{ "aboutHtml": "

GTE-large — Alibaba DAMO Academy o‘qitgan katta hajmli General Text Embeddings modeli. U asosan BERT tuzilmasiga tayangan inglizcha matn enkoderi bo‘lib, har kirish uchun 1024 o‘lchamli zich ifoda beradi. Ishlab chiqaruvchi GTE oilasini turli sohalardagi dolzarblik bo‘yicha matn juftlarida o‘qitadi va qidiruv, semantik matn o‘xshashligi hamda matnni qayta reytinglashni keyingi qo‘llanishlar deb ko‘rsatadi.

Bo‘lim mazmunini ifodalang

Qidiruv so‘rovi bilan taqqoslanadigan inglizcha parchani tayyorlang. Texnik hisobotlar to‘plamida qisqa bo‘limning mavzusi, kuzatuvi va tegishli shartini saqlang. Faqat muayyan yuklama ostidagi tebranishni tavsiflaydigan hisobot umumiy tebranish bayonotini qoldirish o‘rniga shu shartni saqlashi kerak.

Saqlangan vektorlar bilan manba identifikatorlari, hujjat sarlavhalari va bo‘lim joylarini saqlang. So‘rov nomzod bo‘limlarni topishi, asl hisobot esa uskuna, shart va xulosani tekshirish uchun mavjud qolishi mumkin. Embedding parcha mazmunining raqamli ifodasini beradi.

Pooling, normallash va vektordan foydalanish

E’lon qilingan usul diqqat niqobi bilan token ifodalarining o‘rtachasini olib, to‘ldiruvchilarni o‘rtachadan chiqaradi. U ixtiyoriy L2 normallash va undan keyin normallangan vektorlarni skalyar ko‘paytma bilan taqqoslashni ham ko‘rsatadi. Sentence Transformers misoli kosinus o‘xshashligidan foydalanadi.

To‘plam va so‘rov tomonida bir xil tayyorlash, pooling va taqqoslash sozlamalarini saqlang. Har ifoda 1024 komponentni o‘z ichiga oladi. Ko‘proq komponent nashr tafsilotidir; bu GTE-large vektorlarini 768 o‘lchamli GTE-base ifodasi yoki boshqa 1024 o‘lchamli enkoder bilan aralashtirish mumkinligini isbotlamaydi.

Qayta reytinglash jarayoni so‘rov embeddingini allaqachon topilgan nomzod embeddinglar to‘plami bilan taqqoslab, yozuvlarni qayta tartiblashi mumkin. Shu vektor taqqoslashini so‘rov–parcha juftini birgalikda baholaydigan alohida modeldan ajrating.

Kirish uzunligi va ingliz tili cheklovi

Karta aynan shu modelni inglizcha matn bilan ochiq cheklaydi va uzun kirishlarni ko‘pi bilan 512 tokengacha kesadi. Uzun texnik hisobotni izchil bo‘limlarga bo‘ling; har bo‘lim mustaqil tushunilishi uchun yetarli mahalliy kontekstni saqlang. Gap oldingi shart yoki cheklovga bog‘liq bo‘lsa, sig‘ganda shu shart yoki cheklovni ayni indekslangan birlikda saqlang.

Token chegarasi ko‘rinadigan so‘zlar sonidan farq qiladi. Tayyorlangan bo‘lim uzunligini tekshiring va mo‘ljallangan qidiruvga kerakli tafsilot kesishdan keyin qoladigan qismdan tashqarida joylashmaganiga ishonch hosil qiling.

Topilgan dalilni tekshiring

Hisobot qidiruv ilovasida nomzod bo‘limlarni manba joylari bilan ko‘rsating va haqiqiy mazmunini tekshiring. O‘xshash kuzatuvlar turli sabab yoki tavsiyalarga ega bo‘lishi mumkin. Vektor balli tanlangan taqqoslash sozlamasida nomzodlarni tartiblashga yordam beradi; hisobot xulosasi yangi vaziyatga tegishli ekanini isbotlamaydi.

RAG uchun tanlangan bo‘limlarni javob yozish bosqichiga manba konteksti sifatida bering va hujjat bog‘liqliklarini saqlang. Aynan GTE-large profili 1024 o‘lchamli inglizcha zich embeddinglar va 512 tokenli kirish chegarasini beradi. Keyingi ko‘p tilli va kengaytirilgan kontekstli GTE nashrlarining alohida identifikatsiyasi bor; ularning til qamrovi yoki kirish sozlamalari shu checkpointning oila nomidan kelib chiqmaydi.

", "aboutText": "GTE-large — Alibaba DAMO Academy o‘qitgan katta hajmli General Text Embeddings modeli. U asosan BERT tuzilmasiga tayangan inglizcha matn enkoderi bo‘lib, har kirish uchun 1024 o‘lchamli zich ifoda beradi. Ishlab chiqaruvchi GTE oilasini turli sohalardagi dolzarblik bo‘yicha matn juftlarida o‘qitadi va qidiruv, semantik matn o‘xshashligi hamda matnni qayta reytinglashni keyingi qo‘llanishlar deb ko‘rsatadi.\n\n### Bo‘lim mazmunini ifodalang\n\nQidiruv so‘rovi bilan taqqoslanadigan inglizcha parchani tayyorlang. Texnik hisobotlar to‘plamida qisqa bo‘limning mavzusi, kuzatuvi va tegishli shartini saqlang. Faqat muayyan yuklama ostidagi tebranishni tavsiflaydigan hisobot umumiy tebranish bayonotini qoldirish o‘rniga shu shartni saqlashi kerak.\n\nSaqlangan vektorlar bilan manba identifikatorlari, hujjat sarlavhalari va bo‘lim joylarini saqlang. So‘rov nomzod bo‘limlarni topishi, asl hisobot esa uskuna, shart va xulosani tekshirish uchun mavjud qolishi mumkin. Embedding parcha mazmunining raqamli ifodasini beradi.\n\n### Pooling, normallash va vektordan foydalanish\n\nE’lon qilingan usul diqqat niqobi bilan token ifodalarining o‘rtachasini olib, to‘ldiruvchilarni o‘rtachadan chiqaradi. U ixtiyoriy L2 normallash va undan keyin normallangan vektorlarni skalyar ko‘paytma bilan taqqoslashni ham ko‘rsatadi. Sentence Transformers misoli kosinus o‘xshashligidan foydalanadi.\n\nTo‘plam va so‘rov tomonida bir xil tayyorlash, pooling va taqqoslash sozlamalarini saqlang. Har ifoda 1024 komponentni o‘z ichiga oladi. Ko‘proq komponent nashr tafsilotidir; bu GTE-large vektorlarini 768 o‘lchamli GTE-base ifodasi yoki boshqa 1024 o‘lchamli enkoder bilan aralashtirish mumkinligini isbotlamaydi.\n\nQayta reytinglash jarayoni so‘rov embeddingini allaqachon topilgan nomzod embeddinglar to‘plami bilan taqqoslab, yozuvlarni qayta tartiblashi mumkin. Shu vektor taqqoslashini so‘rov–parcha juftini birgalikda baholaydigan alohida modeldan ajrating.\n\n### Kirish uzunligi va ingliz tili cheklovi\n\nKarta aynan shu modelni inglizcha matn bilan ochiq cheklaydi va uzun kirishlarni ko‘pi bilan 512 tokengacha kesadi. Uzun texnik hisobotni izchil bo‘limlarga bo‘ling; har bo‘lim mustaqil tushunilishi uchun yetarli mahalliy kontekstni saqlang. Gap oldingi shart yoki cheklovga bog‘liq bo‘lsa, sig‘ganda shu shart yoki cheklovni ayni indekslangan birlikda saqlang.\n\nToken chegarasi ko‘rinadigan so‘zlar sonidan farq qiladi. Tayyorlangan bo‘lim uzunligini tekshiring va mo‘ljallangan qidiruvga kerakli tafsilot kesishdan keyin qoladigan qismdan tashqarida joylashmaganiga ishonch hosil qiling.\n\n### Topilgan dalilni tekshiring\n\nHisobot qidiruv ilovasida nomzod bo‘limlarni manba joylari bilan ko‘rsating va haqiqiy mazmunini tekshiring. O‘xshash kuzatuvlar turli sabab yoki tavsiyalarga ega bo‘lishi mumkin. Vektor balli tanlangan taqqoslash sozlamasida nomzodlarni tartiblashga yordam beradi; hisobot xulosasi yangi vaziyatga tegishli ekanini isbotlamaydi.\n\nRAG uchun tanlangan bo‘limlarni javob yozish bosqichiga manba konteksti sifatida bering va hujjat bog‘liqliklarini saqlang. Aynan GTE-large profili 1024 o‘lchamli inglizcha zich embeddinglar va 512 tokenli kirish chegarasini beradi. Keyingi ko‘p tilli va kengaytirilgan kontekstli GTE nashrlarining alohida identifikatsiyasi bor; ularning til qamrovi yoki kirish sozlamalari shu checkpointning oila nomidan kelib chiqmaydi.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "bfc5a80ff6b6fd6538ee01993bd2ce6d747d954ad73af43359bb758b6303e249", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

Thenlper: GTE-Large

Thenlper /thenlper/gte-large

Boshlash

Alibaba DAMO Academy GTE-large inglizcha matnni qidiruv, semantik o‘xshashlik va embeddingga asoslangan qayta reytinglash uchun 1024 o‘lchamli vektorlarga aylantiradi. Rasmiy usul o‘rtacha pooling, ixtiyoriy normallash va 512 tokenli maksimal ketma-ketlik uzunligidan foydalanadi.

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.

GTE-large — Alibaba DAMO Academy o‘qitgan katta hajmli General Text Embeddings modeli. U asosan BERT tuzilmasiga tayangan inglizcha matn enkoderi bo‘lib, har kirish uchun 1024 o‘lchamli zich ifoda beradi. Ishlab chiqaruvchi GTE oilasini turli sohalardagi dolzarblik bo‘yicha matn juftlarida o‘qitadi va qidiruv, semantik matn o‘xshashligi hamda matnni qayta reytinglashni keyingi qo‘llanishlar deb ko‘rsatadi.

Bo‘lim mazmunini ifodalang

Qidiruv so‘rovi bilan taqqoslanadigan inglizcha parchani tayyorlang. Texnik hisobotlar to‘plamida qisqa bo‘limning mavzusi, kuzatuvi va tegishli shartini saqlang. Faqat muayyan yuklama ostidagi tebranishni tavsiflaydigan hisobot umumiy tebranish bayonotini qoldirish o‘rniga shu shartni saqlashi kerak.

Saqlangan vektorlar bilan manba identifikatorlari, hujjat sarlavhalari va bo‘lim joylarini saqlang. So‘rov nomzod bo‘limlarni topishi, asl hisobot esa uskuna, shart va xulosani tekshirish uchun mavjud qolishi mumkin. Embedding parcha mazmunining raqamli ifodasini beradi.

Pooling, normallash va vektordan foydalanish

E’lon qilingan usul diqqat niqobi bilan token ifodalarining o‘rtachasini olib, to‘ldiruvchilarni o‘rtachadan chiqaradi. U ixtiyoriy L2 normallash va undan keyin normallangan vektorlarni skalyar ko‘paytma bilan taqqoslashni ham ko‘rsatadi. Sentence Transformers misoli kosinus o‘xshashligidan foydalanadi.

To‘plam va so‘rov tomonida bir xil tayyorlash, pooling va taqqoslash sozlamalarini saqlang. Har ifoda 1024 komponentni o‘z ichiga oladi. Ko‘proq komponent nashr tafsilotidir; bu GTE-large vektorlarini 768 o‘lchamli GTE-base ifodasi yoki boshqa 1024 o‘lchamli enkoder bilan aralashtirish mumkinligini isbotlamaydi.

Qayta reytinglash jarayoni so‘rov embeddingini allaqachon topilgan nomzod embeddinglar to‘plami bilan taqqoslab, yozuvlarni qayta tartiblashi mumkin. Shu vektor taqqoslashini so‘rov–parcha juftini birgalikda baholaydigan alohida modeldan ajrating.

Kirish uzunligi va ingliz tili cheklovi

Karta aynan shu modelni inglizcha matn bilan ochiq cheklaydi va uzun kirishlarni ko‘pi bilan 512 tokengacha kesadi. Uzun texnik hisobotni izchil bo‘limlarga bo‘ling; har bo‘lim mustaqil tushunilishi uchun yetarli mahalliy kontekstni saqlang. Gap oldingi shart yoki cheklovga bog‘liq bo‘lsa, sig‘ganda shu shart yoki cheklovni ayni indekslangan birlikda saqlang.

Token chegarasi ko‘rinadigan so‘zlar sonidan farq qiladi. Tayyorlangan bo‘lim uzunligini tekshiring va mo‘ljallangan qidiruvga kerakli tafsilot kesishdan keyin qoladigan qismdan tashqarida joylashmaganiga ishonch hosil qiling.

Topilgan dalilni tekshiring

Hisobot qidiruv ilovasida nomzod bo‘limlarni manba joylari bilan ko‘rsating va haqiqiy mazmunini tekshiring. O‘xshash kuzatuvlar turli sabab yoki tavsiyalarga ega bo‘lishi mumkin. Vektor balli tanlangan taqqoslash sozlamasida nomzodlarni tartiblashga yordam beradi; hisobot xulosasi yangi vaziyatga tegishli ekanini isbotlamaydi.

RAG uchun tanlangan bo‘limlarni javob yozish bosqichiga manba konteksti sifatida bering va hujjat bog‘liqliklarini saqlang. Aynan GTE-large profili 1024 o‘lchamli inglizcha zich embeddinglar va 512 tokenli kirish chegarasini beradi. Keyingi ko‘p tilli va kengaytirilgan kontekstli GTE nashrlarining alohida identifikatsiyasi bor; ularning til qamrovi yoki kirish sozlamalari shu checkpointning oila nomidan kelib chiqmaydi.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Embeddinglar POST /v1/embeddings
Kirish
text, token_ids
Chiqish
embedding
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
thenlper/gte-large
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: 512 · 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 talablari512
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
Ko‘rsatilmagan Tezkor rejim tanlanganda
Flex rejimi
Ko‘rsatilmagan flex rejimi tanlanganda
Maksimal fikrlash darajasi
Ko‘rsatilmagan Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.00002 / 1K token
Chiqish · Chiqish tokenlari $0 / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

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

So‘rov namunalari

Ailoot Ultra · Embeddinglar

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

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

thenlper/gte-large

", "method_identity": { "effective_request_model_id": "thenlper/gte-large", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "thenlper/gte-large", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|thenlper%2Fgte-large", "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": [ "thenlper/gte-large" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 512, "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": 512, "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": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"thenlper/gte-large\", \"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='thenlper/gte-large',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": null, "flex_multiplier": null, "fmt": "embeddings", "hints": [ "Keep every vector index and dimension.", "Multimodal model name does not admit image objects on this exact path." ], "http": "POST", "in": 0.02, "input": [ "text", "token_ids" ], "label": "Эмбеддинги", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "thenlper/gte-large", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } } ], "model_id": "thenlper/gte-large", "nativeProvenance": { "input_facts_canonical_sha256": "8438b7e63e60e74edd8e049cad9eb26b8b55e3df0637067affd968c0eca5a91b", "input_html_sha256": "8a92192f13f19787334769c8ee2e2317a577cfdb60e3e8edebb13b6d2f3d9bad", "private_audit_sha256": "f858b4f69eaa5029fcb77c8794ac64f8da41358f36aca87b3e2f622bdf7fff48", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/thenlper-gte-large/", "description": "Alibaba DAMO Academy GTE-large inglizcha matnni qidiruv, semantik o‘xshashlik va embeddingga asoslangan qayta reytinglash uchun 1024 o‘lchamli vektorlarga aylantiradi. Rasmiy usul o‘rtacha pooling, ixtiyoriy normallash va 512 tokenli maksimal ketma-ketlik uzunligidan foydalanadi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/thenlper-gte-large/index.md", "title": "Thenlper: GTE-Large — Ailoot", "url": "https://ailoot.ai/models/uz/thenlper-gte-large/" }, "rawModel": { "about": "GTE-large — Alibaba DAMO Academy o‘qitgan katta hajmli General Text Embeddings modeli. U asosan BERT tuzilmasiga tayangan inglizcha matn enkoderi bo‘lib, har kirish uchun 1024 o‘lchamli zich ifoda beradi. Ishlab chiqaruvchi GTE oilasini turli sohalardagi dolzarblik bo‘yicha matn juftlarida o‘qitadi va qidiruv, semantik matn o‘xshashligi hamda matnni qayta reytinglashni keyingi qo‘llanishlar deb ko‘rsatadi.\n\n### Bo‘lim mazmunini ifodalang\n\nQidiruv so‘rovi bilan taqqoslanadigan inglizcha parchani tayyorlang. Texnik hisobotlar to‘plamida qisqa bo‘limning mavzusi, kuzatuvi va tegishli shartini saqlang. Faqat muayyan yuklama ostidagi tebranishni tavsiflaydigan hisobot umumiy tebranish bayonotini qoldirish o‘rniga shu shartni saqlashi kerak.\n\nSaqlangan vektorlar bilan manba identifikatorlari, hujjat sarlavhalari va bo‘lim joylarini saqlang. So‘rov nomzod bo‘limlarni topishi, asl hisobot esa uskuna, shart va xulosani tekshirish uchun mavjud qolishi mumkin. Embedding parcha mazmunining raqamli ifodasini beradi.\n\n### Pooling, normallash va vektordan foydalanish\n\nE’lon qilingan usul diqqat niqobi bilan token ifodalarining o‘rtachasini olib, to‘ldiruvchilarni o‘rtachadan chiqaradi. U ixtiyoriy L2 normallash va undan keyin normallangan vektorlarni skalyar ko‘paytma bilan taqqoslashni ham ko‘rsatadi. Sentence Transformers misoli kosinus o‘xshashligidan foydalanadi.\n\nTo‘plam va so‘rov tomonida bir xil tayyorlash, pooling va taqqoslash sozlamalarini saqlang. Har ifoda 1024 komponentni o‘z ichiga oladi. Ko‘proq komponent nashr tafsilotidir; bu GTE-large vektorlarini 768 o‘lchamli GTE-base ifodasi yoki boshqa 1024 o‘lchamli enkoder bilan aralashtirish mumkinligini isbotlamaydi.\n\nQayta reytinglash jarayoni so‘rov embeddingini allaqachon topilgan nomzod embeddinglar to‘plami bilan taqqoslab, yozuvlarni qayta tartiblashi mumkin. Shu vektor taqqoslashini so‘rov–parcha juftini birgalikda baholaydigan alohida modeldan ajrating.\n\n### Kirish uzunligi va ingliz tili cheklovi\n\nKarta aynan shu modelni inglizcha matn bilan ochiq cheklaydi va uzun kirishlarni ko‘pi bilan 512 tokengacha kesadi. Uzun texnik hisobotni izchil bo‘limlarga bo‘ling; har bo‘lim mustaqil tushunilishi uchun yetarli mahalliy kontekstni saqlang. Gap oldingi shart yoki cheklovga bog‘liq bo‘lsa, sig‘ganda shu shart yoki cheklovni ayni indekslangan birlikda saqlang.\n\nToken chegarasi ko‘rinadigan so‘zlar sonidan farq qiladi. Tayyorlangan bo‘lim uzunligini tekshiring va mo‘ljallangan qidiruvga kerakli tafsilot kesishdan keyin qoladigan qismdan tashqarida joylashmaganiga ishonch hosil qiling.\n\n### Topilgan dalilni tekshiring\n\nHisobot qidiruv ilovasida nomzod bo‘limlarni manba joylari bilan ko‘rsating va haqiqiy mazmunini tekshiring. O‘xshash kuzatuvlar turli sabab yoki tavsiyalarga ega bo‘lishi mumkin. Vektor balli tanlangan taqqoslash sozlamasida nomzodlarni tartiblashga yordam beradi; hisobot xulosasi yangi vaziyatga tegishli ekanini isbotlamaydi.\n\nRAG uchun tanlangan bo‘limlarni javob yozish bosqichiga manba konteksti sifatida bering va hujjat bog‘liqliklarini saqlang. Aynan GTE-large profili 1024 o‘lchamli inglizcha zich embeddinglar va 512 tokenli kirish chegarasini beradi. Keyingi ko‘p tilli va kengaytirilgan kontekstli GTE nashrlarining alohida identifikatsiyasi bor; ularning til qamrovi yoki kirish sozlamalari shu checkpointning oila nomidan kelib chiqmaydi.", "alias_of": null, "aliases": [], "author": "Thenlper", "available": null, "canonical_model": "thenlper/gte-large", "capabilities": { "context_length": 512, "manufacturer_input": [ "text" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": null, "entity_type": "model", "formats": [ "embeddings" ], "id": "thenlper/gte-large", "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": [ "thenlper/gte-large" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "budget": { "maximum": 512, "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": 512, "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": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"thenlper/gte-large\", \"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='thenlper/gte-large',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": null, "flex_multiplier": null, "fmt": "embeddings", "hints": [ "Keep every vector index and dimension.", "Multimodal model name does not admit image objects on this exact path." ], "http": "POST", "in": 0.02, "input": [ "text", "token_ids" ], "label": "Эмбеддинги", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "request_model_id": "thenlper/gte-large", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00002000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000002", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] } ], "model_aliases": [ { "canonical_model_id": "thenlper/gte-large", "request_model_id": "thenlper/gte-large", "route_label": "Ultra" } ], "name": "Thenlper: GTE-Large", "price": { "in": 0.02, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.02, "sort_out": 0, "varies": false }, "public_id": "thenlper/gte-large", "short": "Alibaba DAMO Academy GTE-large inglizcha matnni qidiruv, semantik o‘xshashlik va embeddingga asoslangan qayta reytinglash uchun 1024 o‘lchamli vektorlarga aylantiradi. Rasmiy usul o‘rtacha pooling, ixtiyoriy normallash va 512 tokenli maksimal ketma-ketlik uzunligidan foydalanadi." }, "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": "Alibaba DAMO Academy GTE-large inglizcha matnni qidiruv, semantik o‘xshashlik va embeddingga asoslangan qayta reytinglash uchun 1024 o‘lchamli vektorlarga aylantiradi. Rasmiy usul o‘rtacha pooling, ixtiyoriy normallash va 512 tokenli maksimal ketma-ketlik uzunligidan foydalanadi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "de5435d6e5b09acf2ca58ee9cca080ad969f0927cc56e4d47e0db1a378d34983", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "45df3ece619318202dc6ffd0a5a46f81360a4ef6cac52d57b9fcf7b8a4b750f8" }