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  "aboutHtml": "<p>Llama Nemotron Embed VL 1B V2 er NVIDIAs embeddingmodel til multimodal søgning som grundlag for spørgsmål og svar. Den kan repræsentere et dokument som tekst, et billede eller en kombination af billede og tekst. En tekstforespørgsel kan derefter bruges til at finde relaterede dokumenter. Dens offentliggjorte profil understøtter dokumentbilleder med tekst, tabeller, diagrammer og infografik og returnerer embeddingvektorer.</p><h4>Forbered dokumentelementer, der kan identificeres</h4><p>Hold hvert input knyttet til det kildedokument, den side eller den passage, det repræsenterer. Til en visuel side bevares billedet og eventuel tilhørende tekst. Til en tekstpassage bevares titlen eller afsnitsbetegnelsen, når det hjælper med at fortolke et senere match. Disse kildeidentifikatorer gør et fundet element nyttigt som belæg frem for kun som et numerisk lighedsresultat.</p><p>Vælg elementernes afgrænsning omkring det materiale, en bruger skal finde. Et diagram og dets forklaring kan høre sammen; sider uden forbindelse til hinanden bør bevare separate identiteter. Gennemgå, om inputtet er læsbart, og om teksten eller billedet indeholder den information, som den tilsigtede forespørgsel skal hente.</p><h4>Tekstforespørgsler og vektorsøgning</h4><p>Modellens rolle er at producere vektorer til søgning med tekst som den forespørgselsform, profilen beskriver. Hold forespørgslen og de indekserede elementer i en kompatibel embeddingopsætning. Applikationen lagrer vektorerne, sammenligner dem og returnerer de tilhørende kildeelementer. Det fundne dokument er fortsat det materiale, der skal undersøges eller sendes videre til et senere trin, der genererer et svar.</p><p>Et nyttigt testsæt indeholder de spørgsmål, brugerne faktisk stiller, og de sider, der skal findes. Gennemgå forkerte eller manglende match i forhold til kildeindholdet og elementernes afgrænsning. Bevar den fulde dokumentidentifikator sammen med enhver valgt passage eller ethvert valgt billede, så resultatet kan kontrolleres i sin sammenhæng.</p><h4>Inputgrænse og søgeresultat</h4><p>Den dokumenterede hostede profil angiver op til 131.072 tokens pr. input. Opdel større kildesamlinger i separate, mærkede input, og bevar deres forbindelser. Profilens vektoroutput er hverken et genereret svar i prosa eller en transskription; et senere trin i applikationen kan bruge de fundne belæg til at besvare et spørgsmål.</p><p>Et færdigt søgeresultat forbinder forespørgslen, de udvalgte kildeelementer og deres identifikatorer. Registrer den embeddingmodel og inputforberedelse, der bruges til et indeks, så senere ændringer kan skelnes fra ændringer i kildesamlingen.</p>",
  "aboutText": "Llama Nemotron Embed VL 1B V2 er NVIDIAs embeddingmodel til multimodal søgning som grundlag for spørgsmål og svar. Den kan repræsentere et dokument som tekst, et billede eller en kombination af billede og tekst. En tekstforespørgsel kan derefter bruges til at finde relaterede dokumenter. Dens offentliggjorte profil understøtter dokumentbilleder med tekst, tabeller, diagrammer og infografik og returnerer embeddingvektorer.\n\n### Forbered dokumentelementer, der kan identificeres\n\nHold hvert input knyttet til det kildedokument, den side eller den passage, det repræsenterer. Til en visuel side bevares billedet og eventuel tilhørende tekst. Til en tekstpassage bevares titlen eller afsnitsbetegnelsen, når det hjælper med at fortolke et senere match. Disse kildeidentifikatorer gør et fundet element nyttigt som belæg frem for kun som et numerisk lighedsresultat.\n\nVælg elementernes afgrænsning omkring det materiale, en bruger skal finde. Et diagram og dets forklaring kan høre sammen; sider uden forbindelse til hinanden bør bevare separate identiteter. Gennemgå, om inputtet er læsbart, og om teksten eller billedet indeholder den information, som den tilsigtede forespørgsel skal hente.\n\n### Tekstforespørgsler og vektorsøgning\n\nModellens rolle er at producere vektorer til søgning med tekst som den forespørgselsform, profilen beskriver. Hold forespørgslen og de indekserede elementer i en kompatibel embeddingopsætning. Applikationen lagrer vektorerne, sammenligner dem og returnerer de tilhørende kildeelementer. Det fundne dokument er fortsat det materiale, der skal undersøges eller sendes videre til et senere trin, der genererer et svar.\n\nEt nyttigt testsæt indeholder de spørgsmål, brugerne faktisk stiller, og de sider, der skal findes. Gennemgå forkerte eller manglende match i forhold til kildeindholdet og elementernes afgrænsning. Bevar den fulde dokumentidentifikator sammen med enhver valgt passage eller ethvert valgt billede, så resultatet kan kontrolleres i sin sammenhæng.\n\n### Inputgrænse og søgeresultat\n\nDen dokumenterede hostede profil angiver op til 131.072 tokens pr. input. Opdel større kildesamlinger i separate, mærkede input, og bevar deres forbindelser. Profilens vektoroutput er hverken et genereret svar i prosa eller en transskription; et senere trin i applikationen kan bruge de fundne belæg til at besvare et spørgsmål.\n\nEt færdigt søgeresultat forbinder forespørgslen, de udvalgte kildeelementer og deres identifikatorer. Registrer den embeddingmodel og inputforberedelse, der bruges til et indeks, så senere ændringer kan skelnes fra ændringer i kildesamlingen.