#!/usr/bin/env python from __future__ import annotations import argparse import json from pathlib import Path from typing import Any def _as_list(value: Any) -> list[str]: if isinstance(value, list): return [str(item) for item in value if str(item).strip()] if isinstance(value, str) and value.strip(): return [value.strip()] return [] def _evaluate_case(case: dict[str, Any]) -> dict[str, Any]: case_id = str(case.get("id", "case")) actual_response = str(case.get("actual_response", "")) actual_tools = {str(item) for item in _as_list(case.get("actual_tools"))} expected_contains = _as_list(case.get("expected_contains")) expected_tools = {str(item) for item in _as_list(case.get("expected_tools"))} missing_text = [needle for needle in expected_contains if needle not in actual_response] missing_tools = sorted(expected_tools - actual_tools) passed = not missing_text and not missing_tools return { "id": case_id, "passed": passed, "missing_text": missing_text, "missing_tools": missing_tools, } def _evaluate_results( *, dataset_path: Path, results: list[dict[str, Any]], strict: bool, min_pass_rate: float | None, max_failed: int | None, min_cases: int | None, duplicate_ids: list[str], missing_expected_tools_ids: list[str], ) -> dict[str, Any]: passed = sum(1 for row in results if bool(row.get("passed"))) failed = len(results) - passed pass_rate = (passed / len(results)) if results else 0.0 accepted = (failed == 0) if strict else (passed >= failed) failure_reasons: list[str] = [] if strict and failed > 0: failure_reasons.append("strict_failed_cases") if not strict and passed < failed: failure_reasons.append("non_strict_majority_failed") if min_pass_rate is not None and pass_rate < min_pass_rate: accepted = False failure_reasons.append("pass_rate_below_threshold") if max_failed is not None and failed > max_failed: accepted = False failure_reasons.append("failed_count_above_threshold") if min_cases is not None and len(results) < min_cases: accepted = False failure_reasons.append("insufficient_case_count") if duplicate_ids: accepted = False failure_reasons.append("duplicate_case_ids") if missing_expected_tools_ids: accepted = False failure_reasons.append("missing_expected_tools") return { "dataset": str(dataset_path), "strict": strict, "thresholds": { "min_pass_rate": min_pass_rate, "max_failed": max_failed, "min_cases": min_cases, }, "case_count": len(results), "passed": passed, "failed": failed, "pass_rate": pass_rate, "accepted": accepted, "failure_reasons": failure_reasons, "duplicate_ids": duplicate_ids, "missing_expected_tools_ids": missing_expected_tools_ids, "results": results, } def main() -> int: parser = argparse.ArgumentParser(description="Run deterministic evaluation dataset checks.") parser.add_argument("dataset", help="Path to dataset JSON") parser.add_argument("--output", default="") parser.add_argument("--strict", action="store_true") parser.add_argument( "--min-pass-rate", type=float, default=None, help="Optional minimum pass-rate acceptance threshold in [0.0, 1.0].", ) parser.add_argument( "--max-failed", type=int, default=None, help="Optional maximum failed-case acceptance threshold (>= 0).", ) parser.add_argument( "--min-cases", type=int, default=None, help="Optional minimum number of evaluation cases required.", ) parser.add_argument( "--require-unique-ids", action="store_true", help="Fail if case IDs are duplicated.", ) parser.add_argument( "--require-expected-tools", action="store_true", help="Fail if any case has an empty expected_tools list.", ) args = parser.parse_args() dataset_path = Path(args.dataset) if args.min_pass_rate is not None and (args.min_pass_rate < 0.0 or args.min_pass_rate > 1.0): raise SystemExit("--min-pass-rate must be between 0.0 and 1.0.") if args.max_failed is not None and args.max_failed < 0: raise SystemExit("--max-failed must be >= 0.") if args.min_cases is not None and args.min_cases < 0: raise SystemExit("--min-cases must be >= 0.") payload = json.loads(dataset_path.read_text(encoding="utf-8")) cases = payload.get("cases", []) if isinstance(payload, dict) else [] if not isinstance(cases, list): raise SystemExit("Dataset format error: expected top-level object with 'cases' list.") results = [_evaluate_case(case) for case in cases if isinstance(case, dict)] case_ids = [str(case.get("id", "")).strip() for case in cases if isinstance(case, dict)] id_counts: dict[str, int] = {} for case_id in case_ids: if not case_id: continue id_counts[case_id] = id_counts.get(case_id, 0) + 1 duplicate_ids = sorted(case_id for case_id, count in id_counts.items() if count > 1) if not args.require_unique_ids: duplicate_ids = [] missing_expected_tools_ids: list[str] = [] if args.require_expected_tools: for case in cases: if not isinstance(case, dict): continue expected_tools = _as_list(case.get("expected_tools")) if expected_tools: continue missing_expected_tools_ids.append(str(case.get("id", "case"))) summary = _evaluate_results( dataset_path=dataset_path, results=results, strict=bool(args.strict), min_pass_rate=args.min_pass_rate, max_failed=args.max_failed, min_cases=args.min_cases, duplicate_ids=duplicate_ids, missing_expected_tools_ids=missing_expected_tools_ids, ) text = json.dumps(summary, indent=2) print(text) if args.output: out_path = Path(args.output) out_path.parent.mkdir(parents=True, exist_ok=True) out_path.write_text(text, encoding="utf-8") return 0 if summary["accepted"] else 1 if __name__ == "__main__": raise SystemExit(main())