Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / scripts/quantum_hyperloop.py |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 5cd0bbf744b0aee9445913337e852889d9a8098e |
| Branch | codex/build-online-github-ani-7v4kug |
| Path or archive member | scripts/quantum_hyperloop.py |
| Original SHA-256 | 28117e2d107afd19fc805e4ac4ced358dec350e53062d5fc4ecea44d612ec76d |
| Git blob SHA | 804fc7a4e5438605cca5504095380c00b78d781f |
| Reading-copy SHA-256 | 28117e2d107afd19fc805e4ac4ced358dec350e53062d5fc4ecea44d612ec76d |
Recovered source transcript
Download complete source text ↓122 lines · 3.5 KB
"""CLI utility to execute large-scale quantum stress harnesses.
The command bootstraps the marketing intelligence engine, derives a quantum
stress plan for a given prompt, and then uses the deployment harness to
simulate repeated node deployments. It is designed for deterministic runs but
supports Monte Carlo sampling when the operator wants additional variance.
"""
from __future__ import annotations
import argparse
import json
from typing import Any
from services.quantum_harness import QuantumDeploymentHarness
from services.seeder_api.intelligence import (
MarketingIntelligenceEngine,
MiniQuantumSimulator,
QuantumSimulationProcess,
)
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Quantum deployment stress harness")
parser.add_argument("prompt", help="Marketing or revenue prompt to evaluate")
parser.add_argument(
"--campaign-limit",
type=int,
default=3,
help="Number of ranked campaigns to feed into the quantum planner",
)
parser.add_argument(
"--plan-limit",
type=int,
default=1,
help="Number of quantum plans to evaluate",
)
parser.add_argument(
"--nodes",
type=int,
default=8,
help="Virtual node count for deployment simulation",
)
parser.add_argument(
"--agents",
type=int,
default=1_000_000,
help="Target agents per rollout scenario",
)
parser.add_argument(
"--iterations",
type=int,
default=3,
help="Number of harness passes to execute",
)
parser.add_argument(
"--shots",
type=int,
default=2048,
help="Shot count passed to the simulator (if supported)",
)
parser.add_argument(
"--monte-carlo",
action="store_true",
help="Enable Monte Carlo sampling instead of deterministic amplitudes",
)
parser.add_argument(
"--pretty",
action="store_true",
help="Pretty-print the JSON response",
)
return parser
def main(argv: list[str] | None = None) -> int:
parser = build_parser()
args = parser.parse_args(argv)
engine = MarketingIntelligenceEngine.from_file()
ranked = engine.recommend_statistical(args.prompt, limit=max(1, args.campaign_limit))
simulator = MiniQuantumSimulator(shots=max(1, args.shots))
process = QuantumSimulationProcess(simulator=simulator)
plans = process.plans(args.prompt, ranked, limit=max(1, args.plan_limit))
if not plans:
parser.error("No quantum plan available for the supplied prompt")
harness = QuantumDeploymentHarness(simulator=simulator)
payload: dict[str, Any] = {
"prompt": args.prompt,
"campaign_limit": args.campaign_limit,
"plan_limit": args.plan_limit,
"nodes": args.nodes,
"target_agents": args.agents,
"iterations": args.iterations,
"monte_carlo": args.monte_carlo,
"plans": [],
}
for plan in plans:
harness_result = harness.stress(
plan,
nodes=max(1, args.nodes),
target_agents=max(1, args.agents),
iterations=max(1, args.iterations),
shots=max(1, args.shots),
monte_carlo=args.monte_carlo,
)
payload["plans"].append(harness_result)
if args.pretty:
print(json.dumps(payload, indent=2, sort_keys=True))
else:
print(json.dumps(payload))
return 0
if __name__ == "__main__": # pragma: no cover - CLI entry point
raise SystemExit(main())