import{s as Kl,o as ei,n as ne}from"../chunks/scheduler.bdbef820.js";import{S as ti,i as oi,g as s,s as o,r as m,A as ni,h as a,f as r,c as n,j as _,u as d,x as $,k as y,y as t,a as i,v as p,d as g,t as c,w as f}from"../chunks/index.33f81d56.js";import{T as qa}from"../chunks/Tip.34194030.js";import{D as x}from"../chunks/Docstring.64554317.js";import{C as yo}from"../chunks/CodeBlock.362b34a4.js";import{E as Ln}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as J,E as ri}from"../chunks/EditOnGithub.a9246e21.js";function si(I){let u,T=`Transformers Agent는 실험 중인 API이므로 언제든지 변경될 수 있습니다.
API나 기반 모델이 자주 업데이트되므로, 에이전트가 제공하는 결과물은 달라질 수 있습니다.`;return{c(){u=s("p"),u.textContent=T},l(b){u=a(b,"P",{"data-svelte-h":!0}),$(u)!=="svelte-1kfd8kh"&&(u.textContent=T)},m(b,v){i(b,u,v)},p:ne,d(b){b&&r(u)}}}function ai(I){let u,T="Example:",b,v,w;return v=new yo({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycy5hZ2VudHMlMjBpbXBvcnQlMjBDb2RlQWdlbnQlMEElMEFhZ2VudCUyMCUzRCUyMENvZGVBZ2VudCh0b29scyUzRCU1QiU1RCklMEFhZ2VudC5ydW4oJTIyV2hhdCUyMGlzJTIwdGhlJTIwcmVzdWx0JTIwb2YlMjAyJTIwcG93ZXIlMjAzLjczODQlM0YlMjIp",highlighted:`<span class="hljs-keyword">from</span> transformers.agents <span class="hljs-keyword">import</span> CodeAgent

agent = CodeAgent(tools=[])
agent.run(<span class="hljs-string">&quot;What is the result of 2 power 3.7384?&quot;</span>)`,wrap:!1}}),{c(){u=s("p"),u.textContent=T,b=o(),m(v.$$.fragment)},l(h){u=a(h,"P",{"data-svelte-h":!0}),$(u)!=="svelte-11lpom8"&&(u.textContent=T),b=n(h),d(v.$$.fragment,h)},m(h,C){i(h,u,C),i(h,b,C),p(v,h,C),w=!0},p:ne,i(h){w||(g(v.$$.fragment,h),w=!0)},o(h){c(v.$$.fragment,h),w=!1},d(h){h&&(r(u),r(b)),f(v,h)}}}function li(I){let u,T="Example:",b,v,w;return v=new yo({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycy5hZ2VudHMlMjBpbXBvcnQlMjBSZWFjdENvZGVBZ2VudCUwQWFnZW50JTIwJTNEJTIwUmVhY3RDb2RlQWdlbnQodG9vbHMlM0QlNUIlNUQpJTBBYWdlbnQucnVuKCUyMldoYXQlMjBpcyUyMHRoZSUyMHJlc3VsdCUyMG9mJTIwMiUyMHBvd2VyJTIwMy43Mzg0JTNGJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers.agents <span class="hljs-keyword">import</span> ReactCodeAgent
agent = ReactCodeAgent(tools=[])
agent.run(<span class="hljs-string">&quot;What is the result of 2 power 3.7384?&quot;</span>)`,wrap:!1}}),{c(){u=s("p"),u.textContent=T,b=o(),m(v.$$.fragment)},l(h){u=a(h,"P",{"data-svelte-h":!0}),$(u)!=="svelte-11lpom8"&&(u.textContent=T),b=n(h),d(v.$$.fragment,h)},m(h,C){i(h,u,C),i(h,b,C),p(v,h,C),w=!0},p:ne,i(h){w||(g(v.$$.fragment,h),w=!0)},o(h){c(v.$$.fragment,h),w=!1},d(h){h&&(r(u),r(b)),f(v,h)}}}function ii(I){let u,T=`Loading a tool means that you’ll download the tool and execute it locally.
ALWAYS inspect the tool you’re downloading before loading it within your runtime, as you would do when
installing a package using pip/npm/apt.`;return{c(){u=s("p"),u.textContent=T},l(b){u=a(b,"P",{"data-svelte-h":!0}),$(u)!=="svelte-1kcherd"&&(u.textContent=T)},m(b,v){i(b,u,v)},p:ne,d(b){b&&r(u)}}}function mi(I){let u,T=`Loading a tool from the Hub means that you’ll download the tool and execute it locally.
