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  "aboutHtml": "<p>LFM2.5-Embedding-350M — ko‘p tilli va tillararo qidiruv uchun LiquidAI’ning zich bi-enkoderi. U har matn uchun bitta 1024 o‘lchamli vektor hosil qiladi va o‘n bir tildagi qisqa kontekstli qidiruvga mo‘ljallangan. 350M deb nomlangan model oilasi LFM2.5-350M-Base’ning ikki yo‘nalishli moslashuvidan foydalanadi; batafsil model jadvali shu embedding nashri uchun taxminan 354 million parametrni ko‘rsatadi.</p><h4>Ikki yo‘nalishli qidiruv ifodasi</h4><p>Ishlab chiqaruvchi asosni diqqat va mahalliy konvolutsiyalar tokenning ikkala tomonidagi kontekstdan foydalana oladigan qilib moslashtiradi. CLS uslubidagi pooling qayta ishlangan matnni bitta zich vektorga aylantiradi. Model jadvali o‘n yetti qatlamni tavsiflaydi: o‘nta konvolutsiya bloki, oltita diqqat bloki va pooling bosqichi. Bu bazaviy modelning matn yaratish vazifasi emas, o‘z tayyorlashi va chiqishiga ega qidiruv enkoderidir.</p><p>Alohida nomlangan LFM2.5-ColBERT-350M kech bosqichdagi o‘zaro ta’sir uchun har token vektorlarini saqlaydi. Shu ifodaga boshqa indeks va taqqoslash usuli kerak. Bu yerdagi Embedding modeli hujjat yoki so‘rov uchun bitta vektor beradi; hujjatlashtirilgan taqqoslash kosinus o‘xshashligidir.</p><h4>To‘g‘ri asimmetrik promptlardan foydalaning</h4><p>Qidiruv so‘rovlarini “query:”, manba parchalarini “document:” bilan tayyorlang. Ishlab chiqaruvchi bu promptlarni konfiguratsiyada saqlaydi va rasmiy jarayon mos prompt nomini qo‘llaydi. Model shu prefikslar bilan o‘qitilgani uchun har tomonda tegishli rolni ishlatish alohida tavsiya qilinadi. Bu hujjat prefiksi E5 modellarining “passage:” qoidasidan farq qiladi.</p><p>Amaliy mahsulot qidiruv to‘plami qo‘llanadigan tillardagi tavsiflarni saqlashi mumkin. Foydalanuvchining qidiruv ifodasini so‘rov, har mahsulot tavsifini hujjat sifatida tayyorlang. Qidiruv natijasi tegishli yozuvni qaytarishi uchun asl matn, mahsulot identifikatori va tilni vektor bilan saqlang. Qo‘llab-quvvatlash to‘plamida savolga tegishli siyosat yoki ko‘rsatmani aloqasiz qisqa yorliqqa almashtirmasdan saqlang.</p><h4>Tillar va parcha hajmi</h4><p>Hujjatlashtirilgan tillar to‘plami ingliz, ispan, nemis, fransuz, italyan, portugal, arab, shved, norveg, yapon va koreys tillaridir. Ishlab chiqaruvchi bir til ichida ham, tillar chegarasi orqali ham qidiruvni ochiq maqsad qiladi. Mo‘ljallangan auditoriyadan haqiqiy savollar tayyorlang va qaytgan hujjat matnini shu tillarda tekshiring.</p><p>Hujjat uzunligi spetsifikatsiyasi 512 token. Uzun manbani topilgan natijaga kerakli sarlavha, ta’rif va shartlarni saqlaydigan izchil parchalarga bo‘ling. Rol prefiksi va qo‘shilgan kontekst tayyorlangan kirishga kiradi. FAQ javobi, mahsulot izohi yoki texnik hujjatning aniq yo‘naltirilgan bo‘limi butun haddan katta qo‘llanmadan ko‘ra mos birlikdir.</p><h4>O‘qitish va moslashtirish</h4><p>Ishlab chiqaruvchi uch o‘qitish bosqichini tavsiflaydi: inglizcha kontrastiv dastlabki o‘qitish, ko‘p tilli va tillararo distillatsiya, so‘ng qidirib olingan murakkab salbiy misollar bilan nozik sozlash. Hosil bo‘lgan model mahsulot kataloglari, FAQ va qo‘llab-quvvatlash to‘plamlari, fayl yoki qayd qidiruvi va korporativ bilim qidiruviga mo‘ljallangan. Bular matn ifodalari uchun vazifalar, muayyan ilova joriy qilinganining da’volari emas.