import{s as Bt,o as Qt,n as St}from"../chunks/scheduler.6efaaf90.js";import{S as Rt,i as Et,e as u,s,c as i,h as Lt,a as r,d as l,b as a,f as vt,g as p,j as d,k as Gt,l as Vt,m as n,n as o,t as M,o as m,p as y}from"../chunks/index.eb3e1f0f.js";import{T as qt}from"../chunks/Tip.292c2c3d.js";import{C as Yt,H as U,E as Ht}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.bc356b46.js";import{C as f}from"../chunks/CodeBlock.1ecb727a.js";function Nt(Ue){let c,g="We also recommend sharing a volume with the Docker container (<code>volume=$PWD/data</code>) to avoid downloading weights every run.";return{c(){c=u("p"),c.innerHTML=g},l(T){c=r(T,"P",{"data-svelte-h":!0}),d(c)!=="svelte-jhx6oa"&&(c.innerHTML=g)},m(T,de){n(T,c,de)},p:St,d(T){T&&l(c)}}}function _t(Ue){let c,g,T,de,h,fe,J,Te,$,we,b,ut=`The easiest way to get started with TEI is to use one of the official Docker containers
(see <a href="supported_models">Supported models and hardware</a> to choose the right container).`,ge,j,rt='Hence one needs to install Docker following their <a href="https://docs.docker.com/get-docker/" rel="nofollow">installation instructions</a>.',he,C,dt=`TEI supports inference both on GPU and CPU. If you plan on using a GPU, make sure to check that your hardware is supported by checking <a href="https://github.com/huggingface/text-embeddings-inference?tab=readme-ov-file#docker-images" rel="nofollow">this table</a>.
Next, install the <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html" rel="nofollow">NVIDIA Container Toolkit</a>. NVIDIA drivers on your device need to be compatible with CUDA version 12.2 or higher.`,Je,I,$e,Z,ct='Next it’s time to deploy your model. Let’s say you want to use <a href="https://huggingface.co/Qwen/Qwen3-Embedding-0.6B" rel="nofollow"><code>Qwen/Qwen3-Embedding-0.6B</code></a>. Here’s how you can do this:',be,W,je,w,Ce,k,Ie,A,Ut="Inference can be performed in 3 ways: using cURL, or via the <code>InferenceClient</code> or <code>OpenAI</code> Python SDKs.",Ze,x,We,v,ft="To send a POST request to the TEI endpoint using cURL, you can run the following command:",ke,G,Ae,B,xe,Q,Tt='To run inference using Python, you can either use the <a href="https://huggingface.co/docs/huggingface_hub/en/index" rel="nofollow"><code>huggingface_hub</code></a> Python SDK (recommended) or the <code>openai</code> Python SDK.',ve,S,Ge,R,wt="You can install it via pip as <code>pip install --upgrade --quiet huggingface_hub</code>, and then run:",Be,E,Qe,L,Se,V,gt='To send requests to the <a href="https://platform.openai.com/docs/api-reference/embeddings/create" rel="nofollow">OpenAI Embeddings API</a> exposed on Text Embeddings Inference (TEI) with the OpenAI Python SDK, you can install it as <code>pip install --upgrade openai</code>, and then run the following snippet:',Re,q,Ee,Y,ht="Alternatively, you can also send the request with cURL as follows:",Le,H,Ve,N,qe,_,Jt="TEI also supports re-ranker and classic sequence classification models.",Ye,X,He,z,$t=`Rerankers, also called cross-encoders, are sequence classification models with a single class that score the similarity between a query and a text. See <a href="https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83" rel="nofollow">this blogpost</a> by
the LlamaIndex team to understand how you can use re-rankers models in your RAG pipeline to improve
downstream performance.`,Ne,D,bt='Let’s say you want to use <a href="https://huggingface.co/BAAI/bge-reranker-large" rel="nofollow"><code>BAAI/bge-reranker-large</code></a>. First, you can deploy it like so:',_e,P,Xe,F,jt="Once you have deployed a model, you can use the <code>rerank</code> endpoint to rank the similarity between a query and a list of texts. With <code>cURL</code> this can be done like so:",ze,O,De,K,Pe,ee,Ct='You can also use classic Sequence Classification models like <a href="https://huggingface.co/SamLowe/roberta-base-go_emotions" rel="nofollow"><code>SamLowe/roberta-base-go_emotions</code></a>:',Fe,te,Oe,le,It="Once you have deployed the model you can use the <code>predict</code> endpoint to get the emotions most associated with an input:",Ke,ne,et,se,tt,ae,Zt="You can send multiple inputs in a batch. For example, for embeddings:",lt,ie,nt,pe,Wt="And for Sequence Classification:",st,oe,at,Me,it,me,kt=`To deploy Text Embeddings Inference in an air-gapped environment, first download the weights and then mount them inside
the container using a volume.`,pt,ye,At="For example:",ot,ue,Mt,re,mt,ce,yt;return h=new Yt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),J=new U({props:{title:"Quick Tour",local:"quick-tour",headingTag:"h1"}}),$=new U({props:{title:"Set up",local:"set-up",headingTag:"h2"}}),I=new U({props:{title:"Deploy",local:"deploy",headingTag:"h2"}}),W=new f({props:{code:"bW9kZWwlM0RRd2VuJTJGUXdlbjMtRW1iZWRkaW5nLTAuNkIlMEF2b2x1bWUlM0QlMjRQV0QlMkZkYXRhJTBBJTBBZG9ja2VyJTIwcnVuJTIwLS1ncHVzJTIwYWxsJTIwLXAlMjA4MDgwJTNBODAlMjAtdiUyMCUyNHZvbHVtZSUzQSUyRmRhdGElMjAtLXB1bGwlMjBhbHdheXMlMjBnaGNyLmlvJTJGaHVnZ2luZ2ZhY2UlMkZ0ZXh0LWVtYmVkZGluZ3MtaW5mZXJlbmNlJTNBMS44JTIwLS1tb2RlbC1pZCUyMCUyNG1vZGVs",highlighted:`model=Qwen/Qwen3-Embedding-0.6B
volume=$PWD/data

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.8 --model-id $model`,wrap:!1}}),w=new qt({props:{$$slots:{default:[Nt]},$$scope:{ctx:Ue}}}),k=new U({props:{title:"Inference",local:"inference",headingTag:"h2"}}),x=new U({props:{title:"cURL",local:"curl",headingTag:"h4"}}),G=new f({props:{code:"Y3VybCUyMDEyNy4wLjAuMSUzQTgwODAlMkZlbWJlZCUyMCU1QyUwQSUyMCUyMCUyMCUyMC1YJTIwUE9TVCUyMCU1QyUwQSUyMCUyMCUyMCUyMC1kJTIwJyU3QiUyMmlucHV0cyUyMiUzQSUyMldoYXQlMjBpcyUyMERlZXAlMjBMZWFybmluZyUzRiUyMiU3RCclMjAlNUMlMEElMjAlMjAlMjAlMjAtSCUyMCdDb250ZW50LVR5cGUlM0ElMjBhcHBsaWNhdGlvbiUyRmpzb24n",highlighted:`curl 127.0.0.1:8080/embed \\
