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Nemotron 3 Embed 1B — mazmun bo‘yicha qidiruv va parchalarni solishtirish uchun NVIDIA’ning matn modeli. U savollar, hujjatlar va dastur kodini zich sonli vektorlarga kodlaydi. Bu ifodalar katta to‘plamlar orasidan kerakli kontekstni topish, u bilan yordamchi javobini to‘ldirish yoki agentning keyingi qadami uchun oldin saqlangan axborotni olishda ishlatiladi.

Ko‘p tilli bilimlar va kod

Model ko‘p tilli va tillararo qidiruv uchun mo‘ljallangan: savol va mos material turli tillarda yozilgan bo‘lishi mumkin. Rus tili ingliz, ukrain, xitoy, yapon, arab va boshqalar bilan birga e’lon qilingan 34 til to‘plamiga kiradi. Bu texnik hujjatlar va foydalanuvchi murojaatlari bitta tilga keltirilmagan umumiy bilim bazalari uchun foydali.

Vazifalar orasida hujjatlar bo‘yicha qidiruv, matnlarni solishtirish va so‘z bilan berilgan tavsifdan kod olish bor. Katta kirish oynasi uzun hujjatlar, hajmli dasturiy kontekst va agentning bir nechta qadami tarixi bilan ishlashga imkon beradi. Topilgan material keyingi jarayonga kontekst sifatida qaytariladi; yakuniy tushuntirishlar va javoblarni tizimning keyingi komponenti shakllantiradi.

Matndan vektorga

Kirish matn satrlaridan iborat. Har bir satr uchun 2048 ta sonli bitta vektor olinadi. Ketma-ketlikning maksimal uzunligi — 32768 token; uzunroq materialni qismlarga ajratish yoki qisqartirish zarur. Kirish tayyorlashda savol va hujjat farqlanadi: model ularning rollarini solishtirishga o‘qitilgan, shuning uchun bu ikki vazifadagi bir xil satr vazifasi hisobga olinmasdan shakllantirilmasligi kerak.

Vektorlar uzunligi bo‘yicha normallashtiriladi. Bunday ifodada skalyar ko‘paytma va kosinus o‘xshashligi teng solishtirish usulini beradi. To‘liq vektorning dastlabki 1024 yoki 512 koordinatasini qoldirish mumkin, lekin kesishdan keyin qayta normallashtirish kerak. Bu natijani qayta ishlash xususiyati; o‘lchamni to‘plam va so‘rovlar uchun mos holda tanlash lozim.

Ixcham model qanday tuzilgan

Enkoder taxminan 1,14 mlrd parametrni o‘z ichiga oladi. U Ministral 3 3B asosidagi modeldan ikki bosqichli tuzilmaviy qisqartirish va distillyatsiya orqali olingan. Ikki yo‘nalishli diqqat butun kirishni hisobga oladi, so‘ng token ifodalari umumiy vektorga o‘rtachalashtiriladi.

NVIDIA BF16 va NVFP4 variantlarini chiqaradi. Ularning ifoda fazolari yaqin, lekin variantni almashtirish o‘z hujjatlarida tekshirishni talab qiladi. Sonli o‘xshashlik ham, kontekst uzunligi ham qidiruv har doim to‘g‘ri parchani tanlashini kafolatlamaydi.

", "aboutText": "Nemotron 3 Embed 1B — mazmun bo‘yicha qidiruv va parchalarni solishtirish uchun NVIDIA’ning matn modeli. U savollar, hujjatlar va dastur kodini zich sonli vektorlarga kodlaydi. Bu ifodalar katta to‘plamlar orasidan kerakli kontekstni topish, u bilan yordamchi javobini to‘ldirish yoki agentning keyingi qadami uchun oldin saqlangan axborotni olishda ishlatiladi.\n\n### Ko‘p tilli bilimlar va kod\n\nModel ko‘p tilli va tillararo qidiruv uchun mo‘ljallangan: savol va mos material turli tillarda yozilgan bo‘lishi mumkin. Rus tili ingliz, ukrain, xitoy, yapon, arab va boshqalar bilan birga e’lon qilingan 34 til to‘plamiga kiradi. Bu texnik hujjatlar va foydalanuvchi murojaatlari bitta tilga keltirilmagan umumiy bilim bazalari uchun foydali.\n\nVazifalar orasida hujjatlar bo‘yicha qidiruv, matnlarni solishtirish va so‘z bilan berilgan tavsifdan kod olish bor. Katta kirish oynasi uzun hujjatlar, hajmli dasturiy kontekst va agentning bir nechta qadami tarixi bilan ishlashga imkon beradi. Topilgan material keyingi jarayonga kontekst sifatida qaytariladi; yakuniy tushuntirishlar va javoblarni tizimning keyingi komponenti shakllantiradi.\n\n### Matndan vektorga\n\nKirish matn satrlaridan iborat. Har bir satr uchun 2048 ta sonli bitta vektor olinadi. Ketma-ketlikning maksimal uzunligi — 32768 token; uzunroq materialni qismlarga ajratish yoki qisqartirish zarur. Kirish tayyorlashda savol va hujjat farqlanadi: model ularning rollarini solishtirishga o‘qitilgan, shuning uchun bu ikki vazifadagi bir xil satr vazifasi hisobga olinmasdan shakllantirilmasligi kerak.\n\nVektorlar uzunligi bo‘yicha normallashtiriladi. Bunday ifodada skalyar ko‘paytma va kosinus o‘xshashligi teng solishtirish usulini beradi. To‘liq vektorning dastlabki 1024 yoki 512 koordinatasini qoldirish mumkin, lekin kesishdan keyin qayta normallashtirish kerak. Bu natijani qayta ishlash xususiyati; o‘lchamni to‘plam va so‘rovlar uchun mos holda tanlash lozim.\n\n### Ixcham model qanday tuzilgan\n\nEnkoder taxminan 1,14 mlrd parametrni o‘z ichiga oladi. U Ministral 3 3B asosidagi modeldan ikki bosqichli tuzilmaviy qisqartirish va distillyatsiya orqali olingan. Ikki yo‘nalishli diqqat butun kirishni hisobga oladi, so‘ng token ifodalari umumiy vektorga o‘rtachalashtiriladi.\n\nNVIDIA BF16 va NVFP4 variantlarini chiqaradi. Ularning ifoda fazolari yaqin, lekin variantni almashtirish o‘z hujjatlarida tekshirishni talab qiladi. Sonli o‘xshashlik ham, kontekst uzunligi ham qidiruv har doim to‘g‘ri parchani tanlashini kafolatlamaydi.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "e51bf635c644d8ab32ca4a5d94da11976ab1b498d55a3686c8ba785db0f743f3", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
NV