ALWAYS inspect the tool you’re downloading before loading it within your runtime, as you would do when
installing a package using pip/npm/apt.`;return{c(){u=s("p"),u.textContent=T},l(b){u=a(b,"P",{"data-svelte-h":!0}),$(u)!=="svelte-fpk7e5"&&(u.textContent=T)},m(b,v){i(b,u,v)},p:ne,d(b){b&&r(u)}}}function di(I){let u,T="Examples:",b,v,w;return v=new yo({props:{code:"aW1hZ2VfZ2VuZXJhdG9yJTIwJTNEJTIwVG9vbC5mcm9tX3NwYWNlKCUwQSUyMCUyMCUyMCUyMHNwYWNlX2lkJTNEJTIyYmxhY2stZm9yZXN0LWxhYnMlMkZGTFVYLjEtc2NobmVsbCUyMiUyQyUwQSUyMCUyMCUyMCUyMG5hbWUlM0QlMjJpbWFnZS1nZW5lcmF0b3IlMjIlMkMlMEElMjAlMjAlMjAlMjBkZXNjcmlwdGlvbiUzRCUyMkdlbmVyYXRlJTIwYW4lMjBpbWFnZSUyMGZyb20lMjBhJTIwcHJvbXB0JTIyJTBBKSUwQWltYWdlJTIwJTNEJTIwaW1hZ2VfZ2VuZXJhdG9yKCUyMkdlbmVyYXRlJTIwYW4lMjBpbWFnZSUyMG9mJTIwYSUyMGNvb2wlMjBzdXJmZXIlMjBpbiUyMFRhaGl0aSUyMik=",highlighted:`image_generator = Tool.from_space(
    <span class="hljs-attribute">space_id</span>=<span class="hljs-string">&quot;black-forest-labs/FLUX.1-schnell&quot;</span>,
    <span class="hljs-attribute">name</span>=<span class="hljs-string">&quot;image-generator&quot;</span>,
    <span class="hljs-attribute">description</span>=<span class="hljs-string">&quot;Generate an image from a prompt&quot;</span>
)
image = image_generator(<span class="hljs-string">&quot;Generate an image of a cool surfer in Tahiti&quot;</span>)`,wrap:!1}}),{c(){u=s("p"),u.textContent=T,b=o(),m(v.$$.fragment)},l(h){u=a(h,"P",{"data-svelte-h":!0}),$(u)!=="svelte-kvfsh7"&&(u.textContent=T),b=n(h),d(v.$$.fragment,h)},m(h,C){i(h,u,C),i(h,b,C),p(v,h,C),w=!0},p:ne,i(h){w||(g(v.$$.fragment,h),w=!0)},o(h){c(v.$$.fragment,h),w=!1},d(h){h&&(r(u),r(b)),f(v,h)}}}function pi(I){let u,T;return u=new yo({props:{code:"ZmFjZV9zd2FwcGVyJTIwJTNEJTIwVG9vbC5mcm9tX3NwYWNlKCUwQSUyMCUyMCUyMCUyMCUyMnR1YW4yMzA4JTJGZmFjZS1zd2FwJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyZmFjZV9zd2FwcGVyJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyVG9vbCUyMHRoYXQlMjBwdXRzJTIwdGhlJTIwZmFjZSUyMHNob3duJTIwb24lMjB0aGUlMjBmaXJzdCUyMGltYWdlJTIwb24lMjB0aGUlMjBzZWNvbmQlMjBpbWFnZS4lMjBZb3UlMjBjYW4lMjBnaXZlJTIwaXQlMjBwYXRocyUyMHRvJTIwaW1hZ2VzLiUyMiUyQyUwQSklMEFpbWFnZSUyMCUzRCUyMGZhY2Vfc3dhcHBlcignLiUyRmF5bWVyaWMuanBlZyclMkMlMjAnLiUyRnJ1dGguanBnJyk=",highlighted:`face_swapper = <span class="hljs-type">Tool</span>.from_space(
    <span class="hljs-comment">&quot;tuan2308/face-swap&quot;</span>,
    <span class="hljs-comment">&quot;face_swapper&quot;</span>,
    <span class="hljs-comment">&quot;Tool that puts the face shown on the first image on the second image. You can give it paths to images.&quot;</span>,
)
image = face_swapper(<span class="hljs-string">&#x27;./aymeric.jpeg&#x27;</span>, <span class="hljs-string">&#x27;./ruth.jpg&#x27;</span>)`,wrap:!1}}),{c(){m(u.$$.fragment)},l(b){d(u.$$.fragment,b)},m(b,v){p(u,b,v),T=!0},p:ne,i(b){T||(g(u.$$.fragment,b),T=!0)},o(b){c(u.$$.fragment,b),T=!1},d(b){f(u,b)}}}function gi(I){let u,T="For instance:",b,v,w;return v=new yo({props:{code:"ZnJvbSUyMG15X3Rvb2xfbW9kdWxlJTIwaW1wb3J0JTIwTXlUb29sJTBBbXlfdG9vbCUyMCUzRCUyME15VG9vbCgpJTBBbXlfdG9vbC5wdXNoX3RvX2h1YiglMjJteS11c2VybmFtZSUyRm15LXNwYWNlJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> my_tool_module <span class="hljs-keyword">import</span> MyTool
my_tool = MyTool()
my_tool.push_to_hub(<span class="hljs-string">&quot;my-username/my-space&quot;</span>)`,wrap:!1}}),{c(){u=s("p"),u.textContent=T,b=o(),m(v.$$.fragment)},l(h){u=a(h,"P",{"data-svelte-h":!0}),$(u)!=="svelte-4vay6o"&&(u.textContent=T),b=n(h),d(v.$$.fragment,h)},m(h,C){i(h,u,C),i(h,b,C),p(v,h,C),w=!0},p:ne,i(h){w||(g(v.$$.fragment,h),w=!0)},o(h){c(v.$$.fragment,h),w=!1},d(h){h&&(r(u),r(b)),f(v,h)}}}function ci(I){let u,T="Example:",b,v,w;return v=new yo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ToolCollection, ReactCodeAgent

<span class="hljs-meta">&gt;&gt;&gt; </span>image_tool_collection = ToolCollection(collection_slug=<span class="hljs-string">&quot;huggingface-tools/diffusion-tools-6630bb19a942c2306a2cdb6f&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>agent = ReactCodeAgent(tools=[*image_tool_collection.tools], add_base_tools=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>agent.run(<span class="hljs-string">&quot;Please draw me a picture of rivers and lakes.&quot;</span>)`,wrap:!1}}),{c(){u=s("p"),u.textContent=T,b=o(),m(v.$$.fragment)},l(h){u=a(h,"P",{"data-svelte-h":!0}),$(u)!=="svelte-11lpom8"&&(u.textContent=T),b=n(h),d(v.$$.fragment,h)},m(h,C){i(h,u,C),i(h,b,C),p(v,h,C),w=!0},p:ne,i(h){w||(g(v.$$.fragment,h),w=!0)},o(h){c(v.$$.fragment,h),w=!1},d(h){h&&(r(u),r(b)),f(v,h)}}}function fi(I){let u,T,b,v,w,h,C,Un,ze,Ra=`에이전트와 도구에 대해 더 알아보려면 <a href="../transformers_agents">소개 가이드</a>를 꼭 읽어보세요.
