{ "aboutHtml": "

rerank-2.5 ialah pemeringkat semula Voyage AI yang mematuhi arahan, direka untuk memperhalusi set calon carian. Ia memeriksa pertanyaan dan setiap dokumen bersama-sama sebagai pengekod silang, kemudian mengembalikan susunan kerelevanan. Keluaran pengeluar menekankan kawalan bahasa semula jadi terhadap perkara yang dianggap relevan, pemerolehan maklumat berbilang bahasa dan konteks pertanyaan–dokumen yang lebih panjang daripada edisi rerank-2 terdahulu.

Berikan calon yang perlu dibandingkan kepada model

Input ialah pertanyaan dan senarai dokumen teks calon. Ini boleh berupa petikan yang dipilih oleh carian pembenaman atau carian leksikal peringkat pertama. Pemeringkat semula menilai teks yang dibekalkan tanpa mencipta pembenaman korpus, jadi tugas praktikalnya ialah memperhalusi calon yang telah ditemukan oleh carian awal.

Kekalkan susunan calon asal dan pengecam sumber apabila menyediakan senarai itu. Indeks yang dikembalikan merujuk kedudukan input tersebut. Untuk tugasan RAG, bekalkan petikan yang mungkin benar-benar digunakan oleh peringkat jawapan, termasuk tajuk atau syarat yang diperlukan untuk mentafsirkannya. Dokumen berkaitan yang tiada dalam set calon yang dihantar tidak boleh muncul sebagai hasil baharu yang ditemukan melalui pemeringkatan semula.

Nyatakan pengertian kerelevanan yang dimaksudkan

Arahan boleh ditambahkan selepas atau sebelum pertanyaan. Keluaran menerangkan tiga peranan berguna: menekankan bahagian tertentu dokumen, mengutamakan jenis sumber dan menjelaskan kekaburan pertanyaan dengan konteks aplikasi. Ini mengubah tugasan kerelevanan yang diberikan kepada model.

Untuk carian teknikal, contoh arahan ialah “Utamakan petikan yang menerangkan tingkah laku kegagalan dan pemulihan; gambaran keseluruhan pengenalan adalah sekunder.” Untuk koleksi penyelidikan, nyatakan sama ada tajuk, kaedah atau hasil paling penting. Untuk istilah yang kabur, nyatakan bidang subjek carian. Pastikan soalan dan arahan serasi, supaya satu tidak meminta topik luas sementara yang lain mengubah tugasan secara senyap.

Keutamaan ini memandu penskoran kerelevanan. Jika aplikasi mempunyai keperluan kelayakan yang ketat—seperti versi sumber, tarikh atau skop akses—sediakan set calon yang layak secara berasingan. Arahan kerelevanan model dan sekatan koleksi deterministik memenuhi tujuan yang berbeza.

Output berperingkat dan penggunaan hasil kemudian

Hasil disusun mengikut kerelevanan menurun dan menyertakan skor kerelevanan titik apung serta indeks input asal calon. Pilihan top-k memilih bilangan hasil teratas yang perlu dikembalikan; tanpanya, spesifikasi pengeluar mengembalikan hasil bagi semua calon yang dihantar. Tetapan return-documents pilihan menyertakan teks sumber yang sepadan; jika tidak, respons mengandungi indeks dan skor.

Gunakan susunan itu untuk memilih petikan bagi paparan atau peringkat jawapan kemudian, sambil memastikan identiti sumbernya utuh. Periksa teks sebenar apabila menilai pemeringkatan: skor ialah ukuran kerelevanan kepada pertanyaan yang dihantar, bukannya jawapan yang dijana atau pengesahan bahawa dakwaan dokumen adalah benar. Ini menjadikan model berguna untuk memperhalusi bahan carian teknikal, undang-undang, kewangan, perubatan, perbualan dan berbilang bahasa tanpa menukar pemeringkatan menjadi kesimpulan bidang.

Konteks, belanjawan jumlah token dan pemangkasan

Had pertanyaan yang didokumenkan ialah 8,000 token, termasuk arahan yang diletakkan dalam pertanyaan. Pertanyaan bersama mana-mana satu dokumen calon mesti muat dalam 32,000 token, dan senarai dokumen boleh mengandungi paling banyak 1,000 item. Jumlah peruntukan ialah 600,000 token, dikira sebagai token pertanyaan didarab bilangan dokumen, ditambah semua token dokumen. Oleh itu, arahan panjang dikira berulang kali merentas pasangan calon.

