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  "aboutHtml": "<p>Voyage 3.5 Lite semantik qidiruv uchun matnni embeddinglarga aylantiradi. Voyage AI bu nashrni kechikish va xarajat bo‘yicha optimallashtirilgan deb ta’riflaydi; shu sababli u kiruvchi savollardan tez-tez embedding oladigan yoki qidiriladigan parchalarning katta to‘plamini saqlaydigan qidiruv jarayoniga nomzod bo‘la oladi. Chiqish berilgan matnning sonli ifodasi bo‘lib, qidiruv ilovasi uni manba materiali uchun saqlangan vektorlar bilan solishtiradi.</p><p>Chegaralari foydalanuvchi vazifasiga mos to‘plamdan boshlang. Yordam markazida bitta parcha tartib-taomil va uning dastlabki shartlarini tushuntirishi mumkin. Muhokama arxivida javob nimaga tegishli ekanini aniqlash uchun javoblar zanjirining yetarli qismini saqlang. Natija o‘qiladigan dalilga olib borishi kerak, shuning uchun har bir embedding bilan asl matn, barqaror manba havolasi va hujjat metama’lumotlarini saqlang.</p><p>Sinov so‘rovlarini foydalanuvchilar qanday so‘rasa, shunday yozing. «Hisob-fakturadagi manzilni qayerda o‘zgartiraman?» savoli tegishli maqola sarlavhasini ko‘chirishdan foydaliroq qidiruv tekshiruvidir. Qisqa savollar, boshqa ifodalar, soha qisqartmalari va bir nechta tushunchani tilga oladigan holatlarni kiriting. Qaytarilgan parchalarni tekshiring va topilmagan natija manba kontenti yetishmasligi, yomon parcha chegaralari yoki qidiruv tanlovidan kelib chiqqanini aniqlang.</p><p>Ishlab chiqaruvchi 32 000 tokenli kontekst oynasini hujjatlashtiradi. Bu sig‘im katta matnli kirishlarga imkon beradi, lekin parcha uzunligi qidiruv jarayoni uchun loyihaviy tanlov bo‘lib qoladi. Ixcham javobni zarur shartlar bilan birga saqlang; ta’rifni u tushuntiradigan atamadan uzib qo‘ymang. Manbadan o‘qiladigan matnni ajrating va foydalanuvchi izlayotgan ma’lumotni yashirsa, har sahifada takrorlanadigan navigatsiyani olib tashlang.</p><p>Voyage 3.5 Lite 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik ifodalar vektor indeksi uchun qo‘shimcha tanlovlar beradi. Kichikroq yoki kvantlangan ifodani to‘liq aniqlikdagi boshlang‘ich variant uchun ishlatilgan ayni savollar va korpusda sinang. So‘rov vektorlari mos sozlamalardan foydalanishi uchun tanlangan model, o‘lcham va ma’lumot turini indeks bilan birga qayd eting.</p><p>Ishlab chiqaruvchining qidiruv jarayonida indekslangan parchalarni hujjatlar, kiruvchi qidiruvlarni esa so‘rovlar sifatida belgilang. Bir nechta parchani birgalikda qayta ishlaganda har bir qaytarilgan vektorning kirish matni bilan bog‘lanishini saqlang. Muvaffaqiyatli embedding operatsiyasi vektorlarni beradi; ularni to‘g‘ri yozuvlarga joylash va foydalanuvchi natijani ochganda manba parchasini ko‘rsatish baribir ilovaning vazifasi.</p><p>Bu nashrdan qidiriladigan yordam xizmati to‘plami, hujjat topish vositasi yoki yordamchining qidiruv bosqichi uchun foydalaning. Ko‘p tilli materialda faqat bitta inglizcha sinovga tayanmasdan, o‘z to‘plamingizdagi til birikmalarini baholang. Tegishli moslikni to‘g‘ri kontekstda filtrlash va ko‘rsatish uchun ruxsatlar va til metama’lumotlari ilovaga ochiq bo‘lsin.</p><p>O‘sayotgan to‘plamlarda o‘zgargan parchalar vektorlarini manba versiyalari bilan birga yangilang. Oson mosliklar, qiyin qayta ifodalangan savollar va javoblari turli o‘xshash hujjatlarni qamrab oladigan haqiqiy savollar to‘plamini saqlang. Kutilgan yuklama ostida qidiruv sifati va to‘liq qidiruv jarayonining xatti-harakatini o‘lchang. Ishlab chiqaruvchining Lite nashriga bergan tavsifi nomzod tanlashga yordam beradi; yakuniy o‘lcham, parchalarni ajratish strategiyasi va indeks sozlamalari esa o‘z materialingizdagi natijalardan kelib chiqadi.</p>",
