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- min-width: 0;
- padding: 0.72rem 0.75rem 0 0;
- }
- .faith-step + .faith-step {
- padding-left: 0.75rem;
- border-left: 1px solid #c9deef;
- }
- .faith-step strong {
- display: block;
- margin-bottom: 0.18rem;
- color: var(--faith-blue);
- font-size: 0.75rem;
- }
- .faith-step p {
- margin: 0;
- color: var(--faith-muted);
- font-size: 0.7rem;
- line-height: 1.55;
- }
- .faith-section-title {
- display: flex;
- align-items: baseline;
- gap: 0.55rem;
- margin: 1.45rem 0 0.72rem !important;
- color: var(--faith-ink);
- font-size: 1rem !important;
- line-height: 1.45;
- }
- .faith-section-title span {
- color: var(--faith-muted);
- font-size: 0.68rem;
- font-weight: 400;
- }
- .faith-case-grid {
- display: grid;
- grid-template-columns: repeat(2, minmax(0, 1fr));
- gap: 0.75rem;
- }
- .faith-case {
- min-width: 0;
- padding: 0.85rem;
- border: 1px solid var(--faith-border);
- border-radius: 7px;
- background: #fff;
- }
- .faith-case.is-good {
- border-top: 4px solid var(--faith-green);
- }
- .faith-case.is-bad {
- border-top: 4px solid var(--faith-red);
- }
- .faith-case__head {
- display: flex;
- align-items: flex-start;
- justify-content: space-between;
- gap: 0.6rem;
- margin-bottom: 0.55rem;
- }
- .faith-case__head h6 {
- margin: 0 !important;
- color: var(--faith-ink);
- font-size: 0.79rem !important;
- line-height: 1.4;
- }
- .faith-case__score {
- flex: 0 0 auto;
- padding: 0.18rem 0.4rem;
- border-radius: 4px;
- background: var(--faith-green-soft);
- color: var(--faith-green);
- font-size: 0.7rem;
- font-weight: 800;
- white-space: nowrap;
- }
- .faith-case.is-bad .faith-case__score {
- background: var(--faith-red-soft);
- color: var(--faith-red);
- }
- .faith-case__answer {
- margin: 0 0 0.55rem !important;
- padding: 0.55rem 0.62rem;
- border-left: 3px solid #9aa8b8;
- background: var(--faith-surface);
- color: var(--faith-ink);
- font-size: 0.7rem;
- line-height: 1.55;
- }
- .faith-case__meta {
- display: flex;
- flex-wrap: wrap;
- gap: 0.35rem;
- margin-bottom: 0.5rem;
- }
- .faith-case__meta span {
- padding: 0.16rem 0.35rem;
- border: 1px solid var(--faith-border);
- border-radius: 4px;
- color: var(--faith-muted);
- font-size: 0.65rem;
- font-weight: 700;
- }
- .faith-case__conclusion {
- margin: 0 !important;
- color: var(--faith-muted);
- font-size: 0.68rem;
- line-height: 1.5;
- }
- .faith-case__conclusion strong {
- color: var(--faith-ink);
- }
- .faith-distinction {
- display: grid;
- grid-template-columns: repeat(2, minmax(0, 1fr));
- gap: 0;
- margin-top: 1.15rem;
- border: 1px solid var(--faith-border);
- border-radius: 6px;
- overflow: hidden;
- background: #fff;
- }
- .faith-distinction > div {
- padding: 0.82rem 0.9rem;
- }
- .faith-distinction > div + div {
- border-left: 1px solid var(--faith-border);
- }
- .faith-distinction h6 {
- margin: 0 0 0.28rem !important;
- color: var(--faith-ink);
- font-size: 0.75rem !important;
- }
- .faith-distinction p {
- margin: 0 !important;
- color: var(--faith-muted);
- font-size: 0.68rem;
- line-height: 1.55;
- }
- .faith-distinction strong {
- color: var(--faith-blue);
- }
- .faith-source-note {
- margin: 0.45rem 0 0 !important;
- color: var(--faith-muted);
- font-size: 0.62rem;
- line-height: 1.45;
- }
- .model-tag {
- display: inline-flex;
- align-items: center;
- justify-content: center;
- min-height: 1.25rem;
- padding: 0.12rem 0.35rem;
- border-radius: 4px;
- color: #fff;
- font-size: 0.58rem;
- font-weight: 800;
- line-height: 1.25;
- white-space: nowrap;
- }
- .model-tag--retriever {
- background: #596579;
- }
- .model-tag--rag {
- background: var(--faith-blue);
- }
- .model-tag--llm {
- background: #7650a8;
- }
- .model-tag--embedding {
- background: #087f8c;
- }
- .model-tag--compute {
- background: #4f5b6c;
- }
- .model-tag--human {
- background: var(--faith-amber);
- }
- .model-prompt {
- display: grid;
- gap: 0.38rem;
- margin-top: 0.65rem;
- padding: 0.55rem 0.62rem;
- border: 1px dashed #cbbbe2;
- border-radius: 5px;
- background: #fbf9ff;
- text-align: left;
- }
- .model-prompt--inside {
- margin: 0 0 0.55rem;
- }
- .model-prompt__row {
- display: grid;
