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  "aboutHtml": "<p>Voyage Code 3 — manba kodini topishga ixtisoslashgan embedding modeli. Ishlab chiqaruvchi tabiiy tildan kodga, koddan kodga va docstring’dan kodga qidiruv vazifalarini ta’riflaydi: savol dasturiy yechimni, kod parchasi tegishli mantiqni, funksiya tavsifi esa mos kodni topishi mumkin. Model tahrirlangan fayl yoki tayyor dastur emas, qidiruv vektorlarini yaratadi.</p><p>Repozitoriy materialini mazmunli kod birliklari atrofida tayyorlang. Funksiyani uning nomi, signaturasi va maqsadini tushuntiradigan izohlar bilan birga saqlang. Kattaroq komponent uchun operatsiyani tushunishga kerakli yaqin ta’riflarni kiriting. Qidiruv natijasi embedding olingan aynan o‘sha manbani ochishi uchun repozitoriy, tahrir, fayl yo‘li va satr joylashuvini alohida saqlang.</p><p>Tabiiy tildagi qidiruv xatti-harakatni tavsiflashi mumkin, masalan, loyihaning yuklangan faylni saqlashdan oldin tekshiradigan qismini topish. Kodga asoslangan so‘rov boshqa joyda vazifasi bo‘yicha mos funksiya topilishi kerak bo‘lgan funksiyani berishi mumkin. Haqiqiy dasturlash vazifalaridan namunaviy qidiruvlarni tanlang, jumladan notanish nomlar, o‘xshash dasturiy yechimlar va ma’nosi atrofdagi kodga bog‘liq yordamchi vositalarni.</p><p>32 000 tokenli kontekst oynasi katta kod parchalarini sig‘diradi. Bu joydan maqsadli foydalaning: to‘liq funksiya va uning tegishli bog‘liqliklari boshqaruv oqimining yarmidan ixtiyoriy kesilgan blokdan ko‘proq ma’lumot berishi mumkin. Manba formatini o‘qiladigan saqlang va ma’lumot ajratishda dasturlash tili sintaksisini buzmay saqlang. Manba fayli kirish oynasidan katta bo‘lsa, uni foydali tuzilmaviy chegaralarda bo‘ling va parchalar bilan ularning asosiy fayli o‘rtasidagi bog‘lanishni saqlang.</p><p>Voyage Code 3 2 048, 1 024, 512 yoki 256 o‘lchamli embeddinglarni, shuningdek suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni qo‘llab-quvvatlaydi. Bu sozlamalar repozitoriy indeksining turli hajmlari va qidiruv konfiguratsiyalariga mos keladi. Ixcham ifodani kattaroq boshlang‘ich variant bilan ayni haqiqiy qidiruvlarda baholang, to‘g‘ri funksiya yoki fayl foydali natijalar orasida qolishini tekshiring.</p><p>Dasturlash yordamchisi uchun avval tegishli manba parchalarini toping, so‘ng ularni fayl manzillari bilan birga javob berish yoki o‘zgartirish kiritishga mas’ul komponentga uzating. O‘xshashlik mosligi kontekst tanlashga yordam beradi; u ikkita dasturiy yechimni bir-birining o‘rniga ishlatish mumkinligini isbotlamaydi. Tahrirga yo‘l ko‘rsatishda ishlatishdan oldin topilgan koddagi kirishlar, yon ta’sirlar va atrofdagi shartlarni tekshiring.</p><p>Repozitoriyning aloqasiz versiyalarini metama’lumotlar orqali ajrating va indekslangan tahrirlarni asl manbasiga kuzatish imkonini saqlang. Funksiya ko‘chsa yoki xatti-harakati o‘zgarsa, uning vektori va manba joylashuvini birgalikda yangilang. Indekslangan manba va uning so‘rovlari ayni embedding sozlamalariga ega bo‘lishi kerak. Indeksni ko‘chirishda natijalarni mavjud savollar to‘plami bilan solishtiring, model yoki vektor ifodasidagi o‘zgarish dasturchi ko‘radigan kodni sezdirmasdan o‘zgartirmasin.</p><p>Foydali tekshiruvlar: so‘rov faqat test nomini emas, haqiqiy dasturiy yechimni topadimi; docstring o‘ziga mos funksiyani topadimi; ma’no jihatidan tegishli kod tashqi ko‘rinishigina o‘xshash parchadan ajratiladimi. Dasturchilar dalillarni tekshirishi uchun qidiruv natijasida qaytarilgan manba matni va joylashuvini saqlang. Shu tariqa modelning kodga yo‘naltirilgan embeddinglari amaliy yo‘l topish va kontekst tanlash vositasiga aylanadi.</p>",
