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  "aboutHtml": "<p>Llama Nemotron Embed VL 1B V2 ialah model embedding NVIDIA untuk pemerolehan semula soal jawab multimodal. Ia boleh mewakili dokumen sebagai teks, imej atau gabungan imej dan teks. Pertanyaan teks kemudian boleh digunakan untuk mendapatkan dokumen berkaitan. Profil yang diterbitkan menyokong imej dokumen yang mengandungi teks, jadual, carta dan infografik serta mengembalikan vektor embedding.</p><h4>Sediakan item dokumen yang dapat dikenal pasti</h4><p>Pastikan setiap input dipautkan kepada dokumen sumber, halaman atau petikan yang diwakilinya. Untuk halaman visual, simpan imej dan sebarang teks iringan yang tergolong bersamanya. Untuk petikan teks, simpan tajuk atau label bahagian apabila membantu mentafsir padanan kemudian. Pengecam sumber ini menjadikan item yang diperoleh semula berguna sebagai bukti, bukannya hanya hasil keserupaan berangka.</p><p>Pilih sempadan item berdasarkan bahan yang perlu ditemui pengguna. Carta dan penjelasannya mungkin perlu bersama; halaman tidak berkaitan patut mengekalkan identiti berasingan. Semak sama ada input boleh dibaca dan sama ada teks atau imej mengandungi maklumat yang dimaksudkan untuk diperoleh semula oleh pertanyaan sasaran.</p><h4>Pertanyaan teks dan pemerolehan semula vektor</h4><p>Peranan model ialah menghasilkan vektor untuk pemerolehan semula, dengan teks sebagai bentuk pertanyaan yang diterangkan profil. Pastikan pertanyaan dan item berindeks menggunakan tetapan embedding serasi. Aplikasi menyimpan vektor, membandingkannya dan mengembalikan item sumber sepadan. Dokumen yang diperoleh semula kekal bahan untuk diperiksa atau diberikan kepada langkah penjanaan jawapan kemudian.</p><p>Set ujian berguna menyertakan soalan sebenar pengguna dan halaman yang patut ditemui. Semak padanan salah atau yang tiada berdasarkan kandungan sumber serta sempadan item. Simpan pengecam dokumen penuh bersama mana-mana petikan atau imej terpilih supaya hasil dapat diperiksa dalam konteks.</p><h4>Peruntukan input dan artifak carian</h4><p>Profil perkhidmatan dihoskan yang didokumenkan menyenaraikan sehingga 131,072 token bagi setiap input. Bahagikan set sumber lebih besar kepada input berlabel berasingan dan simpan hubungannya. Output vektor profil ini bukan jawapan prosa terjana atau transkrip; peringkat aplikasi kemudian boleh menggunakan bukti yang diperoleh semula untuk menjawab soalan.</p><p>Artifak pemerolehan semula lengkap menghubungkan pertanyaan, item sumber terpilih dan pengecamnya. Simpan model embedding serta penyediaan input yang digunakan untuk indeks supaya perubahan kemudian dapat dibezakan daripada perubahan koleksi sumber.</p>",
  "aboutText": "Llama Nemotron Embed VL 1B V2 ialah model embedding NVIDIA untuk pemerolehan semula soal jawab multimodal. Ia boleh mewakili dokumen sebagai teks, imej atau gabungan imej dan teks. Pertanyaan teks kemudian boleh digunakan untuk mendapatkan dokumen berkaitan. Profil yang diterbitkan menyokong imej dokumen yang mengandungi teks, jadual, carta dan infografik serta mengembalikan vektor embedding.\n\n### Sediakan item dokumen yang dapat dikenal pasti\n\nPastikan setiap input dipautkan kepada dokumen sumber, halaman atau petikan yang diwakilinya. Untuk halaman visual, simpan imej dan sebarang teks iringan yang tergolong bersamanya. Untuk petikan teks, simpan tajuk atau label bahagian apabila membantu mentafsir padanan kemudian. Pengecam sumber ini menjadikan item yang diperoleh semula berguna sebagai bukti, bukannya hanya hasil keserupaan berangka.\n\nPilih sempadan item berdasarkan bahan yang perlu ditemui pengguna. Carta dan penjelasannya mungkin perlu bersama; halaman tidak berkaitan patut mengekalkan identiti berasingan. Semak sama ada input boleh dibaca dan sama ada teks atau imej mengandungi maklumat yang dimaksudkan untuk diperoleh semula oleh pertanyaan sasaran.\n\n### Pertanyaan teks dan pemerolehan semula vektor\n\nPeranan model ialah menghasilkan vektor untuk pemerolehan semula, dengan teks sebagai bentuk pertanyaan yang diterangkan profil. Pastikan pertanyaan dan item berindeks menggunakan tetapan embedding serasi. Aplikasi menyimpan vektor, membandingkannya dan mengembalikan item sumber sepadan. Dokumen yang diperoleh semula kekal bahan untuk diperiksa atau diberikan kepada langkah penjanaan jawapan kemudian.