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# This file is distributed under the same license as the Xinference package.
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#: ../../source/models/model_abilities/embed.rst:5
msgid "Embeddings"
msgstr "嵌入"

#: ../../source/models/model_abilities/embed.rst:8
msgid "Learn how to create text embeddings in Xinference."
msgstr "學習如何在 Xinference 中建立文字嵌入。"

#: ../../source/models/model_abilities/embed.rst:12
msgid "Introduction"
msgstr "Introduction"

#: ../../source/models/model_abilities/embed.rst:14
msgid ""
"Text embeddings are used to quantify how related different pieces of text"
" are. They can be used in various applications including search, "
"clustering, recommendations, anomaly detection, diversity measurement, "
"and classification."
msgstr "文本嵌入用於量化不同文字之間的相關性。它們可以應用於各種應用程式，包括搜尋、聚類、推薦、異常檢測、多樣性度量和分類。"

#: ../../source/models/model_abilities/embed.rst:17
msgid ""
"An embedding is a vector of floating point numbers. The proximity between"
" two vectors serves as an indicator of their similarity. Less distance "
"implies a higher correlation, while a larger distance indicates reduced "
"correlation."
msgstr "嵌入是一組浮點數的向量。兩個向量之間的接近程度可以作為它們相似性的指標。距離越小表示相關性越高，而距離越大則表示相關性降低。"

#: ../../source/models/model_abilities/embed.rst:20
msgid ""
"Embedding models in Xinference can be invoked through the Embeddings API "
"to create embeddings. The Embeddings API mimics OpenAI's `create "
"embeddings API <https://platform.openai.com/docs/api-"
"reference/embeddings/create>`_."
msgstr ""
"通過 Embeddings API 在 Xinference 中嵌入模型可以被調用，以創建嵌入。Embeddings API 模仿了 OpenAI"
" 的 `create embeddings API <https://platform.openai.com/docs/api-"
"reference/embeddings/create>`_。"

#: ../../source/models/model_abilities/embed.rst:27
msgid "API ENDPOINT"
msgstr "API 端點"

#: ../../source/models/model_abilities/embed.rst:28
msgid "OpenAI-compatible ENDPOINT"
msgstr "OpenAI 兼容端點"

#: ../../source/models/model_abilities/embed.rst:30
msgid "Embeddings API"
msgstr "Embeddings API"

#: ../../source/models/model_abilities/embed.rst:31
msgid "/v1/embeddings"
msgstr "/v1/embeddings"

#: ../../source/models/model_abilities/embed.rst:35
msgid "Supported models"
msgstr "支援的模型列表"

#: ../../source/models/model_abilities/embed.rst:37
msgid ""
"You can examine all the :ref:`builtin embedding models in Xinference "
"<models_embedding_index>`."
msgstr "你可以查看所有 :ref:`Xinference 內建中的嵌入模型 <models_embedding_index>`。"

#: ../../source/models/model_abilities/embed.rst:40
msgid "Embedding engines"
msgstr "嵌入引擎"

#: ../../source/models/model_abilities/embed.rst:42
msgid ""
"When launching an embedding model, you can pick the serving engine with "
"the ``model_engine`` parameter (``--model-engine`` on the command line):"
msgstr "啟動嵌入模型時，可以透過 ``model_engine`` 參數（命令列上為 ``--model-engine``）選擇服務引擎："

#: ../../source/models/model_abilities/embed.rst:45
msgid ""
"``sentence_transformers``: the default engine, available for all "
"embedding models."
msgstr "``sentence_transformers``：預設引擎，適用於所有嵌入模型。"

#: ../../source/models/model_abilities/embed.rst:47
msgid ""
"``vllm``: high-throughput serving for supported model families — "
"currently models whose names start with ``bge``, ``gte``, ``text2vec``, "
"``m3e``, ``Qwen3``, or ``bce`` (e.g. ``bce-embedding-base_v1``)."
msgstr ""
"``vllm``：為支援的模型家族提供高吞吐量服務——目前為名稱以 ``bge``、 ``gte``、 ``text2vec``、 "
"``m3e``、 ``Qwen3`` 或 ``bce`` 開頭的模型（例如 ``bce-embedding-base_v1``）。"

