Buckets:
| 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 $,k as y,y as t,a as i,v as p,d as g,t as c,w as f}from"../chunks/index.33f81d56.js";import{T as Ra}from"../chunks/Tip.34194030.js";import{D as x}from"../chunks/Docstring.64554317.js";import{C as yo}from"../chunks/CodeBlock.362b34a4.js";import{E as Ln}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as J,E as ri}from"../chunks/EditOnGithub.a9246e21.js";function si(I){let u,T=`Transformers Agent는 실험 중인 API이므로 언제든지 변경될 수 있습니다. | |
| API나 기반 모델이 자주 업데이트되므로, 에이전트가 제공하는 결과물은 달라질 수 있습니다.`;return{c(){u=s("p"),u.textContent=T},l(b){u=a(b,"P",{"data-svelte-h":!0}),$(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">"What is the result of 2 power 3.7384?"</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">"What is the result of 2 power 3.7384?"</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">"black-forest-labs/FLUX.1-schnell"</span>, | |
| <span class="hljs-attribute">name</span>=<span class="hljs-string">"image-generator"</span>, | |
| <span class="hljs-attribute">description</span>=<span class="hljs-string">"Generate an image from a prompt"</span> | |
| ) | |
| image = image_generator(<span class="hljs-string">"Generate an image of a cool surfer in Tahiti"</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">"tuan2308/face-swap"</span>, | |
| <span class="hljs-comment">"face_swapper"</span>, | |
| <span class="hljs-comment">"Tool that puts the face shown on the first image on the second image. You can give it paths to images."</span>, | |
| ) | |
| image = face_swapper(<span class="hljs-string">'./aymeric.jpeg'</span>, <span class="hljs-string">'./ruth.jpg'</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">"my-username/my-space"</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">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ToolCollection, ReactCodeAgent | |
| <span class="hljs-meta">>>> </span>image_tool_collection = ToolCollection(collection_slug=<span class="hljs-string">"huggingface-tools/diffusion-tools-6630bb19a942c2306a2cdb6f"</span>) | |
| <span class="hljs-meta">>>> </span>agent = ReactCodeAgent(tools=[*image_tool_collection.tools], add_base_tools=<span class="hljs-literal">True</span>) | |
| <span class="hljs-meta">>>> </span>agent.run(<span class="hljs-string">"Please draw me a picture of rivers and lakes."</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,qa=`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_35339/ko/main_classes/agent#transformers.Agent">Agent</a> 클래스를 기반으로 두 가지 유형의 에이전트를 제공합니다:',En,Se,Wa='<li><a href="/docs/transformers/pr_35339/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_35339/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_35339/ko/main_classes/agent#transformers.ReactJsonAgent">ReactJsonAgent</a>는 도구 호출을 JSON으로 작성합니다.</li> <li><a href="/docs/transformers/pr_35339/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,Rn,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,qn,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,Ro,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,qo,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>"text-classifier"</code> or <code>"image_generator"</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_35339/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_35339/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_35339/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_35339/ko/main_classes/agent#transformers.Tool">Tool</a> from a langchain tool.',Rs,Z,wt,qs,Fo,fl='Creates a <a href="/docs/transformers/pr_35339/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_35339/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_35339/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_35339/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_35339/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,Rt,_a,pn,Jl="Instantiates the <code>pre_processor</code>, <code>model</code> and <code>post_processor</code> if necessary.",er,qt,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,Rl="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,ql=`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,R,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 Ra({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_35339/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>) — 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]) — Arguments passed to the Tool.",name:"arguments"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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>) — Output of the LLM",name:"llm_output"},{anchor:"transformers.Agent.extract_action.split_token",description:"<strong>split_token</strong> (<code>str</code>) — Separator for the action. Should match the example in the system prompt.",name:"split_token"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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_35339/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_35339/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_35339/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_35339/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>) — 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>) — 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>) — | |
