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  "aboutHtml": "<p>rerank-2.5 er Voyage AIs instruksjonsfølgende omrangeringsmodell, utviklet for å forbedre et sett med søkekandidater. Den undersøker spørringen og hvert dokument samlet som en kryssenkoder og returnerer deretter en relevansrekkefølge. Produsentens utgivelse fremhever styring på naturlig språk over hva som regnes som relevant, flerspråklig innhenting og en lengre spørring–dokument-kontekst enn den foregående rerank-2-utgaven.</p><h4>Gi modellen kandidatene som skal sammenlignes</h4><p>Inndataene er en spørring og en liste over kandidattekstdokumenter. Disse kan være tekstutdrag valgt av embedding-søk eller et leksikalsk søk i første trinn. Omrangeringsmodellen vurderer den innsendte teksten fremfor å lage embeddings for et korpus, så dens praktiske oppgave er å forbedre kandidatene det innledende søket allerede fant.</p><p>Behold den opprinnelige kandidatrekkefølgen og kildeidentifikatorene når du forbereder listen. De returnerte indeksene viser til disse inndataposisjonene. For en RAG-oppgave gir du tekstutdragene svartrinnet faktisk kan bruke, inkludert tittelen eller forbeholdene som trengs for å tolke dem. Et relevant dokument som mangler fra det innsendte kandidatsettet, kan ikke dukke opp som et nyfunnet resultat av omrangeringen.</p><h4>Angi den tiltenkte forståelsen av relevans</h4><p>Instruksjoner kan legges til etter eller før spørringen. Utgivelsen beskriver tre nyttige roller: fremheve en bestemt del av et dokument, foretrekke en kildetype og gjøre spørringen entydig med applikasjonskontekst. Disse endrer relevansoppgaven modellen får.</p><p>For et teknisk søk er en illustrerende instruksjon «Prioriter tekstutdrag som forklarer feil- og gjenopprettingsoppførselen; innledende oversikter er sekundære.» For en forskningssamling angir du om tittelen, metoden eller resultatet er viktigst. For et tvetydig begrep angir du fagområdet søket gjelder. Hold spørsmålet og instruksjonen forenlige, slik at den ene ikke ber om et bredt tema mens den andre umerkelig endrer oppgaven.</p><p>Disse preferansene styrer relevansskåringen. Hvis applikasjonen har strenge kvalifikasjonskrav – som kildeversjon, dato eller tilgangsomfang – forbereder du det kvalifiserte kandidatsettet separat. Modellens relevansinstruksjon og en deterministisk begrensning av samlingen dekker forskjellige formål.</p><h4>Rangerte utdata og senere bruk av resultatet</h4><p>Resultatene ordnes etter synkende relevans og inneholder en relevansskår som flyttall og kandidatens opprinnelige inndataindeks. Valget top-k velger hvor mange av de fremste resultatene som skal returneres; uten det returnerer produsentens spesifikasjon resultater for alle innsendte kandidater. Et valgfritt return-documents-valg inkluderer den tilsvarende kildeteksten; ellers inneholder svaret indekser og skårer.</p><p>Bruk rekkefølgen til å velge tekstutdrag for visning eller et senere svartrinn, og bevar kildeidentiteten deres. Undersøk den faktiske teksten når du vurderer rangeringen: skåren er et mål på relevans for den innsendte spørringen, ikke et generert svar eller en bekreftelse på at dokumentets påstander er korrekte. Dette gjør modellen nyttig for å forbedre teknisk, juridisk, finansielt, medisinsk, samtalerelatert og flerspråklig søkemateriale uten å gjøre rangering til en faglig konklusjon.</p><h4>Kontekst, totalt tokenbudsjett og avkorting</h4><p>Den dokumenterte spørringsgrensen er 8 000 tokener, inkludert instruksjoner plassert i spørringen. Spørringen pluss ett hvilket som helst kandidatdokument må få plass innenfor 32 000 tokener, og dokumentlisten kan inneholde maksimalt 1000 elementer. Den totale tildelingen er 600 000 tokener, telt som spørringstokener multiplisert med antall dokumenter, pluss alle dokumenttokener. En lang instruksjon telles derfor gjentatte ganger på tvers av kandidatparene.</p><p>Antallet i top-k for utdata endrer ikke antall kandidater i denne beregningen av inndatatokener. Standardavkorting forkorter for lange spørringer eller dokumenter for å oppfylle kontekstgrensen; når den deaktiveres, returnerer en for lang spørring eller et for langt par en feil. Forbered tekstutdrag som inneholder svaret, bevisst, og hold det relevante forbeholdet innenfor teksten modellen vil se. Vurder den forbedrede rekkefølgen på representative spørsmål og instruksjoner, og send deretter de valgte opprinnelige tekstutdragene til neste trinn.</p>",
