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  "aboutHtml": "<p>Gemini Embedding 2 Preview — Google’ning asl multimodal embedding oilasidagi nomlangan Preview nashri. U matn, rasm, video, audio va PDF’ni umumiy raqamli makonga o‘tkazadi; semantik ma’no 100 dan ortiq tilda ifodalanadi. Chiqish embeddinglar, ya’ni bog‘liq materialni taqqoslash va topish uchun vektorlardir. Bu Preview identifikatsiyasi barqaror Gemini Embedding 2 nashridan alohida qoladi; vaznlar o‘zaro almashtirilishini yoki bir xil vektor makonini anglatmaydi.</p><p>Dastlabki e’lon modalitlararo qidiruv, tasniflash, klasterlash, hissiy ohang tahlili va qidiruv bilan boyitilgan yaratishni keyingi qo‘llanishlar sifatida tavsiflaydi. Matn so‘rovi tegishli vizual elementni topishi, rasm yoki klip ham qidiruv manbasi bo‘lishi mumkin. Ilova ifodalarni saqlaydi, taqqoslaydi va tegishli yozuvlarni qaytaradi; alohida til modeli topilgan manba materialidan javob yozish uchun foydalanishi mumkin. Embeddingning o‘zida tabiiy tildagi izoh yoki transkript yo‘q.</p><p>Uning o‘z model sahifasi 8 192 tokenli kirish chegarasi va 128 dan 3 072 gacha chiqish o‘lchamlarini hujjatlashtiradi; tavsiya etilgan hajmlar 768, 1 536 va 3 072. Preview e’loni odatiy qiymatni 3 072 deb beradi va ifodaning qisqaroq boshlang‘ich qismida foydali ma’lumotni saqlaydigan Matryoshka Representation Learning’ni tavsiflaydi. Uzunliklari mosligi uchungina ifodalarni birlashtirish o‘rniga, taqqoslanadigan vektorlar uchun bitta tanlangan o‘lchamni saqlang va model nashrini to‘plam bilan qayd eting.</p><p>Preview e’loni bir so‘rovda oltitagacha PNG yoki JPEG rasmi, 120 soniyagacha MP4 yoki MOV videosi va olti sahifagacha PDF hujjatlarini hujjatlashtiradi. U oraliq matn transkripsiyasisiz bevosita audio embeddingni ham hujjatlashtiradi. Bu spetsifikatsiyalar keyingi barqaror qo‘llanmadan emas, Preview’ning o‘z e’lonidan olingan. E’lon aynan shu Preview profili uchun har bir joriy audio davomiyligi, kodek yoki kadr tanlash chegarasini belgilamaydi.</p><p>Aralash multimodal kirish ifodaga rasm va izohi kabi media o‘rtasidagi bog‘liqlikni aks ettirish imkonini beradi. Mahsulot katalogida foto va tasdiqlangan tavsif bitta topiladigan obyektni tasvirlasa, ularni birga bering. Hujjat to‘plamida manbani aniq yo‘naltirilgan parcha yoki sahifalarga bo‘ling va sarlavhasi hamda o‘rnini saqlang. Media kutubxonasida foydalanuvchilar topishi kerak bo‘lgan hodisaga mos klipni tanlang va asl vaqt oralig‘ini saqlangan yozuv bilan qoldiring.</p><p>Foydali qidiruv vazifasi mo‘ljallangan bog‘liqlikni aniq qiladi. Izoh izlaydigan savol, vizual o‘xshash elementlar qidiruvi va mavzu bo‘yicha klasterlash bir manba to‘plamidan foydalansa ham turli vazifalardir. Mazmunni izchil tayyorlang, nashr va o‘lchamni qayd eting va har vektorni manbasiga bog‘lang. O‘xshashlik ilova tekshirishi uchun nomzodlar beradi; ikki yozuv bir faktni aytishini yoki topilgan manba savolga to‘liq javob berishini isbotlamaydi.</p><p>Amaliy media qidiruvida tanlangan rasm yoki video parchasini ifodalang va kerakli mazmun tavsifi bilan taqqoslang. Foydalanuvchi ko‘rib chiqishi uchun mos manbani izohi yoki vaqt oralig‘i bilan ko‘rsating. Bilim yordamchisida yozma javob yaratishdan oldin tegishli sahifa yoki parchani toping. Preview modelining roli shu tanlash bosqichlarini qo‘llaydigan multimodal raqamli ifodadir; saqlash, reyting qoidalari va yakuniy javob esa tashqi jarayonning qismlari bo‘lib qoladi.</p>",
  "aboutText": "Gemini Embedding 2 Preview — Google’ning asl multimodal embedding oilasidagi nomlangan Preview nashri. U matn, rasm, video, audio va PDF’ni umumiy raqamli makonga o‘tkazadi; semantik ma’no 100 dan ortiq tilda ifodalanadi. Chiqish embeddinglar, ya’ni bog‘liq materialni taqqoslash va topish uchun vektorlardir. Bu Preview identifikatsiyasi barqaror Gemini Embedding 2 nashridan alohida qoladi; vaznlar o‘zaro almashtirilishini yoki bir xil vektor makonini anglatmaydi.