import{s as wa,o as xa,n as S}from"../chunks/scheduler.9bc65507.js";import{S as Aa,i as Ma,g as i,s as r,r as c,A as ka,h as m,f as o,c as n,j as w,u as g,x as v,k as x,y as t,a as d,v as f,d as u,t as h,w as $}from"../chunks/index.707bf1b6.js";import{T as Ln}from"../chunks/Tip.c2ecdbf4.js";import{D as A}from"../chunks/Docstring.17db21ae.js";import{C as Jt}from"../chunks/CodeBlock.54a9f38d.js";import{E as Lt}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H}from"../chunks/Heading.342b1fa6.js";function Ia(k){let l,T=`Transformers Agents は実験的な API であり、いつでも変更される可能性があります。エージェントから返される結果
API または基礎となるモデルは変更される傾向があるため、変更される可能性があります。`;return{c(){l=i("p"),l.textContent=T},l(_){l=m(_,"P",{"data-svelte-h":!0}),v(l)!=="svelte-1j35sfa"&&(l.textContent=T)},m(_,p){d(_,l,p)},p:S,d(_){_&&o(l)}}}function Ca(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEhmQWdlbnQlMEElMEFhZ2VudCUyMCUzRCUyMEhmQWdlbnQoJTIyaHR0cHMlM0ElMkYlMkZhcGktaW5mZXJlbmNlLmh1Z2dpbmdmYWNlLmNvJTJGbW9kZWxzJTJGYmlnY29kZSUyRnN0YXJjb2RlciUyMiklMEFhZ2VudC5ydW4oJTIySXMlMjB0aGUlMjBmb2xsb3dpbmclMjAlNjB0ZXh0JTYwJTIwKGluJTIwU3BhbmlzaCklMjBwb3NpdGl2ZSUyMG9yJTIwbmVnYXRpdmUlM0YlMjIlMkMlMjB0ZXh0JTNEJTIyJUMyJUExRXN0ZSUyMGVzJTIwdW4lMjBBUEklMjBtdXklMjBhZ3JhZGFibGUhJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HfAgent

agent = HfAgent(<span class="hljs-string">&quot;https://api-inference.huggingface.co/models/bigcode/starcoder&quot;</span>)
agent.run(<span class="hljs-string">&quot;Is the following \`text\` (in Spanish) positive or negative?&quot;</span>, text=<span class="hljs-string">&quot;¡Este es un API muy agradable!&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function Ha(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer, LocalAgent

checkpoint = <span class="hljs-string">&quot;bigcode/starcoder&quot;</span>
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map=<span class="hljs-string">&quot;auto&quot;</span>, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

agent = LocalAgent(model, tokenizer)
agent.run(<span class="hljs-string">&quot;Draw me a picture of rivers and lakes.&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function ja(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwTG9jYWxBZ2VudCUwQSUwQWFnZW50JTIwJTNEJTIwTG9jYWxBZ2VudC5mcm9tX3ByZXRyYWluZWQoJTIyYmlnY29kZSUyRnN0YXJjb2RlciUyMiUyQyUyMGRldmljZV9tYXAlM0QlMjJhdXRvJTIyJTJDJTIwdG9yY2hfZHR5cGUlM0R0b3JjaC5iZmxvYXQxNiklMEFhZ2VudC5ydW4oJTIyRHJhdyUyMG1lJTIwYSUyMHBpY3R1cmUlMjBvZiUyMHJpdmVycyUyMGFuZCUyMGxha2VzLiUyMik=",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> LocalAgent

agent = LocalAgent.from_pretrained(<span class="hljs-string">&quot;bigcode/starcoder&quot;</span>, device_map=<span class="hljs-string">&quot;auto&quot;</span>, torch_dtype=torch.bfloat16)
agent.run(<span class="hljs-string">&quot;Draw me a picture of rivers and lakes.&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function La(k){let l,T=`The openAI models are used in generation mode, so even for the <code>chat()</code> API, it’s better to use models like
<code>&quot;text-davinci-003&quot;</code> over the chat-GPT variant. Proper support for chat-GPT models will come in a next version.`;return{c(){l=i("p"),l.innerHTML=T},l(_){l=m(_,"P",{"data-svelte-h":!0}),v(l)!=="svelte-3idp4e"&&(l.innerHTML=T)},m(_,p){d(_,l,p)},p:S,d(_){_&&o(l)}}}function Ja(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyME9wZW5BaUFnZW50JTBBJTBBYWdlbnQlMjAlM0QlMjBPcGVuQWlBZ2VudChtb2RlbCUzRCUyMnRleHQtZGF2aW5jaS0wMDMlMjIlMkMlMjBhcGlfa2V5JTNEeHh4KSUwQWFnZW50LnJ1biglMjJJcyUyMHRoZSUyMGZvbGxvd2luZyUyMCU2MHRleHQlNjAlMjAoaW4lMjBTcGFuaXNoKSUyMHBvc2l0aXZlJTIwb3IlMjBuZWdhdGl2ZSUzRiUyMiUyQyUyMHRleHQlM0QlMjIlQzIlQTFFc3RlJTIwZXMlMjB1biUyMEFQSSUyMG11eSUyMGFncmFkYWJsZSElMjIp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> OpenAiAgent

