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  "aboutHtml": "<p>multi-qa-mpnet-base-dot-v1 — savol yoki so‘rov uchun matn parchalarini topishga mo‘ljallangan Sentence Transformers modeli. O‘qitishda taxminan 215 million savol–javob jufti ishlatilgan. Har bir kirish 768 o‘lchamli zich vektorga aylanadi; CLS pooling va skalyar ko‘paytma bilan baholash aynan shu nashrni belgilaydi.</p><h4>Savol va nomzod parchalarni tayyorlang</h4><p>So‘rovni ma’lumotga bo‘lgan ehtiyoj sifatida yozing, korpus yozuvlarini esa o‘qiladigan matn parchalari qilib tayyorlang. Masalan, yetkazib berish manzilini o‘zgartirish haqidagi savolni manzilni o‘zgartirish, buyurtmani bekor qilish va yetkazib berishni kuzatishga oid alohida paragraflar bilan taqqoslash mumkin. Manzilni o‘zgartirish parchasida tegishli shartlarni, jumladan buyurtma allaqachon jo‘natilgan-jo‘natilmaganini saqlang.</p><p>So‘rov va parchalarni aynan shu model bilan kodlang. E’lon qilingan misol buni query/document prefiksisiz bevosita bajaradi va parchalarni skalyar ko‘paytma ballari bo‘yicha tartiblaydi. Reytinglangan natija haqiqatan indekslangan materialni ko‘rsata olishi uchun har bir vektorni asl parcha va hujjat identifikatoriga bog‘lang.</p><h4>CLS pooling va skalyar ko‘paytma nashri</h4><p>Modelning rasmiy usuli CLS pooling deb ataladigan birinchi token ifodasini oladi. Texnik jadval embeddinglar normallanmaganini ochiq aytadi va mos baholash funksiyasi sifatida skalyar ko‘paytmani ko‘rsatadi. To‘plam va so‘rov tomonini sozlashda shu usulni saqlang.</p><p>Bu vektorlarni L2 normallash baholash geometriyasini o‘zgartiradi. Normallangan all-mpnet-base-v2 profili boshqa usuldan foydalanadi va MPNet oilasi hamda 768 komponentli chiqishni bo‘lishsa ham alohida checkpointdir. Ularni faqat vektor uzunligidan tanlash o‘rniga, aniq nashr va taqqoslash funksiyasini saqlangan to‘plam bilan birga saqlang.</p><h4>Uzunlik chegarasi va uzunroq parchalar</h4><p>Ko‘rsatilgan kirish chegarasi 512 ta word piece birligi; uzunroq matn kesiladi. Ishlab chiqaruvchi o‘qitish kirishlari atigi 250 ta word piece birligigacha yetganini va uzunroq matn yomonroq natija berishi mumkinligini ham qayd etadi. Demak, tokenizer qabul qiladigan uzunlik va o‘qitish uzunligi ogohlantirishi turli amaliy chegaralarni tavsiflaydi.</p><p>Katta yordam maqolasi uchun muayyan savollarga javob beradigan qisqa bo‘limlar tayyorlang. Mavzuni aniqlasa sarlavhani saqlang; qoidani to‘g‘ri tushunishga kerakli istisno bilan birga qoldiring. Bitta to‘liq sahifali kirish kesilish tufayli keyingi materialni yo‘qotishi, haddan kichik bo‘lak esa uni foydali qiladigan shartni yo‘qotishi mumkin.</p><h4>Reytingdan qidiruv va RAG’da foydalaning</h4><p>Zich vektorlar birinchi qidiruv bosqichini qo‘llab-quvvatlaydi. Eng yuqori balli nomzod parchalarni manba havolalari bilan qaytaring, so‘ng haqiqiy savolni qamrashini tekshiring. RAG jarayonida shu parchalarni javob yozish bosqichiga manba materiali sifatida bering; qidiruvning o‘zi javob yozmaydi.</p><p>Natijalar sonini tanlash va reytingni baholash uchun tegishli parchalari ma’lum namunaviy savollardan foydalaning. Manzilni o‘zgartirish va joriy manzilni ko‘rish kabi lug‘ati o‘xshash, niyati boshqa misollarni kiriting. Ballarni to‘g‘rilikning kalibrlangan ehtimoli emas, bir xil model va baholash sozlamasidagi nomzodlarga nisbatan talqin qiling.</p><p>Bu checkpointning foydali chiqishi semantik qidiruv uchun savol va parcha embedding makonidir. Normallanmagan vektorlar, CLS pooling, skalyar ko‘paytma balli va e’lon qilingan uzun kirish ogohlantirishi — natijalarni tayyorlash va ishlatishda saqlanishi kerak bo‘lgan tafsilotlar.</p>",
  "aboutText": "multi-qa-mpnet-base-dot-v1 — savol yoki so‘rov uchun matn parchalarini topishga mo‘ljallangan Sentence Transformers modeli. O‘qitishda taxminan 215 million savol–javob jufti ishlatilgan. Har bir kirish 768 o‘lchamli zich vektorga aylanadi; CLS pooling va skalyar ko‘paytma bilan baholash aynan shu nashrni belgilaydi.\n\n### Savol va nomzod parchalarni tayyorlang\n\nSo‘rovni ma’lumotga bo‘lgan ehtiyoj sifatida yozing, korpus yozuvlarini esa o‘qiladigan matn parchalari qilib tayyorlang. Masalan, yetkazib berish manzilini o‘zgartirish haqidagi savolni manzilni o‘zgartirish, buyurtmani bekor qilish va yetkazib berishni kuzatishga oid alohida paragraflar bilan taqqoslash mumkin. Manzilni o‘zgartirish parchasida tegishli shartlarni, jumladan buyurtma allaqachon jo‘natilgan-jo‘natilmaganini saqlang.