8.6 KiB
Python
240 lines
8.6 KiB
Python
"""
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Story 写入提交后的统一异步调度：插图、章节物化、可选 memory compaction。
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enqueue 失败不回滚已提交数据，仅记录日志；依赖后续触发或重试收敛。
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"""
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from __future__ import annotations
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import threading
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Any, cast
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import redis
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from app.core.config import settings
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from app.core.logging import get_logger
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from app.core.memoir_pipeline_progress import merge_pipeline_run
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from app.core.memory_compaction_schedule import schedule_memory_compaction_run
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logger = get_logger(__name__)
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_redis_client: redis.Redis | None = None
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_redis_lock = threading.Lock()
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def _story_image_enqueue_key(story_id: str) -> str:
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return f"enqueue:story-image:{story_id}"
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def _get_redis() -> redis.Redis:
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"""进程内复用单个 Redis 客户端，避免重复创建连接池。"""
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global _redis_client
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if _redis_client is None:
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with _redis_lock:
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if _redis_client is None:
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_redis_client = redis.from_url(
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settings.redis_url, decode_responses=True
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)
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return _redis_client
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@dataclass
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class PostCommitResult:
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enqueued_story_image_count: int = 0
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enqueued_chapter_recompose_count: int = 0
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compaction_scheduled: bool = False
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quality_pass_scheduled: bool = False
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errors: list[str] = field(default_factory=list)
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def enqueue_story_post_commit_effects(
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*,
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user_id: str,
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story_ids: set[str],
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chapter_ids: set[str],
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trigger_source: str,
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need_image: bool = True,
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need_recompose: bool = True,
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need_compaction: bool = False,
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need_quality_pass: bool = False,
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compaction_extra: dict[str, Any] | None = None,
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memoir_correlation_id: str | None = None,
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) -> PostCommitResult:
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"""
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Unified post-commit fan-out: story images, chapter recompose, compaction,
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and quality pass. story_ids 为空则跳过 image；chapter_ids 为空则跳过 recompose。
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need_compaction=True 时仅按 user_id 调度 compaction（不依赖 story/chapter 集合）。
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"""
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result = PostCommitResult()
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r = _get_redis()
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ttl = int(settings.story_image_enqueue_dedup_ttl)
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if need_image and story_ids:
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from app.tasks.story_image_tasks import (
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generate_story_image as gen_story_image_task,
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)
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for sid in sorted(story_ids):
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key = _story_image_enqueue_key(sid)
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try:
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if not r.set(key, "1", nx=True, ex=ttl):
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logger.debug(
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"story_image enqueue skipped (dedup): story={} trigger={}",
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sid,
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trigger_source,
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)
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continue
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except Exception as exc:
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logger.warning(
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"story_image enqueue dedup redis failed, allowing enqueue: "
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"story={} err={}",
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sid,
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exc,
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)
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try:
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img_ar = cast(Any, gen_story_image_task).delay(
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sid, memoir_correlation_id=memoir_correlation_id
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)
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result.enqueued_story_image_count += 1
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tid_img = getattr(img_ar, "id", None)
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if memoir_correlation_id and tid_img:
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merge_pipeline_run(
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memoir_correlation_id,
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{
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"fanout": {
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"story_images": [
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{
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"story_id": sid,
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"task_id": str(tid_img),
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"status": "enqueued",
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}
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]
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},
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},
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)
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except Exception as exc:
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logger.warning(
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"generate_story_image.delay failed story={} trigger={}: {}",
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sid,
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trigger_source,
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exc,
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)
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result.errors.append(f"generate_story_image:{sid}:{exc}")
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try:
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r.delete(key)
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except Exception as e:
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logger.debug("Redis key 清理失败: {}", e)
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if need_recompose and chapter_ids:
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from app.tasks.chapter_compose_tasks import (
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recompose_chapter as recompose_chapter_task,
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)
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cd = int(settings.recompose_chapter_delay_seconds)
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for cid in sorted(chapter_ids):
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try:
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rkwargs: dict[str, Any] = {}
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if memoir_correlation_id:
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rkwargs["memoir_correlation_id"] = memoir_correlation_id
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rec_ar = cast(Any, recompose_chapter_task).apply_async(
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args=[cid],
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kwargs=rkwargs,
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countdown=max(0, cd),
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)
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result.enqueued_chapter_recompose_count += 1
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tid_rec = getattr(rec_ar, "id", None)
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if memoir_correlation_id and tid_rec:
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merge_pipeline_run(
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memoir_correlation_id,
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{
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"fanout": {
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"recompose_chapters": [
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{
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"chapter_id": cid,
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"task_id": str(tid_rec),
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"status": "enqueued",
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}
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]
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},
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},
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)
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except Exception as exc:
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logger.warning(
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"recompose_chapter.apply_async failed chapter={} trigger={}: {}",
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cid,
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trigger_source,
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exc,
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)
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result.errors.append(f"recompose_chapter:{cid}:{exc}")
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if need_compaction:
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try:
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ctx: dict[str, Any] = {
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"trigger_source": trigger_source,
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"trigger_time": datetime.now(timezone.utc).isoformat(),
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"story_ids": sorted(story_ids),
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"chapter_ids": sorted(chapter_ids),
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}
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if compaction_extra:
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ctx.update(compaction_extra)
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schedule_memory_compaction_run(user_id, ctx)
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result.compaction_scheduled = True
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if memoir_correlation_id:
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merge_pipeline_run(
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memoir_correlation_id,
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{
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"fanout": {
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"compaction": {
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"status": "scheduled",
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"note": "debounce",
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},
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},
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},
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)
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except Exception as exc:
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logger.warning(
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"schedule_memory_compaction_run failed user_id={} trigger={}: {}",
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user_id,
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trigger_source,
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exc,
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)
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result.errors.append(f"compaction:{exc}")
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if need_quality_pass and settings.memoir_quality_pass_enabled and story_ids:
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try:
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from app.tasks.memoir_quality_pass_tasks import (
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memoir_quality_pass as quality_pass_task,
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)
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cd = int(settings.memoir_quality_pass_delay_seconds)
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qp_ar = cast(Any, quality_pass_task).apply_async(
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args=[user_id, sorted(story_ids), sorted(chapter_ids)],
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kwargs={"memoir_correlation_id": memoir_correlation_id},
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countdown=max(0, cd),
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)
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result.quality_pass_scheduled = True
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tid_qp = getattr(qp_ar, "id", None)
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if memoir_correlation_id and tid_qp:
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merge_pipeline_run(
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memoir_correlation_id,
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{
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"fanout": {
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"quality_pass": {
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"task_id": str(tid_qp),
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"status": "enqueued",
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},
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},
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},
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)
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except Exception as exc:
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logger.warning(
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"memoir_quality_pass enqueue failed user_id={} trigger={}: {}",
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user_id,
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trigger_source,
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exc,
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)
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result.errors.append(f"quality_pass:{exc}")
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return result
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