Nemotron 3 Embed 1B

NVIDIA /nvidia/nemotron-3-embed-1b

Boshlash

Nemotron 3 Embed 1B matnlarni ko‘p tilli qidiruv, kod qidirish va RAG uchun vektorlarga aylantiradi. 32768 tokengacha ketma-ketliklarni qayta ishlab, 2048 ta sondan iborat ifoda beradi.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
embedding
Model haqida

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

Nemotron 3 Embed 1B — mazmun bo‘yicha qidiruv va parchalarni solishtirish uchun NVIDIA’ning matn modeli. U savollar, hujjatlar va dastur kodini zich sonli vektorlarga kodlaydi. Bu ifodalar katta to‘plamlar orasidan kerakli kontekstni topish, u bilan yordamchi javobini to‘ldirish yoki agentning keyingi qadami uchun oldin saqlangan axborotni olishda ishlatiladi.

Ko‘p tilli bilimlar va kod

Model ko‘p tilli va tillararo qidiruv uchun mo‘ljallangan: savol va mos material turli tillarda yozilgan bo‘lishi mumkin. Rus tili ingliz, ukrain, xitoy, yapon, arab va boshqalar bilan birga e’lon qilingan 34 til to‘plamiga kiradi. Bu texnik hujjatlar va foydalanuvchi murojaatlari bitta tilga keltirilmagan umumiy bilim bazalari uchun foydali.

Vazifalar orasida hujjatlar bo‘yicha qidiruv, matnlarni solishtirish va so‘z bilan berilgan tavsifdan kod olish bor. Katta kirish oynasi uzun hujjatlar, hajmli dasturiy kontekst va agentning bir nechta qadami tarixi bilan ishlashga imkon beradi. Topilgan material keyingi jarayonga kontekst sifatida qaytariladi; yakuniy tushuntirishlar va javoblarni tizimning keyingi komponenti shakllantiradi.

Matndan vektorga

Kirish matn satrlaridan iborat. Har bir satr uchun 2048 ta sonli bitta vektor olinadi. Ketma-ketlikning maksimal uzunligi — 32768 token; uzunroq materialni qismlarga ajratish yoki qisqartirish zarur. Kirish tayyorlashda savol va hujjat farqlanadi: model ularning rollarini solishtirishga o‘qitilgan, shuning uchun bu ikki vazifadagi bir xil satr vazifasi hisobga olinmasdan shakllantirilmasligi kerak.

Vektorlar uzunligi bo‘yicha normallashtiriladi. Bunday ifodada skalyar ko‘paytma va kosinus o‘xshashligi teng solishtirish usulini beradi. To‘liq vektorning dastlabki 1024 yoki 512 koordinatasini qoldirish mumkin, lekin kesishdan keyin qayta normallashtirish kerak. Bu natijani qayta ishlash xususiyati; o‘lchamni to‘plam va so‘rovlar uchun mos holda tanlash lozim.

Ixcham model qanday tuzilgan

Enkoder taxminan 1,14 mlrd parametrni o‘z ichiga oladi. U Ministral 3 3B asosidagi modeldan ikki bosqichli tuzilmaviy qisqartirish va distillyatsiya orqali olingan. Ikki yo‘nalishli diqqat butun kirishni hisobga oladi, so‘ng token ifodalari umumiy vektorga o‘rtachalashtiriladi.

NVIDIA BF16 va NVFP4 variantlarini chiqaradi. Ularning ifoda fazolari yaqin, lekin variantni almashtirish o‘z hujjatlarida tekshirishni talab qiladi. Sonli o‘xshashlik ham, kontekst uzunligi ham qidiruv har doim to‘g‘ri parchani tanlashini kafolatlamaydi.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · Embeddinglar POST /v1/embeddings
Kirish
text
Chiqish
embedding
Kontekst
4 096 tokens
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
nvidia/nemotron-3-embed-1b
Standart—
MaydoninputTurstring|string[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Shartlar: Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.
Standart—
Maydoninput_typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
query, passage
Standartquery
Maydonencoding_formatTurstringTalabIxtiyoriyQiymatlar va cheklovlar
float, base64
Standartfloat
MaydontruncateTurstringTalabIxtiyoriyQiymatlar va cheklovlar
NONE, START, END
Standart—
MaydondimensionsTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydonuserTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, input
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json

Hayot sikli: exact retained published method

Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.

Tariflar

Ailoot usullari tariflari

Ailoot EmbeddinglarPOST /v1/embeddings
Kirish
0,0001 US$ / 1K

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 · Embeddinglar

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "nvidia/nemotron-3-embed-1b", "input": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["input"]=[text]\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 · Embeddinglar

json

Barcha tayyor natijalarni saqlash

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

base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.

Asl javobni tekshirish kerak bo‘lsa

Embedding vazifalari uchun endpoint qo‘shilmaydi.

Javob maydonlari
MaydonTur
Maydondata[].indexTurinteger
Maydondata[].embeddingTurfinite float[]|base64 float32 vector
MaydonmodelTurstring
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 nvidia/nemotron-3-embed-1b, nemotron-3-embed, nemotron-3-embed-latest

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

nvidia/nemotron-3-embed-1b, nemotron-3-embed, nemotron-3-embed-latest

", "method_identity": { "effective_request_model_id": "nvidia/nemotron-3-embed-1b", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "nvidia/nemotron-3-embed-1b", "route_label": "Core" }, "method_key": "Core|embeddings|/v1/embeddings|nvidia%2Fnemotron-3-embed-1b", "ordinal": 0, "rawMethod": { "cache_read": null, "cache_write": null, "cache_write_1h": null, "cache_write_5m": null, "conditional_meter": null, "contract": { "data": { "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.", "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": [ "nonempty string", "nonempty string[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.", "family": "embedding", "fields": [ { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]|base64 float32 vector" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "nvidia/nemotron-3-embed-1b" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "conditions": "Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]" }, { "availability": "source_interface", "default": "query", "default_known": true, "enum": [ "query", "passage" ], "enum_known": true, "name": "input_type", "required": false, "type": "string" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float", "base64" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "NONE", "START", "END" ], "enum_known": true, "name": "truncate", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "image objects", "data URI text inputs", "token integer arrays on this NVIDIA adapter" ] }, "hints": [ "Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": null, "required": [ "model", "input" ] }, "lifecycle": { "mode": "exact retained published method" }, "method": { "http": "POST", "path": "/v1/embeddings" }, "operation_modes": [ "embeddings" ], "output": { "family": [ "embedding" ], "format": "indexed vectors JSON", "multioutputs": "retain every actual response output" } }, "ctx": 4096, "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/nemotron-3-embed-1b\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "img_in": null, "img_out": null, "in": 0.1, "input": [ "text" ], "label": "Embeddinglar", "max_out": 0, "max_tool_calls": null, "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "pricing_profiles": [ { "dimensions": {}, "label": "Core", "units": [ { "amount": 0.1, "amount_unit": "usd_per_million_tokens", "key": "in", "label": "Input", "side": "in" }, { "amount": 0, "amount_unit": "usd_per_million_tokens", "key": "out", "label": "Output", "side": "out" } ] } ], "request_model_id": "nvidia/nemotron-3-embed-1b", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } }, { "html": "
NV

Nemotron 3 Embed 1B

NVIDIA /nemotron-3-embed

Boshlash

Nemotron 3 Embed 1B matnlarni ko‘p tilli qidiruv, kod qidirish va RAG uchun vektorlarga aylantiradi. 32768 tokengacha ketma-ketliklarni qayta ishlab, 2048 ta sondan iborat ifoda beradi.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
embedding
Model haqida

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

Nemotron 3 Embed 1B — mazmun bo‘yicha qidiruv va parchalarni solishtirish uchun NVIDIA’ning matn modeli. U savollar, hujjatlar va dastur kodini zich sonli vektorlarga kodlaydi. Bu ifodalar katta to‘plamlar orasidan kerakli kontekstni topish, u bilan yordamchi javobini to‘ldirish yoki agentning keyingi qadami uchun oldin saqlangan axborotni olishda ishlatiladi.

Ko‘p tilli bilimlar va kod

Model ko‘p tilli va tillararo qidiruv uchun mo‘ljallangan: savol va mos material turli tillarda yozilgan bo‘lishi mumkin. Rus tili ingliz, ukrain, xitoy, yapon, arab va boshqalar bilan birga e’lon qilingan 34 til to‘plamiga kiradi. Bu texnik hujjatlar va foydalanuvchi murojaatlari bitta tilga keltirilmagan umumiy bilim bazalari uchun foydali.

