{ "aboutHtml": "

voyage-4-lite — Voyage AI’ning Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding modeli. U umumiy maqsadli qidiruv uchun so‘rov va hujjatlarni raqamli vektorlar sifatida ifodalaydi. Oilaning farqlovchi xususiyati voyage-4, voyage-4-large va voyage-4-nano bilan umumiy embedding makoni bo‘lib, so‘rov va hujjat modellarini alohida tanlash imkonini beradi.

Qidiruv rollaridan boshlang

Foydalanuvchining ma’lumot ehtiyojini so‘rov, qidiriladigan materialni hujjatlar sifatida tayyorlang. Voyage qidiruv uchun query/document kirish turlarini tavsiya qiladi; e’lon qilingan usul matnni vektorlashtirganda tegishli qidiruv ko‘rsatmasini qo‘shadi.

Xodimlar qo‘llanmasi to‘plamida foydalanilmagan ta’til kunlarini keyingi davrga o‘tkazish haqidagi so‘rov siyosatning muayyan bo‘limlari bilan taqqoslanishi kerak. Indekslangan parchada qoida, shartlar va tegishli istisnolarni saqlang. Ilova haqiqiy siyosat matnini qaytara olishi uchun asl hujjat va bo‘lim identifikatorini vektor yonida saqlang.

Kirish turi ko‘rsatilmasa, matn bevosita ifodalanadi; query/document rollari esa uni qidiruvga moslaydi. Ishlab chiqaruvchi shu rol sozlamalari bilan va ularsiz yaratilgan embeddinglar mosligini aytadi, ammo izchil tayyorlash siyosati to‘plamni baholashni osonlashtiradi.

Voyage 4 matn oilasidagi umumiy makon

Voyage asimmetrik qidiruv jarayonini ochiq hujjatlashtiradi: hujjatlarni voyage-4-large bilan kodlab, voyage-4-lite’dan olingan so‘rov vektorlari bilan qidirish mumkin. Bu ikki model vektor uzunligi bir xil ekanidan chiqarilgan xulosa emas, oila darajasidagi moslik bayonotidir.

To‘plam va so‘rov tomonida tanlangan vektor o‘lchami va ifodasi mos bo‘lsin. Umumiy makon shu nomlangan Voyage 4 matn oilasi ichida mavjud hujjatlarni qayta vektorlashtirmasdan so‘rov modelini almashtirishga ham imkon beradi. So‘rov nashrini tanlashda hosil bo‘lgan reytingni namunaviy savollar bilan baholang.

Lite nashrining roli kichikroq, kechikishga e’tibor qaratilgan so‘rov va matn embedding variantidir. Ishlab chiqaruvchi mixture-of-experts arxitekturasini aynan voyage-4-large’ga tegishli deydi; oila nomi Lite uchun shu arxitekturani isbotlamaydi.

Kontekst, o‘lchamlar va natija formati

E’lon qilingan kontekst uzunligi har matn uchun 32 000 token. Odatiy vektor 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 ham mavjud. Ishlab chiqaruvchining matn embedding jarayoni voyage-4-lite uchun suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda bitlari zichlangan ikkilik ifodalarni hujjatlashtiradi.

Standart suzuvchi nuqtali vektorlarda Voyage o‘xshashlik yo‘riqnomasi birlik uzunlikdagi embeddinglarni tavsiflaydi; kosinus va skalyar ko‘paytma o‘xshashligi teng bo‘ladi. Kvantlangan shakllar tegishli ifoda ishlovini talab qiladi. Masalan, bitlari zichlangan ikkilik ro‘yxat har baytda sakkiz embedding bitini saqlaydi; ro‘yxat uzunligi semantik vektor o‘lchamidan farq qiladi.

Voyage’ning hujjatlashtirilgan batch usulida aynan Lite nashri uchun bitta ro‘yxatda ko‘pi bilan 1000 matn va jami bir million token bo‘ladi. Haddan uzun kirishning odatiy xulqi alohida matnni kontekst chegarasida kesadi; muqobil sozlama haddan uzun kirishni rad etadi. Batch va har matn chegaralari alohida.

