{ "aboutHtml": "

Cohere Rerank 3.5 — nomzod hujjatlar va yarim tuzilmali matnni dolzarbligi bo‘yicha saralash uchun ko‘p tilli model. U har bir nomzodni so‘rovga nisbatan baholaydi va dolzarblik ballari bilan qayta tartiblangan natijalar ro‘yxatini qaytaradi. U kalit so‘zli yoki semantik qidiruvdan olingan nomzodlarni yaxshilashi va qidiruv bilan boyitilgan javob uchun parchalarni tanlashi mumkin. Uning vazifasi erkin shakldagi javob yaratish yoki yangi korpus indeksini tuzish emas, dalillar tartibini yaxshilashdir.

Ishlab chiqaruvchining aniq identifikatori rerank-v3.5. U Rerank 4 Pro va Fast’ning 32K kontekstidan farqli 4096 tokenli kontekstdan foydalanadi. So‘rov va hujjat tokenlari har bir nomzodga tegishli shu oynada birgalikda hisoblanadi. Modelga xos tayyorgarlik qo‘llanmasi so‘rovni dastlabki 2048 tokenidan keyin kesadi va so‘rov hisobga olinmasidan oldingi hujjat bo‘laklarini 4093 token deb tavsiflaydi. So‘rovni maqsadga yo‘naltiring va so‘ralgan munosabatni baholash uchun kerakli ma’lumotni o‘z ichiga olgan nomzod parchalarni tayyorlang.

So‘rov va nomzod matn satrlari ro‘yxatini taqdim eting. Har bir natijada asl hujjat o‘rnini ko‘rsatadigan indeks va dolzarblik balli bor; top_n qancha saralangan natijani saqlashni tanlaydi. U berilmasa, hujjatlashtirilgan ishlab chiqaruvchi jarayoni barcha natijalarni qaytaradi. Eng yuqori parcha asl siyosat, mahsulot yozuvi yoki xabargacha kuzatilishi uchun hujjat identifikatorlarini balldan mustaqil saqlang.

Rerank 3.5 inglizcha va boshqa tillardagi materialni qo‘shib, 100 dan ortiq tilni qo‘llab-quvvatlaydi. So‘rov ehtiyojning bir nechta jihatini, masalan mahsulot, foydalanuvchi vaziyati va kerakli tartibni qamrab olishi mumkin. Tuzilmali yozuvlar uchun V2 relizi xom hujjat obyektlarini olib tashlagan va YAML satrlarini tavsiya qiladi. Foydali maydonlarni puxta tartibda saqlang, hal qiluvchi mazmunni kamroq tegishli metama’lumotdan oldin qo‘ying, chunki uzun satrlar kesilishi mumkin. Bu sarlavha va mazmun, mahsulot xususiyatlari yoki xabar mavzusi va tanasi uchun mos keladi.

V2 ishlab chiqaruvchi ma’lumotnomasi hujjatni kesish uchun max_tokens_per_doc’dan foydalanadi; standart qiymat 4096. Cohere tayyorgarlik qo‘llanmasi uzun hujjatlarni bo‘laklashni ham tavsiflaydi; haqiqiy kirish tanlangan jarayon va uning hujjat sozlamalariga bog‘liq. Ko‘proq nazorat uchun uzun manbaning har bir qismi ko‘rinib qoladi deb taxmin qilish o‘rniga, identifikatsiya qilinadigan parchalarni o‘zingiz tayyorlang. Javobni o‘z ichiga olgan parchani faqat uning so‘zlariga o‘xshash parchadan ajratish uchun yetarli atrof kontekstini saqlang.

So‘rov unumdorligi uchun joriy ma’lumotnoma bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni tavsiya etadi. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq qiymatlari so‘rov hamda nomzodlarga bog‘liq va to‘g‘rilikning chiziqli shkalasi emas. Foydali dalillarni topish uchun saralashdan foydalaning. Ball chegarasi kerak bo‘lsa, u haqiqiy qidiruv vazifasini aks ettirishi uchun sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalarni tanlang.

