{ "aboutHtml": "

voyage-multimodal-3.5 — matn, rasm va video bo‘ylab qidiruv uchun Voyage AI embedding modeli. U aralash ketma-ketlikdagi matn va vizual mazmunni bitta transformer enkoderi orqali qayta ishlab, to‘liq multimodal kirishni umumiy vektor makonida ifodalaydi. 3.5 nashri video kadrlarini bevosita qo‘llab-quvvatlash va moslashuvchan embedding o‘lchamlarini qo‘shadi.

Matn va tasvirlar orasidagi bog‘liqlikni saqlang

Hujjat sahifasi yoki bo‘limini foydali vizual ma’lumotni saqlaydigan shaklda tayyorlang. Grafik va qisqa izohi bor slaydni slayd rasmi hamda unga tegishli matn bilan ayni tartibli kirishda ifodalash mumkin. PDF asosidagi to‘plamda hujjatlashtirilgan vizual kirish sahifa skrinshoti yoki hujjatning boshqa rasmli ifodasidir.

Hisobot grafigida sarlavha, o‘q yorliqlari va shartli belgilar izohini rasmda saqlang. Tasvirni talqin qilish uchun kerakli kontekstni bersa, tushuntiruvchi matnni ilova qiling. Topilgan nomzod faqat hujjat nomini qaytarish o‘rniga tegishli sahifani ocha olishi uchun sahifa raqami va manba identifikatorini natijaviy vektor yonida saqlang.

Ishlab chiqaruvchi kirish formati matn, rasm va video ketma-ketliklarini qo‘llaydi. E’lon qilingan rasm formatlari PNG, JPEG, WebP va GIF, video uchun esa MP4. Hujjatlashtirilgan usulda vizual mazmunni URL yoki Base64 ma’lumotlari orqali berish mumkin; bitta so‘rov ichida bir ifoda uslubini izchil saqlang.

Butun uzun video o‘rniga sahnani toping

Model videoni tartibli kadrlar ketma-ketligi sifatida ifodalaydi. Ishlab chiqaruvchi uzun videoni bog‘liq sahnalarga ajratish, sahna chegaralarini mavjud transkript vaqt belgilari bilan moslash va tegishli transkript matnini saqlashni tavsiya qiladi. Pastroq tasvir aniqligi yoki kamaytirilgan kadr tezligi semantik jihatdan uzluksiz sahnani kontekst budjetiga sig‘dirishga yordam berishi mumkin.

O‘quv videolari to‘plamida so‘rov muayyan boshqaruv elementini sozlashni ko‘rsatadigan sahnani so‘rashi mumkin. Har indekslangan sahnani vaqt oralig‘iga bog‘lang. Topilgan vektorni yozuvdagi hamma narsaning yozma tavsifi deb bilish o‘rniga, mos sahnani qaytaring va haqiqiy amalni ko‘rish uchun kadrlar yoki asl klipni tekshiring.

Transkript matni multimodal kirishning bir qismi sifatida tayyorlansa hissa qo‘shishi mumkin. Video kadrlarini embedding qilishning o‘zi nutq transkriptini bermaydi.

Kontekst va tayyorlash chegaralari

Ko‘rsatilgan kontekst uzunligi har bir kirish uchun 32 000 token. Voyage e’lon qilingan hisoblashda har 560 rasm pikseli va har 1120 video pikselini bitta token deb hisoblaydi; vizual mazmun shu budjetni matn bilan bo‘lishadi. Hujjatlashtirilgan chegaralarga har rasm uchun 16 million piksel va 20 MB, har video uchun 20 MB ham kiradi. Bitta ro‘yxatda ko‘pi bilan 1000 kirish va jami 320 000 token bo‘ladi.

Odatiy kesish sozlamasi haddan uzun kirishni kesadi; chegara rasm ichiga tushsa, butun rasm tashlanadi. Demak, to‘liq sahifa yoki sahnalarni budjet ichida tayyorlash kirish mazmunini oldindan bilishni osonlashtiradi. Hujjatlashtirilgan muqobil sozlama haddan uzun kirishni kesish o‘rniga rad etadi.

Vektor o‘lchamlari va qidiruv rollari

Odatiy chiqish o‘lchami 1024; bu nashr uchun 256, 512 va 2048 o‘lcham ham hujjatlashtirilgan. E’londa Matryoshka embeddinglari hamda suzuvchi nuqtali, ishorali yoki ishorasiz 8 bitli va ikkilik ifodalar tasvirlanadi. Tanlangan o‘lcham va ifodani indeks bilan saqlang.

Voyage qidiruv usuli query va document kirishlarini ajratadi. So‘rov matn yoki rasm bo‘lishi, indekslangan hujjatlar esa matn va tasvirlarni birlashtirishi mumkin. Har to‘liq kirish ketma-ketligini bitta vektor ifodalaydi; reytinglangan natija tekshiriladigan bo‘lib qolishi uchun manba sahifasi, rasm yoki sahna bog‘liqligini saqlang.

Qaytgan vizual manbadan foydalaning

Qidiruv bosqichi so‘rovga tegishli hisobot sahifasi, jadval, slayd yoki sahnani topishi mumkin. Keyin javob yozish yoki tahlil bosqichi shu manba materialini o‘qiydi. Jadvalda natijani ishlatishdan oldin haqiqiy sarlavhalar va qiymatlarni, sahnada esa tegishli kadrlar va bog‘liq matnni tekshiring.

voyage-multimodal-3’dan asosiy farq — 3.5 nashrining videoni bevosita qo‘llab-quvvatlashi va qo‘shimcha ifoda tanlovlari. Bular manba materialini topish uchun embedding imkoniyatlari; chiqish yaratilgan rasm, video yoki hikoyaviy javob emas, raqamli vektorlardir.

