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0f67fc2 | 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 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# source tree.
"""Token Optimiser Environment Client."""
from typing import Dict
from openenv.core import EnvClient
from openenv.core.client_types import StepResult
from openenv.core.env_server.types import State
from .models import TokenOptimiserAction, TokenOptimiserObservation, TokenOptimiserState
class TokenOptimiserEnv(
EnvClient[TokenOptimiserAction, TokenOptimiserObservation, TokenOptimiserState]
):
"""
Client for the Token Optimiser Environment.
This client maintains a persistent WebSocket connection to the environment server,
enabling efficient multi-step interactions with lower latency.
Each client instance has its own dedicated environment session on the server.
Example:
>>> # Connect to a running server
>>> with TokenOptimiserEnv(base_url="http://localhost:8000") as client:
... result = client.reset()
... print(result.observation.llm_response)
...
... result = client.step(TokenOptimiserAction(optimized_prompt="Explain ML briefly"))
... print(result.observation.llm_response)
Example with Docker:
>>> # Automatically start container and connect
>>> client = TokenOptimiserEnv.from_docker_image("token_optimiser-env:latest")
>>> try:
... result = client.reset()
... result = client.step(TokenOptimiserAction(optimized_prompt="Test prompt"))
... finally:
... client.close()
"""
def _step_payload(self, action: TokenOptimiserAction) -> Dict:
"""
Convert TokenOptimiserAction to JSON payload for step message.
Args:
action: TokenOptimiserAction instance
Returns:
Dictionary representation suitable for JSON encoding
"""
return {
"optimized_prompt": action.optimized_prompt,
}
def _parse_result(self, payload: Dict) -> StepResult[TokenOptimiserObservation]:
"""
Parse server response into StepResult[TokenOptimiserObservation].
Args:
payload: JSON response data from server
Returns:
StepResult with TokenOptimiserObservation
"""
obs_data = payload.get("observation", {})
observation = TokenOptimiserObservation(
llm_response=obs_data.get("llm_response", ""),
input_tokens=obs_data.get("input_tokens", 0),
output_tokens=obs_data.get("output_tokens", 0),
reward=obs_data.get("reward", 0.0),
)
return StepResult(
observation=observation,
reward=payload.get("reward", 0.0),
done=payload.get("done", False),
)
def _parse_state(self, payload: Dict) -> TokenOptimiserState:
"""
Parse server response into TokenOptimiserState object.
Args:
payload: JSON response from state request
Returns:
TokenOptimiserState object with episode information
"""
return TokenOptimiserState(
episode_id=payload.get("episode_id"),
step_count=payload.get("step_count", 0),
original_prompt=payload.get("original_prompt", ""),
task_difficulty=payload.get("task_difficulty", "easy"),
task_index=payload.get("task_index", 0)
) |