  "aboutText": "Voyage 3.5 Lite semantik qidiruv uchun matnni embeddinglarga aylantiradi. Voyage AI bu nashrni kechikish va xarajat bo‘yicha optimallashtirilgan deb ta’riflaydi; shu sababli u kiruvchi savollardan tez-tez embedding oladigan yoki qidiriladigan parchalarning katta to‘plamini saqlaydigan qidiruv jarayoniga nomzod bo‘la oladi. Chiqish berilgan matnning sonli ifodasi bo‘lib, qidiruv ilovasi uni manba materiali uchun saqlangan vektorlar bilan solishtiradi.\n\nChegaralari foydalanuvchi vazifasiga mos to‘plamdan boshlang. Yordam markazida bitta parcha tartib-taomil va uning dastlabki shartlarini tushuntirishi mumkin. Muhokama arxivida javob nimaga tegishli ekanini aniqlash uchun javoblar zanjirining yetarli qismini saqlang. Natija o‘qiladigan dalilga olib borishi kerak, shuning uchun har bir embedding bilan asl matn, barqaror manba havolasi va hujjat metama’lumotlarini saqlang.\n\nSinov so‘rovlarini foydalanuvchilar qanday so‘rasa, shunday yozing. «Hisob-fakturadagi manzilni qayerda o‘zgartiraman?» savoli tegishli maqola sarlavhasini ko‘chirishdan foydaliroq qidiruv tekshiruvidir. Qisqa savollar, boshqa ifodalar, soha qisqartmalari va bir nechta tushunchani tilga oladigan holatlarni kiriting. Qaytarilgan parchalarni tekshiring va topilmagan natija manba kontenti yetishmasligi, yomon parcha chegaralari yoki qidiruv tanlovidan kelib chiqqanini aniqlang.\n\nIshlab chiqaruvchi 32 000 tokenli kontekst oynasini hujjatlashtiradi. Bu sig‘im katta matnli kirishlarga imkon beradi, lekin parcha uzunligi qidiruv jarayoni uchun loyihaviy tanlov bo‘lib qoladi. Ixcham javobni zarur shartlar bilan birga saqlang; ta’rifni u tushuntiradigan atamadan uzib qo‘ymang. Manbadan o‘qiladigan matnni ajrating va foydalanuvchi izlayotgan ma’lumotni yashirsa, har sahifada takrorlanadigan navigatsiyani olib tashlang.\n\nVoyage 3.5 Lite 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik ifodalar vektor indeksi uchun qo‘shimcha tanlovlar beradi. Kichikroq yoki kvantlangan ifodani to‘liq aniqlikdagi boshlang‘ich variant uchun ishlatilgan ayni savollar va korpusda sinang. So‘rov vektorlari mos sozlamalardan foydalanishi uchun tanlangan model, o‘lcham va ma’lumot turini indeks bilan birga qayd eting.\n\nIshlab chiqaruvchining qidiruv jarayonida indekslangan parchalarni hujjatlar, kiruvchi qidiruvlarni esa so‘rovlar sifatida belgilang. Bir nechta parchani birgalikda qayta ishlaganda har bir qaytarilgan vektorning kirish matni bilan bog‘lanishini saqlang. Muvaffaqiyatli embedding operatsiyasi vektorlarni beradi; ularni to‘g‘ri yozuvlarga joylash va foydalanuvchi natijani ochganda manba parchasini ko‘rsatish baribir ilovaning vazifasi.\n\nBu nashrdan qidiriladigan yordam xizmati to‘plami, hujjat topish vositasi yoki yordamchining qidiruv bosqichi uchun foydalaning. Ko‘p tilli materialda faqat bitta inglizcha sinovga tayanmasdan, o‘z to‘plamingizdagi til birikmalarini baholang. Tegishli moslikni to‘g‘ri kontekstda filtrlash va ko‘rsatish uchun ruxsatlar va til metama’lumotlari ilovaga ochiq bo‘lsin.