\n\nSo‘rov va parchalarni aynan shu model bilan kodlang. E’lon qilingan misol buni query/document prefiksisiz bevosita bajaradi va parchalarni skalyar ko‘paytma ballari bo‘yicha tartiblaydi. Reytinglangan natija haqiqatan indekslangan materialni ko‘rsata olishi uchun har bir vektorni asl parcha va hujjat identifikatoriga bog‘lang.\n\n### CLS pooling va skalyar ko‘paytma nashri\n\nModelning rasmiy usuli CLS pooling deb ataladigan birinchi token ifodasini oladi. Texnik jadval embeddinglar normallanmaganini ochiq aytadi va mos baholash funksiyasi sifatida skalyar ko‘paytmani ko‘rsatadi. To‘plam va so‘rov tomonini sozlashda shu usulni saqlang.\n\nBu vektorlarni L2 normallash baholash geometriyasini o‘zgartiradi. Normallangan all-mpnet-base-v2 profili boshqa usuldan foydalanadi va MPNet oilasi hamda 768 komponentli chiqishni bo‘lishsa ham alohida checkpointdir. Ularni faqat vektor uzunligidan tanlash o‘rniga, aniq nashr va taqqoslash funksiyasini saqlangan to‘plam bilan birga saqlang.\n\n### Uzunlik chegarasi va uzunroq parchalar\n\nKo‘rsatilgan kirish chegarasi 512 ta word piece birligi; uzunroq matn kesiladi. Ishlab chiqaruvchi o‘qitish kirishlari atigi 250 ta word piece birligigacha yetganini va uzunroq matn yomonroq natija berishi mumkinligini ham qayd etadi. Demak, tokenizer qabul qiladigan uzunlik va o‘qitish uzunligi ogohlantirishi turli amaliy chegaralarni tavsiflaydi.\n\nKatta yordam maqolasi uchun muayyan savollarga javob beradigan qisqa bo‘limlar tayyorlang. Mavzuni aniqlasa sarlavhani saqlang; qoidani to‘g‘ri tushunishga kerakli istisno bilan birga qoldiring. Bitta to‘liq sahifali kirish kesilish tufayli keyingi materialni yo‘qotishi, haddan kichik bo‘lak esa uni foydali qiladigan shartni yo‘qotishi mumkin.\n\n### Reytingdan qidiruv va RAG’da foydalaning\n\nZich vektorlar birinchi qidiruv bosqichini qo‘llab-quvvatlaydi. Eng yuqori balli nomzod parchalarni manba havolalari bilan qaytaring, so‘ng haqiqiy savolni qamrashini tekshiring. RAG jarayonida shu parchalarni javob yozish bosqichiga manba materiali sifatida bering; qidiruvning o‘zi javob yozmaydi.\n\nNatijalar sonini tanlash va reytingni baholash uchun tegishli parchalari ma’lum namunaviy savollardan foydalaning. Manzilni o‘zgartirish va joriy manzilni ko‘rish kabi lug‘ati o‘xshash, niyati boshqa misollarni kiriting. Ballarni to‘g‘rilikning kalibrlangan ehtimoli emas, bir xil model va baholash sozlamasidagi nomzodlarga nisbatan talqin qiling.\n\nBu checkpointning foydali chiqishi semantik qidiruv uchun savol va parcha embedding makonidir. Normallanmagan vektorlar, CLS pooling, skalyar ko‘paytma balli va e’lon qilingan uzun kirish ogohlantirishi — natijalarni tayyorlash va ishlatishda saqlanishi kerak bo‘lgan tafsilotlar.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Sentence Transformers: multi-qa-mpnet-base-dot-v1</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Sentence Transformers /</span><code>sentence-transformers/multi-qa-mpnet-base-dot-v1</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|sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1\">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\">Sentence Transformers multi-qa-mpnet-base-dot-v1 savollar va parchalarni semantik qidiruv uchun 768 o‘lchamli vektorlarga kodlaydi. U CLS pooling va skalyar ko‘paytma bilan baholashga mo‘ljallangan normallanmagan embeddinglardan foydalanadi; kirish chegarasi 512 ta word piece birligi.</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>multi-qa-mpnet-base-dot-v1 — savol yoki so‘rov uchun matn parchalarini topishga mo‘ljallangan Sentence Transformers modeli. O‘qitishda taxminan 215 million savol–javob jufti ishlatilgan. Har bir kirish 768 o‘lchamli zich vektorga aylanadi; CLS pooling va skalyar ko‘paytma bilan baholash aynan shu nashrni belgilaydi.</p><h4>Savol va nomzod parchalarni tayyorlang</h4><p>So‘rovni ma’lumotga bo‘lgan ehtiyoj sifatida yozing, korpus yozuvlarini esa o‘qiladigan matn parchalari qilib tayyorlang. Masalan, yetkazib berish manzilini o‘zgartirish haqidagi savolni manzilni o‘zgartirish, buyurtmani bekor qilish va yetkazib berishni kuzatishga oid alohida paragraflar bilan taqqoslash mumkin. Manzilni o‘zgartirish parchasida tegishli shartlarni, jumladan buyurtma allaqachon jo‘natilgan-jo‘natilmaganini saqlang.