  "aboutText": "Voyage Code 3 — manba kodini topishga ixtisoslashgan embedding modeli. Ishlab chiqaruvchi tabiiy tildan kodga, koddan kodga va docstring’dan kodga qidiruv vazifalarini ta’riflaydi: savol dasturiy yechimni, kod parchasi tegishli mantiqni, funksiya tavsifi esa mos kodni topishi mumkin. Model tahrirlangan fayl yoki tayyor dastur emas, qidiruv vektorlarini yaratadi.\n\nRepozitoriy materialini mazmunli kod birliklari atrofida tayyorlang. Funksiyani uning nomi, signaturasi va maqsadini tushuntiradigan izohlar bilan birga saqlang. Kattaroq komponent uchun operatsiyani tushunishga kerakli yaqin ta’riflarni kiriting. Qidiruv natijasi embedding olingan aynan o‘sha manbani ochishi uchun repozitoriy, tahrir, fayl yo‘li va satr joylashuvini alohida saqlang.\n\nTabiiy tildagi qidiruv xatti-harakatni tavsiflashi mumkin, masalan, loyihaning yuklangan faylni saqlashdan oldin tekshiradigan qismini topish. Kodga asoslangan so‘rov boshqa joyda vazifasi bo‘yicha mos funksiya topilishi kerak bo‘lgan funksiyani berishi mumkin. Haqiqiy dasturlash vazifalaridan namunaviy qidiruvlarni tanlang, jumladan notanish nomlar, o‘xshash dasturiy yechimlar va ma’nosi atrofdagi kodga bog‘liq yordamchi vositalarni.\n\n32 000 tokenli kontekst oynasi katta kod parchalarini sig‘diradi. Bu joydan maqsadli foydalaning: to‘liq funksiya va uning tegishli bog‘liqliklari boshqaruv oqimining yarmidan ixtiyoriy kesilgan blokdan ko‘proq ma’lumot berishi mumkin. Manba formatini o‘qiladigan saqlang va ma’lumot ajratishda dasturlash tili sintaksisini buzmay saqlang. Manba fayli kirish oynasidan katta bo‘lsa, uni foydali tuzilmaviy chegaralarda bo‘ling va parchalar bilan ularning asosiy fayli o‘rtasidagi bog‘lanishni saqlang.\n\nVoyage Code 3 2 048, 1 024, 512 yoki 256 o‘lchamli embeddinglarni, shuningdek suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni qo‘llab-quvvatlaydi. Bu sozlamalar repozitoriy indeksining turli hajmlari va qidiruv konfiguratsiyalariga mos keladi. Ixcham ifodani kattaroq boshlang‘ich variant bilan ayni haqiqiy qidiruvlarda baholang, to‘g‘ri funksiya yoki fayl foydali natijalar orasida qolishini tekshiring.\n\nDasturlash yordamchisi uchun avval tegishli manba parchalarini toping, so‘ng ularni fayl manzillari bilan birga javob berish yoki o‘zgartirish kiritishga mas’ul komponentga uzating. O‘xshashlik mosligi kontekst tanlashga yordam beradi; u ikkita dasturiy yechimni bir-birining o‘rniga ishlatish mumkinligini isbotlamaydi. Tahrirga yo‘l ko‘rsatishda ishlatishdan oldin topilgan koddagi kirishlar, yon ta’sirlar va atrofdagi shartlarni tekshiring.\n\nRepozitoriyning aloqasiz versiyalarini metama’lumotlar orqali ajrating va indekslangan tahrirlarni asl manbasiga kuzatish imkonini saqlang. Funksiya ko‘chsa yoki xatti-harakati o‘zgarsa, uning vektori va manba joylashuvini birgalikda yangilang. Indekslangan manba va uning so‘rovlari ayni embedding sozlamalariga ega bo‘lishi kerak. Indeksni ko‘chirishda natijalarni mavjud savollar to‘plami bilan solishtiring, model yoki vektor ifodasidagi o‘zgarish dasturchi ko‘radigan kodni sezdirmasdan o‘zgartirmasin.\n\nFoydali tekshiruvlar: so‘rov faqat test nomini emas, haqiqiy dasturiy yechimni topadimi; docstring o‘ziga mos funksiyani topadimi; ma’no jihatidan tegishli kod tashqi ko‘rinishigina o‘xshash parchadan ajratiladimi. Dasturchilar dalillarni tekshirishi uchun qidiruv natijasida qaytarilgan manba matni va joylashuvini saqlang. Shu tariqa modelning kodga yo‘naltirilgan embeddinglari amaliy yo‘l topish va kontekst tanlash vositasiga aylanadi.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Voyage AI: Voyage Code 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-code-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-code-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 Code 3 matndan kodga, koddan kodga va docstring’dan kodga qidiruv uchun embeddinglar yaratadi. 