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a4fab68 b2896f3 a4fab68 | 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 | from __future__ import annotations
from difflib import SequenceMatcher
from typing import Any, Optional
from uuid import uuid4
from openenv.core.env_server.interfaces import Environment
from data_loader import build_expected, load_dataset, split_difficulty
from kb import build_knowledge_base
from models import SupportAction, SupportObservation, SupportState
from tasks import grade_task, task_for_difficulty
class CustomerSupportEnvironment(Environment[SupportAction, SupportObservation, SupportState]):
SUPPORTS_CONCURRENT_SESSIONS = True
def __init__(self, csv_path: str = "dataset.csv", max_steps: int = 6):
super().__init__()
self.df = load_dataset(csv_path)
self.splits = split_difficulty(self.df)
self.kb = build_knowledge_base(self.df)
self.max_steps = max_steps
self._episodes = self.df.reset_index(drop=True)
self._cursor = 0
self._expected: dict[str, Any] | None = None
self._history: list[dict[str, Any]] = []
self._difficulty_filter: Optional[str] = None
self._final_score = 0.0
self._done = False
self._task_id = ""
self._state = SupportState(episode_id=str(uuid4()), step_count=0)
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
difficulty: Optional[str] = None,
index: Optional[int] = None,
**kwargs: Any,
) -> SupportObservation:
del seed, kwargs
if difficulty is not None:
normalized = difficulty.strip().lower()
if normalized not in self.splits:
raise ValueError(f"Unknown difficulty: {difficulty}")
self._difficulty_filter = normalized
self._episodes = self.splits[normalized].reset_index(drop=True)
elif self._difficulty_filter is None:
self._episodes = self.df.reset_index(drop=True)
if len(self._episodes) == 0:
raise ValueError("No episodes found for the requested filter")
if index is None:
self._cursor = self._cursor % len(self._episodes)
else:
self._cursor = int(index) % len(self._episodes)
row = self._episodes.iloc[self._cursor]
self._cursor = (self._cursor + 1) % len(self._episodes)
self._expected = build_expected(row)
self._task_id = task_for_difficulty(self._expected["difficulty"]).task_id
self._history = []
self._done = False
self._final_score = 0.0
self._state = SupportState(
episode_id=episode_id or str(uuid4()),
step_count=0,
difficulty_filter=self._difficulty_filter,
current_index=self._cursor,
task_id=self._task_id,
score_so_far=0.0,
final_score=0.0,
done=False,
)
return self._make_observation(reward=0.0, done=False, feedback="Environment reset")
def step(
self,
action: SupportAction,
timeout_s: Optional[float] = None,
**kwargs: Any,
) -> SupportObservation:
del timeout_s, kwargs
if self._expected is None:
# HTTP mode can be stateless across requests; initialize lazily.
self.reset()
if self._done:
return self._make_observation(reward=0.0, done=True, feedback="Episode already done")
self._state.step_count += 1
reward = -0.01 * self._state.step_count
feedback_parts: list[str] = ["time_penalty"]
if action.action_type == "classify":
if _norm(action.content) == _norm(self._expected["expected_category"]):
reward += 0.3
feedback_parts.append("classification_correct")
else:
reward -= 0.1
feedback_parts.append("classification_incorrect")
elif action.action_type == "search_kb":
if _norm(action.content) == _norm(self._expected["kb_id"]):
reward += 0.2
feedback_parts.append("kb_match")
else:
reward -= 0.05
feedback_parts.append("kb_mismatch")
elif action.action_type == "respond":
similarity = SequenceMatcher(
None,
_norm(action.content),
_norm(self._expected["expected_response"]),
).ratio()
reward += 0.45 * similarity
if _is_polite(action.content):
reward += 0.1
feedback_parts.append("polite")
if similarity < 0.25:
reward -= 0.2
feedback_parts.append("hallucination_risk")
self._done = True
feedback_parts.append("terminal_respond")
elif action.action_type == "escalate":
if bool(self._expected["requires_escalation"]):
reward += 0.3
feedback_parts.append("escalation_correct")
else:
reward -= 0.2
feedback_parts.append("unnecessary_escalation")
self._done = True
feedback_parts.append("terminal_escalate")
if self._state.step_count >= self.max_steps:
self._done = True
feedback_parts.append("max_steps")
event = {
"step": self._state.step_count,
"action_type": action.action_type,
"content": action.content,
"reward": round(reward, 4),
}
self._history.append(event)
if self._done:
self._final_score = grade_task(self._task_id, self._expected, self._history)
self._state.final_score = self._final_score
self._state.score_so_far = max(0.0, min(1.0, self._state.score_so_far + max(reward, 0.0) / 2.0))
self._state.done = self._done
return self._make_observation(
reward=round(reward, 4),
done=self._done,
feedback=",".join(feedback_parts),
)
@property
def state(self) -> SupportState:
return self._state
def _make_observation(self, reward: float, done: bool, feedback: str) -> SupportObservation:
if self._expected is None:
raise RuntimeError("Environment is not initialized")
return SupportObservation(
ticket_id=self._expected["ticket_id"],
task_id=self._task_id,
difficulty=self._expected["difficulty"],
query=self._expected["query"],
kb_id=self._expected["kb_id"],
requires_escalation=self._expected["requires_escalation"],
history=list(self._history),
done=done,
reward=reward,
feedback=feedback,
metadata={
"expected_category": self._expected["expected_category"],
"expected_action": self._expected["expected_action"],
"expected_response": self._expected["expected_response"],
"kb_id": self._expected["kb_id"],
"requires_escalation": self._expected["requires_escalation"],
"final_score": self._final_score,
},
)
def _norm(text: str) -> str:
return " ".join(str(text).strip().lower().split())
def _is_polite(text: str) -> bool:
lowered = _norm(text)
return any(token in lowered for token in ("thank", "please", "assist", "apolog")) |