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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=\"Schließen\">×</button></div><div class=\"idline\"><span>Google /</span><code>google/gemini-embedding-001</code><button type=\"button\" id=\"copy-id\">ID kopieren</button></div><nav class=\"card-jump-nav\" aria-label=\"Bereiche der Modellkarte\"><button type=\"button\" data-card-jump=\"overview\">Einstieg</button><button type=\"button\" data-card-jump=\"methods\">Eingaben und Methoden</button><button type=\"button\" data-card-jump=\"pricing\">Tarife</button><button type=\"button\" data-card-jump=\"examples\">Beispiele</button><button type=\"button\" data-card-jump=\"results\">Ergebnisse</button><button type=\"button\" data-card-jump=\"aliases\">Aliasnamen</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API-Methoden\"><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\">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\">Einstieg</h3><p class=\"mdesc-full\">Text oder Token-IDs zu Vektoren mit float-Kodierung und angegebenen 20,000 Tokens gehostetem Kontext.</p><dl class=\"card-io\" data-card-io><div><dt>Zulässige Eingaben</dt><dd>Text · token_ids</dd></div><div><dt>Ergebnisse</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>Über das Modell</summary><p class=\"note-s\" data-model-method-boundary>Diese Beschreibung erläutert die Fähigkeiten des Modells. Die über Ailoot verfügbaren Eingaben, Ausgaben und Optionen sind unten für die ausgewählte Methode aufgeführt.</p><div class=\"mabout\" id=\"m-about\"><p>google/gemini-embedding-001 stellt Text als Retrieval-Vektoren dar. Ein praktischer Einsatz findet technische Passagen, wenn eine Frage anders formuliert ist als das Dokument. Die Ailoot-Route nennt 20,000 Tokens gehosteten Kontext und akzeptiert Textstrings, Stringarrays oder Token-ID-Arrays. Pro Anfrage ist eine einheitliche Eingabeart zu verwenden.</p><p>Das Ergebnis enthält indizierte Vektoren mit endlichen Gleitkommazahlen. Der Vertrag veröffentlicht keine Standardvektorbreite und schließt dimensions-Überschreibungen ausdrücklich aus. Bestimme die Indexbreite aus der tatsächlichen Vektorlänge und halte sie für diesen Index fest. encoding_format unterstützt float. Ein Aufgabentyp- oder Anfrage-/Dokumentschalter fehlt. Bereite Fragen und Dokumente konsistent vor und halte beim Indexaufbau oder Update genaues Modell und Vektorbreite zusammen.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">In Ailoot verfügbar · Eingaben und Methoden</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Embeddings</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Eingabe</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Ausgabe</dt><dd><code>embedding</code></dd></div><div><dt>Kontext</dt><dd>20 000 tokens</dd></div><div><dt>Ausgabeformat</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Feld</th><th>Typ</th><th>Anforderung</th><th>Werte und Limits</th><th>Standard</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>string</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Erforderlich</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Erforderlich</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</span><div>Mindestanzahl Elemente: 1</div><div>Bedingungen: Nicht leerer, homogener Batch. Token-IDs sind nichtnegative Ganzzahlen.</div><div>Eingabebudget: Maximum: 20 000 · Texteinheit: UTF8-Bytes · Tokeneinheit: Elemente des Token-Arrays</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>string</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Optional</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>integer</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Nicht unterstützt</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>json_value</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Optional</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</span><div>Bedingungen: Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>json_value</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Optional</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</span><div>Bedingungen: Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>json_value</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Optional</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</span><div>Bedingungen: Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.</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=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span><code>object</code></td><td data-cell-label=\"Anforderung\"><span class=\"card-cell-label\">Anforderung</span>Optional</td><td data-cell-label=\"Werte und Limits\"><span class=\"card-cell-label\">Werte und Limits</span><div>Bedingungen: provider.options abgelehnt</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>Kombinationen von Eingabematerialien</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Methodenparameter und Lebenszyklus</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</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=\"Vertrag\"><span class=\"card-cell-label\">Vertrag</span>text, token_ids</td></tr></tbody></table></div><p>Lebenszyklus: <code>synchronous_json</code></p><p class=\"note-s\">Jeden Vektorindex und jede Dimension beibehalten.