\n\nDastlabki e’lon modalitlararo qidiruv, tasniflash, klasterlash, hissiy ohang tahlili va qidiruv bilan boyitilgan yaratishni keyingi qo‘llanishlar sifatida tavsiflaydi. Matn so‘rovi tegishli vizual elementni topishi, rasm yoki klip ham qidiruv manbasi bo‘lishi mumkin. Ilova ifodalarni saqlaydi, taqqoslaydi va tegishli yozuvlarni qaytaradi; alohida til modeli topilgan manba materialidan javob yozish uchun foydalanishi mumkin. Embeddingning o‘zida tabiiy tildagi izoh yoki transkript yo‘q.\n\nUning o‘z model sahifasi 8 192 tokenli kirish chegarasi va 128 dan 3 072 gacha chiqish o‘lchamlarini hujjatlashtiradi; tavsiya etilgan hajmlar 768, 1 536 va 3 072. Preview e’loni odatiy qiymatni 3 072 deb beradi va ifodaning qisqaroq boshlang‘ich qismida foydali ma’lumotni saqlaydigan Matryoshka Representation Learning’ni tavsiflaydi. Uzunliklari mosligi uchungina ifodalarni birlashtirish o‘rniga, taqqoslanadigan vektorlar uchun bitta tanlangan o‘lchamni saqlang va model nashrini to‘plam bilan qayd eting.\n\nPreview e’loni bir so‘rovda oltitagacha PNG yoki JPEG rasmi, 120 soniyagacha MP4 yoki MOV videosi va olti sahifagacha PDF hujjatlarini hujjatlashtiradi. U oraliq matn transkripsiyasisiz bevosita audio embeddingni ham hujjatlashtiradi. Bu spetsifikatsiyalar keyingi barqaror qo‘llanmadan emas, Preview’ning o‘z e’lonidan olingan. E’lon aynan shu Preview profili uchun har bir joriy audio davomiyligi, kodek yoki kadr tanlash chegarasini belgilamaydi.\n\nAralash multimodal kirish ifodaga rasm va izohi kabi media o‘rtasidagi bog‘liqlikni aks ettirish imkonini beradi. Mahsulot katalogida foto va tasdiqlangan tavsif bitta topiladigan obyektni tasvirlasa, ularni birga bering. Hujjat to‘plamida manbani aniq yo‘naltirilgan parcha yoki sahifalarga bo‘ling va sarlavhasi hamda o‘rnini saqlang. Media kutubxonasida foydalanuvchilar topishi kerak bo‘lgan hodisaga mos klipni tanlang va asl vaqt oralig‘ini saqlangan yozuv bilan qoldiring.\n\nFoydali qidiruv vazifasi mo‘ljallangan bog‘liqlikni aniq qiladi. Izoh izlaydigan savol, vizual o‘xshash elementlar qidiruvi va mavzu bo‘yicha klasterlash bir manba to‘plamidan foydalansa ham turli vazifalardir. Mazmunni izchil tayyorlang, nashr va o‘lchamni qayd eting va har vektorni manbasiga bog‘lang. O‘xshashlik ilova tekshirishi uchun nomzodlar beradi; ikki yozuv bir faktni aytishini yoki topilgan manba savolga to‘liq javob berishini isbotlamaydi.\n\nAmaliy media qidiruvida tanlangan rasm yoki video parchasini ifodalang va kerakli mazmun tavsifi bilan taqqoslang. Foydalanuvchi ko‘rib chiqishi uchun mos manbani izohi yoki vaqt oralig‘i bilan ko‘rsating. Bilim yordamchisida yozma javob yaratishdan oldin tegishli sahifa yoki parchani toping. Preview modelining roli shu tanlash bosqichlarini qo‘llaydigan multimodal raqamli ifodadir; saqlash, reyting qoidalari va yakuniy javob esa tashqi jarayonning qismlari bo‘lib qoladi.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">G</span><h2 id=\"sheet-title\">Google: Gemini Embedding 2 Preview</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Google /</span><code>google/gemini-embedding-2-preview</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|google%2Fgemini-embedding-2-preview\">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\">Gemini Embedding 2 Preview matn, rasm, audio, video va PDF’ni bitta multimodal makonda raqamli vektorlarga aylantiradi. Alohida hujjatlashtirilgan Preview profili modalitlararo qidiruv va taqqoslash, 8 192 tokenli kirish hamda moslashuvchan vektor o‘lchamlarini qo‘llaydi.</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>Gemini Embedding 2 Preview — Google’ning asl multimodal embedding oilasidagi nomlangan Preview nashri. U matn, rasm, video, audio va PDF’ni umumiy raqamli makonga o‘tkazadi; semantik ma’no 100 dan ortiq tilda ifodalanadi. Chiqish embeddinglar, ya’ni bog‘liq materialni taqqoslash va topish uchun vektorlardir. Bu Preview identifikatsiyasi barqaror Gemini Embedding 2 nashridan alohida qoladi; vaznlar o‘zaro almashtirilishini yoki bir xil vektor makonini anglatmaydi.