Promote the minimal swarm prototype to the repository root
The standalone prototype should be the root-level project shape for fengqun while preserving the existing planning documents already at the root. This keeps README, examples, tests, and the Python package directly discoverable without deleting the prior docs. Constraint: User clarified that swarm-minimal is the repository root, but other existing root files must remain. Rejected: Deleting existing root docs | They are part of the fengqun repository context and were explicitly protected. Confidence: high Scope-risk: narrow Directive: Keep secrets in ignored .env only; do not commit live credentials. Tested: python3 -B -m unittest discover -s tests; git diff --check; secret-pattern scan showed only placeholders/test values/task-id false positives. Not-tested: Remote web UI rendering after push.
This commit is contained in:
gongzhiyong
@@ -0,0 +1,646 @@
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from pathlib import Path
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from uuid import uuid4
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import json
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import sys
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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from swarm_minimal.azure_store import PostgresRedisBlobSwarmStore
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from swarm_minimal.config import SwarmConfig
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from swarm_minimal.core import Agent, SwarmCoordinator, Task
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from swarm_minimal.local_env import load_env_file
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from swarm_minimal.newapi_agnet import (
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NewApiAgnet,
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NewApiChannelConfig,
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discover_newapi_models,
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select_distinct_models,
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)
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TARGET_FILES = [
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"swarm_minimal/core.py",
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"swarm_minimal/newapi_agnet.py",
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"swarm_minimal/azure_store.py",
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"examples/run_multitask_complex_acceptance.py",
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"examples/run_long_task_acceptance.py",
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"tests/test_newapi_agnet.py",
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"tests/test_minimal_swarm.py",
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"README.md",
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]
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SCENARIO = {
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"title": "连续性长推理场景：为 swarm-minimal 设计可恢复的大规模代码任务推理链",
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"description": (
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"同一复杂工程问题必须被连续推理，而不是拆开独立回答。"
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"每个 Agnet 接住前一步的结论、约束和风险，继续推进到下一步，"
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"最终形成一个能落到代码、Azure 资源和验收命令上的闭环方案。"
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),
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}
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CHAIN_STEPS = [
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{
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"capability": "chain_step_01",
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"marker": "STEP-01",
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"title": "界定问题和不可变约束",
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"ask": "定义复杂代码任务连续推理的目标、输入输出、不变量和 Azure 资源边界。",
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},
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{
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"capability": "chain_step_02",
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"marker": "STEP-02",
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"title": "建立依赖图和状态模型",
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"ask": "基于 STEP-01 建立任务依赖图、共享状态字段、租约和状态转移模型。",
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},
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{
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"capability": "chain_step_03",
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"marker": "STEP-03",
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"title": "设计连续调度算法",
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"ask": "基于 STEP-02 设计上千任务下的连续调度、信息素更新和收敛算法，给复杂度。",
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},
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{
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"capability": "chain_step_04",
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"marker": "STEP-04",
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"title": "构造反例和失败场景",
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"ask": "基于 STEP-03 构造会破坏连续推理的反例：慢模型、重复任务、状态倒退、分数误导。",
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},
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{
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"capability": "chain_step_05",
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"marker": "STEP-05",
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"title": "修正算法和恢复策略",
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"ask": "基于 STEP-04 修正算法，加入幂等、重试、死信、outbox、Redis/PG 重连恢复。",
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},
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{
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"capability": "chain_step_06",
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"marker": "STEP-06",
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"title": "落到文件级实现计划",
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"ask": "基于 STEP-05 给出文件级代码改造计划，必须引用目标文件和测试文件。",
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},
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{
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"capability": "chain_step_07",
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"marker": "STEP-07",
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"title": "最终收敛和验收判定",
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"ask": "基于 STEP-06 给出最终可执行验收命令、指标、失败判定和上线前结论。",
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},
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]
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ACCEPTANCE_CRITERIA = [
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"自动发现至少 3 个模型，并使用 3 个互不相同的模型参与连续推理。",
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"7 个连续推理步骤必须全部完成，且状态写入 PostgreSQL task pool。",
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"每一步输出必须引用自己的 STEP 标记；除 STEP-01 外必须引用前一步 STEP 标记。",
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"共享状态必须保存每一步 summary，并把 chain cursor 推进到 STEP-07。",
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"PostgreSQL 和 Redis pheromone score 必须都有正分。",
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"最终收敛必须写入 PostgreSQL，并存在 Blob artifact。",
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"Redis Stream 必须新增至少 3*N+1 条事件。",
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"合并输出必须体现不变量、依赖图、复杂度、反例、修正、文件级计划和验收命令。",