32 000 tokenli oyna, tanlanadigan vektor o‘lchamlari va kvantlangan chiqish repozitoriy qidiruvi hamda dasturlash yordamchilari uchun kod kontekstini tanlashni 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 Code 3 — manba kodini topishga ixtisoslashgan embedding modeli. Ishlab chiqaruvchi tabiiy tildan kodga, koddan kodga va docstring’dan kodga qidiruv vazifalarini ta’riflaydi: savol dasturiy yechimni, kod parchasi tegishli mantiqni, funksiya tavsifi esa mos kodni topishi mumkin. Model tahrirlangan fayl yoki tayyor dastur emas, qidiruv vektorlarini yaratadi.</p><p>Repozitoriy materialini mazmunli kod birliklari atrofida tayyorlang. Funksiyani uning nomi, signaturasi va maqsadini tushuntiradigan izohlar bilan birga saqlang. Kattaroq komponent uchun operatsiyani tushunishga kerakli yaqin ta’riflarni kiriting. Qidiruv natijasi embedding olingan aynan o‘sha manbani ochishi uchun repozitoriy, tahrir, fayl yo‘li va satr joylashuvini alohida saqlang.</p><p>Tabiiy tildagi qidiruv xatti-harakatni tavsiflashi mumkin, masalan, loyihaning yuklangan faylni saqlashdan oldin tekshiradigan qismini topish. Kodga asoslangan so‘rov boshqa joyda vazifasi bo‘yicha mos funksiya topilishi kerak bo‘lgan funksiyani berishi mumkin. Haqiqiy dasturlash vazifalaridan namunaviy qidiruvlarni tanlang, jumladan notanish nomlar, o‘xshash dasturiy yechimlar va ma’nosi atrofdagi kodga bog‘liq yordamchi vositalarni.</p><p>32 000 tokenli kontekst oynasi katta kod parchalarini sig‘diradi. Bu joydan maqsadli foydalaning: to‘liq funksiya va uning tegishli bog‘liqliklari boshqaruv oqimining yarmidan ixtiyoriy kesilgan blokdan ko‘proq ma’lumot berishi mumkin. Manba formatini o‘qiladigan saqlang va ma’lumot ajratishda dasturlash tili sintaksisini buzmay saqlang. Manba fayli kirish oynasidan katta bo‘lsa, uni foydali tuzilmaviy chegaralarda bo‘ling va parchalar bilan ularning asosiy fayli o‘rtasidagi bog‘lanishni saqlang.</p><p>Voyage Code 3 2 048, 1 024, 512 yoki 256 o‘lchamli embeddinglarni, shuningdek suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni qo‘llab-quvvatlaydi. Bu sozlamalar repozitoriy indeksining turli hajmlari va qidiruv konfiguratsiyalariga mos keladi. Ixcham ifodani kattaroq boshlang‘ich variant bilan ayni haqiqiy qidiruvlarda baholang, to‘g‘ri funksiya yoki fayl foydali natijalar orasida qolishini tekshiring.</p><p>Dasturlash yordamchisi uchun avval tegishli manba parchalarini toping, so‘ng ularni fayl manzillari bilan birga javob berish yoki o‘zgartirish kiritishga mas’ul komponentga uzating. O‘xshashlik mosligi kontekst tanlashga yordam beradi; u ikkita dasturiy yechimni bir-birining o‘rniga ishlatish mumkinligini isbotlamaydi. Tahrirga yo‘l ko‘rsatishda ishlatishdan oldin topilgan koddagi kirishlar, yon ta’sirlar va atrofdagi shartlarni tekshiring.</p><p>Repozitoriyning aloqasiz versiyalarini metama’lumotlar orqali ajrating va indekslangan tahrirlarni asl manbasiga kuzatish imkonini saqlang. Funksiya ko‘chsa yoki xatti-harakati o‘zgarsa, uning vektori va manba joylashuvini birgalikda yangilang. Indekslangan manba va uning so‘rovlari ayni embedding sozlamalariga ega bo‘lishi kerak. Indeksni ko‘chirishda natijalarni mavjud savollar to‘plami bilan solishtiring, model yoki vektor ifodasidagi o‘zgarish dasturchi ko‘radigan kodni sezdirmasdan o‘zgartirmasin.</p><p>Foydali tekshiruvlar: so‘rov faqat test nomini emas, haqiqiy dasturiy yechimni topadimi; docstring o‘ziga mos funksiyani topadimi; ma’no jihatidan tegishli kod tashqi ko‘rinishigina o‘xshash parchadan ajratiladimi. Dasturchilar dalillarni tekshirishi uchun qidiruv natijasida qaytarilgan manba matni va joylashuvini saqlang. Shu tariqa modelning kodga yo‘naltirilgan embeddinglari amaliy yo‘l topish va kontekst tanlash vositasiga aylanadi.