{ "aboutHtml": "

rerank-2.5-lite adalah anggota ringan dari rilis reranker 2.5 Voyage AI. Pembuatnya mendeskripsikannya sebagai model generalis yang menyeimbangkan latensi dan kualitas relevansi, dengan kepatuhan terhadap instruksi bahasa alami serta dukungan multibahasa. Masukannya berupa kueri dan teks kandidat, sedangkan keluarannya berupa urutan kandidat yang disempurnakan beserta skor relevansi.

Keputusan relevansi tahap kedua yang ringan

Model Lite memproses setiap pasangan kueri-dokumen secara bersamaan sebagai cross-encoder. Model ini dapat mengikuti pencarian embedding atau leksikal tahap pertama, dengan menyempurnakan bagian teks yang dipilih tahap tersebut. Ini berbeda dari menyiapkan indeks embedding yang dapat digunakan kembali: reranker memeriksa kueri dan teks dokumen yang dikirim secara bersamaan untuk keputusan relevansi saat ini.

Untuk pencarian bantuan interaktif, kirim penjelasan kandidat yang mungkin menjawab pertanyaan pengguna. Pertahankan cukup banyak dari setiap bagian teks untuk menyampaikan perilaku dan kondisi yang menyertainya, serta jaga keterkaitan indeks masukan dengan item sumber. Kumpulan kandidat yang terarah membantu memperjelas tugas pemeringkatan ulang sambil menjaga bukti hasil pengambilan tetap dapat digunakan untuk ditampilkan atau untuk jawaban berikutnya.

Instruksi menyatakan hal yang ingin diprioritaskan pengguna

Seperti rerank-2.5, Lite menerima instruksi bahasa alami sebelum atau sesudah kueri. Contoh pembuat menggunakannya untuk menekankan komponen dokumen, memilih jenis sumber yang disukai, atau menambahkan konteks pada kata yang ambigu. Aplikasi dapat menyediakan preferensi relevansi tetap, atau pertanyaan saat ini dapat menyertakan preferensi yang lebih spesifik.

Contoh kueri dukungan dapat menanyakan bagian teks mana yang menjelaskan suatu kesalahan, dengan instruksi untuk memprioritaskan langkah pemecahan masalah daripada deskripsi produk umum. Pencarian dokumen dapat meminta redaksi kebijakan asli, bukan pembahasan tentang kebijakan tersebut. Deskripsikan bukti yang dituju dan jaga instruksi tetap ringkas. Instruksi memengaruhi skor relevansi; siapkan batasan versi, tanggal, atau izin yang ketat dalam pemilihan kandidat, alih-alih memperlakukan preferensi relevansi sebagai filter yang dijamin.

Baca peringkat dan kembalikan bagian teks sumber

Keluaran diurutkan berdasarkan relevansi menurun. Setiap hasil mencakup indeks dokumen asli dan skor floating-point; pengaturan return-documents opsional juga mengembalikan teks masukan yang bersesuaian. Opsi top-k menentukan jumlah hasil teratas yang dikembalikan, sementara tidak menyertakannya mengembalikan peringkat untuk semua kandidat yang dikirim.

Gunakan indeks tersebut untuk memperoleh kembali bagian teks sumber dan pengidentifikasinya. Kandidat yang diperingkat ulang dapat menjadi hasil pencarian atau bukti untuk langkah RAG berikutnya, tetapi keluarannya adalah pengurutan teks yang diberikan, bukan jawaban yang ditulis ulang. Evaluasi apakah bagian teks teratas memenuhi pertanyaan sekaligus instruksi relevansi, termasuk pada bahasa dan jenis dokumen yang benar-benar ada dalam koleksi.

Batas dan anggaran kandidat praktis

Edisi 2.5 Lite memiliki maksimum kueri terdokumentasi 8.000 token dan batas 32.000 token untuk kueri ditambah setiap dokumen individual. Model ini berbeda dari model rerank-2-lite sebelumnya dan konteksnya yang lebih kecil. Maksimum 1.000 dokumen dapat dikirim, dan total masukan dibatasi 600.000 token berdasarkan rumus: token kueri dikalikan jumlah kandidat, ditambah jumlah token seluruh dokumen kandidat.

