"""
Celery 用：按批次将 transcript 写入 Story，并标记 Chapter 需物化（markdown_compose_dirty）。

同步路径不执行 compose；物化由 commit 后 `recompose_chapter` 异步完成。
"""

from __future__ import annotations

import json
import re
import time
import uuid
from typing import Any

from sqlalchemy import select
from sqlalchemy.orm import Session, joinedload

from app.agents.memoir.narrative_agent import NarrativeAgent
from app.agents.memoir.prompts import (
    format_evidence_chunks_for_prompt,
    format_narrative_user_content,
)
from app.agents.stage_constants import (
    CATEGORY_TO_CHAT_STAGE,
    CHAPTER_CATEGORIES,
    CHAT_STAGES,
    STAGE_TO_ORDER,
)
from app.agents.memoir.story_route_agent import (
    PLAN_BATCH_MAX_SEGMENTS,
    StoryBatchPlan,
    StoryRouteAgent,
)
from app.agents.state_schema import MemoirStateSchema
from app.core.config import settings
from app.core.dependencies import get_embedding_provider
from app.core.logging import get_logger
from app.features.memoir.cover_eligibility import chapter_needs_cover_enqueue
from app.features.memoir.memoir_images.settings import MemoirImageSettings
from app.features.memoir.models import Chapter
from app.features.memoir.narrative_to_markdown import narrative_to_markdown
from app.features.memoir.narrative_safety import (
    body_contains_prompt_artifact,
    evidence_leakage_heuristic,
    evidence_scene_anchor_leak,
    strip_evidence_for_overlap_check,
)
from app.features.memoir.oral_normalize import (
    apply_oral_rules,
    normalize_oral_for_memoir,
)
from app.features.memoir.repo import (
    mark_chapter_dirty_sync,
    reorder_chapter_story_links_by_life_order_sync,
)
from app.features.memory.repo import retrieve_evidence_sync
from app.features.story.models import Story
from app.features.story.sync_write import (
    append_story_version_sync,
    count_story_versions_sync,
    create_story_with_version_sync,
    ensure_chapter_story_link_sync,
    list_active_stories_for_user_sync,
)

logger = get_logger(__name__)

# 标题中若出现下列多字履历表述，则必须在 hay（正文+口述+传入标题的 slots）中逐字出现，否则剔除无果片段或降级占位
_MEMOIR_TITLE_HAY_GROUNDING_PHRASES: tuple[str, ...] = (
    "晋升旅长",
    "晋升为旅长",
    "晋升师长",
    "晋升军长",
    "旅长职务",
    "师长职务",
)

# summary 章节跨阶段汇总 slots 时的上限（防叙事 prompt 膨胀）
MAX_SUMMARY_SLOT_KEYS = 80
MAX_SUMMARY_SLOT_CHARS = 12_000


def _slot_snippets_for_narrative(
    *,
    state: MemoirStateSchema,
    chapter_category: str,
    user_id: str,
) -> dict[str, str]:
    """按章节类目收集 slot 片段；summary 时跨 CHAT_STAGES 汇总并做 key/字符上限。"""
    slot_snippets: dict[str, str] = {}
    if chapter_category == "summary":
        total_chars = 0
        keys_added = 0
        capped = False
        for chat_stage_key in CHAT_STAGES:
            if keys_added >= MAX_SUMMARY_SLOT_KEYS:
                capped = True
                break
            stage_slots = state.slots.get(chat_stage_key, {}) or {}
            for key in sorted(stage_slots.keys()):
                if keys_added >= MAX_SUMMARY_SLOT_KEYS:
                    capped = True
                    break
                value = stage_slots[key]
                snip = getattr(value, "snippet", None) or (
                    value.get("snippet") if isinstance(value, dict) else None
                )
                if not snip:
                    continue
                composite = f"{chat_stage_key}_{key}"
                s = str(snip).strip()
                if total_chars + len(s) > MAX_SUMMARY_SLOT_CHARS:
                    remain = MAX_SUMMARY_SLOT_CHARS - total_chars
                    if remain > 32:
                        slot_snippets[composite] = s[:remain] + "…"
                    capped = True
                    break
                slot_snippets[composite] = s
                total_chars += len(s)
                keys_added += 1
            if capped:
                break
        if capped:
            logger.info(
                "event=summary_slot_snippets_capped user_id={} keys={} chars={}",
                user_id,
                len(slot_snippets),
                total_chars,
            )
        return slot_snippets