#: ../../source/models/model_abilities/embed.rst:50
msgid ""
"``flag``: FlagEmbedding-based engine; also supports hybrid (sparse+dense)"
" output, see the FAQ below."
msgstr "``flag``：以 FlagEmbedding 為基礎的引擎；也支援混合（稀疏＋稠密）輸出，參見下方常見問題。"

#: ../../source/models/model_abilities/embed.rst:52
msgid "``llama.cpp``: serve GGUF-format embedding models."
msgstr "``llama.cpp``：提供 GGUF 格式嵌入模型的服務。"

#: ../../source/models/model_abilities/embed.rst:55
msgid "Truncating input"
msgstr "截斷輸入"

#: ../../source/models/model_abilities/embed.rst:57
msgid ""
"The Embeddings API accepts an optional ``truncate_prompt_tokens`` "
"parameter to cap the token length of each input before encoding:"
msgstr ""
"Embeddings API 接受一個可選的 ``truncate_prompt_tokens`` 參數，用於在編碼前限制每個輸入的 token "
"長度："

#: ../../source/models/model_abilities/embed.rst:60
msgid "unset / ``null``: no truncation."
msgstr "未設定 / ``null``：不截斷。"

#: ../../source/models/model_abilities/embed.rst:61
msgid "a positive integer ``N``: truncate each input to at most ``N`` tokens."
msgstr "正整數 ``N``：將每個輸入截斷至最多 ``N`` 個 token。"

#: ../../source/models/model_abilities/embed.rst:62
msgid "``-1``: truncate to the model's own maximum input length."
msgstr "``-1``：截斷至模型自身的最大輸入長度。"

#: ../../source/models/model_abilities/embed.rst:77
msgid "Quickstart"
msgstr "快速入門"

#: ../../source/models/model_abilities/embed.rst:79
msgid ""
"We can try Embeddings API out either via cURL, OpenAI Client, or "
"Xinference's python client:"
msgstr "我們可以透過 cURL、OpenAI Client 或 Xinference 的 Python 客戶端來嘗試 Embeddings API。"

#: ../../source/models/model_abilities/embed.rst:140
msgid "FAQ"
msgstr "常見問題"

#: ../../source/models/model_abilities/embed.rst:143
msgid "Does the LLM in Xinference support Embeddings API?"
msgstr "LLM 在 Xinference 中是否支援 Embeddings API？"

#: ../../source/models/model_abilities/embed.rst:145
msgid ""
"No. Xinference doesn't provide embed API for LLMs due to considerations "
"of performance."
msgstr "不支援。基於效能考量，Xinference 不為 LLM 提供嵌入 API。"

#: ../../source/models/model_abilities/embed.rst:149
msgid "Does Embeddings API provides integration method for LangChain?"
msgstr "Embeddings API 是否提供了與 LangChain 的整合方法？"

#: ../../source/models/model_abilities/embed.rst:151
msgid ""
"Yes, you can refer to the related sections in LangChain's respective "
"official Xinference documentation. Here is the link: `Text Embedding "
"Models: Xinference "
"<https://python.langchain.com/docs/integrations/text_embedding/xinference>`_"
msgstr ""
"是的，你可以參考LangChain相關部分的官方Xinference文件。這裡是連結：`Text Embedding Models: "
"Xinference "
"<https://python.langchain.com/docs/integrations/text_embedding/xinference>`_"

#: ../../source/models/model_abilities/embed.rst:156
msgid "Does Embeddings API support hrbrid model?"
msgstr "Embeddings API 是否支援混合（hybrid）模型？"

#: ../../source/models/model_abilities/embed.rst:158
msgid ""
"Yes, you can use ``flag`` as the engine to deploy the model and call "
"Embeddings API by setting the extra parameter ``return_parse=True`` which"
" will return sparse vectors."
msgstr ""
"支援。你可以使用 ``flag`` 作為引擎部署模型，並在呼叫 Embeddings API 時設定額外參數 "
"``return_parse=True``，即可回傳稀疏向量。"

#~ msgid ""
#~ "You can find more examples of "
#~ "``embed`` ability in the tutorial "
#~ "notebook:"
#~ msgstr "你可以在教學筆記本中找到更多關於 ``embed`` 能力的範例。"

#~ msgid "LangChain Streamlit Doc Chat"
#~ msgstr "LangChain Streamlit 文件對話"

#~ msgid "Learn from an example demonstrating how to use embed API via LangChain"
#~ msgstr "從一個範例中學習如何透過 LangChain 使用嵌入 API"

