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  "aboutHtml": "<p>Voyage Context 3 hujjatlarni qidirish uchun kontekst hisobga olingan bo‘lak embeddinglarini yaratadi. Har bir bo‘lak o‘z vektorini oladi, model esa ayni hujjatdan berilgan boshqa bo‘laklarni ham hisobga oladi. Bu qidiruv tizimiga mahalliy parcha bilan birga hujjatning kengroq kontekstini, masalan, manbaning boshqa joyida tanishtirilgan obyekt yoki mavzuni ifodalash imkonini beradi.</p><p>Har bir hujjatni o‘qiladigan matn bo‘laklarining tartiblangan ketma-ketligi sifatida tayyorlang. Bitta hujjat bo‘laklarini birga tuting va asl tartibini saqlang. Ishlab chiqaruvchining kontekstli jarayonida har bir ichki guruh bitta manbaning bo‘laklarini, alohida guruhlar esa alohida manbalarni o‘z ichiga oladi. Bu guruhlash kirish ma’nosining bir qismi: u modelga qaysi atrof matni ifodalanayotgan hujjatga tegishli ekanini bildiradi.</p><p>Foydali holat — boshida kompaniya nomi aytilgan, keyingi xatboshida esa uning o‘sishi tushuntirilgan hisobot. Shu kompaniyaning o‘sishini qidirishda aniq xatboshi ham, hujjatni aniqlovchi ma’lumot ham kerak. Natija parchani ko‘rsatib, to‘liq manbaga havola berishi uchun yaratilgan vektor bilan asl bo‘lak matni va uning asosiy hujjati metama’lumotlarini saqlang.</p><p>Ishlab chiqaruvchining ma’lumotnomasida bitta so‘rov bo‘yicha 120 000 tagacha token, ko‘pi bilan 1 000 kirish guruhi va jami 16 000 bo‘lak hujjatlashtirilgan. Model qo‘llanmasi alohida ravishda har bir bo‘lak uchun 32 000 tokenli oynani ko‘rsatadi. Material tayyorlashda bu ko‘lamlarni ajrating: bitta bo‘lak chegarasi va butun so‘rov chegarasi turli kirish hajmlarini bildiradi. Har bir bo‘lak ayni so‘rovda o‘z maksimumini to‘ldira oladi deb o‘ylash o‘rniga, manbani bo‘lgandan keyin umumiy hajmni tekshiring.</p><p>Hujjat qidiruvi uchun guruhlangan parchalarni hujjat kirishi sifatida belgilang. Har bir qidiruv savolini alohida so‘rov guruhi qilib tayyorlang. Ishlab chiqaruvchi berilgan hujjat bo‘laklari o‘zaro ustma-ust tushmasligini tavsiya qiladi; bu kontekstli ifodaga har bir parchada matnni takrorlamasdan atrof ma’lumotini olib yurish imkonini beradi. Indeksni yozishda hujjat guruhi, bo‘lak o‘rni va qaytarilgan vektor o‘rtasidagi moslikni saqlang.</p><p>Voyage Context 3 suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik chiqish tanlovlari bilan 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Indeks ifodasini tanlang va uni ayni manba hujjatlari hamda savollarda baholang. So‘rov ifodasi va saqlangan vektorlar izchil qolishi uchun tanlangan o‘lcham va ma’lumot turini indeks bilan birga saqlang.</p><p>Yaratilgan vektorlardan hisobotlar, ilmiy maqolalar, shartnomalar yoki texnik qo‘llanmalar bilan ishlaydigan yordamchining parcha qidiruvi, hujjat topish yoki qidiruv bosqichida foydalaning. Natija aniq parchani ko‘rsatishi, manba havolasi esa hujjatning boshqa joyidagi ta’rif va shartlarga kirish berishi mumkin. Hujjat versiyalari va ruxsatlarini vektorlar bilan birga ilova metama’lumotlari sifatida saqlang.</p><p>Mahalliy detalni ham, hujjat miqyosidagi kontekstni ham talab qiladigan savollarni sinang. Turli manbalardagi o‘xshash parchalar, faqat boshida tanishtirilgan nomlangan obyektlar va dalillari bir nechta bo‘limni qamrab oladigan savollarni kiriting. Kutilgan bo‘lak topilishini va ilova uni talqin qilish uchun yetarli atrof matnini ko‘rsatishini tekshiring. Manba o‘zgarganda, kontekstli bo‘laklar yagona izchil versiyaga mos kelishi uchun tegishli hujjat guruhini maqsadli ravishda qayta yarating.</p>",
  "aboutText": "Voyage Context 3 hujjatlarni qidirish uchun kontekst hisobga olingan bo‘lak embeddinglarini yaratadi. Har bir bo‘lak o‘z vektorini oladi, model esa ayni hujjatdan berilgan boshqa bo‘laklarni ham hisobga oladi. Bu qidiruv tizimiga mahalliy parcha bilan birga hujjatning kengroq kontekstini, masalan, manbaning boshqa joyida tanishtirilgan obyekt yoki mavzuni ifodalash imkonini beradi.\n\nHar bir hujjatni o‘qiladigan matn bo‘laklarining tartiblangan ketma-ketligi sifatida tayyorlang. Bitta hujjat bo‘laklarini birga tuting va asl tartibini saqlang. Ishlab chiqaruvchining kontekstli jarayonida har bir ichki guruh bitta manbaning bo‘laklarini, alohida guruhlar esa alohida manbalarni o‘z ichiga oladi. Bu guruhlash kirish ma’nosining bir qismi: u modelga qaysi atrof matni ifodalanayotgan hujjatga tegishli ekanini bildiradi.