    chat_stage = CATEGORY_TO_CHAT_STAGE.get(chapter_category, chapter_category)
    stage_slots = state.slots.get(chat_stage, {}) or {}
    for key in sorted(stage_slots.keys()):
        value = stage_slots[key]
        snip = getattr(value, "snippet", None) or (
            value.get("snippet") if isinstance(value, dict) else None
        )
        if snip:
            slot_snippets[key] = str(snip).strip()
    return slot_snippets


def _placeholder_title(chapter_category: str) -> str:
    return CHAPTER_CATEGORIES.get(chapter_category, chapter_category)


def _title_slots_filtered_for_generation(
    slot_snippets: dict[str, str], *, md: str, oral_scope: str
) -> dict[str, str]:
    """仅保留与正文或本批口述有文本重叠的 slot，降低档案/历史 slot 串台到标题。"""
    if not settings.memoir_title_slots_require_body_or_oral_match:
        return dict(slot_snippets)
    hay = f"{(md or '').strip()}\n{(oral_scope or '').strip()}"
    if not hay.strip():
        return {}
    out: dict[str, str] = {}
    for k, v in (slot_snippets or {}).items():
        if k == "content_excerpt":
            continue
        s = (v or "").strip()
        if len(s) < 2:
            continue
        if s in hay:
            out[k] = s
            continue
        prefix = s[: min(12, len(s))]
        if len(prefix) >= 4 and prefix in hay:
            out[k] = s
    return out


def _title_hay_for_grounding(
    merged_slots: dict[str, str], md: str, oral_scope: str
) -> str:
    """与标题模型可见材料一致的依据串（用于事后逐字 grounding）。"""
    parts: list[str] = [(md or "").strip(), (oral_scope or "").strip()]
    for k, v in (merged_slots or {}).items():
        if k == "content_excerpt":
            continue
        if (v or "").strip():
            parts.append(str(v).strip())
    return "\n".join(p for p in parts if p)


def _strip_ungrounded_title_segments(
    title: str,
    hay: str,
    *,
    chapter_category: str,
) -> str:
    """
    按 · / • 分节丢弃含未落地履历短语的小节；全部丢弃则占位。
    """
    if not settings.memoir_title_hay_grounding_strict_phrases_enabled:
        return (title or "").strip() or _placeholder_title(chapter_category)
    t = (title or "").strip()
    h = (hay or "").strip()
    if not t:
        return _placeholder_title(chapter_category)
    segments = [s.strip() for s in re.split(r"\s*[·•]\s*", t) if s.strip()]
    if not segments:
        segments = [t]
    kept: list[str] = []
    for seg in segments:
        bad = any(
            phrase in seg and phrase not in h
            for phrase in _MEMOIR_TITLE_HAY_GROUNDING_PHRASES
        )
        if bad:
            logger.info(
                "event=memoir_title_segment_ungrounded segment_preview={} chapter_category={}",
                seg[:40],
                chapter_category,
            )
            continue
        kept.append(seg)
    if not kept:
        return _placeholder_title(chapter_category)
    if len(kept) == 1:
        return kept[0]
    return " · ".join(kept)


def _maybe_generate_title(
    narrative_agent: "NarrativeAgent",
    *,
    chapter_category: str,
    md: str,
    slot_snippets: dict[str, str],
    user_profile: str,
    user_birth_year: int | None,
    llm: Any,
    oral_scope: str = "",
    narrow_profile_for_title: bool = True,
) -> str:
    """Generate a title only when body is long enough; otherwise return placeholder."""
    body_len = len((md or "").strip())
    if body_len < settings.story_title_min_body_chars:
        return _placeholder_title(chapter_category)
    content_excerpt = (md or "").strip()[:300]
    merged_slots = _title_slots_filtered_for_generation(
        slot_snippets, md=md, oral_scope=oral_scope
    )
    if content_excerpt and "content_excerpt" not in merged_slots:
        merged_slots["content_excerpt"] = content_excerpt
    # 标题默认不注入完整档案，仅年龄提示仍可用（来自 birth_year）
    profile_for_title = "" if narrow_profile_for_title else user_profile
    raw_title = narrative_agent.generate_title(
        stage=chapter_category,
        emotion="neutral",
        slots=merged_slots,
        user_profile=profile_for_title,
        birth_year=user_birth_year,
        llm=llm,
    )
    hay = _title_hay_for_grounding(merged_slots, md, oral_scope)
    return _strip_ungrounded_title_segments(
        raw_title, hay, chapter_category=chapter_category
    )


