用 Ragas 指标比较 VertexAI 提供的模型在基于 RAG 的问答任务上的表现

本教程是关于如何将 Vertex AI 模型与 Ragas 一起使用的三部分系列的一部分。建议你先阅读 入门：Ragas 与 Vertex AI；即使你还没有跟过那篇，也完全没问题。你可以通过点击查看 Align LLM Metrics 教程。

概述

在本教程中，你将学习如何使用 Ragas 为 问答（QA） 任务给不同 LLM 模型打分并评测。然后可视化并比较评测结果，以选择生成式模型。

开始

安装依赖

%pip install --upgrade --user --quiet langchain-core langchain-google-vertexai langchain ragas rouge_score

重启运行时

要在这个 Jupyter runtime 中使用新安装的包，必须重启运行时。你可以运行下面的 cell 来完成，它会重启当前 kernel。

重启可能需要一分钟或更久。重启完成后，继续下一步。

import IPython

app = IPython.Application.instance()
app.kernel.do_shutdown(True)

认证你的 notebook 环境（仅 Colab）

如果你在 Google Colab 上运行本 notebook，请运行下面的 cell 来认证你的环境。

import sys

if "google.colab" in sys.modules:
    from google.colab import auth

    auth.authenticate_user()

设置 Google Cloud 项目信息并初始化 Vertex AI SDK

PROJECT_ID = "[your-project-id]"  # @param {type:"string"}
LOCATION = "us-central1"  # @param {type:"string"}

if not PROJECT_ID or PROJECT_ID == "[your-project-id]":
    raise ValueError("Please set your PROJECT_ID")


import vertexai

vertexai.init(project=PROJECT_ID, location=LOCATION)

辅助函数

下面是一些用于展示评测报告和可视化评测结果的辅助函数。

import pandas as pd
import plotly.graph_objects as go
from IPython.display import HTML, Markdown, display


def display_eval_report(eval_result, metrics=None):
    """Display the evaluation results."""

    title, summary_metrics, report_df = eval_result
    metrics_df = pd.DataFrame.from_dict(summary_metrics, orient="index").T
    if metrics:
        metrics_df = metrics_df.filter(
            [
                metric
                for metric in metrics_df.columns
                if any(selected_metric in metric for selected_metric in metrics)
            ]
        )
        report_df = report_df.filter(
            [
                metric
                for metric in report_df.columns
                if any(selected_metric in metric for selected_metric in metrics)
            ]
        )

    # Display the title with Markdown for emphasis
    display(Markdown(f"## {title}"))

    # Display the metrics DataFrame
    display(Markdown("### Summary Metrics"))
    display(metrics_df)

    # Display the detailed report DataFrame
    display(Markdown("### Report Metrics"))
    display(report_df)


def plot_radar_plot(eval_results, max_score=5, metrics=None):
    fig = go.Figure()

    for eval_result in eval_results:
        title, summary_metrics, report_df = eval_result

        if metrics:
            summary_metrics = {
                k: summary_metrics[k]
                for k, v in summary_metrics.items()
                if any(selected_metric in k for selected_metric in metrics)
            }

        fig.add_trace(
            go.Scatterpolar(
                r=list(summary_metrics.values()),
                theta=list(summary_metrics.keys()),
                fill="toself",
                name=title,
            )
        )

    fig.update_layout(
        polar=dict(radialaxis=dict(visible=True, range=[0, max_score])), showlegend=True
    )

    fig.show()


def plot_bar_plot(eval_results, metrics=None):
    fig = go.Figure()
    data = []

    for eval_result in eval_results:
        title, summary_metrics, _ = eval_result
        if metrics:
            summary_metrics = {
                k: summary_metrics[k]
                for k, v in summary_metrics.items()
                if any(selected_metric in k for selected_metric in metrics)
            }

        data.append(
            go.Bar(
                x=list(summary_metrics.keys()),
                y=list(summary_metrics.values()),
                name=title,
            )
        )

    fig = go.Figure(data=data)

    # Change the bar mode
    fig.update_layout(barmode="group")
    fig.show()

用 Ragas 指标设置评测

定义 evaluator_llm

要使用基于模型的指标，首先定义你的 evaluator LLM 和 embeddings。

from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper
from langchain_google_vertexai import VertexAI, VertexAIEmbeddings


evaluator_llm = LangchainLLMWrapper(VertexAI(model_name="gemini-pro"))
evaluator_embeddings = LangchainEmbeddingsWrapper(VertexAIEmbeddings(model_name="text-embedding-004"))

