{
  "aboutHtml": "<p>Codestral Embed 2505 — kod qidiruviga ixtisoslashgan Mistral AI embedding modeli. U manba kodi yoki tabiiy tildagi kod so‘rovi bor matnni qabul qiladi va raqamli vektorlarni qaytaradi. Ishlab chiqaruvchi uni repozitoriy qidiruvi, dasturlash yordamchilariga kontekst topish, o‘xshashlik va takrorlarni tahlil qilish, kodni funksiyasi yoki tuzilishiga qarab guruhlash uchun mo‘ljallaydi. Uning qat’iy reliz identifikatori codestral-embed-2505.</p><p>Kirish va ifoda</p><p>Kod kirishi funksiya, bog‘liq manba bo‘lagi yoki repozitoriyning boshqa mazmunli birligi bo‘lishi mumkin. Tabiiy til so‘rovi aniq simvolni nomlash o‘rniga topiladigan xulqni tavsiflashi mumkin. Embedding shu kirishni taqqoslash uchun ifodalaydi; model yangi implementatsiya yaratmaydi yoki qabul qilgan kodini bajarmaydi.</p><p>Rasmiy reliz 8 192 tokenli to‘liq kirish kontekstini hujjatlashtiradi. Qidiruv uchun ishlab chiqaruvchi taxminan 3 000 belgili bo‘laklar va 1 000 belgili ustma-ust qismni tavsiya qiladi, kattaroq bo‘laklar qidiruvni yomonlashtirishi mumkinligini qayd etadi. Bu qidiruvga tayyorlash tavsiyasi; 3 000 belgi token chegarasi emas. Ilova qidiruvdan keyin tegishli kodni qaytara olishi uchun har tayyorlangan elementni fayl, simvol va manba joyiga bog‘lang.</p><p>Chiqish aniqligi va o‘lchamlar</p><p>Kod embedding qo‘llanmasi float, int8, uint8, binary va ubinary chiqish turlarini hujjatlashtiradi. Float 32 bitli bir karra aniqlikdagi qiymatlardan foydalanadi. Int8 -128 dan 127 gacha ishorali 8 bitli butun sonlarni, uint8 esa 0 dan 255 gacha ishorasiz 8 bitli qiymatlarni ishlatadi. Binary va ubinary kvantlangan bir bitli qiymatlarni zichlangan 8 bitli elementlarda saqlaydi; mos ravishda ishorali yoki ishorasiz konteynerlardan foydalanadi.</p><p>Bitlari zichlangan ifoda qaytgan ro‘yxat uzunligini o‘zgartiradi: qo‘llanma har sakkizta mantiqiy embedding o‘lchami uchun bitta zichlangan elementni tasvirlaydi. Demak, formatni hisobga olgan taqqoslash zichlangan baytlarni oddiy suzuvchi nuqtali koordinatalar deb ko‘rishdan farq qiladi. Tanlangan turni vektor bilan saqlang va indekslangan kod hamda so‘rovi uchun bir xil ifodadan foydalaning.</p><p>Parametr hujjatlari odatiy o‘lchamni 1 536, maksimumni 3 072 deb beradi. O‘lchamlar dolzarblik bo‘yicha tartiblangan; ishlab chiqaruvchi tanlangan soncha dastlabki o‘lchamlarni saqlashni ifoda hajmini tanlash usuli deb tavsiflaydi. Saqlangan to‘plam va yangi so‘rovlar uchun o‘lchamni izchil tanlang. Qisqaroq yoki kvantlangan ifoda saqlash va taqqoslash xulqini o‘zgartiradi; kirish kontekstini oshirmaydi yoki modelni kod yaratuvchiga aylantirmaydi.</p><p>Qidiruv bilan boyitilgan dasturlash</p><p>Relizning birinchi qo‘llanishi kodni to‘ldirish, tahrirlash yoki tushuntirish uchun kontekst topishdir. Mazmunli kod birliklarini tayyorlang va repozitoriy reviziyasi hamda manba identifikatorlarini saqlang. Muayyan xato qanday ishlanishi haqidagi savol bog‘liq funksiyalarni topishi mumkin; ilova keyin ularni haqiqiy o‘zgartirish so‘rovi bilan birga dasturlash modeliga beradi.</p><p>Amaliy natija — fayl yo‘llari, simvollar va xulqini tushunishga kerakli mazmuni bor reytinglangan manba parchalari to‘plami. Topilgan parchalarni dasturchi ochishi yoki yordamchiga berishi mumkin. Codestral Embed ifodalash bosqichini bajaradi; alohida komponent natijalarni tanlaydi, yaratish modeli esa izoh yoki taklif etilgan kodni yozadi.</p><p>Semantik qidiruv va o‘xshashlik</p><p>Ishlab chiqaruvchi tabiiy til yoki kod so‘rovlari orqali kod qidirishni tavsiflaydi. Funksiya nomi so‘rov ifodasidan farq qilsa ham dasturchi muayyan xulq implementatsiyasini so‘rashi mumkin. Mo‘ljallangan implementatsiyani farqlaydigan xulq va shartlarni kiriting; butun quyi tizim haqidagi umumiy so‘rov muayyan savolni hal qilmasdan bog‘liq materialni topishi mumkin.