    -X POST \\
    -d <span class="hljs-string">&#x27;{&quot;inputs&quot;:&quot;What is Deep Learning?&quot;}&#x27;</span> \\
    -H <span class="hljs-string">&#x27;Content-Type: application/json&#x27;</span>`,wrap:!1}}),B=new U({props:{title:"Python",local:"python",headingTag:"h4"}}),S=new U({props:{title:"huggingface_hub",local:"huggingfacehub",headingTag:"h5"}}),E=new f({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient

client = InferenceClient()

embedding = client.feature_extraction(<span class="hljs-string">&quot;What is deep learning?&quot;</span>,
                                      model=<span class="hljs-string">&quot;http://localhost:8080/embed&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-built_in">len</span>(embedding[<span class="hljs-number">0</span>]))`,wrap:!1}}),L=new U({props:{title:"OpenAI",local:"openai",headingTag:"h4"}}),q=new f({props:{code:"aW1wb3J0JTIwb3MlMEFmcm9tJTIwb3BlbmFpJTIwaW1wb3J0JTIwT3BlbkFJJTBBJTBBY2xpZW50JTIwJTNEJTIwT3BlbkFJKGJhc2VfdXJsJTNEJTIyaHR0cCUzQSUyRiUyRmxvY2FsaG9zdCUzQTgwODAlMkZ2MSUyMiUyQyUyMGFwaV9rZXklM0QlMjAlMjItJTIyKSUwQSUwQXJlc3BvbnNlJTIwJTNEJTIwY2xpZW50LmVtYmVkZGluZ3MuY3JlYXRlKCUwQSUyMCUyMCUyMCUyMG1vZGVsJTNEJTIydGV4dC1lbWJlZGRpbmdzLWluZmVyZW5jZSUyMiUyQyUwQSUyMCUyMCUyMCUyMGlucHV0JTNEJTIyV2hhdCUyMGlzJTIwRGVlcCUyMExlYXJuaW5nJTNGJTIyJTJDJTBBKSUwQSUwQXByaW50KHJlc3BvbnNlLmRhdGElNUIwJTVELmVtYmVkZGluZyk=",highlighted:`<span class="hljs-keyword">import</span> os
<span class="hljs-keyword">from</span> openai <span class="hljs-keyword">import</span> OpenAI

client = OpenAI(base_url=<span class="hljs-string">&quot;http://localhost:8080/v1&quot;</span>, api_key= <span class="hljs-string">&quot;-&quot;</span>)

response = client.embeddings.create(
    model=<span class="hljs-string">&quot;text-embeddings-inference&quot;</span>,
    <span class="hljs-built_in">input</span>=<span class="hljs-string">&quot;What is Deep Learning?&quot;</span>,
)

<span class="hljs-built_in">print</span>(response.data[<span class="hljs-number">0</span>].embedding)`,wrap:!1}}),H=new f({props:{code:"Y3VybCUyMGh0dHAlM0ElMkYlMkZsb2NhbGhvc3QlM0E4MDgwJTJGdjElMkZlbWJlZGRpbmdzJTIwJTVDJTBBJTIwJTIwLUglMjAlMjJDb250ZW50LVR5cGUlM0ElMjBhcHBsaWNhdGlvbiUyRmpzb24lMjIlMjAlNUMlMEElMjAlMjAtZCUyMCclN0IlMEElMjAlMjAlMjAlMjAlMjJpbnB1dCUyMiUzQSUyMCUyMldoYXQlMjBpcyUyMERlZXAlMjBMZWFybmluZyUzRiUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMm1vZGVsJTIyJTNBJTIwJTIydGV4dC1lbWJlZGRpbmdzLWluZmVyZW5jZSUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMmVuY29kaW5nX2Zvcm1hdCUyMiUzQSUyMCUyMmZsb2F0JTIyJTBBJTIwJTIwJTdEJw==",highlighted:`curl http://localhost:8080/v1/embeddings \\