Bilangan output top-k tidak mengubah bilangan calon dalam pengiraan token input itu. Pemangkasan lalai memendekkan pertanyaan atau dokumen yang terlalu panjang untuk memenuhi had konteks; mematikannya menyebabkan pertanyaan atau pasangan yang terlalu panjang mengembalikan ralat. Sediakan petikan yang mengandungi jawapan dengan sengaja dan kekalkan syarat berkaitan dalam teks yang akan dilihat oleh model. Nilai susunan yang diperhalusi pada soalan dan arahan yang mewakili, kemudian teruskan petikan asal yang dipilih ke peringkat seterusnya.

", "aboutText": "rerank-2.5 ialah pemeringkat semula Voyage AI yang mematuhi arahan, direka untuk memperhalusi set calon carian. Ia memeriksa pertanyaan dan setiap dokumen bersama-sama sebagai pengekod silang, kemudian mengembalikan susunan kerelevanan. Keluaran pengeluar menekankan kawalan bahasa semula jadi terhadap perkara yang dianggap relevan, pemerolehan maklumat berbilang bahasa dan konteks pertanyaan–dokumen yang lebih panjang daripada edisi rerank-2 terdahulu.\n\n### Berikan calon yang perlu dibandingkan kepada model\n\nInput ialah pertanyaan dan senarai dokumen teks calon. Ini boleh berupa petikan yang dipilih oleh carian pembenaman atau carian leksikal peringkat pertama. Pemeringkat semula menilai teks yang dibekalkan tanpa mencipta pembenaman korpus, jadi tugas praktikalnya ialah memperhalusi calon yang telah ditemukan oleh carian awal.\n\nKekalkan susunan calon asal dan pengecam sumber apabila menyediakan senarai itu. Indeks yang dikembalikan merujuk kedudukan input tersebut. Untuk tugasan RAG, bekalkan petikan yang mungkin benar-benar digunakan oleh peringkat jawapan, termasuk tajuk atau syarat yang diperlukan untuk mentafsirkannya. Dokumen berkaitan yang tiada dalam set calon yang dihantar tidak boleh muncul sebagai hasil baharu yang ditemukan melalui pemeringkatan semula.\n\n### Nyatakan pengertian kerelevanan yang dimaksudkan\n\nArahan boleh ditambahkan selepas atau sebelum pertanyaan. Keluaran menerangkan tiga peranan berguna: menekankan bahagian tertentu dokumen, mengutamakan jenis sumber dan menjelaskan kekaburan pertanyaan dengan konteks aplikasi. Ini mengubah tugasan kerelevanan yang diberikan kepada model.\n\nUntuk carian teknikal, contoh arahan ialah “Utamakan petikan yang menerangkan tingkah laku kegagalan dan pemulihan; gambaran keseluruhan pengenalan adalah sekunder.” Untuk koleksi penyelidikan, nyatakan sama ada tajuk, kaedah atau hasil paling penting. Untuk istilah yang kabur, nyatakan bidang subjek carian. Pastikan soalan dan arahan serasi, supaya satu tidak meminta topik luas sementara yang lain mengubah tugasan secara senyap.\n\nKeutamaan ini memandu penskoran kerelevanan. Jika aplikasi mempunyai keperluan kelayakan yang ketat—seperti versi sumber, tarikh atau skop akses—sediakan set calon yang layak secara berasingan. Arahan kerelevanan model dan sekatan koleksi deterministik memenuhi tujuan yang berbeza.\n\n### Output berperingkat dan penggunaan hasil kemudian\n\nHasil disusun mengikut kerelevanan menurun dan menyertakan skor kerelevanan titik apung serta indeks input asal calon. Pilihan top-k memilih bilangan hasil teratas yang perlu dikembalikan; tanpanya, spesifikasi pengeluar mengembalikan hasil bagi semua calon yang dihantar. Tetapan return-documents pilihan menyertakan teks sumber yang sepadan; jika tidak, respons mengandungi indeks dan skor.\n\nGunakan susunan itu untuk memilih petikan bagi paparan atau peringkat jawapan kemudian, sambil memastikan identiti sumbernya utuh. Periksa teks sebenar apabila menilai pemeringkatan: skor ialah ukuran kerelevanan kepada pertanyaan yang dihantar, bukannya jawapan yang dijana atau pengesahan bahawa dakwaan dokumen adalah benar. Ini menjadikan model berguna untuk memperhalusi bahan carian teknikal, undang-undang, kewangan, perubatan, perbualan dan berbilang bahasa tanpa menukar pemeringkatan menjadi kesimpulan bidang.\n\n### Konteks, belanjawan jumlah token dan pemangkasan\n\nHad pertanyaan yang didokumenkan ialah 8,000 token, termasuk arahan yang diletakkan dalam pertanyaan. Pertanyaan bersama mana-mana satu dokumen calon mesti muat dalam 32,000 token, dan senarai dokumen boleh mengandungi paling banyak 1,000 item. Jumlah peruntukan ialah 600,000 token, dikira sebagai token pertanyaan didarab bilangan dokumen, ditambah semua token dokumen. Oleh itu, arahan panjang dikira berulang kali merentas pasangan calon.\n\nBilangan output top-k tidak mengubah bilangan calon dalam pengiraan token input itu. Pemangkasan lalai memendekkan pertanyaan atau dokumen yang terlalu panjang untuk memenuhi had konteks; mematikannya menyebabkan pertanyaan atau pasangan yang terlalu panjang mengembalikan ralat. Sediakan petikan yang mengandungi jawapan dengan sengaja dan kekalkan syarat berkaitan dalam teks yang akan dilihat oleh model. Nilai susunan yang diperhalusi pada soalan dan arahan yang mewakili, kemudian teruskan petikan asal yang dipilih ke peringkat seterusnya.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "9cffcdb0ecf6e0c93fd308340569f344a36db625401167dd2b9d2bc7a90efc2d", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "ms", "methods": [ { "html": "
•

