aac484463d
回忆录 Story 流水线（同步） - 同步路径仅写入 Story 与章节关联，改为 mark_chapter_dirty_sync，不再内联 compose - 物化由 Celery recompose_chapter 异步完成；compose 不变量与异常时保留 dirty 的语义在 repo 中补充说明 - Evidence：大批次时降低 top_k；路由候选 story 携带 char_count/version_count；append 超长/版本过多时强制新开 story - 叙事 prompt：relevant_chunks 去重，减少重复证据噪声 - 叙事回退与忠实度 gate：返回 fallback 类型并记录结构化日志（含耗时、JSON 有效性等） Post-commit 与任务编排 - 新增 post_commit.enqueue_story_post_commit_effects：统一派发 generate_story_image（Redis 去重）、延迟 recompose_chapter、可选 memory compaction - memoir_tasks / story_service / story_image_tasks 改为调用 post-commit 入口；主图回填后按关联章节重算并调度物化与 compacs（锁委托、Redis 单例、ASR to_thread） - 更新 test_narrative_pipeline 以适配 _apply_narrative_fallbacks 返回值
691 lines
23 KiB
Python
691 lines
23 KiB
Python
"""
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Celery 用：按批次将 transcript 写入 Story，并标记 Chapter 需物化（markdown_compose_dirty）。
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同步路径不执行 compose；物化由 commit 后 `recompose_chapter` 异步完成。
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"""
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from __future__ import annotations
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import json
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import time
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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 (
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STAGE_TO_ORDER,
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format_evidence_chunks_for_prompt,
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format_narrative_user_content,
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)
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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.config import settings
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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.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 (
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mark_chapter_dirty_sync,
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reorder_chapter_story_links_by_life_order_sync,
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)
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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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count_story_versions_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 _fidelity_fallback_json(oral: str, existing_canonical: str | None) -> str:
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"""忠实度未通过时的安全回退：续写场景保留旧文 + 本段口述，避免只剩一句。"""
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o = (oral or "").strip()[:15000]
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ex = (existing_canonical or "").strip()[:15000]
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if ex and o:
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return json.dumps(
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{"paragraphs": [{"content": ex}, {"content": o}]},
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ensure_ascii=False,
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)
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if ex:
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return json.dumps(
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{"paragraphs": [{"content": ex}]},
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ensure_ascii=False,
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)
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return json.dumps(
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{"paragraphs": [{"content": o}]},
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ensure_ascii=False,
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)
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def _gate_narrative_fidelity(
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oral_text: str,
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narrative_raw: str,
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llm: Any,
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*,
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existing_canonical: str | None = None,
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) -> tuple[str, str]:
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"""返回 (文本, fallback 原因)；忠实度不通过时第二项为 fidelity_failed。"""
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from app.agents.memoir.fidelity_check_agent import FidelityCheckAgent
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if not settings.memoir_fidelity_check_enabled or not llm:
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return narrative_raw, "none"
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agent = FidelityCheckAgent()
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ex = (existing_canonical or "").strip() or None
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if agent.passes(
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oral_text=oral_text,
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narrative_json=narrative_raw,
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llm=llm,
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existing_canonical_markdown=ex,
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):
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return narrative_raw, "none"
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logger.warning(
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"event=fidelity_gate_fallback oral_len={} merge={}",
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len((oral_text or "").strip()),
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bool(ex),
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)
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o = (oral_text or "").strip()
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if not o and not ex:
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return narrative_raw, "none"
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return _fidelity_fallback_json(o, ex), "fidelity_failed"
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def _should_fallback_to_transcript(md: str, oral: str) -> bool:
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"""模型输出相对口述明显过短时回退为口述原文（防「1999」类压缩）。"""
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o = (oral or "").strip()
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if not o:
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return False
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m = (md or "").strip()
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if not m:
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return True
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if len(o) < 12:
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return len(m) < len(o)
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ratio = float(settings.memoir_narrative_fallback_body_ratio)
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min_abs = int(settings.memoir_narrative_fallback_min_chars)
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threshold = max(min_abs, int(len(o) * ratio))
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return len(m) < threshold