</p><p class=\"note-s\">Ein multimodaler Modellname erlaubt auf genau diesem Pfad keine Bildobjekte.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Tarife</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarifangaben erweitern die Anfrageoptionen nicht. Unterstützte Parameter stehen bei der ausgewählten Methode.</p><dl data-price-conditions><dt>Schnellmodus</dt><dd>Standardtarif <span class=\"note-s\">Wenn der Schnellmodus ausgewählt ist</span></dd><dt>Flex-Modus</dt><dd>Standardtarif <span class=\"note-s\">Wenn der Modus flex ausgewählt ist</span></dd><dt>Maximaler Denkaufwand</dt><dd>Standardtarif <span class=\"note-s\">Wenn beim maximalen Denkaufwand die Standardanpassung (default) verwendet wird</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot-Tarife</h4><h4>Preise nach Konfiguration</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Konfiguration</th><th>Tarifstaffel</th><th>Gebühr</th></tr></thead><tbody><tr><th data-cell-label=\"Konfiguration\" scope=\"row\"><span class=\"card-cell-label\">Konfiguration</span>Basistarife für Token</th><td data-cell-label=\"Tarifstaffel\"><span class=\"card-cell-label\">Tarifstaffel</span>Grundtarif</td><td data-cell-label=\"Gebühr\"><span class=\"card-cell-label\">Gebühr</span><div><span>Eingabe · Eingabetoken</span> <b>$0.0003</b> <span class=\"price-unit\">/ 1K Tokens</span></div><div><span>Ausgabe · Ausgabetoken</span> <b>$0</b> <span class=\"price-unit\">/ 1K Tokens</span></div><div><span>Eingabe · Cache lesen</span> <b>—</b> <span class=\"price-unit\">/ 1K Tokens</span></div><div><span>Eingabe · In Cache schreiben</span> <b>—</b> <span class=\"price-unit\">/ 1K Tokens</span></div><div><span>Eingabe · In Cache schreiben, 1 Stunde</span> <b>—</b> <span class=\"price-unit\">/ 1K Tokens</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot reserviert vor einer Anfrage einen geschätzten Betrag. Nach dem Ergebnis rechnet Ailoot die endgültige Gebühr anhand des gemessenen Verbrauchs ab. Bei Streaming erfolgt die Abrechnung anhand des abschließenden Verbrauchsdatensatzes. <a href=\"/docs/de-at/billing/reserve/\">So funktionieren Reservierung und Abrechnung →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Beispielanfragen</h3><h4>Ailoot Ultra · Embeddings</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Codevarianten\"><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=\"Code kopieren\"><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\">Ergebnisse abrufen und speichern</h3><h4>Ailoot Ultra · Embeddings</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Alle fertiggestellten Ergebnisse aufbewahren</h5><p class=\"note-s\">Bewahren Sie jede vollständig zurückgegebene Antwort, Datei und jeden Inhaltsteil auf.</p><p class=\"note-s\">Jeden Vektor mit seinem Index beibehalten. Die Anzahl der Vektoren muss der Anzahl der Eingaben entsprechen; die Dimensionen müssen konsistent sein.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Wenn die ursprüngliche Antwort geprüft werden muss</h5><p class=\"note-s\">Es wird kein Endpunkt für Embedding-Aufträge eingeführt.</p></div><details class=\"card-about\" data-output-fields><summary>Antwortfelder</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Feld</th><th>Typ</th></tr></thead><tbody><tr><th data-cell-label=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span>string</td></tr><tr><th data-cell-label=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span>string</td></tr><tr><th data-cell-label=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span>integer</td></tr><tr><th data-cell-label=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span>finite float[]</td></tr><tr><th data-cell-label=\"Feld\" scope=\"row\"><span class=\"card-cell-label\">Feld</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Typ\" data-field-type><span class=\"card-cell-label\">Typ</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Fehler und nächste Schritte</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Antwort</th><th>Bedingung</th><th>Nächster Schritt</th></tr></thead><tbody><tr><th data-cell-label=\"Antwort\" scope=\"row\"><span class=\"card-cell-label\">Antwort</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Bedingung\"><span class=\"card-cell-label\">Bedingung</span>Pflichtfelder, Darstellung des Eingabematerials oder Parameterwert ungültig</td><td data-cell-label=\"Nächster Schritt\"><span class=\"card-cell-label\">Nächster Schritt</span>Die angegebenen Felder vor einer neuen Erstellungsanfrage korrigieren.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Aliasnamen</h3><h4>Modellaliasnamen</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>Aliasnamen für Methodenpfade</h4><p class=\"note-s\">Verwenden Sie die oben angezeigten Methodenpfade.</p><h4>Zugehörige Aufrufer-IDs</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": "Pflichtfelder, Darstellung des Eingabematerials oder Parameterwert ungültig",
                "http_status": 400,
                "recovery": "Die angegebenen Felder vor einer neuen Erstellungsanfrage korrigieren.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Es wird kein Endpunkt für Embedding-Aufträge eingeführt."
