chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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"""
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Show how sandbox agents can be exposed as tools to a normal orchestrator.
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Each sandbox reviewer gets its own isolated workspace. The outer orchestrator
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does not inspect files directly. It calls the reviewers as tools and combines
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their outputs with a normal Python function tool.
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"""
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import argparse
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import asyncio
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import json
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import sys
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from pathlib import Path
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from typing import Literal
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from openai.types.shared import Reasoning
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from pydantic import BaseModel, Field
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from agents import Agent, ModelSettings, Runner, function_tool
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from agents.run import RunConfig
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from agents.sandbox import SandboxAgent, SandboxRunConfig
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from agents.sandbox.sandboxes.unix_local import UnixLocalSandboxClient
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if __package__ is None or __package__ == "":
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sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
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from examples.sandbox.misc.example_support import text_manifest, tool_call_name
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from examples.sandbox.misc.workspace_shell import WorkspaceShellCapability
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DEFAULT_QUESTION = (
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"Review the Acme renewal materials and give me a short recommendation for the deal desk. "
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"Include pricing risk, rollout risk, and the most important next step."
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)
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class PricingPacketReview(BaseModel):
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requested_discount_percent: int = Field(
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description="Exact requested discount percentage from pricing_summary.md."
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)
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requested_term_months: int = Field(
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description="Exact requested renewal term in months from pricing_summary.md."
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)
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pricing_risk: Literal["low", "medium", "high"]
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summary: str = Field(description="Short pricing risk summary grounded in the reviewed files.")
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recommended_next_step: str = Field(
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description="Most important commercial next step for the deal desk."
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)
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evidence_files: list[str] = Field(
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description="File names that support the review.", min_length=1
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)
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class RolloutRiskReview(BaseModel):
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rollout_risk: Literal["low", "medium", "high"]
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summary: str = Field(description="Short rollout risk summary grounded in the reviewed files.")
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blockers: list[str] = Field(description="Concrete rollout blockers from the reviewed files.")
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recommended_next_step: str = Field(
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description="Most important delivery next step for the deal desk."
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)
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evidence_files: list[str] = Field(
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description="File names that support the review.", min_length=1
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)
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async def _structured_tool_output_extractor(result) -> str:
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final_output = result.final_output
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if isinstance(final_output, BaseModel):
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return json.dumps(final_output.model_dump(mode="json"), sort_keys=True)
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return str(final_output)
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@function_tool
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def get_discount_approval_rule(discount_percent: int) -> str:
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"""Return the internal approver required for a proposed discount."""
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if discount_percent <= 10:
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return "Discounts up to 10 percent can be approved by the account executive."
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if discount_percent <= 15:
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return "Discounts from 11 to 15 percent require regional sales director approval."
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return "Discounts above 15 percent require finance and regional sales director approval."
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async def main(model: str, question: str) -> None:
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# This manifest is visible only to the pricing reviewer.
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pricing_manifest = text_manifest(
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{
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"pricing_summary.md": (
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"# Pricing summary\n\n"
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"- Current annual contract: $220,000.\n"
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"- Requested renewal term: 24 months.\n"
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"- Requested discount: 15 percent.\n"
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"- Account executive target discount band: 8 to 10 percent.\n"
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),
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"commercial_notes.md": (
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"# Commercial notes\n\n"
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"- The customer expanded from 120 to 170 paid seats in the last 6 months.\n"
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"- Procurement asked for one final concession to close before quarter end.\n"
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),
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}
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)
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# This separate manifest is visible only to the rollout reviewer.