  "aboutText": "rerank-2.5 er Voyage AIs instruksjonsfølgende omrangeringsmodell, utviklet for å forbedre et sett med søkekandidater. Den undersøker spørringen og hvert dokument samlet som en kryssenkoder og returnerer deretter en relevansrekkefølge. Produsentens utgivelse fremhever styring på naturlig språk over hva som regnes som relevant, flerspråklig innhenting og en lengre spørring–dokument-kontekst enn den foregående rerank-2-utgaven.\n\n### Gi modellen kandidatene som skal sammenlignes\n\nInndataene er en spørring og en liste over kandidattekstdokumenter. Disse kan være tekstutdrag valgt av embedding-søk eller et leksikalsk søk i første trinn. Omrangeringsmodellen vurderer den innsendte teksten fremfor å lage embeddings for et korpus, så dens praktiske oppgave er å forbedre kandidatene det innledende søket allerede fant.\n\nBehold den opprinnelige kandidatrekkefølgen og kildeidentifikatorene når du forbereder listen. De returnerte indeksene viser til disse inndataposisjonene. For en RAG-oppgave gir du tekstutdragene svartrinnet faktisk kan bruke, inkludert tittelen eller forbeholdene som trengs for å tolke dem. Et relevant dokument som mangler fra det innsendte kandidatsettet, kan ikke dukke opp som et nyfunnet resultat av omrangeringen.\n\n### Angi den tiltenkte forståelsen av relevans\n\nInstruksjoner kan legges til etter eller før spørringen. Utgivelsen beskriver tre nyttige roller: fremheve en bestemt del av et dokument, foretrekke en kildetype og gjøre spørringen entydig med applikasjonskontekst. Disse endrer relevansoppgaven modellen får.\n\nFor et teknisk søk er en illustrerende instruksjon «Prioriter tekstutdrag som forklarer feil- og gjenopprettingsoppførselen; innledende oversikter er sekundære.» For en forskningssamling angir du om tittelen, metoden eller resultatet er viktigst. For et tvetydig begrep angir du fagområdet søket gjelder. Hold spørsmålet og instruksjonen forenlige, slik at den ene ikke ber om et bredt tema mens den andre umerkelig endrer oppgaven.\n\nDisse preferansene styrer relevansskåringen. Hvis applikasjonen har strenge kvalifikasjonskrav – som kildeversjon, dato eller tilgangsomfang – forbereder du det kvalifiserte kandidatsettet separat. Modellens relevansinstruksjon og en deterministisk begrensning av samlingen dekker forskjellige formål.\n\n### Rangerte utdata og senere bruk av resultatet\n\nResultatene ordnes etter synkende relevans og inneholder en relevansskår som flyttall og kandidatens opprinnelige inndataindeks. Valget top-k velger hvor mange av de fremste resultatene som skal returneres; uten det returnerer produsentens spesifikasjon resultater for alle innsendte kandidater. Et valgfritt return-documents-valg inkluderer den tilsvarende kildeteksten; ellers inneholder svaret indekser og skårer.\n\nBruk rekkefølgen til å velge tekstutdrag for visning eller et senere svartrinn, og bevar kildeidentiteten deres. Undersøk den faktiske teksten når du vurderer rangeringen: skåren er et mål på relevans for den innsendte spørringen, ikke et generert svar eller en bekreftelse på at dokumentets påstander er korrekte. Dette gjør modellen nyttig for å forbedre teknisk, juridisk, finansielt, medisinsk, samtalerelatert og flerspråklig søkemateriale uten å gjøre rangering til en faglig konklusjon.\n\n### Kontekst, totalt tokenbudsjett og avkorting\n\nDen dokumenterte spørringsgrensen er 8 000 tokener, inkludert instruksjoner plassert i spørringen. Spørringen pluss ett hvilket som helst kandidatdokument må få plass innenfor 32 000 tokener, og dokumentlisten kan inneholde maksimalt 1000 elementer. Den totale tildelingen er 600 000 tokener, telt som spørringstokener multiplisert med antall dokumenter, pluss alle dokumenttokener. En lang instruksjon telles derfor gjentatte ganger på tvers av kandidatparene.\n\nAntallet i top-k for utdata endrer ikke antall kandidater i denne beregningen av inndatatokener. Standardavkorting forkorter for lange spørringer eller dokumenter for å oppfylle kontekstgrensen; når den deaktiveres, returnerer en for lang spørring eller et for langt par en feil. Forbered tekstutdrag som inneholder svaret, bevisst, og hold det relevante forbeholdet innenfor teksten modellen vil se. Vurder den forbedrede rekkefølgen på representative spørsmål og instruksjoner, og send deretter de valgte opprinnelige tekstutdragene til neste trinn.