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  "aboutHtml": "<p>Nemotron 3 Embed 1B ialah model teks NVIDIA untuk carian berdasarkan makna dan pemadanan petikan. Model mengekodkan soalan, dokumen dan kod program kepada vektor angka padat. Perwakilan ini digunakan untuk mencari konteks yang diperlukan dalam koleksi besar, melengkapkan respons pembantu dengannya atau mendapatkan maklumat yang disimpan sebelum ini untuk langkah ejen seterusnya.</p><h4>Pengetahuan berbilang bahasa dan kod</h4><p>Model direka untuk carian berbilang bahasa dan rentas bahasa: soalan dan bahan yang sesuai boleh ditulis dalam bahasa berbeza. Bahasa Rusia termasuk dalam set 34 bahasa yang diterbitkan, bersama bahasa Inggeris, Ukraine, Cina, Jepun, Arab dan bahasa lain. Ini berguna untuk pangkalan pengetahuan bersama yang dokumen teknikal dan permintaan penggunanya tidak diseragamkan kepada satu bahasa.</p><p>Antara tugasan ialah carian dokumentasi, pemadanan teks dan pencarian kod berdasarkan penerangan bertulis. Tetingkap input besar membolehkan dokumen panjang, konteks perisian yang banyak dan sejarah beberapa langkah ejen digunakan. Bahan yang ditemui dikembalikan kepada proses seterusnya sebagai konteks; penerangan dan respons akhir dihasilkan oleh komponen sistem yang berikutnya.</p><h4>Daripada teks kepada vektor</h4><p>Input terdiri daripada rentetan teks. Satu vektor dengan 2048 nombor dihasilkan bagi setiap rentetan. Panjang urutan maksimum ialah 32768 token; bahan lebih panjang perlu dibahagikan atau dipendekkan. Penyediaan input membezakan soalan dengan dokumen: model dilatih untuk memadankan peranan masing-masing, jadi rentetan yang sama dalam dua tugasan ini tidak patut disediakan tanpa mengambil kira tujuannya.</p><p>Vektor dinormalkan mengikut panjang. Dengan perwakilan sebegini, hasil darab skalar dan keserupaan kosinus memberikan cara perbandingan yang setara. Koordinat 1024 atau 512 yang pertama daripada vektor penuh boleh dikekalkan, tetapi penormalan perlu dilakukan semula selepas pemotongan. Ini ialah sifat pemprosesan hasil; dimensi perlu dipilih secara konsisten untuk koleksi dan pertanyaan.</p><h4>Struktur model padat</h4><p>Pengekod mengandungi kira-kira 1,14 bilion parameter. Pengekod dihasilkan daripada model berasaskan Ministral 3 3B melalui dua peringkat pengecilan struktur dan penyulingan. Perhatian dua hala mengambil kira keseluruhan input, kemudian perwakilan token diambil purata menjadi vektor bersama.</p><p>NVIDIA mengeluarkan varian BF16 dan NVFP4. Ruang perwakilannya hampir serupa, tetapi pertukaran varian memerlukan semakan menggunakan dokumen sendiri. Keserupaan angka mahupun panjang konteks tidak menjamin bahawa carian sentiasa memilih petikan yang betul.</p>",
  "aboutText": "Nemotron 3 Embed 1B ialah model teks NVIDIA untuk carian berdasarkan makna dan pemadanan petikan. Model mengekodkan soalan, dokumen dan kod program kepada vektor angka padat. Perwakilan ini digunakan untuk mencari konteks yang diperlukan dalam koleksi besar, melengkapkan respons pembantu dengannya atau mendapatkan maklumat yang disimpan sebelum ini untuk langkah ejen seterusnya.\n\n### Pengetahuan berbilang bahasa dan kod\n\nModel direka untuk carian berbilang bahasa dan rentas bahasa: soalan dan bahan yang sesuai boleh ditulis dalam bahasa berbeza. Bahasa Rusia termasuk dalam set 34 bahasa yang diterbitkan, bersama bahasa Inggeris, Ukraine, Cina, Jepun, Arab dan bahasa lain. Ini berguna untuk pangkalan pengetahuan bersama yang dokumen teknikal dan permintaan penggunanya tidak diseragamkan kepada satu bahasa.\n\nAntara tugasan ialah carian dokumentasi, pemadanan teks dan pencarian kod berdasarkan penerangan bertulis. Tetingkap input besar membolehkan dokumen panjang, konteks perisian yang banyak dan sejarah beberapa langkah ejen digunakan. Bahan yang ditemui dikembalikan kepada proses seterusnya sebagai konteks; penerangan dan respons akhir dihasilkan oleh komponen sistem yang berikutnya.\n\n### Daripada teks kepada vektor\n\nInput terdiri daripada rentetan teks. Satu vektor dengan 2048 nombor dihasilkan bagi setiap rentetan. Panjang urutan maksimum ialah 32768 token; bahan lebih panjang perlu dibahagikan atau dipendekkan. Penyediaan input membezakan soalan dengan dokumen: model dilatih untuk memadankan peranan masing-masing, jadi rentetan yang sama dalam dua tugasan ini tidak patut disediakan tanpa mengambil kira tujuannya.\n\nVektor dinormalkan mengikut panjang. Dengan perwakilan sebegini, hasil darab skalar dan keserupaan kosinus memberikan cara perbandingan yang setara. Koordinat 1024 atau 512 yang pertama daripada vektor penuh boleh dikekalkan, tetapi penormalan perlu dilakukan semula selepas pemotongan. Ini ialah sifat pemprosesan hasil; dimensi perlu dipilih secara konsisten untuk koleksi dan pertanyaan.\n\n### Struktur model padat\n\nPengekod mengandungi kira-kira 1,14 bilion parameter. Pengekod dihasilkan daripada model berasaskan Ministral 3 3B melalui dua peringkat pengecilan struktur dan penyulingan. Perhatian dua hala mengambil kira keseluruhan input, kemudian perwakilan token diambil purata menjadi vektor bersama.