| from typing import Any, Dict |
|
|
| from openenv.core import EnvClient |
| from openenv.core.client_types import StepResult |
|
|
| from my_env.models import MyAction, MyObservation, MyState |
|
|
|
|
| class MyEnv(EnvClient[MyAction, MyObservation, MyState]): |
| """Client for the personal assistant environment.""" |
|
|
| def _step_payload(self, action: MyAction) -> Dict[str, Any]: |
| return { |
| "tool_name": action.tool_name, |
| "tool_args": action.tool_args, |
| } |
|
|
| def _parse_result(self, payload: Dict[str, Any]) -> StepResult[MyObservation]: |
| obs_data = payload.get("observation", {}) |
| observation = MyObservation( |
| result=obs_data.get("result", ""), |
| available_tools=obs_data.get("available_tools", []), |
| task_completed=obs_data.get("task_completed", False), |
| 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]) -> MyState: |
| return MyState( |
| episode_id=payload.get("episode_id"), |
| step_count=payload.get("step_count", 0), |
| task_description=payload.get("task_description", ""), |
| history=payload.get("history", []), |
| ) |
|
|