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">VoyageAI by MongoDB: rerank-2.5</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Lukk\">×</button></div><div class=\"idline\"><span>VoyageAI by MongoDB /</span><code>voyageai/rerank-2.5</code><button type=\"button\" id=\"copy-id\">Kopier ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Seksjoner i modellkortet\"><button type=\"button\" data-card-jump=\"overview\">Start</button><button type=\"button\" data-card-jump=\"methods\">Inndata &amp; metoder</button><button type=\"button\" data-card-jump=\"pricing\">Priser</button><button type=\"button\" data-card-jump=\"examples\">Eksempler</button><button type=\"button\" data-card-jump=\"results\">Resultater</button><button type=\"button\" data-card-jump=\"aliases\">Aliaser</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API-metoder\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|rerank|/v1/rerank|voyageai%2Frerank-2.5\">Omrangering</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Start</h3><p class=\"mdesc-full\">rerank-2.5 omordner et innsendt sett med tekstkandidater etter relevans for en spørring og valgfrie instruksjoner på naturlig språk. Den er en flerspråklig, kvalitetsorientert omrangeringsmodell med en spørring–dokument-kontekst på 32 000 tokener og rangerte skårer knyttet til de opprinnelige kandidatindeksene.</p><dl class=\"card-io\" data-card-io><div><dt>Godkjente inndata</dt><dd>Tekst</dd></div><div><dt>Resultater</dt><dd>Rangerte resultater</dd></div></dl><details class=\"card-about\"><summary>Om modellen</summary><p class=\"note-s\" data-model-method-boundary>Denne beskrivelsen dekker modellens funksjoner. Tilgjengelige inndata, utdata og alternativer i Ailoot er oppført nedenfor for den valgte metoden.</p><div class=\"mabout\" id=\"m-about\"><p>rerank-2.5 er Voyage AIs instruksjonsfølgende omrangeringsmodell, utviklet for å forbedre et sett med søkekandidater. Den undersøker spørringen og hvert dokument samlet som en kryssenkoder og returnerer deretter en relevansrekkefølge. Produsentens utgivelse fremhever styring på naturlig språk over hva som regnes som relevant, flerspråklig innhenting og en lengre spørring–dokument-kontekst enn den foregående rerank-2-utgaven.</p><h4>Gi modellen kandidatene som skal sammenlignes</h4><p>Inndataene er en spørring og en liste over kandidattekstdokumenter. Disse kan være tekstutdrag valgt av embedding-søk eller et leksikalsk søk i første trinn. Omrangeringsmodellen vurderer den innsendte teksten fremfor å lage embeddings for et korpus, så dens praktiske oppgave er å forbedre kandidatene det innledende søket allerede fant.</p><p>Behold den opprinnelige kandidatrekkefølgen og kildeidentifikatorene når du forbereder listen. De returnerte indeksene viser til disse inndataposisjonene. For en RAG-oppgave gir du tekstutdragene svartrinnet faktisk kan bruke, inkludert tittelen eller forbeholdene som trengs for å tolke dem. Et relevant dokument som mangler fra det innsendte kandidatsettet, kan ikke dukke opp som et nyfunnet resultat av omrangeringen.</p><h4>Angi den tiltenkte forståelsen av relevans</h4><p>Instruksjoner kan legges til etter eller før spørringen. Utgivelsen beskriver tre nyttige roller: fremheve en bestemt del av et dokument, foretrekke en kildetype og gjøre spørringen entydig med applikasjonskontekst. Disse endrer relevansoppgaven modellen får.</p><p>For et teknisk søk er en illustrerende instruksjon «Prioriter tekstutdrag som forklarer feil- og gjenopprettingsoppførselen; innledende oversikter er sekundære.» For en forskningssamling angir du om tittelen, metoden eller resultatet er viktigst. For et tvetydig begrep angir du fagområdet søket gjelder. Hold spørsmålet og instruksjonen forenlige, slik at den ene ikke ber om et bredt tema mens den andre umerkelig endrer oppgaven.</p><p>Disse preferansene styrer relevansskåringen. Hvis applikasjonen har strenge kvalifikasjonskrav – som kildeversjon, dato eller tilgangsomfang – forbereder du det kvalifiserte kandidatsettet separat. Modellens relevansinstruksjon og en deterministisk begrensning av samlingen dekker forskjellige formål.