\n\nFoydali holat — boshida kompaniya nomi aytilgan, keyingi xatboshida esa uning o‘sishi tushuntirilgan hisobot. Shu kompaniyaning o‘sishini qidirishda aniq xatboshi ham, hujjatni aniqlovchi ma’lumot ham kerak. Natija parchani ko‘rsatib, to‘liq manbaga havola berishi uchun yaratilgan vektor bilan asl bo‘lak matni va uning asosiy hujjati metama’lumotlarini saqlang.\n\nIshlab chiqaruvchining ma’lumotnomasida bitta so‘rov bo‘yicha 120 000 tagacha token, ko‘pi bilan 1 000 kirish guruhi va jami 16 000 bo‘lak hujjatlashtirilgan. Model qo‘llanmasi alohida ravishda har bir bo‘lak uchun 32 000 tokenli oynani ko‘rsatadi. Material tayyorlashda bu ko‘lamlarni ajrating: bitta bo‘lak chegarasi va butun so‘rov chegarasi turli kirish hajmlarini bildiradi. Har bir bo‘lak ayni so‘rovda o‘z maksimumini to‘ldira oladi deb o‘ylash o‘rniga, manbani bo‘lgandan keyin umumiy hajmni tekshiring.\n\nHujjat qidiruvi uchun guruhlangan parchalarni hujjat kirishi sifatida belgilang. Har bir qidiruv savolini alohida so‘rov guruhi qilib tayyorlang. Ishlab chiqaruvchi berilgan hujjat bo‘laklari o‘zaro ustma-ust tushmasligini tavsiya qiladi; bu kontekstli ifodaga har bir parchada matnni takrorlamasdan atrof ma’lumotini olib yurish imkonini beradi. Indeksni yozishda hujjat guruhi, bo‘lak o‘rni va qaytarilgan vektor o‘rtasidagi moslikni saqlang.\n\nVoyage Context 3 suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik chiqish tanlovlari bilan 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Indeks ifodasini tanlang va uni ayni manba hujjatlari hamda savollarda baholang. So‘rov ifodasi va saqlangan vektorlar izchil qolishi uchun tanlangan o‘lcham va ma’lumot turini indeks bilan birga saqlang.\n\nYaratilgan vektorlardan hisobotlar, ilmiy maqolalar, shartnomalar yoki texnik qo‘llanmalar bilan ishlaydigan yordamchining parcha qidiruvi, hujjat topish yoki qidiruv bosqichida foydalaning. Natija aniq parchani ko‘rsatishi, manba havolasi esa hujjatning boshqa joyidagi ta’rif va shartlarga kirish berishi mumkin. Hujjat versiyalari va ruxsatlarini vektorlar bilan birga ilova metama’lumotlari sifatida saqlang.\n\nMahalliy detalni ham, hujjat miqyosidagi kontekstni ham talab qiladigan savollarni sinang. Turli manbalardagi o‘xshash parchalar, faqat boshida tanishtirilgan nomlangan obyektlar va dalillari bir nechta bo‘limni qamrab oladigan savollarni kiriting. Kutilgan bo‘lak topilishini va ilova uni talqin qilish uchun yetarli atrof matnini ko‘rsatishini tekshiring. Manba o‘zgarganda, kontekstli bo‘laklar yagona izchil versiyaga mos kelishi uchun tegishli hujjat guruhini maqsadli ravishda qayta yarating.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Voyage AI: Voyage Context 3</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Voyage AI /</span><code>voyage-context-3</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|voyage-context-3\">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\">Voyage Context 3 hujjatning boshqa bo‘laklari kontekstini hisobga olgan holda har bir bo‘lak uchun vektor yaratadi. U tanlanadigan o‘lchamlar va kvantlangan embeddinglar bilan uzun hujjatlarda aniq parchalarni topishni qo‘llab-quvvatlaydi.</p><dl class=\"card-io\" data-card-io><div><dt>Qabul qilinadigan kirish ma’lumotlari</dt><dd>Matn</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>Voyage Context 3 hujjatlarni qidirish uchun kontekst hisobga olingan bo‘lak embeddinglarini yaratadi. Har bir bo‘lak o‘z vektorini oladi, model esa ayni hujjatdan berilgan boshqa bo‘laklarni ham hisobga oladi. Bu qidiruv tizimiga mahalliy parcha bilan birga hujjatning kengroq kontekstini, masalan, manbaning boshqa joyida tanishtirilgan obyekt yoki mavzuni ifodalash imkonini beradi.</p><p>Har bir hujjatni o‘qiladigan matn bo‘laklarining tartiblangan ketma-ketligi sifatida tayyorlang. Bitta hujjat bo‘laklarini birga tuting va asl tartibini saqlang. Ishlab chiqaruvchining kontekstli jarayonida har bir ichki guruh bitta manbaning bo‘laklarini, alohida guruhlar esa alohida manbalarni o‘z ichiga oladi. Bu guruhlash kirish ma’nosining bir qismi: u modelga qaysi atrof matni ifodalanayotgan hujjatga tegishli ekanini bildiradi.