{ "aboutHtml": "

Cohere Rerank 4 Fast — pastroq kechikish va yuqoriroq o‘tkazuvchanlikli qidiruv vazifalariga mo‘ljallangan yengilroq Rerank 4 nashri. U so‘rov va nomzod hujjatlarni qabul qilib, dolzarblik tartibi va ballarini qaytaradi. U interaktiv qidiruv, takroriy dalil tanlash va mavjud natijalar ro‘yxatini qayta tartiblash kerak bo‘lgan ilovalar uchun foydali. Fast qidiruv sifati ustuvor bo‘lgan murakkabroq holatlar uchun mo‘ljallangan Rerank 4 Pro’dan alohida nashr bo‘lib qoladi.

Ishlab chiqaruvchi kodi rerank-v4.0-fast. Pro kabi uning konteksti 32,768 token va ko‘p tilli qamrovi 100 dan ortiq tildir. So‘rov va bitta hujjat kontekstni bo‘lishadi; bu vektor o‘lchami yoki butun indekslangan korpus uchun byudjet emas. Modelga berilmagan manbadan hujjatlar topishini kutish o‘rniga, taqqoslanadigan nomzod matnlarni taqdim eting.

Hujjatlashtirilgan natija asl hujjat indeksi va relevance_score’ni o‘z ichiga oladi. top_n qancha natija qaytarilishini tanlaydi, u berilmasa esa to‘liq saralangan ro‘yxat qaytadi. Yangi tartib haqiqiy parchalarga birlashtirilishi uchun kirishlar yonida manba identifikatorlarini saqlang. Mahsulot qidiruvida so‘rovda kerakli mahsulot xususiyatlarini tavsiflang va nomzod tavsiflarni qayta saralang; yuqori o‘rin uni hosil qilgan mahsulot yozuvi bilan bog‘liq qolishi kerak.

Model inglizcha va boshqa tillardagi hujjatlar hamda yarim tuzilmali yozuvlar bilan ishlay oladi. V2 jarayonida hujjat kirishlari satrlardir, Cohere esa tuzilmali ma’lumotni YAML sifatida ifodalashni tavsiya etadi. Muhim maydonlarni oldinga qo‘ying va tartibini saqlang, shunda kesish dolzarblikni baholash uchun kerakli mazmunni olib tashlamaydi. Qo‘llab-quvvatlash yozuvi sarlavha, mahsulot va tartib matnini; xabar esa mavzu va tanasini saqlashi mumkin. Bunday aniq munosabatlar saralovchiga mos kalit so‘zlar ro‘yxatidan ortiq axborot beradi.

Cohere tayyorgarlik qo‘llanmasidagi modelga xos so‘rov chegarasi 16,384 token bo‘lib, so‘rov va hujjat umumiy kontekstdan foydalanadi. Qo‘llanmada uzunroq hujjat bo‘laklarini qayta ishlash tasvirlangan, V2 ma’lumotnomasi esa max_tokens_per_doc orqali hujjatni standart 4096 tokengacha kesishni alohida belgilaydi. Modelning butun 32K oynasi doimo ishlatiladi deb taxmin qilish o‘rniga, haqiqiy hujjat sozlamasini tekshiring. Haqiqiy qidiruv vazifasi uchun tegishli parchalar yoki mos hujjat byudjetini tayyorlang.

Tavsiya etilgan unumdorlik uchun Cohere bitta so‘rovda 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq nomzod to‘plamlarini identifikatorlarini saqlagan holda boshqariladigan guruhlarga ajrating. Chiqish ballari 0 dan 1 gacha normallashtiriladi va so‘rov hamda parchalarga bog‘liq. Ularning tartibi asosiy natijadir; ball dolzarblikning chiziqli ko‘paytmasi yoki parcha to‘g‘riligiga isbot emas. Har qanday filtrlash chegarasini sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.

Foydali Fast natijasi — qidiruv namoyishini darhol boshqaradigan yoki javob yozish bosqichiga dalil tanlaydigan saralangan ro‘yxat. Manba matnini ball bilan birga saqlang va yuqori natijalar so‘ralgan shartlarga haqiqatan javob berishini tekshiring. Fast vazifasi yangi tushuntirish yoki embedding vektori yaratish emas, qidiruv saralashidir. Dolzarblik xatti-harakatini takomillashtirganda nomlangan nashr va kirish tayyorgarligini izchil saqlang.

