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href=https://doc.tryfastgpt.ai/docs/development/upgrading/484/>V4.8.4(包含升级脚本)</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/483/>V4.8.3</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/482/>V4.8.2</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/481/>V4.8.1(包含升级脚本)</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/48/>V4.8</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/471/>V4.7.1(包含升级脚本)</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/47/>V4.7（需要初始化）</a></li><li><a class=sidebar-nested-link href=https://doc.tryfastgpt.ai/docs/development/upgrading/469/>V4.6.9(包含升级脚本)</a></li><li><a class=sidebar-nested-link 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Table of Contents</button><nav id=toc-mobile><ul class=dropdown-menu><li><ul><li><a href=#理解向量>理解向量</a><ul><li><a href=#提高向量搜索精度的方法>提高向量搜索精度的方法</a></li></ul></li><li><a href=#fastgpt-构建知识库方案>FastGPT 构建知识库方案</a><ul><li><a href=#数据存储结构>数据存储结构</a></li><li><a href=#向量存储结构>向量存储结构</a></li><li><a href=#多向量的目的和使用方式>多向量的目的和使用方式</a></li><li><a href=#检索方案>检索方案</a></li></ul></li><li><a href=#搜索参数>搜索参数</a><ul><li><a href=#搜索模式>搜索模式</a></li><li><a href=#搜索过滤>搜索过滤</a></li><li><a href=#问题优化>问题优化</a></li></ul></li></ul></li></ul></nav></div><div class="docs-content col-12 col-xl-9 mt-0"><div class="mb-0 d-flex"><i class="material-icons title-icon me-2">language</i><h1 class="content-title mb-0">知识库搜索方案和参数</h1></div><p class="lead mb-3">本节会详细介绍 FastGPT 知识库结构设计，理解其 QA 的存储格式和多向量映射，以便更好的构建知识库。同时会介绍每个搜索参数的功能。这篇介绍主要以使用为主，详细原理不多介绍。</p><div id=content class=main-content data-bs-spy=scroll data-bs-root-margin="0px 0px -65%" data-bs-target=#toc-mobile><div data-prismjs-copy data-prismjs-copy-success data-prismjs-copy-error><h2 id=理解向量>理解向量 <a href=#%e7%90%86%e8%a7%a3%e5%90%91%e9%87%8f class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><p>FastGPT 采用了 RAG 中的 Embedding 方案构建知识库，要使用好 FastGPT 需要简单的理解<code>Embedding</code>向量是如何工作的及其特点。</p><p>人类的文字、图片、视频等媒介是无法直接被计算机理解的，要想让计算机理解两段文字是否有相似性、相关性，通常需要将它们转成计算机可以理解的语言，向量是其中的一种方式。</p><p>向量可以简单理解为一个数字数组，两个向量之间可以通过数学公式得出一个<code>距离</code>，距离越小代表两个向量的相似度越大。从而映射到文字、图片、视频等媒介上，可以用来判断两个媒介之间的相似度。向量搜索便是利用了这个原理。</p><p>而由于文字是有多种类型，并且拥有成千上万种组合方式，因此在转成向量进行相似度匹配时，很难保障其精确性。在向量方案构建的知识库中，通常使用<code>topk</code>召回的方式，也就是查找前<code>k</code>个最相似的内容，丢给大模型去做更进一步的<code>语义判断</code>、<code>逻辑推理</code>和<code>归纳总结</code>，从而实现知识库问答。因此，在知识库问答中，向量搜索的环节是最为重要的。</p><p>影响向量搜索精度的因素非常多，主要包括：向量模型的质量、数据的质量（长度，完整性，多样性）、检索器的精度（速度与精度之间的取舍）。与数据质量对应的就是检索词的质量。</p><p>检索器的精度比较容易解决，向量模型的训练略复杂，因此数据和检索词质量优化成了一个重要的环节。</p><h3 id=提高向量搜索精度的方法>提高向量搜索精度的方法 <a href=#%e6%8f%90%e9%ab%98%e5%90%91%e9%87%8f%e6%90%9c%e7%b4%a2%e7%b2%be%e5%ba%a6%e7%9a%84%e6%96%b9%e6%b3%95 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><ol><li>更好分词分段：当一段话的结构和语义是完整的，并且是单一的，精度也会提高。因此，许多系统都会优化分词器，尽可能的保障每组数据的完整性。</li><li>精简<code>index</code>的内容，减少向量内容的长度：当<code>index</code>的内容更少，更准确时，检索精度自然会提高。但与此同时，会牺牲一定的检索范围，适合答案较为严格的场景。</li><li>丰富<code>index</code>的数量，可以为同一个<code>chunk</code>内容增加多组<code>index</code>。</li><li>优化检索词：在实际使用过程中，用户的问题通常是模糊的或是缺失的，并不一定是完整清晰的问题。因此优化用户的问题（检索词）很大程度上也可以提高精度。</li><li>微调向量模型：由于市面上直接使用的向量模型都是通用型模型，在特定领域的检索精度并不高，因此微调向量模型可以很大程度上提高专业领域的检索效果。