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  "aboutHtml": "<p>Codestral Embed 2505 是 Mistral AI 專門用於程式碼檢索的嵌入模型。它接收包含原始碼或自然語言程式碼查詢的文字，並回傳數值向量。製造商將其定位於程式碼庫搜尋、程式設計助理上下文檢索、相似度與重複分析，以及依功能或結構分組程式碼。固定發布識別碼是 codestral-embed-2505。</p><p>輸入與表示</p><p>程式碼輸入可以是函式、相關來源片段，或程式碼庫中的其他有意義單位。自然語言查詢可描述要尋找的行為，不必指出確切符號名稱。嵌入表示該輸入以供比較；模型不會產生新實作，也不會執行收到的程式碼。</p><p>官方發布資料記載完整輸入上下文為 8,192 token。檢索時，製造商建議約 3,000 字元的區塊搭配 1,000 字元重疊，並指出較大區塊可能降低檢索效果。這些是檢索準備建議，不是 3,000 字元的 token 上限。請為每個準備項目保留檔案、符號與來源位置，讓應用程式在搜尋後回傳相關程式碼。</p><p>輸出精度與維度</p><p>程式碼嵌入指南記載 float、int8、uint8、binary 與 ubinary 輸出型別。float 使用 32 位元單精度值。int8 使用 -128 至 127 的有號 8 位元整數，uint8 則使用 0 至 255 的無號 8 位元值。binary 與 ubinary 將量化後的單位元值打包在 8 位元元素中，分別採用有號與無號容器。</p><p>位元打包表示會改變回傳清單長度：指南說明，每八個邏輯嵌入維度對應一個打包元素。因此，辨識格式的比較不同於將打包位元組當成一般浮點座標。請將選定型別與向量一起保留，並為索引程式碼及其查詢使用相同表示。</p><p>參數文件列出預設維度為 1,536、最大為 3,072。維度依相關性排序，製造商說明可保留前面選定數量的維度，以選擇表示大小。儲存資料集合與新查詢應一致選擇維度。較短或量化表示會改變儲存及比較行為，不會增加輸入上下文，也不會將模型變成程式碼生成器。</p><p>檢索增強程式設計</p><p>發布資料的第一個案例，是為程式碼補全、編輯或解釋檢索上下文。請準備有意義的程式碼單位，並保留程式碼庫版本與來源識別碼。詢問某種錯誤如何處理，可檢索相關函式，再由應用程式連同實際變更需求提供給程式設計模型。</p><p>實用結果是一組附排名的來源片段，包含檔案路徑、符號，以及理解行為所需的內容。開發者可開啟檢索片段，或將其交給助理。Codestral Embed 執行表示步驟；另一個元件選擇結果，生成模型才會撰寫解釋或建議程式碼。</p><p>語意搜尋與相似度</p><p>製造商描述可用自然語言或程式碼查詢搜尋程式碼。即使函式名稱與請求措辭不同，開發者仍能尋找某個行為的實作。請納入區別預期實作的行為與條件；對整個子系統的一般查詢，可能只檢索相關資料，無法解決具體問題。</p><p>發布資料也描述，即使詞彙不同，仍可找出近似重複或功能相似的程式碼。這類結果能協助找到可重用實作，或識別需要進一步審查的程式碼。嵌入空間中的相似度是檢視來源的訊號，不代表兩個片段在每種輸入下都有相同行為。</p><p>分群、分析與比較</p><p>程式碼嵌入可用於依功能或結構分組程式碼，並檢視程式碼庫組成。請讓原始片段與識別碼持續可用，供人員或後續應用程式步驟解讀群組。群組是基於表示的分組，不是自動驗證過的架構描述。</p><p>製造商的浮點向量範例使用單位長度正規化嵌入，餘弦相似度、內積與歐幾里得距離會產生一致排名。量化及位元打包輸出應保留自己的格式處理，不應沿用原始浮點座標的假設。公開端點也保留輸入索引，有助於維持每個向量與來源項目的連結。</p><p>版本與實際邊界</p><p>確切模型頁面列出嵌入與批次流程，並標示 8k 上下文，而發布資料提供 8,192 token 的數值。請在整個資料集合與查詢流程中保留 2505 版本名稱、選定輸出維度及型別。這是面向程式碼的向量輸出，不同於 Codestral 文字補全，也不同於固定維度的 Mistral Embed 文字模型。</p><p>請準備包含相關行為的程式碼單位，保留來源位置，並明確說明要尋找的問題。有用產物是連結實際程式碼、可供檢索的表示；它支援下一個解釋、編輯或開發者審查步驟，但不自行執行那些步驟。</p>",
  "aboutText": "Codestral Embed 2505 是 Mistral AI 專門用於程式碼檢索的嵌入模型。它接收包含原始碼或自然語言程式碼查詢的文字，並回傳數值向量。製造商將其定位於程式碼庫搜尋、程式設計助理上下文檢索、相似度與重複分析，以及依功能或結構分組程式碼。固定發布識別碼是 codestral-embed-2505。\n\n輸入與表示\n\n程式碼輸入可以是函式、相關來源片段，或程式碼庫中的其他有意義單位。自然語言查詢可描述要尋找的行為，不必指出確切符號名稱。嵌入表示該輸入以供比較；模型不會產生新實作，也不會執行收到的程式碼。\n\n官方發布資料記載完整輸入上下文為 8,192 token。檢索時，製造商建議約 3,000 字元的區塊搭配 1,000 字元重疊，並指出較大區塊可能降低檢索效果。這些是檢索準備建議，不是 3,000 字元的 token 上限。請為每個準備項目保留檔案、符號與來源位置，讓應用程式在搜尋後回傳相關程式碼。\n\n輸出精度與維度\n\n程式碼嵌入指南記載 float、int8、uint8、binary 與 ubinary 輸出型別。float 使用 32 位元單精度值。int8 使用 -128 至 127 的有號 8 位元整數，uint8 則使用 0 至 255 的無號 8 位元值。binary 與 ubinary 將量化後的單位元值打包在 8 位元元素中，分別採用有號與無號容器。\n\n位元打包表示會改變回傳清單長度：指南說明，每八個邏輯嵌入維度對應一個打包元素。因此，辨識格式的比較不同於將打包位元組當成一般浮點座標。請將選定型別與向量一起保留，並為索引程式碼及其查詢使用相同表示。\n\n參數文件列出預設維度為 1,536、最大為 3,072。維度依相關性排序，製造商說明可保留前面選定數量的維度，以選擇表示大小。儲存資料集合與新查詢應一致選擇維度。較短或量化表示會改變儲存及比較行為，不會增加輸入上下文，也不會將模型變成程式碼生成器。\n\n檢索增強程式設計\n\n發布資料的第一個案例，是為程式碼補全、編輯或解釋檢索上下文。請準備有意義的程式碼單位，並保留程式碼庫版本與來源識別碼。詢問某種錯誤如何處理，可檢索相關函式，再由應用程式連同實際變更需求提供給程式設計模型。\n\n實用結果是一組附排名的來源片段，包含檔案路徑、符號，以及理解行為所需的內容。開發者可開啟檢索片段，或將其交給助理。Codestral Embed 執行表示步驟；另一個元件選擇結果，生成模型才會撰寫解釋或建議程式碼。\n\n語意搜尋與相似度\n\n製造商描述可用自然語言或程式碼查詢搜尋程式碼。即使函式名稱與請求措辭不同，開發者仍能尋找某個行為的實作。請納入區別預期實作的行為與條件；對整個子系統的一般查詢，可能只檢索相關資料，無法解決具體問題。