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  "aboutHtml": "<p>Voyage 3 Large — hujjatlar to‘plamidan tegishli ma’lumotni topish uchun matn embedding modeli. U berilgan har bir matnni sonli vektor sifatida ifodalaydi; bu ilovaga qidiruv so‘rovini so‘zlari boshqacha, lekin ma’nosi yaqin parchalar bilan solishtirish imkonini beradi. Voyage AI bu nashrni texnik hujjatlar, suhbatlar va uzun hujjatlar kabi materiallar bo‘yicha umumiy maqsadli hamda ko‘p tilli qidiruv uchun taklif qiladi.</p><p>Qidiriladigan to‘plamni odamlar haqiqatda qayta topishi kerak bo‘lgan ma’lumotdan tuzing. Mahsulot bilim bazasida imkoniyat tavsifini uning dastlabki shartlari va tegishli istisnolari bilan birga saqlang. Yig‘ilish yozuvlarida gapiruvchi yorliqlari va mavzuni tushunishga yetarli atrofdagi muhokamani saqlang. Katta manbani foydali chegaralarda bo‘ling, har bir vektor bilan birga manba sarlavhasi, hujjat identifikatori va parcha joylashuvini saqlang.</p><p>Qidiruv savolini so‘rov, indekslanadigan materialni esa hujjatlar sifatida tayyorlang. Ishlab chiqaruvchining matn embedding jarayonida kirish turi modelga matnning qidiruvdagi vazifasini bildiradi. Masalan, «A’zo xavfsizlik kalitini yo‘qotgach kirishni qanday tiklashi mumkin?» so‘rovini tiklash tartibini tushuntiradigan yordam xizmati parchalari bilan, hatto ular boshqa iboralardan foydalansa ham, solishtirish mumkin. Qidiruv natijasi moslikni asoslaydigan dalillarni ko‘rsatishi uchun asl parchani saqlang.</p><p>Modelning kontekst oynasi 32 000 token. Uzun kirish butun bo‘limni saqlashi mumkin, ammo butun hujjat har doim ham eng foydali qidiruv birligi bo‘lavermaydi. Keng qamrovli vektor to‘g‘ri faylga yo‘naltirishi, lekin aniq javobni topishni o‘quvchining o‘ziga qoldirishi mumkin. Parcha chegaralarini ko‘rsatmoqchi bo‘lgan natija atrofida tanlang, sarlavhalar, shartlar va ta’riflarni ular tushuntiradigan kontent bilan birga saqlang.</p><p>Voyage 3 Large 2 048, 1 024, 512 yoki 256 o‘lchamli vektorlarni qo‘llab-quvvatlaydi. Ishlab chiqaruvchi suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni ham hujjatlashtiradi. Bu tanlovlar qidiruv tizimiga vektordagi tafsilot darajasini indeks hajmi va qidiruv xatti-harakati bilan solishtirish imkonini beradi. Mo‘ljallangan vektor ombori qo‘llab-quvvatlaydigan formatni tanlang, so‘ng ishlab chiqarish indeksining sozlamalarini tanlashdan oldin uni haqiqiy korpus va namunaviy savollarda baholang.</p><p>Ko‘p tilli qidiruv uchun foydalanuvchilar ishlatadigan tillarda sinov savollarini kiriting va topilgan manba parchalarini asl tilida tekshiring. Umumiy qidiruv interfeysi bir nechta til to‘plamiga xizmat qilishi mumkin, hujjat metama’lumotlari esa manba tili va versiyasini baribir ko‘rsatadi. Ma’lumot ajratishda mazmunli nomlar, qisqartmalar va soha atamalarini saqlang; embedding olishdan oldin ularni umumiy xulosa bilan almashtirmang.</p><p>Vektorlardan qidiruv sahifasi, bilim yordamchisi yoki hujjat tanlash bosqichi uchun nomzod parchalarni topishda foydalaning. Indekslangan to‘plam va uning so‘rovlari uchun ayni model va vektor sozlamalarini saqlang. Embedding modelini almashtirish yoki vektor o‘lchamini o‘zgartirishda tegishli indeksni maqsadli ravishda qayta tuzing va solishtiring; vektorning uzunligi o‘zi qanday yaratilganini aniqlamaydi.</p><p>Natijani foydali qidiruv bo‘yicha baholang: to‘g‘ri parcha chiqyaptimi, yaqin, ammo chalg‘ituvchi parchalar undan yuqori turibdimi va ko‘rsatilgan parcha javobni o‘z ichiga olyaptimi. Kutilgan manbalari ma’lum haqiqiy savollarning kichik to‘plamini saqlang, jumladan noaniq iboralar va deyarli bir xil hujjatlar uchun. Embedding qidiriladigan ifodani beradi; moslikni foydalanishga yaroqli natijaga aylantirish uchun kerakli manba matni, ruxsatlar va havolalarni ilova saqlaydi.</p>",
