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Qwen3-Embedding-8B — zich Qwen3 asos modelidan qurilgan Qwen’ning 8B matn embedding modeli. U suhbat javobini yaratish o‘rniga matnni sonli vektorlar bilan ifodalaydi. Qwen qo‘llab-quvvatlanadigan sohalar qatorida matn olish, kod olish, tasniflash, klasterlash va ikki tilli matn juftlarini topishni sanaydi. Qidirish tizimi bu vektorlar orqali savolni tegishli parchalar bilan taqqoslaydi; hujjat to‘plami semantik bog‘liq materialni topish yoki yozuvlarni guruhlarga tashkil qilish uchun foydalanadi.

Aniq model 32K tokenli ketma-ketlik uzunligi va 4 096 o‘lchamgacha embeddinglarni qo‘llab-quvvatlaydi. Matryoshka Representation Learning orqali 32 dan 4 096 gacha foydalanuvchi belgilaydigan chiqish o‘lchamlarini ham qo‘llaydi. Bu vektor uzunligini ongli dizayn tanloviga aylantiradi: qisqaroq vektor ifoda hajmini o‘zgartiradi, to‘liq chiqish esa modelning maksimal o‘lchamligini saqlaydi. Bitta indeksda taqqoslanadigan hujjat va so‘rovlar uchun tanlangan o‘lchamni izchil saqlang. Qwen3-Embedding-4B mavjud 8B to‘plamga almashinadigan vektor manbasi emas, boshqa maksimal o‘lchamga ega alohida versiyadir.

Ishlab chiqaruvchi dasturlash tillarini ham o‘z ichiga olgan 100 dan ortiq til, ko‘p tilli, tillararo va kod qidirishni qo‘llashni tasvirlaydi. Amaliy to‘plam bir nechta tildagi hujjatlarni saqlab, so‘rovlar tegishli ma’noni izlashiga yoki tabiiy til savoli uchun indekslangan manba kodi parchalariga ega bo‘lishi mumkin. Kirishda muhim identifikatorlar, sarlavhalar va soha atamalarini saqlang. Kodda shu kontekst savolning bir qismi bo‘lsa, funksiya yoki modul atrofidagi izohni parcha bilan birga saqlang. Chiqish vektor ifodasi bo‘lib qoladi; mos hujjat matnini qaytarish qidirish ilovasining keyingi bosqichidir.

Model yo‘riqnomani hisobga oladi. Qwen qidirish vazifasiga mos bitta jumlalik ko‘rsatmani, ko‘p tilli foydalanishda esa uni ingliz tilida yozishni tavsiya qiladi. Bevosita misoli so‘rovni Instruct: ortidan vazifa tavsifi va Query: ortidan savol keladigan shaklda formatlaydi. Shu misolda qidiriladigan hujjatlarga ayni ko‘rsatma prefiksi kerak emas. Mos asl topshiriq foydalanuvchi xatosini izohlaydigan texnik parchalarni topishni tasvirlab, muayyan xato tavsifini so‘rovga joylashi mumkin. Ko‘rsatma qidiruv qaysi semantik aloqani afzal ko‘rishi kerakligini bildiradi.

Qwen misollari so‘rovlar va hujjatlarni alohida kodlaydi, vektorlarini kosinus o‘xshashligi bilan taqqoslaydi va oxirgi tokenning yashirin holatini yig‘ib, skalyar ko‘paytma taqqoslashidan oldin embeddingni normallashtiradigan quyi darajali tartibni ko‘rsatadi. Foydali jarayon har parcha vektorini manba identifikatori bilan birga saqlaydi, tayyor so‘rovni saqlangan vektorlar bilan taqqoslaydi va mos parchalarni o‘xshashlik tartibida qaytaradi. Misoldagi 8 192 tokenli kesish sozlamasi modelning hujjatlashtirilgan 32K ketma-ketlik uzunligidan farqli tanlangan kirishni qayta ishlash sozlamasidir. Uzun kirishda nima saqlanishini ko‘rib chiqing.

Embedding va qayta saralash modellari Qwen relizidagi turli komponentlardir. Embedding modeli bitta matn segmentini ifodalab, dastlabki o‘xshashlik qidirishga imkon beradi. Alohida reranker so‘rov va nomzod parchalar o‘rtasidagi aloqani baholab, tartibni aniqlashtiradi. Qwen ikki modulni birlashtirishni tasvirlaydi, lekin Qwen3-Embedding-8B o‘zi reranker’ning tegishlilik bahosi yoki yozma javob emas, embeddinglarni qaytaradi. Qidirishdan keyin javob berishda mos manba matnini saqlab, shu dalilni javob bosqichiga o‘tkazing. Qidiriladigan to‘plam uchun izchil model, o‘lcham va so‘rov ko‘rsatmasini tanlab, saqlang.

", "aboutText": "Qwen3-Embedding-8B — zich Qwen3 asos modelidan qurilgan Qwen’ning 8B matn embedding modeli. U suhbat javobini yaratish o‘rniga matnni sonli vektorlar bilan ifodalaydi. Qwen qo‘llab-quvvatlanadigan sohalar qatorida matn olish, kod olish, tasniflash, klasterlash va ikki tilli matn juftlarini topishni sanaydi. Qidirish tizimi bu vektorlar orqali savolni tegishli parchalar bilan taqqoslaydi; hujjat to‘plami semantik bog‘liq materialni topish yoki yozuvlarni guruhlarga tashkil qilish uchun foydalanadi.\n\nAniq model 32K tokenli ketma-ketlik uzunligi va 4 096 o‘lchamgacha embeddinglarni qo‘llab-quvvatlaydi. Matryoshka Representation Learning orqali 32 dan 4 096 gacha foydalanuvchi belgilaydigan chiqish o‘lchamlarini ham qo‘llaydi. Bu vektor uzunligini ongli dizayn tanloviga aylantiradi: qisqaroq vektor ifoda hajmini o‘zgartiradi, to‘liq chiqish esa modelning maksimal o‘lchamligini saqlaydi. Bitta indeksda taqqoslanadigan hujjat va so‘rovlar uchun tanlangan o‘lchamni izchil saqlang. Qwen3-Embedding-4B mavjud 8B to‘plamga almashinadigan vektor manbasi emas, boshqa maksimal o‘lchamga ega alohida versiyadir.\n\nIshlab chiqaruvchi dasturlash tillarini ham o‘z ichiga olgan 100 dan ortiq til, ko‘p tilli, tillararo va kod qidirishni qo‘llashni tasvirlaydi. Amaliy to‘plam bir nechta tildagi hujjatlarni saqlab, so‘rovlar tegishli ma’noni izlashiga yoki tabiiy til savoli uchun indekslangan manba kodi parchalariga ega bo‘lishi mumkin. Kirishda muhim identifikatorlar, sarlavhalar va soha atamalarini saqlang. Kodda shu kontekst savolning bir qismi bo‘lsa, funksiya yoki modul atrofidagi izohni parcha bilan birga saqlang. Chiqish vektor ifodasi bo‘lib qoladi; mos hujjat matnini qaytarish qidirish ilovasining keyingi bosqichidir.\n\nModel yo‘riqnomani hisobga oladi. Qwen qidirish vazifasiga mos bitta jumlalik ko‘rsatmani, ko‘p tilli foydalanishda esa uni ingliz tilida yozishni tavsiya qiladi. Bevosita misoli so‘rovni Instruct: ortidan vazifa tavsifi va Query: ortidan savol keladigan shaklda formatlaydi. Shu misolda qidiriladigan hujjatlarga ayni ko‘rsatma prefiksi kerak emas. Mos asl topshiriq foydalanuvchi xatosini izohlaydigan texnik parchalarni topishni tasvirlab, muayyan xato tavsifini so‘rovga joylashi mumkin. Ko‘rsatma qidiruv qaysi semantik aloqani afzal ko‘rishi kerakligini bildiradi.\n\nQwen misollari so‘rovlar va hujjatlarni alohida kodlaydi, vektorlarini kosinus o‘xshashligi bilan taqqoslaydi va oxirgi tokenning yashirin holatini yig‘ib, skalyar ko‘paytma taqqoslashidan oldin embeddingni normallashtiradigan quyi darajali tartibni ko‘rsatadi. Foydali jarayon har parcha vektorini manba identifikatori bilan birga saqlaydi, tayyor so‘rovni saqlangan vektorlar bilan taqqoslaydi va mos parchalarni o‘xshashlik tartibida qaytaradi. Misoldagi 8 192 tokenli kesish sozlamasi modelning hujjatlashtirilgan 32K ketma-ketlik uzunligidan farqli tanlangan kirishni qayta ishlash sozlamasidir. Uzun kirishda nima saqlanishini ko‘rib chiqing.\n\nEmbedding va qayta saralash modellari Qwen relizidagi turli komponentlardir. Embedding modeli bitta matn segmentini ifodalab, dastlabki o‘xshashlik qidirishga imkon beradi. Alohida reranker so‘rov va nomzod parchalar o‘rtasidagi aloqani baholab, tartibni aniqlashtiradi. Qwen ikki modulni birlashtirishni tasvirlaydi, lekin Qwen3-Embedding-8B o‘zi reranker’ning tegishlilik bahosi yoki yozma javob emas, embeddinglarni qaytaradi. Qidirishdan keyin javob berishda mos manba matnini saqlab, shu dalilni javob bosqichiga o‘tkazing. Qidiriladigan to‘plam uchun izchil model, o‘lcham va so‘rov ko‘rsatmasini tanlab, saqlang.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "6e3dd3e8898fa7ec2d1ac3533eaedb9c5c789e301e8992d9b4fe3ee67bf35b56", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
Al

Qwen: Qwen3 Embedding 8B

Alibaba /qwen/qwen3-embedding-8b

Boshlash

Qwen3-Embedding-8B matnni ko‘p tilli qidirish, kod olish, tasniflash va klasterlash uchun semantik vektorlarga aylantiradi. U 32K tokenli ketma-ketlik uzunligi va 32 dan 4 096 gacha maxsus embedding o‘lchamlarini qo‘llab-quvvatlaydi.

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.

Qwen3-Embedding-8B — zich Qwen3 asos modelidan qurilgan Qwen’ning 8B matn embedding modeli. U suhbat javobini yaratish o‘rniga matnni sonli vektorlar bilan ifodalaydi. Qwen qo‘llab-quvvatlanadigan sohalar qatorida matn olish, kod olish, tasniflash, klasterlash va ikki tilli matn juftlarini topishni sanaydi. Qidirish tizimi bu vektorlar orqali savolni tegishli parchalar bilan taqqoslaydi; hujjat to‘plami semantik bog‘liq materialni topish yoki yozuvlarni guruhlarga tashkil qilish uchun foydalanadi.

Aniq model 32K tokenli ketma-ketlik uzunligi va 4 096 o‘lchamgacha embeddinglarni qo‘llab-quvvatlaydi. Matryoshka Representation Learning orqali 32 dan 4 096 gacha foydalanuvchi belgilaydigan chiqish o‘lchamlarini ham qo‘llaydi. Bu vektor uzunligini ongli dizayn tanloviga aylantiradi: qisqaroq vektor ifoda hajmini o‘zgartiradi, to‘liq chiqish esa modelning maksimal o‘lchamligini saqlaydi. Bitta indeksda taqqoslanadigan hujjat va so‘rovlar uchun tanlangan o‘lchamni izchil saqlang. Qwen3-Embedding-4B mavjud 8B to‘plamga almashinadigan vektor manbasi emas, boshqa maksimal o‘lchamga ega alohida versiyadir.

