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ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
2026-08-20 13:12:50 +00:00

65 lines
2.0 KiB
Python

"""Run the deterministic reference agent or evaluate external structured predictions."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
try:
from dotenv import load_dotenv
load_dotenv()
except ImportError:
pass
from agent import DeterministicReportingAgent
from evaluator import evaluate, expected_by_task, load_json
from reporting_tools import ReportingEnvironment
ROOT = Path(__file__).parent
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--predictions",
help="Evaluate an external agent's JSON predictions instead of the reference agent.",
)
parser.add_argument("--output", help="Optionally save predictions and scores as JSON.")
parser.add_argument(
"--tolerance",
type=float,
default=float(os.getenv("PUBLIC_HEALTH_EVAL_TOLERANCE", "0.01")),
help="Absolute tolerance for numeric answers (default: 0.01).",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
expected = expected_by_task(ROOT / "expected_answers.json")
if args.predictions:
predictions = load_json(args.predictions)
else:
tasks = load_json(ROOT / "tasks.json")
environment = ReportingEnvironment(ROOT / "data" / "synthetic_reports.csv")
agent = DeterministicReportingAgent(environment, expected)
predictions = [agent.run(task) for task in tasks]
report = evaluate(predictions, expected, tolerance=args.tolerance)
for task in report["tasks"]:
print(f"{task['task_id']:<30} {task['score']}/{task['max_score']}")
print("-" * 36)
print(f"TOTAL{'':<25} {report['score']}/{report['max_score']}")
if args.output:
payload = {"predictions": predictions, "evaluation": report}
Path(args.output).write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
if __name__ == "__main__":
main()