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OpenEnv Pydantic models for the env/ stack.
Matches the env/environment data shape: observations with prompt, target_name,
step, paper_fold_json; actions as fold dicts with from/to/assignment.
"""
from typing import Optional
from pydantic import ConfigDict, Field
from openenv.core.env_server.types import Action, Observation, State
class OrigamiAction(Action):
"""One fold operation — from_point, to_point, assignment."""
model_config = ConfigDict(populate_by_name=True)
from_point: list[float] = Field(
alias="from",
description="[x, y] start point of the crease",
)
to_point: list[float] = Field(
alias="to",
description="[x, y] end point of the crease",
)
assignment: str = Field(
description="'M' (mountain) or 'V' (valley)",
)
class OrigamiObservation(Observation):
"""Observation from env.environment — prompt, target, step, paper state."""
prompt: str = Field(default="", description="LLM prompt for the current step")
target_name: str = Field(default="", description="Name of the target (.fold stem)")
step: int = Field(default=0, ge=0, description="Current step index")
paper_fold_json: dict = Field(
default_factory=dict,
description="Graph edges (crease pattern state)",
)
class OrigamiState(State):
"""Server-side episode state."""
paper: dict = Field(default_factory=dict, description="Paper state")
target: Optional[str] = Field(default=None, description="Target name")
step: int = Field(default=0, ge=0, description="Step count")
mode: str = Field(default="step", description="'step' or 'code_as_policy'")
__all__ = ["OrigamiAction", "OrigamiObservation", "OrigamiState"]
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