            },
            "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": "Jeden Vektor mit seinem Index beibehalten. Die Anzahl der Vektoren muss der Anzahl der Eingaben entsprechen; die Dimensionen müssen konsistent sein.",
              "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-Bytes",
                  "token_unit": "Elemente des Token-Arrays"
                },
                "conditions": "Nicht leerer, homogener Batch. Token-IDs sind nichtnegative Ganzzahlen.",
                "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": "Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options abgelehnt",
                "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": [
            "Jeden Vektorindex und jede Dimension beibehalten.",
            "Ein multimodaler Modellname erlaubt auf genau diesem Pfad keine Bildobjekte."
          ],
          "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": "Basistarife für Token",
            "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": "Schnellmodus",
            "value": "Standardtarif",
            "when": "Wenn der Schnellmodus ausgewählt ist"
          },
          {
            "label": "Flex-Modus",
            "value": "Standardtarif",
            "when": "Wenn der Modus flex ausgewählt ist"
          },
          {
            "label": "Maximaler Denkaufwand",
            "value": "Standardtarif",
            "when": "Wenn beim maximalen Denkaufwand die Standardanpassung (default) verwendet wird"
          }
        ],
        "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": "33aa4c63924c5c183b1ed2cf849b023f812a98a9ea7d31e854aa5751ba8db334",
    "input_html_sha256": "29b9831c540b507f9e0f8bcc771aa61345dd9d74b0d583632c4674bde365b745",
    "private_audit_sha256": "182d7adce491fedcc07f31d1a6b449a509752df7052eee6c32e7695d51f8c3d0",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/de-at/google-gemini-embedding-001/",
    "description": "Text oder Token-IDs zu Vektoren mit float-Kodierung und angegebenen 20,000 Tokens gehostetem Kontext.",
    "locale": "de-at",
    "md_url": "https://ailoot.ai/models/de-at/google-gemini-embedding-001/index.md",
    "title": "Google: Gemini Embedding 001 — Ailoot",
    "url": "https://ailoot.ai/models/de-at/google-gemini-embedding-001/"
  },
  "rawModel": {
    "about": "Gemini Embedding 001 ist Googles Text-Embedding-Modell zur Repräsentation schriftlichen Materials als Vektoren. Seine veröffentlichte Rolle umfasst mehrere Domänen, darunter Wissenschaft, juristische Texte, Finanzen und Programmierung. Text ist die Eingabe und Embedding-Vektoren sind die Ausgabe; eine Suchanwendung verwendet diese Vektoren, um verwandte Quellelemente zu finden.\n\n### Identifizierbare Quellelemente vorbereiten\n\nWählen Sie eine Passage oder ein Dokumentelement mit den Informationen, die Nutzer finden müssen. Bewahren Sie Dokumentname, Abschnittsbezeichnung und Quellkennung. Ein Codeelement kann seine Datei und den Zweck im umgebenden Kontext erhalten; eine technische Passage kann die zum Verständnis nötige Terminologie und Einheiten bewahren. Diese Bezeichnungen machen eine spätere Übereinstimmung als Beleg nützlich statt nur als Ähnlichkeitswert.\n\nHalten Sie Elementgrenzen sinnvoll. Eine Definition und ihre Erklärung können zusammengehören, während sachfremde Themen eigene Identitäten behalten sollten. Bewahren Sie den Originaltext beim Vektor, damit ein abgerufenes Element geprüft oder an einen späteren Antwortgenerierungsschritt übergeben werden kann.\n\n### Vektoren in einem Suchworkflow verwenden\n\nHalten Sie Anfrage und indiziertes Material in einem kompatiblen Embedding-Aufbau. Die Anwendung speichert die Vektoren, vergleicht sie und gibt die zugehörigen Quellelemente zurück. Prüfen Sie repräsentative Anfragen anhand der Passagen, die gefunden werden sollten, einschließlich schwierigen Vokabulars und verschiedener Formulierungen desselben Bedarfs.\n\nEin nützliches Suchergebnis enthält Quellkennung und relevanten Text. Bewahren Sie für eine spätere Antwort die Passage, die jede Feststellung unterstützt. Das Embedding-Modell liefert die Repräsentation; ein separater Anwendungsschritt führt die Suche aus oder erzeugt anhand der abgerufenen Belege eine Prosaantwort.\n\n### Eingabekontingent und Fortführung des Index\n\nDas dokumentierte gehostete Profil akzeptiert bis zu 20.000 Tokens pro Eingabe. Teilen Sie längere Sammlungen in separate, bezeichnete Elemente auf und bewahren Sie ihre Beziehungen. Protokollieren Sie das für einen Index verwendete Modell und die Vorbereitung, damit Änderungen am Embedding-Aufbau von Änderungen der Quellsammlung unterschieden werden können.\n\nEin abgeschlossenes Suchartefakt verknüpft die Anfrage mit den ausgewählten Quellpassagen und ihren Kennungen. Prüfen Sie falsche oder fehlende Übereinstimmungen anhand des tatsächlichen Textes und der Elementgrenzen und übernehmen Sie die relevanten Belege anschließend in die Aufgabe nach dem Informationsabruf.",
    "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": "Die vollständigen Preisangaben zur Konfiguration finden Sie in den Details."