\n\nNVIDIA mengeluarkan varian BF16 dan NVFP4. Ruang perwakilannya hampir serupa, tetapi pertukaran varian memerlukan semakan menggunakan dokumen sendiri. Keserupaan angka mahupun panjang konteks tidak menjamin bahawa carian sentiasa memilih petikan yang betul.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">NV</span><h2 id=\"sheet-title\">Nemotron 3 Embed 1B</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Tutup\">×</button></div><div class=\"idline\"><span>NVIDIA /</span><code>nvidia/nemotron-3-embed-1b</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=\"Core|embeddings|/v1/embeddings|nvidia%2Fnemotron-3-embed-1b\">Pembenaman</button></div><div class=\"idline\"><label for=\"card-model-id\">ID model</label><select class=\"sel\" id=\"card-model-id\" style=\"min-width:0;max-width:100%\"><option value=\"Core|embeddings|/v1/embeddings|nvidia%2Fnemotron-3-embed-1b\" selected>nvidia/nemotron-3-embed-1b</option><option value=\"Core|embeddings|/v1/embeddings|nemotron-3-embed\">nemotron-3-embed</option><option value=\"Core|embeddings|/v1/embeddings|nemotron-3-embed-latest\">nemotron-3-embed-latest</option></select></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\">Nemotron 3 Embed 1B menukarkan teks kepada vektor untuk carian berbilang bahasa, carian kod dan RAG. Memproses urutan sehingga 32768 token dan menghasilkan perwakilan dengan dimensi 2048 nombor.</p><dl class=\"card-io\" data-card-io><div><dt>Input yang diterima</dt><dd>Teks</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>Nemotron 3 Embed 1B ialah model teks NVIDIA untuk carian berdasarkan makna dan pemadanan petikan. Model mengekodkan soalan, dokumen dan kod program kepada vektor angka padat. Perwakilan ini digunakan untuk mencari konteks yang diperlukan dalam koleksi besar, melengkapkan respons pembantu dengannya atau mendapatkan maklumat yang disimpan sebelum ini untuk langkah ejen seterusnya.</p><h4>Pengetahuan berbilang bahasa dan kod</h4><p>Model direka untuk carian berbilang bahasa dan rentas bahasa: soalan dan bahan yang sesuai boleh ditulis dalam bahasa berbeza. Bahasa Rusia termasuk dalam set 34 bahasa yang diterbitkan, bersama bahasa Inggeris, Ukraine, Cina, Jepun, Arab dan bahasa lain. Ini berguna untuk pangkalan pengetahuan bersama yang dokumen teknikal dan permintaan penggunanya tidak diseragamkan kepada satu bahasa.</p><p>Antara tugasan ialah carian dokumentasi, pemadanan teks dan pencarian kod berdasarkan penerangan bertulis. Tetingkap input besar membolehkan dokumen panjang, konteks perisian yang banyak dan sejarah beberapa langkah ejen digunakan. Bahan yang ditemui dikembalikan kepada proses seterusnya sebagai konteks; penerangan dan respons akhir dihasilkan oleh komponen sistem yang berikutnya.</p><h4>Daripada teks kepada vektor</h4><p>Input terdiri daripada rentetan teks. Satu vektor dengan 2048 nombor dihasilkan bagi setiap rentetan. Panjang urutan maksimum ialah 32768 token; bahan lebih panjang perlu dibahagikan atau dipendekkan. Penyediaan input membezakan soalan dengan dokumen: model dilatih untuk memadankan peranan masing-masing, jadi rentetan yang sama dalam dua tugasan ini tidak patut disediakan tanpa mengambil kira tujuannya.</p><p>Vektor dinormalkan mengikut panjang. Dengan perwakilan sebegini, hasil darab skalar dan keserupaan kosinus memberikan cara perbandingan yang setara. Koordinat 1024 atau 512 yang pertama daripada vektor penuh boleh dikekalkan, tetapi penormalan perlu dilakukan semula selepas pemotongan. Ini ialah sifat pemprosesan hasil; dimensi perlu dipilih secara konsisten untuk koleksi dan pertanyaan.</p><h4>Struktur model padat</h4><p>Pengekod mengandungi kira-kira 1,14 bilion parameter. Pengekod dihasilkan daripada model berasaskan Ministral 3 3B melalui dua peringkat pengecilan struktur dan penyulingan. Perhatian dua hala mengambil kira keseluruhan input, kemudian perwakilan token diambil purata menjadi vektor bersama.</p><p>NVIDIA mengeluarkan varian BF16 dan NVFP4. Ruang perwakilannya hampir serupa, tetapi pertukaran varian memerlukan semakan menggunakan dokumen sendiri. Keserupaan angka mahupun panjang konteks tidak menjamin bahawa carian sentiasa memilih petikan yang betul.</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 · Pembenaman</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Input</dt><dd><code>text</code></dd></div><div><dt>Output</dt><dd><code>embedding</code></dd></div><div><dt>Konteks</dt><dd>4,096 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/nemotron-3-embed-1b</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[]</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: Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.