def _route_segment_texts(category_segments: list) -> list[tuple[str, str]]:
    """批量路由 plan_batch：每段仅做规则归一，避免 N 次 LLM。"""
    out: list[tuple[str, str]] = []
    for seg in category_segments:
        raw = seg.user_input_text or ""
        if (
            settings.memoir_oral_normalize_enabled
            and (settings.memoir_oral_normalize_mode or "rules").strip().lower()
            != "off"
        ):
            t = apply_oral_rules(raw)
        else:
            t = raw
        out.append((str(seg.id), t))
    return out


def _fidelity_fallback_json(oral: str, existing_canonical: str | None) -> str:
    """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。"""
    o = (oral or "").strip()[:15000]
    ex = (existing_canonical or "").strip()[:15000]
    if ex and o:
        return json.dumps(
            {"paragraphs": [{"content": ex}, {"content": o}]},
            ensure_ascii=False,
        )
    if ex:
        return json.dumps(
            {"paragraphs": [{"content": ex}]},
            ensure_ascii=False,
        )
    return json.dumps(
        {"paragraphs": [{"content": o}]},
        ensure_ascii=False,
    )


def _gate_narrative_fidelity(
    oral_text: str,
    narrative_raw: str,
    llm: Any,
    *,
    existing_canonical: str | None = None,
) -> tuple[str, str]:
    """返回 (文本, fallback 原因)；忠实度不通过时第二项为 fidelity_failed。"""
    from app.agents.memoir.fidelity_check_agent import FidelityCheckAgent

    if not settings.memoir_fidelity_check_enabled or not llm:
        return narrative_raw, "none"
    agent = FidelityCheckAgent()
    ex = (existing_canonical or "").strip() or None
    is_append = bool(ex)
    if agent.passes(
        oral_text=oral_text,
        narrative_json=narrative_raw,
        llm=llm,
        existing_canonical_markdown=ex,
        is_append=is_append,
    ):
        return narrative_raw, "none"
    logger.warning(
        "event=fidelity_gate_fallback oral_len={} merge={}",
        len((oral_text or "").strip()),
        bool(ex),
    )
    o = (oral_text or "").strip()
    if not o and not ex:
        return narrative_raw, "none"
    return _fidelity_fallback_json(o, ex), "fidelity_failed"


def _apply_narrative_body_safety(
    md: str,
    *,
    oral: str,
    existing_for_narrative: str,
    evidence_text: str,
    chapter_category: str,
) -> tuple[str, str]:
    """prompt 标记或摘录子串疑似渗入正文时，回退为口述/旧文拼接。"""
    m = (md or "").strip()
    ex = (existing_for_narrative or "").strip()
    o = (oral or "").strip()
    min_len = int(settings.memoir_narrative_evidence_overlap_min_chars)
    ev_plain = strip_evidence_for_overlap_check(evidence_text)
    if m and body_contains_prompt_artifact(m):
        logger.warning(
            "event=narrative_invariant_failed reason=prompt_artifact chapter_category={}",
            chapter_category,
        )
        return _coalesce_story_markdown("", oral, existing_for_narrative), (
            "prompt_artifact_in_body"
        )
    if (
        m
        and evidence_text.strip()
        and evidence_leakage_heuristic(m, ev_plain, o, ex, min_len)
    ):
        logger.warning(
            "event=narrative_invariant_failed reason=evidence_leak chapter_category={}",
            chapter_category,
        )
        return _coalesce_story_markdown("", oral, existing_for_narrative), (
            "evidence_leak_heuristic"
        )
    if (
        settings.memoir_evidence_scene_anchor_check_enabled
        and m
        and evidence_text.strip()
        and evidence_scene_anchor_leak(m, ev_plain, o, ex)
    ):
        logger.warning(
            "event=narrative_invariant_failed reason=evidence_scene_anchor chapter_category={}",
            chapter_category,
        )
        return _coalesce_story_markdown("", oral, existing_for_narrative), (
            "evidence_scene_anchor"
        )
    return m, "none"


