Source: https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/
Canonical: https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/
Language: uz

# Nemotron 3 Embed 1B

[Markdown](https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/index.md) · [JSON](https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/facts.json)

## [Nemotron 3 Embed 1B](https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/)

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.

- [**Ailoot · Embeddinglar** `POST /v1/embeddings` `nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/#method-0)
- [**Ailoot · Embeddinglar** `POST /v1/embeddings` `nemotron-3-embed`](https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/#method-1)
- [**Ailoot · Embeddinglar** `POST /v1/embeddings` `nemotron-3-embed-latest`](https://ailoot.ai/models/uz/nvidia-nemotron-3-embed-1b/#method-2)

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 · Embeddinglar** `POST /v1/embeddings` `nvidia/nemotron-3-embed-1b`

[`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings)

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

Model ID

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

### Ailoot da mavjud · kirish ma’lumotlari va usullar

**Ailoot · Embeddinglar** `POST /v1/embeddings`

Kirish

`text`

Chiqish

`embedding`

Kontekst

4 096 tokens

Chiqish formati

json

| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
| --- | --- | --- | --- | --- |
| Maydon`model` | Tur`string` | TalabMajburiy | Qiymatlar va cheklovlar nvidia/nemotron-3-embed-1b | Standart— |
| Maydon`input` | Tur`string\|string[]` | TalabMajburiy | Qiymatlar 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— |
| Maydon`input_type` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar query, passage | Standartquery |
| Maydon`encoding_format` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar float, base64 | Standartfloat |
| Maydon`truncate` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar NONE, START, END | Standart— |
| Maydon`dimensions` | Tur`integer` | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 | Standart— |
| Maydon`user` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |

Kirish materiallari birikmalari

**text\_to\_embedding**

Usul parametrlari va hayot sikli

| Parametr | Parametr talablari |
| --- | --- |
| Parametr`material_types` | Parametr talablaritext |
| Parametr`required` | Parametr talablarimodel, input |
| Parametr`optional_controls` | Parametr talablarinull |
| Parametr`content_type` | Parametr 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

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 →](https://ailoot.ai/docs/uz/billing/reserve/)

### So‘rov namunalari

#### Ailoot · Embeddinglar

Embedding namunasi

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nvidia/nemotron-3-embed-1b", "input": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]=[text]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))
PY_RESULT
```

### 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

| Maydon | Tur |
| --- | --- |
| Maydon`data[].index` | Turinteger |
| Maydon`data[].embedding` | Turfinite float\[\]\|base64 float32 vector |
| Maydon`model` | Turstring |
| Maydon`usage` | Turobject |

Xatolar va keyingi qadamlar

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

### Taxalluslar

#### Model taxalluslari

**Ailoot** [`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings), [`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings), [`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

#### Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

#### Chaqirish uchun tegishli ID lar

[`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings), [`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings), [`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

**Ailoot · Embeddinglar** `POST /v1/embeddings` `nemotron-3-embed`

[`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings)

NVIDIA /`nemotron-3-embed`

Model ID

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

### Ailoot da mavjud · kirish ma’lumotlari va usullar

**Ailoot · Embeddinglar** `POST /v1/embeddings`

Kirish

`text`

Chiqish

`embedding`

Kontekst

4 096 tokens

Chiqish formati

json

| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
| --- | --- | --- | --- | --- |
| Maydon`model` | Tur`string` | TalabMajburiy | Qiymatlar va cheklovlar nemotron-3-embed | Standart— |
| Maydon`input` | Tur`string\|string[]` | TalabMajburiy | Qiymatlar 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— |
| Maydon`input_type` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar query, passage | Standartquery |
| Maydon`encoding_format` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar float, base64 | Standartfloat |
| Maydon`truncate` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar NONE, START, END | Standart— |
| Maydon`dimensions` | Tur`integer` | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 | Standart— |
| Maydon`user` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |

Kirish materiallari birikmalari

**text\_to\_embedding**

Usul parametrlari va hayot sikli

| Parametr | Parametr talablari |
| --- | --- |
| Parametr`material_types` | Parametr talablaritext |
| Parametr`required` | Parametr talablarimodel, input |
| Parametr`optional_controls` | Parametr talablarinull |
| Parametr`content_type` | Parametr 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

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 →](https://ailoot.ai/docs/uz/billing/reserve/)

### So‘rov namunalari

#### Ailoot · Embeddinglar

Embedding namunasi

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nemotron-3-embed", "input": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]=[text]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))
PY_RESULT
```

### 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

| Maydon | Tur |
| --- | --- |
| Maydon`data[].index` | Turinteger |
| Maydon`data[].embedding` | Turfinite float\[\]\|base64 float32 vector |
| Maydon`model` | Turstring |
| Maydon`usage` | Turobject |

Xatolar va keyingi qadamlar

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

### Taxalluslar

#### Model taxalluslari

**Ailoot** [`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings), [`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings), [`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

#### Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

#### Chaqirish uchun tegishli ID lar

[`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings), [`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings), [`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

**Ailoot · Embeddinglar** `POST /v1/embeddings` `nemotron-3-embed-latest`

[`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

NVIDIA /`nemotron-3-embed-latest`

Model ID

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

### Ailoot da mavjud · kirish ma’lumotlari va usullar

**Ailoot · Embeddinglar** `POST /v1/embeddings`

Kirish

`text`

Chiqish

`embedding`

Kontekst

4 096 tokens

Chiqish formati

json

| Maydon | Tur | Talab | Qiymatlar va cheklovlar | Standart |
| --- | --- | --- | --- | --- |
| Maydon`model` | Tur`string` | TalabMajburiy | Qiymatlar va cheklovlar nemotron-3-embed-latest | Standart— |
| Maydon`input` | Tur`string\|string[]` | TalabMajburiy | Qiymatlar 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— |
| Maydon`input_type` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar query, passage | Standartquery |
| Maydon`encoding_format` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar float, base64 | Standartfloat |
| Maydon`truncate` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar NONE, START, END | Standart— |
| Maydon`dimensions` | Tur`integer` | TalabIxtiyoriy | Qiymatlar va cheklovlar Eng kam: 1 | Standart— |
| Maydon`user` | Tur`string` | TalabIxtiyoriy | Qiymatlar va cheklovlar | Standart— |

Kirish materiallari birikmalari

**text\_to\_embedding**

Usul parametrlari va hayot sikli

| Parametr | Parametr talablari |
| --- | --- |
| Parametr`material_types` | Parametr talablaritext |
| Parametr`required` | Parametr talablarimodel, input |
| Parametr`optional_controls` | Parametr talablarinull |
| Parametr`content_type` | Parametr 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

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 →](https://ailoot.ai/docs/uz/billing/reserve/)

### So‘rov namunalari

#### Ailoot · Embeddinglar

Embedding namunasi

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "nemotron-3-embed-latest", "input": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["input"]=[text]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")}))
PY_RESULT
```

### 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

| Maydon | Tur |
| --- | --- |
| Maydon`data[].index` | Turinteger |
| Maydon`data[].embedding` | Turfinite float\[\]\|base64 float32 vector |
| Maydon`model` | Turstring |
| Maydon`usage` | Turobject |

Xatolar va keyingi qadamlar

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

### Taxalluslar

#### Model taxalluslari

**Ailoot** [`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings), [`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings), [`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

#### Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

#### Chaqirish uchun tegishli ID lar

[`nvidia/nemotron-3-embed-1b`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nvidia%2Fnemotron-3-embed-1b&fmt=embeddings), [`nemotron-3-embed`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed&fmt=embeddings), [`nemotron-3-embed-latest`](https://ailoot.ai/models/uz/#m=nvidia%2Fnemotron-3-embed-1b&route=Core&caller=nemotron-3-embed-latest&fmt=embeddings)

FAQ · nvidia/nemotron-3-embed-1b

Qanday kirish ma’lumotlari va natijalar mavjud?

`POST /v1/embeddings` · `nvidia/nemotron-3-embed-1b`: `text` → `embedding` `POST /v1/embeddings` · `nemotron-3-embed`: `text` → `embedding` `POST /v1/embeddings` · `nemotron-3-embed-latest`: `text` → `embedding`

So‘rov qaysi maydonlardan foydalanadi?

`POST /v1/embeddings` · `nvidia/nemotron-3-embed-1b`: `model`, `input`, `input_type`, `encoding_format`, `truncate`, `dimensions`, `user` `POST /v1/embeddings` · `nemotron-3-embed`: `model`, `input`, `input_type`, `encoding_format`, `truncate`, `dimensions`, `user` `POST /v1/embeddings` · `nemotron-3-embed-latest`: `model`, `input`, `input_type`, `encoding_format`, `truncate`, `dimensions`, `user`

Qanday cheklovlar amal qiladi?

`POST /v1/embeddings` · `nvidia/nemotron-3-embed-1b`: `ctx`: `4096`; `input.min_items`: `1`; `dimensions.minimum`: `1` `POST /v1/embeddings` · `nemotron-3-embed`: `ctx`: `4096`; `input.min_items`: `1`; `dimensions.minimum`: `1` `POST /v1/embeddings` · `nemotron-3-embed-latest`: `ctx`: `4096`; `input.min_items`: `1`; `dimensions.minimum`: `1`

Ailoot’ni Claude Code, OpenCode, Hermes yoki ilovangizga ulang.

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