Spaces:
Sleeping
Sleeping
File size: 8,132 Bytes
bd67155 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Dict, List
ROOT = Path(__file__).resolve().parent.parent
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from support_ops_env.env import SupportOpsEnv
from support_ops_env.models import Action, BaselineResult, Observation, TicketObservation
from support_ops_env.tasks import list_task_ids
CONTEXT_PRIORITY = [
"account_security",
"billing_activity",
"tax_status",
"payout_hold",
"appeal_state",
"campaign_deadline",
"payment_status",
]
def choose_next_action(observation: Observation) -> Action:
if observation.queue_mode and not observation.current_queue_order:
ranking = rank_tickets(observation.tickets)
return Action(action_type="rank_queue", value=",".join(ranking))
for ticket in observation.tickets:
next_context = missing_high_value_context(ticket)
if next_context:
return Action(action_type="request_context", target=ticket.ticket_id, value=next_context)
for ticket in observation.tickets:
priority = infer_priority(ticket)
if ticket.selected_priority != priority:
return Action(action_type="set_priority", target=ticket.ticket_id, value=priority)
for ticket in observation.tickets:
route = infer_route(ticket)
if ticket.selected_route != route:
return Action(action_type="set_route", target=ticket.ticket_id, value=route)
for ticket in observation.tickets:
resolution = infer_resolution(ticket)
if ticket.selected_resolution != resolution:
return Action(action_type="set_resolution", target=ticket.ticket_id, value=resolution)
for ticket in observation.tickets:
escalation = infer_escalation(ticket)
if ticket.escalation_team != escalation:
return Action(action_type="escalate", target=ticket.ticket_id, value=escalation)
return Action(action_type="finalize")
def missing_high_value_context(ticket: TicketObservation) -> str | None:
discovered = set(ticket.discovered_context)
haystack = flattened_text(ticket)
candidates: List[str] = infer_required_context(ticket)
for key in CONTEXT_PRIORITY:
if key in candidates and key not in discovered:
return key
return None
def infer_required_context(ticket: TicketObservation) -> List[str]:
text = flattened_text(ticket)
if "payout" in text or "w-9" in text or "bank details" in text or "funds released" in text:
return ["tax_status", "payout_hold"]
if "appeal" in text or "auto-removed" in text or "monetization is paused" in text:
return ["appeal_state", "campaign_deadline"]
if "duplicate charge" in text or "refund" in text:
return ["payment_status"]
if (
"login" in text
or "ad spend" in text
or "unfamiliar campaigns" in text
or "taken over" in text
or "recovery email was changed" in text
):
return ["account_security", "billing_activity"]
return []
def infer_priority(ticket: TicketObservation) -> str:
text = flattened_text(ticket)
if (
"critical" in text
or "$1,900" in text
or "unauthorized ad spend" in text
or "impossible travel" in text
or "recovery email was changed" in text
):
return "urgent"
if "campaign begins in 18 hours" in text or "monetization is paused" in text:
return "high"
if "w-9 expired" in text or "monthly payout" in text:
return "high"
return "normal"
def infer_route(ticket: TicketObservation) -> str:
text = flattened_text(ticket)
if (
"account takeover" in text
or "new devices" in text
or "recovery email was changed" in text
or "unfamiliar campaigns" in text
or "unauthorized ad spend" in text
or "losing access" in text
):
return "account_security"
if "w-9 expired" in text or "compliance hold" in text:
return "monetization_compliance"
if "auto-removed" in text or "human yet" in text:
return "policy_appeals"
if "duplicate charge" in text or "automatically refundable" in text:
return "billing_refunds"
return "general_support"
def infer_resolution(ticket: TicketObservation) -> str:
text = flattened_text(ticket)
if (
"account takeover" in text
or "new devices" in text
or "impossible travel" in text
or "unfamiliar campaigns" in text
or "losing access" in text
):
return "temporary_lock_and_manual_recovery"
if "w-9 expired" in text or "compliance hold" in text:
return "request_tax_renewal"
if "auto-removed" in text or "sponsored campaign begins" in text:
return "expedited_human_review"
if "duplicate charge" in text or "automatically refundable" in text:
return "approve_refund"
return "request_more_info"
def infer_escalation(ticket: TicketObservation) -> str | None:
text = flattened_text(ticket)
if (
"account takeover" in text
or "critical" in text
or "impossible travel" in text
or "unfamiliar campaigns" in text
or "losing access" in text
):
return "security_specialist"
return None
def rank_tickets(tickets: List[TicketObservation]) -> List[str]:
scored = []
for ticket in tickets:
text = flattened_text(ticket)
score = 0
if "critical" in text or "account takeover" in text or "$1,900" in text or "unfamiliar campaigns" in text:
score += 100
if "campaign begins in 18 hours" in text or "sponsored campaign" in text:
score += 60
if "duplicate charge" in text:
score += 20
if ticket.visible_context.get("sla_hours_remaining") == "1":
score += 30
if ticket.visible_context.get("sla_hours_remaining") == "4":
score += 10
scored.append((score, ticket.ticket_id))
scored.sort(key=lambda item: (-item[0], item[1]))
return [ticket_id for _, ticket_id in scored]
def flattened_text(ticket: TicketObservation) -> str:
parts = [
ticket.summary,
json.dumps(ticket.visible_context, sort_keys=True),
json.dumps(ticket.discovered_context, sort_keys=True),
]
return " ".join(parts).lower()
def main() -> None:
parser = argparse.ArgumentParser(description="Run a deterministic rule-based baseline over all tasks.")
parser.add_argument("--output", default="rule_baseline_results.json", help="Path to write JSON results")
args = parser.parse_args()
results: List[BaselineResult] = []
for task_id in list_task_ids():
env = SupportOpsEnv(task_id=task_id)
observation = env.reset()
done = False
transcript: List[Dict[str, object]] = []
last_info: Dict[str, object] = {}
while not done:
action = choose_next_action(observation)
observation, reward, done, info = env.step(action)
transcript.append(
{
"action": action.model_dump(),
"reward": reward.model_dump(),
"task_score": info["task_score"],
"done": done,
}
)
last_info = info
results.append(
BaselineResult(
task_id=task_id,
difficulty=observation.difficulty,
score=float(last_info.get("task_score", 0.0)),
steps=int(last_info.get("step_count", 0)),
transcript=transcript,
)
)
payload = {
"baseline": "rule_based",
"average_score": round(sum(item.score for item in results) / len(results), 4),
"results": [item.model_dump() for item in results],
}
output_path = Path(args.output)
output_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
print(json.dumps(payload, indent=2))
if __name__ == "__main__":
main()
|