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  "aboutHtml": "<p>google/gemini-embedding-001 representerer tekst som vektorer for gjenfinning. Et praktisk bruksområde er relevant teknisk passasje når spørsmålet bruker andre ord enn dokumentet. Ailoot-ruten oppgir driftet kontekst på 20,000 tokens og tar tekststrenger, lister av strenger eller token-ID-er, med én konsekvent inndatatype per forespørsel.</p><p>Resultatet inneholder indekserte vektorer med endelige flyttall. Kontrakten publiserer ingen standardbredde og utelukker uttrykkelig dimensions-overstyring. Fastsett indeksbredde fra faktisk returnert vektorlengde og behold den i indeksen. encoding_format støtter float. Ingen oppgavetype- eller spørring/dokumentvelger tilbys. Bruk konsekvent klargjøring for spørsmål og dokumenter, og behold eksakt modell og vektorbredde sammen ved oppbygging eller oppdatering av indeks.</p>",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">G</span><h2 id=\"sheet-title\">Google: Gemini Embedding 001</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Lukk\">×</button></div><div class=\"idline\"><span>Google /</span><code>google/gemini-embedding-001</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|embeddings|/v1/embeddings|google%2Fgemini-embedding-001\">Vektorrepresentasjoner</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\">Tekst eller token-ID-er til vektorer med float-koding og oppført driftet 20,000-tokenkontekst.</p><dl class=\"card-io\" data-card-io><div><dt>Godkjente inndata</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 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>google/gemini-embedding-001 representerer tekst som vektorer for gjenfinning. Et praktisk bruksområde er relevant teknisk passasje når spørsmålet bruker andre ord enn dokumentet. Ailoot-ruten oppgir driftet kontekst på 20,000 tokens og tar tekststrenger, lister av strenger eller token-ID-er, med én konsekvent inndatatype per forespørsel.</p><p>Resultatet inneholder indekserte vektorer med endelige flyttall. Kontrakten publiserer ingen standardbredde og utelukker uttrykkelig dimensions-overstyring. Fastsett indeksbredde fra faktisk returnert vektorlengde og behold den i indeksen. encoding_format støtter float. Ingen oppgavetype- eller spørring/dokumentvelger tilbys. Bruk konsekvent klargjøring for spørsmål og dokumenter, og behold eksakt modell og vektorbredde sammen ved oppbygging eller oppdatering av indeks.</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 · Vektorrepresentasjoner</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Inndatatyper</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Utdatatyper</dt><dd><code>embedding</code></dd></div><div><dt>Kontekst</dt><dd>20 000 tokens</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\">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>google/gemini-embedding-001</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>Påkrevd</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>Minste antall elementer: 1</div><div>Vilkår: Ikke-tom batch med ensartede elementer. Token-ID-er er ikke-negative heltall.</div><div>Inndatabudsjett: Maksimum: 20 000 · Tekstenhet: UTF8-byte · Tokenenhet: elementer i token-arrayet</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>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</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>Støttes ikke</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=\"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>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>Vilkår: Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.</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>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>Vilkår: Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.</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>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>Vilkår: Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.</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>Valgfritt</td><td data-cell-label=\"Verdier &amp; grenser\"><span class=\"card-cell-label\">Verdier &amp; grenser</span><div>Vilkår: provider.options avvises</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>Kombinasjoner av inngående materialer</summary><p class=\"note-s\"><b>text_to_embedding</b></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, 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=\"Avtale\"><span class=\"card-cell-label\">Avtale</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=\"Avtale\"><span class=\"card-cell-label\">Avtale</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=\"Avtale\"><span class=\"card-cell-label\">Avtale</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=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>20000</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=\"Avtale\"><span class=\"card-cell-label\">Avtale</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=\"Avtale\"><span class=\"card-cell-label\">Avtale</span>text, token_ids</td></tr></tbody></table></div><p>Livssyklus: <code>synchronous_json</code></p><p class=\"note-s\">Bevar hver vektors indeks og dimensjon.</p><p class=\"note-s\">Et multimodalt modellnavn innebærer ikke at bildeobjekter er tillatt på denne nøyaktige stien.</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>Standardpris <span class=\"note-s\">Når hurtigmodus er valgt</span></dd><dt>Flex-modus</dt><dd>Standardpris <span class=\"note-s\">Når flex-modus er valgt</span></dd><dt>Maksimal resonneringsinnsats</dt><dd>Standardpris <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><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Konfigurasjon</th><th>Prisplan</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=\"Kostnad\"><span class=\"card-cell-label\">Kostnad</span><div><span>Inndata · Inngående tokener</span> <b>$0.0003</b> <span class=\"price-unit\">/ 1K tokener</span></div><div><span>Utdata · Utgående tokener</span> <b>$0</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></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 · Vektorrepresentasjoner</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-1486-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1486-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1486-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1486-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=\"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-1486-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1486-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;google/gemini-embedding-001&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-1486-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1486-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;google/gemini-embedding-001&#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\">Motta og lagre resultater</h3><h4>Ailoot Ultra · Vektorrepresentasjoner</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\">Bevar hver vektor sammen med indeksen. Antall vektorer må være lik antall inndata; dimensjonene må være konsistente.