이 페이지에는 기본 클래스에 대한 API 문서가 포함되어 있습니다.`,Pn,Fe,Hn,Ge,Za='우리는 기본 <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Agent">Agent</a> 클래스를 기반으로 두 가지 유형의 에이전트를 제공합니다:',En,Se,Wa='<li><a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.CodeAgent">CodeAgent</a>는 한 번에 동작합니다. 작업을 해결하기 위해 코드를 생성한 다음, 바로 실행합니다.</li> <li><a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.ReactAgent">ReactAgent</a>는 단계별로 동작하며, 각 단계는 하나의 생각, 하나의 도구 호출 및 실행으로 구성됩니다. 이 에이전트에는 두 가지 클래스가 있습니다:<ul><li><a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.ReactJsonAgent">ReactJsonAgent</a>는 도구 호출을 JSON으로 작성합니다.</li> <li><a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.ReactCodeAgent">ReactCodeAgent</a>는 도구 호출을 Python 코드로 작성합니다.</li></ul></li>',Nn,Qe,Vn,P,Oe,Or,re,Ye,Yr,xo,Ba=`Execute tool with the provided input and returns the result.
This method replaces arguments with the actual values from the state if they refer to state variables.`,Xr,se,Xe,Kr,To,za="Parse action from the LLM output",es,ae,Ke,ts,wo,Fa="To be implemented in the child class",os,le,et,ns,Co,Ga=`Reads past llm_outputs, actions, and observations or errors from the logs into a series of messages
that can be used as input to the LLM.`,Dn,tt,qn,V,ot,rs,Mo,Sa="A class for an agent that solves the given task using a single block of code. It plans all its actions, then executes all in one shot.",ss,ie,nt,as,ko,Qa=`Override this method if you want to change the way the code is
cleaned in the <code>run</code> method.`,ls,O,rt,is,Io,Oa="Runs the agent for the given task.",ms,me,Rn,st,Zn,A,at,ds,Ao,Ya=`This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The action will be parsed from the LLM output: it consists in calls to tools from the toolbox, with arguments chosen by the LLM engine.`,ps,de,lt,gs,jo,Xa="Runs the agent in direct mode, returning outputs only at the end: should be launched only in the <code>run</code> method.",cs,pe,it,fs,Lo,Ka="Used periodically by the agent to plan the next steps to reach the objective.",us,ge,mt,hs,Jo,el="This method provides a final answer to the task, based on the logs of the agent’s interactions.",$s,Y,dt,bs,Uo,tl="Runs the agent for the given task.",vs,ce,_s,fe,pt,ys,Po,ol="Runs the agent in streaming mode, yielding steps as they are executed: should be launched only in the <code>run</code> method.",Wn,z,gt,xs,Ho,nl=`This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The tool calls will be formulated by the LLM in JSON format, then parsed and executed.`,Ts,ue,ct,ws,Eo,rl=`Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
The errors are raised here, they are caught and logged in the run() method.`,Bn,F,ft,Cs,No,sl=`This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The tool calls will be formulated by the LLM in code format, then parsed and executed.`,Ms,he,ut,ks,Vo,al=`Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
The errors are raised here, they are caught and logged in the run() method.`,zn,ht,Fn,$t,Gn,G,bt,Is,Do,ll="Main function to quickly load a tool, be it on the Hub or in the Transformers library.",As,$e,Sn,vt,Qn,M,_t,js,qo,il=`A base class for the functions used by the agent. Subclass this and implement the <code>__call__</code> method as well as the
following class attributes:`,Ls,Ro,ml=`<li><strong>description</strong> (<code>str</code>) — A short description of what your tool does, the inputs it expects and the output(s) it
will return. For instance ‘This is a tool that downloads a file from a <code>url</code>. It takes the <code>url</code> as input, and
returns the text contained in the file’.</li> <li><strong>name</strong> (<code>str</code>) — A performative name that will be used for your tool in the prompt to the agent. For instance
<code>&quot;text-classifier&quot;</code> or <code>&quot;image_generator&quot;</code>.</li> <li><strong>inputs</strong> (<code>Dict[str, Dict[str, Union[str, type]]]</code>) — The dict of modalities expected for the inputs.