\n\nSet ujian berguna menyertakan soalan sebenar pengguna dan halaman yang patut ditemui. Semak padanan salah atau yang tiada berdasarkan kandungan sumber serta sempadan item. Simpan pengecam dokumen penuh bersama mana-mana petikan atau imej terpilih supaya hasil dapat diperiksa dalam konteks.\n\n### Peruntukan input dan artifak carian\n\nProfil perkhidmatan dihoskan yang didokumenkan menyenaraikan sehingga 131,072 token bagi setiap input. Bahagikan set sumber lebih besar kepada input berlabel berasingan dan simpan hubungannya. Output vektor profil ini bukan jawapan prosa terjana atau transkrip; peringkat aplikasi kemudian boleh menggunakan bukti yang diperoleh semula untuk menjawab soalan.\n\nArtifak pemerolehan semula lengkap menghubungkan pertanyaan, item sumber terpilih dan pengecamnya. Simpan model embedding serta penyediaan input yang digunakan untuk indeks supaya perubahan kemudian dapat dibezakan daripada perubahan koleksi sumber.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">NV</span><h2 id=\"sheet-title\">NVIDIA: Llama Nemotron Embed VL 1B V2 (free)</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Tutup\">×</button></div><div class=\"idline\"><span>NVIDIA /</span><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code><button type=\"button\" id=\"copy-id\">Salin ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Bahagian kad model\"><button type=\"button\" data-card-jump=\"overview\">Pengenalan</button><button type=\"button\" data-card-jump=\"methods\">Input &amp; kaedah</button><button type=\"button\" data-card-jump=\"pricing\">Pelan harga</button><button type=\"button\" data-card-jump=\"examples\">Contoh</button><button type=\"button\" data-card-jump=\"results\">Hasil</button><button type=\"button\" data-card-jump=\"aliases\">Alias</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"Kaedah API\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree\">Pembenaman</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\">Pengenalan</h3><p class=\"mdesc-full\">Llama Nemotron Embed VL 1B V2 ialah model embedding multimodal NVIDIA untuk pemerolehan semula. Ia menukarkan teks, imej atau input dokumen gabungan kepada vektor dan menyokong carian pertanyaan teks terhadap dokumen yang mengandungi jadual, carta, teks dan infografik.</p><dl class=\"card-io\" data-card-io><div><dt>Input yang diterima</dt><dd>Teks · token_ids</dd></div><div><dt>Hasil</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>Tentang model</summary><p class=\"note-s\" data-model-method-boundary>Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.</p><div class=\"mabout\" id=\"m-about\"><p>Llama Nemotron Embed VL 1B V2 ialah model embedding NVIDIA untuk pemerolehan semula soal jawab multimodal. Ia boleh mewakili dokumen sebagai teks, imej atau gabungan imej dan teks. Pertanyaan teks kemudian boleh digunakan untuk mendapatkan dokumen berkaitan. Profil yang diterbitkan menyokong imej dokumen yang mengandungi teks, jadual, carta dan infografik serta mengembalikan vektor embedding.</p><h4>Sediakan item dokumen yang dapat dikenal pasti</h4><p>Pastikan setiap input dipautkan kepada dokumen sumber, halaman atau petikan yang diwakilinya. Untuk halaman visual, simpan imej dan sebarang teks iringan yang tergolong bersamanya. Untuk petikan teks, simpan tajuk atau label bahagian apabila membantu mentafsir padanan kemudian. Pengecam sumber ini menjadikan item yang diperoleh semula berguna sebagai bukti, bukannya hanya hasil keserupaan berangka.</p><p>Pilih sempadan item berdasarkan bahan yang perlu ditemui pengguna. Carta dan penjelasannya mungkin perlu bersama; halaman tidak berkaitan patut mengekalkan identiti berasingan. Semak sama ada input boleh dibaca dan sama ada teks atau imej mengandungi maklumat yang dimaksudkan untuk diperoleh semula oleh pertanyaan sasaran.</p><h4>Pertanyaan teks dan pemerolehan semula vektor</h4><p>Peranan model ialah menghasilkan vektor untuk pemerolehan semula, dengan teks sebagai bentuk pertanyaan yang diterangkan profil. Pastikan pertanyaan dan item berindeks menggunakan tetapan embedding serasi. Aplikasi menyimpan vektor, membandingkannya dan mengembalikan item sumber sepadan. Dokumen yang diperoleh semula kekal bahan untuk diperiksa atau diberikan kepada langkah penjanaan jawapan kemudian.</p><p>Set ujian berguna menyertakan soalan sebenar pengguna dan halaman yang patut ditemui. Semak padanan salah atau yang tiada berdasarkan kandungan sumber serta sempadan item. Simpan pengecam dokumen penuh bersama mana-mana petikan atau imej terpilih supaya hasil dapat diperiksa dalam konteks.