</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_type</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>query, passage</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>query</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, base64</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>float</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\">truncate</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>NONE, START, END</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\">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>Pilihan</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=\"source_interface\"><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>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></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>material_types</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>text</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>optional_controls</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>null</td></tr><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></tbody></table></div><p>Kitar hayat: <code>exact retained published method</code></p><p class=\"note-s\">Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pelan harga</h3><h4>Kadar kaedah Ailoot</h4><section class=\"meth\" data-route=\"Core\"><header><b><span class=\"alias\">Ailoot</span> Pembenaman</b><code>POST /v1/embeddings</code></header><dl><div><dt>Input</dt><dd>USD 0.0001 <em>/ 1K</em></dd></div></dl></section><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 · 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-527-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-527-panel-0\" aria-selected=\"true\" tabindex=\"0\">Contoh pembenaman</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-527-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-527-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\n: &quot;${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}&quot;\nexport AILOOT_TEXT_FILE\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;nvidia/nemotron-3-embed-1b&quot;, &quot;input&quot;: []}&#39;)\nf=Path(os.environ[&quot;AILOOT_TEXT_FILE&quot;])\nif not f.is_file():raise SystemExit(&quot;Choose your actual UTF-8 source material&quot;)\ntext=f.read_text(encoding=&quot;utf-8&quot;)\nif not text.strip():raise SystemExit(&quot;Source material must contain text&quot;)\np[&quot;input&quot;]=[text]\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></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Terima dan simpan hasil</h3><h4>Ailoot · 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\">Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.</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\">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[]|base64 float32 vector</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\">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=\"Core\"><b>Ailoot</b> <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nvidia%2Fnemotron-3-embed-1b&amp;fmt=embeddings\"><code>nvidia/nemotron-3-embed-1b</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed&amp;fmt=embeddings\"><code>nemotron-3-embed</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed-latest&amp;fmt=embeddings\"><code>nemotron-3-embed-latest</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%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nvidia%2Fnemotron-3-embed-1b&amp;fmt=embeddings\"><code>nvidia/nemotron-3-embed-1b</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed&amp;fmt=embeddings\"><code>nemotron-3-embed</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed-latest&amp;fmt=embeddings\"><code>nemotron-3-embed-latest</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "nvidia/nemotron-3-embed-1b",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "nvidia/nemotron-3-embed-1b",
        "route_label": "Core"
      },
      "method_key": "Core|embeddings|/v1/embeddings|nvidia%2Fnemotron-3-embed-1b",
      "ordinal": 0,
      "rawMethod": {
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "contract": {
          "data": {
            "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.",
            "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": [
                  "nonempty string",
                  "nonempty string[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.",
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]|base64 float32 vector"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nvidia/nemotron-3-embed-1b"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "conditions": "Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]"
              },
              {
                "availability": "source_interface",
                "default": "query",
                "default_known": true,
                "enum": [
                  "query",
                  "passage"
                ],
                "enum_known": true,
                "name": "input_type",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float",
                  "base64"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "NONE",
                  "START",
                  "END"
                ],
                "enum_known": true,
                "name": "truncate",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "image objects",
              "data URI text inputs",
              "token integer arrays on this NVIDIA adapter"
            ]
          },
          "hints": [
            "Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan."
