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  "aboutHtml": "<p>E5-Large-v2 — intfloat’ning 1024 o‘lchamli chiqishga ega yigirma to‘rt qatlamli inglizcha matn embedding modeli. U so‘rov va parchalarni qidiruv hamda boshqa embedding vazifalari uchun raqamli ifodalashga mo‘ljallangan. E5 tayyorlash qoidasi uning o‘qitilishining bir qismi: kirish matnida rol prefiksi bo‘ladi va kodlangan token ifodalari shu matn uchun bitta vektorga birlashtiriladi.</p><h4>So‘rov va parcha rollarini ochiq ko‘rsating</h4><p>Asimmetrik qidiruvda savolni “query:” bilan, korpus matnini “passage:” bilan boshlang. Bu rollar foydalanuvchi izlayotgan narsani unga javob berishi mumkin bo‘lgan materialdan ajratadi. Simmetrik taqqoslash, qayta ifodalarni qidirish, klasterlash yoki tasniflash xususiyatlari uchun ishlab chiqaruvchi embedding qilinayotgan matnlarda “query:”ni tavsiya qiladi.</p><p>Amaliy to‘plam inglizcha qo‘llab-quvvatlash izohlarini indekslashi mumkin. “query: What should I check before replacing the connector?” kabi savol tayyorlang va uni tekshiruvlar hamda istisnolarni tavsiflovchi manba parchalari bilan juftlang. Parcha matni o‘z tegishli prefiksini saqlaydi. Natija foydalanuvchi o‘qishi kerak bo‘lgan materialni qaytara olishi uchun har bir vektor yonida manba identifikatori va asl ifodani saqlang.</p><p>Prefikslar vazifaning bir tomonidan olib tashlanadigan bezak yorliqlari emas. Ishlab chiqaruvchi ularni tashlash qidiruv sifatini pasaytirishini aytadi, chunki model shu tayyorlash bilan o‘qitilgan. Korpusni yaratishda va keyin so‘rov embeddingini olishda tanlangan rolni izchil qo‘llang.</p><h4>O‘rtacha pooling va normallangan vektorlar</h4><p>Rasmiy kodlash misoli diqqat niqobi yordamida to‘ldiruvchi pozitsiyalarni chiqarib tashlab, token ifodalarining o‘rtachasini oladi. So‘ng natijaviy vektorni normallaydi. Normallangan so‘rov va parcha vektorlari skalyar ko‘paytmasi nomzodlarni tartiblash uchun kosinus o‘xshashligini beradi. Bu o‘rtacha pooling usuli E5-Large-v2 ga tegishli; boshqa oiladagi birinchi token pooling usuli alohida tayyorlash tanlovidir.</p><p>1024 ta chiqish qiymati embeddingni tashkil qiladi; ular yozma javob yoki bevosita toifa yorlig‘i emas. Qidiruv bosqichi bu qiymatlarni mos korpus ifodalari bilan taqqoslaydi, keyingi bosqich esa tanlangan parcha matnidan foydalanadi. Vektorlar klasterlash yoki tasniflagich xususiyatlari bo‘lsa, har bir vektorning u xususiyatlarini ifodalayotgan kirish yozuvi bilan bog‘liqligini saqlang.</p><h4>Oyna ichidagi inglizcha matn</h4><p>Model kartasi faqat inglizcha matnni va uzun kirishlarni ko‘pi bilan 512 tokengacha kesishni belgilaydi. Har hujjat parchasi shu oyna ichida, jumladan rol prefiksi va foydali manba konteksti bilan, izchil bo‘lsin. Uzun qo‘llanmani alohida topilganda sarlavha, shart va ko‘rsatmalari tushuniladigan qismlar sifatida tayyorlash kerak.</p><p>Masalan, sozlamani tasvirlovchi bandga shu sozlama ta’rifi va keyingi gapdagi istisno kerak bo‘lishi mumkin. Faqat belgilar soniga qarab bo‘lish o‘rniga, shu kichik birlikni birga saqlang. Yangilanish to‘g‘ri materialni almashtirishi uchun asosiy hujjat va manba versiyasini parcha bilan saqlang.</p><h4>Reytingni qidiruv dalili sifatida o‘qing</h4><p>Ishlab chiqaruvchi kosinus ballari tartibi ularning mutlaq qiymatidan muhimroq ekanini tushuntiradi. Ballni parcha savolga javob berishining kalibrlangan ehtimoli deb talqin qilmaslik kerak. Nechta natijani saqlash yoki nomzodlar to‘plamini qayerda filtrlashni tanlashda javob borligi ma’lum parchalar va ularga yaqin aloqasiz materiallarni tekshiring.</p><p>Foydali chiqish — korpus yoki so‘rov ifodasi, semantik taqqoslash signali yoki boshqa vazifa uchun raqamli xususiyatlar. Aynan shu inglizcha Large v2 checkpointi E5-Base-v2 va Multilingual-E5-Large’dan alohida qoladi. O‘lchamlar tengligining o‘zi ularning vektorlarini hujjatlashtirilgan yagona embedding makoniga aylantirmaydi.</p>",
