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#: ../../source/user_guide/feature_guide/structured_output.md:1
msgid "Structured Output Guide"
msgstr "结构化输出指南"

#: ../../source/user_guide/feature_guide/structured_output.md:3
msgid "Overview"
msgstr "概述"

#: ../../source/user_guide/feature_guide/structured_output.md:5
#, fuzzy
msgid "What is structured output?"
msgstr "什么是结构化输出？"

#~ msgid ""
#~ "LLMs can be unpredictable when you "
#~ "need output in specific formats. Think"
#~ " of asking a model to generate "
#~ "JSON - without guidance, it might "
#~ "produce valid text that breaks JSON "
#~ "specification. **Structured Output (also "
#~ "called Guided Decoding)** enables LLMs "
#~ "to generate outputs that follow a "
#~ "desired structure while preserving the "
#~ "non-deterministic nature of the system."
#~ msgstr ""
#~ "当你需要特定格式输出时，大型语言模型（LLMs）可能表现出不可预测性。比如让模型生成 "
#~ "JSON，如果没有指导，模型可能会生成有效的文本，但这些文本却不符合 JSON "
#~ "规范。**结构化输出（也称为引导解码）** 能让大型语言模型生成符合预期结构的输出，同时保留系统的非确定性特性。"

#~ msgid ""
#~ "In simple terms, structured decoding "
#~ "gives LLMs a “template” to follow. "
#~ "Users provide a schema that “influences”"
#~ " the model’s output, ensuring compliance"
#~ " with the desired structure."
#~ msgstr "简单来说，结构化解码为LLM提供了一个“模板”来遵循。用户提供一个模式来“影响”模型的输出，从而确保输出符合期望的结构。"

#~ msgid "![structured decoding](./images/structured_output_1.png)"
#~ msgstr "![结构化解码](./images/structured_output_1.png)"

#~ msgid "structured decoding"
#~ msgstr "结构化解码"

#~ msgid "Structured Output in vllm-kunlun"
#~ msgstr "vllm-kunlun 中的结构化输出"

#~ msgid ""
#~ "Currently, vllm-kunlun supports **xgrammar**"
#~ " and **guidance** backend for structured"
#~ " output with vllm v1 engine."
#~ msgstr "目前，vllm-kunlun 支持 vllm v1 引擎的结构化输出，后端包括 **xgrammar** 和 **guidance**。"

#~ msgid ""
#~ "XGrammar introduces a new technique that"
#~ " batch constrained decoding via pushdown"
#~ " automaton (PDA). You can think of"
#~ " a PDA as a “collection of "
#~ "FSMs, and each FSM represents a "
#~ "context-free grammar (CFG).” One "
#~ "significant advantage of PDA is its "
#~ "recursive nature, allowing us to execute"
#~ " multiple state transitions. They also "
#~ "include additional optimisation (for those "
#~ "who are interested) to reduce grammar"
#~ " compilation overhead. Besides, you can "
#~ "also find more details about guidance"
#~ " by yourself."
#~ msgstr ""
#~ "XGrammar 引入了一种通过下推自动机（PDA）进行批量约束解码的新技术。你可以把 PDA "
#~ "理解为“有限状态机（FSM）的集合，每个 FSM 代表一个上下文无关文法（CFG）。” PDA "
#~ "的一个重要优点是其递归特性，使我们能够执行多次状态转移。此外，PDA "
#~ "还包含了额外的优化（供感兴趣的用户参考），以减少语法编译的开销。除此之外，你还可以自己找到更多关于指导的信息。"

#~ msgid "How to Use Structured Output?"
#~ msgstr "如何使用结构化输出？"

#~ msgid "Online Inference"
#~ msgstr "在线推理"

#~ msgid ""
#~ "You can also generate structured outputs"
#~ " using the OpenAI's Completions and "
#~ "Chat API. The following parameters are"
#~ " supported, which must be added as"
#~ " extra parameters:"
#~ msgstr "你也可以使用 OpenAI 的 Completions 和 Chat API 生成结构化输出。支持以下参数，这些参数必须作为额外参数添加："

#~ msgid "`guided_choice`: the output will be exactly one of the choices."
#~ msgstr "`guided_choice`：输出将会是其中一个选项。"

#~ msgid "`guided_regex`: the output will follow the regex pattern."
#~ msgstr "`guided_regex`：输出将遵循正则表达式模式。"

#~ msgid "`guided_json`: the output will follow the JSON schema."
#~ msgstr "`guided_json`：输出将遵循 JSON 架构。"

#~ msgid "`guided_grammar`: the output will follow the context free grammar."
#~ msgstr "`guided_grammar`：输出将遵循上下文无关文法。"

#~ msgid ""
#~ "Structured outputs are supported by "
#~ "default in the OpenAI-Compatible Server."
#~ " You can choose to specify the "
#~ "backend to use by setting the "
#~ "`--guided-decoding-backend` flag to vllm"
#~ " serve. The default backend is "
#~ "`auto`, which will try to choose "
#~ "an appropriate backend based on the "
#~ "details of the request. You may "
#~ "also choose a specific backend, along"
#~ " with some options."
#~ msgstr ""
#~ "OpenAI 兼容服务器默认支持结构化输出。你可以通过设置 `--guided-decoding-"
#~ "backend` 标志为 vllm serve 来指定要使用的后端。默认后端为 "
#~ "`auto`，它会根据请求的详细信息尝试选择合适的后端。你也可以选择特定的后端，并设置一些选项。"

#~ msgid ""
#~ "Now let´s see an example for each"
#~ " of the cases, starting with the "
#~ "guided_choice, as it´s the easiest one:"
#~ msgstr "现在让我们来看每种情况的示例，首先是 guided_choice，因为它是最简单的："

#~ msgid ""
#~ "The next example shows how to use"
#~ " the guided_regex. The idea is to "
#~ "generate an email address, given a "
#~ "simple regex template:"
#~ msgstr "下一个例子展示了如何使用 guided_regex。其思路是基于一个简单的正则表达式模板生成一个电子邮件地址："

#~ msgid ""
#~ "One of the most relevant features "
#~ "in structured text generation is the "
#~ "option to generate a valid JSON "
#~ "with pre-defined fields and formats. "
#~ "For this we can use the "
#~ "guided_json parameter in two different "
#~ "ways:"
#~ msgstr ""
#~ "在结构化文本生成中，最相关的特性之一是能够生成具有预定义字段和格式的有效 JSON。为此，我们可以通过两种不同的方式使用 "
#~ "guided_json 参数："

#~ msgid "Using a JSON Schema."
#~ msgstr "使用 JSON 架构。"

#~ msgid "Defining a Pydantic model and then extracting the JSON Schema from it."
#~ msgstr "定义一个 Pydantic 模型，然后从中提取 JSON Schema。"

#~ msgid ""
#~ "The next example shows how to use"
#~ " the guided_json parameter with a "
#~ "Pydantic model:"
#~ msgstr "下一个示例展示了如何将 guided_json 参数与 Pydantic 模型一起使用："

#~ msgid ""
#~ "Finally we have the guided_grammar "
#~ "option, which is probably the most "
#~ "difficult to use, but it´s really "
#~ "powerful. It allows us to define "
#~ "complete languages like SQL queries. It"
#~ " works by using a context free "
#~ "EBNF grammar. As an example, we "
#~ "can use to define a specific "
#~ "format of simplified SQL queries:"
#~ msgstr ""
#~ "最后，我们有 guided_grammar 选项，这可能是最难使用的，但它非常强大。它允许我们定义完整的语言，比如"
#~ " SQL 查询。它通过使用上下文无关的 EBNF 语法来实现。例如，我们可以用它来定义一种简化"
#~ " SQL 查询的特定格式："

#~ msgid ""
#~ "Find more examples [here](https://github.com"
#~ "/vllm-"
#~ "project/vllm/blob/main/examples/offline_inference/structured_outputs.py)."
#~ msgstr ""
#~ "在[这里](https://github.com/vllm-"
#~ "project/vllm/blob/main/examples/offline_inference/structured_outputs.py)可以找到更多示例。"

#~ msgid "Offline Inference"
#~ msgstr "离线推理"

#~ msgid ""
#~ "To use Structured Output, we'll need "
#~ "to configure the guided decoding using"
#~ " the class `GuidedDecodingParams` inside "
#~ "`SamplingParams`. The main available options"
#~ " inside `GuidedDecodingParams` are:"
#~ msgstr ""
#~ "要使用结构化输出，我们需要在 `SamplingParams` 内通过 "
#~ "`GuidedDecodingParams` 类配置引导解码。`GuidedDecodingParams` "
#~ "中主要可用的选项有："

#~ msgid "json"
#~ msgstr "json"

#~ msgid "regex"
#~ msgstr "正则表达式"

#~ msgid "choice"
#~ msgstr "选择"

#~ msgid "grammar"
#~ msgstr "语法"

#~ msgid "One example for the usage of the choice parameter is shown below:"
#~ msgstr "choice 参数用法的一个示例如下："

#~ msgid ""
#~ "Find more examples of other usages "
#~ "[here](https://github.com/vllm-"
#~ "project/vllm/blob/main/examples/offline_inference/structured_outputs.py)."
#~ msgstr ""
#~ "查看更多其他用法的示例 [在这里](https://github.com/vllm-"
#~ "project/vllm/blob/main/examples/offline_inference/structured_outputs.py)。"