          ],
          "input": {
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional_controls": null,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "exact retained published method"
          },
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "operation_modes": [
            "embeddings"
          ],
          "output": {
            "family": [
              "embedding"
            ],
            "format": "indexed vectors JSON",
            "multioutputs": "retain every actual response output"
          }
        },
        "ctx": 4096,
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/nemotron-3-embed-1b\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.1,
        "input": [
          "text"
        ],
        "label": "Pembenaman",
        "max_out": 0,
        "max_tool_calls": null,
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "pricing_profiles": [
          {
            "dimensions": {},
            "label": "Core",
            "units": [
              {
                "amount": 0.1,
                "amount_unit": "usd_per_million_tokens",
                "key": "in",
                "label": "Input",
                "side": "in"
              },
              {
                "amount": 0,
                "amount_unit": "usd_per_million_tokens",
                "key": "out",
                "label": "Output",
                "side": "out"
              }
            ]
          }
        ],
        "request_model_id": "nvidia/nemotron-3-embed-1b",
        "route_label": "Core",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    },
    {
      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">NV</span><h2 id=\"sheet-title\">Nemotron 3 Embed 1B</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Tutup\">×</button></div><div class=\"idline\"><span>NVIDIA /</span><code>nemotron-3-embed</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=\"Core|embeddings|/v1/embeddings|nemotron-3-embed\">Pembenaman</button></div><div class=\"idline\"><label for=\"card-model-id\">ID model</label><select class=\"sel\" id=\"card-model-id\" style=\"min-width:0;max-width:100%\"><option value=\"Core|embeddings|/v1/embeddings|nvidia%2Fnemotron-3-embed-1b\">nvidia/nemotron-3-embed-1b</option><option value=\"Core|embeddings|/v1/embeddings|nemotron-3-embed\" selected>nemotron-3-embed</option><option value=\"Core|embeddings|/v1/embeddings|nemotron-3-embed-latest\">nemotron-3-embed-latest</option></select></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\">Nemotron 3 Embed 1B menukarkan teks kepada vektor untuk carian berbilang bahasa, carian kod dan RAG. Memproses urutan sehingga 32768 token dan menghasilkan perwakilan dengan dimensi 2048 nombor.</p><dl class=\"card-io\" data-card-io><div><dt>Input yang diterima</dt><dd>Teks</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>Nemotron 3 Embed 1B ialah model teks NVIDIA untuk carian berdasarkan makna dan pemadanan petikan. Model mengekodkan soalan, dokumen dan kod program kepada vektor angka padat. Perwakilan ini digunakan untuk mencari konteks yang diperlukan dalam koleksi besar, melengkapkan respons pembantu dengannya atau mendapatkan maklumat yang disimpan sebelum ini untuk langkah ejen seterusnya.</p><h4>Pengetahuan berbilang bahasa dan kod</h4><p>Model direka untuk carian berbilang bahasa dan rentas bahasa: soalan dan bahan yang sesuai boleh ditulis dalam bahasa berbeza. Bahasa Rusia termasuk dalam set 34 bahasa yang diterbitkan, bersama bahasa Inggeris, Ukraine, Cina, Jepun, Arab dan bahasa lain. Ini berguna untuk pangkalan pengetahuan bersama yang dokumen teknikal dan permintaan penggunanya tidak diseragamkan kepada satu bahasa.</p><p>Antara tugasan ialah carian dokumentasi, pemadanan teks dan pencarian kod berdasarkan penerangan bertulis. Tetingkap input besar membolehkan dokumen panjang, konteks perisian yang banyak dan sejarah beberapa langkah ejen digunakan. Bahan yang ditemui dikembalikan kepada proses seterusnya sebagai konteks; penerangan dan respons akhir dihasilkan oleh komponen sistem yang berikutnya.</p><h4>Daripada teks kepada vektor</h4><p>Input terdiri daripada rentetan teks. Satu vektor dengan 2048 nombor dihasilkan bagi setiap rentetan. Panjang urutan maksimum ialah 32768 token; bahan lebih panjang perlu dibahagikan atau dipendekkan. Penyediaan input membezakan soalan dengan dokumen: model dilatih untuk memadankan peranan masing-masing, jadi rentetan yang sama dalam dua tugasan ini tidak patut disediakan tanpa mengambil kira tujuannya.</p><p>Vektor dinormalkan mengikut panjang. Dengan perwakilan sebegini, hasil darab skalar dan keserupaan kosinus memberikan cara perbandingan yang setara. Koordinat 1024 atau 512 yang pertama daripada vektor penuh boleh dikekalkan, tetapi penormalan perlu dilakukan semula selepas pemotongan. Ini ialah sifat pemprosesan hasil; dimensi perlu dipilih secara konsisten untuk koleksi dan pertanyaan.</p><h4>Struktur model padat</h4><p>Pengekod mengandungi kira-kira 1,14 bilion parameter. Pengekod dihasilkan daripada model berasaskan Ministral 3 3B melalui dua peringkat pengecilan struktur dan penyulingan. Perhatian dua hala mengambil kira keseluruhan input, kemudian perwakilan token diambil purata menjadi vektor bersama.</p><p>NVIDIA mengeluarkan varian BF16 dan NVFP4. Ruang perwakilannya hampir serupa, tetapi pertukaran varian memerlukan semakan menggunakan dokumen sendiri. Keserupaan angka mahupun panjang konteks tidak menjamin bahawa carian sentiasa memilih petikan yang betul.</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 · Pembenaman</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Input</dt><dd><code>text</code></dd></div><div><dt>Output</dt><dd><code>embedding</code></dd></div><div><dt>Konteks</dt><dd>4,096 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>nemotron-3-embed</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[]</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: Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.