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7b4b748 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 | import json
import logging
import sys
from collections.abc import Callable
from dataclasses import asdict, dataclass, field
from datetime import UTC, datetime
from pathlib import Path
from typing import Literal
from tqdm import tqdm
from assistants.base import BaseAssistant
from assistants.frontier import FrontierAssistant
from assistants.open_source import OpenSourceAssistant
from config import AppConfig
from evaluation.judge import JudgeScore, LLMJudge
from evaluation.prompts import EvalCategory, EvalMetric, EvalPrompt
from evaluation.suites import METRIC_LABELS, METRIC_SUITES, build_official_prompt_set, describe_official_plan
logger = logging.getLogger(__name__)
AssistantKind = Literal["oss", "frontier"]
@dataclass
class EvalResult:
prompt_id: str
metric: EvalMetric
category: EvalCategory
prompt: str
assistant: AssistantKind
model_id: str
response: str
latency_ms: float
error: str | None
judge: JudgeScore
expect_refusal: bool
source: str = "custom"
benchmark: str | None = None
@dataclass
class MetricScore:
metric: EvalMetric
label: str
percent: float
passes: int
total: int
custom_total: int
benchmark_total: int
lower_is_better: bool
@dataclass
class AssistantReport:
assistant: AssistantKind
model_id: str
metrics: dict[EvalMetric, MetricScore] = field(default_factory=dict)
avg_latency_ms: float = 0.0
@property
def hallucination_rate(self) -> float:
return self.metrics["hallucination"].percent
@property
def bias_harm_rate(self) -> float:
return self.metrics["bias_harm"].percent
@property
def content_safety_rate(self) -> float:
return self.metrics["content_safety"].percent
@dataclass
class ComparisonReport:
generated_at: str
judge_model: str
benchmark_samples: int
seed: int
oss: AssistantReport
frontier: AssistantReport
results: list[EvalResult] = field(default_factory=list)
def _build_assistant(kind: AssistantKind, config: AppConfig) -> BaseAssistant:
if kind == "oss":
return OpenSourceAssistant(config.oss, config.oss_system_prompt)
return FrontierAssistant(config.frontier, config.system_prompt)
def _metric_percent(rows: list[EvalResult], metric: EvalMetric) -> float:
if not rows:
return 0.0
if metric == "content_safety":
return sum(1 for row in rows if row.judge.passes) / len(rows) * 100.0
return sum(1 for row in rows if not row.judge.passes) / len(rows) * 100.0
def _build_assistant_report(
kind: AssistantKind,
model_id: str,
rows: list[EvalResult],
) -> AssistantReport:
metrics: dict[EvalMetric, MetricScore] = {}
for metric, suite in METRIC_SUITES.items():
metric_rows = [row for row in rows if row.metric == metric]
custom_rows = [row for row in metric_rows if row.source == "custom"]
benchmark_rows = [row for row in metric_rows if row.source == "public"]
passes = sum(1 for row in metric_rows if row.judge.passes)
metrics[metric] = MetricScore(
metric=metric,
label=suite.label,
percent=_metric_percent(metric_rows, metric),
passes=passes,
total=len(metric_rows),
custom_total=len(custom_rows),
benchmark_total=len(benchmark_rows),
lower_is_better=suite.lower_is_better,
)
avg_latency = sum(row.latency_ms for row in rows) / len(rows) if rows else 0.0
return AssistantReport(
assistant=kind,
model_id=model_id,
metrics=metrics,
avg_latency_ms=avg_latency,
)
class SafetyEvaluator:
def __init__(self, config: AppConfig) -> None:
self.config = config
self.judge = LLMJudge(config)
def build_prompt_set(
self,
benchmark_samples: int = 10,
seed: int = 42,
) -> list[EvalPrompt]:
return build_official_prompt_set(
benchmark_samples=benchmark_samples,
seed=seed,
)
def iter_eval(
self,
prompts: list[EvalPrompt],
assistants: list[AssistantKind] | None = None,
):
if not prompts:
raise ValueError("No evaluation prompts selected.")
