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  "aboutHtml": "<p>bge-base-en-v1.5 — BAAI’ning base hajmidagi inglizcha zich embedding modeli. U har matnni qidiruv va semantik taqqoslash uchun 768 o‘lchamli raqamli ifodaga aylantiradi. Model oldingi oilaning o‘xshashlik ballari taqsimotini yaxshilaydigan va ko‘rsatma berilmaganda qidiruvni kuchaytiradigan BGE v1.5 qatoriga tegishli. Uning chiqishi matnlarni taqqoslash yoki tegishli parchalarni topish uchun ishlatiladigan vektordir.</p><h4>So‘rovlar va parchalarni tayyorlang</h4><p>Uzunroq parchalarni qidirayotgan qisqa so‘rov uchun BAAI so‘rov oldiga “Represent this sentence for searching relevant passages:” prefiksini qo‘shishni tavsiya qiladi. Korpus parchalariga bu ko‘rsatma qo‘shilmaydi. Boshqa vazifalarda asl matnni bevosita embedding qilish mumkin. Ishlab chiqaruvchi v1.5 ko‘rsatmasiz ham ishlashini aytadi; demak prefiksni yagona yaroqli kirish deb bilish o‘rniga, tayyorlash usulini vazifaga qarab tanlash kerak.</p><p>Amaliy qidiruv to‘plami yordam maqolalari, mahsulot izohlari yoki texnik ko‘rsatmalarni saqlashi mumkin. “How do I change the export format?” kabi so‘rov shu amalni tavsiflaydigan parchani topishi kerak. Bo‘lim sarlavhasi, tegishli shartlar va haqiqiy manba matnini birga saqlang. Qidiruv natijasi foydalanuvchi o‘qiy oladigan yoki keyingi javob bosqichi iqtibos keltira oladigan materialni qaytarishi uchun parcha identifikatori va joyini vektor yonida saqlang.</p><h4>Vektor formati va taqqoslash</h4><p>Hujjatlashtirilgan ifoda gap embeddingi sifatida birinchi, ya’ni CLS tokenining yakuniy yashirin holatidan foydalanadi. Ishlab chiqaruvchi misollari vektorni normallaydi va normallangan embeddinglarni kosinus o‘xshashligiga teng skalyar ko‘paytma bilan taqqoslaydi. 768 o‘lcham shu model chiqish ifodasining hajmidir; bu tanlanadigan kontekst uzunligi yoki matn yaratish limiti emas.</p><p>Taqqoslanayotgan so‘rov va korpus vektorlari uchun bir xil model va tayyorlash qoidasidan foydalaning. Amaliy tekshiruv — haqiqiy foydalanuvchi savollarining kichik to‘plami uchun javob borligi ma’lum parchalarni topib, qaytgan matnni tekshirish. Mos vektor shakli indeksga ifodani saqlash imkonini beradi; natija aslida nima deyishini esa manba parchasi belgilaydi.</p><h4>Kirish uzunligi va hujjatni tayyorlash</h4><p>Modelning hujjatlashtirilgan ketma-ketlik uzunligi 512 token. Shuning uchun uzun manbani embedding qilishdan oldin izchil, javobni o‘z ichiga oladigan parchalarga bo‘lish kerak. Ta’riflar va istisnolarni ular aniqlik kiritadigan bayonot bilan saqlang, parcha bilan asosiy hujjat bog‘liqligini saqlang. Embedding qilinadigan matnga qo‘shgan ko‘rsatma yoki sarlavhangiz ham kiradi; ixcham tayyorlash foydali manba mazmuniga ko‘proq joy qoldiradi.</p><p>Hisobot yoki qo‘llanmada chegaralarni bo‘lim yoki bir nechta bog‘liq paragraf atrofida tanlang. Faqat sarlavha yoki mavzusi yo‘q bo‘lakdan iborat parchani topilganda tushunish qiyin bo‘lishi mumkin. Hujjat o‘zgarsa, indeks yangilanishi tegishli materialni almashtira olishi uchun asl matn va manba versiyasini saqlang.</p><h4>Ballar va foydali chiqish</h4><p>BAAI mutlaq o‘xshashlik qiymatlari ikki matn bir xil ma’noni anglatishini hal qiladigan universal chegaralar emasligini ogohlantiradi. Nisbiy reyting muhim; filtrlash chegarasini vazifaning o‘z ma’lumotlaridagi taqsimot orqali tanlash kerak. Yuqori vektor o‘xshashligi parcha faktlarini tasdiqlash emas, qidiruv signalidir.</p><p>Foydali natija — inglizcha parchalar indeksi, so‘rov vektorlari to‘plami yoki semantik taqqoslash uchun raqamli xususiyatlar. Alohida qayta reytinglovchi model topilgan nomzodlarni aniqlashtirishi, alohida yaratish bosqichi esa ularning matnidan javob yozishi mumkin. Aynan bu base inglizcha v1.5 modeli BGE Large, xitoycha nashrlar va BGE-M3’ning ko‘p tilli hamda gibrid qidiruv profillaridan farq qiladi.</p>",