| Any keyword argument to send to the agent when evaluating the code.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L639"}}),me=new Ln({props:{anchor:"transformers.CodeAgent.run.example",$$slots:{default:[ai]},$$scope:{ctx:I}}}),st=new J({props:{title:"React agents",local:"react-agents ][ transformers.ReactAgent",headingTag:"h3"}}),at=new x({props:{name:"class transformers.ReactAgent",anchor:"transformers.ReactAgent",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:"plan_type",val:": typing.Optional[str] = None"},{name:"planning_interval",val:": typing.Optional[int] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L716"}}),lt=new x({props:{name:"direct_run",anchor:"transformers.ReactAgent.direct_run",parameters:[{name:"task",val:": str"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L844"}}),it=new x({props:{name:"planning_step",anchor:"transformers.ReactAgent.planning_step",parameters:[{name:"task",val:""},{name:"is_first_step",val:": bool = False"},{name:"iteration",val:": int = None"}],parametersDescription:[{anchor:"transformers.ReactAgent.planning_step.task",description:"<strong>task</strong> (<code>str</code>) — The task to perform",name:"task"},{anchor:"transformers.ReactAgent.planning_step.is_first_step",description:"<strong>is_first_step</strong> (<code>bool</code>) — If this step is not the first one, the plan should be an update over a previous plan.",name:"is_first_step"},{anchor:"transformers.ReactAgent.planning_step.iteration",description:"<strong>iteration</strong> (<code>int</code>) — The number of the current step, used as an indication for the LLM.",name:"iteration"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L884"}}),mt=new x({props:{name:"provide_final_answer",anchor:"transformers.ReactAgent.provide_final_answer",parameters:[{name:"task",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L755"}}),dt=new x({props:{name:"run",anchor:"transformers.ReactAgent.run",parameters:[{name:"task",val:": str"},{name:"stream",val:": bool = False"},{name:"reset",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ReactAgent.run.task",description:"<strong>task</strong> (<code>str</code>) — The task to perform",name:"task"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L777"}}),ce=new Ln({props:{anchor:"transformers.ReactAgent.run.example",$$slots:{default:[li]},$$scope:{ctx:I}}}),pt=new x({props:{name:"stream_run",anchor:"transformers.ReactAgent.stream_run",parameters:[{name:"task",val:": str"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L804"}}),gt=new x({props:{name:"class transformers.ReactJsonAgent",anchor:"transformers.ReactJsonAgent",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:"planning_interval",val:": typing.Optional[int] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L984"}}),ct=new x({props:{name:"step",anchor:"transformers.ReactJsonAgent.step",parameters:[{name:"log_entry",val:": typing.Dict[str, typing.Any]"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L1017"}}),ft=new x({props:{name:"class transformers.ReactCodeAgent",anchor:"transformers.ReactCodeAgent",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:"planning_interval",val:": typing.Optional[int] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L1095"}}),ut=new x({props:{name:"step",anchor:"transformers.ReactCodeAgent.step",parameters:[{name:"log_entry",val:": typing.Dict[str, typing.Any]"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/agents.py#L1142"}}),ht=new J({props:{title:"Tools",local:"tools",headingTag:"h2"}}),$t=new J({props:{title:"load_tool",local:"loadtool ][ transformers.load_tool",headingTag:"h3"}}),bt=new x({props:{name:"transformers.load_tool",anchor:"transformers.load_tool",parameters:[{name:"task_or_repo_id",val:""},{name:"model_repo_id",val:" = None"},{name:"token",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.load_tool.task_or_repo_id",description:`<strong>task_or_repo_id</strong> (<code>str</code>) — | |
| The task for which to load the tool or a repo ID of a tool on the Hub. Tasks implemented in Transformers | |
| are:</p> | |
| <ul> | |
| <li><code>"document_question_answering"</code></li> | |
| <li><code>"image_question_answering"</code></li> | |
| <li><code>"speech_to_text"</code></li> | |
| <li><code>"text_to_speech"</code></li> | |
| <li><code>"translation"</code></li> | |
| </ul>`,name:"task_or_repo_id"},{anchor:"transformers.load_tool.model_repo_id",description:`<strong>model_repo_id</strong> (<code>str</code>, <em>optional</em>) — | |
| Use this argument to use a different model than the default one for the tool you selected.`,name:"model_repo_id"},{anchor:"transformers.load_tool.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) — | |
| The token to identify you on hf.co. If unset, will use the token generated when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.load_tool.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) — | |
| Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as | |
| <code>cache_dir</code>, <code>revision</code>, <code>subfolder</code>) will be used when downloading the files for your tool, and the others | |
