feat(api): 访谈路径轻量门控、Memoir Phase1 批处理与叙事/记忆管线加固

- 新增 utterance_substance：短时/应答/元话语可跳过记忆检索、阶段 LLM 与资料抽取 LLM；可配置
- 输入归一化：LLM 模式默认仅语音/ASR；配置项写入 .env.example
- Memoir Phase1：可选 batch LLM 一次性抽取+分类（失败回退逐段）；Extraction 空槽位时阶段与 current_stage 对齐，prompt 约束收紧
- 叙事与忠实度：narrative_safety、证据重叠/场合锚点、标题 slots 与履历短语 grounded；fidelity 解析失败 fail-open 可配置
- 章节管线：锁 TTL 上调、锁竞争 Celery 重试、Phase2 immediate singleflight 等；story_pipeline_sync / chapter_compose / memoir_tasks 联动
- Memory：compaction / repo / summarizer / evidence 小修；事实 FTS 未命中是否回退最近事实可配置
- 新增 memoir_pipeline_trace；补充 memoir_reliability 文档与多项回归/门控测试

api/app/agents/memoir/batch_phase1_prep.py

@@ -0,0 +1,114 @@
+"""
+Phase1 批处理：一次 LLM 调用完成多段的抽取 + 章节分类（与逐段循环语义对齐）。
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, List
+
+from app.agents.memoir.prompts import get_batch_memoir_phase1_prep_prompt
+from app.agents.state_schema import MemoirStateSchema
+from app.core.config import settings
+from app.core.json_utils import extract_json_payload
+from app.core.langchain_llm import invoke_json_object
+from app.core.logging import get_logger
+from app.features.conversation.models import Segment
+
+logger = get_logger(__name__)
+
+STAGE_ALLOWED_SLOTS: Dict[str, frozenset[str]] = {
+    "childhood": frozenset(
+        {"place", "people", "daily_life", "emotion", "turning_event"}
+    ),
+    "education": frozenset({"school", "city", "motivation", "challenge", "change"}),
+    "career": frozenset({"job", "environment", "decision", "pressure", "growth"}),
+    "family": frozenset(
+        {"relationship", "conflict", "support", "responsibility", "change"}
+    ),
+    "belief": frozenset({"value", "regret", "pride", "lesson"}),
+}
+
+
+def _slots_snapshot(state: MemoirStateSchema) -> dict:
+    snap: dict = {}
+    for stage, buckets in (state.slots or {}).items():
+        snap[stage] = {}
+        for k, v in (buckets or {}).items():
+            if hasattr(v, "snippet"):
+                sn = getattr(v, "snippet", None) or ""
+            elif isinstance(v, dict):
+                sn = (
+                    (v.get("snippet") or "")
+                    if isinstance(v.get("snippet"), str)
+                    else ""
+                )
+            else:
+                sn = ""
+            snap[stage][k] = (sn or "")[:120]
+    return snap
+
+
+@dataclass(frozen=True)
+class BatchPhase1SegmentRow:
+    detected_stage: str
+    slots: Dict[str, str]
+    chapter_category_raw: str
+
+
+def run_batch_phase1_prep(
+    segments: List[Segment],
+    state: MemoirStateSchema,
+    llm: Any,
+) -> Dict[str, BatchPhase1SegmentRow]:
+    """对 segments 顺序批量调用 LLM；返回 id → 行。id 集合必须与入参完全一致。"""
+    if not llm:
+        raise ValueError("batch phase1 requires llm")
+    if not segments:
+        return {}
+    items = [(str(s.id), (s.user_input_text or "").strip()) for s in segments]
+    prompt = get_batch_memoir_phase1_prep_prompt(
+        system_current_stage=state.current_stage or "childhood",
+        slots_snapshot=_slots_snapshot(state),
+        segment_items=items,
+    )
+    raw = invoke_json_object(
+        llm,
+        prompt,
+        max_tokens=int(settings.memoir_phase1_batch_llm_max_tokens),
+        agent="BatchPhase1Prep.run",
+    )
+    parsed = json.loads(extract_json_payload(raw))
+    rows = parsed.get("segments") or []
+    if not isinstance(rows, list):
+        raise ValueError("batch phase1: segments must be a list")
+
+    by_id: Dict[str, BatchPhase1SegmentRow] = {}
+    for row in rows:
+        if not isinstance(row, dict):
+            continue
+        sid = str(row.get("id", "")).strip()
+        if not sid:
+            continue
+        ds = str(row.get("detected_stage", "") or "").strip().lower()
+        slots_raw = row.get("slots") or {}
+        slots: Dict[str, str] = {}
+        if isinstance(slots_raw, dict):
+            for k, v in slots_raw.items():
+                if k and isinstance(k, str):
+                    slots[k] = v if isinstance(v, str) else str(v)
+        cat_raw = str(row.get("chapter_category", row.get("category", "")) or "")
+        by_id[sid] = BatchPhase1SegmentRow(
+            detected_stage=ds or (state.current_stage or "childhood"),
+            slots=slots,
+            chapter_category_raw=cat_raw,
+        )
+
+    expected = {str(s.id) for s in segments}
+    if by_id.keys() != expected:
+        missing = expected - by_id.keys()
+        extra = by_id.keys() - expected
+        logger.warning("batch phase1 id mismatch missing={} extra={}", missing, extra)
+        raise ValueError("batch phase1 response segment ids do not match input")
+    return by_id