{ "aboutHtml": "

Cohere Rerank 4 Fast ialah edisi Rerank 4 yang lebih ringan, ditujukan kepada tugasan dapatan maklumat dengan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Model ini menerima pertanyaan dan dokumen calon, kemudian mengembalikan susunan dan skor kerelevanannya. Model ini berguna untuk carian interaktif, pemilihan bukti berulang dan aplikasi yang perlu menyusun semula senarai hasil sedia ada. Fast kekal sebagai edisi berbeza daripada Rerank 4 Pro, yang ditujukan kepada kes lebih kompleks apabila kualiti dapatan maklumat menjadi keutamaan.

Kod pengeluar ialah rerank-v4.0-fast. Seperti Pro, model ini mempunyai konteks 32,768 token dan liputan berbilang bahasa merangkumi lebih daripada 100 bahasa. Pertanyaan dan satu dokumen berkongsi konteks; ia bukan dimensi vektor atau bajet bagi seluruh korpus berindeks. Bekalkan teks calon yang perlu dibandingkan, tanpa mengharapkan model mendapatkan dokumen daripada sumber yang tidak diberikan kepadanya.

Hasil yang didokumenkan menyertakan indeks dokumen asal dan relevance_score. top_n memilih bilangan hasil yang dikembalikan, manakala penggugurannya mengembalikan senarai berperingkat penuh. Simpan pengenal sumber di sisi input supaya susunan baharu boleh dipadankan dengan petikan sebenar. Untuk carian produk, terangkan atribut produk yang diperlukan dalam pertanyaan dan peringkatkan semula penerangan calon; kedudukan yang lebih tinggi patut kekal terhubung dengan rekod produk yang menghasilkannya.

Model ini boleh menggunakan dokumen bahasa Inggeris dan bahasa lain serta rekod separa berstruktur. Dalam aliran kerja V2, input dokumen ialah rentetan, dan Cohere mengesyorkan perwakilan data berstruktur sebagai YAML. Letakkan medan penting dahulu dan kekalkan susunannya supaya pemotongan tidak membuang kandungan yang diperlukan untuk menilai kerelevanan. Rekod sokongan boleh membawa tajuk, produk dan teks prosedur; mesej boleh membawa subjek dan isinya. Hubungan yang dinyatakan ini memberi pemeringkat maklumat melangkaui senarai kata kunci sepadan.

Had pertanyaan khusus model dalam panduan penyediaan Cohere ialah 16,384 token, dengan pertanyaan dan dokumen menggunakan konteks bersama. Panduan itu menerangkan pemprosesan cebisan dokumen lebih panjang, manakala rujukan V2 secara berasingan menetapkan lalai pemotongan dokumen kepada 4096 token melalui max_tokens_per_doc. Semak tetapan dokumen berkesan itu, tanpa menganggap seluruh tetingkap model 32K sentiasa digunakan. Sediakan petikan relevan atau bajet dokumen yang sesuai untuk tugasan dapatan maklumat sebenar.

Untuk prestasi yang disyorkan, Cohere menasihatkan agar tidak melebihi 1,000 dokumen dalam satu permintaan. Bahagikan set calon lebih besar kepada kumpulan yang mudah dikendalikan sambil mengekalkan pengenalnya. Skor output dinormalkan dari 0 hingga 1 dan bergantung pada pertanyaan serta petikan. Susunannya ialah hasil utama; skor bukan gandaan linear kerelevanan atau bukti bahawa petikan itu betul. Tentukan sebarang ambang penapisan melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain tersebut.

Hasil Fast yang berguna ialah senarai berperingkat yang boleh terus memacu paparan carian atau memilih bukti untuk peringkat penulisan jawapan. Simpan teks sumber bersama skor dan semak sama ada hasil teratas benar-benar menjawab syarat yang diminta. Peranan Fast ialah pemeringkatan dapatan maklumat, bukannya penjanaan penjelasan baharu atau vektor embedding. Kekalkan edisi bernama dan penyediaan input yang konsisten apabila memperhalus tingkah laku kerelevanan.