agent = OpenAiAgent(model=<span class="hljs-string">&quot;text-davinci-003&quot;</span>, api_key=xxx)
agent.run(<span class="hljs-string">&quot;Is the following \`text\` (in Spanish) positive or negative?&quot;</span>, text=<span class="hljs-string">&quot;¡Este es un API muy agradable!&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function Pa(k){let l,T=`The openAI models are used in generation mode, so even for the <code>chat()</code> API, it’s better to use models like
<code>&quot;text-davinci-003&quot;</code> over the chat-GPT variant. Proper support for chat-GPT models will come in a next version.`;return{c(){l=i("p"),l.innerHTML=T},l(_){l=m(_,"P",{"data-svelte-h":!0}),v(l)!=="svelte-3idp4e"&&(l.innerHTML=T)},m(_,p){d(_,l,p)},p:S,d(_){_&&o(l)}}}function Ua(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF6dXJlT3BlbkFpQWdlbnQlMEElMEFhZ2VudCUyMCUzRCUyMEF6dXJlQWlBZ2VudChkZXBsb3ltZW50X2lkJTNEJTIyRGF2aW5jaS0wMDMlMjIlMkMlMjBhcGlfa2V5JTNEeHh4JTJDJTIwcmVzb3VyY2VfbmFtZSUzRHl5eSklMEFhZ2VudC5ydW4oJTIySXMlMjB0aGUlMjBmb2xsb3dpbmclMjAlNjB0ZXh0JTYwJTIwKGluJTIwU3BhbmlzaCklMjBwb3NpdGl2ZSUyMG9yJTIwbmVnYXRpdmUlM0YlMjIlMkMlMjB0ZXh0JTNEJTIyJUMyJUExRXN0ZSUyMGVzJTIwdW4lMjBBUEklMjBtdXklMjBhZ3JhZGFibGUhJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AzureOpenAiAgent

agent = AzureAiAgent(deployment_id=<span class="hljs-string">&quot;Davinci-003&quot;</span>, api_key=xxx, resource_name=yyy)
agent.run(<span class="hljs-string">&quot;Is the following \`text\` (in Spanish) positive or negative?&quot;</span>, text=<span class="hljs-string">&quot;¡Este es un API muy agradable!&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function qa(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEhmQWdlbnQlMEElMEFhZ2VudCUyMCUzRCUyMEhmQWdlbnQoJTIyaHR0cHMlM0ElMkYlMkZhcGktaW5mZXJlbmNlLmh1Z2dpbmdmYWNlLmNvJTJGbW9kZWxzJTJGYmlnY29kZSUyRnN0YXJjb2RlciUyMiklMEFhZ2VudC5jaGF0KCUyMkRyYXclMjBtZSUyMGElMjBwaWN0dXJlJTIwb2YlMjByaXZlcnMlMjBhbmQlMjBsYWtlcyUyMiklMEElMEFhZ2VudC5jaGF0KCUyMlRyYW5zZm9ybSUyMHRoZSUyMHBpY3R1cmUlMjBzbyUyMHRoYXQlMjB0aGVyZSUyMGlzJTIwYSUyMHJvY2slMjBpbiUyMHRoZXJlJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HfAgent

agent = HfAgent(<span class="hljs-string">&quot;https://api-inference.huggingface.co/models/bigcode/starcoder&quot;</span>)
agent.chat(<span class="hljs-string">&quot;Draw me a picture of rivers and lakes&quot;</span>)

agent.chat(<span class="hljs-string">&quot;Transform the picture so that there is a rock in there&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function Ea(k){let l,T="Example:",_,p,b;return p=new Jt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEhmQWdlbnQlMEElMEFhZ2VudCUyMCUzRCUyMEhmQWdlbnQoJTIyaHR0cHMlM0ElMkYlMkZhcGktaW5mZXJlbmNlLmh1Z2dpbmdmYWNlLmNvJTJGbW9kZWxzJTJGYmlnY29kZSUyRnN0YXJjb2RlciUyMiklMEFhZ2VudC5ydW4oJTIyRHJhdyUyMG1lJTIwYSUyMHBpY3R1cmUlMjBvZiUyMHJpdmVycyUyMGFuZCUyMGxha2VzJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> HfAgent