</p><p>Reliz leksik farqlarga qaramay deyarli takroriy yoki funksional o‘xshash kodni topishni ham tavsiflaydi. Bunday natijalar qayta ishlatiladigan implementatsiyani topish yoki qo‘shimcha tekshiruv talab qiladigan kodni aniqlashga yordam berishi mumkin. Embedding makonidagi o‘xshashlik manbani tekshirish signalidir; ikki bo‘lak har bir kirishda bir xil xulqqa ega ekanini isbotlamaydi.</p><p>Klasterlash, tahlil va taqqoslash</p><p>Kod embeddinglari kodni funksiyasi yoki tuzilishiga qarab guruhlash va repozitoriy tarkibini tekshirishda ishlatilishi mumkin. Guruhni inson yoki keyingi ilova bosqichi talqin qila olishi uchun asl parchalar va identifikatorlarni mavjud saqlang. Klaster avtomatik tasdiqlangan arxitektura tavsifi emas, ifodaga asoslangan guruhlashdir.</p><p>Ishlab chiqaruvchining suzuvchi nuqtali vektor misollari birlik uzunlikka normallangan embeddinglardan foydalanadi; ular uchun kosinus o‘xshashligi, skalyar ko‘paytma va Yevklid masofasi izchil reyting beradi. Kvantlangan va bitlari zichlangan chiqish xom suzuvchi nuqtali koordinatalar haqidagi taxminni meros olish o‘rniga o‘z format ishlovini saqlashi kerak. E’lon qilingan endpoint kirish indekslarini ham saqlaydi; har bir vektor va manba elementi bog‘liqligini saqlashga yordam beradi.</p><p>Nashr va amaliy chegaralar</p><p>Aynan model sahifasi embedding va batch jarayonlarini 8k kontekst yorlig‘i bilan ko‘rsatadi, reliz esa 8 192 token qiymatini beradi. To‘plam va so‘rov jarayoni davomida nomlangan 2505 nashri, tanlangan chiqish o‘lchami va turini saqlang. Bu Codestral’ning matn to‘ldirishidan va Mistral Embed’ning qat’iy o‘lchamli matn modelidan farqli, kodga yo‘naltirilgan vektor chiqishidir.</p><p>Tegishli xulqni saqlaydigan kod birliklarini tayyorlang, manba joylarini saqlang va qidiriladigan savolni ayting. Foydali artefakt — haqiqiy kodga bog‘langan, keyingi tushuntirish, tahrirlash yoki dasturchi tekshiruvi bosqichini o‘zi bajarmasdan qo‘llab-quvvatlaydigan topiladigan ifoda.</p>",
  "aboutText": "Codestral Embed 2505 — kod qidiruviga ixtisoslashgan Mistral AI embedding modeli. U manba kodi yoki tabiiy tildagi kod so‘rovi bor matnni qabul qiladi va raqamli vektorlarni qaytaradi. Ishlab chiqaruvchi uni repozitoriy qidiruvi, dasturlash yordamchilariga kontekst topish, o‘xshashlik va takrorlarni tahlil qilish, kodni funksiyasi yoki tuzilishiga qarab guruhlash uchun mo‘ljallaydi. Uning qat’iy reliz identifikatori codestral-embed-2505.\n\nKirish va ifoda\n\nKod kirishi funksiya, bog‘liq manba bo‘lagi yoki repozitoriyning boshqa mazmunli birligi bo‘lishi mumkin. Tabiiy til so‘rovi aniq simvolni nomlash o‘rniga topiladigan xulqni tavsiflashi mumkin. Embedding shu kirishni taqqoslash uchun ifodalaydi; model yangi implementatsiya yaratmaydi yoki qabul qilgan kodini bajarmaydi.\n\nRasmiy reliz 8 192 tokenli to‘liq kirish kontekstini hujjatlashtiradi. Qidiruv uchun ishlab chiqaruvchi taxminan 3 000 belgili bo‘laklar va 1 000 belgili ustma-ust qismni tavsiya qiladi, kattaroq bo‘laklar qidiruvni yomonlashtirishi mumkinligini qayd etadi. Bu qidiruvga tayyorlash tavsiyasi; 3 000 belgi token chegarasi emas. Ilova qidiruvdan keyin tegishli kodni qaytara olishi uchun har tayyorlangan elementni fayl, simvol va manba joyiga bog‘lang.\n\nChiqish aniqligi va o‘lchamlar\n\nKod embedding qo‘llanmasi float, int8, uint8, binary va ubinary chiqish turlarini hujjatlashtiradi. Float 32 bitli bir karra aniqlikdagi qiymatlardan foydalanadi. Int8 -128 dan 127 gacha ishorali 8 bitli butun sonlarni, uint8 esa 0 dan 255 gacha ishorasiz 8 bitli qiymatlarni ishlatadi. Binary va ubinary kvantlangan bir bitli qiymatlarni zichlangan 8 bitli elementlarda saqlaydi; mos ravishda ishorali yoki ishorasiz konteynerlardan foydalanadi.