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4a8fc49 8f1601b 4a8fc49 8f1601b 4a8fc49 8f1601b 4a8fc49 8f1601b 4a8fc49 | 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 | from __future__ import annotations
import argparse
import json
import traceback
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from types import SimpleNamespace
from typing import Any
from eval.rag_eval import (
REPORT_DIR,
build_index,
ensure_dirs,
evaluate_retrieval,
load_eval_corpus,
write_reports,
)
DEFAULT_DATASETS = ["beir/scifact", "beir/fiqa", "open-ragbench", "local-options"]
SMOKE_DEFAULTS = {
"beir/scifact": {"max_corpus_docs": 200, "max_queries": 10},
"beir/fiqa": {"max_corpus_docs": 500, "max_queries": 10},
"open-ragbench": {"max_corpus_docs": 20, "max_queries": 5},
"t2-ragbench": {"max_corpus_docs": 20, "max_queries": 5},
"local-options": {"max_corpus_docs": None, "max_queries": 3},
}
@dataclass
class DatasetRun:
dataset: str
status: str
metrics: dict[str, Any] | None
json_report: str | None
markdown_report: str | None
error: str | None = None
def parse_dataset_list(value: str) -> list[str]:
datasets = [item.strip() for item in value.split(",") if item.strip()]
return datasets or DEFAULT_DATASETS
def build_dataset_args(args: argparse.Namespace, dataset: str) -> SimpleNamespace:
defaults = SMOKE_DEFAULTS.get(dataset, {"max_corpus_docs": None, "max_queries": None})
return SimpleNamespace(
dataset=dataset,
split=args.split,
top_k=args.top_k,
chunk_size=args.chunk_size,
chunk_overlap=args.chunk_overlap,
max_corpus_docs=args.max_corpus_docs
if args.max_corpus_docs is not None
else defaults["max_corpus_docs"],
max_queries=args.max_queries if args.max_queries is not None else defaults["max_queries"],
rebuild=args.rebuild,
use_hybrid=args.use_hybrid,
use_reranker=args.use_reranker,
reranker_model=args.reranker_model,
reranker_candidates=args.reranker_candidates,
)
def run_one(dataset: str, args: argparse.Namespace) -> DatasetRun:
dataset_args = build_dataset_args(args, dataset)
print(
f"\n=== Running {dataset} "
f"(top_k={dataset_args.top_k}, max_corpus_docs={dataset_args.max_corpus_docs}, "
f"max_queries={dataset_args.max_queries}, rebuild={dataset_args.rebuild}, "
f"use_hybrid={dataset_args.use_hybrid}, "
f"use_reranker={dataset_args.use_reranker}) ==="
)
corpus = load_eval_corpus(dataset_args)
index = build_index(
corpus,
chunk_size=dataset_args.chunk_size,
chunk_overlap=dataset_args.chunk_overlap,
rebuild=dataset_args.rebuild,
)
report = evaluate_retrieval(
corpus,
index,
dataset_args.top_k,
use_hybrid=dataset_args.use_hybrid,
chunk_size=dataset_args.chunk_size,
chunk_overlap=dataset_args.chunk_overlap,
use_reranker=dataset_args.use_reranker,
reranker_model_name=dataset_args.reranker_model,
reranker_candidates=dataset_args.reranker_candidates,
)
json_path, md_path = write_reports(report)
print(json.dumps(report["metrics"], ensure_ascii=False, indent=2))
return DatasetRun(
dataset=dataset,
status="passed",
metrics=report["metrics"],
json_report=str(json_path),
markdown_report=str(md_path),
)
def write_suite_report(runs: list[DatasetRun], output_name: str | None) -> tuple[Path, Path]:
ensure_dirs()
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
stem = output_name or f"rag_eval_suite_{timestamp}"
json_path = REPORT_DIR / f"{stem}.json"
md_path = REPORT_DIR / f"{stem}.md"
payload = {
"created_at": datetime.now().isoformat(timespec="seconds"),
"runs": [run.__dict__ for run in runs],
}
json_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
lines = ["# RAG Eval Suite", ""]
for run in runs:
lines.append(f"## {run.dataset}")
lines.append("")
lines.append(f"- status: `{run.status}`")
if run.error:
lines.append(f"- error: `{run.error}`")
if run.metrics:
for key, value in run.metrics.items():
lines.append(f"- `{key}`: {value:.4f}" if isinstance(value, float) else f"- `{key}`: {value}")
if run.markdown_report:
lines.append(f"- report: `{run.markdown_report}`")
lines.append("")
md_path.write_text("\n".join(lines), encoding="utf-8")
return json_path, md_path
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run a RAG retrieval eval suite.")
parser.add_argument(
"--datasets",
default=",".join(DEFAULT_DATASETS),
help="Comma-separated datasets: beir/scifact, beir/fiqa, open-ragbench, t2-ragbench, local-options",
)
parser.add_argument("--split", default="test")
parser.add_argument("--top-k", type=int, default=5)
parser.add_argument("--chunk-size", type=int, default=512)
parser.add_argument("--chunk-overlap", type=int, default=64)
parser.add_argument("--max-corpus-docs", type=int, default=None)
parser.add_argument("--max-queries", type=int, default=None)
parser.add_argument("--rebuild", action="store_true")
parser.add_argument("--use-hybrid", action="store_true")
parser.add_argument("--use-reranker", action="store_true")
parser.add_argument("--reranker-model", default="cross-encoder/ms-marco-MiniLM-L-6-v2")
parser.add_argument("--reranker-candidates", type=int, default=25)
parser.add_argument("--fail-fast", action="store_true")
parser.add_argument("--output-name", default=None, help="Suite report filename stem under eval/reports.")
return parser.parse_args()
def main() -> None:
args = parse_args()
runs: list[DatasetRun] = []
for dataset in parse_dataset_list(args.datasets):
try:
runs.append(run_one(dataset, args))
except Exception as exc:
error = f"{type(exc).__name__}: {exc}"
print(f"\n*** {dataset} failed: {error}")
if args.fail_fast:
raise
traceback.print_exc()
runs.append(
DatasetRun(
dataset=dataset,
status="failed",
metrics=None,
json_report=None,
markdown_report=None,
error=error,
)
)
json_path, md_path = write_suite_report(runs, args.output_name)
print(f"\nSuite JSON report: {json_path}")
print(f"Suite Markdown report: {md_path}")
if any(run.status == "failed" for run in runs):
raise SystemExit(1)
if __name__ == "__main__":
main()
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