</li></ol><h2 id=fastgpt-构建知识库方案>FastGPT 构建知识库方案 <a href=#fastgpt-%e6%9e%84%e5%bb%ba%e7%9f%a5%e8%af%86%e5%ba%93%e6%96%b9%e6%a1%88 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><h3 id=数据存储结构>数据存储结构 <a href=#%e6%95%b0%e6%8d%ae%e5%ad%98%e5%82%a8%e7%bb%93%e6%9e%84 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><p>在 FastGPT 中，整个知识库由库、集合和数据 3 部分组成。集合可以简单理解为一个<code>文件</code>。一个<code>库</code>中可以包含多个<code>集合</code>，一个<code>集合</code>中可以包含多组<code>数据</code>。最小的搜索单位是<code>库</code>，也就是说，知识库搜索时，是对整个<code>库</code>进行搜索，而集合仅是为了对数据进行分类管理，与搜索效果无关。（起码目前还是）</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/dataset_tree.png alt loading=lazy class=medium-zoom-image></p><h3 id=向量存储结构>向量存储结构 <a href=#%e5%90%91%e9%87%8f%e5%ad%98%e5%82%a8%e7%bb%93%e6%9e%84 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><p>FastGPT 采用了<code>PostgresSQL</code>的<code>PG Vector</code>插件作为向量检索器，索引为<code>HNSW</code>。且<code>PostgresSQL</code>仅用于向量检索（该引擎可以替换成其它数据库），<code>MongoDB</code>用于其他数据的存取。</p><p>在<code>MongoDB</code>的<code>dataset.datas</code>表中，会存储向量原数据的信息，同时有一个<code>indexes</code>字段，会记录其对应的向量ID，这是一个数组，也就是说，一组数据可以对应多个向量。</p><p>在<code>PostgresSQL</code>的表中，设置一个<code>vector</code>字段用于存储向量。在检索时，会先召回向量，再根据向量的ID，去<code>MongoDB</code>中寻找原数据内容，如果对应了同一组原数据，则进行合并，向量得分取最高得分。</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/datasetSetting1.png alt loading=lazy class=medium-zoom-image></p><h3 id=多向量的目的和使用方式>多向量的目的和使用方式 <a href=#%e5%a4%9a%e5%90%91%e9%87%8f%e7%9a%84%e7%9b%ae%e7%9a%84%e5%92%8c%e4%bd%bf%e7%94%a8%e6%96%b9%e5%bc%8f class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><p>在一组向量中，内容的长度和语义的丰富度通常是矛盾的，无法兼得。因此，FastGPT 采用了多向量映射的方式，将一组数据映射到多组向量中，从而保障数据的完整性和语义的丰富度。</p><p>你可以为一组较长的文本，添加多组向量，从而在检索时，只要其中一组向量被检索到，该数据也将被召回。</p><p>意味着，你可以通过标注数据块的方式，不断提高数据块的精度。</p><h3 id=检索方案>检索方案 <a href=#%e6%a3%80%e7%b4%a2%e6%96%b9%e6%a1%88 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><ol><li>通过<code>问题优化</code>实现指代消除和问题扩展，从而增加连续对话的检索能力以及语义丰富度。</li><li>通过<code>Concat query</code>来增加<code>Rerank</code>连续对话的时，排序的准确性。</li><li>通过<code>RRF</code>合并方式，综合多个渠道的检索效果。</li><li>通过<code>Rerank</code>来二次排序，提高精度。</li></ol><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/dataset_search_process.png alt loading=lazy class=medium-zoom-image></p><h2 id=搜索参数>搜索参数 <a href=#%e6%90%9c%e7%b4%a2%e5%8f%82%e6%95%b0 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h2><table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/dataset_search_params1.png alt loading=lazy class=medium-zoom-image></td><td><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/dataset_search_params2.png alt loading=lazy class=medium-zoom-image></td><td><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/dataset_search_params3.png alt loading=lazy class=medium-zoom-image></td></tr></tbody></table><h3 id=搜索模式>搜索模式 <a href=#%e6%90%9c%e7%b4%a2%e6%a8%a1%e5%bc%8f class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><h4 id=语义检索>语义检索 <a href=#%e8%af%ad%e4%b9%89%e6%a3%80%e7%b4%a2 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>语义检索是通过向量距离，计算用户问题与知识库内容的距离，从而得出“相似度”，当然这并不是语文上的相似度，而是数学上的。</p><p>优点：</p><ul><li>相近语义理解</li><li>跨多语言理解（例如输入中文问题匹配英文知识点）</li><li>多模态理解（文本，图片，音视频等）</li></ul><p>缺点：</p><ul><li>依赖模型训练效果</li><li>精度不稳定</li><li>受关键词和句子完整度影响</li></ul><h4 id=全文检索>全文检索 <a href=#%e5%85%a8%e6%96%87%e6%a3%80%e7%b4%a2 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>采用传统的全文检索方式。