- grid-template-columns: auto minmax(0, 1fr);
- gap: 0.48rem;
- align-items: start;
- }
- .model-prompt__row + .model-prompt__row {
- padding-top: 0.38rem;
- border-top: 1px solid #e4dcef;
- }
- .model-prompt__row p {
- margin: 0 !important;
- color: var(--faith-muted);
- font-size: 0.62rem;
- line-height: 1.5;
- }
- .model-prompt__row strong {
- color: var(--faith-ink);
- }
- .model-prompt__output {
- color: #7650a8;
- font-weight: 700;
- }
- .model-label {
- display: flex;
- flex-wrap: wrap;
- gap: 0.28rem;
- align-items: center;
- }
- .context-relevance-guide .faith-figure {
- border-top-color: var(--faith-blue);
- }
- .context-relevance-guide .faith-score {
- border-color: var(--faith-blue);
- background: var(--faith-blue-soft);
- color: var(--faith-blue);
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- .context-recall-guide .faith-figure {
- border-top-color: var(--faith-amber);
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- .context-recall-guide .faith-step-label {
- background: var(--faith-amber);
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- .context-recall-guide .faith-score {
- border-color: var(--faith-amber);
- background: var(--faith-amber-soft);
- color: var(--faith-amber);
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- .recall-map {
- display: grid;
- grid-template-columns: 112px minmax(0, 1fr);
- gap: 0.7rem;
- align-items: stretch;
- }
- .recall-question,
- .recall-compare {
- min-width: 0;
- padding: 0.72rem;
- border: 1px solid var(--faith-border);
- border-radius: 6px;
- }
- .recall-question {
- display: flex;
- flex-direction: column;
- justify-content: center;
- border-color: #d7c89e;
- background: var(--faith-amber-soft);
- }
- .recall-question p {
- margin: 0 !important;
- font-size: 0.72rem;
- line-height: 1.65;
- }
- .recall-compare {
- background: var(--faith-surface);
- }
- .recall-head,
- .recall-row {
- display: grid;
- grid-template-columns: minmax(0, 1fr) 3.35rem minmax(0, 1fr);
- gap: 0.42rem;
- align-items: stretch;
- }
- .recall-head {
- align-items: center;
- margin-bottom: 0.45rem;
- color: var(--faith-muted);
- font-size: 0.66rem;
- text-align: center;
- }
- .recall-head strong:first-child {
- color: var(--faith-blue);
- }
- .recall-head strong:last-child {
- color: var(--faith-amber);
- }
- .recall-head b {
- display: inline-flex;
- align-items: center;
- justify-content: center;
- min-height: 1.55rem;
- border-radius: 4px;
- background: #e8edf3;
- color: var(--faith-ink);
- font-size: 0.68rem;
- }
- .recall-row {
- margin-top: 0.4rem;
- font-size: 0.64rem;
- line-height: 1.45;
- }
- .recall-row > div {
- min-width: 0;
- padding: 0.48rem;
- border: 1px solid #d6e7f5;
- border-radius: 5px;
- background: #fff;
- }
- .recall-row > div:last-child {
- border-color: #ead7b3;
- background: #fffdf8;
- }
- .recall-row em {
- display: inline-flex;
- align-items: center;
- justify-content: center;
- min-width: 1.75rem;
- min-height: 1.25rem;
- margin-right: 0.28rem;
- border-radius: 4px;
- background: var(--faith-blue);
- color: #fff;
- font-size: 0.61rem;
- font-style: normal;
- font-weight: 800;
- }
- .recall-row > div:last-child em {
- background: var(--faith-amber);
- }
- .recall-row > span {
- display: flex;
- align-items: center;
- justify-content: center;
- border-radius: 4px;
- background: var(--faith-green-soft);
- color: var(--faith-green);
- font-size: 0.61rem;
- font-weight: 800;
- text-align: center;
- }
- .recall-row.is-missing > div:first-child {
- border-style: dashed;
- border-color: #d8dee7;
- background: #f4f6f8;
- color: var(--faith-muted);
- }
- .recall-row.is-missing > div:last-child {
- border-color: #f0c7c4;
- background: var(--faith-red-soft);
- }
- .recall-row.is-missing > span {
- background: var(--faith-red-soft);
- color: var(--faith-red);
- }
- .recall-noise {
- margin: 0.52rem 0 0 !important;
- padding: 0.42rem 0.5rem;
- border-left: 3px solid #8995a5;
- background: #fff;
- color: var(--faith-muted);
- font-size: 0.62rem;
- line-height: 1.5;
- }
- .recall-result {
- display: flex;
- align-items: center;
- justify-content: space-between;
- gap: 0.8rem;
- margin-top: 0.72rem;
- padding: 0.68rem 0.78rem;