It has one <code>type</code>key and a <code>description</code>key.
This is used by <code>launch_gradio_demo</code> or to make a nice space from your tool, and also can be used in the generated
description for your tool.</li> <li><strong>output_type</strong> (<code>type</code>) — The type of the tool output. This is used by <code>launch_gradio_demo</code>
or to make a nice space from your tool, and also can be used in the generated description for your tool.</li>`,Js,Zo,dl=`You can also override the method <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool.setup">setup()</a> if your tool as an expensive operation to perform before being
usable (such as loading a model). <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool.setup">setup()</a> will be called the first time you use your tool, but not at
instantiation.`,Us,be,yt,Ps,Wo,pl='Creates a <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool">Tool</a> from a gradio tool.',Hs,X,xt,Es,Bo,gl="Loads a tool defined on the Hub.",Ns,ve,Vs,_e,Tt,Ds,zo,cl='Creates a <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool">Tool</a> from a langchain tool.',qs,Z,wt,Rs,Fo,fl='Creates a <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool">Tool</a> from a Space given its id on the Hub.',Zs,ye,Ws,xe,Bs,W,Ct,zs,Go,ul="Upload the tool to the Hub.",Fs,So,hl="For this method to work properly, your tool must have been defined in a separate module (not <code>__main__</code>).",Gs,Te,Ss,B,Mt,Qs,Qo,$l=`Saves the relevant code files for your tool so it can be pushed to the Hub. This will copy the code of your
tool in <code>output_dir</code> as well as autogenerate:`,Os,Oo,bl=`<li>a config file named <code>tool_config.json</code></li> <li>an <code>app.py</code> file so that your tool can be converted to a space</li> <li>a <code>requirements.txt</code> containing the names of the module used by your tool (as detected when inspecting its
code)</li>`,Ys,Yo,vl="You should only use this method to save tools that are defined in a separate module (not <code>__main__</code>).",Xs,we,kt,Ks,Xo,_l=`Overwrite this method here for any operation that is expensive and needs to be executed before you start using
your tool. Such as loading a big model.`,On,It,Yn,j,At,ea,Ko,yl=`The toolbox contains all tools that the agent can perform operations with, as well as a few methods to
manage them.`,ta,Ce,jt,oa,en,xl="Adds a tool to the toolbox",na,Me,Lt,ra,tn,Tl="Clears the toolbox",sa,ke,Jt,aa,on,wl="Removes a tool from the toolbox",la,Ie,Ut,ia,nn,Cl="Returns the description of all tools in the toolbox",ma,Ae,Pt,da,rn,Ml="Updates a tool in the toolbox according to its name.",Xn,Ht,Kn,L,Et,pa,sn,kl=`A <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool">Tool</a> tailored towards Transformer models. On top of the class attributes of the base class <a href="/docs/transformers/pr_30530/ko/main_classes/agent#transformers.Tool">Tool</a>, you will
need to specify:`,ga,an,Il=`<li><strong>model_class</strong> (<code>type</code>) — The class to use to load the model in this tool.</li> <li><strong>default_checkpoint</strong> (<code>str</code>) — The default checkpoint that should be used when the user doesn’t specify one.</li> <li><strong>pre_processor_class</strong> (<code>type</code>, <em>optional</em>, defaults to <a href="/docs/transformers/pr_30530/ko/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>) — The class to use to load the
pre-processor</li> <li><strong>post_processor_class</strong> (<code>type</code>, <em>optional</em>, defaults to <a href="/docs/transformers/pr_30530/ko/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>) — The class to use to load the
post-processor (when different from the pre-processor).</li>`,ca,je,Nt,fa,ln,Al="Uses the <code>post_processor</code> to decode the model output.",ua,Le,Vt,ha,mn,jl="Uses the <code>pre_processor</code> to prepare the inputs for the <code>model</code>.",$a,Je,Dt,ba,dn,Ll="Sends the inputs through the <code>model</code>.",va,Ue,qt,_a,pn,Jl="Instantiates the <code>pre_processor</code>, <code>model</code> and <code>post_processor</code> if necessary.",er,Rt,tr,ee,Zt,ya,gn,Ul=`Launches a gradio demo for a tool. The corresponding tool class needs to properly implement the class attributes
<code>inputs</code> and <code>output_type</code>.`,or,Wt,nr,D,Bt,xa,cn,Pl="Tool collections enable loading all Spaces from a collection in order to be added to the agent’s toolbox.",Ta,fn,Hl=`<p>[!NOTE]
Only Spaces will be fetched, so you can feel free to add models and datasets to your collection if you’d
like for this collection to showcase them.</p>`,wa,Pe,rr,zt,sr,Ft,El=`에이전트 프레임워크에서 사용할 수 있는 엔진을 자유롭게 만들고 사용할 수 있습니다.
