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Running on Zero
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483b7d0 | 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 | from __future__ import annotations
import json
import time
from collections.abc import Sequence
from dataclasses import asdict, dataclass, field
from datetime import UTC, datetime
from pathlib import Path
from typing import TYPE_CHECKING, Any
from secrag.core.config import Settings, get_settings
from secrag.core.logging import get_logger
from secrag.engine import QueryEngine, build_engine
from secrag.evaluation import metrics
from secrag.evaluation.goldens import load_goldens
from secrag.retrieval.store import SearchFilter
if TYPE_CHECKING:
from rich.console import Console
log = get_logger(__name__)
@dataclass(frozen=True, slots=True)
class Configuration:
name: str
arms: tuple[str, ...]
reranker: str
description: str
CONFIGURATIONS: tuple[Configuration, ...] = (
Configuration("dense only", ("dense",), "none", "BGE bi-encoder, no fusion"),
Configuration("bm25 only", ("bm25",), "none", "Okapi BM25 lexical baseline"),
Configuration("splade only", ("splade",), "none", "Learned sparse expansion"),
Configuration("dense + bm25", ("dense", "bm25"), "none", "Two-arm RRF"),
Configuration("dense + bm25 + splade", ("dense", "bm25", "splade"), "none", "Three-arm RRF"),
Configuration("hybrid + LTR", ("dense", "bm25", "splade"), "ltr", "LambdaMART over features"),
Configuration(
"hybrid + cross-encoder",
("dense", "bm25", "splade"),
"cross_encoder",
"MiniLM cross-encoder rerank",
),
)
def resolve_arms(arms: Sequence[str], *, enable_splade: bool) -> tuple[str, ...]:
return tuple(a for a in arms if a != "splade" or enable_splade)
@dataclass(slots=True)
class ConfigurationResult:
name: str
description: str
arms: list[str] = field(default_factory=list)
reranker: str = "none"
hit_rate: float = 0.0
ndcg: float = 0.0
mrr: float = 0.0
precision: float = 0.0
mean_latency_ms: float = 0.0
p95_latency_ms: float = 0.0
n_queries: int = 0
available: bool = True
in_sample: bool = False
note: str = ""
def _percentile(values: Sequence[float], pct: float) -> float:
if not values:
return 0.0
ordered = sorted(values)
index = min(len(ordered) - 1, round((pct / 100.0) * (len(ordered) - 1)))
return ordered[index]
async def run_benchmark(
*,
settings: Settings | None = None,
engine: QueryEngine | None = None,
report_path: Path | None = None,
console: Console | None = None,
) -> list[ConfigurationResult]:
settings = settings or get_settings()
engine = engine or build_engine(settings)
engine.retriever.ensure_ready()
cases = load_goldens(settings=settings)
k = settings.eval_k
results: list[ConfigurationResult] = []
for config in CONFIGURATIONS:
arms = resolve_arms(config.arms, enable_splade=settings.enable_splade)
if not arms:
results.append(
ConfigurationResult(
name=config.name,
description=config.description,
arms=list(config.arms),
reranker=config.reranker,
available=False,
note="SPLADE disabled in this deployment",
)
)
continue
reduced = arms != config.arms
if config.reranker == "ltr" and not engine.retriever.reranker("ltr").is_available:
results.append(
ConfigurationResult(
name=config.name,
description=config.description,
arms=list(config.arms),
reranker=config.reranker,
available=False,
note="LTR model not trained. Run: secrag train-ltr",
)
)
continue
accumulator = metrics.MetricAccumulator()
latencies: list[float] = []
for case in cases:
flt = SearchFilter(tickers=case.companies, fiscal_years=case.fiscal_years)
started = time.perf_counter()
retrieved = engine.retriever.retrieve(
case.question,
top_k=k,
flt=flt,
arms=arms,
reranker=config.reranker,
)
latencies.append((time.perf_counter() - started) * 1000.0)
relevance = metrics.relevance_vector(
retrieved.chunks, case.expected_sections, case.expected_terms
)
accumulator.add("hit_rate", metrics.hit_rate_at_k(relevance, k))
accumulator.add("ndcg", metrics.ndcg_at_k(relevance, k))
accumulator.add("mrr", metrics.reciprocal_rank(relevance))
accumulator.add("precision", metrics.precision_at_k(relevance, k))
results.append(
ConfigurationResult(
name=config.name,
description=config.description,
arms=list(arms),
reranker=config.reranker,
note="ran without the SPLADE arm" if reduced else "",
in_sample=config.reranker == "ltr",
hit_rate=round(accumulator.mean("hit_rate"), 4),
ndcg=round(accumulator.mean("ndcg"), 4),
mrr=round(accumulator.mean("mrr"), 4),
precision=round(accumulator.mean("precision"), 4),
mean_latency_ms=round(sum(latencies) / len(latencies), 2) if latencies else 0.0,
p95_latency_ms=round(_percentile(latencies, 95), 2),
n_queries=len(cases),
)
)
log.info("benchmark_config_done", config=config.name, ndcg=results[-1].ndcg)
payload = {
"generated_at": datetime.now(UTC).isoformat(),
"corpus_chunks": engine.retriever.corpus_size,
"n_queries": len(cases),
"eval_k": k,
"models": {
"dense": settings.dense_model,
"sparse": settings.sparse_model,
"reranker": settings.rerank_model,
},
"configurations": [asdict(r) for r in results],
"markdown": to_markdown(results, k),
}
path = report_path or (settings.project_root / "evals" / "reports" / "benchmark.json")
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
if console is not None:
_render(results, console, k)
console.print(f"[dim]Report written to {path}[/dim]")
return results
def to_markdown(results: Sequence[ConfigurationResult], k: int) -> str:
lines = [
f"| Configuration | nDCG@{k} | Hit@{k} | MRR | P@{k} | Mean ms | p95 ms |",
"|---|---:|---:|---:|---:|---:|---:|",
]
has_in_sample = False
for result in results:
if not result.available:
lines.append(f"| {result.name} | _{result.note}_ | | | | | |")
continue
label = result.name + (" \\*" if result.in_sample else "")
if result.note == "ran without the SPLADE arm":
label += " \\*\\*"
has_in_sample = has_in_sample or result.in_sample
lines.append(
f"| {label} | {result.ndcg:.3f} | {result.hit_rate:.3f} | "
f"{result.mrr:.3f} | {result.precision:.3f} | "
f"{result.mean_latency_ms:.0f} | {result.p95_latency_ms:.0f} |"
)
if any(r.note == "ran without the SPLADE arm" for r in results if r.available):
lines.append("")
lines.append(
"\\*\\* Ran with the SPLADE arm disabled, so this row reflects dense plus BM25 only."
)
if has_in_sample:
lines.append("")
lines.append(
"\\* Trained on these same queries, so this row is training-set "
"performance and is optimistic. Its honest grouped cross-validation "
"score is reported separately below."
)
return "\n".join(lines)
def _render(results: Sequence[ConfigurationResult], console: Console, k: int) -> None:
from rich.table import Table
table = Table(title=f"Retrieval ablation (n={results[0].n_queries if results else 0} queries)")
table.add_column("Configuration")
for column in (f"nDCG@{k}", f"Hit@{k}", "MRR", f"P@{k}", "mean ms", "p95 ms"):
table.add_column(column, justify="right")
best_ndcg = max((r.ndcg for r in results if r.available and not r.in_sample), default=0.0)
for result in results:
if not result.available:
table.add_row(result.name, f"[dim]{result.note}[/dim]", "", "", "", "", "")
continue
highlight = "[bold green]" if result.ndcg == best_ndcg else ""
suffix = " [yellow](in-sample)[/yellow]" if result.in_sample else ""
if result.note == "ran without the SPLADE arm":
suffix += " [dim](no splade)[/dim]"
closing = "[/bold green]" if highlight else ""
table.add_row(
result.name + suffix,
f"{highlight}{result.ndcg:.3f}{closing}",
f"{result.hit_rate:.3f}",
f"{result.mrr:.3f}",
f"{result.precision:.3f}",
f"{result.mean_latency_ms:.0f}",
f"{result.p95_latency_ms:.0f}",
)
console.print(table)
def _summary_payload(results: Sequence[ConfigurationResult]) -> dict[str, Any]:
return {r.name: {"ndcg": r.ndcg, "mrr": r.mrr} for r in results if r.available}
|