- border: 1px solid #e2c993;
- border-radius: 6px;
- background: var(--faith-amber-soft);
- }
- .recall-result p {
- margin: 0 !important;
- color: var(--faith-muted);
- font-size: 0.69rem;
- line-height: 1.5;
- text-align: left;
- }
- .recall-result p > strong {
- color: var(--faith-amber);
- }
- .recall-result > strong {
- flex: 0 0 auto;
- color: var(--faith-amber);
- font-size: 0.92rem;
- white-space: nowrap;
- }
- .context-metric-guide .faith-case.is-mid {
- border-top: 4px solid var(--faith-amber);
- }
- .context-metric-guide .faith-case.is-mid .faith-case__score {
- background: var(--faith-amber-soft);
- color: var(--faith-amber);
- }
- .context-metric-guide .faith-case__answer {
- min-height: 3.35rem;
- }
- .context-metric-guide .faith-distinction {
- margin-top: 0.85rem;
- }
- .relevancy-guide .faith-figure {
- border-top-color: #087f8c;
- }
- .relevancy-guide .faith-step-label {
- background: #087f8c;
- }
- .relevancy-guide .faith-score {
- border-color: #087f8c;
- background: #eefafb;
- color: #087f8c;
- }
- .relevancy-average-box {
- min-width: 13.5rem;
- padding: 0.7rem 0.8rem;
- border: 2px solid #087f8c;
- border-radius: 6px;
- background: #eefafb;
- text-align: center;
- }
- .relevancy-average-box span {
- display: block;
- color: var(--faith-muted);
- font-size: 0.62rem;
- font-weight: 700;
- }
- .relevancy-average-box .model-tag {
- display: inline-flex;
- margin-right: 0.22rem;
- color: #fff;
- font-size: 0.58rem;
- vertical-align: middle;
- }
- .relevancy-average-box strong {
- display: block;
- margin: 0.2rem 0;
- color: #087f8c;
- font-size: 0.83rem;
- line-height: 1.4;
- }
- .relevancy-average-box p {
- margin: 0 !important;
- color: var(--faith-ink);
- font-size: 0.66rem;
- line-height: 1.45;
- }
- .relevancy-average-box b {
- color: #087f8c;
- font-size: 0.82rem;
- }
- .relevancy-chain-note {
- margin: 0.65rem 0 0 !important;
- padding: 0.55rem 0.65rem;
- border-radius: 5px;
- background: #fff;
- color: var(--faith-muted);
- font-size: 0.69rem;
- line-height: 1.55;
- }
- .relevancy-chain-note strong {
- color: #087f8c;
- }
- .relevancy-case-intro {
- margin: -0.25rem 0 0.7rem !important;
- color: var(--faith-muted);
- font-size: 0.68rem;
- line-height: 1.5;
- }
- .relevancy-case-grid {
- display: grid;
- grid-template-columns: repeat(3, minmax(0, 1fr));
- gap: 0.7rem;
- }
- .relevancy-guide .faith-case {
- padding: 0.75rem;
- }
- .relevancy-case__reverse {
- margin: 0 0 0.45rem !important;
- color: var(--faith-muted);
- font-size: 0.66rem;
- line-height: 1.5;
- }
- .relevancy-case__reverse strong {
- color: var(--faith-ink);
- }
- .relevancy-case__math {
- margin: 0 0 0.48rem;
- padding: 0.36rem 0.42rem;
- border-radius: 4px;
- background: var(--faith-surface);
- color: var(--faith-muted);
- font-size: 0.63rem;
- font-weight: 700;
- line-height: 1.45;
- text-align: center;
- }
- .relevancy-guide .faith-case.is-high {
- border-top: 4px solid var(--faith-green);
- }
- .relevancy-guide .faith-case.is-mid {
- border-top: 4px solid var(--faith-amber);
- }
- .relevancy-guide .faith-case.is-low {
- border-top: 4px solid var(--faith-red);
- }
- .relevancy-guide .faith-case.is-mid .faith-case__score {
- background: var(--faith-amber-soft);
- color: var(--faith-amber);
- }
- .relevancy-guide .faith-case.is-low .faith-case__score {
- background: var(--faith-red-soft);
- color: var(--faith-red);
- }
- .relevancy-caution {
- display: flex;
- gap: 0.65rem;
- align-items: flex-start;
- margin-top: 0.8rem;
- padding: 0.68rem 0.78rem;
- border-left: 4px solid var(--faith-amber);
- background: var(--faith-amber-soft);
- color: var(--faith-muted);
- font-size: 0.7rem;
- line-height: 1.55;
- }
- .relevancy-caution strong {
- flex: 0 0 auto;
- color: var(--faith-amber);
- }
- .relevancy-caution p {
- margin: 0 !important;
- }
- @media screen and (max-width: 900px) {
- .faith-flow {
- grid-template-columns: 1fr;
- }
- .relevancy-case-grid {
- grid-template-columns: 1fr;
- }
- .faith-arrow {
- min-height: 2.3rem;
- }
- .faith-arrow::after {
- content: "↓";
- }
- }
- @media screen and (max-width: 640px) {
- .faith-figure {
- padding: 0.75rem;
- }
- .faith-backcheck,
- .faith-steps,
- .faith-case-grid,
