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?
164 lines
5.6 KiB
Python
164 lines
5.6 KiB
Python
"""Durable conversation turn persistence + Redis cache sync (feature layer)."""
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from __future__ import annotations
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import uuid
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from dataclasses import dataclass
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from datetime import datetime, timedelta, timezone
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from typing import Any
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core import redis as redis_core
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from app.core.logging import get_logger
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from app.features.conversation import repo
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from app.features.conversation.models import ConversationMessage
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from app.features.conversation.session_history import (
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conversation_messages_to_redis_history,
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)
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logger = get_logger(__name__)
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# 与 LLM / 客户端约定:多段助手消息用 [SPLIT] 拼接,便于拆成多条气泡与多段 TTS
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AI_RESPONSE_SEGMENT_JOIN = "[SPLIT]"
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@dataclass(frozen=True)
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class HumanAiTurnIds:
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"""Durable ids for one user + assistant pair in conversation_messages."""
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human_message_id: str
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assistant_message_id: str
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def _utc_now() -> datetime:
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return datetime.now(timezone.utc)
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class ConversationHistoryStore:
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def __init__(self, db: AsyncSession):
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self._db = db
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async def load_canonical_history(
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self, conversation_id: str
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) -> list[dict[str, Any]]:
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rows = await repo.get_conversation_messages(conversation_id, self._db)
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return conversation_messages_to_redis_history(rows)
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async def _touch_conversation(
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self, conversation_id: str, *, occurred_at: datetime
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) -> None:
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conversation = await repo.get_conversation(conversation_id, self._db)
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if conversation is None:
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return
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current = getattr(conversation, "last_message_at", None)
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if current is None or current < occurred_at:
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conversation.last_message_at = occurred_at
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async def _sync_redis_from_db(self, conversation_id: str) -> None:
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hist = await self.load_canonical_history(conversation_id)
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await redis_core.redis_service.set_conversation_history(conversation_id, hist)
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async def _sync_redis_best_effort(self, conversation_id: str) -> None:
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try:
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await self._sync_redis_from_db(conversation_id)
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except Exception as exc:
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logger.warning("conversation history cache sync skipped: {}", exc)
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async def record_ai_only_turn(
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self, conversation_id: str, responses: list[str]
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) -> str | None:
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if not responses:
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return None
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combined = AI_RESPONSE_SEGMENT_JOIN.join(responses)
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created_at = _utc_now()
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msg = ConversationMessage(
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id=str(uuid.uuid4()),
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conversation_id=conversation_id,
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role="ai",
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content=combined,
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message_type="text",
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created_at=created_at,
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)
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repo.add_conversation_message(msg, self._db)
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await self._touch_conversation(conversation_id, occurred_at=created_at)
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await self._db.commit()
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await self._sync_redis_best_effort(conversation_id)
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return msg.id
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async def record_human_ai_turn(
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self,
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conversation_id: str,
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user_message: str,
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responses: list[str],
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*,
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user_message_timestamp: datetime | None,
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is_from_voice: bool,
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voice_session_id: str | None,
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audio_duration_seconds: int | None,
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tts_audio_urls: list[str] | None,
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segment_id: str | None,
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memory_retrieval_trace: dict | None = None,
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) -> HumanAiTurnIds | None:
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if not responses:
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return None
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human_ts = user_message_timestamp or _utc_now()
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if human_ts.tzinfo is None:
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human_ts = human_ts.replace(tzinfo=timezone.utc)
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ai_ts = human_ts + timedelta(microseconds=1)
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human_type = "audio" if is_from_voice else "text"
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human = ConversationMessage(
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id=str(uuid.uuid4()),
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conversation_id=conversation_id,
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role="human",
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content=user_message,
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message_type=human_type,
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voice_session_id=voice_session_id,
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duration_seconds=audio_duration_seconds
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if audio_duration_seconds is not None and audio_duration_seconds > 0
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else None,
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segment_id=segment_id,
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created_at=human_ts,
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)
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combined = AI_RESPONSE_SEGMENT_JOIN.join(responses)
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ai = ConversationMessage(
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id=str(uuid.uuid4()),
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conversation_id=conversation_id,
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role="ai",
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content=combined,
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message_type="text",
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tts_audio_urls=tts_audio_urls if tts_audio_urls else None,
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segment_id=segment_id,
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created_at=ai_ts,
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memory_retrieval_trace_json=memory_retrieval_trace,
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)
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repo.add_conversation_message(human, self._db)
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repo.add_conversation_message(ai, self._db)
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await self._touch_conversation(conversation_id, occurred_at=ai_ts)
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await self._db.commit()
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await self._sync_redis_best_effort(conversation_id)
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return HumanAiTurnIds(
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human_message_id=str(human.id),
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assistant_message_id=str(ai.id),
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)
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async def attach_ai_tts_audio_urls(
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self,
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conversation_id: str,
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*,
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tts_audio_urls: list[str],
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segment_id: str | None = None,
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) -> None:
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if not tts_audio_urls:
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return
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row = await repo.set_latest_ai_message_tts_audio_urls(
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conversation_id,
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self._db,
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tts_audio_urls=tts_audio_urls,
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segment_id=segment_id,
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)
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if row is None:
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return
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await self._db.commit()
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await self._sync_redis_best_effort(conversation_id)
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