{ "aboutHtml": "

voyage-code-4 — repozitoriy qidiruvi va dasturlash agenti uchun manba topishga mo‘ljallangan Voyage AI’ning kodga ixtisoslashgan embedding modeli. U boshlang‘ich nuqta ma’lum identifikator emas, savol, belgi yoki vazifa tavsifi bo‘lganda tegishli manbani topishga yordam beradi. U qidiruv uchun kod va matnli ma’lumotni raqamli vektorlar sifatida ifodalaydi; repozitoriyni tahrirlamaydi va tuzatish yaratmaydi.

Tekshirilayotgan muammo orqali kodni toping

Ishlab chiqaruvchi tugallangan pull requestlardan saralangan, tabiiy til so‘rovlarini kod bilan bog‘laydigan o‘qitish korpusini tavsiflaydi. Bu asosan manba fayllari va ularning docstringlari atrofida tuzilgan o‘qitish misollaridan muhim farq: qidiruv vazifasi tekshiriladigan xulqdan boshlanishi mumkin.

“Bo‘sh natija muvaffaqiyatli operatsiyaning muvaffaqiyatsiz deb ko‘rsatilishiga sabab bo‘ladigan joyni toping” kabi so‘rov tayyorlang. O‘qiladigan manba bo‘limlarini fayl yo‘llari va tegishli izohlar bilan indekslang. Funksiya xulqini tushuntirsa, uning atrofidagi shartlarni saqlang. Qaytgan nomzod o‘quvchi yoki agentni o‘zi ifodalayotgan manba bo‘limiga olib borishi kerak.

Semantik qidiruv aniq identifikator va to‘liq matn qidiruvini to‘ldirishi mumkin. Funksiya nomi allaqachon ma’lum bo‘lsa, aniq qidiruv foydali qoladi. Faqat muammo belgisi ma’lum bo‘lsa, vektor qidiruvi tekshirish va boshqa so‘rov bilan aniqlashtirish uchun nomzodlar berishi mumkin.

Query va document rollari

Voyage qidiruv yo‘riqnomasi query/document kirish turlari orqali so‘rovni korpus hujjatidan ajratadi. Kod qidirishda muammo yoki vazifa tavsifini query, indekslangan manba yoki hujjatlarni document sifatida ishlating. E’lon qilingan usul embedding paytida rolga xos qidiruv ko‘rsatmalarini qo‘shadi.

Kodning asl formati, nomlari va tegishli hujjatlari vektordan tashqarida mavjud bo‘lsin. Manba bo‘limida fayl yo‘li va joyini, dasturlash qo‘llanmasida sarlavhani saqlang. Qidiruvdan keyin tahrir tanlashdan oldin haqiqiy kod va uni chaqiradigan joylarni tekshiring. Nomzod dolzarbligi u bildirilgan muammoning yagona sababi ekanini isbotlamaydi.

Kontekst va ifoda tanlovlari

Aynan modelning e’lon qilingan kontekst uzunligi 32 000 token. Odatiy embedding 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 o‘lcham ham qo‘llanadi. E’londa 32 bitli suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda ikkilik ifodalar hujjatlashtirilgan.

To‘plam uchun bitta o‘lcham va ifodani tanlang, ularni metama’lumot bilan saqlang. Suzuvchi nuqtali vektorlar va kvantlangan ifodalar tegishli saqlash va taqqoslash ishlovini talab qiladi. Ikkilik ifoda vektor ma’lumotini oddiy suzuvchi nuqtali koordinatalar ro‘yxatidan boshqacha kodlaydi; qidiruv natijalarini tayyorlashda shu farqni saqlang.

Uzun faylda manba joylarini qayta topish mumkin bo‘lgan izchil bo‘limlarni tanlang. Funksiyaga shartni tushuntirish uchun yonidagi tur ta’rifi yoki izoh kerak bo‘lishi mumkin, butun haddan katta fayl esa aloqasiz materialni ham saqlashi mumkin. Maksimal kontekstni afzal bo‘lak hajmi deb bilish o‘rniga, bo‘limni qidiriladigan vazifaga moslang.

Topilgan manbani dalil sifatida ishlating

Amaliy dasturlash agenti jarayoni nomzod manbani topadi, shu fayllarni o‘qiydi, tegishli havolalarni kuzatadi va tekshiruv davomida qidiruvni toraytiradi. Topilgan matn mulohaza yuritish yoki tahrirlash bosqichiga kontekst bo‘ladi. Nomzod to‘g‘ri manba bo‘yicha tekshirilishi uchun uni indekslangan repozitoriy reviziyasiga bog‘liq saqlang.

voyage-code-4’ning nashrga xos hissasi — dasturlash agentlari ishlatadigan muammodan kodni topish bosqichiga e’tibor, moslashuvchan o‘lchamlar va ifodalar. Chiqish qidiruv vektori; tashxis, tahrirlash va tasdiqlash esa haqiqiy repozitoriyga asoslangan alohida vazifalar bo‘lib qoladi.

", "aboutText": "voyage-code-4 — repozitoriy qidiruvi va dasturlash agenti uchun manba topishga mo‘ljallangan Voyage AI’ning kodga ixtisoslashgan embedding modeli. U boshlang‘ich nuqta ma’lum identifikator emas, savol, belgi yoki vazifa tavsifi bo‘lganda tegishli manbani topishga yordam beradi. U qidiruv uchun kod va matnli ma’lumotni raqamli vektorlar sifatida ifodalaydi; repozitoriyni tahrirlamaydi va tuzatish yaratmaydi.\n\n### Tekshirilayotgan muammo orqali kodni toping\n\nIshlab chiqaruvchi tugallangan pull requestlardan saralangan, tabiiy til so‘rovlarini kod bilan bog‘laydigan o‘qitish korpusini tavsiflaydi. Bu asosan manba fayllari va ularning docstringlari atrofida tuzilgan o‘qitish misollaridan muhim farq: qidiruv vazifasi tekshiriladigan xulqdan boshlanishi mumkin.\n\n“Bo‘sh natija muvaffaqiyatli operatsiyaning muvaffaqiyatsiz deb ko‘rsatilishiga sabab bo‘ladigan joyni toping” kabi so‘rov tayyorlang. O‘qiladigan manba bo‘limlarini fayl yo‘llari va tegishli izohlar bilan indekslang. Funksiya xulqini tushuntirsa, uning atrofidagi shartlarni saqlang. Qaytgan nomzod o‘quvchi yoki agentni o‘zi ifodalayotgan manba bo‘limiga olib borishi kerak.\n\nSemantik qidiruv aniq identifikator va to‘liq matn qidiruvini to‘ldirishi mumkin. Funksiya nomi allaqachon ma’lum bo‘lsa, aniq qidiruv foydali qoladi. Faqat muammo belgisi ma’lum bo‘lsa, vektor qidiruvi tekshirish va boshqa so‘rov bilan aniqlashtirish uchun nomzodlar berishi mumkin.\n\n### Query va document rollari\n\nVoyage qidiruv yo‘riqnomasi query/document kirish turlari orqali so‘rovni korpus hujjatidan ajratadi. Kod qidirishda muammo yoki vazifa tavsifini query, indekslangan manba yoki hujjatlarni document sifatida ishlating. E’lon qilingan usul embedding paytida rolga xos qidiruv ko‘rsatmalarini qo‘shadi.\n\nKodning asl formati, nomlari va tegishli hujjatlari vektordan tashqarida mavjud bo‘lsin. Manba bo‘limida fayl yo‘li va joyini, dasturlash qo‘llanmasida sarlavhani saqlang. Qidiruvdan keyin tahrir tanlashdan oldin haqiqiy kod va uni chaqiradigan joylarni tekshiring. Nomzod dolzarbligi u bildirilgan muammoning yagona sababi ekanini isbotlamaydi.\n\n### Kontekst va ifoda tanlovlari\n\nAynan modelning e’lon qilingan kontekst uzunligi 32 000 token. Odatiy embedding 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 o‘lcham ham qo‘llanadi. E’londa 32 bitli suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda ikkilik ifodalar hujjatlashtirilgan.\n\nTo‘plam uchun bitta o‘lcham va ifodani tanlang, ularni metama’lumot bilan saqlang. Suzuvchi nuqtali vektorlar va kvantlangan ifodalar tegishli saqlash va taqqoslash ishlovini talab qiladi. Ikkilik ifoda vektor ma’lumotini oddiy suzuvchi nuqtali koordinatalar ro‘yxatidan boshqacha kodlaydi; qidiruv natijalarini tayyorlashda shu farqni saqlang.\n\nUzun faylda manba joylarini qayta topish mumkin bo‘lgan izchil bo‘limlarni tanlang. Funksiyaga shartni tushuntirish uchun yonidagi tur ta’rifi yoki izoh kerak bo‘lishi mumkin, butun haddan katta fayl esa aloqasiz materialni ham saqlashi mumkin. Maksimal kontekstni afzal bo‘lak hajmi deb bilish o‘rniga, bo‘limni qidiriladigan vazifaga moslang.\n\n### Topilgan manbani dalil sifatida ishlating\n\nAmaliy dasturlash agenti jarayoni nomzod manbani topadi, shu fayllarni o‘qiydi, tegishli havolalarni kuzatadi va tekshiruv davomida qidiruvni toraytiradi. Topilgan matn mulohaza yuritish yoki tahrirlash bosqichiga kontekst bo‘ladi. Nomzod to‘g‘ri manba bo‘yicha tekshirilishi uchun uni indekslangan repozitoriy reviziyasiga bog‘liq saqlang.\n\nvoyage-code-4’ning nashrga xos hissasi — dasturlash agentlari ishlatadigan muammodan kodni topish bosqichiga e’tibor, moslashuvchan o‘lchamlar va ifodalar. Chiqish qidiruv vektori; tashxis, tahrirlash va tasdiqlash esa haqiqiy repozitoriyga asoslangan alohida vazifalar bo‘lib qoladi.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "dd33b0f3b6b8aeff092ec2b4bcf80099d598923481efa48da5cabfe49b892880", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

VoyageAI by MongoDB: voyage-code-4

VoyageAI by MongoDB /voyageai/voyage-code-4

Boshlash

Voyage AI voyage-code-4 — savollar, muammo tavsiflari va dasturlash agenti maqsadlaridan tegishli manbani topish uchun kod embedding modeli. U 32 000 tokenli kirishlar va 256, 512, 1024 yoki 2048 o‘lchamni, suzuvchi nuqtali hamda kvantlangan ifodalarni qo‘llaydi.

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-code-4 — repozitoriy qidiruvi va dasturlash agenti uchun manba topishga mo‘ljallangan Voyage AI’ning kodga ixtisoslashgan embedding modeli. U boshlang‘ich nuqta ma’lum identifikator emas, savol, belgi yoki vazifa tavsifi bo‘lganda tegishli manbani topishga yordam beradi. U qidiruv uchun kod va matnli ma’lumotni raqamli vektorlar sifatida ifodalaydi; repozitoriyni tahrirlamaydi va tuzatish yaratmaydi.

Tekshirilayotgan muammo orqali kodni toping

Ishlab chiqaruvchi tugallangan pull requestlardan saralangan, tabiiy til so‘rovlarini kod bilan bog‘laydigan o‘qitish korpusini tavsiflaydi. Bu asosan manba fayllari va ularning docstringlari atrofida tuzilgan o‘qitish misollaridan muhim farq: qidiruv vazifasi tekshiriladigan xulqdan boshlanishi mumkin.