", "aboutText": "Cohere Rerank 4 Fast ialah edisi Rerank 4 yang lebih ringan, ditujukan kepada tugasan dapatan maklumat dengan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Model ini menerima pertanyaan dan dokumen calon, kemudian mengembalikan susunan dan skor kerelevanannya. Model ini berguna untuk carian interaktif, pemilihan bukti berulang dan aplikasi yang perlu menyusun semula senarai hasil sedia ada. Fast kekal sebagai edisi berbeza daripada Rerank 4 Pro, yang ditujukan kepada kes lebih kompleks apabila kualiti dapatan maklumat menjadi keutamaan.\n\nKod pengeluar ialah rerank-v4.0-fast. Seperti Pro, model ini mempunyai konteks 32,768 token dan liputan berbilang bahasa merangkumi lebih daripada 100 bahasa. Pertanyaan dan satu dokumen berkongsi konteks; ia bukan dimensi vektor atau bajet bagi seluruh korpus berindeks. Bekalkan teks calon yang perlu dibandingkan, tanpa mengharapkan model mendapatkan dokumen daripada sumber yang tidak diberikan kepadanya.\n\nHasil yang didokumenkan menyertakan indeks dokumen asal dan relevance_score. top_n memilih bilangan hasil yang dikembalikan, manakala penggugurannya mengembalikan senarai berperingkat penuh. Simpan pengenal sumber di sisi input supaya susunan baharu boleh dipadankan dengan petikan sebenar. Untuk carian produk, terangkan atribut produk yang diperlukan dalam pertanyaan dan peringkatkan semula penerangan calon; kedudukan yang lebih tinggi patut kekal terhubung dengan rekod produk yang menghasilkannya.\n\nModel ini boleh menggunakan dokumen bahasa Inggeris dan bahasa lain serta rekod separa berstruktur. Dalam aliran kerja V2, input dokumen ialah rentetan, dan Cohere mengesyorkan perwakilan data berstruktur sebagai YAML. Letakkan medan penting dahulu dan kekalkan susunannya supaya pemotongan tidak membuang kandungan yang diperlukan untuk menilai kerelevanan. Rekod sokongan boleh membawa tajuk, produk dan teks prosedur; mesej boleh membawa subjek dan isinya. Hubungan yang dinyatakan ini memberi pemeringkat maklumat melangkaui senarai kata kunci sepadan.\n\nHad pertanyaan khusus model dalam panduan penyediaan Cohere ialah 16,384 token, dengan pertanyaan dan dokumen menggunakan konteks bersama. Panduan itu menerangkan pemprosesan cebisan dokumen lebih panjang, manakala rujukan V2 secara berasingan menetapkan lalai pemotongan dokumen kepada 4096 token melalui max_tokens_per_doc. Semak tetapan dokumen berkesan itu, tanpa menganggap seluruh tetingkap model 32K sentiasa digunakan. Sediakan petikan relevan atau bajet dokumen yang sesuai untuk tugasan dapatan maklumat sebenar.\n\nUntuk prestasi yang disyorkan, Cohere menasihatkan agar tidak melebihi 1,000 dokumen dalam satu permintaan. Bahagikan set calon lebih besar kepada kumpulan yang mudah dikendalikan sambil mengekalkan pengenalnya. Skor output dinormalkan dari 0 hingga 1 dan bergantung pada pertanyaan serta petikan. Susunannya ialah hasil utama; skor bukan gandaan linear kerelevanan atau bukti bahawa petikan itu betul. Tentukan sebarang ambang penapisan melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain tersebut.\n\nHasil Fast yang berguna ialah senarai berperingkat yang boleh terus memacu paparan carian atau memilih bukti untuk peringkat penulisan jawapan. Simpan teks sumber bersama skor dan semak sama ada hasil teratas benar-benar menjawab syarat yang diminta. Peranan Fast ialah pemeringkatan dapatan maklumat, bukannya penjanaan penjelasan baharu atau vektor embedding. Kekalkan edisi bernama dan penyediaan input yang konsisten apabila memperhalus tingkah laku kerelevanan.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "e0d3d494aaec6123c3c1f83db6b307b308b4883b8d1eefc63bfe2b7d52e4f372", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "ms", "methods": [ { "html": "
•

Cohere: Rerank 4 Fast

Cohere /cohere/rerank-4-fast

Pengenalan

Cohere Rerank 4 Fast menyusun dokumen calon mengikut kerelevanan kepada pertanyaan, dengan tumpuan pada dapatan maklumat berkependaman rendah dan berdaya pemprosesan tinggi. Model ini menyokong konteks 32K, lebih daripada 100 bahasa dan teks separa berstruktur untuk carian dan aliran kerja yang diperkaya dengan dapatan carian.

Input yang diterima
Teks
Hasil
Hasil mengikut kedudukan
Tentang model

Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.

Cohere Rerank 4 Fast ialah edisi Rerank 4 yang lebih ringan, ditujukan kepada tugasan dapatan maklumat dengan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Model ini menerima pertanyaan dan dokumen calon, kemudian mengembalikan susunan dan skor kerelevanannya. Model ini berguna untuk carian interaktif, pemilihan bukti berulang dan aplikasi yang perlu menyusun semula senarai hasil sedia ada. Fast kekal sebagai edisi berbeza daripada Rerank 4 Pro, yang ditujukan kepada kes lebih kompleks apabila kualiti dapatan maklumat menjadi keutamaan.

Kod pengeluar ialah rerank-v4.0-fast. Seperti Pro, model ini mempunyai konteks 32,768 token dan liputan berbilang bahasa merangkumi lebih daripada 100 bahasa. Pertanyaan dan satu dokumen berkongsi konteks; ia bukan dimensi vektor atau bajet bagi seluruh korpus berindeks. Bekalkan teks calon yang perlu dibandingkan, tanpa mengharapkan model mendapatkan dokumen daripada sumber yang tidak diberikan kepadanya.

Hasil yang didokumenkan menyertakan indeks dokumen asal dan relevance_score. top_n memilih bilangan hasil yang dikembalikan, manakala penggugurannya mengembalikan senarai berperingkat penuh. Simpan pengenal sumber di sisi input supaya susunan baharu boleh dipadankan dengan petikan sebenar. Untuk carian produk, terangkan atribut produk yang diperlukan dalam pertanyaan dan peringkatkan semula penerangan calon; kedudukan yang lebih tinggi patut kekal terhubung dengan rekod produk yang menghasilkannya.

Model ini boleh menggunakan dokumen bahasa Inggeris dan bahasa lain serta rekod separa berstruktur. Dalam aliran kerja V2, input dokumen ialah rentetan, dan Cohere mengesyorkan perwakilan data berstruktur sebagai YAML. Letakkan medan penting dahulu dan kekalkan susunannya supaya pemotongan tidak membuang kandungan yang diperlukan untuk menilai kerelevanan. Rekod sokongan boleh membawa tajuk, produk dan teks prosedur; mesej boleh membawa subjek dan isinya. Hubungan yang dinyatakan ini memberi pemeringkat maklumat melangkaui senarai kata kunci sepadan.

Had pertanyaan khusus model dalam panduan penyediaan Cohere ialah 16,384 token, dengan pertanyaan dan dokumen menggunakan konteks bersama. Panduan itu menerangkan pemprosesan cebisan dokumen lebih panjang, manakala rujukan V2 secara berasingan menetapkan lalai pemotongan dokumen kepada 4096 token melalui max_tokens_per_doc. Semak tetapan dokumen berkesan itu, tanpa menganggap seluruh tetingkap model 32K sentiasa digunakan. Sediakan petikan relevan atau bajet dokumen yang sesuai untuk tugasan dapatan maklumat sebenar.

Untuk prestasi yang disyorkan, Cohere menasihatkan agar tidak melebihi 1,000 dokumen dalam satu permintaan. Bahagikan set calon lebih besar kepada kumpulan yang mudah dikendalikan sambil mengekalkan pengenalnya. Skor output dinormalkan dari 0 hingga 1 dan bergantung pada pertanyaan serta petikan. Susunannya ialah hasil utama; skor bukan gandaan linear kerelevanan atau bukti bahawa petikan itu betul. Tentukan sebarang ambang penapisan melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain tersebut.

