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
136 lines
4.4 KiB
Python
136 lines
4.4 KiB
Python
"""Celery:memory compaction(近重复 chunk 软排除)。"""
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from __future__ import annotations
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import asyncio
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import time
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from datetime import datetime
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from typing import Any
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from celery import shared_task
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from app.core.config import settings
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from app.core.db import AsyncSessionLocal
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from app.core.logging import get_logger
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from app.core.memory_compaction_schedule import (
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finalize_memory_compaction_run,
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read_debounce_deadline_ts,
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release_scheduler_gate,
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schedule_memory_compaction_run,
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set_incremental_cursor_pair,
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)
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from app.core.redis_lock import acquire_redis_lock, release_redis_lock
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from app.features.memory.repo import list_users_with_recent_chunks
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from app.features.memory.service import MemoryService
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logger = get_logger(__name__)
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async def _list_users_with_recent_chunks_async(hours: int) -> list[str]:
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async with AsyncSessionLocal() as db:
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return await list_users_with_recent_chunks(db, hours=hours)
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async def _run_memory_compaction_async(
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user_id: str,
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context: dict[str, Any] | None,
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) -> dict[str, Any]:
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async with AsyncSessionLocal() as db:
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service = MemoryService(db)
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out = await service.compact_user(user_id, context)
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await db.commit()
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return out
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@shared_task
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def memory_compaction_sweep() -> dict[str, Any]:
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"""Beat:为近期有记忆写入的用户调度 compaction(debounce 仍由 schedule 合并)。"""
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t0 = time.perf_counter()
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if not settings.memory_compaction_enabled:
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return {"skipped": True, "reason": "disabled"}
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hours = int(settings.memory_compaction_sweep_recent_hours)
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user_ids = asyncio.run(_list_users_with_recent_chunks_async(hours))
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ctx_base: dict[str, Any] = {"trigger_source": "beat", "sweep_hours": hours}
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for uid in user_ids:
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schedule_memory_compaction_run(uid, dict(ctx_base))
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ms = (time.perf_counter() - t0) * 1000
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logger.info(
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"event=memory_compaction_sweep_done hours={} scheduled_users={} duration_ms={:.1f} "
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"msg=记忆压缩定时扫描已调度",
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hours,
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len(user_ids),
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ms,
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)
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return {"scheduled": len(user_ids), "user_ids": user_ids}
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@shared_task(bind=True, max_retries=12, default_retry_delay=20)
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def memory_compaction_run(
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self, user_id: str, context: dict[str, Any] | None = None
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) -> dict[str, Any]:
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run_t0 = time.perf_counter()
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if not settings.memory_compaction_enabled:
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return {"skipped": True, "reason": "disabled"}
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ctx = dict(context or {})
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deadline = read_debounce_deadline_ts(user_id)
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now = time.time()
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if deadline is not None and now < deadline:
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delay = max(1.0, deadline - now)
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raise self.retry(countdown=int(delay))
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lock = acquire_redis_lock(
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f"lock:memory_compaction:{user_id}",
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ttl_seconds=settings.memory_compaction_lock_ttl_seconds,
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)
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if lock is None:
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ms = (time.perf_counter() - run_t0) * 1000
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logger.info(
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"event=memory_compaction_skipped user_id={} reason=lock_not_acquired "
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"duration_ms={:.1f} msg=记忆压缩跳过(未拿到锁)",
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user_id,
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ms,
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)
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out = {"skipped": True, "reason": "lock_not_acquired"}
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finalize_memory_compaction_run(
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user_id,
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observed_deadline_ts=deadline,
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context=ctx,
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)
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return out
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try:
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out = asyncio.run(_run_memory_compaction_async(user_id, ctx))
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if out.get("new_cursor_ts") and out.get("new_cursor_id") is not None:
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set_incremental_cursor_pair(
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user_id,
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datetime.fromisoformat(out["new_cursor_ts"]),
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str(out["new_cursor_id"]),
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)
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finalize_memory_compaction_run(
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user_id,
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observed_deadline_ts=deadline,
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context=ctx,
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)
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ms = (time.perf_counter() - run_t0) * 1000
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logger.info(
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"event=memory_compaction_done user_id={} duration_ms={:.1f} msg=记忆压缩运行完成",
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user_id,
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ms,
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)
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return out
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except Exception as exc:
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ms = (time.perf_counter() - run_t0) * 1000
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logger.warning(
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"event=memory_compaction_failed user_id={} duration_ms={:.1f} err={} "
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"msg=记忆压缩运行失败",
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user_id,
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ms,
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exc,
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)
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release_scheduler_gate(user_id)
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raise self.retry(exc=exc) from exc
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finally:
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release_redis_lock(lock)
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