  "aboutText": "E5-Large-v2 — intfloat’ning 1024 o‘lchamli chiqishga ega yigirma to‘rt qatlamli inglizcha matn embedding modeli. U so‘rov va parchalarni qidiruv hamda boshqa embedding vazifalari uchun raqamli ifodalashga mo‘ljallangan. E5 tayyorlash qoidasi uning o‘qitilishining bir qismi: kirish matnida rol prefiksi bo‘ladi va kodlangan token ifodalari shu matn uchun bitta vektorga birlashtiriladi.\n\n### So‘rov va parcha rollarini ochiq ko‘rsating\n\nAsimmetrik qidiruvda savolni “query:” bilan, korpus matnini “passage:” bilan boshlang. Bu rollar foydalanuvchi izlayotgan narsani unga javob berishi mumkin bo‘lgan materialdan ajratadi. Simmetrik taqqoslash, qayta ifodalarni qidirish, klasterlash yoki tasniflash xususiyatlari uchun ishlab chiqaruvchi embedding qilinayotgan matnlarda “query:”ni tavsiya qiladi.\n\nAmaliy to‘plam inglizcha qo‘llab-quvvatlash izohlarini indekslashi mumkin. “query: What should I check before replacing the connector?” kabi savol tayyorlang va uni tekshiruvlar hamda istisnolarni tavsiflovchi manba parchalari bilan juftlang. Parcha matni o‘z tegishli prefiksini saqlaydi. Natija foydalanuvchi o‘qishi kerak bo‘lgan materialni qaytara olishi uchun har bir vektor yonida manba identifikatori va asl ifodani saqlang.\n\nPrefikslar vazifaning bir tomonidan olib tashlanadigan bezak yorliqlari emas. Ishlab chiqaruvchi ularni tashlash qidiruv sifatini pasaytirishini aytadi, chunki model shu tayyorlash bilan o‘qitilgan. Korpusni yaratishda va keyin so‘rov embeddingini olishda tanlangan rolni izchil qo‘llang.\n\n### O‘rtacha pooling va normallangan vektorlar\n\nRasmiy kodlash misoli diqqat niqobi yordamida to‘ldiruvchi pozitsiyalarni chiqarib tashlab, token ifodalarining o‘rtachasini oladi. So‘ng natijaviy vektorni normallaydi. Normallangan so‘rov va parcha vektorlari skalyar ko‘paytmasi nomzodlarni tartiblash uchun kosinus o‘xshashligini beradi. Bu o‘rtacha pooling usuli E5-Large-v2 ga tegishli; boshqa oiladagi birinchi token pooling usuli alohida tayyorlash tanlovidir.\n\n1024 ta chiqish qiymati embeddingni tashkil qiladi; ular yozma javob yoki bevosita toifa yorlig‘i emas. Qidiruv bosqichi bu qiymatlarni mos korpus ifodalari bilan taqqoslaydi, keyingi bosqich esa tanlangan parcha matnidan foydalanadi. Vektorlar klasterlash yoki tasniflagich xususiyatlari bo‘lsa, har bir vektorning u xususiyatlarini ifodalayotgan kirish yozuvi bilan bog‘liqligini saqlang.\n\n### Oyna ichidagi inglizcha matn\n\nModel kartasi faqat inglizcha matnni va uzun kirishlarni ko‘pi bilan 512 tokengacha kesishni belgilaydi. Har hujjat parchasi shu oyna ichida, jumladan rol prefiksi va foydali manba konteksti bilan, izchil bo‘lsin. Uzun qo‘llanmani alohida topilganda sarlavha, shart va ko‘rsatmalari tushuniladigan qismlar sifatida tayyorlash kerak.\n\nMasalan, sozlamani tasvirlovchi bandga shu sozlama ta’rifi va keyingi gapdagi istisno kerak bo‘lishi mumkin. Faqat belgilar soniga qarab bo‘lish o‘rniga, shu kichik birlikni birga saqlang. Yangilanish to‘g‘ri materialni almashtirishi uchun asosiy hujjat va manba versiyasini parcha bilan saqlang.\n\n### Reytingni qidiruv dalili sifatida o‘qing\n\nIshlab chiqaruvchi kosinus ballari tartibi ularning mutlaq qiymatidan muhimroq ekanini tushuntiradi. Ballni parcha savolga javob berishining kalibrlangan ehtimoli deb talqin qilmaslik kerak. Nechta natijani saqlash yoki nomzodlar to‘plamini qayerda filtrlashni tanlashda javob borligi ma’lum parchalar va ularga yaqin aloqasiz materiallarni tekshiring.\n\nFoydali chiqish — korpus yoki so‘rov ifodasi, semantik taqqoslash signali yoki boshqa vazifa uchun raqamli xususiyatlar. Aynan shu inglizcha Large v2 checkpointi E5-Base-v2 va Multilingual-E5-Large’dan alohida qoladi. O‘lchamlar tengligining o‘zi ularning vektorlarini hujjatlashtirilgan yagona embedding makoniga aylantirmaydi.