  "aboutText": "Voyage 3 Large — hujjatlar to‘plamidan tegishli ma’lumotni topish uchun matn embedding modeli. U berilgan har bir matnni sonli vektor sifatida ifodalaydi; bu ilovaga qidiruv so‘rovini so‘zlari boshqacha, lekin ma’nosi yaqin parchalar bilan solishtirish imkonini beradi. Voyage AI bu nashrni texnik hujjatlar, suhbatlar va uzun hujjatlar kabi materiallar bo‘yicha umumiy maqsadli hamda ko‘p tilli qidiruv uchun taklif qiladi.\n\nQidiriladigan to‘plamni odamlar haqiqatda qayta topishi kerak bo‘lgan ma’lumotdan tuzing. Mahsulot bilim bazasida imkoniyat tavsifini uning dastlabki shartlari va tegishli istisnolari bilan birga saqlang. Yig‘ilish yozuvlarida gapiruvchi yorliqlari va mavzuni tushunishga yetarli atrofdagi muhokamani saqlang. Katta manbani foydali chegaralarda bo‘ling, har bir vektor bilan birga manba sarlavhasi, hujjat identifikatori va parcha joylashuvini saqlang.\n\nQidiruv savolini so‘rov, indekslanadigan materialni esa hujjatlar sifatida tayyorlang. Ishlab chiqaruvchining matn embedding jarayonida kirish turi modelga matnning qidiruvdagi vazifasini bildiradi. Masalan, «A’zo xavfsizlik kalitini yo‘qotgach kirishni qanday tiklashi mumkin?» so‘rovini tiklash tartibini tushuntiradigan yordam xizmati parchalari bilan, hatto ular boshqa iboralardan foydalansa ham, solishtirish mumkin. Qidiruv natijasi moslikni asoslaydigan dalillarni ko‘rsatishi uchun asl parchani saqlang.\n\nModelning kontekst oynasi 32 000 token. Uzun kirish butun bo‘limni saqlashi mumkin, ammo butun hujjat har doim ham eng foydali qidiruv birligi bo‘lavermaydi. Keng qamrovli vektor to‘g‘ri faylga yo‘naltirishi, lekin aniq javobni topishni o‘quvchining o‘ziga qoldirishi mumkin. Parcha chegaralarini ko‘rsatmoqchi bo‘lgan natija atrofida tanlang, sarlavhalar, shartlar va ta’riflarni ular tushuntiradigan kontent bilan birga saqlang.\n\nVoyage 3 Large 2 048, 1 024, 512 yoki 256 o‘lchamli vektorlarni qo‘llab-quvvatlaydi. Ishlab chiqaruvchi suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni ham hujjatlashtiradi. Bu tanlovlar qidiruv tizimiga vektordagi tafsilot darajasini indeks hajmi va qidiruv xatti-harakati bilan solishtirish imkonini beradi. Mo‘ljallangan vektor ombori qo‘llab-quvvatlaydigan formatni tanlang, so‘ng ishlab chiqarish indeksining sozlamalarini tanlashdan oldin uni haqiqiy korpus va namunaviy savollarda baholang.\n\nKo‘p tilli qidiruv uchun foydalanuvchilar ishlatadigan tillarda sinov savollarini kiriting va topilgan manba parchalarini asl tilida tekshiring. Umumiy qidiruv interfeysi bir nechta til to‘plamiga xizmat qilishi mumkin, hujjat metama’lumotlari esa manba tili va versiyasini baribir ko‘rsatadi. Ma’lumot ajratishda mazmunli nomlar, qisqartmalar va soha atamalarini saqlang; embedding olishdan oldin ularni umumiy xulosa bilan almashtirmang.