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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 "学习如何在 Xinference 中创建文本嵌入。"

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

#: ../../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 "文本嵌入用于量化不同文本之间的相关性。它们可以应用于各种应用程序，包括搜索、聚类、推荐、异常检测、多样性度量和分类。"

#: ../../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 "嵌入是一组浮点数的向量。两个向量之间的接近程度可以作为它们相似性的指标。距离越小表示相关性越高，而距离越大则表示相关性降低。"

#: ../../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 ""
"通过 Embeddings API 在 Xinference 中嵌入模型可以被调用，以创建嵌入。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 端点"

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

#: ../../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 "支持的模型列表"

#: ../../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 内置中的嵌入模型 <models_embedding_index>`。"

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

#: ../../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 "启动嵌入模型时，可以通过 ``model_engine`` 参数（命令行中为 ``--model-engine``）选择推理引擎："

#: ../../source/models/model_abilities/embed.rst:45
msgid ""
"``sentence_transformers``: the default engine, available for all "
"embedding models."
msgstr "``sentence_transformers``：默认引擎，适用于所有嵌入模型。"

#: ../../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``：为受支持的模型系列提供高吞吐推理——目前支持名称以 ``bge``、 ``gte``、 ``text2vec``、 "
"``m3e``、 ``Qwen3`` 或 ``bce`` 开头的模型（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``：基于 FlagEmbedding 的引擎；还支持混合（稀疏+稠密）输出，参见下方常见问题。"

#: ../../source/models/model_abilities/embed.rst:52
msgid "``llama.cpp``: serve GGUF-format embedding models."
msgstr "``llama.cpp``：用于部署 GGUF 格式的嵌入模型。"

#: ../../source/models/model_abilities/embed.rst:55
msgid "Truncating input"
msgstr "截断输入"

#: ../../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 接受可选的 ``truncate_prompt_tokens`` 参数，用于在编码前限制每条输入的 token 长度："

#: ../../source/models/model_abilities/embed.rst:60
msgid "unset / ``null``: no truncation."
msgstr "不设置 / ``null``：不截断。"

#: ../../source/models/model_abilities/embed.rst:61
msgid "a positive integer ``N``: truncate each input to at most ``N`` tokens."
msgstr "正整数 ``N``：将每条输入截断到最多 ``N`` 个 token。"

#: ../../source/models/model_abilities/embed.rst:62
msgid "``-1``: truncate to the model's own maximum input length."
msgstr "``-1``：截断到模型自身的最大输入长度。"

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

#: ../../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 "我们可以通过 cURL、OpenAI Client 或 Xinference 的 Python 客户端来尝试 Embeddings API。"

#: ../../source/models/model_abilities/embed.rst:140
msgid "FAQ"
msgstr "常见问题"

#: ../../source/models/model_abilities/embed.rst:143
msgid "Does the LLM in Xinference support Embeddings API?"
msgstr "LLM 在 Xinference 中是否支持 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 "不支持，Xinference由于性能考虑，并没有提供 LLMs 嵌入 API。"

#: ../../source/models/model_abilities/embed.rst:149
msgid "Does Embeddings API provides integration method for LangChain?"
msgstr "Embeddings API 是否提供了与 LangChain 的集成方法？"

#: ../../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，你可以参考LangChain相关部分的官方Xinference文档。这里是链接：`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 是否支持混合（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 ""
"支持。可以使用 ``flag`` 引擎部署模型，并在调用 Embeddings API 时设置额外参数 "
"``return_parse=True``，即可返回稀疏向量。"

#~ msgid ""
#~ "You can find more examples of "
#~ "``embed`` ability in the tutorial "
#~ "notebook:"
#~ msgstr "你可以在教程笔记本中找到更多关于 ``embed`` 能力的示例。"

#~ msgid "LangChain Streamlit Doc Chat"
#~ msgstr "LangChain Streamlit 文档对话"

#~ msgid "Learn from an example demonstrating how to use embed API via LangChain"
#~ msgstr "从一个示例中学习如何通过 LangChain 使用嵌入 API"