Untuk penggunaan yang sensitif terhadap latensi, FAQ pembuat merekomendasikan rerank-2.5-lite dengan tidak lebih dari 200.000 total token per permintaan. Ini merupakan panduan praktis di bawah maksimum yang dinyatakan, bukan jendela per dokumen yang berbeda. Kueri atau instruksi lebih panjang dihitung sekali untuk setiap pasangan kandidat, sehingga jumlah kandidat dan panjang kueri sama-sama memengaruhi total.

Truncation aktif secara default dan memendekkan masukan kueri/dokumen yang terlalu panjang agar muat; menonaktifkannya mengembalikan kesalahan untuk kueri atau pasangan yang terlalu panjang. Siapkan kandidat ringkas yang memuat jawaban secara sengaja agar bukti berguna tidak tertinggal di luar batas pemotongan. Memilih lebih sedikit hasil keluaran melalui top-k tidak menghapus kandidat lain yang diberikan dari perhitungan token. Periksa bagian teks sumber yang dikembalikan dan sempurnakan persiapan masukan berdasarkan pertanyaan aktual yang perlu dijawab antarmuka pencarian.

", "aboutText": "rerank-2.5-lite adalah anggota ringan dari rilis reranker 2.5 Voyage AI. Pembuatnya mendeskripsikannya sebagai model generalis yang menyeimbangkan latensi dan kualitas relevansi, dengan kepatuhan terhadap instruksi bahasa alami serta dukungan multibahasa. Masukannya berupa kueri dan teks kandidat, sedangkan keluarannya berupa urutan kandidat yang disempurnakan beserta skor relevansi.\n\n### Keputusan relevansi tahap kedua yang ringan\n\nModel Lite memproses setiap pasangan kueri-dokumen secara bersamaan sebagai cross-encoder. Model ini dapat mengikuti pencarian embedding atau leksikal tahap pertama, dengan menyempurnakan bagian teks yang dipilih tahap tersebut. Ini berbeda dari menyiapkan indeks embedding yang dapat digunakan kembali: reranker memeriksa kueri dan teks dokumen yang dikirim secara bersamaan untuk keputusan relevansi saat ini.\n\nUntuk pencarian bantuan interaktif, kirim penjelasan kandidat yang mungkin menjawab pertanyaan pengguna. Pertahankan cukup banyak dari setiap bagian teks untuk menyampaikan perilaku dan kondisi yang menyertainya, serta jaga keterkaitan indeks masukan dengan item sumber. Kumpulan kandidat yang terarah membantu memperjelas tugas pemeringkatan ulang sambil menjaga bukti hasil pengambilan tetap dapat digunakan untuk ditampilkan atau untuk jawaban berikutnya.\n\n### Instruksi menyatakan hal yang ingin diprioritaskan pengguna\n\nSeperti rerank-2.5, Lite menerima instruksi bahasa alami sebelum atau sesudah kueri. Contoh pembuat menggunakannya untuk menekankan komponen dokumen, memilih jenis sumber yang disukai, atau menambahkan konteks pada kata yang ambigu. Aplikasi dapat menyediakan preferensi relevansi tetap, atau pertanyaan saat ini dapat menyertakan preferensi yang lebih spesifik.\n\nContoh kueri dukungan dapat menanyakan bagian teks mana yang menjelaskan suatu kesalahan, dengan instruksi untuk memprioritaskan langkah pemecahan masalah daripada deskripsi produk umum. Pencarian dokumen dapat meminta redaksi kebijakan asli, bukan pembahasan tentang kebijakan tersebut. Deskripsikan bukti yang dituju dan jaga instruksi tetap ringkas. Instruksi memengaruhi skor relevansi; siapkan batasan versi, tanggal, atau izin yang ketat dalam pemilihan kandidat, alih-alih memperlakukan preferensi relevansi sebagai filter yang dijamin.