{ "aboutHtml": "

Savol bo‘yicha kerakli sahifani topish

Llama Nemotron Embed VL 1B v2 NVIDIA tomonidan savolga javob berishga yordam beradigan materiallarni qidirish uchun ishlab chiqilgan. So‘rov matn bilan beriladi. Ailoot’ning joriy katalogida model uchun matnli kirish taqdim etilgan: savol va material matn sifatida kodlanadi. Ishlab chiqaruvchi sahifa tasviri yoki ajratib olingan matn bilan birga rasm ko‘rinishidagi hujjatni ham tavsiflaydi. Bunday vizual variantlar tasvirlarni qo‘llab-quvvatlaydigan alohida metodni talab qiladi. Tegishli kirishda model jadvallar, grafiklar va infografikani umumiy vektorlar fazosida ifodalaydi.

Bunday qidiruv katta hujjatlar to‘plami, ichki ma’lumotnomalar va tadqiqot materiallari bo‘yicha savol-javob yordamchilariga mos keladi. Kodlash natijasi — sonli vektor. Topilgan sahifa yoki parchani keyin javob shakllantiradigan modelga uzatish mumkin: Embed VL’ning o‘zi kontekst qidirish bosqichini bajaradi.

Ishlab chiqaruvchidagi hujjat variantlari

So‘rov va hujjat mustaqil kodlanadi. Shu sababli to‘plamni oldindan qayta ishlash, vektorlarni boshlang‘ich sahifalar bilan birga saqlash, yangi savolda esa faqat so‘rov vektorini hisoblash mumkin. Vektorlar o‘xshashligi materiallarni mazmun bo‘yicha tanlashga yordam beradi. So‘rov va hujjatning matnli rollari farq qiladi, ma’lumot tayyorlashda buni saqlash muhim.

Ifoda hajmi va kirish uzunligi

Har bir kirish 2048 tagacha sondan iborat bitta vektorga aylantiriladi. Matnli rejimda 8192 token budjeti ishlatiladi. Ishlab chiqaruvchining rasm va matn birikmali vizual metodi uchun ikkala qism kiradigan 10240 tokenlik umumiy budjet tavsiflangan; bu Ailoot metodining matn limiti emas. Tasvir bo‘laklarga ajratiladi: tavsiflangan konfiguratsiyada oltitagacha bo‘lak va butun sahifaning kichraytirilgan nusxasi, birga taxminan 1792 vizual token ruxsat etiladi. Uzun matnlarni bo‘lish yoki qisqartirish kerak.

Til qismi Llama 3.2 1B, vizual qismi esa SigLIP2 asosida qurilgan. Ular kirish tokenlari ifodalarini o‘rtachalashtirib, yagona vektor beradigan enkoderga birlashtirilgan. Kontrastiv o‘qitish savolni mos sahifaga yaqinlashtiradi va nomuvofiq materiallardan uzoqlashtiradi. Qidiruv ehtimoliy bo‘lib qoladi: vektorlar yaqinligi tanlangan parcha to‘g‘ri javobni o‘z ichiga olishini kafolatlamaydi.

", "aboutText": "### Savol bo‘yicha kerakli sahifani topish\n\nLlama Nemotron Embed VL 1B v2 NVIDIA tomonidan savolga javob berishga yordam beradigan materiallarni qidirish uchun ishlab chiqilgan. So‘rov matn bilan beriladi. Ailoot’ning joriy katalogida model uchun matnli kirish taqdim etilgan: savol va material matn sifatida kodlanadi. Ishlab chiqaruvchi sahifa tasviri yoki ajratib olingan matn bilan birga rasm ko‘rinishidagi hujjatni ham tavsiflaydi. Bunday vizual variantlar tasvirlarni qo‘llab-quvvatlaydigan alohida metodni talab qiladi. Tegishli kirishda model jadvallar, grafiklar va infografikani umumiy vektorlar fazosida ifodalaydi.\n\nBunday qidiruv katta hujjatlar to‘plami, ichki ma’lumotnomalar va tadqiqot materiallari bo‘yicha savol-javob yordamchilariga mos keladi. Kodlash natijasi — sonli vektor. Topilgan sahifa yoki parchani keyin javob shakllantiradigan modelga uzatish mumkin: Embed VL’ning o‘zi kontekst qidirish bosqichini bajaradi.\n\n### Ishlab chiqaruvchidagi hujjat variantlari\n\n- Matnli hujjat: alohida bo‘lim, parcha yoki sahifaning allaqachon tanilgan matni.\n- Tasvir: matni, jadvallari va grafik elementlari bilan to‘liq sahifa.\n- Matnli tasvir: sahifaning vizual ko‘rinishi va matn mazmuni bitta ifodada birlashtiriladi.\n\nSo‘rov va hujjat mustaqil kodlanadi. Shu sababli to‘plamni oldindan qayta ishlash, vektorlarni boshlang‘ich sahifalar bilan birga saqlash, yangi savolda esa faqat so‘rov vektorini hisoblash mumkin. Vektorlar o‘xshashligi materiallarni mazmun bo‘yicha tanlashga yordam beradi. So‘rov va hujjatning matnli rollari farq qiladi, ma’lumot tayyorlashda buni saqlash muhim.\n\n### Ifoda hajmi va kirish uzunligi\n\nHar bir kirish 2048 tagacha sondan iborat bitta vektorga aylantiriladi. Matnli rejimda 8192 token budjeti ishlatiladi. Ishlab chiqaruvchining rasm va matn birikmali vizual metodi uchun ikkala qism kiradigan 10240 tokenlik umumiy budjet tavsiflangan; bu Ailoot metodining matn limiti emas. Tasvir bo‘laklarga ajratiladi: tavsiflangan konfiguratsiyada oltitagacha bo‘lak va butun sahifaning kichraytirilgan nusxasi, birga taxminan 1792 vizual token ruxsat etiladi. Uzun matnlarni bo‘lish yoki qisqartirish kerak.\n\nTil qismi Llama 3.2 1B, vizual qismi esa SigLIP2 asosida qurilgan. Ular kirish tokenlari ifodalarini o‘rtachalashtirib, yagona vektor beradigan enkoderga birlashtirilgan. Kontrastiv o‘qitish savolni mos sahifaga yaqinlashtiradi va nomuvofiq materiallardan uzoqlashtiradi. Qidiruv ehtimoliy bo‘lib qoladi: vektorlar yaqinligi tanlangan parcha to‘g‘ri javobni o‘z ichiga olishini kafolatlamaydi.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "5113f1597d5fea19a4e967c9112c86e8ebc140dae29333fbbc079924055d617a", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
NV

Llama Nemotron Embed VL 1B v2

NVIDIA /nvidia/llama-nemotron-embed-vl-1b-v2

Boshlash

Llama Nemotron Embed VL v2 hujjatlar bo‘yicha semantik qidiruv uchun vektorlar yaratadi. Ailoot’ning joriy katalogida so‘rovlar va materiallar uchun matnli kirish taqdim etilgan.

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.

Savol bo‘yicha kerakli sahifani topish

Llama Nemotron Embed VL 1B v2 NVIDIA tomonidan savolga javob berishga yordam beradigan materiallarni qidirish uchun ishlab chiqilgan. So‘rov matn bilan beriladi. Ailoot’ning joriy katalogida model uchun matnli kirish taqdim etilgan: savol va material matn sifatida kodlanadi. Ishlab chiqaruvchi sahifa tasviri yoki ajratib olingan matn bilan birga rasm ko‘rinishidagi hujjatni ham tavsiflaydi. Bunday vizual variantlar tasvirlarni qo‘llab-quvvatlaydigan alohida metodni talab qiladi. Tegishli kirishda model jadvallar, grafiklar va infografikani umumiy vektorlar fazosida ifodalaydi.

Bunday qidiruv katta hujjatlar to‘plami, ichki ma’lumotnomalar va tadqiqot materiallari bo‘yicha savol-javob yordamchilariga mos keladi. Kodlash natijasi — sonli vektor. Topilgan sahifa yoki parchani keyin javob shakllantiradigan modelga uzatish mumkin: Embed VL’ning o‘zi kontekst qidirish bosqichini bajaradi.

Ishlab chiqaruvchidagi hujjat variantlari

  • Matnli hujjat: alohida bo‘lim, parcha yoki sahifaning allaqachon tanilgan matni.
  • Tasvir: matni, jadvallari va grafik elementlari bilan to‘liq sahifa.
  • Matnli tasvir: sahifaning vizual ko‘rinishi va matn mazmuni bitta ifodada birlashtiriladi.

