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ce9edc2 45cf526 ce9edc2 45cf526 ce9edc2 45cf526 ce9edc2 | 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 | """Text-first training environment wrapper for FraudShield."""
from __future__ import annotations
import json
from dataclasses import dataclass
from typing import Any
from config import EnvironmentConfig, RewardWeights
from fraudshield_env import FraudShieldEnvironment
from models import ActionTypeEnum, FraudCheckAction, ResolutionEnum
from reward import RewardBreakdown, build_reward_breakdown
from utils import approximate_token_count, extract_json_object
CANONICAL_INVESTIGATION_ALIASES = [
"merchant_profile",
"customer_profile",
"network_graph",
"payment_trace",
"policy_review",
]
INVESTIGATION_ALIAS_TO_ACTION = {
"merchant_profile": ActionTypeEnum.FETCH_MERCHANT_PROFILE,
"fetch_merchant_profile": ActionTypeEnum.FETCH_MERCHANT_PROFILE,
"customer_profile": ActionTypeEnum.FETCH_CUSTOMER_PROFILE,
"fetch_customer_profile": ActionTypeEnum.FETCH_CUSTOMER_PROFILE,
"network_graph": ActionTypeEnum.FETCH_NETWORK_GRAPH,
"fetch_network_graph": ActionTypeEnum.FETCH_NETWORK_GRAPH,
"device_intel": ActionTypeEnum.FETCH_NETWORK_GRAPH,
"payment_trace": ActionTypeEnum.REVIEW_TRANSACTION,
"fulfillment_review": ActionTypeEnum.REVIEW_TRANSACTION,
"review_transaction": ActionTypeEnum.REVIEW_TRANSACTION,
"policy_review": ActionTypeEnum.CHECK_POLICY,
"check_policy": ActionTypeEnum.CHECK_POLICY,
}
ACTION_TYPE_TO_CANONICAL_ALIAS = {
ActionTypeEnum.FETCH_MERCHANT_PROFILE: "merchant_profile",
ActionTypeEnum.FETCH_CUSTOMER_PROFILE: "customer_profile",
ActionTypeEnum.FETCH_NETWORK_GRAPH: "network_graph",
ActionTypeEnum.REVIEW_TRANSACTION: "payment_trace",
ActionTypeEnum.CHECK_POLICY: "policy_review",
}
def build_fraudshield_prompt(observation) -> str:
"""Build the canonical prompt used for both training and inference."""
payload = {
"case_id": observation.case_id,
"task_name": observation.task_name.value,
"visible_panels": observation.visible_panels,
"revealed_evidence": observation.revealed_evidence,
"linked_case_ids": observation.linked_case_ids,
"remaining_steps": observation.remaining_steps,
"remaining_sla": observation.remaining_sla,
"note_required": observation.note_required,
"allowed_actions": [action.value for action in observation.allowed_actions],
"case_summary": observation.case_summary.model_dump(mode="json"),
"app_context": observation.app_context,
}
available = observation.app_context.get("available_investigations", CANONICAL_INVESTIGATION_ALIASES)
return (
"You are a fraud analyst in a multi-step training environment. "
"Return JSON only. Use visible evidence, investigation budget, and prior evidence carefully.\n\n"
f"Visible observation:\n{json.dumps(payload, sort_keys=True)}\n\n"
f"Valid investigation aliases: {available}\n"
"JSON schema: "
'{"action_type":"investigate|decide","investigation_target":"alias_or_null",'
'"decision":"fraud|legitimate|null","confidence":0.0,"reasoning":"one sentence"}'
)
@dataclass
class TextStepResult:
"""Structured step output for text-based RL loops."""
prompt: str
response_text: str
next_prompt: str
done: bool
reward: float
reward_breakdown: RewardBreakdown
info: dict[str, Any]
class FraudShieldTextEnvironment:
"""Wrap ``FraudShieldEnvironment`` as a text-in/text-out RL environment."""
def __init__(
self,
env_config: EnvironmentConfig | None = None,
reward_weights: RewardWeights | None = None,
):
self.env_config = env_config or EnvironmentConfig()
self.reward_weights = reward_weights or RewardWeights()
self.env = FraudShieldEnvironment(data_path=self.env_config.data_path, seed=self.env_config.seed)
self.env.load_data()
self.current_observation = None
self.current_task = self.env_config.default_task
self.initial_step_budget = self.env_config.max_rollout_steps
self.action_history: list[str] = []
def reset(self, task: str | None = None) -> str:
"""Reset the wrapped environment and return the initial prompt."""