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Hvis det opprinnelige svaret må kontrolleres</h5><p class=\"note-s\">Det innføres ikke noe endepunkt for embedding-jobber.</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>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>Ugyldige påkrevde felter, materialrepresentasjon eller parameterverdi</td><td data-cell-label=\"Neste trinn\"><span class=\"card-cell-label\">Neste trinn</span>Korriger de angitte feltene før en ny opprettelsesforespørsel.</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=google%2Fgemini-embedding-001&amp;route=Ultra&amp;caller=google%2Fgemini-embedding-001&amp;fmt=embeddings\"><code>google/gemini-embedding-001</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=google%2Fgemini-embedding-001&amp;route=Ultra&amp;caller=google%2Fgemini-embedding-001&amp;fmt=embeddings\"><code>google/gemini-embedding-001</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "google/gemini-embedding-001",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "google/gemini-embedding-001",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|google%2Fgemini-embedding-001",
      "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 påkrevde felter, materialrepresentasjon eller parameterverdi",
                "http_status": 400,
                "recovery": "Korriger de angitte feltene før en ny opprettelsesforespørsel.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Det innføres ikke noe endepunkt for embedding-jobber."
            },
            "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": "Bevar hver vektor sammen med indeksen. Antall vektorer må være lik antall inndata; dimensjonene må være konsistente.",
              "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": [
                  "google/gemini-embedding-001"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 20000,
                  "text_unit": "UTF8-byte",
                  "token_unit": "elementer i token-arrayet"
                },
                "conditions": "Ikke-tom batch med ensartede elementer. Token-ID-er er ikke-negative heltall.",
                "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 tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options avvises",
                "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": [
            "Bevar hver vektors indeks og dimensjon.",
            "Et multimodalt modellnavn innebærer ikke at bildeobjekter er tillatt på denne nøyaktige stien."
          ],
          "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": 20000,
            "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": 20000,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Grunnpriser for tokener",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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\": \"google/gemini-embedding-001\", \"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='google/gemini-embedding-001',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": 0.3,
        "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": "Hurtigmodus",
            "value": "Standardpris",
            "when": "Når hurtigmodus er valgt"
          },
          {
            "label": "Flex-modus",
            "value": "Standardpris",
            "when": "Når flex-modus er valgt"
          },
          {
            "label": "Maksimal resonneringsinnsats",
            "value": "Standardpris",
            "when": "Når justeringen «default» brukes for maksimal resonneringsinnsats"
          }
        ],
        "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": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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": "google/gemini-embedding-001",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "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": "google/gemini-embedding-001",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "de1e0024c71607ac5286f079d47009673a7dfa4c0765f032dee87e90aabeec78",
    "input_html_sha256": "1b985eafee1d1e9c8a04e4bf6f7bab39e9bb8efd164d42ef96694c8dcabb8554",
    "private_audit_sha256": "d5b5ede1904c229c78c11d87834634ae4033170f8246086c984ccc4f49a27ae2",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/nb/google-gemini-embedding-001/",
    "description": "Tekst eller token-ID-er til vektorer med float-koding og oppført driftet 20,000-tokenkontekst.",
    "locale": "nb",
    "md_url": "https://ailoot.ai/models/nb/google-gemini-embedding-001/index.md",
    "title": "Google: Gemini Embedding 001 — Ailoot",
    "url": "https://ailoot.ai/models/nb/google-gemini-embedding-001/"
  },
  "rawModel": {
    "about": "Gemini Embedding 001 er Googles tekstembeddingmodell for å representere skriftlig materiale som vektorer. Det publiserte bruksområdet dekker flere fagområder, blant annet vitenskap, juridisk tekst, finans og programmering. Tekst er inndata og embeddingvektorer er utdata; en gjenfinningsapplikasjon bruker vektorene til å finne relaterte kildeelementer.\n\n### Klargjør kildeelementer som fortsatt kan identifiseres\n\nVelg en passasje eller et dokumentelement som inneholder informasjonen brukeren trenger å finne. Bevar dokumentnavn, seksjonsbetegnelse og kildeidentifikator. Et kodeelement kan beholde filen og sammenhengen som forklarer formålet; en teknisk passasje kan beholde terminologien og enhetene som trengs for å tolke den. Disse betegnelsene gjør et senere treff nyttig som belegg fremfor bare som en likhetsskår.\n\nHold elementgrensene meningsfulle. En definisjon og forklaringen til den kan høre sammen, mens emner uten innbyrdes forbindelse bør ha separate identiteter. Oppbevar originalteksten sammen med vektoren slik at et hentet element kan undersøkes eller sendes videre til et senere trinn for svargenerering.