</p><h4>Rangerte utdata og senere bruk av resultatet</h4><p>Resultatene ordnes etter synkende relevans og inneholder en relevansskår som flyttall og kandidatens opprinnelige inndataindeks. Valget top-k velger hvor mange av de fremste resultatene som skal returneres; uten det returnerer produsentens spesifikasjon resultater for alle innsendte kandidater. Et valgfritt return-documents-valg inkluderer den tilsvarende kildeteksten; ellers inneholder svaret indekser og skårer.</p><p>Bruk rekkefølgen til å velge tekstutdrag for visning eller et senere svartrinn, og bevar kildeidentiteten deres. Undersøk den faktiske teksten når du vurderer rangeringen: skåren er et mål på relevans for den innsendte spørringen, ikke et generert svar eller en bekreftelse på at dokumentets påstander er korrekte. Dette gjør modellen nyttig for å forbedre teknisk, juridisk, finansielt, medisinsk, samtalerelatert og flerspråklig søkemateriale uten å gjøre rangering til en faglig konklusjon.</p><h4>Kontekst, totalt tokenbudsjett og avkorting</h4><p>Den dokumenterte spørringsgrensen er 8 000 tokener, inkludert instruksjoner plassert i spørringen. Spørringen pluss ett hvilket som helst kandidatdokument må få plass innenfor 32 000 tokener, og dokumentlisten kan inneholde maksimalt 1000 elementer. Den totale tildelingen er 600 000 tokener, telt som spørringstokener multiplisert med antall dokumenter, pluss alle dokumenttokener. En lang instruksjon telles derfor gjentatte ganger på tvers av kandidatparene.</p><p>Antallet i top-k for utdata endrer ikke antall kandidater i denne beregningen av inndatatokener. Standardavkorting forkorter for lange spørringer eller dokumenter for å oppfylle kontekstgrensen; når den deaktiveres, returnerer en for lang spørring eller et for langt par en feil. Forbered tekstutdrag som inneholder svaret, bevisst, og hold det relevante forbeholdet innenfor teksten modellen vil se. Vurder den forbedrede rekkefølgen på representative spørsmål og instruksjoner, og send deretter de valgte opprinnelige tekstutdragene til neste trinn.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Tilgjengelig i Ailoot · inndata &amp; metoder</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Omrangering</b> <code>POST /v1/rerank</code></summary><dl class=\"method-capabilities\"><div><dt>Inndatatyper</dt><dd><code>text</code></dd></div><div><dt>Utdatatyper</dt><dd><code>rerank</code></dd></div><div><dt>Utdataformat</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Felt</th><th>Type</th><th>Krav</th><th>Verdier &amp; grenser</th><th>Standard</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">documents</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>array</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Påkrevd</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Påkrevd</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>voyageai/rerank-2.5</div></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>schema</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">query</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Påkrevd</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">top_n</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>integer</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>Minimum: 1</div></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>schema</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">documents[].text</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">documents[].image</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Kombinasjoner av inngående materialer</summary><p class=\"note-s\"><b>text_rerank</b> · Påkrevd: query, documents array with minItems1</p></details><details class=\"note-s\" data-method-contract><summary>Metodeparametere og livssyklus</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Avtale</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>model, query, documents</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>text</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>optional_controls</code></th><td data-cell-label=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>provider, session_id, top_n, trace, user</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>multimodal_input_binding</code></th><td data-cell-label=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>null</td></tr></tbody></table></div><p>Livssyklus: <code>synchronous</code></p><p class=\"note-s\">Send den eksakte modell-ID-en og behold hele svaret i det opprinnelige formatet.