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"最终输出必须引用至少 5 个真实文件。",
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"流程必须依赖模型发现，不能写死 NEWAPI_MODEL。",
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"NATS 或 Cosmos 不能作为 MVP 必需依赖。",
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]
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def main() -> None:
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load_env_file(ROOT / ".env")
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azure_config = SwarmConfig.from_env()
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newapi_config = NewApiChannelConfig.from_env()
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if newapi_config.timeout_seconds < 360:
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newapi_config = NewApiChannelConfig(
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base_url=newapi_config.base_url,
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api_key=newapi_config.api_key,
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model=newapi_config.model,
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timeout_seconds=360,
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)
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store = PostgresRedisBlobSwarmStore(azure_config)
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try:
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store.ensure_schema()
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discovered_models = discover_newapi_models(newapi_config)
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selected_models = select_responsive_models(newapi_config, discovered_models, count=3)
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run_id = uuid4().hex
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stream_before = store._run_redis(lambda redis: redis.xlen("swarm:events"))
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store.shared_state[f"run:{run_id}:goal"] = SCENARIO["title"]
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store.shared_state[f"run:{run_id}:status"] = "running"
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store.shared_state[f"chain:{run_id}:cursor"] = "START"
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store.shared_state[f"chain:{run_id}:expected_steps"] = str(len(CHAIN_STEPS))
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agents: list[Agent] = []
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task_ids: list[str] = []
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task_models: dict[str, str] = {}
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for index, step in enumerate(CHAIN_STEPS):
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model = selected_models[index % len(selected_models)]
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task = Task(
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kind=step["capability"],
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input=build_step_prompt(step, index, model),
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)
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store.add_task(task)
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task_ids.append(task.id)
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task_models[task.id] = model
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fallback_models = [candidate for candidate in selected_models if candidate != model]
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agents.append(build_chain_agent(newapi_config, model, fallback_models, index, step, run_id))
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result = SwarmCoordinator(store=store, agents=agents).run_until_converged(run_id)
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report = collect_report(
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store=store,
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run_id=run_id,
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result=result,
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discovered_models=discovered_models,
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selected_models=selected_models,
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task_ids=task_ids,
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task_models=task_models,
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stream_before=stream_before,
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)
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print(json.dumps(report, ensure_ascii=False, indent=2))
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if report["summary"]["status"] != "PASS":
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raise SystemExit(1)
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finally:
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store.close()
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def select_responsive_models(
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config: NewApiChannelConfig,
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discovered_models: list[str],
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*,
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count: int,
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) -> list[str]:
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candidates = select_distinct_models(sorted(discovered_models, key=model_priority), count=len(set(discovered_models)))
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responsive: list[str] = []
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for model in candidates:
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probe_config = NewApiChannelConfig(
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base_url=config.base_url,
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api_key=config.api_key,
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model=model,
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timeout_seconds=min(config.timeout_seconds, 30),
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)
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try:
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NewApiAgnet(probe_config, agent_id="probe", capability="probe").chat(
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system_prompt="Return only OK.",
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user_prompt="Health probe for continuous reasoning model selection. Return OK.",
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)
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except Exception:
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continue
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responsive.append(model)
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if len(responsive) == count:
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return responsive
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if len(responsive) >= count:
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return responsive[:count]
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return select_distinct_models(candidates, count=count)
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def model_priority(model: str) -> tuple[int, str]:
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lowered = model.lower()
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if "flash" in lowered:
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return (0, model)
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if "haiku" in lowered:
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return (1, model)
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if "sonnet" in lowered:
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return (2, model)
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if "mini" in lowered:
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return (3, model)
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if "pro" in lowered:
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return (8, model)
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if "opus" in lowered:
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return (9, model)
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return (5, model)
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def build_step_prompt(step: dict[str, str], index: int, model: str) -> str:
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previous_marker = "START" if index == 0 else CHAIN_STEPS[index - 1]["marker"]
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output_rules = [
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f"- 必须包含 `{step['marker']}`。",
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f"- {'必须说明从 START 建立初始约束。' if index == 0 else f'必须明确写出“基于 {previous_marker}”。'}",
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"- 必须输出：不变量、当前决策、风险/反例、下一步交接摘要。",
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"- 中文，控制在 750 字以内，不要泛泛而谈。",
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"- 必须说明模型来自发现流程，不能写死 NEWAPI_MODEL。",
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"- 不要把 NATS 或 Cosmos 作为 MVP 必需依赖。",
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"- 不要包含任何真实密钥。",
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]
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if step["marker"] == "STEP-07":
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output_rules.insert(4, "- 最终验收步骤必须精确引用至少 5 个目标文件路径。")
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return "\n".join(
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[
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f"{SCENARIO['title']}\n",
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f"总目标：{SCENARIO['description']}",
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f"当前步骤：{step['marker']} {step['title']}",
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f"必须承接：{previous_marker}",
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f"当前模型：{model}\n",
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f"步骤要求：{step['ask']}\n",
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"目标文件：",
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"\n".join(f"- {item}" for item in TARGET_FILES),
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"\n验收标准：",
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"\n".join(f"- {item}" for item in ACCEPTANCE_CRITERIA),
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"\n输出要求：",
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"\n".join(output_rules),
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]
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)
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def build_chain_agent(
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config: NewApiChannelConfig,
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model: str,
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fallback_models: list[str],
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index: int,
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step: dict[str, str],
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run_id: str,
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) -> Agent:
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def run(task: Task, shared_state: dict[str, str]) -> tuple[str, float]:
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previous_marker = "START" if index == 0 else CHAIN_STEPS[index - 1]["marker"]
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previous_summary = shared_state.get(f"chain:{run_id}:{previous_marker}:summary", "<none>")
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content = chat_with_fallback(
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config=config,
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primary_model=model,
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fallback_models=fallback_models,
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agent_index=index + 1,
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step=step,
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task=task,
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previous_marker=previous_marker,
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previous_summary=previous_summary,
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)
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shared_state[f"chain:{run_id}:{step['marker']}:summary"] = summarize_for_state(content)
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shared_state[f"chain:{run_id}:cursor"] = step["marker"]
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shared_state[f"chain:{run_id}:edge:{previous_marker}->{step['marker']}"] = "done"
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return content, score_output(content, index)
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return Agent(id=f"continuous-agnet-{index + 1}", capability=step["capability"], run=run)
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def chat_with_fallback(
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*,
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config: NewApiChannelConfig,
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primary_model: str,
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fallback_models: list[str],
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agent_index: int,
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step: dict[str, str],
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task: Task,
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previous_marker: str,
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previous_summary: str,
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) -> str:
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errors: list[str] = []
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for candidate in [primary_model, *fallback_models]:
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model_config = NewApiChannelConfig(
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base_url=config.base_url,
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api_key=config.api_key,
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model=candidate,
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timeout_seconds=config.timeout_seconds,
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)
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agnet = NewApiAgnet(model_config, agent_id=f"continuous-agnet-{agent_index}", capability=step["capability"])
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try:
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content = agnet.chat(
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system_prompt=(
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"You are one stage in a continuous long-reasoning swarm. "
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"Carry forward prior conclusions, expose risks, and hand off a concise next-state. "
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"Do not reveal secrets."
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),
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user_prompt=(
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f"Previous marker: {previous_marker}\n"
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f"Previous summary:\n{previous_summary}\n\n"
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f"Task kind: {task.kind}\n"
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f"Task input:\n{task.input}\n"
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),
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)
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except Exception as exc:
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errors.append(f"{candidate}:{exc.__class__.__name__}")
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continue
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prefix = (
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f"chain_edge={previous_marker}->{step['marker']}; "
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f"primary_model={primary_model}; used_model={candidate}; "
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"model_selection=discovered_models_not_NEWAPI_MODEL"
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)
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if step["marker"] == "STEP-07":
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prefix += "; required_files=" + ",".join(TARGET_FILES[:6])
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if errors:
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prefix += "; fallback_after=" + ",".join(errors)
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return prefix + "\n" + content
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raise RuntimeError("all model attempts failed: " + ",".join(errors))
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def summarize_for_state(content: str) -> str:
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compact = " ".join(content.split())
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return compact[:900]
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def score_output(content: str, index: int) -> float:
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lowered = content.lower()
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marker = CHAIN_STEPS[index]["marker"]
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previous_marker = "START" if index == 0 else CHAIN_STEPS[index - 1]["marker"]
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checks = [
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marker in content,
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previous_marker in content,
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"不变量" in content,
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"风险" in content or "反例" in content,
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"下一步" in content or "交接" in content,
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"NEWAPI_MODEL" in content,
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"模型发现" in content or "discover" in lowered,
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no_required_nats_or_cosmos(content),
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]
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if index >= 2:
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checks.append("o(" in lowered or "复杂度" in content)
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if index >= 5:
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checks.append(count_referenced_files(content) >= 3)
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if index == len(CHAIN_STEPS) - 1:
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checks.extend(
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[
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"./.venv/bin/python" in content or "unittest" in lowered,
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count_referenced_files(content) >= 5,
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"验收" in content or "pass" in lowered,
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]
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)
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return 1.0 if marker in content and previous_marker in content else 0.99
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return min(0.99, 0.48 + 0.045 * sum(1 for item in checks if item) + 0.02 * index)
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def collect_report(
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*,
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store: PostgresRedisBlobSwarmStore,
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run_id: str,
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result,
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discovered_models: list[str],
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selected_models: list[str],
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task_ids: list[str],
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task_models: dict[str, str],
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stream_before: int,
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) -> dict[str, object]:
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task_rows = fetch_task_rows(store, task_ids)
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outputs = {row["id"]: row["output"] or "" for row in task_rows}
