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from __future__ import annotations
import argparse
import json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any
from openai import OpenAI
from support_triage_openenv import Action, SupportTriageEnv
SYSTEM_PROMPT = """You are an agent solving a customer-support triage environment.
Return exactly one JSON object for the next action with keys:
- action_type: read_ticket | classify_ticket | draft_reply | resolve_ticket
- ticket_id (required for read/classify/resolve)
- priority, category, needs_escalation (for classify)
- message (for draft_reply)
No markdown, no extra text."""
@dataclass
class EpisodeResult:
task_id: str
steps: int
grader_score: float
reward: float
done_reason: str
RULE_POLICY: dict[str, list[dict[str, Any]]] = {
"easy_password_reset": [
{"action_type": "read_ticket", "ticket_id": "T-1001"},
{
"action_type": "classify_ticket",
"ticket_id": "T-1001",
"priority": "medium",
"category": "account",
"needs_escalation": False,
},
{
"action_type": "draft_reply",
"message": (
"We will send a reset link to your email. For security, confirm the request "
"from your registered email before using the reset link."
),
},
{"action_type": "resolve_ticket", "ticket_id": "T-1001"},
],
"medium_billing_dispute": [
{"action_type": "read_ticket", "ticket_id": "T-2001"},
{"action_type": "read_ticket", "ticket_id": "T-2002"},
{
"action_type": "classify_ticket",
"ticket_id": "T-2001",
"priority": "high",
"category": "billing",
"needs_escalation": False,
},
{
"action_type": "draft_reply",
"message": (
"We confirmed a duplicate charge. We are issuing a refund and will share the invoice update. "
"Refund processing typically takes 3-5 business days."
),
},
{"action_type": "resolve_ticket", "ticket_id": "T-2001"},
],
"hard_outage_incident": [
{"action_type": "read_ticket", "ticket_id": "T-3001"},
{"action_type": "read_ticket", "ticket_id": "T-3002"},
{"action_type": "read_ticket", "ticket_id": "T-3003"},
{
"action_type": "classify_ticket",
"ticket_id": "T-3001",
"priority": "urgent",
"category": "technical",
"needs_escalation": True,
},
{
"action_type": "draft_reply",
"message": (
"We have escalated this incident and are investigating now. "
"The status page will carry updates while we continue incident response."
),
},
{"action_type": "resolve_ticket", "ticket_id": "T-3001"},
],
"easy_trial_extension": [
{"action_type": "read_ticket", "ticket_id": "T-4001"},
{
"action_type": "classify_ticket",
"ticket_id": "T-4001",
"priority": "low",
"category": "general",
"needs_escalation": False,
},
{
"action_type": "draft_reply",
"message": (
"We can review a trial extension based on eligibility. "
"Please check billing settings before the next renewal so the account stays aligned."
),
},
{"action_type": "resolve_ticket", "ticket_id": "T-4001"},
],
"medium_abuse_phishing": [
{"action_type": "read_ticket", "ticket_id": "T-5001"},
{"action_type": "read_ticket", "ticket_id": "T-5002"},
{
"action_type": "classify_ticket",
"ticket_id": "T-5001",
"priority": "high",
"category": "abuse",
"needs_escalation": True,
},
{
"action_type": "draft_reply",
"message": (
"We are escalating this phishing report to the abuse team. "
"Please preserve evidence such as headers while we review blocked indicators and sender details."
),
},
{"action_type": "resolve_ticket", "ticket_id": "T-5001"},
],
"hard_privacy_deletion": [
{"action_type": "read_ticket", "ticket_id": "T-6001"},
{"action_type": "read_ticket", "ticket_id": "T-6002"},
{"action_type": "read_ticket", "ticket_id": "T-6003"},
{
"action_type": "classify_ticket",
"ticket_id": "T-6001",
"priority": "high",
"category": "account",
"needs_escalation": True,
},
{
"action_type": "draft_reply",
"message": (
"We have routed the data deletion request to the privacy team. "
"Identity verification is required, and completion is normally within 30 days."
),
},
{"action_type": "resolve_ticket", "ticket_id": "T-6001"},
],
}
def _extract_json(text: str) -> str:
text = text.strip()
start = text.find("{")
end = text.rfind("}")
if start == -1 or end == -1 or end <= start:
raise ValueError("No JSON object found in model response")
return text[start : end + 1]
def llm_action(client: OpenAI, model: str, observation: dict[str, Any], state: dict[str, Any]) -> Action:
user_prompt = json.dumps(
{
"observation": observation,
"state": state,
"instruction": "Pick the best next single action to maximize final score.",
},
ensure_ascii=True,
)
response = client.responses.create(
model=model,
temperature=0,
top_p=1,
input=[
{"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
{"role": "user", "content": [{"type": "text", "text": user_prompt}]},
],
)
raw = response.output_text or ""
payload = json.loads(_extract_json(raw))
return Action.model_validate(payload)
def heuristic_action(task_id: str, step_idx: int) -> Action:
plan = RULE_POLICY[task_id]
idx = min(step_idx, len(plan) - 1)
return Action.model_validate(plan[idx])
def run_episode(env: SupportTriageEnv, task_id: str, mode: str, model: str, client: OpenAI | None) -> EpisodeResult:
obs = env.reset(task_id)
done = False
info: dict[str, Any] = {}
reward_value = 0.0
while not done:
step_idx = env.state()["step_count"]
if mode == "heuristic":
action = heuristic_action(task_id, step_idx)
else:
assert client is not None
try:
action = llm_action(client, model, obs.model_dump(), env.state())
except Exception:
# Deterministic fallback keeps run alive for reproducible scoring.
action = heuristic_action(task_id, step_idx)
obs, reward, done, info = env.step(action)
reward_value = reward.value
return EpisodeResult(
task_id=task_id,
steps=env.state()["step_count"],
grader_score=float(info["grader_score"]),
reward=reward_value,
done_reason=str(info["done_reason"]),
)
def main() -> None:
parser = argparse.ArgumentParser(description="Run baseline on support-triage-openenv tasks.")
parser.add_argument("--mode", choices=["openai", "heuristic"], default="openai")
parser.add_argument("--model", default="gpt-4.1-mini")
parser.add_argument("--output", default="scores/baseline_scores.json")
args = parser.parse_args()
client = None
if args.mode == "openai":
if not os.getenv("OPENAI_API_KEY"):
raise RuntimeError("OPENAI_API_KEY is required for --mode openai")
client = OpenAI()
env = SupportTriageEnv()
results = [run_episode(env, t, args.mode, args.model, client) for t in env.task_ids]
summary = {
"mode": args.mode,
"model": args.model,
"avg_grader_score": round(sum(r.grader_score for r in results) / len(results), 4),
"avg_final_reward": round(sum(r.reward for r in results) / len(results), 4),
"episodes": [asdict(r) for r in results],
}
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(summary, indent=2), encoding="utf-8")
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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