{ "aboutHtml": "

all-mpnet-base-v2 — gaplar va qisqa paragraflarni 768 o‘lchamli zich vektorlar sifatida ifodalash uchun Sentence Transformers modeli. U MPNet bazaviy modelidan bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan. Ishlab chiqaruvchi ma’lumot qidirish, klasterlash va gaplar o‘xshashligini mo‘ljallangan foydalanishlar deb ko‘rsatadi.

Gap yoki qisqa paragrafni kodlang

Taqqoslanadigan matnni tayyorlang, asosiy fikr va uni farqlaydigan tafsilotlarni saqlang. Ichki hujjatlar to‘plamida qisqa bo‘lim uskuna almashtirishni qanday so‘rash yoki texnik xizmat hisobotida qaysi ma’lumot bo‘lishini tavsiflashi mumkin. Hujjat sarlavhasi va manba joyi vektor yonida mavjud bo‘lsin.

Ayni model bilan embedding qilingan so‘rov ifodasi boshqa bo‘lsa ham bog‘liq bo‘limlarni topishi mumkin. O‘quvchiga kerakli haqiqiy ko‘rsatmani aniqlash uchun shu bo‘limlarni tekshiring. O‘xshashlik jarayonida qidiruvdan keyin nomzod gaplarning o‘zini taqqoslang, ayniqsa birida boshqasida yo‘q istisno yoki shart bo‘lsa.

O‘rtacha pooling va normallash

Rasmiy kodlash misoli diqqat niqobi bilan kontekstli token ifodalarining o‘rtachasini oladi, to‘ldiruvchilarni o‘rtachadan chiqaradi. So‘ng natijaviy gap embeddinglarini L2 normallaydi. To‘plam va keyingi so‘rovlar uchun vektorlar yaratishda ikkala bosqichni ham saqlang.

Har vektor 768 komponentga ega. Kosinus o‘xshashligi shu normallangan ifodalarni taqqoslashi mumkin; manba matni esa natija ortidagi dalilni beradi. Komponentlar soni bir xilligi bu modelni multi-qa-mpnet-base-dot-v1 bilan mos qilmaydi: alohida nashr skalyar ko‘paytma bilan baholash uchun CLS pooling va normallanmagan vektorlarni belgilaydi.

Odatiy kesish va parchani tayyorlash

Ishlab chiqaruvchi 384 ta word piece birligidan ortiq kirish odatda kesilishini aytadi. Tegishli bo‘limni tokenizer chegarasi ichida saqlang. Uzun tartib-taomilda mazmunli bo‘lim chegaralarida bo‘ling va qadam nimaga tegishli ekanini tushuntirish uchun yetarli mahalliy kontekst qo‘shing. “Submit within five days” kabi bo‘lakka muddatni boshlaydigan hodisa yoki jarayon kerak.

O‘qitish jarayoni ketma-ketliklarni 128 token bilan cheklagan; bu e’lon qilingan 384 ta word piece birlikli odatiy kodlash sozlamasidan alohida. Uzunroq paragraflar yoki to‘plamga xos lug‘atni baholashda namunaviy materialdan foydalaning. Kirishga ko‘proq matn sig‘ishi har tafsilot qidiruv natijasiga bir xil ta’sir qilishini isbotlamaydi.

Qidiruv, guruhlash va taqqoslash

Qidiruvda reytinglangan nomzodlar asl bo‘limlarni qaytara olishi uchun vektor-hujjat bog‘liqligini saqlang. RAG jarayonida tanlangan bo‘limlar javob yozish bosqichiga kontekst bo‘ladi. Model raqamli ifodalarni beradi; manbali javob yaratmaydi va qaysi hujjat vakolatli manba ekanini hal qilmaydi.

Klasterlashda yorliq berishdan oldin har taklif etilgan guruhdagi yozuvlarni taqqoslang. Texnik xizmat qaydlari to‘plami o‘xshash tildan foydalanib, turli qurilmalar yoki nosozlik shartlariga tegishli bo‘lishi mumkin. Faqat ballga tayanish o‘rniga, farqlovchi tafsilotlarni kodlangan matnga kiriting va qaytgan materialda tekshiring.

Aynan all-mpnet-base-v2 checkpointi o‘z o‘rtacha pooling, normallash va odatiy kesish sozlamalariga ega inglizcha gap embedding profilidir. Sozlamalarni indekslangan to‘plam bilan saqlang va vektorlardan vazifaga muhim manba parchalarini topish hamda taqqoslash yordami sifatida foydalaning.

", "aboutText": "all-mpnet-base-v2 — gaplar va qisqa paragraflarni 768 o‘lchamli zich vektorlar sifatida ifodalash uchun Sentence Transformers modeli. U MPNet bazaviy modelidan bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan. Ishlab chiqaruvchi ma’lumot qidirish, klasterlash va gaplar o‘xshashligini mo‘ljallangan foydalanishlar deb ko‘rsatadi.\n\n### Gap yoki qisqa paragrafni kodlang\n\nTaqqoslanadigan matnni tayyorlang, asosiy fikr va uni farqlaydigan tafsilotlarni saqlang. Ichki hujjatlar to‘plamida qisqa bo‘lim uskuna almashtirishni qanday so‘rash yoki texnik xizmat hisobotida qaysi ma’lumot bo‘lishini tavsiflashi mumkin. Hujjat sarlavhasi va manba joyi vektor yonida mavjud bo‘lsin.\n\nAyni model bilan embedding qilingan so‘rov ifodasi boshqa bo‘lsa ham bog‘liq bo‘limlarni topishi mumkin. O‘quvchiga kerakli haqiqiy ko‘rsatmani aniqlash uchun shu bo‘limlarni tekshiring. O‘xshashlik jarayonida qidiruvdan keyin nomzod gaplarning o‘zini taqqoslang, ayniqsa birida boshqasida yo‘q istisno yoki shart bo‘lsa.\n\n### O‘rtacha pooling va normallash\n\nRasmiy kodlash misoli diqqat niqobi bilan kontekstli token ifodalarining o‘rtachasini oladi, to‘ldiruvchilarni o‘rtachadan chiqaradi. So‘ng natijaviy gap embeddinglarini L2 normallaydi. To‘plam va keyingi so‘rovlar uchun vektorlar yaratishda ikkala bosqichni ham saqlang.\n\nHar vektor 768 komponentga ega. Kosinus o‘xshashligi shu normallangan ifodalarni taqqoslashi mumkin; manba matni esa natija ortidagi dalilni beradi. Komponentlar soni bir xilligi bu modelni multi-qa-mpnet-base-dot-v1 bilan mos qilmaydi: alohida nashr skalyar ko‘paytma bilan baholash uchun CLS pooling va normallanmagan vektorlarni belgilaydi.\n\n### Odatiy kesish va parchani tayyorlash\n\nIshlab chiqaruvchi 384 ta word piece birligidan ortiq kirish odatda kesilishini aytadi. Tegishli bo‘limni tokenizer chegarasi ichida saqlang. Uzun tartib-taomilda mazmunli bo‘lim chegaralarida bo‘ling va qadam nimaga tegishli ekanini tushuntirish uchun yetarli mahalliy kontekst qo‘shing. “Submit within five days” kabi bo‘lakka muddatni boshlaydigan hodisa yoki jarayon kerak.\n\nO‘qitish jarayoni ketma-ketliklarni 128 token bilan cheklagan; bu e’lon qilingan 384 ta word piece birlikli odatiy kodlash sozlamasidan alohida. Uzunroq paragraflar yoki to‘plamga xos lug‘atni baholashda namunaviy materialdan foydalaning. Kirishga ko‘proq matn sig‘ishi har tafsilot qidiruv natijasiga bir xil ta’sir qilishini isbotlamaydi.\n\n### Qidiruv, guruhlash va taqqoslash\n\nQidiruvda reytinglangan nomzodlar asl bo‘limlarni qaytara olishi uchun vektor-hujjat bog‘liqligini saqlang. RAG jarayonida tanlangan bo‘limlar javob yozish bosqichiga kontekst bo‘ladi. Model raqamli ifodalarni beradi; manbali javob yaratmaydi va qaysi hujjat vakolatli manba ekanini hal qilmaydi.\n\nKlasterlashda yorliq berishdan oldin har taklif etilgan guruhdagi yozuvlarni taqqoslang. Texnik xizmat qaydlari to‘plami o‘xshash tildan foydalanib, turli qurilmalar yoki nosozlik shartlariga tegishli bo‘lishi mumkin. Faqat ballga tayanish o‘rniga, farqlovchi tafsilotlarni kodlangan matnga kiriting va qaytgan materialda tekshiring.\n\nAynan all-mpnet-base-v2 checkpointi o‘z o‘rtacha pooling, normallash va odatiy kesish sozlamalariga ega inglizcha gap embedding profilidir. Sozlamalarni indekslangan to‘plam bilan saqlang va vektorlardan vazifaga muhim manba parchalarini topish hamda taqqoslash yordami sifatida foydalaning.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "120f46420ab23b72165a1677ec5e7261eb03ec46e57f67a8ad4edcd5b62f0d86", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

Sentence Transformers: all-mpnet-base-v2

Sentence Transformers /sentence-transformers/all-mpnet-base-v2

Boshlash

Sentence Transformers all-mpnet-base-v2 gaplar va qisqa paragraflarni qidiruv, klasterlash va o‘xshashlik uchun 768 o‘lchamli vektorlarga aylantiradi. E’lon qilingan usul o‘rtacha pooling va L2 normallashdan foydalanadi; odatiy kesish chegarasi 384 ta word piece birligi.

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.

all-mpnet-base-v2 — gaplar va qisqa paragraflarni 768 o‘lchamli zich vektorlar sifatida ifodalash uchun Sentence Transformers modeli. U MPNet bazaviy modelidan bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan. Ishlab chiqaruvchi ma’lumot qidirish, klasterlash va gaplar o‘xshashligini mo‘ljallangan foydalanishlar deb ko‘rsatadi.

Gap yoki qisqa paragrafni kodlang

Taqqoslanadigan matnni tayyorlang, asosiy fikr va uni farqlaydigan tafsilotlarni saqlang. Ichki hujjatlar to‘plamida qisqa bo‘lim uskuna almashtirishni qanday so‘rash yoki texnik xizmat hisobotida qaysi ma’lumot bo‘lishini tavsiflashi mumkin. Hujjat sarlavhasi va manba joyi vektor yonida mavjud bo‘lsin.

Ayni model bilan embedding qilingan so‘rov ifodasi boshqa bo‘lsa ham bog‘liq bo‘limlarni topishi mumkin. O‘quvchiga kerakli haqiqiy ko‘rsatmani aniqlash uchun shu bo‘limlarni tekshiring. O‘xshashlik jarayonida qidiruvdan keyin nomzod gaplarning o‘zini taqqoslang, ayniqsa birida boshqasida yo‘q istisno yoki shart bo‘lsa.

O‘rtacha pooling va normallash

Rasmiy kodlash misoli diqqat niqobi bilan kontekstli token ifodalarining o‘rtachasini oladi, to‘ldiruvchilarni o‘rtachadan chiqaradi. So‘ng natijaviy gap embeddinglarini L2 normallaydi. To‘plam va keyingi so‘rovlar uchun vektorlar yaratishda ikkala bosqichni ham saqlang.

Har vektor 768 komponentga ega. Kosinus o‘xshashligi shu normallangan ifodalarni taqqoslashi mumkin; manba matni esa natija ortidagi dalilni beradi. Komponentlar soni bir xilligi bu modelni multi-qa-mpnet-base-dot-v1 bilan mos qilmaydi: alohida nashr skalyar ko‘paytma bilan baholash uchun CLS pooling va normallanmagan vektorlarni belgilaydi.

Odatiy kesish va parchani tayyorlash

Ishlab chiqaruvchi 384 ta word piece birligidan ortiq kirish odatda kesilishini aytadi. Tegishli bo‘limni tokenizer chegarasi ichida saqlang. Uzun tartib-taomilda mazmunli bo‘lim chegaralarida bo‘ling va qadam nimaga tegishli ekanini tushuntirish uchun yetarli mahalliy kontekst qo‘shing. “Submit within five days” kabi bo‘lakka muddatni boshlaydigan hodisa yoki jarayon kerak.

O‘qitish jarayoni ketma-ketliklarni 128 token bilan cheklagan; bu e’lon qilingan 384 ta word piece birlikli odatiy kodlash sozlamasidan alohida. Uzunroq paragraflar yoki to‘plamga xos lug‘atni baholashda namunaviy materialdan foydalaning. Kirishga ko‘proq matn sig‘ishi har tafsilot qidiruv natijasiga bir xil ta’sir qilishini isbotlamaydi.

Qidiruv, guruhlash va taqqoslash

Qidiruvda reytinglangan nomzodlar asl bo‘limlarni qaytara olishi uchun vektor-hujjat bog‘liqligini saqlang. RAG jarayonida tanlangan bo‘limlar javob yozish bosqichiga kontekst bo‘ladi. Model raqamli ifodalarni beradi; manbali javob yaratmaydi va qaysi hujjat vakolatli manba ekanini hal qilmaydi.

Klasterlashda yorliq berishdan oldin har taklif etilgan guruhdagi yozuvlarni taqqoslang. Texnik xizmat qaydlari to‘plami o‘xshash tildan foydalanib, turli qurilmalar yoki nosozlik shartlariga tegishli bo‘lishi mumkin. Faqat ballga tayanish o‘rniga, farqlovchi tafsilotlarni kodlangan matnga kiriting va qaytgan materialda tekshiring.

Aynan all-mpnet-base-v2 checkpointi o‘z o‘rtacha pooling, normallash va odatiy kesish sozlamalariga ega inglizcha gap embedding profilidir. Sozlamalarni indekslangan to‘plam bilan saqlang va vektorlardan vazifaga muhim manba parchalarini topish hamda taqqoslash yordami sifatida foydalaning.

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
sentence-transformers/all-mpnet-base-v2
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 $0.00001 / 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": "sentence-transformers/all-mpnet-base-v2", "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 sentence-transformers/all-mpnet-base-v2

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

sentence-transformers/all-mpnet-base-v2

", "method_identity": { "effective_request_model_id": "sentence-transformers/all-mpnet-base-v2", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "sentence-transformers/all-mpnet-base-v2", "route_label": "Ultra" }, "method_key": "Ultra|embeddings|/v1/embeddings|sentence-transformers%2Fall-mpnet-base-v2", "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": [ "sentence-transformers/all-mpnet-base-v2" ], "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": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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\": \"sentence-transformers/all-mpnet-base-v2\", \"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='sentence-transformers/all-mpnet-base-v2',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": 0.01, "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": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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": "sentence-transformers/all-mpnet-base-v2", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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": "sentence-transformers/all-mpnet-base-v2", "nativeProvenance": { "input_facts_canonical_sha256": "2615b46e7d9ed5ea806bd6bbf6c9e2afbb2ff52ea520cc32de9faa82bee6d325", "input_html_sha256": "58fd2aa8cfdd539cbf296bc7626727e2c91c5a2923fde1c3ed749dc290f0c85f", "private_audit_sha256": "847762fe46507c6cf3716bd2f58f334ccb7e55287a6afd322809d18f2126e342", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/sentence-transformers-all-mpnet-base-v2/", "description": "Sentence Transformers all-mpnet-base-v2 gaplar va qisqa paragraflarni qidiruv, klasterlash va o‘xshashlik uchun 768 o‘lchamli vektorlarga aylantiradi. E’lon qilingan usul o‘rtacha pooling va L2 normallashdan foydalanadi; odatiy kesish chegarasi 384 ta word piece birligi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/sentence-transformers-all-mpnet-base-v2/index.md", "title": "Sentence Transformers: all-mpnet-base-v2 — Ailoot", "url": "https://ailoot.ai/models/uz/sentence-transformers-all-mpnet-base-v2/" }, "rawModel": { "about": "all-mpnet-base-v2 — gaplar va qisqa paragraflarni 768 o‘lchamli zich vektorlar sifatida ifodalash uchun Sentence Transformers modeli. U MPNet bazaviy modelidan bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan. Ishlab chiqaruvchi ma’lumot qidirish, klasterlash va gaplar o‘xshashligini mo‘ljallangan foydalanishlar deb ko‘rsatadi.\n\n### Gap yoki qisqa paragrafni kodlang\n\nTaqqoslanadigan matnni tayyorlang, asosiy fikr va uni farqlaydigan tafsilotlarni saqlang. Ichki hujjatlar to‘plamida qisqa bo‘lim uskuna almashtirishni qanday so‘rash yoki texnik xizmat hisobotida qaysi ma’lumot bo‘lishini tavsiflashi mumkin. Hujjat sarlavhasi va manba joyi vektor yonida mavjud bo‘lsin.\n\nAyni model bilan embedding qilingan so‘rov ifodasi boshqa bo‘lsa ham bog‘liq bo‘limlarni topishi mumkin. O‘quvchiga kerakli haqiqiy ko‘rsatmani aniqlash uchun shu bo‘limlarni tekshiring. O‘xshashlik jarayonida qidiruvdan keyin nomzod gaplarning o‘zini taqqoslang, ayniqsa birida boshqasida yo‘q istisno yoki shart bo‘lsa.\n\n### O‘rtacha pooling va normallash\n\nRasmiy kodlash misoli diqqat niqobi bilan kontekstli token ifodalarining o‘rtachasini oladi, to‘ldiruvchilarni o‘rtachadan chiqaradi. So‘ng natijaviy gap embeddinglarini L2 normallaydi. To‘plam va keyingi so‘rovlar uchun vektorlar yaratishda ikkala bosqichni ham saqlang.\n\nHar vektor 768 komponentga ega. Kosinus o‘xshashligi shu normallangan ifodalarni taqqoslashi mumkin; manba matni esa natija ortidagi dalilni beradi. Komponentlar soni bir xilligi bu modelni multi-qa-mpnet-base-dot-v1 bilan mos qilmaydi: alohida nashr skalyar ko‘paytma bilan baholash uchun CLS pooling va normallanmagan vektorlarni belgilaydi.