Qidiruv natijalarini tayyorlang va tekshiring

Uzun manba materialini dalil sifatida qaytariladigan izchil bo‘limlarga bo‘ling. 32 000 tokenli kontekst uzun kirishga imkon beradi, lekin foydali qidiruv birligi baribir tegishli manba bo‘limiga olib borishi kerak. Qoidani talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni kiriting.

RAG uchun nomzod bo‘limlarni toping va asl matnini manba bog‘liqliklari bilan javob yozish bosqichiga bering. Embedding modeli vektorlar hosil qiladi, siyosat javobini yaratmaydi. O‘xshashlik tanlangan model va taqqoslash sozlamasida nomzodlarni reytinglaydi; aynan so‘ralgan shart uchun haqiqiy parchani ko‘rib chiqing.

voyage-4-lite’ning nashrga xos foydali birikmasi — kechikishga e’tibor qaratilgan matn modeli, moslashuvchan ifodalar, 32 000 tokenli kontekst va Voyage 4 matn oilasi ichidagi hujjatlashtirilgan moslik. Bu xususiyatlarni indekslangan to‘plam bilan saqlang va tashqi ilovani qo‘llab-quvvatlash uchun manbaga tayangan qidiruv natijalaridan foydalaning.

", "aboutText": "voyage-4-lite — Voyage AI’ning Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding modeli. U umumiy maqsadli qidiruv uchun so‘rov va hujjatlarni raqamli vektorlar sifatida ifodalaydi. Oilaning farqlovchi xususiyati voyage-4, voyage-4-large va voyage-4-nano bilan umumiy embedding makoni bo‘lib, so‘rov va hujjat modellarini alohida tanlash imkonini beradi.\n\n### Qidiruv rollaridan boshlang\n\nFoydalanuvchining ma’lumot ehtiyojini so‘rov, qidiriladigan materialni hujjatlar sifatida tayyorlang. Voyage qidiruv uchun query/document kirish turlarini tavsiya qiladi; e’lon qilingan usul matnni vektorlashtirganda tegishli qidiruv ko‘rsatmasini qo‘shadi.\n\nXodimlar qo‘llanmasi to‘plamida foydalanilmagan ta’til kunlarini keyingi davrga o‘tkazish haqidagi so‘rov siyosatning muayyan bo‘limlari bilan taqqoslanishi kerak. Indekslangan parchada qoida, shartlar va tegishli istisnolarni saqlang. Ilova haqiqiy siyosat matnini qaytara olishi uchun asl hujjat va bo‘lim identifikatorini vektor yonida saqlang.\n\nKirish turi ko‘rsatilmasa, matn bevosita ifodalanadi; query/document rollari esa uni qidiruvga moslaydi. Ishlab chiqaruvchi shu rol sozlamalari bilan va ularsiz yaratilgan embeddinglar mosligini aytadi, ammo izchil tayyorlash siyosati to‘plamni baholashni osonlashtiradi.\n\n### Voyage 4 matn oilasidagi umumiy makon\n\nVoyage asimmetrik qidiruv jarayonini ochiq hujjatlashtiradi: hujjatlarni voyage-4-large bilan kodlab, voyage-4-lite’dan olingan so‘rov vektorlari bilan qidirish mumkin. Bu ikki model vektor uzunligi bir xil ekanidan chiqarilgan xulosa emas, oila darajasidagi moslik bayonotidir.\n\nTo‘plam va so‘rov tomonida tanlangan vektor o‘lchami va ifodasi mos bo‘lsin. Umumiy makon shu nomlangan Voyage 4 matn oilasi ichida mavjud hujjatlarni qayta vektorlashtirmasdan so‘rov modelini almashtirishga ham imkon beradi. So‘rov nashrini tanlashda hosil bo‘lgan reytingni namunaviy savollar bilan baholang.\n\nLite nashrining roli kichikroq, kechikishga e’tibor qaratilgan so‘rov va matn embedding variantidir. Ishlab chiqaruvchi mixture-of-experts arxitekturasini aynan voyage-4-large’ga tegishli deydi; oila nomi Lite uchun shu arxitekturani isbotlamaydi.\n\n### Kontekst, o‘lchamlar va natija formati\n\nE’lon qilingan kontekst uzunligi har matn uchun 32 000 token. Odatiy vektor 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 ham mavjud. Ishlab chiqaruvchining matn embedding jarayoni voyage-4-lite uchun suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda bitlari zichlangan ikkilik ifodalarni hujjatlashtiradi.\n\nStandart suzuvchi nuqtali vektorlarda Voyage o‘xshashlik yo‘riqnomasi birlik uzunlikdagi embeddinglarni tavsiflaydi; kosinus va skalyar ko‘paytma o‘xshashligi teng bo‘ladi. Kvantlangan shakllar tegishli ifoda ishlovini talab qiladi. Masalan, bitlari zichlangan ikkilik ro‘yxat har baytda sakkiz embedding bitini saqlaydi; ro‘yxat uzunligi semantik vektor o‘lchamidan farq qiladi.\n\nVoyage’ning hujjatlashtirilgan batch usulida aynan Lite nashri uchun bitta ro‘yxatda ko‘pi bilan 1000 matn va jami bir million token bo‘ladi. Haddan uzun kirishning odatiy xulqi alohida matnni kontekst chegarasida kesadi; muqobil sozlama haddan uzun kirishni rad etadi. Batch va har matn chegaralari alohida.\n\n### Qidiruv natijalarini tayyorlang va tekshiring\n\nUzun manba materialini dalil sifatida qaytariladigan izchil bo‘limlarga bo‘ling. 32 000 tokenli kontekst uzun kirishga imkon beradi, lekin foydali qidiruv birligi baribir tegishli manba bo‘limiga olib borishi kerak. Qoidani talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni kiriting.\n\nRAG uchun nomzod bo‘limlarni toping va asl matnini manba bog‘liqliklari bilan javob yozish bosqichiga bering. Embedding modeli vektorlar hosil qiladi, siyosat javobini yaratmaydi. O‘xshashlik tanlangan model va taqqoslash sozlamasida nomzodlarni reytinglaydi; aynan so‘ralgan shart uchun haqiqiy parchani ko‘rib chiqing.\n\nvoyage-4-lite’ning nashrga xos foydali birikmasi — kechikishga e’tibor qaratilgan matn modeli, moslashuvchan ifodalar, 32 000 tokenli kontekst va Voyage 4 matn oilasi ichidagi hujjatlashtirilgan moslik. Bu xususiyatlarni indekslangan to‘plam bilan saqlang va tashqi ilovani qo‘llab-quvvatlash uchun manbaga tayangan qidiruv natijalaridan foydalaning.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "5caa89a014801143b016f6b3a0369cbdb5d07f78ba9ea6e10f5d5fb8ffbe0334", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

