{ "aboutHtml": "

BGE-M3 — uch turdagi ko‘p qirralilikka mo‘ljallangan BAAI’ning ko‘p tilli qidiruv modeli: bir nechta qidiruv funksiyasi, bir nechta til va turli kirish uzunliklari. Ishlab chiqaruvchi yuzdan ortiq til va 8192 tokengacha kirishni qo‘llashni hujjatlashtiradi. Yagona model zich vektorlar, siyrak leksik vaznlar va ko‘p vektorli ifodalarni berishi mumkin; shu bois qidiruv dizayni bir nechta dalil turini tanlashi yoki birlashtirishi mumkin.

Qidiruv uchun uch ifoda

Zich rejim matnni bitta 1024 o‘lchamli vektorga o‘tkazadi. U so‘rov va parcha ifodalagan ma’noni taqqoslashni qo‘llab-quvvatlaydi. Siyrak rejim leksik moslashtirish uchun matnda bor tokenlarga vazn beradi. Ko‘p vektorli rejim matnni bir nechta vektor bilan ifodalaydi va so‘rov hamda hujjatni maydaroq darajada taqqoslash uchun ColBERT uslubidagi o‘zaro ta’sirdan foydalanadi.

Bu chiqishlarning qidiruv indeksidagi rollari turlicha. Zich vektor parcha uchun bitta ifoda beradi; siyrak token-vazn xaritasi leksik signallarni saqlaydi; bir nechta vektor boshqa taqqoslash tuzilmasini beradi. Ishlab chiqaruvchi misollari tanlangan ifodalarni qaytarish va matn juftlari uchun ball hisoblashni ko‘rsatadi. Tanlangan ifoda va baholash usulini qidiruv vazifasining ongli belgilangan qismi deb ko‘ring.

Ko‘p tilli matn va kirishni tayyorlash

BGE-M3 oldingi BGE modellarida ishlatilgan qidiruv ko‘rsatmasini qo‘shishni talab qilmaydi. So‘rov va haqiqiy manba parchalarini bevosita tayyorlang. Ko‘p tilli bilimlar to‘plami har parchani asl tilida, hujjat identifikatori va locale qiymati bilan saqlashi mumkin. Natijalarni tekshirishda mo‘ljallangan tillar aralashmasidan haqiqiy foydalanuvchi savollarini oling.

Amaliy vazifa — bir nechta tilda saqlangan siyosat izohlari, mahsulot yordami yoki texnik hujjatlarni qidirish. Savol uchun muhim nomlar, model raqamlari va atamalarni saqlang. Leksik ifoda shu atamalar bo‘yicha moslikni saqlashga, zich ifoda esa semantik dalil berishga yordam berishi mumkin. Topilgan parcha o‘quvchi tekshira oladigan matn va manba joyini o‘z ichiga olishi kerak.

Uzunroq hujjatlar va bo‘laklash darajasi

8192 tokenli kirish oynasi modelga qisqa gaplardan uzunroq hujjatlargacha materialni ifodalash imkonini beradi. Bo‘laklash darajasini qidiruv qaytarishi kerak bo‘lgan javobga qarab tanlang. To‘liq bo‘lim ta’rif va uning istisnolarini saqlashi, kichikroq parcha esa muayyan ko‘rsatmani topishni osonlashtirishi mumkin. Uzun oyna shu tanlovlarga imkon beradi, lekin vektor baribir haqiqatan kodlangan materialga bog‘liq qoladi.

Har parchani asosiy hujjat va versiyasiga bog‘lang. Uzun hisobotda tegishli son yoki xulosaning sarlavhasi va atrofidagi izohni saqlang. Dalil hujjat ichida bo‘lsa, faqat qidiruv natijasi sarlavhasiga tayanmang. Manba o‘zgarsa, tegishli qidiruv ifodalarini yangilash uchun yetarli metama’lumotni saqlang.

Gibrid ballar va keyingi foydalanish

Ishlab chiqaruvchi gibrid qidiruvdan so‘ng alohida qayta reytinglash bosqichini tavsiya qiladi. Misollar zich, siyrak va ko‘p vektorli ballarni hisoblab, vaznlangan birikmalar tuzadi. Birikmani to‘plam savollari va lug‘atiga qarab tanlang; misolda berilgan vazn har korpus uchun universal sozlama emas.

Foydali natija — manba parchalari mavjud qolgan, ifoda va reyting usuli qayd etilgan nomzodlar to‘plami. Alohida qayta reytinglovchi model shu to‘plamni aniqlashtirishi, alohida matn yaratish bosqichi esa parcha mazmunidan foydalanishi mumkin. BGE-M3’ning siyrak va ko‘p vektorli imkoniyatlari ishlab chiqaruvchi modelining rejimlaridir; zich embedding, leksik chiqish va juftlik balli o‘zaro almashtiriladigan massivlar emas, turli artefaktlardir.

", "aboutText": "BGE-M3 — uch turdagi ko‘p qirralilikka mo‘ljallangan BAAI’ning ko‘p tilli qidiruv modeli: bir nechta qidiruv funksiyasi, bir nechta til va turli kirish uzunliklari. Ishlab chiqaruvchi yuzdan ortiq til va 8192 tokengacha kirishni qo‘llashni hujjatlashtiradi. Yagona model zich vektorlar, siyrak leksik vaznlar va ko‘p vektorli ifodalarni berishi mumkin; shu bois qidiruv dizayni bir nechta dalil turini tanlashi yoki birlashtirishi mumkin.\n\n### Qidiruv uchun uch ifoda\n\nZich rejim matnni bitta 1024 o‘lchamli vektorga o‘tkazadi. U so‘rov va parcha ifodalagan ma’noni taqqoslashni qo‘llab-quvvatlaydi. Siyrak rejim leksik moslashtirish uchun matnda bor tokenlarga vazn beradi. Ko‘p vektorli rejim matnni bir nechta vektor bilan ifodalaydi va so‘rov hamda hujjatni maydaroq darajada taqqoslash uchun ColBERT uslubidagi o‘zaro ta’sirdan foydalanadi.\n\nBu chiqishlarning qidiruv indeksidagi rollari turlicha. Zich vektor parcha uchun bitta ifoda beradi; siyrak token-vazn xaritasi leksik signallarni saqlaydi; bir nechta vektor boshqa taqqoslash tuzilmasini beradi. Ishlab chiqaruvchi misollari tanlangan ifodalarni qaytarish va matn juftlari uchun ball hisoblashni ko‘rsatadi. Tanlangan ifoda va baholash usulini qidiruv vazifasining ongli belgilangan qismi deb ko‘ring.\n\n### Ko‘p tilli matn va kirishni tayyorlash\n\nBGE-M3 oldingi BGE modellarida ishlatilgan qidiruv ko‘rsatmasini qo‘shishni talab qilmaydi. So‘rov va haqiqiy manba parchalarini bevosita tayyorlang. Ko‘p tilli bilimlar to‘plami har parchani asl tilida, hujjat identifikatori va locale qiymati bilan saqlashi mumkin. Natijalarni tekshirishda mo‘ljallangan tillar aralashmasidan haqiqiy foydalanuvchi savollarini oling.\n\nAmaliy vazifa — bir nechta tilda saqlangan siyosat izohlari, mahsulot yordami yoki texnik hujjatlarni qidirish. Savol uchun muhim nomlar, model raqamlari va atamalarni saqlang. Leksik ifoda shu atamalar bo‘yicha moslikni saqlashga, zich ifoda esa semantik dalil berishga yordam berishi mumkin. Topilgan parcha o‘quvchi tekshira oladigan matn va manba joyini o‘z ichiga olishi kerak.\n\n### Uzunroq hujjatlar va bo‘laklash darajasi\n\n8192 tokenli kirish oynasi modelga qisqa gaplardan uzunroq hujjatlargacha materialni ifodalash imkonini beradi. Bo‘laklash darajasini qidiruv qaytarishi kerak bo‘lgan javobga qarab tanlang. To‘liq bo‘lim ta’rif va uning istisnolarini saqlashi, kichikroq parcha esa muayyan ko‘rsatmani topishni osonlashtirishi mumkin. Uzun oyna shu tanlovlarga imkon beradi, lekin vektor baribir haqiqatan kodlangan materialga bog‘liq qoladi.\n\nHar parchani asosiy hujjat va versiyasiga bog‘lang. Uzun hisobotda tegishli son yoki xulosaning sarlavhasi va atrofidagi izohni saqlang. Dalil hujjat ichida bo‘lsa, faqat qidiruv natijasi sarlavhasiga tayanmang. Manba o‘zgarsa, tegishli qidiruv ifodalarini yangilash uchun yetarli metama’lumotni saqlang.\n\n### Gibrid ballar va keyingi foydalanish\n\nIshlab chiqaruvchi gibrid qidiruvdan so‘ng alohida qayta reytinglash bosqichini tavsiya qiladi. Misollar zich, siyrak va ko‘p vektorli ballarni hisoblab, vaznlangan birikmalar tuzadi. Birikmani to‘plam savollari va lug‘atiga qarab tanlang; misolda berilgan vazn har korpus uchun universal sozlama emas.\n\nFoydali natija — manba parchalari mavjud qolgan, ifoda va reyting usuli qayd etilgan nomzodlar to‘plami. Alohida qayta reytinglovchi model shu to‘plamni aniqlashtirishi, alohida matn yaratish bosqichi esa parcha mazmunidan foydalanishi mumkin. BGE-M3’ning siyrak va ko‘p vektorli imkoniyatlari ishlab chiqaruvchi modelining rejimlaridir; zich embedding, leksik chiqish va juftlik balli o‘zaro almashtiriladigan massivlar emas, turli artefaktlardir.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "d014cc09a2888da99f81492756cc2e5dc17daa67ce76a3a006caeb8d1048f619", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

BAAI: bge-m3

BAAI /baai/bge-m3

Boshlash

BAAI BGE-M3 100 dan ortiq tilda zich, siyrak leksik va koʻp vektorli qidiruvni qoʻllaydi. Uning zich chiqishi 1024 oʻlchamga ega, 8192 tokenli oynasi esa majburiy soʻrov koʻrsatmasisiz jumlalardan tortib uzunroq hujjatlargacha sigʻdiradi.

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.

BGE-M3 — uch turdagi ko‘p qirralilikka mo‘ljallangan BAAI’ning ko‘p tilli qidiruv modeli: bir nechta qidiruv funksiyasi, bir nechta til va turli kirish uzunliklari. Ishlab chiqaruvchi yuzdan ortiq til va 8192 tokengacha kirishni qo‘llashni hujjatlashtiradi. Yagona model zich vektorlar, siyrak leksik vaznlar va ko‘p vektorli ifodalarni berishi mumkin; shu bois qidiruv dizayni bir nechta dalil turini tanlashi yoki birlashtirishi mumkin.

Qidiruv uchun uch ifoda

Zich rejim matnni bitta 1024 o‘lchamli vektorga o‘tkazadi. U so‘rov va parcha ifodalagan ma’noni taqqoslashni qo‘llab-quvvatlaydi. Siyrak rejim leksik moslashtirish uchun matnda bor tokenlarga vazn beradi. Ko‘p vektorli rejim matnni bir nechta vektor bilan ifodalaydi va so‘rov hamda hujjatni maydaroq darajada taqqoslash uchun ColBERT uslubidagi o‘zaro ta’sirdan foydalanadi.

Bu chiqishlarning qidiruv indeksidagi rollari turlicha. Zich vektor parcha uchun bitta ifoda beradi; siyrak token-vazn xaritasi leksik signallarni saqlaydi; bir nechta vektor boshqa taqqoslash tuzilmasini beradi. Ishlab chiqaruvchi misollari tanlangan ifodalarni qaytarish va matn juftlari uchun ball hisoblashni ko‘rsatadi. Tanlangan ifoda va baholash usulini qidiruv vazifasining ongli belgilangan qismi deb ko‘ring.

Ko‘p tilli matn va kirishni tayyorlash

BGE-M3 oldingi BGE modellarida ishlatilgan qidiruv ko‘rsatmasini qo‘shishni talab qilmaydi. So‘rov va haqiqiy manba parchalarini bevosita tayyorlang. Ko‘p tilli bilimlar to‘plami har parchani asl tilida, hujjat identifikatori va locale qiymati bilan saqlashi mumkin. Natijalarni tekshirishda mo‘ljallangan tillar aralashmasidan haqiqiy foydalanuvchi savollarini oling.

Amaliy vazifa — bir nechta tilda saqlangan siyosat izohlari, mahsulot yordami yoki texnik hujjatlarni qidirish. Savol uchun muhim nomlar, model raqamlari va atamalarni saqlang. Leksik ifoda shu atamalar bo‘yicha moslikni saqlashga, zich ifoda esa semantik dalil berishga yordam berishi mumkin. Topilgan parcha o‘quvchi tekshira oladigan matn va manba joyini o‘z ichiga olishi kerak.

Uzunroq hujjatlar va bo‘laklash darajasi

8192 tokenli kirish oynasi modelga qisqa gaplardan uzunroq hujjatlargacha materialni ifodalash imkonini beradi. Bo‘laklash darajasini qidiruv qaytarishi kerak bo‘lgan javobga qarab tanlang. To‘liq bo‘lim ta’rif va uning istisnolarini saqlashi, kichikroq parcha esa muayyan ko‘rsatmani topishni osonlashtirishi mumkin. Uzun oyna shu tanlovlarga imkon beradi, lekin vektor baribir haqiqatan kodlangan materialga bog‘liq qoladi.

Har parchani asosiy hujjat va versiyasiga bog‘lang. Uzun hisobotda tegishli son yoki xulosaning sarlavhasi va atrofidagi izohni saqlang. Dalil hujjat ichida bo‘lsa, faqat qidiruv natijasi sarlavhasiga tayanmang. Manba o‘zgarsa, tegishli qidiruv ifodalarini yangilash uchun yetarli metama’lumotni saqlang.

Gibrid ballar va keyingi foydalanish

Ishlab chiqaruvchi gibrid qidiruvdan so‘ng alohida qayta reytinglash bosqichini tavsiya qiladi. Misollar zich, siyrak va ko‘p vektorli ballarni hisoblab, vaznlangan birikmalar tuzadi. Birikmani to‘plam savollari va lug‘atiga qarab tanlang; misolda berilgan vazn har korpus uchun universal sozlama emas.

Foydali natija — manba parchalari mavjud qolgan, ifoda va reyting usuli qayd etilgan nomzodlar to‘plami. Alohida qayta reytinglovchi model shu to‘plamni aniqlashtirishi, alohida matn yaratish bosqichi esa parcha mazmunidan foydalanishi mumkin. BGE-M3’ning siyrak va ko‘p vektorli imkoniyatlari ishlab chiqaruvchi modelining rejimlaridir; zich embedding, leksik chiqish va juftlik balli o‘zaro almashtiriladigan massivlar emas, turli artefaktlardir.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Embeddinglar POST /v1/embeddings
Kirish
text, token_ids
Chiqish
embedding
Kontekst
8 194 tokens
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
baai/bge-m3
Standart—
MaydoninputTurstring|string[]|integer[]|integer[][]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Shartlar: Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.
Kirish budjeti: Eng ko‘p: 8 194 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari
Standart—
Maydonencoding_formatTurstringTalabIxtiyoriyQiymatlar va cheklovlar
float
Standartfloat
MaydondimensionsTurintegerTalabQo‘llab-quvvatlanmaydiQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydonuserTurjson_valueTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.
Standart—
Maydonsession_idTurjson_valueTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.
Standart—
MaydontraceTurjson_valueTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.
Standart—
MaydonproviderTurobjectTalabIxtiyoriyQiymatlar va cheklovlar
Shartlar: provider.options rad etildi
Standart—
Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, input
Parametrinput_shapesParametr talablaristring, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]
Parametrencoding_formatParametr talablarifloat
Parametrdimensions_overrideParametr talablarifalse
Parametrmax_input_budget_unitsParametr talablari8194
Parametrmultimodal_objectsParametr talablarifalse
Parametrmaterial_typesParametr talablaritext, token_ids

Hayot sikli: synchronous_json

Har bir vektorning indeksi va o‘lchamini saqlang.

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

Tariflar

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

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

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.00004 / 1K token
Chiqish · Chiqish tokenlari $0 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

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

So‘rov namunalari

Ailoot Ultra · Embeddinglar

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "baai/bge-m3", "input": ["First document", "Second document"]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))\nPY_RESULT\n

Natijalarni olish va saqlash

Ailoot Ultra · Embeddinglar

json

Barcha tayyor natijalarni saqlash

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

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

Asl javobni tekshirish kerak bo‘lsa

Embedding vazifalari uchun endpoint qo‘shilmaydi.

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

Taxalluslar

Model taxalluslari

Ailoot Ultra baai/bge-m3

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

baai/bge-m3

", "method_identity": { "effective_request_model_id": "baai/bge-m3", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "baai/bge-m3", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|baai%2Fbge-m3", "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": [ "baai/bge-m3" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 8194, "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": 8194, "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": 8194, "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\": \"baai/bge-m3\", \"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='baai/bge-m3',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": "baai/bge-m3", "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": "baai/bge-m3", "nativeProvenance": { "input_facts_canonical_sha256": "0ec3c537778c00549d33f9b734e049f4171fb9f87a5afb47b3149852c0250774", "input_html_sha256": "aa6bbf7c8f938f275a490b5563d789bcb46e34c5449e240412ab6a74b346f011", "private_audit_sha256": "774a217cf685d9b7d829fab87bbeeff57472e23926b8ff289e463898a71d490f", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/baai-bge-m3/", "description": "BAAI BGE-M3 100 dan ortiq tilda zich, siyrak leksik va koʻp vektorli qidiruvni qoʻllaydi. Uning zich chiqishi 1024 oʻlchamga ega, 8192 tokenli oynasi esa majburiy soʻrov koʻrsatmasisiz jumlalardan tortib uzunroq hujjatlargacha sigʻdiradi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/baai-bge-m3/index.md", "title": "BAAI: bge-m3 — Ailoot", "url": "https://ailoot.ai/models/uz/baai-bge-m3/" }, "rawModel": { "about": "BGE-M3 — uch turdagi ko‘p qirralilikka mo‘ljallangan BAAI’ning ko‘p tilli qidiruv modeli: bir nechta qidiruv funksiyasi, bir nechta til va turli kirish uzunliklari. Ishlab chiqaruvchi yuzdan ortiq til va 8192 tokengacha kirishni qo‘llashni hujjatlashtiradi. Yagona model zich vektorlar, siyrak leksik vaznlar va ko‘p vektorli ifodalarni berishi mumkin; shu bois qidiruv dizayni bir nechta dalil turini tanlashi yoki birlashtirishi mumkin.\n\n### Qidiruv uchun uch ifoda\n\nZich rejim matnni bitta 1024 o‘lchamli vektorga o‘tkazadi. U so‘rov va parcha ifodalagan ma’noni taqqoslashni qo‘llab-quvvatlaydi. Siyrak rejim leksik moslashtirish uchun matnda bor tokenlarga vazn beradi. Ko‘p vektorli rejim matnni bir nechta vektor bilan ifodalaydi va so‘rov hamda hujjatni maydaroq darajada taqqoslash uchun ColBERT uslubidagi o‘zaro ta’sirdan foydalanadi.\n\nBu chiqishlarning qidiruv indeksidagi rollari turlicha. Zich vektor parcha uchun bitta ifoda beradi; siyrak token-vazn xaritasi leksik signallarni saqlaydi; bir nechta vektor boshqa taqqoslash tuzilmasini beradi. Ishlab chiqaruvchi misollari tanlangan ifodalarni qaytarish va matn juftlari uchun ball hisoblashni ko‘rsatadi. Tanlangan ifoda va baholash usulini qidiruv vazifasining ongli belgilangan qismi deb ko‘ring.