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026df2c | 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 | from dataclasses import dataclass, field
from typing import Any, Dict, List, Literal, Optional
from pydantic import BaseModel, Field, model_validator
class Observation(BaseModel):
case_id: str
track: str
customer_message: str
conversation_history: List[Dict[str, str]]
known_facts: Dict[str, Any]
required_slots: List[str]
available_actions: List[str]
turn_number: int
@property
def ticket_id(self) -> str:
return self.case_id
@property
def task_id(self) -> str:
return str(self.known_facts.get("difficulty", ""))
@property
def ticket_text(self) -> str:
return self.customer_message
@property
def knowledge_base(self) -> List[Dict[str, Any]]:
kb = self.known_facts.get("knowledge_base", [])
return kb if isinstance(kb, list) else []
@property
def available_categories(self) -> List[str]:
categories = self.known_facts.get("available_categories", [])
return categories if isinstance(categories, list) else []
class Action(BaseModel):
action_type: Literal[
"ask_for_details",
"take_action",
"respond_to_user",
"escalate_case",
"close_case",
]
message: Optional[str] = None
fields_requested: List[str] = Field(default_factory=list)
operation: Optional[str] = None
target: Optional[str] = None
# Legacy compatibility with the original helpdesk action schema.
category: Optional[str] = None
faq_id: Optional[str] = None
@model_validator(mode="after")
def _validate_canonical_shape(self) -> "Action":
if self.action_type == "take_action" and not self.operation:
raise ValueError("take_action requires operation")
return self
LegacyActionType = Literal[
"classify",
"lookup_faq",
"ask_clarification",
"reply",
"escalate",
"resolve_ticket",
]
def normalize_action(raw: Dict[str, Any]) -> Action:
action_type = str(raw.get("action_type", "")).strip()
if action_type == "classify":
return Action(
action_type="take_action",
operation="classify",
category=raw.get("category"),
message=raw.get("message"),
faq_id=raw.get("faq_id"),
)
if action_type == "lookup_faq":
return Action(
action_type="take_action",
operation="lookup_faq",
faq_id=raw.get("faq_id"),
message=raw.get("message"),
category=raw.get("category"),
)
if action_type == "ask_clarification":
return Action(
action_type="ask_for_details",
fields_requested=list(raw.get("fields_requested") or ["issue_details"]),
message=raw.get("message"),
)
if action_type == "reply":
return Action(
action_type="respond_to_user",
message=raw.get("message"),
)
if action_type == "escalate":
return Action(
action_type="escalate_case",
target=raw.get("target") or "human_agent",
message=raw.get("message"),
)
if action_type == "resolve_ticket":
return Action(
action_type="close_case",
operation=raw.get("operation") or "resolve_with_guidance",
message=raw.get("message"),
)
return Action(**raw)
class Reward(BaseModel):
value: float = Field(ge=0.0, le=1.0)
correctness: float
safety: float
resolution: float
efficiency: float
penalties: float
done: bool
info: Dict[str, Any]
@property
def escalation_accuracy(self) -> float:
return float(self.info.get("escalation_accuracy", self.correctness))
@dataclass
class TicketState:
ticket_id: str
track: str
required_slots: List[str] = field(default_factory=list)
collected_slots: Dict[str, Any] = field(default_factory=dict)
issue_resolved: bool = False
clarification_received: bool = False
escalated: bool = False
turns_used: int = 0
correct_faq_retrieved: bool = False
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