71fbd39e32
Route all memory ingest/retrieve/enrichment/compaction through async MemoryService. Remove legacy sync memory implementations (ingest/retrieve/compaction); Celery and memoir Phase2 call asyncio.run into MemoryService-backed helpers. Memoir Phase1 batch ingest uses MemoryService.ingest_transcripts_batch; drop chapters. evidence_bundle_json mirror (Alembic 0015). Evaluation uses snapshot/link-only bundles; raise EvidenceClosureMissing instead of partial/fallback lineage tiers. Split memoir state into NarrativeCoverageState and InterviewControlState; delete the _interview_meta_store adapter layer. Remove rolling-query and recent-fact fallback settings from config and evidence assembly. Update judges, docs, tests, and PlaygroundPage alignment. Made-with: Cursor
312 lines
9.7 KiB
Python
312 lines
9.7 KiB
Python
"""
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回忆录状态服务：get_or_create_state、update_slot、mark_stage_complete 等。
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供 memoir service、conversation ws 使用；Celery 任务内使用同步版本。
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"""
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import uuid
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from typing import Dict, List
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.orm import Session
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from app.agents.stage_constants import (
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chat_bucket,
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normalize_chat_stage,
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)
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from app.agents.state_schema import (
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KnownFact,
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MemoirStateSchema,
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PersonaThread,
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SlotData,
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default_state,
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narrative_coverage_state,
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)
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from app.core.config import settings
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from app.features.memoir.models import MemoirState as MemoirStateModel
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def _slots_snapshot_for_merge(raw: Dict[str, Dict] | None) -> Dict[str, Dict]:
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"""浅拷贝 slots，避免就地改 JSON 列同一 dict 引用导致 ORM 不标记 dirty。"""
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if not raw or not isinstance(raw, dict):
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return {}
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return {k: dict(v or {}) for k, v in raw.items()}
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def coerce_memoir_state(model: MemoirStateModel) -> MemoirStateSchema:
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"""把 ORM 行投影成 MemoirStateSchema；控制元数据来自独立列。"""
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raw_slots = model.slots if isinstance(model.slots, dict) else None
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clean_slots = raw_slots or dict(default_state().slots)
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known_raw = (
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model.known_facts_json if isinstance(model.known_facts_json, list) else []
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)
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persona_raw = (
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model.persona_threads_json
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if isinstance(model.persona_threads_json, list)
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else []
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)
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recent_raw = (
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model.recent_questions_json
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if isinstance(model.recent_questions_json, list)
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else []
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)
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return MemoirStateSchema.model_validate(
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{
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"stage_order": model.stage_order or default_state().stage_order,
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"current_stage": model.current_stage,
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"covered_stages": model.covered_stages or [],
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"slots": clean_slots,
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"known_facts": [x for x in known_raw if isinstance(x, dict)],
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"persona_threads": [x for x in persona_raw if isinstance(x, dict)],
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"recent_questions": [str(x).strip() for x in recent_raw if str(x).strip()],
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}
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)
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async def get_or_create_state(user_id: str, db: AsyncSession) -> MemoirStateSchema:
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stmt = select(MemoirStateModel).where(MemoirStateModel.user_id == user_id)
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result = await db.execute(stmt)
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state = result.scalar_one_or_none()
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if state:
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return coerce_memoir_state(state)
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default = default_state()
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state = MemoirStateModel(
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id=str(uuid.uuid4()),
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user_id=user_id,
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stage_order=default.stage_order,
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current_stage=default.current_stage,
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covered_stages=default.covered_stages,
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slots={
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k: {sk: sv.model_dump() for sk, sv in v.items()}
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for k, v in default.slots.items()
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},
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)
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db.add(state)
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await db.commit()
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await db.refresh(state)
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return coerce_memoir_state(state)
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def _apply_current_stage_policy(
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state: MemoirStateModel,
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stage_norm: str,
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*,
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memoir_batch: bool,
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) -> None:
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"""按 memoir_extraction_updates_current_stage 与 chat_bucket 真值表更新 current_stage。"""
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current_from_db = state.current_stage or "childhood"
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if not memoir_batch:
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state.current_stage = stage_norm
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return
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if not settings.memoir_extraction_updates_current_stage:
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return
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cur_b = chat_bucket(state.current_stage or current_from_db)
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new_b = chat_bucket(stage_norm)
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if new_b == cur_b:
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state.current_stage = stage_norm
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async def update_slot(
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user_id: str,
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stage: str,
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slot_name: str,
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snippet: str,
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segment_ids: List[str],
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db: AsyncSession,
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*,
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memoir_batch: bool = False,
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) -> MemoirStateSchema:
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stmt = (
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select(MemoirStateModel)
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.where(MemoirStateModel.user_id == user_id)
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.with_for_update()
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)
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result = await db.execute(stmt)
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state = result.scalar_one_or_none()
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if not state:
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await get_or_create_state(user_id, db)
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result = await db.execute(stmt)
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state = result.scalar_one()
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current_from_db = state.current_stage or "childhood"
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stage_norm = normalize_chat_stage(
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stage,
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fallback=current_from_db,
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log_context={"user_id": user_id},
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)
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slots = _slots_snapshot_for_merge(
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state.slots if isinstance(state.slots, dict) else None
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)
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stage_slots = dict(slots.get(stage_norm, {}) or {})
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existing = stage_slots.get(slot_name, {})
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merged_segment_ids = list({*(existing.get("segment_ids") or []), *segment_ids})
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stage_slots[slot_name] = SlotData(
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snippet=snippet, segment_ids=merged_segment_ids
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).model_dump()
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slots[stage_norm] = stage_slots
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state.slots = slots