Amaliy qo‘llab-quvvatlash jarayoni shikastlangan buyum haqidagi savol uchun qaytarish siyosati bo‘limlarini qayta saralab, tanlangan manba matnini javob yozish bosqichiga uzatishi mumkin. Elektron pochta qidiruvi xabarlarni aniq muammo bo‘yicha saralashi va har bir natija bilan asl xabar identifikatorini saqlashi mumkin. Foydali artefakt — tartiblangan dalillar ro‘yxati. Keyingi modellarning chegaralarini uning o‘rniga qo‘llash o‘rniga, Rerank 3.5’ning qisqaroq konteksti va V2 hujjat tayyorgarligini shu natija bilan bog‘lab saqlang.

", "aboutText": "Cohere Rerank 3.5 — nomzod hujjatlar va yarim tuzilmali matnni dolzarbligi bo‘yicha saralash uchun ko‘p tilli model. U har bir nomzodni so‘rovga nisbatan baholaydi va dolzarblik ballari bilan qayta tartiblangan natijalar ro‘yxatini qaytaradi. U kalit so‘zli yoki semantik qidiruvdan olingan nomzodlarni yaxshilashi va qidiruv bilan boyitilgan javob uchun parchalarni tanlashi mumkin. Uning vazifasi erkin shakldagi javob yaratish yoki yangi korpus indeksini tuzish emas, dalillar tartibini yaxshilashdir.\n\nIshlab chiqaruvchining aniq identifikatori rerank-v3.5. U Rerank 4 Pro va Fast’ning 32K kontekstidan farqli 4096 tokenli kontekstdan foydalanadi. So‘rov va hujjat tokenlari har bir nomzodga tegishli shu oynada birgalikda hisoblanadi. Modelga xos tayyorgarlik qo‘llanmasi so‘rovni dastlabki 2048 tokenidan keyin kesadi va so‘rov hisobga olinmasidan oldingi hujjat bo‘laklarini 4093 token deb tavsiflaydi. So‘rovni maqsadga yo‘naltiring va so‘ralgan munosabatni baholash uchun kerakli ma’lumotni o‘z ichiga olgan nomzod parchalarni tayyorlang.\n\nSo‘rov va nomzod matn satrlari ro‘yxatini taqdim eting. Har bir natijada asl hujjat o‘rnini ko‘rsatadigan indeks va dolzarblik balli bor; top_n qancha saralangan natijani saqlashni tanlaydi. U berilmasa, hujjatlashtirilgan ishlab chiqaruvchi jarayoni barcha natijalarni qaytaradi. Eng yuqori parcha asl siyosat, mahsulot yozuvi yoki xabargacha kuzatilishi uchun hujjat identifikatorlarini balldan mustaqil saqlang.\n\nRerank 3.5 inglizcha va boshqa tillardagi materialni qo‘shib, 100 dan ortiq tilni qo‘llab-quvvatlaydi. So‘rov ehtiyojning bir nechta jihatini, masalan mahsulot, foydalanuvchi vaziyati va kerakli tartibni qamrab olishi mumkin. Tuzilmali yozuvlar uchun V2 relizi xom hujjat obyektlarini olib tashlagan va YAML satrlarini tavsiya qiladi. Foydali maydonlarni puxta tartibda saqlang, hal qiluvchi mazmunni kamroq tegishli metama’lumotdan oldin qo‘ying, chunki uzun satrlar kesilishi mumkin. Bu sarlavha va mazmun, mahsulot xususiyatlari yoki xabar mavzusi va tanasi uchun mos keladi.\n\nV2 ishlab chiqaruvchi ma’lumotnomasi hujjatni kesish uchun max_tokens_per_doc’dan foydalanadi; standart qiymat 4096. Cohere tayyorgarlik qo‘llanmasi uzun hujjatlarni bo‘laklashni ham tavsiflaydi; haqiqiy kirish tanlangan jarayon va uning hujjat sozlamalariga bog‘liq. Ko‘proq nazorat uchun uzun manbaning har bir qismi ko‘rinib qoladi deb taxmin qilish o‘rniga, identifikatsiya qilinadigan parchalarni o‘zingiz tayyorlang. Javobni o‘z ichiga olgan parchani faqat uning so‘zlariga o‘xshash parchadan ajratish uchun yetarli atrof kontekstini saqlang.\n\nSo‘rov unumdorligi uchun joriy ma’lumotnoma bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni tavsiya etadi. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq qiymatlari so‘rov hamda nomzodlarga bog‘liq va to‘g‘rilikning chiziqli shkalasi emas. Foydali dalillarni topish uchun saralashdan foydalaning. Ball chegarasi kerak bo‘lsa, u haqiqiy qidiruv vazifasini aks ettirishi uchun sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalarni tanlang.\n\nAmaliy qo‘llab-quvvatlash jarayoni shikastlangan buyum haqidagi savol uchun qaytarish siyosati bo‘limlarini qayta saralab, tanlangan manba matnini javob yozish bosqichiga uzatishi mumkin. Elektron pochta qidiruvi xabarlarni aniq muammo bo‘yicha saralashi va har bir natija bilan asl xabar identifikatorini saqlashi mumkin. Foydali artefakt — tartiblangan dalillar ro‘yxati. Keyingi modellarning chegaralarini uning o‘rniga qo‘llash o‘rniga, Rerank 3.5’ning qisqaroq konteksti va V2 hujjat tayyorgarligini shu natija bilan bog‘lab saqlang.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "16d18607d94e4c7b1a86134cbe871be12b7dd9e6787b66082ff67f331842ee3d", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
•