Hasil Fast yang berguna ialah senarai berperingkat yang boleh terus memacu paparan carian atau memilih bukti untuk peringkat penulisan jawapan. Simpan teks sumber bersama skor dan semak sama ada hasil teratas benar-benar menjawab syarat yang diminta. Peranan Fast ialah pemeringkatan dapatan maklumat, bukannya penjanaan penjelasan baharu atau vektor embedding. Kekalkan edisi bernama dan penyediaan input yang konsisten apabila memperhalus tingkah laku kerelevanan.

Tersedia di Ailoot · input & kaedah

Ailoot Ultra · Pemeringkatan semula POST /v1/rerank
Input
text
Output
rerank
Format output
json
MedanJenisKeperluanNilai & hadLalai
MedandocumentsJenisarrayKeperluanDiperlukanNilai & hadLalai—
MedanmodelJenisstringKeperluanDiperlukanNilai & had
cohere/rerank-4-fast
Lalai—
MedanproviderJenisschemaKeperluanPilihanNilai & hadLalai—
MedanqueryJenisstringKeperluanDiperlukanNilai & hadLalai—
Medansession_idJenisstringKeperluanPilihanNilai & hadLalai—
Medantop_nJenisintegerKeperluanPilihanNilai & had
Minimum: 1
Lalai—
MedantraceJenisschemaKeperluanPilihanNilai & hadLalai—
MedanuserJenisstringKeperluanPilihanNilai & hadLalai—
Medandocuments[].textJenisstringKeperluanPilihanNilai & hadLalai—
Medandocuments[].imageJenisstringKeperluanPilihanNilai & hadLalai—
Gabungan bahan input

text_rerank · Diperlukan: query, documents array with minItems1

Parameter kaedah dan kitar hayat
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, query, documents
Parametermaterial_typesKontraktext
Parameteroptional_controlsKontrakprovider, session_id, top_n, trace, user
Parametermultimodal_input_bindingKontraknull

Kitar hayat: synchronous

Hantar ID model yang tepat dan simpan respons natif yang lengkap.

Pelan harga

Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.

Mod pantas
Tidak dinyatakan Apabila mod pantas dipilih
Mod flex
Tidak dinyatakan Apabila mod flex dipilih
Usaha penaakulan maksimum
Tidak dinyatakan Apabila usaha penaakulan maksimum menggunakan pelarasan default

Kadar Ailoot

Harga mengikut konfigurasi

Padankan setiap caj dengan syarat yang dipaparkan. Gunakan satu kadar bagi setiap set konfigurasi alternatif.

KonfigurasiJadual kadarUnit pengebilanCaj
KonfigurasiKadar asas tokenJadual kadarKadar asasUnit pengebilan—Caj
Input · Token input — / 1K token
Output · Token output — / 1K token
Input · Bacaan cache — / 1K token
Input · Penulisan cache — / 1K token
Input · Penulisan cache, 1 jam — / 1K token
KonfigurasiUltraJadual kadar—Unit pengebilanbagi setiap unit carianCaj
total · unit carian $0.004 bagi setiap unit carian

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →

Contoh permintaan

Ailoot Ultra · Pemeringkatan semula

{\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}

Terima dan simpan hasil

Ailoot Ultra · Pemeringkatan semula

json

Simpan semua hasil yang selesai

Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.

Setiap indeks hasil sebenar, relevance_score terhingga dan objek dokumen asal dikekalkan. Indeks mengikat tatasusunan dokumen asal. Keseluruhan JSON asal disimpan dan hanya dikembalikan selepas penyelesaian caj.

Jika respons asal perlu disemak

Simpan operasi asal dan balasan penuh/separa; jangan ulang POST.

Medan respons
MedanJenis
MedanmodelJenisstring
MedanidJenisstring
MedanproviderJenisstring
Medanresults[].indexJenisinteger
Medanresults[].relevance_scoreJenisnumber
Medanresults[].document.textJenisstring
Medanresults[].document.imageJenisstring
Medanusage.search_unitsJenisinteger
Medanusage.total_tokensJenisinteger
Medanusage.costJenisnumber
Ralat dan langkah seterusnya
ResponsSyaratLangkah seterusnya
Respons400 · invalid_request_errorSyaratMedan wajib, bentuk perwakilan atau nilai tidak sahLangkah seterusnyaBetulkan medan permintaan yang dinyatakan.