Vazifalar orasida hujjatlar bo‘yicha qidiruv, matnlarni solishtirish va so‘z bilan berilgan tavsifdan kod olish bor. Katta kirish oynasi uzun hujjatlar, hajmli dasturiy kontekst va agentning bir nechta qadami tarixi bilan ishlashga imkon beradi. Topilgan material keyingi jarayonga kontekst sifatida qaytariladi; yakuniy tushuntirishlar va javoblarni tizimning keyingi komponenti shakllantiradi.

Matndan vektorga

Kirish matn satrlaridan iborat. Har bir satr uchun 2048 ta sonli bitta vektor olinadi. Ketma-ketlikning maksimal uzunligi — 32768 token; uzunroq materialni qismlarga ajratish yoki qisqartirish zarur. Kirish tayyorlashda savol va hujjat farqlanadi: model ularning rollarini solishtirishga o‘qitilgan, shuning uchun bu ikki vazifadagi bir xil satr vazifasi hisobga olinmasdan shakllantirilmasligi kerak.

Vektorlar uzunligi bo‘yicha normallashtiriladi. Bunday ifodada skalyar ko‘paytma va kosinus o‘xshashligi teng solishtirish usulini beradi. To‘liq vektorning dastlabki 1024 yoki 512 koordinatasini qoldirish mumkin, lekin kesishdan keyin qayta normallashtirish kerak. Bu natijani qayta ishlash xususiyati; o‘lchamni to‘plam va so‘rovlar uchun mos holda tanlash lozim.

Ixcham model qanday tuzilgan

Enkoder taxminan 1,14 mlrd parametrni o‘z ichiga oladi. U Ministral 3 3B asosidagi modeldan ikki bosqichli tuzilmaviy qisqartirish va distillyatsiya orqali olingan. Ikki yo‘nalishli diqqat butun kirishni hisobga oladi, so‘ng token ifodalari umumiy vektorga o‘rtachalashtiriladi.

NVIDIA BF16 va NVFP4 variantlarini chiqaradi. Ularning ifoda fazolari yaqin, lekin variantni almashtirish o‘z hujjatlarida tekshirishni talab qiladi. Sonli o‘xshashlik ham, kontekst uzunligi ham qidiruv har doim to‘g‘ri parchani tanlashini kafolatlamaydi.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · Embeddinglar POST /v1/embeddings
Kirish
text
Chiqish
embedding
Kontekst
4 096 tokens
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
nemotron-3-embed
Standart—
MaydoninputTurstring|string[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Shartlar: Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.
Standart—
Maydoninput_typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
query, passage
Standartquery
Maydonencoding_formatTurstringTalabIxtiyoriyQiymatlar va cheklovlar
float, base64
Standartfloat
MaydontruncateTurstringTalabIxtiyoriyQiymatlar va cheklovlar
NONE, START, END
Standart—
MaydondimensionsTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydonuserTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, input
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json

Hayot sikli: exact retained published method

Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.

Tariflar

Ailoot usullari tariflari

Ailoot EmbeddinglarPOST /v1/embeddings
Kirish
0,0001 US$ / 1K

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 · Embeddinglar

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "nemotron-3-embed", "input": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["input"]=[text]\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 · Embeddinglar

json

Barcha tayyor natijalarni saqlash

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

base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.

Asl javobni tekshirish kerak bo‘lsa

Embedding vazifalari uchun endpoint qo‘shilmaydi.

Javob maydonlari
MaydonTur
Maydondata[].indexTurinteger
Maydondata[].embeddingTurfinite float[]|base64 float32 vector
MaydonmodelTurstring
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 nvidia/nemotron-3-embed-1b, nemotron-3-embed, nemotron-3-embed-latest

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

nvidia/nemotron-3-embed-1b, nemotron-3-embed, nemotron-3-embed-latest

", "method_identity": { "effective_request_model_id": "nemotron-3-embed", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "nemotron-3-embed", "route_label": "Core" }, "method_key": "Core|embeddings|/v1/embeddings|nemotron-3-embed", "ordinal": 1, "rawMethod": { "cache_read": null, "cache_write": null, "cache_write_1h": null, "cache_write_5m": null, "conditional_meter": null, "contract": { "data": { "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.", "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": [ "nonempty string", "nonempty string[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.", "family": "embedding", "fields": [ { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]|base64 float32 vector" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "nemotron-3-embed" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "conditions": "Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]" }, { "availability": "source_interface", "default": "query", "default_known": true, "enum": [ "query", "passage" ], "enum_known": true, "name": "input_type", "required": false, "type": "string" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float", "base64" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "NONE", "START", "END" ], "enum_known": true, "name": "truncate", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "image objects", "data URI text inputs", "token integer arrays on this NVIDIA adapter" ] }, "hints": [ "Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": null, "required": [ "model", "input" ] }, "lifecycle": { "mode": "exact retained published method" }, "method": { "http": "POST", "path": "/v1/embeddings" }, "operation_modes": [ "embeddings" ], "output": { "family": [ "embedding" ], "format": "indexed vectors JSON", "multioutputs": "retain every actual response output" } }, "ctx": 4096, "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nemotron-3-embed\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "img_in": null, "img_out": null, "in": 0.1, "input": [ "text" ], "label": "Embeddinglar", "max_out": 0, "max_tool_calls": null, "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "pricing_profiles": [ { "dimensions": {}, "label": "Core", "units": [ { "amount": 0.1, "amount_unit": "usd_per_million_tokens", "key": "in", "label": "Input", "side": "in" }, { "amount": 0, "amount_unit": "usd_per_million_tokens", "key": "out", "label": "Output", "side": "out" } ] } ], "request_model_id": "nemotron-3-embed", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } }, { "html": "
NV

Nemotron 3 Embed 1B

NVIDIA /nemotron-3-embed-latest

Boshlash

Nemotron 3 Embed 1B matnlarni ko‘p tilli qidiruv, kod qidirish va RAG uchun vektorlarga aylantiradi. 32768 tokengacha ketma-ketliklarni qayta ishlab, 2048 ta sondan iborat ifoda beradi.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
embedding
Model haqida

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

Nemotron 3 Embed 1B — mazmun bo‘yicha qidiruv va parchalarni solishtirish uchun NVIDIA’ning matn modeli. U savollar, hujjatlar va dastur kodini zich sonli vektorlarga kodlaydi. Bu ifodalar katta to‘plamlar orasidan kerakli kontekstni topish, u bilan yordamchi javobini to‘ldirish yoki agentning keyingi qadami uchun oldin saqlangan axborotni olishda ishlatiladi.

Ko‘p tilli bilimlar va kod

Model ko‘p tilli va tillararo qidiruv uchun mo‘ljallangan: savol va mos material turli tillarda yozilgan bo‘lishi mumkin. Rus tili ingliz, ukrain, xitoy, yapon, arab va boshqalar bilan birga e’lon qilingan 34 til to‘plamiga kiradi. Bu texnik hujjatlar va foydalanuvchi murojaatlari bitta tilga keltirilmagan umumiy bilim bazalari uchun foydali.

Vazifalar orasida hujjatlar bo‘yicha qidiruv, matnlarni solishtirish va so‘z bilan berilgan tavsifdan kod olish bor. Katta kirish oynasi uzun hujjatlar, hajmli dasturiy kontekst va agentning bir nechta qadami tarixi bilan ishlashga imkon beradi. Topilgan material keyingi jarayonga kontekst sifatida qaytariladi; yakuniy tushuntirishlar va javoblarni tizimning keyingi komponenti shakllantiradi.

Matndan vektorga

Kirish matn satrlaridan iborat. Har bir satr uchun 2048 ta sonli bitta vektor olinadi. Ketma-ketlikning maksimal uzunligi — 32768 token; uzunroq materialni qismlarga ajratish yoki qisqartirish zarur. Kirish tayyorlashda savol va hujjat farqlanadi: model ularning rollarini solishtirishga o‘qitilgan, shuning uchun bu ikki vazifadagi bir xil satr vazifasi hisobga olinmasdan shakllantirilmasligi kerak.

Vektorlar uzunligi bo‘yicha normallashtiriladi. Bunday ifodada skalyar ko‘paytma va kosinus o‘xshashligi teng solishtirish usulini beradi. To‘liq vektorning dastlabki 1024 yoki 512 koordinatasini qoldirish mumkin, lekin kesishdan keyin qayta normallashtirish kerak. Bu natijani qayta ishlash xususiyati; o‘lchamni to‘plam va so‘rovlar uchun mos holda tanlash lozim.

Ixcham model qanday tuzilgan

Enkoder taxminan 1,14 mlrd parametrni o‘z ichiga oladi. U Ministral 3 3B asosidagi modeldan ikki bosqichli tuzilmaviy qisqartirish va distillyatsiya orqali olingan. Ikki yo‘nalishli diqqat butun kirishni hisobga oladi, so‘ng token ifodalari umumiy vektorga o‘rtachalashtiriladi.

NVIDIA BF16 va NVFP4 variantlarini chiqaradi. Ularning ifoda fazolari yaqin, lekin variantni almashtirish o‘z hujjatlarida tekshirishni talab qiladi. Sonli o‘xshashlik ham, kontekst uzunligi ham qidiruv har doim to‘g‘ri parchani tanlashini kafolatlamaydi.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot · Embeddinglar POST /v1/embeddings
Kirish
text
Chiqish
embedding
Kontekst
4 096 tokens
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
nemotron-3-embed-latest
Standart—
MaydoninputTurstring|string[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Shartlar: Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.
Standart—
Maydoninput_typeTurstringTalabIxtiyoriyQiymatlar va cheklovlar
query, passage
Standartquery
Maydonencoding_formatTurstringTalabIxtiyoriyQiymatlar va cheklovlar
float, base64
Standartfloat
MaydontruncateTurstringTalabIxtiyoriyQiymatlar va cheklovlar
NONE, START, END
Standart—
MaydondimensionsTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydonuserTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Kirish materiallari birikmalari

text_to_embedding

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrmaterial_typesParametr talablaritext
ParametrrequiredParametr talablarimodel, input
Parametroptional_controlsParametr talablarinull
Parametrcontent_typeParametr talablariapplication/json

Hayot sikli: exact retained published method

Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida.

Tariflar

Ailoot usullari tariflari

Ailoot EmbeddinglarPOST /v1/embeddings
Kirish
0,0001 US$ / 1K

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 · Embeddinglar

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "nemotron-3-embed-latest", "input": []}')\nf=Path(os.environ["AILOOT_TEXT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")\ntext=f.read_text(encoding="utf-8")\nif not text.strip():raise SystemExit("Source material must contain text")\np["input"]=[text]\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 · Embeddinglar

json

Barcha tayyor natijalarni saqlash

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

base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.

Asl javobni tekshirish kerak bo‘lsa

Embedding vazifalari uchun endpoint qo‘shilmaydi.

Javob maydonlari
MaydonTur
Maydondata[].indexTurinteger
Maydondata[].embeddingTurfinite float[]|base64 float32 vector
MaydonmodelTurstring
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 nvidia/nemotron-3-embed-1b, nemotron-3-embed, nemotron-3-embed-latest

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

nvidia/nemotron-3-embed-1b, nemotron-3-embed, nemotron-3-embed-latest

", "method_identity": { "effective_request_model_id": "nemotron-3-embed-latest", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "nemotron-3-embed-latest", "route_label": "Core" }, "method_key": "Core|embeddings|/v1/embeddings|nemotron-3-embed-latest", "ordinal": 2, "rawMethod": { "cache_read": null, "cache_write": null, "cache_write_1h": null, "cache_write_5m": null, "conditional_meter": null, "contract": { "data": { "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.", "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": [ "nonempty string", "nonempty string[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.", "family": "embedding", "fields": [ { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]|base64 float32 vector" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "nemotron-3-embed-latest" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "conditions": "Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]" }, { "availability": "source_interface", "default": "query", "default_known": true, "enum": [ "query", "passage" ], "enum_known": true, "name": "input_type", "required": false, "type": "string" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float", "base64" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "NONE", "START", "END" ], "enum_known": true, "name": "truncate", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "image objects", "data URI text inputs", "token integer arrays on this NVIDIA adapter" ] }, "hints": [ "Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": null, "required": [ "model", "input" ] }, "lifecycle": { "mode": "exact retained published method" }, "method": { "http": "POST", "path": "/v1/embeddings" }, "operation_modes": [ "embeddings" ], "output": { "family": [ "embedding" ], "format": "indexed vectors JSON", "multioutputs": "retain every actual response output" } }, "ctx": 4096, "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nemotron-3-embed-latest\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "img_in": null, "img_out": null, "in": 0.1, "input": [ "text" ], "label": "Embeddinglar", "max_out": 0, "max_tool_calls": null, "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "pricing_profiles": [ { "dimensions": {}, "label": "Core", "units": [ { "amount": 0.1, "amount_unit": "usd_per_million_tokens", "key": "in", "label": "Input", "side": "in" }, { "amount": 0, "amount_unit": "usd_per_million_tokens", "key": "out", "label": "Output", "side": "out" } ] } ], "request_model_id": "nemotron-3-embed-latest", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } } ], "model_id": "nvidia/nemotron-3-embed-1b", "nativeProvenance": { "input_facts_canonical_sha256": "a27ded06b253caebca12a08c972b635c1c6e067c45343da39d955957f614ac31", "input_html_sha256": "6188c37bf73f4cc58d88f43937c7fb2ea7a985c7dc6a371820559ef349ab0c49", "private_audit_sha256": "fe4ba5c89d206fb0e18ab7c48435903290dac9c619f1b3823e3a639c34114c8b", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/", "description": "Nemotron 3 Embed 1B matnlarni ko‘p tilli qidiruv, kod qidirish va RAG uchun vektorlarga aylantiradi. 32768 tokengacha ketma-ketliklarni qayta ishlab, 2048 ta sondan iborat ifoda beradi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/index.md", "title": "Nemotron 3 Embed 1B — Ailoot", "url": "https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/" }, "rawModel": { "about": "Nemotron 3 Embed 1B — mazmun bo‘yicha qidiruv va parchalarni solishtirish uchun NVIDIA’ning matn modeli. U savollar, hujjatlar va dastur kodini zich sonli vektorlarga kodlaydi. Bu ifodalar katta to‘plamlar orasidan kerakli kontekstni topish, u bilan yordamchi javobini to‘ldirish yoki agentning keyingi qadami uchun oldin saqlangan axborotni olishda ishlatiladi.\n\n### Ko‘p tilli bilimlar va kod\n\nModel ko‘p tilli va tillararo qidiruv uchun mo‘ljallangan: savol va mos material turli tillarda yozilgan bo‘lishi mumkin. Rus tili ingliz, ukrain, xitoy, yapon, arab va boshqalar bilan birga e’lon qilingan 34 til to‘plamiga kiradi. Bu texnik hujjatlar va foydalanuvchi murojaatlari bitta tilga keltirilmagan umumiy bilim bazalari uchun foydali.\n\nVazifalar orasida hujjatlar bo‘yicha qidiruv, matnlarni solishtirish va so‘z bilan berilgan tavsifdan kod olish bor. Katta kirish oynasi uzun hujjatlar, hajmli dasturiy kontekst va agentning bir nechta qadami tarixi bilan ishlashga imkon beradi. Topilgan material keyingi jarayonga kontekst sifatida qaytariladi; yakuniy tushuntirishlar va javoblarni tizimning keyingi komponenti shakllantiradi.