VoyageAI by MongoDB: voyage-4-lite

VoyageAI by MongoDB /voyageai/voyage-4-lite

Boshlash

Voyage AI voyage-4-lite — Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding nashri. U 32 000 tokenli kirishlar, vektor o‘lchami uchun to‘rtta tanlov va kvantlangan ifodalarni qo‘llaydi; umumiy embedding makoni boshqa Voyage 4 matn modellari bilan mos.

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-4-lite — Voyage AI’ning Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding modeli. U umumiy maqsadli qidiruv uchun so‘rov va hujjatlarni raqamli vektorlar sifatida ifodalaydi. Oilaning farqlovchi xususiyati voyage-4, voyage-4-large va voyage-4-nano bilan umumiy embedding makoni bo‘lib, so‘rov va hujjat modellarini alohida tanlash imkonini beradi.

Qidiruv rollaridan boshlang

Foydalanuvchining ma’lumot ehtiyojini so‘rov, qidiriladigan materialni hujjatlar sifatida tayyorlang. Voyage qidiruv uchun query/document kirish turlarini tavsiya qiladi; e’lon qilingan usul matnni vektorlashtirganda tegishli qidiruv ko‘rsatmasini qo‘shadi.

Xodimlar qo‘llanmasi to‘plamida foydalanilmagan ta’til kunlarini keyingi davrga o‘tkazish haqidagi so‘rov siyosatning muayyan bo‘limlari bilan taqqoslanishi kerak. Indekslangan parchada qoida, shartlar va tegishli istisnolarni saqlang. Ilova haqiqiy siyosat matnini qaytara olishi uchun asl hujjat va bo‘lim identifikatorini vektor yonida saqlang.

Kirish turi ko‘rsatilmasa, matn bevosita ifodalanadi; query/document rollari esa uni qidiruvga moslaydi. Ishlab chiqaruvchi shu rol sozlamalari bilan va ularsiz yaratilgan embeddinglar mosligini aytadi, ammo izchil tayyorlash siyosati to‘plamni baholashni osonlashtiradi.

Voyage 4 matn oilasidagi umumiy makon

Voyage asimmetrik qidiruv jarayonini ochiq hujjatlashtiradi: hujjatlarni voyage-4-large bilan kodlab, voyage-4-lite’dan olingan so‘rov vektorlari bilan qidirish mumkin. Bu ikki model vektor uzunligi bir xil ekanidan chiqarilgan xulosa emas, oila darajasidagi moslik bayonotidir.

To‘plam va so‘rov tomonida tanlangan vektor o‘lchami va ifodasi mos bo‘lsin. Umumiy makon shu nomlangan Voyage 4 matn oilasi ichida mavjud hujjatlarni qayta vektorlashtirmasdan so‘rov modelini almashtirishga ham imkon beradi. So‘rov nashrini tanlashda hosil bo‘lgan reytingni namunaviy savollar bilan baholang.

Lite nashrining roli kichikroq, kechikishga e’tibor qaratilgan so‘rov va matn embedding variantidir. Ishlab chiqaruvchi mixture-of-experts arxitekturasini aynan voyage-4-large’ga tegishli deydi; oila nomi Lite uchun shu arxitekturani isbotlamaydi.

Kontekst, o‘lchamlar va natija formati

E’lon qilingan kontekst uzunligi har matn uchun 32 000 token. Odatiy vektor 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 ham mavjud. Ishlab chiqaruvchining matn embedding jarayoni voyage-4-lite uchun suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda bitlari zichlangan ikkilik ifodalarni hujjatlashtiradi.

Standart suzuvchi nuqtali vektorlarda Voyage o‘xshashlik yo‘riqnomasi birlik uzunlikdagi embeddinglarni tavsiflaydi; kosinus va skalyar ko‘paytma o‘xshashligi teng bo‘ladi. Kvantlangan shakllar tegishli ifoda ishlovini talab qiladi. Masalan, bitlari zichlangan ikkilik ro‘yxat har baytda sakkiz embedding bitini saqlaydi; ro‘yxat uzunligi semantik vektor o‘lchamidan farq qiladi.

Voyage’ning hujjatlashtirilgan batch usulida aynan Lite nashri uchun bitta ro‘yxatda ko‘pi bilan 1000 matn va jami bir million token bo‘ladi. Haddan uzun kirishning odatiy xulqi alohida matnni kontekst chegarasida kesadi; muqobil sozlama haddan uzun kirishni rad etadi. Batch va har matn chegaralari alohida.

Qidiruv natijalarini tayyorlang va tekshiring

Uzun manba materialini dalil sifatida qaytariladigan izchil bo‘limlarga bo‘ling. 32 000 tokenli kontekst uzun kirishga imkon beradi, lekin foydali qidiruv birligi baribir tegishli manba bo‘limiga olib borishi kerak. Qoidani talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni kiriting.

RAG uchun nomzod bo‘limlarni toping va asl matnini manba bog‘liqliklari bilan javob yozish bosqichiga bering. Embedding modeli vektorlar hosil qiladi, siyosat javobini yaratmaydi. O‘xshashlik tanlangan model va taqqoslash sozlamasida nomzodlarni reytinglaydi; aynan so‘ralgan shart uchun haqiqiy parchani ko‘rib chiqing.

voyage-4-lite’ning nashrga xos foydali birikmasi — kechikishga e’tibor qaratilgan matn modeli, moslashuvchan ifodalar, 32 000 tokenli kontekst va Voyage 4 matn oilasi ichidagi hujjatlashtirilgan moslik. Bu xususiyatlarni indekslangan to‘plam bilan saqlang va tashqi ilovani qo‘llab-quvvatlash uchun manbaga tayangan qidiruv natijalaridan foydalaning.