Alias

Alias model

Ailoot Ultra cohere/rerank-4-fast

Alias laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

ID pemanggil berkaitan

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": "Medan wajib, bentuk perwakilan atau nilai tidak sah", "http_status": 400, "recovery": "Betulkan medan permintaan yang dinyatakan.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Simpan operasi asal dan balasan penuh/separa; jangan ulang POST." }, "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": "Setiap indeks hasil sebenar, relevance_score terhingga dan objek dokumen asal dikekalkan. Indeks mengikat tatasusunan dokumen asal. Keseluruhan JSON asal disimpan dan hanya dikembalikan selepas penyelesaian caj.", "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": [ "Hantar ID model yang tepat dan simpan respons natif yang lengkap." ], "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": "Pemeringkatan semula", "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": "c9a741218fd3359a0f4b431ed4ade49556dcb2ad3af43b22362de7d5a34a8fc0", "input_html_sha256": "a7b16df7cc089e1c1c593a5761d6c4fb35afcee8d63ed299893d52e6714ba4b1", "private_audit_sha256": "36035dc7c62207be077acf1b7dac5a63308d8dc846f868a1cc04779f57e20e03", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/ms/cohere-rerank-4-fast/", "description": "Cohere Rerank 4 Fast menyusun dokumen calon mengikut kerelevanan kepada pertanyaan, dengan tumpuan pada dapatan maklumat berkependaman rendah dan berdaya pemprosesan tinggi. Model ini menyokong konteks 32K, lebih daripada 100 bahasa dan teks separa berstruktur untuk carian dan aliran kerja yang diperkaya dengan dapatan carian.", "locale": "ms", "md_url": "https://ailoot.ai/models/ms/cohere-rerank-4-fast/index.md", "title": "Cohere: Rerank 4 Fast — Ailoot", "url": "https://ailoot.ai/models/ms/cohere-rerank-4-fast/" }, "rawModel": { "about": "Cohere Rerank 4 Fast ialah edisi Rerank 4 yang lebih ringan, ditujukan kepada tugasan dapatan maklumat dengan kependaman lebih rendah dan daya pemprosesan lebih tinggi. Model ini menerima pertanyaan dan dokumen calon, kemudian mengembalikan susunan dan skor kerelevanannya. Model ini berguna untuk carian interaktif, pemilihan bukti berulang dan aplikasi yang perlu menyusun semula senarai hasil sedia ada. Fast kekal sebagai edisi berbeza daripada Rerank 4 Pro, yang ditujukan kepada kes lebih kompleks apabila kualiti dapatan maklumat menjadi keutamaan.\n\nKod pengeluar ialah rerank-v4.0-fast. Seperti Pro, model ini mempunyai konteks 32,768 token dan liputan berbilang bahasa merangkumi lebih daripada 100 bahasa. Pertanyaan dan satu dokumen berkongsi konteks; ia bukan dimensi vektor atau bajet bagi seluruh korpus berindeks. Bekalkan teks calon yang perlu dibandingkan, tanpa mengharapkan model mendapatkan dokumen daripada sumber yang tidak diberikan kepadanya.\n\nHasil yang didokumenkan menyertakan indeks dokumen asal dan relevance_score. top_n memilih bilangan hasil yang dikembalikan, manakala penggugurannya mengembalikan senarai berperingkat penuh. Simpan pengenal sumber di sisi input supaya susunan baharu boleh dipadankan dengan petikan sebenar. Untuk carian produk, terangkan atribut produk yang diperlukan dalam pertanyaan dan peringkatkan semula penerangan calon; kedudukan yang lebih tinggi patut kekal terhubung dengan rekod produk yang menghasilkannya.\n\nModel ini boleh menggunakan dokumen bahasa Inggeris dan bahasa lain serta rekod separa berstruktur. Dalam aliran kerja V2, input dokumen ialah rentetan, dan Cohere mengesyorkan perwakilan data berstruktur sebagai YAML. Letakkan medan penting dahulu dan kekalkan susunannya supaya pemotongan tidak membuang kandungan yang diperlukan untuk menilai kerelevanan. Rekod sokongan boleh membawa tajuk, produk dan teks prosedur; mesej boleh membawa subjek dan isinya. Hubungan yang dinyatakan ini memberi pemeringkat maklumat melangkaui senarai kata kunci sepadan.