</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-code-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>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · Embeddinglar</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-2790-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2790-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · Barcha vektorlar va foydalanish ma’lumotlarini saqlang</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2790-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2790-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${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-code-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-code-3&amp;route=Ultra&amp;caller=voyage-code-3&amp;fmt=embeddings\"><code>voyage-code-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-code-3&amp;route=Ultra&amp;caller=voyage-code-3&amp;fmt=embeddings\"><code>voyage-code-3</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "voyage-code-3",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "voyage-code-3",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|voyage-code-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-code-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-code-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-code-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-code-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-code-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-code-3",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "9ea1c4e087fa25d28ac759ed77ee67ebf1b9570670dff500572c2d0d685020c9",
    "input_html_sha256": "9fbd430ad136340d7ac1f396707922fa3f743cc07414531a0c46d6f2e87d5c5a",
    "private_audit_sha256": "442a15cea6b9dff9d2f01859f631fa9ee0e71608133a973e08c586f32f551794",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/voyage-code-3/",
    "description": "Voyage Code 3 matndan kodga, koddan kodga va docstring’dan kodga qidiruv uchun embeddinglar yaratadi. 32 000 tokenli oyna, tanlanadigan vektor o‘lchamlari va kvantlangan chiqish repozitoriy qidiruvi hamda dasturlash yordamchilari uchun kod kontekstini tanlashni qo‘llab-quvvatlaydi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/voyage-code-3/index.md",
    "title": "Voyage AI: Voyage Code 3 — Ailoot",
    "url": "https://ailoot.ai/models/uz/voyage-code-3/"
  },
  "rawModel": {
    "about": "Voyage Code 3 — manba kodini topishga ixtisoslashgan embedding modeli. Ishlab chiqaruvchi tabiiy tildan kodga, koddan kodga va docstring’dan kodga qidiruv vazifalarini ta’riflaydi: savol dasturiy yechimni, kod parchasi tegishli mantiqni, funksiya tavsifi esa mos kodni topishi mumkin. Model tahrirlangan fayl yoki tayyor dastur emas, qidiruv vektorlarini yaratadi.\n\nRepozitoriy materialini mazmunli kod birliklari atrofida tayyorlang. Funksiyani uning nomi, signaturasi va maqsadini tushuntiradigan izohlar bilan birga saqlang. Kattaroq komponent uchun operatsiyani tushunishga kerakli yaqin ta’riflarni kiriting. Qidiruv natijasi embedding olingan aynan o‘sha manbani ochishi uchun repozitoriy, tahrir, fayl yo‘li va satr joylashuvini alohida saqlang.\n\nTabiiy tildagi qidiruv xatti-harakatni tavsiflashi mumkin, masalan, loyihaning yuklangan faylni saqlashdan oldin tekshiradigan qismini topish. Kodga asoslangan so‘rov boshqa joyda vazifasi bo‘yicha mos funksiya topilishi kerak bo‘lgan funksiyani berishi mumkin. Haqiqiy dasturlash vazifalaridan namunaviy qidiruvlarni tanlang, jumladan notanish nomlar, o‘xshash dasturiy yechimlar va ma’nosi atrofdagi kodga bog‘liq yordamchi vositalarni.\n\n32 000 tokenli kontekst oynasi katta kod parchalarini sig‘diradi. Bu joydan maqsadli foydalaning: to‘liq funksiya va uning tegishli bog‘liqliklari boshqaruv oqimining yarmidan ixtiyoriy kesilgan blokdan ko‘proq ma’lumot berishi mumkin. Manba formatini o‘qiladigan saqlang va ma’lumot ajratishda dasturlash tili sintaksisini buzmay saqlang. Manba fayli kirish oynasidan katta bo‘lsa, uni foydali tuzilmaviy chegaralarda bo‘ling va parchalar bilan ularning asosiy fayli o‘rtasidagi bog‘lanishni saqlang.