</p><p>Ishlab chiqaruvchi misoli so‘rov va hujjat vektorlarini normallab, kosinus o‘xshashligiga teng skalyar ko‘paytma bilan reytinglaydi. Manba materiali tayyorlanganda korpus ifodalarini oldindan hisoblang, keyin qidiruv so‘rovini embedding qiling va nomzod hujjatlarni matni hamda identifikatorlari bilan qaytaring. Raqamli taqqoslash mos ifodalardan foydalanishi uchun tanlangan model va rol qoidasini indeks bilan saqlang.</p><p>Foydali natija — hujjatlashtirilgan tillar to‘plami bo‘ylab manba materialini topadigan ixcham zich qidiruv indeksi yoki so‘rov vektori. Aynan LFM2.5 Embedding nashri yonidagi ColBERT modeli, eski LFM2 qidiruvchilari va tashqi kvantlangan implementatsiyalardan alohida qoladi. Tanlangan parcha matni o‘quvchi yoki keyingi RAG javob bosqichi ishlatadigan dalildir.</p>",
  "aboutText": "LFM2.5-Embedding-350M — ko‘p tilli va tillararo qidiruv uchun LiquidAI’ning zich bi-enkoderi. U har matn uchun bitta 1024 o‘lchamli vektor hosil qiladi va o‘n bir tildagi qisqa kontekstli qidiruvga mo‘ljallangan. 350M deb nomlangan model oilasi LFM2.5-350M-Base’ning ikki yo‘nalishli moslashuvidan foydalanadi; batafsil model jadvali shu embedding nashri uchun taxminan 354 million parametrni ko‘rsatadi.\n\n### Ikki yo‘nalishli qidiruv ifodasi\n\nIshlab chiqaruvchi asosni diqqat va mahalliy konvolutsiyalar tokenning ikkala tomonidagi kontekstdan foydalana oladigan qilib moslashtiradi. CLS uslubidagi pooling qayta ishlangan matnni bitta zich vektorga aylantiradi. Model jadvali o‘n yetti qatlamni tavsiflaydi: o‘nta konvolutsiya bloki, oltita diqqat bloki va pooling bosqichi. Bu bazaviy modelning matn yaratish vazifasi emas, o‘z tayyorlashi va chiqishiga ega qidiruv enkoderidir.\n\nAlohida nomlangan LFM2.5-ColBERT-350M kech bosqichdagi o‘zaro ta’sir uchun har token vektorlarini saqlaydi. Shu ifodaga boshqa indeks va taqqoslash usuli kerak. Bu yerdagi Embedding modeli hujjat yoki so‘rov uchun bitta vektor beradi; hujjatlashtirilgan taqqoslash kosinus o‘xshashligidir.\n\n### To‘g‘ri asimmetrik promptlardan foydalaning\n\nQidiruv so‘rovlarini “query:”, manba parchalarini “document:” bilan tayyorlang. Ishlab chiqaruvchi bu promptlarni konfiguratsiyada saqlaydi va rasmiy jarayon mos prompt nomini qo‘llaydi. Model shu prefikslar bilan o‘qitilgani uchun har tomonda tegishli rolni ishlatish alohida tavsiya qilinadi. Bu hujjat prefiksi E5 modellarining “passage:” qoidasidan farq qiladi.\n\nAmaliy mahsulot qidiruv to‘plami qo‘llanadigan tillardagi tavsiflarni saqlashi mumkin. Foydalanuvchining qidiruv ifodasini so‘rov, har mahsulot tavsifini hujjat sifatida tayyorlang. Qidiruv natijasi tegishli yozuvni qaytarishi uchun asl matn, mahsulot identifikatori va tilni vektor bilan saqlang. Qo‘llab-quvvatlash to‘plamida savolga tegishli siyosat yoki ko‘rsatmani aloqasiz qisqa yorliqqa almashtirmasdan saqlang.\n\n### Tillar va parcha hajmi\n\nHujjatlashtirilgan tillar to‘plami ingliz, ispan, nemis, fransuz, italyan, portugal, arab, shved, norveg, yapon va koreys tillaridir. Ishlab chiqaruvchi bir til ichida ham, tillar chegarasi orqali ham qidiruvni ochiq maqsad qiladi. Mo‘ljallangan auditoriyadan haqiqiy savollar tayyorlang va qaytgan hujjat matnini shu tillarda tekshiring.\n\nHujjat uzunligi spetsifikatsiyasi 512 token. Uzun manbani topilgan natijaga kerakli sarlavha, ta’rif va shartlarni saqlaydigan izchil parchalarga bo‘ling. Rol prefiksi va qo‘shilgan kontekst tayyorlangan kirishga kiradi. FAQ javobi, mahsulot izohi yoki texnik hujjatning aniq yo‘naltirilgan bo‘limi butun haddan katta qo‘llanmadan ko‘ra mos birlikdir.