VoyageAI by MongoDB: rerank-2.5

VoyageAI by MongoDB /voyageai/rerank-2.5

Pengenalan

rerank-2.5 menyusun semula set calon teks yang dibekalkan mengikut kerelevanan kepada pertanyaan dan arahan bahasa semula jadi pilihan. Ia ialah pemeringkat semula berbilang bahasa yang berorientasikan kualiti dengan konteks pertanyaan–dokumen 32,000 token serta skor berperingkat yang dikaitkan dengan indeks calon asal.

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.

rerank-2.5 ialah pemeringkat semula Voyage AI yang mematuhi arahan, direka untuk memperhalusi set calon carian. Ia memeriksa pertanyaan dan setiap dokumen bersama-sama sebagai pengekod silang, kemudian mengembalikan susunan kerelevanan. Keluaran pengeluar menekankan kawalan bahasa semula jadi terhadap perkara yang dianggap relevan, pemerolehan maklumat berbilang bahasa dan konteks pertanyaan–dokumen yang lebih panjang daripada edisi rerank-2 terdahulu.

Berikan calon yang perlu dibandingkan kepada model

Input ialah pertanyaan dan senarai dokumen teks calon. Ini boleh berupa petikan yang dipilih oleh carian pembenaman atau carian leksikal peringkat pertama. Pemeringkat semula menilai teks yang dibekalkan tanpa mencipta pembenaman korpus, jadi tugas praktikalnya ialah memperhalusi calon yang telah ditemukan oleh carian awal.

Kekalkan susunan calon asal dan pengecam sumber apabila menyediakan senarai itu. Indeks yang dikembalikan merujuk kedudukan input tersebut. Untuk tugasan RAG, bekalkan petikan yang mungkin benar-benar digunakan oleh peringkat jawapan, termasuk tajuk atau syarat yang diperlukan untuk mentafsirkannya. Dokumen berkaitan yang tiada dalam set calon yang dihantar tidak boleh muncul sebagai hasil baharu yang ditemukan melalui pemeringkatan semula.

Nyatakan pengertian kerelevanan yang dimaksudkan

Arahan boleh ditambahkan selepas atau sebelum pertanyaan. Keluaran menerangkan tiga peranan berguna: menekankan bahagian tertentu dokumen, mengutamakan jenis sumber dan menjelaskan kekaburan pertanyaan dengan konteks aplikasi. Ini mengubah tugasan kerelevanan yang diberikan kepada model.