</p><h4>Peruntukan input dan artifak carian</h4><p>Profil perkhidmatan dihoskan yang didokumenkan menyenaraikan sehingga 131,072 token bagi setiap input. Bahagikan set sumber lebih besar kepada input berlabel berasingan dan simpan hubungannya. Output vektor profil ini bukan jawapan prosa terjana atau transkrip; peringkat aplikasi kemudian boleh menggunakan bukti yang diperoleh semula untuk menjawab soalan.</p><p>Artifak pemerolehan semula lengkap menghubungkan pertanyaan, item sumber terpilih dan pengecamnya. Simpan model embedding serta penyediaan input yang digunakan untuk indeks supaya perubahan kemudian dapat dibezakan daripada perubahan koleksi sumber.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Tersedia di Ailoot · input &amp; kaedah</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Pembenaman</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Input</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Output</dt><dd><code>embedding</code></dd></div><div><dt>Konteks</dt><dd>131,072 tokens</dd></div><div><dt>Format output</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Medan</th><th>Jenis</th><th>Keperluan</th><th>Nilai &amp; had</th><th>Lalai</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>string</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Diperlukan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>nvidia/llama-nemotron-embed-vl-1b-v2:free</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Diperlukan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Item minimum: 1</div><div>Syarat: Kelompok homogen yang tidak kosong. ID token ialah integer bukan negatif.</div><div>Bajet input: Maksimum: 131,072 · Unit teks: bait UTF8 · Unit token: elemen tatasusunan token</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>string</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>float</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>integer</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Tidak disokong</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Minimum: 1</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>json_value</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Syarat: Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>json_value</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Syarat: Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>json_value</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Syarat: Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span><code>object</code></td><td data-cell-label=\"Keperluan\"><span class=\"card-cell-label\">Keperluan</span>Pilihan</td><td data-cell-label=\"Nilai &amp; had\"><span class=\"card-cell-label\">Nilai &amp; had</span><div>Syarat: provider.options ditolak</div></td><td data-cell-label=\"Lalai\" data-default=\"unspecified\"><span class=\"card-cell-label\">Lalai</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Gabungan bahan input</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Parameter kaedah dan kitar hayat</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>131072</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>multimodal_objects</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</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=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>text, token_ids</td></tr></tbody></table></div><p>Kitar hayat: <code>synchronous_json</code></p><p class=\"note-s\">Simpan setiap indeks dan dimensi vektor.</p><p class=\"note-s\">Nama model multimodal tidak bermakna objek imej dibenarkan pada laluan khusus ini.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pelan harga</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.</p><dl data-price-conditions><dt>Mod pantas</dt><dd>Kadar standard <span class=\"note-s\">Apabila mod pantas dipilih</span></dd><dt>Mod flex</dt><dd>Kadar standard <span class=\"note-s\">Apabila mod flex dipilih</span></dd><dt>Usaha penaakulan maksimum</dt><dd>Kadar standard <span class=\"note-s\">Apabila usaha penaakulan maksimum menggunakan pelarasan default</span></dd></dl></div><div data-declared-tariff-pricing><h4>Kadar Ailoot</h4><h4>Harga mengikut konfigurasi</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Konfigurasi</th><th>Jadual kadar</th><th>Caj</th></tr></thead><tbody><tr><th data-cell-label=\"Konfigurasi\" scope=\"row\"><span class=\"card-cell-label\">Konfigurasi</span>Kadar