\n\nBitlari zichlangan ifoda qaytgan ro‘yxat uzunligini o‘zgartiradi: qo‘llanma har sakkizta mantiqiy embedding o‘lchami uchun bitta zichlangan elementni tasvirlaydi. Demak, formatni hisobga olgan taqqoslash zichlangan baytlarni oddiy suzuvchi nuqtali koordinatalar deb ko‘rishdan farq qiladi. Tanlangan turni vektor bilan saqlang va indekslangan kod hamda so‘rovi uchun bir xil ifodadan foydalaning.\n\nParametr hujjatlari odatiy o‘lchamni 1 536, maksimumni 3 072 deb beradi. O‘lchamlar dolzarblik bo‘yicha tartiblangan; ishlab chiqaruvchi tanlangan soncha dastlabki o‘lchamlarni saqlashni ifoda hajmini tanlash usuli deb tavsiflaydi. Saqlangan to‘plam va yangi so‘rovlar uchun o‘lchamni izchil tanlang. Qisqaroq yoki kvantlangan ifoda saqlash va taqqoslash xulqini o‘zgartiradi; kirish kontekstini oshirmaydi yoki modelni kod yaratuvchiga aylantirmaydi.\n\nQidiruv bilan boyitilgan dasturlash\n\nRelizning birinchi qo‘llanishi kodni to‘ldirish, tahrirlash yoki tushuntirish uchun kontekst topishdir. Mazmunli kod birliklarini tayyorlang va repozitoriy reviziyasi hamda manba identifikatorlarini saqlang. Muayyan xato qanday ishlanishi haqidagi savol bog‘liq funksiyalarni topishi mumkin; ilova keyin ularni haqiqiy o‘zgartirish so‘rovi bilan birga dasturlash modeliga beradi.\n\nAmaliy natija — fayl yo‘llari, simvollar va xulqini tushunishga kerakli mazmuni bor reytinglangan manba parchalari to‘plami. Topilgan parchalarni dasturchi ochishi yoki yordamchiga berishi mumkin. Codestral Embed ifodalash bosqichini bajaradi; alohida komponent natijalarni tanlaydi, yaratish modeli esa izoh yoki taklif etilgan kodni yozadi.\n\nSemantik qidiruv va o‘xshashlik\n\nIshlab chiqaruvchi tabiiy til yoki kod so‘rovlari orqali kod qidirishni tavsiflaydi. Funksiya nomi so‘rov ifodasidan farq qilsa ham dasturchi muayyan xulq implementatsiyasini so‘rashi mumkin. Mo‘ljallangan implementatsiyani farqlaydigan xulq va shartlarni kiriting; butun quyi tizim haqidagi umumiy so‘rov muayyan savolni hal qilmasdan bog‘liq materialni topishi mumkin.\n\nReliz leksik farqlarga qaramay deyarli takroriy yoki funksional o‘xshash kodni topishni ham tavsiflaydi. Bunday natijalar qayta ishlatiladigan implementatsiyani topish yoki qo‘shimcha tekshiruv talab qiladigan kodni aniqlashga yordam berishi mumkin. Embedding makonidagi o‘xshashlik manbani tekshirish signalidir; ikki bo‘lak har bir kirishda bir xil xulqqa ega ekanini isbotlamaydi.\n\nKlasterlash, tahlil va taqqoslash\n\nKod embeddinglari kodni funksiyasi yoki tuzilishiga qarab guruhlash va repozitoriy tarkibini tekshirishda ishlatilishi mumkin. Guruhni inson yoki keyingi ilova bosqichi talqin qila olishi uchun asl parchalar va identifikatorlarni mavjud saqlang. Klaster avtomatik tasdiqlangan arxitektura tavsifi emas, ifodaga asoslangan guruhlashdir.\n\nIshlab chiqaruvchining suzuvchi nuqtali vektor misollari birlik uzunlikka normallangan embeddinglardan foydalanadi; ular uchun kosinus o‘xshashligi, skalyar ko‘paytma va Yevklid masofasi izchil reyting beradi. Kvantlangan va bitlari zichlangan chiqish xom suzuvchi nuqtali koordinatalar haqidagi taxminni meros olish o‘rniga o‘z format ishlovini saqlashi kerak. E’lon qilingan endpoint kirish indekslarini ham saqlaydi; har bir vektor va manba elementi bog‘liqligini saqlashga yordam beradi.\n\nNashr va amaliy chegaralar\n\nAynan model sahifasi embedding va batch jarayonlarini 8k kontekst yorlig‘i bilan ko‘rsatadi, reliz esa 8 192 token qiymatini beradi. To‘plam va so‘rov jarayoni davomida nomlangan 2505 nashri, tanlangan chiqish o‘lchami va turini saqlang. Bu Codestral’ning matn to‘ldirishidan va Mistral Embed’ning qat’iy o‘lchamli matn modelidan farqli, kodga yo‘naltirilgan vektor chiqishidir.\n\nTegishli xulqni saqlaydigan kod birliklarini tayyorlang, manba joylarini saqlang va qidiriladigan savolni ayting. Foydali artefakt — haqiqiy kodga bog‘langan, keyingi tushuntirish, tahrirlash yoki dasturchi tekshiruvi bosqichini o‘zi bajarmasdan qo‘llab-quvvatlaydigan topiladigan ifoda.