Cohere: Rerank v3.5

Cohere /cohere/rerank-v3.5

Boshlash

Cohere Rerank 3.5 nomzod matnlar va yarim tuzilmali yozuvlarni so‘rovga dolzarbligi bo‘yicha qayta tartiblaydi. U javob yoki qidiruv namoyishidan oldin kalit so‘zli yoxud semantik qidiruv natijalarini yaxshilash uchun 100 dan ortiq til va 4096 tokenli kontekstni 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 3.5 — nomzod hujjatlar va yarim tuzilmali matnni dolzarbligi bo‘yicha saralash uchun ko‘p tilli model. U har bir nomzodni so‘rovga nisbatan baholaydi va dolzarblik ballari bilan qayta tartiblangan natijalar ro‘yxatini qaytaradi. U kalit so‘zli yoki semantik qidiruvdan olingan nomzodlarni yaxshilashi va qidiruv bilan boyitilgan javob uchun parchalarni tanlashi mumkin. Uning vazifasi erkin shakldagi javob yaratish yoki yangi korpus indeksini tuzish emas, dalillar tartibini yaxshilashdir.

Ishlab chiqaruvchining aniq identifikatori rerank-v3.5. U Rerank 4 Pro va Fast’ning 32K kontekstidan farqli 4096 tokenli kontekstdan foydalanadi. So‘rov va hujjat tokenlari har bir nomzodga tegishli shu oynada birgalikda hisoblanadi. Modelga xos tayyorgarlik qo‘llanmasi so‘rovni dastlabki 2048 tokenidan keyin kesadi va so‘rov hisobga olinmasidan oldingi hujjat bo‘laklarini 4093 token deb tavsiflaydi. So‘rovni maqsadga yo‘naltiring va so‘ralgan munosabatni baholash uchun kerakli ma’lumotni o‘z ichiga olgan nomzod parchalarni tayyorlang.

