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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 "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 ""

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

#: ../../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:41
msgid "Quickstart"
msgstr "快速入门"

#: ../../source/models/model_abilities/embed.rst:43
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:103
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。"

#: ../../source/models/model_abilities/embed.rst:107
msgid "LangChain Streamlit Doc Chat"
msgstr "LangChain Streamlit 文档对话"

#: ../../source/models/model_abilities/embed.rst:110
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"

#: ../../source/models/model_abilities/embed.rst:114
msgid "FAQ"
msgstr ""

#: ../../source/models/model_abilities/embed.rst:117
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:119
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:123
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:125
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>`_"

