309a051038
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?
417 lines
16 KiB
Python
417 lines
16 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.helpers import get_history_with_window
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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.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.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.config import settings
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from app.core.dependencies import get_llm_provider
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from app.core.logging import get_logger
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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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) -> tuple[str, dict | None]:
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"""按本轮用户话检索记忆：格式化短文本 + 可入库 trace（稳定 id）。"""
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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.retrieval_trace import (
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chat_memory_retrieval_trace_from_bundle,
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)
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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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logger.debug(
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"event=chat_memory_retrieval_skip reason=disabled user_id={}", user_id
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)
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return "", None
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msg = (user_message or "").strip()
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if not msg:
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logger.debug(
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"event=chat_memory_retrieval_skip reason=empty user_id={}", user_id
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)
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return "", None
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try:
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emb = get_embedding_provider()
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ms = MemoryService(db, embedding_provider=emb)
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top_k = settings.chat_memory_top_k
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bundle = await ms.retrieve(user_id, msg, top_k=top_k)
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bd = bundle.model_dump()
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trace = chat_memory_retrieval_trace_from_bundle(
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bd, top_k=top_k, query_len=len(msg)
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)
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text = format_evidence_chunks_for_prompt(bd)
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t = (text or "").strip()
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if not t:
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logger.debug(
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"event=memory_evidence_for_prompt user_id={} formatted_chars=0",
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user_id,
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)
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return "", trace
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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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t = t[: max_c - 3] + "..."
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logger.info(
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"event=memory_evidence_for_prompt user_id={} formatted_chars={}",
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user_id,
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len(t),
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)
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return t, trace
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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 "", None
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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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hw_profile = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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profile_turn_total = hw_profile.turn_total
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if profile_turn_total >= settings.chat_profile_max_turns:
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logger.info(
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"event=chat_profile_cap_skip conversation_id={} "
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"turn_total={} cap={} missing_fields={}",
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conversation_id,
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profile_turn_total,
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settings.chat_profile_max_turns,
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missing,
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)
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else:
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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={} profile_turn_total={}",
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conversation_id,
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missing,
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len(user_message or ""),
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profile_turn_total,
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)
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# Profile 阶段每轮都抽取：短确认语也可能带可推断资料，跳过抽取会导致槽位长期不更新
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extracted = (
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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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)
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logger.info(
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"event=chat_profile_extract conversation_id={} "
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"extracted_keys={} missing_before={}",
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conversation_id,
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list(extracted.keys()) if extracted else [],
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missing,
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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(
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st.current_stage
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)
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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(
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messages=responses, skip_tts=False, memory_retrieval_trace=None
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)
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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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memory_retrieval_trace=None,
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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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is_from_voice=is_from_voice,
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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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occupation = ""
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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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occupation = user.occupation or ""
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memory_evidence_text, mem_trace = await _fetch_interview_memory_evidence(
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db, user_id, normalized_user_message
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)
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profile_birth_year = user.birth_year if user else None
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profile_era_place = ""
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if user:
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profile_era_place = (
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(user.birth_place or user.grew_up_place or "").strip()
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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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occupation=occupation,
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profile_birth_year=profile_birth_year,
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profile_era_place=profile_era_place,
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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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if mem_trace is not None:
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return AgentChatTurn(
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messages=turn.messages,
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skip_tts=turn.skip_tts,
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memory_retrieval_trace=mem_trace,
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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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occupation: str = "",
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profile_birth_year: int | None = None,
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profile_era_place: str = "",
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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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occupation=occupation,
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profile_birth_year=profile_birth_year,
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profile_era_place=profile_era_place,
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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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occupation: str = "",
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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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occupation=occupation,
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)
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