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Priser</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tariffopplysninger legger ikke til forespørselsvalg. Støttede parametere står ved den valgte metoden.</p><dl data-price-conditions><dt>Hurtigmodus</dt><dd>Ikke angitt <span class=\"note-s\">Når hurtigmodus er valgt</span></dd><dt>Flex-modus</dt><dd>Ikke angitt <span class=\"note-s\">Når flex-modus er valgt</span></dd><dt>Maksimal resonneringsinnsats</dt><dd>Ikke angitt <span class=\"note-s\">Når justeringen «default» brukes for maksimal resonneringsinnsats</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot-satser</h4><h4>Priser etter konfigurasjon</h4><p class=\"note-s\">Knytt hver kostnad til de viste vilkårene. Bruk én sats for hvert sett med alternative konfigurasjoner.</p><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Konfigurasjon</th><th>Prisplan</th><th>Faktureringsenhet</th><th>Kostnad</th></tr></thead><tbody><tr><th data-cell-label=\"Konfigurasjon\" scope=\"row\"><span class=\"card-cell-label\">Konfigurasjon</span>Grunnpriser for tokener</th><td data-cell-label=\"Prisplan\"><span class=\"card-cell-label\">Prisplan</span>Grunnsats</td><td data-cell-label=\"Faktureringsenhet\"><span class=\"card-cell-label\">Faktureringsenhet</span>—</td><td data-cell-label=\"Kostnad\"><span class=\"card-cell-label\">Kostnad</span><div><span>Inndata · Inngående tokener</span> <b>—</b> <span class=\"price-unit\">/ 1K tokener</span></div><div><span>Utdata · Utgående tokener</span> <b>—</b> <span class=\"price-unit\">/ 1K tokener</span></div><div><span>Inndata · Lesing fra hurtigbuffer</span> <b>—</b> <span class=\"price-unit\">/ 1K tokener</span></div><div><span>Inndata · Skriving til hurtigbuffer</span> <b>—</b> <span class=\"price-unit\">/ 1K tokener</span></div><div><span>Inndata · Skriving til hurtigbuffer, 1 time</span> <b>—</b> <span class=\"price-unit\">/ 1K tokener</span></div></td></tr><tr><th data-cell-label=\"Konfigurasjon\" scope=\"row\"><span class=\"card-cell-label\">Konfigurasjon</span>Ultra</th><td data-cell-label=\"Prisplan\"><span class=\"card-cell-label\">Prisplan</span>—</td><td data-cell-label=\"Faktureringsenhet\"><span class=\"card-cell-label\">Faktureringsenhet</span>/ 1K tokener</td><td data-cell-label=\"Kostnad\"><span class=\"card-cell-label\">Kostnad</span><div><span>Inndata</span> <b>$0.0001</b> <span class=\"price-unit\">/ 1K tokener</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot reserverer et estimert beløp før en forespørsel. Etter resultatet gjøres det endelige beløpet opp basert på målt forbruk. Oppgjør for strømming bruker den endelige forbruksposten. <a href=\"/docs/nb/billing/reserve/\">Slik fungerer reservasjon og oppgjør →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Eksempelforespørsler</h3><h4>Ailoot Ultra · Omrangering</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kodevarianter\"><button class=\"cg-tab\" id=\"model-code-2697-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2697-panel-0\" aria-selected=\"true\" tabindex=\"0\">Bevart offisiell forespørselsstruktur; dokumentasjonseksempel</button><button class=\"cg-tab\" id=\"model-code-2697-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2697-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Bruk ditt faktiske forespørselsmateriale og bevar hele resultatet</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kopier kode\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2697-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2697-tab-0\"><pre data-lang=\"json\"><code>{\n  &quot;documents&quot;: [\n    &quot;Paris is the capital of France.&quot;,\n    &quot;Berlin is the capital of Germany.&quot;\n  ],\n  &quot;model&quot;: &quot;voyageai/rerank-2.5&quot;,\n  &quot;query&quot;: &quot;What is the capital of France?&quot;,\n  &quot;top_n&quot;: 3\n}</code></pre></div><div class=\"cg-pane\" id=\"model-code-2697-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-2697-tab-1\" hidden><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\n: &quot;${AILOOT_REQUEST_FILE:?Set your actual JSON request file}&quot;\nexport AILOOT_REQUEST_FILE\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(Path(os.environ[&#39;AILOOT_REQUEST_FILE&#39;]).read_text(encoding=&#39;utf-8&#39;))\nif not isinstance(p,dict): raise SystemExit(&#39;Request must be a JSON object&#39;)\np[&#39;model&#39;]=&#39;voyageai/rerank-2.5&#39;\nPath(&#39;request.json&#39;).write_text(json.dumps(p,ensure_ascii=False),encoding=&#39;utf-8&#39;)\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/rerank \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path(&#39;result.json&#39;).read_text()),ensure_ascii=False,indent=2))\nPY_RESULT\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Motta og lagre resultater</h3><h4>Ailoot Ultra · Omrangering</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Behold alle ferdige resultater</h5><p class=\"note-s\">Behold alle fullstendige returnerte svar, filer og innholdsdeler.</p><p class=\"note-s\">Alle faktiske resultatindekser, alle endelige relevance_score-verdier og de opprinnelige dokumentobjektene bevares. Indeksene knytter resultatene til den opprinnelige dokumentmatrisen. Hele det opprinnelige JSON-svaret lagres og returneres først etter oppgjør.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Hvis det opprinnelige svaret må kontrolleres</h5><p class=\"note-s\">Behold den opprinnelige operasjonen og hele eller deler av svaret; ikke gjenta POST.</p></div><details class=\"card-about\" data-output-fields><summary>Svarfelter</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Felt</th><th>Type</th></tr></thead><tbody><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">id</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">results[].index</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>integer</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">results[].relevance_score</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>number</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">results[].document.text</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">results[].document.image</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">usage.search_units</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>integer</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">usage.total_tokens</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>integer</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">usage.cost</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>number</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Feil og neste trinn</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Svar</th><th>Vilkår</th><th>Neste trinn</th></tr></thead><tbody><tr><th data-cell-label=\"Svar\" scope=\"row\"><span class=\"card-cell-label\">Svar</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Vilkår\"><span class=\"card-cell-label\">Vilkår</span>Ugyldig påkrevd felt, representasjon eller verdi</td><td data-cell-label=\"Neste trinn\"><span class=\"card-cell-label\">Neste trinn</span>Korriger det angitte forespørselsfeltet.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Aliaser</h3><h4>Modellaliaser</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=voyageai%2Frerank-2.5&amp;route=Ultra&amp;caller=voyageai%2Frerank-2.5&amp;fmt=rerank\"><code>voyageai/rerank-2.5</code></a></span></p><h4>Aliaser for metodestier</h4><p class=\"note-s\">Bruk metodestiene som vises ovenfor.</p><h4>Tilknyttede anroper-ID-er</h4><p class=\"note-s\"><a href=\"#m=voyageai%2Frerank-2.5&amp;route=Ultra&amp;caller=voyageai%2Frerank-2.5&amp;fmt=rerank\"><code>voyageai/rerank-2.5</code></a></p></section></div>",
      "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": "Ugyldig påkrevd felt, representasjon eller verdi",
                "http_status": 400,
                "recovery": "Korriger det angitte forespørselsfeltet.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "cancel": null,
              "idempotency_header": null,
              "job_retrieve": null,
              "mode": "synchronous",
              "streaming": false,
              "unknown": "Behold den opprinnelige operasjonen og hele eller deler av svaret; ikke gjenta 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": "Alle faktiske resultatindekser, alle endelige relevance_score-verdier og de opprinnelige dokumentobjektene bevares. Indeksene knytter resultatene til den opprinnelige dokumentmatrisen. Hele det opprinnelige JSON-svaret lagres og returneres først etter oppgjør.",
              "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": [
            "Send den eksakte modell-ID-en og behold hele svaret i det opprinnelige formatet."