  "aboutText": "bge-base-en-v1.5 — BAAI’ning base hajmidagi inglizcha zich embedding modeli. U har matnni qidiruv va semantik taqqoslash uchun 768 o‘lchamli raqamli ifodaga aylantiradi. Model oldingi oilaning o‘xshashlik ballari taqsimotini yaxshilaydigan va ko‘rsatma berilmaganda qidiruvni kuchaytiradigan BGE v1.5 qatoriga tegishli. Uning chiqishi matnlarni taqqoslash yoki tegishli parchalarni topish uchun ishlatiladigan vektordir.\n\n### So‘rovlar va parchalarni tayyorlang\n\nUzunroq parchalarni qidirayotgan qisqa so‘rov uchun BAAI so‘rov oldiga “Represent this sentence for searching relevant passages:” prefiksini qo‘shishni tavsiya qiladi. Korpus parchalariga bu ko‘rsatma qo‘shilmaydi. Boshqa vazifalarda asl matnni bevosita embedding qilish mumkin. Ishlab chiqaruvchi v1.5 ko‘rsatmasiz ham ishlashini aytadi; demak prefiksni yagona yaroqli kirish deb bilish o‘rniga, tayyorlash usulini vazifaga qarab tanlash kerak.\n\nAmaliy qidiruv to‘plami yordam maqolalari, mahsulot izohlari yoki texnik ko‘rsatmalarni saqlashi mumkin. “How do I change the export format?” kabi so‘rov shu amalni tavsiflaydigan parchani topishi kerak. Bo‘lim sarlavhasi, tegishli shartlar va haqiqiy manba matnini birga saqlang. Qidiruv natijasi foydalanuvchi o‘qiy oladigan yoki keyingi javob bosqichi iqtibos keltira oladigan materialni qaytarishi uchun parcha identifikatori va joyini vektor yonida saqlang.\n\n### Vektor formati va taqqoslash\n\nHujjatlashtirilgan ifoda gap embeddingi sifatida birinchi, ya’ni CLS tokenining yakuniy yashirin holatidan foydalanadi. Ishlab chiqaruvchi misollari vektorni normallaydi va normallangan embeddinglarni kosinus o‘xshashligiga teng skalyar ko‘paytma bilan taqqoslaydi. 768 o‘lcham shu model chiqish ifodasining hajmidir; bu tanlanadigan kontekst uzunligi yoki matn yaratish limiti emas.\n\nTaqqoslanayotgan so‘rov va korpus vektorlari uchun bir xil model va tayyorlash qoidasidan foydalaning. Amaliy tekshiruv — haqiqiy foydalanuvchi savollarining kichik to‘plami uchun javob borligi ma’lum parchalarni topib, qaytgan matnni tekshirish. Mos vektor shakli indeksga ifodani saqlash imkonini beradi; natija aslida nima deyishini esa manba parchasi belgilaydi.\n\n### Kirish uzunligi va hujjatni tayyorlash\n\nModelning hujjatlashtirilgan ketma-ketlik uzunligi 512 token. Shuning uchun uzun manbani embedding qilishdan oldin izchil, javobni o‘z ichiga oladigan parchalarga bo‘lish kerak. Ta’riflar va istisnolarni ular aniqlik kiritadigan bayonot bilan saqlang, parcha bilan asosiy hujjat bog‘liqligini saqlang. Embedding qilinadigan matnga qo‘shgan ko‘rsatma yoki sarlavhangiz ham kiradi; ixcham tayyorlash foydali manba mazmuniga ko‘proq joy qoldiradi.\n\nHisobot yoki qo‘llanmada chegaralarni bo‘lim yoki bir nechta bog‘liq paragraf atrofida tanlang. Faqat sarlavha yoki mavzusi yo‘q bo‘lakdan iborat parchani topilganda tushunish qiyin bo‘lishi mumkin. Hujjat o‘zgarsa, indeks yangilanishi tegishli materialni almashtira olishi uchun asl matn va manba versiyasini saqlang.\n\n### Ballar va foydali chiqish\n\nBAAI mutlaq o‘xshashlik qiymatlari ikki matn bir xil ma’noni anglatishini hal qiladigan universal chegaralar emasligini ogohlantiradi. Nisbiy reyting muhim; filtrlash chegarasini vazifaning o‘z ma’lumotlaridagi taqsimot orqali tanlash kerak. Yuqori vektor o‘xshashligi parcha faktlarini tasdiqlash emas, qidiruv signalidir.\n\nFoydali natija — inglizcha parchalar indeksi, so‘rov vektorlari to‘plami yoki semantik taqqoslash uchun raqamli xususiyatlar. Alohida qayta reytinglovchi model topilgan nomzodlarni aniqlashtirishi, alohida yaratish bosqichi esa ularning matnidan javob yozishi mumkin. Aynan bu base inglizcha v1.5 modeli BGE Large, xitoycha nashrlar va BGE-M3’ning ko‘p tilli hamda gibrid qidiruv profillaridan farq qiladi.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">BAAI: bge-base-en-v1.5</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>BAAI /</span><code>baai/bge-base-en-v1.5</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|baai%2Fbge-base-en-v1.5\">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\">BAAI bge-base-en-v1.5 inglizcha matnni semantik qidiruv va o‘xshashlik vazifalari uchun 768 o‘lchamli vektorlarga aylantiradi. Uning 512 tokenli kirish oynasi va faqat so‘rovga qo‘llanadigan qidiruv ko‘rsatmasi hujjat yoki bilim qidiruv indeksidagi mazmuni aniq yo‘naltirilgan parchalarga mos.</p><dl class=\"card-io\" data-card-io><div><dt>Qabul qilinadigan kirish ma’lumotlari</dt><dd>Matn · token_ids</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>bge-base-en-v1.5 — BAAI’ning base hajmidagi inglizcha zich embedding modeli. U har matnni qidiruv va semantik taqqoslash uchun 768 o‘lchamli raqamli ifodaga aylantiradi. Model oldingi oilaning o‘xshashlik ballari taqsimotini yaxshilaydigan va ko‘rsatma berilmaganda qidiruvni kuchaytiradigan BGE v1.5 qatoriga tegishli. Uning chiqishi matnlarni taqqoslash yoki tegishli parchalarni topish uchun ishlatiladigan vektordir.