- .faith-distinction {
- grid-template-columns: 1fr;
- }
- .recall-map {
- grid-template-columns: 1fr;
- }
- .recall-question {
- display: block;
- }
- .recall-head,
- .recall-row {
- grid-template-columns: minmax(0, 1fr) 2.65rem minmax(0, 1fr);
- gap: 0.25rem;
- }
- .recall-row > div {
- padding: 0.4rem;
- }
- .recall-row em {
- display: flex;
- width: fit-content;
- margin: 0 0 0.22rem;
- }
- .recall-result {
- align-items: flex-start;
- flex-direction: column;
- }
- .model-prompt__row {
- grid-template-columns: 1fr;
- }
- .faith-backcheck {
- align-items: stretch;
- }
- .faith-step + .faith-step,
- .faith-distinction > div + div {
- padding-left: 0;
- border-left: 0;
- }
- .faith-step + .faith-step {
- margin-top: 0.6rem;
- padding-top: 0.6rem;
- border-top: 1px solid #c9deef;
- }
- .faith-distinction > div + div {
- border-top: 1px solid var(--faith-border);
- }
- .faith-score {
- width: 100%;
- }
- .relevancy-average-box {
- width: 100%;
- min-width: 0;
- }
- .relevancy-caution {
- display: block;
- }
- .relevancy-caution strong {
- display: block;
- margin-bottom: 0.2rem;
- }
- }
- @media print {
- .faith-figure,
- .faith-case,
- .faith-distinction {
- break-inside: avoid;
- box-shadow: none;
- }
- }
- </style>
-
- <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce(((e,_)=>(e<<5)-e+_.charCodeAt(0)),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
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- <h1>5.1 RAG系统评估工具</h1>
- <h2 id="_1">学习目标<a class="headerlink" href="#_1" title="Permanent link">¶</a></h2>
- <ul>
- <li>了解RAG系统评估的指标</li>
- <li>掌握RAGAS框架的原理和使用方式</li>
- </ul>
- <h2 id="_2">一、概述<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
- <p>当我们为某个真实线上系统开发了检索增强生成 (RAG) 应用,那么在此应用正式上线提供服务前,我们需要评估 RAG 的表现到底是怎样的。如果发现现有的 RAG 效果不够理想,可能需要一些新的 RAG 算法流程来改进。在这之前,就需要对 RAG 流程进行评估,得到评估指标,然后才能进行自动化对比,观察改进的流程是否真的有效。</p>
- <h2 id="ragas">二、RAGAS评估框架<a class="headerlink" href="#ragas" title="Permanent link">¶</a></h2>
- <p>RAGAS (Retrieval Augmented Generation Assessment) 我们一般称为 Automated Evaluation of Retrieval Augmented Generation,即检索增强生成的自动评估。Ragas是一个大模型评测框架,可以评估检索增强生成(RAG)的效果,帮助分析模型的输出,了解模型在给定任务上的表现。Github地址: https://github.com/explodinggradients/ragas</p>
- <p>RAGAS的评估主要基于两个方向:检索部分和生成部分,那么针对不同的部分评估的指标也会有所区分。接下来,我们将分别介绍RAGAS评估框架所需的数据,评估指标,实际用例等。</p>
- <h3 id="1">1 数据说明<a class="headerlink" href="#1" title="Permanent link">¶</a></h3>
- <p>最开始的 RAGAs 在评估数据集时,不必依赖人工标注的标准答案,而是通过底层的大语言模型 (LLM) 来进行评估。所以只需要一个带有问题-答案对的评估数据集(QA 对),如:https://huggingface.co/datasets/m-ric/huggingface_doc
- 具体字段:</p>
- <ul>
- <li>question:作为 RAG 管道输入的用户查询,输入。</li>
- <li>answer:从 RAG 管道生成的答案,输出。</li>
- <li>contexts:从用于回答question外部知识源中检索的上下文。</li>
- <li>ground_truths:question的基本事实答案。这是唯一人工注释的信息。</li>
- </ul>
- <hr>
- <h3 id="2">2 评估指标<a class="headerlink" href="#2" title="Permanent link">¶</a></h3>
- <div align="center"><img src="../img/9-7-1.png" style="zoom:40%"><img></div>
- <hr>
- <ul>
- <li><strong>评估检索(context)</strong>的指标:提供了上下文相关性(context_relevancy)和上下文召回率(context_recall),这些可以衡量你的检索系统的性能,即检索的段落是否相关。</li>
- <li><strong>评估生成(answer)</strong>的指标:提供了忠实度(faithfulness),用以衡量生成的信息是否准确无误;以及答案相关性(answer_relevancy),用以衡量答案对问题的切题程度,即模型生成的答案是否恰当。</li>
- </ul>
- <hr>
- <h4 id="21-context-relevance">2.1 上下文相关性 (context relevance)<a class="headerlink" href="#21-context-relevance" title="Permanent link">¶</a></h4>
- <section class="faithfulness-guide context-metric-guide context-relevance-guide" aria-label="上下文相关性图解与示例">
- <figure class="faith-figure">
- <figcaption><span class="faith-step-label">看“干不干净”</span>检索回来的内容里,有多少句真正能回答问题?</figcaption>
- <div class="faith-flow">
- <div class="faith-node faith-node--question">
- <span class="faith-node__label">用户问题 q</span>
- <p>满一年有几天年假?未休年假何时前用完?最小申请单位是多少?</p>
- </div>
- <div class="faith-arrow"><span>检索器</span></div>
- <div class="faith-node faith-node--context">
- <span class="faith-node__label model-label"><span class="model-tag model-tag--retriever">检索器</span>输出:上下文 C1–C4</span>
- <ol class="faith-doc-list">
- <li><span class="faith-id">C1</span><p>满 12 个月后,每年有 <strong>5 天</strong>带薪年假。</p></li>
- <li><span class="faith-id">C2</span><p>未休年假可结转至<strong>次年 3 月 31 日前</strong>。</p></li>
