zengli
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coding-agent


			
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  <h1>5.2 实现企业知识库RAG系统的评估</h1>

<h2 id="_1">学习目标<a class="headerlink" href="#_1" title="Permanent link">¶</a></h2>
<ul>
<li>理解RAGAS库在RAG（检索增强生成）系统评估中的作用。</li>
<li>掌握如何使用RAGAS评估RAG系统的核心指标。</li>
</ul>
<h2 id="_2">一、功能描述<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
<p><code>ragas_evaluate.py</code>脚本用于评估RAG系统的性能，具体功能包括：</p>
<ol>
<li>
<p><strong>数据集加载</strong>：从JSON文件加载包含问题、答案、上下文和真实答案的评估数据集。</p>
</li>
<li>
<p><strong>数据格式转换</strong>：将JSON数据转换为RAGAS要求的<code>Dataset</code>格式。</p>
</li>
<li>
<p><strong>环境配置</strong>：使用LangChain的OpenAI模型和嵌入模型初始化RAGAS评估环境。</p>
</li>
<li>
<p><strong>评估执行</strong>：计算四个核心指标：</p>
</li>
<li><strong>Faithfulness（忠实度）</strong>：答案是否忠于上下文。</li>
<li><strong>Answer Relevancy（答案相关性）</strong>：答案与问题的匹配程度。</li>
<li><strong>Context Relevancy（上下文相关性）</strong>：上下文是否与问题相关。</li>
<li>
<p><strong>Context Recall（上下文召回率）</strong>：上下文是否包含所有必要信息。</p>
</li>
<li>
<p><strong>结果输出与保存</strong>：打印评估结果并保存为CSV文件，便于后续分析。</p>
</li>
</ol>
<hr>
<h2 id="_3">二、完整代码<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
<div class="highlight"><pre><span></span><code><span class="c1"># 导入pandas库，用于数据处理和保存CSV文件</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="c1"># 导入ragas库的evaluate函数，用于执行RAG评估</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ragas</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluate</span>
<span class="c1"># 导入ragas的评估指标，包括忠实度、答案相关性、上下文相关性和上下文召回率</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ragas.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">faithfulness</span><span class="p">,</span>
    <span class="n">answer_relevancy</span><span class="p">,</span>
    <span class="n">context_relevancy</span><span class="p">,</span>
    <span class="n">context_recall</span>
<span class="p">)</span>
<span class="c1"># 导入datasets库的Dataset类，用于构建RAGAS所需的数据格式</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dataset</span>
<span class="c1"># 导入langchain_openai的嵌入模型和聊天模型，用于评估时的语义计算和推理</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_openai</span><span class="w"> </span><span class="kn">import</span> <span class="n">OpenAIEmbeddings</span><span class="p">,</span> <span class="n">ChatOpenAI</span>
<span class="c1"># 导入json库，用于加载JSON格式的评估数据集</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">json</span>

<span class="c1"># 1. 加载生成的数据集</span>
<span class="c1"># 使用with语句打开JSON文件，确保文件正确关闭，指定编码为utf-8</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"rag_evaluation_dataset.json"</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"utf-8"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="c1"># 将JSON文件内容加载到data变量中，data为包含多个数据条目的列表</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

<span class="c1"># 2. 转换为RAGAS格式</span>
<span class="c1"># 创建字典eval_data，将JSON数据转换为RAGAS要求的字段格式</span>
<span class="n">eval_data</span> <span class="o">=</span> <span class="p">{</span>
    <span class="c1"># 提取每个数据条目的question字段，组成问题列表</span>
    <span class="s2">"question"</span><span class="p">:</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s2">"question"</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span>
    <span class="c1"># 提取每个数据条目的answer字段，组成答案列表</span>
    <span class="s2">"answer"</span><span class="p">:</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s2">"answer"</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span>
    <span class="c1"># 提取每个数据条目的context字段，组成上下文列表（每个context为列表）</span>
    <span class="s2">"contexts"</span><span class="p">:</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s2">"context"</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span>
    <span class="c1"># 提取每个数据条目的ground_truth字段，组成真实答案列表</span>
    <span class="s2">"ground_truth"</span><span class="p">:</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
<span class="p">}</span>
<span class="c1"># 使用Dataset.from_dict将字典转换为RAGAS所需的Dataset对象</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">eval_data</span><span class="p">)</span>

<span class="c1"># 3. 配置RAGAS评估环境</span>
<span class="c1"># 初始化ChatOpenAI模型，指定使用gpt-4模型，并设置OpenAI API密钥</span>
<span class="n">llm</span> <span class="o">=</span> <span class="n">ChatOpenAI</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"gpt-4"</span><span class="p">,</span> <span class="n">openai_api_key</span><span class="o">=</span><span class="s2">"your_openai_api_key"</span><span class="p">)</span>
<span class="c1"># 初始化OpenAI嵌入模型，用于计算语义相似度，设置API密钥</span>
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">OpenAIEmbeddings</span><span class="p">(</span><span class="n">openai_api_key</span><span class="o">=</span><span class="s2">"your_openai_api_key"</span><span class="p">)</span>