“Bo‘sh natija muvaffaqiyatli operatsiyaning muvaffaqiyatsiz deb ko‘rsatilishiga sabab bo‘ladigan joyni toping” kabi so‘rov tayyorlang. O‘qiladigan manba bo‘limlarini fayl yo‘llari va tegishli izohlar bilan indekslang. Funksiya xulqini tushuntirsa, uning atrofidagi shartlarni saqlang. Qaytgan nomzod o‘quvchi yoki agentni o‘zi ifodalayotgan manba bo‘limiga olib borishi kerak.

Semantik qidiruv aniq identifikator va to‘liq matn qidiruvini to‘ldirishi mumkin. Funksiya nomi allaqachon ma’lum bo‘lsa, aniq qidiruv foydali qoladi. Faqat muammo belgisi ma’lum bo‘lsa, vektor qidiruvi tekshirish va boshqa so‘rov bilan aniqlashtirish uchun nomzodlar berishi mumkin.

Query va document rollari

Voyage qidiruv yo‘riqnomasi query/document kirish turlari orqali so‘rovni korpus hujjatidan ajratadi. Kod qidirishda muammo yoki vazifa tavsifini query, indekslangan manba yoki hujjatlarni document sifatida ishlating. E’lon qilingan usul embedding paytida rolga xos qidiruv ko‘rsatmalarini qo‘shadi.

Kodning asl formati, nomlari va tegishli hujjatlari vektordan tashqarida mavjud bo‘lsin. Manba bo‘limida fayl yo‘li va joyini, dasturlash qo‘llanmasida sarlavhani saqlang. Qidiruvdan keyin tahrir tanlashdan oldin haqiqiy kod va uni chaqiradigan joylarni tekshiring. Nomzod dolzarbligi u bildirilgan muammoning yagona sababi ekanini isbotlamaydi.

Kontekst va ifoda tanlovlari

Aynan modelning e’lon qilingan kontekst uzunligi 32 000 token. Odatiy embedding 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 o‘lcham ham qo‘llanadi. E’londa 32 bitli suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda ikkilik ifodalar hujjatlashtirilgan.

To‘plam uchun bitta o‘lcham va ifodani tanlang, ularni metama’lumot bilan saqlang. Suzuvchi nuqtali vektorlar va kvantlangan ifodalar tegishli saqlash va taqqoslash ishlovini talab qiladi. Ikkilik ifoda vektor ma’lumotini oddiy suzuvchi nuqtali koordinatalar ro‘yxatidan boshqacha kodlaydi; qidiruv natijalarini tayyorlashda shu farqni saqlang.

Uzun faylda manba joylarini qayta topish mumkin bo‘lgan izchil bo‘limlarni tanlang. Funksiyaga shartni tushuntirish uchun yonidagi tur ta’rifi yoki izoh kerak bo‘lishi mumkin, butun haddan katta fayl esa aloqasiz materialni ham saqlashi mumkin. Maksimal kontekstni afzal bo‘lak hajmi deb bilish o‘rniga, bo‘limni qidiriladigan vazifaga moslang.

Topilgan manbani dalil sifatida ishlating

Amaliy dasturlash agenti jarayoni nomzod manbani topadi, shu fayllarni o‘qiydi, tegishli havolalarni kuzatadi va tekshiruv davomida qidiruvni toraytiradi. Topilgan matn mulohaza yuritish yoki tahrirlash bosqichiga kontekst bo‘ladi. Nomzod to‘g‘ri manba bo‘yicha tekshirilishi uchun uni indekslangan repozitoriy reviziyasiga bog‘liq saqlang.

voyage-code-4’ning nashrga xos hissasi — dasturlash agentlari ishlatadigan muammodan kodni topish bosqichiga e’tibor, moslashuvchan o‘lchamlar va ifodalar. Chiqish qidiruv vektori; tashxis, tahrirlash va tasdiqlash esa haqiqiy repozitoriyga asoslangan alohida vazifalar bo‘lib qoladi.

