786ebf8ae6
- 对齐「多智能体收敛」与「回忆录 stories-first / markdown-first」方向：收紧运行时契约、 删除过渡兼容路径与双轨逻辑，并同步更新客户端与文档。 - Chat：以 ChatOrchestrator 为实时编排入口；删除独立 conversation_agent，精简 prompts。 - Memoir：删除 memory_agent；MemoirOrchestrator、classification / story_route 与 prompts 收敛到 prepare_batches + run_story_pipeline_for_category_batch 主链路。 - 将 agents 侧 processor 迁入 feature 层为 background_runner，并移除 features 下重复/过时 processor 封装。 - 新增 history_store，强化「conversation_messages 为 DB 真源、Redis 为缓存」模型。 - 调整 models、repo、service、session_history；精简 WS message_types，重构 pipeline 与 router。 - 移除章节占位、整章再生等旧路径；章节列表与封面逻辑要求 story 关联；收紧 cover 资格与 enqueue。 - helpers、repo、service、router、reading_segment_materialize、story_pipeline_sync、pdf_service 等按 canonical markdown / cover_asset_id 收缩；删除 memoir_images/provider 等冗余。 - tasks：memoir_tasks、chapter_cover_tasks 等大幅瘦身；story_image_tasks 等与当前图片任务对齐。 - core：config、logging、redis、task_tracker 小幅调整。 - auth / user / payment / quota：路由或服务侧删减过时接口或逻辑（如 payment router 行数减少）。 - pyproject.toml、development.sh、.env.example / .env.production、README 等同步说明或变量。 - Alembic 0001_initial_schema 微调（与当前 schema 叙事一致的小改动）。 - 回忆录：types / mappers / api、章节页与 memoir 页与后端契约对齐；markdown-renderer 调整。 - 语音：删除 voice/player，voice-segment-store 相应精简。 - api/tests：删除 conftest 及绝大部分既有测试文件（websocket_baseline、conversation、memoir 图片、PDF、SMS 等），属有意收缩/待按 backend-test-system 重建的信号。 - docs：新增多智能体收敛与移除兼容层计划摘要；更新 story-first 设计、backend-test-system、 multi-agent-refactor-plan、实施总结等。 BREAKING CHANGE: 后端对外契约、回忆录章节字段与若干路由/任务行为已变更；大量 API 测试被移除， CI 若依赖这些用例需按新策略补测或调整流水线。
421 lines
13 KiB
Python
421 lines
13 KiB
Python
"""
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Celery 用：按批次将 transcript 写入 Story，并物化 Chapter canonical_markdown。
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"""
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from __future__ import annotations
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import uuid
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.orm import Session, joinedload
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from app.agents.memoir.narrative_agent import NarrativeAgent
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from app.agents.memoir.prompts import STAGE_TO_ORDER, format_evidence_chunks_for_prompt
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from app.agents.memoir.story_route_agent import (
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PLAN_BATCH_MAX_SEGMENTS,
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StoryBatchPlan,
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StoryRouteAgent,
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)
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from app.agents.state_schema import MemoirStateSchema
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from app.core.logging import get_logger
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from app.features.memoir.cover_eligibility import chapter_needs_cover_enqueue
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from app.features.memoir.helpers import _chapter_markdown
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from app.features.memoir.memoir_images.settings import MemoirImageSettings
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from app.features.memoir.models import Chapter
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from app.features.memoir.narrative_to_markdown import narrative_to_markdown
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from app.features.memoir.repo import compose_chapter_from_story_links_sync
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from app.features.memory.repo import retrieve_evidence_sync
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from app.features.story.models import Story
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from app.features.story.sync_write import (
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append_story_version_sync,
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create_story_with_version_sync,
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ensure_chapter_story_link_sync,
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list_active_stories_for_user_sync,
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)
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logger = get_logger(__name__)
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def _is_json_narrative(text: str) -> bool:
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if not text or not text.strip():
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return False
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s = text.strip()
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return s.startswith("{") and "paragraphs" in s
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def _ordered_text_for_segment_ids(
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category_segments: list, segment_ids: list[str]
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) -> str:
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id_to_text = {seg.id: (seg.transcript_text or "") for seg in category_segments}
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return "\n\n".join(id_to_text.get(sid, "") for sid in segment_ids)
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def _apply_narrative_fallbacks(
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narrative_raw: str,
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combined_unit_text: str,
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existing_for_narrative: str,
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existing_chapter_md: str,
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*,
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chapter_category: str,
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) -> str:
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if (
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existing_for_narrative
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and not _is_json_narrative(narrative_raw)
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and len(narrative_raw) < len(existing_for_narrative) * 0.8
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):
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logger.warning("叙事长度异常: 回退为原文追加")
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return f"{existing_for_narrative}\n\n{combined_unit_text}"
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if (
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not existing_for_narrative
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and existing_chapter_md
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and not _is_json_narrative(narrative_raw)
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and len(narrative_raw) < len(existing_chapter_md) * 0.8
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):
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logger.warning(
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"章节级长度异常: 回退为 transcript 追加, category=%s",
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chapter_category,
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)
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return f"{existing_chapter_md}\n\n{combined_unit_text}"
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return narrative_raw
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def _ensure_chapter_record(
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session: Session,
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*,
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user_id: str,
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chapter_category: str,
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title: str,
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source_ids: list[str],
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calculated_order_index: int,
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) -> Chapter:
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stmt_chapter = (
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select(Chapter)
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.where(
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Chapter.user_id == user_id,
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Chapter.category == chapter_category,
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Chapter.is_active == True, # noqa: E712
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)
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.options(
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joinedload(Chapter.images),
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joinedload(Chapter.story_links),
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)
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)
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chapter = session.execute(stmt_chapter).unique().scalar_one_or_none()
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if not chapter:
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chapter = Chapter(
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id=str(uuid.uuid4()),
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user_id=user_id,
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title=title,
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order_index=calculated_order_index,
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status="completed",
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category=chapter_category,
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is_new=True,
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source_segments=source_ids,
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)
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session.add(chapter)
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session.flush()
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else:
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chapter.source_segments = list(
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set((chapter.source_segments or []) + source_ids)
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)
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chapter.is_new = True
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session.flush()
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return chapter
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def _run_batch_plan_writes(