适合查找关键的主谓语等。</p><h4 id=混合检索>混合检索 <a href=#%e6%b7%b7%e5%90%88%e6%a3%80%e7%b4%a2 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>同时使用向量检索和全文检索，并通过 RRF 公式进行两个搜索结果合并，一般情况下搜索结果会更加丰富准确。</p><p>由于混合检索后的查找范围很大，并且无法直接进行相似度过滤，通常需要进行利用重排模型进行一次结果重新排序，并利用重排的得分进行过滤。</p><h4 id=结果重排>结果重排 <a href=#%e7%bb%93%e6%9e%9c%e9%87%8d%e6%8e%92 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>利用<code>ReRank</code>模型对搜索结果进行重排，绝大多数情况下，可以有效提高搜索结果的准确率。不过，重排模型与问题的完整度（主谓语齐全）有一些关系，通常会先走问题优化后再进行搜索-重排。重排后可以得到一个<code>0-1</code>的得分，代表着搜索内容与问题的相关度，该分数通常比向量的得分更加精确，可以根据得分进行过滤。</p><p>FastGPT 会使用 <code>RRF</code> 对重排结果、向量搜索结果、全文检索结果进行合并，得到最终的搜索结果。</p><h3 id=搜索过滤>搜索过滤 <a href=#%e6%90%9c%e7%b4%a2%e8%bf%87%e6%bb%a4 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><h4 id=引用上限>引用上限 <a href=#%e5%bc%95%e7%94%a8%e4%b8%8a%e9%99%90 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>每次搜索最多引用<code>n</code>个<code>tokens</code>的内容。</p><p>之所以不采用<code>top k</code>，是发现在混合知识库（问答库、文档库）时，不同<code>chunk</code>的长度差距很大，会导致<code>top k</code>的结果不稳定，因此采用了<code>tokens</code>的方式进行引用上限的控制。</p><h4 id=最低相关度>最低相关度 <a href=#%e6%9c%80%e4%bd%8e%e7%9b%b8%e5%85%b3%e5%ba%a6 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>一个<code>0-1</code>的数值，会过滤掉一些低相关度的搜索结果。</p><p>该值仅在<code>语义检索</code>或使用<code>结果重排</code>时生效。</p><h3 id=问题优化>问题优化 <a href=#%e9%97%ae%e9%a2%98%e4%bc%98%e5%8c%96 class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h3><h4 id=背景>背景 <a href=#%e8%83%8c%e6%99%af class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>在 RAG 中，我们需要根据输入的问题去数据库里执行 embedding 搜索，查找相关的内容，从而查找到相似的内容（简称知识库搜索）。</p><p>在搜索的过程中，尤其是连续对话的搜索，我们通常会发现后续的问题难以搜索到合适的内容，其中一个原因是知识库搜索只会使用“当前”的问题去执行。看下面的例子：</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/coreferenceResolution2.webp alt loading=lazy class=medium-zoom-image></p><p>用户在提问“第二点是什么”的时候，只会去知识库里查找“第二点是什么”，压根查不到内容。实际上需要查询的是“QA结构是什么”。因此我们需要引入一个【问题优化】模块，来对用户当前的问题进行补全，从而使得知识库搜索能够搜索到合适的内容。使用补全后效果如下：</p><p><img src=https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/coreferenceResolution3.webp alt loading=lazy class=medium-zoom-image></p><h4 id=实现方式>实现方式 <a href=#%e5%ae%9e%e7%8e%b0%e6%96%b9%e5%bc%8f class=anchor aria-hidden=true><i class="material-icons align-middle">link</i></a></h4><p>在进行<code>数据检索</code>前，会先让模型进行<code>指代消除</code>与<code>问题扩展</code>，一方面可以可以解决指代对象不明确问题，同时可以扩展问题的语义丰富度。你可以通过每次对话后的对话详情，查看补全的结果。</p></div><div class="gitinfo d-flex flex-wrap justify-content-between align-items-center opacity-85 pt-3"><div id=edit-this-page class=mt-1><a href=https://github.com/labring/FastGPT/blob/main/docSite/content/zh-cn/docs/guide/knowledge_base/dataset_engine.md alt=知识库搜索方案和参数 rel="noopener noreferrer" target=_blank><span class="me-1 align-text-bottom"><svg width="20" height="20" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg" fill="currentcolor"><path d="M16 .396c-8.839.0-16 7.167-16 16 0 7.073 4.584 13.068 10.937 15.183.803.151 1.093-.344 1.093-.772.0-.38-.009-1.385-.015-2.719-4.453.964-5.391-2.151-5.391-2.151-.729-1.844-1.781-2.339-1.781-2.339-1.448-.989.115-.968.115-.968 1.604.109 2.448 1.645 2.448 1.645 1.427 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