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<title>LightRAG: 简单快速的检索增强生成</title>
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</head>
<body>
<div class="poster-container">
<div class="bg-accent bg-accent-1"></div>
<div class="bg-accent bg-accent-2"></div>
<header class="header">
<h1 class="title">LightRAG: 简单快速的检索增强生成</h1>
<p class="subtitle">跳出"二选一"困境,同时具备图谱的深度和向量检索的速度</p>
</header>
<section class="section">
<h2 class="section-title">
<i class="material-icons">help_outline</i>
RAG系统的两难问题
</h2>
<div class="dilemma-container">
<div class="dilemma-card">
<h3>传统RAG</h3>
<p>使用简单向量检索,成本低、速度快,但答案常常碎片化,无法理解复杂关联</p>
</div>
<div class="dilemma-arrow">
<i class="material-icons">compare_arrows</i>
</div>
<div class="dilemma-card">
<h3>GraphRAG</h3>
<p>能理解数据里复杂的关联,但构建成本高昂,更新困难,检索速度慢</p>
</div>
</div>
</section>
<section class="section">
<h2 class="section-title">
<i class="material-icons">lightbulb</i>
LightRAG的核心思想
</h2>
<p>LightRAG提出了一种轻量级的思路,核心不是去建造一个庞大臃肿的知识图谱,而是用一种更巧妙、更经济的方式,让系统同时具备图谱的深度和向量检索的速度。</p>
<div class="image-container">
<img src="https://sfile.chatglm.cn/moeSlide/image/fd/fd8087af.jpg" alt="LightRAG架构图">
</div>
<div class="dual-retrieval">
<div class="retrieval-level">
<h3><i class="material-icons">filter_1</i> 低层检索</h3>
<p>聚焦实体属性与直接关系,精确检索特定节点或边的信息</p>
</div>
<div class="retrieval-level">
<h3><i class="material-icons">filter_2</i> 高层检索</h3>
<p>聚合跨实体的全局主题,提供对更高层次概念和摘要的洞察</p>
</div>
</div>
</section>
<section class="section">
<h2 class="section-title">
<i class="material-icons">architecture</i>
LightRAG的架构设计
</h2>
<div class="architecture">
<div class="arch-step">
<div class="step-number">1</div>
<div class="step-content">
<h3>实体和关系提取</h3>
<p>使用LLM从文本块中提取实体(节点)和关系(边)</p>
</div>
</div>
<div class="arch-step">
<div class="step-number">2</div>
<div class="step-content">
<h3>LLM生成键值对</h3>
<p>为每个实体和关系生成文本键值对,键是检索索引词,值是总结性文本</p>
</div>
</div>
<div class="arch-step">
<div class="step-number">3</div>
<div class="step-content">
<h3>图谱去重</h3>
<p>合并不同文本块中的相同实体和关系,减小图规模</p>
</div>
</div>
<div class="arch-step">
<div class="step-number">4</div>
<div class="step-content">
<h3>增量更新</h3>
<p>新文档独立处理后与主图谱合并,无需重建整个索引</p>
</div>
</div>
</div>
</section>
<section class="section">
<h2 class="section-title">
<i class="material-icons">compare</i>
LightRAG vs GraphRAG
</h2>
<table class="comparison-table">
<tr>
<th>比较维度</th>
<th>LightRAG</th>
<th>GraphRAG</th>
</tr>
<tr>
<td>检索成本</td>
<td><span class="highlight">极低 (< 100 token)</span></td>
<td>高昂 (610,000 token)</td>
</tr>
<tr>
<td>更新方式</td>
<td><span class="highlight">增量更新</span></td>
<td>全量重建</td>
</tr>
<tr>
<td>检索速度</td>
<td><span class="highlight">快速</span></td>
<td>较慢</td>
</tr>
<tr>
<td>答案多样性</td>
<td><span class="highlight">高 (76.4% vs 23.6%)</span></td>
<td>低</td>
</tr>
<tr>
<td>答案全面性</td>
<td><span class="highlight">高 (83.6% vs 16.4%)</span></td>
<td>低</td>
</tr>
</table>
<div class="image-container">
<img src="https://sfile.chatglm.cn/moeSlide/image/1e/1edc2493.jpg" alt="LightRAG流程图">
</div>
</section>
<section class="section">
<h2 class="section-title">
<i class="material-icons">stars</i>
LightRAG的优势
</h2>
<div class="advantage">
<i class="material-icons">check_circle</i>
<p>检索成本降低99%,同时保持高质量的答案生成</p>
</div>
<div class="advantage">
<i class="material-icons">check_circle</i>
<p>支持增量更新,适合动态数据环境</p>
</div>
<div class="advantage">
<i class="material-icons">check_circle</i>
<p>双层检索机制,兼顾细节与全局信息</p>
</div>
<div class="advantage">
<i class="material-icons">check_circle</i>
<p>在多个评估维度上全面优于现有方法</p>
</div>
</section>
<section class="section">
<h2 class="section-title">
<i class="material-icons">category</i>
适用场景
</h2>
<div class="use-cases">
<div class="use-case">
<i class="material-icons">update</i>
<p>动态数据环境</p>
</div>
<div class="use-case">
<i class="material-icons">speed</i>
<p>高效检索需求</p>
</div>
<div class="use-case">
<i class="material-icons">hub</i>
<p>跨领域综合查询</p>
</div>
<div class="use-case">
<i class="material-icons">question_answer</i>
<p>智能客服系统</p>
</div>
<div class="use-case">
<i class="material-icons">analytics</i>
<p>企业知识管理</p>
</div>
<div class="use-case">
<i class="material-icons">school</i>
<p>学术研究辅助</p>
</div>
</div>
</section>
</div>
</body>
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