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Intfloat: E5-Large-v2</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Intfloat /</span><code>intfloat/e5-large-v2</code><button type=\"button\" id=\"copy-id\">ID ni nusxalash</button></div><nav class=\"card-jump-nav\" aria-label=\"Model kartochkasi bo‘limlari\"><button type=\"button\" data-card-jump=\"overview\">Boshlash</button><button type=\"button\" data-card-jump=\"methods\">Kirish ma’lumotlari va usullar</button><button type=\"button\" data-card-jump=\"pricing\">Tariflar</button><button type=\"button\" data-card-jump=\"examples\">Namunalar</button><button type=\"button\" data-card-jump=\"results\">Natijalar</button><button type=\"button\" data-card-jump=\"aliases\">Taxalluslar</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API usullari\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|intfloat%2Fe5-large-v2\">Embeddinglar</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Boshlash</h3><p class=\"mdesc-full\">intfloat E5-Large-v2 inglizcha so‘rovlar va parchalarni 1024 o‘lchamli vektorlarga kodlaydi. Uning 24 qatlamli modeli qidiruv, semantik o‘xshashlik va raqamli matn xususiyatlari uchun query/passage rollari, o‘rtacha pooling va 512 tokenli kirish chegarasidan foydalanadi.</p><dl class=\"card-io\" data-card-io><div><dt>Qabul qilinadigan kirish ma’lumotlari</dt><dd>Matn · token_ids</dd></div><div><dt>Natijalar</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>Model haqida</summary><p class=\"note-s\" data-model-method-boundary>Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.</p><div class=\"mabout\" id=\"m-about\"><p>E5-Large-v2 — intfloat’ning 1024 o‘lchamli chiqishga ega yigirma to‘rt qatlamli inglizcha matn embedding modeli. U so‘rov va parchalarni qidiruv hamda boshqa embedding vazifalari uchun raqamli ifodalashga mo‘ljallangan. E5 tayyorlash qoidasi uning o‘qitilishining bir qismi: kirish matnida rol prefiksi bo‘ladi va kodlangan token ifodalari shu matn uchun bitta vektorga birlashtiriladi.</p><h4>So‘rov va parcha rollarini ochiq ko‘rsating</h4><p>Asimmetrik qidiruvda savolni “query:” bilan, korpus matnini “passage:” bilan boshlang. Bu rollar foydalanuvchi izlayotgan narsani unga javob berishi mumkin bo‘lgan materialdan ajratadi. Simmetrik taqqoslash, qayta ifodalarni qidirish, klasterlash yoki tasniflash xususiyatlari uchun ishlab chiqaruvchi embedding qilinayotgan matnlarda “query:”ni tavsiya qiladi.</p><p>Amaliy to‘plam inglizcha qo‘llab-quvvatlash izohlarini indekslashi mumkin. “query: What should I check before replacing the connector?” kabi savol tayyorlang va uni tekshiruvlar hamda istisnolarni tavsiflovchi manba parchalari bilan juftlang. Parcha matni o‘z tegishli prefiksini saqlaydi. Natija foydalanuvchi o‘qishi kerak bo‘lgan materialni qaytara olishi uchun har bir vektor yonida manba identifikatori va asl ifodani saqlang.</p><p>Prefikslar vazifaning bir tomonidan olib tashlanadigan bezak yorliqlari emas. Ishlab chiqaruvchi ularni tashlash qidiruv sifatini pasaytirishini aytadi, chunki model shu tayyorlash bilan o‘qitilgan. Korpusni yaratishda va keyin so‘rov embeddingini olishda tanlangan rolni izchil qo‘llang.</p><h4>O‘rtacha pooling va normallangan vektorlar</h4><p>Rasmiy kodlash misoli diqqat niqobi yordamida to‘ldiruvchi pozitsiyalarni chiqarib tashlab, token ifodalarining o‘rtachasini oladi. So‘ng natijaviy vektorni normallaydi. Normallangan so‘rov va parcha vektorlari skalyar ko‘paytmasi nomzodlarni tartiblash uchun kosinus o‘xshashligini beradi. Bu o‘rtacha pooling usuli E5-Large-v2 ga tegishli; boshqa oiladagi birinchi token pooling usuli alohida tayyorlash tanlovidir.</p><p>1024 ta chiqish qiymati embeddingni tashkil qiladi; ular yozma javob yoki bevosita toifa yorlig‘i emas. Qidiruv bosqichi bu qiymatlarni mos korpus ifodalari bilan taqqoslaydi, keyingi bosqich esa tanlangan parcha matnidan foydalanadi. Vektorlar klasterlash yoki tasniflagich xususiyatlari bo‘lsa, har bir vektorning u xususiyatlarini ifodalayotgan kirish yozuvi bilan bog‘liqligini saqlang.</p><h4>Oyna ichidagi inglizcha matn</h4><p>Model kartasi faqat inglizcha matnni va uzun kirishlarni ko‘pi bilan 512 tokengacha kesishni belgilaydi. Har hujjat parchasi shu oyna ichida, jumladan rol prefiksi va foydali manba konteksti bilan, izchil bo‘lsin. Uzun qo‘llanmani alohida topilganda sarlavha, shart va ko‘rsatmalari tushuniladigan qismlar sifatida tayyorlash kerak.