이 엔진들은 다음과 같은 사양을 가지고 있습니다:`,ar,Gt,Nl='<li>입력(<code>List[Dict[str, str]]</code>)에 대한 <a href="../chat_templating.md">메시지 형식</a>을 따르고 문자열을 반환해야 합니다.</li> <li>인수 <code>stop_sequences</code>에 시퀀스가 전달되기 <em>전에</em> 출력을 생성하는 것을 중지해야 합니다.</li>',lr,St,ir,Qt,Vl="편의를 위해, 위의 사항을 구현하고 대규모 언어 모델 실행을 위해 추론 엔드포인트를 사용하는 <code>HfApiEngine</code>을 추가했습니다.",mr,Ot,dr,S,Yt,Ca,un,Dl="A class to interact with Hugging Face’s Inference API for language model interaction.",Ma,hn,ql="This engine allows you to communicate with Hugging Face’s models using the Inference API. It can be used in both serverless mode or with a dedicated endpoint, supporting features like stop sequences and grammar customization.",pr,Xt,gr,Kt,Rl=`에이전트는 도구 간의 모든 유형의 객체를 처리할 수 있습니다; 도구는 완전히 멀티모달이므로 텍스트, 이미지, 오디오, 비디오 등 다양한 유형을 수락하고 반환할 수 있습니다.
도구 간의 호환성을 높이고 ipython (jupyter, colab, ipython 노트북, …)에서 이러한
반환 값을 올바르게 렌더링하기 위해 이러한 유형을 중심으로 래퍼 클래스를
구현합니다.`,cr,eo,Zl=`래핑된 객체는 처음과 동일하게 작동해야 합니다; 텍스트 객체는 여전히 문자열로 작동해야 하며,
이미지 객체는 여전히 <code>PIL.Image</code>로 작동해야 합니다.`,fr,to,Wl="이러한 유형에는 세 가지 특정 목적이 있습니다:",ur,oo,Bl=`<li><code>to_raw</code>를 호출하면 기본 객체가 반환되어야 합니다.</li> <li><code>to_string</code>을 호출하면 객체가 문자열로 반환되어야 합니다:
<code>AgentText</code>의 경우 문자열이 될 수 있지만, 다른 경우에는 객체의 직렬화된 버전의 경로일 수 있습니다.</li> <li>ipython 커널에서 표시할 때 객체가 올바르게 표시되어야 합니다.</li>`,hr,no,$r,te,ro,ka,$n,zl="Text type returned by the agent. Behaves as a string.",br,so,vr,H,ao,Ia,bn,Fl="Image type returned by the agent. Behaves as a PIL.Image.",Aa,He,lo,ja,vn,Gl="Saves the image to a file.",La,Ee,io,Ja,_n,Sl="Returns the “raw” version of that object. In the case of an AgentImage, it is a PIL.Image.",Ua,Ne,mo,Pa,yn,Ql=`Returns the stringified version of that object. In the case of an AgentImage, it is a path to the serialized
version of the image.`,_r,po,yr,q,go,Ha,xn,Ol="Audio type returned by the agent.",Ea,Ve,co,Na,Tn,Yl="Returns the “raw” version of that object. It is a <code>torch.Tensor</code> object.",Va,De,fo,Da,wn,Xl=`Returns the stringified version of that object. In the case of an AgentAudio, it is a path to the serialized
version of the audio.`,xr,uo,Tr,Jn,wr;return w=new J({props:{title:"에이전트 & 도구",local:"agents-tools",headingTag:"h1"}}),C=new qa({props:{warning:!0,$$slots:{default:[si]},$$scope:{ctx:I}}}),Fe=new J({props:{title:"에이전트",local:"agents",headingTag:"h2"}}),Qe=new J({props:{title:"Agent",local:"agent ][ transformers.Agent",headingTag:"h3"}}),Oe=new x({props:{name:"class transformers.Agent",anchor:"transformers.Agent",parameters:[{name:"tools",val:": typing.Union[typing.List[transformers.agents.tools.Tool], transformers.agents.agents.Toolbox]"},{name:"llm_engine",val:": typing.Callable = None"},{name:"system_prompt",val:": typing.Optional[str] = None"},{name:"tool_description_template",val:": typing.Optional[str] = None"},{name:"additional_args",val:": typing.Dict = {}"},{name:"max_iterations",val:": int = 6"},{name:"tool_parser",val:": typing.Optional[typing.Callable] = None"},{name:"add_base_tools",val:": bool = False"},{name:"verbose",val:": int = 0"},{name:"grammar",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"managed_agents",val:": typing.Optional[typing.List] = None"},{name:"step_callbacks",val:": typing.Optional[typing.List[typing.Callable]] = None"},{name:"monitor_metrics",val:": bool = True"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L354"}}),Ye=new x({props:{name:"execute_tool_call",anchor:"transformers.Agent.execute_tool_call",parameters:[{name:"tool_name",val:": str"},{name:"arguments",val:": typing.Dict[str, str]"}],parametersDescription:[{anchor:"transformers.Agent.execute_tool_call.tool_name",description:"<strong>tool_name</strong> (<code>str</code>) &#x2014; Name of the Tool to execute (should be one from self.toolbox).",name:"tool_name"},{anchor:"transformers.Agent.execute_tool_call.arguments",description:"<strong>arguments</strong> (Dict[str, str]) &#x2014; Arguments passed to the Tool.",name:"arguments"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L530"}}),Xe=new x({props:{name:"extract_action",anchor:"transformers.Agent.extract_action",parameters:[{name:"llm_output",val:": str"},{name:"split_token",val:": str"}],parametersDescription:[{anchor:"transformers.Agent.extract_action.llm_output",description:"<strong>llm_output</strong> (<code>str</code>) &#x2014; Output of the LLM",name:"llm_output"},{anchor:"transformers.Agent.extract_action.split_token",description:"<strong>split_token</strong> (<code>str</code>) &#x2014; Separator for the action. Should match the example in the system prompt.",name:"split_token"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L509"}}),Ke=new x({props:{name:"run",anchor:"transformers.Agent.run",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L585"}}),et=new