  -H <span class="hljs-string">&quot;Content-Type: application/json&quot;</span> \\
  -d <span class="hljs-string">&#x27;{
    &quot;input&quot;: &quot;What is Deep Learning?&quot;,
    &quot;model&quot;: &quot;text-embeddings-inference&quot;,
    &quot;encoding_format&quot;: &quot;float&quot;
  }&#x27;</span>`,wrap:!1}}),N=new U({props:{title:"Re-rankers and sequence classification",local:"re-rankers-and-sequence-classification",headingTag:"h2"}}),X=new U({props:{title:"Re-rankers",local:"re-rankers",headingTag:"h3"}}),P=new f({props:{code:"bW9kZWwlM0RCQUFJJTJGYmdlLXJlcmFua2VyLWxhcmdlJTBBdm9sdW1lJTNEJTI0UFdEJTJGZGF0YSUwQSUwQWRvY2tlciUyMHJ1biUyMC0tZ3B1cyUyMGFsbCUyMC1wJTIwODA4MCUzQTgwJTIwLXYlMjAlMjR2b2x1bWUlM0ElMkZkYXRhJTIwLS1wdWxsJTIwYWx3YXlzJTIwZ2hjci5pbyUyRmh1Z2dpbmdmYWNlJTJGdGV4dC1lbWJlZGRpbmdzLWluZmVyZW5jZSUzQTEuOCUyMC0tbW9kZWwtaWQlMjAlMjRtb2RlbA==",highlighted:`model=BAAI/bge-reranker-large
volume=$PWD/data

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.8 --model-id $model`,wrap:!1}}),O=new f({props:{code:"Y3VybCUyMDEyNy4wLjAuMSUzQTgwODAlMkZyZXJhbmslMjAlNUMlMEElMjAlMjAlMjAlMjAtWCUyMFBPU1QlMjAlNUMlMEElMjAlMjAlMjAlMjAtZCUyMCclN0IlMjJxdWVyeSUyMiUzQSUyMldoYXQlMjBpcyUyMERlZXAlMjBMZWFybmluZyUzRiUyMiUyQyUyMCUyMnRleHRzJTIyJTNBJTIwJTVCJTIyRGVlcCUyMExlYXJuaW5nJTIwaXMlMjBub3QuLi4lMjIlMkMlMjAlMjJEZWVwJTIwbGVhcm5pbmclMjBpcy4uLiUyMiU1RCUyQyUyMCUyMnJhd19zY29yZXMlMjIlM0ElMjBmYWxzZSU3RCclMjAlNUMlMEElMjAlMjAlMjAlMjAtSCUyMCdDb250ZW50LVR5cGUlM0ElMjBhcHBsaWNhdGlvbiUyRmpzb24n",highlighted:`curl 127.0.0.1:8080/rerank \\
    -X POST \\
    -d <span class="hljs-string">&#x27;{&quot;query&quot;:&quot;What is Deep Learning?&quot;, &quot;texts&quot;: [&quot;Deep Learning is not...&quot;, &quot;Deep learning is...&quot;], &quot;raw_scores&quot;: false}&#x27;</span> \\
    -H <span class="hljs-string">&#x27;Content-Type: application/json&#x27;</span>`,wrap:!1}}),K=new U({props:{title:"Sequence classification models",local:"sequence-classification-models",headingTag:"h3"}}),te=new f({props:{code:"bW9kZWwlM0RTYW1Mb3dlJTJGcm9iZXJ0YS1iYXNlLWdvX2Vtb3Rpb25zJTBBdm9sdW1lJTNEJTI0UFdEJTJGZGF0YSUwQSUwQWRvY2tlciUyMHJ1biUyMC0tZ3B1cyUyMGFsbCUyMC1wJTIwODA4MCUzQTgwJTIwLXYlMjAlMjR2b2x1bWUlM0ElMkZkYXRhJTIwLS1wdWxsJTIwYWx3YXlzJTIwZ2hjci5pbyUyRmh1Z2dpbmdmYWNlJTJGdGV4dC1lbWJlZGRpbmdzLWluZmVyZW5jZSUzQTEuOCUyMC0tbW9kZWwtaWQlMjAlMjRtb2RlbA==",highlighted:`model=SamLowe/roberta-base-go_emotions
volume=$PWD/data

docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.8 --model-id $model`,wrap:!1}}),ne=new f({props:{code:"Y3VybCUyMDEyNy4wLjAuMSUzQTgwODAlMkZwcmVkaWN0JTIwJTVDJTBBJTIwJTIwJTIwJTIwLVglMjBQT1NUJTIwJTVDJTBBJTIwJTIwJTIwJTIwLWQlMjAnJTdCJTIyaW5wdXRzJTIyJTNBJTIySSUyMGxpa2UlMjB5b3UuJTIyJTdEJyUyMCU1QyUwQSUyMCUyMCUyMCUyMC1IJTIwJ0NvbnRlbnQtVHlwZSUzQSUyMGFwcGxpY2F0aW9uJTJGanNvbic=",highlighted:`curl 127.0.0.1:8080/predict \\
    -X POST \\
    -d <span class="hljs-string">&#x27;{&quot;inputs&quot;:&quot;I like you.&quot;}&#x27;</span> \\
    -H <span class="hljs-string">&#x27;Content-Type: application/json&#x27;</span>`,wrap:!1}}),se=new U({props:{title:"Batching",local:"batching",headingTag:"h2"}}),ie=new f({props:{code:"Y3VybCUyMDEyNy4wLjAuMSUzQTgwODAlMkZlbWJlZCUyMCU1QyUwQSUyMCUyMCUyMCUyMC1YJTIwUE9TVCUyMCU1QyUwQSUyMCUyMCUyMCUyMC1kJTIwJyU3QiUyMmlucHV0cyUyMiUzQSU1QiUyMlRvZGF5JTIwaXMlMjBhJTIwbmljZSUyMGRheSUyMiUyQyUyMCUyMkklMjBsaWtlJTIweW91JTIyJTVEJTdEJyUyMCU1QyUwQSUyMCUyMCUyMCUyMC1IJTIwJ0NvbnRlbnQtVHlwZSUzQSUyMGFwcGxpY2F0aW9uJTJGanNvbic=",highlighted:`curl 127.0.0.1:8080/embed \\
    -X POST \\
    -d <span class="hljs-string">&#x27;{&quot;inputs&quot;:[&quot;Today is a nice day&quot;, &quot;I like you&quot;]}&#x27;</span> \\
    -H <span class="hljs-string">&#x27;Content-Type: application/json&#x27;</span>`,wrap:!1}}),oe=new f({props:{code:"Y3VybCUyMDEyNy4wLjAuMSUzQTgwODAlMkZwcmVkaWN0JTIwJTVDJTBBJTIwJTIwJTIwJTIwLVglMjBQT1NUJTIwJTVDJTBBJTIwJTIwJTIwJTIwLWQlMjAnJTdCJTIyaW5wdXRzJTIyJTNBJTVCJTVCJTIySSUyMGxpa2UlMjB5b3UuJTIyJTVEJTJDJTIwJTVCJTIySSUyMGhhdGUlMjBwaW5lYXBwbGVzJTIyJTVEJTVEJTdEJyUyMCU1QyUwQSUyMCUyMCUyMCUyMC1IJTIwJ0NvbnRlbnQtVHlwZSUzQSUyMGFwcGxpY2F0aW9uJTJGanNvbic=",highlighted:`curl 127.0.0.1:8080/predict \\
    -X POST \\
    -d <span class="hljs-string">&#x27;{&quot;inputs&quot;:[[&quot;I like you.&quot;], [&quot;I hate pineapples&quot;]]}&#x27;</span> \\
    -H <span class="hljs-string">&#x27;Content-Type: application/json&#x27;</span>`,wrap:!1}}),Me=new U({props:{title:"Air gapped deployment",local:"air-gapped-deployment",headingTag:"h2"}}),ue=new f({props:{code:"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",highlighted:`<span class="hljs-meta prompt_"># </span><span class="language-bash">(Optional) create a \`models\` directory</span>
mkdir models
cd models
<span class="hljs-meta prompt_">
# </span><span class="language-bash">Make sure you have git-lfs installed (https://git-lfs.com)</span>
git lfs install
git clone https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5
<span class="hljs-meta prompt_">
# </span><span class="language-bash">Set the models directory as the volume path</span>
volume=$PWD
<span class="hljs-meta prompt_">