\n\n發布資料也描述，即使詞彙不同，仍可找出近似重複或功能相似的程式碼。這類結果能協助找到可重用實作，或識別需要進一步審查的程式碼。嵌入空間中的相似度是檢視來源的訊號，不代表兩個片段在每種輸入下都有相同行為。\n\n分群、分析與比較\n\n程式碼嵌入可用於依功能或結構分組程式碼，並檢視程式碼庫組成。請讓原始片段與識別碼持續可用，供人員或後續應用程式步驟解讀群組。群組是基於表示的分組，不是自動驗證過的架構描述。\n\n製造商的浮點向量範例使用單位長度正規化嵌入，餘弦相似度、內積與歐幾里得距離會產生一致排名。量化及位元打包輸出應保留自己的格式處理，不應沿用原始浮點座標的假設。公開端點也保留輸入索引，有助於維持每個向量與來源項目的連結。\n\n版本與實際邊界\n\n確切模型頁面列出嵌入與批次流程，並標示 8k 上下文，而發布資料提供 8,192 token 的數值。請在整個資料集合與查詢流程中保留 2505 版本名稱、選定輸出維度及型別。這是面向程式碼的向量輸出，不同於 Codestral 文字補全，也不同於固定維度的 Mistral Embed 文字模型。\n\n請準備包含相關行為的程式碼單位，保留來源位置，並明確說明要尋找的問題。有用產物是連結實際程式碼、可供檢索的表示；它支援下一個解釋、編輯或開發者審查步驟，但不自行執行那些步驟。",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Mistral: Codestral Embed 2505</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"關閉\">×</button></div><div class=\"idline\"><span>Mistral /</span><code>mistralai/codestral-embed-2505</code><button type=\"button\" id=\"copy-id\">複製 ID</button></div><nav class=\"card-jump-nav\" aria-label=\"模型卡片區段\"><button type=\"button\" data-card-jump=\"overview\">入門</button><button type=\"button\" data-card-jump=\"methods\">輸入與方法</button><button type=\"button\" data-card-jump=\"pricing\">方案</button><button type=\"button\" data-card-jump=\"examples\">範例</button><button type=\"button\" data-card-jump=\"results\">結果</button><button type=\"button\" data-card-jump=\"aliases\">別名</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API 方法\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|mistralai%2Fcodestral-embed-2505\">嵌入向量</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">入門</h3><p class=\"mdesc-full\">Codestral Embed 2505 將程式碼與自然語言查詢表示為向量，用於程式碼庫搜尋及程式設計助理檢索。製造商記載 8,192 token 上下文、最高 3,072 的可設定維度，以及浮點數、整數或位元打包的輸出格式。</p><dl class=\"card-io\" data-card-io><div><dt>接受的輸入</dt><dd>文字 · token_ids</dd></div><div><dt>結果</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>關於模型</summary><p class=\"note-s\" data-model-method-boundary>此說明介紹模型的能力。Ailoot 可用的輸入、輸出與選項，依所選方法列於下方。</p><div class=\"mabout\" id=\"m-about\"><p>Codestral Embed 2505 是 Mistral AI 專門用於程式碼檢索的嵌入模型。它接收包含原始碼或自然語言程式碼查詢的文字，並回傳數值向量。製造商將其定位於程式碼庫搜尋、程式設計助理上下文檢索、相似度與重複分析，以及依功能或結構分組程式碼。固定發布識別碼是 codestral-embed-2505。</p><p>輸入與表示</p><p>程式碼輸入可以是函式、相關來源片段，或程式碼庫中的其他有意義單位。自然語言查詢可描述要尋找的行為，不必指出確切符號名稱。嵌入表示該輸入以供比較；模型不會產生新實作，也不會執行收到的程式碼。</p><p>官方發布資料記載完整輸入上下文為 8,192 token。檢索時，製造商建議約 3,000 字元的區塊搭配 1,000 字元重疊，並指出較大區塊可能降低檢索效果。這些是檢索準備建議，不是 3,000 字元的 token 上限。請為每個準備項目保留檔案、符號與來源位置，讓應用程式在搜尋後回傳相關程式碼。</p><p>輸出精度與維度</p><p>程式碼嵌入指南記載 float、int8、uint8、binary 與 ubinary 輸出型別。float 使用 32 位元單精度值。int8 使用 -128 至 127 的有號 8 位元整數，uint8 則使用 0 至 255 的無號 8 位元值。binary 與 ubinary 將量化後的單位元值打包在 8 位元元素中，分別採用有號與無號容器。</p><p>位元打包表示會改變回傳清單長度：指南說明，每八個邏輯嵌入維度對應一個打包元素。因此，辨識格式的比較不同於將打包位元組當成一般浮點座標。請將選定型別與向量一起保留，並為索引程式碼及其查詢使用相同表示。</p><p>參數文件列出預設維度為 1,536、最大為 3,072。維度依相關性排序，製造商說明可保留前面選定數量的維度，以選擇表示大小。儲存資料集合與新查詢應一致選擇維度。較短或量化表示會改變儲存及比較行為，不會增加輸入上下文，也不會將模型變成程式碼生成器。</p><p>檢索增強程式設計</p><p>發布資料的第一個案例，是為程式碼補全、編輯或解釋檢索上下文。請準備有意義的程式碼單位，並保留程式碼庫版本與來源識別碼。詢問某種錯誤如何處理，可檢索相關函式，再由應用程式連同實際變更需求提供給程式設計模型。</p><p>實用結果是一組附排名的來源片段，包含檔案路徑、符號，以及理解行為所需的內容。開發者可開啟檢索片段，或將其交給助理。Codestral Embed 執行表示步驟；另一個元件選擇結果，生成模型才會撰寫解釋或建議程式碼。</p><p>語意搜尋與相似度</p><p>製造商描述可用自然語言或程式碼查詢搜尋程式碼。即使函式名稱與請求措辭不同，開發者仍能尋找某個行為的實作。請納入區別預期實作的行為與條件；對整個子系統的一般查詢，可能只檢索相關資料，無法解決具體問題。</p><p>發布資料也描述，即使詞彙不同，仍可找出近似重複或功能相似的程式碼。這類結果能協助找到可重用實作，或識別需要進一步審查的程式碼。嵌入空間中的相似度是檢視來源的訊號，不代表兩個片段在每種輸入下都有相同行為。</p><p>分群、分析與比較</p><p>程式碼嵌入可用於依功能或結構分組程式碼，並檢視程式碼庫組成。請讓原始片段與識別碼持續可用，供人員或後續應用程式步驟解讀群組。群組是基於表示的分組，不是自動驗證過的架構描述。</p><p>製造商的浮點向量範例使用單位長度正規化嵌入，餘弦相似度、內積與歐幾里得距離會產生一致排名。量化及位元打包輸出應保留自己的格式處理，不應沿用原始浮點座標的假設。公開端點也保留輸入索引，有助於維持每個向量與來源項目的連結。</p><p>版本與實際邊界</p><p>確切模型頁面列出嵌入與批次流程，並標示 8k 上下文，而發布資料提供 8,192 token 的數值。請在整個資料集合與查詢流程中保留 2505 版本名稱、選定輸出維度及型別。這是面向程式碼的向量輸出，不同於 Codestral 文字補全，也不同於固定維度的 Mistral Embed 文字模型。</p><p>請準備包含相關行為的程式碼單位，保留來源位置，並明確說明要尋找的問題。有用產物是連結實際程式碼、可供檢索的表示；它支援下一個解釋、編輯或開發者審查步驟，但不自行執行那些步驟。</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Ailoot 可用 · 輸入與方法</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · 嵌入向量</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>輸入</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>輸出</dt><dd><code>embedding</code></dd></div><div><dt>上下文</dt><dd>8,192 tokens</dd></div><div><dt>輸出格式</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>欄位</th><th>類型</th><th>要求</th><th>值與限制</th><th>預設值</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>string</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>必填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>mistralai/codestral-embed-2505</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>必填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>項目數下限: 1</div><div>條件: 批次不得為空，且各項目類型須一致。Token ID 須為非負整數。</div><div>輸入額度： 最大值: 8,192 · 文字單位: UTF8 位元組 · Token 單位: token 陣列元素</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>string</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>選填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>float</div></td><td data-cell-label=\"預設值\" data-default=\"known\"><span class=\"card-cell-label\">預設值</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>integer</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>不支援</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>最小值: 1</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>json_value</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>選填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>條件: 來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>json_value</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>選填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>條件: 來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>json_value</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>選填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>條件: 來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span><code>object</code></td><td data-cell-label=\"要求\"><span class=\"card-cell-label\">要求</span>選填</td><td data-cell-label=\"值與限制\"><span class=\"card-cell-label\">值與限制</span><div>條件: provider.options 遭拒絕</div></td><td data-cell-label=\"預設值\" data-default=\"unspecified\"><span