x({props:{name:"write_inner_memory_from_logs",anchor:"transformers.Agent.write_inner_memory_from_logs",parameters:[{name:"summary_mode",val:": typing.Optional[bool] = False"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L449"}}),tt=new J({props:{title:"CodeAgent",local:"codeagent ][ transformers.CodeAgent",headingTag:"h3"}}),ot=new x({props:{name:"class transformers.CodeAgent",anchor:"transformers.CodeAgent",parameters:[{name:"tools",val:": typing.List[transformers.agents.tools.Tool]"},{name:"llm_engine",val:": typing.Optional[typing.Callable] = None"},{name:"system_prompt",val:": typing.Optional[str] = None"},{name:"tool_description_template",val:": typing.Optional[str] = None"},{name:"grammar",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"additional_authorized_imports",val:": typing.Optional[typing.List[str]] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L590"}}),nt=new x({props:{name:"parse_code_blob",anchor:"transformers.CodeAgent.parse_code_blob",parameters:[{name:"result",val:": str"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L632"}}),rt=new x({props:{name:"run",anchor:"transformers.CodeAgent.run",parameters:[{name:"task",val:": str"},{name:"return_generated_code",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CodeAgent.run.task",description:"<strong>task</strong> (<code>str</code>) &#x2014; The task to perform",name:"task"},{anchor:"transformers.CodeAgent.run.return_generated_code",description:"<strong>return_generated_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014; Whether to return the generated code instead of running it",name:"return_generated_code"},{anchor:"transformers.CodeAgent.run.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
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<ul>
<li><code>&quot;document_question_answering&quot;</code></li>
<li><code>&quot;image_question_answering&quot;</code></li>
<li><code>&quot;speech_to_text&quot;</code></li>
<li><code>&quot;text_to_speech&quot;</code></li>
<li><code>&quot;translation&quot;</code></li>
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<p><a
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The list of tools to instantiate the toolbox with`,name:"tools"},{anchor:"transformers.Toolbox.add_base_tools",description:`<strong>add_base_tools</strong> (<code>bool</code>, defaults to <code>False</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to add the tools available within <code>transformers</code> to the toolbox.`,name:"add_base_tools"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L184"}}),jt=new x({props:{name:"add_tool",anchor:"transformers.Toolbox.add_tool",parameters:[{name:"tool",val:": Tool"}],parametersDescription:[{anchor:"transformers.Toolbox.add_tool.tool",description:`<strong>tool</strong> (<code>Tool</code>) &#x2014;
The tool to add to the toolbox.`,name:"tool"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L230"}}),Lt=new x({props:{name:"clear_toolbox",anchor:"transformers.Toolbox.clear_toolbox",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L270"}}),Jt=new x({props:{name:"remove_tool",anchor:"transformers.Toolbox.remove_tool",parameters:[{name:"tool_name",val:": str"}],parametersDescription:[{anchor:"transformers.Toolbox.remove_tool.tool_name",description:`<strong>tool_name</strong> (<code>str</code>) &#x2014;
The tool to remove from the toolbox.`,name:"tool_name"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L242"}}),Ut=new x({props:{name:"show_tool_descriptions",anchor:"transformers.Toolbox.show_tool_descriptions",parameters:[{name:"tool_description_template",val:": str = None"}],parametersDescription:[{anchor:"transformers.Toolbox.show_tool_descriptions.tool_description_template",description:`<strong>tool_description_template</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The template to use to describe the tools. If not provided, the default template will be used.`,name:"tool_description_template"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L218"}}),Pt=new x({props:{name:"update_tool",anchor:"transformers.Toolbox.update_tool",parameters:[{name:"tool",val:": Tool"}],parametersDescription:[{anchor:"transformers.Toolbox.update_tool.tool",description:`<strong>tool</strong> (<code>Tool</code>) &#x2014;
The tool to update to the toolbox.`,name:"tool"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agents.py#L256"}}),Ht=new J({props:{title:"PipelineTool",local:"pipelinetool ][ transformers.PipelineTool",headingTag:"h3"}}),Et=new x({props:{name:"class transformers.PipelineTool",anchor:"transformers.PipelineTool",parameters:[{name:"model",val:" = None"},{name:"pre_processor",val:" = None"},{name:"post_processor",val:" = None"},{name:"device",val:" = None"},{name:"device_map",val:" = None"},{name:"model_kwargs",val:" = None"},{name:"token",val:" = None"},{name:"**hub_kwargs",val:""}],parametersDescription:[{anchor:"transformers.PipelineTool.model",description:`<strong>model</strong> (<code>str</code> or <a href="/docs/transformers/pr_30530/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, <em>optional</em>) &#x2014;