</p><p>Foydali holat — boshida kompaniya nomi aytilgan, keyingi xatboshida esa uning o‘sishi tushuntirilgan hisobot. Shu kompaniyaning o‘sishini qidirishda aniq xatboshi ham, hujjatni aniqlovchi ma’lumot ham kerak. Natija parchani ko‘rsatib, to‘liq manbaga havola berishi uchun yaratilgan vektor bilan asl bo‘lak matni va uning asosiy hujjati metama’lumotlarini saqlang.</p><p>Ishlab chiqaruvchining ma’lumotnomasida bitta so‘rov bo‘yicha 120 000 tagacha token, ko‘pi bilan 1 000 kirish guruhi va jami 16 000 bo‘lak hujjatlashtirilgan. Model qo‘llanmasi alohida ravishda har bir bo‘lak uchun 32 000 tokenli oynani ko‘rsatadi. Material tayyorlashda bu ko‘lamlarni ajrating: bitta bo‘lak chegarasi va butun so‘rov chegarasi turli kirish hajmlarini bildiradi. Har bir bo‘lak ayni so‘rovda o‘z maksimumini to‘ldira oladi deb o‘ylash o‘rniga, manbani bo‘lgandan keyin umumiy hajmni tekshiring.</p><p>Hujjat qidiruvi uchun guruhlangan parchalarni hujjat kirishi sifatida belgilang. Har bir qidiruv savolini alohida so‘rov guruhi qilib tayyorlang. Ishlab chiqaruvchi berilgan hujjat bo‘laklari o‘zaro ustma-ust tushmasligini tavsiya qiladi; bu kontekstli ifodaga har bir parchada matnni takrorlamasdan atrof ma’lumotini olib yurish imkonini beradi. Indeksni yozishda hujjat guruhi, bo‘lak o‘rni va qaytarilgan vektor o‘rtasidagi moslikni saqlang.</p><p>Voyage Context 3 suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik chiqish tanlovlari bilan 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Indeks ifodasini tanlang va uni ayni manba hujjatlari hamda savollarda baholang. So‘rov ifodasi va saqlangan vektorlar izchil qolishi uchun tanlangan o‘lcham va ma’lumot turini indeks bilan birga saqlang.</p><p>Yaratilgan vektorlardan hisobotlar, ilmiy maqolalar, shartnomalar yoki texnik qo‘llanmalar bilan ishlaydigan yordamchining parcha qidiruvi, hujjat topish yoki qidiruv bosqichida foydalaning. Natija aniq parchani ko‘rsatishi, manba havolasi esa hujjatning boshqa joyidagi ta’rif va shartlarga kirish berishi mumkin. Hujjat versiyalari va ruxsatlarini vektorlar bilan birga ilova metama’lumotlari sifatida saqlang.</p><p>Mahalliy detalni ham, hujjat miqyosidagi kontekstni ham talab qiladigan savollarni sinang. Turli manbalardagi o‘xshash parchalar, faqat boshida tanishtirilgan nomlangan obyektlar va dalillari bir nechta bo‘limni qamrab oladigan savollarni kiriting. Kutilgan bo‘lak topilishini va ilova uni talqin qilish uchun yetarli atrof matnini ko‘rsatishini tekshiring. Manba o‘zgarganda, kontekstli bo‘laklar yagona izchil versiyaga mos kelishi uchun tegishli hujjat guruhini maqsadli ravishda qayta yarating.</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></dd></div><div><dt>Chiqish</dt><dd><code>embedding</code></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><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>voyage-context-3</div><div>Shartlar: Ailoot ning aniq model tanlagichidan foydalaning.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr><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 or array&lt;string&gt;</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>Shartlar: Matn yoki matnlar ro‘yxati; qaytarilgan har bir data elementi va uning indeksiga bittadan vektor saqlang.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr></tbody></table></div><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>optional</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span></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</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>conditions</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>Tanlangan model bilan matn yuboring; qaytarilgan har bir vektor va uning indeksini saqlang.