Files
life-echo/api/app/features/conversation/history_store.py
Kevin 309a051038 feat: 回忆录证据血缘与内部评测可追溯,顺带对齐本地评测台与 CI
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?
2026-04-08 15:37:09 +08:00

164 lines
5.6 KiB
Python

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