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session: Session,
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*,
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plan: StoryBatchPlan,
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category_segments: list,
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chapter: Chapter,
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chapter_category: str,
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evidence_text: str,
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existing_chapter_md: str,
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slot_snippets: dict[str, str],
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user_id: str,
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user_profile: str,
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user_birth_year: int | None,
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llm: Any,
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narrative_agent: NarrativeAgent,
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) -> set[str]:
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dispatch_ids: set[str] = set()
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for unit in plan.units:
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unit_text = _ordered_text_for_segment_ids(category_segments, unit.segment_ids)
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new_content_input = (
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f"{unit_text}\n\n【相关记忆摘录】\n{evidence_text}"
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if evidence_text.strip()
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else unit_text
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)
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target_story_id: str | None = None
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existing_for_narrative = ""
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if unit.decision == "append_story" and unit.target_story_id:
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st = session.get(Story, unit.target_story_id)
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if st and st.user_id == user_id:
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target_story_id = st.id
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existing_for_narrative = (st.canonical_markdown or "").strip()
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narrative_raw = narrative_agent.generate_narrative(
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stage=chapter_category,
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slots=slot_snippets,
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new_content=new_content_input,
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existing_content=existing_for_narrative,
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user_profile=user_profile,
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birth_year=user_birth_year,
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llm=llm,
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)
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narrative_raw = _apply_narrative_fallbacks(
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narrative_raw,
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unit_text,
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existing_for_narrative,
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existing_chapter_md,
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chapter_category=chapter_category,
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)
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md = narrative_to_markdown(narrative_raw)
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if not md.strip():
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md = unit_text.strip()
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if target_story_id:
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append_story_version_sync(session, target_story_id, md)
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dispatch_ids.add(target_story_id)
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ensure_chapter_story_link_sync(
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session, chapter_id=chapter.id, story_id=target_story_id
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)
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else:
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story_title = (unit.new_story_title or "").strip()
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if not story_title:
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story_title = narrative_agent.generate_title(
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stage=chapter_category,
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emotion="neutral",
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slots=slot_snippets,
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user_profile=user_profile,
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birth_year=user_birth_year,
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llm=llm,
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)
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st = create_story_with_version_sync(
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session,
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user_id=user_id,
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title=story_title,
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canonical_markdown=md,
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stage=chapter_category,
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)
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dispatch_ids.add(st.id)
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ensure_chapter_story_link_sync(
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session, chapter_id=chapter.id, story_id=st.id
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)
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return dispatch_ids
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def run_story_pipeline_for_category_batch(
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session: Session,
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*,
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user_id: str,
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chapter_category: str,
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category_segments: list,
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state: MemoirStateSchema,
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user_profile: str,
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user_birth_year: int | None,
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llm: Any,
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) -> tuple[Chapter | None, bool, set[str]]:
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"""
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返回 (chapter, needs_cover_enqueue, story_ids_to_dispatch_after_commit)。
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"""
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narrative_agent = NarrativeAgent()
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route_agent = StoryRouteAgent()
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dispatch_ids: set[str] = set()
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segment_texts = [seg.transcript_text or "" for seg in category_segments]
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combined_text = "\n\n".join(segment_texts)
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source_ids = [seg.id for seg in category_segments]
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try:
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evidence = retrieve_evidence_sync(session, user_id, combined_text, top_k=10)
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except Exception as e:
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logger.warning("Evidence 检索跳过: %s", e)
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evidence = {
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"relevant_chunks": [],
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"relevant_summaries": [],
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"relevant_facts": [],
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"timeline_hints": [],
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"relevant_stories": [],
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}
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evidence_text = format_evidence_chunks_for_prompt(evidence)
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new_content_input = (
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f"{combined_text}\n\n【相关记忆摘录】\n{evidence_text}"
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if evidence_text.strip()
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else combined_text
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)
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stmt_chapter = (
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select(Chapter)
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.where(
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Chapter.user_id == user_id,
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Chapter.category == chapter_category,
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Chapter.is_active == True, # noqa: E712
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)
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.options(
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joinedload(Chapter.images),
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joinedload(Chapter.story_links),
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)
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)
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chapter = session.execute(stmt_chapter).unique().scalar_one_or_none()
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slot_snippets: dict[str, str] = {}
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stage_slots = state.slots.get(chapter_category, {}) or {}
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for key, value in stage_slots.items():
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snip = getattr(value, "snippet", None) or (
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value.get("snippet") if isinstance(value, dict) else None