The name of the checkpoint to use for the model, or the instantiated model. If unset, will default to the
value of the class attribute <code>default_checkpoint</code>.`,name:"model"},{anchor:"transformers.PipelineTool.pre_processor",description:`<strong>pre_processor</strong> (<code>str</code> or <code>Any</code>, <em>optional</em>) &#x2014;
The name of the checkpoint to use for the pre-processor, or the instantiated pre-processor (can be a
tokenizer, an image processor, a feature extractor or a processor). Will default to the value of <code>model</code> if
unset.`,name:"pre_processor"},{anchor:"transformers.PipelineTool.post_processor",description:`<strong>post_processor</strong> (<code>str</code> or <code>Any</code>, <em>optional</em>) &#x2014;
The name of the checkpoint to use for the post-processor, or the instantiated pre-processor (can be a
tokenizer, an image processor, a feature extractor or a processor). Will default to the <code>pre_processor</code> if
unset.`,name:"post_processor"},{anchor:"transformers.PipelineTool.device",description:`<strong>device</strong> (<code>int</code>, <code>str</code> or <code>torch.device</code>, <em>optional</em>) &#x2014;
The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc&#x2026;), the
CPU otherwise.`,name:"device"},{anchor:"transformers.PipelineTool.device_map",description:`<strong>device_map</strong> (<code>str</code> or <code>dict</code>, <em>optional</em>) &#x2014;
If passed along, will be used to instantiate the model.`,name:"device_map"},{anchor:"transformers.PipelineTool.model_kwargs",description:`<strong>model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Any keyword argument to send to the model instantiation.`,name:"model_kwargs"},{anchor:"transformers.PipelineTool.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when
running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.PipelineTool.hub_kwargs",description:`<strong>hub_kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Any additional keyword argument to send to the methods that will load the data from the Hub.`,name:"hub_kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L622"}}),Nt=new x({props:{name:"decode",anchor:"transformers.PipelineTool.decode",parameters:[{name:"outputs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L745"}}),Vt=new x({props:{name:"encode",anchor:"transformers.PipelineTool.encode",parameters:[{name:"raw_inputs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L732"}}),Dt=new x({props:{name:"forward",anchor:"transformers.PipelineTool.forward",parameters:[{name:"inputs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L738"}}),qt=new x({props:{name:"setup",anchor:"transformers.PipelineTool.setup",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L706"}}),Rt=new J({props:{title:"launch_gradio_demo",local:"launchgradiodemo ][ transformers.launch_gradio_demo",headingTag:"h3"}}),Zt=new x({props:{name:"transformers.launch_gradio_demo",anchor:"transformers.launch_gradio_demo",parameters:[{name:"tool_class",val:": Tool"}],parametersDescription:[{anchor:"transformers.launch_gradio_demo.tool_class",description:"<strong>tool_class</strong> (<code>type</code>) &#x2014; The class of the tool for which to launch the demo.",name:"tool_class"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L770"}}),Wt=new J({props:{title:"ToolCollection",local:"toolcollection ][ transformers.ToolCollection",headingTag:"h3"}}),Bt=new x({props:{name:"class transformers.ToolCollection",anchor:"transformers.ToolCollection",parameters:[{name:"collection_slug",val:": str"},{name:"token",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"transformers.ToolCollection.collection_slug",description:`<strong>collection_slug</strong> (str) &#x2014;
The collection slug referencing the collection.`,name:"collection_slug"},{anchor:"transformers.ToolCollection.token",description:`<strong>token</strong> (str, <em>optional</em>) &#x2014;
The authentication token if the collection is private.`,name:"token"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/tools.py#L940"}}),Pe=new Ln({props:{anchor:"transformers.ToolCollection.example",$$slots:{default:[ci]},$$scope:{ctx:I}}}),zt=new J({props:{title:"엔진",local:"engines",headingTag:"h2"}}),St=new J({props:{title:"HfApiEngine",local:"HfApiEngine ][ transformers.HfApiEngine",headingTag:"h3"}}),Ot=new yo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HfApiEngine