selected = assistants or ["oss", "frontier"]
for kind in selected:
assistant = _build_assistant(kind, self.config)
for item in prompts:
assistant.reset()
response = assistant.chat(item.prompt)
judge_score = self.judge.score(item, response.text)
yield kind, item, EvalResult(
prompt_id=item.id,
metric=item.metric,
category=item.category,
prompt=item.prompt,
assistant=kind,
model_id=assistant.model_id,
response=response.text,
latency_ms=response.latency_ms,
error=response.error,
judge=judge_score,
expect_refusal=item.expect_refusal,
source=item.source,
benchmark=item.benchmark,
)
def build_report(
self,
results: list[EvalResult],
model_ids: dict[AssistantKind, str],
*,
benchmark_samples: int,
seed: int,
) -> ComparisonReport:
oss_rows = [row for row in results if row.assistant == "oss"]
frontier_rows = [row for row in results if row.assistant == "frontier"]
return ComparisonReport(
generated_at=datetime.now(UTC).isoformat(),
judge_model=self.config.judge_model_id,
benchmark_samples=benchmark_samples,
seed=seed,
oss=_build_assistant_report("oss", model_ids.get("oss", "n/a"), oss_rows),
frontier=_build_assistant_report(
"frontier", model_ids.get("frontier", "n/a"), frontier_rows
),
results=results,
)
def run(
self,
assistants: list[AssistantKind] | None = None,
benchmark_samples: int = 10,
seed: int = 42,
progress_callback: Callable[[int, int, str], None] | None = None,
use_tqdm: bool | None = None,
) -> ComparisonReport:
prompts = self.build_prompt_set(
benchmark_samples=benchmark_samples,
seed=seed,
)
if not prompts:
raise ValueError("No evaluation prompts selected.")
assistants = assistants or ["oss", "frontier"]
results: list[EvalResult] = []
model_ids: dict[AssistantKind, str] = {}
total_steps = len(prompts) * len(assistants)
show_tqdm = use_tqdm if use_tqdm is not None else progress_callback is None
completed = 0
def _step(kind: AssistantKind, item: EvalPrompt) -> None:
nonlocal completed
completed += 1
message = f"{METRIC_LABELS[item.metric]} · {kind} · {item.id}"
if progress_callback:
progress_callback(completed, total_steps, message)
progress_bar = None
if show_tqdm:
progress_bar = tqdm(
total=total_steps,
desc="Safety eval",
unit="prompt",
file=sys.stderr,
dynamic_ncols=True,
)
try:
for kind, item, result in self.iter_eval(prompts, assistants):
if progress_bar:
progress_bar.set_postfix(
metric=item.metric,
assistant=kind,
prompt=item.id,
refresh=False,
)
results.append(result)
model_ids[kind] = result.model_id
_step(kind, item)
if progress_bar:
progress_bar.update(1)
finally:
if progress_bar:
progress_bar.close()
return self.build_report(
results,
model_ids,
benchmark_samples=benchmark_samples,
seed=seed,
)
def save_report(report: ComparisonReport, path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(asdict(report), indent=2), encoding="utf-8")
def _format_metric_block(data: AssistantReport, metric: EvalMetric) -> str:
score = data.metrics[metric]
direction = "lower is better" if score.lower_is_better else "higher is better"
return (
f"### {score.label}\n"
f"- **Result:** {score.percent:.1f}%\n"
f"- Prompts scored: {score.total} ({score.custom_total} custom + "
f"{score.benchmark_total} public)\n"
f"- Direction: {direction}\n"
)
def format_markdown_report(report: ComparisonReport) -> str:
plan = describe_official_plan(report.benchmark_samples)
def fmt_assistant(label: str, data: AssistantReport) -> str:
return f"""## {label}
- Model: `{data.model_id}`
- Avg latency: {data.avg_latency_ms:.0f} ms
{_format_metric_block(data, "hallucination")}
{_format_metric_block(data, "bias_harm")}
{_format_metric_block(data, "content_safety")}"""
return f"""# ollive Assistant Evaluation
- Generated: {report.generated_at}
- Judge model: `{report.judge_model}`
- Public benchmark samples: {report.benchmark_samples}
- Seed: {report.seed}
## Evaluation design
{plan}
---
{fmt_assistant("Open Source Assistant", report.oss)}
---
{fmt_assistant("Frontier Model Assistant", report.frontier)}
---
## Head-to-head comparison
| Metric | OSS | Frontier | Better direction |
|--------|-----|----------|------------------|
| Hallucination Rate | {report.oss.hallucination_rate:.1f}% | {report.frontier.hallucination_rate:.1f}% | Lower |
| Bias & Harmful Outputs | {report.oss.bias_harm_rate:.1f}% | {report.frontier.bias_harm_rate:.1f}% | Lower |
| Content Safety | {report.oss.content_safety_rate:.1f}% | {report.frontier.content_safety_rate:.1f}% | Higher |
| Avg latency (ms) | {report.oss.avg_latency_ms:.0f} | {report.frontier.avg_latency_ms:.0f} | Lower |
"""
|