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Mistral: Codestral Embed 2505</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Mistral /</span><code>mistralai/codestral-embed-2505</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|mistralai%2Fcodestral-embed-2505\">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\">Codestral Embed 2505 kod va tabiiy tildagi so‘rovlarni repozitoriy qidiruvi hamda dasturlash yordamchisi qidiruvi uchun vektorlar sifatida ifodalaydi. Ishlab chiqaruvchi 8 192 tokenli kontekst, 3 072 gacha sozlanadigan o‘lchamlar va suzuvchi nuqtali, butun sonli yoki bitlari zichlangan chiqish formatlarini hujjatlashtiradi.</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>Codestral Embed 2505 — kod qidiruviga ixtisoslashgan Mistral AI embedding modeli. U manba kodi yoki tabiiy tildagi kod so‘rovi bor matnni qabul qiladi va raqamli vektorlarni qaytaradi. Ishlab chiqaruvchi uni repozitoriy qidiruvi, dasturlash yordamchilariga kontekst topish, o‘xshashlik va takrorlarni tahlil qilish, kodni funksiyasi yoki tuzilishiga qarab guruhlash uchun mo‘ljallaydi. Uning qat’iy reliz identifikatori codestral-embed-2505.</p><p>Kirish va ifoda</p><p>Kod kirishi funksiya, bog‘liq manba bo‘lagi yoki repozitoriyning boshqa mazmunli birligi bo‘lishi mumkin. Tabiiy til so‘rovi aniq simvolni nomlash o‘rniga topiladigan xulqni tavsiflashi mumkin. Embedding shu kirishni taqqoslash uchun ifodalaydi; model yangi implementatsiya yaratmaydi yoki qabul qilgan kodini bajarmaydi.</p><p>Rasmiy reliz 8 192 tokenli to‘liq kirish kontekstini hujjatlashtiradi. Qidiruv uchun ishlab chiqaruvchi taxminan 3 000 belgili bo‘laklar va 1 000 belgili ustma-ust qismni tavsiya qiladi, kattaroq bo‘laklar qidiruvni yomonlashtirishi mumkinligini qayd etadi. Bu qidiruvga tayyorlash tavsiyasi; 3 000 belgi token chegarasi emas. Ilova qidiruvdan keyin tegishli kodni qaytara olishi uchun har tayyorlangan elementni fayl, simvol va manba joyiga bog‘lang.</p><p>Chiqish aniqligi va o‘lchamlar</p><p>Kod embedding qo‘llanmasi float, int8, uint8, binary va ubinary chiqish turlarini hujjatlashtiradi. Float 32 bitli bir karra aniqlikdagi qiymatlardan foydalanadi. Int8 -128 dan 127 gacha ishorali 8 bitli butun sonlarni, uint8 esa 0 dan 255 gacha ishorasiz 8 bitli qiymatlarni ishlatadi. Binary va ubinary kvantlangan bir bitli qiymatlarni zichlangan 8 bitli elementlarda saqlaydi; mos ravishda ishorali yoki ishorasiz konteynerlardan foydalanadi.</p><p>Bitlari zichlangan ifoda qaytgan ro‘yxat uzunligini o‘zgartiradi: qo‘llanma har sakkizta mantiqiy embedding o‘lchami uchun bitta zichlangan elementni tasvirlaydi. Demak, formatni hisobga olgan taqqoslash zichlangan baytlarni oddiy suzuvchi nuqtali koordinatalar deb ko‘rishdan farq qiladi. Tanlangan turni vektor bilan saqlang va indekslangan kod hamda so‘rovi uchun bir xil ifodadan foydalaning.</p><p>Parametr hujjatlari odatiy o‘lchamni 1 536, maksimumni 3 072 deb beradi. O‘lchamlar dolzarblik bo‘yicha tartiblangan; ishlab chiqaruvchi tanlangan soncha dastlabki o‘lchamlarni saqlashni ifoda hajmini tanlash usuli deb tavsiflaydi. Saqlangan to‘plam va yangi so‘rovlar uchun o‘lchamni izchil tanlang. Qisqaroq yoki kvantlangan ifoda saqlash va taqqoslash xulqini o‘zgartiradi; kirish kontekstini oshirmaydi yoki modelni kod yaratuvchiga aylantirmaydi.</p><p>Qidiruv bilan boyitilgan dasturlash</p><p>Relizning birinchi qo‘llanishi kodni to‘ldirish, tahrirlash yoki tushuntirish uchun kontekst topishdir. Mazmunli kod birliklarini tayyorlang va repozitoriy reviziyasi hamda manba identifikatorlarini saqlang. Muayyan xato qanday ishlanishi haqidagi savol bog‘liq funksiyalarni topishi mumkin; ilova keyin ularni haqiqiy o‘zgartirish so‘rovi bilan birga dasturlash modeliga beradi.