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1395b2e | 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 | #!/usr/bin/env python3
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"},
],
}
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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