\n\nVektorlardan qidiruv sahifasi, bilim yordamchisi yoki hujjat tanlash bosqichi uchun nomzod parchalarni topishda foydalaning. Indekslangan to‘plam va uning so‘rovlari uchun ayni model va vektor sozlamalarini saqlang. Embedding modelini almashtirish yoki vektor o‘lchamini o‘zgartirishda tegishli indeksni maqsadli ravishda qayta tuzing va solishtiring; vektorning uzunligi o‘zi qanday yaratilganini aniqlamaydi.\n\nNatijani foydali qidiruv bo‘yicha baholang: to‘g‘ri parcha chiqyaptimi, yaqin, ammo chalg‘ituvchi parchalar undan yuqori turibdimi va ko‘rsatilgan parcha javobni o‘z ichiga olyaptimi. Kutilgan manbalari ma’lum haqiqiy savollarning kichik to‘plamini saqlang, jumladan noaniq iboralar va deyarli bir xil hujjatlar uchun. Embedding qidiriladigan ifodani beradi; moslikni foydalanishga yaroqli natijaga aylantirish uchun kerakli manba matni, ruxsatlar va havolalarni ilova saqlaydi.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Voyage AI: Voyage 3 Large</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Voyage AI /</span><code>voyage-3-large</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-3-large\">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 3 Large umumiy maqsadli va ko‘p tilli qidiruv uchun matnni vektorlarga aylantiradi. Uning 32 000 tokenli kirish oynasi, tanlanadigan vektor o‘lchamlari va kvantlangan formatlari qidiriladigan bilim bazalari, hujjatlarni topish va qidiruv bilan boyitilgan generatsiyani 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 3 Large — hujjatlar to‘plamidan tegishli ma’lumotni topish uchun matn embedding modeli. U berilgan har bir matnni sonli vektor sifatida ifodalaydi; bu ilovaga qidiruv so‘rovini so‘zlari boshqacha, lekin ma’nosi yaqin parchalar bilan solishtirish imkonini beradi. Voyage AI bu nashrni texnik hujjatlar, suhbatlar va uzun hujjatlar kabi materiallar bo‘yicha umumiy maqsadli hamda ko‘p tilli qidiruv uchun taklif qiladi.</p><p>Qidiriladigan to‘plamni odamlar haqiqatda qayta topishi kerak bo‘lgan ma’lumotdan tuzing. Mahsulot bilim bazasida imkoniyat tavsifini uning dastlabki shartlari va tegishli istisnolari bilan birga saqlang. Yig‘ilish yozuvlarida gapiruvchi yorliqlari va mavzuni tushunishga yetarli atrofdagi muhokamani saqlang. Katta manbani foydali chegaralarda bo‘ling, har bir vektor bilan birga manba sarlavhasi, hujjat identifikatori va parcha joylashuvini saqlang.</p><p>Qidiruv savolini so‘rov, indekslanadigan materialni esa hujjatlar sifatida tayyorlang. Ishlab chiqaruvchining matn embedding jarayonida kirish turi modelga matnning qidiruvdagi vazifasini bildiradi. Masalan, «A’zo xavfsizlik kalitini yo‘qotgach kirishni qanday tiklashi mumkin?» so‘rovini tiklash tartibini tushuntiradigan yordam xizmati parchalari bilan, hatto ular boshqa iboralardan foydalansa ham, solishtirish mumkin. Qidiruv natijasi moslikni asoslaydigan dalillarni ko‘rsatishi uchun asl parchani saqlang.</p><p>Modelning kontekst oynasi 32 000 token. Uzun kirish butun bo‘limni saqlashi mumkin, ammo butun hujjat har doim ham eng foydali qidiruv birligi bo‘lavermaydi. Keng qamrovli vektor to‘g‘ri faylga yo‘naltirishi, lekin aniq javobni topishni o‘quvchining o‘ziga qoldirishi mumkin. Parcha chegaralarini ko‘rsatmoqchi bo‘lgan natija atrofida tanlang, sarlavhalar, shartlar va ta’riflarni ular tushuntiradigan kontent bilan birga saqlang.