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f577d1f b44d7b0 f577d1f 9737348 f577d1f 9737348 f577d1f 9737348 f577d1f 9737348 f577d1f b44d7b0 f577d1f | 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 | """Grader for the Deceit environment.
Two-stage design:
Stage 1 — exact match (normalized): handles ~80% of cases, zero cost.
Stage 2 — GPT-4o-mini semantic match: only when exact fails, results cached.
"""
from __future__ import annotations
import hashlib
import json
import re
import pathlib
import time
from dataclasses import dataclass
import os
try:
from openai import OpenAI
except ImportError:
OpenAI = None # type: ignore[assignment,misc]
def _default_cache_path() -> pathlib.Path:
"""Use DECEIT_GRADER_CACHE env var, falling back to /tmp."""
env_path = os.environ.get("DECEIT_GRADER_CACHE")
if env_path:
return pathlib.Path(env_path)
return pathlib.Path("/tmp/deceit_grader_cache.json")
@dataclass
class GraderResult:
correct: bool
method: str # "exact" | "semantic" | "abstain"
explanation: str
def _normalize(text: str) -> str:
text = text.lower().strip()
text = re.sub(r"[^\w\s]", "", text)
text = re.sub(r"\s+", " ", text).strip()
return text
class Grader:
"""Checks agent answers against ground truth with caching."""
def __init__(
self,
cache_path: str | pathlib.Path | None = None,
openai_api_key: str | None = None,
) -> None:
self._cache_path = pathlib.Path(cache_path) if cache_path is not None else _default_cache_path()
self._openai_api_key = openai_api_key
self._cache: dict[str, bool] = {}
if self._cache_path.exists():
try:
self._cache = json.loads(self._cache_path.read_text(encoding="utf-8"))
except (json.JSONDecodeError, OSError):
self._cache = {}
def check(self, answer: str, ground_truth: str) -> GraderResult:
"""Grade answer against ground_truth. Returns GraderResult."""
if not answer:
return GraderResult(correct=False, method="exact", explanation="empty answer")
if _normalize(answer) == _normalize(ground_truth):
return GraderResult(correct=True, method="exact", explanation="normalized exact match")
return self._semantic_check(answer, ground_truth)
def _semantic_check(self, answer: str, ground_truth: str) -> GraderResult:
cache_key = hashlib.sha256(f"{answer}|{ground_truth}".encode()).hexdigest()
if cache_key in self._cache:
correct = self._cache[cache_key]
return GraderResult(
correct=correct,
method="semantic",
explanation="cached semantic match" if correct else "cached semantic mismatch",
)
if not self._openai_api_key:
raise RuntimeError(
"Semantic match required but no OpenAI API key configured. "
"Pass openai_api_key to Grader() or set OPENAI_API_KEY env var."
)
if OpenAI is None:
raise RuntimeError("openai package is not installed. Run: pip install openai")
client = OpenAI(api_key=self._openai_api_key)
prompt = (
f"Is '{answer}' semantically equivalent to '{ground_truth}'? "
"Reply YES or NO only."
)
max_retries = 3
for attempt in range(max_retries):
try:
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}],
max_tokens=5,
temperature=0,
)
break
except Exception as e:
if "429" in str(e) or "RateLimitError" in type(e).__name__:
print(f"[grader] Rate limit hit (attempt {attempt + 1}/{max_retries}), waiting 25s...")
time.sleep(25)
if attempt == max_retries - 1:
raise
else:
raise
verdict = response.choices[0].message.content.strip().upper()
correct = verdict.startswith("YES")
self._cache[cache_key] = correct
self._save_cache()
return GraderResult(
correct=correct,
method="semantic",
explanation="semantic match" if correct else "semantic mismatch",
)
def _save_cache(self) -> None:
self._cache_path.parent.mkdir(parents=True, exist_ok=True)
tmp = self._cache_path.with_suffix(".tmp")
tmp.write_text(json.dumps(self._cache, indent=2), encoding="utf-8")
tmp.replace(self._cache_path)
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