So‘rov va hujjat mustaqil kodlanadi. Shu sababli to‘plamni oldindan qayta ishlash, vektorlarni boshlang‘ich sahifalar bilan birga saqlash, yangi savolda esa faqat so‘rov vektorini hisoblash mumkin. Vektorlar o‘xshashligi materiallarni mazmun bo‘yicha tanlashga yordam beradi. So‘rov va hujjatning matnli rollari farq qiladi, ma’lumot tayyorlashda buni saqlash muhim.

Ifoda hajmi va kirish uzunligi

Har bir kirish 2048 tagacha sondan iborat bitta vektorga aylantiriladi. Matnli rejimda 8192 token budjeti ishlatiladi. Ishlab chiqaruvchining rasm va matn birikmali vizual metodi uchun ikkala qism kiradigan 10240 tokenlik umumiy budjet tavsiflangan; bu Ailoot metodining matn limiti emas. Tasvir bo‘laklarga ajratiladi: tavsiflangan konfiguratsiyada oltitagacha bo‘lak va butun sahifaning kichraytirilgan nusxasi, birga taxminan 1792 vizual token ruxsat etiladi. Uzun matnlarni bo‘lish yoki qisqartirish kerak.

Til qismi Llama 3.2 1B, vizual qismi esa SigLIP2 asosida qurilgan. Ular kirish tokenlari ifodalarini o‘rtachalashtirib, yagona vektor beradigan enkoderga birlashtirilgan. Kontrastiv o‘qitish savolni mos sahifaga yaqinlashtiradi va nomuvofiq materiallardan uzoqlashtiradi. Qidiruv ehtimoliy bo‘lib qoladi: vektorlar yaqinligi tanlangan parcha to‘g‘ri javobni o‘z ichiga olishini 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/llama-nemotron-embed-vl-1b-v2
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/llama-nemotron-embed-vl-1b-v2", "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/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest

", "method_identity": { "effective_request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "route_label": "Core" }, "method_key": "Core|embeddings|/v1/embeddings|nvidia%2Fllama-nemotron-embed-vl-1b-v2", "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/llama-nemotron-embed-vl-1b-v2" ], "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/llama-nemotron-embed-vl-1b-v2\", \"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/llama-nemotron-embed-vl-1b-v2", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } }, { "html": "
NV

Llama Nemotron Embed VL 1B v2

NVIDIA /nemotron-embed-vl

Boshlash

Llama Nemotron Embed VL v2 hujjatlar bo‘yicha semantik qidiruv uchun vektorlar yaratadi. Ailoot’ning joriy katalogida so‘rovlar va materiallar uchun matnli kirish taqdim etilgan.

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.

Savol bo‘yicha kerakli sahifani topish

Llama Nemotron Embed VL 1B v2 NVIDIA tomonidan savolga javob berishga yordam beradigan materiallarni qidirish uchun ishlab chiqilgan. So‘rov matn bilan beriladi. Ailoot’ning joriy katalogida model uchun matnli kirish taqdim etilgan: savol va material matn sifatida kodlanadi. Ishlab chiqaruvchi sahifa tasviri yoki ajratib olingan matn bilan birga rasm ko‘rinishidagi hujjatni ham tavsiflaydi. Bunday vizual variantlar tasvirlarni qo‘llab-quvvatlaydigan alohida metodni talab qiladi. Tegishli kirishda model jadvallar, grafiklar va infografikani umumiy vektorlar fazosida ifodalaydi.

Bunday qidiruv katta hujjatlar to‘plami, ichki ma’lumotnomalar va tadqiqot materiallari bo‘yicha savol-javob yordamchilariga mos keladi. Kodlash natijasi — sonli vektor. Topilgan sahifa yoki parchani keyin javob shakllantiradigan modelga uzatish mumkin: Embed VL’ning o‘zi kontekst qidirish bosqichini bajaradi.

Ishlab chiqaruvchidagi hujjat variantlari

  • Matnli hujjat: alohida bo‘lim, parcha yoki sahifaning allaqachon tanilgan matni.
  • Tasvir: matni, jadvallari va grafik elementlari bilan to‘liq sahifa.
  • Matnli tasvir: sahifaning vizual ko‘rinishi va matn mazmuni bitta ifodada birlashtiriladi.

