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- decision_logs.py: add LIMIT 100 to dedup query to avoid loading all records - ExecutionProcess.tsx: document N+1 API pattern and explain why acceptable - scheduler.py: document why scan_once runs synchronously (thread pool would break fire-and-forget) All 880 tests pass.
272 lines
8.9 KiB
Python
272 lines
8.9 KiB
Python
"""Decision-log service (P3 deepening, S2).
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Pulled out of ``web/routers/intelligent_evals.py`` so the router only handles
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HTTP validation and error translation. The ORM writes and reads now live here.
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"""
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from typing import Any
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from sqlmodel import Session, select
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from agenteval.intelligent_eval.models import IntelligentEvalStatus
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from agenteval.storage.db import (
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IntelligentEvalDB,
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IntelligentEvalDecisionLogDB,
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IntelligentEvalSessionDB,
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)
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def _log_to_dict(log: IntelligentEvalDecisionLogDB) -> dict:
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return {
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"id": log.id,
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"eval_id": log.eval_id,
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"decision_type": log.decision_type,
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"reason": log.reason,
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"context": log.get_context(),
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"cron_id": log.cron_id,
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"created_at": log.created_at.isoformat() if log.created_at else None,
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}
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def _require_eval(eval_id: str, session: Session) -> None:
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eval_db = session.get(IntelligentEvalDB, eval_id)
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if eval_db is None or eval_db.status == IntelligentEvalStatus.DELETED.value:
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raise LookupError(f"intelligent eval {eval_id} not found")
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def _append_row(
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eval_id: str,
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decision_type: str,
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reason: str,
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cron_id: str,
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context: dict[str, Any],
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session: Session,
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) -> dict:
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log = IntelligentEvalDecisionLogDB(
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eval_id=eval_id,
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decision_type=decision_type,
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reason=reason,
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cron_id=cron_id,
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)
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log.set_context(context)
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session.add(log)
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session.commit()
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session.refresh(log)
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return _log_to_dict(log)
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def create_decision_log(
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eval_id: str,
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decision_type: str,
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reason: str,
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cron_id: str,
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context: dict[str, Any],
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session: Session,
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) -> dict:
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"""Agent-reporting entry: create a decision log, or return the existing
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one if the (eval_id, decision_type, context) tuple is already recorded.
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P3 真问题修复 (T8 / Gitea #6): agent 会在同一分钟内重复上报相同决策，
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按 context JSON 去重保表干净。**去重只服务 agent 上报路径**——平台落账
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每次都是新事实，用 ``append_decision_log``，调用方无需知道去重存在。
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Raises ``LookupError`` if eval not found.
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"""
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_require_eval(eval_id, session)
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# Dedupe: same (eval, decision_type, context) → return existing.
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# Limit to 100 records to avoid loading too many into memory; in practice,
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# an eval rarely has more than a few dozen logs of the same type.
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for existing in session.exec(
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select(IntelligentEvalDecisionLogDB)
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.where(
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IntelligentEvalDecisionLogDB.eval_id == eval_id,
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IntelligentEvalDecisionLogDB.decision_type == decision_type,
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)
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.limit(100)
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).all():
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if existing.get_context() == context:
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return _log_to_dict(existing)
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return _append_row(eval_id, decision_type, reason, cron_id, context, session)
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def append_decision_log(
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eval_id: str,
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decision_type: str,
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reason: str,
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cron_id: str,
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context: dict[str, Any],
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session: Session,
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) -> dict:
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"""Platform-bookkeeping entry: 纯追加，不去重。
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平台每次落账都是新事实（attempt 递增、task_id 不同、闸门判定），
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相同 context 也总是追加一行。
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Raises ``LookupError`` if eval not found.
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"""
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_require_eval(eval_id, session)
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return _append_row(eval_id, decision_type, reason, cron_id, context, session)
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def count_decisions(eval_id: str, decision_type: str, session: Session) -> int:
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"""该评估某类型决策日志的条数（平台闸门计数与 attempt 落账的计数原语）。"""
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return len(
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session.exec(
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select(IntelligentEvalDecisionLogDB).where(
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IntelligentEvalDecisionLogDB.eval_id == eval_id,
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IntelligentEvalDecisionLogDB.decision_type == decision_type,
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)
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).all()
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)
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def list_decision_logs(eval_id: str, session: Session) -> list[dict]:
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"""List decision logs for an eval. Raises ``LookupError`` if eval not found."""