</td></tr></tbody></table></div><p>Hayot sikli: <code>synchronous</code></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>$0.0004</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Chiqish · Chiqish tokenlari</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · Embeddinglar</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-2791-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2791-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · Barcha vektorlar va foydalanish ma’lumotlarini saqlang</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2791-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2791-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\ncat &gt; request.json &lt;&lt;&#39;JSON&#39;\n{\n  &quot;model&quot;: &quot;voyage-context-3&quot;,\n  &quot;input&quot;: &quot;The quick brown fox jumps over the lazy dog&quot;\n}\nJSON\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 embeddings.json\n# Keep every data item and its index, the full usage object and response headers.\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Natijalarni olish va saqlash</h3></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=voyage-context-3&amp;route=Ultra&amp;caller=voyage-context-3&amp;fmt=embeddings\"><code>voyage-context-3</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=voyage-context-3&amp;route=Ultra&amp;caller=voyage-context-3&amp;fmt=embeddings\"><code>voyage-context-3</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "voyage-context-3",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "voyage-context-3",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|voyage-context-3",
      "ordinal": 0,
      "rawMethod": {
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Save the complete JSON response, every vector with its index, all usage fields and request headers.",
            "lifecycle": {
              "content": [],
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "mode": "synchronous",
              "status": [],
              "unknown": "Solishtirib tekshirish uchun asl javobni va X-Client-Request-ID qiymatini saqlang. Noaniq javob ayni yaratish so‘rovini qayta yuborishga ruxsat bermaydi.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "To‘liq JSON javobini, har bir vektorni o‘z indeksi bilan, foydalanishga oid barcha maydonlarni va so‘rov sarlavhalarini saqlang.",
              "every_actual_output": true,
              "family": "embedding",
              "fields": [
                {
                  "name": "object",
                  "type": "string"
                },
                {
                  "name": "model",
                  "type": "string"
                },
                {
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "name": "data[].embedding",
                  "type": "array<number>"
                },
                {
                  "name": "usage",
                  "type": "object"
                },
                {
                  "name": "usage.prompt_tokens",
                  "type": "integer"
                },
                {
                  "name": "usage.total_tokens",
                  "type": "integer"
                }
              ]
            },
            "parameters": [
              {
                "conditions": "Ailoot ning aniq model tanlagichidan foydalaning.",
                "default_known": false,
                "enum": [
                  "voyage-context-3"
                ],
                "enum_known": true,
                "maximum_length": null,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "conditions": "Matn yoki matnlar ro‘yxati; qaytarilgan har bir data elementi va uning indeksiga bittadan vektor saqlang.",
                "default_known": false,
                "enum_known": false,
                "maximum_length": null,
                "name": "input",
                "oneOf": [
                  {
                    "minLength": 1,
                    "type": "string"
                  },
                  {
                    "items": {
                      "minLength": 1,
                      "type": "string"
                    },
                    "minItems": 1,
                    "type": "array"
                  }
                ],
                "required": true,
                "type": "string or array<string>"
              }
            ],
            "request_schema": {
              "additionalProperties": true,
              "properties": {
                "input": {
                  "oneOf": [
                    {
                      "minLength": 1,
                      "type": "string"
                    },
                    {