\n\n### Matndan vektorga\n\nKirish matn satrlaridan iborat. Har bir satr uchun 2048 ta sonli bitta vektor olinadi. Ketma-ketlikning maksimal uzunligi — 32768 token; uzunroq materialni qismlarga ajratish yoki qisqartirish zarur. Kirish tayyorlashda savol va hujjat farqlanadi: model ularning rollarini solishtirishga o‘qitilgan, shuning uchun bu ikki vazifadagi bir xil satr vazifasi hisobga olinmasdan shakllantirilmasligi kerak.\n\nVektorlar uzunligi bo‘yicha normallashtiriladi. Bunday ifodada skalyar ko‘paytma va kosinus o‘xshashligi teng solishtirish usulini beradi. To‘liq vektorning dastlabki 1024 yoki 512 koordinatasini qoldirish mumkin, lekin kesishdan keyin qayta normallashtirish kerak. Bu natijani qayta ishlash xususiyati; o‘lchamni to‘plam va so‘rovlar uchun mos holda tanlash lozim.\n\n### Ixcham model qanday tuzilgan\n\nEnkoder taxminan 1,14 mlrd parametrni o‘z ichiga oladi. U Ministral 3 3B asosidagi modeldan ikki bosqichli tuzilmaviy qisqartirish va distillyatsiya orqali olingan. Ikki yo‘nalishli diqqat butun kirishni hisobga oladi, so‘ng token ifodalari umumiy vektorga o‘rtachalashtiriladi.\n\nNVIDIA BF16 va NVFP4 variantlarini chiqaradi. Ularning ifoda fazolari yaqin, lekin variantni almashtirish o‘z hujjatlarida tekshirishni talab qiladi. Sonli o‘xshashlik ham, kontekst uzunligi ham qidiruv har doim to‘g‘ri parchani tanlashini kafolatlamaydi.", "alias_of": null, "aliases": [ "nemotron-3-embed", "nemotron-3-embed-latest" ], "author": "NVIDIA", "ctx": 4096, "display_name": "Nemotron 3 Embed 1B", "formats": [ "embeddings" ], "id": "nvidia/nemotron-3-embed-1b", "input": [ "text" ], "kind": "embedding", "max_out": null, "methods": [ { "cache_read": null, "cache_write": null, "cache_write_1h": null, "cache_write_5m": null, "conditional_meter": null, "contract": { "data": { "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.", "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": [ "nonempty string", "nonempty string[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.", "family": "embedding", "fields": [ { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]|base64 float32 vector" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "nvidia/nemotron-3-embed-1b" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "conditions": "Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]" }, { "availability": "source_interface", "default": "query", "default_known": true, "enum": [ "query", "passage" ], "enum_known": true, "name": "input_type", "required": false, "type": "string" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float", "base64" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "NONE", "START", "END" ], "enum_known": true, "name": "truncate", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "image objects", "data URI text inputs", "token integer arrays on this NVIDIA adapter" ] }, "hints": [ "Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": null, "required": [ "model", "input" ] }, "lifecycle": { "mode": "exact retained published method" }, "method": { "http": "POST", "path": "/v1/embeddings" }, "operation_modes": [ "embeddings" ], "output": { "family": [ "embedding" ], "format": "indexed vectors JSON", "multioutputs": "retain every actual response output" } }, "ctx": 4096, "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/nemotron-3-embed-1b\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "img_in": null, "img_out": null, "in": 0.1, "input": [ "text" ], "label": "Embeddinglar", "max_out": 0, "max_tool_calls": null, "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "pricing_profiles": [ { "dimensions": {}, "label": "Core", "units": [ { "amount": 0.1, "amount_unit": "usd_per_million_tokens", "key": "in", "label": "Input", "side": "in" }, { "amount": 0, "amount_unit": "usd_per_million_tokens", "key": "out", "label": "Output", "side": "out" } ] } ], "request_model_id": "nvidia/nemotron-3-embed-1b", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] }, { "cache_read": null, "cache_write": null, "cache_write_1h": null, "cache_write_5m": null, "conditional_meter": null, "contract": { "data": { "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.", "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": [ "nonempty string", "nonempty string[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.", "family": "embedding", "fields": [ { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]|base64 float32 vector" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "nemotron-3-embed" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "conditions": "Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]" }, { "availability": "source_interface", "default": "query", "default_known": true, "enum": [ "query", "passage" ], "enum_known": true, "name": "input_type", "required": false, "type": "string" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float", "base64" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "NONE", "START", "END" ], "enum_known": true, "name": "truncate", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "image objects", "data URI text inputs", "token integer arrays on this NVIDIA adapter" ] }, "hints": [ "Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": null, "required": [ "model", "input" ] }, "lifecycle": { "mode": "exact retained published method" }, "method": { "http": "POST", "path": "/v1/embeddings" }, "operation_modes": [ "embeddings" ], "output": { "family": [ "embedding" ], "format": "indexed vectors JSON", "multioutputs": "retain