</p><h4>So‘rovlar va parchalarni tayyorlang</h4><p>Uzunroq parchalarni qidirayotgan qisqa so‘rov uchun BAAI so‘rov oldiga “Represent this sentence for searching relevant passages:” prefiksini qo‘shishni tavsiya qiladi. Korpus parchalariga bu ko‘rsatma qo‘shilmaydi. Boshqa vazifalarda asl matnni bevosita embedding qilish mumkin. Ishlab chiqaruvchi v1.5 ko‘rsatmasiz ham ishlashini aytadi; demak prefiksni yagona yaroqli kirish deb bilish o‘rniga, tayyorlash usulini vazifaga qarab tanlash kerak.</p><p>Amaliy qidiruv to‘plami yordam maqolalari, mahsulot izohlari yoki texnik ko‘rsatmalarni saqlashi mumkin. “How do I change the export format?” kabi so‘rov shu amalni tavsiflaydigan parchani topishi kerak. Bo‘lim sarlavhasi, tegishli shartlar va haqiqiy manba matnini birga saqlang. Qidiruv natijasi foydalanuvchi o‘qiy oladigan yoki keyingi javob bosqichi iqtibos keltira oladigan materialni qaytarishi uchun parcha identifikatori va joyini vektor yonida saqlang.</p><h4>Vektor formati va taqqoslash</h4><p>Hujjatlashtirilgan ifoda gap embeddingi sifatida birinchi, ya’ni CLS tokenining yakuniy yashirin holatidan foydalanadi. Ishlab chiqaruvchi misollari vektorni normallaydi va normallangan embeddinglarni kosinus o‘xshashligiga teng skalyar ko‘paytma bilan taqqoslaydi. 768 o‘lcham shu model chiqish ifodasining hajmidir; bu tanlanadigan kontekst uzunligi yoki matn yaratish limiti emas.</p><p>Taqqoslanayotgan so‘rov va korpus vektorlari uchun bir xil model va tayyorlash qoidasidan foydalaning. Amaliy tekshiruv — haqiqiy foydalanuvchi savollarining kichik to‘plami uchun javob borligi ma’lum parchalarni topib, qaytgan matnni tekshirish. Mos vektor shakli indeksga ifodani saqlash imkonini beradi; natija aslida nima deyishini esa manba parchasi belgilaydi.</p><h4>Kirish uzunligi va hujjatni tayyorlash</h4><p>Modelning hujjatlashtirilgan ketma-ketlik uzunligi 512 token. Shuning uchun uzun manbani embedding qilishdan oldin izchil, javobni o‘z ichiga oladigan parchalarga bo‘lish kerak. Ta’riflar va istisnolarni ular aniqlik kiritadigan bayonot bilan saqlang, parcha bilan asosiy hujjat bog‘liqligini saqlang. Embedding qilinadigan matnga qo‘shgan ko‘rsatma yoki sarlavhangiz ham kiradi; ixcham tayyorlash foydali manba mazmuniga ko‘proq joy qoldiradi.</p><p>Hisobot yoki qo‘llanmada chegaralarni bo‘lim yoki bir nechta bog‘liq paragraf atrofida tanlang. Faqat sarlavha yoki mavzusi yo‘q bo‘lakdan iborat parchani topilganda tushunish qiyin bo‘lishi mumkin. Hujjat o‘zgarsa, indeks yangilanishi tegishli materialni almashtira olishi uchun asl matn va manba versiyasini saqlang.</p><h4>Ballar va foydali chiqish</h4><p>BAAI mutlaq o‘xshashlik qiymatlari ikki matn bir xil ma’noni anglatishini hal qiladigan universal chegaralar emasligini ogohlantiradi. Nisbiy reyting muhim; filtrlash chegarasini vazifaning o‘z ma’lumotlaridagi taqsimot orqali tanlash kerak. Yuqori vektor o‘xshashligi parcha faktlarini tasdiqlash emas, qidiruv signalidir.</p><p>Foydali natija — inglizcha parchalar indeksi, so‘rov vektorlari to‘plami yoki semantik taqqoslash uchun raqamli xususiyatlar. Alohida qayta reytinglovchi model topilgan nomzodlarni aniqlashtirishi, alohida yaratish bosqichi esa ularning matnidan javob yozishi mumkin. Aynan bu base inglizcha v1.5 modeli BGE Large, xitoycha nashrlar va BGE-M3’ning ko‘p tilli hamda gibrid qidiruv profillaridan farq qiladi.</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>, <code>token_ids</code></dd></div><div><dt>Chiqish</dt><dd><code>embedding</code></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>baai/bge-base-en-v1.5</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[]|integer[]|integer[][]</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>Elementlarning eng kam soni: 1</div><div>Shartlar: Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.