</p><p>Amaliy natija — fayl yo‘llari, simvollar va xulqini tushunishga kerakli mazmuni bor reytinglangan manba parchalari to‘plami. Topilgan parchalarni dasturchi ochishi yoki yordamchiga berishi mumkin. Codestral Embed ifodalash bosqichini bajaradi; alohida komponent natijalarni tanlaydi, yaratish modeli esa izoh yoki taklif etilgan kodni yozadi.</p><p>Semantik qidiruv va o‘xshashlik</p><p>Ishlab chiqaruvchi tabiiy til yoki kod so‘rovlari orqali kod qidirishni tavsiflaydi. Funksiya nomi so‘rov ifodasidan farq qilsa ham dasturchi muayyan xulq implementatsiyasini so‘rashi mumkin. Mo‘ljallangan implementatsiyani farqlaydigan xulq va shartlarni kiriting; butun quyi tizim haqidagi umumiy so‘rov muayyan savolni hal qilmasdan bog‘liq materialni topishi mumkin.</p><p>Reliz leksik farqlarga qaramay deyarli takroriy yoki funksional o‘xshash kodni topishni ham tavsiflaydi. Bunday natijalar qayta ishlatiladigan implementatsiyani topish yoki qo‘shimcha tekshiruv talab qiladigan kodni aniqlashga yordam berishi mumkin. Embedding makonidagi o‘xshashlik manbani tekshirish signalidir; ikki bo‘lak har bir kirishda bir xil xulqqa ega ekanini isbotlamaydi.</p><p>Klasterlash, tahlil va taqqoslash</p><p>Kod embeddinglari kodni funksiyasi yoki tuzilishiga qarab guruhlash va repozitoriy tarkibini tekshirishda ishlatilishi mumkin. Guruhni inson yoki keyingi ilova bosqichi talqin qila olishi uchun asl parchalar va identifikatorlarni mavjud saqlang. Klaster avtomatik tasdiqlangan arxitektura tavsifi emas, ifodaga asoslangan guruhlashdir.</p><p>Ishlab chiqaruvchining suzuvchi nuqtali vektor misollari birlik uzunlikka normallangan embeddinglardan foydalanadi; ular uchun kosinus o‘xshashligi, skalyar ko‘paytma va Yevklid masofasi izchil reyting beradi. Kvantlangan va bitlari zichlangan chiqish xom suzuvchi nuqtali koordinatalar haqidagi taxminni meros olish o‘rniga o‘z format ishlovini saqlashi kerak. E’lon qilingan endpoint kirish indekslarini ham saqlaydi; har bir vektor va manba elementi bog‘liqligini saqlashga yordam beradi.</p><p>Nashr va amaliy chegaralar</p><p>Aynan model sahifasi embedding va batch jarayonlarini 8k kontekst yorlig‘i bilan ko‘rsatadi, reliz esa 8 192 token qiymatini beradi. To‘plam va so‘rov jarayoni davomida nomlangan 2505 nashri, tanlangan chiqish o‘lchami va turini saqlang. Bu Codestral’ning matn to‘ldirishidan va Mistral Embed’ning qat’iy o‘lchamli matn modelidan farqli, kodga yo‘naltirilgan vektor chiqishidir.</p><p>Tegishli xulqni saqlaydigan kod birliklarini tayyorlang, manba joylarini saqlang va qidiriladigan savolni ayting. Foydali artefakt — haqiqiy kodga bog‘langan, keyingi tushuntirish, tahrirlash yoki dasturchi tekshiruvi bosqichini o‘zi bajarmasdan qo‘llab-quvvatlaydigan topiladigan ifoda.</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>8 192 tokens</dd></div><div><dt>Chiqish formati</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Maydon</th><th>Tur</th><th>Talab</th><th>Qiymatlar va cheklovlar</th><th>Standart</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>mistralai/codestral-embed-2505</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: 8 192 · 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>8192</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>$0.0003</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-1892-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1892-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1892-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1892-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-1892-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1892-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;mistralai/codestral-embed-2505&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-1892-