Untuk carian teknikal, contoh arahan ialah “Utamakan petikan yang menerangkan tingkah laku kegagalan dan pemulihan; gambaran keseluruhan pengenalan adalah sekunder.” Untuk koleksi penyelidikan, nyatakan sama ada tajuk, kaedah atau hasil paling penting. Untuk istilah yang kabur, nyatakan bidang subjek carian. Pastikan soalan dan arahan serasi, supaya satu tidak meminta topik luas sementara yang lain mengubah tugasan secara senyap.

Keutamaan ini memandu penskoran kerelevanan. Jika aplikasi mempunyai keperluan kelayakan yang ketat—seperti versi sumber, tarikh atau skop akses—sediakan set calon yang layak secara berasingan. Arahan kerelevanan model dan sekatan koleksi deterministik memenuhi tujuan yang berbeza.

Output berperingkat dan penggunaan hasil kemudian

Hasil disusun mengikut kerelevanan menurun dan menyertakan skor kerelevanan titik apung serta indeks input asal calon. Pilihan top-k memilih bilangan hasil teratas yang perlu dikembalikan; tanpanya, spesifikasi pengeluar mengembalikan hasil bagi semua calon yang dihantar. Tetapan return-documents pilihan menyertakan teks sumber yang sepadan; jika tidak, respons mengandungi indeks dan skor.

Gunakan susunan itu untuk memilih petikan bagi paparan atau peringkat jawapan kemudian, sambil memastikan identiti sumbernya utuh. Periksa teks sebenar apabila menilai pemeringkatan: skor ialah ukuran kerelevanan kepada pertanyaan yang dihantar, bukannya jawapan yang dijana atau pengesahan bahawa dakwaan dokumen adalah benar. Ini menjadikan model berguna untuk memperhalusi bahan carian teknikal, undang-undang, kewangan, perubatan, perbualan dan berbilang bahasa tanpa menukar pemeringkatan menjadi kesimpulan bidang.

Konteks, belanjawan jumlah token dan pemangkasan

Had pertanyaan yang didokumenkan ialah 8,000 token, termasuk arahan yang diletakkan dalam pertanyaan. Pertanyaan bersama mana-mana satu dokumen calon mesti muat dalam 32,000 token, dan senarai dokumen boleh mengandungi paling banyak 1,000 item. Jumlah peruntukan ialah 600,000 token, dikira sebagai token pertanyaan didarab bilangan dokumen, ditambah semua token dokumen. Oleh itu, arahan panjang dikira berulang kali merentas pasangan calon.

Bilangan output top-k tidak mengubah bilangan calon dalam pengiraan token input itu. Pemangkasan lalai memendekkan pertanyaan atau dokumen yang terlalu panjang untuk memenuhi had konteks; mematikannya menyebabkan pertanyaan atau pasangan yang terlalu panjang mengembalikan ralat. Sediakan petikan yang mengandungi jawapan dengan sengaja dan kekalkan syarat berkaitan dalam teks yang akan dilihat oleh model. Nilai susunan yang diperhalusi pada soalan dan arahan yang mewakili, kemudian teruskan petikan asal yang dipilih ke peringkat seterusnya.

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
voyageai/rerank-2.5
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 pengebilan/ 1K tokenCaj
Input $0.0001 / 1K token

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": "voyageai/rerank-2.5",\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 voyageai/rerank-2.5

Alias laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

ID pemanggil berkaitan

voyageai/rerank-2.5

", "method_identity": { "effective_request_model_id": "voyageai/rerank-2.5", "fmt": "rerank", "http": "POST", "path": "/v1/rerank", "request_model_id": "voyageai/rerank-2.5", "route_label": "Ultra" }, "method_key": "Ultra|rerank|/v1/rerank|voyageai%2Frerank-2.5", "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": "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": [ "voyageai/rerank-2.5" ], "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": [ "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": 32000, "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": [ "voyageai/rerank-2.5" ], "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": "/ 1K tokens" }, "label": "Ultra", "units": [ { "amount": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"voyageai/rerank-2.5\",\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']='voyageai/rerank-2.5'\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": "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": { "input_token": 1e-07 } }, "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": "/ 1K tokens" }, "label": "Ultra", "units": [ { "amount": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ] } ], "request_model_id": "voyageai/rerank-2.5", "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": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ] } } ], "model_id": "voyageai/rerank-2.5", "nativeProvenance": { "input_facts_canonical_sha256": "c50b271e37d36434c1167d5bc5255202659bc5271e7680552b47c67086d7072a", "input_html_sha256": "cedc879364fdcbff68ec1eeb01317f330d66db440d02742d7812fcd1df2d4fb6", "private_audit_sha256": "ff4a0bf42e7523cd4459bb89eeb7c60145116d44881af2d272ade7065e0c741c", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/ms/voyageai-rerank-2-5/", "description": "rerank-2.5 menyusun semula set calon teks yang dibekalkan mengikut kerelevanan kepada pertanyaan dan arahan bahasa semula jadi pilihan. Ia ialah pemeringkat semula berbilang bahasa yang berorientasikan kualiti dengan konteks pertanyaan–dokumen 32,000 token serta skor berperingkat yang dikaitkan dengan indeks calon asal.", "locale": "ms", "md_url": "https://ailoot.ai/models/ms/voyageai-rerank-2-5/index.md", "title": "VoyageAI by MongoDB: rerank-2.5 — Ailoot", "url": "https://ailoot.ai/models/ms/voyageai-rerank-2-5/" }, "rawModel": { "about": "rerank-2.5 ialah pemeringkat semula Voyage AI yang mematuhi arahan, direka untuk memperhalusi set calon carian. Ia memeriksa pertanyaan dan setiap dokumen bersama-sama sebagai pengekod silang, kemudian mengembalikan susunan kerelevanan. Keluaran pengeluar menekankan kawalan bahasa semula jadi terhadap perkara yang dianggap relevan, pemerolehan maklumat berbilang bahasa dan konteks pertanyaan–dokumen yang lebih panjang daripada edisi rerank-2 terdahulu.\n\n### Berikan calon yang perlu dibandingkan kepada model\n\nInput ialah pertanyaan dan senarai dokumen teks calon. Ini boleh berupa petikan yang dipilih oleh carian pembenaman atau carian leksikal peringkat pertama. Pemeringkat semula menilai teks yang dibekalkan tanpa mencipta pembenaman korpus, jadi tugas praktikalnya ialah memperhalusi calon yang telah ditemukan oleh carian awal.\n\nKekalkan susunan calon asal dan pengecam sumber apabila menyediakan senarai itu. Indeks yang dikembalikan merujuk kedudukan input tersebut. Untuk tugasan RAG, bekalkan petikan yang mungkin benar-benar digunakan oleh peringkat jawapan, termasuk tajuk atau syarat yang diperlukan untuk mentafsirkannya. Dokumen berkaitan yang tiada dalam set calon yang dihantar tidak boleh muncul sebagai hasil baharu yang ditemukan melalui pemeringkatan semula.\n\n### Nyatakan pengertian kerelevanan yang dimaksudkan\n\nArahan boleh ditambahkan selepas atau sebelum pertanyaan. Keluaran menerangkan tiga peranan berguna: menekankan bahagian tertentu dokumen, mengutamakan jenis sumber dan menjelaskan kekaburan pertanyaan dengan konteks aplikasi. Ini mengubah tugasan kerelevanan yang diberikan kepada model.\n\nUntuk carian teknikal, contoh arahan ialah “Utamakan petikan yang menerangkan tingkah laku kegagalan dan pemulihan; gambaran keseluruhan pengenalan adalah sekunder.” Untuk koleksi penyelidikan, nyatakan sama ada tajuk, kaedah atau hasil paling penting. Untuk istilah yang kabur, nyatakan bidang subjek carian. Pastikan soalan dan arahan serasi, supaya satu tidak meminta topik luas sementara yang lain mengubah tugasan secara senyap.\n\nKeutamaan ini memandu penskoran kerelevanan. Jika aplikasi mempunyai keperluan kelayakan yang ketat—seperti versi sumber, tarikh atau skop akses—sediakan set calon yang layak secara berasingan. Arahan kerelevanan model dan sekatan koleksi deterministik memenuhi tujuan yang berbeza.