", "aboutText": "voyage-multimodal-3.5 — matn, rasm va video bo‘ylab qidiruv uchun Voyage AI embedding modeli. U aralash ketma-ketlikdagi matn va vizual mazmunni bitta transformer enkoderi orqali qayta ishlab, to‘liq multimodal kirishni umumiy vektor makonida ifodalaydi. 3.5 nashri video kadrlarini bevosita qo‘llab-quvvatlash va moslashuvchan embedding o‘lchamlarini qo‘shadi.\n\n### Matn va tasvirlar orasidagi bog‘liqlikni saqlang\n\nHujjat sahifasi yoki bo‘limini foydali vizual ma’lumotni saqlaydigan shaklda tayyorlang. Grafik va qisqa izohi bor slaydni slayd rasmi hamda unga tegishli matn bilan ayni tartibli kirishda ifodalash mumkin. PDF asosidagi to‘plamda hujjatlashtirilgan vizual kirish sahifa skrinshoti yoki hujjatning boshqa rasmli ifodasidir.\n\nHisobot grafigida sarlavha, o‘q yorliqlari va shartli belgilar izohini rasmda saqlang. Tasvirni talqin qilish uchun kerakli kontekstni bersa, tushuntiruvchi matnni ilova qiling. Topilgan nomzod faqat hujjat nomini qaytarish o‘rniga tegishli sahifani ocha olishi uchun sahifa raqami va manba identifikatorini natijaviy vektor yonida saqlang.\n\nIshlab chiqaruvchi kirish formati matn, rasm va video ketma-ketliklarini qo‘llaydi. E’lon qilingan rasm formatlari PNG, JPEG, WebP va GIF, video uchun esa MP4. Hujjatlashtirilgan usulda vizual mazmunni URL yoki Base64 ma’lumotlari orqali berish mumkin; bitta so‘rov ichida bir ifoda uslubini izchil saqlang.\n\n### Butun uzun video o‘rniga sahnani toping\n\nModel videoni tartibli kadrlar ketma-ketligi sifatida ifodalaydi. Ishlab chiqaruvchi uzun videoni bog‘liq sahnalarga ajratish, sahna chegaralarini mavjud transkript vaqt belgilari bilan moslash va tegishli transkript matnini saqlashni tavsiya qiladi. Pastroq tasvir aniqligi yoki kamaytirilgan kadr tezligi semantik jihatdan uzluksiz sahnani kontekst budjetiga sig‘dirishga yordam berishi mumkin.\n\nO‘quv videolari to‘plamida so‘rov muayyan boshqaruv elementini sozlashni ko‘rsatadigan sahnani so‘rashi mumkin. Har indekslangan sahnani vaqt oralig‘iga bog‘lang. Topilgan vektorni yozuvdagi hamma narsaning yozma tavsifi deb bilish o‘rniga, mos sahnani qaytaring va haqiqiy amalni ko‘rish uchun kadrlar yoki asl klipni tekshiring.\n\nTranskript matni multimodal kirishning bir qismi sifatida tayyorlansa hissa qo‘shishi mumkin. Video kadrlarini embedding qilishning o‘zi nutq transkriptini bermaydi.\n\n### Kontekst va tayyorlash chegaralari\n\nKo‘rsatilgan kontekst uzunligi har bir kirish uchun 32 000 token. Voyage e’lon qilingan hisoblashda har 560 rasm pikseli va har 1120 video pikselini bitta token deb hisoblaydi; vizual mazmun shu budjetni matn bilan bo‘lishadi. Hujjatlashtirilgan chegaralarga har rasm uchun 16 million piksel va 20 MB, har video uchun 20 MB ham kiradi. Bitta ro‘yxatda ko‘pi bilan 1000 kirish va jami 320 000 token bo‘ladi.\n\nOdatiy kesish sozlamasi haddan uzun kirishni kesadi; chegara rasm ichiga tushsa, butun rasm tashlanadi. Demak, to‘liq sahifa yoki sahnalarni budjet ichida tayyorlash kirish mazmunini oldindan bilishni osonlashtiradi. Hujjatlashtirilgan muqobil sozlama haddan uzun kirishni kesish o‘rniga rad etadi.\n\n### Vektor o‘lchamlari va qidiruv rollari\n\nOdatiy chiqish o‘lchami 1024; bu nashr uchun 256, 512 va 2048 o‘lcham ham hujjatlashtirilgan. E’londa Matryoshka embeddinglari hamda suzuvchi nuqtali, ishorali yoki ishorasiz 8 bitli va ikkilik ifodalar tasvirlanadi. Tanlangan o‘lcham va ifodani indeks bilan saqlang.\n\nVoyage qidiruv usuli query va document kirishlarini ajratadi. So‘rov matn yoki rasm bo‘lishi, indekslangan hujjatlar esa matn va tasvirlarni birlashtirishi mumkin. Har to‘liq kirish ketma-ketligini bitta vektor ifodalaydi; reytinglangan natija tekshiriladigan bo‘lib qolishi uchun manba sahifasi, rasm yoki sahna bog‘liqligini saqlang.\n\n### Qaytgan vizual manbadan foydalaning\n\nQidiruv bosqichi so‘rovga tegishli hisobot sahifasi, jadval, slayd yoki sahnani topishi mumkin. Keyin javob yozish yoki tahlil bosqichi shu manba materialini o‘qiydi. Jadvalda natijani ishlatishdan oldin haqiqiy sarlavhalar va qiymatlarni, sahnada esa tegishli kadrlar va bog‘liq matnni tekshiring.\n\nvoyage-multimodal-3’dan asosiy farq — 3.5 nashrining videoni bevosita qo‘llab-quvvatlashi va qo‘shimcha ifoda tanlovlari. Bular manba materialini topish uchun embedding imkoniyatlari; chiqish yaratilgan rasm, video yoki hikoyaviy javob emas, raqamli vektorlardir.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "5af189786840881e721acbec38763340886fd2cd4b6033d6c009bdad43c7bf5a", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

VoyageAI by MongoDB: voyage-multimodal-3.5

VoyageAI by MongoDB /voyageai/voyage-multimodal-3.5

Boshlash

Voyage AI voyage-multimodal-3.5 matn, rasm va video kadrlarini umumiy qidiruv makonida embedding qiladi. U hujjat skrinshotlari, jadvallar va sahnalar kabi aralash mazmunni qayta ishlaydi; kirish 32 000 token, odatiy ifoda esa 1024 o‘lchamli.

Qabul qilinadigan kirish ma’lumotlari
Matn · token_ids
Natijalar
embedding
Model haqida

Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.

voyage-multimodal-3.5 — matn, rasm va video bo‘ylab qidiruv uchun Voyage AI embedding modeli. U aralash ketma-ketlikdagi matn va vizual mazmunni bitta transformer enkoderi orqali qayta ishlab, to‘liq multimodal kirishni umumiy vektor makonida ifodalaydi. 3.5 nashri video kadrlarini bevosita qo‘llab-quvvatlash va moslashuvchan embedding o‘lchamlarini qo‘shadi.

Matn va tasvirlar orasidagi bog‘liqlikni saqlang

Hujjat sahifasi yoki bo‘limini foydali vizual ma’lumotni saqlaydigan shaklda tayyorlang. Grafik va qisqa izohi bor slaydni slayd rasmi hamda unga tegishli matn bilan ayni tartibli kirishda ifodalash mumkin. PDF asosidagi to‘plamda hujjatlashtirilgan vizual kirish sahifa skrinshoti yoki hujjatning boshqa rasmli ifodasidir.

Hisobot grafigida sarlavha, o‘q yorliqlari va shartli belgilar izohini rasmda saqlang. Tasvirni talqin qilish uchun kerakli kontekstni bersa, tushuntiruvchi matnni ilova qiling. Topilgan nomzod faqat hujjat nomini qaytarish o‘rniga tegishli sahifani ocha olishi uchun sahifa raqami va manba identifikatorini natijaviy vektor yonida saqlang.

Ishlab chiqaruvchi kirish formati matn, rasm va video ketma-ketliklarini qo‘llaydi. E’lon qilingan rasm formatlari PNG, JPEG, WebP va GIF, video uchun esa MP4. Hujjatlashtirilgan usulda vizual mazmunni URL yoki Base64 ma’lumotlari orqali berish mumkin; bitta so‘rov ichida bir ifoda uslubini izchil saqlang.

Butun uzun video o‘rniga sahnani toping

Model videoni tartibli kadrlar ketma-ketligi sifatida ifodalaydi. Ishlab chiqaruvchi uzun videoni bog‘liq sahnalarga ajratish, sahna chegaralarini mavjud transkript vaqt belgilari bilan moslash va tegishli transkript matnini saqlashni tavsiya qiladi. Pastroq tasvir aniqligi yoki kamaytirilgan kadr tezligi semantik jihatdan uzluksiz sahnani kontekst budjetiga sig‘dirishga yordam berishi mumkin.

O‘quv videolari to‘plamida so‘rov muayyan boshqaruv elementini sozlashni ko‘rsatadigan sahnani so‘rashi mumkin. Har indekslangan sahnani vaqt oralig‘iga bog‘lang. Topilgan vektorni yozuvdagi hamma narsaning yozma tavsifi deb bilish o‘rniga, mos sahnani qaytaring va haqiqiy amalni ko‘rish uchun kadrlar yoki asl klipni tekshiring.

Transkript matni multimodal kirishning bir qismi sifatida tayyorlansa hissa qo‘shishi mumkin. Video kadrlarini embedding qilishning o‘zi nutq transkriptini bermaydi.

Kontekst va tayyorlash chegaralari

Ko‘rsatilgan kontekst uzunligi har bir kirish uchun 32 000 token. Voyage e’lon qilingan hisoblashda har 560 rasm pikseli va har 1120 video pikselini bitta token deb hisoblaydi; vizual mazmun shu budjetni matn bilan bo‘lishadi. Hujjatlashtirilgan chegaralarga har rasm uchun 16 million piksel va 20 MB, har video uchun 20 MB ham kiradi. Bitta ro‘yxatda ko‘pi bilan 1000 kirish va jami 320 000 token bo‘ladi.

Odatiy kesish sozlamasi haddan uzun kirishni kesadi; chegara rasm ichiga tushsa, butun rasm tashlanadi. Demak, to‘liq sahifa yoki sahnalarni budjet ichida tayyorlash kirish mazmunini oldindan bilishni osonlashtiradi. Hujjatlashtirilgan muqobil sozlama haddan uzun kirishni kesish o‘rniga rad etadi.

Vektor o‘lchamlari va qidiruv rollari

Odatiy chiqish o‘lchami 1024; bu nashr uchun 256, 512 va 2048 o‘lcham ham hujjatlashtirilgan. E’londa Matryoshka embeddinglari hamda suzuvchi nuqtali, ishorali yoki ishorasiz 8 bitli va ikkilik ifodalar tasvirlanadi. Tanlangan o‘lcham va ifodani indeks bilan saqlang.

Voyage qidiruv usuli query va document kirishlarini ajratadi. So‘rov matn yoki rasm bo‘lishi, indekslangan hujjatlar esa matn va tasvirlarni birlashtirishi mumkin. Har to‘liq kirish ketma-ketligini bitta vektor ifodalaydi; reytinglangan natija tekshiriladigan bo‘lib qolishi uchun manba sahifasi, rasm yoki sahna bog‘liqligini saqlang.

Qaytgan vizual manbadan foydalaning

Qidiruv bosqichi so‘rovga tegishli hisobot sahifasi, jadval, slayd yoki sahnani topishi mumkin. Keyin javob yozish yoki tahlil bosqichi shu manba materialini o‘qiydi. Jadvalda natijani ishlatishdan oldin haqiqiy sarlavhalar va qiymatlarni, sahnada esa tegishli kadrlar va bog‘liq matnni tekshiring.

voyage-multimodal-3’dan asosiy farq — 3.5 nashrining videoni bevosita qo‘llab-quvvatlashi va qo‘shimcha ifoda tanlovlari. Bular manba materialini topish uchun embedding imkoniyatlari; chiqish yaratilgan rasm, video yoki hikoyaviy javob emas, raqamli vektorlardir.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Embeddinglar POST /v1/embeddings
Kirish
text, token_ids
Chiqish
embedding
Kontekst
32 000 tokens
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
voyageai/voyage-multimodal-3.5
Standart—
MaydoninputTurstring|string[]|integer[]|integer[][]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Shartlar: Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.
Kirish budjeti: Eng ko‘p: 32 000 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari
Standart—
Maydonencoding_formatTurstringTalabIxtiyoriyQiymatlar va cheklovlar
float
Standartfloat
MaydondimensionsTurintegerTalabQo‘llab-quvvatlanmaydiQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydonuserTurjson_valueTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.
Standart—
Maydonsession_idTurjson_valueTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.
Standart—
MaydontraceTurjson_valueTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.
Standart—
MaydonproviderTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: provider.options rad etildi
Standart—
Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, input
Parametrinput_shapesParametr talablaristring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]
Parametrencoding_formatParametr talablarifloat
Parametrdimensions_overrideParametr talablarifalse
Parametrmax_input_budget_unitsParametr talablari32000
Parametrmultimodal_objectsParametr talablarifalse
Parametrmaterial_typesParametr talablaritext, token_ids

Hayot sikli: synchronous_json

Har bir vektorning indeksi va o‘lchamini saqlang.

Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi.

Tariflar

Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.

Tezkor rejim
Standart tarif Tezkor rejim tanlanganda
Flex rejimi
Standart tarif flex rejimi tanlanganda
Maksimal fikrlash darajasi
Standart tarif Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.00024 / 1K token
Chiqish · Chiqish tokenlari $0 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

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. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · Embeddinglar

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": "voyageai/voyage-multimodal-3.5", "input": ["First document", "Second document"]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))\nPY_RESULT\n

Natijalarni olish va saqlash

Ailoot Ultra · Embeddinglar

json

Barcha tayyor natijalarni saqlash

Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.

Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.

Asl javobni tekshirish kerak bo‘lsa

Embedding vazifalari uchun endpoint qo‘shilmaydi.

Javob maydonlari
MaydonTur
MaydonobjectTurstring
MaydonmodelTurstring
Maydondata[].indexTurinteger
Maydondata[].embeddingTurfinite float[]
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra voyageai/voyage-multimodal-3.5

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

voyageai/voyage-multimodal-3.5

", "method_identity": { "effective_request_model_id": "voyageai/voyage-multimodal-3.5", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "voyageai/voyage-multimodal-3.5", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|voyageai%2Fvoyage-multimodal-3.5", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.", "errors": [ { "code": null, "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri", "http_status": 400, "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/embeddings" }, "job_read": null, "mode": "synchronous_json", "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi." }, "material_combinations": [ { "alternatives": [ "string", "string[]", "nonnegative_integer_token_array", "nonnegative_integer_token_array[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.", "default_dimensions": null, "family": "embedding", "fields": [ { "description": null, "name": "object", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "voyageai/voyage-multimodal-3.5" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 32000, "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": 32000, "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": 32000, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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\": \"voyageai/voyage-multimodal-3.5\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='voyageai/voyage-multimodal-3.5',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n", "label": "Python SDK · all vectors", "language": "python" } ], "family": "openai", "fast_multiplier": 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": 0.24, "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": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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": "voyageai/voyage-multimodal-3.5", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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": "voyageai/voyage-multimodal-3.5", "nativeProvenance": { "input_facts_canonical_sha256": "1f1ee2ae9af8fe0c546dae32399330cb12993dcac44ecd57c3f3a8e65ad1896a", "input_html_sha256": "b8029a94c49216d68cebb1474b498a17054510a062a44eefe01aea6f360ea080", "private_audit_sha256": "385dcd17be7ea49c073a3f1cc9012831d3f01f193af0b40b96dceec1e389ff69", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/voyageai-voyage-multimodal-3-5/", "description": "Voyage AI voyage-multimodal-3.5 matn, rasm va video kadrlarini umumiy qidiruv makonida embedding qiladi. U hujjat skrinshotlari, jadvallar va sahnalar kabi aralash mazmunni qayta ishlaydi; kirish 32 000 token, odatiy ifoda esa 1024 o‘lchamli.