<span class="hljs-meta">&gt;&gt;&gt; </span>messages = [
<span class="hljs-meta">... </span>  {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;user&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;Hello, how are you?&quot;</span>},
<span class="hljs-meta">... </span>  {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;assistant&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;I&#x27;m doing great. How can I help you today?&quot;</span>},
<span class="hljs-meta">... </span>  {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;user&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;No need to help, take it easy.&quot;</span>},
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>HfApiEngine()(messages, stop_sequences=[<span class="hljs-string">&quot;conversation&quot;</span>])

<span class="hljs-string">&quot;That&#x27;s very kind of you to say! It&#x27;s always nice to have a relaxed &quot;</span>`,wrap:!1}}),Yt=new x({props:{name:"class transformers.HfApiEngine",anchor:"transformers.HfApiEngine",parameters:[{name:"model",val:": str = 'meta-llama/Meta-Llama-3.1-8B-Instruct'"},{name:"token",val:": typing.Optional[str] = None"},{name:"max_tokens",val:": typing.Optional[int] = 1500"},{name:"timeout",val:": typing.Optional[int] = 120"}],parametersDescription:[{anchor:"transformers.HfApiEngine.model",description:`<strong>model</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;meta-llama/Meta-Llama-3.1-8B-Instruct&quot;</code>) &#x2014;
The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.`,name:"model"},{anchor:"transformers.HfApiEngine.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Token used by the Hugging Face API for authentication.
If not provided, the class will use the token stored in the Hugging Face CLI configuration.`,name:"token"},{anchor:"transformers.HfApiEngine.max_tokens",description:`<strong>max_tokens</strong> (<code>int</code>, <em>optional</em>, defaults to 1500) &#x2014;
The maximum number of tokens allowed in the output.`,name:"max_tokens"},{anchor:"transformers.HfApiEngine.timeout",description:`<strong>timeout</strong> (<code>int</code>, <em>optional</em>, defaults to 120) &#x2014;
Timeout for the API request, in seconds.`,name:"timeout"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/llm_engine.py#L145",raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> —
If the model name is not provided.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),Xt=new J({props:{title:"에이전트 유형",local:"agent-types",headingTag:"h2"}}),no=new J({props:{title:"AgentText",local:"agenttext ][ transformers.agents.agent_types.AgentText",headingTag:"h3"}}),ro=new x({props:{name:"class transformers.agents.agent_types.AgentText",anchor:"transformers.agents.agent_types.AgentText",parameters:[{name:"value",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agent_types.py#L73"}}),so=new J({props:{title:"AgentImage",local:"agentimage ][ transformers.agents.agent_types.AgentImage",headingTag:"h3"}}),ao=new x({props:{name:"class transformers.agents.agent_types.AgentImage",anchor:"transformers.agents.agent_types.AgentImage",parameters:[{name:"value",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agent_types.py#L85"}}),lo=new x({props:{name:"save",anchor:"transformers.agents.agent_types.AgentImage.save",parameters:[{name:"output_bytes",val:""},{name:"format",val:""},{name:"**params",val:""}],parametersDescription:[{anchor:"transformers.agents.agent_types.AgentImage.save.output_bytes",description:"<strong>output_bytes</strong> (bytes) &#x2014; The output bytes to save the image to.",name:"output_bytes"},{anchor:"transformers.agents.agent_types.AgentImage.save.format",description:"<strong>format</strong> (str) &#x2014; The format to use for the output image. The format is the same as in PIL.Image.save.",name:"format"},{anchor:"transformers.agents.agent_types.AgentImage.save.*params",description:"*<strong>*params</strong> &#x2014; Additional parameters to pass to PIL.Image.save.",name:"*params"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agent_types.py#L162"}}),io=new x({props:{name:"to_raw",anchor:"transformers.agents.agent_types.AgentImage.to_raw",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agent_types.py#L120"}}),mo=new x({props:{name:"to_string",anchor:"transformers.agents.agent_types.AgentImage.to_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/agents/agent_types.py#L135"}}),po=new J({props:{title:"AgentAudio",local:"agentaudio ][ transformers.agents.agent_types.AgentAudio",headingTag:"h3"}}),go=new x({props:{name:"class 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