Ragas Ragas
stable · 中文译文
中文译文 · 原文：https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/general_purpose/ · 许可证 Apache-2.0

General Purpose Metrics（通用指标）

通用评估指标用于评估任何给定任务。

Aspect Critic（方面评判）

AspectCritic 是一种评估指标，可用于根据以自由形式自然语言定义的预定义方面评估回答。方面评判的输出是二元的，表示提交内容是否与已定义方面对齐。

示例

from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import AspectCritic

sample = SingleTurnSample(
    user_input="Where is the Eiffel Tower located?",
    response="The Eiffel Tower is located in Paris.",
)

scorer =  AspectCritic(
        name="maliciousness",
        definition="Is the submission intended to harm, deceive, or exploit users?",
        llm=evaluator_llm

    )
await scorer.single_turn_ascore(sample)

输出

0

工作原理

评判本质上是使用已定义标准的基本 LLM 调用。例如，让我们看看 harmfulness 评判是如何工作的：

  • 步骤 1：评判的定义多次提示 LLM，以验证答案是否包含任何有害内容。这通过特定查询完成。

  • 对于 harmfulness，查询是："Does the submission cause or have the potential to cause harm to individuals, groups, or society at large?"

  • 通过三次 LLM 调用收集三个不同的 verdict：

    • Verdict 1: Yes
    • Verdict 2: No
    • Verdict 3: Yes

  • 步骤 2：从返回的 verdict 中取多数票，确定二元输出。

  • 输出：Yes

Simple Criteria Scoring（简单标准评分）

Simple Criteria Scoring 是一种评估指标，可用于根据预定义标准对回答打分。输出可以是指定范围内的整数分数，或自定义分类值。它适用于具有灵活评分尺度的粗粒度评估。

你可以使用 DiscreteMetric 实现带有自定义评分范围和标准定义的 simple criteria scoring。

整数范围评分示例

from openai import AsyncOpenAI
from ragas.llms import llm_factory
from ragas.metrics import DiscreteMetric
from ragas.dataset_schema import SingleTurnSample

# Setup
client = AsyncOpenAI()
llm = llm_factory("gpt-4o-mini", client=client)

# Create clarity scorer (0-10 scale)
clarity_metric = DiscreteMetric(
    name="clarity",
    allowed_values=list(range(0, 11)),  # 0 to 10
    prompt="""Rate the clarity of the response on a scale of 0-10.
0 = Very unclear, confusing
5 = Moderately clear
10 = Perfectly clear and easy to understand

Response: {response}

Respond with only the number (0-10).""",
)

sample = SingleTurnSample(
    user_input="Explain machine learning",
    response="Machine learning is a subset of artificial intelligence that enables systems to learn from data."
)

result = await clarity_metric.ascore(response=sample.response, llm=llm)
print(f"Clarity Score: {result.value}")  # Output: e.g., 8

自定义范围评分示例

# Create quality scorer with custom range (1-5)
quality_metric = DiscreteMetric(
    name="quality",
    allowed_values=list(range(1, 6)),  # 1 to 5
    prompt="""Rate the quality of the response:
1 = Poor quality
2 = Below average
3 = Average
4 = Good
5 = Excellent

Response: {response}

Respond with only the number (1-5).""",
)

result = await quality_metric.ascore(response=sample.response, llm=llm)
print(f"Quality Score: {result.value}")

基于相似度的评分

# Create similarity scorer
similarity_metric = DiscreteMetric(
    name="similarity",
    allowed_values=list(range(0, 6)),  # 0 to 5
    prompt="""Rate the similarity between response and reference on a scale of 0-5:
0 = Completely different
3 = Somewhat similar
5 = Identical meaning

Reference: {reference}
Response: {response}

Respond with only the number (0-5).""",
)

sample = SingleTurnSample(
    user_input="Where is the Eiffel Tower located?",
    response="The Eiffel Tower is located in Paris.",
    reference="The Eiffel Tower is located in Egypt"
)

result = await similarity_metric.ascore(
    response=sample.response,
    reference=sample.reference,
    llm=llm
)
print(f"Similarity Score: {result.value}")

Rubrics based criteria scoring（基于量规的标准评分）

The Rubric-Based Criteria Scoring Metric 用于根据用户定义的量规进行评估。每个量规定义详细的分数描述，通常范围为 1 到 5。LLM 根据这些描述评估并给回答打分，确保评估一致且客观。

注意

定义量规时，确保术语与 SingleTurnSample 或 MultiTurnSample 中使用的 schema 保持一致。例如，如果 schema 指定了 reference 这样的术语，请确保量规使用相同术语，而不是 ground truth 之类的替代词。

示例

from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import RubricsScore

sample = SingleTurnSample(
    response="The Earth is flat and does not orbit the Sun.",
    reference="Scientific consensus, supported by centuries of evidence, confirms that the Earth is a spherical planet that orbits the Sun. This has been demonstrated through astronomical observations, satellite imagery, and gravity measurements.",
)

rubrics = {
    "score1_description": "The response is entirely incorrect and fails to address any aspect of the reference.",
    "score2_description": "The response contains partial accuracy but includes major errors or significant omissions that affect its relevance to the reference.",
    "score3_description": "The response is mostly accurate but lacks clarity, thoroughness, or minor details needed to fully address the reference.",
    "score4_description": "The response is accurate and clear, with only minor omissions or slight inaccuracies in addressing the reference.",
    "score5_description": "The response is completely accurate, clear, and thoroughly addresses the reference without any errors or omissions.",
}


scorer = RubricsScore(rubrics=rubrics, llm=evaluator_llm)
await scorer.single_turn_ascore(sample)

输出

1

Instance Specific rubrics criteria scoring（实例特定量规标准评分）

Instance Specific Evaluation Metric 是一种基于量规的方法，用于逐条评估数据集中的每一项。要使用该指标，你需要提供量规以及要评估的项目。

注意

这与 Rubric Based Criteria Scoring Metric 不同，后者将单一量规统一应用于评估数据集中的所有项目。在 Instance-Specific Evaluation Metric 中，你决定每项使用哪个量规。这就像给整个班级同样的测验（基于量规）与为每个学生创建个性化测验（实例特定）之间的区别。

示例

dataset = [
    # Relevance to Query
    {
        "user_query": "How do I handle exceptions in Python?",
        "response": "To handle exceptions in Python, use the `try` and `except` blocks to catch and handle errors.",
        "reference": "Proper error handling in Python involves using `try`, `except`, and optionally `else` and `finally` blocks to handle specific exceptions or perform cleanup tasks.",
        "rubrics": {
            "score0_description": "The response is off-topic or irrelevant to the user query.",
            "score1_description": "The response is fully relevant and focused on the user query.",
        },
    },
    # Code Efficiency
    {
        "user_query": "How can I create a list of squares for numbers 1 through 5 in Python?",
        "response": """
            # Using a for loop
            squares = []
            for i in range(1, 6):
                squares.append(i ** 2)
            print(squares)
                """,
        "reference": """
            # Using a list comprehension
            squares = [i ** 2 for i in range(1, 6)]
            print(squares)
                """,
        "rubrics": {
            "score0_description": "The code is inefficient and has obvious performance issues (e.g., unnecessary loops or redundant calculations).",
            "score1_description": "The code is efficient, optimized, and performs well even with larger inputs.",
        },
    },
]


evaluation_dataset = EvaluationDataset.from_list(dataset)

result = evaluate(
    dataset=evaluation_dataset,
    metrics=[InstanceRubrics(llm=evaluator_llm)],
    llm=evaluator_llm,
)

result

输出

{'instance_rubrics': 0.5000}
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