- <li><span class="faith-id">C3</span><p>年假最小申请单位为 <strong>0.5 天</strong>。</p></li>
- <li class="is-noise"><span class="faith-id">C4</span><p>连续病假 2 天须提交医疗证明。</p></li>
- </ol>
- </div>
- <div class="faith-arrow"><span>评估 LLM</span></div>
- <div class="faith-node faith-node--answer">
- <span class="faith-node__label model-label"><span class="model-tag model-tag--llm">评估 LLM</span>输出:必要 / 无关</span>
- <ol class="faith-answer-list">
- <li><span class="faith-id">C1</span><span>回答年假天数:有用</span></li>
- <li><span class="faith-id">C2</span><span>回答结转期限:有用</span></li>
- <li><span class="faith-id">C3</span><span>回答申请单位:有用</span></li>
- <li class="is-unsupported"><span class="faith-id">C4</span><span>讲病假证明:无关</span></li>
- </ol>
- </div>
- </div>
- <div class="model-prompt">
- <div class="model-prompt__row">
- <span class="model-tag model-tag--llm">评估 LLM</span>
- <p><strong>输入:</strong>用户问题 q + 检索上下文 C1–C4 <strong>提示词(简化):</strong>“从上下文中原样提取回答问题所必需的句子;没有则输出‘信息不足’。” <span class="model-prompt__output">输出:C1–C3 有用,C4 无关</span></p>
- </div>
- </div>
- <div class="faith-backcheck">
- <div>
- <p class="faith-backcheck__title"><span class="model-tag model-tag--compute">程序计算</span> 总共检索 4 句,其中 3 句是回答问题所必需的</p>
- <ul class="faith-verdict-list">
- <li>有用句:C1、C2、C3</li>
- <li class="is-no">噪声句:C4</li>
- </ul>
- </div>
- <div class="faith-score"><span>程序计算 · 上下文相关性</span><strong>3 / 4 = 0.75</strong></div>
- </div>
- </figure>
- <div class="faith-explanation">
- <p class="faith-explanation__lead"><strong>一句话抓住核心:</strong>上下文相关性衡量检索结果的“信息密度”。分母是检索回来的全部句子,混入无关内容就会降分。</p>
- <div class="faith-steps">
- <div class="faith-step"><strong>1|数全部检索句</strong><p>C1–C4 共 4 句,作为分母。</p></div>
- <div class="faith-step"><strong>2|抽取必要句</strong><p>LLM 判断 C1–C3 能回答问题,共 3 句。</p></div>
- <div class="faith-step"><strong>3|算有用占比</strong><p>相关性 = 必要句数 / 检索总句数 = 3 / 4。</p></div>
- </div>
- </div>
- <h5 class="faith-section-title">多捞一条无关内容,分数会怎样?</h5>
- <div class="faith-case-grid">
- <article class="faith-case is-good">
- <div class="faith-case__head"><h6>检索 A|只有 C1–C3</h6><span class="faith-case__score">1.00</span></div>
- <p class="faith-case__answer">3 句全部能回答问题:3 / 3 = 1.00</p>
- <p class="faith-case__conclusion"><strong>干净。</strong>没有多余信息。</p>
- </article>
- <article class="faith-case is-mid">
- <div class="faith-case__head"><h6>检索 B|C1–C3 + 病假 C4</h6><span class="faith-case__score">0.75</span></div>
- <p class="faith-case__answer">4 句中只有 3 句有用:3 / 4 = 0.75</p>
- <p class="faith-case__conclusion"><strong>有噪声。</strong>该找的信息都在,但多捞了无关内容。</p>
- </article>
- </div>
- <p class="faith-source-note">记忆口诀:相关性看“捞回来的内容有多少有用”,怕的是多捞废料。本节沿用课件旧版 Ragas 的句子级计算方式。</p>
- </section>
- <hr>
- <h4 id="22-context-recall">2.2 上下文召回率 (context recall)<a class="headerlink" href="#22-context-recall" title="Permanent link">¶</a></h4>
- <section class="faithfulness-guide context-metric-guide context-recall-guide" aria-label="上下文召回率图解与示例">
- <figure class="faith-figure">
- <figcaption><span class="faith-step-label">看“全不全”</span>把 RAG 检索结果与标准答案并排 VS</figcaption>
- <div class="recall-map">
- <div class="recall-question">
- <span class="faith-node__label">用户问题 q</span>
- <p>满一年有几天年假?未休年假何时前用完?最小申请单位是多少?</p>
- </div>
- <div class="recall-compare">
- <div class="recall-head">
- <strong>RAG 检索结果 c(q) <span class="model-tag model-tag--retriever">检索器输出</span></strong>
- <b>VS</b>
- <strong>标准答案 GT <span class="model-tag model-tag--human">人工标注</span></strong>
- </div>
- <div class="model-prompt model-prompt--inside">
- <div class="model-prompt__row">
- <span class="model-tag model-tag--llm">评估 LLM</span>
- <p><strong>输入:</strong>检索上下文 + 标准答案 GT <strong>提示词(简化):</strong>“把标准答案列成可独立核验的事实,并逐条检查:仅凭检索上下文能否推出?输出‘是 / 否’及理由。” <span class="model-prompt__output">输出:G1、G2 支撑,G3 缺失</span></p>
- </div>
- </div>
- <div class="recall-row is-match">
- <div><em>C1</em>满 12 个月后,每年有 5 天年假。</div>
- <span>✓ 支撑</span>
- <div><em>G1</em>满 12 个月后,每年有 5 天年假。</div>
- </div>
- <div class="recall-row is-match">
- <div><em>C2</em>未休年假可结转至次年 3 月 31 日前。</div>
- <span>✓ 支撑</span>
- <div><em>G2</em>未休年假可结转至次年 3 月 31 日前。</div>
- </div>
- <div class="recall-row is-missing">
- <div>所有检索内容均未提到</div>