</p><p>Dastlabki e’lon modalitlararo qidiruv, tasniflash, klasterlash, hissiy ohang tahlili va qidiruv bilan boyitilgan yaratishni keyingi qo‘llanishlar sifatida tavsiflaydi. Matn so‘rovi tegishli vizual elementni topishi, rasm yoki klip ham qidiruv manbasi bo‘lishi mumkin. Ilova ifodalarni saqlaydi, taqqoslaydi va tegishli yozuvlarni qaytaradi; alohida til modeli topilgan manba materialidan javob yozish uchun foydalanishi mumkin. Embeddingning o‘zida tabiiy tildagi izoh yoki transkript yo‘q.</p><p>Uning o‘z model sahifasi 8 192 tokenli kirish chegarasi va 128 dan 3 072 gacha chiqish o‘lchamlarini hujjatlashtiradi; tavsiya etilgan hajmlar 768, 1 536 va 3 072. Preview e’loni odatiy qiymatni 3 072 deb beradi va ifodaning qisqaroq boshlang‘ich qismida foydali ma’lumotni saqlaydigan Matryoshka Representation Learning’ni tavsiflaydi. Uzunliklari mosligi uchungina ifodalarni birlashtirish o‘rniga, taqqoslanadigan vektorlar uchun bitta tanlangan o‘lchamni saqlang va model nashrini to‘plam bilan qayd eting.</p><p>Preview e’loni bir so‘rovda oltitagacha PNG yoki JPEG rasmi, 120 soniyagacha MP4 yoki MOV videosi va olti sahifagacha PDF hujjatlarini hujjatlashtiradi. U oraliq matn transkripsiyasisiz bevosita audio embeddingni ham hujjatlashtiradi. Bu spetsifikatsiyalar keyingi barqaror qo‘llanmadan emas, Preview’ning o‘z e’lonidan olingan. E’lon aynan shu Preview profili uchun har bir joriy audio davomiyligi, kodek yoki kadr tanlash chegarasini belgilamaydi.</p><p>Aralash multimodal kirish ifodaga rasm va izohi kabi media o‘rtasidagi bog‘liqlikni aks ettirish imkonini beradi. Mahsulot katalogida foto va tasdiqlangan tavsif bitta topiladigan obyektni tasvirlasa, ularni birga bering. Hujjat to‘plamida manbani aniq yo‘naltirilgan parcha yoki sahifalarga bo‘ling va sarlavhasi hamda o‘rnini saqlang. Media kutubxonasida foydalanuvchilar topishi kerak bo‘lgan hodisaga mos klipni tanlang va asl vaqt oralig‘ini saqlangan yozuv bilan qoldiring.</p><p>Foydali qidiruv vazifasi mo‘ljallangan bog‘liqlikni aniq qiladi. Izoh izlaydigan savol, vizual o‘xshash elementlar qidiruvi va mavzu bo‘yicha klasterlash bir manba to‘plamidan foydalansa ham turli vazifalardir. Mazmunni izchil tayyorlang, nashr va o‘lchamni qayd eting va har vektorni manbasiga bog‘lang. O‘xshashlik ilova tekshirishi uchun nomzodlar beradi; ikki yozuv bir faktni aytishini yoki topilgan manba savolga to‘liq javob berishini isbotlamaydi.</p><p>Amaliy media qidiruvida tanlangan rasm yoki video parchasini ifodalang va kerakli mazmun tavsifi bilan taqqoslang. Foydalanuvchi ko‘rib chiqishi uchun mos manbani izohi yoki vaqt oralig‘i bilan ko‘rsating. Bilim yordamchisida yozma javob yaratishdan oldin tegishli sahifa yoki parchani toping. Preview modelining roli shu tanlash bosqichlarini qo‘llaydigan multimodal raqamli ifodadir; saqlash, reyting qoidalari va yakuniy javob esa tashqi jarayonning qismlari bo‘lib qoladi.</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><div><dt>Kontekst</dt><dd>8 192 tokens</dd></div><div><dt>Chiqish formati</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Maydon</th><th>Tur</th><th>Talab</th><th>Qiymatlar va cheklovlar</th><th>Standart</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>google/gemini-embedding-2-preview</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string|string[]</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></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Kirish materiallari birikmalari</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Usul parametrlari va hayot sikli</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parametr</th><th>Parametr talablari</th></tr></thead><tbody><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>content_type</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>application/json</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>required</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>model, input</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>input_shapes</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>string, string[]</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></tbody></table></div><p>Hayot sikli: <code>synchronous_json</code></p><p class=\"note-s\">Qaytarilgan har bir vektor va indeksni saqlang.