self.current_task = task or self.current_task
result = self.env.reset(task=self.current_task)
self.current_observation = result.observation
self.initial_step_budget = result.info.get("max_steps", self.env_config.max_rollout_steps)
self.action_history = []
return self.build_prompt(self.current_observation)
def build_prompt(self, observation) -> str:
"""Build the prompt shown to an LLM policy."""
return build_fraudshield_prompt(observation)
def parse_response(self, response_text: str) -> tuple[FraudCheckAction, dict[str, Any], bool, bool]:
"""Convert model output into a typed environment action."""
parse_failed = False
required_fields_present = True
try:
payload = extract_json_object(response_text)
except Exception:
parse_failed = True
required_fields_present = False
payload = {
"action_type": "investigate",
"investigation_target": "payment_trace",
"decision": None,
"confidence": 0.0,
"reasoning": "Fallback after invalid output.",
}
action_type = str(payload.get("action_type", "")).strip().lower()
reasoning = str(payload.get("reasoning", "")).strip()
if not reasoning:
required_fields_present = False
reasoning = "Fallback after missing reasoning."
if action_type == "investigate":
alias = str(payload.get("investigation_target", "")).strip().lower()
if not alias:
required_fields_present = False
alias = "payment_trace"
mapped_action = INVESTIGATION_ALIAS_TO_ACTION.get(alias, ActionTypeEnum.REVIEW_TRANSACTION)
action = FraudCheckAction(case_id=self.current_observation.case_id, action_type=mapped_action, reasoning=reasoning)
elif action_type == "decide":
decision = str(payload.get("decision", "")).strip().lower()
confidence = float(payload.get("confidence") or 0.5)
if decision not in {"fraud", "legitimate"}:
required_fields_present = False
decision = "fraud"
if self.current_observation.note_required:
action = FraudCheckAction(
case_id=self.current_observation.case_id,
action_type=ActionTypeEnum.ADD_CASE_NOTE,
note_text=f"Decision summary: {reasoning}",
)
else:
resolution = self._decision_to_resolution(decision, confidence)
action = FraudCheckAction(
case_id=self.current_observation.case_id,
action_type=ActionTypeEnum.RESOLVE_CASE,
resolution=resolution,
reasoning=reasoning,
)
else:
required_fields_present = False
action = FraudCheckAction(
case_id=self.current_observation.case_id,
action_type=ActionTypeEnum.REVIEW_TRANSACTION,
reasoning="Fallback after unsupported action type.",
)
return action, payload, parse_failed, required_fields_present
def step(self, response_text: str) -> TextStepResult:
"""Step the environment using raw model text."""
prompt = self.build_prompt(self.current_observation)
action, payload, parse_failed, required_fields_present = self.parse_response(response_text)
env_step = self.env.step(action)
self.action_history.append(action.action_type.value)
self.current_observation = env_step.observation
token_count = approximate_token_count(prompt + response_text)
breakdown = build_reward_breakdown(
env_reward_value=env_step.reward.value,
is_correct=env_step.reward.is_correct,
done=env_step.done,
action_type=action.action_type,
resolution=action.resolution,
reasoning=action.reasoning if action.action_type != ActionTypeEnum.ADD_CASE_NOTE else action.note_text or "",
revealed_evidence=env_step.observation.revealed_evidence,
remaining_steps=env_step.observation.remaining_steps,
initial_budget=self.initial_step_budget,
token_count=token_count,
parse_failed=parse_failed,
required_fields_present=required_fields_present,
action_history=self.action_history[:-1],
weights=self.reward_weights,
)
next_prompt = self.build_prompt(self.current_observation)
return TextStepResult(
prompt=prompt,
response_text=response_text,
next_prompt=next_prompt,
done=env_step.done,
reward=breakdown.total_reward,
reward_breakdown=breakdown,
info={
"payload": payload,
"env_reward": env_step.reward.model_dump(mode="json"),
"state": self.env.state().model_dump(mode="json"),
},
)
def _decision_to_resolution(self, decision: str, confidence: float) -> ResolutionEnum:
if decision == "legitimate":
if confidence >= 0.75 or self.current_observation.task_name.value == "easy":
return ResolutionEnum.APPROVE
return ResolutionEnum.REQUEST_DOCS
if confidence < 0.70:
return ResolutionEnum.HOLD
return ResolutionEnum.BLOCK
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