\n\n### O‘qitish va moslashtirish\n\nIshlab chiqaruvchi uch o‘qitish bosqichini tavsiflaydi: inglizcha kontrastiv dastlabki o‘qitish, ko‘p tilli va tillararo distillatsiya, so‘ng qidirib olingan murakkab salbiy misollar bilan nozik sozlash. Hosil bo‘lgan model mahsulot kataloglari, FAQ va qo‘llab-quvvatlash to‘plamlari, fayl yoki qayd qidiruvi va korporativ bilim qidiruviga mo‘ljallangan. Bular matn ifodalari uchun vazifalar, muayyan ilova joriy qilinganining da’volari emas.\n\nIshlab chiqaruvchi misoli so‘rov va hujjat vektorlarini normallab, kosinus o‘xshashligiga teng skalyar ko‘paytma bilan reytinglaydi. Manba materiali tayyorlanganda korpus ifodalarini oldindan hisoblang, keyin qidiruv so‘rovini embedding qiling va nomzod hujjatlarni matni hamda identifikatorlari bilan qaytaring. Raqamli taqqoslash mos ifodalardan foydalanishi uchun tanlangan model va rol qoidasini indeks bilan saqlang.\n\nFoydali natija — hujjatlashtirilgan tillar to‘plami bo‘ylab manba materialini topadigan ixcham zich qidiruv indeksi yoki so‘rov vektori. Aynan LFM2.5 Embedding nashri yonidagi ColBERT modeli, eski LFM2 qidiruvchilari va tashqi kvantlangan implementatsiyalardan alohida qoladi. Tanlangan parcha matni o‘quvchi yoki keyingi RAG javob bosqichi ishlatadigan dalildir.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">LiquidAI: LFM2.5-Embedding-350M (free)</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>LiquidAI /</span><code>liquid/lfm-2.5-embedding-350m:free</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|liquid%2Flfm-2.5-embedding-350m%3Afree\">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\">LiquidAI LFM2.5-Embedding-350M koʻp tilli va tillararo qidiruv uchun har bir matnga bitta 1024 oʻlchamli vektor yaratadi. U 11 tilga va qisqa, 512 tokenli kirishlarga yoʻnaltirilgan; soʻrov va hujjat prefikslari alohida.</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>LFM2.5-Embedding-350M — ko‘p tilli va tillararo qidiruv uchun LiquidAI’ning zich bi-enkoderi. U har matn uchun bitta 1024 o‘lchamli vektor hosil qiladi va o‘n bir tildagi qisqa kontekstli qidiruvga mo‘ljallangan. 350M deb nomlangan model oilasi LFM2.5-350M-Base’ning ikki yo‘nalishli moslashuvidan foydalanadi; batafsil model jadvali shu embedding nashri uchun taxminan 354 million parametrni ko‘rsatadi.</p><h4>Ikki yo‘nalishli qidiruv ifodasi</h4><p>Ishlab chiqaruvchi asosni diqqat va mahalliy konvolutsiyalar tokenning ikkala tomonidagi kontekstdan foydalana oladigan qilib moslashtiradi. CLS uslubidagi pooling qayta ishlangan matnni bitta zich vektorga aylantiradi. Model jadvali o‘n yetti qatlamni tavsiflaydi: o‘nta konvolutsiya bloki, oltita diqqat bloki va pooling bosqichi. Bu bazaviy modelning matn yaratish vazifasi emas, o‘z tayyorlashi va chiqishiga ega qidiruv enkoderidir.</p><p>Alohida nomlangan LFM2.5-ColBERT-350M kech bosqichdagi o‘zaro ta’sir uchun har token vektorlarini saqlaydi. Shu ifodaga boshqa indeks va taqqoslash usuli kerak. Bu yerdagi Embedding modeli hujjat yoki so‘rov uchun bitta vektor beradi; hujjatlashtirilgan taqqoslash kosinus o‘xshashligidir.</p><h4>To‘g‘ri asimmetrik promptlardan foydalaning</h4><p>Qidiruv so‘rovlarini “query:”, manba parchalarini “document:” bilan tayyorlang. Ishlab chiqaruvchi bu promptlarni konfiguratsiyada saqlaydi va rasmiy jarayon mos prompt nomini qo‘llaydi. Model shu prefikslar bilan o‘qitilgani uchun har tomonda tegishli rolni ishlatish alohida tavsiya qilinadi. Bu hujjat prefiksi E5 modellarining “passage:” qoidasidan farq qiladi.