- <span>✕ 缺失</span>
- <div><em>G3</em>年假最小申请单位为 0.5 天。</div>
- </div>
- <p class="recall-noise"><strong>C4|额外检索:</strong>连续病假 2 天须提交医疗证明。它是噪声,但不影响召回率。</p>
- </div>
- </div>
- <div class="recall-result">
- <p><strong>核对结果:</strong>G1、G2 已找回,G3 缺失;标准答案一共需要 3 条事实。</p>
- <strong><span class="model-tag model-tag--compute">程序计算</span> 上下文召回率 = 2 / 3 ≈ 0.67</strong>
- </div>
- </figure>
- <div class="faith-explanation">
- <p class="faith-explanation__lead"><strong>一句话抓住核心:</strong>上下文召回率先拆标准答案 GT,不是拆 AI 生成答案;它检查标准答案需要的事实是否都被检索回来。</p>
- <div class="faith-steps">
- <div class="faith-step"><strong>1|列出标准答案事实</strong><p>评估 LLM 在同一次判断中列出 G1–G3,共 3 条事实,作为分母。</p></div>
- <div class="faith-step"><strong>2|逐条找依据</strong><p>在检索上下文中回查,G1、G2 有依据,G3 缺失。</p></div>
- <div class="faith-step"><strong>3|算找回比例</strong><p>召回率 = 被支撑的 GT 事实数 / GT 事实总数 = 2 / 3。</p></div>
- </div>
- </div>
- <h5 class="faith-section-title">多一条噪声不扣分,漏一个要点才扣分</h5>
- <div class="faith-case-grid">
- <article class="faith-case is-good">
- <div class="faith-case__head"><h6>检索 A|C1–C4 全部返回</h6><span class="faith-case__score">1.00</span></div>
- <p class="faith-case__answer">G1、G2、G3 全有依据:3 / 3 = 1.00</p>
- <p class="faith-case__conclusion"><strong>找全了。</strong>即使多了病假 C4,也不降低召回率。</p>
- </article>
- <article class="faith-case is-bad">
- <div class="faith-case__head"><h6>检索 B|只有 C1、C2、C4</h6><span class="faith-case__score">0.67</span></div>
- <p class="faith-case__answer">G3“最小 0.5 天”没有依据:2 / 3 ≈ 0.67</p>
- <p class="faith-case__conclusion"><strong>漏要点了。</strong>缺少标准答案所需的一条事实。</p>
- </article>
- </div>
- <div class="faith-distinction">
- <div>
- <h6>上下文相关性:怕“多”</h6>
- <p>分母是<strong>全部检索句</strong>。无关内容越多,结果越不干净,分数越低。</p>
- </div>
- <div>
- <h6>上下文召回率:怕“少”</h6>
- <p>分母是<strong>标准答案事实</strong>。需要的信息漏得越多,分数越低。</p>
- </div>
- </div>
- <p class="faith-source-note">记忆口诀:相关性看“捞得干不干净”,召回率看“该捞的找全没有”。</p>
- </section>
- <hr>
- <h4 id="23-faithfulness">2.3 忠实度(faithfulness)<a class="headerlink" href="#23-faithfulness" title="Permanent link">¶</a></h4>
- <section class="faithfulness-guide" aria-label="忠实度图解与示例">
- <figure class="faith-figure">
- <figcaption><span class="faith-step-label">先看图</span>答案拆成 S1–S3,再逐条回看检索上下文 C1–C4</figcaption>
- <div class="faith-flow">
- <div class="faith-node faith-node--question">
- <span class="faith-node__label">用户问题 q</span>
- <p>我在公司连续工作满一年,今年有多少天带薪年假?</p>
- </div>
- <div class="faith-arrow"><span>检索器</span></div>
- <div class="faith-node faith-node--context">
- <span class="faith-node__label model-label"><span class="model-tag model-tag--retriever">检索器</span>输出:上下文 c(q)</span>
- <ol class="faith-doc-list">
- <li><span class="faith-id">C1</span><p>连续服务满 12 个月后,每个自然年度享有 <strong>5 天带薪年假</strong>。</p></li>
- <li><span class="faith-id">C2</span><p>未休完的年假经批准,可结转至<strong>次年 3 月 31 日前</strong>使用。</p></li>
- <li><span class="faith-id">C3</span><p>年假可以分次申请,最小申请单位为 <strong>0.5 天</strong>。</p></li>
- <li class="is-noise"><span class="faith-id">C4</span><p>连续病假达到 2 个工作日,须提交医疗机构证明。</p></li>
- </ol>
- </div>
- <div class="faith-arrow"><span>RAG LLM 生成<br>评估 LLM 拆分</span></div>
- <div class="faith-node faith-node--answer">
- <span class="faith-node__label model-label"><span class="model-tag model-tag--llm">评估 LLM ①</span>输出:答案事实 S(a(q))</span>
- <ol class="faith-answer-list">
- <li><span class="faith-id">S1</span><span>满一年有 <strong>5 天</strong>带薪年假。</span></li>
- <li><span class="faith-id">S2</span><span>未休年假可结转至次年 3 月 31 日前。</span></li>
- <li class="is-unsupported"><span class="faith-id">S3</span><span>每次最多只能连续休 <strong>3 天</strong>。</span></li>
- </ol>
- </div>
- </div>
- <div class="model-prompt">
- <div class="model-prompt__row">
- <span class="model-tag model-tag--rag">RAG 生成 LLM</span>
- <p><strong>输入:</strong>用户问题 q + 检索上下文 c(q) <strong>提示词(简化):</strong>“请仅依据检索上下文回答用户问题。” <span class="model-prompt__output">输出:AI 答案 a(q)</span></p>
- </div>
- <div class="model-prompt__row">
- <span class="model-tag model-tag--llm">评估 LLM ①</span>
- <p><strong>输入:</strong>问题 q + AI 答案 a(q) <strong>提示词(简化):</strong>“把答案拆成可独立判断真假的事实陈述。” <span class="model-prompt__output">输出:S1–S3</span></p>
- </div>
- <div class="model-prompt__row">
- <span class="model-tag model-tag--llm">评估 LLM ②</span>
