69a673e6c6
Chat 访谈 - 新增 persona 系统（default / warm_listener / curious_guide）与 background_voice 语气层 - 回复长度由 compute_reply_plan 统一决策（brief / standard / expanded），融合信息密度启发式 - 输入净稿（input_normalize）：编排层可选 rules/llm 归一用户口语后再喂模型与记忆检索 - 记忆证据注入：按用户话检索 memory evidence 并注入 prompt Memoir 回忆录 - 口述归一（oral_normalize）：segment 原文保留，story 管线取派生净稿作叙事输入 - segment 入队批次门闸：累计字数 + 最长等待秒数，减少零碎提交 - fidelity_check / prompts / narrative_agent 微调 - Alembic 0005：清理跨章节 story 外键 Infra - Dockerfile 加入 ffmpeg - pyproject.toml 新增依赖并同步 uv.lock - .env.example / .env.production 补全新配置项 Tests - 新增 test_background_voice、test_chat_input_normalize、test_experience_regressions - 扩展 test_interview_prompts、test_interview_reply_length、test_story_route_oral_invariant Made-with: Cursor
333 lines
13 KiB
Python
333 lines
13 KiB
Python
"""
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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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from sqlalchemy.ext.asyncio import AsyncSession
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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.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.core.config import settings
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from app.core.dependencies import get_llm_provider
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from app.features.conversation.input_normalize import normalize_chat_input_for_agent
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from app.features.memoir.state_service import get_or_create_state, switch_stage
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def _llm_for_chat_input_normalize():
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try:
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p = get_llm_provider()
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return getattr(p, "langchain_llm", None)
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except Exception:
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return None
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if TYPE_CHECKING:
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from app.features.user.models import User
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logger = get_logger(__name__)
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_UNAUTH_TURN = AgentChatTurn(
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messages=["暂时没法继续对话，请先登录后再试。"], skip_tts=True
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)
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async def _fetch_interview_memory_evidence(
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db: AsyncSession,
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user_id: str,
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user_message: str,
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) -> str:
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"""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。"""
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from app.core.dependencies import get_embedding_provider
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from app.features.memory.evidence_format import format_evidence_chunks_for_prompt
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from app.features.memory.service import MemoryService
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if not settings.chat_memory_retrieval_enabled:
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return ""
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msg = (user_message or "").strip()
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if not msg:
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return ""
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try:
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ms = MemoryService(db, embedding_provider=get_embedding_provider())
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bundle = await ms.retrieve(user_id, msg, top_k=settings.chat_memory_top_k)
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text = format_evidence_chunks_for_prompt(bundle.model_dump())
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t = (text or "").strip()
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if not t:
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return ""
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max_c = settings.chat_memory_evidence_max_chars
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if len(t) > max_c:
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return t[: max_c - 3] + "..."
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return t
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except Exception as e:
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try:
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await db.rollback()
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except Exception as rollback_error:
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logger.warning("访谈记忆检索失败后回滚也失败: {}", rollback_error)
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logger.warning("访谈记忆检索失败: {}", e)
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return ""
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class ChatOrchestrator:
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"""
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聊天编排器：根据用户资料完成度路由到 ProfileAgent 或 InterviewAgent。
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不直接写入 Redis/DB；由 WS pipeline / ConversationHistoryStore 落库并同步缓存。
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"""
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def __init__(self):
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self.profile_agent = ProfileAgent()
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self.interview_agent = InterviewAgent()
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async def process_user_message(
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self,
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conversation_id: str,
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user_message: str,
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user: Optional["User"],
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conversation, # 用于更新 conversation_stage
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is_from_voice: bool,
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voice_session_id: Optional[str],
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db: AsyncSession,
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apply_extracted_profile_fn,
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get_missing_profile_fields_fn,
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get_filled_profile_fields_fn,
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user_message_timestamp: Optional[datetime] = None,
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audio_duration_seconds: Optional[int] = None,
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) -> AgentChatTurn:
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"""
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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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if extracted:
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await apply_extracted_profile_fn(user, extracted, db)
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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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return AgentChatTurn(
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messages=["不好意思刚才没接住，你再说一遍好吗？"],
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skip_tts=False,
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)
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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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llm_n = None
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if settings.chat_input_normalize_enabled and (
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(settings.chat_input_normalize_mode or "").strip().lower() == "llm"
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):
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llm_n = _llm_for_chat_input_normalize()
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normalized_user_message = normalize_chat_input_for_agent(
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user_message or "",
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llm=llm_n,
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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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normalized_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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from app.agents.chat.background_voice import infer_background_voice
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from app.agents.chat.prompts_profile import format_user_profile_context
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user_profile_context = ""
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background_voice = "default"
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if user:
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user_profile_context = format_user_profile_context(
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birth_year=user.birth_year,
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birth_place=user.birth_place,
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grew_up_place=user.grew_up_place,
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occupation=user.occupation,
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)
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background_voice = infer_background_voice(user.occupation)
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memory_evidence_text = await _fetch_interview_memory_evidence(
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db, user_id, normalized_user_message
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)
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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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memory_evidence_text=memory_evidence_text,
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background_voice=background_voice,
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normalized_user_message=normalized_user_message,
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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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user_message: str,
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missing_fields: List[str],
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conversation_id: Optional[str] = None,
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):
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"""委托 ProfileAgent 提取资料"""
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return await self.profile_agent.extract_profile_from_message(
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user_message, missing_fields, conversation_id=conversation_id
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)
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async def generate_profile_followup(
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self,
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conversation_id: str,
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user_message: str,
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missing_fields: List[str],
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filled_fields: dict,
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nickname: str = "",
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is_from_voice: bool = False,
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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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) -> List[str]:
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"""委托 ProfileAgent 生成资料追问（持久化由调用方负责）。"""
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return 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=missing_fields,
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filled_fields=filled_fields,
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nickname=nickname,
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)
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async def generate_profile_greeting(
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self,
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conversation_id: str,
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missing_fields: List[str],
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nickname: str = "",
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) -> List[str]:
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"""委托 ProfileAgent 生成资料收集开场白（持久化由调用方负责）。"""
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return await self.profile_agent.generate_profile_greeting(
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conversation_id=conversation_id,
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missing_fields=missing_fields,
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nickname=nickname,
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)
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async def generate_response_with_state(
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self,
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conversation_id: str,
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user_message: str,
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memoir_state: MemoirStateSchema,
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user_profile_context: str = "",
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is_from_voice: bool = False,
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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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memory_evidence_text: str = "",
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background_voice: str = "default",
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normalized_user_message: 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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conversation_id=conversation_id,
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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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memory_evidence_text=memory_evidence_text,
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background_voice=background_voice,
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normalized_user_message=normalized_user_message,
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)
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def detect_user_stage(self, user_message: str) -> str:
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"""委托 InterviewAgent 检测用户阶段"""
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return self.interview_agent._detect_user_stage(user_message)
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async def generate_opening_message(
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self,
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conversation_id: str,
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memoir_state: MemoirStateSchema,
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user_profile_context: str = "",
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background_voice: str = "default",
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) -> List[str]:
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"""
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委托 InterviewAgent 生成访谈开场白（持久化由调用方 ConversationHistoryStore 负责）。
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"""
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return await self.interview_agent.generate_opening_message(
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conversation_id=conversation_id,
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memoir_state=memoir_state,
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user_profile_context=user_profile_context,
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background_voice=background_voice,
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)
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