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21c7db9 | 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 | """OpenEnv-compatible wrapper around local env service.
The wrapper intentionally exposes meaningful clinician-facing tool methods for
LLM policy training instead of a single opaque ``step(action)`` interface.
"""
from __future__ import annotations
from typing import Any, Literal
from app.env.client import PolyGuardEnvClient
try:
from openenv import GenericEnvClient
except Exception: # noqa: BLE001
GenericEnvClient = None # type: ignore[assignment]
class LocalOpenEnvWrapper:
def __init__(self, base_url: str = "http://127.0.0.1:8100") -> None:
self.http_client = PolyGuardEnvClient(base_url=base_url)
self.base_url = base_url
self._sync_client: Any = None
if GenericEnvClient is not None:
try:
self._sync_client = GenericEnvClient(base_url=base_url).sync()
self._sync_client.connect()
except Exception: # noqa: BLE001
self._sync_client = None
def reset(self, **kwargs: Any) -> dict[str, Any]:
if self._sync_client is not None:
result = self._sync_client.reset(**kwargs)
return {
"observation": result.observation,
"reward": result.reward,
"done": result.done,
}
return self.http_client.reset(**kwargs)
def step(self, action: dict[str, Any]) -> dict[str, Any]:
if self._sync_client is not None:
result = self._sync_client.step(action)
return {
"observation": result.observation,
"reward": result.reward,
"done": result.done,
}
return self.http_client.step(action)
def state(self) -> dict[str, Any]:
if self._sync_client is not None:
return self._sync_client.state()
return self.http_client.state()
def trace(self) -> list[dict[str, Any]]:
return self.http_client.trace()
def legal_actions(self) -> list[dict[str, Any]]:
return self.http_client.legal_actions()
def reward_breakdown(self) -> dict[str, Any]:
return self.http_client.reward_breakdown()
def uncertainty(self) -> dict[str, Any]:
return self.http_client.uncertainty()
def inspect_regimen(self) -> dict[str, Any]:
"""Return a compact clinical snapshot of the active case."""
state = self.state()
patient = state.get("patient", {})
risk_summary = state.get("risk_summary", {})
meds = patient.get("medications", [])
return {
"patient_id": patient.get("patient_id"),
"age": patient.get("age"),
"comorbidities": patient.get("comorbidities", []),
"medication_count": len(meds),
"medications": meds,
"risk_summary": risk_summary,
"burden_score": state.get("burden_score"),
"step_count": state.get("step_count"),
"max_steps": state.get("max_steps"),
}
def evaluate_candidate(self, candidate_id: str) -> dict[str, Any]:
"""Lookup a legal candidate action by candidate id."""
candidates = self.legal_actions()
for candidate in candidates:
if candidate.get("candidate_id") == candidate_id:
return candidate
return {"candidate_id": candidate_id, "found": False}
def _execute_action(
self,
mode: str,
action_type: str,
target_drug: str | None = None,
replacement_drug: str | None = None,
dose_bucket: str = "NA",
taper_days: int | None = None,
monitoring_plan: str | None = None,
candidate_id: str = "cand_manual",
confidence: float = 0.65,
rationale_brief: str = "tool_action",
) -> dict[str, Any]:
payload = {
"mode": mode,
"action_type": action_type,
"target_drug": target_drug,
"replacement_drug": replacement_drug,
"dose_bucket": dose_bucket,
"taper_days": taper_days,
"monitoring_plan": monitoring_plan,
"candidate_id": candidate_id,
"confidence": confidence,
"rationale_brief": rationale_brief,
}
return self.step(payload)
def stop_drug(self, target_drug: str, taper_days: int | None = None, candidate_id: str = "cand_stop_tool") -> dict[str, Any]:
"""Issue STOP_DRUG action for a single medication."""
return self._execute_action(
mode="REGIMEN_OPT",
action_type="STOP_DRUG",
target_drug=target_drug,
taper_days=taper_days,
candidate_id=candidate_id,
rationale_brief=f"stop_drug:{target_drug}",
)
def substitute_drug(
self,
target_drug: str,
replacement_drug: str,
candidate_id: str = "cand_substitute_tool",
) -> dict[str, Any]:
"""Issue SUBSTITUTE_WITHIN_CLASS action."""
return self._execute_action(
mode="REGIMEN_OPT",
action_type="SUBSTITUTE_WITHIN_CLASS",
target_drug=target_drug,
replacement_drug=replacement_drug,
candidate_id=candidate_id,
rationale_brief=f"substitute:{target_drug}->{replacement_drug}",
)
def start_taper(self, target_drug: str, taper_days: int = 14, candidate_id: str = "cand_taper_start_tool") -> dict[str, Any]:
"""Issue TAPER_INITIATE action."""
return self._execute_action(
mode="REGIMEN_OPT",
action_type="TAPER_INITIATE",
target_drug=target_drug,
taper_days=taper_days,
candidate_id=candidate_id,
rationale_brief=f"taper_start:{target_drug}",
)
def continue_taper(self, target_drug: str, taper_days: int = 7, candidate_id: str = "cand_taper_continue_tool") -> dict[str, Any]:
"""Issue TAPER_CONTINUE action."""
return self._execute_action(
mode="REGIMEN_OPT",
action_type="TAPER_CONTINUE",
target_drug=target_drug,
taper_days=taper_days,
candidate_id=candidate_id,
rationale_brief=f"taper_continue:{target_drug}",
)
def adjust_dose(
self,
target_drug: str,
direction: Literal["increase", "reduce", "hold"],
candidate_id: str = "cand_adjust_dose_tool",
) -> dict[str, Any]:
"""Adjust dose bucket with an explicit direction."""
if direction == "increase":
action_type = "INCREASE_DOSE_BUCKET"
dose_bucket = "HIGH"
elif direction == "reduce":
action_type = "REDUCE_DOSE_BUCKET"
dose_bucket = "LOW"
else:
action_type = "DOSE_HOLD"
dose_bucket = "HOLD"
return self._execute_action(
mode="DOSE_OPT",
action_type=action_type,
target_drug=target_drug,
dose_bucket=dose_bucket,
candidate_id=candidate_id,
rationale_brief=f"adjust_dose:{direction}:{target_drug}",
)
def request_review(
self,
review_type: Literal["pharmacist", "specialist"] = "specialist",
candidate_id: str = "cand_review_tool",
) -> dict[str, Any]:
"""Request human review when uncertainty or legality concerns are high."""
action_type = "REQUEST_PHARMACIST_REVIEW" if review_type == "pharmacist" else "REQUEST_SPECIALIST_REVIEW"
return self._execute_action(
mode="ABSTAIN_REVIEW",
action_type=action_type,
candidate_id=candidate_id,
rationale_brief=f"request_review:{review_type}",
)
def finish_case(self, candidate_id: str = "cand_finish_tool") -> dict[str, Any]:
"""Close the episode with a conservative keep action."""
return self._execute_action(
mode="REGIMEN_OPT",
action_type="KEEP_REGIMEN",
candidate_id=candidate_id,
rationale_brief="finish_case",
)
def close(self) -> None:
if self._sync_client is not None:
try:
self._sync_client.close()
except Exception: # noqa: BLE001
pass
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