</p><p>Voyage 3 Large 2 048, 1 024, 512 yoki 256 o‘lchamli vektorlarni qo‘llab-quvvatlaydi. Ishlab chiqaruvchi suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni ham hujjatlashtiradi. Bu tanlovlar qidiruv tizimiga vektordagi tafsilot darajasini indeks hajmi va qidiruv xatti-harakati bilan solishtirish imkonini beradi. Mo‘ljallangan vektor ombori qo‘llab-quvvatlaydigan formatni tanlang, so‘ng ishlab chiqarish indeksining sozlamalarini tanlashdan oldin uni haqiqiy korpus va namunaviy savollarda baholang.</p><p>Ko‘p tilli qidiruv uchun foydalanuvchilar ishlatadigan tillarda sinov savollarini kiriting va topilgan manba parchalarini asl tilida tekshiring. Umumiy qidiruv interfeysi bir nechta til to‘plamiga xizmat qilishi mumkin, hujjat metama’lumotlari esa manba tili va versiyasini baribir ko‘rsatadi. Ma’lumot ajratishda mazmunli nomlar, qisqartmalar va soha atamalarini saqlang; embedding olishdan oldin ularni umumiy xulosa bilan almashtirmang.</p><p>Vektorlardan qidiruv sahifasi, bilim yordamchisi yoki hujjat tanlash bosqichi uchun nomzod parchalarni topishda foydalaning. Indekslangan to‘plam va uning so‘rovlari uchun ayni model va vektor sozlamalarini saqlang. Embedding modelini almashtirish yoki vektor o‘lchamini o‘zgartirishda tegishli indeksni maqsadli ravishda qayta tuzing va solishtiring; vektorning uzunligi o‘zi qanday yaratilganini aniqlamaydi.</p><p>Natijani foydali qidiruv bo‘yicha baholang: to‘g‘ri parcha chiqyaptimi, yaqin, ammo chalg‘ituvchi parchalar undan yuqori turibdimi va ko‘rsatilgan parcha javobni o‘z ichiga olyaptimi. Kutilgan manbalari ma’lum haqiqiy savollarning kichik to‘plamini saqlang, jumladan noaniq iboralar va deyarli bir xil hujjatlar uchun. Embedding qidiriladigan ifodani beradi; moslikni foydalanishga yaroqli natijaga aylantirish uchun kerakli manba matni, ruxsatlar va havolalarni ilova saqlaydi.</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-3-large</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>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · Embeddinglar</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-2787-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2787-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · Barcha vektorlar va foydalanish ma’lumotlarini saqlang</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2787-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2787-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${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-3-large&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-3-large&amp;route=Ultra&amp;caller=voyage-3-large&amp;fmt=embeddings\"><code>voyage-3-large</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-3-large&amp;route=Ultra&amp;caller=voyage-3-large&amp;fmt=embeddings\"><code>voyage-3-large</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "voyage-3-large",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "voyage-3-large",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|voyage-3-large",
      "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-3-large"
                ],
                "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-3-large"
                  ],
                  "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-3-large"
              },
              "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-3-large\",\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-3-large",