</p><p class=\"note-s\">Ushbu minimal matn profilidan rasm/audio/fayl obyekti qo‘llab-quvvatlanishi haqida xulosa chiqarilmaydi.</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-1488-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1488-panel-0\" aria-selected=\"true\" tabindex=\"0\">To‘liq minimal JSON so‘rov tanasi</button><button class=\"cg-tab\" id=\"model-code-1488-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1488-panel-1\" aria-selected=\"false\" tabindex=\"-1\">curl · Matn embeddinglari uchun minimal so‘rov</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-1488-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1488-tab-0\"><pre data-lang=\"json\"><code>{\n  &quot;model&quot;: &quot;google/gemini-embedding-2-preview&quot;,\n  &quot;input&quot;: [\n    &quot;First document&quot;,\n    &quot;Second document&quot;\n  ]\n}</code></pre></div><div class=\"cg-pane\" id=\"model-code-1488-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1488-tab-1\" hidden><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\ncat &gt; embedding-request.json &lt;&lt;&#39;JSON_REQUEST&#39;\n{\n  &quot;model&quot;: &quot;google/gemini-embedding-2-preview&quot;,\n  &quot;input&quot;: [\n    &quot;First document&quot;,\n    &quot;Second document&quot;\n  ]\n}\nJSON_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @embedding-request.json -o embedding-response.json\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Natijalarni olish va saqlash</h3><h4>Ailoot Ultra · Embeddinglar</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Barcha tayyor natijalarni saqlash</h5><p class=\"note-s\">Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.</p><p class=\"note-s\">Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Asl javobni tekshirish kerak bo‘lsa</h5><p class=\"note-s\">Embedding vazifalari uchun endpoint qo‘shilmaydi.</p></div><details class=\"card-about\" data-output-fields><summary>Javob maydonlari</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Maydon</th><th>Tur</th></tr></thead><tbody><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>integer</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>finite float[]</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Xatolar va keyingi qadamlar</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Javob</th><th>Shart</th><th>Keyingi qadam</th></tr></thead><tbody><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>model yo‘q yoki JSON noto‘g‘ri tuzilgan.</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Ko‘rsatilgan maydonlarni tuzating.</td></tr><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>413 · <code>invalid_request_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>Sozlangan so‘rov tanasi hajmi limitidan oshib ketdi.</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Haqiqiy material hajmini kamaytiring.</td></tr><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>422 · <code>invalid_request_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>Kirish ma’lumoti yoki boshqaruv parametrlari amaldagi maxsus siyosatga zid.</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Ko‘rsatilgan minimal matn kirishidan meros qilib olingan boshqaruvlarsiz foydalaning.</td></tr><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>503 · <code>billing_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>Ruxsat yoki narx taklifini bog‘lab bo‘lmaydi.</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Javobni saqlang; siyosatni boshqasiga almashtirmang.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Taxalluslar</h3><h4>Model taxalluslari</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=google%2Fgemini-embedding-2-preview&amp;route=Ultra&amp;caller=google%2Fgemini-embedding-2-preview&amp;fmt=embeddings\"><code>google/gemini-embedding-2-preview</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=google%2Fgemini-embedding-2-preview&amp;route=Ultra&amp;caller=google%2Fgemini-embedding-2-preview&amp;fmt=embeddings\"><code>google/gemini-embedding-2-preview</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "google/gemini-embedding-2-preview",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "google/gemini-embedding-2-preview",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|google%2Fgemini-embedding-2-preview",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "model yo‘q yoki JSON noto‘g‘ri tuzilgan.",