                      "items": {
                        "minLength": 1,
                        "type": "string"
                      },
                      "minItems": 1,
                      "type": "array"
                    }
                  ]
                },
                "model": {
                  "enum": [
                    "voyage-context-3"
                  ],
                  "type": "string"
                }
              },
              "required": [
                "model",
                "input"
              ],
              "type": "object"
            },
            "schema_template": {
              "actual_file_preparation_required": false,
              "body": {
                "input": "The quick brown fox jumps over the lazy dog",
                "model": "voyage-context-3"
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [
              "Tanlangan model bilan matn yuboring; qaytarilgan har bir vektor va uning indeksini saqlang."
            ],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [],
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "content": [],
            "create": {
              "http": "POST",
              "path": "/v1/embeddings"
            },
            "mode": "synchronous",
            "status": [],
            "unknown": "Keep the original response and X-Client-Request-ID for reconciliation. An ambiguous response is not permission to submit the same creation again.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "output": {
            "all_results": "Save the complete JSON response, every vector with its index, all usage fields and request headers.",
            "every_actual_output": true,
            "family": "embedding",
            "fields": [
              {
                "name": "object",
                "type": "string"
              },
              {
                "name": "model",
                "type": "string"
              },
              {
                "name": "data[].index",
                "type": "integer"
              },
              {
                "name": "data[].embedding",
                "type": "array<number>"
              },
              {
                "name": "usage",
                "type": "object"
              },
              {
                "name": "usage.prompt_tokens",
                "type": "integer"
              },
              {
                "name": "usage.total_tokens",
                "type": "integer"
              }
            ]
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.0004000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000004",
                "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}\"\ncat > request.json <<'JSON'\n{\n  \"model\": \"voyage-context-3\",\n  \"input\": \"The quick brown fox jumps over the lazy dog\"\n}\nJSON\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 embeddings.json\n# Keep every data item and its index, the full usage object and response headers.\n",
            "label": "curl · save all vectors and usage",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "http": "POST",
        "in": 0.4,
        "input": [
          "text"
        ],
        "label": "Embeddinglar",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "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": "0.0004000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000004",
                "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": "voyage-context-3",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0004000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000004",
            "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": "voyage-context-3",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "758219a4395a5485470256953e72098949e82b9e6f24964a38657de0aa838d65",
    "input_html_sha256": "396f104a49310965c66cea8d377aad86ddbbf920cce8ea1bf3ea308844d11c95",
    "private_audit_sha256": "d25528cd8356bb0ecfbc187032533233b95385d2f01e1b686cb2130edf0f8ecb",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/voyage-context-3/",