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def _coalesce_story_markdown(
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md: str,
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oral: str,
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existing_for_narrative: str,
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) -> str:
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"""落库前对齐正文：空输出或过短回退时，续写场景保留「已有故事 + 本段口述」。"""
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o = (oral or "").strip()
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ex = (existing_for_narrative or "").strip()
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m = (md or "").strip()
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if not m:
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if ex and o:
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return f"{ex}\n\n{o}"
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if o:
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return o
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return ex
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if o and _should_fallback_to_transcript(m, o):
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if ex:
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return f"{ex}\n\n{o}"
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return o
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return m
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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.user_input_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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*,
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chapter_category: str,
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) -> tuple[str, str]:
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"""返回 (文本, fallback_type)；无改写时为 none。"""
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# 整篇合并（JSON）输出异常缩水：回退为旧文 + 本段口述，避免覆盖丢失
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if existing_for_narrative and _is_json_narrative(narrative_raw):
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merged_md = narrative_to_markdown(narrative_raw).strip()
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ex = (existing_for_narrative or "").strip()
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if ex and len(ex) > 400 and len(merged_md) < len(ex) * 0.35:
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logger.warning(
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"event=narrative_fallback reason=merge_shrink action=append_oral "
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"chapter_category={}",
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chapter_category,
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)
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return f"{ex}\n\n{combined_unit_text.strip()}", "merge_shrink"
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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(
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"event=narrative_fallback reason=length_anomaly action=append_raw "
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"chapter_category={}",
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chapter_category,
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)
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return (
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f"{existing_for_narrative}\n\n{combined_unit_text}",
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"coalesce_to_old_plus_oral",
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)
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md_check = narrative_to_markdown(narrative_raw).strip()
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oral = (combined_unit_text or "").strip()
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ex_fb = (existing_for_narrative or "").strip()
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if oral and _should_fallback_to_transcript(md_check, oral):
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if ex_fb:
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logger.warning(
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"event=narrative_fallback reason=body_too_short_vs_oral_merge "
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"chapter_category={} oral_len={} md_len={}",
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chapter_category,
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len(oral),
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len(md_check),
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)
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return f"{ex_fb}\n\n{oral}", "coalesce_to_old_plus_oral"
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logger.warning(
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"event=narrative_fallback reason=body_too_short_vs_oral "
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"chapter_category={} oral_len={} md_len={}",
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chapter_category,
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len(oral),
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len(md_check),
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)
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return oral, "coalesce_to_oral"
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return narrative_raw, "none"
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def _merge_fallback_type(gate_ft: str, apply_ft: str) -> str:
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if apply_ft != "none":
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return apply_ft
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return gate_ft
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def _story_meta_for_route(
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session: Session, candidates: list
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) -> dict[str, dict[str, int]]:
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meta: dict[str, dict[str, int]] = {}
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for s in candidates:
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sid = str(s.id)
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meta[sid] = {
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"char_count": len((s.canonical_markdown or "").strip()),
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"version_count": count_story_versions_sync(session, sid),
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}
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return meta
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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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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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max_chars = int(settings.story_append_max_canonical_chars)
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max_ver = int(settings.story_append_max_versions)
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for unit in plan.units:
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t0 = time.perf_counter()