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_apply_current_stage_policy(state, stage_norm, memoir_batch=memoir_batch)
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await db.commit()
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await db.refresh(state)
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return coerce_memoir_state(state)
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async def mark_stage_complete(
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user_id: str, stage: str, db: AsyncSession
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) -> MemoirStateSchema:
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"""推进 covered_stages 并在当前阶段匹配时尝试进入下一阶段。当前无调用方，预留未来阶段推进逻辑。"""
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stmt = select(MemoirStateModel).where(MemoirStateModel.user_id == user_id)
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result = await db.execute(stmt)
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state = result.scalar_one_or_none()
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if not state:
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return await get_or_create_state(user_id, db)
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covered = state.covered_stages or []
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if stage not in covered:
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covered.append(stage)
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state.covered_stages = covered
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stage_order = state.stage_order or default_state().stage_order
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if state.current_stage == stage:
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try:
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idx = stage_order.index(stage)
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state.current_stage = stage_order[min(idx + 1, len(stage_order) - 1)]
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except ValueError:
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state.current_stage = default_state().current_stage
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await db.commit()
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await db.refresh(state)
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return coerce_memoir_state(state)
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async def get_empty_slots(user_id: str, db: AsyncSession) -> List[str]:
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state = await get_or_create_state(user_id, db)
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return narrative_coverage_state(state).empty_slots_for_current_stage()
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async def switch_stage(
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user_id: str, new_stage: str, db: AsyncSession
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) -> MemoirStateSchema:
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stmt = (
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select(MemoirStateModel)
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.where(MemoirStateModel.user_id == user_id)
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.with_for_update()
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)
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result = await db.execute(stmt)
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state = result.scalar_one_or_none()
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if not state:
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await get_or_create_state(user_id, db)
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result = await db.execute(stmt)
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state = result.scalar_one()
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fb = state.current_stage or "childhood"
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state.current_stage = normalize_chat_stage(
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new_stage, fallback=fb, log_context={"user_id": user_id}
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)
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await db.commit()
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await db.refresh(state)
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return coerce_memoir_state(state)
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async def save_interview_state_meta(
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user_id: str,
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*,
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known_facts: list[KnownFact],
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persona_threads: list[PersonaThread],
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recent_questions: list[str],
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db: AsyncSession,
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) -> MemoirStateSchema:
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stmt = (
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select(MemoirStateModel)
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.where(MemoirStateModel.user_id == user_id)
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.with_for_update()
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)
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result = await db.execute(stmt)
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state = result.scalar_one_or_none()
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if not state:
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await get_or_create_state(user_id, db)
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result = await db.execute(stmt)
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state = result.scalar_one()
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state.known_facts_json = [x.model_dump() for x in known_facts]
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state.persona_threads_json = [x.model_dump() for x in persona_threads]
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state.recent_questions_json = list(recent_questions)
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await db.commit()
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await db.refresh(state)
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return coerce_memoir_state(state)
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def get_or_create_state_sync(user_id: str, db: Session) -> MemoirStateSchema:
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stmt = select(MemoirStateModel).where(MemoirStateModel.user_id == user_id)
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result = db.execute(stmt)
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state = result.scalar_one_or_none()
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if state:
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return coerce_memoir_state(state)
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default = default_state()
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state = MemoirStateModel(
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id=str(uuid.uuid4()),
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user_id=user_id,
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stage_order=default.stage_order,
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current_stage=default.current_stage,
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covered_stages=default.covered_stages,
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slots={
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k: {sk: sv.model_dump() for sk, sv in v.items()}
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for k, v in default.slots.items()
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},
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)
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db.add(state)
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db.commit()
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db.refresh(state)
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return coerce_memoir_state(state)
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def update_slot_sync(
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user_id: str,
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stage: str,
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slot_name: str,
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snippet: str,
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segment_ids: List[str],
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db: Session,
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*,
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memoir_batch: bool = True,
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) -> MemoirStateSchema:
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stmt = (
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select(MemoirStateModel)
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.where(MemoirStateModel.user_id == user_id)
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.with_for_update()
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)
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result = db.execute(stmt)
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state = result.scalar_one_or_none()
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if not state:
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get_or_create_state_sync(user_id, db)
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result = db.execute(stmt)
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state = result.scalar_one()
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current_from_db = state.current_stage or "childhood"
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stage_norm = normalize_chat_stage(
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stage,
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fallback=current_from_db,
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log_context={"user_id": user_id},
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)
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slots = _slots_snapshot_for_merge(
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state.slots if isinstance(state.slots, dict) else None
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)
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stage_slots = dict(slots.get(stage_norm, {}) or {})
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existing = stage_slots.get(slot_name, {})
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merged_segment_ids = list({*(existing.get("segment_ids") or []), *segment_ids})
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stage_slots[slot_name] = SlotData(
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snippet=snippet, segment_ids=merged_segment_ids
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).model_dump()
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slots[stage_norm] = stage_slots
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state.slots = slots
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_apply_current_stage_policy(state, stage_norm, memoir_batch=memoir_batch)
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db.commit()
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db.refresh(state)
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return coerce_memoir_state(state)
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