\n\nO‘sayotgan to‘plamlarda o‘zgargan parchalar vektorlarini manba versiyalari bilan birga yangilang. Oson mosliklar, qiyin qayta ifodalangan savollar va javoblari turli o‘xshash hujjatlarni qamrab oladigan haqiqiy savollar to‘plamini saqlang. Kutilgan yuklama ostida qidiruv sifati va to‘liq qidiruv jarayonining xatti-harakatini o‘lchang. Ishlab chiqaruvchining Lite nashriga bergan tavsifi nomzod tanlashga yordam beradi; yakuniy o‘lcham, parchalarni ajratish strategiyasi va indeks sozlamalari esa o‘z materialingizdagi natijalardan kelib chiqadi.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Voyage AI: Voyage 3.5 Lite</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Voyage AI /</span><code>voyage-3.5-lite</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-3.5-lite\">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 3.5 Lite javob kechikishi va samarali qidiruvga e’tibor qaratgan holda semantik qidiruv uchun matn embeddinglarini yaratadi. U bilim bazasida qidirish, hujjatlarni moslashtirish va RAG uchun 32 000 tokenli kontekst oynasi, to‘rtta vektor hajmi va kvantlangan chiqishni 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 3.5 Lite semantik qidiruv uchun matnni embeddinglarga aylantiradi. Voyage AI bu nashrni kechikish va xarajat bo‘yicha optimallashtirilgan deb ta’riflaydi; shu sababli u kiruvchi savollardan tez-tez embedding oladigan yoki qidiriladigan parchalarning katta to‘plamini saqlaydigan qidiruv jarayoniga nomzod bo‘la oladi. Chiqish berilgan matnning sonli ifodasi bo‘lib, qidiruv ilovasi uni manba materiali uchun saqlangan vektorlar bilan solishtiradi.</p><p>Chegaralari foydalanuvchi vazifasiga mos to‘plamdan boshlang. Yordam markazida bitta parcha tartib-taomil va uning dastlabki shartlarini tushuntirishi mumkin. Muhokama arxivida javob nimaga tegishli ekanini aniqlash uchun javoblar zanjirining yetarli qismini saqlang. Natija o‘qiladigan dalilga olib borishi kerak, shuning uchun har bir embedding bilan asl matn, barqaror manba havolasi va hujjat metama’lumotlarini saqlang.</p><p>Sinov so‘rovlarini foydalanuvchilar qanday so‘rasa, shunday yozing. «Hisob-fakturadagi manzilni qayerda o‘zgartiraman?» savoli tegishli maqola sarlavhasini ko‘chirishdan foydaliroq qidiruv tekshiruvidir. Qisqa savollar, boshqa ifodalar, soha qisqartmalari va bir nechta tushunchani tilga oladigan holatlarni kiriting. Qaytarilgan parchalarni tekshiring va topilmagan natija manba kontenti yetishmasligi, yomon parcha chegaralari yoki qidiruv tanlovidan kelib chiqqanini aniqlang.</p><p>Ishlab chiqaruvchi 32 000 tokenli kontekst oynasini hujjatlashtiradi. Bu sig‘im katta matnli kirishlarga imkon beradi, lekin parcha uzunligi qidiruv jarayoni uchun loyihaviy tanlov bo‘lib qoladi. Ixcham javobni zarur shartlar bilan birga saqlang; ta’rifni u tushuntiradigan atamadan uzib qo‘ymang. Manbadan o‘qiladigan matnni ajrating va foydalanuvchi izlayotgan ma’lumotni yashirsa, har sahifada takrorlanadigan navigatsiyani olib tashlang.</p><p>Voyage 3.5 Lite 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik ifodalar vektor indeksi uchun qo‘shimcha tanlovlar beradi. Kichikroq yoki kvantlangan ifodani to‘liq aniqlikdagi boshlang‘ich variant uchun ishlatilgan ayni savollar va korpusda sinang. So‘rov vektorlari mos sozlamalardan foydalanishi uchun tanlangan model, o‘lcham va ma’lumot turini indeks bilan birga qayd eting.</p><p>Ishlab chiqaruvchining qidiruv jarayonida indekslangan parchalarni hujjatlar, kiruvchi qidiruvlarni esa so‘rovlar sifatida belgilang. Bir nechta parchani birgalikda qayta ishlaganda har bir qaytarilgan vektorning kirish matni bilan bog‘lanishini saqlang. Muvaffaqiyatli embedding operatsiyasi vektorlarni beradi; ularni to‘g‘ri yozuvlarga joylash va foydalanuvchi natijani ochganda manba parchasini ko‘rsatish baribir ilovaning vazifasi.