", "aboutText": "Cohere Rerank 4 Fast — pastroq kechikish va yuqoriroq o‘tkazuvchanlikli qidiruv vazifalariga mo‘ljallangan yengilroq Rerank 4 nashri. U so‘rov va nomzod hujjatlarni qabul qilib, dolzarblik tartibi va ballarini qaytaradi. U interaktiv qidiruv, takroriy dalil tanlash va mavjud natijalar ro‘yxatini qayta tartiblash kerak bo‘lgan ilovalar uchun foydali. Fast qidiruv sifati ustuvor bo‘lgan murakkabroq holatlar uchun mo‘ljallangan Rerank 4 Pro’dan alohida nashr bo‘lib qoladi.\n\nIshlab chiqaruvchi kodi rerank-v4.0-fast. Pro kabi uning konteksti 32,768 token va ko‘p tilli qamrovi 100 dan ortiq tildir. So‘rov va bitta hujjat kontekstni bo‘lishadi; bu vektor o‘lchami yoki butun indekslangan korpus uchun byudjet emas. Modelga berilmagan manbadan hujjatlar topishini kutish o‘rniga, taqqoslanadigan nomzod matnlarni taqdim eting.\n\nHujjatlashtirilgan natija asl hujjat indeksi va relevance_score’ni o‘z ichiga oladi. top_n qancha natija qaytarilishini tanlaydi, u berilmasa esa to‘liq saralangan ro‘yxat qaytadi. Yangi tartib haqiqiy parchalarga birlashtirilishi uchun kirishlar yonida manba identifikatorlarini saqlang. Mahsulot qidiruvida so‘rovda kerakli mahsulot xususiyatlarini tavsiflang va nomzod tavsiflarni qayta saralang; yuqori o‘rin uni hosil qilgan mahsulot yozuvi bilan bog‘liq qolishi kerak.\n\nModel inglizcha va boshqa tillardagi hujjatlar hamda yarim tuzilmali yozuvlar bilan ishlay oladi. V2 jarayonida hujjat kirishlari satrlardir, Cohere esa tuzilmali ma’lumotni YAML sifatida ifodalashni tavsiya etadi. Muhim maydonlarni oldinga qo‘ying va tartibini saqlang, shunda kesish dolzarblikni baholash uchun kerakli mazmunni olib tashlamaydi. Qo‘llab-quvvatlash yozuvi sarlavha, mahsulot va tartib matnini; xabar esa mavzu va tanasini saqlashi mumkin. Bunday aniq munosabatlar saralovchiga mos kalit so‘zlar ro‘yxatidan ortiq axborot beradi.\n\nCohere tayyorgarlik qo‘llanmasidagi modelga xos so‘rov chegarasi 16,384 token bo‘lib, so‘rov va hujjat umumiy kontekstdan foydalanadi. Qo‘llanmada uzunroq hujjat bo‘laklarini qayta ishlash tasvirlangan, V2 ma’lumotnomasi esa max_tokens_per_doc orqali hujjatni standart 4096 tokengacha kesishni alohida belgilaydi. Modelning butun 32K oynasi doimo ishlatiladi deb taxmin qilish o‘rniga, haqiqiy hujjat sozlamasini tekshiring. Haqiqiy qidiruv vazifasi uchun tegishli parchalar yoki mos hujjat byudjetini tayyorlang.\n\nTavsiya etilgan unumdorlik uchun Cohere bitta so‘rovda 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq nomzod to‘plamlarini identifikatorlarini saqlagan holda boshqariladigan guruhlarga ajrating. Chiqish ballari 0 dan 1 gacha normallashtiriladi va so‘rov hamda parchalarga bog‘liq. Ularning tartibi asosiy natijadir; ball dolzarblikning chiziqli ko‘paytmasi yoki parcha to‘g‘riligiga isbot emas. Har qanday filtrlash chegarasini sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.\n\nFoydali Fast natijasi — qidiruv namoyishini darhol boshqaradigan yoki javob yozish bosqichiga dalil tanlaydigan saralangan ro‘yxat. Manba matnini ball bilan birga saqlang va yuqori natijalar so‘ralgan shartlarga haqiqatan javob berishini tekshiring. Fast vazifasi yangi tushuntirish yoki embedding vektori yaratish emas, qidiruv saralashidir. Dolzarblik xatti-harakatini takomillashtirganda nomlangan nashr va kirish tayyorgarligini izchil saqlang.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "f2a0221ede28e0080bd3788d734a875cf4ee21e5fea14cbd05e9ee70b9a6d784", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

Cohere: Rerank 4 Fast

Cohere /cohere/rerank-4-fast

Boshlash

Cohere Rerank 4 Fast past kechikish va yuqori o‘tkazuvchanlikka urg‘u berib, nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U qidiruv va qidiruv bilan boyitilgan jarayonlar uchun 32K kontekst, 100 dan ortiq til hamda yarim tuzilmali matnni qo‘llab-quvvatlaydi.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Saralangan natijalar
Model haqida

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

Cohere Rerank 4 Fast — pastroq kechikish va yuqoriroq o‘tkazuvchanlikli qidiruv vazifalariga mo‘ljallangan yengilroq Rerank 4 nashri. U so‘rov va nomzod hujjatlarni qabul qilib, dolzarblik tartibi va ballarini qaytaradi. U interaktiv qidiruv, takroriy dalil tanlash va mavjud natijalar ro‘yxatini qayta tartiblash kerak bo‘lgan ilovalar uchun foydali. Fast qidiruv sifati ustuvor bo‘lgan murakkabroq holatlar uchun mo‘ljallangan Rerank 4 Pro’dan alohida nashr bo‘lib qoladi.

