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dab441f b9ad6f9 dab441f b9ad6f9 dab441f b9ad6f9 dab441f b9ad6f9 dab441f b9ad6f9 dab441f b9ad6f9 dab441f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 | """Supermail OpenEnv environment implementation."""
from __future__ import annotations
import json
from dataclasses import dataclass
from uuid import uuid4
try:
from openenv.core.env_server.interfaces import Environment
except ImportError: # pragma: no cover - local fallback when OpenEnv is absent
class Environment:
"""Fallback OpenEnv Environment base class."""
try:
from ..models import SupportAction, SupportObservation, SupportState
from ..tasks import ALL_TASKS, FIELD_OPTIONS, TASKS_BY_ID, TaskDefinition
except ImportError: # pragma: no cover
from models import SupportAction, SupportObservation, SupportState
from tasks import ALL_TASKS, FIELD_OPTIONS, TASKS_BY_ID, TaskDefinition
@dataclass(frozen=True)
class StepAssessment:
"""Internal grading result for one agent action."""
reward: float
score: float
done: bool
success: bool
feedback: str
error: str | None
matched_fields: set[str]
class SupermailEnvironment(Environment):
"""Deterministic customer support email triage environment."""
SUPPORTS_CONCURRENT_SESSIONS: bool = True
MIN_SCORE: float = 0.01
MAX_SCORE: float = 0.99
def __init__(self, task_id: str | None = None):
self._requested_task_id = task_id
self._task_order = [task.task_id for task in ALL_TASKS]
self._next_task_index = 0
self._task: TaskDefinition | None = None
self._matched_fields: set[str] = set()
self._history: list[str] = []
self._score = self._bounded_score(0.0)
self._state = SupportState(
episode_id=str(uuid4()),
step_count=0,
score=self._score,
)
@property
def benchmark(self) -> str:
return "supermail"
@property
def task_name(self) -> str:
if self._task is not None:
return self._task.task_id
if self._requested_task_id:
return self._requested_task_id
return self._task_order[self._next_task_index % len(self._task_order)]
def reset(self) -> SupportObservation:
"""Start a fresh episode."""
self._task = self._select_task()
self._matched_fields = set()
self._history = []
self._score = self._bounded_score(0.0)
self._state = SupportState(
episode_id=str(uuid4()),
step_count=0,
task_id=self._task.task_id,
difficulty=self._task.difficulty,
score=self._score,
matched_fields=[],
attempts_remaining=self._task.max_attempts,
)
return self._build_observation(
feedback=(
f"{self._task.guidance} Required fields: "
f"{', '.join(self._task.required_fields)}."
),
reward=0.0,
done=False,
last_action_error=None,
success=False,
)
def step(self, action: SupportAction) -> SupportObservation: # type: ignore[override]
"""Grade one classification attempt and return the next observation."""
if self._task is None:
raise RuntimeError("Call reset() before step().")
self._state.step_count += 1
decision = self._extract_decision(action)
assessment = self._assess(decision)
self._matched_fields = assessment.matched_fields
self._score = assessment.score
self._state.score = assessment.score
self._state.matched_fields = sorted(self._matched_fields)
self._state.attempts_remaining = max(
self._task.max_attempts - self._state.step_count,
0,
)
compact_decision = json.dumps(decision, sort_keys=True)
self._history.append(
"step="
f"{self._state.step_count} decision={compact_decision} "
f"reward={assessment.reward:.2f} score={assessment.score:.2f} "
f"feedback={assessment.feedback}"
)
return self._build_observation(
feedback=assessment.feedback,
reward=assessment.reward,
done=assessment.done,
last_action_error=assessment.error,
success=assessment.success,
)
@property
def state(self) -> SupportState:
"""Return the current environment state."""
return self._state
def close(self) -> None:
"""No-op close hook for API symmetry."""
