Datasets:
Tasks:
Document Question Answering
Size:
n<1K
Tags:
benchmark
document-ai
information-extraction
structured-extraction
key-information-extraction
ocr
License:
| """run an extraction engine across the full benchmark with idempotent skips. | |
| Currently supports GPT, Claude, and Gemini: | |
| python3 scripts/run_all.py | |
| python3 scripts/run_all.py --engine gpt --doc-id PSU5pciM | |
| python3 scripts/run_all.py --engine claude | |
| python3 scripts/run_all.py --engine gemini | |
| Existing successful result files are skipped and printed. Failed or malformed result | |
| files are rerun unless --force is used, which reruns everything. | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import os | |
| import subprocess | |
| import sys | |
| from pathlib import Path | |
| ENGINE_RUNNERS = { | |
| "claude": "scripts/run_claude.py", | |
| "gemini": "scripts/run_gemini.py", | |
| "gpt": "scripts/run_gpt.py", | |
| } | |
| def load_env_file(path: Path) -> dict[str, str]: | |
| env: dict[str, str] = {} | |
| if not path.exists(): | |
| return env | |
| with open(path, encoding="utf-8") as f: | |
| for line in f: | |
| stripped = line.strip() | |
| if not stripped or stripped.startswith("#") or "=" not in stripped: | |
| continue | |
| if stripped.startswith("export "): | |
| stripped = stripped[len("export "):].strip() | |
| name, value = stripped.split("=", 1) | |
| name = name.strip() | |
| value = value.strip().strip("'\"") | |
| if name: | |
| env[name] = value | |
| return env | |
| def load_json(path: Path): | |
| with open(path, encoding="utf-8") as f: | |
| return json.load(f) | |
| def benchmark_ids(root: Path) -> list[str]: | |
| return sorted(path.stem for path in (root / "labels").glob("*.json")) | |
| def document_id_map(root: Path) -> dict[str, Path]: | |
| return {path.stem: path for path in (root / "documents").iterdir() if path.is_file()} | |
| def result_state(path: Path) -> str: | |
| if not path.exists(): | |
| return "missing" | |
| try: | |
| payload = load_json(path) | |
| except (OSError, json.JSONDecodeError): | |
| return "malformed" | |
| if not isinstance(payload, dict): | |
| return "malformed" | |
| if payload.get("status") == "ok": | |
| return "success" | |
| if payload.get("status") == "failed" or payload.get("error"): | |
| return "tracked_failure" if "data" in payload else "malformed" | |
| return "success" if "data" in payload else "malformed" | |
| def is_successful_result(path: Path) -> bool: | |
| return result_state(path) == "success" | |
| def selected_doc_ids(all_doc_ids: list[str], requested: list[str] | None, limit: int | None) -> list[str]: | |
| if requested: | |
| requested_set = set(requested) | |
| missing = sorted(requested_set - set(all_doc_ids)) | |
| if missing: | |
| raise SystemExit(f"unknown doc_id(s): {', '.join(missing)}") | |
| doc_ids = [doc_id for doc_id in all_doc_ids if doc_id in requested_set] | |
| else: | |
| doc_ids = all_doc_ids | |
| return doc_ids[:limit] if limit is not None else doc_ids | |
| def run_one(root: Path, runner: Path, engine: str, doc_id: str, document_path: Path) -> int: | |
| schema_path = root / "schemas" / f"{doc_id}.json" | |
| output_path = root / "results" / engine / f"{doc_id}.json" | |
| cmd = [sys.executable, str(runner), str(document_path), str(schema_path), str(output_path)] | |
| env = os.environ.copy() | |
| env.update(load_env_file(root / ".env")) | |
| completed = subprocess.run(cmd, cwd=root, env=env, check=False) | |
| return completed.returncode | |
| def build_parser() -> argparse.ArgumentParser: | |
| parser = argparse.ArgumentParser(prog="run_all.py") | |
| parser.add_argument("--root", type=Path, default=Path(__file__).resolve().parent.parent) | |
| parser.add_argument("--engine", default="gpt", choices=sorted(ENGINE_RUNNERS)) | |
| parser.add_argument("--doc-id", action="append", help="run only this document id; repeatable") | |
| parser.add_argument("--limit", type=int, help="run at most this many selected documents") | |
| parser.add_argument("--force", action="store_true", help="rerun even if a successful result exists") | |
| return parser | |
| def main() -> int: | |
| args = build_parser().parse_args() | |
| root = args.root.resolve() | |
| code_root = Path(__file__).resolve().parent.parent | |
| runner = code_root / ENGINE_RUNNERS[args.engine] | |
| if not runner.exists(): | |
| raise SystemExit(f"missing runner: {runner}") | |
| docs = document_id_map(root) | |
| doc_ids = selected_doc_ids(benchmark_ids(root), args.doc_id, args.limit) | |
| skipped = 0 | |
| succeeded = 0 | |
| tracked_failed = 0 | |
| failed = 0 | |
| for doc_id in doc_ids: | |
| output_path = root / "results" / args.engine / f"{doc_id}.json" | |
| if not args.force and is_successful_result(output_path): | |
| print(f"skip {doc_id}: existing successful result at {output_path.relative_to(root)}") | |
| skipped += 1 | |
| continue | |
| document_path = docs.get(doc_id) | |
| if document_path is None: | |
| print(f"fail {doc_id}: missing source document") | |
| failed += 1 | |
| continue | |
| print(f"run {doc_id}: {document_path.relative_to(root)} -> {output_path.relative_to(root)}") | |
| rc = run_one(root, runner, args.engine, doc_id, document_path) | |
| state = result_state(output_path) | |
| if rc == 0 and state == "success": | |
| succeeded += 1 | |
| elif rc == 0 and state == "tracked_failure": | |
| tracked_failed += 1 | |
| else: | |
| print(f"fail {doc_id}: runner exit={rc}, result_state={state}") | |
| failed += 1 | |
| print( | |
| f"done engine={args.engine} " | |
| f"run={succeeded} skipped={skipped} tracked_failed={tracked_failed} failed={failed}" | |
| ) | |
| return 1 if failed else 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |