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text-embedding-3-small — OpenAI’ning uchinchi avloddagi kichikroq matn embedding modeli. U yozma materialni odatda 1 536 komponentli sonli vektorlarga aylantiradi. Bu vektorlar keyingi qidiruv yoki tahlil bosqichi uchun matnlar o‘rtasidagi munosabatlarni o‘lchaydi; model kirish matniga suhbat shaklidagi javob yaratmaydi.

Uchinchi avloddagi kattaroq model kabi, u ham qisqaroq embeddinglar uchun ishlab chiqaruvchining dimensions boshqaruv vositasini qo‘llab-quvvatlaydi. O‘qitish qisqartirilgan vektorda tushunchalarni ifodalovchi ma’lumotni saqlashga mo‘ljallangan, o‘lcham tanlovi esa ilovaning sifat va resurs talablariga ta’sir qiladi. Taqqoslanayotgan yozuvlar va so‘rovlarda model hamda tanlangan o‘lchamlar sonini bir xil saqlang.

Amaliy qo‘llanishlardan biri kod qidiruvidir. Ishlab chiqaruvchi qo‘llanmasida manba kodidagi funksiyalarni matn sifatida ifodalash va embedding qilish, so‘ng tabiiy tildagi so‘rovni o‘sha model bilan embedding qilib, hosil bo‘lgan vektorlarni taqqoslash tavsiflangan. Namunaviy natija — manba joylashuvlariga bog‘langan, tegishliligi bo‘yicha tartiblangan funksiyalar to‘plamidir. Hujjat qidiruvida bunga mos natija — matni va asl havolalari o‘quvchi yoki keyingi model uchun mavjud bo‘lib qoladigan parchalar to‘plamidir.

Hujjatlarda qayd etilgan boshqa qo‘llanishlar klasterlash, bog‘liq kontent tavsiyalari, tasniflash, boshqalardan ajralib turadigan yozuvlarni aniqlash va xilma-xillikni o‘lchashni o‘z ichiga oladi. Muayyan vazifaga yo‘naltirilgan xabarlar jarayoni o‘xshash so‘rovlarni guruhlashi yoki ularni toifalar tavsiflari bilan taqqoslashi mumkin. O‘xshashlik natijasi qanday qilib toifa, tavsiya yoki ko‘rib chiqiladigan elementga aylanishini ilova belgilaydi; embeddingning o‘zi sonli ma’lumot bo‘lib qoladi.

OpenAI har bir matn uchun maksimal kirish hajmini 8 192 token deb belgilaydi. Uning embedding API’si satrlar yoki tokenlashtirilgan kirishni va bitta so‘rovdagi bir nechta kirishni qabul qiladi, bo‘sh satrlarni rad etadi va jami 300 000 tokenlik limit qo‘llaydi. Uzun materialni mazmunli parchalar shaklida tayyorlang va ularni talqin qilish uchun kerak bo‘lgan sarlavha yoki ta’riflarni saqlang. Turli xil yozuvlarni o‘zaro aloqasiz tayyorlash qoidalari bilan embedding qilish o‘rniga, izchil manba maydonlari to‘plamidan foydalaning.

Ishlab chiqaruvchi odatda float vektorlar qaytaradi va base64 kodlashni ham hujjatlashtirgan. Qo‘llanma OpenAI embeddinglari birlik uzunligiga normallashtirilganini bildiradi va kosinus o‘xshashligini tavsiya qiladi; skalyar ko‘paytma uni hisoblashi mumkin, Evklid masofasi esa bir xil tartiblashni beradi. Agar uchinchi avlod embeddingi qaytarilgandan so‘ng qo‘lda qisqartirilsa, ushbu qisqartirilgan vektorni qo‘llanmada tasvirlanganidek qayta normallashtiring. dimensions boshqaruv vositasi qo‘lda kesishni odatiy tayyorlash bosqichiga aylantirishning oldini oladi.

Har bir vektor bilan birga model identifikatori, o‘lchamlar soni va manba havolasini qayd eting. Keyingi bosqich uchun foydali natija — ularni hosil qilgan matnga bog‘langan tartiblangan ro‘yxat, guruhlangan to‘plam yoki tasniflash belgilari to‘plamidir. Tanlangan material haqiqatan vazifani qo‘llab-quvvatlashini tekshiring va yetishmayotgan faktlarni semantik bog‘liqlikdan alohida tuting. Shunda keyingi bosqich bu ifodadan muayyan qidirib topish yoki qaror qabul qilish jarayoni uchun foydalanishi mumkin.

", "aboutText": "text-embedding-3-small — OpenAI’ning uchinchi avloddagi kichikroq matn embedding modeli. U yozma materialni odatda 1 536 komponentli sonli vektorlarga aylantiradi. Bu vektorlar keyingi qidiruv yoki tahlil bosqichi uchun matnlar o‘rtasidagi munosabatlarni o‘lchaydi; model kirish matniga suhbat shaklidagi javob yaratmaydi.\n\nUchinchi avloddagi kattaroq model kabi, u ham qisqaroq embeddinglar uchun ishlab chiqaruvchining dimensions boshqaruv vositasini qo‘llab-quvvatlaydi. O‘qitish qisqartirilgan vektorda tushunchalarni ifodalovchi ma’lumotni saqlashga mo‘ljallangan, o‘lcham tanlovi esa ilovaning sifat va resurs talablariga ta’sir qiladi. Taqqoslanayotgan yozuvlar va so‘rovlarda model hamda tanlangan o‘lchamlar sonini bir xil saqlang.\n\nAmaliy qo‘llanishlardan biri kod qidiruvidir. Ishlab chiqaruvchi qo‘llanmasida manba kodidagi funksiyalarni matn sifatida ifodalash va embedding qilish, so‘ng tabiiy tildagi so‘rovni o‘sha model bilan embedding qilib, hosil bo‘lgan vektorlarni taqqoslash tavsiflangan. Namunaviy natija — manba joylashuvlariga bog‘langan, tegishliligi bo‘yicha tartiblangan funksiyalar to‘plamidir. Hujjat qidiruvida bunga mos natija — matni va asl havolalari o‘quvchi yoki keyingi model uchun mavjud bo‘lib qoladigan parchalar to‘plamidir.\n\nHujjatlarda qayd etilgan boshqa qo‘llanishlar klasterlash, bog‘liq kontent tavsiyalari, tasniflash, boshqalardan ajralib turadigan yozuvlarni aniqlash va xilma-xillikni o‘lchashni o‘z ichiga oladi. Muayyan vazifaga yo‘naltirilgan xabarlar jarayoni o‘xshash so‘rovlarni guruhlashi yoki ularni toifalar tavsiflari bilan taqqoslashi mumkin. O‘xshashlik natijasi qanday qilib toifa, tavsiya yoki ko‘rib chiqiladigan elementga aylanishini ilova belgilaydi; embeddingning o‘zi sonli ma’lumot bo‘lib qoladi.\n\nOpenAI har bir matn uchun maksimal kirish hajmini 8 192 token deb belgilaydi. Uning embedding API’si satrlar yoki tokenlashtirilgan kirishni va bitta so‘rovdagi bir nechta kirishni qabul qiladi, bo‘sh satrlarni rad etadi va jami 300 000 tokenlik limit qo‘llaydi. Uzun materialni mazmunli parchalar shaklida tayyorlang va ularni talqin qilish uchun kerak bo‘lgan sarlavha yoki ta’riflarni saqlang. Turli xil yozuvlarni o‘zaro aloqasiz tayyorlash qoidalari bilan embedding qilish o‘rniga, izchil manba maydonlari to‘plamidan foydalaning.\n\nIshlab chiqaruvchi odatda float vektorlar qaytaradi va base64 kodlashni ham hujjatlashtirgan. Qo‘llanma OpenAI embeddinglari birlik uzunligiga normallashtirilganini bildiradi va kosinus o‘xshashligini tavsiya qiladi; skalyar ko‘paytma uni hisoblashi mumkin, Evklid masofasi esa bir xil tartiblashni beradi. Agar uchinchi avlod embeddingi qaytarilgandan so‘ng qo‘lda qisqartirilsa, ushbu qisqartirilgan vektorni qo‘llanmada tasvirlanganidek qayta normallashtiring. dimensions boshqaruv vositasi qo‘lda kesishni odatiy tayyorlash bosqichiga aylantirishning oldini oladi.\n\nHar bir vektor bilan birga model identifikatori, o‘lchamlar soni va manba havolasini qayd eting. Keyingi bosqich uchun foydali natija — ularni hosil qilgan matnga bog‘langan tartiblangan ro‘yxat, guruhlangan to‘plam yoki tasniflash belgilari to‘plamidir. Tanlangan material haqiqatan vazifani qo‘llab-quvvatlashini tekshiring va yetishmayotgan faktlarni semantik bog‘liqlikdan alohida tuting. Shunda keyingi bosqich bu ifodadan muayyan qidirib topish yoki qaror qabul qilish jarayoni uchun foydalanishi mumkin.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "3adf6aaefefa349718bacfcd516f57636bd7944156a0c52d5a814df4df1b23d2", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "

O

OpenAI: Text Embedding 3 Small

OpenAI /openai/text-embedding-3-small

Model ID

Boshlash

OpenAI’ning uchinchi avloddagi kichik matn embedding modeli odatda 1 536 o‘lchamli vektorlar qaytaradi va qisqaroq ifodalarni qo‘llab-quvvatlaydi. Sonli natijadan semantik qidiruv, kodni qidirib topish, guruhlash, tavsiyalar va tasniflash uchun foydalaning.

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.

text-embedding-3-small — OpenAI’ning uchinchi avloddagi kichikroq matn embedding modeli. U yozma materialni odatda 1 536 komponentli sonli vektorlarga aylantiradi. Bu vektorlar keyingi qidiruv yoki tahlil bosqichi uchun matnlar o‘rtasidagi munosabatlarni o‘lchaydi; model kirish matniga suhbat shaklidagi javob yaratmaydi.

Uchinchi avloddagi kattaroq model kabi, u ham qisqaroq embeddinglar uchun ishlab chiqaruvchining dimensions boshqaruv vositasini qo‘llab-quvvatlaydi. O‘qitish qisqartirilgan vektorda tushunchalarni ifodalovchi ma’lumotni saqlashga mo‘ljallangan, o‘lcham tanlovi esa ilovaning sifat va resurs talablariga ta’sir qiladi. Taqqoslanayotgan yozuvlar va so‘rovlarda model hamda tanlangan o‘lchamlar sonini bir xil saqlang.

Amaliy qo‘llanishlardan biri kod qidiruvidir. Ishlab chiqaruvchi qo‘llanmasida manba kodidagi funksiyalarni matn sifatida ifodalash va embedding qilish, so‘ng tabiiy tildagi so‘rovni o‘sha model bilan embedding qilib, hosil bo‘lgan vektorlarni taqqoslash tavsiflangan. Namunaviy natija — manba joylashuvlariga bog‘langan, tegishliligi bo‘yicha tartiblangan funksiyalar to‘plamidir. Hujjat qidiruvida bunga mos natija — matni va asl havolalari o‘quvchi yoki keyingi model uchun mavjud bo‘lib qoladigan parchalar to‘plamidir.

Hujjatlarda qayd etilgan boshqa qo‘llanishlar klasterlash, bog‘liq kontent tavsiyalari, tasniflash, boshqalardan ajralib turadigan yozuvlarni aniqlash va xilma-xillikni o‘lchashni o‘z ichiga oladi. Muayyan vazifaga yo‘naltirilgan xabarlar jarayoni o‘xshash so‘rovlarni guruhlashi yoki ularni toifalar tavsiflari bilan taqqoslashi mumkin. O‘xshashlik natijasi qanday qilib toifa, tavsiya yoki ko‘rib chiqiladigan elementga aylanishini ilova belgilaydi; embeddingning o‘zi sonli ma’lumot bo‘lib qoladi.

OpenAI har bir matn uchun maksimal kirish hajmini 8 192 token deb belgilaydi. Uning embedding API’si satrlar yoki tokenlashtirilgan kirishni va bitta so‘rovdagi bir nechta kirishni qabul qiladi, bo‘sh satrlarni rad etadi va jami 300 000 tokenlik limit qo‘llaydi. Uzun materialni mazmunli parchalar shaklida tayyorlang va ularni talqin qilish uchun kerak bo‘lgan sarlavha yoki ta’riflarni saqlang. Turli xil yozuvlarni o‘zaro aloqasiz tayyorlash qoidalari bilan embedding qilish o‘rniga, izchil manba maydonlari to‘plamidan foydalaning.

Ishlab chiqaruvchi odatda float vektorlar qaytaradi va base64 kodlashni ham hujjatlashtirgan. Qo‘llanma OpenAI embeddinglari birlik uzunligiga normallashtirilganini bildiradi va kosinus o‘xshashligini tavsiya qiladi; skalyar ko‘paytma uni hisoblashi mumkin, Evklid masofasi esa bir xil tartiblashni beradi. Agar uchinchi avlod embeddingi qaytarilgandan so‘ng qo‘lda qisqartirilsa, ushbu qisqartirilgan vektorni qo‘llanmada tasvirlanganidek qayta normallashtiring. dimensions boshqaruv vositasi qo‘lda kesishni odatiy tayyorlash bosqichiga aylantirishning oldini oladi.

Har bir vektor bilan birga model identifikatori, o‘lchamlar soni va manba havolasini qayd eting. Keyingi bosqich uchun foydali natija — ularni hosil qilgan matnga bog‘langan tartiblangan ro‘yxat, guruhlangan to‘plam yoki tasniflash belgilari to‘plamidir. Tanlangan material haqiqatan vazifani qo‘llab-quvvatlashini tekshiring va yetishmayotgan faktlarni semantik bog‘liqlikdan alohida tuting. Shunda keyingi bosqich bu ifodadan muayyan qidirib topish yoki qaror qabul qilish jarayoni uchun foydalanishi mumkin.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Embeddinglar POST /v1/embeddings

Kirish: text, token_ids
Chiqish: embedding
Kontekst: 8 192 tokens
Chiqish formati: json

Maydon	Tur	Talab	Qiymatlar va cheklovlar	Standart
Maydon`model`	Tur`string`	TalabMajburiy	Qiymatlar va cheklovlar openai/text-embedding-3-small	Standart—
Maydon`input`	Tur`string\|string[]\|integer[]\|integer[][]`	TalabMajburiy	Qiymatlar 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: 8 192 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari	Standart—
Maydon`encoding_format`	Tur`string`	TalabIxtiyoriy	Qiymatlar va cheklovlar float	Standartfloat
Maydon`dimensions`	Tur`integer`	TalabQo‘llab-quvvatlanmaydi	Qiymatlar va cheklovlar Eng kam: 1	Standart—
Maydon`user`	Tur`json_value`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.	Standart—
Maydon`session_id`	Tur`json_value`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.	Standart—
Maydon`trace`	Tur`json_value`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.	Standart—
Maydon`provider`	Tur`object`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: provider.options rad etildi	Standart—

Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli

Parametr	Parametr talablari
Parametr`content_type`	Parametr talablariapplication/json
Parametr`required`	Parametr talablarimodel, input
Parametr`input_shapes`	Parametr talablaristring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]
Parametr`encoding_format`	Parametr talablarifloat
Parametr`dimensions_override`	Parametr talablarifalse
Parametr`max_input_budget_units`	Parametr talablari8192
Parametr`multimodal_objects`	Parametr talablarifalse
Parametr`material_types`	Parametr 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

Sozlama	Tariflar jadvali	To‘lov
SozlamaTokenlar uchun bazaviy tariflar	Tariflar jadvaliAsosiy tarif	To‘lov Kirish · Kirish tokenlari $0.00004 / 1K token Chiqish · Chiqish tokenlari $0 / 1K token Kirish · Keshdan o‘qish — / 1K token Kirish · Keshga yozish — / 1K token Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · Embeddinglar

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "openai/text-embedding-3-small", "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

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='openai/text-embedding-3-small',input=["First document","Second document"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\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

Maydon	Tur
Maydon`object`	Turstring
Maydon`model`	Turstring
Maydon`data[].index`	Turinteger
Maydon`data[].embedding`	Turfinite float[]
Maydon`usage`	Turobject

Xatolar va keyingi qadamlar

Javob	Shart	Keyingi qadam
Javob400 · `invalid_request_error`	ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri	Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra openai/text-embedding-3-small, text-embedding-3-small

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

openai/text-embedding-3-small, text-embedding-3-small

", "method_identity": { "effective_request_model_id": "openai/text-embedding-3-small", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "openai/text-embedding-3-small", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|openai%2Ftext-embedding-3-small", "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": [ "openai/text-embedding-3-small" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 8192, "text_unit": "UTF8 baytlari", "token_unit": "Tokenlar massivi elementlari" }, "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]|integer[]|integer[][]" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "unsupported", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "pass_through_metadata", "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "json_value" }, { "availability": "pass_through_metadata", "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "session_id", "required": false, "type": "json_value" }, { "availability": "pass_through_metadata", "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "json_value" }, { "availability": "source_interface", "conditions": "provider.options rad etildi", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "object" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "mixed input kinds", "empty input", "image/audio/file objects", "encoding_format=base64", "dimensions override" ] }, "hints": [ "Har bir vektorning indeksi va o‘lchamini saqlang.", "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi." ], "input": { "content_type": "application/json", "dimensions_override": false, "encoding_format": [ "float" ], "input_shapes": [ "string", "string[]", "nonnegative_integer_token_array", "nonnegative_integer_token_array[]" ], "material_types": [ "text", "token_ids" ], "max_input_budget_units": 8192, "multimodal_objects": false, "required": [ "model", "input" ] }, "lifecycle": { "mode": "synchronous_json", "unknown": "no embedding job endpoint inferred" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/embeddings" }, "method_aliases": [], "operation_modes": [ "text_to_embedding" ], "output": { "default_dimensions": null, "family": "embedding", "format": "json", "http_success": 200, "vectors": "one dense float vector per input; indices/model/usage validated" } }, "ctx": 8192, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"openai/text-embedding-3-small\", \"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='openai/text-embedding-3-small',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n", "label": "Python SDK · all vectors", "language": "python" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "embeddings", "hints": [ "Keep every vector index and dimension.", "Multimodal model name does not admit image objects on this exact path." ], "http": "POST", "in": 0.04, "input": [ "text", "token_ids" ], "label": "Эмбеддинги", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "openai/text-embedding-3-small", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } }, { "html": "

O

OpenAI: Text Embedding 3 Small

OpenAI /text-embedding-3-small

Model ID

Boshlash

OpenAI’ning uchinchi avloddagi kichik matn embedding modeli odatda 1 536 o‘lchamli vektorlar qaytaradi va qisqaroq ifodalarni qo‘llab-quvvatlaydi. Sonli natijadan semantik qidiruv, kodni qidirib topish, guruhlash, tavsiyalar va tasniflash uchun foydalaning.

Qabul qilinadigan kirish ma’lumotlari: Matn
Natijalar: embedding

Model haqida

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

text-embedding-3-small — OpenAI’ning uchinchi avloddagi kichikroq matn embedding modeli. U yozma materialni odatda 1 536 komponentli sonli vektorlarga aylantiradi. Bu vektorlar keyingi qidiruv yoki tahlil bosqichi uchun matnlar o‘rtasidagi munosabatlarni o‘lchaydi; model kirish matniga suhbat shaklidagi javob yaratmaydi.

Uchinchi avloddagi kattaroq model kabi, u ham qisqaroq embeddinglar uchun ishlab chiqaruvchining dimensions boshqaruv vositasini qo‘llab-quvvatlaydi. O‘qitish qisqartirilgan vektorda tushunchalarni ifodalovchi ma’lumotni saqlashga mo‘ljallangan, o‘lcham tanlovi esa ilovaning sifat va resurs talablariga ta’sir qiladi. Taqqoslanayotgan yozuvlar va so‘rovlarda model hamda tanlangan o‘lchamlar sonini bir xil saqlang.

Amaliy qo‘llanishlardan biri kod qidiruvidir. Ishlab chiqaruvchi qo‘llanmasida manba kodidagi funksiyalarni matn sifatida ifodalash va embedding qilish, so‘ng tabiiy tildagi so‘rovni o‘sha model bilan embedding qilib, hosil bo‘lgan vektorlarni taqqoslash tavsiflangan. Namunaviy natija — manba joylashuvlariga bog‘langan, tegishliligi bo‘yicha tartiblangan funksiyalar to‘plamidir. Hujjat qidiruvida bunga mos natija — matni va asl havolalari o‘quvchi yoki keyingi model uchun mavjud bo‘lib qoladigan parchalar to‘plamidir.

Hujjatlarda qayd etilgan boshqa qo‘llanishlar klasterlash, bog‘liq kontent tavsiyalari, tasniflash, boshqalardan ajralib turadigan yozuvlarni aniqlash va xilma-xillikni o‘lchashni o‘z ichiga oladi. Muayyan vazifaga yo‘naltirilgan xabarlar jarayoni o‘xshash so‘rovlarni guruhlashi yoki ularni toifalar tavsiflari bilan taqqoslashi mumkin. O‘xshashlik natijasi qanday qilib toifa, tavsiya yoki ko‘rib chiqiladigan elementga aylanishini ilova belgilaydi; embeddingning o‘zi sonli ma’lumot bo‘lib qoladi.

OpenAI har bir matn uchun maksimal kirish hajmini 8 192 token deb belgilaydi. Uning embedding API’si satrlar yoki tokenlashtirilgan kirishni va bitta so‘rovdagi bir nechta kirishni qabul qiladi, bo‘sh satrlarni rad etadi va jami 300 000 tokenlik limit qo‘llaydi. Uzun materialni mazmunli parchalar shaklida tayyorlang va ularni talqin qilish uchun kerak bo‘lgan sarlavha yoki ta’riflarni saqlang. Turli xil yozuvlarni o‘zaro aloqasiz tayyorlash qoidalari bilan embedding qilish o‘rniga, izchil manba maydonlari to‘plamidan foydalaning.

Ishlab chiqaruvchi odatda float vektorlar qaytaradi va base64 kodlashni ham hujjatlashtirgan. Qo‘llanma OpenAI embeddinglari birlik uzunligiga normallashtirilganini bildiradi va kosinus o‘xshashligini tavsiya qiladi; skalyar ko‘paytma uni hisoblashi mumkin, Evklid masofasi esa bir xil tartiblashni beradi. Agar uchinchi avlod embeddingi qaytarilgandan so‘ng qo‘lda qisqartirilsa, ushbu qisqartirilgan vektorni qo‘llanmada tasvirlanganidek qayta normallashtiring. dimensions boshqaruv vositasi qo‘lda kesishni odatiy tayyorlash bosqichiga aylantirishning oldini oladi.

Har bir vektor bilan birga model identifikatori, o‘lchamlar soni va manba havolasini qayd eting. Keyingi bosqich uchun foydali natija — ularni hosil qilgan matnga bog‘langan tartiblangan ro‘yxat, guruhlangan to‘plam yoki tasniflash belgilari to‘plamidir. Tanlangan material haqiqatan vazifani qo‘llab-quvvatlashini tekshiring va yetishmayotgan faktlarni semantik bog‘liqlikdan alohida tuting. Shunda keyingi bosqich bu ifodadan muayyan qidirib topish yoki qaror qabul qilish jarayoni uchun foydalanishi mumkin.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Embeddinglar · text-embedding-3-small POST /v1/embeddings

Kirish: text
Chiqish: embedding
Kontekst: 8 192 tokens
Chiqish formati: json

Maydon	Tur	Talab	Qiymatlar va cheklovlar	Standart
Maydon`model`	Tur`string`	TalabMajburiy	Qiymatlar va cheklovlar text-embedding-3-small	Standart—
Maydon`input`	Tur`string\|string[]\|integer[]\|integer[][]`	TalabMajburiy	Qiymatlar 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: 8 192 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari	Standart—
Maydon`encoding_format`	Tur`string`	TalabIxtiyoriy	Qiymatlar va cheklovlar float	Standartfloat
Maydon`dimensions`	Tur`integer`	TalabQo‘llab-quvvatlanmaydi	Qiymatlar va cheklovlar Eng kam: 1	Standart—
Maydon`user`	Tur`json_value`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan modelga bog‘liq.	Standart—
Maydon`session_id`	Tur`json_value`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan modelga bog‘liq.	Standart—
Maydon`trace`	Tur`json_value`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan modelga bog‘liq.	Standart—
Maydon`provider`	Tur`object`	TalabIxtiyoriy	Qiymatlar va cheklovlar Shartlar: provider.options rad etildi	Standart—

Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli

Parametr	Parametr talablari
Parametr`content_type`	Parametr talablariapplication/json
Parametr`required`	Parametr talablarimodel, input
Parametr`input_shapes`	Parametr talablaristring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]
Parametr`encoding_format`	Parametr talablarifloat
Parametr`dimensions_override`	Parametr talablarifalse
Parametr`max_input_budget_units`	Parametr talablari8192
Parametr`multimodal_objects`	Parametr talablarifalse
Parametr`material_types`	Parametr talablaritext
Parametr`text_file_material`	Parametr talablariHaqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring.

Hayot sikli: synchronous_json

Aniq model ID sini va majburiy maydonlarni yuboring.

Qaytarilgan har bir element va uning dastlabki identifikatorlarini saqlang.

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

Sozlama	Tariflar jadvali	To‘lov
SozlamaTokenlar uchun bazaviy tariflar	Tariflar jadvaliAsosiy tarif	To‘lov Kirish · Kirish tokenlari $0.00004 / 1K token Chiqish · Chiqish tokenlari $0 / 1K token Kirish · Keshdan o‘qish — / 1K token Kirish · Keshga yozish — / 1K token Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · Embeddinglar · text-embedding-3-small

{\n  "model": "text-embedding-3-small",\n  "input": [\n    "First document",\n    "Second document"\n  ],\n  "encoding_format": "float"\n}

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n    raise SystemExit('Choose a document containing text')\npayload = {'model': 'text-embedding-3-small', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}\npayload['input'] = [text]\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nfor vector in result.get('data', []):\n    print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))\nPY_RESULT\n

import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n    raise SystemExit('Choose a document containing text')\npayload = {'model': 'text-embedding-3-small', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}\npayload['input'] = [text]\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n    raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'Authorization': 'Bearer PLACEHOLDER_KEY', 'Content-Type': 'application/json'}\nheaders['Authorization'] = 'Bearer ' + api_key\nrequest = Request('https://api.ailoot.ai/v1/embeddings', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n    with urlopen(request, timeout=120) as response:\n        raw = response.read()\n        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n    Path('result.json').write_bytes(error.read())\n    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n    raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nfor vector in result.get('data', []):\n    print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))\n

Natijalarni olish va saqlash

Ailoot Ultra · Embeddinglar · text-embedding-3-small

json

Barcha tayyor natijalarni saqlash

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

Har bir chekli vektorni dastlabki indeksi bilan saqlang. Barcha o‘lchamlarni va to‘liq usage obyektini saqlang.

Asl javobni tekshirish kerak bo‘lsa

Embedding vazifalari uchun endpoint qo‘shilmaydi.

Javob maydonlari

Maydon	Tur
Maydon`object`	Turstring
Maydon`model`	Turstring
Maydon`data[].index`	Turinteger
Maydon`data[].embedding`	Turfinite float[]
Maydon`usage`	Turobject

Xatolar va keyingi qadamlar

Javob	Shart	Keyingi qadam
Javob400 · `invalid_request_error`	ShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri	Keyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra openai/text-embedding-3-small, text-embedding-3-small

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

openai/text-embedding-3-small, text-embedding-3-small

", "method_identity": { "effective_request_model_id": "text-embedding-3-small", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "text-embedding-3-small", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|text-embedding-3-small", "ordinal": 1, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every finite vector with its original index. Preserve all dimensions and the complete usage object.", "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 chekli vektorni dastlabki indeksi bilan saqlang. Barcha o‘lchamlarni va to‘liq usage obyektini saqlang.", "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": [ "text-embedding-3-small" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 8192, "text_unit": "UTF8 baytlari", "token_unit": "Tokenlar massivi elementlari" }, "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]|integer[]|integer[][]" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "unsupported", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "pass_through_metadata", "conditions": "Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan 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": "Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan 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": "Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan 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" } ], "unsupported": [ "mixed input kinds", "empty input", "image/audio/file objects", "encoding_format=base64", "dimensions override" ] }, "hints": [ "Aniq model ID sini va majburiy maydonlarni yuboring.", "Qaytarilgan har bir element va uning dastlabki identifikatorlarini saqlang." ], "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" ], "max_input_budget_units": 8192, "multimodal_objects": false, "required": [ "model", "input" ], "text_file_material": "Haqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring." }, "lifecycle": { "mode": "synchronous_json", "unknown": "no embedding job endpoint inferred" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/embeddings" }, "method_aliases": [], "official_sources": [], "operation_modes": [ "text_to_embedding" ], "output": { "default_dimensions": null, "family": "embedding", "format": "json", "http_success": 200, "vectors": "one dense float vector per input; indices/model/usage validated" } }, "ctx": 8192, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "{\n \"model\": \"text-embedding-3-small\",\n \"input\": [\n \"First document\",\n \"Second document\"\n ],\n \"encoding_format\": \"float\"\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'text-embedding-3-small', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}\npayload['input'] = [text]\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nfor vector in result.get('data', []):\n print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'text-embedding-3-small', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}\npayload['input'] = [text]\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'Authorization': 'Bearer PLACEHOLDER_KEY', 'Content-Type': 'application/json'}\nheaders['Authorization'] = 'Bearer ' + api_key\nrequest = Request('https://api.ailoot.ai/v1/embeddings', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nfor vector in result.get('data', []):\n print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "embeddings", "hints": [ "Use the text of your own UTF-8 document as the request material.", "Save the complete result, including usage and every returned output." ], "http": "POST", "in": 0.04, "input": [ "text" ], "label": "Embeddinglar · text-embedding-3-small", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "official_sources": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "text-embedding-3-small", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } } ], "model_id": "openai/text-embedding-3-small", "nativeProvenance": { "input_facts_canonical_sha256": "252fa6d34bbf6101c945dedfae9f98b516e756f8045a600c1851780086570aed", "input_html_sha256": "c2baca5ab5f6b605c53465d19abf22d5c01c007c84fce90ba835433f9710e61b", "private_audit_sha256": "f57f3b127b175bff2431e33012046fafbfbd83d716522c45b12e2fcae3f1db6b", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/openai-text-embedding-3-small/", "description": "OpenAI’ning uchinchi avloddagi kichik matn embedding modeli odatda 1 536 o‘lchamli vektorlar qaytaradi va qisqaroq ifodalarni qo‘llab-quvvatlaydi. Sonli natijadan semantik qidiruv, kodni qidirib topish, guruhlash, tavsiyalar va tasniflash uchun foydalaning.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/openai-text-embedding-3-small/index.md", "title": "OpenAI: Text Embedding 3 Small — Ailoot", "url": "https://ailoot.ai/models/uz/openai-text-embedding-3-small/" }, "rawModel": { "about": "text-embedding-3-small — OpenAI’ning uchinchi avloddagi kichikroq matn embedding modeli. U yozma materialni odatda 1 536 komponentli sonli vektorlarga aylantiradi. Bu vektorlar keyingi qidiruv yoki tahlil bosqichi uchun matnlar o‘rtasidagi munosabatlarni o‘lchaydi; model kirish matniga suhbat shaklidagi javob yaratmaydi.\n\nUchinchi avloddagi kattaroq model kabi, u ham qisqaroq embeddinglar uchun ishlab chiqaruvchining dimensions boshqaruv vositasini qo‘llab-quvvatlaydi. O‘qitish qisqartirilgan vektorda tushunchalarni ifodalovchi ma’lumotni saqlashga mo‘ljallangan, o‘lcham tanlovi esa ilovaning sifat va resurs talablariga ta’sir qiladi. Taqqoslanayotgan yozuvlar va so‘rovlarda model hamda tanlangan o‘lchamlar sonini bir xil saqlang.\n\nAmaliy qo‘llanishlardan biri kod qidiruvidir. Ishlab chiqaruvchi qo‘llanmasida manba kodidagi funksiyalarni matn sifatida ifodalash va embedding qilish, so‘ng tabiiy tildagi so‘rovni o‘sha model bilan embedding qilib, hosil bo‘lgan vektorlarni taqqoslash tavsiflangan. Namunaviy natija — manba joylashuvlariga bog‘langan, tegishliligi bo‘yicha tartiblangan funksiyalar to‘plamidir. Hujjat qidiruvida bunga mos natija — matni va asl havolalari o‘quvchi yoki keyingi model uchun mavjud bo‘lib qoladigan parchalar to‘plamidir.\n\nHujjatlarda qayd etilgan boshqa qo‘llanishlar klasterlash, bog‘liq kontent tavsiyalari, tasniflash, boshqalardan ajralib turadigan yozuvlarni aniqlash va xilma-xillikni o‘lchashni o‘z ichiga oladi. Muayyan vazifaga yo‘naltirilgan xabarlar jarayoni o‘xshash so‘rovlarni guruhlashi yoki ularni toifalar tavsiflari bilan taqqoslashi mumkin. O‘xshashlik natijasi qanday qilib toifa, tavsiya yoki ko‘rib chiqiladigan elementga aylanishini ilova belgilaydi; embeddingning o‘zi sonli ma’lumot bo‘lib qoladi.\n\nOpenAI har bir matn uchun maksimal kirish hajmini 8 192 token deb belgilaydi. Uning embedding API’si satrlar yoki tokenlashtirilgan kirishni va bitta so‘rovdagi bir nechta kirishni qabul qiladi, bo‘sh satrlarni rad etadi va jami 300 000 tokenlik limit qo‘llaydi. Uzun materialni mazmunli parchalar shaklida tayyorlang va ularni talqin qilish uchun kerak bo‘lgan sarlavha yoki ta’riflarni saqlang. Turli xil yozuvlarni o‘zaro aloqasiz tayyorlash qoidalari bilan embedding qilish o‘rniga, izchil manba maydonlari to‘plamidan foydalaning.\n\nIshlab chiqaruvchi odatda float vektorlar qaytaradi va base64 kodlashni ham hujjatlashtirgan. Qo‘llanma OpenAI embeddinglari birlik uzunligiga normallashtirilganini bildiradi va kosinus o‘xshashligini tavsiya qiladi; skalyar ko‘paytma uni hisoblashi mumkin, Evklid masofasi esa bir xil tartiblashni beradi. Agar uchinchi avlod embeddingi qaytarilgandan so‘ng qo‘lda qisqartirilsa, ushbu qisqartirilgan vektorni qo‘llanmada tasvirlanganidek qayta normallashtiring. dimensions boshqaruv vositasi qo‘lda kesishni odatiy tayyorlash bosqichiga aylantirishning oldini oladi.\n\nHar bir vektor bilan birga model identifikatori, o‘lchamlar soni va manba havolasini qayd eting. Keyingi bosqich uchun foydali natija — ularni hosil qilgan matnga bog‘langan tartiblangan ro‘yxat, guruhlangan to‘plam yoki tasniflash belgilari to‘plamidir. Tanlangan material haqiqatan vazifani qo‘llab-quvvatlashini tekshiring va yetishmayotgan faktlarni semantik bog‘liqlikdan alohida tuting. Shunda keyingi bosqich bu ifodadan muayyan qidirib topish yoki qaror qabul qilish jarayoni uchun foydalanishi mumkin.", "alias_of": null, "aliases": [ "text-embedding-3-small" ], "author": "OpenAI", "available": null, "canonical_model": "openai/text-embedding-3-small", "capabilities": { "context_length": 8192, "manufacturer_input": [ "text" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." }, { "label": "Ultra", "rates": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": 8192, "entity_type": "model", "formats": [ "embeddings" ], "id": "openai/text-embedding-3-small", "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": [ "openai/text-embedding-3-small" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 8192, "text_unit": "UTF8 baytlari", "token_unit": "Tokenlar massivi elementlari" }, "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]|integer[]|integer[][]" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "unsupported", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "pass_through_metadata", "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "json_value" }, { "availability": "pass_through_metadata", "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "session_id", "required": false, "type": "json_value" }, { "availability": "pass_through_metadata", "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "json_value" }, { "availability": "source_interface", "conditions": "provider.options rad etildi", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "object" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "mixed input kinds", "empty input", "image/audio/file objects", "encoding_format=base64", "dimensions override" ] }, "hints": [ "Har bir vektorning indeksi va o‘lchamini saqlang.", "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi." ], "input": { "content_type": "application/json", "dimensions_override": false, "encoding_format": [ "float" ], "input_shapes": [ "string", "string[]", "nonnegative_integer_token_array", "nonnegative_integer_token_array[]" ], "material_types": [ "text", "token_ids" ], "max_input_budget_units": 8192, "multimodal_objects": false, "required": [ "model", "input" ] }, "lifecycle": { "mode": "synchronous_json", "unknown": "no embedding job endpoint inferred" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/embeddings" }, "method_aliases": [], "operation_modes": [ "text_to_embedding" ], "output": { "default_dimensions": null, "family": "embedding", "format": "json", "http_success": 200, "vectors": "one dense float vector per input; indices/model/usage validated" } }, "ctx": 8192, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"openai/text-embedding-3-small\", \"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='openai/text-embedding-3-small',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n", "label": "Python SDK · all vectors", "language": "python" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "embeddings", "hints": [ "Keep every vector index and dimension.", "Multimodal model name does not admit image objects