</p><p>Masalan, sozlamani tasvirlovchi bandga shu sozlama ta’rifi va keyingi gapdagi istisno kerak bo‘lishi mumkin. Faqat belgilar soniga qarab bo‘lish o‘rniga, shu kichik birlikni birga saqlang. Yangilanish to‘g‘ri materialni almashtirishi uchun asosiy hujjat va manba versiyasini parcha bilan saqlang.</p><h4>Reytingni qidiruv dalili sifatida o‘qing</h4><p>Ishlab chiqaruvchi kosinus ballari tartibi ularning mutlaq qiymatidan muhimroq ekanini tushuntiradi. Ballni parcha savolga javob berishining kalibrlangan ehtimoli deb talqin qilmaslik kerak. Nechta natijani saqlash yoki nomzodlar to‘plamini qayerda filtrlashni tanlashda javob borligi ma’lum parchalar va ularga yaqin aloqasiz materiallarni tekshiring.</p><p>Foydali chiqish — korpus yoki so‘rov ifodasi, semantik taqqoslash signali yoki boshqa vazifa uchun raqamli xususiyatlar. Aynan shu inglizcha Large v2 checkpointi E5-Base-v2 va Multilingual-E5-Large’dan alohida qoladi. O‘lchamlar tengligining o‘zi ularning vektorlarini hujjatlashtirilgan yagona embedding makoniga aylantirmaydi.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Ailoot da mavjud · kirish ma’lumotlari va usullar</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Embeddinglar</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Kirish</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Chiqish</dt><dd><code>embedding</code></dd></div><div><dt>Chiqish formati</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Maydon</th><th>Tur</th><th>Talab</th><th>Qiymatlar va cheklovlar</th><th>Standart</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>intfloat/e5-large-v2</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Elementlarning eng kam soni: 1</div><div>Shartlar: Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.</div><div>Kirish budjeti: Eng ko‘p: 512 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>float</div></td><td data-cell-label=\"Standart\" data-default=\"known\"><span class=\"card-cell-label\">Standart</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>integer</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Qo‘llab-quvvatlanmaydi</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Eng kam: 1</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>object</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: provider.options rad etildi</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Kirish materiallari birikmalari</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Usul parametrlari va hayot sikli</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parametr</th><th>Parametr talablari</th></tr></thead><tbody><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>content_type</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>application/json</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>required</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>model, input</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>input_shapes</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>encoding_format</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>float</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>dimensions_override</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>max_input_budget_units</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>512</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>multimodal_objects</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>material_types</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>text, token_ids</td></tr></tbody></table></div><p>Hayot sikli: <code>synchronous_json</code></p><p class=\"note-s\">Har bir vektorning indeksi va o‘lchamini saqlang.</p><p class=\"note-s\">Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Tariflar</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.