          ],
          "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": "Omrangering",
        "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,
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            "dimensions": {
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        "request_model_id": "voyageai/rerank-2.5",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": null,
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            "billing_input": true,
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            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
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            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
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            "key": "output_price",
            "label": "Output tokens",
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            "native_quantity": 1,
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            "public_unit_label": "/ 1K tokens",
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            "billing_input": true,
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            "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",
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            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
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          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
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            "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,
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          },
          {
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            "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",
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        ]
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    }
  ],
  "model_id": "voyageai/rerank-2.5",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "6d86c95afa8189ba243bc008c7c23e053024115c6f8dd713da64d07a8ab6e81e",
    "input_html_sha256": "962a320e46127df8e38cb67891bfc038c75ee1c9299c1e142fb25592c4c08486",
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    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/nb/voyageai-rerank-2-5/",
    "description": "rerank-2.5 omordner et innsendt sett med tekstkandidater etter relevans for en spørring og valgfrie instruksjoner på naturlig språk. Den er en flerspråklig, kvalitetsorientert omrangeringsmodell med en spørring–dokument-kontekst på 32 000 tokener og rangerte skårer knyttet til de opprinnelige kandidatindeksene.",
    "locale": "nb",
    "md_url": "https://ailoot.ai/models/nb/voyageai-rerank-2-5/index.md",
    "title": "VoyageAI by MongoDB: rerank-2.5 — Ailoot",
    "url": "https://ailoot.ai/models/nb/voyageai-rerank-2-5/"
  },
  "rawModel": {
    "about": "rerank-2.5 er Voyage AIs instruksjonsfølgende omrangeringsmodell, utviklet for å forbedre et sett med søkekandidater. Den undersøker spørringen og hvert dokument samlet som en kryssenkoder og returnerer deretter en relevansrekkefølge. Produsentens utgivelse fremhever styring på naturlig språk over hva som regnes som relevant, flerspråklig innhenting og en lengre spørring–dokument-kontekst enn den foregående rerank-2-utgaven.\n\n### Gi modellen kandidatene som skal sammenlignes\n\nInndataene er en spørring og en liste over kandidattekstdokumenter. Disse kan være tekstutdrag valgt av embedding-søk eller et leksikalsk søk i første trinn. Omrangeringsmodellen vurderer den innsendte teksten fremfor å lage embeddings for et korpus, så dens praktiske oppgave er å forbedre kandidatene det innledende søket allerede fant.\n\nBehold den opprinnelige kandidatrekkefølgen og kildeidentifikatorene når du forbereder listen. De returnerte indeksene viser til disse inndataposisjonene. For en RAG-oppgave gir du tekstutdragene svartrinnet faktisk kan bruke, inkludert tittelen eller forbeholdene som trengs for å tolke dem. Et relevant dokument som mangler fra det innsendte kandidatsettet, kan ikke dukke opp som et nyfunnet resultat av omrangeringen.\n\n### Angi den tiltenkte forståelsen av relevans\n\nInstruksjoner kan legges til etter eller før spørringen. Utgivelsen beskriver tre nyttige roller: fremheve en bestemt del av et dokument, foretrekke en kildetype og gjøre spørringen entydig med applikasjonskontekst. Disse endrer relevansoppgaven modellen får.\n\nFor et teknisk søk er en illustrerende instruksjon «Prioriter tekstutdrag som forklarer feil- og gjenopprettingsoppførselen; innledende oversikter er sekundære.» For en forskningssamling angir du om tittelen, metoden eller resultatet er viktigst. For et tvetydig begrep angir du fagområdet søket gjelder. Hold spørsmålet og instruksjonen forenlige, slik at den ene ikke ber om et bredt tema mens den andre umerkelig endrer oppgaven.