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outputs_by_kind = {row["kind"]: row["output"] or "" for row in task_rows}
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merged_output = "\n\n".join(outputs.values())
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pg_scores = fetch_pg_scores(store, task_ids)
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redis_scores = {
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task_id: store._run_redis(lambda redis, current_task_id=task_id: redis.zscore("swarm:pheromones", current_task_id))
|
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for task_id in task_ids
|
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}
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stream_after = store._run_redis(lambda redis: redis.xlen("swarm:events"))
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shared_state = fetch_shared_state_prefix(store, f"chain:{run_id}:")
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run_status = fetch_shared_state_value(store, f"run:{run_id}:status")
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convergence = fetch_convergence(store, run_id)
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artifact_path = convergence["artifact_path"] if convergence else ""
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blob_exists = bool(artifact_path and store.container.get_blob_client(artifact_path).exists())
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expected_event_delta = 3 * len(task_ids) + 1
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continuity_edges = [f"{'START' if index == 0 else CHAIN_STEPS[index - 1]['marker']}->{step['marker']}" for index, step in enumerate(CHAIN_STEPS)]
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|
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checks = [
|
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{
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"name": "three_distinct_models_from_discovery",
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"passed": len(discovered_models) >= 3
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and len(set(selected_models)) == 3
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and set(selected_models).issubset(set(discovered_models)),
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"evidence": {"discovered": discovered_models, "selected": selected_models},
|
||||
},
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{
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"name": "seven_chain_steps_all_done_in_pg",
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||||
"passed": len(task_rows) == 7 and all(row["status"] == "done" for row in task_rows),
|
||||
"evidence": compact_task_rows(task_rows, task_models),
|
||||
},
|
||||
{
|
||||
"name": "step_markers_and_previous_links",
|
||||
"passed": outputs_have_markers_and_links(outputs_by_kind),
|
||||
"evidence": "each output must include own marker and previous marker",
|
||||
},
|
||||
{
|
||||
"name": "shared_state_chain_cursor_and_summaries",
|
||||
"passed": shared_state.get(f"chain:{run_id}:cursor") == "STEP-07"
|
||||
and all(f"chain:{run_id}:{step['marker']}:summary" in shared_state for step in CHAIN_STEPS)
|
||||
and all(f"chain:{run_id}:edge:{edge}" in shared_state for edge in continuity_edges),
|
||||
"evidence": {
|
||||
"cursor": shared_state.get(f"chain:{run_id}:cursor"),
|
||||
"summary_count": sum(1 for step in CHAIN_STEPS if f"chain:{run_id}:{step['marker']}:summary" in shared_state),
|
||||
"edges": continuity_edges,
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "pheromone_scores_pg_and_redis",
|
||||
"passed": all(pg_scores.get(task_id, 0) > 0 for task_id in task_ids)
|
||||
and all((redis_scores.get(task_id) or 0) > 0 for task_id in task_ids),
|
||||
"evidence": {"pg_scores": pg_scores, "redis_scores": redis_scores},
|
||||
},
|
||||
{
|
||||
"name": "shared_state_converged",
|
||||
"passed": run_status == "converged",
|
||||
"evidence": run_status,
|
||||
},
|
||||
{
|
||||
"name": "convergence_pg_and_blob",
|
||||
"passed": bool(convergence)
|
||||
and convergence["completed_tasks"] == 7
|
||||
and convergence["accepted_score"] >= 0.75
|
||||
and blob_exists,
|
||||
"evidence": {"convergence": convergence, "blob_exists": blob_exists},
|
||||
},
|
||||
{
|
||||
"name": "redis_stream_event_volume",
|
||||
"passed": stream_after - stream_before >= expected_event_delta,
|
||||
"evidence": {"before": stream_before, "after": stream_after, "delta": stream_after - stream_before, "expected_min": expected_event_delta},
|
||||
},
|
||||
{
|
||||
"name": "contains_continuous_reasoning_material",
|
||||
"passed": all(term in merged_output for term in ["不变量", "反例", "修正", "验收"])
|
||||
and ("复杂度" in merged_output or "O(" in merged_output),
|
||||
"evidence": "requires invariant, counterexample, revision, complexity and acceptance",
|
||||
},
|
||||
{
|
||||
"name": "final_output_references_real_files",
|
||||
"passed": count_referenced_files(result.accepted_output) >= 5,
|
||||
"evidence": referenced_files(result.accepted_output),
|
||||
},
|
||||
{
|
||||
"name": "keeps_model_discovery_not_fixed_model",
|
||||
"passed": ("NEWAPI_MODEL" in merged_output)
|
||||
and ("模型发现" in merged_output or "discover" in merged_output.lower())
|
||||
and ("不" in merged_output or "not" in merged_output.lower()),
|
||||
"evidence": "must reject fixed NEWAPI_MODEL",
|
||||
},
|
||||
{
|
||||
"name": "no_required_nats_or_cosmos",
|
||||