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">NV</span><h2 id=\"sheet-title\">NVIDIA: Llama Nemotron Embed VL 1B V2 (free)</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Luk\">×</button></div><div class=\"idline\"><span>NVIDIA /</span><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code><button type=\"button\" id=\"copy-id\">Kopiér ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Modelkortets afsnit\"><button type=\"button\" data-card-jump=\"overview\">Start</button><button type=\"button\" data-card-jump=\"methods\">Input og 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|embeddings|/v1/embeddings|nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree\">Embeddings</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\">Llama Nemotron Embed VL 1B V2 er NVIDIAs multimodale embeddingmodel til søgning. Den omdanner tekst, billeder eller kombinerede dokumentinput til vektorer og understøtter søgning med tekstforespørgsler i dokumenter med tabeller, diagrammer, tekst og infografik.</p><dl class=\"card-io\" data-card-io><div><dt>Accepterede input</dt><dd>Tekst · token_ids</dd></div><div><dt>Resultater</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>Om modellen</summary><p class=\"note-s\" data-model-method-boundary>Denne beskrivelse dækker modellens funktioner. Tilgængelige input, output og indstillinger i Ailoot står nedenfor for den valgte metode.</p><div class=\"mabout\" id=\"m-about\"><p>Llama Nemotron Embed VL 1B V2 er NVIDIAs embeddingmodel til multimodal søgning som grundlag for spørgsmål og svar. Den kan repræsentere et dokument som tekst, et billede eller en kombination af billede og tekst. En tekstforespørgsel kan derefter bruges til at finde relaterede dokumenter. Dens offentliggjorte profil understøtter dokumentbilleder med tekst, tabeller, diagrammer og infografik og returnerer embeddingvektorer.</p><h4>Forbered dokumentelementer, der kan identificeres</h4><p>Hold hvert input knyttet til det kildedokument, den side eller den passage, det repræsenterer. Til en visuel side bevares billedet og eventuel tilhørende tekst. Til en tekstpassage bevares titlen eller afsnitsbetegnelsen, når det hjælper med at fortolke et senere match. Disse kildeidentifikatorer gør et fundet element nyttigt som belæg frem for kun som et numerisk lighedsresultat.</p><p>Vælg elementernes afgrænsning omkring det materiale, en bruger skal finde. Et diagram og dets forklaring kan høre sammen; sider uden forbindelse til hinanden bør bevare separate identiteter. Gennemgå, om inputtet er læsbart, og om teksten eller billedet indeholder den information, som den tilsigtede forespørgsel skal hente.</p><h4>Tekstforespørgsler og vektorsøgning</h4><p>Modellens rolle er at producere vektorer til søgning med tekst som den forespørgselsform, profilen beskriver. Hold forespørgslen og de indekserede elementer i en kompatibel embeddingopsætning. Applikationen lagrer vektorerne, sammenligner dem og returnerer de tilhørende kildeelementer. Det fundne dokument er fortsat det materiale, der skal undersøges eller sendes videre til et senere trin, der genererer et svar.</p><p>Et nyttigt testsæt indeholder de spørgsmål, brugerne faktisk stiller, og de sider, der skal findes. Gennemgå forkerte eller manglende match i forhold til kildeindholdet og elementernes afgrænsning. Bevar den fulde dokumentidentifikator sammen med enhver valgt passage eller ethvert valgt billede, så resultatet kan kontrolleres i sin sammenhæng.</p><h4>Inputgrænse og søgeresultat</h4><p>Den dokumenterede hostede profil angiver op til 131.072 tokens pr. input. Opdel større kildesamlinger i separate, mærkede input, og bevar deres forbindelser. Profilens vektoroutput er hverken et genereret svar i prosa eller en transskription; et senere trin i applikationen kan bruge de fundne belæg til at besvare et spørgsmål.</p><p>Et færdigt søgeresultat forbinder forespørgslen, de udvalgte kildeelementer og deres identifikatorer. Registrer den embeddingmodel og inputforberedelse, der bruges til et indeks, så senere ændringer kan skelnes fra ændringer i kildesamlingen.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Tilgængelig i Ailoot · input og metoder</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Embeddings</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Inputtyper</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Outputtyper</dt><dd><code>embedding</code></dd></div><div><dt>Kontekst</dt><dd>131.072 tokens</dd></div><div><dt>Outputformat</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>Værdier og grænser</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\">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>Påkrævet</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>nvidia/llama-nemotron-embed-vl-1b-v2:free</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\">input</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Påkrævet</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>Minimumsantal elementer: 1</div><div>Betingelser: Ikke-tom batch med ensartede elementer. Token-ID’er er ikke-negative heltal.</div><div>Inputbudget: Maksimum: 131.072 · Tekstenhed: UTF8-bytes · Tokenenhed: elementer i et tokenarray</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\">encoding_format</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>Valgfrit</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>float</div></td><td data-cell-label=\"Standard\" data-default=\"known\"><span class=\"card-cell-label\">Standard</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">dimensions</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>Ikke understøttet</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</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=\"pass_through_metadata\"><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>json_value</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfrit</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>Betingelser: Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.</div></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><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>json_value</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfrit</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>Betingelser: Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.</div></td><td data-cell-label=\"Standard\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standard</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><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>json_value</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfrit</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>Betingelser: Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.</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>object</code></td><td data-cell-label=\"Krav\"><span class=\"card-cell-label\">Krav</span>Valgfrit</td><td data-cell-label=\"Værdier og grænser\"><span class=\"card-cell-label\">Værdier og grænser</span><div>Betingelser: provider.options afvist</div></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>Kombinationer af inputmaterialer</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Metodeparametre og livscyklus</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Kontrakt</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=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</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=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>model, input</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>input_shapes</code></th><td data-cell-label=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>encoding_format</code></th><td data-cell-label=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>float</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>dimensions_override</code></th><td data-cell-label=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>max_input_budget_units</code></th><td data-cell-label=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>131072</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>multimodal_objects</code></th><td data-cell-label=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>false</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=\"Kontrakt\"><span class=\"card-cell-label\">Kontrakt</span>text, token_ids</td></tr></tbody></table></div><p>Livscyklus: <code>synchronous_json</code></p><p class=\"note-s\">Gem hver vektors indeks og dimension.</p><p class=\"note-s\">Et multimodalt modelnavn giver ikke adgang til at bruge billedobjekter på denne præcise sti.</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>Tarifoplysninger tilføjer ikke anmodningsmuligheder. Understøttede parametre står ved den valgte metode.</p><dl data-price-conditions><dt>Hurtig tilstand</dt><dd>Standardpris <span class=\"note-s\">Når hurtig tilstand er valgt</span></dd><dt>flex-tilstand</dt><dd>Standardpris <span class=\"note-s\">Når flex-tilstanden er valgt</span></dd><dt>Maksimal ræsonneringsindsats</dt><dd>Standardpris <span class=\"note-s\">Når default-justeringen anvendes ved maksimal ræsonneringsindsats</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot-takster</h4><h4>Priser efter konfiguration</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Konfiguration</th><th>Takstplan</th><th>Beløb</th></tr></thead><tbody><tr><th data-cell-label=\"Konfiguration\" scope=\"row\"><span class=\"card-cell-label\">Konfiguration</span>Grundtakster for tokens</th><td data-cell-label=\"Takstplan\"><span class=\"card-cell-label\">Takstplan</span>Grundtakst</td><td data-cell-label=\"Beløb\"><span class=\"card-cell-label\">Beløb</span><div><span>Input · Inputtokens</span> <b>$0</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Output · Outputtokens</span> <b>$0</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cachelæsning</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cacheskrivning</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cacheskrivning, 1 time</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot reserverer et estimeret beløb før en anmodning. Efter resultatet afregnes det endelige beløb ud fra det målte forbrug. Ved streaming bruges den endelige forbrugsregistrering til afregningen. <a href=\"/docs/da/billing/reserve/\">Sådan fungerer reservation og afregning →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Eksempelanmodninger</h3><h4>Ailoot Ultra · Embeddings</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-1992-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1992-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1992-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1992-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kopiér 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-1992-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1992-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;nvidia/llama-nemotron-embed-vl-1b-v2:free&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\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\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1992-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1992-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;nvidia/llama-nemotron-embed-vl-1b-v2:free&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Hent og gem resultater</h3><h4>Ailoot Ultra · Embeddings</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Behold alle færdige resultater</h5><p class=\"note-s\">Gem hvert fuldstændigt returneret svar, hver fil og hver indholdsdel.</p><p class=\"note-s\">Gem hver vektor med dens indeks. Antallet af vektorer skal svare til antallet af input; dimensionerne skal være ensartede.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Hvis det oprindelige svar skal kontrolleres</h5><p class=\"note-s\">Der indføres intet endpoint til embedding-job.</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\">object</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\">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\">data[].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\">data[].embedding</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>finite float[]</td></tr><tr><th data-cell-label=\"Felt\" scope=\"row\"><span class=\"card-cell-label\">Felt</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Fejl og næste trin</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Svar</th><th>Betingelse</th><th>Næste trin</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=\"Betingelse\"><span class=\"card-cell-label\">Betingelse</span>Ugyldige obligatoriske felter, materialerepræsentation eller parameterværdi</td><td data-cell-label=\"Næste trin\"><span class=\"card-cell-label\">Næste trin</span>Ret de angivne felter før en ny oprettelsesanmodning.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Aliaser</h3><h4>Modelaliaser</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;route=Ultra&amp;caller=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;fmt=embeddings\"><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code></a></span></p><h4>Aliaser for metodestier</h4><p class=\"note-s\">Brug metodestierne vist ovenfor.</p><h4>Relaterede kalder-ID’er</h4><p class=\"note-s\"><a href=\"#m=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;route=Ultra&amp;caller=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;fmt=embeddings\"><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Ugyldige obligatoriske felter, materialerepræsentation eller parameterværdi",
                "http_status": 400,
                "recovery": "Ret de angivne felter før en ny oprettelsesanmodning.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Der indføres intet endpoint til embedding-job."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Gem hver vektor med dens indeks. Antallet af vektorer skal svare til antallet af input; dimensionerne skal være ensartede.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nvidia/llama-nemotron-embed-vl-1b-v2:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 131072,
                  "text_unit": "UTF8-bytes",
                  "token_unit": "elementer i et tokenarray"
                },
                "conditions": "Ikke-tom batch med ensartede elementer. Token-ID’er er ikke-negative heltal.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options afvist",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Gem hver vektors indeks og dimension.",
            "Et multimodalt modelnavn giver ikke adgang til at bruge billedobjekter på denne præcise sti."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 131072,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 131072,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Grundtakster for tokens",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "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"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2:free\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\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\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/llama-nemotron-embed-vl-1b-v2:free',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Hurtig tilstand",
            "value": "Standardpris",
            "when": "Når hurtig tilstand er valgt"
          },
          {
            "label": "flex-tilstand",
            "value": "Standardpris",
            "when": "Når flex-tilstanden er valgt"
          },
          {
            "label": "Maksimal ræsonneringsindsats",
            "value": "Standardpris",
            "when": "Når default-justeringen anvendes ved maksimal ræsonneringsindsats"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "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"
              }
            ]
          }
        ],
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "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": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "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"
          }
        ]
      }
    }
  ],
  "model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "ee70806743e4639d2ab29fe8db06cfd9a1b55776289ec31adbbc9a68791d39e8",
    "input_html_sha256": "e22c2d2c55c254fc6e82834cc6e0dd9bbd9e66b8d9b1ebc9e81329eeaa2dff80",
    "private_audit_sha256": "0e37ff31030363afd634aea3dfeb23f6fcace41204ec994296f0bc710c84ab3e",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/da/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/",
    "description": "Llama Nemotron Embed VL 1B V2 er NVIDIAs multimodale embeddingmodel til søgning. Den omdanner tekst, billeder eller kombinerede dokumentinput til vektorer og understøtter søgning med tekstforespørgsler i dokumenter med tabeller, diagrammer, tekst og infografik.",
    "locale": "da",
    "md_url": "https://ailoot.ai/models/da/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/index.md",
    "title": "NVIDIA: Llama Nemotron Embed VL 1B V2 (free) — Ailoot",
    "url": "https://ailoot.ai/models/da/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/"
  },
  "rawModel": {