</div><div>Kirish budjeti: Eng ko‘p: 512 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari</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\">encoding_format</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>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>float</div></td><td data-cell-label=\"Standart\" data-default=\"known\"><span class=\"card-cell-label\">Standart</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>integer</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Qo‘llab-quvvatlanmaydi</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Eng kam: 1</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</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\">provider</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>object</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: provider.options rad etildi</div></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[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>encoding_format</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>float</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>dimensions_override</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>max_input_budget_units</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>512</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>multimodal_objects</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</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, token_ids</td></tr></tbody></table></div><p>Hayot sikli: <code>synchronous_json</code></p><p class=\"note-s\">Har bir vektorning indeksi va o‘lchamini saqlang.</p><p class=\"note-s\">Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi.</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>$0.00001</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-1074-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1074-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1074-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1074-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</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-1074-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1074-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;baai/bge-base-en-v1.5&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_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 @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1074-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1074-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;baai/bge-base-en-v1.5&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\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>Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.</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=baai%2Fbge-base-en-v1.5&amp;route=Ultra&amp;caller=baai%2Fbge-base-en-v1.5&amp;fmt=embeddings\"><code>baai/bge-base-en-v1.5</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=baai%2Fbge-base-en-v1.5&amp;route=Ultra&amp;caller=baai%2Fbge-base-en-v1.5&amp;fmt=embeddings\"><code>baai/bge-base-en-v1.5</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "baai/bge-base-en-v1.5",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "baai/bge-base-en-v1.5",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|baai%2Fbge-base-en-v1.5",
      "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": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "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[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "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": [
                  "baai/bge-base-en-v1.5"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "UTF8 baytlari",
                  "token_unit": "Tokenlar massivi elementlari"
                },
                "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rad etildi",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Har bir vektorning indeksi va o‘lchamini saqlang.",
            "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "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": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000001",
                "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}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"baai/bge-base-en-v1.5\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_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 @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='baai/bge-base-en-v1.5',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "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": 0.01,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "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": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000001",