</p><dl data-price-conditions><dt>Tezkor rejim</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">Tezkor rejim tanlanganda</span></dd><dt>Flex rejimi</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">flex rejimi tanlanganda</span></dd><dt>Maksimal fikrlash darajasi</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot tariflari</h4><h4>Sozlamalar bo‘yicha narxlar</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Sozlama</th><th>Tariflar jadvali</th><th>To‘lov</th></tr></thead><tbody><tr><th data-cell-label=\"Sozlama\" scope=\"row\"><span class=\"card-cell-label\">Sozlama</span>Tokenlar uchun bazaviy tariflar</th><td data-cell-label=\"Tariflar jadvali\"><span class=\"card-cell-label\">Tariflar jadvali</span>Asosiy tarif</td><td data-cell-label=\"To‘lov\"><span class=\"card-cell-label\">To‘lov</span><div><span>Kirish · Kirish tokenlari</span> <b>$0.00002</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Chiqish · Chiqish tokenlari</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · Embeddinglar</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-1765-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1765-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1765-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1765-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-1765-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1765-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;intfloat/e5-large-v2&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1765-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1765-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;intfloat/e5-large-v2&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Natijalarni olish va saqlash</h3><h4>Ailoot Ultra · Embeddinglar</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Barcha tayyor natijalarni saqlash</h5><p class=\"note-s\">Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.</p><p class=\"note-s\">Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Asl javobni tekshirish kerak bo‘lsa</h5><p class=\"note-s\">Embedding vazifalari uchun endpoint qo‘shilmaydi.</p></div><details class=\"card-about\" data-output-fields><summary>Javob maydonlari</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Maydon</th><th>Tur</th></tr></thead><tbody><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>integer</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>finite float[]</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Xatolar va keyingi qadamlar</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Javob</th><th>Shart</th><th>Keyingi qadam</th></tr></thead><tbody><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Taxalluslar</h3><h4>Model taxalluslari</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=intfloat%2Fe5-large-v2&amp;route=Ultra&amp;caller=intfloat%2Fe5-large-v2&amp;fmt=embeddings\"><code>intfloat/e5-large-v2</code></a></span></p><h4>Usul yo‘llari taxalluslari</h4><p class=\"note-s\">Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.</p><h4>Chaqirish uchun tegishli ID lar</h4><p class=\"note-s\"><a href=\"#m=intfloat%2Fe5-large-v2&amp;route=Ultra&amp;caller=intfloat%2Fe5-large-v2&amp;fmt=embeddings\"><code>intfloat/e5-large-v2</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "intfloat/e5-large-v2",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "intfloat/e5-large-v2",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|intfloat%2Fe5-large-v2",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "intfloat/e5-large-v2"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "UTF8 baytlari",
                  "token_unit": "Tokenlar massivi elementlari"
                },
                "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rad etildi",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Har bir vektorning indeksi va o‘lchamini saqlang.",