</p><p>Amaliy mahsulot qidiruv to‘plami qo‘llanadigan tillardagi tavsiflarni saqlashi mumkin. Foydalanuvchining qidiruv ifodasini so‘rov, har mahsulot tavsifini hujjat sifatida tayyorlang. Qidiruv natijasi tegishli yozuvni qaytarishi uchun asl matn, mahsulot identifikatori va tilni vektor bilan saqlang. Qo‘llab-quvvatlash to‘plamida savolga tegishli siyosat yoki ko‘rsatmani aloqasiz qisqa yorliqqa almashtirmasdan saqlang.</p><h4>Tillar va parcha hajmi</h4><p>Hujjatlashtirilgan tillar to‘plami ingliz, ispan, nemis, fransuz, italyan, portugal, arab, shved, norveg, yapon va koreys tillaridir. Ishlab chiqaruvchi bir til ichida ham, tillar chegarasi orqali ham qidiruvni ochiq maqsad qiladi. Mo‘ljallangan auditoriyadan haqiqiy savollar tayyorlang va qaytgan hujjat matnini shu tillarda tekshiring.</p><p>Hujjat uzunligi spetsifikatsiyasi 512 token. Uzun manbani topilgan natijaga kerakli sarlavha, ta’rif va shartlarni saqlaydigan izchil parchalarga bo‘ling. Rol prefiksi va qo‘shilgan kontekst tayyorlangan kirishga kiradi. FAQ javobi, mahsulot izohi yoki texnik hujjatning aniq yo‘naltirilgan bo‘limi butun haddan katta qo‘llanmadan ko‘ra mos birlikdir.</p><h4>O‘qitish va moslashtirish</h4><p>Ishlab chiqaruvchi uch o‘qitish bosqichini tavsiflaydi: inglizcha kontrastiv dastlabki o‘qitish, ko‘p tilli va tillararo distillatsiya, so‘ng qidirib olingan murakkab salbiy misollar bilan nozik sozlash. Hosil bo‘lgan model mahsulot kataloglari, FAQ va qo‘llab-quvvatlash to‘plamlari, fayl yoki qayd qidiruvi va korporativ bilim qidiruviga mo‘ljallangan. Bular matn ifodalari uchun vazifalar, muayyan ilova joriy qilinganining da’volari emas.</p><p>Ishlab chiqaruvchi misoli so‘rov va hujjat vektorlarini normallab, kosinus o‘xshashligiga teng skalyar ko‘paytma bilan reytinglaydi. Manba materiali tayyorlanganda korpus ifodalarini oldindan hisoblang, keyin qidiruv so‘rovini embedding qiling va nomzod hujjatlarni matni hamda identifikatorlari bilan qaytaring. Raqamli taqqoslash mos ifodalardan foydalanishi uchun tanlangan model va rol qoidasini indeks bilan saqlang.</p><p>Foydali natija — hujjatlashtirilgan tillar to‘plami bo‘ylab manba materialini topadigan ixcham zich qidiruv indeksi yoki so‘rov vektori. Aynan LFM2.5 Embedding nashri yonidagi ColBERT modeli, eski LFM2 qidiruvchilari va tashqi kvantlangan implementatsiyalardan alohida qoladi. Tanlangan parcha matni o‘quvchi yoki keyingi RAG javob bosqichi ishlatadigan dalildir.</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>Kontekst</dt><dd>512 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>liquid/lfm-2.5-embedding-350m:free</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>Standart tarif <span class=\"note-s\">Tezkor rejim tanlanganda</span></dd><dt>Flex rejimi</dt><dd>Standart tarif <span class=\"note-s\">flex rejimi tanlanganda</span></dd><dt>Maksimal fikrlash darajasi</dt><dd>Standart tarif <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</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-1793-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1793-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1793-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1793-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-1793-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1793-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;liquid/lfm-2.5-embedding-350m:free&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-1793-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1793-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;liquid/lfm-2.5-embedding-350m:free&#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=liquid%2Flfm-2.5-embedding-350m%3Afree&amp;route=Ultra&amp;caller