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unit_text = _ordered_text_for_segment_ids(category_segments, unit.segment_ids)
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new_content_input = format_narrative_user_content(unit_text, evidence_text)
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target_story_id: str | None = None
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existing_for_narrative = ""
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decision_source = "batch_plan"
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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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canon = (st.canonical_markdown or "").strip()
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vc = count_story_versions_sync(session, str(st.id))
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if len(canon) > max_chars or vc >= max_ver:
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logger.info(
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"event=append_overflow_to_new story_id={} canonical_chars={} "
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"versions={} decision_source=batch_plan",
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str(st.id),
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len(canon),
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vc,
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)
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target_story_id = None
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existing_for_narrative = ""
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decision_source = "forced_new_due_to_append_limit"
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else:
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target_story_id = st.id
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existing_for_narrative = canon
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raw_gen = 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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json_invalid = False
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s0 = (raw_gen or "").strip()
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if s0.startswith("{") and "paragraphs" in s0:
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try:
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json.loads(s0)
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except json.JSONDecodeError:
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json_invalid = True
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narrative_raw, fb_gate = _gate_narrative_fidelity(
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unit_text,
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raw_gen,
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llm,
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existing_canonical=existing_for_narrative or None,
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)
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narrative_raw, fb_apply = _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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chapter_category=chapter_category,
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)
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fallback_type = _merge_fallback_type(fb_gate, fb_apply)
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if json_invalid and fallback_type == "none":
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fallback_type = "json_invalid"
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md = _coalesce_story_markdown(
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narrative_to_markdown(narrative_raw).strip(),
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unit_text.strip(),
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existing_for_narrative or "",
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)
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if target_story_id:
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append_story_version_sync(session, str(target_story_id), md)
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dispatch_ids.add(str(target_story_id))
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ensure_chapter_story_link_sync(
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session, chapter_id=str(chapter.id), story_id=str(target_story_id)
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)
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sid_log = target_story_id
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is_append = True
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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(str(st.id))
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ensure_chapter_story_link_sync(
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session, chapter_id=str(chapter.id), story_id=str(st.id)
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)
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sid_log = st.id
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is_append = False
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elapsed = time.perf_counter() - t0
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logger.info(
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"event=story_generated route_type=batch decision_source={} route_decision={} "
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"unit_segments={} used_evidence={} narrative_json_valid={} fidelity_passed={} "
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"fallback_type={} oral_len={} md_len={} chapter_category={} is_append={} "
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"story_id={} seconds={:.3f}",
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decision_source,
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unit.decision,
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len(unit.segment_ids),
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bool(evidence_text.strip()),
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_is_json_narrative(raw_gen),
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fb_gate == "none",
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fallback_type,
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len(unit_text.strip()),
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len(md.strip()),
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chapter_category,
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is_append,
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sid_log,
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elapsed,
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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.user_input_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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n_units = len(category_segments)
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top_k = int(settings.evidence_top_k_default)
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if n_units > int(settings.evidence_large_batch_threshold):
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top_k = int(settings.evidence_top_k_large_batch)