Ragas 指标

选择并定义与你的应用最相关的指标。

from ragas import evaluate
from ragas.metrics import ContextPrecision, Faithfulness, RubricsScore, RougeScore

rouge_score = RougeScore()

helpfulness_rubrics = {
    "score1_description": "Response is useless/irrelevant, contains inaccurate/deceptive/misleading information, and/or contains harmful/offensive content. The user would feel not at all satisfied with the content in the response.",
    "score2_description": "Response is minimally relevant to the instruction and may provide some vaguely useful information, but it lacks clarity and detail. It might contain minor inaccuracies. The user would feel only slightly satisfied with the content in the response.",
    "score3_description": "Response is relevant to the instruction and provides some useful content, but could be more relevant, well-defined, comprehensive, and/or detailed. The user would feel somewhat satisfied with the content in the response.",
    "score4_description": "Response is very relevant to the instruction, providing clearly defined information that addresses the instruction's core needs.  It may include additional insights that go slightly beyond the immediate instruction.  The user would feel quite satisfied with the content in the response.",
    "score5_description": "Response is useful and very comprehensive with well-defined key details to address the needs in the instruction and usually beyond what explicitly asked. The user would feel very satisfied with the content in the response.",
}

rubrics_score = RubricsScore(name="helpfulness", rubrics=helpfulness_rubrics)
context_precision = ContextPrecision(llm=evaluator_llm)
faithfulness = Faithfulness(llm=evaluator_llm)

准备你的数据集

要用 Ragas 指标进行评测，你需要把数据转换成 EvaluationDataset，这是 Ragas 的核心数据类型。关于其结构的更多细节，请参阅 Ragas 文档。

# questions or query from user
user_inputs = [
    "Which part of the brain does short-term memory seem to rely on?",
    "What provided the Roman senate with exuberance?",
    "What area did the Hasan-jalalians command?",
]

# retrieved data used in answer generation
retrieved_contexts = [
    ["Short-term memory is supported by transient patterns of neuronal communication, dependent on regions of the frontal lobe (especially dorsolateral prefrontal cortex) and the parietal lobe. Long-term memory, on the other hand, is maintained by more stable and permanent changes in neural connections widely spread throughout the brain. The hippocampus is essential (for learning new information) to the consolidation of information from short-term to long-term memory, although it does not seem to store information itself. Without the hippocampus, new memories are unable to be stored into long-term memory, as learned from patient Henry Molaison after removal of both his hippocampi, and there will be a very short attention span. Furthermore, it may be involved in changing neural connections for a period of three months or more after the initial learning."],
    ["In 62 BC, Pompey returned victorious from Asia. The Senate, elated by its successes against Catiline, refused to ratify the arrangements that Pompey had made. Pompey, in effect, became powerless. Thus, when Julius Caesar returned from a governorship in Spain in 61 BC, he found it easy to make an arrangement with Pompey. Caesar and Pompey, along with Crassus, established a private agreement, now known as the First Triumvirate. Under the agreement, Pompey's arrangements would be ratified. Caesar would be elected consul in 59 BC, and would then serve as governor of Gaul for five years. Crassus was promised a future consulship."],
    ["The Seljuk Empire soon started to collapse. In the early 12th century, Armenian princes of the Zakarid noble family drove out the Seljuk Turks and established a semi-independent Armenian principality in Northern and Eastern Armenia, known as Zakarid Armenia, which lasted under the patronage of the Georgian Kingdom. The noble family of Orbelians shared control with the Zakarids in various parts of the country, especially in Syunik and Vayots Dzor, while the Armenian family of Hasan-Jalalians controlled provinces of Artsakh and Utik as the Kingdom of Artsakh."],
]

# expected responses or ground truth
references = [
    "frontal lobe and the parietal lobe",
    "Due to successes against Catiline.",
    "The Hasan-Jalalians commanded the area of Artsakh and Utik.",
]

from vertexai.generative_models import GenerativeModel

generation_config = {
    "max_output_tokens": 128,
    "temperature": 0.1,
}

model_a_name = "gemini-1.5-pro"
model_b_name = "gemini-1.0-pro"

gemini_model_15 = GenerativeModel(
    model_a_name,
    generation_config=generation_config,
)

gemini_model_1 = GenerativeModel(
    model_b_name,
    generation_config=generation_config,
)

responses_a = []
responses_b = []