\n\n### Baca peringkat dan kembalikan bagian teks sumber\n\nKeluaran diurutkan berdasarkan relevansi menurun. Setiap hasil mencakup indeks dokumen asli dan skor floating-point; pengaturan return-documents opsional juga mengembalikan teks masukan yang bersesuaian. Opsi top-k menentukan jumlah hasil teratas yang dikembalikan, sementara tidak menyertakannya mengembalikan peringkat untuk semua kandidat yang dikirim.\n\nGunakan indeks tersebut untuk memperoleh kembali bagian teks sumber dan pengidentifikasinya. Kandidat yang diperingkat ulang dapat menjadi hasil pencarian atau bukti untuk langkah RAG berikutnya, tetapi keluarannya adalah pengurutan teks yang diberikan, bukan jawaban yang ditulis ulang. Evaluasi apakah bagian teks teratas memenuhi pertanyaan sekaligus instruksi relevansi, termasuk pada bahasa dan jenis dokumen yang benar-benar ada dalam koleksi.\n\n### Batas dan anggaran kandidat praktis\n\nEdisi 2.5 Lite memiliki maksimum kueri terdokumentasi 8.000 token dan batas 32.000 token untuk kueri ditambah setiap dokumen individual. Model ini berbeda dari model rerank-2-lite sebelumnya dan konteksnya yang lebih kecil. Maksimum 1.000 dokumen dapat dikirim, dan total masukan dibatasi 600.000 token berdasarkan rumus: token kueri dikalikan jumlah kandidat, ditambah jumlah token seluruh dokumen kandidat.\n\nUntuk penggunaan yang sensitif terhadap latensi, FAQ pembuat merekomendasikan rerank-2.5-lite dengan tidak lebih dari 200.000 total token per permintaan. Ini merupakan panduan praktis di bawah maksimum yang dinyatakan, bukan jendela per dokumen yang berbeda. Kueri atau instruksi lebih panjang dihitung sekali untuk setiap pasangan kandidat, sehingga jumlah kandidat dan panjang kueri sama-sama memengaruhi total.\n\nTruncation aktif secara default dan memendekkan masukan kueri/dokumen yang terlalu panjang agar muat; menonaktifkannya mengembalikan kesalahan untuk kueri atau pasangan yang terlalu panjang. Siapkan kandidat ringkas yang memuat jawaban secara sengaja agar bukti berguna tidak tertinggal di luar batas pemotongan. Memilih lebih sedikit hasil keluaran melalui top-k tidak menghapus kandidat lain yang diberikan dari perhitungan token. Periksa bagian teks sumber yang dikembalikan dan sempurnakan persiapan masukan berdasarkan pertanyaan aktual yang perlu dijawab antarmuka pencarian.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "64578783ee12924ab9016ad12a91dded4abc2a257790678dfd8852335f2925ac", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "id", "methods": [ { "html": "
•

VoyageAI by MongoDB: rerank-2.5-lite

VoyageAI by MongoDB /voyageai/rerank-2.5-lite

Mulai

rerank-2.5-lite adalah reranker Voyage AI yang mengikuti instruksi dan berorientasi pada latensi untuk kumpulan kandidat teks yang diberikan. Model ini mempertahankan konteks kueri-dokumen 32.000 token dan dukungan multibahasa, serta mengembalikan daftar terurut skor relevansi dan indeks dokumen asli.

Masukan yang diterima
Teks
Hasil
Hasil berperingkat
Tentang model

Deskripsi ini mencakup kemampuan model. Masukan, keluaran, dan opsi yang tersedia di Ailoot tercantum di bawah untuk metode yang dipilih.

rerank-2.5-lite adalah anggota ringan dari rilis reranker 2.5 Voyage AI. Pembuatnya mendeskripsikannya sebagai model generalis yang menyeimbangkan latensi dan kualitas relevansi, dengan kepatuhan terhadap instruksi bahasa alami serta dukungan multibahasa. Masukannya berupa kueri dan teks kandidat, sedangkan keluarannya berupa urutan kandidat yang disempurnakan beserta skor relevansi.

Keputusan relevansi tahap kedua yang ringan

Model Lite memproses setiap pasangan kueri-dokumen secara bersamaan sebagai cross-encoder. Model ini dapat mengikuti pencarian embedding atau leksikal tahap pertama, dengan menyempurnakan bagian teks yang dipilih tahap tersebut. Ini berbeda dari menyiapkan indeks embedding yang dapat digunakan kembali: reranker memeriksa kueri dan teks dokumen yang dikirim secara bersamaan untuk keputusan relevansi saat ini.

Untuk pencarian bantuan interaktif, kirim penjelasan kandidat yang mungkin menjawab pertanyaan pengguna. Pertahankan cukup banyak dari setiap bagian teks untuk menyampaikan perilaku dan kondisi yang menyertainya, serta jaga keterkaitan indeks masukan dengan item sumber. Kumpulan kandidat yang terarah membantu memperjelas tugas pemeringkatan ulang sambil menjaga bukti hasil pengambilan tetap dapat digunakan untuk ditampilkan atau untuk jawaban berikutnya.

Instruksi menyatakan hal yang ingin diprioritaskan pengguna

Seperti rerank-2.5, Lite menerima instruksi bahasa alami sebelum atau sesudah kueri. Contoh pembuat menggunakannya untuk menekankan komponen dokumen, memilih jenis sumber yang disukai, atau menambahkan konteks pada kata yang ambigu. Aplikasi dapat menyediakan preferensi relevansi tetap, atau pertanyaan saat ini dapat menyertakan preferensi yang lebih spesifik.

Contoh kueri dukungan dapat menanyakan bagian teks mana yang menjelaskan suatu kesalahan, dengan instruksi untuk memprioritaskan langkah pemecahan masalah daripada deskripsi produk umum. Pencarian dokumen dapat meminta redaksi kebijakan asli, bukan pembahasan tentang kebijakan tersebut. Deskripsikan bukti yang dituju dan jaga instruksi tetap ringkas. Instruksi memengaruhi skor relevansi; siapkan batasan versi, tanggal, atau izin yang ketat dalam pemilihan kandidat, alih-alih memperlakukan preferensi relevansi sebagai filter yang dijamin.

Baca peringkat dan kembalikan bagian teks sumber

Keluaran diurutkan berdasarkan relevansi menurun. Setiap hasil mencakup indeks dokumen asli dan skor floating-point; pengaturan return-documents opsional juga mengembalikan teks masukan yang bersesuaian. Opsi top-k menentukan jumlah hasil teratas yang dikembalikan, sementara tidak menyertakannya mengembalikan peringkat untuk semua kandidat yang dikirim.

Gunakan indeks tersebut untuk memperoleh kembali bagian teks sumber dan pengidentifikasinya. Kandidat yang diperingkat ulang dapat menjadi hasil pencarian atau bukti untuk langkah RAG berikutnya, tetapi keluarannya adalah pengurutan teks yang diberikan, bukan jawaban yang ditulis ulang. Evaluasi apakah bagian teks teratas memenuhi pertanyaan sekaligus instruksi relevansi, termasuk pada bahasa dan jenis dokumen yang benar-benar ada dalam koleksi.

Batas dan anggaran kandidat praktis

Edisi 2.5 Lite memiliki maksimum kueri terdokumentasi 8.000 token dan batas 32.000 token untuk kueri ditambah setiap dokumen individual. Model ini berbeda dari model rerank-2-lite sebelumnya dan konteksnya yang lebih kecil. Maksimum 1.000 dokumen dapat dikirim, dan total masukan dibatasi 600.000 token berdasarkan rumus: token kueri dikalikan jumlah kandidat, ditambah jumlah token seluruh dokumen kandidat.

Untuk penggunaan yang sensitif terhadap latensi, FAQ pembuat merekomendasikan rerank-2.5-lite dengan tidak lebih dari 200.000 total token per permintaan. Ini merupakan panduan praktis di bawah maksimum yang dinyatakan, bukan jendela per dokumen yang berbeda. Kueri atau instruksi lebih panjang dihitung sekali untuk setiap pasangan kandidat, sehingga jumlah kandidat dan panjang kueri sama-sama memengaruhi total.

Truncation aktif secara default dan memendekkan masukan kueri/dokumen yang terlalu panjang agar muat; menonaktifkannya mengembalikan kesalahan untuk kueri atau pasangan yang terlalu panjang. Siapkan kandidat ringkas yang memuat jawaban secara sengaja agar bukti berguna tidak tertinggal di luar batas pemotongan. Memilih lebih sedikit hasil keluaran melalui top-k tidak menghapus kandidat lain yang diberikan dari perhitungan token. Periksa bagian teks sumber yang dikembalikan dan sempurnakan persiapan masukan berdasarkan pertanyaan aktual yang perlu dijawab antarmuka pencarian.

Tersedia di Ailoot · masukan & metode

Ailoot Ultra · Pemeringkatan ulang POST /v1/rerank
Masukan
text
Keluaran
rerank
Format keluaran
json
KolomJenisPersyaratanNilai & batasanBawaan
KolomdocumentsJenisarrayPersyaratanWajibNilai & batasanBawaan—
KolommodelJenisstringPersyaratanWajibNilai & batasan
voyageai/rerank-2.5-lite
Bawaan—
KolomproviderJenisschemaPersyaratanOpsionalNilai & batasanBawaan—
KolomqueryJenisstringPersyaratanWajibNilai & batasanBawaan—
Kolomsession_idJenisstringPersyaratanOpsionalNilai & batasanBawaan—
Kolomtop_nJenisintegerPersyaratanOpsionalNilai & batasan
Minimum: 1
Bawaan—
KolomtraceJenisschemaPersyaratanOpsionalNilai & batasanBawaan—
KolomuserJenisstringPersyaratanOpsionalNilai & batasanBawaan—
Kolomdocuments[].textJenisstringPersyaratanOpsionalNilai & batasanBawaan—
Kolomdocuments[].imageJenisstringPersyaratanOpsionalNilai & batasanBawaan—
Kombinasi bahan masukan

text_rerank · Wajib: query, documents array with minItems1

Parameter metode dan siklus hidup
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, query, documents
Parametermaterial_typesKontraktext
Parameteroptional_controlsKontrakprovider, session_id, top_n, trace, user
Parametermultimodal_input_bindingKontraknull

Siklus hidup: synchronous

Kirim ID model yang persis dan simpan respons native secara utuh.

Paket

Referensi tarif tidak menambah opsi permintaan. Parameter yang didukung tercantum pada metode yang dipilih.

Mode Fast
Tidak ditentukan Saat mode fast dipilih
Mode Flex
Tidak ditentukan Saat mode flex dipilih
Upaya penalaran maksimum
Tidak ditentukan Saat upaya penalaran maksimum menggunakan penyesuaian default

Tarif Ailoot

Harga menurut konfigurasi

Cocokkan setiap biaya dengan kondisi yang ditampilkan. Gunakan satu tarif untuk setiap kelompok konfigurasi alternatif.

KonfigurasiJadwal tarifSatuan penagihanBiaya
KonfigurasiTarif dasar tokenJadwal tarifTarif dasarSatuan penagihan—Biaya
Masukan · Token masukan — / 1K token
Keluaran · Token keluaran — / 1K token
Masukan · Pembacaan cache — / 1K token
Masukan · Penulisan cache — / 1K token
Masukan · Penulisan cache, 1 jam — / 1K token
KonfigurasiUltraJadwal tarif—Satuan penagihan/ 1K tokenBiaya
Masukan $0.00004 / 1K token

Ailoot mencadangkan perkiraan dana sebelum permintaan. Setelah hasil diterima, tagihan akhir diselesaikan berdasarkan penggunaan yang terukur. Penyelesaian tagihan streaming menggunakan catatan penggunaan akhir. Cara kerja cadangan dan penyelesaian tagihan →

Contoh permintaan

Ailoot Ultra · Pemeringkatan ulang

{\n  "documents": [\n    "Paris is the capital of France.",\n    "Berlin is the capital of Germany."\n  ],\n  "model": "voyageai/rerank-2.5-lite",\n  "query": "What is the capital of France?",\n  "top_n": 3\n}

Terima dan simpan hasil

Ailoot Ultra · Pemeringkatan ulang

json

Simpan semua hasil yang selesai

Simpan setiap respons lengkap, berkas, dan bagian konten yang dikembalikan.

