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import random
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from ..graders.category_grader import grade_classification, grade_information_collection
from ..graders.faq_grader import (
grade_escalation,
grade_faq_retrieval,
grade_operation_choice,
)
from ..graders.resolution_grader import grade_case_closure, grade_resolution
from ..graders.score_utils import ensure_open_unit_interval
from ..models import Action, Observation, Reward, TicketState
from ..user_simulator import UserSimulator
def _data_dir() -> Path:
return Path(__file__).resolve().parent.parent / "data"
class HelpdeskEnv:
def __init__(self):
data_dir = _data_dir()
tickets_dir = data_dir / "tickets"
with open(data_dir / "knowledge_base.json", "r", encoding="utf-8") as f:
self.kb: List[Dict[str, str]] = json.load(f)
with open(tickets_dir / "easy.json", "r", encoding="utf-8") as f:
self.easy_tickets: List[Dict[str, Any]] = json.load(f)
with open(tickets_dir / "medium.json", "r", encoding="utf-8") as f:
self.medium_tickets: List[Dict[str, Any]] = json.load(f)
with open(tickets_dir / "hard.json", "r", encoding="utf-8") as f:
self.hard_tickets: List[Dict[str, Any]] = json.load(f)
self.current_ticket: Optional[Dict[str, Any]] = None
self.ticket_state: Optional[TicketState] = None
self.user_sim: Optional[UserSimulator] = None
self.task_id: str = "easy"
self.turn_number: int = 0
self.conversation_history: List[Dict[str, str]] = []
self.action_history: List[str] = []
def reset(self, task_id: str = "easy") -> Observation:
pool_map = {
"easy": self.easy_tickets,
"medium": self.medium_tickets,
"hard": self.hard_tickets,
}
if task_id not in pool_map:
raise ValueError("task_id must be one of: easy, medium, hard")
self.task_id = task_id
self.current_ticket = random.choice(pool_map[task_id])
self.ticket_state = TicketState(
ticket_id=self.current_ticket["id"],
track=self._infer_track(self.current_ticket),
required_slots=self._required_slots(self.current_ticket, task_id),
)
self.user_sim = UserSimulator(self.current_ticket) if task_id == "hard" else None
self.turn_number = 0
self.conversation_history = []
self.action_history = []
return self.state()
def step(self, action: Action) -> Tuple[Observation, Reward, bool, Dict[str, Any]]:
if self.current_ticket is None or self.ticket_state is None:
raise RuntimeError("Environment not initialized. Call reset() first.")
current_ticket = self.current_ticket
ticket_state = self.ticket_state
canonical_action = action
self.turn_number += 1
ticket_state.turns_used += 1
self.action_history.append(canonical_action.action_type)
self._track_collected_slots(canonical_action)
action_content = (
canonical_action.message
or canonical_action.operation
or canonical_action.target
or canonical_action.action_type
)
self.conversation_history.append({"role": "agent", "content": action_content})
done = False
metrics: Dict[str, float] = {
"correctness": 0.0,
"safety": 1.0,
"resolution": 0.0,
"efficiency": 0.0,
"penalties": 0.0,
}
info: Dict[str, Any] = {
"action_type": canonical_action.action_type,
"operation": canonical_action.operation,
"target": canonical_action.target,
}
if canonical_action.action_type == "ask_for_details":
metrics["correctness"] = self._grade_detail_request(canonical_action)
if self.task_id == "hard" and self.user_sim is not None:
user_response = self.user_sim.respond(canonical_action.message or "")
self.conversation_history.append({"role": "user", "content": user_response})
ticket_state.clarification_received = self.user_sim.clarification_given
info["user_response"] = user_response
elif canonical_action.action_type == "take_action":
correctness, resolved = self._grade_take_action(canonical_action)
metrics["correctness"] = correctness
ticket_state.issue_resolved = resolved
if resolved:
metrics["resolution"] = grade_resolution(ticket_state)
done = True
elif canonical_action.action_type == "respond_to_user":
metrics["correctness"] = self._grade_response(canonical_action)