Ishlab chiqaruvchi kodi rerank-v4.0-fast. Pro kabi uning konteksti 32,768 token va ko‘p tilli qamrovi 100 dan ortiq tildir. So‘rov va bitta hujjat kontekstni bo‘lishadi; bu vektor o‘lchami yoki butun indekslangan korpus uchun byudjet emas. Modelga berilmagan manbadan hujjatlar topishini kutish o‘rniga, taqqoslanadigan nomzod matnlarni taqdim eting.

Hujjatlashtirilgan natija asl hujjat indeksi va relevance_score’ni o‘z ichiga oladi. top_n qancha natija qaytarilishini tanlaydi, u berilmasa esa to‘liq saralangan ro‘yxat qaytadi. Yangi tartib haqiqiy parchalarga birlashtirilishi uchun kirishlar yonida manba identifikatorlarini saqlang. Mahsulot qidiruvida so‘rovda kerakli mahsulot xususiyatlarini tavsiflang va nomzod tavsiflarni qayta saralang; yuqori o‘rin uni hosil qilgan mahsulot yozuvi bilan bog‘liq qolishi kerak.

Model inglizcha va boshqa tillardagi hujjatlar hamda yarim tuzilmali yozuvlar bilan ishlay oladi. V2 jarayonida hujjat kirishlari satrlardir, Cohere esa tuzilmali ma’lumotni YAML sifatida ifodalashni tavsiya etadi. Muhim maydonlarni oldinga qo‘ying va tartibini saqlang, shunda kesish dolzarblikni baholash uchun kerakli mazmunni olib tashlamaydi. Qo‘llab-quvvatlash yozuvi sarlavha, mahsulot va tartib matnini; xabar esa mavzu va tanasini saqlashi mumkin. Bunday aniq munosabatlar saralovchiga mos kalit so‘zlar ro‘yxatidan ortiq axborot beradi.

Cohere tayyorgarlik qo‘llanmasidagi modelga xos so‘rov chegarasi 16,384 token bo‘lib, so‘rov va hujjat umumiy kontekstdan foydalanadi. Qo‘llanmada uzunroq hujjat bo‘laklarini qayta ishlash tasvirlangan, V2 ma’lumotnomasi esa max_tokens_per_doc orqali hujjatni standart 4096 tokengacha kesishni alohida belgilaydi. Modelning butun 32K oynasi doimo ishlatiladi deb taxmin qilish o‘rniga, haqiqiy hujjat sozlamasini tekshiring. Haqiqiy qidiruv vazifasi uchun tegishli parchalar yoki mos hujjat byudjetini tayyorlang.

Tavsiya etilgan unumdorlik uchun Cohere bitta so‘rovda 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq nomzod to‘plamlarini identifikatorlarini saqlagan holda boshqariladigan guruhlarga ajrating. Chiqish ballari 0 dan 1 gacha normallashtiriladi va so‘rov hamda parchalarga bog‘liq. Ularning tartibi asosiy natijadir; ball dolzarblikning chiziqli ko‘paytmasi yoki parcha to‘g‘riligiga isbot emas. Har qanday filtrlash chegarasini sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.

Foydali Fast natijasi — qidiruv namoyishini darhol boshqaradigan yoki javob yozish bosqichiga dalil tanlaydigan saralangan ro‘yxat. Manba matnini ball bilan birga saqlang va yuqori natijalar so‘ralgan shartlarga haqiqatan javob berishini tekshiring. Fast vazifasi yangi tushuntirish yoki embedding vektori yaratish emas, qidiruv saralashidir. Dolzarblik xatti-harakatini takomillashtirganda nomlangan nashr va kirish tayyorgarligini izchil saqlang.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Qayta saralash POST /v1/rerank
Kirish
text
Chiqish
rerank
Chiqish formati
json
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydondocumentsTurarrayTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
cohere/rerank-4-fast
Standart—
MaydonproviderTurschemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonqueryTurstringTalabMajburiyQiymatlar va cheklovlarStandart—
Maydonsession_idTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontop_nTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydontraceTurschemaTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonuserTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydondocuments[].textTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydondocuments[].imageTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Kirish materiallari birikmalari

text_rerank · Majburiy: query, documents array with minItems1

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, query, documents
Parametrmaterial_typesParametr talablaritext
Parametroptional_controlsParametr talablariprovider, session_id, top_n, trace, user
Parametrmultimodal_input_bindingParametr talablarinull

Hayot sikli: synchronous

Aniq model ID sini yuboring va to‘liq asl javobni saqlang.