                "http_status": 400,
                "recovery": "Ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              },
              {
                "code": null,
                "condition": "Sozlangan so‘rov tanasi hajmi limitidan oshib ketdi.",
                "http_status": 413,
                "recovery": "Haqiqiy material hajmini kamaytiring.",
                "type": "invalid_request_error"
              },
              {
                "code": null,
                "condition": "Kirish ma’lumoti yoki boshqaruv parametrlari amaldagi maxsus siyosatga zid.",
                "http_status": 422,
                "recovery": "Ko‘rsatilgan minimal matn kirishidan meros qilib olingan boshqaruvlarsiz foydalaning.",
                "type": "invalid_request_error"
              },
              {
                "code": null,
                "condition": "Ruxsat yoki narx taklifini bog‘lab bo‘lmaydi.",
                "http_status": 503,
                "recovery": "Javobni saqlang; siyosatni boshqasiga almashtirmang.",
                "type": "billing_error"
              }
            ],
            "example_body": {
              "input": [
                "First document",
                "Second document"
              ],
              "model": "google/gemini-embedding-2-preview"
            },
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "google/gemini-embedding-2-preview"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "input",
                "required": true,
                "type": "string|string[]"
              }
            ],
            "profile_binding": "Minimal current Source subset: only model and text input. Optional controls and another model dimensions/token limits are not inherited.",
            "request_schema": {
              "additionalProperties": false,
              "properties": {
                "input": {
                  "oneOf": [
                    {
                      "minLength": 1,
                      "type": "string"
                    },
                    {
                      "items": {
                        "minLength": 1,
                        "type": "string"
                      },
                      "minItems": 1,
                      "type": "array"
                    }
                  ]
                },
                "model": {
                  "enum": [
                    "google/gemini-embedding-2-preview"
                  ],
                  "type": "string"
                }
              },
              "required": [
                "model",
                "input"
              ],
              "type": "object"
            },
            "schema_template": {
              "input": [
                "First document",
                "Second document"
              ],
              "model": "google/gemini-embedding-2-preview"
            },
            "unsupported": [
              "image/audio/file objects in this minimal text profile",
              "dimensions (current Native43 supports_dimensions:false)",
              "foreign model-specific dimensions or token limits"
            ]
          },
          "hints": [
            "Qaytarilgan har bir vektor va indeksni saqlang.",
            "Ushbu minimal matn profilidan rasm/audio/fayl obyekti qo‘llab-quvvatlanishi haqida xulosa chiqarilmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "input_shapes": [
              "string",
              "string[]"
            ],
            "material_types": [
              "text"
            ],
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "Keep every vector, index, model and usage field. Current dedicated Source validates count, finite float elements and consistent actual dimensions."