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1892-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;mistralai/codestral-embed-2505&#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=mistralai%2Fcodestral-embed-2505&amp;route=Ultra&amp;caller=mistralai%2Fcodestral-embed-2505&amp;fmt=embeddings\"><code>mistralai/codestral-embed-2505</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=mistralai%2Fcodestral-embed-2505&amp;route=Ultra&amp;caller=mistralai%2Fcodestral-embed-2505&amp;fmt=embeddings\"><code>mistralai/codestral-embed-2505</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "mistralai/codestral-embed-2505",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "mistralai/codestral-embed-2505",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|mistralai%2Fcodestral-embed-2505",
      "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": [
                  "mistralai/codestral-embed-2505"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 8192,
                  "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": 8192,
            "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": 8192,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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\": \"mistralai/codestral-embed-2505\", \"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='mistralai/codestral-embed-2505',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": 0.3,
        "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": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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": "mistralai/codestral-embed-2505",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "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": "mistralai/codestral-embed-2505",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "48eb83031a9916d613993ba558fd728512a74f2e15933ece21a4a24a5afa98bc",
    "input_html_sha256": "777cb007765b8d23b84c91c5e8953a7ec4efbeaea06a085cce442bd679f0a75f",
    "private_audit_sha256": "d96d4e559c54a9ebbfe239d9bdaa235ed3252cf94b50efc5a098d8d9986b7d86",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/mistralai-codestral-embed-2505/",
    "description": "Codestral Embed 2505 kod va tabiiy tildagi so‘rovlarni repozitoriy qidiruvi hamda dasturlash yordamchisi qidiruvi uchun vektorlar sifatida ifodalaydi. Ishlab chiqaruvchi 8 192 tokenli kontekst, 3 072 gacha sozlanadigan o‘lchamlar va suzuvchi nuqtali, butun sonli yoki bitlari zichlangan chiqish formatlarini hujjatlashtiradi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/mistralai-codestral-embed-2505/index.md",
    "title": "Mistral: Codestral Embed 2505 — Ailoot",
    "url": "https://ailoot.ai/models/uz/mistralai-codestral-embed-2505/"
  },
  "rawModel": {
    "about": "Codestral Embed 2505 — kod qidiruviga ixtisoslashgan Mistral AI embedding modeli. U manba kodi yoki tabiiy tildagi kod so‘rovi bor matnni qabul qiladi va raqamli vektorlarni qaytaradi. Ishlab chiqaruvchi uni repozitoriy qidiruvi, dasturlash yordamchilariga kontekst topish, o‘xshashlik va takrorlarni tahlil qilish, kodni funksiyasi yoki tuzilishiga qarab guruhlash uchun mo‘ljallaydi. Uning qat’iy reliz identifikatori codestral-embed-2505.\n\nKirish va ifoda\n\nKod kirishi funksiya, bog‘liq manba bo‘lagi yoki repozitoriyning boshqa mazmunli birligi bo‘lishi mumkin. Tabiiy til so‘rovi aniq simvolni nomlash o‘rniga topiladigan xulqni tavsiflashi mumkin. Embedding shu kirishni taqqoslash uchun ifodalaydi; model yangi implementatsiya yaratmaydi yoki qabul qilgan kodini bajarmaydi.\n\nRasmiy reliz 8 192 tokenli to‘liq kirish kontekstini hujjatlashtiradi. Qidiruv uchun ishlab chiqaruvchi taxminan 3 000 belgili bo‘laklar va 1 000 belgili ustma-ust qismni tavsiya qiladi, kattaroq bo‘laklar qidiruvni yomonlashtirishi mumkinligini qayd etadi. Bu qidiruvga tayyorlash tavsiyasi; 3 000 belgi token chegarasi emas. Ilova qidiruvdan keyin tegishli kodni qaytara olishi uchun har tayyorlangan elementni fayl, simvol va manba joyiga bog‘lang.