        "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-3-large",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "ce98047e6d3300db4679ce975e2189bba6558663ae670fa46743fd9572d4edbe",
    "input_html_sha256": "0e6c7f0c9f1d892ae9155368c44b59ffd89be4782f73f84c0fbaa0c7b705d019",
    "private_audit_sha256": "c2cde31bab7d7f4267ef498b84f38f8d933cd8509a143e2352efa441af2191b3",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/voyage-3-large/",
    "description": "Voyage 3 Large umumiy maqsadli va ko‘p tilli qidiruv uchun matnni vektorlarga aylantiradi. Uning 32 000 tokenli kirish oynasi, tanlanadigan vektor o‘lchamlari va kvantlangan formatlari qidiriladigan bilim bazalari, hujjatlarni topish va qidiruv bilan boyitilgan generatsiyani qo‘llab-quvvatlaydi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/voyage-3-large/index.md",
    "title": "Voyage AI: Voyage 3 Large — Ailoot",
    "url": "https://ailoot.ai/models/uz/voyage-3-large/"
  },
  "rawModel": {
    "about": "Voyage 3 Large — hujjatlar to‘plamidan tegishli ma’lumotni topish uchun matn embedding modeli. U berilgan har bir matnni sonli vektor sifatida ifodalaydi; bu ilovaga qidiruv so‘rovini so‘zlari boshqacha, lekin ma’nosi yaqin parchalar bilan solishtirish imkonini beradi. Voyage AI bu nashrni texnik hujjatlar, suhbatlar va uzun hujjatlar kabi materiallar bo‘yicha umumiy maqsadli hamda ko‘p tilli qidiruv uchun taklif qiladi.\n\nQidiriladigan to‘plamni odamlar haqiqatda qayta topishi kerak bo‘lgan ma’lumotdan tuzing. Mahsulot bilim bazasida imkoniyat tavsifini uning dastlabki shartlari va tegishli istisnolari bilan birga saqlang. Yig‘ilish yozuvlarida gapiruvchi yorliqlari va mavzuni tushunishga yetarli atrofdagi muhokamani saqlang. Katta manbani foydali chegaralarda bo‘ling, har bir vektor bilan birga manba sarlavhasi, hujjat identifikatori va parcha joylashuvini saqlang.\n\nQidiruv savolini so‘rov, indekslanadigan materialni esa hujjatlar sifatida tayyorlang. Ishlab chiqaruvchining matn embedding jarayonida kirish turi modelga matnning qidiruvdagi vazifasini bildiradi. Masalan, «A’zo xavfsizlik kalitini yo‘qotgach kirishni qanday tiklashi mumkin?» so‘rovini tiklash tartibini tushuntiradigan yordam xizmati parchalari bilan, hatto ular boshqa iboralardan foydalansa ham, solishtirish mumkin. Qidiruv natijasi moslikni asoslaydigan dalillarni ko‘rsatishi uchun asl parchani saqlang.\n\nModelning kontekst oynasi 32 000 token. Uzun kirish butun bo‘limni saqlashi mumkin, ammo butun hujjat har doim ham eng foydali qidiruv birligi bo‘lavermaydi. Keng qamrovli vektor to‘g‘ri faylga yo‘naltirishi, lekin aniq javobni topishni o‘quvchining o‘ziga qoldirishi mumkin. Parcha chegaralarini ko‘rsatmoqchi bo‘lgan natija atrofida tanlang, sarlavhalar, shartlar va ta’riflarni ular tushuntiradigan kontent bilan birga saqlang.\n\nVoyage 3 Large 2 048, 1 024, 512 yoki 256 o‘lchamli vektorlarni qo‘llab-quvvatlaydi. Ishlab chiqaruvchi suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni ham hujjatlashtiradi. Bu tanlovlar qidiruv tizimiga vektordagi tafsilot darajasini indeks hajmi va qidiruv xatti-harakati bilan solishtirish imkonini beradi. Mo‘ljallangan vektor ombori qo‘llab-quvvatlaydigan formatni tanlang, so‘ng ishlab chiqarish indeksining sozlamalarini tanlashdan oldin uni haqiqiy korpus va namunaviy savollarda baholang.