every actual response output" } }, "ctx": 4096, "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nemotron-3-embed\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "img_in": null, "img_out": null, "in": 0.1, "input": [ "text" ], "label": "Embeddinglar", "max_out": 0, "max_tool_calls": null, "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "pricing_profiles": [ { "dimensions": {}, "label": "Core", "units": [ { "amount": 0.1, "amount_unit": "usd_per_million_tokens", "key": "in", "label": "Input", "side": "in" }, { "amount": 0, "amount_unit": "usd_per_million_tokens", "key": "out", "label": "Output", "side": "out" } ] } ], "request_model_id": "nemotron-3-embed", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] }, { "cache_read": null, "cache_write": null, "cache_write_1h": null, "cache_write_5m": null, "conditional_meter": null, "contract": { "data": { "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.", "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": [ "nonempty string", "nonempty string[]" ], "mode": "text_to_embedding" } ], "operation_modes": [ "single_embedding", "batched_embeddings" ], "outputs": { "all_results": "base64 vektorlarni faqat shu format so‘ralganda dekodlang; har bir vektor va uning dastlabki indeksini saqlang.", "family": "embedding", "fields": [ { "description": null, "name": "data[].index", "type": "integer" }, { "description": null, "name": "data[].embedding", "type": "finite float[]|base64 float32 vector" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "nemotron-3-embed-latest" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "conditions": "Har bir satr chet bo‘shliqlari olib tashlangach bo‘sh bo‘lmasligi kerak; data URI lar rad etiladi; qabul qilingan budjet tegishli UTF8 baytlarini o‘lchaydi.", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "input", "required": true, "type": "string|string[]" }, { "availability": "source_interface", "default": "query", "default_known": true, "enum": [ "query", "passage" ], "enum_known": true, "name": "input_type", "required": false, "type": "string" }, { "availability": "source_interface", "default": "float", "default_known": true, "enum": [ "float", "base64" ], "enum_known": true, "name": "encoding_format", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "NONE", "START", "END" ], "enum_known": true, "name": "truncate", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "minimum": 1, "name": "dimensions", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "user", "required": false, "type": "string" } ], "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.", "unsupported": [ "image objects", "data URI text inputs", "token integer arrays on this NVIDIA adapter" ] }, "hints": [ "Metod tuzilishlari va IDlari aniq; yetkazib beruvchining imkoniyati so‘rovni qabul qilishga ruxsatdan alohida." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": null, "required": [ "model", "input" ] }, "lifecycle": { "mode": "exact retained published method" }, "method": { "http": "POST", "path": "/v1/embeddings" }, "operation_modes": [ "embeddings" ], "output": { "family": [ "embedding" ], "format": "indexed vectors JSON", "multioutputs": "retain every actual response output" } }, "ctx": 4096, "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nemotron-3-embed-latest\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "embeddings", "http": "POST", "img_in": null, "img_out": null, "in": 0.1, "input": [ "text" ], "label": "Embeddinglar", "max_out": 0, "max_tool_calls": null, "out": 0, "output": [ "embedding" ], "path": "/v1/embeddings", "pricing_profiles": [ { "dimensions": {}, "label": "Core", "units": [ { "amount": 0.1, "amount_unit": "usd_per_million_tokens", "key": "in", "label": "Input", "side": "in" }, { "amount": 0, "amount_unit": "usd_per_million_tokens", "key": "out", "label": "Output", "side": "out" } ] } ], "request_model_id": "nemotron-3-embed-latest", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } ], "model_aliases": [ { "canonical_model_id": "nvidia/nemotron-3-embed-1b", "request_model_id": "nvidia/nemotron-3-embed-1b", "route_label": "Core" }, { "canonical_model_id": "nvidia/nemotron-3-embed-1b", "request_model_id": "nemotron-3-embed", "route_label": "Core" }, { "canonical_model_id": "nvidia/nemotron-3-embed-1b", "request_model_id": "nemotron-3-embed-latest", "route_label": "Core" } ], "name": "nvidia/nemotron-3-embed-1b", "price": { "in": 0.1, "in_label": null, "out": null, "out_label": null, "sort_in": 0.1, "sort_out": null, "varies": false }, "short": "Nemotron 3 Embed 1B matnlarni ko‘p tilli qidiruv, kod qidirish va RAG uchun vektorlarga aylantiradi. 32768 tokengacha ketma-ketliklarni qayta ishlab, 2048 ta sondan iborat ifoda beradi." }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 }, { "contract_json_pointer": "/methods/1/rawMethod/contract", "html_json_pointer": "/methods/1/html", "method_ordinal": 1 }, { "contract_json_pointer": "/methods/2/rawMethod/contract", "html_json_pointer": "/methods/2/html", "method_ordinal": 2 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod", "/methods/1/rawMethod", "/methods/2/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "Nemotron 3 Embed 1B matnlarni ko‘p tilli qidiruv, kod qidirish va RAG uchun vektorlarga aylantiradi. 32768 tokengacha ketma-ketliklarni qayta ishlab, 2048 ta sondan iborat ifoda beradi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "2ec4c5aebed658a07a5b619bbc77874c0ea3641e860990b6035b7407acea1899", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "54f6dc2126357b284729777b9840aa8b71b6302cc068a23d7672b1ae41df4658" }