</p><p>Bu nashrdan qidiriladigan yordam xizmati to‘plami, hujjat topish vositasi yoki yordamchining qidiruv bosqichi uchun foydalaning. Ko‘p tilli materialda faqat bitta inglizcha sinovga tayanmasdan, o‘z to‘plamingizdagi til birikmalarini baholang. Tegishli moslikni to‘g‘ri kontekstda filtrlash va ko‘rsatish uchun ruxsatlar va til metama’lumotlari ilovaga ochiq bo‘lsin.</p><p>O‘sayotgan to‘plamlarda o‘zgargan parchalar vektorlarini manba versiyalari bilan birga yangilang. Oson mosliklar, qiyin qayta ifodalangan savollar va javoblari turli o‘xshash hujjatlarni qamrab oladigan haqiqiy savollar to‘plamini saqlang. Kutilgan yuklama ostida qidiruv sifati va to‘liq qidiruv jarayonining xatti-harakatini o‘lchang. Ishlab chiqaruvchining Lite nashriga bergan tavsifi nomzod tanlashga yordam beradi; yakuniy o‘lcham, parchalarni ajratish strategiyasi va indeks sozlamalari esa o‘z materialingizdagi natijalardan kelib chiqadi.</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-3.5-lite</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>$0.0001</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Chiqish · Chiqish tokenlari</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · Embeddinglar</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-2788-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2788-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · Barcha vektorlar va foydalanish ma’lumotlarini saqlang</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2788-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2788-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${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-3.5-lite&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-3.5-lite&amp;route=Ultra&amp;caller=voyage-3.5-lite&amp;fmt=embeddings\"><code>voyage-3.5-lite</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-3.5-lite&amp;route=Ultra&amp;caller=voyage-3.5-lite&amp;fmt=embeddings\"><code>voyage-3.5-lite</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "voyage-3.5-lite",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "voyage-3.5-lite",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|voyage-3.5-lite",
      "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-3.5-lite"
                ],
                "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-3.5-lite"
                  ],
                  "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-3.5-lite"
              },
              "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": "0.0001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000001",
                "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-3.5-lite\",\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": 0.1,
        "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": "0.0001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000001",
                "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-3.5-lite",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000001",
            "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-3.5-lite",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "31196f49152fc4e3d657e61623339db5811efbe72f056fa9be8104d8f25eaede",
    "input_html_sha256": "ec6e1a2e7fc827869c697180b422da5ce017e9fc2c632fd8bbb29d6f9575d707",