Tariflar

Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.

Tezkor rejim
Ko‘rsatilmagan Tezkor rejim tanlanganda
Flex rejimi
Ko‘rsatilmagan flex rejimi tanlanganda
Maksimal fikrlash darajasi
Ko‘rsatilmagan Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

Har bir to‘lovni ko‘rsatilgan shartlarga moslang. Har bir muqobil sozlamalar to‘plami uchun bitta tarifdan foydalaning.

SozlamaTariflar jadvaliHisob birligiTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifHisob birligi—To‘lov
Kirish · Kirish tokenlari — / 1K token
Chiqish · Chiqish tokenlari — / 1K token
Kirish · Keshdan o‘qish — / 1K token
Kirish · Keshga yozish — / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token
SozlamaUltraTariflar jadvali—Hisob birligiqidiruv birligi uchunTo‘lov
total · Qidiruv birligi $0.004 qidiruv birligi uchun

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

So‘rov namunalari

Ailoot Ultra · Qayta saralash

{\n  "documents": [\n    "Paris is the capital of France.",\n    "Berlin is the capital of Germany."\n  ],\n  "model": "cohere/rerank-4-fast",\n  "query": "What is the capital of France?",\n  "top_n": 3\n}

Natijalarni olish va saqlash

Ailoot Ultra · Qayta saralash

json

Barcha tayyor natijalarni saqlash

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

Har bir haqiqiy natija indeksi, chekli relevance_score va dastlabki hujjat obyekti saqlanadi. Indekslar dastlabki hujjatlar massiviga bog‘lanadi. To‘liq dastlabki JSON saqlanadi va faqat hisob-kitob yakunlangach qaytariladi.

Asl javobni tekshirish kerak bo‘lsa

Dastlabki operatsiyani va to‘liq/qisman javobni saqlang; POST ni qayta yubormang.

Javob maydonlari
MaydonTur
MaydonmodelTurstring
MaydonidTurstring
MaydonproviderTurstring
Maydonresults[].indexTurinteger
Maydonresults[].relevance_scoreTurnumber
Maydonresults[].document.textTurstring
Maydonresults[].document.imageTurstring
Maydonusage.search_unitsTurinteger
Maydonusage.total_tokensTurinteger
Maydonusage.costTurnumber
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydon, ko‘rinish yoki qiymat yaroqsizKeyingi qadamKo‘rsatilgan so‘rov maydonini tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra cohere/rerank-4-fast