# Template for creating the prompt
template = """Answer the question based only on the following context:
{context}

Question: {query}
"""

# Iterate through each user input and corresponding context
for i in range(len(user_inputs)):
    # Join the list of retrieved contexts into a single string
    context_str = "\n".join(retrieved_contexts[i])

    # Create prompt Generate response for Gemini 1.5 pro model
    gemini_15_prompt = template.format(context=context_str, query=user_inputs[i])

    gemini_15_response = gemini_model_15.generate_content(gemini_15_prompt)
    responses_a.append(gemini_15_response.text)

    # Create prompt Generate response for Gemini 1 pro model
    gemini_1_prompt = template.format(context=context_str, query=user_inputs[i])

    gemini_1_response = gemini_model_1.generate_content(gemini_1_prompt)
    responses_b.append(gemini_1_response.text)

把这些转换成 Ragas EvaluationDataset：

from ragas.dataset_schema import SingleTurnSample, EvaluationDataset

n = len(user_inputs)

samples_a = []
samples_b = []

for i in range(n):
    sample_a = SingleTurnSample(
        user_input=user_inputs[i],
        retrieved_contexts=retrieved_contexts[i],
        response=responses_a[i],
        reference=references[i],
    )
    sample_b = SingleTurnSample(
        user_input=user_inputs[i],
        retrieved_contexts=retrieved_contexts[i],
        response=responses_b[i],
        reference=references[i],
    )

    samples_a.append(sample_a)
    samples_b.append(sample_b)

ragas_eval_dataset_a = EvaluationDataset(samples=samples_a)
ragas_eval_dataset_b = EvaluationDataset(samples=samples_b)

ragas_eval_dataset_a.to_pandas()

输出

ragas_eval_dataset_b.to_pandas()

输出

运行评测

通过把数据集和所需指标列表传给 evaluate 函数，用 Ragas 评测这些数据集：

from ragas import evaluate

ragas_metrics = [
    context_precision,
    faithfulness,
    rouge_score,
    rubrics_score,
]

ragas_result_rag_a = evaluate(
    dataset=ragas_eval_dataset_a, metrics=ragas_metrics, llm=evaluator_llm
)

ragas_result_rag_b = evaluate(
    dataset=ragas_eval_dataset_b, metrics=ragas_metrics, llm=evaluator_llm
)

Evaluating: 100%|██████████| 12/12 [00:00<?, ?it/s]

Evaluating: 100%|██████████| 12/12 [00:00<?, ?it/s]

把结果包装进 Google 的 EvalResult 结构：

from vertexai.evaluation import EvalResult

result_rag_a = EvalResult(
    summary_metrics=ragas_result_rag_a._repr_dict,
    metrics_table=ragas_result_rag_a.to_pandas(),
)

result_rag_b = EvalResult(
    summary_metrics=ragas_result_rag_b._repr_dict,
    metrics_table=ragas_result_rag_b.to_pandas(),
)

比较评测结果

查看汇总结果

如果你想在一张表中查看所有评测指标的全面汇总，只需调用 display_eval_report() 辅助函数。

display_eval_report(
    eval_result=(
        f"{model_a_name} Eval Result",
        result_rag_a.summary_metrics,
        result_rag_a.metrics_table,
    ),
)

输出

gemini-1.5-pro Eval Result

Summary Metrics

Report Metrics

display_eval_report(
    (
        f"{model_b_name} Eval Result",
        result_rag_b.summary_metrics,
        result_rag_b.metrics_table,
    )
)

输出

gemini-1.0-pro Eval Result

Summary Metrics

Report Metrics

可视化评测结果

eval_results = []

eval_results.append(
    (model_a_name, result_rag_a.summary_metrics, result_rag_a.metrics_table)
)
eval_results.append(
    (model_b_name, result_rag_b.summary_metrics, result_rag_b.metrics_table)
)

plot_radar_plot(eval_results, max_score=5)

plot_bar_plot(eval_results)

查看本系列其他教程：

• Ragas with Vertex AI：学习如何将 Vertex AI 模型与 Ragas 一起使用，以评测你的 LLM 工作流。
• Align LLM Metrics：训练并对齐你的 LLM 评测器，使其更贴近人类判断。