So‘rov va hujjat mustaqil kodlanadi. Shu sababli to‘plamni oldindan qayta ishlash, vektorlarni boshlang‘ich sahifalar bilan birga saqlash, yangi savolda esa faqat so‘rov vektorini hisoblash mumkin. Vektorlar o‘xshashligi materiallarni mazmun bo‘yicha tanlashga yordam beradi. So‘rov va hujjatning matnli rollari farq qiladi, ma’lumot tayyorlashda buni saqlash muhim.

Ifoda hajmi va kirish uzunligi

Har bir kirish 2048 tagacha sondan iborat bitta vektorga aylantiriladi. Matnli rejimda 8192 token budjeti ishlatiladi. Ishlab chiqaruvchining rasm va matn birikmali vizual metodi uchun ikkala qism kiradigan 10240 tokenlik umumiy budjet tavsiflangan; bu Ailoot metodining matn limiti emas. Tasvir bo‘laklarga ajratiladi: tavsiflangan konfiguratsiyada oltitagacha bo‘lak va butun sahifaning kichraytirilgan nusxasi, birga taxminan 1792 vizual token ruxsat etiladi. Uzun matnlarni bo‘lish yoki qisqartirish kerak.

Til qismi Llama 3.2 1B, vizual qismi esa SigLIP2 asosida qurilgan. Ular kirish tokenlari ifodalarini o‘rtachalashtirib, yagona vektor beradigan enkoderga birlashtirilgan. Kontrastiv o‘qitish savolni mos sahifaga yaqinlashtiradi va nomuvofiq materiallardan uzoqlashtiradi. Qidiruv ehtimoliy bo‘lib qoladi: vektorlar yaqinligi tanlangan parcha to‘g‘ri javobni o‘z ichiga olishini 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-embed-vl
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-embed-vl", "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/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest

", "method_identity": { "effective_request_model_id": "nemotron-embed-vl", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "nemotron-embed-vl", "route_label": "Core" }, "method_key": "Core|embeddings|/v1/embeddings|nemotron-embed-vl", "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-embed-vl" ], "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-embed-vl\", \"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-embed-vl", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } }, { "html": "
NV

Llama Nemotron Embed VL 1B v2

NVIDIA /nemotron-embed-vl-latest

Boshlash

Llama Nemotron Embed VL v2 hujjatlar bo‘yicha semantik qidiruv uchun vektorlar yaratadi. Ailoot’ning joriy katalogida so‘rovlar va materiallar uchun matnli kirish taqdim etilgan.

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.

Savol bo‘yicha kerakli sahifani topish

Llama Nemotron Embed VL 1B v2 NVIDIA tomonidan savolga javob berishga yordam beradigan materiallarni qidirish uchun ishlab chiqilgan. So‘rov matn bilan beriladi. Ailoot’ning joriy katalogida model uchun matnli kirish taqdim etilgan: savol va material matn sifatida kodlanadi. Ishlab chiqaruvchi sahifa tasviri yoki ajratib olingan matn bilan birga rasm ko‘rinishidagi hujjatni ham tavsiflaydi. Bunday vizual variantlar tasvirlarni qo‘llab-quvvatlaydigan alohida metodni talab qiladi. Tegishli kirishda model jadvallar, grafiklar va infografikani umumiy vektorlar fazosida ifodalaydi.

Bunday qidiruv katta hujjatlar to‘plami, ichki ma’lumotnomalar va tadqiqot materiallari bo‘yicha savol-javob yordamchilariga mos keladi. Kodlash natijasi — sonli vektor. Topilgan sahifa yoki parchani keyin javob shakllantiradigan modelga uzatish mumkin: Embed VL’ning o‘zi kontekst qidirish bosqichini bajaradi.

Ishlab chiqaruvchidagi hujjat variantlari

  • Matnli hujjat: alohida bo‘lim, parcha yoki sahifaning allaqachon tanilgan matni.
  • Tasvir: matni, jadvallari va grafik elementlari bilan to‘liq sahifa.
  • Matnli tasvir: sahifaning vizual ko‘rinishi va matn mazmuni bitta ifodada birlashtiriladi.