def _coalesce_story_markdown(
    md: str,
    oral: str,
    existing_for_narrative: str,
) -> str:
    """落库前对齐正文：空输出时续写场景保留「已有故事 + 本段口述」。"""
    o = (oral or "").strip()
    ex = (existing_for_narrative or "").strip()
    m = (md or "").strip()
    if not m:
        if ex and o:
            return f"{ex}\n\n{o}"
        if o:
            return o
        return ex
    return m


def _is_json_narrative(text: str) -> bool:
    if not text or not text.strip():
        return False
    s = text.strip()
    return s.startswith("{") and "paragraphs" in s


def _ordered_text_for_segment_ids(
    category_segments: list, segment_ids: list[str]
) -> str:
    id_to_text = {seg.id: (seg.user_input_text or "") for seg in category_segments}
    return "\n\n".join(id_to_text.get(sid, "") for sid in segment_ids)


def _apply_narrative_fallbacks(
    narrative_raw: str,
    combined_unit_text: str,
    existing_for_narrative: str,
    *,
    chapter_category: str,
) -> tuple[str, str]:
    """返回 (文本, fallback_type)；无改写时为 none。

    仅防 merge/append 场景下模型输出极端缩水（丢旧内容），不再按口述字数比例回退。
    """
    if existing_for_narrative and _is_json_narrative(narrative_raw):
        merged_md = narrative_to_markdown(narrative_raw).strip()
        ex = (existing_for_narrative or "").strip()
        if ex and len(ex) > 400 and len(merged_md) < len(ex) * 0.25:
            logger.warning(
                "event=narrative_fallback reason=merge_shrink action=append_oral "
                "chapter_category={}",
                chapter_category,
            )
            return f"{ex}\n\n{combined_unit_text.strip()}", "merge_shrink"

    if (
        existing_for_narrative
        and not _is_json_narrative(narrative_raw)
        and len(narrative_raw) < len(existing_for_narrative) * 0.5
    ):
        logger.warning(
            "event=narrative_fallback reason=length_anomaly action=append_raw "
            "chapter_category={}",
            chapter_category,
        )
        return (
            f"{existing_for_narrative}\n\n{combined_unit_text}",
            "coalesce_to_old_plus_oral",
        )

    return narrative_raw, "none"


def _merge_fallback_type(gate_ft: str, apply_ft: str) -> str:
    if apply_ft != "none":
        return apply_ft
    return gate_ft


def _story_meta_for_route(
    session: Session, candidates: list
) -> dict[str, dict[str, int]]:
    meta: dict[str, dict[str, int]] = {}
    for s in candidates:
        sid = str(s.id)
        meta[sid] = {
            "char_count": len((s.canonical_markdown or "").strip()),
            "version_count": count_story_versions_sync(session, sid),
        }
    return meta


def _ensure_chapter_record(
    session: Session,
    *,
    user_id: str,
    chapter_category: str,
    title: str,
    source_ids: list[str],
    calculated_order_index: int,
) -> Chapter:
    stmt_chapter = (
        select(Chapter)
        .where(
            Chapter.user_id == user_id,
            Chapter.category == chapter_category,
            Chapter.is_active == True,  # noqa: E712
        )
        .options(
            joinedload(Chapter.images),
            joinedload(Chapter.story_links),
        )
    )
    chapter = session.execute(stmt_chapter).unique().scalar_one_or_none()
    if not chapter:
        chapter = Chapter(
            id=str(uuid.uuid4()),
            user_id=user_id,
            title=title,
            order_index=calculated_order_index,
            status="completed",
            category=chapter_category,
            is_new=True,
            source_segments=source_ids,
        )
        session.add(chapter)
        session.flush()
    else:
        chapter.source_segments = list(
            set((chapter.source_segments or []) + source_ids)
        )
        chapter.is_new = True
        session.flush()
    return chapter