Ishlab chiqaruvchi dasturlash tillarini ham o‘z ichiga olgan 100 dan ortiq til, ko‘p tilli, tillararo va kod qidirishni qo‘llashni tasvirlaydi. Amaliy to‘plam bir nechta tildagi hujjatlarni saqlab, so‘rovlar tegishli ma’noni izlashiga yoki tabiiy til savoli uchun indekslangan manba kodi parchalariga ega bo‘lishi mumkin. Kirishda muhim identifikatorlar, sarlavhalar va soha atamalarini saqlang. Kodda shu kontekst savolning bir qismi bo‘lsa, funksiya yoki modul atrofidagi izohni parcha bilan birga saqlang. Chiqish vektor ifodasi bo‘lib qoladi; mos hujjat matnini qaytarish qidirish ilovasining keyingi bosqichidir.

Model yo‘riqnomani hisobga oladi. Qwen qidirish vazifasiga mos bitta jumlalik ko‘rsatmani, ko‘p tilli foydalanishda esa uni ingliz tilida yozishni tavsiya qiladi. Bevosita misoli so‘rovni Instruct: ortidan vazifa tavsifi va Query: ortidan savol keladigan shaklda formatlaydi. Shu misolda qidiriladigan hujjatlarga ayni ko‘rsatma prefiksi kerak emas. Mos asl topshiriq foydalanuvchi xatosini izohlaydigan texnik parchalarni topishni tasvirlab, muayyan xato tavsifini so‘rovga joylashi mumkin. Ko‘rsatma qidiruv qaysi semantik aloqani afzal ko‘rishi kerakligini bildiradi.

Qwen misollari so‘rovlar va hujjatlarni alohida kodlaydi, vektorlarini kosinus o‘xshashligi bilan taqqoslaydi va oxirgi tokenning yashirin holatini yig‘ib, skalyar ko‘paytma taqqoslashidan oldin embeddingni normallashtiradigan quyi darajali tartibni ko‘rsatadi. Foydali jarayon har parcha vektorini manba identifikatori bilan birga saqlaydi, tayyor so‘rovni saqlangan vektorlar bilan taqqoslaydi va mos parchalarni o‘xshashlik tartibida qaytaradi. Misoldagi 8 192 tokenli kesish sozlamasi modelning hujjatlashtirilgan 32K ketma-ketlik uzunligidan farqli tanlangan kirishni qayta ishlash sozlamasidir. Uzun kirishda nima saqlanishini ko‘rib chiqing.

Embedding va qayta saralash modellari Qwen relizidagi turli komponentlardir. Embedding modeli bitta matn segmentini ifodalab, dastlabki o‘xshashlik qidirishga imkon beradi. Alohida reranker so‘rov va nomzod parchalar o‘rtasidagi aloqani baholab, tartibni aniqlashtiradi. Qwen ikki modulni birlashtirishni tasvirlaydi, lekin Qwen3-Embedding-8B o‘zi reranker’ning tegishlilik bahosi yoki yozma javob emas, embeddinglarni qaytaradi. Qidirishdan keyin javob berishda mos manba matnini saqlab, shu dalilni javob bosqichiga o‘tkazing. Qidiriladigan to‘plam uchun izchil model, o‘lcham va so‘rov ko‘rsatmasini tanlab, saqlang.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Embeddinglar POST /v1/embeddings
Kirish
text, token_ids
Chiqish
embedding
Kontekst
32 768 tokens
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
qwen/qwen3-embedding-8b
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: 32 768 · 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 talablari32768
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 $0.00002 / 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": "qwen/qwen3-embedding-8b", "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 qwen/qwen3-embedding-8b

