fix(ai): F1-5a 沙箱 batch-run 接入 runtime_context (W1 / 阶段 A 主体)
Neo F1-5 反馈: "让沙箱起到其真正的作用. 真正的模拟日期, 仅能看到沙箱设定日期 及之前日期的数据, 并运行 AI 的各个业务." 调研发现 (4 个并行子代理): batch-run 端点 _run_batch 是空壳 stub (只 logger.info, 实际不跑 AI), GUC apply_runtime_session_vars 0 处调用 (dead code), 7 张业务表 6 张有 runtime 复合索引唯独 ai_run_logs 漏建, App2/2a 3 行 _calc_date_range 漏传 ref_date. 本 commit (F1-5a 阶段 A 主体, F1-5b 后续完整 zqyy_app RLS 视图层): 后端核心: - admin_service.py: _run_batch 真实化 (Semaphore(5)+asyncio.gather+ return_exceptions=True+ctx_snapshot 防漂移); estimate 入口抓 RuntimeContext 快照, confirm 取出传给 worker - admin_ai.py: confirm_batch_run lazy 注入 dispatcher - admin_service.retry_trigger_job: INSERT 落 runtime_mode + sandbox_instance_id 列 (用 runtime_insert_columns helper) - runtime_context.py: get_runtime_context 加 bind_to_session 参数, 激活 GUC app.current_business_date / app.current_runtime_mode - run_log_service.create_log: 启用 bind_to_session=True 试点 App2/2a 3 行 ref_date 修复: - app2_finance_prompt.py:817 储值卡余额变化板块 - app2_finance_prompt.py:841 日粒度 series + 异常检测窗口 - app2a_finance_area_prompt.py:466 区域日粒度 series DB: - migrations/20260505__ai_run_logs_runtime_index.sql: 补 (site_id, runtime_mode, sandbox_instance_id, created_at DESC) 复合索引 前端: - AIOperations.tsx: 顶部加 sandbox 模式提示条 (Alert 显示 sandbox_date + sandbox_instance_id + 影响范围 + 切回 live 入口) 未做 (留 F1-5b 完整 zqyy_app RLS 视图层一并): - B1 admin_service 6 处 CURRENT_DATE -> business_date - B2 fdw_queries 异常分支兜底 - GUC 完整传递 (fdw_queries / page_context 等) - 测试 3 套 (.gitignore:71 排除, F2-2 入仓时 commit) - P20 SPEC \xa76/\xa710/\xa711/\xa715 (F1-5b 完整收口后同步更准确) Neo 决策: docs/_overview/wave1-findings/F1-5-impl-decisions.md 详见 docs/audit/changes/2026-05-05__wave1_f1_5a_sandbox_batch_run.md
This commit is contained in:
@@ -13,9 +13,18 @@ import asyncio
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import logging
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import uuid
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from datetime import datetime, timezone, timedelta
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from typing import TYPE_CHECKING, Any
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from app.ai.budget_tracker import BudgetTracker
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from app.database import get_connection
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from app.services.runtime_context import (
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RuntimeContext,
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get_runtime_context,
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runtime_insert_columns,
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)
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if TYPE_CHECKING:
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from app.ai.dispatcher import AIDispatcher
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logger = logging.getLogger(__name__)
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@@ -25,13 +34,22 @@ AVG_TOKENS_PER_CALL = 2000
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# 批量执行内存存储 TTL（秒）
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_BATCH_TTL_SECONDS = 600 # 10 分钟
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# F1-5a 批量执行并发上限
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# Neo 决策 N=5（与 dispatcher 现有 circuit_breaker / rate_limiter 配合，不打爆 DashScope 1000 RPM 限制）
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_BATCH_CONCURRENCY = 5
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class AdminAIService:
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"""AI 监控后台聚合服务。"""
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def __init__(self, budget_tracker: BudgetTracker | None = None) -> None:
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self._budget = budget_tracker
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self._batch_store: dict[str, dict] = {} # batch_id → {params, expires_at}
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self._batch_store: dict[str, dict] = {} # batch_id → {params, ctx_snapshot, expires_at}
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self._dispatcher: AIDispatcher | None = None # F1-5a: lifespan 启动时注入
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def set_dispatcher(self, dispatcher: AIDispatcher) -> None:
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"""F1-5a: lifespan 启动时注入 dispatcher，用于 _run_batch 实际执行 AI 调用。"""
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self._dispatcher = dispatcher
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# ── Dashboard ─────────────────────────────────────────
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@@ -344,29 +362,37 @@ class AdminAIService:
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return _row_to_dict(cols, row)
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async def retry_trigger_job(self, job_id: int) -> int:
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"""创建新 trigger_job（is_forced=true），返回新 job_id。"""
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"""创建新 trigger_job（is_forced=true），返回新 job_id。
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F1-5a: INSERT 显式落 runtime_mode + sandbox_instance_id，
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与原 trigger_job 的 runtime 上下文保持一致（避免依赖默认值导致重试时丢失 sandbox 标记）。
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"""
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original = await self.get_trigger_job(job_id)
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if original is None:
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raise ValueError(f"trigger_job {job_id} 不存在")
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site_id = original["site_id"]
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cols, placeholders, runtime_params = runtime_insert_columns(site_id)
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conn = get_connection()
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try:
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with conn.cursor() as cur:
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cur.execute(
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"""
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f"""
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INSERT INTO biz.ai_trigger_jobs
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(event_type, member_id, site_id, connector_type,
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payload, app_chain, is_forced, status)
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VALUES (%s, %s, %s, %s, %s, %s, true, 'pending')
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payload, app_chain, is_forced, status, {cols})
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VALUES (%s, %s, %s, %s, %s, %s, true, 'pending', {placeholders})
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RETURNING id
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""",
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(
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original["event_type"],
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original.get("member_id"),
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original["site_id"],
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site_id,
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original.get("connector_type", "feiqiu"),
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original.get("payload"),
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original.get("app_chain"),
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*runtime_params,
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),
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)
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new_id = cur.fetchone()[0]
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@@ -576,22 +602,41 @@ class AdminAIService:
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async def estimate_batch(
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self, app_types: list[str], member_ids: list[int], site_id: int,
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) -> dict:
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"""生成 batch_id，存入内存（TTL 10min），返回预估。"""
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"""生成 batch_id，存入内存（TTL 10min），返回预估。
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F1-5a: estimate 阶段抓 RuntimeContext 快照存入 _batch_store，
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confirm 时取出传给 _run_batch，避免 estimate→confirm 间 Neo 切 sandbox 模式造成数据漂移污染。
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"""
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self._cleanup_expired_batches()
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batch_id = uuid.uuid4().hex
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estimated_calls = len(app_types) * len(member_ids)
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estimated_tokens = estimated_calls * AVG_TOKENS_PER_CALL
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# F1-5a: 抓 ctx_snapshot
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ctx_snapshot = get_runtime_context(site_id)
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self._batch_store[batch_id] = {
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"params": {
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"app_types": app_types,
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"member_ids": member_ids,
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"site_id": site_id,
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"batch_id": batch_id, # 用于 _run_batch 内 triggered_by 标注
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},
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"ctx_snapshot": ctx_snapshot,
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"expires_at": datetime.now(timezone.utc) + timedelta(seconds=_BATCH_TTL_SECONDS),
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}
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logger.info(
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"批量执行预估: batch_id=%s apps=%s members=%d site_id=%s mode=%s sandbox_date=%s",
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batch_id,
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app_types,
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len(member_ids),
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site_id,
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ctx_snapshot.mode,
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ctx_snapshot.sandbox_date.isoformat() if ctx_snapshot.sandbox_date else None,
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)
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return {
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"batch_id": batch_id,
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"estimated_calls": estimated_calls,
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@@ -599,7 +644,7 @@ class AdminAIService:
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}
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async def confirm_batch(self, batch_id: str) -> None:
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"""取出参数，异步执行批量调用。"""
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"""取出参数 + ctx_snapshot，异步执行批量调用。"""
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self._cleanup_expired_batches()
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entry = self._batch_store.pop(batch_id, None)
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@@ -607,28 +652,77 @@ class AdminAIService:
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raise ValueError(f"batch_id 无效或已过期: {batch_id}")
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params = entry["params"]
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ctx_snapshot: RuntimeContext = entry["ctx_snapshot"]
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logger.info(
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"批量执行确认: batch_id=%s apps=%s members=%d site_id=%s",