=liquid%2Flfm-2.5-embedding-350m%3Afree&amp;fmt=embeddings\"><code>liquid/lfm-2.5-embedding-350m:free</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=liquid%2Flfm-2.5-embedding-350m%3Afree&amp;route=Ultra&amp;caller=liquid%2Flfm-2.5-embedding-350m%3Afree&amp;fmt=embeddings\"><code>liquid/lfm-2.5-embedding-350m:free</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "liquid/lfm-2.5-embedding-350m:free",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "liquid/lfm-2.5-embedding-350m:free",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|liquid%2Flfm-2.5-embedding-350m%3Afree",
      "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": 1024,
              "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": [
                  "liquid/lfm-2.5-embedding-350m:free"
                ],
                "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": 1024,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 512,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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\": \"liquid/lfm-2.5-embedding-350m:free\", \"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='liquid/lfm-2.5-embedding-350m:free',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": 1,
        "flex_multiplier": 1,
        "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,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Tezkor rejim",
            "value": "Standart tarif",
            "when": "Tezkor rejim tanlanganda"
          },
          {
            "label": "Flex rejimi",
            "value": "Standart tarif",
            "when": "flex rejimi tanlanganda"
          },
          {
            "label": "Maksimal fikrlash darajasi",
            "value": "Standart tarif",
            "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda"
          }
        ],
        "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",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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": "liquid/lfm-2.5-embedding-350m:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "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": "liquid/lfm-2.5-embedding-350m:free",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "ed33c2a76220dda25aa48ad18a42b2baee7ca00818d7880618551ac97627eb0c",
    "input_html_sha256": "c5804f3b13a6b144ad5870c13aa587252b5488c52403fa2b707e3156b478a988",
    "private_audit_sha256": "c6fea537cd063c379ae695bbcae1f8ce666412e6aca73426a0baf2b23d2622c1",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/liquid-lfm-2-5-embedding-350m-u003a-free/",
    "description": "LiquidAI LFM2.5-Embedding-350M koʻp tilli va tillararo qidiruv uchun har bir matnga bitta 1024 oʻlchamli vektor yaratadi. U 11 tilga va qisqa, 512 tokenli kirishlarga yoʻnaltirilgan; soʻrov va hujjat prefikslari alohida.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/liquid-lfm-2-5-embedding-350m-u003a-free/index.md",
    "title": "LiquidAI: LFM2.5-Embedding-350M (free) — Ailoot",
    "url": "https://ailoot.ai/models/uz/liquid-lfm-2-5-embedding-350m-u003a-free/"
  },
  "rawModel": {