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-code-4
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-code-4", "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-code-4

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

voyageai/voyage-code-4

", "method_identity": { "effective_request_model_id": "voyageai/voyage-code-4", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "voyageai/voyage-code-4", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|voyageai%2Fvoyage-code-4", "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-code-4" ], "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-code-4\", \"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-code-4',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-code-4", "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-code-4", "nativeProvenance": { "input_facts_canonical_sha256": "01624cfc622f65851485ae5ea37d2e871126ccf711268fa319d5705282ce037f", "input_html_sha256": "4e9c0b6ecbfd19fc82507df60adc1acb9876e9d20ae51ccb21b3d12a497e990b", "private_audit_sha256": "e534d30a8f6fdc1e223402191b6b4bf32c54e67464bd9682651dd37275ea1ccb", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/voyageai-voyage-code-4/", "description": "Voyage AI voyage-code-4 — savollar, muammo tavsiflari va dasturlash agenti maqsadlaridan tegishli manbani topish uchun kod embedding modeli. U 32 000 tokenli kirishlar va 256, 512, 1024 yoki 2048 o‘lchamni, suzuvchi nuqtali hamda kvantlangan ifodalarni qo‘llaydi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/voyageai-voyage-code-4/index.md", "title": "VoyageAI by MongoDB: voyage-code-4 — Ailoot", "url": "https://ailoot.ai/models/uz/voyageai-voyage-code-4/" }, "rawModel": { "about": "voyage-code-4 — repozitoriy qidiruvi va dasturlash agenti uchun manba topishga mo‘ljallangan Voyage AI’ning kodga ixtisoslashgan embedding modeli. U boshlang‘ich nuqta ma’lum identifikator emas, savol, belgi yoki vazifa tavsifi bo‘lganda tegishli manbani topishga yordam beradi. U qidiruv uchun kod va matnli ma’lumotni raqamli vektorlar sifatida ifodalaydi; repozitoriyni tahrirlamaydi va tuzatish yaratmaydi.\n\n### Tekshirilayotgan muammo orqali kodni toping\n\nIshlab chiqaruvchi tugallangan pull requestlardan saralangan, tabiiy til so‘rovlarini kod bilan bog‘laydigan o‘qitish korpusini tavsiflaydi. Bu asosan manba fayllari va ularning docstringlari atrofida tuzilgan o‘qitish misollaridan muhim farq: qidiruv vazifasi tekshiriladigan xulqdan boshlanishi mumkin.\n\n“Bo‘sh natija muvaffaqiyatli operatsiyaning muvaffaqiyatsiz deb ko‘rsatilishiga sabab bo‘ladigan joyni toping” kabi so‘rov tayyorlang. O‘qiladigan manba bo‘limlarini fayl yo‘llari va tegishli izohlar bilan indekslang. Funksiya xulqini tushuntirsa, uning atrofidagi shartlarni saqlang. Qaytgan nomzod o‘quvchi yoki agentni o‘zi ifodalayotgan manba bo‘limiga olib borishi kerak.\n\nSemantik qidiruv aniq identifikator va to‘liq matn qidiruvini to‘ldirishi mumkin. Funksiya nomi allaqachon ma’lum bo‘lsa, aniq qidiruv foydali qoladi. Faqat muammo belgisi ma’lum bo‘lsa, vektor qidiruvi tekshirish va boshqa so‘rov bilan aniqlashtirish uchun nomzodlar berishi mumkin.\n\n### Query va document rollari\n\nVoyage qidiruv yo‘riqnomasi query/document kirish turlari orqali so‘rovni korpus hujjatidan ajratadi. Kod qidirishda muammo yoki vazifa tavsifini query, indekslangan manba yoki hujjatlarni document sifatida ishlating. E’lon qilingan usul embedding paytida rolga xos qidiruv ko‘rsatmalarini qo‘shadi.