\n\nChiqish aniqligi va o‘lchamlar\n\nKod embedding qo‘llanmasi float, int8, uint8, binary va ubinary chiqish turlarini hujjatlashtiradi. Float 32 bitli bir karra aniqlikdagi qiymatlardan foydalanadi. Int8 -128 dan 127 gacha ishorali 8 bitli butun sonlarni, uint8 esa 0 dan 255 gacha ishorasiz 8 bitli qiymatlarni ishlatadi. Binary va ubinary kvantlangan bir bitli qiymatlarni zichlangan 8 bitli elementlarda saqlaydi; mos ravishda ishorali yoki ishorasiz konteynerlardan foydalanadi.\n\nBitlari zichlangan ifoda qaytgan ro‘yxat uzunligini o‘zgartiradi: qo‘llanma har sakkizta mantiqiy embedding o‘lchami uchun bitta zichlangan elementni tasvirlaydi. Demak, formatni hisobga olgan taqqoslash zichlangan baytlarni oddiy suzuvchi nuqtali koordinatalar deb ko‘rishdan farq qiladi. Tanlangan turni vektor bilan saqlang va indekslangan kod hamda so‘rovi uchun bir xil ifodadan foydalaning.\n\nParametr hujjatlari odatiy o‘lchamni 1 536, maksimumni 3 072 deb beradi. O‘lchamlar dolzarblik bo‘yicha tartiblangan; ishlab chiqaruvchi tanlangan soncha dastlabki o‘lchamlarni saqlashni ifoda hajmini tanlash usuli deb tavsiflaydi. Saqlangan to‘plam va yangi so‘rovlar uchun o‘lchamni izchil tanlang. Qisqaroq yoki kvantlangan ifoda saqlash va taqqoslash xulqini o‘zgartiradi; kirish kontekstini oshirmaydi yoki modelni kod yaratuvchiga aylantirmaydi.\n\nQidiruv bilan boyitilgan dasturlash\n\nRelizning birinchi qo‘llanishi kodni to‘ldirish, tahrirlash yoki tushuntirish uchun kontekst topishdir. Mazmunli kod birliklarini tayyorlang va repozitoriy reviziyasi hamda manba identifikatorlarini saqlang. Muayyan xato qanday ishlanishi haqidagi savol bog‘liq funksiyalarni topishi mumkin; ilova keyin ularni haqiqiy o‘zgartirish so‘rovi bilan birga dasturlash modeliga beradi.\n\nAmaliy natija — fayl yo‘llari, simvollar va xulqini tushunishga kerakli mazmuni bor reytinglangan manba parchalari to‘plami. Topilgan parchalarni dasturchi ochishi yoki yordamchiga berishi mumkin. Codestral Embed ifodalash bosqichini bajaradi; alohida komponent natijalarni tanlaydi, yaratish modeli esa izoh yoki taklif etilgan kodni yozadi.\n\nSemantik qidiruv va o‘xshashlik\n\nIshlab chiqaruvchi tabiiy til yoki kod so‘rovlari orqali kod qidirishni tavsiflaydi. Funksiya nomi so‘rov ifodasidan farq qilsa ham dasturchi muayyan xulq implementatsiyasini so‘rashi mumkin. Mo‘ljallangan implementatsiyani farqlaydigan xulq va shartlarni kiriting; butun quyi tizim haqidagi umumiy so‘rov muayyan savolni hal qilmasdan bog‘liq materialni topishi mumkin.\n\nReliz leksik farqlarga qaramay deyarli takroriy yoki funksional o‘xshash kodni topishni ham tavsiflaydi. Bunday natijalar qayta ishlatiladigan implementatsiyani topish yoki qo‘shimcha tekshiruv talab qiladigan kodni aniqlashga yordam berishi mumkin. Embedding makonidagi o‘xshashlik manbani tekshirish signalidir; ikki bo‘lak har bir kirishda bir xil xulqqa ega ekanini isbotlamaydi.\n\nKlasterlash, tahlil va taqqoslash\n\nKod embeddinglari kodni funksiyasi yoki tuzilishiga qarab guruhlash va repozitoriy tarkibini tekshirishda ishlatilishi mumkin. Guruhni inson yoki keyingi ilova bosqichi talqin qila olishi uchun asl parchalar va identifikatorlarni mavjud saqlang. Klaster avtomatik tasdiqlangan arxitektura tavsifi emas, ifodaga asoslangan guruhlashdir.\n\nIshlab chiqaruvchining suzuvchi nuqtali vektor misollari birlik uzunlikka normallangan embeddinglardan foydalanadi; ular uchun kosinus o‘xshashligi, skalyar ko‘paytma va Yevklid masofasi izchil reyting beradi. Kvantlangan va bitlari zichlangan chiqish xom suzuvchi nuqtali koordinatalar haqidagi taxminni meros olish o‘rniga o‘z format ishlovini saqlashi kerak. E’lon qilingan endpoint kirish indekslarini ham saqlaydi; har bir vektor va manba elementi bog‘liqligini saqlashga yordam beradi.