\n\n### Odatiy kesish va parchani tayyorlash\n\nIshlab chiqaruvchi 384 ta word piece birligidan ortiq kirish odatda kesilishini aytadi. Tegishli bo‘limni tokenizer chegarasi ichida saqlang. Uzun tartib-taomilda mazmunli bo‘lim chegaralarida bo‘ling va qadam nimaga tegishli ekanini tushuntirish uchun yetarli mahalliy kontekst qo‘shing. “Submit within five days” kabi bo‘lakka muddatni boshlaydigan hodisa yoki jarayon kerak.\n\nO‘qitish jarayoni ketma-ketliklarni 128 token bilan cheklagan; bu e’lon qilingan 384 ta word piece birlikli odatiy kodlash sozlamasidan alohida. Uzunroq paragraflar yoki to‘plamga xos lug‘atni baholashda namunaviy materialdan foydalaning. Kirishga ko‘proq matn sig‘ishi har tafsilot qidiruv natijasiga bir xil ta’sir qilishini isbotlamaydi.\n\n### Qidiruv, guruhlash va taqqoslash\n\nQidiruvda reytinglangan nomzodlar asl bo‘limlarni qaytara olishi uchun vektor-hujjat bog‘liqligini saqlang. RAG jarayonida tanlangan bo‘limlar javob yozish bosqichiga kontekst bo‘ladi. Model raqamli ifodalarni beradi; manbali javob yaratmaydi va qaysi hujjat vakolatli manba ekanini hal qilmaydi.\n\nKlasterlashda yorliq berishdan oldin har taklif etilgan guruhdagi yozuvlarni taqqoslang. Texnik xizmat qaydlari to‘plami o‘xshash tildan foydalanib, turli qurilmalar yoki nosozlik shartlariga tegishli bo‘lishi mumkin. Faqat ballga tayanish o‘rniga, farqlovchi tafsilotlarni kodlangan matnga kiriting va qaytgan materialda tekshiring.\n\nAynan all-mpnet-base-v2 checkpointi o‘z o‘rtacha pooling, normallash va odatiy kesish sozlamalariga ega inglizcha gap embedding profilidir. Sozlamalarni indekslangan to‘plam bilan saqlang va vektorlardan vazifaga muhim manba parchalarini topish hamda taqqoslash yordami sifatida foydalaning.", "alias_of": null, "aliases": [], "author": "Sentence Transformers", "available": null, "canonical_model": "sentence-transformers/all-mpnet-base-v2", "capabilities": { "context_length": 512, "manufacturer_input": [ "text" ], "manufacturer_output": [ "embeddings" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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": "sentence-transformers/all-mpnet-base-v2", "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": [ "sentence-transformers/all-mpnet-base-v2" ], "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": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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\": \"sentence-transformers/all-mpnet-base-v2\", \"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='sentence-transformers/all-mpnet-base-v2',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": 0.01, "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": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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": "sentence-transformers/all-mpnet-base-v2", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.00001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.00000001", "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": "sentence-transformers/all-mpnet-base-v2", "request_model_id": "sentence-transformers/all-mpnet-base-v2", "route_label": "Ultra" } ], "name": "Sentence Transformers: all-mpnet-base-v2", "price": { "in": 0.01, "in_label": null, "out": 0, "out_label": null, "scope_label": "Token rates", "sort_in": 0.01, "sort_out": 0, "varies": false }, "public_id": "sentence-transformers/all-mpnet-base-v2", "short": "Sentence Transformers all-mpnet-base-v2 gaplar va qisqa paragraflarni qidiruv, klasterlash va o‘xshashlik uchun 768 o‘lchamli vektorlarga aylantiradi. E’lon qilingan usul o‘rtacha pooling va L2 normallashdan foydalanadi; odatiy kesish chegarasi 384 ta word piece birligi." }, "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": "Sentence Transformers all-mpnet-base-v2 gaplar va qisqa paragraflarni qidiruv, klasterlash va o‘xshashlik uchun 768 o‘lchamli vektorlarga aylantiradi. E’lon qilingan usul o‘rtacha pooling va L2 normallashdan foydalanadi; odatiy kesish chegarasi 384 ta word piece birligi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "2fa0a64298f8bfd75e638399f536ebb6829660fa85d9ea0b7bef77ab0c32e352", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "52368756ecc29db2b1035831e7f9f9d39d91c5c4a5e93666b606ed30fb6b2968" }