</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_type</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>query, passage</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>query</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, base64</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>float</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\">truncate</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>NONE, START, END</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\">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>Pilihan</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=\"source_interface\"><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>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></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>material_types</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>text</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>optional_controls</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>null</td></tr><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></tbody></table></div><p>Kitar hayat: <code>exact retained published method</code></p><p class=\"note-s\">Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pelan harga</h3><h4>Kadar kaedah Ailoot</h4><section class=\"meth\" data-route=\"Core\"><header><b><span class=\"alias\">Ailoot</span> Pembenaman</b><code>POST /v1/embeddings</code></header><dl><div><dt>Input</dt><dd>USD 0.0001 <em>/ 1K</em></dd></div></dl></section><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 · 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-528-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-528-panel-0\" aria-selected=\"true\" tabindex=\"0\">Contoh pembenaman</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-528-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-528-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\n: &quot;${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}&quot;\nexport AILOOT_TEXT_FILE\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;nemotron-3-embed&quot;, &quot;input&quot;: []}&#39;)\nf=Path(os.environ[&quot;AILOOT_TEXT_FILE&quot;])\nif not f.is_file():raise SystemExit(&quot;Choose your actual UTF-8 source material&quot;)\ntext=f.read_text(encoding=&quot;utf-8&quot;)\nif not text.strip():raise SystemExit(&quot;Source material must contain text&quot;)\np[&quot;input&quot;]=[text]\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></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Terima dan simpan hasil</h3><h4>Ailoot · 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\">Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.</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\">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[]|base64 float32 vector</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\">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=\"Core\"><b>Ailoot</b> <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nvidia%2Fnemotron-3-embed-1b&amp;fmt=embeddings\"><code>nvidia/nemotron-3-embed-1b</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed&amp;fmt=embeddings\"><code>nemotron-3-embed</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed-latest&amp;fmt=embeddings\"><code>nemotron-3-embed-latest</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%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nvidia%2Fnemotron-3-embed-1b&amp;fmt=embeddings\"><code>nvidia/nemotron-3-embed-1b</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed&amp;fmt=embeddings\"><code>nemotron-3-embed</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed-latest&amp;fmt=embeddings\"><code>nemotron-3-embed-latest</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "nemotron-3-embed",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "nemotron-3-embed",
        "route_label": "Core"
      },
      "method_key": "Core|embeddings|/v1/embeddings|nemotron-3-embed",
      "ordinal": 1,
      "rawMethod": {
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "contract": {
          "data": {
            "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.",
            "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": [
                  "nonempty string",
                  "nonempty string[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.",
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]|base64 float32 vector"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nemotron-3-embed"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "conditions": "Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]"
              },
              {
                "availability": "source_interface",
                "default": "query",
                "default_known": true,
                "enum": [
                  "query",
                  "passage"
                ],
                "enum_known": true,
                "name": "input_type",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float",
                  "base64"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "NONE",
                  "START",
                  "END"
                ],
                "enum_known": true,
                "name": "truncate",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "image objects",
              "data URI text inputs",
              "token integer arrays on this NVIDIA adapter"
            ]
          },
          "hints": [
            "Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan."
          ],
          "input": {
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional_controls": null,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "exact retained published method"
          },
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "operation_modes": [
            "embeddings"
          ],
          "output": {
            "family": [
              "embedding"
            ],
            "format": "indexed vectors JSON",
            "multioutputs": "retain every actual response output"
          }
        },
        "ctx": 4096,
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nemotron-3-embed\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.1,
        "input": [
          "text"
        ],
        "label": "Pembenaman",
        "max_out": 0,
        "max_tool_calls": null,
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "pricing_profiles": [
          {
            "dimensions": {},
            "label": "Core",
            "units": [
              {
                "amount": 0.1,
                "amount_unit": "usd_per_million_tokens",
                "key": "in",
                "label": "Input",
                "side": "in"
              },
              {
                "amount": 0,
                "amount_unit": "usd_per_million_tokens",
                "key": "out",
                "label": "Output",
                "side": "out"
              }
            ]
          }
        ],
        "request_model_id": "nemotron-3-embed",