class=\"card-cell-label\">預設值</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>輸入素材組合</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>方法參數與生命週期</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>參數</th><th>規格</th></tr></thead><tbody><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>content_type</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>application/json</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>required</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>model, input</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>input_shapes</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>encoding_format</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>float</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>dimensions_override</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>false</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>max_input_budget_units</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>8192</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>multimodal_objects</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>false</td></tr><tr><th data-cell-label=\"參數\" scope=\"row\"><span class=\"card-cell-label\">參數</span><code>material_types</code></th><td data-cell-label=\"規格\"><span class=\"card-cell-label\">規格</span>text, token_ids</td></tr></tbody></table></div><p>生命週期： <code>synchronous_json</code></p><p class=\"note-s\">保留每個向量的索引與維度。</p><p class=\"note-s\">即使模型名稱標示為多模態，此指定路徑也不接受圖片物件。</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">方案</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>費率資訊不會增加請求選項。支援的參數列於所選方法中。</p><dl data-price-conditions><dt>快速模式</dt><dd>標準費率 <span class=\"note-s\">選取快速模式時</span></dd><dt>flex 模式</dt><dd>標準費率 <span class=\"note-s\">選取 flex 模式時</span></dd><dt>最高推理強度</dt><dd>標準費率 <span class=\"note-s\">最高推理強度採用 default 調整值時</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot 費率</h4><h4>各設定的價格</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>設定</th><th>費率表</th><th>費用</th></tr></thead><tbody><tr><th data-cell-label=\"設定\" scope=\"row\"><span class=\"card-cell-label\">設定</span>詞元基本費率</th><td data-cell-label=\"費率表\"><span class=\"card-cell-label\">費率表</span>基本費率</td><td data-cell-label=\"費用\"><span class=\"card-cell-label\">費用</span><div><span>輸入 · 輸入 Token</span> <b>$0.0003</b> <span class=\"price-unit\">/ 1K Token</span></div><div><span>輸出 · 輸出 Token</span> <b>$0</b> <span class=\"price-unit\">/ 1K Token</span></div><div><span>輸入 · 讀取快取</span> <b>—</b> <span class=\"price-unit\">/ 1K Token</span></div><div><span>輸入 · 寫入快取</span> <b>—</b> <span class=\"price-unit\">/ 1K Token</span></div><div><span>輸入 · 寫入快取，1 小時</span> <b>—</b> <span class=\"price-unit\">/ 1K Token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot 會在請求前預留估算金額。取得結果後，依實測用量結算最終費用。串流結算採用最終用量紀錄。<a href=\"/docs/zh-hant-tw/billing/reserve/\">預留金額與結算的運作方式 →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">請求範例</h3><h4>Ailoot Ultra · 嵌入向量</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"程式碼版本\"><button class=\"cg-tab\" id=\"model-code-1892-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1892-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1892-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1892-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"複製程式碼\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-1892-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1892-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;mistralai/codestral-embed-2505&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1892-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1892-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;mistralai/codestral-embed-2505&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">接收並儲存結果</h3><h4>Ailoot Ultra · 嵌入向量</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>保留所有已完成的結果</h5><p class=\"note-s\">請保留所有完整傳回的回應、檔案與內容區塊。</p><p class=\"note-s\">保留每個向量及其索引。向量數量必須與輸入數量相同，且維度必須一致。</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>需要檢查原始回應時</h5><p class=\"note-s\">此流程未引入嵌入向量任務端點。</p></div><details class=\"card-about\" data-output-fields><summary>回應欄位</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>欄位</th><th>類型</th></tr></thead><tbody><tr><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span>string</td></tr><tr><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span>string</td></tr><tr><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span>integer</td></tr><tr><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span>finite float[]</td></tr><tr><th data-cell-label=\"欄位\" scope=\"row\"><span class=\"card-cell-label\">欄位</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"類型\" data-field-type><span class=\"card-cell-label\">類型</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>錯誤與後續步驟</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>回應</th><th>條件</th><th>下一步</th></tr></thead><tbody><tr><th data-cell-label=\"回應\" scope=\"row\"><span class=\"card-cell-label\">回應</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"條件\"><span class=\"card-cell-label\">條件</span>必要欄位、素材表示方式或參數值無效</td><td data-cell-label=\"下一步\"><span class=\"card-cell-label\">下一步</span>提出新的建立請求前，先修正指定欄位。</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">別名</h3><h4>模型別名</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=mistralai%2Fcodestral-embed-2505&amp;route=Ultra&amp;caller=mistralai%2Fcodestral-embed-2505&amp;fmt=embeddings\"><code>mistralai/codestral-embed-2505</code></a></span></p><h4>方法路徑別名</h4><p class=\"note-s\">請使用上方列出的 API 方法路徑。</p><h4>相關呼叫用 ID</h4><p class=\"note-s\"><a href=\"#m=mistralai%2Fcodestral-embed-2505&amp;route=Ultra&amp;caller=mistralai%2Fcodestral-embed-2505&amp;fmt=embeddings\"><code>mistralai/codestral-embed-2505</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "mistralai/codestral-embed-2505",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "mistralai/codestral-embed-2505",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|mistralai%2Fcodestral-embed-2505",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "必要欄位、素材表示方式或參數值無效",
                "http_status": 400,
                "recovery": "提出新的建立請求前，先修正指定欄位。",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "此流程未引入嵌入向量任務端點。"
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "保留每個向量及其索引。向量數量必須與輸入數量相同，且維度必須一致。",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "mistralai/codestral-embed-2505"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 8192,