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/voyageai-voyage-multimodal-3-5/index.md", "title": "VoyageAI by MongoDB: voyage-multimodal-3.5 — Ailoot", "url": "https://ailoot.ai/models/uz/voyageai-voyage-multimodal-3-5/" }, "rawModel": { "about": "voyage-multimodal-3.5 — matn, rasm va video bo‘ylab qidiruv uchun Voyage AI embedding modeli. U aralash ketma-ketlikdagi matn va vizual mazmunni bitta transformer enkoderi orqali qayta ishlab, to‘liq multimodal kirishni umumiy vektor makonida ifodalaydi. 3.5 nashri video kadrlarini bevosita qo‘llab-quvvatlash va moslashuvchan embedding o‘lchamlarini qo‘shadi.\n\n### Matn va tasvirlar orasidagi bog‘liqlikni saqlang\n\nHujjat sahifasi yoki bo‘limini foydali vizual ma’lumotni saqlaydigan shaklda tayyorlang. Grafik va qisqa izohi bor slaydni slayd rasmi hamda unga tegishli matn bilan ayni tartibli kirishda ifodalash mumkin. PDF asosidagi to‘plamda hujjatlashtirilgan vizual kirish sahifa skrinshoti yoki hujjatning boshqa rasmli ifodasidir.\n\nHisobot grafigida sarlavha, o‘q yorliqlari va shartli belgilar izohini rasmda saqlang. Tasvirni talqin qilish uchun kerakli kontekstni bersa, tushuntiruvchi matnni ilova qiling. Topilgan nomzod faqat hujjat nomini qaytarish o‘rniga tegishli sahifani ocha olishi uchun sahifa raqami va manba identifikatorini natijaviy vektor yonida saqlang.\n\nIshlab chiqaruvchi kirish formati matn, rasm va video ketma-ketliklarini qo‘llaydi. E’lon qilingan rasm formatlari PNG, JPEG, WebP va GIF, video uchun esa MP4. Hujjatlashtirilgan usulda vizual mazmunni URL yoki Base64 ma’lumotlari orqali berish mumkin; bitta so‘rov ichida bir ifoda uslubini izchil saqlang.\n\n### Butun uzun video o‘rniga sahnani toping\n\nModel videoni tartibli kadrlar ketma-ketligi sifatida ifodalaydi. Ishlab chiqaruvchi uzun videoni bog‘liq sahnalarga ajratish, sahna chegaralarini mavjud transkript vaqt belgilari bilan moslash va tegishli transkript matnini saqlashni tavsiya qiladi. Pastroq tasvir aniqligi yoki kamaytirilgan kadr tezligi semantik jihatdan uzluksiz sahnani kontekst budjetiga sig‘dirishga yordam berishi mumkin.\n\nO‘quv videolari to‘plamida so‘rov muayyan boshqaruv elementini sozlashni ko‘rsatadigan sahnani so‘rashi mumkin. Har indekslangan sahnani vaqt oralig‘iga bog‘lang. Topilgan vektorni yozuvdagi hamma narsaning yozma tavsifi deb bilish o‘rniga, mos sahnani qaytaring va haqiqiy amalni ko‘rish uchun kadrlar yoki asl klipni tekshiring.\n\nTranskript matni multimodal kirishning bir qismi sifatida tayyorlansa hissa qo‘shishi mumkin. Video kadrlarini embedding qilishning o‘zi nutq transkriptini bermaydi.\n\n### Kontekst va tayyorlash chegaralari\n\nKo‘rsatilgan kontekst uzunligi har bir kirish uchun 32 000 token. Voyage e’lon qilingan hisoblashda har 560 rasm pikseli va har 1120 video pikselini bitta token deb hisoblaydi; vizual mazmun shu budjetni matn bilan bo‘lishadi. Hujjatlashtirilgan chegaralarga har rasm uchun 16 million piksel va 20 MB, har video uchun 20 MB ham kiradi. Bitta ro‘yxatda ko‘pi bilan 1000 kirish va jami 320 000 token bo‘ladi.\n\nOdatiy kesish sozlamasi haddan uzun kirishni kesadi; chegara rasm ichiga tushsa, butun rasm tashlanadi. Demak, to‘liq sahifa yoki sahnalarni budjet ichida tayyorlash kirish mazmunini oldindan bilishni osonlashtiradi. Hujjatlashtirilgan muqobil sozlama haddan uzun kirishni kesish o‘rniga rad etadi.\n\n### Vektor o‘lchamlari va qidiruv rollari\n\nOdatiy chiqish o‘lchami 1024; bu nashr uchun 256, 512 va 2048 o‘lcham ham hujjatlashtirilgan. E’londa Matryoshka embeddinglari hamda suzuvchi nuqtali, ishorali yoki ishorasiz 8 bitli va ikkilik ifodalar tasvirlanadi. Tanlangan o‘lcham va ifodani indeks bilan saqlang.\n\nVoyage qidiruv usuli query va document kirishlarini ajratadi. So‘rov matn yoki rasm bo‘lishi, indekslangan hujjatlar esa matn va tasvirlarni birlashtirishi mumkin. Har to‘liq kirish ketma-ketligini bitta vektor ifodalaydi; reytinglangan natija tekshiriladigan bo‘lib qolishi uchun manba sahifasi, rasm yoki sahna bog‘liqligini saqlang.