# </span><span class="language-bash">Mount the models directory inside the container with a volume and <span class="hljs-built_in">set</span> the model ID</span>
docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.8 --model-id /data/gte-base-en-v1.5`,wrap:!1}}),re=new Ht({props:{source:"https://github.com/huggingface/text-embeddings-inference/blob/main/docs/source/en/quick_tour.md"}}),{c(){c=u("meta"),g=s(),T=u("p"),de=s(),i(h.$$.fragment),fe=s(),i(J.$$.fragment),Te=s(),i($.$$.fragment),we=s(),b=u("p"),b.innerHTML=ut,ge=s(),j=u("p"),j.innerHTML=rt,he=s(),C=u("p"),C.innerHTML=dt,Je=s(),i(I.$$.fragment),$e=s(),Z=u("p"),Z.innerHTML=ct,be=s(),i(W.$$.fragment),je=s(),i(w.$$.fragment),Ce=s(),i(k.$$.fragment),Ie=s(),A=u("p"),A.innerHTML=Ut,Ze=s(),i(x.$$.fragment),We=s(),v=u("p"),v.textContent=ft,ke=s(),i(G.$$.fragment),Ae=s(),i(B.$$.fragment),xe=s(),Q=u("p"),Q.innerHTML=Tt,ve=s(),i(S.$$.fragment),Ge=s(),R=u("p"),R.innerHTML=wt,Be=s(),i(E.$$.fragment),Qe=s(),i(L.$$.fragment),Se=s(),V=u("p"),V.innerHTML=gt,Re=s(),i(q.$$.fragment),Ee=s(),Y=u("p"),Y.textContent=ht,Le=s(),i(H.$$.fragment),Ve=s(),i(N.$$.fragment),qe=s(),_=u("p"),_.textContent=Jt,Ye=s(),i(X.$$.fragment),He=s(),z=u("p"),z.innerHTML=$t,Ne=s(),D=u("p"),D.innerHTML=bt,_e=s(),i(P.$$.fragment),Xe=s(),F=u("p"),F.innerHTML=jt,ze=s(),i(O.$$.fragment),De=s(),i(K.$$.fragment),Pe=s(),ee=u("p"),ee.innerHTML=Ct,Fe=s(),i(te.$$.fragment),Oe=s(),le=u("p"),le.innerHTML=It,Ke=s(),i(ne.$$.fragment),et=s(),i(se.$$.fragment),tt=s(),ae=u("p"),ae.textContent=Zt,lt=s(),i(ie.$$.fragment),nt=s(),pe=u("p"),pe.textContent=Wt,st=s(),i(oe.$$.fragment),at=s(),i(Me.$$.fragment),it=s(),me=u("p"),me.textContent=kt,pt=s(),ye=u("p"),ye.textContent=At,ot=s(),i(ue.$$.fragment),Mt=s(),i(re.$$.fragment),mt=s(),ce=u("p"),this.h()},l(e){const t=Lt("svelte-u9bgzb",document.head);c=r(t,"META",{name:!0,content:!0}),t.forEach(l),g=a(e),T=r(e,"P",{}),vt(T).forEach(l),de=a(e),p(h.$$.fragment,e),fe=a(e),p(J.$$.fragment,e),Te=a(e),p($.$$.fragment,e),we=a(e),b=r(e,"P",{"data-svelte-h":!0}),d(b)!=="svelte-18xzp3u"&&(b.innerHTML=ut),ge=a(e),j=r(e,"P",{"data-svelte-h":!0}),d(j)!=="svelte-1snuvj1"&&(j.innerHTML=rt),he=a(e),C=r(e,"P",{"data-svelte-h":!0}),d(C)!=="svelte-9dwn57"&&(C.innerHTML=dt),Je=a(e),p(I.$$.fragment,e),$e=a(e),Z=r(e,"P",{"data-svelte-h":!0}),d(Z)!=="svelte-16wjhv4"&&(Z.innerHTML=ct),be=a(e),p(W.$$.fragment,e),je=a(e),p(w.$$.fragment,e),Ce=a(e),p(k.$$.fragment,e),Ie=a(e),A=r(e,"P",{"data-svelte-h":!0}),d(A)!=="svelte-o03r4k"&&(A.innerHTML=Ut),Ze=a(e),p(x.$$.fragment,e),We=a(e),v=r(e,"P",{"data-svelte-h":!0}),d(v)!=="svelte-es03af"&&(v.textContent=ft),ke=a(e),p(G.$$.fragment,e),Ae=a(e),p(B.$$.fragment,e),xe=a(e),Q=r(e,"P",{"data-svelte-h":!0}),d(Q)!=="svelte-1s4gfrj"&&(Q.innerHTML=Tt),ve=a(e),p(S.$$.fragment,e),Ge=a(e),R=r(e,"P",{"data-svelte-h":!0}),d(R)!=="svelte-1pmfr0a"&&(R.innerHTML=wt),Be=a(e),p(E.$$.fragment,e),Qe=a(e),p(L.