if self.task_id == "hard" and self.user_sim is not None:
user_response = self.user_sim.respond(canonical_action.message or "")
self.conversation_history.append({"role": "user", "content": user_response})
ticket_state.issue_resolved = self.user_sim.confirm_resolved()
info["user_response"] = user_response
elif canonical_action.action_type == "escalate_case":
metrics["correctness"] = grade_escalation(
True,
bool(current_ticket.get("should_escalate", False)),
)
ticket_state.escalated = True
metrics["resolution"] = metrics["correctness"]
info["escalation_accuracy"] = metrics["correctness"]
done = True
elif canonical_action.action_type == "close_case":
if self.task_id == "hard" and self.user_sim is not None:
ticket_state.issue_resolved = self.user_sim.confirm_resolved()
metrics["resolution"] = grade_case_closure(ticket_state)
if metrics["resolution"] <= 0.001 and not ticket_state.escalated:
metrics["penalties"] -= 0.20
done = True
metrics["safety"] = self._grade_safety(canonical_action, metrics)
metrics["efficiency"] = self._grade_efficiency(done)
reward = self._calculate_reward(metrics, done=done)
info.update(
{
"ticket_id": ticket_state.ticket_id,
"task_id": self.task_id,
"track": ticket_state.track,
"turn_number": self.turn_number,
}
)
return self.state(), reward, done, info
def _infer_track(self, ticket: Dict[str, Any]) -> str:
category = (
ticket.get("issue_category")
or ticket.get("gold_category")
or ticket.get("difficulty")
or self.task_id
)
return str(category).strip().lower().replace(" ", "_")
def _required_slots(self, ticket: Dict[str, Any], task_id: str) -> List[str]:
if task_id == "easy":
return ["issue_category"]
if task_id == "medium":
return ["faq_or_escalation_decision"]
return ["issue_details", "resolution_confirmation"]
def _track_collected_slots(self, action: Action) -> None:
if self.ticket_state is None:
return
for field_name in action.fields_requested:
self.ticket_state.collected_slots[field_name] = "requested"
if action.operation:
self.ticket_state.collected_slots["last_operation"] = action.operation
if action.target:
self.ticket_state.collected_slots["escalation_target"] = action.target
def _grade_detail_request(self, action: Action) -> float:
if self.ticket_state is None:
return ensure_open_unit_interval(0.0)
if not action.fields_requested and not action.message:
return ensure_open_unit_interval(0.0)
if not self.ticket_state.required_slots:
return ensure_open_unit_interval(0.5)
info_score = grade_information_collection(
action.fields_requested,
self.ticket_state.required_slots,
)
if self.task_id != "hard" and info_score <= 0.001:
return ensure_open_unit_interval(0.5)
return ensure_open_unit_interval(info_score)
def _grade_take_action(self, action: Action) -> Tuple[float, bool]:
if self.current_ticket is None:
return ensure_open_unit_interval(0.0), False
operation = (action.operation or "").strip().lower()
current_ticket = self.current_ticket
if operation in {"classify_issue", "classify"}:
gold_category = current_ticket.get("gold_category", "")
score = grade_classification(action.category or "", gold_category)
resolved = (action.category or "").strip().lower() == str(gold_category).strip().lower()
return score, resolved
if operation == "lookup_faq":
gold_faq_id = current_ticket.get("gold_faq_id", "")
score = grade_faq_retrieval(action.faq_id or "", gold_faq_id)
if self.ticket_state is not None and (action.faq_id or "").strip() == str(gold_faq_id).strip():
self.ticket_state.correct_faq_retrieved = True
return score, False
if operation == "resolve_with_guidance":
resolved = bool(
self.ticket_state
and self.ticket_state.correct_faq_retrieved
and (self.task_id != "hard" or self.ticket_state.clarification_received)
)
return ensure_open_unit_interval(1.0 if resolved else 0.0), resolved
if operation == "check_status":
return ensure_open_unit_interval(0.5), False
banking_operations = {
"check_payment",
"check_refund",
"check_kyc",
"secure_account",
"troubleshoot_upi",
}
