eval/run_eval_suite.py

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()