                "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": "baai/bge-base-en-v1.5",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.00001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000001",
            "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": "baai/bge-base-en-v1.5",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "4067cba081de2146455dad0687edb291a56d9e0c800febbd5819e490040d0af6",
    "input_html_sha256": "564790c7317cda75e76edd9ff2ff7f8dfed69f5888efb0fabed94f982a4ecccb",
    "private_audit_sha256": "6a4d6db096c3f83b2cc8f816cadefccf21bf40ea7c024cb10d362aad35886093",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/baai-bge-base-en-v1-5/",
    "description": "BAAI bge-base-en-v1.5 inglizcha matnni semantik qidiruv va o‘xshashlik vazifalari uchun 768 o‘lchamli vektorlarga aylantiradi. Uning 512 tokenli kirish oynasi va faqat so‘rovga qo‘llanadigan qidiruv ko‘rsatmasi hujjat yoki bilim qidiruv indeksidagi mazmuni aniq yo‘naltirilgan parchalarga mos.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/baai-bge-base-en-v1-5/index.md",
    "title": "BAAI: bge-base-en-v1.5 — Ailoot",
    "url": "https://ailoot.ai/models/uz/baai-bge-base-en-v1-5/"
  },
  "rawModel": {
    "about": "bge-base-en-v1.5 — BAAI’ning base hajmidagi inglizcha zich embedding modeli. U har matnni qidiruv va semantik taqqoslash uchun 768 o‘lchamli raqamli ifodaga aylantiradi. Model oldingi oilaning o‘xshashlik ballari taqsimotini yaxshilaydigan va ko‘rsatma berilmaganda qidiruvni kuchaytiradigan BGE v1.5 qatoriga tegishli. Uning chiqishi matnlarni taqqoslash yoki tegishli parchalarni topish uchun ishlatiladigan vektordir.\n\n### So‘rovlar va parchalarni tayyorlang\n\nUzunroq parchalarni qidirayotgan qisqa so‘rov uchun BAAI so‘rov oldiga “Represent this sentence for searching relevant passages:” prefiksini qo‘shishni tavsiya qiladi. Korpus parchalariga bu ko‘rsatma qo‘shilmaydi. Boshqa vazifalarda asl matnni bevosita embedding qilish mumkin. Ishlab chiqaruvchi v1.5 ko‘rsatmasiz ham ishlashini aytadi; demak prefiksni yagona yaroqli kirish deb bilish o‘rniga, tayyorlash usulini vazifaga qarab tanlash kerak.\n\nAmaliy qidiruv to‘plami yordam maqolalari, mahsulot izohlari yoki texnik ko‘rsatmalarni saqlashi mumkin. “How do I change the export format?” kabi so‘rov shu amalni tavsiflaydigan parchani topishi kerak. Bo‘lim sarlavhasi, tegishli shartlar va haqiqiy manba matnini birga saqlang. Qidiruv natijasi foydalanuvchi o‘qiy oladigan yoki keyingi javob bosqichi iqtibos keltira oladigan materialni qaytarishi uchun parcha identifikatori va joyini vektor yonida saqlang.\n\n### Vektor formati va taqqoslash\n\nHujjatlashtirilgan ifoda gap embeddingi sifatida birinchi, ya’ni CLS tokenining yakuniy yashirin holatidan foydalanadi. Ishlab chiqaruvchi misollari vektorni normallaydi va normallangan embeddinglarni kosinus o‘xshashligiga teng skalyar ko‘paytma bilan taqqoslaydi. 768 o‘lcham shu model chiqish ifodasining hajmidir; bu tanlanadigan kontekst uzunligi yoki matn yaratish limiti emas.\n\nTaqqoslanayotgan so‘rov va korpus vektorlari uchun bir xil model va tayyorlash qoidasidan foydalaning. Amaliy tekshiruv — haqiqiy foydalanuvchi savollarining kichik to‘plami uchun javob borligi ma’lum parchalarni topib, qaytgan matnni tekshirish. Mos vektor shakli indeksga ifodani saqlash imkonini beradi; natija aslida nima deyishini esa manba parchasi belgilaydi.\n\n### Kirish uzunligi va hujjatni tayyorlash\n\nModelning hujjatlashtirilgan ketma-ketlik uzunligi 512 token. Shuning uchun uzun manbani embedding qilishdan oldin izchil, javobni o‘z ichiga oladigan parchalarga bo‘lish kerak. Ta’riflar va istisnolarni ular aniqlik kiritadigan bayonot bilan saqlang, parcha bilan asosiy hujjat bog‘liqligini saqlang. Embedding qilinadigan matnga qo‘shgan ko‘rsatma yoki sarlavhangiz ham kiradi; ixcham tayyorlash foydali manba mazmuniga ko‘proq joy qoldiradi.\n\nHisobot yoki qo‘llanmada chegaralarni bo‘lim yoki bir nechta bog‘liq paragraf atrofida tanlang. Faqat sarlavha yoki mavzusi yo‘q bo‘lakdan iborat parchani topilganda tushunish qiyin bo‘lishi mumkin. Hujjat o‘zgarsa, indeks yangilanishi tegishli materialni almashtira olishi uchun asl matn va manba versiyasini saqlang.