"""Fraud Hunter Env — EnvClient subclass for training-side use.""" from __future__ import annotations from typing import Any, Dict from openenv.core import EnvClient from openenv.core.client_types import StepResult from openenv.core.env_server.types import State from .models import FraudHunterAction, FraudHunterObservation class FraudHunterEnv(EnvClient[FraudHunterAction, FraudHunterObservation, State]): """ WebSocket client for the Fraud Hunter Env server. Example: >>> with FraudHunterEnv(base_url="http://localhost:8000") as env: ... r = env.reset() ... r = env.step(FraudHunterAction(kind="query_corporate", entity_name="Acme")) ... print(r.reward, r.observation.tool_output) """ def _step_payload(self, action: FraudHunterAction) -> Dict[str, Any]: # Pydantic v2 serialisation, dropping None fields for compact WS frames. return action.model_dump(exclude_none=True, mode="json") def _parse_result(self, payload: Dict[str, Any]) -> StepResult[FraudHunterObservation]: obs_data = payload.get("observation", {}) or {} observation = FraudHunterObservation( case_brief=obs_data.get("case_brief"), tool_output=obs_data.get("tool_output"), base64_document=obs_data.get("base64_document"), grader_feedback=obs_data.get("grader_feedback"), evidence_graph=obs_data.get("evidence_graph"), step_count=obs_data.get("step_count", 0), budget_remaining=obs_data.get("budget_remaining", 0), difficulty_tier=obs_data.get("difficulty_tier", 1), info=obs_data.get("info"), done=payload.get("done", False), reward=payload.get("reward"), ) return StepResult( observation=observation, reward=payload.get("reward"), done=payload.get("done", False), ) def _parse_state(self, payload: Dict[str, Any]) -> State: return State( episode_id=payload.get("episode_id"), step_count=payload.get("step_count", 0), )