        "route_label": "Core",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    },
    {
      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">NV</span><h2 id=\"sheet-title\">Nemotron 3 Embed 1B</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Tutup\">×</button></div><div class=\"idline\"><span>NVIDIA /</span><code>nemotron-3-embed-latest</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=\"Core|embeddings|/v1/embeddings|nemotron-3-embed-latest\">Pembenaman</button></div><div class=\"idline\"><label for=\"card-model-id\">ID model</label><select class=\"sel\" id=\"card-model-id\" style=\"min-width:0;max-width:100%\"><option value=\"Core|embeddings|/v1/embeddings|nvidia%2Fnemotron-3-embed-1b\">nvidia/nemotron-3-embed-1b</option><option value=\"Core|embeddings|/v1/embeddings|nemotron-3-embed\">nemotron-3-embed</option><option value=\"Core|embeddings|/v1/embeddings|nemotron-3-embed-latest\" selected>nemotron-3-embed-latest</option></select></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\">Nemotron 3 Embed 1B menukarkan teks kepada vektor untuk carian berbilang bahasa, carian kod dan RAG. Memproses urutan sehingga 32768 token dan menghasilkan perwakilan dengan dimensi 2048 nombor.</p><dl class=\"card-io\" data-card-io><div><dt>Input yang diterima</dt><dd>Teks</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>Nemotron 3 Embed 1B ialah model teks NVIDIA untuk carian berdasarkan makna dan pemadanan petikan. Model mengekodkan soalan, dokumen dan kod program kepada vektor angka padat. Perwakilan ini digunakan untuk mencari konteks yang diperlukan dalam koleksi besar, melengkapkan respons pembantu dengannya atau mendapatkan maklumat yang disimpan sebelum ini untuk langkah ejen seterusnya.</p><h4>Pengetahuan berbilang bahasa dan kod</h4><p>Model direka untuk carian berbilang bahasa dan rentas bahasa: soalan dan bahan yang sesuai boleh ditulis dalam bahasa berbeza. Bahasa Rusia termasuk dalam set 34 bahasa yang diterbitkan, bersama bahasa Inggeris, Ukraine, Cina, Jepun, Arab dan bahasa lain. Ini berguna untuk pangkalan pengetahuan bersama yang dokumen teknikal dan permintaan penggunanya tidak diseragamkan kepada satu bahasa.</p><p>Antara tugasan ialah carian dokumentasi, pemadanan teks dan pencarian kod berdasarkan penerangan bertulis. Tetingkap input besar membolehkan dokumen panjang, konteks perisian yang banyak dan sejarah beberapa langkah ejen digunakan. Bahan yang ditemui dikembalikan kepada proses seterusnya sebagai konteks; penerangan dan respons akhir dihasilkan oleh komponen sistem yang berikutnya.</p><h4>Daripada teks kepada vektor</h4><p>Input terdiri daripada rentetan teks. Satu vektor dengan 2048 nombor dihasilkan bagi setiap rentetan. Panjang urutan maksimum ialah 32768 token; bahan lebih panjang perlu dibahagikan atau dipendekkan. Penyediaan input membezakan soalan dengan dokumen: model dilatih untuk memadankan peranan masing-masing, jadi rentetan yang sama dalam dua tugasan ini tidak patut disediakan tanpa mengambil kira tujuannya.</p><p>Vektor dinormalkan mengikut panjang. Dengan perwakilan sebegini, hasil darab skalar dan keserupaan kosinus memberikan cara perbandingan yang setara. Koordinat 1024 atau 512 yang pertama daripada vektor penuh boleh dikekalkan, tetapi penormalan perlu dilakukan semula selepas pemotongan. Ini ialah sifat pemprosesan hasil; dimensi perlu dipilih secara konsisten untuk koleksi dan pertanyaan.</p><h4>Struktur model padat</h4><p>Pengekod mengandungi kira-kira 1,14 bilion parameter. Pengekod dihasilkan daripada model berasaskan Ministral 3 3B melalui dua peringkat pengecilan struktur dan penyulingan. Perhatian dua hala mengambil kira keseluruhan input, kemudian perwakilan token diambil purata menjadi vektor bersama.</p><p>NVIDIA mengeluarkan varian BF16 dan NVFP4. Ruang perwakilannya hampir serupa, tetapi pertukaran varian memerlukan semakan menggunakan dokumen sendiri. Keserupaan angka mahupun panjang konteks tidak menjamin bahawa carian sentiasa memilih petikan yang betul.</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 · Pembenaman</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Input</dt><dd><code>text</code></dd></div><div><dt>Output</dt><dd><code>embedding</code></dd></div><div><dt>Konteks</dt><dd>4,096 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>nemotron-3-embed-latest</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[]</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: Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.</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_type</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>query, passage</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>query</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, base64</div></td><td data-cell-label=\"Lalai\" data-default=\"known\"><span class=\"card-cell-label\">Lalai</span>float</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\">truncate</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>NONE, START, END</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\">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>Pilihan</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=\"source_interface\"><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>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></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>material_types</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>text</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>optional_controls</code></th><td data-cell-label=\"Kontrak\"><span class=\"card-cell-label\">Kontrak</span>null</td></tr><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></tbody></table></div><p>Kitar hayat: <code>exact retained published method</code></p><p class=\"note-s\">Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pelan harga</h3><h4>Kadar kaedah Ailoot</h4><section class=\"meth\" data-route=\"Core\"><header><b><span class=\"alias\">Ailoot</span> Pembenaman</b><code>POST /v1/embeddings</code></header><dl><div><dt>Input</dt><dd>USD 0.0001 <em>/ 1K</em></dd></div></dl></section><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 · 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-529-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-529-panel-0\" aria-selected=\"true\" tabindex=\"0\">Contoh pembenaman</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-529-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-529-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\n: &quot;${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}&quot;\nexport AILOOT_TEXT_FILE\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;nemotron-3-embed-latest&quot;, &quot;input&quot;: []}&#39;)\nf=Path(os.environ[&quot;AILOOT_TEXT_FILE&quot;])\nif not f.is_file():raise SystemExit(&quot;Choose your actual UTF-8 source material&quot;)\ntext=f.read_text(encoding=&quot;utf-8&quot;)\nif not text.strip():raise SystemExit(&quot;Source material must contain text&quot;)\np[&quot;input&quot;]=[text]\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></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Terima dan simpan hasil</h3><h4>Ailoot · 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\">Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.