<span class="c1"># 4. 执行评估</span>
<span class="c1"># 调用evaluate函数，传入数据集、评估指标、LLM模型和嵌入模型</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span>
    <span class="c1"># 传入转换好的Dataset对象</span>
    <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
    <span class="c1"># 指定使用的评估指标列表</span>
    <span class="n">metrics</span><span class="o">=</span><span class="p">[</span>
        <span class="n">faithfulness</span><span class="p">,</span>  <span class="c1"># 忠实度：答案是否基于上下文</span>
        <span class="n">answer_relevancy</span><span class="p">,</span>  <span class="c1"># 答案相关性：答案与问题的匹配度</span>
        <span class="n">context_relevancy</span><span class="p">,</span>  <span class="c1"># 上下文相关性：上下文是否仅包含相关信息</span>
        <span class="n">context_recall</span>  <span class="c1"># 上下文召回率：上下文是否包含所有必要信息</span>
    <span class="p">],</span>
    <span class="c1"># 传入配置好的LLM模型</span>
    <span class="n">llm</span><span class="o">=</span><span class="n">llm</span><span class="p">,</span>
    <span class="c1"># 传入配置好的嵌入模型</span>
    <span class="n">embeddings</span><span class="o">=</span><span class="n">embeddings</span>
<span class="p">)</span>

<span class="c1"># 5. 输出并保存结果</span>
<span class="c1"># 打印评估结果标题</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"RAGAS评估结果："</span><span class="p">)</span>
<span class="c1"># 打印评估结果，包含各指标的分数</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="c1"># 将评估结果转换为pandas DataFrame，便于保存</span>
<span class="n">result_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">result</span><span class="p">])</span>
<span class="c1"># 将DataFrame保存为CSV文件，文件名为ragas_evaluation_results.csv，不保存索引</span>
<span class="n">result_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"ragas_evaluation_results.csv"</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</code></pre></div>
<h2 id="_4">三、说明<a class="headerlink" href="#_4" title="Permanent link">¶</a></h2>
<ol>
<li>
<p><strong>数据集格式</strong>：</p>
</li>
<li>
<p>RAGAS要求数据集包含<code>question</code>、<code>answer</code>、<code>contexts</code>（列表格式）和<code>ground_truth</code>四个字段。</p>
</li>
<li>
<p><code>contexts</code>必须是列表，即使每个问题只对应一个上下文（如<code>["context text"]</code>）。</p>
</li>
<li>
<p><strong>评估指标</strong>：</p>
</li>
<li>
<p><strong>Faithfulness</strong>：依赖LLM（如gpt-4）判断答案是否基于上下文，避免生成无关内容。</p>
</li>
<li><strong>Answer Relevancy</strong>：使用嵌入模型（OpenAIEmbeddings）计算答案与问题的语义相似度。</li>
<li><strong>Context Relevancy</strong>：评估上下文是否仅包含与问题相关的信息，减少冗余。</li>
<li>
<p><strong>Context Recall</strong>：检查上下文是否覆盖所有必要信息，需高质量的<code>ground_truth</code>支持。</p>
</li>
<li>
<p><strong>环境配置</strong>：</p>
</li>
<li>
<p><code>ChatOpenAI</code>用于生成评估所需的推理（如判断忠实度），需指定模型（如gpt-4）和API密钥。</p>
</li>
<li><code>OpenAIEmbeddings</code>用于计算语义相似度（如答案相关性），需确保API密钥有效。</li>
<li>
<p>可替换为其他LLM（如通义千问），需适配LangChain的模型接口。</p>
</li>
<li>
<p><strong>结果分析</strong>：</p>
</li>
<li>
<p>评估结果为字典，包含各指标的分数（0-1，1为最佳），如：</p>
<div class="highlight"><pre><span></span><code><span class="p">{</span><span class="s1">'faithfulness'</span><span class="p">:</span> <span class="mf">0.95</span><span class="p">,</span> <span class="s1">'answer_relevancy'</span><span class="p">:</span> <span class="mf">0.92</span><span class="p">,</span> <span class="s1">'context_relevancy'</span><span class="p">:</span> <span class="mf">0.90</span><span class="p">,</span> <span class="s1">'context_recall'</span><span class="p">:</span> <span class="mf">0.93</span><span class="p">}</span>
</code></pre></div>
</li>
<li>
<p>结果保存为CSV文件（<code>ragas_evaluation_results.csv</code>），便于统计分析和多次运行比较。</p>
</li>
<li>
<p><strong>迁移性</strong>：</p>
</li>
<li>
<p>代码结构通用，可用于任何RAG系统评估，只需替换数据集和LLM配置。</p>
</li>
<li>可扩展指标（如<code>answer_correctness</code>）或添加自定义数据处理逻辑（如过滤低质量数据）。</li>
</ol>
<hr>
<h2 id="_5">章节总结<a class="headerlink" href="#_5" title="Permanent link">¶</a></h2>
<p>本制度文档通过<code>ragas_evaluate.py</code>代码，详细介绍了使用RAGAS评估RAG系统的完整流程。</p>


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