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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> <h1 class="relative group"><a id="多步骤-agent-是如何工作的" 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="#多步骤-agent-是如何工作的"><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>多步骤 agent 是如何工作的?</span></h1> <p data-svelte-h="svelte-gm4bn2">ReAct 框架(<a href="https://huggingface.co/papers/2210.03629" rel="nofollow">Yao et al., 2022</a>)是目前构建 agent 的主要方法。</p> <p data-svelte-h="svelte-ktrkfc">该名称基于两个词的组合:“Reason” (推理)和 “Act” (行动)。实际上,遵循此架构的 agent 将根据需要尽可能多的步骤来解决其任务,每个步骤包括一个推理步骤,然后是一个行动步骤,在该步骤中,它制定工具调用,使其更接近解决手头的任务。</p> <p data-svelte-h="svelte-lhy2wj">ReAct 过程涉及保留过去步骤的记忆。</p> <blockquote class="tip" data-svelte-h="svelte-1syzs5b"><p>阅读 <a href="https://huggingface.co/blog/open-source-llms-as-agents" rel="nofollow">Open-source LLMs as LangChain Agents</a> 博客文章以了解更多关于多步 agent 的信息。</p></blockquote> <p data-svelte-h="svelte-1ghba8i">以下是其工作原理的视频概述:</p> <div class="flex justify-center" data-svelte-h="svelte-4ra9xq"><img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Agent_ManimCE.gif"> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Agent_ManimCE.gif"></div> <p data-svelte-h="svelte-1qm66iv"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open-source-llms-as-agents/ReAct.png" alt="ReAct agent 的框架"></p> <p data-svelte-h="svelte-1j4ubj4">我们实现了两个版本的 ToolCallingAgent:</p> <ul data-svelte-h="svelte-1qm16ow"><li><a href="/docs/smolagents/pr_2321/zh/reference/agents#smolagents.ToolCallingAgent">ToolCallingAgent</a> 在其输出中生成 JSON 格式的工具调用。</li> <li><a href="/docs/smolagents/pr_2321/zh/reference/agents#smolagents.CodeAgent">CodeAgent</a> 是一种新型的 ToolCallingAgent,它生成代码块形式的工具调用,这对于具有强大编码性能的 LLM 非常有效。</li></ul> <a class="!text-gray-400 !no-underline text-sm flex 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