            "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.00002000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000002",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"intfloat/e5-large-v2\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='intfloat/e5-large-v2',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.02,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00002000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000002",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "intfloat/e5-large-v2",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.00002000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000002",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    }
  ],
  "model_id": "intfloat/e5-large-v2",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "751c884fc384034764fc1f95bd8491f56a06578af0d545fc5faa1b709701ce31",
    "input_html_sha256": "43668a76cb600cd4629eff2afc64a3fa5bb9e83a185a2a2a8d27b07b2a3dbe38",
    "private_audit_sha256": "445faf8c26202a5c1ca15cfeb266cd044a2d139c9757039af6c3b5235c405eb8",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/intfloat-e5-large-v2/",
    "description": "intfloat E5-Large-v2 inglizcha so‘rovlar va parchalarni 1024 o‘lchamli vektorlarga kodlaydi. Uning 24 qatlamli modeli qidiruv, semantik o‘xshashlik va raqamli matn xususiyatlari uchun query/passage rollari, o‘rtacha pooling va 512 tokenli kirish chegarasidan foydalanadi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/intfloat-e5-large-v2/index.md",
    "title": "Intfloat: E5-Large-v2 — Ailoot",
    "url": "https://ailoot.ai/models/uz/intfloat-e5-large-v2/"
  },
  "rawModel": {
    "about": "E5-Large-v2 — intfloat’ning 1024 o‘lchamli chiqishga ega yigirma to‘rt qatlamli inglizcha matn embedding modeli. U so‘rov va parchalarni qidiruv hamda boshqa embedding vazifalari uchun raqamli ifodalashga mo‘ljallangan. E5 tayyorlash qoidasi uning o‘qitilishining bir qismi: kirish matnida rol prefiksi bo‘ladi va kodlangan token ifodalari shu matn uchun bitta vektorga birlashtiriladi.\n\n### So‘rov va parcha rollarini ochiq ko‘rsating\n\nAsimmetrik qidiruvda savolni “query:” bilan, korpus matnini “passage:” bilan boshlang. Bu rollar foydalanuvchi izlayotgan narsani unga javob berishi mumkin bo‘lgan materialdan ajratadi. Simmetrik taqqoslash, qayta ifodalarni qidirish, klasterlash yoki tasniflash xususiyatlari uchun ishlab chiqaruvchi embedding qilinayotgan matnlarda “query:”ni tavsiya qiladi.\n\nAmaliy to‘plam inglizcha qo‘llab-quvvatlash izohlarini indekslashi mumkin. “query: What should I check before replacing the connector?” kabi savol tayyorlang va uni tekshiruvlar hamda istisnolarni tavsiflovchi manba parchalari bilan juftlang. Parcha matni o‘z tegishli prefiksini saqlaydi. Natija foydalanuvchi o‘qishi kerak bo‘lgan materialni qaytara olishi uchun har bir vektor yonida manba identifikatori va asl ifodani saqlang.\n\nPrefikslar vazifaning bir tomonidan olib tashlanadigan bezak yorliqlari emas. Ishlab chiqaruvchi ularni tashlash qidiruv sifatini pasaytirishini aytadi, chunki model shu tayyorlash bilan o‘qitilgan. Korpusni yaratishda va keyin so‘rov embeddingini olishda tanlangan rolni izchil qo‘llang.\n\n### O‘rtacha pooling va normallangan vektorlar\n\nRasmiy kodlash misoli diqqat niqobi yordamida to‘ldiruvchi pozitsiyalarni chiqarib tashlab, token ifodalarining o‘rtachasini oladi. So‘ng natijaviy vektorni normallaydi. Normallangan so‘rov va parcha vektorlari skalyar ko‘paytmasi nomzodlarni tartiblash uchun kosinus o‘xshashligini beradi. Bu o‘rtacha pooling usuli E5-Large-v2 ga tegishli; boshqa oiladagi birinchi token pooling usuli alohida tayyorlash tanlovidir.\n\n1024 ta chiqish qiymati embeddingni tashkil qiladi; ular yozma javob yoki bevosita toifa yorlig‘i emas. Qidiruv bosqichi bu qiymatlarni mos korpus ifodalari bilan taqqoslaydi, keyingi bosqich esa tanlangan parcha matnidan foydalanadi. Vektorlar klasterlash yoki tasniflagich xususiyatlari bo‘lsa, har bir vektorning u xususiyatlarini ifodalayotgan kirish yozuvi bilan bog‘liqligini saqlang.\n\n### Oyna ichidagi inglizcha matn\n\nModel kartasi faqat inglizcha matnni va uzun kirishlarni ko‘pi bilan 512 tokengacha kesishni belgilaydi. Har hujjat parchasi shu oyna ichida, jumladan rol prefiksi va foydali manba konteksti bilan, izchil bo‘lsin. Uzun qo‘llanmani alohida topilganda sarlavha, shart va ko‘rsatmalari tushuniladigan qismlar sifatida tayyorlash kerak.