on this exact path." ], "http": "POST", "in": 0.04, "input": [ "text", "token_ids" ], "label": "Эмбеддинги", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "openai/text-embedding-3-small", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every finite vector with its original index. Preserve all dimensions and the complete usage object.", "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 chekli vektorni dastlabki indeksi bilan saqlang. Barcha o‘lchamlarni va to‘liq usage obyektini saqlang.", "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": [ "text-embedding-3-small" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 8192, "text_unit": "UTF8 baytlari", "token_unit": "Tokenlar massivi elementlari" }, "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]|integer[]|integer[][]" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "unsupported", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "pass_through_metadata", "conditions": "Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan 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": "Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan 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": "Ixtiyoriy JSON metama’lumotlari; dastlabki tizimdagi aniq semantika tanlangan 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" } ], "unsupported": [ "mixed input kinds", "empty input", "image/audio/file objects", "encoding_format=base64", "dimensions override" ] }, "hints": [ "Aniq model ID sini va majburiy maydonlarni yuboring.", "Qaytarilgan har bir element va uning dastlabki identifikatorlarini saqlang." ], "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" ], "max_input_budget_units": 8192, "multimodal_objects": false, "required": [ "model", "input" ], "text_file_material": "Haqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring." }, "lifecycle": { "mode": "synchronous_json", "unknown": "no embedding job endpoint inferred" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/embeddings" }, "method_aliases": [], "official_sources": [], "operation_modes": [ "text_to_embedding" ], "output": { "default_dimensions": null, "family": "embedding", "format": "json", "http_success": 200, "vectors": "one dense float vector per input; indices/model/usage validated" } }, "ctx": 8192, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "{\n \"model\": \"text-embedding-3-small\",\n \"input\": [\n \"First document\",\n \"Second document\"\n ],\n \"encoding_format\": \"float\"\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'text-embedding-3-small', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}\npayload['input'] = [text]\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nfor vector in result.get('data', []):\n print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'text-embedding-3-small', 'input': ['First document', 'Second document'], 'encoding_format': 'float'}\npayload['input'] = [text]\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'Authorization': 'Bearer PLACEHOLDER_KEY', 'Content-Type': 'application/json'}\nheaders['Authorization'] = 'Bearer ' + api_key\nrequest = Request('https://api.ailoot.ai/v1/embeddings', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nfor vector in result.get('data', []):\n print('Vector', vector.get('index'), json.dumps(vector.get('embedding')))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "embeddings", "hints": [ "Use the text of your own UTF-8 document as the request material.", "Save the complete result, including usage and every returned output." ], "http": "POST", "in": 0.04, "input": [ "text" ], "label": "Embeddinglar · text-embedding-3-small", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "official_sources": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Tezkor rejim", "value": "Standart tarif", "when": "Tezkor rejim tanlanganda" }, { "label": "Flex rejimi", "value": "Standart tarif", "when": "flex rejimi tanlanganda" }, { "label": "Maksimal fikrlash darajasi", "value": "Standart tarif", "when": "Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "text-embedding-3-small", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "model_aliases": [ { "canonical_model_id": "openai/text-embedding-3-small", "request_model_id": "openai/text-embedding-3-small", "route_label": "Ultra" }, { "canonical_model_id": "openai/text-embedding-3-small", "request_model_id": "text-embedding-3-small", "route_label": "Ultra" } ], "name": "OpenAI: Text Embedding 3 Small", "price": { "in": 0.04, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.04, "sort_out": 0, "varies": false }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Эмбеддинги · Base token rates", "units": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "public_id": "openai/text-embedding-3-small", "short": "OpenAI’ning uchinchi avloddagi kichik matn embedding modeli odatda 1 536 o‘lchamli vektorlar qaytaradi va qisqaroq ifodalarni qo‘llab-quvvatlaydi. Sonli natijadan semantik qidiruv, kodni qidirib topish, guruhlash, tavsiyalar va tasniflash uchun foydalaning." }, "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 }, { "contract_json_pointer": "/methods/1/rawMethod/contract", "html_json_pointer": "/methods/1/html", "method_ordinal": 1 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod", "/methods/1/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "OpenAI’ning uchinchi avloddagi kichik matn embedding modeli odatda 1 536 o‘lchamli vektorlar qaytaradi va qisqaroq ifodalarni qo‘llab-quvvatlaydi. Sonli natijadan semantik qidiruv, kodni qidirib topish, guruhlash, tavsiyalar va tasniflash uchun foydalaning.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "cee69da7d774961105b7770a94e6daf4213ba58680c5179871539380a3f394ef", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "ea5066ab888836810f3ab8da094838d8060bd005d123d982db8f4f2ae56c60bf" }