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RAG知识库召回率
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<h1 class="post-title">RAG知识库召回率</h1>
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上次更新: 2024-03-28 12:10:10
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<h1 id="通用的RAG方案的召回率"><a href="#通用的RAG方案的召回率" class="headerlink" title="通用的RAG方案的召回率"></a>通用的RAG方案的召回率</h1><p>不一定召回率越高越好,对于准确性也需要考虑,通用的召回率大概在<code>70%</code>,不论是<code>dify</code>还是<code>fastgpt</code>,对于生产场景,要求起码到<code>90%</code>的水平</p>
<h2 id="意图分类"><a href="#意图分类" class="headerlink" title="意图分类"></a>意图分类</h2><p>类似 <code>xxx企业的董事长是谁</code>,一定不能打错,类似<code>QA</code>,上一代的客服系统必备的</p>
<p>针对有明确答案的召回一般都是比较准确的,但是针对有日期、数字类的一般效果会非常不好,可以考虑采用<code>nl2sql</code>的方案,使用<code>fine-tuning</code>,能够做到召回率<code>90%</code>,有专门的模型针对这块,例如微软的<code>RAT-SQL</code>模型。</p>
<p>针对<code>excel</code>类型的文件,直接把数据存到一张<code>宽表</code>中,不要跨表,降低复杂度。针对<code>nl2sql</code>的开源模型的能力基本都是<code>单表</code>,基本可以达到预期</p>
<p>针对某些场景例如<code>2021年的xxxx</code>,同样的<code>2022年的xxxx</code>也发生了,使用<code>embedding</code>容易给错误召回的,而<code>ES</code>的效果会更好。相当于通过从用户的问题中,捕捉关键词,然后通过这些关键词去库里召回。</p>
<p>针对<code>LLM</code>模型来说,数据治理变的很重要,在数据切分的过程中,需要符合人的逻辑去<code>分词</code>。按照人的逻辑,例如:表格、章节、目录……</p>
<h2 id="分词"><a href="#分词" class="headerlink" title="分词"></a>分词</h2><p><code>chunk</code><code>size</code>越大,召回越少</p>
<h2 id="embedding"><a href="#embedding" class="headerlink" title="embedding"></a>embedding</h2><p><code>embedding方案</code>可以考虑引入<code>link memory</code>,可以提高<code>10%-20%</code>,在医疗文献领域,根据参考材料写综述是非常专业的。</p>
<ul>
<li>文档切分是浅层信息类似chatPDF数据中加入了总结、归纳的内容</li>
<li>大模型生成摘要,总结上下文,再进行检索。</li>
</ul>
<h1 id="如何提升召回率"><a href="#如何提升召回率" class="headerlink" title="如何提升召回率"></a>如何提升召回率</h1><h1 id="embedding哪个算法好"><a href="#embedding哪个算法好" class="headerlink" title="embedding哪个算法好"></a>embedding哪个算法好</h1><h1 id="国内知识库方案哪些底座比较好"><a href="#国内知识库方案哪些底座比较好" class="headerlink" title="国内知识库方案哪些底座比较好"></a>国内知识库方案哪些底座比较好</h1><h1 id="RAG的发展方向"><a href="#RAG的发展方向" class="headerlink" title="RAG的发展方向"></a>RAG的发展方向</h1>
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<p class="motto">重拾纯粹的写作</p>
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<ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#%E9%80%9A%E7%94%A8%E7%9A%84RAG%E6%96%B9%E6%A1%88%E7%9A%84%E5%8F%AC%E5%9B%9E%E7%8E%87"><span class="toc-number">1.</span> <span class="toc-text">通用的RAG方案的召回率</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E6%84%8F%E5%9B%BE%E5%88%86%E7%B1%BB"><span class="toc-number">1.1.</span> <span class="toc-text">意图分类</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E5%88%86%E8%AF%8D"><span class="toc-number">1.2.</span> <span class="toc-text">分词</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#embedding"><span class="toc-number">1.3.</span> <span class="toc-text">embedding</span></a></li></ol></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%A6%82%E4%BD%95%E6%8F%90%E5%8D%87%E5%8F%AC%E5%9B%9E%E7%8E%87"><span class="toc-number">2.</span> <span class="toc-text">如何提升召回率</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#embedding%E5%93%AA%E4%B8%AA%E7%AE%97%E6%B3%95%E5%A5%BD"><span class="toc-number">3.</span> <span class="toc-text">embedding哪个算法好</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%9B%BD%E5%86%85%E7%9F%A5%E8%AF%86%E5%BA%93%E6%96%B9%E6%A1%88%E5%93%AA%E4%BA%9B%E5%BA%95%E5%BA%A7%E6%AF%94%E8%BE%83%E5%A5%BD"><span class="toc-number">4.</span> <span class="toc-text">国内知识库方案哪些底座比较好</span></a></li><li class="toc-item toc-level-1"><a class="toc-link" href="#RAG%E7%9A%84%E5%8F%91%E5%B1%95%E6%96%B9%E5%90%91"><span class="toc-number">5.</span> <span class="toc-text">RAG的发展方向</span></a></li></ol>
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