\n\nHad pertanyaan khusus model dalam panduan penyediaan Cohere ialah 16,384 token, dengan pertanyaan dan dokumen menggunakan konteks bersama. Panduan itu menerangkan pemprosesan cebisan dokumen lebih panjang, manakala rujukan V2 secara berasingan menetapkan lalai pemotongan dokumen kepada 4096 token melalui max_tokens_per_doc. Semak tetapan dokumen berkesan itu, tanpa menganggap seluruh tetingkap model 32K sentiasa digunakan. Sediakan petikan relevan atau bajet dokumen yang sesuai untuk tugasan dapatan maklumat sebenar.\n\nUntuk prestasi yang disyorkan, Cohere menasihatkan agar tidak melebihi 1,000 dokumen dalam satu permintaan. Bahagikan set calon lebih besar kepada kumpulan yang mudah dikendalikan sambil mengekalkan pengenalnya. Skor output dinormalkan dari 0 hingga 1 dan bergantung pada pertanyaan serta petikan. Susunannya ialah hasil utama; skor bukan gandaan linear kerelevanan atau bukti bahawa petikan itu betul. Tentukan sebarang ambang penapisan melalui pertanyaan yang mewakili penggunaan dan petikan yang hampir relevan dalam domain tersebut.\n\nHasil Fast yang berguna ialah senarai berperingkat yang boleh terus memacu paparan carian atau memilih bukti untuk peringkat penulisan jawapan. Simpan teks sumber bersama skor dan semak sama ada hasil teratas benar-benar menjawab syarat yang diminta. Peranan Fast ialah pemeringkatan dapatan maklumat, bukannya penjanaan penjelasan baharu atau vektor embedding. Kekalkan edisi bernama dan penyediaan input yang konsisten apabila memperhalus tingkah laku kerelevanan.", "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": "Maklumat lengkap tentang harga konfigurasi terdapat dalam butiran." } ], "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": "Medan wajib, bentuk perwakilan atau nilai tidak sah", "http_status": 400, "recovery": "Betulkan medan permintaan yang dinyatakan.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Simpan operasi asal dan balasan penuh/separa; jangan ulang POST." }, "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": "Setiap indeks hasil sebenar, relevance_score terhingga dan objek dokumen asal dikekalkan. Indeks mengikat tatasusunan dokumen asal. Keseluruhan JSON asal disimpan dan hanya dikembalikan selepas penyelesaian caj.", "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": [ "Hantar ID model yang tepat dan simpan respons natif yang lengkap." ], "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": "Pemeringkatan semula", "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 menyusun dokumen calon mengikut kerelevanan kepada pertanyaan, dengan tumpuan pada dapatan maklumat berkependaman rendah dan berdaya pemprosesan tinggi. Model ini menyokong konteks 32K, lebih daripada 100 bahasa dan teks separa berstruktur untuk carian dan aliran kerja yang diperkaya dengan dapatan carian." }, "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 menyusun dokumen calon mengikut kerelevanan kepada pertanyaan, dengan tumpuan pada dapatan maklumat berkependaman rendah dan berdaya pemprosesan tinggi. Model ini menyokong konteks 32K, lebih daripada 100 bahasa dan teks separa berstruktur untuk carian dan aliran kerja yang diperkaya dengan dapatan carian.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "9e5465d60b33dc7a18afbd3ea19539b1b1c731609c8b5d7c41132076bb3df471", "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460" }, "sourceRevision": "16ebb2fca313c3c682ea05ec1fa1e1086743320621c68cfbde599c8e27d995fa" }