agent = HfAgent(<span class="hljs-string">&quot;https://api-inference.huggingface.co/models/bigcode/starcoder&quot;</span>)
agent.run(<span class="hljs-string">&quot;Draw me a picture of rivers and lakes&quot;</span>)`,wrap:!1}}),{c(){l=i("p"),l.textContent=T,_=r(),c(p.$$.fragment)},l(a){l=m(a,"P",{"data-svelte-h":!0}),v(l)!=="svelte-11lpom8"&&(l.textContent=T),_=n(a),g(p.$$.fragment,a)},m(a,y){d(a,l,y),d(a,_,y),f(p,a,y),b=!0},p:S,i(a){b||(u(p.$$.fragment,a),b=!0)},o(a){h(p.$$.fragment,a),b=!1},d(a){a&&(o(l),o(_)),$(p,a)}}}function Za(k){let l,T,_,p,b,a,y,yo,He,Jn=`エージェントとツールの詳細については、<a href="../transformers_agents">入門ガイド</a> を必ずお読みください。このページ
基礎となるクラスの API ドキュメントが含まれています。`,To,je,wo,Le,Pn='私たちは 3 種類のエージェントを提供します。<a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.HfAgent">HfAgent</a> はオープンソース モデルの推論エンドポイントを使用し、<a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.LocalAgent">LocalAgent</a> は選択したモデルをローカルで使用し、<a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.OpenAiAgent">OpenAiAgent</a> は OpenAI クローズド モデルを使用します。',xo,Je,Ao,z,Pe,vr,Pt,Un="Agent that uses an inference endpoint to generate code.",br,re,Mo,Ue,ko,J,qe,yr,Ut,qn="Agent that uses a local model and tokenizer to generate code.",Tr,ne,wr,D,Ee,xr,qt,En="Convenience method to build a <code>LocalAgent</code> from a pretrained checkpoint.",Ar,ae,Io,Ze,Co,P,We,Mr,Et,Zn="Agent that uses the openai API to generate code.",kr,se,Ir,le,Ho,Re,jo,U,ze,Cr,Zt,Wn=`Agent that uses Azure OpenAI to generate code. See the <a href="https://learn.microsoft.com/en-us/azure/cognitive-services/openai/" rel="nofollow">official
documentation</a> to learn how to deploy an openAI
model on Azure`,Hr,ie,jr,me,Lo,Ge,Jo,j,Ve,Lr,Wt,Rn="Base class for all agents which contains the main API methods.",Jr,B,Ne,Pr,Rt,zn="Sends a new request to the agent in a chat. Will use the previous ones in its history.",Ur,de,qr,Y,De,Er,zt,Gn="Sends a request to the agent.",Zr,pe,Wr,ce,Be,Rr,Gt,Vn='Clears the history of prior calls to <a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.Agent.chat">chat()</a>.',Po,Ye,Uo,Fe,qo,Q,Se,zr,Vt,Nn="Main function to quickly load a tool, be it on the Hub or in the Transformers library.",Eo,Qe,Zo,M,Xe,Gr,Nt,Dn=`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:`,Vr,Dt,Bn=`<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>List[str]</code>) — The list of modalities expected for the inputs (in the same order as in the call).
Modalitiies should be <code>&quot;text&quot;</code>, <code>&quot;image&quot;</code> or <code>&quot;audio&quot;</code>. This is only used by <code>launch_gradio_demo</code> or to make a
nice space from your tool.</li> <li><strong>outputs</strong> (<code>List[str]</code>) — The list of modalities returned but the tool (in the same order as the return of the
call method). Modalitiies should be <code>&quot;text&quot;</code>, <code>&quot;image&quot;</code> or <code>&quot;audio&quot;</code>. This is only used by <code>launch_gradio_demo</code>
or to make a nice space from your tool.</li>`,Nr,Bt,Yn=`You can also override the method <a href="/docs/transformers/v4.36.0/ja/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/v4.36.0/ja/main_classes/agent#transformers.Tool.setup">setup()</a> will be called the first time you use your tool, but not at
instantiation.`,Dr,ge,Oe,Br,Yt,Fn='Creates a <a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.Tool">Tool</a> from a gradio tool.',Yr,fe,Ke,Fr,Ft,Sn="Loads a tool defined on the Hub.",Sr,ue,et,Qr,St,Qn="Upload the tool to the Hub.",Xr,R,tt,Or,Qt,Xn=`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:`,Kr,Xt,On=`<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>`,en,Ot,Kn="You should only use this method to save tools that are defined in a separate module (not <code>__main__</code>).",tn,he,ot,on,Kt,ea=`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.`,Wo,rt,Ro,I,nt,rn,eo,ta=`A <a href="/docs/transformers/v4.36.0/ja/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/v4.36.0/ja/main_classes/agent#transformers.Tool">Tool</a>, you will