Setiap indeks hasil aktual, relevance_score yang bernilai berhingga, dan objek dokumen asli dipertahankan. Indeks mengacu pada array dokumen asli. Seluruh JSON asli disimpan dan hanya dikembalikan setelah penyelesaian tagihan.

Jika respons asli perlu diperiksa

Simpan operasi awal dan balasan lengkap/sebagian; jangan ulangi POST.

Kolom respons
KolomJenis
KolommodelJenisstring
KolomidJenisstring
KolomproviderJenisstring
Kolomresults[].indexJenisinteger
Kolomresults[].relevance_scoreJenisnumber
Kolomresults[].document.textJenisstring
Kolomresults[].document.imageJenisstring
Kolomusage.search_unitsJenisinteger
Kolomusage.total_tokensJenisinteger
Kolomusage.costJenisnumber
Kesalahan dan langkah berikutnya
ResponsKondisiLangkah berikutnya
Respons400 · invalid_request_errorKondisiBidang wajib, representasi, atau nilai tidak validLangkah berikutnyaPerbaiki bidang permintaan yang ditunjukkan.

Alias

Alias model

Ailoot Ultra voyageai/rerank-2.5-lite

Alias jalur metode

Gunakan jalur metode yang ditampilkan di atas.

ID pemanggil terkait

voyageai/rerank-2.5-lite

", "method_identity": { "effective_request_model_id": "voyageai/rerank-2.5-lite", "fmt": "rerank", "http": "POST", "path": "/v1/rerank", "request_model_id": "voyageai/rerank-2.5-lite", "route_label": "Ultra" }, "method_key": "Ultra|rerank|/v1/rerank|voyageai%2Frerank-2.5-lite", "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": "Bidang wajib, representasi, atau nilai tidak valid", "http_status": 400, "recovery": "Perbaiki bidang permintaan yang ditunjukkan.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Simpan operasi awal dan balasan lengkap/sebagian; jangan ulangi 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 aktual, relevance_score yang bernilai berhingga, dan objek dokumen asli dipertahankan. Indeks mengacu pada array dokumen asli. Seluruh JSON asli disimpan dan hanya dikembalikan setelah penyelesaian tagihan.", "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-lite" ], "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": [ "Kirim ID model yang persis dan simpan respons native secara utuh." ], "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-lite" ], "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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-lite\",\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-lite'\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 ulang", "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": 4e-08 } }, "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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-lite", "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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-lite", "nativeProvenance": { "input_facts_canonical_sha256": "f127fffe183a2cbbce4e91e3b5640101335431b94c2b88a30c503d35fdbe6aba", "input_html_sha256": "c3c480876d6c50bb0d6a6271cc088d95f4703d0e13c3a7076e40717f4241fb46", "private_audit_sha256": "ac26e22f47735d6f07888da31e0f3e31abd077847f04287a0dd7354adda729ab", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/id/voyageai-rerank-2-5-lite/", "description": "rerank-2.5-lite adalah reranker Voyage AI yang mengikuti instruksi dan berorientasi pada latensi untuk kumpulan kandidat teks yang diberikan. Model ini mempertahankan konteks kueri-dokumen 32.000 token dan dukungan multibahasa, serta mengembalikan daftar terurut skor relevansi dan indeks dokumen asli.", "locale": "id", "md_url": "https://ailoot.ai/models/id/voyageai-rerank-2-5-lite/index.md", "title": "VoyageAI by MongoDB: rerank-2.5-lite — Ailoot", "url": "https://ailoot.ai/models/id/voyageai-rerank-2-5-lite/" }, "rawModel": { "about": "rerank-2.5-lite adalah anggota ringan dari rilis reranker 2.5 Voyage AI. Pembuatnya mendeskripsikannya sebagai model generalis yang menyeimbangkan latensi dan kualitas relevansi, dengan kepatuhan terhadap instruksi bahasa alami serta dukungan multibahasa. Masukannya berupa kueri dan teks kandidat, sedangkan keluarannya berupa urutan kandidat yang disempurnakan beserta skor relevansi.