So‘rov va nomzod matn satrlari ro‘yxatini taqdim eting. Har bir natijada asl hujjat o‘rnini ko‘rsatadigan indeks va dolzarblik balli bor; top_n qancha saralangan natijani saqlashni tanlaydi. U berilmasa, hujjatlashtirilgan ishlab chiqaruvchi jarayoni barcha natijalarni qaytaradi. Eng yuqori parcha asl siyosat, mahsulot yozuvi yoki xabargacha kuzatilishi uchun hujjat identifikatorlarini balldan mustaqil saqlang.

Rerank 3.5 inglizcha va boshqa tillardagi materialni qo‘shib, 100 dan ortiq tilni qo‘llab-quvvatlaydi. So‘rov ehtiyojning bir nechta jihatini, masalan mahsulot, foydalanuvchi vaziyati va kerakli tartibni qamrab olishi mumkin. Tuzilmali yozuvlar uchun V2 relizi xom hujjat obyektlarini olib tashlagan va YAML satrlarini tavsiya qiladi. Foydali maydonlarni puxta tartibda saqlang, hal qiluvchi mazmunni kamroq tegishli metama’lumotdan oldin qo‘ying, chunki uzun satrlar kesilishi mumkin. Bu sarlavha va mazmun, mahsulot xususiyatlari yoki xabar mavzusi va tanasi uchun mos keladi.

V2 ishlab chiqaruvchi ma’lumotnomasi hujjatni kesish uchun max_tokens_per_doc’dan foydalanadi; standart qiymat 4096. Cohere tayyorgarlik qo‘llanmasi uzun hujjatlarni bo‘laklashni ham tavsiflaydi; haqiqiy kirish tanlangan jarayon va uning hujjat sozlamalariga bog‘liq. Ko‘proq nazorat uchun uzun manbaning har bir qismi ko‘rinib qoladi deb taxmin qilish o‘rniga, identifikatsiya qilinadigan parchalarni o‘zingiz tayyorlang. Javobni o‘z ichiga olgan parchani faqat uning so‘zlariga o‘xshash parchadan ajratish uchun yetarli atrof kontekstini saqlang.

So‘rov unumdorligi uchun joriy ma’lumotnoma bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni tavsiya etadi. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq qiymatlari so‘rov hamda nomzodlarga bog‘liq va to‘g‘rilikning chiziqli shkalasi emas. Foydali dalillarni topish uchun saralashdan foydalaning. Ball chegarasi kerak bo‘lsa, u haqiqiy qidiruv vazifasini aks ettirishi uchun sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalarni tanlang.

Amaliy qo‘llab-quvvatlash jarayoni shikastlangan buyum haqidagi savol uchun qaytarish siyosati bo‘limlarini qayta saralab, tanlangan manba matnini javob yozish bosqichiga uzatishi mumkin. Elektron pochta qidiruvi xabarlarni aniq muammo bo‘yicha saralashi va har bir natija bilan asl xabar identifikatorini saqlashi mumkin. Foydali artefakt — tartiblangan dalillar ro‘yxati. Keyingi modellarning chegaralarini uning o‘rniga qo‘llash o‘rniga, Rerank 3.5’ning qisqaroq konteksti va V2 hujjat tayyorgarligini shu natija bilan bog‘lab 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-v3.5
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 $0.002 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-v3.5",\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-v3.5