    "about": "LFM2.5-Embedding-350M — ko‘p tilli va tillararo qidiruv uchun LiquidAI’ning zich bi-enkoderi. U har matn uchun bitta 1024 o‘lchamli vektor hosil qiladi va o‘n bir tildagi qisqa kontekstli qidiruvga mo‘ljallangan. 350M deb nomlangan model oilasi LFM2.5-350M-Base’ning ikki yo‘nalishli moslashuvidan foydalanadi; batafsil model jadvali shu embedding nashri uchun taxminan 354 million parametrni ko‘rsatadi.\n\n### Ikki yo‘nalishli qidiruv ifodasi\n\nIshlab chiqaruvchi asosni diqqat va mahalliy konvolutsiyalar tokenning ikkala tomonidagi kontekstdan foydalana oladigan qilib moslashtiradi. CLS uslubidagi pooling qayta ishlangan matnni bitta zich vektorga aylantiradi. Model jadvali o‘n yetti qatlamni tavsiflaydi: o‘nta konvolutsiya bloki, oltita diqqat bloki va pooling bosqichi. Bu bazaviy modelning matn yaratish vazifasi emas, o‘z tayyorlashi va chiqishiga ega qidiruv enkoderidir.\n\nAlohida nomlangan LFM2.5-ColBERT-350M kech bosqichdagi o‘zaro ta’sir uchun har token vektorlarini saqlaydi. Shu ifodaga boshqa indeks va taqqoslash usuli kerak. Bu yerdagi Embedding modeli hujjat yoki so‘rov uchun bitta vektor beradi; hujjatlashtirilgan taqqoslash kosinus o‘xshashligidir.\n\n### To‘g‘ri asimmetrik promptlardan foydalaning\n\nQidiruv so‘rovlarini “query:”, manba parchalarini “document:” bilan tayyorlang. Ishlab chiqaruvchi bu promptlarni konfiguratsiyada saqlaydi va rasmiy jarayon mos prompt nomini qo‘llaydi. Model shu prefikslar bilan o‘qitilgani uchun har tomonda tegishli rolni ishlatish alohida tavsiya qilinadi. Bu hujjat prefiksi E5 modellarining “passage:” qoidasidan farq qiladi.\n\nAmaliy mahsulot qidiruv to‘plami qo‘llanadigan tillardagi tavsiflarni saqlashi mumkin. Foydalanuvchining qidiruv ifodasini so‘rov, har mahsulot tavsifini hujjat sifatida tayyorlang. Qidiruv natijasi tegishli yozuvni qaytarishi uchun asl matn, mahsulot identifikatori va tilni vektor bilan saqlang. Qo‘llab-quvvatlash to‘plamida savolga tegishli siyosat yoki ko‘rsatmani aloqasiz qisqa yorliqqa almashtirmasdan saqlang.\n\n### Tillar va parcha hajmi\n\nHujjatlashtirilgan tillar to‘plami ingliz, ispan, nemis, fransuz, italyan, portugal, arab, shved, norveg, yapon va koreys tillaridir. Ishlab chiqaruvchi bir til ichida ham, tillar chegarasi orqali ham qidiruvni ochiq maqsad qiladi. Mo‘ljallangan auditoriyadan haqiqiy savollar tayyorlang va qaytgan hujjat matnini shu tillarda tekshiring.\n\nHujjat uzunligi spetsifikatsiyasi 512 token. Uzun manbani topilgan natijaga kerakli sarlavha, ta’rif va shartlarni saqlaydigan izchil parchalarga bo‘ling. Rol prefiksi va qo‘shilgan kontekst tayyorlangan kirishga kiradi. FAQ javobi, mahsulot izohi yoki texnik hujjatning aniq yo‘naltirilgan bo‘limi butun haddan katta qo‘llanmadan ko‘ra mos birlikdir.\n\n### O‘qitish va moslashtirish\n\nIshlab chiqaruvchi uch o‘qitish bosqichini tavsiflaydi: inglizcha kontrastiv dastlabki o‘qitish, ko‘p tilli va tillararo distillatsiya, so‘ng qidirib olingan murakkab salbiy misollar bilan nozik sozlash. Hosil bo‘lgan model mahsulot kataloglari, FAQ va qo‘llab-quvvatlash to‘plamlari, fayl yoki qayd qidiruvi va korporativ bilim qidiruviga mo‘ljallangan. Bular matn ifodalari uchun vazifalar, muayyan ilova joriy qilinganining da’volari emas.\n\nIshlab chiqaruvchi misoli so‘rov va hujjat vektorlarini normallab, kosinus o‘xshashligiga teng skalyar ko‘paytma bilan reytinglaydi. Manba materiali tayyorlanganda korpus ifodalarini oldindan hisoblang, keyin qidiruv so‘rovini embedding qiling va nomzod hujjatlarni matni hamda identifikatorlari bilan qaytaring. Raqamli taqqoslash mos ifodalardan foydalanishi uchun tanlangan model va rol qoidasini indeks bilan saqlang.\n\nFoydali natija — hujjatlashtirilgan tillar to‘plami bo‘ylab manba materialini topadigan ixcham zich qidiruv indeksi yoki so‘rov vektori. Aynan LFM2.5 Embedding nashri yonidagi ColBERT modeli, eski LFM2 qidiruvchilari va tashqi kvantlangan implementatsiyalardan alohida qoladi. Tanlangan parcha matni o‘quvchi yoki keyingi RAG javob bosqichi ishlatadigan dalildir.",