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_require_eval(eval_id, session)
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logs = session.exec(
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select(IntelligentEvalDecisionLogDB)
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.where(IntelligentEvalDecisionLogDB.eval_id == eval_id)
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.order_by(IntelligentEvalDecisionLogDB.created_at.desc())
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).all()
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return [_log_to_dict(log) for log in logs]
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def _supplement_executing(
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ev: IntelligentEvalDB,
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sessions: list,
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completed: int,
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estimated: int,
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types: set[str],
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session: Session,
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) -> int:
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"""Supplement decision logs for EXECUTING evals."""
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added = 0
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if "execute_session" not in types and completed < estimated:
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_append_row(
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ev.id,
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"execute_session",
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"平台兜底：时段欠账需执行会话",
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"platform",
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{"platform_supplemented": True, "completed": completed, "estimated": estimated},
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session,
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)
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added += 1
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elif "start_analysis" not in types and sessions and completed >= estimated:
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_append_row(
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ev.id,
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"start_analysis",
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"平台兜底：所有会话已完成开始分析",
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"platform",
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{"platform_supplemented": True, "completed": completed, "estimated": estimated},
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session,
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)
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added += 1
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return added
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def _supplement_completed(
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ev: IntelligentEvalDB,
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sessions: list,
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completed: int,
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estimated: int,
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plan: dict,
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types: set[str],
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session: Session,
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) -> int:
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"""Supplement decision logs for COMPLETED evals (historical backfill)."""
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added = 0
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if "execute_session" not in types:
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slots = plan.get("time_distribution") or []
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if slots:
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for slot in slots:
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_append_row(
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ev.id,
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"execute_session",
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f"平台兜底：时段{slot.get('time_slot', '')}执行会话（历史回填）",
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"platform",
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{
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"platform_supplemented": True,
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"time_slot": slot.get("time_slot"),
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"sessions": slot.get("sessions"),
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"completed": completed,
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"estimated": estimated,
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},
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session,
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)
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added += 1
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else:
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_append_row(
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ev.id,
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"execute_session",
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"平台兜底：执行会话（历史回填）",
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"platform",
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{"platform_supplemented": True, "completed": completed, "estimated": estimated},
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session,
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)
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added += 1
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if "start_analysis" not in types and sessions:
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_append_row(
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ev.id,
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"start_analysis",
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"平台兜底：所有会话已完成开始分析（历史回填）",
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"platform",
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{"platform_supplemented": True, "completed": completed, "estimated": estimated},
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session,
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)
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added += 1
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return added
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def supplement_decision_logs(session: Session) -> int:
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"""Platform audit backfill for decision logs.
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方案③的决策日志由 OpenClaw agent 上报（LLM 自主，尽力而为）——异常路径
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（如卡死恢复后重试）agent 可能跳过上报，导致决策过程页面为空。这里按评估
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状态推导决策并补录：
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- EXECUTING：欠账（completed < estimated）补 execute_session，所有会话
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完成后补 start_analysis。
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- COMPLETED：历史评估/异常路径可能完全没有决策日志，回填 execute_session
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（按 plan 时段逐条）+ start_analysis，让旧报告也有决策过程可看。
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只补"该类型缺失"的，不重复；且只记录状态，不改变 agent 的实际执行。
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Returns:
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补录的决策日志条数。
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"""
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evals = session.exec(
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select(IntelligentEvalDB).where(
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IntelligentEvalDB.status.in_(
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[
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IntelligentEvalStatus.EXECUTING.value,
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IntelligentEvalStatus.COMPLETED.value,
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]
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)
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)
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).all()
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added = 0
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for ev in evals:
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plan = ev.get_plan() if ev.plan else {}
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estimated = plan.get("estimated_sessions", 0)
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sessions = session.exec(
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select(IntelligentEvalSessionDB).where(IntelligentEvalSessionDB.eval_id == ev.id)
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).all()
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completed = sum(1 for s in sessions if s.status == "completed")
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types = {
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x.decision_type
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for x in session.exec(
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select(IntelligentEvalDecisionLogDB).where(IntelligentEvalDecisionLogDB.eval_id == ev.id)
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).all()
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}
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if ev.status == IntelligentEvalStatus.EXECUTING.value:
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added += _supplement_executing(ev, sessions, completed, estimated, types, session)
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elif ev.status == IntelligentEvalStatus.COMPLETED.value:
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added += _supplement_completed(ev, sessions, completed, estimated, plan, types, session)
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return added
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