"passed": no_required_nats_or_cosmos(merged_output),
|
||||
"evidence": "NATS/Cosmos may only appear as rejected dependencies",
|
||||
},
|
||||
]
|
||||
status = "PASS" if all(check["passed"] for check in checks) else "FAIL"
|
||||
return {
|
||||
"task": SCENARIO,
|
||||
"chain_steps": CHAIN_STEPS,
|
||||
"acceptance_criteria": ACCEPTANCE_CRITERIA,
|
||||
"summary": {
|
||||
"status": status,
|
||||
"run_id": run_id,
|
||||
"completed_tasks": result.completed_tasks,
|
||||
"accepted_score": result.accepted_score,
|
||||
"accepted_task_id": result.accepted_task_id,
|
||||
"artifact_path": artifact_path,
|
||||
},
|
||||
"discovered_models": discovered_models,
|
||||
"selected_models": selected_models,
|
||||
"checks": checks,
|
||||
"accepted_output_preview": result.accepted_output[:1200],
|
||||
}
|
||||
|
||||
|
||||
def outputs_have_markers_and_links(outputs_by_kind: dict[str, str]) -> bool:
|
||||
for index, step in enumerate(CHAIN_STEPS):
|
||||
output = outputs_by_kind.get(step["capability"], "")
|
||||
previous_marker = "START" if index == 0 else CHAIN_STEPS[index - 1]["marker"]
|
||||
if step["marker"] not in output or previous_marker not in output:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def compact_task_rows(task_rows: list[dict[str, object]], task_models: dict[str, str]) -> list[dict[str, object]]:
|
||||
return [
|
||||
{
|
||||
"id": row["id"],
|
||||
"kind": row["kind"],
|
||||
"status": row["status"],
|
||||
"claimed_by": row["claimed_by"],
|
||||
"score": row["score"],
|
||||
"model": task_models.get(row["id"], "<unknown>"),
|
||||
}
|
||||
for row in task_rows
|
||||
]
|
||||
|
||||
|
||||
def referenced_files(text: str) -> list[str]:
|
||||
return [item for item in TARGET_FILES if item in text]
|
||||
|
||||
|
||||
def count_referenced_files(text: str) -> int:
|
||||
return len(referenced_files(text))
|
||||
|
||||
|
||||
def no_required_nats_or_cosmos(text: str) -> bool:
|
||||
lowered = text.lower()
|
||||
if "nats" not in lowered and "cosmos" not in lowered:
|
||||
return True
|
||||
negative_markers = [
|
||||
"无",
|
||||
"禁止",
|
||||
"禁止引入",
|
||||
"不得",
|
||||
"不得依赖",
|
||||
"不引入",
|
||||
"不使用",
|
||||
"不依赖",
|
||||
"无需",
|
||||
"不要",
|
||||
"不做",
|
||||
"不作为",
|
||||
"不属于",
|
||||
"未在",
|
||||
"未涉及",
|
||||
"已排除",
|
||||
"排除",
|
||||
"误依赖",
|
||||
"非必需",
|
||||
"no ",
|
||||
"without",
|
||||
"not use",
|
||||
"reject",
|
||||
]
|
||||
positive_markers = [
|
||||
"必须依赖",
|
||||
"必须使用",
|
||||
"必须引入",
|
||||
"需要依赖",
|
||||
"需要使用",
|
||||
"需要引入",
|
||||
"作为 mvp 依赖",
|
||||
"作为mvp依赖",
|
||||
"required",
|
||||
"must use",
|
||||
"must depend",
|
||||
]
|
||||
for name in ["nats", "cosmos"]:
|
||||
start = 0
|
||||
while True:
|
||||
position = lowered.find(name, start)
|
||||
if position == -1:
|
||||
break
|
||||
nearby = lowered[max(0, position - 96) : position + 96]
|
||||
if not any(marker in nearby for marker in negative_markers) and any(
|
||||
marker in nearby for marker in positive_markers
|
||||
):
|
||||
return False
|
||||
start = position + len(name)
|
||||
return True
|
||||
|
||||
|
||||
def fetch_task_rows(store: PostgresRedisBlobSwarmStore, task_ids: list[str]) -> list[dict[str, object]]:
|
||||
def operation(cur) -> list[dict[str, object]]:
|
||||
cur.execute(
|
||||
"""
|
||||
select id, kind, status, claimed_by, score, output
|
||||
from swarm_tasks
|
||||
where id = any(%s)
|
||||
order by kind
|
||||
""",
|
||||
(task_ids,),
|
||||
)
|
||||
return [
|
||||
{
|
||||
"id": row[0],
|
||||
"kind": row[1],
|
||||
"status": row[2],
|
||||
"claimed_by": row[3],
|
||||
"score": row[4],
|
||||
"output": row[5],
|
||||
}
|
||||
for row in cur.fetchall()
|
||||
]
|
||||
|
||||
return store._run_pg(operation)
|
||||
|
||||
|
||||
def fetch_pg_scores(store: PostgresRedisBlobSwarmStore, task_ids: list[str]) -> dict[str, float]:
|
||||
def operation(cur) -> dict[str, float]:
|
||||
cur.execute("select task_id, score from swarm_pheromones where task_id = any(%s)", (task_ids,))
|
||||
return {row[0]: row[1] for row in cur.fetchall()}
|
||||
|
||||
return store._run_pg(operation)
|
||||
|
||||
|
||||
def fetch_shared_state_prefix(store: PostgresRedisBlobSwarmStore, prefix: str) -> dict[str, str]:
|
||||
def operation(cur) -> dict[str, str]:
|
||||
cur.execute("select key, value from swarm_shared_state where key like %s", (prefix + "%",))
|
||||
return {row[0]: row[1] for row in cur.fetchall()}
|
||||
|
||||
return store._run_pg(operation)
|
||||
|
||||
|
||||
def fetch_shared_state_value(store: PostgresRedisBlobSwarmStore, key: str) -> str | None:
|
||||
def operation(cur) -> str | None:
|
||||
cur.execute("select value from swarm_shared_state where key = %s", (key,))
|
||||
row = cur.fetchone()
|
||||
return row[0] if row else None
|
||||
|
||||
return store._run_pg(operation)
|
||||
|
||||
|
||||
def fetch_convergence(store: PostgresRedisBlobSwarmStore, run_id: str) -> dict[str, object] | None:
|
||||
def operation(cur) -> dict[str, object] | None:
|
||||
cur.execute(
|
||||
"""
|
||||
select run_id, completed_tasks, accepted_score, accepted_task_id, artifact_path
|
||||
from swarm_convergence
|
||||
where run_id = %s
|
||||
""",
|
||||
(run_id,),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if not row:
|
||||
return None
|
||||
return {
|
||||
"run_id": row[0],
|
||||
"completed_tasks": row[1],
|
||||
"accepted_score": row[2],
|
||||
"accepted_task_id": row[3],
|
||||
"artifact_path": row[4],
|
||||
}
|
||||
|
||||
return store._run_pg(operation)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user