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
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Kevin
@@ -3,6 +3,7 @@ ChatOrchestrator：AI 回复用户模块的编排层
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负责路由（Profile vs Interview）、调用 Specialist Agent；持久化由 feature 层 ConversationHistoryStore 完成。
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"""
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import time
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from datetime import datetime
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from typing import TYPE_CHECKING, List, Optional
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@@ -12,8 +13,13 @@ from app.agents.chat.agent_turn import AgentChatTurn
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from app.agents.chat.interview_agent import InterviewAgent
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from app.agents.chat.profile_agent import ProfileAgent
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from app.agents.state_schema import MemoirStateSchema
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from app.core.agent_logging import agent_summary_enabled, log_agent_detail
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from app.core.logging import get_logger
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from app.features.memoir.state_service import get_or_create_state
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from app.agents.chat.stage_detection import (
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detect_primary_life_stage,
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life_stage_display_name,
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)
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from app.features.memoir.state_service import get_or_create_state, switch_stage
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if TYPE_CHECKING:
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from app.features.user.models import User
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@@ -54,12 +60,21 @@ class ChatOrchestrator:
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处理用户消息，返回 AI 回复（分段 + 是否跳过 TTS）。
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根据 missing_fields 路由到 ProfileAgent 或 InterviewAgent。
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"""
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t0 = time.perf_counter()
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# --- 资料收集模式 ---
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if user:
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missing = get_missing_profile_fields_fn(user)
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if missing:
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try:
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log_agent_detail(
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logger,
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"ChatOrchestrator route=profile conversation_id={} "
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"missing_fields={} user_msg_len={}",
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conversation_id,
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missing,
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len(user_message or ""),
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)
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extracted = await self.profile_agent.extract_profile_from_message(
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user_message, missing, conversation_id=conversation_id
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)
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@@ -68,13 +83,26 @@ class ChatOrchestrator:
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remaining = get_missing_profile_fields_fn(user)
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filled = get_filled_profile_fields_fn(user)
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interview_stage_hint = ""
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if not remaining:
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st = await get_or_create_state(user.id, db)
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interview_stage_hint = life_stage_display_name(st.current_stage)
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responses = await self.profile_agent.generate_profile_followup(
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conversation_id=conversation_id,
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user_message=user_message,
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missing_fields=remaining,
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filled_fields=filled,
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nickname=user.nickname or "",
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interview_stage_hint=interview_stage_hint,
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)
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if agent_summary_enabled():
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logger.info(
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"ChatOrchestrator.process_user_message route=profile "
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"duration_ms={:.2f} conversation_id={} response_segments={}",
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(time.perf_counter() - t0) * 1000,
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conversation_id,
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len(responses),
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)
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return AgentChatTurn(messages=responses, skip_tts=False)
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except Exception as e:
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logger.error(f"资料收集处理失败: {e}", exc_info=True)
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@@ -82,9 +110,30 @@ class ChatOrchestrator:
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# --- 正式访谈模式 ---
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user_id = user.id if user else None
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if not user_id:
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if agent_summary_enabled():
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logger.info(
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"ChatOrchestrator.process_user_message route=unauth "
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"duration_ms={:.2f} conversation_id={}",
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(time.perf_counter() - t0) * 1000,
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conversation_id,
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)
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return _UNAUTH_TURN
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log_agent_detail(
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logger,
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"ChatOrchestrator route=interview conversation_id={} user_msg_len={}",
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conversation_id,
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len(user_message or ""),
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)
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state = await get_or_create_state(user_id, db)
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detected = await detect_primary_life_stage(
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user_message,
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state.current_stage,
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self.interview_agent.llm,
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)
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if detected != state.current_stage:
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state = await switch_stage(user_id, detected, db)
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if conversation and conversation.conversation_stage != state.current_stage:
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conversation.conversation_stage = state.current_stage
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await db.commit()
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@@ -100,12 +149,24 @@ class ChatOrchestrator:
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occupation=user.occupation,
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)
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return await self.interview_agent.generate_response_with_state(
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turn = await self.interview_agent.generate_response_with_state(
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conversation_id=conversation_id,
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user_message=user_message,
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memoir_state=state,
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user_profile_context=user_profile_context,
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detected_user_stage=detected,
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)
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if agent_summary_enabled():
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logger.info(
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"ChatOrchestrator.process_user_message route=interview "
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"duration_ms={:.2f} conversation_id={} stage={} response_segments={} skip_tts={}",
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(time.perf_counter() - t0) * 1000,
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conversation_id,
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state.current_stage,
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len(turn.messages),
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turn.skip_tts,
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)
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return turn
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async def extract_profile_from_message(
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self,
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@@ -162,6 +223,7 @@ class ChatOrchestrator:
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voice_session_id: str | None = None,
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user_message_timestamp: datetime | None = None,
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audio_duration_seconds: int | None = None,
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detected_user_stage: str | None = None,
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) -> AgentChatTurn:
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"""委托 InterviewAgent 生成访谈回复（持久化由调用方负责）。"""
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return await self.interview_agent.generate_response_with_state(
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@@ -169,6 +231,7 @@ class ChatOrchestrator:
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user_message=user_message,
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memoir_state=memoir_state,
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user_profile_context=user_profile_context,
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detected_user_stage=detected_user_stage,
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)
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def detect_user_stage(self, user_message: str) -> str:
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