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

qwen/qwen3-embedding-8b

", "method_identity": { "effective_request_model_id": "qwen/qwen3-embedding-8b", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "qwen/qwen3-embedding-8b", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|qwen%2Fqwen3-embedding-8b", "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": [ "qwen/qwen3-embedding-8b" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 32768, "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": 32768, "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": 32768, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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\": \"qwen/qwen3-embedding-8b\", \"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='qwen/qwen3-embedding-8b',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": 0.02, "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": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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": "qwen/qwen3-embedding-8b", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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": "qwen/qwen3-embedding-8b", "nativeProvenance": { "input_facts_canonical_sha256": "826b028dd0f00b0c70b40e249f7f5a780e0247acd210ce3d8a0f62eb4b3b8212", "input_html_sha256": "8e165c0729613dd7b3b464e8f7af64418cee2dae38c6466deb566d90fe3a7696", "private_audit_sha256": "d8bfcb5a70a8899ec786282b402606a77e7c08ec6e7e1c45a6cfae61a5069c28", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/qwen-qwen3-embedding-8b/", "description": "Qwen3-Embedding-8B matnni ko‘p tilli qidirish, kod olish, tasniflash va klasterlash uchun semantik vektorlarga aylantiradi. U 32K tokenli ketma-ketlik uzunligi va 32 dan 4 096 gacha maxsus embedding o‘lchamlarini qo‘llab-quvvatlaydi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/qwen-qwen3-embedding-8b/index.md", "title": "Qwen: Qwen3 Embedding 8B — Ailoot", "url": "https://ailoot.ai/models/uz/qwen-qwen3-embedding-8b/" }, "rawModel": { "about": "Qwen3-Embedding-8B — zich Qwen3 asos modelidan qurilgan Qwen’ning 8B matn embedding modeli. U suhbat javobini yaratish o‘rniga matnni sonli vektorlar bilan ifodalaydi. Qwen qo‘llab-quvvatlanadigan sohalar qatorida matn olish, kod olish, tasniflash, klasterlash va ikki tilli matn juftlarini topishni sanaydi. Qidirish tizimi bu vektorlar orqali savolni tegishli parchalar bilan taqqoslaydi; hujjat to‘plami semantik bog‘liq materialni topish yoki yozuvlarni guruhlarga tashkil qilish uchun foydalanadi.\n\nAniq model 32K tokenli ketma-ketlik uzunligi va 4 096 o‘lchamgacha embeddinglarni qo‘llab-quvvatlaydi. Matryoshka Representation Learning orqali 32 dan 4 096 gacha foydalanuvchi belgilaydigan chiqish o‘lchamlarini ham qo‘llaydi. Bu vektor uzunligini ongli dizayn tanloviga aylantiradi: qisqaroq vektor ifoda hajmini o‘zgartiradi, to‘liq chiqish esa modelning maksimal o‘lchamligini saqlaydi. Bitta indeksda taqqoslanadigan hujjat va so‘rovlar uchun tanlangan o‘lchamni izchil saqlang. Qwen3-Embedding-4B mavjud 8B to‘plamga almashinadigan vektor manbasi emas, boshqa maksimal o‘lchamga ega alohida versiyadir.\n\nIshlab chiqaruvchi dasturlash tillarini ham o‘z ichiga olgan 100 dan ortiq til, ko‘p tilli, tillararo va kod qidirishni qo‘llashni tasvirlaydi. Amaliy to‘plam bir nechta tildagi hujjatlarni saqlab, so‘rovlar tegishli ma’noni izlashiga yoki tabiiy til savoli uchun indekslangan manba kodi parchalariga ega bo‘lishi mumkin. Kirishda muhim identifikatorlar, sarlavhalar va soha atamalarini saqlang. Kodda shu kontekst savolning bir qismi bo‘lsa, funksiya yoki modul atrofidagi izohni parcha bilan birga saqlang. Chiqish vektor ifodasi bo‘lib qoladi; mos hujjat matnini qaytarish qidirish ilovasining keyingi bosqichidir.