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

cohere/rerank-4-fast

", "method_identity": { "effective_request_model_id": "cohere/rerank-4-fast", "fmt": "rerank", "http": "POST", "path": "/v1/rerank", "request_model_id": "cohere/rerank-4-fast", "route_label": "Ultra" }, "method_key": "Ultra|rerank|/v1/rerank|cohere%2Frerank-4-fast", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "errors": [ { "code": null, "condition": "Majburiy maydon, ko‘rinish yoki qiymat yaroqsiz", "http_status": 400, "recovery": "Ko‘rsatilgan so‘rov maydonini tuzating.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Dastlabki operatsiyani va to‘liq/qisman javobni saqlang; POST ni qayta yubormang." }, "material_combinations": [ { "accepted": [ "string documents", "objects with text; image only for separately exact-supported multimodal model" ], "mode": "text_rerank", "required": [ "query", "documents array with minItems1" ] } ], "operation_modes": [ "rerank" ], "outputs": { "all_results": "Har bir haqiqiy natija indeksi, chekli relevance_score va dastlabki hujjat obyekti saqlanadi. Indekslar dastlabki hujjatlar massiviga bog‘lanadi. To‘liq dastlabki JSON saqlanadi va faqat hisob-kitob yakunlangach qaytariladi.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "enum": null, "enum_known": false, "minItems": 1, "name": "documents", "required": true, "type": "array" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "cohere/rerank-4-fast" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "The search query to rerank documents against", "enum": null, "enum_known": false, "name": "query", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "enum": null, "enum_known": false, "maxLength": 256, "name": "session_id", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Number of most relevant documents to return", "enum": null, "enum_known": false, "minimum": 1, "name": "top_n", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "enum": null, "enum_known": false, "maxLength": 256, "name": "user", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "documents[].text", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Official family image carrier is remote http/https URL or data:image URI; only for exact multimodal-model bindings.", "enum": null, "enum_known": false, "name": "documents[].image", "required": false, "type": "string" } ], "unsupported": [ "stream=true" ] }, "hints": [ "Aniq model ID sini yuboring va to‘liq asl javobni saqlang." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "multimodal_input_binding": null, "optional_controls": [ "provider", "session_id", "top_n", "trace", "user" ], "required": [ "model", "query", "documents" ] }, "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Preserve original operation and full/partial reply; do not replay POST." }, "manufacturer_capability": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "method": { "http": "POST", "path": "/v1/rerank" }, "method_aliases": [], "operation_modes": [ "rerank" ], "output": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "schema": { "request": { "description": "Rerank request input", "example": { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "cohere/rerank-v3.5", "query": "What is the capital of France?", "top_n": 3 }, "properties": { "documents": { "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "example": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "items": { "anyOf": [ { "type": "string" }, { "description": "A structured document with optional text and/or image content. At least one of `text` or `image` must be provided.", "properties": { "image": { "description": "An image associated with the document, as a remote URL (http/https) or a base64-encoded data URI (data:image/...).", "example": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8b/Phytogenic.png", "type": "string" }, "text": { "description": "The document text", "example": "AI enables robots to perceive, plan, and act autonomously.", "type": "string" } }, "type": "object" } ], "description": "A document to rerank. Either a plain string, or a structured object with optional `text` and/or `image`." }, "minItems": 1, "type": "array" }, "model": { "enum": [ "cohere/rerank-4-fast" ], "type": "string" }, "provider": { "allOf": [ { "$ref": "#/components/schemas/ProviderPreferences" }, { "description": "Provider routing preferences for the request." } ] }, "query": { "description": "The search query to rerank documents against", "example": "What is the capital of France?", "type": "string" }, "session_id": { "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "example": "session-1234", "maxLength": 256, "type": "string" }, "top_n": { "description": "Number of most relevant documents to return", "example": 3, "minimum": 1, "type": "integer" }, "trace": { "$ref": "#/components/schemas/TraceConfig" }, "user": { "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "example": "user-1234", "maxLength": 256, "type": "string" } }, "required": [ "model", "query", "documents" ], "type": "object" }, "response": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"cohere/rerank-4-fast\",\n \"query\": \"What is the capital of France?