\n\nKo‘p tilli qidiruv uchun foydalanuvchilar ishlatadigan tillarda sinov savollarini kiriting va topilgan manba parchalarini asl tilida tekshiring. Umumiy qidiruv interfeysi bir nechta til to‘plamiga xizmat qilishi mumkin, hujjat metama’lumotlari esa manba tili va versiyasini baribir ko‘rsatadi. Ma’lumot ajratishda mazmunli nomlar, qisqartmalar va soha atamalarini saqlang; embedding olishdan oldin ularni umumiy xulosa bilan almashtirmang.\n\nVektorlardan qidiruv sahifasi, bilim yordamchisi yoki hujjat tanlash bosqichi uchun nomzod parchalarni topishda foydalaning. Indekslangan to‘plam va uning so‘rovlari uchun ayni model va vektor sozlamalarini saqlang. Embedding modelini almashtirish yoki vektor o‘lchamini o‘zgartirishda tegishli indeksni maqsadli ravishda qayta tuzing va solishtiring; vektorning uzunligi o‘zi qanday yaratilganini aniqlamaydi.\n\nNatijani foydali qidiruv bo‘yicha baholang: to‘g‘ri parcha chiqyaptimi, yaqin, ammo chalg‘ituvchi parchalar undan yuqori turibdimi va ko‘rsatilgan parcha javobni o‘z ichiga olyaptimi. Kutilgan manbalari ma’lum haqiqiy savollarning kichik to‘plamini saqlang, jumladan noaniq iboralar va deyarli bir xil hujjatlar uchun. Embedding qidiriladigan ifodani beradi; moslikni foydalanishga yaroqli natijaga aylantirish uchun kerakli manba matni, ruxsatlar va havolalarni ilova saqlaydi.",
    "about_sha256": "f8a14551c12ad8f26e408dfc995cf463d79d159d38d8d98b1790a59c1789dcc3",
    "alias_of": null,
    "aliases": [],
    "author": "Voyage AI",
    "capabilities": {
      "context_length": 32000,
      "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 3 Large",
    "formats": [
      "embeddings"
    ],
    "id": "voyage-3-large",
    "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-3-large"
                ],
                "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-3-large"
                  ],
                  "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-3-large"
              },
              "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-3-large\",\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-3-large",
        "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-3-large",
        "request_model_id": "voyage-3-large",
        "route_label": "Ultra"
      }
    ],
    "name": "Voyage AI: Voyage 3 Large",
    "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 3 Large umumiy maqsadli va ko‘p tilli qidiruv uchun matnni vektorlarga aylantiradi. Uning 32 000 tokenli kirish oynasi, tanlanadigan vektor o‘lchamlari va kvantlangan formatlari qidiriladigan bilim bazalari, hujjatlarni topish va qidiruv bilan boyitilgan generatsiyani qo‘llab-quvvatlaydi.",
    "short_sha256": "97e0e5100c1133c3a38a116210c6492617852a455c2338779be2ecc6f0754b10"
  },
  "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 3 Large umumiy maqsadli va ko‘p tilli qidiruv uchun matnni vektorlarga aylantiradi. Uning 32 000 tokenli kirish oynasi, tanlanadigan vektor o‘lchamlari va kvantlangan formatlari qidiriladigan bilim bazalari, hujjatlarni topish va qidiruv bilan boyitilgan generatsiyani qo‘llab-quvvatlaydi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "224df50a44b82dcc3f4aaa6ccd371b993e3d54c8d54559eb939cc6a61ca8a64c",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "9565a0470f5523a2333ecb36ed2637f3866246e9d0624ed415517328ad6e6878"
}