\n\nModel yo‘riqnomani hisobga oladi. Qwen qidirish vazifasiga mos bitta jumlalik ko‘rsatmani, ko‘p tilli foydalanishda esa uni ingliz tilida yozishni tavsiya qiladi. Bevosita misoli so‘rovni Instruct: ortidan vazifa tavsifi va Query: ortidan savol keladigan shaklda formatlaydi. Shu misolda qidiriladigan hujjatlarga ayni ko‘rsatma prefiksi kerak emas. Mos asl topshiriq foydalanuvchi xatosini izohlaydigan texnik parchalarni topishni tasvirlab, muayyan xato tavsifini so‘rovga joylashi mumkin. Ko‘rsatma qidiruv qaysi semantik aloqani afzal ko‘rishi kerakligini bildiradi.\n\nQwen misollari so‘rovlar va hujjatlarni alohida kodlaydi, vektorlarini kosinus o‘xshashligi bilan taqqoslaydi va oxirgi tokenning yashirin holatini yig‘ib, skalyar ko‘paytma taqqoslashidan oldin embeddingni normallashtiradigan quyi darajali tartibni ko‘rsatadi. Foydali jarayon har parcha vektorini manba identifikatori bilan birga saqlaydi, tayyor so‘rovni saqlangan vektorlar bilan taqqoslaydi va mos parchalarni o‘xshashlik tartibida qaytaradi. Misoldagi 8 192 tokenli kesish sozlamasi modelning hujjatlashtirilgan 32K ketma-ketlik uzunligidan farqli tanlangan kirishni qayta ishlash sozlamasidir. Uzun kirishda nima saqlanishini ko‘rib chiqing.\n\nEmbedding va qayta saralash modellari Qwen relizidagi turli komponentlardir. Embedding modeli bitta matn segmentini ifodalab, dastlabki o‘xshashlik qidirishga imkon beradi. Alohida reranker so‘rov va nomzod parchalar o‘rtasidagi aloqani baholab, tartibni aniqlashtiradi. Qwen ikki modulni birlashtirishni tasvirlaydi, lekin Qwen3-Embedding-8B o‘zi reranker’ning tegishlilik bahosi yoki yozma javob emas, embeddinglarni qaytaradi. Qidirishdan keyin javob berishda mos manba matnini saqlab, shu dalilni javob bosqichiga o‘tkazing. Qidiriladigan to‘plam uchun izchil model, o‘lcham va so‘rov ko‘rsatmasini tanlab, saqlang.", "alias_of": null, "aliases": [], "author": "Alibaba", "available": null, "canonical_model": "qwen/qwen3-embedding-8b", "capabilities": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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": 32768, "entity_type": "model", "formats": [ "embeddings" ], "id": "qwen/qwen3-embedding-8b", "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": [ "qwen/qwen3-embedding-8b" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 32768, "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": 32768, "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": 32768, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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\": \"qwen/qwen3-embedding-8b\", \"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='qwen/qwen3-embedding-8b',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": 0.02, "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": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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": "qwen/qwen3-embedding-8b", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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": "qwen/qwen3-embedding-8b", "request_model_id": "qwen/qwen3-embedding-8b", "route_label": "Ultra" } ], "name": "Qwen: Qwen3 Embedding 8B", "price": { "in": 0.02, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.02, "sort_out": 0, "varies": false }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Эмбеддинги · Base token rates", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "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": "qwen/qwen3-embedding-8b", "short": "Qwen3-Embedding-8B matnni ko‘p tilli qidirish, kod olish, tasniflash va klasterlash uchun semantik vektorlarga aylantiradi. U 32K tokenli ketma-ketlik uzunligi va 32 dan 4 096 gacha maxsus embedding o‘lchamlarini qo‘llab-quvvatlaydi." }, "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": "Qwen3-Embedding-8B matnni ko‘p tilli qidirish, kod olish, tasniflash va klasterlash uchun semantik vektorlarga aylantiradi. U 32K tokenli ketma-ketlik uzunligi va 32 dan 4 096 gacha maxsus embedding o‘lchamlarini qo‘llab-quvvatlaydi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "162df2f02f9bded697e7c3c32c9eeb7b11a1e69511b8a387fdc18c4812648373", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "a0e59ffa4f76b954ba5f7c81d48808d662d6e1840b734b60a5a76ac335a6d070" }