\n\n### Bruk vektorer i en gjenfinningsflyt\n\nHold forespørselen og det indekserte materialet i et kompatibelt embeddingoppsett. Applikasjonen lagrer vektorene, sammenligner dem og returnerer de tilsvarende kildeelementene. Gå gjennom representative forespørsler opp mot passasjene som skal finnes, inkludert vanskelig ordforråd og ulike måter å uttrykke samme behov på.\n\nEt nyttig gjenfinningsresultat inneholder kildeidentifikatoren og relevant tekst. Til et senere svar bevares passasjen som støtter hvert funn. Embeddingmodellen leverer representasjonen; et separat applikasjonstrinn utfører søket eller genererer et svar i prosa fra de hentede kildebeleggene.\n\n### Inndatagrense og kontinuitet i indeksen\n\nDen dokumenterte driftede tjenesteprofilen godtar opptil 20 000 tokener per inndataelement. Del lengre samlinger i separate, merkede elementer, og bevar forbindelsene mellom dem. Registrer modellen og klargjøringen som brukes for en indeks, slik at endringer i embeddingoppsettet kan skilles fra endringer i kildesamlingen.\n\nEt ferdig søkeresultat knytter forespørselen til de valgte kildepassasjene og identifikatorene deres. Undersøk feilaktige eller manglende treff opp mot den faktiske teksten og elementgrensene, og ta deretter med det relevante belegget til oppgaven som følger etter gjenfinningen.",
    "alias_of": null,
    "aliases": [],
    "author": "Google",
    "available": null,
    "canonical_model": "google/gemini-embedding-001",
    "capabilities": {
      "context_length": 20000,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "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": "Fullstendige priser for konfigurasjonen finnes i detaljene."
      }
    ],
    "ctx": 20000,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "google/gemini-embedding-001",
    "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 påkrevde felter, materialrepresentasjon eller parameterverdi",
                "http_status": 400,
                "recovery": "Korriger de angitte feltene før en ny opprettelsesforespørsel.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Det innføres ikke noe endepunkt for embedding-jobber."
            },
            "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": "Bevar hver vektor sammen med indeksen. Antall vektorer må være lik antall inndata; dimensjonene må være konsistente.",
              "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": [
                  "google/gemini-embedding-001"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 20000,
                  "text_unit": "UTF8-byte",
                  "token_unit": "elementer i token-arrayet"
                },
                "conditions": "Ikke-tom batch med ensartede elementer. Token-ID-er er ikke-negative heltall.",
                "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 tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Kilden tillater dette JSON-metadatafeltet; den nøyaktige semantikken hos den underliggende leverandøren er modellavhengig.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options avvises",
                "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": [
            "Bevar hver vektors indeks og dimensjon.",
            "Et multimodalt modellnavn innebærer ikke at bildeobjekter er tillatt på denne nøyaktige stien."
          ],
          "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": 20000,
            "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": 20000,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Grunnpriser for tokener",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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\": \"google/gemini-embedding-001\", \"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='google/gemini-embedding-001',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": 0.3,
        "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": "Hurtigmodus",
            "value": "Standardpris",
            "when": "Når hurtigmodus er valgt"
          },
          {
            "label": "Flex-modus",
            "value": "Standardpris",
            "when": "Når flex-modus er valgt"
          },
          {
            "label": "Maksimal resonneringsinnsats",
            "value": "Standardpris",
            "when": "Når justeringen «default» brukes for maksimal resonneringsinnsats"
          }
        ],
        "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": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "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": "google/gemini-embedding-001",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
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        ]
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    ],
    "model_aliases": [
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        "canonical_model_id": "google/gemini-embedding-001",
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    ],
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        "dimensions": {
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        "label": "Эмбеддинги · Base token rates",
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            "label": "Cache write, 1 hour",
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    "short": "Gemini Embedding 001 er Googles tekstembeddingmodell for gjenfinning innen områder som vitenskap, juridisk materiale, finans og kode. Den omdanner tekst til vektorer, med en dokumentert grense i den driftede tjenesteprofilen på opptil 20 000 tokener per inndataelement."
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    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
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  "representationNotes": {
    "current_reference": {
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      "methods": [
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          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
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    "raw_input": {
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      "json_pointers": [
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        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
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  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Tekst eller token-ID-er til vektorer med float-koding og oppført driftet 20,000-tokenkontekst.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
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    "renderer_sha256": "c8cb5a92c10b47373d294dee7465d66796cd4710c6626fd81459dc0625fc6d26"
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}