def _run_batch_plan_writes(
    session: Session,
    *,
    plan: StoryBatchPlan,
    category_segments: list,
    chapter: Chapter,
    chapter_category: str,
    evidence_text: str,
    slot_snippets: dict[str, str],
    user_id: str,
    user_profile: str,
    user_birth_year: int | None,
    llm: Any,
    narrative_agent: NarrativeAgent,
    background_voice: str = "default",
    occupation: str = "",
    memoir_correlation_id: str | None = None,
) -> set[str]:
    dispatch_ids: set[str] = set()
    max_chars = int(settings.story_append_max_canonical_chars)
    max_ver = int(settings.story_append_max_versions)
    for unit in plan.units:
        t0 = time.perf_counter()
        unit_text = _ordered_text_for_segment_ids(category_segments, unit.segment_ids)
        oral_unit = normalize_oral_for_memoir(unit_text, llm=llm)
        ut_raw = (unit_text or "").strip()
        ut_norm = (oral_unit or "").strip()
        if ut_raw != ut_norm:
            logger.info(
                "event=oral_normalized context=batch_unit raw_len={} norm_len={}",
                len(ut_raw),
                len(ut_norm),
            )
        new_content_input = format_narrative_user_content(oral_unit, evidence_text)

        target_story_id: str | None = None
        existing_for_narrative = ""
        decision_source = "batch_plan"
        if unit.decision == "append_story" and unit.target_story_id:
            st = session.get(Story, unit.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=batch_plan",
                        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,
            background_voice=background_voice,
            occupation=occupation,
            fallback_plain_oral=ut_norm,
        )
        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(
            oral_unit,
            raw_gen,
            llm,
            existing_canonical=existing_for_narrative or None,
        )
        narrative_raw, fb_apply = _apply_narrative_fallbacks(
            narrative_raw,
            oral_unit,
            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(),
            oral_unit.strip(),
            existing_for_narrative or "",
        )
        md, inv_fb = _apply_narrative_body_safety(
            md,
            oral=oral_unit,
            existing_for_narrative=existing_for_narrative or "",
            evidence_text=evidence_text,
            chapter_category=chapter_category,
        )
        if inv_fb != "none":
            fallback_type = (
                inv_fb if fallback_type == "none" else f"{fallback_type}+{inv_fb}"
            )

        if target_story_id:
            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 = _maybe_generate_title(
                narrative_agent,
                chapter_category=chapter_category,
                md=md,
                slot_snippets=slot_snippets,
                user_profile=user_profile,
                user_birth_year=user_birth_year,
                llm=llm,
                oral_scope=ut_norm,
            )
            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 memoir_correlation_id={} route_type=batch "
            "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} oral_normalize_changed={}",
            memoir_correlation_id or "",
            decision_source,
            unit.decision,
            len(unit.segment_ids),
            bool(evidence_text.strip()),
            _is_json_narrative(raw_gen),
            fb_gate == "none",
            fallback_type,
            len(ut_norm),
            len(md.strip()),
            chapter_category,
            is_append,
            sid_log,
            elapsed,
            ut_raw != ut_norm,
        )
    return dispatch_ids


def run_story_pipeline_for_category_batch(
    session: Session,
    *,
    user_id: str,
    chapter_category: str,
    category_segments: list,
    state: MemoirStateSchema,
    user_profile: str,
    user_birth_year: int | None,
    llm: Any,
    background_voice: str = "default",
    occupation: str = "",
    memoir_correlation_id: str | None = None,
) -> tuple[Chapter | None, bool, set[str]]:
    """
    返回 (chapter, needs_cover_enqueue, story_ids_to_dispatch_after_commit)。
    """
    narrative_agent = NarrativeAgent()
    route_agent = StoryRouteAgent()
    dispatch_ids: set[str] = set()

    segment_texts = [seg.user_input_text or "" for seg in category_segments]
    combined_text = "\n\n".join(segment_texts)
    source_ids = [seg.id for seg in category_segments]

    n_units = len(category_segments)
    top_k = int(settings.evidence_top_k_default)
    if n_units > int(settings.evidence_large_batch_threshold):
        top_k = int(settings.evidence_top_k_large_batch)
    emb = get_embedding_provider()
    embedding_available = emb.is_available()
    try:
        evidence = retrieve_evidence_sync(
            session,
            user_id,
            combined_text,
            top_k=top_k,
            embedding_provider=emb,
        )
    except Exception as e:
        logger.warning("Evidence 检索跳过: {}", e)
        evidence = {
            "relevant_chunks": [],
            "relevant_summaries": [],
            "relevant_facts": [],
            "timeline_hints": [],
            "relevant_stories": [],
        }