\",\n \"top_n\": 3\n}", "label": "Retained official request structure; documentary example", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_REQUEST_FILE:?Set your actual JSON request file}\"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-fast'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n", "label": "Use your actual request material and retain the complete result", "language": "bash", "material_policy": "Use real owner-supplied state/questions or query/documents. No invented file ID, document, image URL, job ID or returned result." } ], "family": "rerank", "fast_multiplier": null, "flex_multiplier": null, "fmt": "rerank", "http": "POST", "in": null, "input": [ "text" ], "label": "Qayta saralash", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "rerank", "out": null, "output": [ "rerank" ], "path": "/v1/rerank", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "search_unit": 0.004 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "request_model_id": "cohere/rerank-4-fast", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } } ], "model_id": "cohere/rerank-4-fast", "nativeProvenance": { "input_facts_canonical_sha256": "6de46e47f7e5c3c3ac638b62a1059e59e0132156036ebd7429e35b9e7f38509d", "input_html_sha256": "da1f15fb9d0cb7099339b8a22c837ac800b7985aa43258e79f97b1721cc67be9", "private_audit_sha256": "9480df268c13d9f06b55f5df22786c03cb904475330e82524e83464035bbdf48", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/cohere-rerank-4-fast/", "description": "Cohere Rerank 4 Fast past kechikish va yuqori o‘tkazuvchanlikka urg‘u berib, nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U qidiruv va qidiruv bilan boyitilgan jarayonlar uchun 32K kontekst, 100 dan ortiq til hamda yarim tuzilmali matnni qo‘llab-quvvatlaydi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/cohere-rerank-4-fast/index.md", "title": "Cohere: Rerank 4 Fast — Ailoot", "url": "https://ailoot.ai/models/uz/cohere-rerank-4-fast/" }, "rawModel": { "about": "Cohere Rerank 4 Fast — pastroq kechikish va yuqoriroq o‘tkazuvchanlikli qidiruv vazifalariga mo‘ljallangan yengilroq Rerank 4 nashri. U so‘rov va nomzod hujjatlarni qabul qilib, dolzarblik tartibi va ballarini qaytaradi. U interaktiv qidiruv, takroriy dalil tanlash va mavjud natijalar ro‘yxatini qayta tartiblash kerak bo‘lgan ilovalar uchun foydali. Fast qidiruv sifati ustuvor bo‘lgan murakkabroq holatlar uchun mo‘ljallangan Rerank 4 Pro’dan alohida nashr bo‘lib qoladi.\n\nIshlab chiqaruvchi kodi rerank-v4.0-fast. Pro kabi uning konteksti 32,768 token va ko‘p tilli qamrovi 100 dan ortiq tildir. So‘rov va bitta hujjat kontekstni bo‘lishadi; bu vektor o‘lchami yoki butun indekslangan korpus uchun byudjet emas. Modelga berilmagan manbadan hujjatlar topishini kutish o‘rniga, taqqoslanadigan nomzod matnlarni taqdim eting.\n\nHujjatlashtirilgan natija asl hujjat indeksi va relevance_score’ni o‘z ichiga oladi. top_n qancha natija qaytarilishini tanlaydi, u berilmasa esa to‘liq saralangan ro‘yxat qaytadi. Yangi tartib haqiqiy parchalarga birlashtirilishi uchun kirishlar yonida manba identifikatorlarini saqlang. Mahsulot qidiruvida so‘rovda kerakli mahsulot xususiyatlarini tavsiflang va nomzod tavsiflarni qayta saralang; yuqori o‘rin uni hosil qilgan mahsulot yozuvi bilan bog‘liq qolishi kerak.\n\nModel inglizcha va boshqa tillardagi hujjatlar hamda yarim tuzilmali yozuvlar bilan ishlay oladi. V2 jarayonida hujjat kirishlari satrlardir, Cohere esa tuzilmali ma’lumotni YAML sifatida ifodalashni tavsiya etadi. Muhim maydonlarni oldinga qo‘ying va tartibini saqlang, shunda kesish dolzarblikni baholash uchun kerakli mazmunni olib tashlamaydi. Qo‘llab-quvvatlash yozuvi sarlavha, mahsulot va tartib matnini; xabar esa mavzu va tanasini saqlashi mumkin. Bunday aniq munosabatlar saralovchiga mos kalit so‘zlar ro‘yxatidan ortiq axborot beradi.\n\nCohere tayyorgarlik qo‘llanmasidagi modelga xos so‘rov chegarasi 16,384 token bo‘lib, so‘rov va hujjat umumiy kontekstdan foydalanadi. Qo‘llanmada uzunroq hujjat bo‘laklarini qayta ishlash tasvirlangan, V2 ma’lumotnomasi esa max_tokens_per_doc orqali hujjatni standart 4096 tokengacha kesishni alohida belgilaydi. Modelning butun 32K oynasi doimo ishlatiladi deb taxmin qilish o‘rniga, haqiqiy hujjat sozlamasini tekshiring. Haqiqiy qidiruv vazifasi uchun tegishli parchalar yoki mos hujjat byudjetini tayyorlang.\n\nTavsiya etilgan unumdorlik uchun Cohere bitta so‘rovda 1,000 dan ortiq hujjat yubormaslikni maslahat beradi. Kattaroq nomzod to‘plamlarini identifikatorlarini saqlagan holda boshqariladigan guruhlarga ajrating. Chiqish ballari 0 dan 1 gacha normallashtiriladi va so‘rov hamda parchalarga bog‘liq. Ularning tartibi asosiy natijadir; ball dolzarblikning chiziqli ko‘paytmasi yoki parcha to‘g‘riligiga isbot emas. Har qanday filtrlash chegarasini sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalar bilan belgilang.\n\nFoydali Fast natijasi — qidiruv namoyishini darhol boshqaradigan yoki javob yozish bosqichiga dalil tanlaydigan saralangan ro‘yxat. Manba matnini ball bilan birga saqlang va yuqori natijalar so‘ralgan shartlarga haqiqatan javob berishini tekshiring. Fast vazifasi yangi tushuntirish yoki embedding vektori yaratish emas, qidiruv saralashidir. Dolzarblik xatti-harakatini takomillashtirganda nomlangan nashr va kirish tayyorgarligini izchil saqlang.", "alias_of": null, "aliases": [], "author": "Cohere", "available": null, "canonical_model": "cohere/rerank-4-fast", "capabilities": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ], "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": null, "entity_type": "model", "formats": [ "rerank" ], "id": "cohere/rerank-4-fast", "input": [ "text" ], "kind": "rerank", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "errors": [ { "code": null, "condition": "Majburiy maydon, ko‘rinish yoki qiymat yaroqsiz", "http_status": 400, "recovery": "Ko‘rsatilgan so‘rov maydonini tuzating.