\n\nVoyage Code 3 2 048, 1 024, 512 yoki 256 o‘lchamli embeddinglarni, shuningdek suzuvchi nuqtali, ishorali yoki ishorasiz sakkiz bitli hamda ikkilik ifodalarni qo‘llab-quvvatlaydi. Bu sozlamalar repozitoriy indeksining turli hajmlari va qidiruv konfiguratsiyalariga mos keladi. Ixcham ifodani kattaroq boshlang‘ich variant bilan ayni haqiqiy qidiruvlarda baholang, to‘g‘ri funksiya yoki fayl foydali natijalar orasida qolishini tekshiring.\n\nDasturlash yordamchisi uchun avval tegishli manba parchalarini toping, so‘ng ularni fayl manzillari bilan birga javob berish yoki o‘zgartirish kiritishga mas’ul komponentga uzating. O‘xshashlik mosligi kontekst tanlashga yordam beradi; u ikkita dasturiy yechimni bir-birining o‘rniga ishlatish mumkinligini isbotlamaydi. Tahrirga yo‘l ko‘rsatishda ishlatishdan oldin topilgan koddagi kirishlar, yon ta’sirlar va atrofdagi shartlarni tekshiring.\n\nRepozitoriyning aloqasiz versiyalarini metama’lumotlar orqali ajrating va indekslangan tahrirlarni asl manbasiga kuzatish imkonini saqlang. Funksiya ko‘chsa yoki xatti-harakati o‘zgarsa, uning vektori va manba joylashuvini birgalikda yangilang. Indekslangan manba va uning so‘rovlari ayni embedding sozlamalariga ega bo‘lishi kerak. Indeksni ko‘chirishda natijalarni mavjud savollar to‘plami bilan solishtiring, model yoki vektor ifodasidagi o‘zgarish dasturchi ko‘radigan kodni sezdirmasdan o‘zgartirmasin.\n\nFoydali tekshiruvlar: so‘rov faqat test nomini emas, haqiqiy dasturiy yechimni topadimi; docstring o‘ziga mos funksiyani topadimi; ma’no jihatidan tegishli kod tashqi ko‘rinishigina o‘xshash parchadan ajratiladimi. Dasturchilar dalillarni tekshirishi uchun qidiruv natijasida qaytarilgan manba matni va joylashuvini saqlang. Shu tariqa modelning kodga yo‘naltirilgan embeddinglari amaliy yo‘l topish va kontekst tanlash vositasiga aylanadi.",
    "about_sha256": "7a62faecaa1a469a228fb832e8fd51d0fb1ee7fc0232e975614f45428c1f130f",
    "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 Code 3",
    "formats": [
      "embeddings"
    ],
    "id": "voyage-code-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-code-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-code-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-code-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-code-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-code-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-code-3",
        "request_model_id": "voyage-code-3",
        "route_label": "Ultra"
      }
    ],
    "name": "Voyage AI: Voyage Code 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 Code 3 matndan kodga, koddan kodga va docstring’dan kodga qidiruv uchun embeddinglar yaratadi. 32 000 tokenli oyna, tanlanadigan vektor o‘lchamlari va kvantlangan chiqish repozitoriy qidiruvi hamda dasturlash yordamchilari uchun kod kontekstini tanlashni qo‘llab-quvvatlaydi.",
    "short_sha256": "d4d092b4a7555efe0f233ac899b7e43901dde1d9465c4a647af52200c6d5d963"
  },
  "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 Code 3 matndan kodga, koddan kodga va docstring’dan kodga qidiruv uchun embeddinglar yaratadi. 32 000 tokenli oyna, tanlanadigan vektor o‘lchamlari va kvantlangan chiqish repozitoriy qidiruvi hamda dasturlash yordamchilari uchun kod kontekstini tanlashni qo‘llab-quvvatlaydi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "4ef2dab9b0b7b5a020cedbd72560df40e0b205a47dd56b5cf7cbc58aa5e6fc3e",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "6c302e3d53eac2d7ea558787a8dd865e44e2d713e62fe7c475b0f1e9d5886194"
}