</p><p>So‘rov va parchalarni aynan shu model bilan kodlang. E’lon qilingan misol buni query/document prefiksisiz bevosita bajaradi va parchalarni skalyar ko‘paytma ballari bo‘yicha tartiblaydi. Reytinglangan natija haqiqatan indekslangan materialni ko‘rsata olishi uchun har bir vektorni asl parcha va hujjat identifikatoriga bog‘lang.</p><h4>CLS pooling va skalyar ko‘paytma nashri</h4><p>Modelning rasmiy usuli CLS pooling deb ataladigan birinchi token ifodasini oladi. Texnik jadval embeddinglar normallanmaganini ochiq aytadi va mos baholash funksiyasi sifatida skalyar ko‘paytmani ko‘rsatadi. To‘plam va so‘rov tomonini sozlashda shu usulni saqlang.</p><p>Bu vektorlarni L2 normallash baholash geometriyasini o‘zgartiradi. Normallangan all-mpnet-base-v2 profili boshqa usuldan foydalanadi va MPNet oilasi hamda 768 komponentli chiqishni bo‘lishsa ham alohida checkpointdir. Ularni faqat vektor uzunligidan tanlash o‘rniga, aniq nashr va taqqoslash funksiyasini saqlangan to‘plam bilan birga saqlang.</p><h4>Uzunlik chegarasi va uzunroq parchalar</h4><p>Ko‘rsatilgan kirish chegarasi 512 ta word piece birligi; uzunroq matn kesiladi. Ishlab chiqaruvchi o‘qitish kirishlari atigi 250 ta word piece birligigacha yetganini va uzunroq matn yomonroq natija berishi mumkinligini ham qayd etadi. Demak, tokenizer qabul qiladigan uzunlik va o‘qitish uzunligi ogohlantirishi turli amaliy chegaralarni tavsiflaydi.</p><p>Katta yordam maqolasi uchun muayyan savollarga javob beradigan qisqa bo‘limlar tayyorlang. Mavzuni aniqlasa sarlavhani saqlang; qoidani to‘g‘ri tushunishga kerakli istisno bilan birga qoldiring. Bitta to‘liq sahifali kirish kesilish tufayli keyingi materialni yo‘qotishi, haddan kichik bo‘lak esa uni foydali qiladigan shartni yo‘qotishi mumkin.</p><h4>Reytingdan qidiruv va RAG’da foydalaning</h4><p>Zich vektorlar birinchi qidiruv bosqichini qo‘llab-quvvatlaydi. Eng yuqori balli nomzod parchalarni manba havolalari bilan qaytaring, so‘ng haqiqiy savolni qamrashini tekshiring. RAG jarayonida shu parchalarni javob yozish bosqichiga manba materiali sifatida bering; qidiruvning o‘zi javob yozmaydi.</p><p>Natijalar sonini tanlash va reytingni baholash uchun tegishli parchalari ma’lum namunaviy savollardan foydalaning. Manzilni o‘zgartirish va joriy manzilni ko‘rish kabi lug‘ati o‘xshash, niyati boshqa misollarni kiriting. Ballarni to‘g‘rilikning kalibrlangan ehtimoli emas, bir xil model va baholash sozlamasidagi nomzodlarga nisbatan talqin qiling.</p><p>Bu checkpointning foydali chiqishi semantik qidiruv uchun savol va parcha embedding makonidir. Normallanmagan vektorlar, CLS pooling, skalyar ko‘paytma balli va e’lon qilingan uzun kirish ogohlantirishi — natijalarni tayyorlash va ishlatishda saqlanishi kerak bo‘lgan tafsilotlar.</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>sentence-transformers/multi-qa-mpnet-base-dot-v1</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-2599-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2599-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-2599-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2599-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-2599-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2599-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;sentence-transformers/multi-qa-mpnet-base-dot-v1&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-2599-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-2599-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;sentence-transformers/multi-qa-mpnet-base-dot-v1&#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=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;route=Ultra&amp;caller=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;fmt=embeddings\"><code>sentence-transformers/multi-qa-mpnet-base-dot-v1</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=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;route=Ultra&amp;caller=sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1&amp;fmt=embeddings\"><code>sentence-transformers/multi-qa-mpnet-base-dot-v1</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|sentence-transformers%2Fmulti-qa-mpnet-base-dot-v1",
      "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": [
                  "sentence-transformers/multi-qa-mpnet-base-dot-v1"
                ],