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

cohere/rerank-v3.5

", "method_identity": { "effective_request_model_id": "cohere/rerank-v3.5", "fmt": "rerank", "http": "POST", "path": "/v1/rerank", "request_model_id": "cohere/rerank-v3.5", "route_label": "Ultra" }, "method_key": "Ultra|rerank|/v1/rerank|cohere%2Frerank-v3.5", "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-v3.5" ], "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": 4096, "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-v3.5" ], "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": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5\",\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-v3.5'\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": 0.002 } }, "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": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5", "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": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5", "nativeProvenance": { "input_facts_canonical_sha256": "0e62e6cfe2ac8c0ee12e4e3585c68575d48ea71111ef5201607fc4db3099b40c", "input_html_sha256": "9fddfecf94136cec1c1c38ffbe8733e2e214eda52034df99dba48f262b6c54df", "private_audit_sha256": "a3b3578cd0665e6bc93ccd7868b90b18bb553655a9f97a53fcbb3bf244d3443a", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/cohere-rerank-v3-5/", "description": "Cohere Rerank 3.5 nomzod matnlar va yarim tuzilmali yozuvlarni so‘rovga dolzarbligi bo‘yicha qayta tartiblaydi. U javob yoki qidiruv namoyishidan oldin kalit so‘zli yoxud semantik qidiruv natijalarini yaxshilash uchun 100 dan ortiq til va 4096 tokenli kontekstni qo‘llab-quvvatlaydi.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/cohere-rerank-v3-5/index.md", "title": "Cohere: Rerank v3.5 — Ailoot", "url": "https://ailoot.ai/models/uz/cohere-rerank-v3-5/" }, "rawModel": { "about": "Cohere Rerank 3.5 — nomzod hujjatlar va yarim tuzilmali matnni dolzarbligi bo‘yicha saralash uchun ko‘p tilli model. U har bir nomzodni so‘rovga nisbatan baholaydi va dolzarblik ballari bilan qayta tartiblangan natijalar ro‘yxatini qaytaradi. U kalit so‘zli yoki semantik qidiruvdan olingan nomzodlarni yaxshilashi va qidiruv bilan boyitilgan javob uchun parchalarni tanlashi mumkin. Uning vazifasi erkin shakldagi javob yaratish yoki yangi korpus indeksini tuzish emas, dalillar tartibini yaxshilashdir.\n\nIshlab chiqaruvchining aniq identifikatori rerank-v3.5. U Rerank 4 Pro va Fast’ning 32K kontekstidan farqli 4096 tokenli kontekstdan foydalanadi. So‘rov va hujjat tokenlari har bir nomzodga tegishli shu oynada birgalikda hisoblanadi. Modelga xos tayyorgarlik qo‘llanmasi so‘rovni dastlabki 2048 tokenidan keyin kesadi va so‘rov hisobga olinmasidan oldingi hujjat bo‘laklarini 4093 token deb tavsiflaydi. So‘rovni maqsadga yo‘naltiring va so‘ralgan munosabatni baholash uchun kerakli ma’lumotni o‘z ichiga olgan nomzod parchalarni tayyorlang.\n\nSo‘rov va nomzod matn satrlari ro‘yxatini taqdim eting. Har bir natijada asl hujjat o‘rnini ko‘rsatadigan indeks va dolzarblik balli bor; top_n qancha saralangan natijani saqlashni tanlaydi. U berilmasa, hujjatlashtirilgan ishlab chiqaruvchi jarayoni barcha natijalarni qaytaradi. Eng yuqori parcha asl siyosat, mahsulot yozuvi yoki xabargacha kuzatilishi uchun hujjat identifikatorlarini balldan mustaqil saqlang.\n\nRerank 3.5 inglizcha va boshqa tillardagi materialni qo‘shib, 100 dan ortiq tilni qo‘llab-quvvatlaydi. So‘rov ehtiyojning bir nechta jihatini, masalan mahsulot, foydalanuvchi vaziyati va kerakli tartibni qamrab olishi mumkin. Tuzilmali yozuvlar uchun V2 relizi xom hujjat obyektlarini olib tashlagan va YAML satrlarini tavsiya qiladi. Foydali maydonlarni puxta tartibda saqlang, hal qiluvchi mazmunni kamroq tegishli metama’lumotdan oldin qo‘ying, chunki uzun satrlar kesilishi mumkin. Bu sarlavha va mazmun, mahsulot xususiyatlari yoki xabar mavzusi va tanasi uchun mos keladi.