</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\">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[]|base64 float32 vector</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\">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=\"Core\"><b>Ailoot</b> <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nvidia%2Fnemotron-3-embed-1b&amp;fmt=embeddings\"><code>nvidia/nemotron-3-embed-1b</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed&amp;fmt=embeddings\"><code>nemotron-3-embed</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed-latest&amp;fmt=embeddings\"><code>nemotron-3-embed-latest</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%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nvidia%2Fnemotron-3-embed-1b&amp;fmt=embeddings\"><code>nvidia/nemotron-3-embed-1b</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed&amp;fmt=embeddings\"><code>nemotron-3-embed</code></a>, <a href=\"#m=nvidia%2Fnemotron-3-embed-1b&amp;route=Core&amp;caller=nemotron-3-embed-latest&amp;fmt=embeddings\"><code>nemotron-3-embed-latest</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "nemotron-3-embed-latest",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "nemotron-3-embed-latest",
        "route_label": "Core"
      },
      "method_key": "Core|embeddings|/v1/embeddings|nemotron-3-embed-latest",
      "ordinal": 2,
      "rawMethod": {
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "contract": {
          "data": {
            "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.",
            "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": [
                  "nonempty string",
                  "nonempty string[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.",
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]|base64 float32 vector"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nemotron-3-embed-latest"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "conditions": "Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]"
              },
              {
                "availability": "source_interface",
                "default": "query",
                "default_known": true,
                "enum": [
                  "query",
                  "passage"
                ],
                "enum_known": true,
                "name": "input_type",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float",
                  "base64"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
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    "description": "Nemotron 3 Embed 1B menukarkan teks kepada vektor untuk carian berbilang bahasa, carian kod dan RAG. Memproses urutan sehingga 32768 token dan menghasilkan perwakilan dengan dimensi 2048 nombor.",
    "locale": "ms",
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    "title": "Nemotron 3 Embed 1B — Ailoot",
    "url": "https://ailoot.ai/models/ms/nvidia-nemotron-3-embed-1b/"
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    "about": "Nemotron 3 Embed 1B ialah model teks NVIDIA untuk carian berdasarkan makna dan pemadanan petikan. Model mengekodkan soalan, dokumen dan kod program kepada vektor angka padat. Perwakilan ini digunakan untuk mencari konteks yang diperlukan dalam koleksi besar, melengkapkan respons pembantu dengannya atau mendapatkan maklumat yang disimpan sebelum ini untuk langkah ejen seterusnya.\n\n### Pengetahuan berbilang bahasa dan kod\n\nModel direka untuk carian berbilang bahasa dan rentas bahasa: soalan dan bahan yang sesuai boleh ditulis dalam bahasa berbeza. Bahasa Rusia termasuk dalam set 34 bahasa yang diterbitkan, bersama bahasa Inggeris, Ukraine, Cina, Jepun, Arab dan bahasa lain. Ini berguna untuk pangkalan pengetahuan bersama yang dokumen teknikal dan permintaan penggunanya tidak diseragamkan kepada satu bahasa.\n\nAntara tugasan ialah carian dokumentasi, pemadanan teks dan pencarian kod berdasarkan penerangan bertulis. Tetingkap input besar membolehkan dokumen panjang, konteks perisian yang banyak dan sejarah beberapa langkah ejen digunakan. Bahan yang ditemui dikembalikan kepada proses seterusnya sebagai konteks; penerangan dan respons akhir dihasilkan oleh komponen sistem yang berikutnya.\n\n### Daripada teks kepada vektor\n\nInput terdiri daripada rentetan teks. Satu vektor dengan 2048 nombor dihasilkan bagi setiap rentetan. Panjang urutan maksimum ialah 32768 token; bahan lebih panjang perlu dibahagikan atau dipendekkan. Penyediaan input membezakan soalan dengan dokumen: model dilatih untuk memadankan peranan masing-masing, jadi rentetan yang sama dalam dua tugasan ini tidak patut disediakan tanpa mengambil kira tujuannya.\n\nVektor dinormalkan mengikut panjang. Dengan perwakilan sebegini, hasil darab skalar dan keserupaan kosinus memberikan cara perbandingan yang setara. Koordinat 1024 atau 512 yang pertama daripada vektor penuh boleh dikekalkan, tetapi penormalan perlu dilakukan semula selepas pemotongan. Ini ialah sifat pemprosesan hasil; dimensi perlu dipilih secara konsisten untuk koleksi dan pertanyaan.\n\n### Struktur model padat\n\nPengekod mengandungi kira-kira 1,14 bilion parameter. Pengekod dihasilkan daripada model berasaskan Ministral 3 3B melalui dua peringkat pengecilan struktur dan penyulingan. Perhatian dua hala mengambil kira keseluruhan input, kemudian perwakilan token diambil purata menjadi vektor bersama.\n\nNVIDIA mengeluarkan varian BF16 dan NVFP4. Ruang perwakilannya hampir serupa, tetapi pertukaran varian memerlukan semakan menggunakan dokumen sendiri. Keserupaan angka mahupun panjang konteks tidak menjamin bahawa carian sentiasa memilih petikan yang betul.",
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            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nvidia/nemotron-3-embed-1b\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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",
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            "units": [
              {
                "amount": 0.1,
                "amount_unit": "usd_per_million_tokens",
                "key": "in",
                "label": "Input",
                "side": "in"
              },
              {
                "amount": 0,
                "amount_unit": "usd_per_million_tokens",
                "key": "out",
                "label": "Output",
                "side": "out"
              }
            ]
          }
        ],
        "request_model_id": "nemotron-3-embed",
        "route_label": "Core",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      },
      {
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "contract": {
          "data": {
            "all_results": "Decode base64 vectors only when that format was requested; retain every vector and its original index.",
            "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": [
                  "nonempty string",
                  "nonempty string[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Nyahkod vektor base64 hanya apabila format tersebut diminta; simpan setiap vektor berserta indeks asalnya.",
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]|base64 float32 vector"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "nemotron-3-embed-latest"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "conditions": "Setiap rentetan mesti tidak kosong selepas trim; URI data ditolak; belanjawan yang diterima mengukur bait UTF8 yang berkaitan.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]"
              },
              {
                "availability": "source_interface",
                "default": "query",
                "default_known": true,
                "enum": [
                  "query",
                  "passage"
                ],
                "enum_known": true,
                "name": "input_type",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float",
                  "base64"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "NONE",
                  "START",
                  "END"
                ],
                "enum_known": true,
                "name": "truncate",
                "required": false,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "image objects",
              "data URI text inputs",
              "token integer arrays on this NVIDIA adapter"
            ]
          },
          "hints": [
            "Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan."
          ],
          "input": {
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional_controls": null,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "exact retained published method"
          },
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "operation_modes": [
            "embeddings"
          ],
          "output": {
            "family": [
              "embedding"
            ],
            "format": "indexed vectors JSON",
            "multioutputs": "retain every actual response output"
          }
        },
        "ctx": 4096,
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"nemotron-3-embed-latest\", \"input\": []}')\nf=Path(os.environ[\"AILOOT_TEXT_FILE\"])\nif not f.is_file():raise SystemExit(\"Choose your actual UTF-8 source material\")\ntext=f.read_text(encoding=\"utf-8\")\nif not text.strip():raise SystemExit(\"Source material must contain text\")\np[\"input\"]=[text]\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": "Embed your source material",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.1,
        "input": [
          "text"
        ],
        "label": "Pembenaman",
        "max_out": 0,
        "max_tool_calls": null,
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "pricing_profiles": [
          {
            "dimensions": {},
            "label": "Core",
            "units": [
              {
                "amount": 0.1,
                "amount_unit": "usd_per_million_tokens",
                "key": "in",
                "label": "Input",
                "side": "in"
              },
              {
                "amount": 0,
                "amount_unit": "usd_per_million_tokens",
                "key": "out",
                "label": "Output",
                "side": "out"
              }
            ]
          }
        ],
        "request_model_id": "nemotron-3-embed-latest",
        "route_label": "Core",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "nvidia/nemotron-3-embed-1b",
        "request_model_id": "nvidia/nemotron-3-embed-1b",
        "route_label": "Core"
      },
      {
        "canonical_model_id": "nvidia/nemotron-3-embed-1b",
        "request_model_id": "nemotron-3-embed",
        "route_label": "Core"
      },
      {
        "canonical_model_id": "nvidia/nemotron-3-embed-1b",
        "request_model_id": "nemotron-3-embed-latest",
        "route_label": "Core"
      }
    ],
    "name": "nvidia/nemotron-3-embed-1b",
    "price": {
      "in": 0.1,
      "in_label": null,
      "out": null,
      "out_label": null,
      "sort_in": 0.1,
      "sort_out": null,
      "varies": false
    },
    "short": "Nemotron 3 Embed 1B menukarkan teks kepada vektor untuk carian berbilang bahasa, carian kod dan RAG. Memproses urutan sehingga 32768 token dan menghasilkan perwakilan dengan dimensi 2048 nombor."
  },
  "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
        },
        {
          "contract_json_pointer": "/methods/1/rawMethod/contract",
          "html_json_pointer": "/methods/1/html",
          "method_ordinal": 1
        },
        {
          "contract_json_pointer": "/methods/2/rawMethod/contract",
          "html_json_pointer": "/methods/2/html",
          "method_ordinal": 2
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod",
        "/methods/1/rawMethod",
        "/methods/2/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Nemotron 3 Embed 1B menukarkan teks kepada vektor untuk carian berbilang bahasa, carian kod dan RAG. Memproses urutan sehingga 32768 token dan menghasilkan perwakilan dengan dimensi 2048 nombor.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "bb6c4948bd2b2f406f00ca2197644a7f62682c26d0b72338f91db651bc49e077",
    "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460"
  },
  "sourceRevision": "6077b251dbbceeb88a0641fb9c6459eac4f8c783b0526ea0e71d3405fe44af2b"
}