"""SearchQA environment adapter for ReflACT."""
from __future__ import annotations
import json
import os
from skillopt.gradient.deep_probe import generate_deep_probe_instruction
from skillopt.datasets.base import BatchSpec
from skillopt.envs.base import EnvAdapter
from skillopt.envs.searchqa.dataloader import SearchQADataLoader
from skillopt.envs.searchqa.rollout import run_batch
from skillopt.gradient.reflect import run_minibatch_reflect
from skillopt.model import get_student_backend
class SearchQAAdapter(EnvAdapter):
"""SearchQA environment adapter."""
def __init__(
self,
split_dir: str = "",
data_path: str = "",
split_mode: str = "ratio",
split_ratio: str = "2:1:7",
split_seed: int = 42,
split_output_dir: str = "",
max_turns: int = 1,
exec_timeout: int = 120,
workers: int = 64,
analyst_workers: int = 16,
failure_only: bool = False,
minibatch_size: int = 8,
edit_budget: int = 4,
seed: int = 42,
limit: int = 0,
use_deep_reflect: bool = False,
deep_reflect_failures: int = 4,
deep_reflect_successes: int = 2,
) -> None:
self.max_turns = max_turns
self.exec_timeout = exec_timeout
self.workers = workers
self.analyst_workers = analyst_workers
self.failure_only = failure_only
self.minibatch_size = minibatch_size
self.edit_budget = edit_budget
self.use_deep_reflect = use_deep_reflect
self.deep_reflect_failures = deep_reflect_failures
self.deep_reflect_successes = deep_reflect_successes
self.dataloader = SearchQADataLoader(
split_dir=split_dir,
data_path=data_path,
split_mode=split_mode,
split_ratio=split_ratio,
split_seed=split_seed,
split_output_dir=split_output_dir,
seed=seed,
limit=limit,
)
def setup(self, cfg: dict) -> None:
super().setup(cfg)
self.dataloader.setup(cfg)
def get_dataloader(self):
return self.dataloader
def build_env_from_batch(self, batch: BatchSpec, **kwargs):
return list(batch.payload or [])
def build_train_env(self, batch_size: int, seed: int, **kwargs):
batch = self.dataloader.build_train_batch(batch_size=batch_size, seed=seed, **kwargs)
return self.build_env_from_batch(batch, **kwargs)
def build_eval_env(self, env_num: int, split: str, seed: int, **kwargs):
batch = self.dataloader.build_eval_batch(env_num=env_num, split=split, seed=seed, **kwargs)
return self.build_env_from_batch(batch, **kwargs)
def rollout(
self,
env_manager, # actually list[dict] for SearchQA
skill_content: str,
out_dir: str,
**kwargs,
) -> list[dict]:
"""Run QA agent on items. Resume-aware."""
items: list[dict] = env_manager # type alias for clarity
return run_batch(
items=items,
out_root=out_dir,
skill_content=skill_content,
max_turns=self.max_turns,
exec_timeout=self.exec_timeout,
workers=self.workers,
diagnostic_mode=kwargs.get("diagnostic_mode", False),
diagnostic_instruction=kwargs.get("diagnostic_instruction", ""),
diagnostic_trace_context_by_id=kwargs.get("diagnostic_trace_context_by_id"),
task_timeout=self.exec_timeout,
)
def reflect(
self,
results: list[dict],
skill_content: str,
out_dir: str,
**kwargs,
) -> list[dict | None]:
prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
random_seed = kwargs.get("random_seed")
step_buffer_context = kwargs.get("step_buffer_context", "")
meta_skill_context = kwargs.get("meta_skill_context", "")
return run_minibatch_reflect(
results=results,
skill_content=skill_content,
prediction_dir=prediction_dir,
patches_dir=patches_dir,
workers=self.analyst_workers,
failure_only=self.failure_only,
minibatch_size=self.minibatch_size,
edit_budget=self.edit_budget,
random_seed=random_seed,
error_system=self.get_error_minibatch_prompt(),
success_system=self.get_success_minibatch_prompt(),
step_buffer_context=step_buffer_context,
meta_skill_context=meta_skill_context,
update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
