feat(api+app): 对话阶段化、回忆录流水线与客户端会话体验

- DB: segments 用户输入文本（Alembic 0002）
- Chat: 阶段检测/阶段提示/回复限制，编排与访谈/画像 prompts 调整
- Memoir: 忠实度检查 agent，叙事与分类等链路更新
- Core: agent 日志、Alembic 启动、LangChain/日志/配置等
- Story: time_hints；Memory 检索与相关测试
- Expo: 助手头像、会话页与消息拆分、实时会话与文案/i18n
- Docs/scripts/tests: 迁移脚本、LLM JSON/记忆检索文档、新增单测

api/app/agents/memoir/classification_agent.py

@@ -8,14 +8,17 @@ ClassificationAgent：将内容分类到 8 个章节类别，或判定无价值
 
 from __future__ import annotations
 
+import json
 import re
 from typing import Any, Optional
 
 from app.agents.memoir.prompts import (
     CHAPTER_CATEGORIES,
-    get_chapter_classification_prompt,
+    get_chapter_classification_json_prompt,
 )
+from app.core.langchain_llm import invoke_json_object
 from app.core.logging import get_logger
+from app.features.memoir.memoir_images.json_payload import extract_json_payload
 
 logger = get_logger(__name__)
 
@@ -89,6 +92,20 @@ def _normalize_llm_category(raw: str) -> str:
     return s
 
 
+def _parse_category_from_llm_response(raw: str) -> str:
+    """优先解析 JSON ``{"category": "..."}``，失败则按纯文本 key 处理。"""
+    s = (raw or "").strip()
+    if not s:
+        return ""
+    try:
+        data = json.loads(extract_json_payload(s))
+        if isinstance(data, dict) and "category" in data:
+            return _normalize_llm_category(str(data["category"]))
+    except (json.JSONDecodeError, TypeError, ValueError):
+        pass
+    return _normalize_llm_category(s)
+
+
 class ClassificationAgent:
     """将内容分类到 8 个章节类别之一，或判定无价值返回 None"""
 
@@ -105,7 +122,7 @@ class ClassificationAgent:
         """
         if _looks_like_fragment_only(text):
             logger.debug(
-                "零散档案/极短标签句，跳过回忆录 Story: text_len=%s text=%s",
+                "零散档案/极短标签句，跳过回忆录 Story: text_len={} text={}",
                 len(text or ""),
                 text or "",
             )
@@ -113,12 +130,17 @@ class ClassificationAgent:
 
         if llm:
             try:
-                prompt = get_chapter_classification_prompt(text)
-                response = llm.invoke(prompt)
-                category = _normalize_llm_category(response.content or "")
+                prompt = get_chapter_classification_json_prompt(text)
+                raw = invoke_json_object(
+                    llm,
+                    prompt,
+                    max_tokens=256,
+                    agent="ClassificationAgent.classify",
+                )
+                category = _parse_category_from_llm_response(raw)
                 if category == "none":
                     logger.debug(
-                        "LLM 判定内容不足以成篇，跳过: text_len=%s text=%s",
+                        "LLM 判定内容不足以成篇，跳过: text_len={} text={}",
                         len(text or ""),
                         text or "",
                     )
@@ -126,7 +148,7 @@ class ClassificationAgent:
                 if category in CHAPTER_CATEGORIES:
                     return category
             except Exception as e:
-                logger.warning("ClassificationAgent LLM 章节分类失败: %s", e)
+                logger.warning("ClassificationAgent LLM 章节分类失败: {}", e)
 
         stage = _detect_stage(text, fallback_stage)
         return _STAGE_TO_DEFAULT_CATEGORY.get(