feat(api): 回忆录管线简化、路由延迟池与相关加固

- Phase1/2：移除 MemoirOrchestrator.run 与 process_memoir_segments 别名；文档改为 process_memoir_phase1。
- 槽位校验集中到 stage_constants（filter_stage_slots），批处理与顺序路径及 state_service 写库一致。
- StoryRoute：no_llm/parse_error/invalid_target 保守 new_story；短篇护栏不覆盖这些 fallback。
- Phase2 低置信单路径可选延迟（StoryPipelineResult.deferred）：不写 Chapter/Story，Segment 记录 defer 元数据，冷却内不重复消费；上限后停自动重试，Phase1 同类目新段唤醒池内段。
- Alembic 0017：segments 表 narrative_defer_* 列。
- ProfileAgent：经 LlmGateway/注入 Provider 统一聊天与 JSON，新增测试。
- ImagePromptOrchestrator：LLM 初始化失败可依配置降级或硬失败；补充策略测试。
- 配套单测与 README/本地开发文档表述更新。

Co-authored-by: Cursor <cursoragent@cursor.com>

api/app/agents/chat/profile_agent.py

@@ -22,7 +22,6 @@ from app.agents.chat.reply_limits import (
 from app.agents.chat.schemas import ProfileExtractionOutput
 from app.core.agent_logging import agent_span, log_agent_payload, log_agent_summary
 from app.core.config import settings
-from app.core.dependencies import get_llm_provider
 from app.core.llm_call import allm_json_call
 from app.core.llm_gateway import LlmGateway, LlmUseCase
 from app.core.logging import get_logger
@@ -31,11 +30,53 @@ from app.ports.llm import LLMProvider
 logger = get_logger(__name__)
 
 
-def _get_langchain_llm():
-    try:
-        return LlmGateway().langchain_llm_for(LlmUseCase("chat.profile"))
-    except Exception:
-        return None
+class _ProviderBackedProfileGateway:
+    def __init__(self, provider: LLMProvider) -> None:
+        self._provider = provider
+
+    async def chat_text(
+        self,
+        messages: list[dict],
+        *,
+        use_case: LlmUseCase | None = None,
+        temperature: float | None = None,
+        model: str | None = None,
+        max_tokens: int | None = None,
+    ) -> str:
+        resolved_temperature = temperature
+        if resolved_temperature is None:
+            resolved_temperature = (
+                use_case.temperature
+                if use_case and use_case.temperature is not None
+                else 0.7
+            )
+        return await self._provider.complete(
+            messages,
+            temperature=resolved_temperature,
+            model=model if model is not None else (use_case.model if use_case else None),
+            max_tokens=(
+                max_tokens
+                if max_tokens is not None
+                else (use_case.max_tokens if use_case else None)
+            ),
+        )
+
+    async def json_object(
+        self,
+        prompt: str,
+        schema: type[ProfileExtractionOutput],
+        *,
+        use_case: LlmUseCase,
+        fallback_factory: Any = None,
+    ) -> ProfileExtractionOutput:
+        return await allm_json_call(
+            getattr(self._provider, "langchain_llm", None),
+            prompt,
+            schema,
+            max_tokens=use_case.max_tokens or 1024,
+            agent=use_case.name,
+            fallback_factory=fallback_factory,
+        )
 
 
 def _langchain_messages_to_port(messages: List[Any]) -> list[dict]:
@@ -66,14 +107,17 @@ def _message_contents_char_count(messages: List[Any]) -> int:
 class ProfileAgent:
     """用户资料收集 Specialist Agent"""
 
-    def __init__(self, llm_provider: LLMProvider | None = None):
-        self._llm_provider = llm_provider
-        self.llm = _get_langchain_llm()
-
-    def _provider(self) -> LLMProvider:
-        if self._llm_provider is not None:
-            return self._llm_provider
-        return get_llm_provider()
+    def __init__(
+        self,
+        llm_provider: LLMProvider | None = None,
+        llm_gateway: Any | None = None,
+    ) -> None:
+        if llm_gateway is not None:
+            self._llm_gateway = llm_gateway
+        elif llm_provider is not None:
+            self._llm_gateway = _ProviderBackedProfileGateway(llm_provider)
+        else:
+            self._llm_gateway = LlmGateway()
 
     async def _invoke_chat(
         self,
@@ -88,8 +132,9 @@ class ProfileAgent:
         with agent_span(
             logger, f"{agent_name}.llm", conversation_id=conversation_id or ""
         ):
-            response_text = await self._provider().complete(
+            response_text = await self._llm_gateway.chat_text(
                 port_messages,
+                use_case=LlmUseCase("chat.profile", max_tokens=max_tokens),
                 max_tokens=max_tokens,
             )
         logger.info(
@@ -130,7 +175,7 @@ class ProfileAgent:
         conversation_id: Optional[str] = None,
     ) -> Dict[str, Any]:
         """从用户消息中提取资料字段，不持久化"""
-        if not self.llm or not missing_fields:
+        if not missing_fields:
             return {}
         recent_dialogue = ""
         if conversation_id:
@@ -151,12 +196,13 @@ class ProfileAgent:
             prompt = get_profile_extraction_prompt(
                 user_message, missing_fields, recent_dialogue=recent_dialogue or None
             )
-            parsed = await allm_json_call(
-                self.llm,
+            parsed = await self._llm_gateway.json_object(
                 prompt,
                 ProfileExtractionOutput,
-                max_tokens=settings.chat_profile_extract_max_tokens,
-                agent="ProfileAgent.extract_profile_from_message",
+                use_case=LlmUseCase(
+                    "ProfileAgent.extract_profile_from_message",
+                    max_tokens=settings.chat_profile_extract_max_tokens,
+                ),
                 fallback_factory=lambda: ProfileExtractionOutput(),
             )
             result = {}
@@ -197,8 +243,6 @@ class ProfileAgent:
         interview_stage_hint: str = "",
     ) -> List[str]:
         """生成资料追问回复，不持久化（由 Orchestrator 负责）"""
-        if not self.llm:
-            return ["谢谢！还能告诉我更多吗？"]
         try:
             prompt = get_profile_followup_prompt(
                 missing_fields,
@@ -260,8 +304,6 @@ class ProfileAgent:
         nickname: str = "",
     ) -> List[str]:
         """生成资料收集开场白，不持久化（由 Orchestrator 负责）"""
-        if not self.llm:
-            return ["你好！在开始之前，能告诉我你是哪一年出生的吗？"]
         try:
             prompt = get_profile_greeting_prompt(missing_fields, nickname)
             hw = await get_history_with_window(