    "alias_of": null,
    "aliases": [],
    "author": "LiquidAI",
    "available": null,
    "canonical_model": "liquid/lfm-2.5-embedding-350m:free",
    "capabilities": {
      "context_length": 512,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "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": 512,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "liquid/lfm-2.5-embedding-350m:free",
    "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": 1024,
              "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": [
                  "liquid/lfm-2.5-embedding-350m:free"
                ],
                "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": 1024,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 512,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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\": \"liquid/lfm-2.5-embedding-350m:free\", \"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='liquid/lfm-2.5-embedding-350m:free',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": 1,
        "flex_multiplier": 1,
        "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,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Tezkor rejim",
            "value": "Standart tarif",
            "when": "Tezkor rejim tanlanganda"
          },
          {
            "label": "Flex rejimi",
            "value": "Standart tarif",
            "when": "flex rejimi tanlanganda"
          },
          {
            "label": "Maksimal fikrlash darajasi",
            "value": "Standart tarif",
            "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda"
          }
        ],
        "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",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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": "liquid/lfm-2.5-embedding-350m:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "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": "liquid/lfm-2.5-embedding-350m:free",
        "request_model_id": "liquid/lfm-2.5-embedding-350m:free",
        "route_label": "Ultra"
      }
    ],
    "name": "LiquidAI: LFM2.5-Embedding-350M (free)",
    "price": {
      "in": 0,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0,
      "sort_out": 0,
      "varies": false
    },
    "pricing_profiles": [
      {
        "dimensions": {
          "regime": "base"
        },
        "label": "Эмбеддинги · Base token rates",
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "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"
          }
        ]
      }
    ],
    "public_id": "liquid/lfm-2.5-embedding-350m:free",
    "short": "LiquidAI LFM2.5-Embedding-350M koʻp tilli va tillararo qidiruv uchun har bir matnga bitta 1024 oʻlchamli vektor yaratadi. U 11 tilga va qisqa, 512 tokenli kirishlarga yoʻnaltirilgan; soʻrov va hujjat prefikslari alohida."
  },
  "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": "LiquidAI LFM2.5-Embedding-350M koʻp tilli va tillararo qidiruv uchun har bir matnga bitta 1024 oʻlchamli vektor yaratadi. U 11 tilga va qisqa, 512 tokenli kirishlarga yoʻnaltirilgan; soʻrov va hujjat prefikslari alohida.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "387b31817686a7fafb74b30acb46c4ba48dc103c55d8c7c87a91955c2b2f7f25",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "8dc5f71d3d321046b0bf37b8f9c458b8599765858920793e093434dde2f6b3a8"
}