    "about": "Llama Nemotron Embed VL 1B V2 er NVIDIAs embeddingmodel til multimodal søgning som grundlag for spørgsmål og svar. Den kan repræsentere et dokument som tekst, et billede eller en kombination af billede og tekst. En tekstforespørgsel kan derefter bruges til at finde relaterede dokumenter. Dens offentliggjorte profil understøtter dokumentbilleder med tekst, tabeller, diagrammer og infografik og returnerer embeddingvektorer.\n\n### Forbered dokumentelementer, der kan identificeres\n\nHold hvert input knyttet til det kildedokument, den side eller den passage, det repræsenterer. Til en visuel side bevares billedet og eventuel tilhørende tekst. Til en tekstpassage bevares titlen eller afsnitsbetegnelsen, når det hjælper med at fortolke et senere match. Disse kildeidentifikatorer gør et fundet element nyttigt som belæg frem for kun som et numerisk lighedsresultat.\n\nVælg elementernes afgrænsning omkring det materiale, en bruger skal finde. Et diagram og dets forklaring kan høre sammen; sider uden forbindelse til hinanden bør bevare separate identiteter. Gennemgå, om inputtet er læsbart, og om teksten eller billedet indeholder den information, som den tilsigtede forespørgsel skal hente.\n\n### Tekstforespørgsler og vektorsøgning\n\nModellens rolle er at producere vektorer til søgning med tekst som den forespørgselsform, profilen beskriver. Hold forespørgslen og de indekserede elementer i en kompatibel embeddingopsætning. Applikationen lagrer vektorerne, sammenligner dem og returnerer de tilhørende kildeelementer. Det fundne dokument er fortsat det materiale, der skal undersøges eller sendes videre til et senere trin, der genererer et svar.\n\nEt nyttigt testsæt indeholder de spørgsmål, brugerne faktisk stiller, og de sider, der skal findes. Gennemgå forkerte eller manglende match i forhold til kildeindholdet og elementernes afgrænsning. Bevar den fulde dokumentidentifikator sammen med enhver valgt passage eller ethvert valgt billede, så resultatet kan kontrolleres i sin sammenhæng.\n\n### Inputgrænse og søgeresultat\n\nDen dokumenterede hostede profil angiver op til 131.072 tokens pr. input. Opdel større kildesamlinger i separate, mærkede input, og bevar deres forbindelser. Profilens vektoroutput er hverken et genereret svar i prosa eller en transskription; et senere trin i applikationen kan bruge de fundne belæg til at besvare et spørgsmål.\n\nEt færdigt søgeresultat forbinder forespørgslen, de udvalgte kildeelementer og deres identifikatorer. Registrer den embeddingmodel og inputforberedelse, der bruges til et indeks, så senere ændringer kan skelnes fra ændringer i kildesamlingen.",
    "alias_of": null,
    "aliases": [],
    "author": "NVIDIA",
    "available": null,
    "canonical_model": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
    "capabilities": {
      "context_length": 131072,
      "manufacturer_input": [
        "text",
        "image"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "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": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "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"
          }
        ],
        "summary": "De fulde prisoplysninger for konfigurationen findes under detaljer."
      }
    ],
    "ctx": 131072,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Ugyldige obligatoriske felter, materialerepræsentation eller parameterværdi",
                "http_status": 400,
                "recovery": "Ret de angivne felter før en ny oprettelsesanmodning.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Der indføres intet endpoint til embedding-job."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Gem hver vektor med dens indeks. Antallet af vektorer skal svare til antallet af input; dimensionerne skal være ensartede.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nvidia/llama-nemotron-embed-vl-1b-v2:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 131072,
                  "text_unit": "UTF8-bytes",
                  "token_unit": "elementer i et tokenarray"
                },
                "conditions": "Ikke-tom batch med ensartede elementer. Token-ID’er er ikke-negative heltal.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillader dette JSON-metadatafelt; den præcise betydning hos den bagvedliggende udbyder afhænger af modellen.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options afvist",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Gem hver vektors indeks og dimension.",
            "Et multimodalt modelnavn giver ikke adgang til at bruge billedobjekter på denne præcise sti."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 131072,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 131072,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Grundtakster for tokens",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "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": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "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"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2:free\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\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\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/llama-nemotron-embed-vl-1b-v2:free',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Hurtig tilstand",
            "value": "Standardpris",
            "when": "Når hurtig tilstand er valgt"
          },
          {
            "label": "flex-tilstand",
            "value": "Standardpris",
            "when": "Når flex-tilstanden er valgt"
          },
          {
            "label": "Maksimal ræsonneringsindsats",
            "value": "Standardpris",
            "when": "Når default-justeringen anvendes ved maksimal ræsonneringsindsats"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
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