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-4-lite
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 $0.00004 / 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-4-lite", "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-4-lite

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

voyageai/voyage-4-lite

", "method_identity": { "effective_request_model_id": "voyageai/voyage-4-lite", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "voyageai/voyage-4-lite", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|voyageai%2Fvoyage-4-lite", "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-4-lite" ], "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite\", \"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-4-lite',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": 0.04, "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite", "nativeProvenance": { "input_facts_canonical_sha256": "5dffa1fc00d3c665d12b39f9daa4c7c22b94d0df0c9bd26edd88bd10358c3a74", "input_html_sha256": "76d7850bd59f60ae3e8ce7ffadd89ef5e818ba5a0750cfc541cc5637b5dfc0be", "private_audit_sha256": "e9f36e6ee79b0abeabf4807a825abf3c93092fda318fe9f183d366cf474dab78", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/voyageai-voyage-4-lite/", "description": "Voyage AI voyage-4-lite — Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding nashri. U 32 000 tokenli kirishlar, vektor o‘lchami uchun to‘rtta tanlov va kvantlangan ifodalarni qo‘llaydi; umumiy embedding makoni boshqa Voyage 4 matn modellari bilan mos.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/voyageai-voyage-4-lite/index.md", "title": "VoyageAI by MongoDB: voyage-4-lite — Ailoot", "url": "https://ailoot.ai/models/uz/voyageai-voyage-4-lite/" }, "rawModel": { "about": "voyage-4-lite — Voyage AI’ning Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding modeli. U umumiy maqsadli qidiruv uchun so‘rov va hujjatlarni raqamli vektorlar sifatida ifodalaydi. Oilaning farqlovchi xususiyati voyage-4, voyage-4-large va voyage-4-nano bilan umumiy embedding makoni bo‘lib, so‘rov va hujjat modellarini alohida tanlash imkonini beradi.\n\n### Qidiruv rollaridan boshlang\n\nFoydalanuvchining ma’lumot ehtiyojini so‘rov, qidiriladigan materialni hujjatlar sifatida tayyorlang. Voyage qidiruv uchun query/document kirish turlarini tavsiya qiladi; e’lon qilingan usul matnni vektorlashtirganda tegishli qidiruv ko‘rsatmasini qo‘shadi.\n\nXodimlar qo‘llanmasi to‘plamida foydalanilmagan ta’til kunlarini keyingi davrga o‘tkazish haqidagi so‘rov siyosatning muayyan bo‘limlari bilan taqqoslanishi kerak. Indekslangan parchada qoida, shartlar va tegishli istisnolarni saqlang. Ilova haqiqiy siyosat matnini qaytara olishi uchun asl hujjat va bo‘lim identifikatorini vektor yonida saqlang.\n\nKirish turi ko‘rsatilmasa, matn bevosita ifodalanadi; query/document rollari esa uni qidiruvga moslaydi. Ishlab chiqaruvchi shu rol sozlamalari bilan va ularsiz yaratilgan embeddinglar mosligini aytadi, ammo izchil tayyorlash siyosati to‘plamni baholashni osonlashtiradi.\n\n### Voyage 4 matn oilasidagi umumiy makon\n\nVoyage asimmetrik qidiruv jarayonini ochiq hujjatlashtiradi: hujjatlarni voyage-4-large bilan kodlab, voyage-4-lite’dan olingan so‘rov vektorlari bilan qidirish mumkin. Bu ikki model vektor uzunligi bir xil ekanidan chiqarilgan xulosa emas, oila darajasidagi moslik bayonotidir.\n\nTo‘plam va so‘rov tomonida tanlangan vektor o‘lchami va ifodasi mos bo‘lsin. Umumiy makon shu nomlangan Voyage 4 matn oilasi ichida mavjud hujjatlarni qayta vektorlashtirmasdan so‘rov modelini almashtirishga ham imkon beradi. So‘rov nashrini tanlashda hosil bo‘lgan reytingni namunaviy savollar bilan baholang.\n\nLite nashrining roli kichikroq, kechikishga e’tibor qaratilgan so‘rov va matn embedding variantidir. Ishlab chiqaruvchi mixture-of-experts arxitekturasini aynan voyage-4-large’ga tegishli deydi; oila nomi Lite uchun shu arxitekturani isbotlamaydi.