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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 13:47:49
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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>
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2024-03-28 13:12:04 +08:00
<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><p>首先是针对搜索query来说，通过文本框的<code>打字输入</code>的角度，人类一定是倾向于偷懒，只输入关键词，这就是给表达真实意图带来的难度。</p>
<p>语音交互，才有可能让大家使用起来更舒服。</p>
<h2 id="数据预处理"><a href="#数据预处理" class="headerlink" title="数据预处理"></a>数据预处理</h2><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>
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<h3 id="分词-chunk"><a href="#分词-chunk" class="headerlink" title="分词 chunk"></a>分词 chunk</h3><p><code>chunk</code>的<code>size</code>越大，召回越少。但是分的越精细，同样会损失上下文连贯性</p>
<h1 id="意图分类"><a href="#意图分类" class="headerlink" title="意图分类"></a>意图分类</h1><p>用户提问的内容，很短、缩写的情况，如何命中用户的<code>真实意图</code>。</p>
<h2 id="缓存库"><a href="#缓存库" class="headerlink" title="缓存库"></a>缓存库</h2><p>可以做一个缓存库，直接命中返回结构就行，不需要走<code>LLM</code></p>
<h2 id="QA"><a href="#QA" class="headerlink" title="QA"></a>QA</h2><p>处理成<code>QA</code>的格式，类似 <code>xxx企业的董事长是谁</code>，答案是必须正确的，上一代的客服系统必备。直接数据级别的匹配就行,召回一般都是比较准确的。</p>
<h2 id="关键词-词典-迭代"><a href="#关键词-词典-迭代" class="headerlink" title="关键词+词典+迭代"></a>关键词+词典+迭代</h2><p>成本非常低，可控。思路是帮用户模糊，先提取关键词，再结合词典的词条解释，让表达更加的领域话、专业化，再扔给大语言模型。</p>
<p>例如：某个提问<code>什么是RAG</code>，首先<code>RAG</code>对应的解释是：<code>增强向量检索的知识库</code>，那对应又引入的新的<code>关键词</code>，继续递归对新的<code>关键词</code>给出解释，通常迭代个<code>2-5轮</code>，就会有非常好的召回效果。字典里面通常会定义：同义词、类别、上下关系。私用飞书的话，内部标准自带一个<code>词典</code>应用，统一的业务领域的知识、语言体系。并且提供<code>api</code>从词典中提取关键字。直接通过关键字，把词条的内容读取出来。文本提取<code>关键词</code>也有很多开源模型能够提取，但是针对某些专业领域，一些开源不一定具备这样的提取能力。</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<code>关键词</code>去库里召回。这样的<code>关键词</code>，可以不断的积累<code>字典</code>,针对关键词给解释。</p>
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2024-03-28 12:32:12 +08:00
<p>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>
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<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>
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<h2 id="知识图谱"><a href="#知识图谱" class="headerlink" title="知识图谱"></a>知识图谱</h2><p>难点在于构建知识图谱，例如<code>dify</code>在针对某些回答不满意的情况，可以对回复进行修正。这样的话会让下次回答更加可控</p>
<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><p>目前openai的最好，能够输入1536个输入，输出结果能切分到700多个维度。针对模型来说，重要性并不是很高。可以考虑替代方案。差距不会非常大，从实际角度来说，并不是很重要。</p>
<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><p><code>qWen</code> 和 gpt 做过对齐，然后 <code>14B</code>、<code>72B int4</code> 效果就比较好，虽然比较慢，针对中文语料还是比较好的。</p>
<p>另一个就是 <code>yi-34b</code> 也还行。</p>
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<h1 id="如何设计大模型测试样本"><a href="#如何设计大模型测试样本" class="headerlink" title="如何设计大模型测试样本"></a>如何设计大模型测试样本</h1><p>从用户的提问当中，选取50-100个场景，作为<code>大模型</code>选择、<code>知识库</code>效果的测评的数据集，例如需要测<code>text2sql</code>的场景的模型效果，首先就是要找到哪些查询的频次最高。</p>
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<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><ol>
<li>知识库作为专业领域的基座，大模型一定是无法覆盖的</li>
<li>从用户的角度来说，大厂商可能会说服开发者去接入自家agent，贡献领域知识</li>
<li>逐渐变成 Agent 场景下的底层设施，有清晰地 SOP，目标明确</li>
</ol>
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<span>目录</span>
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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></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><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86"><span class="toc-number">2.1.</span> <span class="toc-text">数据预处理</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#%E5%88%86%E8%AF%8D-chunk"><span class="toc-number">2.1.1.</span> <span class="toc-text">分词 chunk</span></a></li></ol></li></ol></li><li class="toc-item toc-level-1"><a class="toc-link" href="#%E6%84%8F%E5%9B%BE%E5%88%86%E7%B1%BB"><span class="toc-number">3.</span> <span class="toc-text">意图分类</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%BC%93%E5%AD%98%E5%BA%93"><span class="toc-number">3.1.</span> <span class="toc-text">缓存库</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#QA"><span class="toc-number">3.2.</span> <span class="toc-text">QA</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E5%85%B3%E9%94%AE%E8%AF%8D-%E8%AF%8D%E5%85%B8-%E8%BF%AD%E4%BB%A3"><span class="toc-number">3.3.</span> <span class="toc-text">关键词+词典+迭代</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1"><span class="toc-number">3.4.</span> <span class="toc-text">知识图谱</span></a></li></ol></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">4.</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">5.</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="#%E5%A6%82%E4%BD%95%E8%AE%BE%E8%AE%A1%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%B5%8B%E8%AF%95%E6%A0%B7%E6%9C%AC"><span class="toc-number">6.</span> <span class="toc-text">如何设计大模型测试样本</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">7.</span> <span class="toc-text">RAG的发展方向</span></a></li></ol>
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