                "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",
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                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
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                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
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                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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                "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,
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                "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,
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                "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\": \"sentence-transformers/multi-qa-mpnet-base-dot-v1\", \"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='sentence-transformers/multi-qa-mpnet-base-dot-v1',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",
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                "billing_input": true,
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                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
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              },
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
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                "key": "cache_read_price",
                "label": "Cache read",
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                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
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        ]
      }
    }
  ],
  "model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "6892f9ad38e34c5b82cf74534b01d07b62b0213516b6e89666eac21091bf27f3",
    "input_html_sha256": "4b14c2eeeb8aad59e0c3f435882c76c8b0cb46ea147e7cf256b82f5dd8662c0b",
    "private_audit_sha256": "464b86f52d1228e03b15623a06401f14b4960bd7e656c6466c1517b5c1018c61",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/sentence-transformers-multi-qa-mpnet-base-dot-v1/",
    "description": "Sentence Transformers multi-qa-mpnet-base-dot-v1 savollar va parchalarni semantik qidiruv uchun 768 o‘lchamli vektorlarga kodlaydi. U CLS pooling va skalyar ko‘paytma bilan baholashga mo‘ljallangan normallanmagan embeddinglardan foydalanadi; kirish chegarasi 512 ta word piece birligi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/sentence-transformers-multi-qa-mpnet-base-dot-v1/index.md",
    "title": "Sentence Transformers: multi-qa-mpnet-base-dot-v1 — Ailoot",
    "url": "https://ailoot.ai/models/uz/sentence-transformers-multi-qa-mpnet-base-dot-v1/"
  },
  "rawModel": {
    "about": "multi-qa-mpnet-base-dot-v1 — savol yoki so‘rov uchun matn parchalarini topishga mo‘ljallangan Sentence Transformers modeli. O‘qitishda taxminan 215 million savol–javob jufti ishlatilgan. Har bir kirish 768 o‘lchamli zich vektorga aylanadi; CLS pooling va skalyar ko‘paytma bilan baholash aynan shu nashrni belgilaydi.\n\n### Savol va nomzod parchalarni tayyorlang\n\nSo‘rovni ma’lumotga bo‘lgan ehtiyoj sifatida yozing, korpus yozuvlarini esa o‘qiladigan matn parchalari qilib tayyorlang. Masalan, yetkazib berish manzilini o‘zgartirish haqidagi savolni manzilni o‘zgartirish, buyurtmani bekor qilish va yetkazib berishni kuzatishga oid alohida paragraflar bilan taqqoslash mumkin. Manzilni o‘zgartirish parchasida tegishli shartlarni, jumladan buyurtma allaqachon jo‘natilgan-jo‘natilmaganini saqlang.\n\nSo‘rov va parchalarni aynan shu model bilan kodlang. E’lon qilingan misol buni query/document prefiksisiz bevosita bajaradi va parchalarni skalyar ko‘paytma ballari bo‘yicha tartiblaydi. Reytinglangan natija haqiqatan indekslangan materialni ko‘rsata olishi uchun har bir vektorni asl parcha va hujjat identifikatoriga bog‘lang.\n\n### CLS pooling va skalyar ko‘paytma nashri\n\nModelning rasmiy usuli CLS pooling deb ataladigan birinchi token ifodasini oladi. Texnik jadval embeddinglar normallanmaganini ochiq aytadi va mos baholash funksiyasi sifatida skalyar ko‘paytmani ko‘rsatadi. To‘plam va so‘rov tomonini sozlashda shu usulni saqlang.