So‘rov va hujjat mustaqil kodlanadi. Shu sababli to‘plamni oldindan qayta ishlash, vektorlarni boshlang‘ich sahifalar bilan birga saqlash, yangi savolda esa faqat so‘rov vektorini hisoblash mumkin. Vektorlar o‘xshashligi materiallarni mazmun bo‘yicha tanlashga yordam beradi. So‘rov va hujjatning matnli rollari farq qiladi, ma’lumot tayyorlashda buni saqlash muhim.

Ifoda hajmi va kirish uzunligi

Har bir kirish 2048 tagacha sondan iborat bitta vektorga aylantiriladi. Matnli rejimda 8192 token budjeti ishlatiladi. Ishlab chiqaruvchining rasm va matn birikmali vizual metodi uchun ikkala qism kiradigan 10240 tokenlik umumiy budjet tavsiflangan; bu Ailoot metodining matn limiti emas. Tasvir bo‘laklarga ajratiladi: tavsiflangan konfiguratsiyada oltitagacha bo‘lak va butun sahifaning kichraytirilgan nusxasi, birga taxminan 1792 vizual token ruxsat etiladi. Uzun matnlarni bo‘lish yoki qisqartirish kerak.

Til qismi Llama 3.2 1B, vizual qismi esa SigLIP2 asosida qurilgan. Ular kirish tokenlari ifodalarini o‘rtachalashtirib, yagona vektor beradigan enkoderga birlashtirilgan. Kontrastiv o‘qitish savolni mos sahifaga yaqinlashtiradi va nomuvofiq materiallardan uzoqlashtiradi. Qidiruv ehtimoliy bo‘lib qoladi: vektorlar yaqinligi tanlangan parcha to‘g‘ri javobni o‘z ichiga olishini 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-embed-vl-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-embed-vl-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/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

nvidia/llama-nemotron-embed-vl-1b-v2, nemotron-embed-vl, nemotron-embed-vl-latest