$$.fragment,e),Se=a(e),V=r(e,"P",{"data-svelte-h":!0}),d(V)!=="svelte-8klmrb"&&(V.innerHTML=gt),Re=a(e),p(q.$$.fragment,e),Ee=a(e),Y=r(e,"P",{"data-svelte-h":!0}),d(Y)!=="svelte-18myjae"&&(Y.textContent=ht),Le=a(e),p(H.$$.fragment,e),Ve=a(e),p(N.$$.fragment,e),qe=a(e),_=r(e,"P",{"data-svelte-h":!0}),d(_)!=="svelte-e90q9w"&&(_.textContent=Jt),Ye=a(e),p(X.$$.fragment,e),He=a(e),z=r(e,"P",{"data-svelte-h":!0}),d(z)!=="svelte-10to1lh"&&(z.innerHTML=$t),Ne=a(e),D=r(e,"P",{"data-svelte-h":!0}),d(D)!=="svelte-fs3cz1"&&(D.innerHTML=bt),_e=a(e),p(P.$$.fragment,e),Xe=a(e),F=r(e,"P",{"data-svelte-h":!0}),d(F)!=="svelte-14ssuk2"&&(F.innerHTML=jt),ze=a(e),p(O.$$.fragment,e),De=a(e),p(K.$$.fragment,e),Pe=a(e),ee=r(e,"P",{"data-svelte-h":!0}),d(ee)!=="svelte-2pw5r1"&&(ee.innerHTML=Ct),Fe=a(e),p(te.$$.fragment,e),Oe=a(e),le=r(e,"P",{"data-svelte-h":!0}),d(le)!=="svelte-1fovlx2"&&(le.innerHTML=It),Ke=a(e),p(ne.$$.fragment,e),et=a(e),p(se.$$.fragment,e),tt=a(e),ae=r(e,"P",{"data-svelte-h":!0}),d(ae)!=="svelte-825na6"&&(ae.textContent=Zt),lt=a(e),p(ie.$$.fragment,e),nt=a(e),pe=r(e,"P",{"data-svelte-h":!0}),d(pe)!=="svelte-8ydrrf"&&(pe.textContent=Wt),st=a(e),p(oe.$$.fragment,e),at=a(e),p(Me.$$.fragment,e),it=a(e),me=r(e,"P",{"data-svelte-h":!0}),d(me)!=="svelte-jgaxx5"&&(me.textContent=kt),pt=a(e),ye=r(e,"P",{"data-svelte-h":!0}),d(ye)!=="svelte-1gkqha7"&&(ye.textContent=At),ot=a(e),p(ue.$$.fragment,e),Mt=a(e),p(re.$$.fragment,e),mt=a(e),ce=r(e,"P",{}),vt(ce).forEach(l),this.h()},h(){Gt(c,"name","hf:doc:metadata"),Gt(c,"content",Xt)},m(e,t){Vt(document.head,c),n(e,g,t),n(e,T,t),n(e,de,t),o(h,e,t),n(e,fe,t),o(J,e,t),n(e,Te,t),o($,e,t),n(e,we,t),n(e,b,t),n(e,ge,t),n(e,j,t),n(e,he,t),n(e,C,t),n(e,Je,t),o(I,e,t),n(e,$e,t),n(e,Z,t),n(e,be,t),o(W,e,t),n(e,je,t),o(w,e,t),n(e,Ce,t),o(k,e,t),n(e,Ie,t),n(e,A,t),n(e,Ze,t),o(x,e,t),n(e,We,t),n(e,v,t),n(e,ke,t),o(G,e,t),n(e,Ae,t),o(B,e,t),n(e,xe,t),n(e,Q,t),n(e,ve,t),o(S,e,t),n(e,Ge,t),n(e,R,t),n(e,Be,t),o(E,e,t),n(e,Qe,t),o(L,e,t),n(e,Se,t),n(e,V,t),n(e,Re,t),o(q,e,t),n(e,Ee,t),n(e,Y,t),n(e,Le,t),o(H,e,t),n(e,Ve,t),o(N,e,t),n(e,qe,t),n(e,_,t),n(e,Ye,t),o(X,e,t),n(e,He,t),n(e,z,t),n(e,Ne,t),n(e,D,t),n(e,_e,t),o(P,e,t),n(e,Xe,t),n(e,F,t),n(e,ze,t),o(O,e,t),n(e,De,t),o(K,e,t),n(e,Pe,t),n(e,ee,t),n(e,Fe,t),o(te,e,t),n(e,Oe,t),n(e,le,t),n(e,Ke,t),o(ne,e,t),n(e,et,t),o(se,e,t),n(e,tt,t),n(e,ae,t),n(e,lt,t),o(ie,e,t),n(e,nt,t),n(e,pe,t),n(e,st,t),o(oe,e,t),n(e,at,t),o(Me,e,t),n(e,it,t),n(e,me,t),n(e,pt,t),n(e,ye,t),n(e,ot,t),o(ue,e,t),n(e,Mt,t),o(re,e,t),n(e,mt,t),n(e,ce,t),yt=!0},p(e,[t]){const xt={};t&2&&(xt.$$scope={dirty:t,ctx:e}),w.$set(xt)},i(e){yt||(M(h.$$.fragment,e),M(J.$$.fragment,e),M($.$$.fragment,e),M(I.$$.fragment,e),M(W.$$.fragment,e),M(w.$$.fragment,e),M(k.$$.fragment,e),M(x.