    "private_audit_sha256": "cd72d502aae6d23b097765cffd60ea2ebe4fc610b7390123059440c1b1d1a5e0",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/voyage-3-5-lite/",
    "description": "Voyage 3.5 Lite javob kechikishi va samarali qidiruvga e’tibor qaratgan holda semantik qidiruv uchun matn embeddinglarini yaratadi. U bilim bazasida qidirish, hujjatlarni moslashtirish va RAG uchun 32 000 tokenli kontekst oynasi, to‘rtta vektor hajmi va kvantlangan chiqishni qo‘llab-quvvatlaydi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/voyage-3-5-lite/index.md",
    "title": "Voyage AI: Voyage 3.5 Lite — Ailoot",
    "url": "https://ailoot.ai/models/uz/voyage-3-5-lite/"
  },
  "rawModel": {
    "about": "Voyage 3.5 Lite semantik qidiruv uchun matnni embeddinglarga aylantiradi. Voyage AI bu nashrni kechikish va xarajat bo‘yicha optimallashtirilgan deb ta’riflaydi; shu sababli u kiruvchi savollardan tez-tez embedding oladigan yoki qidiriladigan parchalarning katta to‘plamini saqlaydigan qidiruv jarayoniga nomzod bo‘la oladi. Chiqish berilgan matnning sonli ifodasi bo‘lib, qidiruv ilovasi uni manba materiali uchun saqlangan vektorlar bilan solishtiradi.\n\nChegaralari foydalanuvchi vazifasiga mos to‘plamdan boshlang. Yordam markazida bitta parcha tartib-taomil va uning dastlabki shartlarini tushuntirishi mumkin. Muhokama arxivida javob nimaga tegishli ekanini aniqlash uchun javoblar zanjirining yetarli qismini saqlang. Natija o‘qiladigan dalilga olib borishi kerak, shuning uchun har bir embedding bilan asl matn, barqaror manba havolasi va hujjat metama’lumotlarini saqlang.\n\nSinov so‘rovlarini foydalanuvchilar qanday so‘rasa, shunday yozing. «Hisob-fakturadagi manzilni qayerda o‘zgartiraman?» savoli tegishli maqola sarlavhasini ko‘chirishdan foydaliroq qidiruv tekshiruvidir. Qisqa savollar, boshqa ifodalar, soha qisqartmalari va bir nechta tushunchani tilga oladigan holatlarni kiriting. Qaytarilgan parchalarni tekshiring va topilmagan natija manba kontenti yetishmasligi, yomon parcha chegaralari yoki qidiruv tanlovidan kelib chiqqanini aniqlang.\n\nIshlab chiqaruvchi 32 000 tokenli kontekst oynasini hujjatlashtiradi. Bu sig‘im katta matnli kirishlarga imkon beradi, lekin parcha uzunligi qidiruv jarayoni uchun loyihaviy tanlov bo‘lib qoladi. Ixcham javobni zarur shartlar bilan birga saqlang; ta’rifni u tushuntiradigan atamadan uzib qo‘ymang. Manbadan o‘qiladigan matnni ajrating va foydalanuvchi izlayotgan ma’lumotni yashirsa, har sahifada takrorlanadigan navigatsiyani olib tashlang.\n\nVoyage 3.5 Lite 2 048, 1 024, 512 va 256 o‘lchamni qo‘llab-quvvatlaydi. Suzuvchi nuqtali, sakkiz bitli butun sonli va ikkilik ifodalar vektor indeksi uchun qo‘shimcha tanlovlar beradi. Kichikroq yoki kvantlangan ifodani to‘liq aniqlikdagi boshlang‘ich variant uchun ishlatilgan ayni savollar va korpusda sinang. So‘rov vektorlari mos sozlamalardan foydalanishi uchun tanlangan model, o‘lcham va ma’lumot turini indeks bilan birga qayd eting.\n\nIshlab chiqaruvchining qidiruv jarayonida indekslangan parchalarni hujjatlar, kiruvchi qidiruvlarni esa so‘rovlar sifatida belgilang. Bir nechta parchani birgalikda qayta ishlaganda har bir qaytarilgan vektorning kirish matni bilan bog‘lanishini saqlang. Muvaffaqiyatli embedding operatsiyasi vektorlarni beradi; ularni to‘g‘ri yozuvlarga joylash va foydalanuvchi natijani ochganda manba parchasini ko‘rsatish baribir ilovaning vazifasi.