\n\n### Keputusan relevansi tahap kedua yang ringan\n\nModel Lite memproses setiap pasangan kueri-dokumen secara bersamaan sebagai cross-encoder. Model ini dapat mengikuti pencarian embedding atau leksikal tahap pertama, dengan menyempurnakan bagian teks yang dipilih tahap tersebut. Ini berbeda dari menyiapkan indeks embedding yang dapat digunakan kembali: reranker memeriksa kueri dan teks dokumen yang dikirim secara bersamaan untuk keputusan relevansi saat ini.\n\nUntuk pencarian bantuan interaktif, kirim penjelasan kandidat yang mungkin menjawab pertanyaan pengguna. Pertahankan cukup banyak dari setiap bagian teks untuk menyampaikan perilaku dan kondisi yang menyertainya, serta jaga keterkaitan indeks masukan dengan item sumber. Kumpulan kandidat yang terarah membantu memperjelas tugas pemeringkatan ulang sambil menjaga bukti hasil pengambilan tetap dapat digunakan untuk ditampilkan atau untuk jawaban berikutnya.\n\n### Instruksi menyatakan hal yang ingin diprioritaskan pengguna\n\nSeperti rerank-2.5, Lite menerima instruksi bahasa alami sebelum atau sesudah kueri. Contoh pembuat menggunakannya untuk menekankan komponen dokumen, memilih jenis sumber yang disukai, atau menambahkan konteks pada kata yang ambigu. Aplikasi dapat menyediakan preferensi relevansi tetap, atau pertanyaan saat ini dapat menyertakan preferensi yang lebih spesifik.\n\nContoh kueri dukungan dapat menanyakan bagian teks mana yang menjelaskan suatu kesalahan, dengan instruksi untuk memprioritaskan langkah pemecahan masalah daripada deskripsi produk umum. Pencarian dokumen dapat meminta redaksi kebijakan asli, bukan pembahasan tentang kebijakan tersebut. Deskripsikan bukti yang dituju dan jaga instruksi tetap ringkas. Instruksi memengaruhi skor relevansi; siapkan batasan versi, tanggal, atau izin yang ketat dalam pemilihan kandidat, alih-alih memperlakukan preferensi relevansi sebagai filter yang dijamin.\n\n### Baca peringkat dan kembalikan bagian teks sumber\n\nKeluaran diurutkan berdasarkan relevansi menurun. Setiap hasil mencakup indeks dokumen asli dan skor floating-point; pengaturan return-documents opsional juga mengembalikan teks masukan yang bersesuaian. Opsi top-k menentukan jumlah hasil teratas yang dikembalikan, sementara tidak menyertakannya mengembalikan peringkat untuk semua kandidat yang dikirim.\n\nGunakan indeks tersebut untuk memperoleh kembali bagian teks sumber dan pengidentifikasinya. Kandidat yang diperingkat ulang dapat menjadi hasil pencarian atau bukti untuk langkah RAG berikutnya, tetapi keluarannya adalah pengurutan teks yang diberikan, bukan jawaban yang ditulis ulang. Evaluasi apakah bagian teks teratas memenuhi pertanyaan sekaligus instruksi relevansi, termasuk pada bahasa dan jenis dokumen yang benar-benar ada dalam koleksi.\n\n### Batas dan anggaran kandidat praktis\n\nEdisi 2.5 Lite memiliki maksimum kueri terdokumentasi 8.000 token dan batas 32.000 token untuk kueri ditambah setiap dokumen individual. Model ini berbeda dari model rerank-2-lite sebelumnya dan konteksnya yang lebih kecil. Maksimum 1.000 dokumen dapat dikirim, dan total masukan dibatasi 600.000 token berdasarkan rumus: token kueri dikalikan jumlah kandidat, ditambah jumlah token seluruh dokumen kandidat.\n\nUntuk penggunaan yang sensitif terhadap latensi, FAQ pembuat merekomendasikan rerank-2.5-lite dengan tidak lebih dari 200.000 total token per permintaan. Ini merupakan panduan praktis di bawah maksimum yang dinyatakan, bukan jendela per dokumen yang berbeda. Kueri atau instruksi lebih panjang dihitung sekali untuk setiap pasangan kandidat, sehingga jumlah kandidat dan panjang kueri sama-sama memengaruhi total.