    "description": "Voyage Context 3 hujjatning boshqa bo‘laklari kontekstini hisobga olgan holda har bir bo‘lak uchun vektor yaratadi. U tanlanadigan o‘lchamlar va kvantlangan embeddinglar bilan uzun hujjatlarda aniq parchalarni topishni qo‘llab-quvvatlaydi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/voyage-context-3/index.md",
    "title": "Voyage AI: Voyage Context 3 — Ailoot",
    "url": "https://ailoot.ai/models/uz/voyage-context-3/"
  },
  "rawModel": {
    "about": "Voyage Context 3 hujjatlarni qidirish uchun kontekst hisobga olingan bo‘lak embeddinglarini yaratadi. Har bir bo‘lak o‘z vektorini oladi, model esa ayni hujjatdan berilgan boshqa bo‘laklarni ham hisobga oladi. Bu qidiruv tizimiga mahalliy parcha bilan birga hujjatning kengroq kontekstini, masalan, manbaning boshqa joyida tanishtirilgan obyekt yoki mavzuni ifodalash imkonini beradi.\n\nHar bir hujjatni o‘qiladigan matn bo‘laklarining tartiblangan ketma-ketligi sifatida tayyorlang. Bitta hujjat bo‘laklarini birga tuting va asl tartibini saqlang. Ishlab chiqaruvchining kontekstli jarayonida har bir ichki guruh bitta manbaning bo‘laklarini, alohida guruhlar esa alohida manbalarni o‘z ichiga oladi. Bu guruhlash kirish ma’nosining bir qismi: u modelga qaysi atrof matni ifodalanayotgan hujjatga tegishli ekanini bildiradi.\n\nFoydali holat — boshida kompaniya nomi aytilgan, keyingi xatboshida esa uning o‘sishi tushuntirilgan hisobot. Shu kompaniyaning o‘sishini qidirishda aniq xatboshi ham, hujjatni aniqlovchi ma’lumot ham kerak. Natija parchani ko‘rsatib, to‘liq manbaga havola berishi uchun yaratilgan vektor bilan asl bo‘lak matni va uning asosiy hujjati metama’lumotlarini saqlang.\n\nIshlab chiqaruvchining ma’lumotnomasida bitta so‘rov bo‘yicha 120 000 tagacha token, ko‘pi bilan 1 000 kirish guruhi va jami 16 000 bo‘lak hujjatlashtirilgan. Model qo‘llanmasi alohida ravishda har bir bo‘lak uchun 32 000 tokenli oynani ko‘rsatadi. Material tayyorlashda bu ko‘lamlarni ajrating: bitta bo‘lak chegarasi va butun so‘rov chegarasi turli kirish hajmlarini bildiradi. Har bir bo‘lak ayni so‘rovda o‘z maksimumini to‘ldira oladi deb o‘ylash o‘rniga, manbani bo‘lgandan keyin umumiy hajmni tekshiring.\n\nHujjat qidiruvi uchun guruhlangan parchalarni hujjat kirishi sifatida belgilang. Har bir qidiruv savolini alohida so‘rov guruhi qilib tayyorlang. Ishlab chiqaruvchi berilgan hujjat bo‘laklari o‘zaro ustma-ust tushmasligini tavsiya qiladi; bu kontekstli ifodaga har bir parchada matnni takrorlamasdan atrof ma’lumotini olib yurish imkonini beradi. Indeksni yozishda hujjat guruhi, bo‘lak o‘rni va qaytarilgan vektor o‘rtasidagi moslikni saqlang.\n\nVoyage Context 3 suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik chiqish tanlovlari bilan 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Indeks ifodasini tanlang va uni ayni manba hujjatlari hamda savollarda baholang. So‘rov ifodasi va saqlangan vektorlar izchil qolishi uchun tanlangan o‘lcham va ma’lumot turini indeks bilan birga saqlang.\n\nYaratilgan vektorlardan hisobotlar, ilmiy maqolalar, shartnomalar yoki texnik qo‘llanmalar bilan ishlaydigan yordamchining parcha qidiruvi, hujjat topish yoki qidiruv bosqichida foydalaning. Natija aniq parchani ko‘rsatishi, manba havolasi esa hujjatning boshqa joyidagi ta’rif va shartlarga kirish berishi mumkin. Hujjat versiyalari va ruxsatlarini vektorlar bilan birga ilova metama’lumotlari sifatida saqlang.\n\nMahalliy detalni ham, hujjat miqyosidagi kontekstni ham talab qiladigan savollarni sinang. Turli manbalardagi o‘xshash parchalar, faqat boshida tanishtirilgan nomlangan obyektlar va dalillari bir nechta bo‘limni qamrab oladigan savollarni kiriting. Kutilgan bo‘lak topilishini va ilova uni talqin qilish uchun yetarli atrof matnini ko‘rsatishini tekshiring. Manba o‘zgarganda, kontekstli bo‘laklar yagona izchil versiyaga mos kelishi uchun tegishli hujjat guruhini maqsadli ravishda qayta yarating.",
    "about_sha256": "cbe672f81d248e79c90f9384b4d5a3ff311043f96a3b07b7eb8b4ede52d2cd43",
    "alias_of": null,
    "aliases": [],
    "author": "Voyage AI",
    "capabilities": {
      "context_length": 120000,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.0004000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000004",
            "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,