                  "text_unit": "UTF8 位元組",
                  "token_unit": "token 陣列元素"
                },
                "conditions": "批次不得為空，且各項目類型須一致。Token ID 須為非負整數。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options 遭拒絕",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "保留每個向量的索引與維度。",
            "即使模型名稱標示為多模態，此指定路徑也不接受圖片物件。"
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 8192,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 8192,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "詞元基本費率",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"mistralai/codestral-embed-2505\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='mistralai/codestral-embed-2505',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.3,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "快速模式",
            "value": "標準費率",
            "when": "選取快速模式時"
          },
          {
            "label": "flex 模式",
            "value": "標準費率",
            "when": "選取 flex 模式時"
          },
          {
            "label": "最高推理強度",
            "value": "標準費率",
            "when": "最高推理強度採用 default 調整值時"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "mistralai/codestral-embed-2505",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    }
  ],
  "model_id": "mistralai/codestral-embed-2505",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "acf0f6d74502ed0f3306b84d9070441f3e474bbba208741fb6351e7699833474",
    "input_html_sha256": "c2f680dbdd5d9f86548117c49d233a2b4b457a4df6c9e1851cc83a6a6acce4c8",
    "private_audit_sha256": "5775450b3da9bc0900aad81c2d5e0ff4c08c15fcc8b3915b5c2a0a6a1546e26e",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/zh-hant-tw/mistralai-codestral-embed-2505/",
    "description": "Codestral Embed 2505 將程式碼與自然語言查詢表示為向量，用於程式碼庫搜尋及程式設計助理檢索。製造商記載 8,192 token 上下文、最高 3,072 的可設定維度，以及浮點數、整數或位元打包的輸出格式。",
    "locale": "zh-hant-tw",
    "md_url": "https://ailoot.ai/models/zh-hant-tw/mistralai-codestral-embed-2505/index.md",
    "title": "Mistral: Codestral Embed 2505 — Ailoot",
    "url": "https://ailoot.ai/models/zh-hant-tw/mistralai-codestral-embed-2505/"
  },
  "rawModel": {
    "about": "Codestral Embed 2505 是 Mistral AI 專門用於程式碼檢索的嵌入模型。它接收包含原始碼或自然語言程式碼查詢的文字，並回傳數值向量。製造商將其定位於程式碼庫搜尋、程式設計助理上下文檢索、相似度與重複分析，以及依功能或結構分組程式碼。固定發布識別碼是 codestral-embed-2505。\n\n輸入與表示\n\n程式碼輸入可以是函式、相關來源片段，或程式碼庫中的其他有意義單位。自然語言查詢可描述要尋找的行為，不必指出確切符號名稱。嵌入表示該輸入以供比較；模型不會產生新實作，也不會執行收到的程式碼。\n\n官方發布資料記載完整輸入上下文為 8,192 token。檢索時，製造商建議約 3,000 字元的區塊搭配 1,000 字元重疊，並指出較大區塊可能降低檢索效果。這些是檢索準備建議，不是 3,000 字元的 token 上限。請為每個準備項目保留檔案、符號與來源位置，讓應用程式在搜尋後回傳相關程式碼。\n\n輸出精度與維度\n\n程式碼嵌入指南記載 float、int8、uint8、binary 與 ubinary 輸出型別。float 使用 32 位元單精度值。int8 使用 -128 至 127 的有號 8 位元整數，uint8 則使用 0 至 255 的無號 8 位元值。binary 與 ubinary 將量化後的單位元值打包在 8 位元元素中，分別採用有號與無號容器。\n\n位元打包表示會改變回傳清單長度：指南說明，每八個邏輯嵌入維度對應一個打包元素。因此，辨識格式的比較不同於將打包位元組當成一般浮點座標。請將選定型別與向量一起保留，並為索引程式碼及其查詢使用相同表示。\n\n參數文件列出預設維度為 1,536、最大為 3,072。維度依相關性排序，製造商說明可保留前面選定數量的維度，以選擇表示大小。儲存資料集合與新查詢應一致選擇維度。較短或量化表示會改變儲存及比較行為，不會增加輸入上下文，也不會將模型變成程式碼生成器。