          }
        },
        "ctx": 8192,
        "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": "{\n  \"model\": \"google/gemini-embedding-2-preview\",\n  \"input\": [\n    \"First document\",\n    \"Second document\"\n  ]\n}",
            "label": "Complete minimal JSON body",
            "language": "json"
          },
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\ncat > embedding-request.json <<'JSON_REQUEST'\n{\n  \"model\": \"google/gemini-embedding-2-preview\",\n  \"input\": [\n    \"First document\",\n    \"Second document\"\n  ]\n}\nJSON_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @embedding-request.json -o embedding-response.json\n",
            "label": "curl · minimal text embeddings",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.4,
        "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": "google/gemini-embedding-2-preview",
        "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": "google/gemini-embedding-2-preview",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "04489086f248ef5b3510c560adc0a95e0e8a5ee69ec75a8b14a8aa95a37b7247",
    "input_html_sha256": "e50a1ef79189e15d54ca35465d47daba197893033619adf8df4e22f6c4e2dd2b",
    "private_audit_sha256": "0891c18196babce8e6b59d948c278d330fd29b738da16497ae66905949da197c",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/google-gemini-embedding-2-preview/",
    "description": "Gemini Embedding 2 Preview matn, rasm, audio, video va PDF’ni bitta multimodal makonda raqamli vektorlarga aylantiradi. Alohida hujjatlashtirilgan Preview profili modalitlararo qidiruv va taqqoslash, 8 192 tokenli kirish hamda moslashuvchan vektor o‘lchamlarini qo‘llaydi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/google-gemini-embedding-2-preview/index.md",
    "title": "Google: Gemini Embedding 2 Preview — Ailoot",
    "url": "https://ailoot.ai/models/uz/google-gemini-embedding-2-preview/"
  },
  "rawModel": {
    "about": "Gemini Embedding 2 Preview — Google’ning asl multimodal embedding oilasidagi nomlangan Preview nashri. U matn, rasm, video, audio va PDF’ni umumiy raqamli makonga o‘tkazadi; semantik ma’no 100 dan ortiq tilda ifodalanadi. Chiqish embeddinglar, ya’ni bog‘liq materialni taqqoslash va topish uchun vektorlardir. Bu Preview identifikatsiyasi barqaror Gemini Embedding 2 nashridan alohida qoladi; vaznlar o‘zaro almashtirilishini yoki bir xil vektor makonini anglatmaydi.\n\nDastlabki e’lon modalitlararo qidiruv, tasniflash, klasterlash, hissiy ohang tahlili va qidiruv bilan boyitilgan yaratishni keyingi qo‘llanishlar sifatida tavsiflaydi. Matn so‘rovi tegishli vizual elementni topishi, rasm yoki klip ham qidiruv manbasi bo‘lishi mumkin. Ilova ifodalarni saqlaydi, taqqoslaydi va tegishli yozuvlarni qaytaradi; alohida til modeli topilgan manba materialidan javob yozish uchun foydalanishi mumkin. Embeddingning o‘zida tabiiy tildagi izoh yoki transkript yo‘q.\n\nUning o‘z model sahifasi 8 192 tokenli kirish chegarasi va 128 dan 3 072 gacha chiqish o‘lchamlarini hujjatlashtiradi; tavsiya etilgan hajmlar 768, 1 536 va 3 072. Preview e’loni odatiy qiymatni 3 072 deb beradi va ifodaning qisqaroq boshlang‘ich qismida foydali ma’lumotni saqlaydigan Matryoshka Representation Learning’ni tavsiflaydi. Uzunliklari mosligi uchungina ifodalarni birlashtirish o‘rniga, taqqoslanadigan vektorlar uchun bitta tanlangan o‘lchamni saqlang va model nashrini to‘plam bilan qayd eting.\n\nPreview e’loni bir so‘rovda oltitagacha PNG yoki JPEG rasmi, 120 soniyagacha MP4 yoki MOV videosi va olti sahifagacha PDF hujjatlarini hujjatlashtiradi. U oraliq matn transkripsiyasisiz bevosita audio embeddingni ham hujjatlashtiradi. Bu spetsifikatsiyalar keyingi barqaror qo‘llanmadan emas, Preview’ning o‘z e’lonidan olingan. E’lon aynan shu Preview profili uchun har bir joriy audio davomiyligi, kodek yoki kadr tanlash chegarasini belgilamaydi.