    "display_name": "Voyage AI: Voyage Context 3",
    "formats": [
      "embeddings"
    ],
    "id": "voyage-context-3",
    "input": [
      "text"
    ],
    "kind": "embedding",
    "locale": "en",
    "max_out": null,
    "methods": [
      {
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Save the complete JSON response, every vector with its index, all usage fields and request headers.",
            "lifecycle": {
              "content": [],
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "mode": "synchronous",
              "status": [],
              "unknown": "Solishtirib tekshirish uchun asl javobni va X-Client-Request-ID qiymatini saqlang. Noaniq javob ayni yaratish so‘rovini qayta yuborishga ruxsat bermaydi.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "To‘liq JSON javobini, har bir vektorni o‘z indeksi bilan, foydalanishga oid barcha maydonlarni va so‘rov sarlavhalarini saqlang.",
              "every_actual_output": true,
              "family": "embedding",
              "fields": [
                {
                  "name": "object",
                  "type": "string"
                },
                {
                  "name": "model",
                  "type": "string"
                },
                {
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "name": "data[].embedding",
                  "type": "array<number>"
                },
                {
                  "name": "usage",
                  "type": "object"
                },
                {
                  "name": "usage.prompt_tokens",
                  "type": "integer"
                },
                {
                  "name": "usage.total_tokens",
                  "type": "integer"
                }
              ]
            },
            "parameters": [
              {
                "conditions": "Ailoot ning aniq model tanlagichidan foydalaning.",
                "default_known": false,
                "enum": [
                  "voyage-context-3"
                ],
                "enum_known": true,
                "maximum_length": null,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "conditions": "Matn yoki matnlar ro‘yxati; qaytarilgan har bir data elementi va uning indeksiga bittadan vektor saqlang.",
                "default_known": false,
                "enum_known": false,
                "maximum_length": null,
                "name": "input",
                "oneOf": [
                  {
                    "minLength": 1,
                    "type": "string"
                  },
                  {
                    "items": {
                      "minLength": 1,
                      "type": "string"
                    },
                    "minItems": 1,
                    "type": "array"
                  }
                ],
                "required": true,
                "type": "string or array<string>"
              }
            ],
            "request_schema": {
              "additionalProperties": true,
              "properties": {
                "input": {
                  "oneOf": [
                    {
                      "minLength": 1,
                      "type": "string"
                    },
                    {
                      "items": {
                        "minLength": 1,
                        "type": "string"
                      },
                      "minItems": 1,
                      "type": "array"
                    }
                  ]
                },
                "model": {
                  "enum": [
                    "voyage-context-3"
                  ],
                  "type": "string"
                }
              },
              "required": [
                "model",
                "input"
              ],
              "type": "object"
            },
            "schema_template": {
              "actual_file_preparation_required": false,
              "body": {
                "input": "The quick brown fox jumps over the lazy dog",
                "model": "voyage-context-3"
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [
              "Tanlangan model bilan matn yuboring; qaytarilgan har bir vektor va uning indeksini saqlang."