    logger.info(
        "memoir_evidence_retrieved user_id={} chunks={} facts={} summaries={} stories={} vector_ok={}",
        user_id,
        len(evidence.get("relevant_chunks") or []),
        len(evidence.get("relevant_facts") or []),
        len(evidence.get("relevant_summaries") or []),
        len(evidence.get("relevant_stories") or []),
        embedding_available,
    )

    evidence_text = format_evidence_chunks_for_prompt(evidence)
    oral_for_memoir = normalize_oral_for_memoir(combined_text, llm=llm)
    ct_raw = (combined_text or "").strip()
    om_norm = (oral_for_memoir or "").strip()
    if ct_raw != om_norm:
        logger.info(
            "event=oral_normalized context=category_batch raw_len={} norm_len={}",
            len(ct_raw),
            len(om_norm),
        )
    new_content_input = format_narrative_user_content(oral_for_memoir, evidence_text)
    logger.info(
        "event=memoir_story_pipeline_start memoir_correlation_id={} user_id={} "
        "chapter_category={} segment_count={}",
        memoir_correlation_id or "",
        user_id,
        chapter_category,
        len(category_segments),
    )

    stmt_chapter = (
        select(Chapter)
        .where(
            Chapter.user_id == user_id,
            Chapter.category == chapter_category,
            Chapter.is_active == True,  # noqa: E712
        )
        .options(
            joinedload(Chapter.images),
            joinedload(Chapter.story_links),
        )
    )
    chapter = session.execute(stmt_chapter).unique().scalar_one_or_none()

    slot_snippets = _slot_snippets_for_narrative(
        state=state,
        chapter_category=chapter_category,
        user_id=user_id,
    )

    title = chapter.title if chapter else _placeholder_title(chapter_category)

    # 仅同 chapter_category（story.stage）的 Story 可作为 append 候选，避免跨章节链接导致多章内容相同
    all_stories = list_active_stories_for_user_sync(session, user_id)
    candidates = [s for s in all_stories if s.stage == chapter_category]
    valid_ids = {str(s.id) for s in candidates}
    story_meta = _story_meta_for_route(session, candidates)

    # Story route 仅依据本批用户口述；evidence 只进入叙事/合并，不参与 new/append 判定。
    route_transcript = oral_for_memoir

    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 = _route_segment_texts(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,
            background_voice=background_voice,
            occupation=occupation,
            memoir_correlation_id=memoir_correlation_id,
        )
    else:
        route = route_agent.decide(
            chapter_category=chapter_category,
            chapter_title=title,
            batch_transcript=route_transcript,
            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,
            background_voice=background_voice,
            occupation=occupation,
            fallback_plain_oral=om_norm,
        )
        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(
            oral_for_memoir,
            raw_gen,
            llm,
            existing_canonical=existing_for_narrative or None,
        )

        narrative_raw, fb_apply = _apply_narrative_fallbacks(
            narrative_raw,
            oral_for_memoir,
            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(),
            oral_for_memoir.strip(),
            existing_for_narrative or "",
        )
        md, inv_fb = _apply_narrative_body_safety(
            md,
            oral=oral_for_memoir,
            existing_for_narrative=existing_for_narrative or "",
            evidence_text=evidence_text,
            chapter_category=chapter_category,
        )
        if inv_fb != "none":
            fallback_type = (
                inv_fb if fallback_type == "none" else f"{fallback_type}+{inv_fb}"
            )

        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 = _maybe_generate_title(
                narrative_agent,
                chapter_category=chapter_category,
                md=md,
                slot_snippets=slot_snippets,
                user_profile=user_profile,
                user_birth_year=user_birth_year,
                llm=llm,
                oral_scope=om_norm,
            )
            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 memoir_correlation_id={} 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} oral_normalize_changed={}",
            memoir_correlation_id or "",
            decision_source,
            route.decision,
            len(category_segments),
            bool(evidence_text.strip()),
            _is_json_narrative(raw_gen),
            fb_gate == "none",
            fallback_type,
            len(om_norm),
            len(md.strip()),
            chapter_category,
            is_append,
            sid_log,
            elapsed,
            ct_raw != om_norm,
        )

    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