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rollout_manifest = text_manifest(
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{
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"rollout_plan.md": (
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"# Rollout plan\n\n"
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"- Customer wants a 30-day rollout for three new regional teams.\n"
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"- Regional admins have not completed training yet.\n"
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"- SSO migration is scheduled for the second week of the rollout.\n"
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),
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"support_history.md": (
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"# Support history\n\n"
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"- Two high-priority onboarding tickets were closed in the last quarter.\n"
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"- No open production incidents.\n"
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"- Customer success manager asked for a phased launch if the contract closes.\n"
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),
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}
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)
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pricing_agent = SandboxAgent(
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name="Pricing Packet Reviewer",
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model=model,
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instructions=(
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"You inspect renewal pricing documents and return a structured commercial review. "
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"Inspect the files before answering and extract the exact requested discount percent "
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"and renewal term from pricing_summary.md. "
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"Use the shell tool before answering. requested_discount_percent must match the exact "
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"integer in pricing_summary.md. requested_term_months must match the exact renewal "
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"term from pricing_summary.md. Do not introduce any facts, incidents, or numbers that "
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"are not present in pricing_summary.md or commercial_notes.md. evidence_files must "
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"list only files you actually inspected."
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),
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default_manifest=pricing_manifest,
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capabilities=[WorkspaceShellCapability()],
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model_settings=ModelSettings(tool_choice="required", reasoning=Reasoning(effort="none")),
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output_type=PricingPacketReview,
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)
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rollout_agent = SandboxAgent(
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name="Rollout Risk Reviewer",
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model=model,
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instructions=(
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"You inspect rollout plans and return a structured delivery review. Inspect the files "
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"before answering and keep the output tightly grounded in the rollout documents. "
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"Use the shell tool before answering. blockers must only contain issues that appear in "
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"rollout_plan.md or support_history.md. Do not introduce any extra numbers, incidents, "
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"or stakeholders beyond those files. evidence_files must list only files you actually "
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"inspected."
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),
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default_manifest=rollout_manifest,
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capabilities=[WorkspaceShellCapability()],
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model_settings=ModelSettings(tool_choice="required", reasoning=Reasoning(effort="none")),
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output_type=RolloutRiskReview,
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)
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# Each sandbox-backed tool gets its own run configuration so the workspaces stay isolated.
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pricing_run_config = RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient()))
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rollout_run_config = RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient()))
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orchestrator = Agent(
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name="Revenue Operations Coordinator",
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model=model,
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instructions=(
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"You coordinate renewal reviews. Before answering, you must use all three tools: "
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"`review_pricing_packet`, `review_rollout_risk`, and `get_discount_approval_rule`. "
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"The review tools return JSON. Use the exact `requested_discount_percent` field from "
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"`review_pricing_packet` when calling `get_discount_approval_rule`. In the final "
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"recommendation, use only facts and numbers that appear in the tool outputs, and do "
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"not add any extra incidents, price points, or contract terms."
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),
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model_settings=ModelSettings(tool_choice="required", reasoning=Reasoning(effort="none")),
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tools=[
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pricing_agent.as_tool(
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tool_name="review_pricing_packet",
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tool_description="Inspect the pricing packet and summarize commercial risk.",
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custom_output_extractor=_structured_tool_output_extractor,
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run_config=pricing_run_config,
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max_turns=6,
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),
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rollout_agent.as_tool(
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tool_name="review_rollout_risk",
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tool_description="Inspect the rollout packet and summarize implementation risk.",
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custom_output_extractor=_structured_tool_output_extractor,
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run_config=rollout_run_config,
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max_turns=6,
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),
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get_discount_approval_rule,
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],
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)
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result = await Runner.run(orchestrator, question, max_turns=8)
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tool_names = [
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tool_call_name(item.raw_item)
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for item in result.new_items
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if getattr(item, "type", None) == "tool_call_item"
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]
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if tool_names:
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print(f"[tools used] {', '.join(tool_names)}")
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print(result.final_output)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default="gpt-5.6-sol", help="Model name to use.")
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parser.add_argument("--question", default=DEFAULT_QUESTION, help="Prompt to send to the agent.")
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args = parser.parse_args()
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asyncio.run(main(args.model, args.question))
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