- <p><strong>输入:</strong>检索上下文 c(q) + S1–S3 <strong>提示词(简化):</strong>“仅依据上下文,逐条判断陈述是否成立;输出‘是 / 否’及理由。” <span class="model-prompt__output">输出:S1、S2 支持,S3 不支持</span></p>
- </div>
- </div>
- <div class="faith-backcheck">
- <div>
- <p class="faith-backcheck__title"><span class="model-tag model-tag--llm">评估 LLM ②</span> 逐条核验:仅凭 C1–C4,能否推出这条陈述?</p>
- <ul class="faith-verdict-list">
- <li>S1 ← C1:支持,计入 V</li>
- <li>S2 ← C2:支持,计入 V</li>
- <li class="is-no">S3:无来源,不计入 V</li>
- </ul>
- </div>
- <div class="faith-score"><span>程序计算 · 忠实度</span><strong>F = 2 / 3 ≈ 0.67</strong></div>
- </div>
- </figure>
- <div class="faith-explanation">
- <p class="faith-explanation__lead"><strong>一句话抓住核心:</strong>被拆分的是 RAG 最终生成的答案 a(q),不是检索上下文 c(q);忠实度只检查“答案中写出的每条事实,有没有检索依据”。</p>
- <div class="faith-steps">
- <div class="faith-step"><strong>1|拆答案</strong><p>把生成答案拆成可独立判断真假的事实陈述,得到集合 S。</p></div>
- <div class="faith-step"><strong>2|找依据</strong><p>让评估 LLM 将每条陈述与原始检索上下文逐一比对。</p></div>
- <div class="faith-step"><strong>3|算比例</strong><p>有依据的陈述组成 V,最终 F = |V| / |S|。</p></div>
- </div>
- </div>
- <h5 class="faith-section-title">同一批检索结果,不同回答会得到什么分数?</h5>
- <div class="faith-case-grid">
- <article class="faith-case is-good">
- <div class="faith-case__head"><h6>案例 1|只使用部分检索信息</h6><span class="faith-case__score">F = 1.00</span></div>
- <p class="faith-case__answer">“工作满一年后,每年有 5 天带薪年假。”</p>
- <div class="faith-case__meta"><span>S = 1</span><span>V = 1</span><span>S1 ← C1</span></div>
- <p class="faith-case__conclusion"><strong>没用完 C2–C4,不扣分。</strong>忠实度不要求把检索到的所有内容都写进答案。</p>
- </article>
- <article class="faith-case is-good">
- <div class="faith-case__head"><h6>案例 2|同义改写,事实不变</h6><span class="faith-case__score">F = 1.00</span></div>
- <p class="faith-case__answer">“连续任职 12 个月,即可享受每年 5 个工作日的带薪休假。”</p>
- <div class="faith-case__meta"><span>S = 1</span><span>V = 1</span><span>S1 ← C1</span></div>
- <p class="faith-case__conclusion"><strong>表达改变,不扣分。</strong>只要事实含义与检索上下文一致,就能被支撑。</p>
- </article>
- <article class="faith-case is-bad">
- <div class="faith-case__head"><h6>案例 3|把关键数字改错</h6><span class="faith-case__score">F = 0.00</span></div>
- <p class="faith-case__answer">“工作满一年后,每年有 7 天带薪年假。”</p>
- <div class="faith-case__meta"><span>S = 1</span><span>V = 0</span><span>与 C1 矛盾</span></div>
- <p class="faith-case__conclusion"><strong>“7 天”没有依据。</strong>即使句式很像原文,只要关键事实被改动,这条陈述就不通过。</p>
- </article>
- <article class="faith-case is-bad">
- <div class="faith-case__head"><h6>案例 4|凭空增加新规定</h6><span class="faith-case__score">F = 0.50</span></div>
- <p class="faith-case__answer">“满一年有 5 天年假;每次最多只能连续休 3 天。”</p>
- <div class="faith-case__meta"><span>S = 2</span><span>V = 1</span><span>1 条有依据,1 条无来源</span></div>
- <p class="faith-case__conclusion"><strong>新增事实会拉低分数。</strong>第一条由 C1 支撑,第二条在 C1–C4 中都找不到。</p>
- </article>
- </div>
- <div class="faith-distinction">
- <div>
- <h6>忠实度 ≠ 文档本身是否正确</h6>
- <p>即使检索文档里的制度写错了,模型原样复述仍可能得到高忠实度,因为它忠实于检索上下文。</p>
- </div>
- <div>
- <h6>忠实度 ≠ 答案相关性</h6>
- <p>若回答“连续病假两天需提交证明”,内容完全来自 C4,<strong>忠实度可为 1</strong>;但没有回答年假问题,答案相关性很低。</p>
- </div>
- </div>
- <p class="faith-source-note">记忆口诀:忠实度看“有没有依据”,答案相关性看“有没有答题”。</p>
- </section>
- <hr>
- <h4 id="24-answer-relevancy">2.4 答案相关性(answer relevancy)<a class="headerlink" href="#24-answer-relevancy" title="Permanent link">¶</a></h4>
- <section class="faithfulness-guide relevancy-guide" aria-label="答案相关性图解与示例">
- <figure class="faith-figure">
- <figcaption><span class="faith-step-label">一条链记住</span>答案反推问 → 原问逐个比 → 相似求平均 → 得到 AR</figcaption>
- <div class="faith-flow">
- <div class="faith-node faith-node--question">
- <span class="faith-node__label">原始问题 q</span>
- <p>我在公司工作满一年,今年有多少天带薪年假?</p>
- </div>
- <div class="faith-arrow"><span>RAG 生成 LLM</span></div>
- <div class="faith-node faith-node--context">
- <span class="faith-node__label model-label"><span class="model-tag model-tag--rag">RAG 生成 LLM</span>AI 答案 a(q)</span>
- <p>连续工作满一年后,每个自然年度享有 <strong>5 天带薪年假</strong>。</p>
- </div>
- <div class="faith-arrow"><span>评估 LLM</span></div>
- <div class="faith-node faith-node--answer">
- <span class="faith-node__label model-label"><span class="model-tag model-tag--llm">评估 LLM</span>输出:反推问题 q1–q3</span>
