feat: 回忆录证据血缘与内部评测可追溯，顺带对齐本地评测台与 CI

数据库与模型：新增多版迁移（章节证据快照、对话血缘、记忆事实/时间线 lineage 等），把「成稿 ↔ 对话/记忆」的溯源信息落到表结构里。
业务链路：会话与 WS、回忆录/故事流水线、记忆写入与 enrichment 等跟着接上线索与快照；新增章节证据快照与评测侧 EvalTraceService 等模块，方便组评审用的证据包。
内部评测：自动化 run 与手工 memoir 评审共用可追溯证据；rubric/ judge 相关脚本与文档有配套调整。
app-eval-web：Memoir/实验详情里能展开看证据摘要与 evidence_trace（含对话轮次 id）；Vite 代理与 development.sh 注入的 API 端口与当前默认内部评测端口一致，避免改端口后页面连错服务。
工程杂项：GitHub Actions / 仓库说明有更新；各适配器与支付/配额/plan 等多处为小改动或跟随主改动的收尾；新增/扩充了?

api/app/agents/chat/orchestrator.py

@@ -13,15 +13,15 @@ from app.agents.chat.agent_turn import AgentChatTurn
 from app.agents.chat.helpers import get_history_with_window
 from app.agents.chat.interview_agent import InterviewAgent
 from app.agents.chat.profile_agent import ProfileAgent
-from app.agents.state_schema import MemoirStateSchema
-from app.core.agent_logging import agent_summary_enabled, log_agent_detail
-from app.core.logging import get_logger
 from app.agents.chat.stage_detection import (
     detect_primary_life_stage,
     life_stage_display_name,
 )
+from app.agents.state_schema import MemoirStateSchema
+from app.core.agent_logging import agent_summary_enabled, log_agent_detail
 from app.core.config import settings
 from app.core.dependencies import get_llm_provider
+from app.core.logging import get_logger
 from app.features.conversation.input_normalize import normalize_chat_input_for_agent
 from app.features.memoir.state_service import get_or_create_state, switch_stage
 
@@ -48,28 +48,35 @@ async def _fetch_interview_memory_evidence(
     db: AsyncSession,
     user_id: str,
     user_message: str,
-) -> str:
-    """按本轮用户话检索记忆，格式化为短文本；失败或未启用时返回空串。"""
+) -> tuple[str, dict | None]:
+    """按本轮用户话检索记忆：格式化短文本 + 可入库 trace（稳定 id）。"""
     from app.core.dependencies import get_embedding_provider
     from app.features.memory.evidence_format import format_evidence_chunks_for_prompt
+    from app.features.memory.retrieval_trace import (
+        chat_memory_retrieval_trace_from_bundle,
+    )
     from app.features.memory.service import MemoryService
 
     if not settings.chat_memory_retrieval_enabled:
         logger.debug(
             "event=chat_memory_retrieval_skip reason=disabled user_id={}", user_id
         )
-        return ""
+        return "", None
     msg = (user_message or "").strip()
     if not msg:
         logger.debug(
             "event=chat_memory_retrieval_skip reason=empty user_id={}", user_id
         )
-        return ""
+        return "", None
     try:
         emb = get_embedding_provider()
         ms = MemoryService(db, embedding_provider=emb)
-        bundle = await ms.retrieve(user_id, msg, top_k=settings.chat_memory_top_k)
+        top_k = settings.chat_memory_top_k
+        bundle = await ms.retrieve(user_id, msg, top_k=top_k)
         bd = bundle.model_dump()
+        trace = chat_memory_retrieval_trace_from_bundle(
+            bd, top_k=top_k, query_len=len(msg)
+        )
         text = format_evidence_chunks_for_prompt(bd)
         t = (text or "").strip()
         if not t:
@@ -77,7 +84,7 @@ async def _fetch_interview_memory_evidence(
                 "event=memory_evidence_for_prompt user_id={} formatted_chars=0",
                 user_id,
             )
-            return ""
+            return "", trace
         max_c = settings.chat_memory_evidence_max_chars
         if len(t) > max_c:
             t = t[: max_c - 3] + "..."
@@ -86,14 +93,14 @@ async def _fetch_interview_memory_evidence(
             user_id,
             len(t),
         )
-        return t
+        return t, trace
     except Exception as e:
         try:
             await db.rollback()
         except Exception as rollback_error:
             logger.warning("访谈记忆检索失败后回滚也失败: {}", rollback_error)
         logger.warning("访谈记忆检索失败: {}", e)
-        return ""
+        return "", None
 
 
 class ChatOrchestrator:
@@ -197,12 +204,15 @@ class ChatOrchestrator:
                                 conversation_id,
                                 len(responses),
                             )
-                        return AgentChatTurn(messages=responses, skip_tts=False)
+                        return AgentChatTurn(
+                            messages=responses, skip_tts=False, memory_retrieval_trace=None
+                        )
                     except Exception as e:
                         logger.error(f"资料收集处理失败: {e}", exc_info=True)
                         return AgentChatTurn(
                             messages=["不好意思刚才没接住，你再说一遍好吗？"],
                             skip_tts=False,
+                            memory_retrieval_trace=None,
                         )
 
         # --- 正式访谈模式 ---
@@ -262,10 +272,17 @@ class ChatOrchestrator:
             background_voice = infer_background_voice(user.occupation)
             occupation = user.occupation or ""
 
-        memory_evidence_text = await _fetch_interview_memory_evidence(
+        memory_evidence_text, mem_trace = await _fetch_interview_memory_evidence(
             db, user_id, normalized_user_message
         )
 
+        profile_birth_year = user.birth_year if user else None
+        profile_era_place = ""
+        if user:
+            profile_era_place = (
+                (user.birth_place or user.grew_up_place or "").strip()
+            )
+
         turn = await self.interview_agent.generate_response_with_state(
             conversation_id=conversation_id,
             user_message=user_message,
@@ -276,6 +293,8 @@ class ChatOrchestrator:
             background_voice=background_voice,
             normalized_user_message=normalized_user_message,
             occupation=occupation,
+            profile_birth_year=profile_birth_year,
+            profile_era_place=profile_era_place,
         )
         if agent_summary_enabled():
             logger.info(
@@ -287,6 +306,12 @@ class ChatOrchestrator:
                 len(turn.messages),
                 turn.skip_tts,
             )
+        if mem_trace is not None:
+            return AgentChatTurn(
+                messages=turn.messages,
+                skip_tts=turn.skip_tts,
+                memory_retrieval_trace=mem_trace,
+            )
         return turn
 
     async def extract_profile_from_message(
@@ -349,6 +374,8 @@ class ChatOrchestrator:
         background_voice: str = "default",
         normalized_user_message: str | None = None,
         occupation: str = "",
+        profile_birth_year: int | None = None,
+        profile_era_place: str = "",
     ) -> AgentChatTurn:
         """委托 InterviewAgent 生成访谈回复（持久化由调用方负责）。"""
         return await self.interview_agent.generate_response_with_state(
@@ -361,6 +388,8 @@ class ChatOrchestrator:
             background_voice=background_voice,
             normalized_user_message=normalized_user_message,
             occupation=occupation,
+            profile_birth_year=profile_birth_year,
+            profile_era_place=profile_era_place,
         )
 
     def detect_user_stage(self, user_message: str) -> str: