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30}) 31 32console.log(message.content[0].text)`:`const response = await fetch('${t}', { 33 method: 'POST', 34 headers: { 35 'x-api-key': process.env.${n.apiKeyEnv}, 36 'anthropic-version': '2023-06-01', 37 'Content-Type': 'application/json', 38 }, 39 body: JSON.stringify({ 40 model: '${n.modelName}', 41 max_tokens: 1024, 42 messages: [{ role: 'user', content: '${a}' }], 43 }), 44}) 45 46const data = await response.json() 47console.log(data.content[0].text)`}if("gemini"===t){let t=`${n.baseUrl}${n.endpointPath}?key=$${n.apiKeyEnv}`,a="Explain quantum entanglement in one paragraph.";if("curl"===e){let e=JSON.stringify({contents:[{parts:[{text:a}]}]},null,2);return`curl '${t}' \\ 48 -H 'Content-Type: application/json' \\ 49 -d '${e.replace(/\n/g,"\n ")}'`}return"python"===e?`import google.generativeai as genai 50 51genai.configure(api_key="<YOUR_API_KEY>") 52 53model = genai.GenerativeModel("${n.modelName}") 54response = model.generate_content("${a}") 55 56print(response.text)`:"typescript"===e?`import { GoogleGenerativeAI } from '@google/generative-ai' 57 58const genAI = new GoogleGenerativeAI(process.env.${n.apiKeyEnv}!) 59const model = genAI.getGenerativeModel({ model: '${n.modelName}' }) 60 61const result = await model.generateContent('${a}') 62console.log(result.response.text())`:`const response = await fetch('${t}', { 63 method: 'POST', 64 headers: { 'Content-Type': 'application/json' }, 65 body: JSON.stringify({ 66 contents: [{ parts: [{ text: '${a}' }] }], 67 }), 68}) 69 70const data = await response.json() 71console.log(data.candidates[0].content.parts[0].text)`}if("embeddings"===t||"jina-rerank"===t){let t=`${n.baseUrl}${n.endpointPath}`,a="The food was delicious and the waiterâ¦";if("curl"===e){let e=JSON.stringify({model:n.modelName,input:a},null,2);return`curl ${t} \\ 72 -H "Authorization: Bearer $${n.apiKeyEnv}" \\ 73 -H "Content-Type: application/json" \\ 74 -d '${e.replace(/\n/g,"\n ")}'`}return"python"===e?`from openai import OpenAI 75 76client = OpenAI(base_url="${n.baseUrl}/v1", api_key="<YOUR_API_KEY>") 77 78response = client.embeddings.create( 79 model="${n.modelName}", 80 input="${a}", 81) 82 83print(response.data[0].embedding[:8])`:"typescript"===e?`import OpenAI from 'openai' 84 85const client = new OpenAI({ 86 baseURL: '${n.baseUrl}/v1', 87 apiKey: process.env.${n.apiKeyEnv}, 88}) 89 90const response = await client.embeddings.create({ 91 model: '${n.modelName}', 92 input: '${a}', 93}) 94 95console.log(response.data[0].embedding.slice(0, 8))`:`const response = await fetch('${t}', { 96 method: 'POST', 97 headers: { 98 Authorization: \`Bearer \${process.env.${n.apiKeyEnv}}\`, 99 'Content-Type': 'application/json', 100 }, 101 body: JSON.stringify({ 102 model: '${n.modelName}', 103 input: '${a}', 104 }), 105}) 106 107const data = await response.json() 108console.log(data.data[0].embedding.slice(0, 8))`}if("image-generation"===t){let t=`${n.baseUrl}${n.endpointPath}`,a="A serene koi pond at sunset, ukiyo-e style.";if("curl"===e){let e=JSON.stringify({model:n.modelName,prompt:a,size:"1024x1024",n:1},null,2);return`curl ${t} \\ 109 -H "Authorization: Bearer $${n.apiKeyEnv}" \\ 110 -H "Content-Type: application/json" \\ 111 -d '${e.replace(/\n/g,"\n ")}'`}return"python"===e?`from openai import OpenAI 112 113client = OpenAI(base_url="${n.baseUrl}/v1", api_key="<YOUR_API_KEY>") 114 115response = client.images.generate( 116 model="${n.modelName}", 117 prompt="${a}", 118 size="1024x1024", 119 n=1, 120) 121 122print(response.data[0].url)`:"typescript"===e?`import OpenAI from 'openai' 123 124const client = new OpenAI({ 125 baseURL: '${n.baseUrl}/v1', 126 apiKey: process.env.${n.apiKeyEnv}, 127}) 128 129const response = await client.images.generate({ 130 model: '${n.modelName}', 131 prompt: '${a}', 132 size: '1024x1024', 133 n: 1, 134}) 135 136console.log(response.data[0].url)`:`const response = await fetch('${t}', { 137 method: 'POST', 138 headers: { 139 Authorization: \`Bearer \${process.env.${n.apiKeyEnv}}\`, 140 'Content-Type': 'application/json', 141 }, 142 body: JSON.stringify({ 143 model: '${n.modelName}', 144 prompt: '${a}', 145 size: '1024x1024', 146 n: 1, 147 }), 148}) 149 150const data = await response.json() 151console.log(data.data[0].url)`}return a=`${n.baseUrl}${n.endpointPath}`,r="openai-response"===n.endpointType,s=/^o[1-4]|reasoning|thinking|deepseek-r/i.test(n.modelName),i="Explain quantum entanglement in one paragraph.",o=r?JSON.stringify({model:n.modelName,input:i},null,2):JSON.stringify({model:n.modelName,messages:[{role:"user",content:i}],...s?{}:{temperature:.7}}
151,null,2),l=r?"responses.create":"chat.completions.create","curl"===e?`curl ${a} \\ 152 -H "Authorization: Bearer $${n.apiKeyEnv}" \\ 153 -H "Content-Type: application/json" \\ 154 -d '${o.replace(/\n/g,"\n ")}'`:"python"===e?["from openai import OpenAI\n\nclient = OpenAI(",` base_url="${n.baseUrl}/v1",`,' api_key="<YOUR_API_KEY>",\n)\n',r?`response = client.${l}( 155 model="${n.modelName}", 156 input="${i}", 157) 158 159print(response.output_text)`:`completion = client.${l}( 160 model="${n.modelName}", 161 messages=[ 162 {"role": "user", "content": "${i}"} 163 ], 164) 165 166print(completion.choices[0].message.content)`].join("\n"):"typescript"===e?["import OpenAI from 'openai'\n\nconst client = new OpenAI({",` baseURL: '${n.baseUrl}/v1',`,` apiKey: process.env.${n.apiKeyEnv},`,"})\n",r?`const response = await client.${l}({ 167 model: '${n.modelName}', 168 input: '${i}', 169}) 170 171console.log(response.output_text)`:`const completion = await client.${l}({ 172 model: '${n.modelName}', 173 messages: [{ role: 'user', content: '${i}' }], 174}) 175 176console.log(completion.choices[0].message.content)`].join("\n"):`const response = await fetch('${a}', { 177 method: 'POST', 178 headers: { 179 Authorization: \`Bearer \${process.env.${n.apiKeyEnv}}\`, 180 'Content-Type': 'application/json', 181 }, 182 body: JSON.stringify(${o}), 183}) 184 185const data = await response.json() 186console.log(data)`}(l,c.type,{baseUrl:r,apiKeyEnv:"NEW_API_KEY",modelName:e.model.model_name||"",endpointType:c.type,endpointPath:c.path});return(0,a.jsxs)("section",{children:[(0,a.jsx)(em,{icon:U,children:t("Code samples")}),(0,a.jsxs)("div",{className:"flex flex-wrap items-center gap-2",children:[s.length>1&&(0,a.jsx)(C.tU,{value:i,onValueChange:o,children:(0,a.jsx)(C.j7,{className:"bg-muted/40 h-8 p-0.5",children:s.map(e=>(0,a.jsx)(C.Xi,{value:e.type,className:"h-7 px-2.5 text-xs",children:e.type},e.type))})}),(0,a.jsx)(C.tU,{value:l,onValueChange:e=>d(e),className:"ml-auto",children:(0,a.jsx)(C.j7,{className:"bg-muted/40 h-8 p-0.5",children:Object.keys(ea).map(e=>(0,a.jsx)(C.Xi,{value:e,className:"h-7 px-2.5 text-xs",children:ea[e]},e))})})]}),(0,a.jsx)("div",{className:"mt-3",children:(0,a.jsx)(q.NG,{code:m,language:er[l],children:(0,a.jsx)(q.gZ,{})})}),(0,a.jsxs)("p",{className:"text-muted-foreground mt-2 text-xs",children:[t("Replace")," ",(0,a.jsx)("code",{className:"bg-muted rounded px-1 py-0.5 font-mono text-[11px]",children:"<YOUR_API_KEY>"})," ",t("with the API key from your token settings.")]})]})}function ei(e){let{t}=(0,N.Bd)(),n=(0,y.useMemo)(()=>{let t;return"reasoning"===(t=et(e.model))?Q:"embedding"===t?Z:"image"===t?X:"video"===t?ee:W},[e.model]);return 0===n.length?null:(0,a.jsxs)("section",{children:[(0,a.jsx)(em,{icon:B,children:t("Supported parameters")}),(0,a.jsx)(j.hm,{className:j.n$.sectionContainer,headerRowClassName:j.n$.mutedHeaderRow,data:n,getRowKey:e=>e.name,getRowClassName:()=>"hover:bg-muted/20",columns:[{id:"parameter",header:t("Parameter"),className:"h-9 w-44",cellClassName:j.n$.topCell,cell:e=>(0,a.jsxs)("div",{className:"flex items-center gap-1.5",children:[(0,a.jsx)("code",{className:"font-mono text-sm font-medium",children:e.name}),e.required&&(0,a.jsx)(V.E,{variant:"outline",className:"h-6 border-rose-500/40 px-2 text-sm text-rose-600 dar
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