asas token</th><td data-cell-label=\"Jadual kadar\"><span class=\"card-cell-label\">Jadual kadar</span>Kadar asas</td><td data-cell-label=\"Caj\"><span class=\"card-cell-label\">Caj</span><div><span>Input · Token input</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Output · Token output</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Input · Bacaan cache</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Input · Penulisan cache</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Input · Penulisan cache, 1 jam</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. <a href=\"/docs/ms/billing/reserve/\">Cara rizab dan penyelesaian caj berfungsi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Contoh permintaan</h3><h4>Ailoot Ultra · Pembenaman</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Pilihan kod\"><button class=\"cg-tab\" id=\"model-code-1992-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1992-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1992-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1992-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Salin kod\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-1992-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1992-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;nvidia/llama-nemotron-embed-vl-1b-v2:free&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1992-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1992-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;nvidia/llama-nemotron-embed-vl-1b-v2:free&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Terima dan simpan hasil</h3><h4>Ailoot Ultra · Pembenaman</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Simpan semua hasil yang selesai</h5><p class=\"note-s\">Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan.</p><p class=\"note-s\">Simpan setiap vektor bersama indeksnya. Bilangan vektor mesti sama dengan bilangan input; dimensi mesti konsisten.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Jika respons asal perlu disemak</h5><p class=\"note-s\">Tiada titik akhir tugas pembenaman diperkenalkan.</p></div><details class=\"card-about\" data-output-fields><summary>Medan respons</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Medan</th><th>Jenis</th></tr></thead><tbody><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span>string</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span>string</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span>integer</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span>finite float[]</td></tr><tr><th data-cell-label=\"Medan\" scope=\"row\"><span class=\"card-cell-label\">Medan</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Jenis\" data-field-type><span class=\"card-cell-label\">Jenis</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Ralat dan langkah seterusnya</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Respons</th><th>Syarat</th><th>Langkah seterusnya</th></tr></thead><tbody><tr><th data-cell-label=\"Respons\" scope=\"row\"><span class=\"card-cell-label\">Respons</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Syarat\"><span class=\"card-cell-label\">Syarat</span>Medan wajib, perwakilan bahan atau nilai parameter tidak sah</td><td data-cell-label=\"Langkah seterusnya\"><span class=\"card-cell-label\">Langkah seterusnya</span>Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Alias</h3><h4>Alias model</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;route=Ultra&amp;caller=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;fmt=embeddings\"><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code></a></span></p><h4>Alias laluan kaedah</h4><p class=\"note-s\">Gunakan laluan kaedah yang dipaparkan di atas.</p><h4>ID pemanggil berkaitan</h4><p class=\"note-s\"><a href=\"#m=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;route=Ultra&amp;caller=nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree&amp;fmt=embeddings\"><code>nvidia/llama-nemotron-embed-vl-1b-v2:free</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|nvidia%2Fllama-nemotron-embed-vl-1b-v2%3Afree",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Medan wajib, perwakilan bahan atau nilai parameter tidak sah",
                "http_status": 400,
                "recovery": "Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Tiada titik akhir tugas pembenaman diperkenalkan."