| will be passed along to its init.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L825"}}),$e=new Ra({props:{warning:!0,$$slots:{default:[ii]},$$scope:{ctx:I}}}),vt=new J({props:{title:"Tool",local:"tool ][ transformers.Tool",headingTag:"h3"}}),_t=new x({props:{name:"class transformers.Tool",anchor:"transformers.Tool",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L108"}}),yt=new x({props:{name:"from_gradio",anchor:"transformers.Tool.from_gradio",parameters:[{name:"gradio_tool",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L537"}}),xt=new x({props:{name:"from_hub",anchor:"transformers.Tool.from_hub",parameters:[{name:"repo_id",val:": str"},{name:"token",val:": typing.Optional[str] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.Tool.from_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) — | |
| The name of the repo on the Hub where your tool is defined.`,name:"repo_id"},{anchor:"transformers.Tool.from_hub.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) — | |
| The token to identify you on hf.co. If unset, will use the token generated when running | |
| <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.Tool.from_hub.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) — | |
| Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as | |
| <code>cache_dir</code>, <code>revision</code>, <code>subfolder</code>) will be used when downloading the files for your tool, and the | |
| others will be passed along to its init.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L253"}}),ve=new Ra({props:{warning:!0,$$slots:{default:[mi]},$$scope:{ctx:I}}}),Tt=new x({props:{name:"from_langchain",anchor:"transformers.Tool.from_langchain",parameters:[{name:"langchain_tool",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L558"}}),wt=new x({props:{name:"from_space",anchor:"transformers.Tool.from_space",parameters:[{name:"space_id",val:": str"},{name:"name",val:": str"},{name:"description",val:": str"},{name:"api_name",val:": typing.Optional[str] = None"},{name:"token",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"transformers.Tool.from_space.space_id",description:`<strong>space_id</strong> (<code>str</code>) — | |
| The id of the Space on the Hub.`,name:"space_id"},{anchor:"transformers.Tool.from_space.name",description:`<strong>name</strong> (<code>str</code>) — | |
| The name of the tool.`,name:"name"},{anchor:"transformers.Tool.from_space.description",description:`<strong>description</strong> (<code>str</code>) — | |
| The description of the tool.`,name:"description"},{anchor:"transformers.Tool.from_space.api_name",description:`<strong>api_name</strong> (<code>str</code>, <em>optional</em>) — | |
| The specific api_name to use, if the space has several tabs. If not precised, will default to the first available api.`,name:"api_name"},{anchor:"transformers.Tool.from_space.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) — | |
| Add your token to access private spaces or increase your GPU quotas.`,name:"token"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L421",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>The Space, as a tool.</p> | |
| `,returnType:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p><a | |
| href="/docs/transformers/pr_35339/ko/main_classes/agent#transformers.Tool" | |
| >Tool</a></p> | |
| `}}),ye=new Ln({props:{anchor:"transformers.Tool.from_space.example",$$slots:{default:[di]},$$scope:{ctx:I}}}),xe=new Ln({props:{anchor:"transformers.Tool.from_space.example-2",$$slots:{default:[pi]},$$scope:{ctx:I}}}),Ct=new x({props:{name:"push_to_hub",anchor:"transformers.Tool.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"commit_message",val:": str = 'Upload tool'"},{name:"private",val:": typing.Optional[bool] = None"},{name:"token",val:": typing.Union[str, bool, NoneType] = None"},{name:"create_pr",val:": bool = False"}],parametersDescription:[{anchor:"transformers.Tool.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) — | |
| The name of the repository you want to push your tool to. It should contain your organization name when | |
| pushing to a given organization.`,name:"repo_id"},{anchor:"transformers.Tool.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"Upload tool"</code>) — | |
| Message to commit while pushing.`,name:"commit_message"},{anchor:"transformers.Tool.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) — | |
| Whether to make the repo private. If <code>None</code> (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.`,name:"private"},{anchor:"transformers.Tool.push_to_hub.token",description:`<strong>token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) — | |
| 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.Tool.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether or not to create a PR with the uploaded files or directly commit.`,name:"create_pr"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L364"}}),Te=new Ln({props:{anchor:"transformers.Tool.push_to_hub.example",$$slots:{default:[gi]},$$scope:{ctx:I}}}),Mt=new x({props:{name:"save",anchor:"transformers.Tool.save",parameters:[{name:"output_dir",val:""}],parametersDescription:[{anchor:"transformers.Tool.save.output_dir",description:"<strong>output_dir</strong> (<code>str</code>) — The folder in which you want to save your tool.",name:"output_dir"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L193"}}),kt=new x({props:{name:"setup",anchor:"transformers.Tool.setup",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L186"}}),It=new J({props:{title:"Toolbox",local:"toolbox ][ transformers.Toolbox",headingTag:"h3"}}),At=new x({props:{name:"class transformers.Toolbox",anchor:"transformers.Toolbox",parameters:[{name:"tools",val:": typing.List[transformers.agents.tools.Tool]"},{name:"add_base_tools",val:": bool = False"}],parametersDescription:[{anchor:"transformers.Toolbox.tools",description:`<strong>tools</strong> (<code>List[Tool]</code>) — | |