\n\nV2 ishlab chiqaruvchi ma’lumotnomasi hujjatni kesish uchun max_tokens_per_doc’dan foydalanadi; standart qiymat 4096. Cohere tayyorgarlik qo‘llanmasi uzun hujjatlarni bo‘laklashni ham tavsiflaydi; haqiqiy kirish tanlangan jarayon va uning hujjat sozlamalariga bog‘liq. Ko‘proq nazorat uchun uzun manbaning har bir qismi ko‘rinib qoladi deb taxmin qilish o‘rniga, identifikatsiya qilinadigan parchalarni o‘zingiz tayyorlang. Javobni o‘z ichiga olgan parchani faqat uning so‘zlariga o‘xshash parchadan ajratish uchun yetarli atrof kontekstini saqlang.\n\nSo‘rov unumdorligi uchun joriy ma’lumotnoma bir vaqtning o‘zida 1,000 dan ortiq hujjat yubormaslikni tavsiya etadi. Dolzarblik ballari 0 va 1 orasida normallashtiriladi, biroq qiymatlari so‘rov hamda nomzodlarga bog‘liq va to‘g‘rilikning chiziqli shkalasi emas. Foydali dalillarni topish uchun saralashdan foydalaning. Ball chegarasi kerak bo‘lsa, u haqiqiy qidiruv vazifasini aks ettirishi uchun sohani ifodalovchi namunaviy so‘rovlar va chegaraviy dolzarb parchalarni tanlang.\n\nAmaliy qo‘llab-quvvatlash jarayoni shikastlangan buyum haqidagi savol uchun qaytarish siyosati bo‘limlarini qayta saralab, tanlangan manba matnini javob yozish bosqichiga uzatishi mumkin. Elektron pochta qidiruvi xabarlarni aniq muammo bo‘yicha saralashi va har bir natija bilan asl xabar identifikatorini saqlashi mumkin. Foydali artefakt — tartiblangan dalillar ro‘yxati. Keyingi modellarning chegaralarini uning o‘rniga qo‘llash o‘rniga, Rerank 3.5’ning qisqaroq konteksti va V2 hujjat tayyorgarligini shu natija bilan bog‘lab saqlang.", "alias_of": null, "aliases": [], "author": "Cohere", "available": null, "canonical_model": "cohere/rerank-v3.5", "capabilities": { "context_length": 4096, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5", "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-v3.5" ], "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": 4096, "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-v3.5" ], "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": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5\",\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-v3.5'\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": 0.002 } }, "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": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5", "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": "0.002", "amount_unit": "usd_per_declared_unit", "billing_input": true, "estimate_only": false, "key": "search_unit", "label": "search unit", "native_amount_usd_per_unit": "0.002", "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-v3.5", "request_model_id": "cohere/rerank-v3.5", "route_label": "Ultra" } ], "name": "Cohere: Rerank v3.5", "price": { "in": null, "in_label": null, "out": null, "out_label": null, "sort_in": null, "sort_out": null, "varies": true }, "public_id": "cohere/rerank-v3.5", "short": "Cohere Rerank 3.5 nomzod matnlar va yarim tuzilmali yozuvlarni so‘rovga dolzarbligi bo‘yicha qayta tartiblaydi. U javob yoki qidiruv namoyishidan oldin kalit so‘zli yoxud semantik qidiruv natijalarini yaxshilash uchun 100 dan ortiq til va 4096 tokenli kontekstni 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 3.5 nomzod matnlar va yarim tuzilmali yozuvlarni so‘rovga dolzarbligi bo‘yicha qayta tartiblaydi. U javob yoki qidiruv namoyishidan oldin kalit so‘zli yoxud semantik qidiruv natijalarini yaxshilash uchun 100 dan ortiq til va 4096 tokenli kontekstni qo‘llab-quvvatlaydi.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "c3348b26593902d55b1f2a14a11035bed6b4c4502dd801990a1a1e6a5be7099b", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "3c3c280807187a29be9cafae6e63aebe26476993de3c59a609f28b63a1a3a761" }