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)
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if snip:
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slot_snippets[key] = snip
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title = chapter.title if chapter else f"{chapter_category} 回忆"
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existing_chapter_md = _chapter_markdown(chapter) if chapter else ""
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if not chapter:
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title = narrative_agent.generate_title(
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stage=chapter_category,
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emotion="neutral",
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slots=slot_snippets,
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user_profile=user_profile,
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birth_year=user_birth_year,
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llm=llm,
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)
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candidates = list_active_stories_for_user_sync(session, user_id)
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valid_ids = {s.id for s in candidates}
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batch_for_route = (
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f"{combined_text}\n\n{evidence_text}"
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if evidence_text.strip()
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else combined_text
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)
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calculated_order_index = STAGE_TO_ORDER.get(chapter_category, 999)
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use_batch_plan = (
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llm
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and len(category_segments) >= 2
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and len(category_segments) <= PLAN_BATCH_MAX_SEGMENTS
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)
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plan: StoryBatchPlan | None = None
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if use_batch_plan:
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segs = [(seg.id, seg.transcript_text or "") for seg in category_segments]
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plan = route_agent.plan_batch(
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chapter_category=chapter_category,
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chapter_title=title,
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segments=segs,
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candidate_stories=candidates,
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llm=llm,
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valid_story_ids=valid_ids,
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)
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chapter = _ensure_chapter_record(
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session,
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user_id=user_id,
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chapter_category=chapter_category,
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title=title,
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source_ids=source_ids,
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calculated_order_index=calculated_order_index,
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)
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if plan is not None:
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dispatch_ids = _run_batch_plan_writes(
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session,
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plan=plan,
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category_segments=category_segments,
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chapter=chapter,
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chapter_category=chapter_category,
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evidence_text=evidence_text,
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existing_chapter_md=existing_chapter_md,
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slot_snippets=slot_snippets,
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user_id=user_id,
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user_profile=user_profile,
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user_birth_year=user_birth_year,
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llm=llm,
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narrative_agent=narrative_agent,
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)
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else:
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route = route_agent.decide(
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chapter_category=chapter_category,
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chapter_title=title,
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batch_transcript=batch_for_route,
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candidate_stories=candidates,
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llm=llm,
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valid_story_ids=valid_ids,
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)
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target_story_id: str | None = None
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existing_for_narrative = ""
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if route.decision == "append_story" and route.target_story_id:
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st = session.get(Story, route.target_story_id)
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if st and st.user_id == user_id:
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target_story_id = st.id
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existing_for_narrative = (st.canonical_markdown or "").strip()
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narrative_raw = narrative_agent.generate_narrative(
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stage=chapter_category,
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slots=slot_snippets,
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new_content=new_content_input,
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existing_content=existing_for_narrative,
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user_profile=user_profile,
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birth_year=user_birth_year,
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llm=llm,
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)
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narrative_raw = _apply_narrative_fallbacks(
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narrative_raw,
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combined_text,
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existing_for_narrative,
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existing_chapter_md,
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chapter_category=chapter_category,
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)
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md = narrative_to_markdown(narrative_raw)
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if not md.strip():
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md = combined_text.strip()
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do_append = target_story_id is not None
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if do_append:
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append_story_version_sync(session, target_story_id, md)
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dispatch_ids.add(target_story_id)
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ensure_chapter_story_link_sync(
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session, chapter_id=chapter.id, story_id=target_story_id
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)
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else:
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story_title = (route.new_story_title or "").strip()
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if not story_title:
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story_title = narrative_agent.generate_title(
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stage=chapter_category,
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emotion="neutral",
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slots=slot_snippets,
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user_profile=user_profile,
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birth_year=user_birth_year,
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llm=llm,
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)
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st = create_story_with_version_sync(
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session,
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user_id=user_id,
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title=story_title,
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canonical_markdown=md,
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stage=chapter_category,
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)
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dispatch_ids.add(st.id)
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ensure_chapter_story_link_sync(
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session, chapter_id=chapter.id, story_id=st.id
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)
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compose_chapter_from_story_links_sync(session, chapter.id)
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session.flush()
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image_settings = MemoirImageSettings.from_env()
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needs_cover = image_settings.enabled and chapter_needs_cover_enqueue(chapter)
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return chapter, needs_cover, dispatch_ids
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