\n\nKodning asl formati, nomlari va tegishli hujjatlari vektordan tashqarida mavjud bo‘lsin. Manba bo‘limida fayl yo‘li va joyini, dasturlash qo‘llanmasida sarlavhani saqlang. Qidiruvdan keyin tahrir tanlashdan oldin haqiqiy kod va uni chaqiradigan joylarni tekshiring. Nomzod dolzarbligi u bildirilgan muammoning yagona sababi ekanini isbotlamaydi.\n\n### Kontekst va ifoda tanlovlari\n\nAynan modelning e’lon qilingan kontekst uzunligi 32 000 token. Odatiy embedding 1024 o‘lchamli; Matryoshka o‘rganishi orqali 256, 512 va 2048 o‘lcham ham qo‘llanadi. E’londa 32 bitli suzuvchi nuqtali, ishorali va ishorasiz 8 bitli butun sonli hamda ikkilik ifodalar hujjatlashtirilgan.\n\nTo‘plam uchun bitta o‘lcham va ifodani tanlang, ularni metama’lumot bilan saqlang. Suzuvchi nuqtali vektorlar va kvantlangan ifodalar tegishli saqlash va taqqoslash ishlovini talab qiladi. Ikkilik ifoda vektor ma’lumotini oddiy suzuvchi nuqtali koordinatalar ro‘yxatidan boshqacha kodlaydi; qidiruv natijalarini tayyorlashda shu farqni saqlang.\n\nUzun faylda manba joylarini qayta topish mumkin bo‘lgan izchil bo‘limlarni tanlang. Funksiyaga shartni tushuntirish uchun yonidagi tur ta’rifi yoki izoh kerak bo‘lishi mumkin, butun haddan katta fayl esa aloqasiz materialni ham saqlashi mumkin. Maksimal kontekstni afzal bo‘lak hajmi deb bilish o‘rniga, bo‘limni qidiriladigan vazifaga moslang.\n\n### Topilgan manbani dalil sifatida ishlating\n\nAmaliy dasturlash agenti jarayoni nomzod manbani topadi, shu fayllarni o‘qiydi, tegishli havolalarni kuzatadi va tekshiruv davomida qidiruvni toraytiradi. Topilgan matn mulohaza yuritish yoki tahrirlash bosqichiga kontekst bo‘ladi. Nomzod to‘g‘ri manba bo‘yicha tekshirilishi uchun uni indekslangan repozitoriy reviziyasiga bog‘liq saqlang.\n\nvoyage-code-4’ning nashrga xos hissasi — dasturlash agentlari ishlatadigan muammodan kodni topish bosqichiga e’tibor, moslashuvchan o‘lchamlar va ifodalar. Chiqish qidiruv vektori; tashxis, tahrirlash va tasdiqlash esa haqiqiy repozitoriyga asoslangan alohida vazifalar bo‘lib qoladi.", "alias_of": null, "aliases": [], "author": "VoyageAI by MongoDB", "available": null, "canonical_model": "voyageai/voyage-code-4", "capabilities": { "context_length": 32000, "manufacturer_input": [ "text" ], "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-code-4", "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-code-4" ], "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-code-4\", \"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-code-4',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-code-4", "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-code-4", "request_model_id": "voyageai/voyage-code-4", "route_label": "Ultra" } ], "name": "VoyageAI by MongoDB: voyage-code-4", "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-code-4", "short": "Voyage AI voyage-code-4 — savollar, muammo tavsiflari va dasturlash agenti maqsadlaridan tegishli manbani topish uchun kod embedding modeli. U 32 000 tokenli kirishlar va 256, 512, 1024 yoki 2048 o‘lchamni, suzuvchi nuqtali hamda kvantlangan ifodalarni qo‘llaydi." }, "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-code-4 — savollar, muammo tavsiflari va dasturlash agenti maqsadlaridan tegishli manbani topish uchun kod embedding modeli. U 32 000 tokenli kirishlar va 256, 512, 1024 yoki 2048 o‘lchamni, suzuvchi nuqtali hamda kvantlangan ifodalarni qo‘llaydi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "308d01bea575b97176120e53d06f19746c259286bc5e9102eb4e862f3fef64eb", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "e8e026a15f8c0b968e74cf66838544ea9f39c21e61b14631744d3dd9d3923c8e" }