            ],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [],
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "content": [],
            "create": {
              "http": "POST",
              "path": "/v1/embeddings"
            },
            "mode": "synchronous",
            "status": [],
            "unknown": "Keep the original response and X-Client-Request-ID for reconciliation. An ambiguous response is not permission to submit the same creation again.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "output": {
            "all_results": "Save the complete JSON response, every vector with its index, all usage fields and request headers.",
            "every_actual_output": true,
            "family": "embedding",
            "fields": [
              {
                "name": "object",
                "type": "string"
              },
              {
                "name": "model",
                "type": "string"
              },
              {
                "name": "data[].index",
                "type": "integer"
              },
              {
                "name": "data[].embedding",
                "type": "array<number>"
              },
              {
                "name": "usage",
                "type": "object"
              },
              {
                "name": "usage.prompt_tokens",
                "type": "integer"
              },
              {
                "name": "usage.total_tokens",
                "type": "integer"
              }
            ]
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.0004000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000004",
                "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}\"\ncat > request.json <<'JSON'\n{\n  \"model\": \"voyage-context-3\",\n  \"input\": \"The quick brown fox jumps over the lazy dog\"\n}\nJSON\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 embeddings.json\n# Keep every data item and its index, the full usage object and response headers.\n",
            "label": "curl · save all vectors and usage",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "http": "POST",
        "in": 0.4,
        "input": [
          "text"
        ],
        "label": "Embeddinglar",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "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": "0.0004000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000004",
                "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": "voyage-context-3",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0004000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000004",
            "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": "voyage-context-3",
        "request_model_id": "voyage-context-3",
        "route_label": "Ultra"
      }
    ],
    "name": "Voyage AI: Voyage Context 3",
    "price": {
      "in": 0.4,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.4,
      "sort_out": 0,
      "varies": false
    },
    "short": "Voyage Context 3 hujjatning boshqa bo‘laklari kontekstini hisobga olgan holda har bir bo‘lak uchun vektor yaratadi. U tanlanadigan o‘lchamlar va kvantlangan embeddinglar bilan uzun hujjatlarda aniq parchalarni topishni qo‘llab-quvvatlaydi.",
    "short_sha256": "ecf5347d318cafaa85e440415592285b9b3d6c51afeb2467a37418c7b94e3dd3"
  },
  "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": "Voyage Context 3 hujjatning boshqa bo‘laklari kontekstini hisobga olgan holda har bir bo‘lak uchun vektor yaratadi. U tanlanadigan o‘lchamlar va kvantlangan embeddinglar bilan uzun hujjatlarda aniq parchalarni topishni qo‘llab-quvvatlaydi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "62c8aae3d2a7ade9ff0c3db2b8924d80f6fa7fde31d212d6c4df19c35cd807d8",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "08da86e1124584026d24eed295b49bfb71b625535b806fa390307aa6823f2ed3"
}