\n\nTruncation aktif secara default dan memendekkan masukan kueri/dokumen yang terlalu panjang agar muat; menonaktifkannya mengembalikan kesalahan untuk kueri atau pasangan yang terlalu panjang. Siapkan kandidat ringkas yang memuat jawaban secara sengaja agar bukti berguna tidak tertinggal di luar batas pemotongan. Memilih lebih sedikit hasil keluaran melalui top-k tidak menghapus kandidat lain yang diberikan dari perhitungan token. Periksa bagian teks sumber yang dikembalikan dan sempurnakan persiapan masukan berdasarkan pertanyaan aktual yang perlu dijawab antarmuka pencarian.", "alias_of": null, "aliases": [], "author": "VoyageAI by MongoDB", "available": null, "canonical_model": "voyageai/rerank-2.5-lite", "capabilities": { "context_length": 32000, "manufacturer_input": [ "text" ], "manufacturer_output": [ "rerank" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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": "Informasi lengkap harga konfigurasi tersedia di bagian detail." } ], "ctx": null, "entity_type": "model", "formats": [ "rerank" ], "id": "voyageai/rerank-2.5-lite", "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": "Bidang wajib, representasi, atau nilai tidak valid", "http_status": 400, "recovery": "Perbaiki bidang permintaan yang ditunjukkan.", "type": "invalid_request_error" } ], "hints": [], "ignored_fields": [], "lifecycle": { "cancel": null, "idempotency_header": null, "job_retrieve": null, "mode": "synchronous", "streaming": false, "unknown": "Simpan operasi awal dan balasan lengkap/sebagian; jangan ulangi 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 aktual, relevance_score yang bernilai berhingga, dan objek dokumen asli dipertahankan. Indeks mengacu pada array dokumen asli. Seluruh JSON asli disimpan dan hanya dikembalikan setelah penyelesaian tagihan.", "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-lite" ], "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": [ "Kirim ID model yang persis dan simpan respons native secara utuh." ], "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-lite" ], "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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-lite\",\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-lite'\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 ulang", "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": 4e-08 } }, "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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-lite", "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": "0.00004000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.00000004", "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-lite", "request_model_id": "voyageai/rerank-2.5-lite", "route_label": "Ultra" } ], "name": "VoyageAI by MongoDB: rerank-2.5-lite", "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-lite", "short": "rerank-2.5-lite adalah reranker Voyage AI yang mengikuti instruksi dan berorientasi pada latensi untuk kumpulan kandidat teks yang diberikan. Model ini mempertahankan konteks kueri-dokumen 32.000 token dan dukungan multibahasa, serta mengembalikan daftar terurut skor relevansi dan indeks dokumen asli." }, "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-lite adalah reranker Voyage AI yang mengikuti instruksi dan berorientasi pada latensi untuk kumpulan kandidat teks yang diberikan. Model ini mempertahankan konteks kueri-dokumen 32.000 token dan dukungan multibahasa, serta mengembalikan daftar terurut skor relevansi dan indeks dokumen asli.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "940ca3b02f47334293c5f7b14241baee5587852abb7d46aa11b14d4e632910a8", "renderer_sha256": "2a0471475a29000c23db326ca1e315df73dee7524f6bc2907818ee3458813f2d" }, "sourceRevision": "092df93a3baa0979b644797f8a31000b3fc00e9ebf69c30f24a2ec99a37c110e" }