运行你的第一次实验
本教程带你使用 @experiment 装饰器和本地 CSV 后端，运行第一次 Ragas 实验。
前置条件
- Python 3.9+
- 已安装 Ragas（见安装）
Hello World 👋
1. 安装（如果还没有安装）
pip install ragas
2. 创建 hello_world.py
把下面内容复制到新文件并保存为 hello_world.py：
import numpy as np
from ragas import Dataset, experiment
from ragas.metrics import MetricResult, discrete_metric
# Define a custom metric for accuracy
@discrete_metric(name="accuracy_score", allowed_values=["pass", "fail"])
def accuracy_score(response: str, expected: str):
result = "pass" if expected.lower().strip() == response.lower().strip() else "fail"
return MetricResult(value=result, reason=f"Match: {result == 'pass'}")
# Mock application endpoint that simulates an AI application response
def mock_app_endpoint(**kwargs) -> str:
return np.random.choice(["Paris", "4", "Blue Whale", "Einstein", "Python"])
# Create an experiment that uses the mock application endpoint and the accuracy metric
@experiment()
async def run_experiment(row):
response = mock_app_endpoint(query=row.get("query"))
accuracy = accuracy_score.score(response=response, expected=row.get("expected_output"))
return {**row, "response": response, "accuracy": accuracy.value}
if __name__ == "__main__":
import asyncio
# Create dataset inline
dataset = Dataset(name="test_dataset", backend="local/csv", root_dir=".")
test_data = [
{"query": "What is the capital of France?", "expected_output": "Paris"},
{"query": "What is 2 + 2?", "expected_output": "4"},
{"query": "What is the largest animal?", "expected_output": "Blue Whale"},
{"query": "Who developed the theory of relativity?", "expected_output": "Einstein"},
{"query": "What programming language is named after a snake?", "expected_output": "Python"},
]
for sample in test_data:
dataset.append(sample)
dataset.save()
# Run experiment
_ = asyncio.run(run_experiment.arun(dataset, name="first_experiment"))
3. 检查生成的文件
tree .
你应该会看到：
├── datasets
│ └── test_dataset.csv
└── experiments
└── first_experiment.csv
4. 查看第一次实验的结果
open experiments/first_experiment.csv
输出预览：