| 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>) — | |
| 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_35339/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>) — | |
| The tool to add to the toolbox.`,name:"tool"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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_35339/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>) — | |
| The tool to remove from the toolbox.`,name:"tool_name"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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>) — | |
| 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_35339/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>) — | |
| The tool to update to the toolbox.`,name:"tool"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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_35339/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, <em>optional</em>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc…), 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>) — | |
| 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>) — | |
| 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>) — | |
| 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>) — | |
| 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_35339/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_35339/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_35339/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_35339/src/transformers/agents/tools.py#L738"}}),Rt=new x({props:{name:"setup",anchor:"transformers.PipelineTool.setup",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_35339/src/transformers/agents/tools.py#L706"}}),qt=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>) — The class of the tool for which to launch the demo.",name:"tool_class"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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) — | |
| The collection slug referencing the collection.`,name:"collection_slug"},{anchor:"transformers.ToolCollection.token",description:`<strong>token</strong> (str, <em>optional</em>) — | |
| The authentication token if the collection is private.`,name:"token"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HfApiEngine | |
| <span class="hljs-meta">>>> </span>messages = [ | |
| <span class="hljs-meta">... </span> {<span class="hljs-string">"role"</span>: <span class="hljs-string">"user"</span>, <span class="hljs-string">"content"</span>: <span class="hljs-string">"Hello, how are you?"</span>}, | |
| <span class="hljs-meta">... </span> {<span class="hljs-string">"role"</span>: <span class="hljs-string">"assistant"</span>, <span class="hljs-string">"content"</span>: <span class="hljs-string">"I'm doing great. How can I help you today?"</span>}, | |
| <span class="hljs-meta">... </span> {<span class="hljs-string">"role"</span>: <span class="hljs-string">"user"</span>, <span class="hljs-string">"content"</span>: <span class="hljs-string">"No need to help, take it easy."</span>}, | |
| <span class="hljs-meta">... </span>] | |
| <span class="hljs-meta">>>> </span>HfApiEngine()(messages, stop_sequences=[<span class="hljs-string">"conversation"</span>]) | |
| <span class="hljs-string">"That's very kind of you to say! It's always nice to have a relaxed "</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>"meta-llama/Meta-Llama-3.1-8B-Instruct"</code>) — | |
| 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>) — | |
| 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) — | |
| 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) — | |
| Timeout for the API request, in seconds.`,name:"timeout"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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_35339/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_35339/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) — The output bytes to save the image to.",name:"output_bytes"},{anchor:"transformers.agents.agent_types.AgentImage.save.format",description:"<strong>format</strong> (str) — 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> — Additional parameters to pass to PIL.Image.save.",name:"*params"}],source:"https://github.com/huggingface/transformers/blob/vr_35339/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_35339/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_35339/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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Xet Storage Details
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- 86.1 kB
- Xet hash:
- 819dac67bc5cfc105284259b474623571ed09a13a486e6d4bf624d446c3e1b6b
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Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.