\n\n### Ko‘p tilli matn va kirishni tayyorlash\n\nBGE-M3 oldingi BGE modellarida ishlatilgan qidiruv ko‘rsatmasini qo‘shishni talab qilmaydi. So‘rov va haqiqiy manba parchalarini bevosita tayyorlang. Ko‘p tilli bilimlar to‘plami har parchani asl tilida, hujjat identifikatori va locale qiymati bilan saqlashi mumkin. Natijalarni tekshirishda mo‘ljallangan tillar aralashmasidan haqiqiy foydalanuvchi savollarini oling.\n\nAmaliy vazifa — bir nechta tilda saqlangan siyosat izohlari, mahsulot yordami yoki texnik hujjatlarni qidirish. Savol uchun muhim nomlar, model raqamlari va atamalarni saqlang. Leksik ifoda shu atamalar bo‘yicha moslikni saqlashga, zich ifoda esa semantik dalil berishga yordam berishi mumkin. Topilgan parcha o‘quvchi tekshira oladigan matn va manba joyini o‘z ichiga olishi kerak.\n\n### Uzunroq hujjatlar va bo‘laklash darajasi\n\n8192 tokenli kirish oynasi modelga qisqa gaplardan uzunroq hujjatlargacha materialni ifodalash imkonini beradi. Bo‘laklash darajasini qidiruv qaytarishi kerak bo‘lgan javobga qarab tanlang. To‘liq bo‘lim ta’rif va uning istisnolarini saqlashi, kichikroq parcha esa muayyan ko‘rsatmani topishni osonlashtirishi mumkin. Uzun oyna shu tanlovlarga imkon beradi, lekin vektor baribir haqiqatan kodlangan materialga bog‘liq qoladi.\n\nHar parchani asosiy hujjat va versiyasiga bog‘lang. Uzun hisobotda tegishli son yoki xulosaning sarlavhasi va atrofidagi izohni saqlang. Dalil hujjat ichida bo‘lsa, faqat qidiruv natijasi sarlavhasiga tayanmang. Manba o‘zgarsa, tegishli qidiruv ifodalarini yangilash uchun yetarli metama’lumotni saqlang.\n\n### Gibrid ballar va keyingi foydalanish\n\nIshlab chiqaruvchi gibrid qidiruvdan so‘ng alohida qayta reytinglash bosqichini tavsiya qiladi. Misollar zich, siyrak va ko‘p vektorli ballarni hisoblab, vaznlangan birikmalar tuzadi. Birikmani to‘plam savollari va lug‘atiga qarab tanlang; misolda berilgan vazn har korpus uchun universal sozlama emas.\n\nFoydali natija — manba parchalari mavjud qolgan, ifoda va reyting usuli qayd etilgan nomzodlar to‘plami. Alohida qayta reytinglovchi model shu to‘plamni aniqlashtirishi, alohida matn yaratish bosqichi esa parcha mazmunidan foydalanishi mumkin. BGE-M3’ning siyrak va ko‘p vektorli imkoniyatlari ishlab chiqaruvchi modelining rejimlaridir; zich embedding, leksik chiqish va juftlik balli o‘zaro almashtiriladigan massivlar emas, turli artefaktlardir.", "alias_of": null, "aliases": [], "author": "BAAI", "available": null, "canonical_model": "baai/bge-m3", "capabilities": { "context_length": 8194, "manufacturer_input": [ "text" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000004", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": 8194, "entity_type": "model", "formats": [ "embeddings" ], "id": "baai/bge-m3", "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": [ "baai/bge-m3" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 8194, "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": 8194, "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": 8194, "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\": \"baai/bge-m3\", \"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='baai/bge-m3',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": "baai/bge-m3", "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": "baai/bge-m3", "request_model_id": "baai/bge-m3", "route_label": "Ultra" } ], "name": "BAAI: bge-m3", "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": "baai/bge-m3", "short": "BAAI BGE-M3 100 dan ortiq tilda zich, siyrak leksik va koʻp vektorli qidiruvni qoʻllaydi. Uning zich chiqishi 1024 oʻlchamga ega, 8192 tokenli oynasi esa majburiy soʻrov koʻrsatmasisiz jumlalardan tortib uzunroq hujjatlargacha sigʻdiradi." }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "BAAI BGE-M3 100 dan ortiq tilda zich, siyrak leksik va koʻp vektorli qidiruvni qoʻllaydi. Uning zich chiqishi 1024 oʻlchamga ega, 8192 tokenli oynasi esa majburiy soʻrov koʻrsatmasisiz jumlalardan tortib uzunroq hujjatlargacha sigʻdiradi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "02c6f15b4b38ee7eff78659e848448898438793010873aa2362dd19bb59ff131", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "1906b2a3ec3bf733266234e15f910e1ff97f614842243770f36f3409637c5bc3" }