\n\n### Kontekst, o‘lchamlar va natija formati\n\nE’lon qilingan kontekst uzunligi har matn uchun 32 000 token. Odatiy vektor 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 ham mavjud. Ishlab chiqaruvchining matn embedding jarayoni voyage-4-lite uchun suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda bitlari zichlangan ikkilik ifodalarni hujjatlashtiradi.\n\nStandart suzuvchi nuqtali vektorlarda Voyage o‘xshashlik yo‘riqnomasi birlik uzunlikdagi embeddinglarni tavsiflaydi; kosinus va skalyar ko‘paytma o‘xshashligi teng bo‘ladi. Kvantlangan shakllar tegishli ifoda ishlovini talab qiladi. Masalan, bitlari zichlangan ikkilik ro‘yxat har baytda sakkiz embedding bitini saqlaydi; ro‘yxat uzunligi semantik vektor o‘lchamidan farq qiladi.\n\nVoyage’ning hujjatlashtirilgan batch usulida aynan Lite nashri uchun bitta ro‘yxatda ko‘pi bilan 1000 matn va jami bir million token bo‘ladi. Haddan uzun kirishning odatiy xulqi alohida matnni kontekst chegarasida kesadi; muqobil sozlama haddan uzun kirishni rad etadi. Batch va har matn chegaralari alohida.\n\n### Qidiruv natijalarini tayyorlang va tekshiring\n\nUzun manba materialini dalil sifatida qaytariladigan izchil bo‘limlarga bo‘ling. 32 000 tokenli kontekst uzun kirishga imkon beradi, lekin foydali qidiruv birligi baribir tegishli manba bo‘limiga olib borishi kerak. Qoidani talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni kiriting.\n\nRAG uchun nomzod bo‘limlarni toping va asl matnini manba bog‘liqliklari bilan javob yozish bosqichiga bering. Embedding modeli vektorlar hosil qiladi, siyosat javobini yaratmaydi. O‘xshashlik tanlangan model va taqqoslash sozlamasida nomzodlarni reytinglaydi; aynan so‘ralgan shart uchun haqiqiy parchani ko‘rib chiqing.\n\nvoyage-4-lite’ning nashrga xos foydali birikmasi — kechikishga e’tibor qaratilgan matn modeli, moslashuvchan ifodalar, 32 000 tokenli kontekst va Voyage 4 matn oilasi ichidagi hujjatlashtirilgan moslik. Bu xususiyatlarni indekslangan to‘plam bilan saqlang va tashqi ilovani qo‘llab-quvvatlash uchun manbaga tayangan qidiruv natijalaridan foydalaning.", "alias_of": null, "aliases": [], "author": "VoyageAI by MongoDB", "available": null, "canonical_model": "voyageai/voyage-4-lite", "capabilities": { "context_length": 32000, "manufacturer_input": [ "text" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite", "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-4-lite" ], "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite\", \"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-4-lite',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": 0.04, "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite", "request_model_id": "voyageai/voyage-4-lite", "route_label": "Ultra" } ], "name": "VoyageAI by MongoDB: voyage-4-lite", "price": { "in": 0.04, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.04, "sort_out": 0, "varies": false }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Эмбеддинги · Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "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-4-lite", "short": "Voyage AI voyage-4-lite — Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding nashri. U 32 000 tokenli kirishlar, vektor o‘lchami uchun to‘rtta tanlov va kvantlangan ifodalarni qo‘llaydi; umumiy embedding makoni boshqa Voyage 4 matn modellari bilan mos." }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "Voyage AI voyage-4-lite — Voyage 4 oilasidagi kechikish vaqtiga yo‘naltirilgan matn embedding nashri. U 32 000 tokenli kirishlar, vektor o‘lchami uchun to‘rtta tanlov va kvantlangan ifodalarni qo‘llaydi; umumiy embedding makoni boshqa Voyage 4 matn modellari bilan mos.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "243eb086f2247ebe71bd3de7bdd3e296c0e3b691ba50ea807c281d7983464523", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "d14b331ae2c78e05205cc7a55daf0873448bbcb24b50af3bd024ae0ec1a21603" }