            },
            "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": "Simpan setiap vektor bersama indeksnya. Bilangan vektor mesti sama dengan bilangan input; dimensi mesti konsisten.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nvidia/llama-nemotron-embed-vl-1b-v2:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 131072,
                  "text_unit": "bait UTF8",
                  "token_unit": "elemen tatasusunan token"
                },
                "conditions": "Kelompok homogen yang tidak kosong. ID token ialah integer bukan negatif.",
                "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": "Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options ditolak",
                "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": [
            "Simpan setiap indeks dan dimensi vektor.",
            "Nama model multimodal tidak bermakna objek imej dibenarkan pada laluan khusus ini."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 131072,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 131072,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Kadar asas token",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2:free\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/llama-nemotron-embed-vl-1b-v2:free',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Mod pantas",
            "value": "Kadar standard",
            "when": "Apabila mod pantas dipilih"
          },
          {
            "label": "Mod flex",
            "value": "Kadar standard",
            "when": "Apabila mod flex dipilih"
          },
          {
            "label": "Usaha penaakulan maksimum",
            "value": "Kadar standard",
            "when": "Apabila usaha penaakulan maksimum menggunakan pelarasan default"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
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          }
        ],
        "request_model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
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            "amount_unit": "usd_per_thousand_tokens",
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            "public_unit_label": "/ 1K tokens",
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            "amount_unit": "usd_per_thousand_tokens",
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  ],
  "model_id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "53f4235c46cff333e238cb9348d7723678f9353eb15f824b91f414b57301e780",
    "input_html_sha256": "a024d2f9f2d07a996714cfcfb4d78ad5ef3b0c4e9fcaf9857d3e1efdb21c97f7",
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    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/ms/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/",
    "description": "Llama Nemotron Embed VL 1B V2 ialah model embedding multimodal NVIDIA untuk pemerolehan semula. Ia menukarkan teks, imej atau input dokumen gabungan kepada vektor dan menyokong carian pertanyaan teks terhadap dokumen yang mengandungi jadual, carta, teks dan infografik.",
    "locale": "ms",
    "md_url": "https://ailoot.ai/models/ms/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/index.md",
    "title": "NVIDIA: Llama Nemotron Embed VL 1B V2 (free) — Ailoot",
    "url": "https://ailoot.ai/models/ms/nvidia-llama-nemotron-embed-vl-1b-v2-u003a-free/"
  },
  "rawModel": {
    "about": "Llama Nemotron Embed VL 1B V2 ialah model embedding NVIDIA untuk pemerolehan semula soal jawab multimodal. Ia boleh mewakili dokumen sebagai teks, imej atau gabungan imej dan teks. Pertanyaan teks kemudian boleh digunakan untuk mendapatkan dokumen berkaitan. Profil yang diterbitkan menyokong imej dokumen yang mengandungi teks, jadual, carta dan infografik serta mengembalikan vektor embedding.\n\n### Sediakan item dokumen yang dapat dikenal pasti\n\nPastikan setiap input dipautkan kepada dokumen sumber, halaman atau petikan yang diwakilinya. Untuk halaman visual, simpan imej dan sebarang teks iringan yang tergolong bersamanya. Untuk petikan teks, simpan tajuk atau label bahagian apabila membantu mentafsir padanan kemudian. Pengecam sumber ini menjadikan item yang diperoleh semula berguna sebagai bukti, bukannya hanya hasil keserupaan berangka.\n\nPilih sempadan item berdasarkan bahan yang perlu ditemui pengguna. Carta dan penjelasannya mungkin perlu bersama; halaman tidak berkaitan patut mengekalkan identiti berasingan. Semak sama ada input boleh dibaca dan sama ada teks atau imej mengandungi maklumat yang dimaksudkan untuk diperoleh semula oleh pertanyaan sasaran.