\n\nAralash multimodal kirish ifodaga rasm va izohi kabi media o‘rtasidagi bog‘liqlikni aks ettirish imkonini beradi. Mahsulot katalogida foto va tasdiqlangan tavsif bitta topiladigan obyektni tasvirlasa, ularni birga bering. Hujjat to‘plamida manbani aniq yo‘naltirilgan parcha yoki sahifalarga bo‘ling va sarlavhasi hamda o‘rnini saqlang. Media kutubxonasida foydalanuvchilar topishi kerak bo‘lgan hodisaga mos klipni tanlang va asl vaqt oralig‘ini saqlangan yozuv bilan qoldiring.\n\nFoydali qidiruv vazifasi mo‘ljallangan bog‘liqlikni aniq qiladi. Izoh izlaydigan savol, vizual o‘xshash elementlar qidiruvi va mavzu bo‘yicha klasterlash bir manba to‘plamidan foydalansa ham turli vazifalardir. Mazmunni izchil tayyorlang, nashr va o‘lchamni qayd eting va har vektorni manbasiga bog‘lang. O‘xshashlik ilova tekshirishi uchun nomzodlar beradi; ikki yozuv bir faktni aytishini yoki topilgan manba savolga to‘liq javob berishini isbotlamaydi.\n\nAmaliy media qidiruvida tanlangan rasm yoki video parchasini ifodalang va kerakli mazmun tavsifi bilan taqqoslang. Foydalanuvchi ko‘rib chiqishi uchun mos manbani izohi yoki vaqt oralig‘i bilan ko‘rsating. Bilim yordamchisida yozma javob yaratishdan oldin tegishli sahifa yoki parchani toping. Preview modelining roli shu tanlash bosqichlarini qo‘llaydigan multimodal raqamli ifodadir; saqlash, reyting qoidalari va yakuniy javob esa tashqi jarayonning qismlari bo‘lib qoladi.",
    "alias_of": null,
    "aliases": [],
    "author": "Google",
    "capabilities": {
      "context_length": 8192,
      "manufacturer_input": [
        "text",
        "image",
        "file",
        "audio",
        "video"
      ],
      "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": 8192,
    "formats": [
      "embeddings"
    ],
    "id": "google/gemini-embedding-2-preview",
    "input": [
      "text"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "model yo‘q yoki JSON noto‘g‘ri tuzilgan.",
                "http_status": 400,
                "recovery": "Ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              },
              {
                "code": null,
                "condition": "Sozlangan so‘rov tanasi hajmi limitidan oshib ketdi.",
                "http_status": 413,
                "recovery": "Haqiqiy material hajmini kamaytiring.",
                "type": "invalid_request_error"
              },
              {
                "code": null,
                "condition": "Kirish ma’lumoti yoki boshqaruv parametrlari amaldagi maxsus siyosatga zid.",
                "http_status": 422,
                "recovery": "Ko‘rsatilgan minimal matn kirishidan meros qilib olingan boshqaruvlarsiz foydalaning.",
                "type": "invalid_request_error"
              },
              {
                "code": null,
                "condition": "Ruxsat yoki narx taklifini bog‘lab bo‘lmaydi.",
                "http_status": 503,
                "recovery": "Javobni saqlang; siyosatni boshqasiga almashtirmang.",
                "type": "billing_error"
              }
            ],
            "example_body": {
              "input": [
                "First document",
                "Second document"
              ],
              "model": "google/gemini-embedding-2-preview"
            },
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "google/gemini-embedding-2-preview"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "input",
                "required": true,
                "type": "string|string[]"
              }
            ],
            "profile_binding": "Minimal current Source subset: only model and text input. Optional controls and another model dimensions/token limits are not inherited.",
            "request_schema": {
              "additionalProperties": false,
              "properties": {
                "input": {
                  "oneOf": [
                    {
                      "minLength": 1,
                      "type": "string"
                    },
                    {
                      "items": {
                        "minLength": 1,
                        "type": "string"
                      },
                      "minItems": 1,
                      "type": "array"
                    }
                  ]