\n\n檢索增強程式設計\n\n發布資料的第一個案例，是為程式碼補全、編輯或解釋檢索上下文。請準備有意義的程式碼單位，並保留程式碼庫版本與來源識別碼。詢問某種錯誤如何處理，可檢索相關函式，再由應用程式連同實際變更需求提供給程式設計模型。\n\n實用結果是一組附排名的來源片段，包含檔案路徑、符號，以及理解行為所需的內容。開發者可開啟檢索片段，或將其交給助理。Codestral Embed 執行表示步驟；另一個元件選擇結果，生成模型才會撰寫解釋或建議程式碼。\n\n語意搜尋與相似度\n\n製造商描述可用自然語言或程式碼查詢搜尋程式碼。即使函式名稱與請求措辭不同，開發者仍能尋找某個行為的實作。請納入區別預期實作的行為與條件；對整個子系統的一般查詢，可能只檢索相關資料，無法解決具體問題。\n\n發布資料也描述，即使詞彙不同，仍可找出近似重複或功能相似的程式碼。這類結果能協助找到可重用實作，或識別需要進一步審查的程式碼。嵌入空間中的相似度是檢視來源的訊號，不代表兩個片段在每種輸入下都有相同行為。\n\n分群、分析與比較\n\n程式碼嵌入可用於依功能或結構分組程式碼，並檢視程式碼庫組成。請讓原始片段與識別碼持續可用，供人員或後續應用程式步驟解讀群組。群組是基於表示的分組，不是自動驗證過的架構描述。\n\n製造商的浮點向量範例使用單位長度正規化嵌入，餘弦相似度、內積與歐幾里得距離會產生一致排名。量化及位元打包輸出應保留自己的格式處理，不應沿用原始浮點座標的假設。公開端點也保留輸入索引，有助於維持每個向量與來源項目的連結。\n\n版本與實際邊界\n\n確切模型頁面列出嵌入與批次流程，並標示 8k 上下文，而發布資料提供 8,192 token 的數值。請在整個資料集合與查詢流程中保留 2505 版本名稱、選定輸出維度及型別。這是面向程式碼的向量輸出，不同於 Codestral 文字補全，也不同於固定維度的 Mistral Embed 文字模型。\n\n請準備包含相關行為的程式碼單位，保留來源位置，並明確說明要尋找的問題。有用產物是連結實際程式碼、可供檢索的表示；它支援下一個解釋、編輯或開發者審查步驟，但不自行執行那些步驟。",
    "alias_of": null,
    "aliases": [],
    "author": "Mistral",
    "available": null,
    "canonical_model": "mistralai/codestral-embed-2505",
    "capabilities": {
      "context_length": 8192,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "完整的設定價格資訊請見詳細資料。"
      }
    ],
    "ctx": 8192,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "mistralai/codestral-embed-2505",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "必要欄位、素材表示方式或參數值無效",
                "http_status": 400,
                "recovery": "提出新的建立請求前，先修正指定欄位。",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "此流程未引入嵌入向量任務端點。"
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "保留每個向量及其索引。向量數量必須與輸入數量相同，且維度必須一致。",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "mistralai/codestral-embed-2505"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 8192,
                  "text_unit": "UTF8 位元組",
                  "token_unit": "token 陣列元素"
                },
                "conditions": "批次不得為空，且各項目類型須一致。Token ID 須為非負整數。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "來源允許此 JSON 中繼資料欄位；確切的上游語意依模型而異。",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options 遭拒絕",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "保留每個向量的索引與維度。",
            "即使模型名稱標示為多模態，此指定路徑也不接受圖片物件。"
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 8192,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 8192,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "詞元基本費率",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"mistralai/codestral-embed-2505\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='mistralai/codestral-embed-2505',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.3,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "快速模式",
            "value": "標準費率",
            "when": "選取快速模式時"
          },
          {
            "label": "flex 模式",
            "value": "標準費率",
            "when": "選取 flex 模式時"
          },
          {
            "label": "最高推理強度",
            "value": "標準費率",
            "when": "最高推理強度採用 default 調整值時"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
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