\n\n### Pertanyaan teks dan pemerolehan semula vektor\n\nPeranan model ialah menghasilkan vektor untuk pemerolehan semula, dengan teks sebagai bentuk pertanyaan yang diterangkan profil. Pastikan pertanyaan dan item berindeks menggunakan tetapan embedding serasi. Aplikasi menyimpan vektor, membandingkannya dan mengembalikan item sumber sepadan. Dokumen yang diperoleh semula kekal bahan untuk diperiksa atau diberikan kepada langkah penjanaan jawapan kemudian.\n\nSet ujian berguna menyertakan soalan sebenar pengguna dan halaman yang patut ditemui. Semak padanan salah atau yang tiada berdasarkan kandungan sumber serta sempadan item. Simpan pengecam dokumen penuh bersama mana-mana petikan atau imej terpilih supaya hasil dapat diperiksa dalam konteks.\n\n### Peruntukan input dan artifak carian\n\nProfil perkhidmatan dihoskan yang didokumenkan menyenaraikan sehingga 131,072 token bagi setiap input. Bahagikan set sumber lebih besar kepada input berlabel berasingan dan simpan hubungannya. Output vektor profil ini bukan jawapan prosa terjana atau transkrip; peringkat aplikasi kemudian boleh menggunakan bukti yang diperoleh semula untuk menjawab soalan.\n\nArtifak pemerolehan semula lengkap menghubungkan pertanyaan, item sumber terpilih dan pengecamnya. Simpan model embedding serta penyediaan input yang digunakan untuk indeks supaya perubahan kemudian dapat dibezakan daripada perubahan koleksi sumber.",
    "alias_of": null,
    "aliases": [],
    "author": "NVIDIA",
    "available": null,
    "canonical_model": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
    "capabilities": {
      "context_length": 131072,
      "manufacturer_input": [
        "text",
        "image"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Maklumat lengkap tentang harga konfigurasi terdapat dalam butiran."
      }
    ],
    "ctx": 131072,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "nvidia/llama-nemotron-embed-vl-1b-v2:free",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Medan wajib, perwakilan bahan atau nilai parameter tidak sah",
                "http_status": 400,
                "recovery": "Betulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Tiada titik akhir tugas pembenaman diperkenalkan."
            },
            "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": "Simpan setiap vektor bersama indeksnya. Bilangan vektor mesti sama dengan bilangan input; dimensi mesti konsisten.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nvidia/llama-nemotron-embed-vl-1b-v2:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 131072,
                  "text_unit": "bait UTF8",
                  "token_unit": "elemen tatasusunan token"
                },
                "conditions": "Kelompok homogen yang tidak kosong. ID token ialah integer bukan negatif.",
                "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": "Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Sumber membenarkan medan metadata JSON ini; semantik tepat di pihak huluan bergantung pada model.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options ditolak",
                "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": [
            "Simpan setiap indeks dan dimensi vektor.",
            "Nama model multimodal tidak bermakna objek imej dibenarkan pada laluan khusus ini."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 131072,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 131072,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Kadar asas token",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/llama-nemotron-embed-vl-1b-v2:free\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='nvidia/llama-nemotron-embed-vl-1b-v2:free',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Mod pantas",
            "value": "Kadar standard",
            "when": "Apabila mod pantas dipilih"
          },
          {
            "label": "Mod flex",
            "value": "Kadar standard",
            "when": "Apabila mod flex dipilih"
          },
          {
            "label": "Usaha penaakulan maksimum",
            "value": "Kadar standard",
            "when": "Apabila usaha penaakulan maksimum menggunakan pelarasan default"
          }
        ],
        "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": [
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