- <ol class="faith-doc-list">
- <li><span class="faith-id">q1</span><p>满一年有几天带薪年假?</p></li>
- <li><span class="faith-id">q2</span><p>任职 12 个月后的年假天数是多少?</p></li>
- <li><span class="faith-id">q3</span><p>满一周年可享多少天年假?</p></li>
- </ol>
- </div>
- </div>
- <div class="model-prompt">
- <div class="model-prompt__row">
- <span class="model-tag model-tag--rag">RAG 生成 LLM</span>
- <p><strong>输入:</strong>用户问题 q + 检索上下文 c(q) <strong>提示词(简化):</strong>“请仅依据检索上下文回答用户问题。” <span class="model-prompt__output">输出:AI 答案 a(q)</span></p>
- </div>
- <div class="model-prompt__row">
- <span class="model-tag model-tag--llm">评估 LLM</span>
- <p><strong>输入:</strong>AI 答案 a(q) <strong>提示词(简化):</strong>“仅根据这份答案,反推一个它最可能在回答的问题,并判断答案是否回避作答。” <span class="model-prompt__output">同一提示运行 3 次,输出 q1–q3</span></p>
- </div>
- <div class="model-prompt__row">
- <span class="model-tag model-tag--embedding">向量模型</span>
- <p>将原问题 q 与 q1–q3 转成向量;随后由程序计算余弦相似度。<strong>这里不是让 LLM 主观打分。</strong></p>
- </div>
- </div>
- <div class="faith-backcheck">
- <div>
- <p class="faith-backcheck__title"><span class="model-tag model-tag--compute">程序计算</span> 用向量逐一比较,得到 3 个余弦相似度</p>
- <ul class="faith-verdict-list">
- <li>sim(q, q1) = 0.96</li>
- <li>sim(q, q2) = 0.94</li>
- <li>sim(q, q3) = 0.92</li>
- </ul>
- </div>
- <div class="relevancy-average-box">
- <span><span class="model-tag model-tag--compute">程序计算</span> 相加后除以 3</span>
- <strong>(0.96 + 0.94 + 0.92) ÷ 3 = 0.94</strong>
- <p><b>0.94</b> 就是这一个答案的最终 AR</p>
- </div>
- </div>
- </figure>
- <div class="faith-explanation">
- <p class="faith-explanation__lead"><strong>AR 是什么?</strong>AR = Answer Relevancy,是“这一个答案”的最终相关性分数。多个相似度求完平均后,流程就结束;这个平均值就是最终 AR。</p>
- <div class="faith-steps">
- <div class="faith-step"><strong>1|一个答案 → 多个问题</strong><p>评估 LLM 根据当前答案反推 q1、q2、q3。</p></div>
- <div class="faith-step"><strong>2|多个问题 → 多个相似度</strong><p>每个反推问题分别与原问题 q 比较,得到 0.96、0.94、0.92。</p></div>
- <div class="faith-step"><strong>3|多个相似度 → 一个 AR</strong><p>把三个数相加再除以 3,平均值 0.94 就是最终分数。</p></div>
- </div>
- <p class="relevancy-chain-note"><strong>为什么取平均?</strong>同一答案可能反推出措辞不同的问题。平均值把多次比较汇总成一个分数,也能减少某一次反推措辞偶然性的影响。</p>
- </div>
- <h5 class="faith-section-title">换一个答案,再把上面的完整流程重新跑一遍</h5>
- <p class="relevancy-case-intro">下面是回答 A、B、C 三个独立案例。每个回答都会各自反推 3 个问题、算出 3 个相似度,再取平均得到自己的最终 AR;它们不是上方的 q1、q2、q3。</p>
- <div class="relevancy-case-grid">
- <article class="faith-case is-high">
- <div class="faith-case__head"><h6>回答 A|直接且完整</h6><span class="faith-case__score">最终 AR ≈ 0.94</span></div>
- <p class="faith-case__answer">“满一年后,每年有 5 天带薪年假。”</p>
- <p class="relevancy-case__reverse"><strong>反推方向:</strong>大多仍在问“满一年有几天年假?”</p>
- <div class="relevancy-case__math">0.96、0.94、0.92 → 平均 0.94</div>
- <p class="faith-case__conclusion"><strong>高相关。</strong>完整回应了原问题。</p>
- </article>
- <article class="faith-case is-mid">
- <div class="faith-case__head"><h6>回答 B|只答了一部分</h6><span class="faith-case__score">最终 AR ≈ 0.62</span></div>
- <p class="faith-case__answer">“员工满一年后,可以享受带薪年假。”</p>
- <p class="relevancy-case__reverse"><strong>反推方向:</strong>更像在问“满一年后能否休年假?”</p>
- <div class="relevancy-case__math">0.66、0.62、0.58 → 平均 0.62</div>
- <p class="faith-case__conclusion"><strong>中等相关。</strong>丢失了“多少天”。</p>
- </article>
- <article class="faith-case is-low">
- <div class="faith-case__head"><h6>回答 C|答非所问</h6><span class="faith-case__score">最终 AR ≈ 0.11</span></div>
- <p class="faith-case__answer">“连续病假两天,需要提交医疗机构证明。”</p>
- <p class="relevancy-case__reverse"><strong>反推方向:</strong>都会围绕“病假需要什么证明?”</p>
- <div class="relevancy-case__math">0.12、0.11、0.10 → 平均 0.11</div>
- <p class="faith-case__conclusion"><strong>低相关。</strong>与原问题方向不同。</p>
- </article>
- </div>
- <div class="relevancy-caution">
- <strong>别混淆</strong>
- <p>“满一年有 7 天年假”仍可能得到较高的答案相关性,因为它确实在回答“多少天”;但“7 天”没有检索依据,所以忠实度低。</p>
- </div>
- <p class="faith-source-note">记忆口诀:答案反推问,原问逐个比,相似求平均,最终就是 AR。以上分数为示意值;若答案是“不知道”等回避式回答,Ragas 会将结果置为 0。</p>
- </section>
- <h2 id="_3">本章小结<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
- <ul>
- <li>本章节主要介绍了RAG系统如何实现评估的方式,重点分析了RAGAS系统中:上下文相关性、上下文召回率、忠实度以及答案相关性的相关指标。</li>
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