", "method_identity": { "effective_request_model_id": "nemotron-embed-vl-latest", "fmt": "embeddings", "http": "POST", "path": "/v1/embeddings", "request_model_id": "nemotron-embed-vl-latest", "route_label": "Core" }, "method_key": "Core|embeddings|/v1/embeddings|nemotron-embed-vl-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-embed-vl-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-embed-vl-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-embed-vl-latest", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } } ], "model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "nativeProvenance": { "input_facts_canonical_sha256": "12b56a738a8a9ee44462194ef6af48012261a92073e4fb05f19aa570fef6e80a", "input_html_sha256": "8c93b346b26d075f9256df2fdead350f9ce49fea51eaff90b920beea25525c12", "private_audit_sha256": "4ab600ef5886f32006a13e4d2f33f150b4b3cd7b0ab9b3b9b1e512573953fe26", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/nvidia-llama-nemotron-embed-vl-1b-v2/", "description": "Llama Nemotron Embed VL v2 hujjatlar bo‘yicha semantik qidiruv uchun vektorlar yaratadi. Ailoot’ning joriy katalogida so‘rovlar va materiallar uchun matnli kirish taqdim etilgan.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/nvidia-llama-nemotron-embed-vl-1b-v2/index.md", "title": "Llama Nemotron Embed VL 1B v2 — Ailoot", "url": "https://ailoot.ai/models/uz/nvidia-llama-nemotron-embed-vl-1b-v2/" }, "rawModel": { "about": "### Savol bo‘yicha kerakli sahifani topish\n\nLlama Nemotron Embed VL 1B v2 NVIDIA tomonidan savolga javob berishga yordam beradigan materiallarni qidirish uchun ishlab chiqilgan. So‘rov matn bilan beriladi. Ailoot’ning joriy katalogida model uchun matnli kirish taqdim etilgan: savol va material matn sifatida kodlanadi. Ishlab chiqaruvchi sahifa tasviri yoki ajratib olingan matn bilan birga rasm ko‘rinishidagi hujjatni ham tavsiflaydi. Bunday vizual variantlar tasvirlarni qo‘llab-quvvatlaydigan alohida metodni talab qiladi. Tegishli kirishda model jadvallar, grafiklar va infografikani umumiy vektorlar fazosida ifodalaydi.\n\nBunday qidiruv katta hujjatlar to‘plami, ichki ma’lumotnomalar va tadqiqot materiallari bo‘yicha savol-javob yordamchilariga mos keladi. Kodlash natijasi — sonli vektor. Topilgan sahifa yoki parchani keyin javob shakllantiradigan modelga uzatish mumkin: Embed VL’ning o‘zi kontekst qidirish bosqichini bajaradi.\n\n### Ishlab chiqaruvchidagi hujjat variantlari\n\n- Matnli hujjat: alohida bo‘lim, parcha yoki sahifaning allaqachon tanilgan matni.\n- Tasvir: matni, jadvallari va grafik elementlari bilan to‘liq sahifa.\n- Matnli tasvir: sahifaning vizual ko‘rinishi va matn mazmuni bitta ifodada birlashtiriladi.\n\nSo‘rov va hujjat mustaqil kodlanadi. Shu sababli to‘plamni oldindan qayta ishlash, vektorlarni boshlang‘ich sahifalar bilan birga saqlash, yangi savolda esa faqat so‘rov vektorini hisoblash mumkin. Vektorlar o‘xshashligi materiallarni mazmun bo‘yicha tanlashga yordam beradi. So‘rov va hujjatning matnli rollari farq qiladi, ma’lumot tayyorlashda buni saqlash muhim.\n\n### Ifoda hajmi va kirish uzunligi\n\nHar bir kirish 2048 tagacha sondan iborat bitta vektorga aylantiriladi. Matnli rejimda 8192 token budjeti ishlatiladi. Ishlab chiqaruvchining rasm va matn birikmali vizual metodi uchun ikkala qism kiradigan 10240 tokenlik umumiy budjet tavsiflangan; bu Ailoot metodining matn limiti emas. Tasvir bo‘laklarga ajratiladi: tavsiflangan konfiguratsiyada oltitagacha bo‘lak va butun sahifaning kichraytirilgan nusxasi, birga taxminan 1792 vizual token ruxsat etiladi. Uzun matnlarni bo‘lish yoki qisqartirish kerak.\n\nTil qismi Llama 3.2 1B, vizual qismi esa SigLIP2 asosida qurilgan. Ular kirish tokenlari ifodalarini o‘rtachalashtirib, yagona vektor beradigan enkoderga birlashtirilgan. Kontrastiv o‘qitish savolni mos sahifaga yaqinlashtiradi va nomuvofiq materiallardan uzoqlashtiradi. Qidiruv ehtimoliy bo‘lib qoladi: vektorlar yaqinligi tanlangan parcha to‘g‘ri javobni o‘z ichiga olishini kafolatlamaydi.", "alias_of": null, "aliases": [ "nemotron-embed-vl", "nemotron-embed-vl-latest" ], "author": "NVIDIA", "ctx": 4096, "display_name": "Llama Nemotron Embed VL 1B v2", "formats": [ "embeddings" ], "id": "nvidia/llama-nemotron-embed-vl-1b-v2", "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/llama-nemotron-embed-vl-1b-v2" ], "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/llama-nemotron-embed-vl-1b-v2\", \"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/llama-nemotron-embed-vl-1b-v2", "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-embed-vl" ], "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-embed-vl\", \"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-embed-vl", "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-embed-vl-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-embed-vl-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-embed-vl-latest", "route_label": "Core", "status_reads": [], "tiers": [], "tool_prices": null, "unit": "token", "units": [] } ], "model_aliases": [ { "canonical_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "route_label": "Core" }, { "canonical_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "request_model_id": "nemotron-embed-vl", "route_label": "Core" }, { "canonical_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2", "request_model_id": "nemotron-embed-vl-latest", "route_label": "Core" } ], "name": "nvidia/llama-nemotron-embed-vl-1b-v2", "price": { "in": 0.1, "in_label": null, "out": null, "out_label": null, "sort_in": 0.1, "sort_out": null, "varies": false }, "short": "Llama Nemotron Embed VL v2 hujjatlar bo‘yicha semantik qidiruv uchun vektorlar yaratadi. Ailoot’ning joriy katalogida so‘rovlar va materiallar uchun matnli kirish taqdim etilgan." }, "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": "Llama Nemotron Embed VL v2 hujjatlar bo‘yicha semantik qidiruv uchun vektorlar yaratadi. Ailoot’ning joriy katalogida so‘rovlar va materiallar uchun matnli kirish taqdim etilgan.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "2eafc26fa1a3ee8bd0d51f669ee81c90308b023132093d2dbf7c4aa8b25000d3", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "5fcfe416a1f6848f9c9f5151eeb8b66efa6dc6174e85f6a4740b1890959b5118" }