\n\n### Ballar va foydali chiqish\n\nBAAI mutlaq o‘xshashlik qiymatlari ikki matn bir xil ma’noni anglatishini hal qiladigan universal chegaralar emasligini ogohlantiradi. Nisbiy reyting muhim; filtrlash chegarasini vazifaning o‘z ma’lumotlaridagi taqsimot orqali tanlash kerak. Yuqori vektor o‘xshashligi parcha faktlarini tasdiqlash emas, qidiruv signalidir.\n\nFoydali natija — inglizcha parchalar indeksi, so‘rov vektorlari to‘plami yoki semantik taqqoslash uchun raqamli xususiyatlar. Alohida qayta reytinglovchi model topilgan nomzodlarni aniqlashtirishi, alohida yaratish bosqichi esa ularning matnidan javob yozishi mumkin. Aynan bu base inglizcha v1.5 modeli BGE Large, xitoycha nashrlar va BGE-M3’ning ko‘p tilli hamda gibrid qidiruv profillaridan farq qiladi.",
    "alias_of": null,
    "aliases": [],
    "author": "BAAI",
    "available": null,
    "canonical_model": "baai/bge-base-en-v1.5",
    "capabilities": {
      "context_length": 512,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.00001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000001",
            "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,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "baai/bge-base-en-v1.5",
    "input": [
      "text",
      "token_ids"
    ],
    "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": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "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[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "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": [
                  "baai/bge-base-en-v1.5"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "UTF8 baytlari",
                  "token_unit": "Tokenlar massivi elementlari"
                },
                "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rad etildi",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Har bir vektorning indeksi va o‘lchamini saqlang.",
            "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "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": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000001",
                "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}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"baai/bge-base-en-v1.5\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_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 @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='baai/bge-base-en-v1.5',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "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": 0.01,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "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": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000001",
                "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": "baai/bge-base-en-v1.5",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.00001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000001",
            "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": "baai/bge-base-en-v1.5",
        "request_model_id": "baai/bge-base-en-v1.5",
        "route_label": "Ultra"
      }
    ],
    "name": "BAAI: bge-base-en-v1.5",
    "price": {
      "in": 0.01,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.01,
      "sort_out": 0,
      "varies": false
    },
    "public_id": "baai/bge-base-en-v1.5",
    "short": "BAAI bge-base-en-v1.5 inglizcha matnni semantik qidiruv va o‘xshashlik vazifalari uchun 768 o‘lchamli vektorlarga aylantiradi. Uning 512 tokenli kirish oynasi va faqat so‘rovga qo‘llanadigan qidiruv ko‘rsatmasi hujjat yoki bilim qidiruv indeksidagi mazmuni aniq yo‘naltirilgan parchalarga mos."
  },
  "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": "BAAI bge-base-en-v1.5 inglizcha matnni semantik qidiruv va o‘xshashlik vazifalari uchun 768 o‘lchamli vektorlarga aylantiradi. Uning 512 tokenli kirish oynasi va faqat so‘rovga qo‘llanadigan qidiruv ko‘rsatmasi hujjat yoki bilim qidiruv indeksidagi mazmuni aniq yo‘naltirilgan parchalarga mos.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "1235bdf0587f5d247090eaf9bb939dc2f7b788da51286a4c3024e3350314dd76",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "26e131708832467f55e391d4447a8869e6e85a2c6b1ce701393b4dad76f422d4"
}