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try:
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evidence = retrieve_evidence_sync(session, user_id, combined_text, top_k=top_k)
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except Exception as e:
|
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logger.warning("Evidence 检索跳过: {}", 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 = format_narrative_user_content(combined_text, evidence_text)
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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),
|
||
joinedload(Chapter.story_links),
|
||
)
|
||
)
|
||
chapter = session.execute(stmt_chapter).unique().scalar_one_or_none()
|
||
|
||
slot_snippets: dict[str, str] = {}
|
||
stage_slots = state.slots.get(chapter_category, {}) or {}
|
||
for key, value in stage_slots.items():
|
||
snip = getattr(value, "snippet", None) or (
|
||
value.get("snippet") if isinstance(value, dict) else None
|
||
)
|
||
if snip:
|
||
slot_snippets[key] = snip
|
||
|
||
title = chapter.title if chapter else f"{chapter_category} 回忆"
|
||
|
||
if not chapter:
|
||
title = narrative_agent.generate_title(
|
||
stage=chapter_category,
|
||
emotion="neutral",
|
||
slots=slot_snippets,
|
||
user_profile=user_profile,
|
||
birth_year=user_birth_year,
|
||
llm=llm,
|
||
)
|
||
|
||
candidates = list_active_stories_for_user_sync(session, user_id)
|
||
valid_ids = {str(s.id) for s in candidates}
|
||
story_meta = _story_meta_for_route(session, candidates)
|
||
|
||
batch_for_route = (
|
||
f"{combined_text}\n\n{evidence_text}"
|
||
if evidence_text.strip()
|
||
else combined_text
|
||
)
|
||
|
||
calculated_order_index = STAGE_TO_ORDER.get(chapter_category, 999)
|
||
|
||
use_batch_plan = (
|
||
llm
|
||
and len(category_segments) >= 2
|
||
and len(category_segments) <= PLAN_BATCH_MAX_SEGMENTS
|
||
)
|
||
plan: StoryBatchPlan | None = None
|
||
if use_batch_plan:
|
||
segs = [(seg.id, seg.user_input_text or "") for seg in category_segments]
|
||
plan = route_agent.plan_batch(
|
||
chapter_category=chapter_category,
|
||
chapter_title=title,
|
||
segments=segs,
|
||
candidate_stories=candidates,
|
||
llm=llm,
|
||
valid_story_ids=valid_ids,
|
||
story_meta=story_meta,
|
||
)
|
||
|
||
chapter = _ensure_chapter_record(
|
||
session,
|
||
user_id=user_id,
|
||
chapter_category=chapter_category,
|
||
title=title,
|
||
source_ids=source_ids,
|
||
calculated_order_index=calculated_order_index,
|
||
)
|
||
|
||
if plan is not None:
|
||
dispatch_ids = _run_batch_plan_writes(
|
||
session,
|
||
plan=plan,
|
||
category_segments=category_segments,
|
||
chapter=chapter,
|
||
chapter_category=chapter_category,
|
||
evidence_text=evidence_text,
|
||
slot_snippets=slot_snippets,
|
||
user_id=user_id,
|
||
user_profile=user_profile,
|
||
user_birth_year=user_birth_year,
|
||
llm=llm,
|
||
narrative_agent=narrative_agent,
|
||
)
|
||
else:
|
||
route = route_agent.decide(
|
||
chapter_category=chapter_category,
|
||
chapter_title=title,
|
||
batch_transcript=batch_for_route,
|
||
candidate_stories=candidates,
|
||
llm=llm,
|
||
valid_story_ids=valid_ids,
|
||
story_meta=story_meta,
|
||
)
|
||
|
||
t0 = time.perf_counter()
|
||
target_story_id: str | None = None
|
||
existing_for_narrative = ""
|
||
decision_source = "fallback_no_llm" if not llm else "single_decide"
|
||
max_chars = int(settings.story_append_max_canonical_chars)
|
||
max_ver = int(settings.story_append_max_versions)
|
||
if route.decision == "append_story" and route.target_story_id:
|
||
st = session.get(Story, route.target_story_id)
|
||
if st and st.user_id == user_id:
|
||
canon = (st.canonical_markdown or "").strip()
|
||
vc = count_story_versions_sync(session, str(st.id))
|
||
if len(canon) > max_chars or vc >= max_ver:
|
||
logger.info(
|
||
"event=append_overflow_to_new story_id={} canonical_chars={} "
|
||
"versions={} decision_source=single_decide",
|
||
str(st.id),
|
||
len(canon),
|
||
vc,
|
||
)
|
||
target_story_id = None
|
||
existing_for_narrative = ""
|
||
decision_source = "forced_new_due_to_append_limit"
|
||
else:
|
||
target_story_id = st.id
|
||
existing_for_narrative = canon
|
||
|
||
raw_gen = narrative_agent.generate_narrative(
|
||
stage=chapter_category,
|
||
slots=slot_snippets,
|
||
new_content=new_content_input,
|
||
existing_content=existing_for_narrative,
|
||
user_profile=user_profile,
|
||
birth_year=user_birth_year,
|
||
llm=llm,
|
||
)
|
||
json_invalid = False
|
||
s0 = (raw_gen or "").strip()
|
||
if s0.startswith("{") and "paragraphs" in s0:
|
||
try:
|
||
json.loads(s0)
|
||
except json.JSONDecodeError:
|
||
json_invalid = True
|
||
|
||
narrative_raw, fb_gate = _gate_narrative_fidelity(
|
||
combined_text,
|
||
raw_gen,
|
||
llm,
|
||
existing_canonical=existing_for_narrative or None,
|
||
)
|
||
|
||
narrative_raw, fb_apply = _apply_narrative_fallbacks(
|
||
narrative_raw,
|
||
combined_text,
|
||
existing_for_narrative,
|
||
chapter_category=chapter_category,
|
||
)
|
||
fallback_type = _merge_fallback_type(fb_gate, fb_apply)
|
||
if json_invalid and fallback_type == "none":
|
||
fallback_type = "json_invalid"
|
||
|
||
md = _coalesce_story_markdown(
|
||
narrative_to_markdown(narrative_raw).strip(),
|
||
combined_text.strip(),
|
||
existing_for_narrative or "",
|
||
)
|
||
|
||
do_append = target_story_id is not None
|
||
|
||
if do_append:
|
||
append_story_version_sync(session, str(target_story_id), md)
|
||
dispatch_ids.add(str(target_story_id))
|
||
ensure_chapter_story_link_sync(
|
||
session, chapter_id=str(chapter.id), story_id=str(target_story_id)
|
||
)
|
||
sid_log = target_story_id
|
||
is_append = True
|
||
else:
|
||
story_title = (route.new_story_title or "").strip()
|
||
if not story_title:
|
||
story_title = narrative_agent.generate_title(
|
||
stage=chapter_category,
|
||
emotion="neutral",
|
||
slots=slot_snippets,
|
||
user_profile=user_profile,
|
||
birth_year=user_birth_year,
|
||
llm=llm,
|
||
)
|
||
st = create_story_with_version_sync(
|
||
session,
|
||
user_id=user_id,
|
||
title=story_title,
|
||
canonical_markdown=md,
|
||
stage=chapter_category,
|
||
)
|
||
dispatch_ids.add(str(st.id))
|
||
ensure_chapter_story_link_sync(
|
||
session, chapter_id=str(chapter.id), story_id=str(st.id)
|
||
)
|
||
sid_log = st.id
|
||
is_append = False
|
||
|
||
elapsed = time.perf_counter() - t0
|
||
logger.info(
|
||
"event=story_generated route_type=single decision_source={} route_decision={} "
|
||
"unit_segments={} used_evidence={} narrative_json_valid={} fidelity_passed={} "
|
||
"fallback_type={} oral_len={} md_len={} chapter_category={} is_append={} "
|
||
"story_id={} seconds={:.3f}",
|
||
decision_source,
|
||
route.decision,
|
||
len(category_segments),
|
||
bool(evidence_text.strip()),
|
||
_is_json_narrative(raw_gen),
|
||
fb_gate == "none",
|
||
fallback_type,
|
||
len(combined_text.strip()),
|
||
len(md.strip()),
|
||
chapter_category,
|
||
is_append,
|
||
sid_log,
|
||
elapsed,
|
||
)
|
||
|
||
reorder_chapter_story_links_by_life_order_sync(session, str(chapter.id))
|
||
mark_chapter_dirty_sync(session, str(chapter.id))
|
||
session.flush()
|
||
|
||
image_settings = MemoirImageSettings.from_env()
|
||
needs_cover = image_settings.enabled and chapter_needs_cover_enqueue(chapter)
|
||
|
||
return chapter, needs_cover, dispatch_ids
|