\n\n### Output berperingkat dan penggunaan hasil kemudian\n\nHasil disusun mengikut kerelevanan menurun dan menyertakan skor kerelevanan titik apung serta indeks input asal calon. Pilihan top-k memilih bilangan hasil teratas yang perlu dikembalikan; tanpanya, spesifikasi pengeluar mengembalikan hasil bagi semua calon yang dihantar. Tetapan return-documents pilihan menyertakan teks sumber yang sepadan; jika tidak, respons mengandungi indeks dan skor.\n\nGunakan susunan itu untuk memilih petikan bagi paparan atau peringkat jawapan kemudian, sambil memastikan identiti sumbernya utuh. Periksa teks sebenar apabila menilai pemeringkatan: skor ialah ukuran kerelevanan kepada pertanyaan yang dihantar, bukannya jawapan yang dijana atau pengesahan bahawa dakwaan dokumen adalah benar. Ini menjadikan model berguna untuk memperhalusi bahan carian teknikal, undang-undang, kewangan, perubatan, perbualan dan berbilang bahasa tanpa menukar pemeringkatan menjadi kesimpulan bidang.\n\n### Konteks, belanjawan jumlah token dan pemangkasan\n\nHad pertanyaan yang didokumenkan ialah 8,000 token, termasuk arahan yang diletakkan dalam pertanyaan. Pertanyaan bersama mana-mana satu dokumen calon mesti muat dalam 32,000 token, dan senarai dokumen boleh mengandungi paling banyak 1,000 item. Jumlah peruntukan ialah 600,000 token, dikira sebagai token pertanyaan didarab bilangan dokumen, ditambah semua token dokumen. Oleh itu, arahan panjang dikira berulang kali merentas pasangan calon.\n\nBilangan output top-k tidak mengubah bilangan calon dalam pengiraan token input itu. Pemangkasan lalai memendekkan pertanyaan atau dokumen yang terlalu panjang untuk memenuhi had konteks; mematikannya menyebabkan pertanyaan atau pasangan yang terlalu panjang mengembalikan ralat. Sediakan petikan yang mengandungi jawapan dengan sengaja dan kekalkan syarat berkaitan dalam teks yang akan dilihat oleh model. Nilai susunan yang diperhalusi pada soalan dan arahan yang mewakili, kemudian teruskan petikan asal yang dipilih ke peringkat seterusnya.", "alias_of": null, "aliases": [], "author": "VoyageAI by MongoDB", "available": null, "canonical_model": "voyageai/rerank-2.5", "capabilities": { "context_length": 32000, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ], "summary": "Maklumat lengkap tentang harga konfigurasi terdapat dalam butiran." } ], "ctx": null, "entity_type": "model", "formats": [ "rerank" ], "id": "voyageai/rerank-2.5", "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": "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": [ "voyageai/rerank-2.5" ], "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": [ "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": 32000, "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": [ "voyageai/rerank-2.5" ], "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": "/ 1K tokens" }, "label": "Ultra", "units": [ { "amount": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ] } ], "examples": [ { "code": "{\n \"documents\": [\n \"Paris is the capital of France.\",\n \"Berlin is the capital of Germany.\"\n ],\n \"model\": \"voyageai/rerank-2.5\",\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']='voyageai/rerank-2.5'\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": "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": { "input_token": 1e-07 } }, "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": "/ 1K tokens" }, "label": "Ultra", "units": [ { "amount": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ] } ], "request_model_id": "voyageai/rerank-2.5", "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": "0.0001000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000001", "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" } ] } ], "model_aliases": [ { "canonical_model_id": "voyageai/rerank-2.5", "request_model_id": "voyageai/rerank-2.5", "route_label": "Ultra" } ], "name": "VoyageAI by MongoDB: rerank-2.5", "price": { "in": null, "in_label": null, "out": null, "out_label": null, "sort_in": null, "sort_out": null, "varies": true }, "public_id": "voyageai/rerank-2.5", "short": "rerank-2.5 menyusun semula set calon teks yang dibekalkan mengikut kerelevanan kepada pertanyaan dan arahan bahasa semula jadi pilihan. Ia ialah pemeringkat semula berbilang bahasa yang berorientasikan kualiti dengan konteks pertanyaan–dokumen 32,000 token serta skor berperingkat yang dikaitkan dengan indeks calon asal." }, "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": "rerank-2.5 menyusun semula set calon teks yang dibekalkan mengikut kerelevanan kepada pertanyaan dan arahan bahasa semula jadi pilihan. Ia ialah pemeringkat semula berbilang bahasa yang berorientasikan kualiti dengan konteks pertanyaan–dokumen 32,000 token serta skor berperingkat yang dikaitkan dengan indeks calon asal.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "1036300cc2d436048a91e7010c7da495f0b9d5d7f79deb26dfd2f053247b322a", "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460" }, "sourceRevision": "2b35b6df0e72eb6642d23c29e4962770971c15f96866cc50548277fc80dfa0a1" }