def _select_task(self) -> TaskDefinition:
if self._requested_task_id:
return TASKS_BY_ID[self._requested_task_id]
task_id = self._task_order[self._next_task_index % len(self._task_order)]
self._next_task_index += 1
return TASKS_BY_ID[task_id]
def _extract_decision(self, action: SupportAction) -> dict[str, str]:
decision: dict[str, str] = {}
for field_name in ("priority", "category", "action"):
value = getattr(action, field_name, None)
if value:
decision[field_name] = value
return decision
def _bounded_score(self, raw_score: float) -> float:
"""Map raw progress into the open interval (0, 1)."""
clamped_raw_score = min(max(raw_score, 0.0), 1.0)
scaled_score = self.MIN_SCORE + (
clamped_raw_score * (self.MAX_SCORE - self.MIN_SCORE)
)
return round(scaled_score, 2)
def _assess(self, decision: dict[str, str]) -> StepAssessment:
if self._task is None:
raise RuntimeError("Task not initialized.")
if not decision:
return StepAssessment(
reward=-0.10,
score=round(self._score, 2),
done=self._state.step_count >= self._task.max_attempts,
success=False,
feedback=(
"No decision fields were submitted. Provide "
+ ", ".join(self._task.required_fields)
+ "."
),
error="empty_action",
matched_fields=set(self._matched_fields),
)
matched_fields = set(self._matched_fields)
newly_matched: list[str] = []
mismatched_fields: list[str] = []
for field_name in self._task.required_fields:
predicted = decision.get(field_name)
if predicted is None:
continue
if predicted == self._task.expected[field_name]:
if field_name not in matched_fields:
newly_matched.append(field_name)
matched_fields.add(field_name)
else:
mismatched_fields.append(field_name)
reward = sum(self._task.field_weights[field] for field in newly_matched)
if mismatched_fields and not newly_matched:
reward -= 0.10
elif not newly_matched and not mismatched_fields:
reward -= 0.02
if self._state.step_count > 3 and matched_fields != set(self._task.required_fields):
reward -= 0.05
raw_score = sum(self._task.field_weights[field] for field in matched_fields)
score = self._bounded_score(raw_score)
success = matched_fields == set(self._task.required_fields)
done = success or self._state.step_count >= self._task.max_attempts
feedback_parts: list[str] = []
if newly_matched:
feedback_parts.append("Matched " + ", ".join(newly_matched) + ".")
if mismatched_fields:
feedback_parts.append("Incorrect " + ", ".join(mismatched_fields) + ".")
remaining_fields = [
field for field in self._task.required_fields if field not in matched_fields
]
if success:
feedback_parts.append("All required fields are correct.")
elif remaining_fields:
feedback_parts.append("Still need " + ", ".join(remaining_fields) + ".")
if done and not success:
feedback_parts.append("Max attempts reached.")
if not feedback_parts:
feedback_parts.append("No new progress.")
return StepAssessment(
reward=round(reward, 2),
score=score,
done=done,
success=success,
feedback=" ".join(feedback_parts),
error=None,
matched_fields=matched_fields,
)
def _build_observation(
self,
*,
feedback: str,
reward: float,
done: bool,
last_action_error: str | None,
success: bool,
) -> SupportObservation:
if self._task is None:
raise RuntimeError("Task not initialized.")
required_allowed_values = {
field_name: FIELD_OPTIONS[field_name]
for field_name in self._task.required_fields
}
return SupportObservation(
task_id=self._task.task_id,
task_type=self._task.difficulty,
benchmark=self._task.benchmark,
objective=self._task.objective,
email=self._task.email,
context=dict(self._task.context),
required_fields=list(self._task.required_fields),
allowed_values=required_allowed_values,
history=list(self._history),
feedback=feedback,
score=round(self._score, 2),
attempts_remaining=max(
self._task.max_attempts - self._state.step_count,
0,
),
done=done,
reward=round(reward, 2),
metadata={
"last_action_error": last_action_error,
"success": success,
"score": round(self._score, 2),
"matched_fields": sorted(self._matched_fields),
},
)
SupportSimEnvironment = SupermailEnvironment
|