                },
                "model": {
                  "enum": [
                    "google/gemini-embedding-2-preview"
                  ],
                  "type": "string"
                }
              },
              "required": [
                "model",
                "input"
              ],
              "type": "object"
            },
            "schema_template": {
              "input": [
                "First document",
                "Second document"
              ],
              "model": "google/gemini-embedding-2-preview"
            },
            "unsupported": [
              "image/audio/file objects in this minimal text profile",
              "dimensions (current Native43 supports_dimensions:false)",
              "foreign model-specific dimensions or token limits"
            ]
          },
          "hints": [
            "Qaytarilgan har bir vektor va indeksni saqlang.",
            "Ushbu minimal matn profilidan rasm/audio/fayl obyekti qo‘llab-quvvatlanishi haqida xulosa chiqarilmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "input_shapes": [
              "string",
              "string[]"
            ],
            "material_types": [
              "text"
            ],
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "Keep every vector, index, model and usage field. Current dedicated Source validates count, finite float elements and consistent actual dimensions."
          }
        },
        "ctx": 8192,
        "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": "{\n  \"model\": \"google/gemini-embedding-2-preview\",\n  \"input\": [\n    \"First document\",\n    \"Second document\"\n  ]\n}",
            "label": "Complete minimal JSON body",
            "language": "json"
          },
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\ncat > embedding-request.json <<'JSON_REQUEST'\n{\n  \"model\": \"google/gemini-embedding-2-preview\",\n  \"input\": [\n    \"First document\",\n    \"Second document\"\n  ]\n}\nJSON_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @embedding-request.json -o embedding-response.json\n",
            "label": "curl · minimal text embeddings",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.4,
        "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": "google/gemini-embedding-2-preview",
        "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",
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            "native_unit": "input_token",
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            "quantity": 1000,
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          {
            "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",
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            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
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            "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": "google/gemini-embedding-2-preview",
        "request_model_id": "google/gemini-embedding-2-preview",
        "route_label": "Ultra"
      }
    ],
    "name": "Google: Gemini Embedding 2 Preview",
    "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": "Gemini Embedding 2 Preview matn, rasm, audio, video va PDF’ni bitta multimodal makonda raqamli vektorlarga aylantiradi. Alohida hujjatlashtirilgan Preview profili modalitlararo qidiruv va taqqoslash, 8 192 tokenli kirish hamda moslashuvchan vektor o‘lchamlarini qo‘llaydi."
  },
  "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": "Gemini Embedding 2 Preview matn, rasm, audio, video va PDF’ni bitta multimodal makonda raqamli vektorlarga aylantiradi. Alohida hujjatlashtirilgan Preview profili modalitlararo qidiruv va taqqoslash, 8 192 tokenli kirish hamda moslashuvchan vektor o‘lchamlarini qo‘llaydi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "0eab73be1421ea2c3f6c5a668832d3b547639f79b6c2fcf7eb77e518a80700b3",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "994aafe49c0f0b30cd8653311472abe044f4db2fbace6b68c7e3f86eb3bc326d"
}