op_score = grade_operation_choice(operation, banking_operations)
return op_score, False
def _grade_response(self, action: Action) -> float:
if not action.message:
return ensure_open_unit_interval(0.0)
if self.task_id == "hard" and self.ticket_state and self.ticket_state.correct_faq_retrieved:
return ensure_open_unit_interval(1.0)
return ensure_open_unit_interval(0.5)
def _grade_safety(self, action: Action, metrics: Dict[str, float]) -> float:
text = (action.message or "").lower()
sensitive_markers = ["otp", "pin", "cvv", "password"]
if any(marker in text for marker in sensitive_markers):
metrics["penalties"] -= 0.50
return ensure_open_unit_interval(0.0)
if action.action_type == "close_case" and metrics["resolution"] <= 0.001:
return ensure_open_unit_interval(0.25)
if action.action_type == "escalate_case":
expected = bool(self.current_ticket and self.current_ticket.get("should_escalate", False))
return ensure_open_unit_interval(1.0 if expected else 0.6)
return ensure_open_unit_interval(1.0)
def _grade_efficiency(self, done: bool) -> float:
max_turns = 1 if self.task_id == "easy" else 2 if self.task_id == "medium" else 6
if not done:
remaining_ratio = max(0.0, 1.0 - (self.turn_number / max_turns))
return ensure_open_unit_interval(round(0.5 * remaining_ratio, 3))
return ensure_open_unit_interval(1.0 - (0.1 * max(0, self.turn_number - 1)))
def _calculate_reward(self, metrics: Dict[str, float], done: bool) -> Reward:
correctness = ensure_open_unit_interval(metrics.get("correctness", 0.0))
safety = ensure_open_unit_interval(metrics.get("safety", 0.0))
resolution = ensure_open_unit_interval(metrics.get("resolution", 0.0))
efficiency = ensure_open_unit_interval(metrics.get("efficiency", 0.0))
penalties = metrics.get("penalties", 0.0)
weighted = (
(0.35 * correctness)
+ (0.30 * safety)
+ (0.20 * resolution)
+ (0.15 * efficiency)
)
recent_actions = self.action_history[-3:]
if len(recent_actions) >= 2 and len(set(recent_actions)) < len(recent_actions):
penalties -= 0.05
case_adjustment = self._case_complexity_adjustment()
final_value = ensure_open_unit_interval(weighted + penalties + case_adjustment)
return Reward(
value=final_value,
correctness=correctness,
safety=safety,
resolution=resolution,
efficiency=efficiency,
penalties=penalties,
done=done,
info={
"turn_number": self.turn_number,
"task_id": self.task_id,
"case_adjustment": case_adjustment,
"escalation_accuracy": metrics.get("escalation_accuracy", correctness),
},
)
def _case_complexity_adjustment(self) -> float:
if self.current_ticket is None:
return 0.0
ticket_id = str(self.current_ticket.get("id", ""))
bucket = sum(ord(char) for char in ticket_id) % 4
return -0.015 * bucket
def _build_known_facts(self) -> Dict[str, Any]:
if self.current_ticket is None or self.ticket_state is None:
return {}
return {
"difficulty": self.current_ticket.get("difficulty", self.task_id),
"knowledge_base": self.kb,
"available_categories": [
"payment_failure",
"refund_delay",
"fraud_complaint",
"kyc_account_restriction",
"upi_pin_or_bank_linking",
],
"clarification_received": self.ticket_state.clarification_received,
"faq_retrieved": self.ticket_state.correct_faq_retrieved,
"issue_resolved": self.ticket_state.issue_resolved,
"collected_slots": self.ticket_state.collected_slots,
}
def state(self) -> Observation:
if self.current_ticket is None or self.ticket_state is None:
raise RuntimeError("Environment not initialized. Call reset() first.")
customer_message = self.current_ticket.get("text") or self.current_ticket.get(
"initial_text", ""
)
return Observation(
case_id=self.current_ticket["id"],
track=self.task_id,
customer_message=customer_message,
conversation_history=self.conversation_history,
known_facts=self._build_known_facts(),
required_slots=self.ticket_state.required_slots,
available_actions=[
"ask_for_details",
"take_action",
"respond_to_user",
"escalate_case",
"close_case",
],
turn_number=self.turn_number,
)
__all__ = ["HelpdeskEnv"]
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