\n\nMasalan, sozlamani tasvirlovchi bandga shu sozlama ta’rifi va keyingi gapdagi istisno kerak bo‘lishi mumkin. Faqat belgilar soniga qarab bo‘lish o‘rniga, shu kichik birlikni birga saqlang. Yangilanish to‘g‘ri materialni almashtirishi uchun asosiy hujjat va manba versiyasini parcha bilan saqlang.\n\n### Reytingni qidiruv dalili sifatida o‘qing\n\nIshlab chiqaruvchi kosinus ballari tartibi ularning mutlaq qiymatidan muhimroq ekanini tushuntiradi. Ballni parcha savolga javob berishining kalibrlangan ehtimoli deb talqin qilmaslik kerak. Nechta natijani saqlash yoki nomzodlar to‘plamini qayerda filtrlashni tanlashda javob borligi ma’lum parchalar va ularga yaqin aloqasiz materiallarni tekshiring.\n\nFoydali chiqish — korpus yoki so‘rov ifodasi, semantik taqqoslash signali yoki boshqa vazifa uchun raqamli xususiyatlar. Aynan shu inglizcha Large v2 checkpointi E5-Base-v2 va Multilingual-E5-Large’dan alohida qoladi. O‘lchamlar tengligining o‘zi ularning vektorlarini hujjatlashtirilgan yagona embedding makoniga aylantirmaydi.",
    "alias_of": null,
    "aliases": [],
    "author": "Intfloat",
    "available": null,
    "canonical_model": "intfloat/e5-large-v2",
    "capabilities": {
      "context_length": 512,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.00002000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000002",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan."
      }
    ],
    "ctx": null,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "intfloat/e5-large-v2",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "intfloat/e5-large-v2"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "UTF8 baytlari",
                  "token_unit": "Tokenlar massivi elementlari"
                },
                "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rad etildi",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Har bir vektorning indeksi va o‘lchamini saqlang.",
            "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.00002000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000002",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"intfloat/e5-large-v2\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='intfloat/e5-large-v2',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.02,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00002000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000002",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "intfloat/e5-large-v2",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.00002000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000002",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "intfloat/e5-large-v2",
        "request_model_id": "intfloat/e5-large-v2",
        "route_label": "Ultra"
      }
    ],
    "name": "Intfloat: E5-Large-v2",
    "price": {
      "in": 0.02,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.02,
      "sort_out": 0,
      "varies": false
    },
    "public_id": "intfloat/e5-large-v2",
    "short": "intfloat E5-Large-v2 inglizcha so‘rovlar va parchalarni 1024 o‘lchamli vektorlarga kodlaydi. Uning 24 qatlamli modeli qidiruv, semantik o‘xshashlik va raqamli matn xususiyatlari uchun query/passage rollari, o‘rtacha pooling va 512 tokenli kirish chegarasidan foydalanadi."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
        {
          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "intfloat E5-Large-v2 inglizcha so‘rovlar va parchalarni 1024 o‘lchamli vektorlarga kodlaydi. Uning 24 qatlamli modeli qidiruv, semantik o‘xshashlik va raqamli matn xususiyatlari uchun query/passage rollari, o‘rtacha pooling va 512 tokenli kirish chegarasidan foydalanadi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "75e39a346dac5841fa1f50f19977ae4daf0f33d88e7c1404a7b554601cdd8d68",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "64cf20e114e6201ac7bf132847aa8611f672e18feaa1e0b940a447dcd13600a7"
}