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"批量执行确认: batch_id=%s apps=%s members=%d site_id=%s mode=%s",
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batch_id,
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params["app_types"],
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len(params["member_ids"]),
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params["site_id"],
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ctx_snapshot.mode,
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)
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# 后台异步执行（具体调用链由路由层注入 dispatcher 处理）
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asyncio.create_task(
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self._run_batch(params["app_types"], params["member_ids"], params["site_id"])
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)
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# F1-5a: 后台异步执行，传入 ctx_snapshot 防止运行时 Neo 切模式
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asyncio.create_task(self._run_batch(params, ctx_snapshot))
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async def _run_batch(
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self, app_types: list[str], member_ids: list[int], site_id: int,
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self, params: dict[str, Any], ctx_snapshot: RuntimeContext,
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) -> None:
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"""后台批量执行（占位实现，实际由 dispatcher 驱动）。"""
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"""F1-5a: 后台批量执行真正实现。
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- Semaphore(_BATCH_CONCURRENCY=5) 限并发，避免打爆 DashScope 限流
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- asyncio.gather + return_exceptions=True：单个 member 失败不连坐
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- context 显式带 business_date（来自 ctx_snapshot），prompt builder 自取沙箱日期
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- triggered_by=f"batch:{batch_id}"：打标 ai_run_logs，Wave 2 进度查询基础
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"""
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if self._dispatcher is None:
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logger.error(
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"批量执行失败: dispatcher 未注入 batch_id=%s",
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params.get("batch_id"),
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)
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return
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app_types: list[str] = params["app_types"]
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member_ids: list[int] = params["member_ids"]
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site_id: int = params["site_id"]
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batch_id: str = params["batch_id"]
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sem = asyncio.Semaphore(_BATCH_CONCURRENCY)
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triggered_by = f"batch:{batch_id}"
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business_date_iso = ctx_snapshot.business_date.isoformat()
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async def _run_one(app_type: str, member_id: int) -> None:
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async with sem:
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try:
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await self._dispatcher.run_single_app(
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app_type=app_type,
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context={
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"site_id": site_id,
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"member_id": member_id,
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"business_date": business_date_iso,
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},
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triggered_by=triggered_by,
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)
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except Exception:
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# 单个失败已写 ai_run_logs，此处仅记录（不连坐其他 member）
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logger.exception(
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"批量执行单步失败 batch_id=%s app=%s member=%s",
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batch_id, app_type, member_id,
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)
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tasks = [
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asyncio.create_task(_run_one(at, mid))
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for at in app_types
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for mid in member_ids
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]
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logger.info(
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"批量执行开始: apps=%s members=%d site_id=%s",
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app_types, len(member_ids), site_id,
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"批量执行开始: batch_id=%s tasks=%d concurrency=%d mode=%s",
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batch_id, len(tasks), _BATCH_CONCURRENCY, ctx_snapshot.mode,
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)
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# 实际执行逻辑在路由层通过 dispatcher.handle_trigger 驱动
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# 此处仅记录日志，避免服务层直接依赖 dispatcher 实例
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await asyncio.gather(*tasks, return_exceptions=True)
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logger.info("批量执行完成: batch_id=%s tasks=%d", batch_id, len(tasks))
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def _cleanup_expired_batches(self) -> None:
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"""清理过期 batch。"""
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