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/extraction_agent.py

@@ -10,7 +10,7 @@ from dataclasses import dataclass
 from typing import Any, Dict
 
 from app.agents.memoir.prompts import get_state_extraction_prompt
-from app.core.langchain_llm import bind_json_object_mode
+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
 
@@ -56,15 +56,19 @@ class ExtractionAgent:
                     for k, v in (stage_slots or {}).items()
                 },
             )
-            json_llm = bind_json_object_mode(llm, max_tokens=1024)
-            response = json_llm.invoke(prompt)
-            parsed = json.loads(extract_json_payload(response.content))
+            raw = invoke_json_object(
+                llm,
+                prompt,
+                max_tokens=1024,
+                agent="ExtractionAgent.extract",
+            )
+            parsed = json.loads(extract_json_payload(raw))
             detected_stage = parsed.get("detected_stage", detected_stage)
             raw_slots = parsed.get("slots", {}) or {}
             extracted_slots = {
                 k: v if isinstance(v, str) else str(v) for k, v in raw_slots.items()
             }
         except (json.JSONDecodeError, Exception) as e:
-            logger.warning("ExtractionAgent LLM 解析失败: %s", e)
+            logger.warning("ExtractionAgent LLM 解析失败: {}", e)
 
         return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)