\n\nDisse preferansene styrer relevansskåringen. Hvis applikasjonen har strenge kvalifikasjonskrav – som kildeversjon, dato eller tilgangsomfang – forbereder du det kvalifiserte kandidatsettet separat. Modellens relevansinstruksjon og en deterministisk begrensning av samlingen dekker forskjellige formål.\n\n### Rangerte utdata og senere bruk av resultatet\n\nResultatene ordnes etter synkende relevans og inneholder en relevansskår som flyttall og kandidatens opprinnelige inndataindeks. Valget top-k velger hvor mange av de fremste resultatene som skal returneres; uten det returnerer produsentens spesifikasjon resultater for alle innsendte kandidater. Et valgfritt return-documents-valg inkluderer den tilsvarende kildeteksten; ellers inneholder svaret indekser og skårer.\n\nBruk rekkefølgen til å velge tekstutdrag for visning eller et senere svartrinn, og bevar kildeidentiteten deres. Undersøk den faktiske teksten når du vurderer rangeringen: skåren er et mål på relevans for den innsendte spørringen, ikke et generert svar eller en bekreftelse på at dokumentets påstander er korrekte. Dette gjør modellen nyttig for å forbedre teknisk, juridisk, finansielt, medisinsk, samtalerelatert og flerspråklig søkemateriale uten å gjøre rangering til en faglig konklusjon.\n\n### Kontekst, totalt tokenbudsjett og avkorting\n\nDen dokumenterte spørringsgrensen er 8 000 tokener, inkludert instruksjoner plassert i spørringen. Spørringen pluss ett hvilket som helst kandidatdokument må få plass innenfor 32 000 tokener, og dokumentlisten kan inneholde maksimalt 1000 elementer. Den totale tildelingen er 600 000 tokener, telt som spørringstokener multiplisert med antall dokumenter, pluss alle dokumenttokener. En lang instruksjon telles derfor gjentatte ganger på tvers av kandidatparene.\n\nAntallet i top-k for utdata endrer ikke antall kandidater i denne beregningen av inndatatokener. Standardavkorting forkorter for lange spørringer eller dokumenter for å oppfylle kontekstgrensen; når den deaktiveres, returnerer en for lang spørring eller et for langt par en feil. Forbered tekstutdrag som inneholder svaret, bevisst, og hold det relevante forbeholdet innenfor teksten modellen vil se. Vurder den forbedrede rekkefølgen på representative spørsmål og instruksjoner, og send deretter de valgte opprinnelige tekstutdragene til neste trinn.",
    "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": [
          {
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            "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": "Fullstendige priser for konfigurasjonen finnes i detaljene."
      }
    ],
    "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": "Ugyldig påkrevd felt, representasjon eller verdi",
                "http_status": 400,
                "recovery": "Korriger det angitte forespørselsfeltet.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "cancel": null,
              "idempotency_header": null,
              "job_retrieve": null,
              "mode": "synchronous",
              "streaming": false,
              "unknown": "Behold den opprinnelige operasjonen og hele eller deler av svaret; ikke gjenta 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": "Alle faktiske resultatindekser, alle endelige relevance_score-verdier og de opprinnelige dokumentobjektene bevares. Indeksene knytter resultatene til den opprinnelige dokumentmatrisen. Hele det opprinnelige JSON-svaret lagres og returneres først etter oppgjør.",
              "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": [
            "Send den eksakte modell-ID-en og behold hele svaret i det opprinnelige formatet."
          ],
          "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",
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              },
              {
                "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": "Omrangering",
        "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 omordner et innsendt sett med tekstkandidater etter relevans for en spørring og valgfrie instruksjoner på naturlig språk. Den er en flerspråklig, kvalitetsorientert omrangeringsmodell med en spørring–dokument-kontekst på 32 000 tokener og rangerte skårer knyttet til de opprinnelige kandidatindeksene."
  },
  "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 omordner et innsendt sett med tekstkandidater etter relevans for en spørring og valgfrie instruksjoner på naturlig språk. Den er en flerspråklig, kvalitetsorientert omrangeringsmodell med en spørring–dokument-kontekst på 32 000 tokener og rangerte skårer knyttet til de opprinnelige kandidatindeksene.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "00c28f8593ebb7b907360908ba2038d786737560fe86cf08948cfd22f2bf419d",
    "renderer_sha256": "c8cb5a92c10b47373d294dee7465d66796cd4710c6626fd81459dc0625fc6d26"
  },
  "sourceRevision": "8772e55835a0116702371bb22e4671d990fd03df188ed542389a1fcc5ae3b7dd"
}