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bcd8636 | 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 | """Core environment logic for DataDetective."""
import random
import uuid
from typing import Any, Optional
from openenv.core.env_server import Environment
try:
from ..models import DataDetectiveAction, DataDetectiveObservation, DataDetectiveState
from .database import create_database, get_schema_info
from .tasks import TASKS, grade_answer
except (ImportError, ModuleNotFoundError):
from models import DataDetectiveAction, DataDetectiveObservation, DataDetectiveState
from server.database import create_database, get_schema_info
from server.tasks import TASKS, grade_answer
class DataDetectiveEnvironment(
Environment[DataDetectiveAction, DataDetectiveObservation, DataDetectiveState]
):
SUPPORTS_CONCURRENT_SESSIONS = True
MAX_STEPS = 30
def __init__(self):
super().__init__()
self._db = None
self._task_id: str = ""
self._step_count: int = 0
self._episode_id: str = ""
self._queries_executed: int = 0
self._state = DataDetectiveState()
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
task_id: Optional[str] = None,
**kwargs: Any,
) -> DataDetectiveObservation:
if seed is not None:
random.seed(seed)
self._episode_id = episode_id or str(uuid.uuid4())
self._task_id = task_id if task_id in TASKS else random.choice(list(TASKS))
self._step_count = 0
self._queries_executed = 0
if self._db is not None:
self._db.close()
self._db = create_database()
task = TASKS[self._task_id]
schema = get_schema_info(self._db)
self._state = DataDetectiveState(
episode_id=self._episode_id,
step_count=0,
task_id=self._task_id,
queries_executed=0,
max_steps=self.MAX_STEPS,
)
return DataDetectiveObservation(
done=False,
reward=None,
output="Environment ready. Run SQL queries to investigate the issue, then submit your answer.",
task_description=task["description"],
schema_info=schema,
step_number=0,
max_steps=self.MAX_STEPS,
message=f"Investigation: {task['title']} [{task['difficulty'].upper()}] -- {self.MAX_STEPS} steps available.",
)
def step(
self,
action: DataDetectiveAction,
timeout_s: Optional[float] = None,
**kwargs: Any,
) -> DataDetectiveObservation:
self._step_count += 1
self._state.step_count = self._step_count
remaining = self.MAX_STEPS - self._step_count
if self._step_count > self.MAX_STEPS:
return self._obs(
done=True, reward=0.0,
output="Maximum steps reached -- investigation ended with no answer submitted.",
message="Out of steps.",
)
atype = (action.action_type or "").strip().lower()
if atype == "query":
return self._handle_query(action.content, remaining)
elif atype == "answer":
return self._handle_answer(action.content)
else:
return self._obs(
done=False, reward=0.0,
output="",
message=f"Unknown action_type '{action.action_type}'. Use 'query' or 'answer'. ({remaining} steps left)",
)
@property
def state(self) -> DataDetectiveState:
return self._state
def close(self) -> None:
if self._db is not None:
self._db.close()
self._db = None
def _obs(self, *, done: bool, reward: float | None, output: str, message: str) -> DataDetectiveObservation:
return DataDetectiveObservation(
done=done,
reward=reward,
output=output,
task_description=TASKS[self._task_id]["description"],
schema_info="",
step_number=self._step_count,
max_steps=self.MAX_STEPS,
message=message,
)
def _handle_query(self, sql: str, remaining: int) -> DataDetectiveObservation:
self._queries_executed += 1
self._state.queries_executed = self._queries_executed
if not sql or not sql.strip():
return self._obs(
done=False, reward=0.0,
output="Empty query -- please provide a valid SQL statement.",
message=f"{remaining} steps left.",
)
try:
cur = self._db.cursor()
cur.execute(sql)
columns = [d[0] for d in cur.description] if cur.description else []
rows = cur.fetchall()
output = _format_table(columns, rows) if rows else "Query returned 0 rows."
except Exception as exc:
output = f"SQL Error: {exc}"
return self._obs(
done=False, reward=0.0,
output=output,
message=f"Query failed. Fix your SQL and retry. ({remaining} steps left)",
)
return self._obs(
done=False, reward=0.0,
output=output,
message=f"{len(rows)} row(s) returned. ({remaining} steps left)",
)
def _handle_answer(self, answer_text: str) -> DataDetectiveObservation:
reward = grade_answer(self._task_id, answer_text)
if reward >= 0.8:
verdict = "Excellent investigation!"
elif reward >= 0.5:
verdict = "Good findings, but some details missing."
else:
verdict = "Several key findings were missed."
return self._obs(
done=True,
reward=reward,
output=f"Score: {reward:.2f} / 1.00 -- {verdict}",
message=f"Investigation complete. Final score: {reward:.2f}",
)
def _format_table(columns: list[str], rows: list, max_rows: int = 100) -> str:
truncated = len(rows) > max_rows
display = rows[:max_rows]
widths = [len(str(c)) for c in columns]
for row in display:
for i, v in enumerate(row):
widths[i] = max(widths[i], min(len(str(v)), 60))
header = " | ".join(str(c).ljust(widths[i]) for i, c in enumerate(columns))
sep = "-+-".join("-" * w for w in widths)
lines = [header, sep]
for row in display:
lines.append(" | ".join(str(v).ljust(widths[i])[:60] for i, v in enumerate(row)))
if truncated:
lines.append(f"... (showing {max_rows} of {len(rows)} rows)")
return "\n".join(lines)
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