)
def deep_reflect(
self,
results: list[dict],
skill_content: str,
out_dir: str,
**kwargs,
) -> list[dict | None]:
if not self.use_deep_reflect:
return []
env_manager = kwargs.get("env_manager")
if not isinstance(env_manager, list):
return []
prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
random_seed = kwargs.get("random_seed")
step_buffer_context = kwargs.get("step_buffer_context", "")
meta_skill_context = kwargs.get("meta_skill_context", "")
codex_backend = get_student_backend() == "codex_exec"
selected_items = self.select_representative_items(
results,
env_manager,
n_failures=self.deep_reflect_failures,
n_successes=self.deep_reflect_successes,
seed=random_seed,
)
if not selected_items:
return []
selected_ids = {str(item["id"]) for item in selected_items}
selected_results = [row for row in results if str(row.get("id")) in selected_ids]
selected_examples = (
self.attach_codex_probe_context(selected_results, prediction_dir)
if codex_backend
else selected_results
)
selected_metadata = [
{
"id": str(item["id"]),
"question_preview": str(item.get("question") or "")[:200],
"has_context": bool(str(item.get("context") or "").strip()),
"n_gold_answers": len(item.get("answers") or []),
}
for item in selected_items
]
deep_dir = os.path.join(out_dir, "deep_reflect")
rollout_dir = os.path.join(deep_dir, "rollout")
patches_dir = os.path.join(deep_dir, "patches")
os.makedirs(deep_dir, exist_ok=True)
print(
f" [2b/6 DEEP REFLECT setup] selected={len(selected_items)} "
f"mode=no_reference_probe"
)
probe = generate_deep_probe_instruction(
skill_content=skill_content,
items=selected_examples,
prediction_dir=prediction_dir,
system_prompt=self.get_codex_deep_probe_prompt() if codex_backend else self.get_deep_probe_prompt(),
step_buffer_context=step_buffer_context,
meta_skill_context=meta_skill_context,
output_requirements=[
"- There is no hidden reference block. Use only the question, provided context, the student's output, and the evaluation result to infer what intermediate state is worth probing.",
"- The instruction must explicitly request a short ... block before the final ....",
"- The readout should focus on likely evidence span, top candidate and runner-up, decisive clue, or a few short intermediate conclusions.",
"- Do not ask for exhaustive copying of the context or a full chain-of-thought.",
"- The instruction text should be ready to append directly to the student's prompt.",
],
)
if not probe:
return []
diagnostic_trace_context_by_id = None
if codex_backend:
selected_items, diagnostic_trace_context_by_id, probe = self.resolve_codex_probe_target(
selected_items=selected_items,
selected_examples=selected_examples,
prediction_dir=prediction_dir,
probe=probe,
)
with open(os.path.join(deep_dir, "probe.json"), "w", encoding="utf-8") as f:
json.dump(
{
**probe,
"selected_examples": selected_metadata,
},
f,
ensure_ascii=False,
indent=2,
)
deep_results = self.rollout(
selected_items,
skill_content,
rollout_dir,
diagnostic_mode=True,
diagnostic_instruction=probe["probe_instruction"],
diagnostic_trace_context_by_id=diagnostic_trace_context_by_id,
)
return run_minibatch_reflect(
results=deep_results,
skill_content=skill_content,
prediction_dir=os.path.join(rollout_dir, "predictions"),
patches_dir=patches_dir,
workers=self.analyst_workers,
failure_only=self.failure_only,
minibatch_size=self.minibatch_size,
edit_budget=self.edit_budget,
random_seed=random_seed,
error_system=self.get_error_minibatch_prompt(),
success_system=self.get_success_minibatch_prompt(),
step_buffer_context=step_buffer_context,
meta_skill_context=meta_skill_context,
update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
)
def get_task_types(self) -> list[str]:
return ["qa"]