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 <link rel="modulepreload" href="/docs/smolagents/pr_2321/zh/_app/immutable/chunks/DocNotebookDropdown.be7b60e3.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Agentic RAG&quot;,&quot;local&quot;:&quot;agentic-rag&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div> <div class="flex space-x-1 " style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"> <div class="relative colab-dropdown "> <button class=" " type="button"> <img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"> </button> </div> <div class="relative colab-dropdown "> <button class=" " type="button"> <img alt="Open In Studio Lab" class="!m-0" src="https://studiolab.sagemaker.aws/studiolab.svg"> </button> </div></div> <h1 class="relative group"><a id="agentic-rag" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#agentic-rag"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Agentic RAG</span></h1> <p data-svelte-h="svelte-mhdtwz">Retrieval-Augmented-Generation (RAG) 是“使用大语言模型（LLM）来回答用户查询，但基于从知识库中检索的信息”。它比使用普通或微调的 LLM 具有许多优势：举几个例子，它允许将答案基于真实事实并减少虚构；它允许提供 LLM 领域特定的知识；并允许对知识库中的信息访问进行精细控制。</p> <p data-svelte-h="svelte-14tubvl">但是，普通的 RAG 存在一些局限性，以下两点尤为突出：</p> <ul data-svelte-h="svelte-16mtzo0"><li>它只执行一次检索步骤：如果结果不好，生成的内容也会不好。</li> <li>语义相似性是以用户查询为参考计算的，这可能不是最优的：例如，用户查询通常是一个问题，而包含真实答案的文档通常是肯定语态，因此其相似性得分会比其他以疑问形式呈现的源文档低，从而导致错失相关信息的风险。</li></ul> <p data-svelte-h="svelte-xmboum">我们可以通过制作一个 RAG agent来缓解这些问题：非常简单，一个配备了检索工具的agent！这个 agent 将
会：✅ 自己构建查询和检索，✅ 如果需要的话会重新检索。</p> <p data-svelte-h="svelte-lrjdxa">因此，它将比普通 RAG 更智能，因为它可以自己构建查询，而不是直接使用用户查询作为参考。这样，它可以更
接近目标文档，从而提高检索的准确性， <a href="https://huggingface.co/papers/2212.10496" rel="nofollow">HyDE</a>。此 agent 可以
使用生成的片段，并在需要时重新检索，就像 <a href="https://docs.llamaindex.ai/en/stable/examples/evaluation/RetryQuery/" rel="nofollow">Self-Query</a>。</p> <p data-svelte-h="svelte-2ch6nl">我们现在开始构建这个系统. 🛠️</p> <p data-svelte-h="svelte-54uamx">运行以下代码以安装所需的依赖包：</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-bash "><!-- HTML_TAG_START -->!pip install smolagents pandas langchain langchain-community sentence-transformers rank_bm25 --upgrade -q<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1rvxl6s">你需要一个有效的 token 作为环境变量 <code>HF_TOKEN</code> 来调用 Inference Providers。我们使用 python-dotenv 来加载它。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> dotenv <span class="hljs-keyword">import</span> load_dotenv
load_dotenv()<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-77u41g">我们首先加载一个知识库以在其上执行 RAG：此数据集是许多 Hugging Face 库的文档页面的汇编，存储为 markdown 格式。我们将仅保留 <code>transformers</code> 库的文档。然后通过处理数据集并将其存储到向量数据库中，为检索器准备知识库。我们将使用 <a href="https://python.langchain.com/docs/introduction/" rel="nofollow">LangChain</a> 来利用其出色的向量数据库工具。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> datasets
<span class="hljs-keyword">from</span> langchain.docstore.document <span class="hljs-keyword">import</span> Document
<span class="hljs-keyword">from</span> langchain.text_splitter <span class="hljs-keyword">import</span> RecursiveCharacterTextSplitter
<span class="hljs-keyword">from</span> langchain_community.retrievers <span class="hljs-keyword">import</span> BM25Retriever
knowledge_base = datasets.load_dataset(<span class="hljs-string">&quot;m-ric/huggingface_doc&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
knowledge_base = knowledge_base.<span class="hljs-built_in">filter</span>(<span class="hljs-keyword">lambda</span> row: row[<span class="hljs-string">&quot;source&quot;</span>].startswith(<span class="hljs-string">&quot;huggingface/transformers&quot;</span>))
source_docs = [
 Document(page_content=doc[<span class="hljs-string">&quot;text&quot;</span>], metadata={<span class="hljs-string">&quot;source&quot;</span>: doc[<span class="hljs-string">&quot;source&quot;</span>].split(<span class="hljs-string">&quot;/&quot;</span>)[<span class="hljs-number">1</span>]})
 <span class="hljs-keyword">for</span> doc <span class="hljs-keyword">in</span> knowledge_base
]
text_splitter = RecursiveCharacterTextSplitter(
 chunk_size=<span class="hljs-number">500</span>,
 chunk_overlap=<span class="hljs-number">50</span>,
 add_start_index=<span class="hljs-literal">True</span>,
 strip_whitespace=<span class="hljs-literal">True</span>,
 separators=[<span class="hljs-string">&quot;\n\n&quot;</span>, <span class="hljs-string">&quot;\n&quot;</span>, <span class="hljs-string">&quot;.&quot;</span>, <span class="hljs-string">&quot; &quot;</span>, <span class="hljs-string">&quot;&quot;</span>],
)
docs_processed = text_splitter.split_documents(source_docs)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1jt4w61">现在文档已准备好。我们来一起构建我们的 agent RAG 系统！