\n\nBu vektorlarni L2 normallash baholash geometriyasini o‘zgartiradi. Normallangan all-mpnet-base-v2 profili boshqa usuldan foydalanadi va MPNet oilasi hamda 768 komponentli chiqishni bo‘lishsa ham alohida checkpointdir. Ularni faqat vektor uzunligidan tanlash o‘rniga, aniq nashr va taqqoslash funksiyasini saqlangan to‘plam bilan birga saqlang.\n\n### Uzunlik chegarasi va uzunroq parchalar\n\nKo‘rsatilgan kirish chegarasi 512 ta word piece birligi; uzunroq matn kesiladi. Ishlab chiqaruvchi o‘qitish kirishlari atigi 250 ta word piece birligigacha yetganini va uzunroq matn yomonroq natija berishi mumkinligini ham qayd etadi. Demak, tokenizer qabul qiladigan uzunlik va o‘qitish uzunligi ogohlantirishi turli amaliy chegaralarni tavsiflaydi.\n\nKatta yordam maqolasi uchun muayyan savollarga javob beradigan qisqa bo‘limlar tayyorlang. Mavzuni aniqlasa sarlavhani saqlang; qoidani to‘g‘ri tushunishga kerakli istisno bilan birga qoldiring. Bitta to‘liq sahifali kirish kesilish tufayli keyingi materialni yo‘qotishi, haddan kichik bo‘lak esa uni foydali qiladigan shartni yo‘qotishi mumkin.\n\n### Reytingdan qidiruv va RAG’da foydalaning\n\nZich vektorlar birinchi qidiruv bosqichini qo‘llab-quvvatlaydi. Eng yuqori balli nomzod parchalarni manba havolalari bilan qaytaring, so‘ng haqiqiy savolni qamrashini tekshiring. RAG jarayonida shu parchalarni javob yozish bosqichiga manba materiali sifatida bering; qidiruvning o‘zi javob yozmaydi.\n\nNatijalar sonini tanlash va reytingni baholash uchun tegishli parchalari ma’lum namunaviy savollardan foydalaning. Manzilni o‘zgartirish va joriy manzilni ko‘rish kabi lug‘ati o‘xshash, niyati boshqa misollarni kiriting. Ballarni to‘g‘rilikning kalibrlangan ehtimoli emas, bir xil model va baholash sozlamasidagi nomzodlarga nisbatan talqin qiling.\n\nBu checkpointning foydali chiqishi semantik qidiruv uchun savol va parcha embedding makonidir. Normallanmagan vektorlar, CLS pooling, skalyar ko‘paytma balli va e’lon qilingan uzun kirish ogohlantirishi — natijalarni tayyorlash va ishlatishda saqlanishi kerak bo‘lgan tafsilotlar.",
    "alias_of": null,
    "aliases": [],
    "author": "Sentence Transformers",
    "available": null,
    "canonical_model": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
    "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": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
    "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": [
                  "sentence-transformers/multi-qa-mpnet-base-dot-v1"
                ],
                "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\": \"sentence-transformers/multi-qa-mpnet-base-dot-v1\", \"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='sentence-transformers/multi-qa-mpnet-base-dot-v1',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"
              },
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                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "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": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "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,
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            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
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            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "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"
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        ]
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    "model_aliases": [
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        "request_model_id": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
        "route_label": "Ultra"
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    "name": "Sentence Transformers: multi-qa-mpnet-base-dot-v1",
    "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": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
    "short": "Sentence Transformers multi-qa-mpnet-base-dot-v1 savollar va parchalarni semantik qidiruv uchun 768 o‘lchamli vektorlarga kodlaydi. U CLS pooling va skalyar ko‘paytma bilan baholashga mo‘ljallangan normallanmagan embeddinglardan foydalanadi; kirish chegarasi 512 ta word piece birligi."
  },
  "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": [
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          "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,
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