      }
    ],
    "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": "Pflichtfelder, Darstellung des Eingabematerials oder Parameterwert ungültig",
                "http_status": 400,
                "recovery": "Die angegebenen Felder vor einer neuen Erstellungsanfrage korrigieren.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Es wird kein Endpunkt für Embedding-Aufträge eingeführt."
            },
            "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": "Jeden Vektor mit seinem Index beibehalten. Die Anzahl der Vektoren muss der Anzahl der Eingaben entsprechen; die Dimensionen müssen konsistent sein.",
              "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-Bytes",
                  "token_unit": "Elemente des Token-Arrays"
                },
                "conditions": "Nicht leerer, homogener Batch. Token-IDs sind nichtnegative Ganzzahlen.",
                "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": "Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Die Quelle lässt dieses JSON-Metadatenfeld zu; die genaue Semantik des vorgelagerten Systems ist modellspezifisch.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options abgelehnt",
                "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": [
            "Jeden Vektorindex und jede Dimension beibehalten.",
            "Ein multimodaler Modellname erlaubt auf genau diesem Pfad keine Bildobjekte."
          ],
          "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": "Basistarife für Token",
            "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": "Schnellmodus",
            "value": "Standardtarif",
            "when": "Wenn der Schnellmodus ausgewählt ist"
          },
          {
            "label": "Flex-Modus",
            "value": "Standardtarif",
            "when": "Wenn der Modus flex ausgewählt ist"
          },
          {
            "label": "Maximaler Denkaufwand",
            "value": "Standardtarif",
            "when": "Wenn beim maximalen Denkaufwand die Standardanpassung (default) verwendet wird"
          }
        ],
        "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_aliases": [
      {
        "canonical_model_id": "google/gemini-embedding-001",
        "request_model_id": "google/gemini-embedding-001",
        "route_label": "Ultra"
      }
    ],
    "name": "Google: Gemini Embedding 001",
    "price": {
      "in": 0.3,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.3,
      "sort_out": 0,
      "varies": false
    },
    "pricing_profiles": [
      {
        "dimensions": {
          "regime": "base"
        },
        "label": "Эмбеддинги · 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"
          }
        ]
      }
    ],
    "public_id": "google/gemini-embedding-001",
    "short": "Gemini Embedding 001 ist Googles Text-Embedding-Modell für die Informationssuche in Domänen wie Wissenschaft, juristischem Material, Finanzen und Code. Es wandelt Text in Vektoren um, mit einem dokumentierten gehosteten Kontingent von bis zu 20.000 Tokens pro Eingabe."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
        {
          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Text oder Token-IDs zu Vektoren mit float-Kodierung und angegebenen 20,000 Tokens gehostetem Kontext.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "383e7e8ceedf0abbe79b3fa29b32a62eea14765b2c426936799f2e33d4444d7a",
    "renderer_sha256": "4915581496a34d8a27d706c57a04aa12aa22b4b136560828b2e1b9b8437d1eb0"
  },
  "sourceRevision": "fe334e652db3b887dbc7320b02745b7a76abe48af2b40452821fd8ced0f8c776"
}