feat(api): 访谈人格/回复长度策略、口述归一、背景语气与输入净稿全链路

Chat 访谈
- 新增 persona 系统（default / warm_listener / curious_guide）与 background_voice 语气层
- 回复长度由 compute_reply_plan 统一决策（brief / standard / expanded），融合信息密度启发式
- 输入净稿（input_normalize）：编排层可选 rules/llm 归一用户口语后再喂模型与记忆检索
- 记忆证据注入：按用户话检索 memory evidence 并注入 prompt

Memoir 回忆录
- 口述归一（oral_normalize）：segment 原文保留，story 管线取派生净稿作叙事输入
- segment 入队批次门闸：累计字数 + 最长等待秒数，减少零碎提交
- fidelity_check / prompts / narrative_agent 微调
- Alembic 0005：清理跨章节 story 外键

Infra
- Dockerfile 加入 ffmpeg
- pyproject.toml 新增依赖并同步 uv.lock
- .env.example / .env.production 补全新配置项

Tests
- 新增 test_background_voice、test_chat_input_normalize、test_experience_regressions
- 扩展 test_interview_prompts、test_interview_reply_length、test_story_route_oral_invariant

Made-with: Cursor

api/app/features/memory/evidence_format.py

@@ -0,0 +1,99 @@
+"""
+将 MemoryService.retrieve / evidence bundle 格式化为 prompt 用短文本（叙事与访谈共用）。
+"""
+
+from __future__ import annotations
+
+import re
+
+
+def _normalize_evidence_line(s: str) -> str:
+    return re.sub(r"\s+", " ", (s or "").strip().lower())
+
+
+def dedupe_evidence_chunk_rows(chunks: list) -> list:
+    """
+    对 relevant_chunks 做稳定去重：按归一化后长度降序 + 原下标，单遍包含判定；
+    复杂度 O(n log n)；输出按原顺序中保留条目的相对顺序稳定。
+    """
+    extracted: list[tuple[int, str, object]] = []
+    for i, c in enumerate(chunks):
+        content = (
+            c.get("content", "") if isinstance(c, dict) else getattr(c, "content", "")
+        )
+        t = (content or "").strip()
+        if not t:
+            continue
+        extracted.append((i, t, c))
+    if len(extracted) <= 1:
+        return [x[2] for x in extracted]
+    extracted.sort(
+        key=lambda x: (-len(_normalize_evidence_line(x[1])), x[0]),
+    )
+    kept_norms: list[str] = []
+    kept: list[tuple[int, object]] = []
+    for orig_idx, text, c in extracted:
+        n = _normalize_evidence_line(text)
+        dup = False
+        for kn in kept_norms:
+            if len(n) <= len(kn) and n in kn:
+                dup = True
+                break
+        if not dup:
+            kept_norms.append(n)
+            kept.append((orig_idx, c))
+    kept.sort(key=lambda x: x[0])
+    return [x[1] for x in kept]
+
+
+def format_evidence_chunks_for_prompt(evidence: dict) -> str:
+    """将 retrieve_evidence / retrieve_evidence_sync 结果格式化为简短文本，供叙事与访谈 prompt 使用。
+
+    包含 chunks、摘要（若有）、confirmed facts、timeline、故事摘要（若有）。
+    """
+    chunks = evidence.get("relevant_chunks") or []
+    chunks = dedupe_evidence_chunk_rows(chunks[:10])
+    summaries = evidence.get("relevant_summaries") or []
+    facts = evidence.get("relevant_facts") or []
+    timeline = evidence.get("timeline_hints") or []
+    stories = evidence.get("relevant_stories") or []
+    parts: list[str] = []
+    for c in chunks:
+        content = (
+            c.get("content", "") if isinstance(c, dict) else getattr(c, "content", "")
+        )
+        if content:
+            parts.append(content.strip())
+    for s in summaries[:3]:
+        if isinstance(s, dict):
+            st = (s.get("content") or "").strip()
+            stype = (s.get("summary_type") or "").strip()
+            if st:
+                label = f"[摘要:{stype}]" if stype else "[摘要]"
+                parts.append(f"{label} {st}")
+    for f in facts[:5]:
+        if isinstance(f, dict):
+            subj = f.get("subject", "")
+            pred = f.get("predicate", "")
+            obj = f.get("object_json", "")
+            if subj or pred:
+                parts.append(f"{subj} {pred} {obj}")
+        else:
+            parts.append(f"{getattr(f, 'subject', '')} {getattr(f, 'predicate', '')}")
+    for t in timeline[:5]:
+        if isinstance(t, dict):
+            title = (t.get("title") or "").strip()
+            year = t.get("event_year")
+            desc = (t.get("description") or "").strip()
+            line = " ".join(
+                x for x in (str(year) if year is not None else "", title, desc) if x
+            )
+            if line:
+                parts.append(line)
+    for st in stories[:3]:
+        if isinstance(st, dict):
+            title = (st.get("title") or "").strip()
+            summ = (st.get("summary") or "").strip()
+            if title or summ:
+                parts.append(" ".join(x for x in (title, summ) if x))
+    return "\n\n".join(parts) if parts else ""