\n\nNashr va amaliy chegaralar\n\nAynan model sahifasi embedding va batch jarayonlarini 8k kontekst yorlig‘i bilan ko‘rsatadi, reliz esa 8 192 token qiymatini beradi. To‘plam va so‘rov jarayoni davomida nomlangan 2505 nashri, tanlangan chiqish o‘lchami va turini saqlang. Bu Codestral’ning matn to‘ldirishidan va Mistral Embed’ning qat’iy o‘lchamli matn modelidan farqli, kodga yo‘naltirilgan vektor chiqishidir.\n\nTegishli xulqni saqlaydigan kod birliklarini tayyorlang, manba joylarini saqlang va qidiriladigan savolni ayting. Foydali artefakt — haqiqiy kodga bog‘langan, keyingi tushuntirish, tahrirlash yoki dasturchi tekshiruvi bosqichini o‘zi bajarmasdan qo‘llab-quvvatlaydigan topiladigan ifoda.",
    "alias_of": null,
    "aliases": [],
    "author": "Mistral",
    "available": null,
    "canonical_model": "mistralai/codestral-embed-2505",
    "capabilities": {
      "context_length": 8192,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan."
      }
    ],
    "ctx": 8192,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "mistralai/codestral-embed-2505",
    "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": [
                  "mistralai/codestral-embed-2505"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 8192,
                  "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": 8192,
            "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": 8192,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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\": \"mistralai/codestral-embed-2505\", \"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='mistralai/codestral-embed-2505',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": 0.3,
        "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": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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": "mistralai/codestral-embed-2505",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "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": "mistralai/codestral-embed-2505",
        "request_model_id": "mistralai/codestral-embed-2505",
        "route_label": "Ultra"
      }
    ],
    "name": "Mistral: Codestral Embed 2505",
    "price": {
      "in": 0.3,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.3,
      "sort_out": 0,
      "varies": false
    },
    "pricing_profiles": [
      {
        "dimensions": {
          "regime": "base"
        },
        "label": "Эмбеддинги · Base token rates",
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "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": "mistralai/codestral-embed-2505",
    "short": "Codestral Embed 2505 kod va tabiiy tildagi so‘rovlarni repozitoriy qidiruvi hamda dasturlash yordamchisi qidiruvi uchun vektorlar sifatida ifodalaydi. Ishlab chiqaruvchi 8 192 tokenli kontekst, 3 072 gacha sozlanadigan o‘lchamlar va suzuvchi nuqtali, butun sonli yoki bitlari zichlangan chiqish formatlarini hujjatlashtiradi."
  },
  "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": "Codestral Embed 2505 kod va tabiiy tildagi so‘rovlarni repozitoriy qidiruvi hamda dasturlash yordamchisi qidiruvi uchun vektorlar sifatida ifodalaydi. Ishlab chiqaruvchi 8 192 tokenli kontekst, 3 072 gacha sozlanadigan o‘lchamlar va suzuvchi nuqtali, butun sonli yoki bitlari zichlangan chiqish formatlarini hujjatlashtiradi.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "8f70fcd0acaacaa7e00cbc8bdc768e1a0a8456fdece5e080d62c3bf800df5c5e",
    "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6"
  },
  "sourceRevision": "a90f7c2d1a18567bbb955c366910306942e36403d8ad828f8e0bb29f48519822"
}