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/tests/test_json_and_memory_utils.py

@@ -0,0 +1,103 @@
+"""JSON 载荷解析、证据格式化、Story 批量规划校验（纯函数）。"""
+
+import pytest
+
+from app.agents.chat.reply_limits import truncate_chat_segments
+
+from app.agents.memoir.classification_agent import _normalize_llm_category
+from app.agents.memoir.prompts import format_evidence_chunks_for_prompt
+from app.agents.memoir.story_route_agent import (
+    StoryBatchPlan,
+    StoryBatchPlanUnit,
+    validate_story_batch_plan,
+)
+from app.features.memoir.memoir_images.json_payload import extract_json_payload
+
+
+def test_extract_json_payload_strips_markdown_fence() -> None:
+    raw = """```json
+{"a": 1}
+```"""
+    assert '"a"' in extract_json_payload(raw)
+
+
+def test_normalize_llm_category_strips_quotes() -> None:
+    assert _normalize_llm_category('"childhood"') == "childhood"
+    assert _normalize_llm_category("`beliefs`") == "beliefs"
+
+
+def test_format_evidence_chunks_includes_timeline() -> None:
+    ev = {
+        "relevant_chunks": [{"content": "chunk1"}],
+        "relevant_facts": [
+            {"subject": "我", "predicate": "生于", "object_json": "1950"}
+        ],
+        "timeline_hints": [
+            {
+                "id": "1",
+                "event_year": 1977,
+                "event_date": None,
+                "title": "恢复高考",
+                "description": "参加了考试",
+            }
+        ],
+        "relevant_summaries": [],
+        "relevant_stories": [],
+    }
+    out = format_evidence_chunks_for_prompt(ev)
+    assert "chunk1" in out
+    assert "1950" in out or "生于" in out
+    assert "1977" in out or "恢复高考" in out
+
+
+def test_validate_story_batch_plan_ok() -> None:
+    ordered = ["s1", "s2"]
+    plan = StoryBatchPlan(
+        units=[
+            StoryBatchPlanUnit(
+                segment_ids=["s1", "s2"],
+                decision="new_story",
+                target_story_id=None,
+                new_story_title="标题",
+                reason=None,
+            )
+        ]
+    )
+    ok, err = validate_story_batch_plan(ordered, plan, valid_story_ids=set())
+    assert ok is True
+    assert err is None
+
+
+def test_truncate_chat_segments() -> None:
+    out = truncate_chat_segments(
+        ["a" * 300, "b"],
+        max_segments=2,
+        max_chars_per_segment=220,
+    )
+    assert out[0] == "a" * 219 + "…"
+    assert len(out[0]) == 220
+    assert out[1] == "b"
+
+
+def test_validate_story_batch_plan_duplicate_segment() -> None:
+    plan = StoryBatchPlan(
+        units=[
+            StoryBatchPlanUnit(
+                segment_ids=["s1"],
+                decision="new_story",
+                target_story_id=None,
+                new_story_title="A",
+                reason=None,
+            ),
+            StoryBatchPlanUnit(
+                segment_ids=["s1"],
+                decision="new_story",
+                target_story_id=None,
+                new_story_title="B",
+                reason=None,
+            ),
+        ]
+    )
+    ok, err = validate_story_batch_plan(["s1", "s1"], plan, valid_story_ids=set())
+    assert ok is False
+    assert err == "duplicate_segment"