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Dastlabki operatsiyani va to‘liq/qisman javobni saqlang; POST ni qayta yubormang." }, "material_combinations": [ { "accepted": [ "string documents", "objects with text; image only for separately exact-supported multimodal model" ], "mode": "text_rerank", "required": [ "query", "documents array with minItems1" ] } ], "operation_modes": [ "rerank" ], "outputs": { "all_results": "Har bir haqiqiy natija indeksi, chekli relevance_score va dastlabki hujjat obyekti saqlanadi. Indekslar dastlabki hujjatlar massiviga bog‘lanadi. To‘liq dastlabki JSON saqlanadi va faqat hisob-kitob yakunlangach qaytariladi.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "enum": null, "enum_known": false, "minItems": 1, "name": "documents", "required": true, "type": "array" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "cohere/rerank-4-fast" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "provider", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "The search query to rerank documents against", "enum": null, "enum_known": false, "name": "query", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "enum": null, "enum_known": false, "maxLength": 256, "name": "session_id", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Number of most relevant documents to return", "enum": null, "enum_known": false, "minimum": 1, "name": "top_n", "required": false, "type": "integer" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "trace", "required": false, "type": "schema" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "enum": null, "enum_known": false, "maxLength": 256, "name": "user", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "documents[].text", "required": false, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "description": "Official family image carrier is remote http/https URL or data:image URI; only for exact multimodal-model bindings.", "enum": null, "enum_known": false, "name": "documents[].image", "required": false, "type": "string" } ], "unsupported": [ "stream=true" ] }, "hints": [ "Aniq model ID sini yuboring va to‘liq asl javobni saqlang." ], "input": { "content_type": "application/json", "material_types": [ "text" ], "multimodal_input_binding": null, "optional_controls": [ "provider", "session_id", "top_n", "trace", "user" ], "required": [ "model", "query", "documents" ] }, "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Preserve original operation and full/partial reply; do not replay POST." }, "manufacturer_capability": { "context_length": 32768, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "method": { "http": "POST", "path": "/v1/rerank" }, "method_aliases": [], "operation_modes": [ "rerank" ], "output": { "all_results": "Every actual result index, finite relevance_score and original document object is retained. Indices bind the original document array. Entire original JSON is stored and returned only after settlement.", "family": "rerank", "fields": [ { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "provider", "type": "string" }, { "description": null, "name": "results[].index", "type": "integer" }, { "description": null, "name": "results[].relevance_score", "type": "number" }, { "description": null, "name": "results[].document.text", "type": "string" }, { "description": null, "name": "results[].document.image", "type": "string" }, { "description": null, "name": "usage.search_units", "type": "integer" }, { "description": null, "name": "usage.total_tokens", "type": "integer" }, { "description": null, "name": "usage.cost", "type": "number" } ], "format": "json", "schema": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } }, "schema": { "request": { "description": "Rerank request input", "example": { "documents": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "model": "cohere/rerank-v3.5", "query": "What is the capital of France?", "top_n": 3 }, "properties": { "documents": { "description": "The list of documents to rerank. Documents may be plain strings, or structured objects with `text` and/or `image` for multimodal models.", "example": [ "Paris is the capital of France.", "Berlin is the capital of Germany." ], "items": { "anyOf": [ { "type": "string" }, { "description": "A structured document with optional text and/or image content. At least one of `text` or `image` must be provided.", "properties": { "image": { "description": "An image associated with the document, as a remote URL (http/https) or a base64-encoded data URI (data:image/...).", "example": "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8b/Phytogenic.png", "type": "string" }, "text": { "description": "The document text", "example": "AI enables robots to perceive, plan, and act autonomously.", "type": "string" } }, "type": "object" } ], "description": "A document to rerank. Either a plain string, or a structured object with optional `text` and/or `image`." }, "minItems": 1, "type": "array" }, "model": { "enum": [ "cohere/rerank-4-fast" ], "type": "string" }, "provider": { "allOf": [ { "$ref": "#/components/schemas/ProviderPreferences" }, { "description": "Provider routing preferences for the request." } ] }, "query": { "description": "The search query to rerank documents against", "example": "What is the capital of France?", "type": "string" }, "session_id": { "description": "A unique identifier for grouping related requests (e.g., a conversation or agent workflow). Used for observability grouping in Broadcast and private logging; never sent to the provider. If provided in both the request body and the x-session-id header, the body value takes precedence. Maximum of 256 characters.", "example": "session-1234", "maxLength": 256, "type": "string" }, "top_n": { "description": "Number of most relevant documents to return", "example": 3, "minimum": 1, "type": "integer" }, "trace": { "$ref": "#/components/schemas/TraceConfig" }, "user": { "description": "A unique identifier representing your end-user. Forwarded to Broadcast and private logging as the end-user id; never sent to the provider.", "example": "user-1234", "maxLength": 256, "type": "string" } }, "required": [ "model", "query", "documents" ], "type": "object" }, "response": { "description": "Rerank response containing ranked results", "example": { "id": "gen-rerank-1234567890-abc", "model": "cohere/rerank-v3.5", "results": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "usage": { "search_units": 1, "total_tokens": 150 } }, "properties": { "id": { "description": "Unique identifier for the rerank response (ORID format)", "example": "gen-rerank-1234567890-abc", "type": "string" }, "model": { "description": "The model used for reranking", "example": "cohere/rerank-v3.5", "type": "string" }, "provider": { "description": "The provider that served the rerank request", "example": "Cohere", "type": "string" }, "results": { "description": "List of rerank results sorted by relevance", "example": [ { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 } ], "items": { "description": "A single rerank result", "example": { "document": { "text": "Paris is the capital of France." }, "index": 0, "relevance_score": 0.98 }, "properties": { "document": { "description": "The document object echoing the original input (text and/or image)", "properties": { "image": { "description": "The image (URL or data URI) from the original document", "example": "https://example.com/image.png", "type": "string" }, "text": { "description": "The document text", "example": "Paris is the capital of France.", "type": "string" } }, "type": "object" }, "index": { "description": "Index of the document in the original input list", "example": 0, "type": "integer" }, "relevance_score": { "description": "Relevance score of the document to the query", "example": 0.98, "format": "double", "type": "number" } }, "required": [ "index", "relevance_score", "document" ], "type": "object" }, "type": "array" }, "usage": { "description": "Usage statistics", "example": { "search_units": 1, "total_tokens": 150 }, "properties": { "cost": { "description": "Cost of the request in credits", "example": 0.001, "format": "double", "type": "number" }, "search_units": { "description": "Number of search units consumed (Cohere billing)", "example": 1, "type": "integer" }, "total_tokens": { "description": "Total number of tokens used", "example": 150, "type": "integer" } }, "type": "object" } }, "required": [ "model", "results" ], "type": "object" } } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"cohere/rerank-4-fast\",\n \"query\": \"What is the capital of France?\",\n \"top_n\": 3\n}", "label": "Retained official request structure; documentary example", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_REQUEST_FILE:?Set your actual JSON request file}\"\nexport AILOOT_REQUEST_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ['AILOOT_REQUEST_FILE']).read_text(encoding='utf-8'))\nif not isinstance(p,dict): raise SystemExit('Request must be a JSON object')\np['model']='cohere/rerank-4-fast'\nPath('request.json').write_text(json.dumps(p,ensure_ascii=False),encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\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\nprint(json.dumps(json.loads(Path('result.json').read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n", "label": "Use your actual request material and retain the complete result", "language": "bash", "material_policy": "Use real owner-supplied state/questions or query/documents. No invented file ID, document, image URL, job ID or returned result." } ], "family": "rerank", "fast_multiplier": null, "flex_multiplier": null, "fmt": "rerank", "http": "POST", "in": null, "input": [ "text" ], "label": "Qayta saralash", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "rerank", "out": null, "output": [ "rerank" ], "path": "/v1/rerank", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "search_unit": 0.004 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "native_endpoint": "rerank", "quoted_unit": "per search unit" }, "label": "Ultra", "units": [ { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "request_model_id": "cohere/rerank-4-fast", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.004", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.004", "native_quantity": 1, "public_unit_label": "per search unit", "quantity": "1", "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "total", "unit": "search_unit" } ] } ], "model_aliases": [ { "canonical_model_id": "cohere/rerank-4-fast", "request_model_id": "cohere/rerank-4-fast", "route_label": "Ultra" } ], "name": "Cohere: Rerank 4 Fast", "price": { "in": null, "in_label": null, "out": null, "out_label": null, "sort_in": null, "sort_out": null, "varies": true }, "public_id": "cohere/rerank-4-fast", "short": "Cohere Rerank 4 Fast past kechikish va yuqori o‘tkazuvchanlikka urg‘u berib, nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U qidiruv va qidiruv bilan boyitilgan jarayonlar uchun 32K kontekst, 100 dan ortiq til hamda yarim tuzilmali matnni qo‘llab-quvvatlaydi." }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "Cohere Rerank 4 Fast past kechikish va yuqori o‘tkazuvchanlikka urg‘u berib, nomzod hujjatlarni so‘rovga dolzarbligi bo‘yicha tartiblaydi. U qidiruv va qidiruv bilan boyitilgan jarayonlar uchun 32K kontekst, 100 dan ortiq til hamda yarim tuzilmali matnni qo‘llab-quvvatlaydi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "5d805a50cd9a0ca44c1d10e7a5f9a1e502df37cd7fb31834e8b5cc0103455618", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "7ec7893aaca3b0ae4475168f128e46bfa9e586257dbf4b3a2e6512d1c7f4c5f3" }