👉 我们只需要一个 RetrieverTool，我们的 agent 可以利用它从知识库中检索信息。</p> <p data-svelte-h="svelte-d65l33">由于我们需要将 vectordb 添加为工具的属性，我们不能简单地使用带有 <code>@tool</code> 装饰器的简单工具构造函数：因此我们将遵循 <a href="../tutorials/tools">tools 教程</a> 中突出显示的高级设置。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> Tool
<span class="hljs-keyword">class</span> <span class="hljs-title class_">RetrieverTool</span>(<span class="hljs-title class_ inherited__">Tool</span>):
 name = <span class="hljs-string">&quot;retriever&quot;</span>
 description = <span class="hljs-string">&quot;Uses semantic search to retrieve the parts of transformers documentation that could be most relevant to answer your query.&quot;</span>
 inputs = {
 <span class="hljs-string">&quot;query&quot;</span>: {
 <span class="hljs-string">&quot;type&quot;</span>: <span class="hljs-string">&quot;string&quot;</span>,
 <span class="hljs-string">&quot;description&quot;</span>: <span class="hljs-string">&quot;The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.&quot;</span>,
 }
 }
 output_type = <span class="hljs-string">&quot;string&quot;</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, docs, **kwargs</span>):
 <span class="hljs-built_in">super</span>().__init__(**kwargs)
 self.retriever = BM25Retriever.from_documents(
 docs, k=<span class="hljs-number">10</span>
 )
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, query: <span class="hljs-built_in">str</span></span>) -&gt; <span class="hljs-built_in">str</span>:
 <span class="hljs-keyword">assert</span> <span class="hljs-built_in">isinstance</span>(query, <span class="hljs-built_in">str</span>), <span class="hljs-string">&quot;Your search query must be a string&quot;</span>
docs = self.retriever.invoke(
 query,
 )
 <span class="hljs-keyword">return</span> <span class="hljs-string">&quot;\nRetrieved documents:\n&quot;</span> + <span class="hljs-string">&quot;&quot;</span>.join(
 [
 <span class="hljs-string">f&quot;\n\n===== Document <span class="hljs-subst">{<span class="hljs-built_in">str</span>(i)}</span> =====\n&quot;</span> + doc.page_content
 <span class="hljs-keyword">for</span> i, doc <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(docs)
 ]
 )
retriever_tool = RetrieverTool(docs_processed)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-8u62jq">BM25 检索方法是一个经典的检索方法，因为它的设置速度非常快。为了提高检索准确性，你可以使用语义搜索，使用文档的向量表示替换 BM25：因此你可以前往 <a href="https://huggingface.co/spaces/mteb/leaderboard" rel="nofollow">MTEB Leaderboard</a> 选择一个好的嵌入模型。</p> <p data-svelte-h="svelte-g2qdob">现在我们已经创建了一个可以从知识库中检索信息的工具，现在我们可以很容易地创建一个利用这个
<code>retriever_tool</code> 的 agent！此 agent 将使用如下参数初始化：</p> <ul data-svelte-h="svelte-1bkhezl"><li><code>tools</code>：代理将能够调用的工具列表。</li> <li><code>model</code>：为代理提供动力的 LLM。</li></ul> <p data-svelte-h="svelte-1i51m6b">我们的 <code>model</code> 必须是一个可调用对象，它接受一个消息的 list 作为输入，并返回文本。它还需要接受一个 stop_sequences 参数，指示何时停止生成。为了方便起见，我们直接使用包中提供的 <code>HfEngine</code> 类来获取调用 Hugging Face 的 Inference API 的 LLM 引擎。</p> <p data-svelte-h="svelte-1bx3er4">接着，我们将使用 <a href="meta-llama/Llama-3.3-70B-Instruct">meta-llama/Llama-3.3-70B-Instruct</a> 作为 llm 引
擎，因为：</p> <ul data-svelte-h="svelte-1n3y97g"><li>它有一个长 128k 上下文，这对处理长源文档很有用。</li> <li>它在 HF 的 Inference API 上始终免费提供！</li></ul> <p data-svelte-h="svelte-1835ngc"><em>Note:</em> 此 Inference API 托管基于各种标准的模型，部署的模型可能会在没有事先通知的情况下进行更新或替换。了解更多信息，请点击<a href="https://huggingface.co/docs/api-inference/supported-models" rel="nofollow">这里</a>。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> InferenceClientModel, CodeAgent
agent = CodeAgent(
 tools=[retriever_tool], model=InferenceClientModel(model_id=<span class="hljs-string">&quot;meta-llama/Llama-3.3-70B-Instruct&quot;</span>), max_steps=<span class="hljs-number">4</span>, verbose=<span class="hljs-literal">True</span>
)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-emwerr">当我们初始化 CodeAgent 时，它已经自动获得了一个默认的系统提示，告诉 LLM 引擎按步骤处理并生成工具调用作为代码片段，但你可以根据需要替换此提示模板。接着，当其 <code>.run()</code> 方法被调用时，代理将负责调用 LLM 引擎，并在循环中执行工具调用，直到工具 <code>final_answer</code> 被调用，而其参数为最终答案。</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START -->agent_output = agent.run(<span class="hljs-string">&quot;For a transformers model training, which is slower, the forward or the backward pass?&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Final output:&quot;</span>)
<span class="hljs-built_in">print</span>(agent_output)<!-- HTML_TAG_END --></pre></div> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/smolagents/blob/main/docs/source/zh/examples/rag.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p>
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