$$.fragment,e),M(G.$$.fragment,e),M(B.$$.fragment,e),M(S.$$.fragment,e),M(E.$$.fragment,e),M(L.$$.fragment,e),M(q.$$.fragment,e),M(H.$$.fragment,e),M(N.$$.fragment,e),M(X.$$.fragment,e),M(P.$$.fragment,e),M(O.$$.fragment,e),M(K.$$.fragment,e),M(te.$$.fragment,e),M(ne.$$.fragment,e),M(se.$$.fragment,e),M(ie.$$.fragment,e),M(oe.$$.fragment,e),M(Me.$$.fragment,e),M(ue.$$.fragment,e),M(re.$$.fragment,e),yt=!0)},o(e){m(h.$$.fragment,e),m(J.$$.fragment,e),m($.$$.fragment,e),m(I.$$.fragment,e),m(W.$$.fragment,e),m(w.$$.fragment,e),m(k.$$.fragment,e),m(x.$$.fragment,e),m(G.$$.fragment,e),m(B.$$.fragment,e),m(S.$$.fragment,e),m(E.$$.fragment,e),m(L.$$.fragment,e),m(q.$$.fragment,e),m(H.$$.fragment,e),m(N.$$.fragment,e),m(X.$$.fragment,e),m(P.$$.fragment,e),m(O.$$.fragment,e),m(K.$$.fragment,e),m(te.$$.fragment,e),m(ne.$$.fragment,e),m(se.$$.fragment,e),m(ie.$$.fragment,e),m(oe.$$.fragment,e),m(Me.$$.fragment,e),m(ue.$$.fragment,e),m(re.$$.fragment,e),yt=!1},d(e){e&&(l(g),l(T),l(de),l(fe),l(Te),l(we),l(b),l(ge),l(j),l(he),l(C),l(Je),l($e),l(Z),l(be),l(je),l(Ce),l(Ie),l(A),l(Ze),l(We),l(v),l(ke),l(Ae),l(xe),l(Q),l(ve),l(Ge),l(R),l(Be),l(Qe),l(Se),l(V),l(Re),l(Ee),l(Y),l(Le),l(Ve),l(qe),l(_),l(Ye),l(He),l(z),l(Ne),l(D),l(_e),l(Xe),l(F),l(ze),l(De),l(Pe),l(ee),l(Fe),l(Oe),l(le),l(Ke),l(et),l(tt),l(ae),l(lt),l(nt),l(pe),l(st),l(at),l(it),l(me),l(pt),l(ye),l(ot),l(Mt),l(mt),l(ce)),l(c),y(h,e),y(J,e),y($,e),y(I,e),y(W,e),y(w,e),y(k,e),y(x,e),y(G,e),y(B,e),y(S,e),y(E,e),y(L,e),y(q,e),y(H,e),y(N,e),y(X,e),y(P,e),y(O,e),y(K,e),y(te,e),y(ne,e),y(se,e),y(ie,e),y(oe,e),y(Me,e),y(ue,e),y(re,e)}}}const Xt='{"title":"Quick Tour","local":"quick-tour","sections":[{"title":"Set up","local":"set-up","sections":[],"depth":2},{"title":"Deploy","local":"deploy","sections":[],"depth":2},{"title":"Inference","local":"inference","sections":[{"title":"cURL","local":"curl","sections":[],"depth":4},{"title":"Python","local":"python","sections":[{"title":"huggingface_hub","local":"huggingfacehub","sections":[],"depth":5}],"depth":4},{"title":"OpenAI","local":"openai","sections":[],"depth":4}],"depth":2},{"title":"Re-rankers and sequence classification","local":"re-rankers-and-sequence-classification","sections":[{"title":"Re-rankers","local":"re-rankers","sections":[],"depth":3},{"title":"Sequence classification models","local":"sequence-classification-models","sections":[],"depth":3}],"depth":2},{"title":"Batching","local":"batching","sections":[],"depth":2},{"title":"Air gapped deployment","local":"air-gapped-deployment","sections":[],"depth":2}],"depth":1}';function zt(Ue){return Qt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class el extends Rt{constructor(c){super(),Et(this,c,zt,_t,Bt,{})}}export{el as component};