need to specify:`,nn,to,oa=`<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/v4.36.0/ja/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/v4.36.0/ja/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>) — The class to use to load the
post-processor (when different from the pre-processor).</li>`,an,$e,at,sn,oo,ra="Uses the <code>post_processor</code> to decode the model output.",ln,_e,st,mn,ro,na="Uses the <code>pre_processor</code> to prepare the inputs for the <code>model</code>.",dn,ve,lt,pn,no,aa="Sends the inputs through the <code>model</code>.",cn,be,it,gn,ao,sa="Instantiates the <code>pre_processor</code>, <code>model</code> and <code>post_processor</code> if necessary.",zo,mt,Go,q,dt,fn,so,la='A <a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.Tool">Tool</a> that will make requests to an inference endpoint.',un,ye,pt,hn,lo,ia=`You can override this method in your custom class of <a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.RemoteTool">RemoteTool</a> to apply some custom post-processing of the
outputs of the endpoint.`,$n,F,ct,_n,io,ma=`Prepare the inputs received for the HTTP client sending data to the endpoint. Positional arguments will be
matched with the signature of the <code>tool_class</code> if it was provided at instantation. Images will be encoded into
bytes.`,vn,mo,da='You can override this method in your custom class of <a href="/docs/transformers/v4.36.0/ja/main_classes/agent#transformers.RemoteTool">RemoteTool</a>.',Vo,gt,No,X,ft,bn,po,pa=`Launches a gradio demo for a tool. The corresponding tool class needs to properly implement the class attributes
<code>inputs</code> and <code>outputs</code>.`,Do,ut,Bo,ht,ca=`エージェントはツール間であらゆる種類のオブジェクトを処理できます。ツールは完全にマルチモーダルであるため、受け取りと返品が可能です
テキスト、画像、オーディオ、ビデオなどのタイプ。ツール間の互換性を高めるためだけでなく、
これらの戻り値を ipython (jupyter、colab、ipython ノートブックなど) で正しくレンダリングするには、ラッパー クラスを実装します。
このタイプの周り。`,Yo,$t,ga=`ラップされたオブジェクトは最初と同じように動作し続けるはずです。テキストオブジェクトは依然として文字列または画像として動作する必要があります
オブジェクトは依然として <code>PIL.Image</code> として動作するはずです。`,Fo,_t,fa="これらのタイプには、次の 3 つの特定の目的があります。",So,vt,ua=`<li>型に対して <code>to_raw</code> を呼び出すと、基になるオブジェクトが返されるはずです</li> <li>型に対して <code>to_string</code> を呼び出すと、オブジェクトを文字列として返す必要があります。<code>AgentText</code> の場合は文字列になる可能性があります。
ただし、他のインスタンスのオブジェクトのシリアル化されたバージョンのパスになります。</li> <li>ipython カーネルで表示すると、オブジェクトが正しく表示されるはずです</li>`,Qo,bt,Xo,O,yt,yn,co,ha="Text type returned by the agent. Behaves as a string.",Oo,Tt,Ko,E,wt,Tn,go,$a="Image type returned by the agent. Behaves as a PIL.Image.",wn,Te,xt,xn,fo,_a="Returns the “raw” version of that object. In the case of an AgentImage, it is a PIL.Image.",An,we,At,Mn,uo,va=`Returns the stringified version of that object. In the case of an AgentImage, it is a path to the serialized
version of the image.`,er,Mt,tr,Z,kt,kn,ho,ba="Audio type returned by the agent.",In,xe,It,Cn,$o,ya="Returns the “raw” version of that object. It is a <code>torch.Tensor</code> object.",Hn,Ae,Ct,jn,_o,Ta=`Returns the stringified version of that object. In the case of an AgentAudio, it is a path to the serialized
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<ul>
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<li><code>&quot;image-captioning&quot;</code></li>
<li><code>&quot;image-question-answering&quot;</code></li>
<li><code>&quot;image-segmentation&quot;</code></li>
<li><code>&quot;speech-to-text&quot;</code></li>
<li><code>&quot;summarization&quot;</code></li>
<li><code>&quot;text-classification&quot;</code></li>
<li><code>&quot;text-question-answering&quot;</code></li>
<li><code>&quot;text-to-speech&quot;</code></li>
<li><code>&quot;translation&quot;</code></li>
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