\n\nBu nashrdan qidiriladigan yordam xizmati to‘plami, hujjat topish vositasi yoki yordamchining qidiruv bosqichi uchun foydalaning. Ko‘p tilli materialda faqat bitta inglizcha sinovga tayanmasdan, o‘z to‘plamingizdagi til birikmalarini baholang. Tegishli moslikni to‘g‘ri kontekstda filtrlash va ko‘rsatish uchun ruxsatlar va til metama’lumotlari ilovaga ochiq bo‘lsin.\n\nO‘sayotgan to‘plamlarda o‘zgargan parchalar vektorlarini manba versiyalari bilan birga yangilang. Oson mosliklar, qiyin qayta ifodalangan savollar va javoblari turli o‘xshash hujjatlarni qamrab oladigan haqiqiy savollar to‘plamini saqlang. Kutilgan yuklama ostida qidiruv sifati va to‘liq qidiruv jarayonining xatti-harakatini o‘lchang. Ishlab chiqaruvchining Lite nashriga bergan tavsifi nomzod tanlashga yordam beradi; yakuniy o‘lcham, parchalarni ajratish strategiyasi va indeks sozlamalari esa o‘z materialingizdagi natijalardan kelib chiqadi.",
    "about_sha256": "6da9d6fd6cfde8d6f5bf8b7aa370b1ddd356c1c7303a01cbc8c52ded9e166385",
    "alias_of": null,
    "aliases": [],
    "author": "Voyage AI",
    "capabilities": {
      "context_length": 32000,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.0001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000001",
            "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 3.5 Lite",
    "formats": [
      "embeddings"
    ],
    "id": "voyage-3.5-lite",
    "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-3.5-lite"
                ],
                "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-3.5-lite"
                  ],
                  "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-3.5-lite"
              },
              "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": "0.0001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000001",
                "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-3.5-lite\",\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": 0.1,
        "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": "0.0001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000001",
                "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-3.5-lite",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000001",
            "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-3.5-lite",
        "request_model_id": "voyage-3.5-lite",
        "route_label": "Ultra"
      }
    ],
    "name": "Voyage AI: Voyage 3.5 Lite",
    "price": {
      "in": 0.1,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.1,
      "sort_out": 0,
      "varies": false
    },
    "short": "Voyage 3.5 Lite javob kechikishi va samarali qidiruvga e’tibor qaratgan holda semantik qidiruv uchun matn embeddinglarini yaratadi. U bilim bazasida qidirish, hujjatlarni moslashtirish va RAG uchun 32 000 tokenli kontekst oynasi, to‘rtta vektor hajmi va kvantlangan chiqishni qo‘llab-quvvatlaydi.",
    "short_sha256": "61db24acd587cfab4f14b7f3c526314d69996c4d48e50298883531c74deb64a7"
  },
  "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 3.5 Lite javob kechikishi va samarali qidiruvga e’tibor qaratgan holda semantik qidiruv uchun matn embeddinglarini yaratadi. U bilim bazasida qidirish, hujjatlarni moslashtirish va RAG uchun 32 000 tokenli kontekst oynasi, to‘rtta vektor hajmi va kvantlangan chiqishni qo‘llab-quvvatlaydi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "c1dac894396a46edf880c890f27519798e2d99b63f6e207f6253306a2cee2a93",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "c06cff3d957d60c416694f6e697fd9f36f85becdeb91766ebbcfe35b6240122d"
}