\n\n### Qaytgan vizual manbadan foydalaning\n\nQidiruv bosqichi so‘rovga tegishli hisobot sahifasi, jadval, slayd yoki sahnani topishi mumkin. Keyin javob yozish yoki tahlil bosqichi shu manba materialini o‘qiydi. Jadvalda natijani ishlatishdan oldin haqiqiy sarlavhalar va qiymatlarni, sahnada esa tegishli kadrlar va bog‘liq matnni tekshiring.\n\nvoyage-multimodal-3’dan asosiy farq — 3.5 nashrining videoni bevosita qo‘llab-quvvatlashi va qo‘shimcha ifoda tanlovlari. Bular manba materialini topish uchun embedding imkoniyatlari; chiqish yaratilgan rasm, video yoki hikoyaviy javob emas, raqamli vektorlardir.", "alias_of": null, "aliases": [], "author": "VoyageAI by MongoDB", "available": null, "canonical_model": "voyageai/voyage-multimodal-3.5", "capabilities": { "context_length": 32000, "manufacturer_input": [ "text", "image" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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": 32000, "entity_type": "model", "formats": [ "embeddings" ], "id": "voyageai/voyage-multimodal-3.5", "input": [ "text", "token_ids" ], "kind": "embedding", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.", "errors": [ { "code": null, "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri", "http_status": 400, "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/embeddings" }, "job_read": null, "mode": "synchronous_json", "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi." }, "material_combinations": [ { "alternatives": [ "string", "string[]", "nonnegative_integer_token_array", "nonnegative_integer_token_array[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.", "default_dimensions": null, "family": "embedding", "fields": [ { "description": null, "name": "object", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "voyageai/voyage-multimodal-3.5" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 32000, "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": 32000, "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": 32000, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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\": \"voyageai/voyage-multimodal-3.5\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='voyageai/voyage-multimodal-3.5',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n", "label": "Python SDK · all vectors", "language": "python" } ], "family": "openai", "fast_multiplier": 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": 0.24, "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": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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": "voyageai/voyage-multimodal-3.5", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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": "voyageai/voyage-multimodal-3.5", "request_model_id": "voyageai/voyage-multimodal-3.5", "route_label": "Ultra" } ], "name": "VoyageAI by MongoDB: voyage-multimodal-3.5", "price": { "in": 0.24, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.24, "sort_out": 0, "varies": false }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Эмбеддинги · Base token rates", "units": [ { "amount": "0.00024000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000024", "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": "voyageai/voyage-multimodal-3.5", "short": "Voyage AI voyage-multimodal-3.5 matn, rasm va video kadrlarini umumiy qidiruv makonida embedding qiladi. U hujjat skrinshotlari, jadvallar va sahnalar kabi aralash mazmunni qayta ishlaydi; kirish 32 000 token, odatiy ifoda esa 1024 o‘lchamli." }, "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": "Voyage AI voyage-multimodal-3.5 matn, rasm va video kadrlarini umumiy qidiruv makonida embedding qiladi. U hujjat skrinshotlari, jadvallar va sahnalar kabi aralash mazmunni qayta ishlaydi; kirish 32 000 token, odatiy ifoda esa 1024 o‘lchamli.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "02780591d76c0c17def4904bb4ff0fea604805c4098ec2615c99e9b00f49570f", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "a8f548d1b9b5711eca476f6b833bc98ecb54867c63b2b1724f9cd4819e054b6b" }